Compare commits
2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 CI Bot
|
148b41a5d6 | ||
|
xiaoxia
|
f34b076dcd |
@@ -1 +0,0 @@
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||||
CI re-trigger after runner add-host/DNS fix. This file is harmless and not referenced.
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@@ -1,105 +0,0 @@
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name: CI Base Image Build
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on:
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push:
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branches:
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- develop
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- main
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paths:
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- 'requirements-base.txt'
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- 'requirements-dev.txt'
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- 'infra/docker/ci.Dockerfile'
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workflow_dispatch:
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inputs:
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reason:
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description: "触发原因"
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required: false
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default: "手动触发 - ci-base 镜像重建"
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concurrency:
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group: ci-base-image-build
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cancel-in-progress: false
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jobs:
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build-ci-base:
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name: Build CI Base Image
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runs-on: runtime-builder
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timeout-minutes: 60
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steps:
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- name: Checkout code
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shell: sh
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env:
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GITHUB_TOKEN: ${{ github.token }}
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run: |
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curl -sH "Authorization: token $GITHUB_TOKEN" \
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"${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/raw/scripts/ci/step_checkout.sh?ref=${GITHUB_SHA}" \
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| bash
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- name: Docker login to Gitea Registry
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shell: sh
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env:
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GITEA_REGISTRY_USER: xiaoxia
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GITEA_REGISTRY_TOKEN: ${{ secrets.REGISTRY_TOKEN }}
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run: |
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set -eu
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for i in 1 2 3; do
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echo "=== Docker login 尝试 $i/3 ==="
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if docker login git.xiaoxiajianji.com -u "${GITEA_REGISTRY_USER}" -p "${GITEA_REGISTRY_TOKEN}"; then
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echo "✅ Docker login successful"
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break
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fi
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echo "❌ Docker login 失败（尝试 $i/3），5s 后重试..."
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sleep 5
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done
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- name: Build and push CI base image
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shell: sh
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run: |
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set -eu
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IMAGE="git.xiaoxiajianji.com/xiaoxia/xiaoxia-saas/ci-base"
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VERSION_TAG="deps-$(date +%Y%m%d-%H%M)-${GITHUB_SHA::8}"
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echo "=== Building CI base image (tags: latest, ${VERSION_TAG}) ==="
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docker build --progress=plain \
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-f infra/docker/ci.Dockerfile \
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-t "${IMAGE}:latest" \
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-t "${IMAGE}:${VERSION_TAG}" \
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.
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echo "✅ Image built successfully"
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echo "=== Pushing ${VERSION_TAG} ==="
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docker push "${IMAGE}:${VERSION_TAG}"
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echo "=== Pushing latest ==="
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docker push "${IMAGE}:latest"
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echo "✅ Pushed to Gitea Registry"
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- name: Verify image
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shell: sh
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run: |
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set -eu
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IMAGE="git.xiaoxiajianji.com/xiaoxia/xiaoxia-saas/ci-base:latest"
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echo "=== Verifying pinned deps in fresh image ==="
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docker run --rm "${IMAGE}" /opt/xiaoxia-ci-venv/bin/python -c \
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"import httpcore, h2, numpy, httpx; print('VERSIONS:', httpcore.__version__, h2.__version__, numpy.__version__, httpx.__version__)"
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|
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- name: Notify result
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if: always()
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continue-on-error: true
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shell: sh
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env:
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CI_NOTIFY_WEBHOOK: ${{ secrets.CI_NOTIFY_WEBHOOK }}
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run: |
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set +e
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if [ "${{ job.status }}" = "success" ]; then
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NOTIFY_MODE=success JOB_NAME="CI Base Image Build" python3 scripts/ci_notify.py
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else
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NOTIFY_MODE=failure JOB_NAME="CI Base Image Build" python3 scripts/ci_notify.py
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fi
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- name: Cleanup
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if: always()
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shell: sh
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run: |
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IMAGE="git.xiaoxiajianji.com/xiaoxia/xiaoxia-saas/ci-base"
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docker rmi "${IMAGE}:latest" 2>/dev/null || true
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echo "Cleanup done"
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@@ -1,51 +0,0 @@
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name: CI Canary Check
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on:
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schedule:
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- cron: '*/30 * * * *'
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workflow_dispatch:
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jobs:
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canary:
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runs-on: ci-l2
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timeout-minutes: 10
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steps:
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- name: Canary (runner -> docker -> network -> gitea)
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run: |
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set -e
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echo "== runner/container basic =="
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date; hostname; whoami
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echo "== gitea api reachability =="
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code=$(curl -s -o /tmp/v.json -w '%{http_code}' -m 15 "$GITHUB_API_URL/version")
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echo "gitea api http_code=$code"
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[ "$code" = "200" ] || { echo "::error::Gitea API unreachable, http_code=$code"; exit 1; }
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cat /tmp/v.json; echo
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echo "== external egress =="
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ext=$(curl -s -o /dev/null -w '%{http_code}' -m 15 https://www.baidu.com || echo 000)
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echo "external http_code=$ext"
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echo "== gitea domain resolves NOT to loopback =="
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set -o pipefail
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ip=$(getent hosts git.xiaoxiajianji.com | awk '{print $1}' | head -1)
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echo "git.xiaoxiajianji.com -> $ip"
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if [ -z "$ip" ]; then
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echo "::error::DNS resolution failed, git.xiaoxiajianji.com unresolvable"; exit 1
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fi
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if [ "$ip" = "127.0.0.1" ] || [ "$ip" = "::1" ]; then
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echo "::error::Gitea domain resolves to loopback inside job container (hosts/DNS leak)"; exit 1
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fi
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echo "CANARY OK"
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- name: Notify failure
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if: failure()
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env:
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CI_NOTIFY_WEBHOOK: ${{ secrets.CI_NOTIFY_WEBHOOK }}
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run: |
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set +e
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if [ -n "$CI_NOTIFY_WEBHOOK" ]; then
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MSG="🚨 CI 金丝雀失败：runner->docker->网络->Gitea 链路异常，时间 $(date '+%Y-%m-%d %H:%M:%S')，请立即检查构建服务器"
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python3 - "$CI_NOTIFY_WEBHOOK" "$MSG" <<'PY'
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import json,sys,urllib.request
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hook,msg=sys.argv[1],sys.argv[2]
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data=json.dumps({"msg_type":"text","content":{"text":msg}}).encode()
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urllib.request.urlopen(urllib.request.Request(hook,data=data,headers={"Content-Type":"application/json"}),timeout=10)
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PY
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fi
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exit 0
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@@ -150,7 +150,6 @@ jobs:
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run: bash scripts/ci/step_timer_start.sh
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- name: Cache pip dependencies
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uses: actions/cache@v4
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continue-on-error: true
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with:
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path: /root/.cache/pip
|
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key: ${{ runner.os }}-pip-style-${{ hashFiles('requirements*.txt') }}
|
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@@ -253,7 +252,6 @@ jobs:
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run: bash scripts/ci/step_timer_start.sh
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- name: Cache pip dependencies
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uses: actions/cache@v4
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continue-on-error: true
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with:
|
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path: /root/.cache/pip
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key: ${{ runner.os }}-pip-security-${{ hashFiles('requirements*.txt') }}
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@@ -283,7 +281,6 @@ jobs:
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sleep 5
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done
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- name: Run security checks
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continue-on-error: true # Security scan is advisory; runner failure must not block deploy
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shell: bash
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ github.token }}
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||||
@@ -334,7 +331,7 @@ jobs:
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PIP_NO_CACHE_DIR: ''
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DATABASE_URL: postgresql+psycopg://postgres:postgres@host.docker.internal:5432/xiaoxia_saas
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USE_IN_MEMORY_DB: 'false'
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CI_USE_SHARED_PG: 'false'
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CI_USE_SHARED_PG: 'true'
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permissions:
|
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contents: read
|
||||
steps:
|
||||
@@ -355,7 +352,6 @@ jobs:
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run: bash scripts/ci/step_timer_start.sh
|
||||
- name: Cache pip dependencies
|
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uses: actions/cache@v4
|
||||
continue-on-error: true
|
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with:
|
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path: /root/.cache/pip
|
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key: ${{ runner.os }}-pip-python-${{ hashFiles('requirements*.txt') }}
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||||
@@ -460,7 +456,6 @@ jobs:
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run: bash scripts/ci/step_install_ffmpeg.sh
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- name: Cache pip dependencies
|
||||
uses: actions/cache@v4
|
||||
continue-on-error: true
|
||||
with:
|
||||
path: /root/.cache/pip
|
||||
key: ${{ runner.os }}-pip-unittests-${{ hashFiles('requirements*.txt') }}
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||||
@@ -518,7 +513,7 @@ jobs:
|
||||
env:
|
||||
DATABASE_URL: postgresql+psycopg://postgres:postgres@host.docker.internal:5432/xiaoxia_saas
|
||||
USE_IN_MEMORY_DB: 'false'
|
||||
CI_USE_SHARED_PG: 'false'
|
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CI_USE_SHARED_PG: 'true'
|
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OSS_ACCESS_KEY_ID: placeholder
|
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OSS_ACCESS_KEY_SECRET: placeholder
|
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OSS_BUCKET_NAME: xiaoxia-autocut
|
||||
@@ -669,7 +664,6 @@ jobs:
|
||||
run: bash scripts/ci/step_timer_start.sh
|
||||
- name: Cache npm dependencies
|
||||
uses: actions/cache@v4
|
||||
continue-on-error: true
|
||||
with:
|
||||
path: /root/.npm
|
||||
key: ${{ runner.os }}-npm-vitest-${{ hashFiles('apps/web/package-lock.json') }}
|
||||
@@ -1170,31 +1164,6 @@ jobs:
|
||||
run: |
|
||||
set +e
|
||||
NOTIFY_MODE=start JOB_NAME="Deploy Staging" python3 scripts/ci_notify.py
|
||||
- name: Render .env from template
|
||||
shell: sh
|
||||
env:
|
||||
STAGING_DATABASE_URL: ${{ secrets.STAGING_DATABASE_URL }}
|
||||
STAGING_REDIS_URL: ${{ secrets.STAGING_REDIS_URL }}
|
||||
STAGING_CELERY_BROKER_URL: ${{ secrets.STAGING_CELERY_BROKER_URL }}
|
||||
STAGING_CELERY_RESULT_BACKEND: ${{ secrets.STAGING_CELERY_RESULT_BACKEND }}
|
||||
STAGING_JWT_SECRET_KEY: ${{ secrets.STAGING_JWT_SECRET_KEY }}
|
||||
STAGING_MINIO_ENDPOINT: ${{ secrets.STAGING_MINIO_ENDPOINT }}
|
||||
STAGING_MINIO_ACCESS_KEY: ${{ secrets.STAGING_MINIO_ACCESS_KEY }}
|
||||
STAGING_MINIO_SECRET_KEY: ${{ secrets.STAGING_MINIO_SECRET_KEY }}
|
||||
STAGING_MINIO_BUCKET: ${{ secrets.STAGING_MINIO_BUCKET }}
|
||||
OSS_ACCESS_KEY_ID: ${{ secrets.OSS_ACCESS_KEY_ID }}
|
||||
OSS_ACCESS_KEY_SECRET: ${{ secrets.OSS_ACCESS_KEY_SECRET }}
|
||||
COSYVOICE_API_KEY: ${{ secrets.COSYVOICE_API_KEY }}
|
||||
DASHSCOPE_API_KEY: ${{ secrets.DASHSCOPE_API_KEY }}
|
||||
MEDIAKIT_API_KEY: ${{ secrets.MEDIAKIT_API_KEY }}
|
||||
run: |
|
||||
set -eu
|
||||
echo "Rendering .env from template + secrets..."
|
||||
bash scripts/render_env.sh staging
|
||||
echo "✅ .env rendered (file contains secrets, not printed to log)"
|
||||
# 验证文件存在且非空
|
||||
test -s .env.rendered
|
||||
echo "✅ .env.rendered validated ($(wc -l < .env.rendered) lines)"
|
||||
- name: Docker login to Registry
|
||||
shell: sh
|
||||
env:
|
||||
@@ -1267,31 +1236,9 @@ jobs:
|
||||
ssh -p "$staging_port" -i "$key_path" -o StrictHostKeyChecking=no "${staging_user}@${staging_host}" "echo SSH_CONNECTION_OK && hostname"
|
||||
echo "SSH connection verified"
|
||||
|
||||
# 配置 Diff 检查：下载服务器当前 .env，对比渲染结果，检测漂移
|
||||
echo "Running config diff check..."
|
||||
scp -P "$staging_port" -i "$key_path" -o StrictHostKeyChecking=no \
|
||||
"${staging_user}@${staging_host}:/var/lib/xiaoxia-saas-staging/.env" .env.current 2>/dev/null \
|
||||
|| touch .env.current # 首次部署时文件不存在，创建空文件
|
||||
bash scripts/config_diff_check.sh .env.rendered .env.current
|
||||
rm -f .env.current
|
||||
echo "Config diff check done"
|
||||
|
||||
# 上传渲染后的 .env 到服务器（替代服务器上旧的 .env）
|
||||
echo "Uploading rendered .env to staging server..."
|
||||
# 备份旧 .env
|
||||
ssh -p "$staging_port" -i "$key_path" -o StrictHostKeyChecking=no "${staging_user}@${staging_host}" \
|
||||
"cp -f /var/lib/xiaoxia-saas-staging/.env /var/lib/xiaoxia-saas-staging/.env.bak.\$(date +%Y%m%d%H%M%S) 2>/dev/null || true"
|
||||
# 上传新 .env
|
||||
scp -P "$staging_port" -i "$key_path" -o StrictHostKeyChecking=no .env.rendered \
|
||||
"${staging_user}@${staging_host}:/var/lib/xiaoxia-saas-staging/.env"
|
||||
echo "✅ .env uploaded to staging server"
|
||||
|
||||
# 通过环境变量传递凭证，避免命令行引号转义问题
|
||||
cat scripts/ci_staging_deploy.sh | ssh -p "$staging_port" -i "$key_path" -o StrictHostKeyChecking=no "${staging_user}@${staging_host}" "IMAGE_TAG=${GITHUB_SHA} ACR_USERNAME=${ACR_USERNAME} ACR_PASSWORD=${ACR_PASSWORD} sh"
|
||||
|
||||
# 清理 CI runner 上的渲染文件
|
||||
rm -f .env.rendered
|
||||
|
||||
- name: Staging health check + auto rollback
|
||||
if: success()
|
||||
shell: sh
|
||||
@@ -1462,7 +1409,7 @@ jobs:
|
||||
- unit-tests
|
||||
- frontend-lint
|
||||
- frontend-unit-test
|
||||
if: github.event_name == 'push' && github.ref_name == 'main' && !failure() && !cancelled()
|
||||
if: startsWith(github.ref, 'refs/tags/v') || (github.event_name == 'push' && github.ref_name == 'main')
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
@@ -1546,7 +1493,11 @@ jobs:
|
||||
set -eu
|
||||
REGISTRY="xiaoxia-registry.cn-hangzhou.cr.aliyuncs.com/xiaoxiakeji"
|
||||
# 根据ref类型设置镜像标签：tag用版本号，分支用分支名+sha
|
||||
TAG_NAME="${GITHUB_SHA}"
|
||||
if [[ "$GITHUB_REF" == refs/tags/* ]]; then
|
||||
TAG_NAME="${GITHUB_REF_NAME}"
|
||||
else
|
||||
TAG_NAME="${GITHUB_REF_NAME}-${GITHUB_SHA::8}"
|
||||
fi
|
||||
IMAGE_TAG="${REGISTRY}/${{ matrix.image_name }}:${TAG_NAME}"
|
||||
CACHE_REF="${REGISTRY}/${{ matrix.cache_name }}:main"
|
||||
|
||||
@@ -1608,7 +1559,7 @@ jobs:
|
||||
concurrency:
|
||||
group: deploy-production-${{ gitea.ref }}
|
||||
cancel-in-progress: false
|
||||
if: github.event_name == 'push' && github.ref_name == 'main'
|
||||
if: startsWith(github.ref, 'refs/tags/v')
|
||||
needs:
|
||||
- build-production
|
||||
steps:
|
||||
@@ -1681,7 +1632,7 @@ jobs:
|
||||
echo "SSH connection verified"
|
||||
|
||||
# 通过环境变量传递凭证，避免命令行引号转义问题
|
||||
cat scripts/ci_production_deploy.sh | ssh -p "$production_port" -i "$key_path" -o StrictHostKeyChecking=no "${production_user}@${production_host}" "IMAGE_TAG=${GITHUB_SHA} ACR_USERNAME=${ACR_USERNAME} ACR_PASSWORD=${ACR_PASSWORD} sh"
|
||||
cat scripts/ci_production_deploy.sh | ssh -p "$production_port" -i "$key_path" -o StrictHostKeyChecking=no "${production_user}@${production_host}" "IMAGE_TAG=${GITHUB_REF_NAME} ACR_USERNAME=${ACR_USERNAME} ACR_PASSWORD=${ACR_PASSWORD} sh"
|
||||
|
||||
- name: Production health check + auto rollback
|
||||
if: success()
|
||||
@@ -1740,7 +1691,7 @@ jobs:
|
||||
name: Production Browser E2E
|
||||
runs-on: runtime-builder
|
||||
timeout-minutes: 15
|
||||
# if: removed - runs after deploy-production succeeds
|
||||
if: startsWith(github.ref, 'refs/tags/v')
|
||||
needs: deploy-production
|
||||
steps:
|
||||
- name: Checkout code
|
||||
@@ -2060,11 +2011,6 @@ jobs:
|
||||
echo " ⏳ $name: pending（审查中，暂不阻塞）"
|
||||
continue
|
||||
fi
|
||||
# Security scan cancelled/failed时不阻塞部署（runner故障不应卡住流水线）
|
||||
if [ "$name" = "validate-security" ] && { [ "$result" = "cancelled" ] || [ "$result" = "failure" ]; }; then
|
||||
echo " ⚠️ $name: $result（安全扫描为非阻塞项，不卡住部署）"
|
||||
continue
|
||||
fi
|
||||
check_job "$name" "$result"
|
||||
done
|
||||
|
||||
|
||||
@@ -1,59 +0,0 @@
|
||||
name: Playwright Base Image Build
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
reason:
|
||||
description: "触发原因"
|
||||
required: false
|
||||
default: "构建 playwright 基础镜像"
|
||||
|
||||
jobs:
|
||||
build-playwright:
|
||||
name: Build Playwright Base Image
|
||||
runs-on: runtime-builder
|
||||
timeout-minutes: 30
|
||||
steps:
|
||||
- name: Docker login to Gitea Registry
|
||||
shell: sh
|
||||
env:
|
||||
GITEA_REGISTRY_USER: xiaoxia
|
||||
GITEA_REGISTRY_TOKEN: ${{ secrets.REGISTRY_TOKEN }}
|
||||
run: |
|
||||
set -eu
|
||||
for i in 1 2 3; do
|
||||
echo "=== Docker login attempt $i/3 ==="
|
||||
if printf '%s' "${GITEA_REGISTRY_TOKEN}" | docker login git.xiaoxiajianji.com -u "${GITEA_REGISTRY_USER}" --password-stdin; then
|
||||
echo "Docker login successful"
|
||||
break
|
||||
fi
|
||||
echo "Docker login failed (attempt $i/3), retrying in 5s..."
|
||||
sleep 5
|
||||
[ $i -eq 3 ] && exit 1
|
||||
done
|
||||
|
||||
- name: Pull, retag and push Playwright image
|
||||
shell: sh
|
||||
run: |
|
||||
set -eu
|
||||
OFFICIAL_IMAGE="mcr.microsoft.com/playwright:v1.45.0-jammy"
|
||||
GITEA_IMAGE="git.xiaoxiajianji.com/xiaoxia/base/playwright:v1.45.0-jammy"
|
||||
|
||||
echo "=== Pulling official Playwright image ==="
|
||||
docker pull "${OFFICIAL_IMAGE}"
|
||||
|
||||
echo "=== Tagging ==="
|
||||
docker tag "${OFFICIAL_IMAGE}" "${GITEA_IMAGE}"
|
||||
|
||||
echo "=== Pushing to Gitea Registry ==="
|
||||
docker push "${GITEA_IMAGE}"
|
||||
|
||||
echo "Done: ${GITEA_IMAGE}"
|
||||
|
||||
- name: Cleanup
|
||||
if: always()
|
||||
shell: sh
|
||||
run: |
|
||||
docker rmi "mcr.microsoft.com/playwright:v1.45.0-jammy" 2>/dev/null || true
|
||||
docker rmi "git.xiaoxiajianji.com/xiaoxia/base/playwright:v1.45.0-jammy" 2>/dev/null || true
|
||||
echo "Cleanup done"
|
||||
@@ -3,7 +3,7 @@ name: PR Auto Scan
|
||||
# 作为短作业模式的兜底，防止事件驱动遗漏
|
||||
on:
|
||||
schedule:
|
||||
# - cron: "*/15 * * * *" # DISABLED: temporarily to stop failure spam (2026-09-02) # 每10分钟扫描一次（脚本自带240s墙钟上限，降频减负）
|
||||
- cron: "*/10 * * * *" # 每10分钟扫描一次（脚本自带240s墙钟上限，降频减负）
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
|
||||
@@ -18,7 +18,7 @@ jobs:
|
||||
name: Auto Approve on CI Green
|
||||
runs-on: ci-check
|
||||
if: github.event_name == 'pull_request' && !github.event.pull_request.draft
|
||||
timeout-minutes: 10 # 等待CI全绿+审批，需要充足时间
|
||||
timeout-minutes: 3 # 长等待模式：等CI全绿后自动合并，不遗漏任何PR
|
||||
steps:
|
||||
- name: Checkout code
|
||||
shell: sh
|
||||
@@ -61,8 +61,7 @@ jobs:
|
||||
name: Auto Merge on CI Green + Approved
|
||||
runs-on: ci-check
|
||||
if: github.event_name == 'pull_request' && !github.event.pull_request.draft && github.event.pull_request.base.ref == 'develop'
|
||||
needs: [auto-approve] # 修复竞态：必须等审批完成后再尝试合并
|
||||
timeout-minutes: 15 # 等待审批+CI就绪+合并，需要充足时间
|
||||
timeout-minutes: 3 # 短作业模式：检查一次，不满足就退出，由pr-auto-scan每5分钟定时兜底
|
||||
steps:
|
||||
- name: Checkout code
|
||||
shell: sh
|
||||
|
||||
@@ -24,11 +24,6 @@ ruff_cache/
|
||||
.env.production
|
||||
.env.staging
|
||||
!.env.example
|
||||
# 配置模板不受忽略规则限制
|
||||
!deploy/configs/.env.staging
|
||||
!deploy/configs/.env.production
|
||||
# 渲染后的 env 文件包含真实密钥，绝不能提交
|
||||
.env.rendered
|
||||
|
||||
# OS / editor
|
||||
.DS_Store
|
||||
@@ -59,4 +54,3 @@ frontend-v21-ui-prototype-final.html
|
||||
!.vscode/settings.json
|
||||
.vscode/extensions.json
|
||||
.coverage
|
||||
.env.current
|
||||
|
||||
@@ -1,26 +0,0 @@
|
||||
"""add sort_order to template_categories
|
||||
|
||||
Revision ID: 061_sort_order
|
||||
Revises: 060_migrate_segments
|
||||
Create Date: 2026-09-02
|
||||
"""
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "061_sort_order"
|
||||
down_revision = "060_migrate_segments"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
op.add_column(
|
||||
"template_categories",
|
||||
sa.Column("sort_order", sa.Integer, nullable=False, server_default="0"),
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
op.drop_column("template_categories", "sort_order")
|
||||
@@ -1,28 +0,0 @@
|
||||
"""re-add edit_plan_id to generation_tasks (align staging with production)
|
||||
|
||||
Revision ID: 062_edit_plan_id
|
||||
Revises: 061_sort_order
|
||||
Create Date: 2026-09-02
|
||||
"""
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "062_edit_plan_id"
|
||||
down_revision = "061_sort_order"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
op.add_column(
|
||||
"generation_tasks",
|
||||
sa.Column("edit_plan_id", sa.String(36), nullable=True),
|
||||
)
|
||||
op.create_index("ix_generation_tasks_edit_plan_id_2", "generation_tasks", ["edit_plan_id"])
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
op.drop_index("ix_generation_tasks_edit_plan_id_2", table_name="generation_tasks")
|
||||
op.drop_column("generation_tasks", "edit_plan_id")
|
||||
@@ -1,46 +0,0 @@
|
||||
"""add video_fingerprint_chunks table for per-chunk fingerprint storage
|
||||
|
||||
Revision ID: 063_fingerprint_chunks
|
||||
Revises: 062_edit_plan_id
|
||||
Create Date: 2026-09-03
|
||||
"""
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "063_fingerprint_chunks"
|
||||
down_revision = "062_edit_plan_id"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
op.create_table(
|
||||
"video_fingerprint_chunks",
|
||||
sa.Column("id", sa.String(36), primary_key=True),
|
||||
sa.Column("video_id", sa.String(36), nullable=False),
|
||||
sa.Column("project_id", sa.String(36), nullable=False),
|
||||
sa.Column("user_id", sa.String(36), nullable=False, server_default=""),
|
||||
sa.Column("start_time_ms", sa.Integer, nullable=False),
|
||||
sa.Column("end_time_ms", sa.Integer, nullable=False),
|
||||
sa.Column("phash_binary", sa.String(16), nullable=False),
|
||||
sa.Column("color_histogram", sa.JSON, nullable=False),
|
||||
sa.Column("frame_count", sa.Integer, nullable=False, server_default="1"),
|
||||
sa.Column(
|
||||
"created_at",
|
||||
sa.DateTime,
|
||||
nullable=False,
|
||||
server_default=sa.func.now(),
|
||||
),
|
||||
)
|
||||
op.create_index("ix_vfc_video_id", "video_fingerprint_chunks", ["video_id"])
|
||||
op.create_index("ix_vfc_project_id", "video_fingerprint_chunks", ["project_id"])
|
||||
op.create_index("ix_vfc_user_id", "video_fingerprint_chunks", ["user_id"])
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
op.drop_index("ix_vfc_user_id", table_name="video_fingerprint_chunks")
|
||||
op.drop_index("ix_vfc_project_id", table_name="video_fingerprint_chunks")
|
||||
op.drop_index("ix_vfc_video_id", table_name="video_fingerprint_chunks")
|
||||
op.drop_table("video_fingerprint_chunks")
|
||||
@@ -290,7 +290,7 @@ def list_assets(
|
||||
else:
|
||||
total = asset_repository.count_by_project_ids(project_ids, status=status_list)
|
||||
# 跨项目分页：逐项目累积直到凑够一页
|
||||
paged_items = []
|
||||
paged_items: list = []
|
||||
offset = skip
|
||||
remaining = limit
|
||||
for pid in project_ids:
|
||||
@@ -579,45 +579,39 @@ def smart_match_assets(
|
||||
filtered_assets = asset_repository.find_by_library(request.library_id, status=["ready"], limit=10000)
|
||||
total_candidates = len(filtered_assets)
|
||||
|
||||
# ── 过滤前置：余量 + 高频使用，过滤在评分/截取 limit 之前完成 ──────────
|
||||
# 旧实现先 smart_select_assets(limit=N) 再对这 N 条做过滤，过滤后不回补，
|
||||
# 当排名靠前的素材恰好都被排除时返回空 items（前端回退全选，smart-match 名存实亡）。
|
||||
# 现在先过滤全量候选，每级过滤后为空/不足则回退上一级，最后才评分截取。
|
||||
# 调用统一智能选素材算法（kind 已在 DB 层过滤，无需重复过滤）
|
||||
results = smart_select_assets(
|
||||
filtered_assets,
|
||||
limit=request.limit,
|
||||
kind=None,
|
||||
)
|
||||
|
||||
# 1) 余量过滤：usable=False（零重复可切区间耗尽且历史区间均达复用上限）的素材排除
|
||||
usable_assets = []
|
||||
exhausted_assets = []
|
||||
for a in filtered_assets:
|
||||
# 结果层过滤：usable=false（零重复可切区间耗尽且历史区间均达复用上限）的素材
|
||||
# 不返回给前端；不动 smart_select_assets 评分逻辑本身
|
||||
filtered_results = []
|
||||
for r in results:
|
||||
try:
|
||||
avail = compute_asset_availability(a)
|
||||
avail = compute_asset_availability(r.asset)
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"smart-match 余量计算失败，按可用处理: asset_id=%s",
|
||||
getattr(a, "id", "?"),
|
||||
getattr(r.asset, "id", "?"),
|
||||
exc_info=True,
|
||||
)
|
||||
avail = None
|
||||
if avail is not None and not avail["usable"]:
|
||||
exhausted_assets.append(a)
|
||||
else:
|
||||
usable_assets.append(a)
|
||||
logger.info(
|
||||
"smart-match 排除已用尽素材: asset_id=%s name=%s",
|
||||
getattr(r.asset, "id", "?"),
|
||||
getattr(r.asset, "name", ""),
|
||||
)
|
||||
continue
|
||||
filtered_results.append(r)
|
||||
|
||||
if exhausted_assets:
|
||||
logger.info(
|
||||
"smart-match 余量过滤: 候选 %d，可切区间耗尽 %d",
|
||||
len(filtered_assets), len(exhausted_assets),
|
||||
)
|
||||
|
||||
# 回退策略：余量过滤后为空（全部耗尽）时，保留全部候选，不返回空结果。
|
||||
# 宁可让用户在已耗尽素材上复用，也比 smart-match 空结果回退全选更可控
|
||||
# （全选同样会选到这些素材，且不经过评分排序）。
|
||||
pool = usable_assets if usable_assets else filtered_assets
|
||||
|
||||
# 2) 高频使用排除：同一素材在最近 5 个视频中出现超过 3 次则排除
|
||||
# 高频使用排除：同一素材在最近 5 个视频中出现超过 3 次则排除
|
||||
MAX_RECENT_USE_COUNT = 3
|
||||
high_freq_assets = set()
|
||||
if pool:
|
||||
asset_ids = [getattr(a, "id", "") for a in pool if getattr(a, "id", "")]
|
||||
if filtered_results:
|
||||
asset_ids = [getattr(r.asset, "id", "") for r in filtered_results if getattr(r.asset, "id", "")]
|
||||
if asset_ids:
|
||||
try:
|
||||
use_counts = get_asset_recent_use_counts(
|
||||
@@ -625,35 +619,27 @@ def smart_match_assets(
|
||||
asset_ids=asset_ids,
|
||||
recent_video_count=5,
|
||||
)
|
||||
for a in pool:
|
||||
aid = getattr(a, "id", "")
|
||||
high_use_excluded = set()
|
||||
for r in filtered_results:
|
||||
aid = getattr(r.asset, "id", "")
|
||||
count = use_counts.get(aid, 0)
|
||||
if count > MAX_RECENT_USE_COUNT:
|
||||
high_freq_assets.add(aid)
|
||||
logger.info(
|
||||
"smart-match 排除高频使用素材: asset_id=%s use_count=%d limit=%d",
|
||||
aid, count, MAX_RECENT_USE_COUNT,
|
||||
)
|
||||
# 回退策略：排除后剩余素材不足（为空或不够 limit）时，
|
||||
# 不再全部排除，保留全部可用素材
|
||||
if high_freq_assets:
|
||||
remaining_count = len(pool) - len(high_freq_assets)
|
||||
enough = request.limit is None or remaining_count >= request.limit
|
||||
if remaining_count > 0 and enough:
|
||||
pool = [a for a in pool if getattr(a, "id", "") not in high_freq_assets]
|
||||
high_use_excluded.add(id(r))
|
||||
else:
|
||||
logger.info(
|
||||
"smart-match 高频排除后素材不足(%d<%s)，保留全部 %d 条",
|
||||
remaining_count,
|
||||
request.limit if request.limit is not None else "不限",
|
||||
len(pool),
|
||||
)
|
||||
pass
|
||||
# 如果排除后不够 limit，放宽到不限制
|
||||
remaining = [r for r in filtered_results if id(r) not in high_use_excluded]
|
||||
if len(remaining) >= request.limit:
|
||||
filtered_results = remaining
|
||||
else:
|
||||
logger.info("smart-match 高频排除后素材不足(%d<%d)，保留全部", len(remaining), request.limit)
|
||||
except Exception:
|
||||
logger.warning("smart-match 高频使用查询失败，跳过排除", exc_info=True)
|
||||
|
||||
# 3) 调用统一智能选素材算法（kind 已在 DB 层过滤，无需重复过滤）
|
||||
results = smart_select_assets(pool, limit=request.limit, kind=None)
|
||||
|
||||
# 扁平结构：SmartMatchItem 继承 AssetResponse，素材字段直接在条目顶层，
|
||||
# 前端无需解析 item.asset 包装层，item.id / item.usable / 余量字段直接可读
|
||||
items = [
|
||||
@@ -662,7 +648,7 @@ def smart_match_assets(
|
||||
score=r.score,
|
||||
breakdown=r.breakdown,
|
||||
)
|
||||
for r in results
|
||||
for r in filtered_results
|
||||
]
|
||||
|
||||
return SmartMatchResponse(items=items, total_candidates=total_candidates)
|
||||
|
||||
@@ -456,7 +456,7 @@ async def wechat_callback(
|
||||
user = user_repository.find_by_id(response.user_id)
|
||||
binding_complete = False
|
||||
if user:
|
||||
binding_complete = bool(
|
||||
binding_complete = (
|
||||
user.phone_verified and user.email_verified and user.email and "@wechat.local" not in user.email
|
||||
)
|
||||
|
||||
|
||||
@@ -513,7 +513,7 @@ def generate_cover(
|
||||
if generation_task_id:
|
||||
try:
|
||||
task = gen_task_repo.get(generation_task_id)
|
||||
if task and getattr(task, "cover_url", ""): # type: ignore[arg-type]
|
||||
if task and getattr(task, "cover_url", ""):
|
||||
cover_url_from_task = task.cover_url
|
||||
logger.info(
|
||||
"[封面生成] 统一管道封面(步骤A-direct): plan_id=%s task_id=%s url=%s",
|
||||
@@ -538,7 +538,7 @@ def generate_cover(
|
||||
gv_task_id = getattr(gv, "generation_task_id", "") or ""
|
||||
if gv_task_id:
|
||||
task_a2 = gen_task_repo.get(gv_task_id)
|
||||
if task_a2 and getattr(task_a2, "cover_url", ""): # type: ignore[arg-type]
|
||||
if task_a2 and getattr(task_a2, "cover_url", ""):
|
||||
cover_url_from_task = task_a2.cover_url
|
||||
logger.info(
|
||||
"[封面生成] 封面(步骤A2-video-task): plan_id=%s video_id=%s url=%s",
|
||||
@@ -747,7 +747,7 @@ def generate_cover(
|
||||
|
||||
if cover_url_from_task:
|
||||
# 标题已在预览视频渲染时烧录（ASS字幕），封面帧自然包含标题
|
||||
cover_data: dict[str, object] = { # type: ignore[no-redef]
|
||||
cover_data = {
|
||||
"type": "ai_frame",
|
||||
"image_url": cover_url_from_task,
|
||||
"frame_time": 0.0,
|
||||
|
||||
@@ -98,7 +98,7 @@ def _resolve_strategy_id_from_template(template_id: str, db: Session, user_id: s
|
||||
try:
|
||||
new_repo = SQLAlchemyEditTemplateRepository(db)
|
||||
new_template = new_repo.get(template_id)
|
||||
if new_template and getattr(new_template, "editing_mode", ""): # type: ignore[arg-type]
|
||||
if new_template and getattr(new_template, "editing_mode", ""):
|
||||
mode = new_template.editing_mode.strip()
|
||||
if mode:
|
||||
logger.info(
|
||||
|
||||
@@ -385,7 +385,7 @@ def create_generation_task(
|
||||
|
||||
use_case = CreateGenerationTaskUseCase(generation_task_repository)
|
||||
count = request.count
|
||||
created_tasks: list = []
|
||||
created_tasks = []
|
||||
failed_tasks = []
|
||||
user_id = authenticated_user.user.id
|
||||
# 同批次任务共享 batch_id，用于视频查重时批次内比对
|
||||
|
||||
@@ -23,10 +23,6 @@ import re
|
||||
from app.auth import AuthenticatedUser, get_current_user
|
||||
from app.core.storage import get_storage_service
|
||||
from app.dependencies import get_asset_repository, get_db_session
|
||||
|
||||
# 默认转场时长（与 worker 端保持一致）
|
||||
_DEFAULT_TRANSITION_DURATION = 0.5
|
||||
|
||||
from app.services.asset_segment_tracker import (
|
||||
REUSE_RATIO_LIMIT,
|
||||
SEGMENT_EDGE_GAP,
|
||||
@@ -41,20 +37,10 @@ from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, Query, s
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.asset_repository import SQLAlchemyAssetRepository
|
||||
from packages.adapters.sqlalchemy_impl.template_clip_config_repository import (
|
||||
SQLAlchemyTemplateClipConfigRepository,
|
||||
)
|
||||
from packages.adapters.sqlalchemy_impl.template_repository import (
|
||||
SQLAlchemyTemplateRepository,
|
||||
)
|
||||
from packages.domain.plan_generator_utils import (
|
||||
_calc_random_start_time,
|
||||
build_scene_segments,
|
||||
extract_scene_points_from_metadata,
|
||||
pick_scene_aware_start,
|
||||
pick_start_in_scene_segment,
|
||||
)
|
||||
from packages.domain.smart_match import SCORE_RANDOM_NOISE_MAX, score_asset
|
||||
from packages.domain.plan_generator_utils import _calc_random_start_time
|
||||
from packages.shared.mediakit_client import get_mediakit_client
|
||||
|
||||
from .dependencies import get_draft_plan_id, get_editor_services
|
||||
@@ -425,25 +411,7 @@ def _get_template_segments(
|
||||
result.append((cc.order, dur_min, dur_max))
|
||||
return sorted(result, key=lambda x: x[0])
|
||||
except Exception:
|
||||
logger.warning("新模板系统查询clip_configs失败（主表可能不存在），直接查clip_configs表", exc_info=True)
|
||||
|
||||
# 兜底：直接查 template_clip_configs 表（片段表有 template_id 外键，不依赖模板主表）
|
||||
try:
|
||||
direct_repo = SQLAlchemyTemplateClipConfigRepository(db)
|
||||
direct_configs = direct_repo.list_by_template(template_id)
|
||||
if direct_configs:
|
||||
result = []
|
||||
for cc in direct_configs:
|
||||
dur_min = _safe_segment_duration(cc.min_duration, _DEFAULT_EDITOR_CLIP_DURATION)
|
||||
dur_max = _safe_segment_duration(
|
||||
cc.max_duration or cc.min_duration,
|
||||
_DEFAULT_EDITOR_CLIP_DURATION,
|
||||
)
|
||||
dur_min, dur_max = min(dur_min, dur_max), max(dur_min, dur_max)
|
||||
result.append((cc.order, dur_min, dur_max))
|
||||
return sorted(result, key=lambda x: x[0])
|
||||
except Exception:
|
||||
logger.warning("直接查clip_configs表也失败，继续回退旧系统", exc_info=True)
|
||||
logger.warning("新模板系统查询clip_configs失败，回退到旧系统", exc_info=True)
|
||||
|
||||
# 回退到旧模板系统（template_segments表）
|
||||
try:
|
||||
@@ -481,12 +449,6 @@ def _recommended_time_conflicts(
|
||||
return False
|
||||
|
||||
|
||||
# 向后兼容别名：镜头段构建/段内取点逻辑已下沉到 packages.domain.plan_generator_utils，
|
||||
# 旧测试与历史代码仍按 clips._build_scene_segments / _pick_start_in_scene_segment 导入
|
||||
_build_scene_segments = build_scene_segments
|
||||
_pick_start_in_scene_segment = pick_start_in_scene_segment
|
||||
|
||||
|
||||
def _get_mediakit_recommendations(
|
||||
asset_ids: list[str],
|
||||
asset_repo,
|
||||
@@ -599,47 +561,6 @@ def _get_mediakit_recommendations(
|
||||
return {}
|
||||
|
||||
|
||||
def _calc_plan_internal_duplicate_rate(clips_data: list[dict]) -> float:
|
||||
"""估算单条成片内部重复率（%）.
|
||||
|
||||
检查本条成片中同一素材是否有重叠的时间区间。
|
||||
重叠时长 / 成片总时长 * 100 = 内部重复率。
|
||||
这是一个轻量估算，不依赖视频指纹；完整查重由 worker 异步完成。
|
||||
"""
|
||||
if not clips_data:
|
||||
return 0.0
|
||||
|
||||
# 按素材分组
|
||||
by_asset: dict[str, list[tuple[float, float]]] = {}
|
||||
total_duration = 0.0
|
||||
for c in clips_data:
|
||||
aid = c.get("asset_id", "")
|
||||
if not aid:
|
||||
continue
|
||||
start = c.get("start_time", 0.0)
|
||||
end = start + c.get("duration", 0.0)
|
||||
by_asset.setdefault(aid, []).append((start, end))
|
||||
total_duration += c.get("duration", 0.0)
|
||||
|
||||
if total_duration <= 0:
|
||||
return 0.0
|
||||
|
||||
# 检查同素材内的区间重叠
|
||||
overlap_duration = 0.0
|
||||
for segments in by_asset.values():
|
||||
if len(segments) < 2:
|
||||
continue
|
||||
segments_sorted = sorted(segments, key=lambda s: s[0])
|
||||
last_end = segments_sorted[0][1]
|
||||
for start, end in segments_sorted[1:]:
|
||||
overlap = max(0.0, min(end, last_end) - start)
|
||||
if overlap > 0:
|
||||
overlap_duration += overlap
|
||||
last_end = max(last_end, end)
|
||||
|
||||
return round(overlap_duration / total_duration * 100, 1)
|
||||
|
||||
|
||||
@router.post("/clips/from-assets", response_model=ClipsFromAssetsResponse)
|
||||
def create_clips_from_assets_editor(
|
||||
template_id: str,
|
||||
@@ -684,26 +605,10 @@ def create_clips_from_assets_editor(
|
||||
# 2. 获取素材实际时长（去重查询）
|
||||
unique_asset_ids = list(dict.fromkeys(asset_ids))
|
||||
asset_durations: dict[str, float] = {}
|
||||
asset_smart_scores: dict[str, float] = {}
|
||||
# 素材 metadata 中缓存的场景切换点（由后台 MediaKit SceneChange 检测写入）：
|
||||
# 有缓存时片段起点从随机镜头段中选取（不同片段来自不同镜头），无缓存回退随机起点
|
||||
asset_scene_points: dict[str, list[float]] = {}
|
||||
for asset_id in unique_asset_ids:
|
||||
asset = asset_repo.get(asset_id)
|
||||
if asset and hasattr(asset, "duration"):
|
||||
asset_durations[asset_id] = float(asset.duration or 0.0)
|
||||
# 计算 smart_match 综合评分，用于候选排序
|
||||
smart_score, _ = score_asset(asset)
|
||||
asset_smart_scores[asset_id] = smart_score
|
||||
# 读取场景切换点缓存（新素材未检测过时为 None，走随机起点兜底）
|
||||
cached_points = extract_scene_points_from_metadata(getattr(asset, "metadata", None))
|
||||
if cached_points:
|
||||
asset_scene_points[asset_id] = cached_points
|
||||
logger.info(
|
||||
"from-assets 场景缓存命中: %d/%d 个素材有场景切换点",
|
||||
len(asset_scene_points),
|
||||
len(unique_asset_ids),
|
||||
)
|
||||
|
||||
# 3. 在内存中计算所有片段数据（使用随机起始时间，不调用MediaKit）
|
||||
# 读取素材 metadata 中持久化的历史已用区间（跨任务/跨调用去重），
|
||||
@@ -736,52 +641,19 @@ def create_clips_from_assets_editor(
|
||||
return False
|
||||
return reused_durations.get(aid, 0.0) / assigned > REUSE_RATIO_LIMIT
|
||||
|
||||
# 素材耗尽标志：某轮循环中所有素材均被跳过时为 True
|
||||
all_assets_exhausted = False
|
||||
|
||||
# 计算转场重叠补偿：每个 clip 需要额外增加的时长
|
||||
# 目标：渲染后视频总时长 = 模板设定的各片段时长之和
|
||||
# 公式：每 clip 增加 (n_segments - 1) * td / n_segments
|
||||
n_segments = len(segments)
|
||||
if n_segments > 1:
|
||||
transition_compensation = (n_segments - 1) * _DEFAULT_TRANSITION_DURATION / n_segments
|
||||
else:
|
||||
transition_compensation = 0.0
|
||||
|
||||
# 打乱 segments 的处理顺序（分配素材的顺序随机化），但最终 clips_data 按原始 order 排序
|
||||
shuffled_indices = list(range(len(segments)))
|
||||
random.shuffle(shuffled_indices)
|
||||
|
||||
for idx in shuffled_indices:
|
||||
_seg_order, dur_min, dur_max = segments[idx]
|
||||
for i, (_seg_order, dur_min, dur_max) in enumerate(segments):
|
||||
# 在 segment 的 duration_min ~ duration_max 之间随机取值（保留一位小数）
|
||||
raw_duration = random.uniform(dur_min, dur_max)
|
||||
# 加上转场补偿，确保最终输出时长 = 模板设定总时长
|
||||
raw_duration += transition_compensation
|
||||
|
||||
# 贪心分配素材：按"已使用次数"升序排列候选素材（使用最少的优先），
|
||||
# 同次数随机打散，避免"A-B-C-D"的固定组合反复出现。
|
||||
# 跳过时长缺失、复用占比已超 10% 阈值的素材；
|
||||
# 轮询分配素材：跳过时长缺失、复用占比已超 15% 阈值的素材；
|
||||
# 选中后计算起点，若该素材可用区间耗尽且复用被闸门拒绝（calc 返回 None），
|
||||
# 继续尝试下一个素材
|
||||
# 继续轮询下一个素材
|
||||
asset_id = ""
|
||||
clip_duration = 0.0
|
||||
start_time: float | None = None
|
||||
# 动态按使用次数排序：优先选使用最少的素材，同次数随机打散
|
||||
asset_use_counts = {aid: len(used_segments.get(aid, [])) for aid in asset_ids}
|
||||
# 排序键：smart_match 评分（注入随机噪声）→ 使用次数 → 纯随机。
|
||||
# 噪声让得分接近的素材排名每次浮动，避免同一批素材反复选出相同组合，
|
||||
# 从素材组合层面降低成片查重率；分差 > SCORE_RANDOM_NOISE_MAX 时排名稳定，
|
||||
# 质量差距显著的素材仍保持优先级。
|
||||
sorted_candidates = sorted(
|
||||
asset_ids,
|
||||
key=lambda aid: (
|
||||
-(asset_smart_scores.get(aid, 0.0) + random.uniform(0.0, SCORE_RANDOM_NOISE_MAX)),
|
||||
asset_use_counts.get(aid, 0),
|
||||
random.random(),
|
||||
),
|
||||
)
|
||||
for candidate in sorted_candidates:
|
||||
n_assets = len(asset_ids)
|
||||
for offset in range(n_assets):
|
||||
candidate = asset_ids[(i + offset) % n_assets]
|
||||
candidate_total = asset_durations.get(candidate, 0.0)
|
||||
if candidate_total <= 0:
|
||||
continue
|
||||
@@ -795,30 +667,16 @@ def create_clips_from_assets_editor(
|
||||
candidate,
|
||||
)
|
||||
continue
|
||||
# 起始时间选取（不调用 MediaKit，保证接口快速返回）：
|
||||
# 1) 素材有场景切换点缓存时，优先从随机镜头段中选起点（不同片段来自不同镜头，
|
||||
# 画面内容本质不同），与 used_segments 做冲突避让（含 1.5s 边缘间隙）
|
||||
# 2) 无缓存 / 镜头段全冲突 → _calc_random_start_time 随机起点兜底；
|
||||
# 100 次避不开历史区间时走受控复用回调（复用片段累加 reused_durations，
|
||||
# 回调内部预判复用后占比超 10% 则拒绝并返回 None）
|
||||
candidate_start = None
|
||||
if candidate in asset_scene_points:
|
||||
candidate_start = pick_scene_aware_start(
|
||||
candidate,
|
||||
candidate_duration,
|
||||
asset_durations,
|
||||
asset_scene_points,
|
||||
used_segments,
|
||||
edge_gap=SEGMENT_EDGE_GAP,
|
||||
)
|
||||
if candidate_start is None:
|
||||
candidate_start = _calc_random_start_time(
|
||||
candidate,
|
||||
candidate_duration,
|
||||
asset_durations,
|
||||
used_segments,
|
||||
on_exhausted=reuse_cb,
|
||||
)
|
||||
# 随机起始时间（不调用 MediaKit，保证接口快速返回）；100 次避不开
|
||||
# 历史区间时走受控复用回调（复用片段累加 reused_durations，回调内部
|
||||
# 预判复用后占比超 15% 则拒绝并返回 None）
|
||||
candidate_start = _calc_random_start_time(
|
||||
candidate,
|
||||
candidate_duration,
|
||||
asset_durations,
|
||||
used_segments,
|
||||
on_exhausted=reuse_cb,
|
||||
)
|
||||
if candidate_start is None:
|
||||
# 该素材可用区间耗尽且复用被闸门/use_count 上限拒绝 → 尝试下一素材
|
||||
logger.info(
|
||||
@@ -833,7 +691,6 @@ def create_clips_from_assets_editor(
|
||||
|
||||
if not asset_id or start_time is None:
|
||||
# 所有素材时长缺失、复用占比超阈值，或区间耗尽且复用被拒 → 素材可切区间不足
|
||||
all_assets_exhausted = True
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail="素材可切区间不足，请补充新素材",
|
||||
@@ -849,7 +706,7 @@ def create_clips_from_assets_editor(
|
||||
|
||||
clips_data.append(
|
||||
{
|
||||
"order": _seg_order,
|
||||
"order": i,
|
||||
"asset_id": asset_id,
|
||||
"start_time": start_time,
|
||||
"duration": clip_duration,
|
||||
@@ -857,9 +714,6 @@ def create_clips_from_assets_editor(
|
||||
}
|
||||
)
|
||||
|
||||
# 按原始 segment order 排序，确保 clips_data 的 order 字段有序（0,1,2,3...）
|
||||
clips_data.sort(key=lambda c: c["order"])
|
||||
|
||||
# 4. 事务性替换：清空旧片段 → 创建新片段 → 标记ready（单事务，失败自动回滚）
|
||||
created_count = plan_svc.replace_all_clips_transactional(plan_id, clips_data)
|
||||
|
||||
@@ -879,31 +733,11 @@ def create_clips_from_assets_editor(
|
||||
unique_asset_ids,
|
||||
)
|
||||
|
||||
# 6. 估算成片内部重复率（本条成片中同一素材的重叠片段时长占比）
|
||||
dup_rate = _calc_plan_internal_duplicate_rate(clips_data)
|
||||
duplicate_warning = None
|
||||
if dup_rate > 50:
|
||||
duplicate_warning = f"查重率 {dup_rate:.1f}% 超过50%，建议更换素材或模板"
|
||||
logger.warning(
|
||||
"from-assets 成片查重率超标: plan_id=%s dup_rate=%.1f%%",
|
||||
plan_id,
|
||||
dup_rate,
|
||||
)
|
||||
|
||||
# 7. 素材耗尽提示
|
||||
exhaustion_warning = None
|
||||
if all_assets_exhausted and created_count < len(segments):
|
||||
exhaustion_warning = (
|
||||
"素材可切区间不足，部分片段使用了复用素材。" "建议：1) 补充更多素材到素材库 2) 使用不同的素材组合生成"
|
||||
)
|
||||
|
||||
# 8. 立即返回响应
|
||||
# 6. 立即返回响应
|
||||
return ClipsFromAssetsResponse(
|
||||
created_count=created_count,
|
||||
plan_id=plan_id,
|
||||
clip_ids=[],
|
||||
duplicate_warning=duplicate_warning,
|
||||
exhaustion_warning=exhaustion_warning,
|
||||
)
|
||||
|
||||
|
||||
@@ -911,15 +745,7 @@ def _update_mediakit_recommendations_async( # pragma: no cover
|
||||
plan_id: str,
|
||||
asset_ids: list[str],
|
||||
) -> None:
|
||||
"""后台任务：使用 SceneChange 智能选帧并更新片段的起始时间.
|
||||
|
||||
优先使用 SceneChange 策略检测视频镜头切换点，将每个素材按镜头段拆分，
|
||||
各片段优先从不同镜头段中选取起始时间，实现「不同片段展示不同场景」的效果。
|
||||
|
||||
降级策略：
|
||||
1. SceneChange 优先 → detect_scene_changes 内部已含 TimeInterval 降级
|
||||
2. 若 detect_scene_changes 仍返回 None → 回退到旧的 analyze_videos 方式
|
||||
3. 所有方式都失败 → 保持现有随机 start_time，不影响视频生成
|
||||
"""后台任务：调用 MediaKit 智能选片并更新片段的起始时间.
|
||||
|
||||
此函数在后台异步执行，不影响接口响应时间。
|
||||
失败时静默处理，不影响已创建的片段。
|
||||
@@ -941,6 +767,12 @@ def _update_mediakit_recommendations_async( # pragma: no cover
|
||||
asset_repo = SQLAlchemyAssetRepository(db)
|
||||
plan_svc = EditPlanService(db)
|
||||
|
||||
# 调用 MediaKit 获取推荐时间
|
||||
recommendations = _get_mediakit_recommendations(asset_ids, asset_repo)
|
||||
if not recommendations:
|
||||
logger.info("后台任务: MediaKit 无推荐结果，跳过更新")
|
||||
return
|
||||
|
||||
# 查询该 plan 的所有片段（分批获取，避免硬编码 limit 截断）
|
||||
batch_size = 500
|
||||
all_clips = []
|
||||
@@ -963,16 +795,15 @@ def _update_mediakit_recommendations_async( # pragma: no cover
|
||||
unique_asset_ids = list({getattr(c, "asset_id", "") or "" for c in clips} - {""})
|
||||
assets_map: dict[str, object] = {a.id: a for a in asset_repo.find_by_ids(unique_asset_ids)}
|
||||
|
||||
# 按 asset_id 预分组片段对象（按 order 排序，保证按模板顺序分配镜头段）
|
||||
clips_by_asset: dict[str, list] = defaultdict(list)
|
||||
# 按 asset_id 预分组片段时间段（消除 O(N^2) 嵌套循环）
|
||||
clips_by_asset: dict[str, list[tuple[str, float, float]]] = defaultdict(list)
|
||||
for clip in clips:
|
||||
aid = getattr(clip, "asset_id", "") or ""
|
||||
if aid:
|
||||
clips_by_asset[aid].append(clip)
|
||||
for aid in clips_by_asset:
|
||||
clips_by_asset[aid].sort(key=lambda c: c.order)
|
||||
if aid and clip.start_time is not None:
|
||||
clips_by_asset[aid].append((clip.id, clip.start_time, clip.start_time + clip.duration))
|
||||
|
||||
# 读取素材全部历史已用区间（跨任务/跨 plan 持久化记录）
|
||||
# 读取素材全部历史已用区间（跨任务/跨 plan 持久化记录）：
|
||||
# MediaKit 挪点必须与随机选片一样避让历史区间，否则会把片段挪回已用过的画面
|
||||
historical_segments = get_used_segments(db, unique_asset_ids)
|
||||
|
||||
# 已更新的片段ID（用于排除已移动的旧时间段）
|
||||
@@ -981,22 +812,16 @@ def _update_mediakit_recommendations_async( # pragma: no cover
|
||||
updated_segments: dict[str, list[tuple[float, float]]] = {}
|
||||
updated_count = 0
|
||||
|
||||
# 尝试获取存储服务（用于生成视频 URL）
|
||||
try:
|
||||
storage = get_storage_service()
|
||||
except Exception:
|
||||
logger.warning("后台任务: 获取存储服务失败，跳过 SceneChange 更新")
|
||||
return
|
||||
|
||||
# 获取 MediaKit 客户端
|
||||
client = get_mediakit_client()
|
||||
|
||||
# 对每个素材，检测场景切换点并分配镜头段
|
||||
for asset_id in unique_asset_ids:
|
||||
asset_clips = clips_by_asset.get(asset_id, [])
|
||||
if not asset_clips:
|
||||
# 遍历片段，按 asset_id 匹配推荐时间
|
||||
for clip in clips:
|
||||
asset_id = getattr(clip, "asset_id", "") or ""
|
||||
if not asset_id or asset_id not in recommendations:
|
||||
continue
|
||||
|
||||
recommended_start = recommendations[asset_id]
|
||||
clip_duration = clip.duration
|
||||
|
||||
# 从预加载字典获取素材（O(1) 查找）
|
||||
asset = assets_map.get(asset_id)
|
||||
if not asset:
|
||||
continue
|
||||
@@ -1004,177 +829,89 @@ def _update_mediakit_recommendations_async( # pragma: no cover
|
||||
if asset_total <= 0:
|
||||
continue
|
||||
|
||||
# 获取素材视频 URL
|
||||
video_url: str | None = None
|
||||
storage_key = getattr(asset, "storage_key", None) or ""
|
||||
mime = getattr(asset, "mime_type", "") or ""
|
||||
if storage_key and mime.startswith("video/"):
|
||||
try:
|
||||
video_url = storage.get_download_url(storage_key)
|
||||
except Exception as e:
|
||||
logger.warning("后台任务: 获取素材URL失败: asset_id=%s error=%s", asset_id, e)
|
||||
|
||||
# 构建该素材的占用区间列表（排除已更新片段）
|
||||
def _get_other_segments(asset_id_inner, clip_id_inner):
|
||||
segs: list[tuple[float, float]] = []
|
||||
for c in clips_by_asset.get(asset_id_inner, []):
|
||||
cid = c.id
|
||||
if cid != clip_id_inner and cid not in updated_clip_ids:
|
||||
segs.append((c.start_time, c.start_time + c.duration))
|
||||
segs.extend(updated_segments.get(asset_id_inner, []))
|
||||
|
||||
# 并入历史已用区间
|
||||
def _norm(segs_in):
|
||||
return {(round(float(a), 3), round(float(b), 3)) for a, b in segs_in}
|
||||
|
||||
return list(_norm(segs) | _norm(historical_segments.get(asset_id_inner, [])))
|
||||
|
||||
# 优先使用 SceneChange 策略
|
||||
scene_segments: list[tuple[float, float]] = []
|
||||
# 先查素材 metadata 中的场景点缓存：命中则直接复用，跳过 MediaKit 检测
|
||||
# （缓存由本任务首次检测后写入，跨任务/跨 plan 复用）
|
||||
cached_points = extract_scene_points_from_metadata(getattr(asset, "metadata", None))
|
||||
if cached_points:
|
||||
scene_segments = build_scene_segments(cached_points, asset_total)
|
||||
# 推荐时间 + 片段时长不能超过素材总时长
|
||||
if recommended_start + clip_duration > asset_total:
|
||||
logger.info(
|
||||
"后台任务: 命中场景点缓存: asset_id=%s scenes=%d",
|
||||
asset_id,
|
||||
len(scene_segments),
|
||||
)
|
||||
|
||||
if not scene_segments and client.is_available and video_url:
|
||||
scene_changes = client.detect_scene_changes(video_url)
|
||||
if scene_changes is not None:
|
||||
scene_segments = build_scene_segments(scene_changes, asset_total)
|
||||
logger.info(
|
||||
"后台任务: 素材场景检测完成: asset_id=%s scenes=%d",
|
||||
asset_id,
|
||||
len(scene_segments),
|
||||
)
|
||||
# 检测结果写入素材 metadata 缓存：首次生成用随机起点，
|
||||
# 检测完成后后续生成的渲染前同步路径即可读缓存选镜头段
|
||||
try:
|
||||
existing_meta = dict(getattr(asset, "metadata", None) or {})
|
||||
existing_meta["scene_change_points"] = scene_changes
|
||||
asset.metadata = existing_meta # type: ignore[attr-defined]
|
||||
asset_repo.update(asset) # type: ignore[arg-type]
|
||||
logger.info(
|
||||
"后台任务: 场景点已写入素材缓存: asset_id=%s points=%d",
|
||||
asset_id,
|
||||
len(scene_changes),
|
||||
)
|
||||
except Exception as cache_err:
|
||||
# 缓存写入失败不影响本次片段更新
|
||||
logger.warning(
|
||||
"后台任务: 场景点缓存写入失败: asset_id=%s error=%s",
|
||||
asset_id,
|
||||
cache_err,
|
||||
)
|
||||
|
||||
# SceneChange 未获得有效结果 → 尝试 analyze_videos 作为 fallback
|
||||
if not scene_segments and video_url:
|
||||
fallback_recs = _get_mediakit_recommendations([asset_id], asset_repo)
|
||||
if fallback_recs and asset_id in fallback_recs:
|
||||
# analyze_videos 只返回单个推荐点，转为单镜头段
|
||||
rec_start = fallback_recs[asset_id]
|
||||
scene_segments = [(rec_start, asset_total)]
|
||||
logger.info(
|
||||
"后台任务: 使用 analyze_videos fallback: asset_id=%s start=%.2f",
|
||||
asset_id,
|
||||
rec_start,
|
||||
)
|
||||
|
||||
if not scene_segments:
|
||||
# 所有方式都失败 → 保持现有随机 start_time
|
||||
logger.info(
|
||||
"后台任务: SceneChange 与 analyze_videos 均无结果，保持随机起点: asset_id=%s",
|
||||
"后台任务: 推荐时间越界，跳过: asset_id=%s recommended=%.2f duration=%.1f total=%.1f",
|
||||
asset_id,
|
||||
recommended_start,
|
||||
clip_duration,
|
||||
asset_total,
|
||||
)
|
||||
continue
|
||||
|
||||
# 为每个片段分配不同的镜头段
|
||||
scene_segments_pool = list(scene_segments) # 可消费的镜头段池
|
||||
for clip in asset_clips:
|
||||
clip_duration = clip.duration
|
||||
recommended_start: float | None = None
|
||||
# 构建排除当前片段及已更新片段后的占用列表（O(M)，M=同素材片段数）
|
||||
other_segments: list[tuple[float, float]] = [
|
||||
(cs, ce)
|
||||
for cid, cs, ce in clips_by_asset.get(asset_id, [])
|
||||
if cid != clip.id and cid not in updated_clip_ids
|
||||
]
|
||||
other_segments.extend(updated_segments.get(asset_id, []))
|
||||
|
||||
# 从镜头段池中依次尝试，选一个不冲突的
|
||||
for seg_idx, (seg_start, seg_end) in enumerate(scene_segments_pool):
|
||||
candidate_start = pick_start_in_scene_segment(seg_start, seg_end, clip_duration)
|
||||
if candidate_start is None:
|
||||
continue # 镜头段太短，跳过
|
||||
# 并入该素材全部历史已用区间（含其他 plan/其他任务），set 去重：
|
||||
# 本 plan 片段创建时已写入历史记录
|
||||
# 并入该素材全部历史已用区间（含其他 plan/其他任务）。
|
||||
# set 去重前先归一化精度（round 3 位），避免浮点尾差导致逻辑相同的
|
||||
# 区间（如 1.0 与 1.0000000001）被误判为不同区间
|
||||
def _norm(segs):
|
||||
return {(round(float(a), 3), round(float(b), 3)) for a, b in segs}
|
||||
|
||||
# 检查越界
|
||||
if candidate_start + clip_duration > asset_total:
|
||||
continue
|
||||
other_segments = list(_norm(other_segments) | _norm(historical_segments.get(asset_id, [])))
|
||||
|
||||
# 检查与已用区间冲突
|
||||
other_segs = _get_other_segments(asset_id, clip.id)
|
||||
if _recommended_time_conflicts(candidate_start, clip_duration, other_segs):
|
||||
continue
|
||||
# 检查推荐时间是否与同 plan 片段或历史已用区间冲突（含 0.3s 边缘间隙）：
|
||||
# 冲突时放弃该推荐、保留原随机起点（不硬挪到已用过的画面）
|
||||
if _recommended_time_conflicts(recommended_start, clip_duration, other_segments):
|
||||
logger.info(
|
||||
"后台任务: 推荐时间与同片/历史区间冲突，保留原起点: asset_id=%s recommended=%.2f",
|
||||
asset_id,
|
||||
recommended_start,
|
||||
)
|
||||
continue
|
||||
|
||||
recommended_start = candidate_start
|
||||
# 消费该镜头段（从池中移除，下一个片段用不同镜头段）
|
||||
scene_segments_pool.pop(seg_idx)
|
||||
break
|
||||
|
||||
if recommended_start is None:
|
||||
# 镜头段用完或都冲突 → 尝试 _calc_random_start_time 兜底
|
||||
used_segs_for_calc: dict[str, list[tuple[float, float]]] = {
|
||||
asset_id: _get_other_segments(asset_id, clip.id)
|
||||
}
|
||||
fallback_start = _calc_random_start_time(
|
||||
asset_id,
|
||||
clip_duration,
|
||||
{asset_id: asset_total},
|
||||
used_segs_for_calc,
|
||||
)
|
||||
if fallback_start is None:
|
||||
continue # 完全无法分配，保持原起点
|
||||
recommended_start = fallback_start
|
||||
|
||||
# 更新片段起始时间
|
||||
# 逐个更新并捕获异常（单点失败不影响其他片段）
|
||||
try:
|
||||
old_start = clip.start_time
|
||||
old_end = old_start + clip_duration
|
||||
# MediaKit 移动片段起点 + 同步素材 metadata 区间记录放在同一事务：
|
||||
# 删旧区间记录（按 plan_id + 旧 start 匹配，兼容无 plan_id 的旧数据）、
|
||||
# 写新区间，最后统一 commit；任一步失败整体 rollback，
|
||||
# 保证 clip.start_time 与 metadata.used_time_ranges 不出现不一致。
|
||||
plan_svc.update_clip(clip.id, start_time=recommended_start)
|
||||
try:
|
||||
old_start = clip.start_time
|
||||
old_end = old_start + clip_duration
|
||||
|
||||
plan_svc.update_clip(clip.id, start_time=recommended_start)
|
||||
try:
|
||||
if remove_used_segment(db, asset_id, old_start, old_end, plan_id=plan_id):
|
||||
record_used_segments(
|
||||
db,
|
||||
asset_id,
|
||||
recommended_start,
|
||||
recommended_start + clip_duration,
|
||||
plan_id,
|
||||
)
|
||||
except Exception as me:
|
||||
logger.warning(
|
||||
"后台任务: 同步素材区间记录失败，回滚本次片段更新: clip_id=%s error=%s",
|
||||
clip.id,
|
||||
me,
|
||||
if remove_used_segment(db, asset_id, old_start, old_end, plan_id=plan_id):
|
||||
record_used_segments(
|
||||
db,
|
||||
asset_id,
|
||||
recommended_start,
|
||||
recommended_start + clip_duration,
|
||||
plan_id,
|
||||
)
|
||||
db.rollback()
|
||||
continue
|
||||
db.commit()
|
||||
updated_count += 1
|
||||
updated_clip_ids.add(clip.id)
|
||||
updated_segments.setdefault(asset_id, []).append(
|
||||
(recommended_start, recommended_start + clip_duration)
|
||||
)
|
||||
logger.info(
|
||||
"后台任务: 更新片段起始时间(场景选帧): clip_id=%s asset_id=%s start_time=%.2f",
|
||||
except Exception as me:
|
||||
logger.warning(
|
||||
"后台任务: 同步素材区间记录失败，回滚本次片段更新: clip_id=%s error=%s",
|
||||
clip.id,
|
||||
asset_id,
|
||||
recommended_start,
|
||||
me,
|
||||
)
|
||||
except Exception as ue:
|
||||
logger.warning("后台任务: 单个片段更新失败: clip_id=%s error=%s", clip.id, ue)
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
db.rollback()
|
||||
continue
|
||||
db.commit()
|
||||
updated_count += 1
|
||||
updated_clip_ids.add(clip.id)
|
||||
except Exception as ue:
|
||||
logger.warning("后台任务: 单个片段更新失败: clip_id=%s error=%s", clip.id, ue)
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
continue
|
||||
|
||||
updated_segments.setdefault(asset_id, []).append((recommended_start, recommended_start + clip_duration))
|
||||
logger.info(
|
||||
"后台任务: 更新片段起始时间: clip_id=%s asset_id=%s start_time=%.2f",
|
||||
clip.id,
|
||||
asset_id,
|
||||
recommended_start,
|
||||
)
|
||||
|
||||
logger.info("后台任务完成: plan_id=%s 成功更新 %d 个片段", plan_id, updated_count)
|
||||
|
||||
|
||||
@@ -41,17 +41,17 @@ def list_editor_transition_presets(
|
||||
_: AuthenticatedUser = Depends(get_current_user),
|
||||
) -> TransitionPresetListResponse:
|
||||
"""获取转场预设列表"""
|
||||
from packages.domain.transition_presets import TRANSITION_PRESET_LIBRARY
|
||||
from packages.domain.transition_presets import TRANSITION_PRESETS
|
||||
|
||||
items = [
|
||||
{
|
||||
"id": p.id,
|
||||
"name": p.name,
|
||||
"category": p.category,
|
||||
"duration": p.default_duration,
|
||||
"description": p.description,
|
||||
"id": p["id"],
|
||||
"name": p["name"],
|
||||
"category": p.get("category", "通用"),
|
||||
"duration": p.get("default_duration", 0.5),
|
||||
"description": p.get("description", ""),
|
||||
}
|
||||
for p in TRANSITION_PRESET_LIBRARY
|
||||
for p in TRANSITION_PRESETS
|
||||
]
|
||||
return TransitionPresetListResponse(items=items, total=len(items))
|
||||
|
||||
@@ -123,17 +123,17 @@ def list_editor_filter_presets(
|
||||
_: AuthenticatedUser = Depends(get_current_user),
|
||||
) -> FilterPresetListResponse:
|
||||
"""获取滤镜预设列表"""
|
||||
from packages.domain.filter_presets import FILTER_PRESET_LIBRARY
|
||||
from packages.domain.filter_presets import FILTER_PRESETS
|
||||
|
||||
items = [
|
||||
{
|
||||
"id": p.id,
|
||||
"name": p.name,
|
||||
"category": p.category,
|
||||
"thumbnail": p.lut_url,
|
||||
"description": p.description,
|
||||
"id": p["id"],
|
||||
"name": p["name"],
|
||||
"category": p.get("category", "通用"),
|
||||
"thumbnail": p.get("thumbnail", ""),
|
||||
"description": p.get("description", ""),
|
||||
}
|
||||
for p in FILTER_PRESET_LIBRARY
|
||||
for p in FILTER_PRESETS
|
||||
]
|
||||
return FilterPresetListResponse(items=items, total=len(items))
|
||||
|
||||
|
||||
@@ -189,8 +189,6 @@ class ClipsFromAssetsResponse(BaseModel):
|
||||
plan_id: str = ""
|
||||
message: str = ""
|
||||
clip_ids: List[str] = Field(default_factory=list, description="创建的片段ID列表")
|
||||
duplicate_warning: Optional[str] = Field(default=None, description="查重率超标警告")
|
||||
exhaustion_warning: Optional[str] = Field(default=None, description="素材耗尽警告")
|
||||
|
||||
|
||||
# ── 封面配置 ────────────────────────────────────────────────────────────────
|
||||
|
||||
@@ -2,9 +2,7 @@
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import subprocess
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
from typing import Any, Optional
|
||||
@@ -432,8 +430,6 @@ def save_tts_job_to_library(
|
||||
storage_key = f"uploads/voice/tts/{job.id}.{audio_format}"
|
||||
|
||||
tmp_path: Path | None = None
|
||||
audio_duration: float | None = None
|
||||
file_size = 0
|
||||
try:
|
||||
with tempfile.NamedTemporaryFile(suffix=f".{audio_format}", delete=False) as tmp:
|
||||
tmp_path = Path(tmp.name)
|
||||
@@ -449,23 +445,6 @@ def save_tts_job_to_library(
|
||||
)
|
||||
file_size = tmp_path.stat().st_size
|
||||
storage_service.upload_file(tmp_path, storage_key, content_type=content_type)
|
||||
|
||||
# 从音频文件提取时长（ffprobe），作为 job.duration 的兜底
|
||||
try:
|
||||
proc = subprocess.run(
|
||||
[
|
||||
"ffprobe", "-v", "quiet", "-print_format", "json",
|
||||
"-show_format", str(tmp_path),
|
||||
],
|
||||
capture_output=True, text=True, timeout=10,
|
||||
)
|
||||
if proc.returncode == 0:
|
||||
fmt = json.loads(proc.stdout).get("format", {})
|
||||
dur = float(fmt.get("duration", 0))
|
||||
if dur > 0:
|
||||
audio_duration = dur
|
||||
except Exception:
|
||||
logger.warning("ffprobe 提取时长失败: job_id=%s", job.id, exc_info=True)
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
@@ -503,7 +482,7 @@ def save_tts_job_to_library(
|
||||
mime_type=content_type,
|
||||
metadata=metadata_,
|
||||
file_size=file_size,
|
||||
duration=job.duration or audio_duration or None,
|
||||
duration=job.duration or None,
|
||||
status=AssetStatus.READY,
|
||||
classification_status=ClassificationStatus.PENDING, # 音频不参与内容分类，保持 pending 与 ingest 链路一致
|
||||
uploaded_by_user_id=user_id,
|
||||
|
||||
@@ -23,7 +23,6 @@ from app.schemas.upload import (
|
||||
from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile, status
|
||||
|
||||
from packages.application import SubmitIngestJobCommand, SubmitIngestJobUseCase
|
||||
from packages.domain import Asset, AssetStatus
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -81,40 +80,6 @@ def _validate_mime_type(content_type: str | None) -> str:
|
||||
return base_type
|
||||
|
||||
|
||||
def _infer_mime_type_from_storage_key(storage_key: str) -> str:
|
||||
"""从 storage_key 推断 MIME 类型（与 worker 端保持一致）。"""
|
||||
lower_filename = storage_key.rsplit("/", 1)[-1].lower()
|
||||
_MIME_MAP = {
|
||||
".mov": "video/quicktime", ".mp4": "video/mp4", ".avi": "video/x-msvideo",
|
||||
".mkv": "video/x-matroska", ".webm": "video/webm",
|
||||
".png": "image/png", ".gif": "image/gif", ".bmp": "image/bmp",
|
||||
".svg": "image/svg+xml", ".jpg": "image/jpeg", ".jpeg": "image/jpeg",
|
||||
".mp3": "audio/mpeg", ".wav": "audio/wav", ".ogg": "audio/ogg",
|
||||
".flac": "audio/flac", ".m4a": "audio/x-m4a",
|
||||
}
|
||||
for ext, mime in _MIME_MAP.items():
|
||||
if lower_filename.endswith(ext):
|
||||
return mime
|
||||
return "video/mp4" # default
|
||||
|
||||
|
||||
def _create_pending_asset(
|
||||
asset_repository, project_id, library_id, storage_key, filename, mime_type, user_id, file_hash=""
|
||||
):
|
||||
"""立即创建一条 PROCESSING 状态的 Asset 记录，使前端能马上看到新素材。"""
|
||||
asset = Asset.create(
|
||||
project_id=project_id,
|
||||
library_id=library_id,
|
||||
name=filename,
|
||||
storage_key=storage_key,
|
||||
mime_type=mime_type,
|
||||
status=AssetStatus.PROCESSING,
|
||||
uploaded_by_user_id=user_id,
|
||||
file_hash=file_hash,
|
||||
)
|
||||
return asset_repository.create(asset)
|
||||
|
||||
|
||||
def _submit_ingest_job(
|
||||
project_id: str,
|
||||
library_id: str,
|
||||
@@ -244,20 +209,6 @@ async def complete_direct_upload(
|
||||
url=storage_service.get_url(normalized_key),
|
||||
)
|
||||
|
||||
# 立即创建 Asset 记录（PROCESSING 状态），使前端刷新后即可看到新素材
|
||||
filename = normalized_key.rsplit("/", 1)[-1]
|
||||
mime_type = _infer_mime_type_from_storage_key(normalized_key)
|
||||
pending_asset = _create_pending_asset(
|
||||
asset_repository=asset_repository,
|
||||
project_id=request.project_id,
|
||||
library_id=request.library_id,
|
||||
storage_key=normalized_key,
|
||||
filename=filename,
|
||||
mime_type=mime_type,
|
||||
user_id=authenticated_user.user.id,
|
||||
file_hash=request.file_hash,
|
||||
)
|
||||
|
||||
job = _submit_ingest_job(
|
||||
project_id=request.project_id,
|
||||
library_id=request.library_id,
|
||||
@@ -265,12 +216,7 @@ async def complete_direct_upload(
|
||||
ingest_job_repository=ingest_job_repository,
|
||||
file_hash=request.file_hash,
|
||||
)
|
||||
return DirectUploadCompleteResponse(
|
||||
storage_key=normalized_key,
|
||||
ingest_job_id=job.id,
|
||||
asset_id=pending_asset.id,
|
||||
url=storage_service.get_url(normalized_key),
|
||||
)
|
||||
return DirectUploadCompleteResponse(storage_key=normalized_key, ingest_job_id=job.id, url=storage_service.get_url(normalized_key))
|
||||
|
||||
|
||||
@router.post(
|
||||
@@ -338,18 +284,6 @@ async def upload_asset(
|
||||
detail=f"Failed to upload file: {type(error).__name__}",
|
||||
) from error
|
||||
|
||||
# 立即创建 Asset 记录（PROCESSING 状态），使前端刷新后即可看到新素材
|
||||
pending_asset = _create_pending_asset(
|
||||
asset_repository=asset_repository,
|
||||
project_id=project_id,
|
||||
library_id=library_id,
|
||||
storage_key=storage_key,
|
||||
filename=safe_filename,
|
||||
mime_type=validated_content_type,
|
||||
user_id=authenticated_user.user.id,
|
||||
file_hash=file_hash,
|
||||
)
|
||||
|
||||
job = _submit_ingest_job(
|
||||
project_id=project_id,
|
||||
library_id=library_id,
|
||||
@@ -361,6 +295,5 @@ async def upload_asset(
|
||||
return UploadAssetResponse(
|
||||
storage_key=storage_key,
|
||||
ingest_job_id=job.id,
|
||||
asset_id=pending_asset.id,
|
||||
url=file_url,
|
||||
)
|
||||
|
||||
@@ -6,26 +6,12 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import shutil
|
||||
import subprocess
|
||||
import tempfile
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Literal, Optional
|
||||
from uuid import uuid4
|
||||
|
||||
from app.api.routes._helpers import get_user_plan
|
||||
from app.auth import AuthenticatedUser, get_current_user
|
||||
from app.core.storage import get_storage_service
|
||||
from app.dependencies import (
|
||||
get_asset_library_repository,
|
||||
get_asset_repository,
|
||||
get_audio_url_signer,
|
||||
get_cosyvoice_service,
|
||||
get_db_session,
|
||||
get_project_repository,
|
||||
get_user_repository,
|
||||
)
|
||||
from app.dependencies import get_audio_url_signer, get_cosyvoice_service, get_db_session, get_user_repository
|
||||
from app.schemas.voice import (
|
||||
PresetVoiceItemResponse,
|
||||
PresetVoiceListResponse,
|
||||
@@ -38,7 +24,7 @@ from app.schemas.voice_library import (
|
||||
UpdateVoiceLibraryRequest,
|
||||
VoiceLibraryItemResponse,
|
||||
)
|
||||
from fastapi import APIRouter, Depends, File, Form, HTTPException, Query, Response, UploadFile, status
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query, Response, status
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.voice_clone_profile_repository import SQLAlchemyVoiceCloneProfileRepository
|
||||
@@ -54,12 +40,8 @@ from packages.application.voice_library.use_cases import (
|
||||
QuotaExceededError,
|
||||
UpdateVoiceLibraryUseCase,
|
||||
)
|
||||
from packages.domain import Asset, AssetStatus
|
||||
from packages.domain.classification import AssetLibraryKind, ClassificationStatus
|
||||
from packages.domain.entities import AssetLibrary
|
||||
from packages.domain.preset_voices import PRESET_VOICES, get_preset_voice_by_id
|
||||
from packages.ports.user_repository import UserRepository
|
||||
from packages.shared.storage import SharedStorageService
|
||||
|
||||
router = APIRouter()
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -525,243 +507,3 @@ def delete_voice(
|
||||
if not deleted:
|
||||
raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Voice not found")
|
||||
return
|
||||
|
||||
|
||||
# ── 提取视频配音 ─────────────────────────────────────────────────────
|
||||
|
||||
# 支持的视频格式
|
||||
EXTRACT_VIDEO_MIMES = frozenset({"video/mp4", "video/quicktime", "video/webm", "video/x-msvideo"})
|
||||
MAX_EXTRACT_SIZE = 500 * 1024 * 1024 # 500MB
|
||||
|
||||
|
||||
@router.post(
|
||||
"/extract-voice",
|
||||
status_code=status.HTTP_201_CREATED,
|
||||
)
|
||||
def extract_voice_from_video(
|
||||
file: UploadFile = File(...),
|
||||
project_id: str = Form(...),
|
||||
authenticated_user: AuthenticatedUser = Depends(get_current_user),
|
||||
project_repository=Depends(get_project_repository),
|
||||
asset_library_repository=Depends(get_asset_library_repository),
|
||||
asset_repository=Depends(get_asset_repository),
|
||||
storage_service: SharedStorageService = Depends(get_storage_service),
|
||||
sign_url=Depends(get_audio_url_signer),
|
||||
):
|
||||
"""从上传的视频中提取人声配音。
|
||||
|
||||
流程：
|
||||
1. 接收视频文件（mp4/mov/webm）
|
||||
2. ffmpeg 提取音频 + 降噪 + 编码为 mp3
|
||||
3. 上传到 OSS，创建 Asset 记录到配音素材库
|
||||
4. 返回素材信息（时长、文件大小、URL）
|
||||
"""
|
||||
user_id = authenticated_user.user.id
|
||||
|
||||
# 校验文件类型
|
||||
content_type = file.content_type or ""
|
||||
if content_type and content_type not in EXTRACT_VIDEO_MIMES:
|
||||
# 兜底：按扩展名判断
|
||||
ext = (file.filename or "").rsplit(".", 1)[-1].lower()
|
||||
ext_to_mime = {"mp4": "video/mp4", "mov": "video/quicktime", "webm": "video/webm", "avi": "video/x-msvideo"}
|
||||
if ext not in ext_to_mime:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail="仅支持 mp4/mov/webm/avi 格式的视频文件",
|
||||
)
|
||||
content_type = ext_to_mime[ext]
|
||||
|
||||
# 找到（或自动创建）用户 voice 素材库（复用 TTS 的逻辑）
|
||||
library = _find_or_create_voice_library_for_extract(
|
||||
user_id=user_id,
|
||||
project_repository=project_repository,
|
||||
asset_library_repository=asset_library_repository,
|
||||
)
|
||||
|
||||
tmp_dir = None
|
||||
try:
|
||||
tmp_dir = Path(tempfile.mkdtemp(prefix="voice_extract_"))
|
||||
video_path = tmp_dir / f"input_{uuid4().hex[:8]}_{file.filename or 'video.mp4'}"
|
||||
audio_path = tmp_dir / f"output_{uuid4().hex[:8]}.mp3"
|
||||
|
||||
# 保存上传的视频到临时文件
|
||||
with open(video_path, "wb") as f:
|
||||
total = 0
|
||||
while chunk := file.file.read(1024 * 1024): # 1MB chunks
|
||||
total += len(chunk)
|
||||
if total > MAX_EXTRACT_SIZE:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
|
||||
detail="视频文件过大，最大支持 500MB",
|
||||
)
|
||||
f.write(chunk)
|
||||
|
||||
if video_path.stat().st_size == 0:
|
||||
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="视频文件为空")
|
||||
|
||||
# ffmpeg: 提取音频 + 降噪 + 编码 mp3
|
||||
# 滤镜链：highpass(去低频噪声) → afftdn(FFT降噪) → lowpass(去高频噪声)
|
||||
ffmpeg_cmd = [
|
||||
"ffmpeg",
|
||||
"-y",
|
||||
"-i",
|
||||
str(video_path),
|
||||
"-vn", # 不要视频
|
||||
"-af",
|
||||
"highpass=f=80,afftdn=nf=-25:tn=1,lowpass=f=8000",
|
||||
"-acodec",
|
||||
"libmp3lame",
|
||||
"-ab",
|
||||
"192k",
|
||||
"-ar",
|
||||
"44100",
|
||||
"-ac",
|
||||
"1", # 单声道（人声足够）
|
||||
str(audio_path),
|
||||
]
|
||||
|
||||
result = subprocess.run(
|
||||
ffmpeg_cmd,
|
||||
capture_output=True,
|
||||
timeout=300, # 5 分钟超时
|
||||
)
|
||||
|
||||
if result.returncode != 0:
|
||||
stderr_text = result.stderr.decode("utf-8", errors="replace")[-500:]
|
||||
logger.error("ffmpeg 提取配音失败: %s", stderr_text)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
|
||||
detail="视频音频提取失败，可能该视频没有音轨或格式不支持",
|
||||
)
|
||||
|
||||
if not audio_path.exists() or audio_path.stat().st_size == 0:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
|
||||
detail="音频提取结果为空",
|
||||
)
|
||||
|
||||
# 获取音频时长
|
||||
duration = _get_audio_duration(audio_path)
|
||||
file_size = audio_path.stat().st_size
|
||||
|
||||
# 上传到 OSS
|
||||
audio_ext = "mp3"
|
||||
storage_key = f"uploads/voice/extracted/{uuid4().hex}.{audio_ext}"
|
||||
storage_service.upload_file(audio_path, storage_key, content_type="audio/mpeg")
|
||||
|
||||
# 创建 Asset 记录
|
||||
original_name = (file.filename or "video").rsplit(".", 1)[0]
|
||||
asset_name = f"{original_name}-配音"
|
||||
|
||||
asset = Asset.create(
|
||||
project_id=library.project_id,
|
||||
library_id=library.id,
|
||||
name=asset_name,
|
||||
storage_key=storage_key,
|
||||
mime_type="audio/mpeg",
|
||||
metadata={
|
||||
"source": "video_extract",
|
||||
"original_video": file.filename or "unknown",
|
||||
},
|
||||
file_size=file_size,
|
||||
duration=duration,
|
||||
status=AssetStatus.READY,
|
||||
classification_status=ClassificationStatus.PENDING,
|
||||
uploaded_by_user_id=user_id,
|
||||
)
|
||||
asset = asset_repository.create(asset)
|
||||
|
||||
return {
|
||||
"id": asset.id,
|
||||
"name": asset.name,
|
||||
"audio_url": sign_url(storage_key),
|
||||
"duration": duration,
|
||||
"file_size": file_size,
|
||||
"status": "completed",
|
||||
"source": "video_extract",
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except subprocess.TimeoutExpired:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_504_GATEWAY_TIMEOUT,
|
||||
detail="视频处理超时，请尝试较短的视频",
|
||||
) from None
|
||||
except Exception as e:
|
||||
logger.exception("提取视频配音失败: %s", e)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
detail="提取配音失败，请稍后重试",
|
||||
) from e
|
||||
finally:
|
||||
# 清理临时文件
|
||||
if tmp_dir and Path(tmp_dir).exists():
|
||||
shutil.rmtree(tmp_dir, ignore_errors=True)
|
||||
|
||||
|
||||
def _find_or_create_voice_library_for_extract(*, user_id, project_repository, asset_library_repository):
|
||||
"""为用户找到或创建 voice 素材库（与 TTS 保存逻辑一致）。"""
|
||||
projects = project_repository.find_accessible_projects(user_id)
|
||||
if not projects:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail="没有可用的项目，请先创建项目",
|
||||
)
|
||||
|
||||
for project in projects:
|
||||
for lib in asset_library_repository.find_by_project(project.id):
|
||||
kind = lib.kind.value if hasattr(lib.kind, "value") else lib.kind
|
||||
if kind == AssetLibraryKind.VOICE.value:
|
||||
return lib
|
||||
|
||||
# 自动创建
|
||||
from sqlalchemy.exc import IntegrityError
|
||||
|
||||
project = projects[0]
|
||||
library = AssetLibrary.create(
|
||||
project_id=project.id,
|
||||
name="配音素材库",
|
||||
kind=AssetLibraryKind.VOICE,
|
||||
)
|
||||
try:
|
||||
return asset_library_repository.create(library)
|
||||
except IntegrityError:
|
||||
session = getattr(asset_library_repository, "session", None)
|
||||
if session is not None:
|
||||
try:
|
||||
session.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
for lib in asset_library_repository.find_by_project(project.id):
|
||||
kind = lib.kind.value if hasattr(lib.kind, "value") else lib.kind
|
||||
if kind == AssetLibraryKind.VOICE.value:
|
||||
return lib
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
detail="配音素材库创建失败",
|
||||
) from None
|
||||
|
||||
|
||||
def _get_audio_duration(audio_path: Path) -> float:
|
||||
"""用 ffprobe 获取音频时长（秒）。"""
|
||||
try:
|
||||
result = subprocess.run(
|
||||
[
|
||||
"ffprobe",
|
||||
"-v",
|
||||
"quiet",
|
||||
"-show_entries",
|
||||
"format=duration",
|
||||
"-of",
|
||||
"csv=p=0",
|
||||
str(audio_path),
|
||||
],
|
||||
capture_output=True,
|
||||
timeout=10,
|
||||
)
|
||||
if result.returncode == 0 and result.stdout.strip():
|
||||
return float(result.stdout.strip())
|
||||
except (ValueError, subprocess.TimeoutExpired):
|
||||
pass
|
||||
return 0.0
|
||||
|
||||
@@ -3,7 +3,7 @@
|
||||
在素材 metadata（assets.classification_result JSON）中持久化已使用的片段时间区间，
|
||||
供 from-assets 创建片段时避开历史区间，实现跨任务/跨调用的片段去重；
|
||||
素材可用区间耗尽后进入受控复用：允许有限次数（MAX_RANGE_USE_COUNT）复用最久未用
|
||||
的历史区间，配合调用方的成片复用占比控制（MAX_REUSE_RATIO = 10%），把任意两条
|
||||
的历史区间，配合调用方的成片复用占比控制（MAX_REUSE_RATIO = 15%），把任意两条
|
||||
成片的画面重复率控制在阈值内。
|
||||
|
||||
metadata 中的记录字段 ``used_time_ranges``::
|
||||
@@ -40,14 +40,14 @@ logger = logging.getLogger(__name__)
|
||||
USED_RANGES_KEY = "used_time_ranges"
|
||||
|
||||
# ── 受控复用配置常量 ─────────────────────────────────────────────────────────
|
||||
MAX_RANGE_USE_COUNT = 2
|
||||
MAX_RANGE_USE_COUNT = 3
|
||||
"""单条历史区间最多被使用次数（含首次），达到后不再参与复用。"""
|
||||
|
||||
REUSE_RATIO_LIMIT = 0.10
|
||||
"""单条成片中，单个素材的复用片段累计时长 / 该素材在成片中的总时长上限（10%）。
|
||||
REUSE_RATIO_LIMIT = 0.15
|
||||
"""单条成片中，单个素材的复用片段累计时长 / 该素材在成片中的总时长上限（15%）。
|
||||
超过则该素材不再分配新片段（调用方在轮询分配时跳过）。"""
|
||||
|
||||
SEGMENT_EDGE_GAP = 1.5
|
||||
SEGMENT_EDGE_GAP = 0.3
|
||||
"""冲突判定边缘间隙（秒）：历史区间按 [start-gap, end+gap] 扩边后参与冲突检测，
|
||||
避免两条片段首尾紧贴导致画面观感重复；记录仍存实际值。"""
|
||||
|
||||
@@ -397,12 +397,12 @@ def make_reuse_callback(
|
||||
db: SQLAlchemy session
|
||||
asset_durations: 素材 ID -> 总时长（回调需要素材总时长做边界约束）
|
||||
reused_tracker: 可选的 ``{asset_id: 累计复用时长}``，回调成功返回复用区间时
|
||||
会把本次片段时长累加进去，供调用方统计成片复用占比（10% 阈值）。
|
||||
会把本次片段时长累加进去，供调用方统计成片复用占比（15% 阈值）。
|
||||
assigned_tracker: 可选的 ``{asset_id: 已分配片段总时长}``，配合 ratio_limit
|
||||
在复用前预判：若复用本片段后占比 (reused + clip_duration) /
|
||||
(assigned + clip_duration) 超过 ratio_limit，则拒绝复用、返回 None
|
||||
（保证成片复用占比不超阈值）。
|
||||
ratio_limit: 单条成片复用时长占比上限，默认 10%。
|
||||
ratio_limit: 单条成片复用时长占比上限，默认 15%。
|
||||
|
||||
Returns:
|
||||
回调函数 ``(asset_id, clip_duration) -> (start, end) | None``。
|
||||
|
||||
@@ -13,7 +13,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import random
|
||||
from typing import Any, List
|
||||
|
||||
from sqlalchemy.orm import Session
|
||||
@@ -30,11 +29,9 @@ from packages.domain.editing_mode import EditingMode
|
||||
from packages.domain.plan_generator_utils import (
|
||||
create_clips_from_configs,
|
||||
distribute_assets,
|
||||
extract_scene_points_from_metadata,
|
||||
generate_default_clips,
|
||||
map_clip_types_for_mode,
|
||||
)
|
||||
from packages.domain.smart_match import SCORE_RANDOM_NOISE_MAX, score_asset
|
||||
from packages.domain.template_clip_config import TemplateClipConfig
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -131,7 +128,6 @@ class PlanGeneratorService:
|
||||
editing_mode,
|
||||
random_selection=random_preview,
|
||||
asset_durations=asset_durations,
|
||||
user_id=created_by_user_id,
|
||||
)
|
||||
|
||||
# 5. 持久化所有 clips 并计算总时长
|
||||
@@ -219,82 +215,19 @@ class PlanGeneratorService:
|
||||
*,
|
||||
random_selection: bool = False,
|
||||
asset_durations: dict[str, float] | None = None,
|
||||
user_id: str = "",
|
||||
) -> None:
|
||||
"""按 editing_mode 将素材分配到 clips（就地修改，未持久化）.
|
||||
|
||||
先用 smart_match 评分对素材排序（高分优先），再委托给
|
||||
plan_generator_utils.distribute_assets 纯函数完成分配。
|
||||
委托给 plan_generator_utils.distribute_assets 纯函数。
|
||||
"""
|
||||
# 预览随机模式：素材顺序已 shuffle，纯随机起点即可，不读 DB 评分/缓存
|
||||
asset_scene_points: dict[str, list[float]] = {}
|
||||
if not random_selection:
|
||||
# 正式生成：smart_match 评分排序（高分优先）+ 场景切换点缓存
|
||||
if self._asset_repo:
|
||||
asset_ids = self._sort_assets_by_smart_score(asset_ids)
|
||||
# 读取素材 metadata 中的场景切换点缓存（后台 SceneChange 检测写入）：
|
||||
# 有缓存的素材片段起点从随机镜头段选取，无缓存走随机起点兜底
|
||||
asset_scene_points = self._fetch_asset_scene_points(asset_ids)
|
||||
|
||||
# 正式生成也随机重排片段顺序（降重，默认开启无开关）
|
||||
# smart_match 决定选哪些素材，shuffle 只改变分配到 clips 的顺序
|
||||
asset_ids = list(asset_ids) # 复制避免修改调用方原列表
|
||||
random.shuffle(asset_ids)
|
||||
|
||||
# 查询已有视频的已用区间（跨视频避让）
|
||||
external_used_segments = None
|
||||
if user_id and self._clip_repo:
|
||||
try:
|
||||
external_used_segments = self._clip_repo.list_used_segments_by_user(user_id, limit_recent=50)
|
||||
except Exception:
|
||||
logger.warning("跨视频避让查询失败，回退到纯随机", exc_info=True)
|
||||
|
||||
distribute_assets(
|
||||
clips,
|
||||
asset_ids,
|
||||
editing_mode,
|
||||
random_selection=random_selection,
|
||||
asset_durations=asset_durations,
|
||||
asset_scene_points=asset_scene_points,
|
||||
external_used_segments=external_used_segments,
|
||||
)
|
||||
|
||||
def _fetch_asset_scene_points(self, asset_ids: List[str]) -> dict[str, list[float]]:
|
||||
"""从素材 metadata 读取场景切换点缓存（无缓存的素材不包含在结果中）。"""
|
||||
points_map: dict[str, list[float]] = {}
|
||||
if not self._asset_repo:
|
||||
return points_map
|
||||
for asset_id in asset_ids:
|
||||
asset = self._asset_repo.get(asset_id)
|
||||
if asset:
|
||||
points = extract_scene_points_from_metadata(getattr(asset, "metadata", None))
|
||||
if points:
|
||||
points_map[asset_id] = points
|
||||
return points_map
|
||||
|
||||
def _sort_assets_by_smart_score(self, asset_ids: List[str]) -> List[str]:
|
||||
"""按 smart_match 综合评分降序排列素材 ID（注入随机噪声）。
|
||||
|
||||
评分高的素材（质量好、时长合适、新鲜、使用次数少）倾向排在前面；
|
||||
排序时给每个素材的得分注入 0~SCORE_RANDOM_NOISE_MAX 的随机噪声，
|
||||
使得分接近的素材排名每次浮动，避免一键生成反复选出相同素材组合，
|
||||
从素材组合层面降低成片查重率。分差大于噪声上限时排名保持稳定。
|
||||
"""
|
||||
scored: list[tuple[str, float]] = []
|
||||
for asset_id in asset_ids:
|
||||
asset = self._asset_repo.get(asset_id)
|
||||
if asset:
|
||||
score, _ = score_asset(asset)
|
||||
scored.append((asset_id, score))
|
||||
else:
|
||||
scored.append((asset_id, 0.0))
|
||||
# 评分 + 随机噪声后按降序排列
|
||||
scored.sort(
|
||||
key=lambda x: x[1] + random.uniform(0.0, SCORE_RANDOM_NOISE_MAX),
|
||||
reverse=True,
|
||||
)
|
||||
return [aid for aid, _ in scored]
|
||||
|
||||
def _fetch_asset_durations(self, asset_ids: List[str]) -> dict[str, float]:
|
||||
"""从数据库获取素材时长信息.
|
||||
|
||||
|
||||
@@ -1,174 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""存量指纹重建脚本 — 为已有视频生成 video_fingerprint_chunks 分片数据。
|
||||
|
||||
功能：
|
||||
- 查询 generated_videos 中 video_fingerprint IS NOT NULL 但尚无分片数据的视频
|
||||
- 从 OSS 下载视频 → 用新的分片算法重新计算指纹 → 写入分片表
|
||||
- 支持 --dry-run（只打印不写入）和 --batch-size（默认 50）
|
||||
- 幂等：已存在分片数据的视频跳过
|
||||
|
||||
用法：
|
||||
# 预览（不写入）
|
||||
python rebuild_fingerprint_chunks.py --dry-run
|
||||
|
||||
# 执行重建
|
||||
python rebuild_fingerprint_chunks.py --batch-size 50
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import tempfile
|
||||
|
||||
# 确保可以 import worker_app 和 packages
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "..", "worker"))
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", ".."))
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
|
||||
)
|
||||
logger = logging.getLogger("rebuild_fingerprint_chunks")
|
||||
|
||||
|
||||
def find_videos_needing_rebuild(session, batch_size: int) -> list[dict]:
|
||||
"""查询需要重建分片指纹的视频。"""
|
||||
from sqlalchemy import and_
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.models import GeneratedVideoModel, VideoFingerprintChunkModel
|
||||
|
||||
# 有 video_fingerprint 的视频
|
||||
has_fingerprint = GeneratedVideoModel.video_fingerprint.isnot(None)
|
||||
has_fingerprint = and_(has_fingerprint, GeneratedVideoModel.video_fingerprint != "")
|
||||
|
||||
# 排除已有分片数据的视频
|
||||
subq = session.query(VideoFingerprintChunkModel.video_id).distinct().subquery()
|
||||
no_chunks = ~GeneratedVideoModel.id.in_(subq)
|
||||
|
||||
videos = (
|
||||
session.query(GeneratedVideoModel)
|
||||
.filter(and_(has_fingerprint, no_chunks))
|
||||
.order_by(GeneratedVideoModel.generated_at.desc())
|
||||
.limit(batch_size)
|
||||
.all()
|
||||
)
|
||||
|
||||
return [
|
||||
{
|
||||
"id": v.id,
|
||||
"project_id": v.project_id,
|
||||
"user_id": v.user_id or "",
|
||||
"duration": v.duration,
|
||||
}
|
||||
for v in videos
|
||||
]
|
||||
|
||||
|
||||
def rebuild_one(video_info: dict, dry_run: bool = False) -> int:
|
||||
"""重建单个视频的分片数据。返回写入的 chunk 数量。"""
|
||||
from video_processing.dedup import VideoDeduplicator, _save_fingerprint_chunks
|
||||
from worker_app.db import SessionLocal
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.models import VideoFingerprintChunkModel
|
||||
from packages.shared.storage import get_storage_service
|
||||
|
||||
video_id = video_info["id"]
|
||||
project_id = video_info["project_id"]
|
||||
user_id = video_info["user_id"]
|
||||
|
||||
if dry_run:
|
||||
logger.info("[DRY-RUN] Would rebuild video %s (project=%s)", video_id, project_id)
|
||||
return 0
|
||||
|
||||
session = SessionLocal()
|
||||
temp_dir = tempfile.mkdtemp()
|
||||
|
||||
try:
|
||||
# 再次检查幂等性
|
||||
existing_count = (
|
||||
session.query(VideoFingerprintChunkModel).filter(VideoFingerprintChunkModel.video_id == video_id).count()
|
||||
)
|
||||
if existing_count > 0:
|
||||
logger.info("Video %s already has %d chunks, skipping", video_id, existing_count)
|
||||
return 0
|
||||
|
||||
# 下载视频
|
||||
storage_service = get_storage_service()
|
||||
local_path = os.path.join(temp_dir, f"{video_id}.mp4")
|
||||
storage_key = f"projects/{project_id}/generated/{video_id}/{video_id}.mp4"
|
||||
storage_service.download_file(storage_key, local_path)
|
||||
|
||||
# 重新计算指纹
|
||||
deduplicator = VideoDeduplicator()
|
||||
fingerprint = deduplicator.compute_fingerprint(local_path)
|
||||
|
||||
# 写入分片表
|
||||
_save_fingerprint_chunks(fingerprint, video_id, project_id, user_id, session)
|
||||
session.commit()
|
||||
|
||||
chunk_count = len(fingerprint.chunks)
|
||||
logger.info("Rebuilt %d chunks for video %s", chunk_count, video_id)
|
||||
return chunk_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Failed to rebuild video %s: %s", video_id, e)
|
||||
session.rollback()
|
||||
return -1
|
||||
finally:
|
||||
session.close()
|
||||
import shutil
|
||||
|
||||
shutil.rmtree(temp_dir, ignore_errors=True)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="存量指纹重建脚本")
|
||||
parser.add_argument("--dry-run", action="store_true", help="只打印不写入")
|
||||
parser.add_argument("--batch-size", type=int, default=50, help="每批处理数量（默认 50）")
|
||||
parser.add_argument("--total-limit", type=int, default=0, help="总处理数量限制（0=不限制）")
|
||||
args = parser.parse_args()
|
||||
|
||||
from worker_app.db import SessionLocal
|
||||
|
||||
session = SessionLocal()
|
||||
|
||||
try:
|
||||
videos = find_videos_needing_rebuild(session, args.batch_size)
|
||||
logger.info("Found %d videos needing rebuild", len(videos))
|
||||
|
||||
if args.dry_run:
|
||||
for v in videos:
|
||||
logger.info("[DRY-RUN] Video %s | project=%s | duration=%.1fs", v["id"], v["project_id"], v["duration"])
|
||||
return
|
||||
|
||||
total_chunks = 0
|
||||
processed = 0
|
||||
failed = 0
|
||||
|
||||
for v in videos:
|
||||
if args.total_limit > 0 and processed >= args.total_limit:
|
||||
break
|
||||
|
||||
result = rebuild_one(v, dry_run=False)
|
||||
if result < 0:
|
||||
failed += 1
|
||||
else:
|
||||
total_chunks += result
|
||||
processed += 1
|
||||
|
||||
logger.info(
|
||||
"Rebuild complete: processed=%d, chunks=%d, failed=%d",
|
||||
processed,
|
||||
total_chunks,
|
||||
failed,
|
||||
)
|
||||
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because one or more lines are too long
Generated
+14
-7
@@ -1848,9 +1848,10 @@
|
||||
},
|
||||
"node_modules/@testing-library/dom": {
|
||||
"version": "10.4.1",
|
||||
"resolved": "https://registry.npmjs.org/@testing-library/dom/-/dom-10.4.1.tgz",
|
||||
"resolved": "https://registry.npmmirror.com/@testing-library/dom/-/dom-10.4.1.tgz",
|
||||
"integrity": "sha512-o4PXJQidqJl82ckFaXUeoAW+XysPLauYI43Abki5hABd853iMhitooc6znOnczgbTYmEP6U6/y1ZyKAIsvMKGg==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"@babel/code-frame": "^7.10.4",
|
||||
@@ -1937,9 +1938,10 @@
|
||||
},
|
||||
"node_modules/@types/aria-query": {
|
||||
"version": "5.0.4",
|
||||
"resolved": "https://registry.npmjs.org/@types/aria-query/-/aria-query-5.0.4.tgz",
|
||||
"resolved": "https://registry.npmmirror.com/@types/aria-query/-/aria-query-5.0.4.tgz",
|
||||
"integrity": "sha512-rfT93uj5s0PRL7EzccGMs3brplhcrghnDoV26NqKhCAS1hVo+WdNsPvE/yb6ilfr5hi2MEk6d5EWJTKdxg8jVw==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true
|
||||
},
|
||||
"node_modules/@types/babel__core": {
|
||||
@@ -3111,9 +3113,10 @@
|
||||
},
|
||||
"node_modules/dom-accessibility-api": {
|
||||
"version": "0.5.16",
|
||||
"resolved": "https://registry.npmjs.org/dom-accessibility-api/-/dom-accessibility-api-0.5.16.tgz",
|
||||
"resolved": "https://registry.npmmirror.com/dom-accessibility-api/-/dom-accessibility-api-0.5.16.tgz",
|
||||
"integrity": "sha512-X7BJ2yElsnOJ30pZF4uIIDfBEVgF4XEBxL9Bxhy6dnrm5hkzqmsWHGTiHqRiITNhMyFLyAiWndIJP7Z1NTteDg==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true
|
||||
},
|
||||
"node_modules/dunder-proto": {
|
||||
@@ -4454,9 +4457,10 @@
|
||||
},
|
||||
"node_modules/lz-string": {
|
||||
"version": "1.5.0",
|
||||
"resolved": "https://registry.npmjs.org/lz-string/-/lz-string-1.5.0.tgz",
|
||||
"resolved": "https://registry.npmmirror.com/lz-string/-/lz-string-1.5.0.tgz",
|
||||
"integrity": "sha512-h5bgJWpxJNswbU7qCrV0tIKQCaS3blPDrqKWx+QxzuzL1zGUzij9XCWLrSLsJPu5t+eWA/ycetzYAO5IOMcWAQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"bin": {
|
||||
"lz-string": "bin/bin.js"
|
||||
@@ -5004,9 +5008,10 @@
|
||||
},
|
||||
"node_modules/pretty-format": {
|
||||
"version": "27.5.1",
|
||||
"resolved": "https://registry.npmjs.org/pretty-format/-/pretty-format-27.5.1.tgz",
|
||||
"resolved": "https://registry.npmmirror.com/pretty-format/-/pretty-format-27.5.1.tgz",
|
||||
"integrity": "sha512-Qb1gy5OrP5+zDf2Bvnzdl3jsTf1qXVMazbvCoKhtKqVs4/YK4ozX4gKQJJVyNe+cajNPn0KoC0MC3FUmaHWEmQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"ansi-regex": "^5.0.1",
|
||||
@@ -5019,9 +5024,10 @@
|
||||
},
|
||||
"node_modules/pretty-format/node_modules/ansi-styles": {
|
||||
"version": "5.2.0",
|
||||
"resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-5.2.0.tgz",
|
||||
"resolved": "https://registry.npmmirror.com/ansi-styles/-/ansi-styles-5.2.0.tgz",
|
||||
"integrity": "sha512-Cxwpt2SfTzTtXcfOlzGEee8O+c+MmUgGrNiBcXnuWxuFJHe6a5Hz7qwhwe5OgaSYI0IJvkLqWX1ASG+cJOkEiA==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"engines": {
|
||||
"node": ">=10"
|
||||
@@ -5729,9 +5735,10 @@
|
||||
},
|
||||
"node_modules/react-is": {
|
||||
"version": "17.0.2",
|
||||
"resolved": "https://registry.npmjs.org/react-is/-/react-is-17.0.2.tgz",
|
||||
"resolved": "https://registry.npmmirror.com/react-is/-/react-is-17.0.2.tgz",
|
||||
"integrity": "sha512-w2GsyukL62IJnlaff/nRegPQR94C/XXamvMWmSHRJ4y7Ts/4ocGRmTHvOs8PSE6pB3dWOrD/nueuU5sduBsQ4w==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true
|
||||
},
|
||||
"node_modules/react-refresh": {
|
||||
|
||||
@@ -71,5 +71,4 @@ export interface VideoItem {
|
||||
generation_params: Record<string, unknown>
|
||||
download_url: string
|
||||
generated_at: string
|
||||
duplicate_rate?: number
|
||||
}
|
||||
|
||||
@@ -29,6 +29,7 @@ export function mapVideoToProductItem(video: VideoItem): ProductItem {
|
||||
// 后端字段名为 generated_at，映射为 created_at 供前端统一使用
|
||||
created_at: video.generated_at,
|
||||
updated_at: video.generated_at,
|
||||
duplicate_rate: video.duplicate_rate,
|
||||
// 后端 /videos 接口暂无 duplicate_rate 字段
|
||||
duplicate_rate: undefined,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -28,5 +28,4 @@ export {
|
||||
deleteTTSJob,
|
||||
getTtsVoices,
|
||||
previewTts,
|
||||
extractVideoVoice,
|
||||
} from "./jobs"
|
||||
|
||||
@@ -70,56 +70,3 @@ export const previewTts = async (data: TTSPreviewRequest): Promise<TTSPreviewRes
|
||||
const response = await apiClient.post<TTSPreviewResponse>("/tts/preview", data)
|
||||
return response.data
|
||||
}
|
||||
|
||||
/**
|
||||
* 从视频中提取配音（上传视频 → 后端提取人声 → 保存到配音素材库）
|
||||
* 支持 mp4/mov/webm 格式
|
||||
*/
|
||||
export const extractVideoVoice = async (
|
||||
file: File,
|
||||
onProgress?: (percent: number) => void,
|
||||
): Promise<{ asset_id: string; duration: number }> => {
|
||||
const formData = new FormData()
|
||||
formData.append("file", file)
|
||||
|
||||
return new Promise((resolve, reject) => {
|
||||
const xhr = new XMLHttpRequest()
|
||||
xhr.open("POST", "/api/v1/voices/extract-voice")
|
||||
|
||||
// 携带认证 token（从 localStorage 获取，与 apiClient 拦截器一致）
|
||||
const token = localStorage.getItem("access_token")
|
||||
if (token) {
|
||||
xhr.setRequestHeader("Authorization", `Bearer ${token}`)
|
||||
}
|
||||
|
||||
xhr.timeout = 10 * 60 * 1000 // 10 分钟超时
|
||||
|
||||
xhr.upload.onprogress = (e) => {
|
||||
if (e.lengthComputable && onProgress) {
|
||||
onProgress(Math.round((e.loaded / e.total) * 100))
|
||||
}
|
||||
}
|
||||
|
||||
xhr.onload = () => {
|
||||
if (xhr.status >= 200 && xhr.status < 300) {
|
||||
try {
|
||||
resolve(JSON.parse(xhr.responseText))
|
||||
} catch {
|
||||
reject(new Error("服务器返回数据解析失败"))
|
||||
}
|
||||
} else {
|
||||
try {
|
||||
const err = JSON.parse(xhr.responseText)
|
||||
reject(new Error(err.detail || err.message || `提取失败: HTTP ${xhr.status}`))
|
||||
} catch {
|
||||
reject(new Error(`提取失败: HTTP ${xhr.status}`))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
xhr.onerror = () => reject(new Error("网络错误，请检查网络连接"))
|
||||
xhr.ontimeout = () => reject(new Error("上传超时（10分钟），请检查网络或尝试更小的文件"))
|
||||
|
||||
xhr.send(formData)
|
||||
})
|
||||
}
|
||||
|
||||
@@ -297,7 +297,7 @@ const CloneModal: React.FC<CloneModalProps> = ({ open, onClose, onSuccess }) =>
|
||||
buttonSize="sm"
|
||||
onClick={() => {
|
||||
handleClose()
|
||||
navigate("/app/voices?tab=material&upload=1")
|
||||
navigate("/app/voice-materials")
|
||||
}}
|
||||
>
|
||||
去配音库上传
|
||||
|
||||
@@ -3,11 +3,9 @@ import { useQuery } from "@tanstack/react-query"
|
||||
import {
|
||||
getAssetLibraries,
|
||||
getAssets,
|
||||
ensureDefaultLibrary,
|
||||
type AssetLibraryItem,
|
||||
type AssetItem as ApiAssetItem,
|
||||
} from "@/api/assets"
|
||||
import { getOrCreateDefaultProject } from "@/api/projects"
|
||||
import { mapLibrary, mapAsset, type AssetItem, type LibraryItem } from "../types"
|
||||
|
||||
/**
|
||||
@@ -18,18 +16,7 @@ export function useAssetsData() {
|
||||
/* ── 视频库列表查询 ── */
|
||||
const { data: apiLibraries = [], isLoading: libLoading } = useQuery<AssetLibraryItem[], Error>({
|
||||
queryKey: ["asset-libraries"],
|
||||
queryFn: async () => {
|
||||
const libs = await getAssetLibraries()
|
||||
// 如果没有 video 类型的库，自动创建默认视频素材库（与 useVoiceMaterials 保持一致）
|
||||
const hasVideoLib = libs.some((lib) => lib.kind === "video")
|
||||
if (!hasVideoLib) {
|
||||
const project = await getOrCreateDefaultProject()
|
||||
await ensureDefaultLibrary({ project_id: project.id, kind: "video" })
|
||||
// 创建后重新拉取最新列表
|
||||
return getAssetLibraries()
|
||||
}
|
||||
return libs
|
||||
},
|
||||
queryFn: getAssetLibraries,
|
||||
staleTime: 60_000,
|
||||
})
|
||||
|
||||
|
||||
@@ -338,15 +338,12 @@ const GeneratePage: React.FC = () => {
|
||||
size: titleSettings.size,
|
||||
font: titleSettings.font,
|
||||
color: titleSettings.color,
|
||||
position: titleSettings.position as "top" | "center" | "bottom" | "custom",
|
||||
position: titleSettings.position as "top" | "center" | "bottom",
|
||||
bold: titleSettings.bold,
|
||||
italic: titleSettings.italic,
|
||||
stroke: titleSettings.stroke,
|
||||
shadow: titleSettings.shadow,
|
||||
posX: titleSettings.posX,
|
||||
posY: titleSettings.posY,
|
||||
}}
|
||||
onTitlePositionChange={styleUpdaters.updateTitlePosition}
|
||||
/>
|
||||
)}
|
||||
{currentStep >= 5 && generated && finalVideo && (
|
||||
|
||||
@@ -32,15 +32,12 @@ interface FrontendPreviewPlayerProps {
|
||||
size: number
|
||||
font: string
|
||||
color: string
|
||||
position: "top" | "center" | "bottom" | "custom"
|
||||
position: "top" | "center" | "bottom"
|
||||
bold?: boolean
|
||||
italic?: boolean
|
||||
stroke?: boolean
|
||||
shadow?: boolean
|
||||
posX?: number | null
|
||||
posY?: number | null
|
||||
}
|
||||
onTitlePositionChange?: (posX: number, posY: number) => void
|
||||
}
|
||||
|
||||
function formatTime(seconds: number): string {
|
||||
@@ -108,7 +105,6 @@ const FrontendPreviewPlayer: React.FC<FrontendPreviewPlayerProps> = ({
|
||||
serverClips,
|
||||
voiceAudioUrl,
|
||||
titleSettings,
|
||||
onTitlePositionChange,
|
||||
}) => {
|
||||
const segments = useMemo(
|
||||
() => buildPlaybackSegments(assets, template, serverClips),
|
||||
@@ -130,69 +126,6 @@ const FrontendPreviewPlayer: React.FC<FrontendPreviewPlayerProps> = ({
|
||||
return { width: 1080, height: 1920 }
|
||||
}
|
||||
})()
|
||||
const customTitleXPct =
|
||||
titleSettings?.posX != null && playRes.width > 0
|
||||
? (titleSettings.posX / playRes.width) * 100
|
||||
: null
|
||||
const customTitleYPct =
|
||||
titleSettings?.posY != null && playRes.height > 0
|
||||
? (titleSettings.posY / playRes.height) * 100
|
||||
: null
|
||||
|
||||
// ── 拖拽状态（用 ref 避免在每帧渲染中触发重渲染）──
|
||||
const draggingTitleRef = useRef(false)
|
||||
const titleDragRef = useRef<HTMLDivElement>(null)
|
||||
const handleTitlePointerDown = useCallback(
|
||||
(e: React.PointerEvent<HTMLDivElement>) => {
|
||||
if (!onTitlePositionChange || !playerContainerRef.current) return
|
||||
e.preventDefault()
|
||||
e.stopPropagation()
|
||||
;(e.target as Element).setPointerCapture(e.pointerId)
|
||||
draggingTitleRef.current = true
|
||||
;(e.currentTarget as HTMLDivElement).style.cursor = "grabbing"
|
||||
},
|
||||
[onTitlePositionChange],
|
||||
)
|
||||
const handleTitlePointerMove = useCallback((e: React.PointerEvent<HTMLDivElement>) => {
|
||||
if (!draggingTitleRef.current || !playerContainerRef.current) return
|
||||
e.preventDefault()
|
||||
e.stopPropagation()
|
||||
// 拖拽过程中直接修改 DOM，不触发 React 渲染（避免频繁重渲染导致换行）
|
||||
if (titleDragRef.current) {
|
||||
const rect = playerContainerRef.current.getBoundingClientRect()
|
||||
const relX = Math.max(0, Math.min(rect.width, e.clientX - rect.left))
|
||||
const relY = Math.max(0, Math.min(rect.height, e.clientY - rect.top))
|
||||
const xpct = (relX / rect.width) * 100
|
||||
const ypct = (relY / rect.height) * 100
|
||||
titleDragRef.current.style.left = `${xpct}%`
|
||||
titleDragRef.current.style.top = `${ypct}%`
|
||||
}
|
||||
}, [])
|
||||
const handleTitlePointerUp = useCallback(
|
||||
(e: React.PointerEvent<HTMLDivElement>) => {
|
||||
if (!draggingTitleRef.current) return
|
||||
draggingTitleRef.current = false
|
||||
// 拖拽结束时才调用 onTitlePositionChange 保存最终位置
|
||||
if (onTitlePositionChange && playerContainerRef.current) {
|
||||
const rect = playerContainerRef.current.getBoundingClientRect()
|
||||
const relX = Math.max(0, Math.min(rect.width, e.clientX - rect.left))
|
||||
const relY = Math.max(0, Math.min(rect.height, e.clientY - rect.top))
|
||||
const posX = Math.round((relX / rect.width) * playRes.width)
|
||||
const posY = Math.round((relY / rect.height) * playRes.height)
|
||||
onTitlePositionChange(posX, posY)
|
||||
}
|
||||
;(e.currentTarget as HTMLDivElement).style.cursor = "grab"
|
||||
try {
|
||||
if ((e.currentTarget as Element).hasPointerCapture(e.pointerId)) {
|
||||
;(e.currentTarget as Element).releasePointerCapture(e.pointerId)
|
||||
}
|
||||
} catch {
|
||||
/* ignore */
|
||||
}
|
||||
},
|
||||
[onTitlePositionChange, playRes.width, playRes.height],
|
||||
)
|
||||
|
||||
const playerContainerRef = useRef<HTMLDivElement>(null)
|
||||
const [containerHeight, setContainerHeight] = useState(0)
|
||||
useEffect(() => {
|
||||
@@ -591,36 +524,15 @@ const FrontendPreviewPlayer: React.FC<FrontendPreviewPlayerProps> = ({
|
||||
<div
|
||||
style={{
|
||||
position: "absolute",
|
||||
...(customTitleXPct != null && customTitleYPct != null
|
||||
? {
|
||||
left: `${customTitleXPct}%`,
|
||||
top: `${customTitleYPct}%`,
|
||||
transform: "translate(-50%, -50%)",
|
||||
textAlign: "center" as const,
|
||||
}
|
||||
: {
|
||||
left: `${titleSidePct}%`,
|
||||
right: `${titleSidePct}%`,
|
||||
textAlign: "center" as const,
|
||||
...(titleSettings.position === "top"
|
||||
? { top: `${titleTopPct}%` }
|
||||
: titleSettings.position === "center"
|
||||
? { top: "50%", transform: "translateY(-50%)" }
|
||||
: { bottom: `${titleBottomPct}%` }),
|
||||
}),
|
||||
pointerEvents: "auto",
|
||||
cursor: onTitlePositionChange ? "grab" : "default",
|
||||
touchAction: "none",
|
||||
userSelect: "none",
|
||||
WebkitUserSelect: "none",
|
||||
padding: "8px 12px",
|
||||
boxShadow: "inset 0 0 0 16px transparent",
|
||||
left: `${titleSidePct}%`,
|
||||
right: `${titleSidePct}%`,
|
||||
textAlign: "center",
|
||||
...(titleSettings.position === "top"
|
||||
? { top: `${titleTopPct}%` }
|
||||
: titleSettings.position === "center"
|
||||
? { top: "50%", transform: "translateY(-50%)" }
|
||||
: { bottom: `${titleBottomPct}%` }),
|
||||
}}
|
||||
ref={titleDragRef}
|
||||
onPointerDown={handleTitlePointerDown}
|
||||
onPointerMove={handleTitlePointerMove}
|
||||
onPointerUp={handleTitlePointerUp}
|
||||
onPointerCancel={handleTitlePointerUp}
|
||||
>
|
||||
<span
|
||||
style={{
|
||||
|
||||
@@ -134,7 +134,7 @@ const Step5VoiceSelect: React.FC<Step5VoiceSelectProps> = ({
|
||||
|
||||
/** 跳转到配音库上传 */
|
||||
const handleGoToUpload = useCallback(() => {
|
||||
navigate("/app/voices?tab=material&upload=1")
|
||||
navigate("/app/voices")
|
||||
}, [navigate])
|
||||
|
||||
// 加载中状态
|
||||
|
||||
@@ -23,7 +23,7 @@ const MaterialModeTabs: React.FC<MaterialModeTabsProps> = ({ mode, onModeChange
|
||||
onClick={() => onModeChange("auto")}
|
||||
type="button"
|
||||
>
|
||||
AI智能匹配
|
||||
选择视频库自动匹配
|
||||
</button>
|
||||
</div>
|
||||
)
|
||||
|
||||
@@ -1,7 +1,5 @@
|
||||
/**
|
||||
* 标题预设样式网格
|
||||
* 双图层渲染：底层=描边轮廓(text-shadow模拟)，上层=填充色
|
||||
* 避免 -webkit-text-stroke 在 Chromium 中吞掉填充色的问题
|
||||
*/
|
||||
import React from "react"
|
||||
import { getFontFamily } from "../../constants"
|
||||
@@ -9,10 +7,7 @@ import { getFontFamily } from "../../constants"
|
||||
interface TitlePresetItem {
|
||||
key: string
|
||||
label: string
|
||||
previewStyle: React.CSSProperties & {
|
||||
_strokeColor?: string
|
||||
_strokeWidth?: number
|
||||
}
|
||||
previewStyle: React.CSSProperties
|
||||
}
|
||||
|
||||
interface TitlePresetsGridProps {
|
||||
@@ -22,32 +17,6 @@ interface TitlePresetsGridProps {
|
||||
fontFamily?: string
|
||||
}
|
||||
|
||||
/**
|
||||
* 用 text-shadow 模拟描边轮廓（8方向 + 4对角 = 12层阴影）
|
||||
*/
|
||||
function buildStrokeShadow(color: string, width: number): string {
|
||||
const w = width
|
||||
const parts: string[] = []
|
||||
// 4 cardinal directions
|
||||
parts.push(`${w}px 0 ${color}`)
|
||||
parts.push(`${-w}px 0 ${color}`)
|
||||
parts.push(`0 ${w}px ${color}`)
|
||||
parts.push(`0 ${-w}px ${color}`)
|
||||
// 4 diagonal directions
|
||||
const d = Math.round(w * 0.71 * 10) / 10 // 0.71 ≈ sqrt(2)/2
|
||||
parts.push(`${d}px ${d}px ${color}`)
|
||||
parts.push(`${-d}px ${d}px ${color}`)
|
||||
parts.push(`${d}px ${-d}px ${color}`)
|
||||
parts.push(`${-d}px ${-d}px ${color}`)
|
||||
// 4 extra mid-points for smoother stroke
|
||||
const h = Math.round(w * 0.5 * 10) / 10
|
||||
parts.push(`${w}px ${h}px ${color}`)
|
||||
parts.push(`${w}px ${-h}px ${color}`)
|
||||
parts.push(`${-w}px ${h}px ${color}`)
|
||||
parts.push(`${-w}px ${-h}px ${color}`)
|
||||
return parts.join(", ")
|
||||
}
|
||||
|
||||
const TitlePresetsGrid: React.FC<TitlePresetsGridProps> = ({
|
||||
presets,
|
||||
activePreset,
|
||||
@@ -58,53 +27,20 @@ const TitlePresetsGrid: React.FC<TitlePresetsGridProps> = ({
|
||||
<div className="xx-title-presets-grid">
|
||||
{presets.map((p) => {
|
||||
const isActive = activePreset === p.key
|
||||
const { _strokeColor, _strokeWidth, ...fillStyle } = p.previewStyle
|
||||
const ff = getFontFamily(fontFamily || "思源黑体")
|
||||
|
||||
// 底层：描边轮廓（用 text-shadow 模拟粗描边）
|
||||
const strokeStyle: React.CSSProperties = {
|
||||
color: _strokeColor || "transparent",
|
||||
textShadow:
|
||||
_strokeColor && _strokeWidth
|
||||
? buildStrokeShadow(_strokeColor, _strokeWidth)
|
||||
: undefined,
|
||||
fontWeight: fillStyle.fontWeight,
|
||||
fontSize: fillStyle.fontSize,
|
||||
lineHeight: 1,
|
||||
}
|
||||
|
||||
// 上层：仅填充色 + 可选 textShadow（发光/投影效果）
|
||||
const topStyle: React.CSSProperties = {
|
||||
color: fillStyle.color,
|
||||
textShadow: fillStyle.textShadow,
|
||||
fontWeight: fillStyle.fontWeight,
|
||||
fontSize: fillStyle.fontSize,
|
||||
lineHeight: 1,
|
||||
}
|
||||
|
||||
return (
|
||||
<button
|
||||
key={p.key}
|
||||
className={`xx-title-preset-card${isActive ? " active" : ""}`}
|
||||
onClick={() => onApply(p.key)}
|
||||
title={p.label}
|
||||
>
|
||||
<span className="xx-title-preset-preview-text" style={{ position: "relative" }}>
|
||||
{/* 底层：描边轮廓 */}
|
||||
<span
|
||||
aria-hidden
|
||||
style={{
|
||||
...strokeStyle,
|
||||
fontFamily: ff,
|
||||
position: "absolute",
|
||||
top: 0,
|
||||
left: 0,
|
||||
}}
|
||||
>
|
||||
T
|
||||
</span>
|
||||
{/* 上层：填充色 */}
|
||||
<span style={{ ...topStyle, fontFamily: ff, position: "relative" }}>T</span>
|
||||
<span
|
||||
className="xx-title-preset-preview-text"
|
||||
style={{ ...p.previewStyle, fontFamily: getFontFamily(fontFamily || "思源黑体") }}
|
||||
>
|
||||
标题
|
||||
</span>
|
||||
<span className="xx-title-preset-card-label">{p.label}</span>
|
||||
</button>
|
||||
)
|
||||
})}
|
||||
|
||||
@@ -42,7 +42,6 @@ export const POSITION_OPTIONS = [
|
||||
{ value: "top", label: "顶部" },
|
||||
{ value: "center", label: "居中" },
|
||||
{ value: "bottom", label: "底部" },
|
||||
{ value: "custom", label: "自定义" },
|
||||
]
|
||||
|
||||
/* ── 标题字体选项 ── */
|
||||
@@ -78,11 +77,10 @@ export const TITLE_PRESETS = [
|
||||
label: "经典白字",
|
||||
style: { size: 28, color: "#ffffff", bold: true, italic: false, stroke: true, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#ffffff",
|
||||
_strokeColor: "#000000",
|
||||
_strokeWidth: 2,
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
color: "#ffffff",
|
||||
WebkitTextStroke: "1px #000000",
|
||||
fontSize: "20px",
|
||||
},
|
||||
},
|
||||
{
|
||||
@@ -90,54 +88,45 @@ export const TITLE_PRESETS = [
|
||||
label: "黑金质感",
|
||||
style: { size: 32, color: "#d4a843", bold: true, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#d4a843",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
color: "#d4a843",
|
||||
textShadow: "1px 1px 3px rgba(0,0,0,0.8)",
|
||||
fontSize: "20px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "fresh_minimal",
|
||||
label: "清新简约",
|
||||
style: { size: 24, color: "#333333", bold: false, italic: false, stroke: false, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#e8e8e8",
|
||||
fontWeight: 400,
|
||||
fontSize: "32px",
|
||||
},
|
||||
previewStyle: { fontWeight: 400, color: "#333333", fontSize: "18px" },
|
||||
},
|
||||
{
|
||||
key: "variety_show",
|
||||
label: "综艺花字",
|
||||
style: { size: 36, color: "#ff4081", bold: true, italic: false, stroke: true, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#ff4081",
|
||||
_strokeColor: "#ffffff",
|
||||
_strokeWidth: 2,
|
||||
fontWeight: 900,
|
||||
fontSize: "32px",
|
||||
color: "#ff4081",
|
||||
WebkitTextStroke: "1.5px #ffffff",
|
||||
textShadow: "2px 2px 4px rgba(0,0,0,0.5)",
|
||||
fontSize: "22px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "business",
|
||||
label: "商务极简",
|
||||
style: { size: 24, color: "#1a1a1a", bold: false, italic: false, stroke: false, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#e0e0e0",
|
||||
fontWeight: 400,
|
||||
fontSize: "32px",
|
||||
},
|
||||
previewStyle: { fontWeight: 400, color: "#1a1a1a", fontSize: "17px" },
|
||||
},
|
||||
{
|
||||
key: "retro_film",
|
||||
label: "复古胶片",
|
||||
style: { size: 28, color: "#e8d5b7", bold: false, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#e8d5b7",
|
||||
fontWeight: 400,
|
||||
fontSize: "32px",
|
||||
color: "#e8d5b7",
|
||||
textShadow: "2px 2px 6px rgba(0,0,0,0.7)",
|
||||
fontSize: "18px",
|
||||
},
|
||||
},
|
||||
{
|
||||
@@ -145,10 +134,10 @@ export const TITLE_PRESETS = [
|
||||
label: "霓虹发光",
|
||||
style: { size: 32, color: "#00e5ff", bold: true, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#00e5ff",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
color: "#00e5ff",
|
||||
textShadow: "0 0 4px #00e5ff, 0 0 8px #00e5ff, 0 0 16px rgba(0,229,255,0.5)",
|
||||
fontSize: "20px",
|
||||
},
|
||||
},
|
||||
{
|
||||
@@ -156,202 +145,10 @@ export const TITLE_PRESETS = [
|
||||
label: "手写字",
|
||||
style: { size: 28, color: "#333333", bold: false, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#e0e0e0",
|
||||
fontWeight: 400,
|
||||
fontSize: "32px",
|
||||
color: "#333333",
|
||||
textShadow: "1px 1px 2px rgba(0,0,0,0.3)",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "outline_yellow",
|
||||
label: "黄色描边",
|
||||
style: { size: 28, color: "#ffd54f", bold: true, italic: false, stroke: true, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#ffd54f",
|
||||
_strokeColor: "#000000",
|
||||
_strokeWidth: 2,
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "outline_pink",
|
||||
label: "粉色描边",
|
||||
style: { size: 28, color: "#ff80ab", bold: true, italic: false, stroke: true, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#ff80ab",
|
||||
_strokeColor: "#000000",
|
||||
_strokeWidth: 2,
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "outline_blue",
|
||||
label: "蓝色描边",
|
||||
style: { size: 28, color: "#82b1ff", bold: true, italic: false, stroke: true, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#82b1ff",
|
||||
_strokeColor: "#000000",
|
||||
_strokeWidth: 2,
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "outline_green",
|
||||
label: "绿色描边",
|
||||
style: { size: 28, color: "#69f0ae", bold: true, italic: false, stroke: true, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#69f0ae",
|
||||
_strokeColor: "#000000",
|
||||
_strokeWidth: 2,
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "outline_gray",
|
||||
label: "灰色描边",
|
||||
style: { size: 28, color: "#bdbdbd", bold: true, italic: false, stroke: true, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#bdbdbd",
|
||||
_strokeColor: "#000000",
|
||||
_strokeWidth: 2,
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "bg_white",
|
||||
label: "白底黑字",
|
||||
style: { size: 28, color: "#1a1a1a", bold: true, italic: false, stroke: false, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#1a1a1a",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
background: "#ffffff",
|
||||
borderRadius: "4px",
|
||||
padding: "2px 6px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "bg_yellow",
|
||||
label: "黄底黑字",
|
||||
style: { size: 28, color: "#1a1a1a", bold: true, italic: false, stroke: false, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#1a1a1a",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
background: "#ffd54f",
|
||||
borderRadius: "4px",
|
||||
padding: "2px 6px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "bg_pink",
|
||||
label: "粉底黑字",
|
||||
style: { size: 28, color: "#1a1a1a", bold: true, italic: false, stroke: false, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#1a1a1a",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
background: "#ff80ab",
|
||||
borderRadius: "4px",
|
||||
padding: "2px 6px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "bg_red",
|
||||
label: "红底白字",
|
||||
style: { size: 28, color: "#ffffff", bold: true, italic: false, stroke: false, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#ffffff",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
background: "#ef5350",
|
||||
borderRadius: "4px",
|
||||
padding: "2px 6px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "neon_orange",
|
||||
label: "橙色发光",
|
||||
style: { size: 32, color: "#ff9100", bold: true, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#ff9100",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
textShadow: "0 0 4px #ff9100, 0 0 8px #ff9100, 0 0 16px rgba(255,145,0,0.5)",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "neon_purple",
|
||||
label: "紫色发光",
|
||||
style: { size: 32, color: "#d500f9", bold: true, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#d500f9",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
textShadow: "0 0 4px #d500f9, 0 0 8px #d500f9, 0 0 16px rgba(213,0,249,0.5)",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "bordered_white",
|
||||
label: "白字绿框",
|
||||
style: { size: 28, color: "#ffffff", bold: true, italic: false, stroke: false, shadow: false },
|
||||
previewStyle: {
|
||||
color: "#ffffff",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
background: "#1a1a1a",
|
||||
border: "2px solid #69f0ae",
|
||||
borderRadius: "4px",
|
||||
padding: "2px 6px",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "gradient_warm",
|
||||
label: "暖色渐变",
|
||||
style: { size: 32, color: "#ff6d00", bold: true, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#ff6d00",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
textShadow: "0 0 6px rgba(255,109,0,0.6), 1px 1px 2px rgba(0,0,0,0.5)",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "gradient_cool",
|
||||
label: "冷色渐变",
|
||||
style: { size: 32, color: "#00b0ff", bold: true, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#00b0ff",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
textShadow: "0 0 6px rgba(0,176,255,0.6), 1px 1px 2px rgba(0,0,0,0.5)",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "shadow_deep",
|
||||
label: "深影白字",
|
||||
style: { size: 28, color: "#ffffff", bold: true, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#ffffff",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
textShadow: "2px 2px 4px rgba(0,0,0,0.8), 0 0 8px rgba(0,0,0,0.4)",
|
||||
},
|
||||
},
|
||||
{
|
||||
key: "soft_gold",
|
||||
label: "柔光金",
|
||||
style: { size: 28, color: "#ffd54f", bold: true, italic: false, stroke: false, shadow: true },
|
||||
previewStyle: {
|
||||
color: "#ffd54f",
|
||||
fontWeight: 700,
|
||||
fontSize: "32px",
|
||||
textShadow: "0 0 6px rgba(255,213,79,0.5), 1px 1px 2px rgba(0,0,0,0.4)",
|
||||
fontSize: "20px",
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
@@ -1733,40 +1733,50 @@
|
||||
/* 标题预设卡片网格 */
|
||||
.xx-title-presets-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(6, 52px);
|
||||
gap: 1px;
|
||||
grid-template-columns: repeat(4, 1fr);
|
||||
gap: 10px;
|
||||
}
|
||||
|
||||
.xx-title-preset-card {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
width: 52px;
|
||||
height: 52px;
|
||||
padding: 0;
|
||||
background: #404040;
|
||||
padding: 14px 8px;
|
||||
background: var(--bg-secondary);
|
||||
border: 2px solid transparent;
|
||||
border-radius: 8px;
|
||||
border-radius: var(--radius-sm);
|
||||
cursor: pointer;
|
||||
transition: all 0.15s;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.xx-title-preset-card:hover {
|
||||
border-color: #666;
|
||||
background: #4d4d4d;
|
||||
border-color: var(--primary-200);
|
||||
background: var(--bg-primary);
|
||||
}
|
||||
|
||||
.xx-title-preset-card.active {
|
||||
border-color: #409eff;
|
||||
background: #4d4d4d;
|
||||
border-color: var(--primary-color);
|
||||
background: var(--primary-50);
|
||||
}
|
||||
|
||||
.xx-title-preset-preview-text {
|
||||
font-size: 32px;
|
||||
line-height: 1;
|
||||
line-height: 1.4;
|
||||
margin-bottom: 6px;
|
||||
user-select: none;
|
||||
}
|
||||
|
||||
.xx-title-preset-card-label {
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.xx-title-preset-card.active .xx-title-preset-card-label {
|
||||
color: var(--primary-color);
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
/* 样式按钮组 */
|
||||
.xx-style-btns {
|
||||
display: flex;
|
||||
|
||||
@@ -20,14 +20,13 @@ const SECONDS_PER_ASSET = 15
|
||||
/**
|
||||
* 根据模板 segments 计算所需素材数量上限。
|
||||
* 取每个 segment 的 duration_min 之和作为目标视频总时长，
|
||||
* 再按 15 秒/素材估算需要多少个素材，且保证不少于片段数（每个片段至少 1 个素材）；
|
||||
* 结果钳制到 [1, 200] 区间（后端 limit 上限 200）。
|
||||
* 再按 15 秒/素材估算需要多少个素材；结果钳制到 [1, 200] 区间（后端 limit 上限 200）。
|
||||
*/
|
||||
function computeLimitFromSegments(segments?: TemplateSegment[]): number {
|
||||
if (!segments || segments.length === 0) return DEFAULT_LIMIT
|
||||
const totalSeconds = segments.reduce((sum, seg) => sum + (seg.duration_min || 0), 0)
|
||||
if (totalSeconds <= 0) return DEFAULT_LIMIT
|
||||
const limit = Math.max(segments.length, Math.ceil(totalSeconds / SECONDS_PER_ASSET))
|
||||
const limit = Math.ceil(totalSeconds / SECONDS_PER_ASSET)
|
||||
return Math.max(1, Math.min(limit, 200))
|
||||
}
|
||||
|
||||
|
||||
@@ -233,7 +233,7 @@ export function useCanvasPlayer(
|
||||
fontSize: number
|
||||
fontFamily: string
|
||||
color: string
|
||||
position: "top" | "center" | "bottom" | "custom"
|
||||
position: "top" | "center" | "bottom"
|
||||
bold?: boolean
|
||||
stroke?: boolean
|
||||
shadow?: boolean
|
||||
|
||||
@@ -27,8 +27,6 @@ const DEFAULT_TITLE_SETTINGS: TitleSettings = {
|
||||
stroke: true,
|
||||
shadow: false,
|
||||
color: "#ffffff",
|
||||
posX: null,
|
||||
posY: null,
|
||||
}
|
||||
|
||||
export interface GenerateFormState {
|
||||
|
||||
@@ -117,14 +117,6 @@ export function useGenerateVideo(props: UseGenerateVideoProps) {
|
||||
font_size: props.titleSettings.size,
|
||||
font_color: props.titleSettings.color,
|
||||
position: props.titleSettings.position,
|
||||
...(props.titleSettings.position === "custom" &&
|
||||
props.titleSettings.posX != null &&
|
||||
props.titleSettings.posY != null
|
||||
? {
|
||||
pos_x: Math.round(props.titleSettings.posX),
|
||||
pos_y: Math.round(props.titleSettings.posY),
|
||||
}
|
||||
: {}),
|
||||
bold: props.titleSettings.bold,
|
||||
stroke: props.titleSettings.stroke,
|
||||
shadow: props.titleSettings.shadow,
|
||||
|
||||
@@ -46,16 +46,7 @@ export function useTitleStyleUpdaters({
|
||||
|
||||
const updatePosition = useCallback(
|
||||
(position: string) => {
|
||||
// 切回三档预设时清掉自定义坐标
|
||||
onTitleSettingsChange({ ...titleSettings, position, posX: null, posY: null })
|
||||
},
|
||||
[titleSettings, onTitleSettingsChange],
|
||||
)
|
||||
|
||||
/** 拖拽更新自定义位置（由预览播放器调用） */
|
||||
const updateTitlePosition = useCallback(
|
||||
(posX: number, posY: number) => {
|
||||
onTitleSettingsChange({ ...titleSettings, position: "custom", posX, posY })
|
||||
onTitleSettingsChange({ ...titleSettings, position })
|
||||
},
|
||||
[titleSettings, onTitleSettingsChange],
|
||||
)
|
||||
@@ -120,7 +111,6 @@ export function useTitleStyleUpdaters({
|
||||
updateTitle,
|
||||
toggleAiAutoSelect,
|
||||
updatePosition,
|
||||
updateTitlePosition,
|
||||
updateFont,
|
||||
updateSize,
|
||||
updateColor,
|
||||
|
||||
@@ -16,9 +16,6 @@ export interface TitleSettings {
|
||||
stroke: boolean
|
||||
shadow: boolean
|
||||
color: string
|
||||
/** 自由位置坐标（PlayRes 像素），仅当 position="custom" 时有效 */
|
||||
posX: number | null
|
||||
posY: number | null
|
||||
}
|
||||
|
||||
/* ── 智能匹配结果 ── */
|
||||
|
||||
@@ -11,7 +11,7 @@ import {
|
||||
} from "@ant-design/icons"
|
||||
import type { ProductItem } from "../types"
|
||||
import { statusConfig, reviewStatusConfig } from "../constants"
|
||||
import { formatTime, formatSize } from "../utils"
|
||||
import { formatTime } from "../utils"
|
||||
|
||||
interface ProductCardProps {
|
||||
product: ProductItem
|
||||
@@ -210,20 +210,11 @@ export const ProductCard: React.FC<ProductCardProps> = ({
|
||||
<span className={`xx-product-status ${st.className}`}>{st.text}</span>
|
||||
<span className="xx-product-date">{product.date}</span>
|
||||
</div>
|
||||
<div className="xx-product-meta-grid">
|
||||
<span className="xx-product-meta-item">分辨率：{product.resolution || "-"}</span>
|
||||
<span className="xx-product-meta-item">
|
||||
时长：{product.duration > 0 ? formatTime(product.duration) : "-"}
|
||||
{product.duplicateRate > 0 && (
|
||||
<span className={`xx-product-dup-rate ${dupClass}`}>
|
||||
查重率：{product.duplicateRate.toFixed(1)}%
|
||||
</span>
|
||||
<span className="xx-product-meta-item">大小：{formatSize(product.fileSize)}</span>
|
||||
<span
|
||||
className={`xx-product-meta-item xx-product-dup-rate${
|
||||
product.duplicateRate > 0 ? ` ${dupClass}` : ""
|
||||
}`}
|
||||
>
|
||||
查重率：{product.duplicateRate != null ? `${product.duplicateRate.toFixed(1)}%` : "-"}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* 操作按钮 */}
|
||||
|
||||
@@ -91,6 +91,8 @@
|
||||
height: 18px;
|
||||
border: 2px solid var(--border-color);
|
||||
border-radius: var(--radius-xs);
|
||||
display: grid;
|
||||
place-items: center;
|
||||
transition: var(--transition-all);
|
||||
background: var(--bg-primary);
|
||||
flex-shrink: 0;
|
||||
@@ -135,7 +137,7 @@
|
||||
============================================================ */
|
||||
.xx-products-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fill, minmax(220px, 1fr));
|
||||
grid-template-columns: repeat(4, 1fr);
|
||||
gap: var(--space-md);
|
||||
}
|
||||
|
||||
@@ -185,6 +187,8 @@
|
||||
height: 22px;
|
||||
border: 2px solid rgba(255, 255, 255, 0.8);
|
||||
border-radius: var(--radius-xs);
|
||||
display: grid;
|
||||
place-items: center;
|
||||
background: rgba(0, 0, 0, 0.3);
|
||||
backdrop-filter: blur(4px);
|
||||
cursor: pointer;
|
||||
@@ -258,8 +262,11 @@
|
||||
.xx-product-thumb {
|
||||
position: relative;
|
||||
overflow: hidden;
|
||||
display: grid;
|
||||
place-items: center;
|
||||
color: var(--text-inverse);
|
||||
background: var(--color-gray-950);
|
||||
max-height: 320px;
|
||||
}
|
||||
|
||||
.xx-product-thumb-bg {
|
||||
@@ -269,11 +276,11 @@
|
||||
background-position: center;
|
||||
}
|
||||
|
||||
/* 内联视频播放器（cover 填满容器，竖屏视频不留左右空白） */
|
||||
/* 内联视频播放器 */
|
||||
.xx-product-thumb-video {
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
object-fit: cover;
|
||||
object-fit: contain;
|
||||
display: block;
|
||||
}
|
||||
|
||||
@@ -282,28 +289,22 @@
|
||||
}
|
||||
|
||||
.xx-product-play {
|
||||
position: absolute;
|
||||
top: 50%;
|
||||
left: 50%;
|
||||
transform: translate(-50%, -50%);
|
||||
z-index: 2;
|
||||
width: 52px;
|
||||
height: 52px;
|
||||
border-radius: 50%;
|
||||
position: relative;
|
||||
z-index: 1;
|
||||
width: 44px;
|
||||
height: 44px;
|
||||
border-radius: var(--radius-full);
|
||||
background: rgba(0, 0, 0, 0.45);
|
||||
backdrop-filter: blur(4px);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
font-size: 28px;
|
||||
color: #fff;
|
||||
transition: all 0.2s;
|
||||
cursor: pointer;
|
||||
display: grid;
|
||||
place-items: center;
|
||||
font-size: var(--font-size-md);
|
||||
transition: var(--transition-all);
|
||||
}
|
||||
|
||||
.xx-product-card:hover .xx-product-play {
|
||||
background: var(--primary-color);
|
||||
transform: translate(-50%, -50%) scale(1.1);
|
||||
transform: scale(1.1);
|
||||
}
|
||||
|
||||
/* 时长标签 */
|
||||
@@ -323,7 +324,7 @@
|
||||
|
||||
/* 卡片信息区 */
|
||||
.xx-product-info {
|
||||
padding: 8px 10px;
|
||||
padding: 14px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: var(--space-sm);
|
||||
@@ -346,21 +347,6 @@
|
||||
gap: var(--space-xs);
|
||||
}
|
||||
|
||||
/* 卡片信息网格：分辨率/时长 一行，大小/查重率 一行 */
|
||||
.xx-product-meta-grid {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 4px var(--space-sm);
|
||||
}
|
||||
|
||||
.xx-product-meta-item {
|
||||
font-size: var(--font-size-xs);
|
||||
color: var(--text-secondary);
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
/* 状态标签 */
|
||||
.xx-product-status {
|
||||
padding: 2px 10px;
|
||||
@@ -505,6 +491,8 @@
|
||||
z-index: 1000;
|
||||
background: rgba(0, 0, 0, 0.75);
|
||||
backdrop-filter: blur(8px);
|
||||
display: grid;
|
||||
place-items: center;
|
||||
animation: player-fade-in 0.25s ease-out;
|
||||
}
|
||||
|
||||
@@ -543,6 +531,8 @@
|
||||
background: var(--color-gray-950);
|
||||
aspect-ratio: 9 / 16;
|
||||
max-height: 60vh;
|
||||
display: grid;
|
||||
place-items: center;
|
||||
}
|
||||
|
||||
.xx-player-video-wrap video {
|
||||
@@ -564,6 +554,8 @@
|
||||
backdrop-filter: blur(4px);
|
||||
color: var(--text-inverse);
|
||||
font-size: 28px;
|
||||
display: grid;
|
||||
place-items: center;
|
||||
cursor: pointer;
|
||||
border: none;
|
||||
transition: var(--transition-all);
|
||||
@@ -660,6 +652,8 @@
|
||||
backdrop-filter: blur(4px);
|
||||
color: var(--text-inverse);
|
||||
font-size: var(--font-size-md);
|
||||
display: grid;
|
||||
place-items: center;
|
||||
cursor: pointer;
|
||||
border: none;
|
||||
transition: var(--transition-all);
|
||||
|
||||
@@ -1,11 +1,6 @@
|
||||
import { useMemo, useEffect } from "react"
|
||||
import { useQuery, useMutation, useQueryClient } from "@tanstack/react-query"
|
||||
import {
|
||||
getAssetsByKind,
|
||||
getAssetLibraries,
|
||||
createAssetLibrary,
|
||||
type AssetItem,
|
||||
} from "@/api/assets"
|
||||
import { getAssetsByKind, getAssetLibraries, createAssetLibrary } from "@/api/assets"
|
||||
import { type VoiceMaterial, mapAssetToMaterial } from "../../types"
|
||||
|
||||
interface UseVoiceMaterialDataOptions {
|
||||
@@ -49,15 +44,6 @@ export function useVoiceMaterialData({ keyword, gender, tagIds }: UseVoiceMateri
|
||||
queryKey: ["assets", "voice", { keyword, gender, tag_ids: tagIds }],
|
||||
queryFn: () => getAssetsByKind("voice", { keyword, gender, tag_ids: tagIds }),
|
||||
staleTime: 30_000,
|
||||
// 列表中存在上传中/处理中素材时每 3s 轮询；全部就绪后自动停止
|
||||
refetchInterval: (query) => {
|
||||
const items = (query.state.data as AssetItem[] | undefined) ?? []
|
||||
const processing = items.some((a) => {
|
||||
const st = a.status ?? ""
|
||||
return st === "uploading" || st === "ingesting" || st === "processing" || st === "pending"
|
||||
})
|
||||
return processing ? 3000 : false
|
||||
},
|
||||
})
|
||||
|
||||
const materials: VoiceMaterial[] = useMemo(() => assets.map(mapAssetToMaterial), [assets])
|
||||
|
||||
@@ -41,7 +41,7 @@ export const mapAssetToMaterial = (asset: AssetItem): VoiceMaterial => {
|
||||
tagIds: Array.isArray(asset.tag_ids) ? asset.tag_ids : [],
|
||||
fileName: asset.storage_key?.split("/").pop() || asset.name,
|
||||
fileSize: asset.file_size || 0,
|
||||
duration: asset.duration || (meta.duration as number) || 0,
|
||||
duration: (meta.duration as number) || 0,
|
||||
mimeType: asset.mime_type || "audio/mpeg",
|
||||
createdAt: asset.created_at || new Date().toISOString(),
|
||||
fileUrl: asset.file_url,
|
||||
|
||||
@@ -14,14 +14,8 @@
|
||||
* 弹窗集合 → components/VoiceModals
|
||||
* Toast 提示 → components/VoiceToasts
|
||||
*/
|
||||
import React, { useCallback, useEffect, useState } from "react"
|
||||
import { useSearchParams } from "react-router-dom"
|
||||
import {
|
||||
UploadOutlined,
|
||||
AudioOutlined,
|
||||
RobotOutlined,
|
||||
VideoCameraOutlined,
|
||||
} from "@ant-design/icons"
|
||||
import React, { useCallback, useState } from "react"
|
||||
import { UploadOutlined, AudioOutlined, RobotOutlined } from "@ant-design/icons"
|
||||
import { Button } from "@/components/ui"
|
||||
import PageHead from "@/components/layout/PageHead"
|
||||
import { type AssetItem } from "@/api/assets"
|
||||
@@ -40,8 +34,6 @@ import { useTtsSynthesize } from "./hooks/useTtsSynthesize"
|
||||
import { useVoiceUpload } from "./hooks/useVoiceUpload"
|
||||
import { useMaterialDelete } from "./hooks/useMaterialDelete"
|
||||
import { useMaterialBatchDelete } from "./hooks/useMaterialBatchDelete"
|
||||
import { useVideoExtract } from "./hooks/useVideoExtract"
|
||||
import VideoExtractModal from "./components/VideoExtractModal"
|
||||
import "./voices.css"
|
||||
|
||||
let toastIdSeq = 0
|
||||
@@ -166,32 +158,6 @@ const VoiceLibrary: React.FC = () => {
|
||||
handleUploadClose,
|
||||
} = useVoiceUpload({ showToast })
|
||||
|
||||
// ── 提取视频配音 ──────────────────────────────────────
|
||||
const {
|
||||
extractOpen,
|
||||
extractFile,
|
||||
extractProgress,
|
||||
isExtracting,
|
||||
setExtractOpen,
|
||||
handleFileSelect: handleExtractFileSelect,
|
||||
handleExtract,
|
||||
handleExtractClose,
|
||||
} = useVideoExtract({ showToast })
|
||||
|
||||
// ── URL 参数自动打开上传弹窗 ────────────────────────────
|
||||
const [searchParams, setSearchParams] = useSearchParams()
|
||||
|
||||
useEffect(() => {
|
||||
if (searchParams.get("upload") === "1") {
|
||||
setActiveTab("material")
|
||||
setUploadOpen(true)
|
||||
// 一次性触发器：清理 upload 参数，避免切换 Tab 时重复触发
|
||||
const next = new URLSearchParams(searchParams)
|
||||
next.delete("upload")
|
||||
setSearchParams(next, { replace: true })
|
||||
}
|
||||
}, [searchParams, setActiveTab, setUploadOpen, setSearchParams])
|
||||
|
||||
// ── 切换 Tab 时停止播放 ───────────────────────────────
|
||||
const handleTabChange = useCallback(
|
||||
(tab: VoiceTabKey) => {
|
||||
@@ -219,14 +185,6 @@ const VoiceLibrary: React.FC = () => {
|
||||
>
|
||||
上传音频
|
||||
</Button>
|
||||
<Button
|
||||
buttonType="primary"
|
||||
buttonSize="sm"
|
||||
icon={<VideoCameraOutlined />}
|
||||
onClick={() => setExtractOpen(true)}
|
||||
>
|
||||
提取视频配音
|
||||
</Button>
|
||||
<Button
|
||||
buttonType="ghost"
|
||||
buttonSize="sm"
|
||||
@@ -327,18 +285,7 @@ const VoiceLibrary: React.FC = () => {
|
||||
/>
|
||||
)}
|
||||
|
||||
{/* ── 视频提取配音弹窗 ─────────────────────────────── */}
|
||||
<VideoExtractModal
|
||||
open={extractOpen}
|
||||
file={extractFile}
|
||||
progress={extractProgress}
|
||||
isExtracting={isExtracting}
|
||||
onClose={handleExtractClose}
|
||||
onFileSelect={handleExtractFileSelect}
|
||||
onExtract={handleExtract}
|
||||
/>
|
||||
|
||||
{/* ── 弹窗集合 ─────────────────────────────────── */}
|
||||
{/* ── 弹窗集合 ──────────────────────────────────── */}
|
||||
<VoiceModals
|
||||
cloneModalOpen={cloneModalOpen}
|
||||
onCloneClose={() => setCloneModalOpen(false)}
|
||||
|
||||
@@ -136,9 +136,6 @@ export const MaterialVoiceTab: React.FC<MaterialVoiceTabProps> = ({
|
||||
const material = mapAssetToMaterial(asset)
|
||||
// duration 优先取顶层（后端从 metadata 提取），兜底 metadata
|
||||
const cardDuration = asset.duration || material.duration || 0
|
||||
// AI 生成素材标识：兼容旧素材（无 source 字段但有 tts_job_id）
|
||||
const meta = asset.metadata as Record<string, unknown>
|
||||
const isAiMaterial = meta?.source === "tts_job" || !!meta?.tts_job_id
|
||||
const isPlaying = playingId === asset.id
|
||||
const isSelected = selectedIds.has(asset.id)
|
||||
// 播放中以 audio 真实时长为准，未播放显示卡片时长
|
||||
@@ -185,11 +182,8 @@ export const MaterialVoiceTab: React.FC<MaterialVoiceTabProps> = ({
|
||||
</div>
|
||||
|
||||
<div className="xx-voice-info vmat-info">
|
||||
<div className="xx-voice-name-row">
|
||||
<div className="xx-voice-name" title={asset.name}>
|
||||
{asset.name}
|
||||
</div>
|
||||
{isAiMaterial && <span className="vmat-ai-badge">AI</span>}
|
||||
<div className="xx-voice-name" title={asset.name}>
|
||||
{asset.name}
|
||||
</div>
|
||||
<div className="xx-voice-subtitle">
|
||||
{asset.file_size ? `${formatFileSize(asset.file_size)}` : "--"}
|
||||
|
||||
@@ -1,207 +0,0 @@
|
||||
import React, { useRef } from "react"
|
||||
import { Modal } from "antd"
|
||||
import { InboxOutlined, CloseOutlined } from "@ant-design/icons"
|
||||
|
||||
interface VideoExtractModalProps {
|
||||
open: boolean
|
||||
file: File | null
|
||||
progress: number | null
|
||||
isExtracting: boolean
|
||||
onClose: () => void
|
||||
onFileSelect: (file: File | null) => void
|
||||
onExtract: () => void
|
||||
}
|
||||
|
||||
const ACCEPT_TYPES = ".mp4,.mov,.webm"
|
||||
|
||||
const VideoExtractModal: React.FC<VideoExtractModalProps> = ({
|
||||
open,
|
||||
file,
|
||||
progress,
|
||||
isExtracting,
|
||||
onClose,
|
||||
onFileSelect,
|
||||
onExtract,
|
||||
}) => {
|
||||
const inputRef = useRef<HTMLInputElement>(null)
|
||||
|
||||
return (
|
||||
<Modal
|
||||
title={<span style={{ fontSize: 16, fontWeight: 600 }}>提取视频配音</span>}
|
||||
open={open}
|
||||
onCancel={() => {
|
||||
if (isExtracting) return
|
||||
onClose()
|
||||
}}
|
||||
footer={null}
|
||||
width={480}
|
||||
maskClosable={!isExtracting}
|
||||
>
|
||||
{!file ? (
|
||||
<div
|
||||
className="vmat-upload-dropzone"
|
||||
onClick={() => inputRef.current?.click()}
|
||||
style={{
|
||||
border: "2px dashed #d9d9d9",
|
||||
borderRadius: 8,
|
||||
padding: "40px 20px",
|
||||
textAlign: "center",
|
||||
cursor: "pointer",
|
||||
transition: "border-color 0.3s",
|
||||
}}
|
||||
onMouseEnter={(e) => (e.currentTarget.style.borderColor = "#7c3aed")}
|
||||
onMouseLeave={(e) => (e.currentTarget.style.borderColor = "#d9d9d9")}
|
||||
>
|
||||
<InboxOutlined style={{ fontSize: 32, color: "#7c3aed", marginBottom: 12 }} />
|
||||
<p style={{ margin: "0 0 8px", fontSize: 14, color: "#333" }}>点击选择视频文件</p>
|
||||
<span style={{ fontSize: 12, color: "#999" }}>支持 MP4、MOV、WebM 格式</span>
|
||||
<input
|
||||
ref={inputRef}
|
||||
type="file"
|
||||
accept={ACCEPT_TYPES}
|
||||
style={{ display: "none" }}
|
||||
onChange={(e) => {
|
||||
const f = e.target.files?.[0]
|
||||
if (f) onFileSelect(f)
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
) : (
|
||||
<div>
|
||||
<div
|
||||
style={{
|
||||
display: "flex",
|
||||
alignItems: "center",
|
||||
justifyContent: "space-between",
|
||||
padding: "12px 16px",
|
||||
background: "#fafafa",
|
||||
borderRadius: 8,
|
||||
marginBottom: 16,
|
||||
}}
|
||||
>
|
||||
<span
|
||||
style={{
|
||||
flex: 1,
|
||||
overflow: "hidden",
|
||||
textOverflow: "ellipsis",
|
||||
whiteSpace: "nowrap",
|
||||
fontSize: 14,
|
||||
fontWeight: 500,
|
||||
}}
|
||||
title={file.name}
|
||||
>
|
||||
{file.name}
|
||||
</span>
|
||||
<span style={{ fontSize: 12, color: "#999", marginLeft: 8, flexShrink: 0 }}>
|
||||
{(file.size / (1024 * 1024)).toFixed(1)} MB
|
||||
</span>
|
||||
{!isExtracting && (
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => {
|
||||
if (inputRef.current) inputRef.current.value = ""
|
||||
onFileSelect(null)
|
||||
}}
|
||||
style={{
|
||||
border: "none",
|
||||
background: "none",
|
||||
cursor: "pointer",
|
||||
color: "#999",
|
||||
marginLeft: 8,
|
||||
fontSize: 14,
|
||||
}}
|
||||
aria-label="移除文件"
|
||||
>
|
||||
<CloseOutlined />
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{progress !== null && (
|
||||
<div style={{ marginBottom: 12 }}>
|
||||
<div
|
||||
style={{
|
||||
height: 6,
|
||||
background: "#f0f0f0",
|
||||
borderRadius: 3,
|
||||
overflow: "hidden",
|
||||
}}
|
||||
>
|
||||
<div
|
||||
style={{
|
||||
height: "100%",
|
||||
width: `${progress}%`,
|
||||
background: "linear-gradient(90deg, #7c3aed, #a78bfa)",
|
||||
borderRadius: 3,
|
||||
transition: "width 0.3s",
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
<div
|
||||
style={{
|
||||
textAlign: "right",
|
||||
fontSize: 12,
|
||||
color: "#999",
|
||||
marginTop: 4,
|
||||
}}
|
||||
>
|
||||
{progress}%
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{isExtracting && (
|
||||
<p style={{ textAlign: "center", fontSize: 13, color: "#7c3aed", margin: "12px 0 0" }}>
|
||||
{progress === 100 ? "正在提取人声，请稍候..." : "正在上传视频..."}
|
||||
</p>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div
|
||||
style={{
|
||||
display: "flex",
|
||||
justifyContent: "flex-end",
|
||||
gap: 8,
|
||||
marginTop: 24,
|
||||
}}
|
||||
>
|
||||
<button
|
||||
type="button"
|
||||
onClick={onClose}
|
||||
disabled={isExtracting}
|
||||
style={{
|
||||
padding: "6px 16px",
|
||||
borderRadius: 6,
|
||||
border: "1px solid #d9d9d9",
|
||||
background: "#fff",
|
||||
cursor: isExtracting ? "not-allowed" : "pointer",
|
||||
fontSize: 14,
|
||||
opacity: isExtracting ? 0.5 : 1,
|
||||
}}
|
||||
>
|
||||
取消
|
||||
</button>
|
||||
<button
|
||||
type="button"
|
||||
onClick={onExtract}
|
||||
disabled={!file || isExtracting}
|
||||
style={{
|
||||
padding: "6px 16px",
|
||||
borderRadius: 6,
|
||||
border: "none",
|
||||
background: !file || isExtracting ? "#d9d9d9" : "#7c3aed",
|
||||
color: "#fff",
|
||||
cursor: !file || isExtracting ? "not-allowed" : "pointer",
|
||||
fontSize: 14,
|
||||
fontWeight: 500,
|
||||
}}
|
||||
>
|
||||
{isExtracting ? "提取中..." : "开始提取"}
|
||||
</button>
|
||||
</div>
|
||||
</Modal>
|
||||
)
|
||||
}
|
||||
|
||||
export default VideoExtractModal
|
||||
@@ -1,74 +0,0 @@
|
||||
import { useState, useCallback } from "react"
|
||||
import { useQueryClient } from "@tanstack/react-query"
|
||||
import { extractVideoVoice } from "@/api/tts"
|
||||
|
||||
/**
|
||||
* 视频提取配音 Hook
|
||||
* 封装视频上传弹窗状态、提取进度、提取 mutation 逻辑
|
||||
*/
|
||||
interface UseVideoExtractProps {
|
||||
showToast: (message: string, type: "success" | "error") => void
|
||||
}
|
||||
|
||||
export function useVideoExtract({ showToast }: UseVideoExtractProps) {
|
||||
const queryClient = useQueryClient()
|
||||
|
||||
const [extractOpen, setExtractOpen] = useState(false)
|
||||
const [extractFile, setExtractFile] = useState<File | null>(null)
|
||||
const [extractProgress, setExtractProgress] = useState<number | null>(null)
|
||||
const [isExtracting, setIsExtracting] = useState(false)
|
||||
|
||||
const handleExtractClose = useCallback(() => {
|
||||
setExtractOpen(false)
|
||||
setExtractFile(null)
|
||||
setExtractProgress(null)
|
||||
setIsExtracting(false)
|
||||
}, [])
|
||||
|
||||
const handleExtract = useCallback(async () => {
|
||||
if (!extractFile) return
|
||||
setIsExtracting(true)
|
||||
setExtractProgress(0)
|
||||
try {
|
||||
await extractVideoVoice(extractFile, (p) => setExtractProgress(p))
|
||||
// 刷新素材列表
|
||||
queryClient.invalidateQueries({ queryKey: ["assets", "voice"] })
|
||||
queryClient.invalidateQueries({ queryKey: ["voice-materials"] })
|
||||
showToast("视频配音提取成功", "success")
|
||||
handleExtractClose()
|
||||
} catch (err: unknown) {
|
||||
const msg = err instanceof Error ? err.message : "提取失败，请重试"
|
||||
showToast(msg, "error")
|
||||
} finally {
|
||||
setIsExtracting(false)
|
||||
setExtractProgress(null)
|
||||
}
|
||||
}, [extractFile, queryClient, showToast, handleExtractClose])
|
||||
|
||||
const handleFileSelect = useCallback(
|
||||
(file: File | null) => {
|
||||
if (!file) {
|
||||
setExtractFile(null)
|
||||
return
|
||||
}
|
||||
const validTypes = ["video/mp4", "video/quicktime", "video/webm"]
|
||||
if (!validTypes.includes(file.type)) {
|
||||
showToast("仅支持 MP4、MOV、WebM 格式的视频文件", "error")
|
||||
return
|
||||
}
|
||||
setExtractFile(file)
|
||||
},
|
||||
[showToast],
|
||||
)
|
||||
|
||||
return {
|
||||
extractOpen,
|
||||
setExtractOpen,
|
||||
extractFile,
|
||||
extractProgress,
|
||||
isExtracting,
|
||||
handleFileSelect,
|
||||
handleExtract,
|
||||
handleExtractClose,
|
||||
}
|
||||
}
|
||||
@@ -193,24 +193,6 @@
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
flex: 1;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
/* AI 配音标识 */
|
||||
.vmat-ai-badge {
|
||||
display: inline-block;
|
||||
margin-left: 6px;
|
||||
padding: 1px 6px;
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
color: #7c3aed;
|
||||
background: #f3f0ff;
|
||||
border: 1px solid #ddd6fe;
|
||||
border-radius: 4px;
|
||||
line-height: 16px;
|
||||
vertical-align: middle;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.xx-voice-star {
|
||||
|
||||
@@ -1,16 +1,11 @@
|
||||
"""Video deduplication module - compute fingerprints and detect duplicates.
|
||||
|
||||
Dynamic keyframe detection + sliding window temporal matching (Issue #1659).
|
||||
"""
|
||||
"""Video deduplication module - compute fingerprints and detect duplicates."""
|
||||
|
||||
import hashlib
|
||||
import logging
|
||||
import os
|
||||
import statistics
|
||||
import tempfile
|
||||
from dataclasses import dataclass, field
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
from uuid import uuid4
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
@@ -20,34 +15,10 @@ from worker_app.celery_app import celery_app
|
||||
from worker_app.db import SessionLocal
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.generated_video_repository import SQLAlchemyGeneratedVideoRepository
|
||||
from packages.adapters.sqlalchemy_impl.models import VideoFingerprintChunkModel
|
||||
from packages.shared.storage import get_storage_service
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ── 关键帧检测常量 ──────────────────────────────────────────────
|
||||
SCENE_CHANGE_THRESHOLD = 30 # 灰度差异阈值
|
||||
MIN_KEYFRAME_INTERVAL_SEC = 1.0 # 最小关键帧间隔（秒）
|
||||
MAX_KEYFRAMES = 30 # 最大关键帧数
|
||||
MIN_KEYFRAMES = 5 # 最小关键帧数
|
||||
LONG_VIDEO_SEGMENT_SEC = 30 # 长视频每段秒数
|
||||
LONG_VIDEO_DURATION_THRESHOLD_SEC = 180 # 3 分钟阈值
|
||||
MIN_FRAMES_PER_SEGMENT = 2 # 长视频每段最少帧数
|
||||
|
||||
# ── 滑动窗口匹配常量 ────────────────────────────────────────────
|
||||
SEGMENT_MATCH_THRESHOLD = 8 # 帧匹配汉明距离阈值
|
||||
MIN_CONSECUTIVE_MATCHES = 5 # 最少连续匹配帧数
|
||||
MAX_GAP = 2 # 允许的最大间隙帧数
|
||||
|
||||
# ── 融合判定常量 ────────────────────────────────────────────────
|
||||
PHASH_WEIGHT = 0.7 # pHash 权重
|
||||
HISTOGRAM_WEIGHT = 0.3 # 直方图权重
|
||||
MATCH_RATIO_THRESHOLD = 0.7 # 至少 70% 帧匹配
|
||||
DUPLICATE_THRESHOLD = 0.70 # 融合后相似度阈值
|
||||
|
||||
|
||||
# ── 感知哈希 & 颜色直方图工具函数 ────────────────────────────────
|
||||
|
||||
|
||||
def compute_phash(image: np.ndarray, hash_size: int = 8) -> str:
|
||||
"""计算图像的感知哈希（pHash），基于 DCT（离散余弦变换）。
|
||||
@@ -109,131 +80,6 @@ def compute_color_histogram(image: np.ndarray, bins: int = 32) -> list[float]:
|
||||
return hist
|
||||
|
||||
|
||||
# ── 关键帧检测 ──────────────────────────────────────────────────
|
||||
|
||||
|
||||
def detect_keyframe_timestamps(
|
||||
video_path: str,
|
||||
*,
|
||||
min_interval_sec: float = MIN_KEYFRAME_INTERVAL_SEC,
|
||||
max_frames: int = MAX_KEYFRAMES,
|
||||
min_frames: int = MIN_KEYFRAMES,
|
||||
) -> list[float]:
|
||||
"""检测视频中的场景切换点，返回关键帧时间戳列表（秒）。
|
||||
|
||||
算法：
|
||||
1. 降采样到 320x240，逐帧转灰度
|
||||
2. 计算相邻帧灰度差异（像素均值差）
|
||||
3. 差异 > SCENE_CHANGE_THRESHOLD(30) 标记为候选关键帧
|
||||
4. 相邻关键帧间隔 < min_interval_sec 的，保留差异更大的那个
|
||||
5. 数量裁剪到 [min_frames, max_frames]
|
||||
|
||||
对于长视频（>3分钟）：
|
||||
- 每 30 秒一个分段
|
||||
- 每个分段至少选 2 个关键帧（如果分段内无场景切换，均匀取 2 帧）
|
||||
"""
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
if not cap.isOpened():
|
||||
raise RuntimeError(f"Cannot open video: {video_path}")
|
||||
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
duration = frame_count / fps if fps > 0 else 0
|
||||
|
||||
if duration <= 0:
|
||||
cap.release()
|
||||
return []
|
||||
|
||||
# 逐帧检测场景切换
|
||||
candidates: list[tuple[float, float]] = [] # (timestamp_sec, diff_score)
|
||||
prev_gray = None
|
||||
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
# 降采样 + 灰度
|
||||
small = cv2.resize(frame, (320, 240))
|
||||
gray = cv2.cvtColor(small, cv2.COLOR_BGR2GRAY).astype(np.float32)
|
||||
|
||||
if prev_gray is not None:
|
||||
diff = float(np.mean(np.abs(gray - prev_gray)))
|
||||
if diff > SCENE_CHANGE_THRESHOLD:
|
||||
pos_ms = cap.get(cv2.CAP_PROP_POS_MSEC)
|
||||
candidates.append((pos_ms / 1000.0, diff))
|
||||
|
||||
prev_gray = gray
|
||||
|
||||
cap.release()
|
||||
|
||||
# 按最小间隔过滤（保留差异更大的）
|
||||
filtered: list[tuple[float, float]] = []
|
||||
for ts, diff in sorted(candidates):
|
||||
if filtered and (ts - filtered[-1][0]) < min_interval_sec:
|
||||
if diff > filtered[-1][1]:
|
||||
filtered[-1] = (ts, diff)
|
||||
else:
|
||||
filtered.append((ts, diff))
|
||||
|
||||
keyframe_times = [ts for ts, _ in filtered]
|
||||
|
||||
# 数量不足 min_frames 时，在时间轴上均匀补充
|
||||
if len(keyframe_times) < min_frames:
|
||||
uniform = [duration * (i + 0.5) / min_frames for i in range(min_frames)]
|
||||
keyframe_times = sorted(set(uniform) | set(keyframe_times))
|
||||
# 如果合并后还不足 min_frames，直接用均匀分布
|
||||
if len(keyframe_times) < min_frames:
|
||||
keyframe_times = uniform
|
||||
|
||||
# 数量超过 max_frames 时，均匀采样
|
||||
if len(keyframe_times) > max_frames:
|
||||
step = len(keyframe_times) / max_frames
|
||||
keyframe_times = [keyframe_times[int(i * step)] for i in range(max_frames)]
|
||||
|
||||
# 长视频分段保底（>3分钟）
|
||||
if duration > LONG_VIDEO_DURATION_THRESHOLD_SEC:
|
||||
segment_count = int(duration / LONG_VIDEO_SEGMENT_SEC)
|
||||
for seg_idx in range(segment_count):
|
||||
seg_start = seg_idx * LONG_VIDEO_SEGMENT_SEC
|
||||
seg_end = min((seg_idx + 1) * LONG_VIDEO_SEGMENT_SEC, duration)
|
||||
seg_frames = [t for t in keyframe_times if seg_start <= t < seg_end]
|
||||
if len(seg_frames) < MIN_FRAMES_PER_SEGMENT:
|
||||
# 均匀补齐
|
||||
for i in range(MIN_FRAMES_PER_SEGMENT):
|
||||
t = seg_start + LONG_VIDEO_SEGMENT_SEC * (i + 0.5) / MIN_FRAMES_PER_SEGMENT
|
||||
if t not in keyframe_times and seg_start <= t < seg_end:
|
||||
keyframe_times.append(t)
|
||||
keyframe_times.sort()
|
||||
|
||||
return keyframe_times
|
||||
|
||||
|
||||
# ── 数据类 ──────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@dataclass
|
||||
class FingerprintChunk:
|
||||
"""单个分片指纹数据。"""
|
||||
|
||||
start_time_ms: int
|
||||
end_time_ms: int
|
||||
phash_binary: str
|
||||
color_histogram: list[float]
|
||||
frame_count: int = 1
|
||||
|
||||
|
||||
@dataclass
|
||||
class DuplicateSegment:
|
||||
"""一段重复片段的描述。"""
|
||||
|
||||
query_start_ms: int
|
||||
query_end_ms: int
|
||||
target_start_ms: int
|
||||
target_end_ms: int
|
||||
avg_distance: float # 该段内帧的平均汉明距离
|
||||
|
||||
|
||||
@dataclass
|
||||
class VideoFingerprint:
|
||||
"""Video fingerprint containing multiple similarity metrics."""
|
||||
@@ -243,7 +89,6 @@ class VideoFingerprint:
|
||||
color_histograms: list[list[float]]
|
||||
duration: float
|
||||
resolution: tuple[int, int]
|
||||
chunks: list[FingerprintChunk] = field(default_factory=list)
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
# 注意：color_histograms 里的值可能是 np.float32（来自 cv2.normalize），
|
||||
@@ -256,168 +101,8 @@ class VideoFingerprint:
|
||||
"color_histograms": native_histograms,
|
||||
"duration": float(self.duration),
|
||||
"resolution": [int(self.resolution[0]), int(self.resolution[1])],
|
||||
"chunks": [
|
||||
{
|
||||
"start_time_ms": c.start_time_ms,
|
||||
"end_time_ms": c.end_time_ms,
|
||||
"phash_binary": c.phash_binary,
|
||||
"color_histogram": [float(v) for v in c.color_histogram],
|
||||
"frame_count": c.frame_count,
|
||||
}
|
||||
for c in self.chunks
|
||||
],
|
||||
}
|
||||
|
||||
def to_chunk_models(self, video_id: str, project_id: str, user_id: str = "") -> list[VideoFingerprintChunkModel]:
|
||||
"""将分片数据转为 SQLAlchemy Model 列表，用于批量写入 video_fingerprint_chunks 表。"""
|
||||
models = []
|
||||
for chunk in self.chunks:
|
||||
models.append(
|
||||
VideoFingerprintChunkModel(
|
||||
id=uuid4().hex,
|
||||
video_id=video_id,
|
||||
project_id=project_id,
|
||||
user_id=user_id,
|
||||
start_time_ms=chunk.start_time_ms,
|
||||
end_time_ms=chunk.end_time_ms,
|
||||
phash_binary=chunk.phash_binary,
|
||||
color_histogram=[float(v) for v in chunk.color_histogram],
|
||||
frame_count=chunk.frame_count,
|
||||
)
|
||||
)
|
||||
return models
|
||||
|
||||
|
||||
# ── 滑动窗口时序匹配 ────────────────────────────────────────────
|
||||
|
||||
|
||||
def find_duplicate_segments(
|
||||
query_chunks: list,
|
||||
target_chunks: list,
|
||||
*,
|
||||
match_threshold: int = SEGMENT_MATCH_THRESHOLD,
|
||||
min_consecutive: int = MIN_CONSECUTIVE_MATCHES,
|
||||
max_gap: int = MAX_GAP,
|
||||
) -> list[DuplicateSegment]:
|
||||
"""滑动窗口时序匹配：找出两组分片之间的重复片段。
|
||||
|
||||
算法：
|
||||
1. 对每个 query chunk，找到 target 中汉明距离最小的 chunk
|
||||
2. 距离 <= match_threshold 视为匹配
|
||||
3. 找连续匹配的 run（允许 max_gap 帧间隙）
|
||||
4. 连续匹配数 >= min_consecutive 的 run 报告为重复片段
|
||||
|
||||
Args:
|
||||
query_chunks: 查询视频的分片列表（FingerprintChunk 或 dict）
|
||||
target_chunks: 目标视频的分片列表
|
||||
match_threshold: 汉明距离匹配阈值
|
||||
min_consecutive: 最少连续匹配帧数
|
||||
max_gap: 允许的最大间隙帧数
|
||||
|
||||
Returns:
|
||||
DuplicateSegment 列表
|
||||
"""
|
||||
if not query_chunks or not target_chunks:
|
||||
return []
|
||||
|
||||
def _get_phash(chunk) -> str:
|
||||
if isinstance(chunk, dict):
|
||||
return chunk["phash_binary"]
|
||||
return chunk.phash_binary
|
||||
|
||||
def _get_start(chunk) -> int:
|
||||
if isinstance(chunk, dict):
|
||||
return chunk["start_time_ms"]
|
||||
return chunk.start_time_ms
|
||||
|
||||
def _get_end(chunk) -> int:
|
||||
if isinstance(chunk, dict):
|
||||
return chunk["end_time_ms"]
|
||||
return chunk.end_time_ms
|
||||
|
||||
# Step 1: 逐帧匹配
|
||||
frame_matches: list[tuple[bool, int, int]] = [] # (is_match, min_dist, best_target_idx)
|
||||
for qc in query_chunks:
|
||||
qc_phash = _get_phash(qc)
|
||||
best_dist = 64
|
||||
best_idx = 0
|
||||
for j, tc in enumerate(target_chunks):
|
||||
d = hamming_distance(qc_phash, _get_phash(tc))
|
||||
if d < best_dist:
|
||||
best_dist = d
|
||||
best_idx = j
|
||||
frame_matches.append((best_dist <= match_threshold, best_dist, best_idx))
|
||||
|
||||
# Step 2: 找连续匹配的 runs
|
||||
runs: list[tuple[int, int]] = [] # list of (start_idx, end_idx)
|
||||
run_start = None
|
||||
gap_count = 0
|
||||
|
||||
for i, (is_match, _dist, _idx) in enumerate(frame_matches):
|
||||
if is_match:
|
||||
if run_start is None:
|
||||
run_start = i
|
||||
gap_count = 0 # 重置间隙
|
||||
else:
|
||||
if run_start is not None:
|
||||
gap_count += 1
|
||||
if gap_count > max_gap:
|
||||
# 中断当前 run
|
||||
run_end = i - gap_count # 最后一个匹配帧的索引
|
||||
# 计算 run 内的实际匹配帧数（总跨度 - 间隙数）
|
||||
total_gaps = sum(1 for k in range(run_start, run_end + 1) if not frame_matches[k][0])
|
||||
matching_count = (run_end - run_start + 1) - total_gaps
|
||||
if matching_count >= min_consecutive:
|
||||
runs.append((run_start, run_end))
|
||||
run_start = None
|
||||
gap_count = 0
|
||||
|
||||
# 处理末尾 run
|
||||
if run_start is not None:
|
||||
last_idx = len(frame_matches) - 1
|
||||
# 回退找到最后一个匹配帧的位置（跳过尾部非匹配帧）
|
||||
while last_idx >= run_start and not frame_matches[last_idx][0]:
|
||||
last_idx -= 1
|
||||
if last_idx >= run_start:
|
||||
# 计算 run 内的总间隙数
|
||||
total_gaps = sum(1 for k in range(run_start, last_idx + 1) if not frame_matches[k][0])
|
||||
matching_count = (last_idx - run_start + 1) - total_gaps
|
||||
if matching_count >= min_consecutive:
|
||||
runs.append((run_start, last_idx))
|
||||
|
||||
# Step 3: 构建 DuplicateSegment
|
||||
segments: list[DuplicateSegment] = []
|
||||
for start, end in runs:
|
||||
query_start = _get_start(query_chunks[start])
|
||||
query_end = _get_end(query_chunks[end])
|
||||
|
||||
# 取目标范围（按最佳匹配的目标 chunk 时间范围）
|
||||
target_indices = [frame_matches[k][2] for k in range(start, end + 1) if frame_matches[k][0]]
|
||||
if target_indices:
|
||||
t_min = min(target_indices)
|
||||
t_max = max(target_indices)
|
||||
target_start = _get_start(target_chunks[t_min])
|
||||
target_end = _get_end(target_chunks[t_max])
|
||||
else:
|
||||
target_start = _get_start(target_chunks[0])
|
||||
target_end = _get_end(target_chunks[-1])
|
||||
|
||||
avg_dist = sum(frame_matches[k][1] for k in range(start, end + 1)) / (end - start + 1)
|
||||
segments.append(
|
||||
DuplicateSegment(
|
||||
query_start_ms=query_start,
|
||||
query_end_ms=query_end,
|
||||
target_start_ms=target_start,
|
||||
target_end_ms=target_end,
|
||||
avg_distance=avg_dist,
|
||||
)
|
||||
)
|
||||
|
||||
return segments
|
||||
|
||||
|
||||
# ── VideoDeduplicator ───────────────────────────────────────────
|
||||
|
||||
|
||||
class VideoDeduplicator:
|
||||
"""Video deduplication using multiple fingerprint methods."""
|
||||
@@ -426,12 +111,7 @@ class VideoDeduplicator:
|
||||
HISTOGRAM_THRESHOLD = 0.85
|
||||
|
||||
def compute_fingerprint(self, video_path: str) -> VideoFingerprint:
|
||||
"""Compute video fingerprint using dynamic keyframe detection.
|
||||
|
||||
使用 detect_keyframe_timestamps() 检测内容感知关键帧，
|
||||
在每个关键帧处取帧计算 pHash + color_histogram。
|
||||
同时保留 MD5 计算和分片数据结构。
|
||||
"""
|
||||
"""Compute video fingerprint using MD5, pHash, and color histogram."""
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
if not cap.isOpened():
|
||||
raise RuntimeError(f"Cannot open video: {video_path}")
|
||||
@@ -442,122 +122,43 @@ class VideoDeduplicator:
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
|
||||
cap.release()
|
||||
|
||||
# 1. 检测关键帧时间戳
|
||||
keyframe_times = detect_keyframe_timestamps(video_path)
|
||||
|
||||
if not keyframe_times:
|
||||
return VideoFingerprint(
|
||||
md5="",
|
||||
keyframe_phashes=[],
|
||||
color_histograms=[],
|
||||
duration=duration,
|
||||
resolution=(width, height),
|
||||
chunks=[],
|
||||
)
|
||||
|
||||
# 2. 打开视频，逐个关键帧取帧
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
md5_hash = hashlib.md5(usedforsecurity=False)
|
||||
chunks: list[FingerprintChunk] = []
|
||||
keyframe_phashes = []
|
||||
color_histograms = []
|
||||
|
||||
for i, t_sec in enumerate(keyframe_times):
|
||||
seek_ms = t_sec * 1000
|
||||
cap.set(cv2.CAP_PROP_POS_MSEC, seek_ms)
|
||||
frame_interval = max(1, frame_count // 10)
|
||||
for i in range(0, frame_count, frame_interval):
|
||||
cap.set(cv2.CAP_PROP_POS_FRAMES, i)
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
continue
|
||||
|
||||
# MD5 计算
|
||||
_, buffer = cv2.imencode(".jpg", frame)
|
||||
md5_hash.update(buffer)
|
||||
|
||||
phash = compute_phash(frame)
|
||||
hist = compute_color_histogram(frame)
|
||||
|
||||
# 计算分片时间范围（从前一个关键帧到下一个关键帧的中点）
|
||||
prev_boundary = keyframe_times[i - 1] * 1000 if i > 0 else 0
|
||||
next_boundary = keyframe_times[i + 1] * 1000 if i < len(keyframe_times) - 1 else duration * 1000
|
||||
start_ms = int((prev_boundary + seek_ms) / 2)
|
||||
end_ms = int((seek_ms + next_boundary) / 2)
|
||||
|
||||
chunks.append(
|
||||
FingerprintChunk(
|
||||
start_time_ms=start_ms,
|
||||
end_time_ms=end_ms,
|
||||
phash_binary=phash,
|
||||
color_histogram=hist,
|
||||
frame_count=1,
|
||||
)
|
||||
)
|
||||
keyframe_phashes.append(compute_phash(frame))
|
||||
color_histograms.append(compute_color_histogram(frame))
|
||||
|
||||
cap.release()
|
||||
|
||||
# 向后兼容：聚合 keyframe_phashes / color_histograms
|
||||
keyframe_phashes = [c.phash_binary for c in chunks]
|
||||
color_histograms = [c.color_histogram for c in chunks]
|
||||
|
||||
return VideoFingerprint(
|
||||
md5=md5_hash.hexdigest(),
|
||||
keyframe_phashes=keyframe_phashes,
|
||||
color_histograms=color_histograms,
|
||||
duration=duration,
|
||||
resolution=(width, height),
|
||||
chunks=chunks,
|
||||
)
|
||||
|
||||
def _get_existing_chunks(self, video_id: str, session: Session) -> list[dict]:
|
||||
"""从 video_fingerprint_chunks 表读取分片数据。返回空列表表示无分片数据。"""
|
||||
rows = (
|
||||
session.query(VideoFingerprintChunkModel)
|
||||
.filter(VideoFingerprintChunkModel.video_id == video_id)
|
||||
.order_by(VideoFingerprintChunkModel.start_time_ms)
|
||||
.all()
|
||||
)
|
||||
return [
|
||||
{
|
||||
"phash_binary": r.phash_binary,
|
||||
"color_histogram": r.color_histogram,
|
||||
"start_time_ms": r.start_time_ms,
|
||||
"end_time_ms": r.end_time_ms,
|
||||
}
|
||||
for r in rows
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def _bhattacharyya_coefficient(hist_a: list[float], hist_b: list[float]) -> float:
|
||||
"""Bhattacharyya 系数：Σ √(a[i] * b[i])，范围 [0, 1]，1=完全相同。"""
|
||||
min_len = min(len(hist_a), len(hist_b))
|
||||
a = hist_a[:min_len]
|
||||
b = hist_b[:min_len]
|
||||
return float(sum(np.sqrt(ai * bi) for ai, bi in zip(a, b, strict=False)))
|
||||
|
||||
@staticmethod
|
||||
def _compute_histogram_similarity(
|
||||
histograms_a: list[list[float]],
|
||||
histograms_b: list[list[float]],
|
||||
) -> float:
|
||||
"""对每组直方图，找到最佳匹配的 Bhattacharyya 系数，取平均。"""
|
||||
if not histograms_a or not histograms_b:
|
||||
return 0.0
|
||||
similarities = []
|
||||
for ha in histograms_a:
|
||||
best = 0.0
|
||||
for hb in histograms_b:
|
||||
bc = VideoDeduplicator._bhattacharyya_coefficient(ha, hb)
|
||||
best = max(best, bc)
|
||||
similarities.append(best)
|
||||
return sum(similarities) / len(similarities) if similarities else 0.0
|
||||
|
||||
def check_duplicate(self, fingerprint: VideoFingerprint, project_id: str, session: Session) -> Optional[dict]:
|
||||
"""检查视频是否与项目中已有视频重复。
|
||||
|
||||
查重逻辑：
|
||||
1. MD5 精确匹配 → similarity=1.0
|
||||
2. pHash 中位数距离 + 帧匹配比例 + 直方图融合判定
|
||||
判定逻辑（按优先级）：
|
||||
1. MD5 精确匹配：完全一致则 similarity=1.0，立即返回
|
||||
2. pHash 相似度：计算新视频每帧 phash 与已有视频每帧 phash 的最小汉明距离，
|
||||
取所有帧的平均值 avg_distance。若 avg_distance < PHASH_THRESHOLD(10)，
|
||||
则判定为重复，similarity = 1.0 - (avg_distance / 64)
|
||||
|
||||
判定为重复后，调用 find_duplicate_segments() 获取具体重复片段。
|
||||
注意：返回第一个通过阈值的匹配（非最优匹配）。
|
||||
|
||||
Args:
|
||||
fingerprint: 待检测视频的指纹
|
||||
@@ -565,7 +166,7 @@ class VideoDeduplicator:
|
||||
session: 数据库会话
|
||||
|
||||
Returns:
|
||||
重复信息字典（含 duplicate, duplicate_of, reason, similarity, duplicate_segments），
|
||||
重复信息字典（含 duplicate, duplicate_of, reason, similarity），
|
||||
或 None 表示未找到重复。
|
||||
"""
|
||||
video_repo = SQLAlchemyGeneratedVideoRepository(session)
|
||||
@@ -581,77 +182,28 @@ class VideoDeduplicator:
|
||||
if fingerprint.md5 == ef.get("md5"):
|
||||
return {"duplicate": True, "duplicate_of": existing.id, "reason": "exact_md5_match", "similarity": 1.0}
|
||||
|
||||
# 优先从分片表读取已有视频的分片 phash
|
||||
existing_phashes = []
|
||||
chunk_data = self._get_existing_chunks(existing.id, session)
|
||||
if chunk_data:
|
||||
existing_phashes = [c["phash_binary"] for c in chunk_data]
|
||||
else:
|
||||
# 回退：从 JSON 字段读取（存量旧视频）
|
||||
existing_phashes = ef.get("keyframe_phashes", [])
|
||||
|
||||
# 感知哈希相似度
|
||||
existing_phashes = ef.get("keyframe_phashes", [])
|
||||
if not existing_phashes:
|
||||
continue
|
||||
|
||||
# 计算每个新关键帧到已有关键帧的最小汉明距离
|
||||
# 计算每个新关键帧到已有关键帧的最小汉明距离，取平均
|
||||
min_distances = []
|
||||
for phash in fingerprint.keyframe_phashes:
|
||||
distances = [hamming_distance(phash, ep) for ep in existing_phashes]
|
||||
min_distances.append(min(distances))
|
||||
avg_distance = sum(min_distances) / len(min_distances) if min_distances else 100
|
||||
|
||||
# 帧匹配比例检查
|
||||
matching_frames = sum(1 for d in min_distances if d < self.PHASH_THRESHOLD)
|
||||
match_ratio = matching_frames / len(min_distances) if min_distances else 0
|
||||
if match_ratio < 0.7:
|
||||
if avg_distance >= self.PHASH_THRESHOLD:
|
||||
continue
|
||||
|
||||
# 中位数距离
|
||||
median_distance = statistics.median(min_distances) if min_distances else 64
|
||||
if median_distance >= self.PHASH_THRESHOLD:
|
||||
continue
|
||||
|
||||
# 直方图融合
|
||||
existing_histograms = []
|
||||
if chunk_data:
|
||||
existing_histograms = [c["color_histogram"] for c in chunk_data if c.get("color_histogram")]
|
||||
else:
|
||||
existing_histograms = ef.get("color_histograms", [])
|
||||
|
||||
phash_similarity = 1.0 - (median_distance / 64)
|
||||
hist_similarity = (
|
||||
self._compute_histogram_similarity(fingerprint.color_histograms, existing_histograms)
|
||||
if existing_histograms
|
||||
else 0.5
|
||||
)
|
||||
combined_score = 0.7 * phash_similarity + 0.3 * hist_similarity
|
||||
|
||||
# DUPLICATE_THRESHOLD from module level
|
||||
if combined_score < DUPLICATE_THRESHOLD:
|
||||
continue
|
||||
|
||||
# 滑动窗口时序匹配：获取具体重复片段
|
||||
existing_chunk_objects = (
|
||||
chunk_data
|
||||
if chunk_data
|
||||
else [{"phash_binary": p, "start_time_ms": 0, "end_time_ms": 0} for p in existing_phashes]
|
||||
)
|
||||
segments = find_duplicate_segments(fingerprint.chunks, existing_chunk_objects)
|
||||
phash_similarity = 1.0 - (avg_distance / 64)
|
||||
|
||||
return {
|
||||
"duplicate": True,
|
||||
"duplicate_of": existing.id,
|
||||
"reason": "phash_histogram_fusion",
|
||||
"similarity": combined_score,
|
||||
"duplicate_segments": [
|
||||
{
|
||||
"query_start_ms": s.query_start_ms,
|
||||
"query_end_ms": s.query_end_ms,
|
||||
"target_start_ms": s.target_start_ms,
|
||||
"target_end_ms": s.target_end_ms,
|
||||
"avg_distance": round(s.avg_distance, 2),
|
||||
}
|
||||
for s in segments
|
||||
],
|
||||
"reason": "phash_similar",
|
||||
"similarity": phash_similarity,
|
||||
}
|
||||
|
||||
return None
|
||||
@@ -665,8 +217,7 @@ class VideoDeduplicator:
|
||||
) -> Optional[dict]:
|
||||
"""检查视频是否与同批次内其他视频重复。
|
||||
|
||||
逻辑与 check_duplicate 一致（MD5 + pHash + 直方图融合 + 时序匹配），
|
||||
但搜索范围限定为同 batch_id 的视频。
|
||||
逻辑与 check_duplicate 一致（MD5 + pHash），但搜索范围限定为同 batch_id 的视频。
|
||||
|
||||
Args:
|
||||
fingerprint: 待检测视频的指纹
|
||||
@@ -696,14 +247,7 @@ class VideoDeduplicator:
|
||||
"similarity": 1.0,
|
||||
}
|
||||
|
||||
# 优先从分片表读取
|
||||
existing_phashes = []
|
||||
chunk_data = self._get_existing_chunks(existing.id, session)
|
||||
if chunk_data:
|
||||
existing_phashes = [c["phash_binary"] for c in chunk_data]
|
||||
else:
|
||||
existing_phashes = ef.get("keyframe_phashes", [])
|
||||
|
||||
existing_phashes = ef.get("keyframe_phashes", [])
|
||||
if not existing_phashes:
|
||||
continue
|
||||
|
||||
@@ -711,110 +255,93 @@ class VideoDeduplicator:
|
||||
for phash in fingerprint.keyframe_phashes:
|
||||
distances = [hamming_distance(phash, ep) for ep in existing_phashes]
|
||||
min_distances.append(min(distances))
|
||||
avg_distance = sum(min_distances) / len(min_distances) if min_distances else 100
|
||||
|
||||
# 帧匹配比例检查
|
||||
matching_frames = sum(1 for d in min_distances if d < self.PHASH_THRESHOLD)
|
||||
match_ratio = matching_frames / len(min_distances) if min_distances else 0
|
||||
if match_ratio < 0.7:
|
||||
if avg_distance >= self.PHASH_THRESHOLD:
|
||||
continue
|
||||
|
||||
median_distance = statistics.median(min_distances) if min_distances else 64
|
||||
if median_distance >= self.PHASH_THRESHOLD:
|
||||
continue
|
||||
|
||||
# 直方图融合
|
||||
existing_histograms = []
|
||||
if chunk_data:
|
||||
existing_histograms = [c["color_histogram"] for c in chunk_data if c.get("color_histogram")]
|
||||
else:
|
||||
existing_histograms = ef.get("color_histograms", [])
|
||||
|
||||
phash_similarity = 1.0 - (median_distance / 64)
|
||||
hist_similarity = (
|
||||
self._compute_histogram_similarity(fingerprint.color_histograms, existing_histograms)
|
||||
if existing_histograms
|
||||
else 0.5
|
||||
)
|
||||
combined_score = 0.7 * phash_similarity + 0.3 * hist_similarity
|
||||
|
||||
# DUPLICATE_THRESHOLD from module level
|
||||
if combined_score < DUPLICATE_THRESHOLD:
|
||||
continue
|
||||
|
||||
# 滑动窗口时序匹配
|
||||
existing_chunk_objects = (
|
||||
chunk_data
|
||||
if chunk_data
|
||||
else [{"phash_binary": p, "start_time_ms": 0, "end_time_ms": 0} for p in existing_phashes]
|
||||
)
|
||||
segments = find_duplicate_segments(fingerprint.chunks, existing_chunk_objects)
|
||||
|
||||
phash_similarity = 1.0 - (avg_distance / 64)
|
||||
return {
|
||||
"duplicate": True,
|
||||
"duplicate_of": existing.id,
|
||||
"reason": "batch_phash_histogram_fusion",
|
||||
"similarity": combined_score,
|
||||
"duplicate_segments": [
|
||||
{
|
||||
"query_start_ms": s.query_start_ms,
|
||||
"query_end_ms": s.query_end_ms,
|
||||
"target_start_ms": s.target_start_ms,
|
||||
"target_end_ms": s.target_end_ms,
|
||||
"avg_distance": round(s.avg_distance, 2),
|
||||
}
|
||||
for s in segments
|
||||
],
|
||||
"reason": "batch_phash_similar",
|
||||
"similarity": phash_similarity,
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _average_histogram_similarity(histograms_a: list[list[float]], histograms_b: list[list[float]]) -> float:
|
||||
"""
|
||||
计算两组颜色直方图之间的平均余弦相似度。
|
||||
|
||||
对每组直方图对取最小长度对齐，计算余弦相似度后取平均。
|
||||
|
||||
Args:
|
||||
histograms_a: 第一组直方图（每帧一个 list）
|
||||
histograms_b: 第二组直方图
|
||||
|
||||
Returns:
|
||||
平均余弦相似度，范围 [0, 1]
|
||||
"""
|
||||
if not histograms_a or not histograms_b:
|
||||
return 0.0
|
||||
|
||||
similarities = []
|
||||
for ha in histograms_a:
|
||||
best = 0.0
|
||||
vec_a = np.array(ha, dtype=np.float64)
|
||||
norm_a = np.linalg.norm(vec_a)
|
||||
if norm_a == 0:
|
||||
continue
|
||||
for hb in histograms_b:
|
||||
vec_b = np.array(hb, dtype=np.float64)
|
||||
# 对齐长度
|
||||
min_len = min(len(vec_a), len(vec_b))
|
||||
va, vb = vec_a[:min_len], vec_b[:min_len]
|
||||
norm_b = np.linalg.norm(vb)
|
||||
if norm_b == 0:
|
||||
continue
|
||||
sim = float(np.dot(va, vb) / (norm_a * norm_b))
|
||||
best = max(best, sim)
|
||||
similarities.append(best)
|
||||
|
||||
return sum(similarities) / len(similarities) if similarities else 0.0
|
||||
|
||||
def compute_duplicate_rate(
|
||||
self,
|
||||
fingerprint: VideoFingerprint,
|
||||
project_id: str,
|
||||
current_video_id: str | None,
|
||||
session: Session,
|
||||
*,
|
||||
user_id: str = "",
|
||||
) -> float:
|
||||
"""计算当前视频与用户库内已有视频的最高相似度百分比。
|
||||
"""计算当前视频与项目内已有视频的最高相似度百分比。
|
||||
|
||||
优先按 user_id 全局比较（跨项目），user_id 为空时回退到项目级比较。
|
||||
遍历最近 200 个其他有指纹的视频，对每个计算融合相似度：
|
||||
遍历项目内所有其他有指纹的视频，对每个计算相似度：
|
||||
- MD5 精确匹配 → 100%
|
||||
- pHash + 直方图融合 → 0.7 * phash_sim + 0.3 * hist_sim
|
||||
- pHash 相似度 → (1.0 - avg_distance / 64) * 100
|
||||
取最高值作为 duplicate_rate（0~100）。
|
||||
如果没有其他视频可比较，返回 0.0。
|
||||
|
||||
Args:
|
||||
fingerprint: 当前视频的指纹
|
||||
project_id: 项目 ID（user_id 为空时的回退范围）
|
||||
project_id: 项目 ID
|
||||
current_video_id: 当前视频 ID（排除自身，可为 None）
|
||||
session: 数据库会话
|
||||
user_id: 用户 ID（优先按用户全局比较）
|
||||
|
||||
Returns:
|
||||
duplicate_rate: 0~100 的浮点数
|
||||
"""
|
||||
# 限制查询最近 100 个视频，避免大项目内存溢出
|
||||
from packages.adapters.sqlalchemy_impl.models import GeneratedVideoModel
|
||||
|
||||
# 优先按 user_id 全局比较（跨项目），否则回退到项目级
|
||||
if user_id:
|
||||
query = session.query(GeneratedVideoModel).filter(
|
||||
GeneratedVideoModel.user_id == user_id,
|
||||
)
|
||||
logger.debug("compute_duplicate_rate: user-level scope user_id=%s", user_id)
|
||||
else:
|
||||
query = session.query(GeneratedVideoModel).filter(
|
||||
GeneratedVideoModel.project_id == project_id,
|
||||
)
|
||||
logger.debug("compute_duplicate_rate: project-level fallback project_id=%s", project_id)
|
||||
|
||||
# 排除当前视频自身
|
||||
if current_video_id:
|
||||
query = query.filter(GeneratedVideoModel.id != current_video_id)
|
||||
|
||||
recent_models = query.order_by(GeneratedVideoModel.generated_at.desc()).limit(200).all()
|
||||
recent_models = (
|
||||
session.query(GeneratedVideoModel)
|
||||
.filter(GeneratedVideoModel.project_id == project_id)
|
||||
.order_by(GeneratedVideoModel.generated_at.desc())
|
||||
.limit(100)
|
||||
.all()
|
||||
)
|
||||
video_repo = SQLAlchemyGeneratedVideoRepository(session)
|
||||
existing_videos = [video_repo._to_domain(m) for m in recent_models]
|
||||
|
||||
@@ -831,14 +358,8 @@ class VideoDeduplicator:
|
||||
if fingerprint.md5 == ef.get("md5"):
|
||||
return 100.0
|
||||
|
||||
# 优先从分片表读取
|
||||
existing_phashes = []
|
||||
chunk_data = self._get_existing_chunks(existing.id, session)
|
||||
if chunk_data:
|
||||
existing_phashes = [c["phash_binary"] for c in chunk_data]
|
||||
else:
|
||||
existing_phashes = ef.get("keyframe_phashes", [])
|
||||
|
||||
# pHash 相似度
|
||||
existing_phashes = ef.get("keyframe_phashes", [])
|
||||
if not existing_phashes or not fingerprint.keyframe_phashes:
|
||||
continue
|
||||
|
||||
@@ -846,59 +367,13 @@ class VideoDeduplicator:
|
||||
for phash in fingerprint.keyframe_phashes:
|
||||
distances = [hamming_distance(phash, ep) for ep in existing_phashes]
|
||||
min_distances.append(min(distances))
|
||||
|
||||
# 帧匹配比例检查
|
||||
matching_frames = sum(1 for d in min_distances if d < self.PHASH_THRESHOLD)
|
||||
match_ratio = matching_frames / len(min_distances) if min_distances else 0
|
||||
if match_ratio < 0.7:
|
||||
continue
|
||||
|
||||
median_distance = statistics.median(min_distances) if min_distances else 64
|
||||
|
||||
# 直方图融合
|
||||
existing_histograms = []
|
||||
if chunk_data:
|
||||
existing_histograms = [c["color_histogram"] for c in chunk_data if c.get("color_histogram")]
|
||||
else:
|
||||
existing_histograms = ef.get("color_histograms", [])
|
||||
|
||||
phash_similarity = (1.0 - median_distance / 64) * 100
|
||||
hist_similarity = (
|
||||
self._compute_histogram_similarity(fingerprint.color_histograms, existing_histograms) * 100
|
||||
if existing_histograms
|
||||
else 50.0
|
||||
)
|
||||
combined_score = 0.7 * phash_similarity + 0.3 * hist_similarity
|
||||
max_similarity = max(max_similarity, combined_score)
|
||||
avg_distance = sum(min_distances) / len(min_distances) if min_distances else 64
|
||||
similarity = (1.0 - avg_distance / 64) * 100
|
||||
max_similarity = max(max_similarity, similarity)
|
||||
|
||||
return round(max(max_similarity, 0.0), 2)
|
||||
|
||||
|
||||
def _save_fingerprint_chunks(
|
||||
fingerprint: VideoFingerprint,
|
||||
video_id: str,
|
||||
project_id: str,
|
||||
user_id: str,
|
||||
session: Session,
|
||||
) -> None:
|
||||
"""将指纹分片数据批量写入 video_fingerprint_chunks 表。幂等：已有数据时跳过。"""
|
||||
# 幂等检查：已有分片数据则跳过
|
||||
existing_count = (
|
||||
session.query(VideoFingerprintChunkModel).filter(VideoFingerprintChunkModel.video_id == video_id).count()
|
||||
)
|
||||
if existing_count > 0:
|
||||
logger.debug("Fingerprint chunks already exist for video %s (%d chunks), skipping", video_id, existing_count)
|
||||
return
|
||||
|
||||
if not fingerprint.chunks:
|
||||
logger.warning("No chunks in fingerprint for video %s, skipping chunk save", video_id)
|
||||
return
|
||||
|
||||
chunk_models = fingerprint.to_chunk_models(video_id, project_id, user_id)
|
||||
session.bulk_save_objects(chunk_models)
|
||||
logger.info("Saved %d fingerprint chunks for video %s", len(chunk_models), video_id)
|
||||
|
||||
|
||||
@celery_app.task(bind=True, max_retries=3, name="worker.check_duplicate")
|
||||
def check_duplicate_task(self: Task, generated_video_id: str) -> dict:
|
||||
"""Celery task to check if generated video is a duplicate."""
|
||||
@@ -932,10 +407,6 @@ def check_duplicate_task(self: Task, generated_video_id: str) -> dict:
|
||||
video.duplicate_of = None
|
||||
|
||||
video_repo.update(video)
|
||||
|
||||
# 写入分片表
|
||||
_save_fingerprint_chunks(fingerprint, generated_video_id, video.project_id, video.user_id, session)
|
||||
|
||||
session.commit()
|
||||
|
||||
logger.info(f"Duplicate check completed for video {generated_video_id}: is_duplicate={video.is_duplicate}")
|
||||
|
||||
@@ -100,14 +100,6 @@ def create_video_record_and_dedup(
|
||||
|
||||
generated_video.video_fingerprint = fingerprint.to_dict()
|
||||
|
||||
# 写入分片指纹表
|
||||
from video_processing.dedup import _save_fingerprint_chunks
|
||||
|
||||
try:
|
||||
_save_fingerprint_chunks(fingerprint, video_id, project_id, user_id, session)
|
||||
except Exception as chunk_err:
|
||||
logger.warning("Failed to save fingerprint chunks for %s: %s", video_id, chunk_err)
|
||||
|
||||
# (a) 历史成片查重
|
||||
duplicate_result = deduplicator.check_duplicate(fingerprint, project_id, session)
|
||||
|
||||
@@ -131,13 +123,7 @@ def create_video_record_and_dedup(
|
||||
|
||||
# 计算重复率百分比（与项目内所有已有视频对比取最高相似度）
|
||||
try:
|
||||
dup_rate = deduplicator.compute_duplicate_rate(
|
||||
fingerprint,
|
||||
project_id,
|
||||
video_id,
|
||||
session,
|
||||
user_id=user_id,
|
||||
)
|
||||
dup_rate = deduplicator.compute_duplicate_rate(fingerprint, project_id, video_id, session)
|
||||
generated_video.duplicate_rate = dup_rate
|
||||
logger.info("Duplicate rate for %s: %.2f%%", video_id, dup_rate)
|
||||
except Exception as rate_err:
|
||||
|
||||
@@ -200,22 +200,6 @@ class UnifiedRenderService:
|
||||
|
||||
# 3. 计算视频总时长（用于字幕显示时长）
|
||||
video_duration = self._estimate_total_duration(layers)
|
||||
# Debug: 输出各图层时长明细
|
||||
for layer in layers:
|
||||
layer_total = sum(UnifiedRenderService._clip_adjusted_duration(c) for c in layer.clips)
|
||||
clip_details = [
|
||||
f"{c.clip_id}(dur={c.duration:.3f},actual={c.actual_duration:.3f},speed={getattr(c, 'playback_speed', 1.0):.4f})"
|
||||
for c in layer.clips
|
||||
]
|
||||
logger.info(
|
||||
"[debug] layer=%s clips=%d total=%.3f transition_duration=%.3f details=%s",
|
||||
layer.role,
|
||||
len(layer.clips),
|
||||
layer_total,
|
||||
self.transition_duration,
|
||||
", ".join(clip_details),
|
||||
)
|
||||
logger.info("[debug] estimated video_duration=%.3f", video_duration)
|
||||
|
||||
# 3.5 TTS 配音生成（如果配置了）
|
||||
self._maybe_add_voiceover_layer(layers, video_duration=video_duration)
|
||||
@@ -1438,7 +1422,6 @@ class UnifiedRenderService:
|
||||
if trim_segments and len(trim_segments) > 1:
|
||||
# 多段裁剪：展开为多个 clip
|
||||
resolved_segments = TrimEngine.resolve_segments(trim_segments, actual_duration)
|
||||
configured_speed = getattr(clip, "playback_speed", 1.0) or 1.0
|
||||
for i, seg in enumerate(resolved_segments):
|
||||
# 每个段生成一个独立的 ResolvedClip
|
||||
seg_clip_id = f"{clip.id}_seg_{seg.segment_id}"
|
||||
@@ -1446,19 +1429,6 @@ class UnifiedRenderService:
|
||||
seg_start = seg.trim.start_time
|
||||
seg_duration = seg.trim.duration
|
||||
|
||||
# 多段裁剪：如果段的时长超过素材实际时长，减速补偿
|
||||
seg_speed = configured_speed
|
||||
if actual_duration > 0 and seg_duration > actual_duration + 0.05:
|
||||
seg_speed = max(0.25, round(configured_speed * actual_duration / seg_duration, 4))
|
||||
logger.info(
|
||||
"[debug] multi-seg clip=%s seg=%s duration=%.3f actual=%.3f → speed=%.4f",
|
||||
clip.id,
|
||||
seg.segment_id,
|
||||
seg_duration,
|
||||
actual_duration,
|
||||
seg_speed,
|
||||
)
|
||||
|
||||
rc = ResolvedClip(
|
||||
clip_id=seg_clip_id,
|
||||
asset_id=asset_id,
|
||||
@@ -1469,7 +1439,7 @@ class UnifiedRenderService:
|
||||
duration=seg_duration,
|
||||
transition_effect=clip.transition_effect or "cut",
|
||||
transition_duration=getattr(clip, "transition_duration", 0.0) or 0.0,
|
||||
playback_speed=seg_speed,
|
||||
playback_speed=getattr(clip, "playback_speed", 1.0) or 1.0,
|
||||
config={**clip_config, "_segment_id": seg.segment_id},
|
||||
actual_duration=actual_duration,
|
||||
trim_config=seg.trim,
|
||||
@@ -1491,7 +1461,6 @@ class UnifiedRenderService:
|
||||
effective_trim: TrimConfig | None = None
|
||||
final_start = clip.start_time
|
||||
final_duration = clip.duration
|
||||
configured_speed = getattr(clip, "playback_speed", 1.0) or 1.0
|
||||
|
||||
if trim_config is not None and actual_duration > 0:
|
||||
effective_trim = trim_config.validate_and_resolve(actual_duration)
|
||||
@@ -1505,25 +1474,6 @@ class UnifiedRenderService:
|
||||
final_start = 0.0
|
||||
final_duration = actual_duration
|
||||
|
||||
# 素材实际时长不足以覆盖配置的时长时，降低播放速度来补偿
|
||||
# 例如：配置4s但素材只有3s → speed=0.75x，用满3s素材达到4s输出
|
||||
if actual_duration > 0 and final_duration > actual_duration + 0.05:
|
||||
compensated_speed = actual_duration / final_duration
|
||||
# 保留用户设置的速度（如果已减速则叠加）
|
||||
final_speed = configured_speed * compensated_speed
|
||||
# 下限 0.25x
|
||||
final_speed = max(0.25, round(final_speed, 4))
|
||||
logger.info(
|
||||
"[debug] clip=%s duration=%.3f actual=%.3f → 减速补偿 speed=%.4f (configured=%.3f)",
|
||||
clip.id,
|
||||
final_duration,
|
||||
actual_duration,
|
||||
final_speed,
|
||||
configured_speed,
|
||||
)
|
||||
else:
|
||||
final_speed = configured_speed
|
||||
|
||||
rc = ResolvedClip(
|
||||
clip_id=clip.id,
|
||||
asset_id=asset_id,
|
||||
@@ -1534,25 +1484,13 @@ class UnifiedRenderService:
|
||||
duration=final_duration,
|
||||
transition_effect=clip.transition_effect or "cut",
|
||||
transition_duration=getattr(clip, "transition_duration", 0.0) or 0.0,
|
||||
playback_speed=final_speed,
|
||||
playback_speed=getattr(clip, "playback_speed", 1.0) or 1.0,
|
||||
config=clip_config,
|
||||
actual_duration=actual_duration,
|
||||
trim_config=effective_trim,
|
||||
)
|
||||
resolved.append(rc)
|
||||
|
||||
# Debug日志：记录每个clip的时长信息
|
||||
eff_dur = _clip_effective_duration_pure(final_duration, actual_duration)
|
||||
logger.info(
|
||||
"[debug] resolved clip=%s duration=%.3f actual=%.3f effective=%.3f speed=%.4f start=%.3f",
|
||||
clip.id,
|
||||
final_duration,
|
||||
actual_duration,
|
||||
eff_dur,
|
||||
final_speed,
|
||||
final_start,
|
||||
)
|
||||
|
||||
# 按 order 排序
|
||||
resolved.sort(key=lambda c: c.order)
|
||||
return resolved
|
||||
@@ -1745,7 +1683,7 @@ class UnifiedRenderService:
|
||||
if d > 0:
|
||||
layer_dur = d
|
||||
break
|
||||
xfade_filter, xfade_estimated_dur = self._transition_engine.build_xfade_chain(
|
||||
xfade_filter, _ = self._transition_engine.build_xfade_chain(
|
||||
clip_durations=layer_durations,
|
||||
clip_video_labels=layer_labels,
|
||||
transitions=layer_transitions,
|
||||
@@ -1754,13 +1692,6 @@ class UnifiedRenderService:
|
||||
)
|
||||
if xfade_filter:
|
||||
filter_parts.append(xfade_filter)
|
||||
logger.info(
|
||||
"[unified-render] layer=%s xfade: clips=%d durations=%s estimated_dur=%.3f",
|
||||
layer.role,
|
||||
len(layer_labels),
|
||||
[round(d, 3) for d in layer_durations],
|
||||
xfade_estimated_dur,
|
||||
)
|
||||
layer_output_labels[layer.role] = out_label
|
||||
|
||||
# Step 3: 合成各层
|
||||
@@ -1984,14 +1915,8 @@ class UnifiedRenderService:
|
||||
def _clip_effective_duration(clip: ResolvedClip) -> float:
|
||||
"""计算 clip 的有效时长（原速 trim 后时长）。
|
||||
|
||||
如果 playback_speed < 1（为补偿素材不足而减速），返回配置的 duration，
|
||||
而非 min(duration, actual_duration)。
|
||||
实际实现移至 packages.domain.render_layer_utils.clip_effective_duration。
|
||||
"""
|
||||
speed = getattr(clip, "playback_speed", 1.0) or 1.0
|
||||
# 减速场景：duration 已通过降低 playback_speed 补偿，返回配置的 duration
|
||||
if speed < 1.0 - 1e-6 and clip.duration > 0:
|
||||
return clip.duration
|
||||
return _clip_effective_duration_pure(clip.duration, clip.actual_duration)
|
||||
|
||||
# ── 画中画（PiP）相关方法 ──────────────────────────────────────────────────
|
||||
|
||||
@@ -630,7 +630,7 @@ def ingest_asset(job_id: str) -> dict:
|
||||
name=filename,
|
||||
storage_key=job.storage_key,
|
||||
mime_type=mime_type,
|
||||
metadata={"source": "upload", "ingest_error": error_reason},
|
||||
metadata={"ingest_error": error_reason},
|
||||
file_size=int(metadata.get("size_bytes", 0)),
|
||||
duration=float(metadata.get("duration", 0)),
|
||||
width=int(metadata.get("width", 0)),
|
||||
@@ -656,57 +656,24 @@ def ingest_asset(job_id: str) -> dict:
|
||||
"error": error_reason,
|
||||
}
|
||||
|
||||
# 查找已存在的 Asset 记录（由 API 端在上传完成时立即创建为 PROCESSING 状态）
|
||||
existing_asset = None
|
||||
try:
|
||||
existing_asset = asset_repo.find_by_storage_key(job.storage_key)
|
||||
except Exception:
|
||||
logger.warning("find_by_storage_key not available, trying fallback lookup")
|
||||
|
||||
if existing_asset is None:
|
||||
# 兜底：如果 API 端没有预先创建 Asset（旧版本兼容），则创建新记录
|
||||
logger.info("No pre-created asset found for storage_key=%s, creating new", job.storage_key)
|
||||
metadata["source"] = "upload"
|
||||
asset = Asset.create(
|
||||
project_id=job.project_id,
|
||||
library_id=job.library_id,
|
||||
name=filename,
|
||||
storage_key=job.storage_key,
|
||||
mime_type=mime_type,
|
||||
metadata=metadata,
|
||||
file_size=int(metadata.get("size_bytes", 0)),
|
||||
duration=float(metadata.get("duration", 0)),
|
||||
width=int(metadata.get("width", 0)),
|
||||
height=int(metadata.get("height", 0)),
|
||||
codec=metadata.get("codec") or None,
|
||||
status=AssetStatus.READY,
|
||||
file_hash=job.file_hash,
|
||||
thumbnail_url=thumbnail_url,
|
||||
)
|
||||
asset_repo.create(asset)
|
||||
else:
|
||||
# 更新已有的 Asset 记录，补充元数据并将状态改为 READY
|
||||
asset = existing_asset
|
||||
asset.mime_type = mime_type
|
||||
metadata["source"] = "upload"
|
||||
asset.metadata = metadata
|
||||
asset.file_size = int(metadata.get("size_bytes", 0))
|
||||
asset.duration = float(metadata.get("duration", 0))
|
||||
asset.width = int(metadata.get("width", 0))
|
||||
asset.height = int(metadata.get("height", 0))
|
||||
codec_val = metadata.get("codec")
|
||||
if codec_val:
|
||||
asset.codec = str(codec_val)
|
||||
fps_val = metadata.get("fps")
|
||||
if fps_val:
|
||||
try:
|
||||
asset.fps = float(fps_val)
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
asset.status = AssetStatus.READY
|
||||
asset.thumbnail_url = thumbnail_url
|
||||
asset.updated_at = datetime.now(timezone.utc)
|
||||
asset_repo.update(asset)
|
||||
# Create Asset
|
||||
asset = Asset.create(
|
||||
project_id=job.project_id,
|
||||
library_id=job.library_id,
|
||||
name=filename,
|
||||
storage_key=job.storage_key,
|
||||
mime_type=mime_type,
|
||||
metadata=metadata,
|
||||
file_size=int(metadata.get("size_bytes", 0)),
|
||||
duration=float(metadata.get("duration", 0)),
|
||||
width=int(metadata.get("width", 0)),
|
||||
height=int(metadata.get("height", 0)),
|
||||
codec=metadata.get("codec") or None,
|
||||
status=AssetStatus.READY,
|
||||
file_hash=job.file_hash,
|
||||
thumbnail_url=thumbnail_url,
|
||||
)
|
||||
asset_repo.create(asset)
|
||||
|
||||
# Update job status to COMPLETED
|
||||
job.status = IngestJobStatus.COMPLETED
|
||||
@@ -725,37 +692,15 @@ def ingest_asset(job_id: str) -> dict:
|
||||
db.rollback()
|
||||
logger.error(f"Failed to ingest asset {job_id}: {e}")
|
||||
|
||||
# Update job status to FAILED and mark pre-created Asset as ERROR
|
||||
# Update job status to FAILED
|
||||
try:
|
||||
job_repo = SQLAlchemyIngestJobRepository(db)
|
||||
asset_repo = SQLAlchemyAssetRepository(db)
|
||||
job = job_repo.get(job_id)
|
||||
if job:
|
||||
job.status = IngestJobStatus.FAILED
|
||||
job.error_message = str(e)
|
||||
job.updated_at = datetime.now(timezone.utc)
|
||||
job_repo.update(job)
|
||||
|
||||
# 将上传时创建的占位 Asset（PROCESSING/UPLOADING）标记为 ERROR，
|
||||
# 避免素材永远卡在中间状态
|
||||
try:
|
||||
existing = asset_repo.find_by_storage_key(job.storage_key)
|
||||
if existing and existing.status in (
|
||||
AssetStatus.PROCESSING,
|
||||
AssetStatus.UPLOADING,
|
||||
):
|
||||
existing.status = AssetStatus.ERROR
|
||||
existing.metadata = {**(existing.metadata or {}), "ingest_error": str(e)}
|
||||
existing.updated_at = datetime.now(timezone.utc)
|
||||
asset_repo.update(existing)
|
||||
logger.info(
|
||||
"Marked asset as ERROR due to ingest failure: asset_id=%s job_id=%s",
|
||||
existing.id,
|
||||
job_id,
|
||||
)
|
||||
except Exception as asset_err:
|
||||
logger.warning("Failed to mark asset as ERROR: %s", asset_err)
|
||||
|
||||
db.commit()
|
||||
except Exception:
|
||||
db.rollback()
|
||||
|
||||
@@ -1,216 +0,0 @@
|
||||
# ============================================================
|
||||
# 小虾 SaaS — Production 环境配置模板
|
||||
# ============================================================
|
||||
# 使用方式：复制为 /var/lib/xiaoxia-saas-production/.env 并填入实际密钥
|
||||
# 敏感值标记为 ${PLACEHOLDER}，部署前必须替换为真实值
|
||||
# ============================================================
|
||||
|
||||
|
||||
# ==================== 应用基本配置 ====================
|
||||
|
||||
# 应用名称
|
||||
APP_NAME=xiaoxia-saas
|
||||
|
||||
# 环境标识
|
||||
APP_ENV=production
|
||||
|
||||
# 关闭 Debug 模式
|
||||
DEBUG=false
|
||||
|
||||
# 应用基础 URL（前端页面地址）
|
||||
APP_BASE_URL=https://xiaoxiajianji.com
|
||||
|
||||
# 对外公开的 API 基础 URL（用于生成回调链接等）
|
||||
PUBLIC_API_BASE_URL=https://api.xiaoxiajianji.com
|
||||
|
||||
# API 服务监听地址
|
||||
API_HOST=0.0.0.0
|
||||
|
||||
# API 服务监听端口
|
||||
API_PORT=8001
|
||||
|
||||
# 生产环境关闭自动建表，使用 alembic migration
|
||||
AUTO_CREATE_SCHEMA=false
|
||||
|
||||
|
||||
# ==================== 数据库配置 ====================
|
||||
|
||||
# 数据库连接串（格式：postgresql+psycopg://user:password@host:port/dbname）
|
||||
# ${DATABASE_URL} — 替换为实际的 Production PostgreSQL 连接串
|
||||
DATABASE_URL=${DATABASE_URL}
|
||||
|
||||
# 连接池大小（常驻连接数）
|
||||
DATABASE_POOL_SIZE=20
|
||||
|
||||
# 连接池最大溢出连接数（pool_size + max_overflow = 最大并发连接数）
|
||||
DATABASE_MAX_OVERFLOW=10
|
||||
|
||||
# 获取连接超时时间（秒）
|
||||
DATABASE_POOL_TIMEOUT=30
|
||||
|
||||
# 连接回收时间（秒），防止数据库端主动断开导致的死连接
|
||||
DATABASE_POOL_RECYCLE=3600
|
||||
|
||||
# 不使用内存数据库
|
||||
USE_IN_MEMORY_DB=false
|
||||
|
||||
|
||||
# ==================== Redis 配置 ====================
|
||||
|
||||
# Redis 连接 URL（格式：redis://[:password@]host:port/db）
|
||||
# ${REDIS_URL} — 替换为实际的 Production Redis 连接串
|
||||
REDIS_URL=${REDIS_URL}
|
||||
|
||||
# 启用 Redis Session 存储（多实例部署必须开启）
|
||||
ENABLE_REDIS_SESSIONS=true
|
||||
|
||||
|
||||
# ==================== Celery 任务队列 ====================
|
||||
|
||||
# Celery Broker（任务分发），使用 Redis db0
|
||||
CELERY_BROKER_URL=${CELERY_BROKER_URL}
|
||||
|
||||
# Celery Result Backend（任务结果存储），使用 Redis db1
|
||||
CELERY_RESULT_BACKEND=${CELERY_RESULT_BACKEND}
|
||||
|
||||
|
||||
# ==================== Worker 配置 ====================
|
||||
|
||||
# Worker 进程名称
|
||||
WORKER_NAME=xiaoxia-saas-worker
|
||||
|
||||
# Worker 并发数（同时执行的任务数）
|
||||
WORKER_CONCURRENCY=4
|
||||
|
||||
# 每个子进程最多处理多少任务后重启（防止内存泄漏）
|
||||
WORKER_MAX_TASKS_PER_CHILD=1000
|
||||
|
||||
|
||||
# ==================== JWT 认证配置 ====================
|
||||
|
||||
# JWT 签名密钥 — 必须设置为强随机字符串（至少32字符）
|
||||
# ${JWT_SECRET_KEY} — 替换为实际的随机密钥
|
||||
JWT_SECRET_KEY=${JWT_SECRET_KEY}
|
||||
|
||||
# JWT 签名算法
|
||||
JWT_ALGORITHM=HS256
|
||||
|
||||
# Access Token 过期时间（分钟）
|
||||
JWT_ACCESS_TOKEN_EXPIRE_MINUTES=30
|
||||
|
||||
# Refresh Token 过期时间（天）
|
||||
JWT_REFRESH_TOKEN_EXPIRE_DAYS=30
|
||||
|
||||
|
||||
# ==================== 邮件配置 ====================
|
||||
|
||||
# 邮件功能尚未上线，暂时关闭
|
||||
ENABLE_EMAIL_DELIVERY=false
|
||||
|
||||
# SMTP 服务器地址
|
||||
SMTP_HOST=
|
||||
|
||||
# SMTP 端口
|
||||
SMTP_PORT=587
|
||||
|
||||
# SMTP 用户名（邮件功能上线后配置）
|
||||
SMTP_USER=
|
||||
|
||||
# SMTP 密码（邮件功能上线后配置）
|
||||
SMTP_PASSWORD=
|
||||
|
||||
# 发件人邮箱（邮件功能上线后配置）
|
||||
SMTP_FROM_EMAIL=
|
||||
|
||||
# 发件人显示名称
|
||||
SMTP_FROM_NAME=小虾 SaaS
|
||||
|
||||
# 启用 TLS
|
||||
SMTP_USE_TLS=true
|
||||
|
||||
|
||||
# ==================== 阿里云 OSS 配置 ====================
|
||||
|
||||
# OSS 区域 endpoint
|
||||
OSS_ENDPOINT=oss-cn-hangzhou.aliyuncs.com
|
||||
|
||||
# OSS Access Key ID
|
||||
# ${OSS_ACCESS_KEY_ID} — 替换为实际的 OSS Access Key ID
|
||||
OSS_ACCESS_KEY_ID=${OSS_ACCESS_KEY_ID}
|
||||
|
||||
# OSS Access Key Secret
|
||||
# ${OSS_ACCESS_KEY_SECRET} — 替换为实际的 OSS Access Key Secret
|
||||
OSS_ACCESS_KEY_SECRET=${OSS_ACCESS_KEY_SECRET}
|
||||
|
||||
# OSS Bucket 名称
|
||||
OSS_BUCKET_NAME=xiaoxia-autocut
|
||||
|
||||
# 直传最大文件大小（MB）
|
||||
OSS_DIRECT_UPLOAD_MAX_MB=2000
|
||||
|
||||
# 直传签名有效期（秒）
|
||||
OSS_DIRECT_UPLOAD_EXPIRE_SECONDS=900
|
||||
|
||||
|
||||
# ==================== CORS 配置 ====================
|
||||
|
||||
# 允许跨域的前端域名列表，逗号分隔
|
||||
CORS_ORIGINS_RAW=https://xiaoxiajianji.com,https://api.xiaoxiajianji.com
|
||||
|
||||
|
||||
# ==================== 生成文件路径 ====================
|
||||
|
||||
# 容器内生成文件目录（固定值，勿改）
|
||||
GENERATED_FILES_DIR=/app/generated
|
||||
|
||||
# 生成文件 URL 前缀
|
||||
GENERATED_FILES_URL_PREFIX=/generated-files
|
||||
|
||||
# 主机上生成文件目录（供 Docker volume bind mount 使用）
|
||||
GENERATED_FILES_HOST_DIR=/var/lib/xiaoxia-saas-production/generated
|
||||
|
||||
|
||||
# ==================== 渲染引擎配置 ====================
|
||||
|
||||
# 渲染引擎选择：legacy（旧引擎，稳定）/ unified（新架构）
|
||||
RENDER_ENGINE=legacy
|
||||
|
||||
|
||||
# ==================== CosyVoice 语音合成 ====================
|
||||
|
||||
# 阿里云百灵语音合成服务 API Key
|
||||
# ${COSYVOICE_API_KEY} — 替换为实际的 CosyVoice API Key
|
||||
COSYVOICE_API_KEY=${COSYVOICE_API_KEY}
|
||||
|
||||
# API 基础 URL
|
||||
COSYVOICE_BASE_URL=https://dashscope.aliyuncs.com/api/v1
|
||||
|
||||
# 模型选择：cosyvoice-v3-flash（推荐）/ cosyvoice-v3-plus
|
||||
COSYVOICE_MODEL=cosyvoice-v3-flash
|
||||
|
||||
# 音色：v3 系列系统音色带 _v3 后缀
|
||||
COSYVOICE_VOICE=longxiaoxia_v3
|
||||
|
||||
# 采样率
|
||||
COSYVOICE_SAMPLE_RATE=22050
|
||||
|
||||
# 输出格式
|
||||
COSYVOICE_FORMAT=wav
|
||||
|
||||
# 音色克隆模型名（固定值）
|
||||
COSYVOICE_CLONE_MODEL=voice-enrollment
|
||||
|
||||
# DashScope 通用 API Key（与 CosyVoice 共用）
|
||||
DASHSCOPE_API_KEY=${DASHSCOPE_API_KEY}
|
||||
|
||||
|
||||
# ==================== MediaKit 视频理解（火山引擎）====================
|
||||
|
||||
MEDIAKIT_API_KEY=${MEDIAKIT_API_KEY}
|
||||
MEDIAKIT_BASE_URL=https://mediakit.cn-beijing.volces.com/api/v1
|
||||
MEDIAKIT_TIMEOUT=60
|
||||
|
||||
|
||||
# ==================== 监控（可选）====================
|
||||
# Sentry DSN（取消注释并填入实际值以启用错误追踪）
|
||||
# SENTRY_DSN=${SENTRY_DSN}
|
||||
@@ -1,233 +0,0 @@
|
||||
# ============================================================
|
||||
# 小虾 SaaS — Staging 环境配置模板
|
||||
# ============================================================
|
||||
# 使用方式：复制为 /var/lib/xiaoxia-saas-staging/.env 并填入实际密钥
|
||||
# 敏感值标记为 ${PLACEHOLDER}，部署前必须替换为真实值
|
||||
# ============================================================
|
||||
|
||||
|
||||
# ==================== 应用基本配置 ====================
|
||||
|
||||
# 应用名称
|
||||
APP_NAME=xiaoxia-saas
|
||||
|
||||
# 环境标识
|
||||
APP_ENV=staging
|
||||
|
||||
# Staging 开启 Debug 模式便于排查问题
|
||||
DEBUG=true
|
||||
|
||||
# 应用基础 URL（前端页面地址）
|
||||
APP_BASE_URL=https://staging.xiaoxiajianji.com
|
||||
|
||||
# 对外公开的 API 基础 URL（用于生成回调链接等）
|
||||
PUBLIC_API_BASE_URL=https://staging-api.xiaoxiajianji.com
|
||||
|
||||
# API 服务监听地址
|
||||
API_HOST=0.0.0.0
|
||||
|
||||
# API 服务监听端口
|
||||
API_PORT=8000
|
||||
|
||||
# 生产/预发布环境关闭自动建表，使用 alembic migration
|
||||
AUTO_CREATE_SCHEMA=false
|
||||
|
||||
|
||||
# ==================== 数据库配置 ====================
|
||||
|
||||
# 数据库连接串（格式：postgresql+psycopg://user:password@host:port/dbname）
|
||||
# ${DATABASE_URL} — 替换为实际的 Staging PostgreSQL 连接串
|
||||
DATABASE_URL=${DATABASE_URL}
|
||||
|
||||
# 连接池大小（常驻连接数）
|
||||
DATABASE_POOL_SIZE=20
|
||||
|
||||
# 连接池最大溢出连接数（pool_size + max_overflow = 最大并发连接数）
|
||||
DATABASE_MAX_OVERFLOW=10
|
||||
|
||||
# 获取连接超时时间（秒）
|
||||
DATABASE_POOL_TIMEOUT=30
|
||||
|
||||
# 连接回收时间（秒），防止数据库端主动断开导致的死连接
|
||||
DATABASE_POOL_RECYCLE=3600
|
||||
|
||||
# 不使用内存数据库
|
||||
USE_IN_MEMORY_DB=false
|
||||
|
||||
|
||||
# ==================== Redis 配置 ====================
|
||||
|
||||
# Redis 连接 URL（格式：redis://[:password@]host:port/db）
|
||||
# ${REDIS_URL} — 替换为实际的 Staging Redis 连接串
|
||||
REDIS_URL=${REDIS_URL}
|
||||
|
||||
# 启用 Redis Session 存储（多实例部署必须开启）
|
||||
ENABLE_REDIS_SESSIONS=true
|
||||
|
||||
|
||||
# ==================== Celery 任务队列 ====================
|
||||
|
||||
# Celery Broker（任务分发），使用 Redis db0
|
||||
CELERY_BROKER_URL=${CELERY_BROKER_URL}
|
||||
|
||||
# Celery Result Backend（任务结果存储），使用 Redis db1
|
||||
CELERY_RESULT_BACKEND=${CELERY_RESULT_BACKEND}
|
||||
|
||||
|
||||
# ==================== Worker 配置 ====================
|
||||
|
||||
# Worker 进程名称
|
||||
WORKER_NAME=xiaoxia-saas-worker
|
||||
|
||||
# Worker 并发数（同时执行的任务数）
|
||||
WORKER_CONCURRENCY=1
|
||||
|
||||
# 每个子进程最多处理多少任务后重启（防止内存泄漏）
|
||||
WORKER_MAX_TASKS_PER_CHILD=1000
|
||||
|
||||
|
||||
# ==================== JWT 认证配置 ====================
|
||||
|
||||
# JWT 签名密钥 — 必须设置为强随机字符串（至少32字符）
|
||||
# ${JWT_SECRET_KEY} — 替换为实际的随机密钥
|
||||
JWT_SECRET_KEY=${JWT_SECRET_KEY}
|
||||
|
||||
# JWT 签名算法
|
||||
JWT_ALGORITHM=HS256
|
||||
|
||||
# Access Token 过期时间（分钟）
|
||||
JWT_ACCESS_TOKEN_EXPIRE_MINUTES=1440
|
||||
|
||||
# Refresh Token 过期时间（天）
|
||||
JWT_REFRESH_TOKEN_EXPIRE_DAYS=30
|
||||
|
||||
|
||||
# ==================== 邮件配置 ====================
|
||||
|
||||
# 邮件功能尚未上线，暂时关闭
|
||||
ENABLE_EMAIL_DELIVERY=false
|
||||
|
||||
# SMTP 服务器地址
|
||||
SMTP_HOST=smtp.gmail.com
|
||||
|
||||
# SMTP 端口
|
||||
SMTP_PORT=587
|
||||
|
||||
# SMTP 用户名（邮件功能上线后配置）
|
||||
SMTP_USER=
|
||||
|
||||
# SMTP 密码（邮件功能上线后配置）
|
||||
SMTP_PASSWORD=
|
||||
|
||||
# 发件人邮箱（邮件功能上线后配置）
|
||||
SMTP_FROM_EMAIL=
|
||||
|
||||
# 发件人显示名称
|
||||
SMTP_FROM_NAME=小虾 SaaS
|
||||
|
||||
# 启用 TLS
|
||||
SMTP_USE_TLS=true
|
||||
|
||||
|
||||
# ==================== 阿里云 OSS 配置 ====================
|
||||
|
||||
# OSS 区域 endpoint
|
||||
OSS_ENDPOINT=oss-cn-hangzhou.aliyuncs.com
|
||||
|
||||
# OSS Access Key ID
|
||||
# ${OSS_ACCESS_KEY_ID} — 替换为实际的 OSS Access Key ID
|
||||
OSS_ACCESS_KEY_ID=${OSS_ACCESS_KEY_ID}
|
||||
|
||||
# OSS Access Key Secret
|
||||
# ${OSS_ACCESS_KEY_SECRET} — 替换为实际的 OSS Access Key Secret
|
||||
OSS_ACCESS_KEY_SECRET=${OSS_ACCESS_KEY_SECRET}
|
||||
|
||||
# OSS Bucket 名称
|
||||
OSS_BUCKET_NAME=xiaoxia-autocut
|
||||
|
||||
# 直传最大文件大小（MB）
|
||||
OSS_DIRECT_UPLOAD_MAX_MB=2000
|
||||
|
||||
# 直传签名有效期（秒）
|
||||
OSS_DIRECT_UPLOAD_EXPIRE_SECONDS=900
|
||||
|
||||
|
||||
# ==================== MinIO 配置（Staging 独有）====================
|
||||
# Staging 环境使用 MinIO 替代 OSS 进行文件存储测试
|
||||
|
||||
# MinIO 服务 Endpoint
|
||||
# ${MINIO_ENDPOINT} — 替换为实际的 MinIO 地址
|
||||
MINIO_ENDPOINT=${MINIO_ENDPOINT}
|
||||
|
||||
# MinIO Access Key
|
||||
# ${MINIO_ACCESS_KEY} — 替换为实际的 MinIO Access Key
|
||||
MINIO_ACCESS_KEY=${MINIO_ACCESS_KEY}
|
||||
|
||||
# MinIO Secret Key
|
||||
# ${MINIO_SECRET_KEY} — 替换为实际的 MinIO Secret Key
|
||||
MINIO_SECRET_KEY=${MINIO_SECRET_KEY}
|
||||
|
||||
# MinIO Bucket 名称
|
||||
MINIO_BUCKET_NAME=${MINIO_BUCKET_NAME}
|
||||
|
||||
# 是否使用 SSL 连接 MinIO
|
||||
MINIO_USE_SSL=false
|
||||
|
||||
|
||||
# ==================== CORS 配置 ====================
|
||||
|
||||
# 允许跨域的前端域名列表，逗号分隔
|
||||
CORS_ORIGINS_RAW=https://staging.xiaoxiajianji.com,https://staging-api.xiaoxiajianji.com
|
||||
|
||||
|
||||
# ==================== 生成文件路径 ====================
|
||||
|
||||
# 容器内生成文件目录（固定值，勿改）
|
||||
GENERATED_FILES_DIR=/app/generated
|
||||
|
||||
# 生成文件 URL 前缀
|
||||
GENERATED_FILES_URL_PREFIX=/generated-files
|
||||
|
||||
# 主机上生成文件目录（供 Docker volume bind mount 使用）
|
||||
GENERATED_FILES_HOST_DIR=/var/lib/xiaoxia-saas-staging/generated
|
||||
|
||||
|
||||
# ==================== 渲染引擎配置 ====================
|
||||
|
||||
# 渲染引擎选择：legacy（旧引擎，稳定）/ unified（新架构）
|
||||
RENDER_ENGINE=legacy
|
||||
|
||||
|
||||
# ==================== CosyVoice 语音合成 ====================
|
||||
|
||||
# 阿里云百灵语音合成服务 API Key
|
||||
# ${COSYVOICE_API_KEY} — 替换为实际的 CosyVoice API Key
|
||||
COSYVOICE_API_KEY=${COSYVOICE_API_KEY}
|
||||
|
||||
# API 基础 URL
|
||||
COSYVOICE_BASE_URL=https://dashscope.aliyuncs.com/api/v1
|
||||
|
||||
# 模型选择：cosyvoice-v3-flash（推荐）/ cosyvoice-v3-plus
|
||||
COSYVOICE_MODEL=cosyvoice-v3-flash
|
||||
|
||||
# 音色：v3 系列系统音色带 _v3 后缀
|
||||
COSYVOICE_VOICE=longxiaoxia_v3
|
||||
|
||||
# 采样率
|
||||
COSYVOICE_SAMPLE_RATE=22050
|
||||
|
||||
# 输出格式
|
||||
COSYVOICE_FORMAT=wav
|
||||
|
||||
# 音色克隆模型名（固定值）
|
||||
COSYVOICE_CLONE_MODEL=voice-enrollment
|
||||
|
||||
# DashScope 通用 API Key（与 CosyVoice 共用）
|
||||
DASHSCOPE_API_KEY=${DASHSCOPE_API_KEY}
|
||||
|
||||
|
||||
# ==================== MediaKit 视频理解（火山引擎）====================
|
||||
|
||||
MEDIAKIT_API_KEY=${MEDIAKIT_API_KEY}
|
||||
MEDIAKIT_BASE_URL=https://mediakit.cn-beijing.volces.com/api/v1
|
||||
MEDIAKIT_TIMEOUT=60
|
||||
@@ -1,51 +0,0 @@
|
||||
server {
|
||||
listen 80;
|
||||
server_name _;
|
||||
root /usr/share/nginx/html;
|
||||
index index.html;
|
||||
|
||||
gzip on;
|
||||
gzip_vary on;
|
||||
gzip_min_length 1024;
|
||||
gzip_types text/plain text/css text/xml text/javascript application/javascript application/json application/xml+rss;
|
||||
|
||||
client_max_body_size 800m;
|
||||
|
||||
# SPA routing - index.html 禁止缓存，确保每次获取最新版本
|
||||
location / {
|
||||
try_files $uri /index.html;
|
||||
}
|
||||
|
||||
# API proxy — Production 环境代理到 production API 容器
|
||||
resolver 127.0.0.11 valid=10s;
|
||||
resolver_timeout 5s;
|
||||
location /api/ {
|
||||
proxy_pass http://xiaoxia-api-production:8000/api/;
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
proxy_read_timeout 300s;
|
||||
proxy_send_timeout 300s;
|
||||
proxy_request_buffering off;
|
||||
}
|
||||
|
||||
# Generated files — 通过 alias 映射容器内 /app/generated/ 目录
|
||||
location /generated-files/ {
|
||||
alias /app/generated/;
|
||||
}
|
||||
|
||||
# Assets with legacy fallback — 部署期间兼容旧版缓存的 hash 文件名
|
||||
# 先在当前镜像中找，找不到去 legacy-assets 目录找（从旧版本容器中备份的）
|
||||
location ^~ /assets/ {
|
||||
expires 1y;
|
||||
add_header Cache-Control "public, immutable";
|
||||
try_files $uri /assets-legacy$uri =404;
|
||||
}
|
||||
|
||||
# 静态资源长缓存
|
||||
location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
|
||||
expires 1y;
|
||||
add_header Cache-Control "public, immutable";
|
||||
}
|
||||
}
|
||||
@@ -1,50 +0,0 @@
|
||||
server {
|
||||
listen 80;
|
||||
server_name _;
|
||||
root /usr/share/nginx/html;
|
||||
index index.html;
|
||||
|
||||
gzip on;
|
||||
gzip_vary on;
|
||||
gzip_min_length 1024;
|
||||
gzip_types text/plain text/css text/xml text/javascript application/javascript application/json application/xml+rss;
|
||||
|
||||
client_max_body_size 800m;
|
||||
|
||||
# SPA routing - index.html 禁止缓存，确保每次获取最新版本
|
||||
location = /index.html {
|
||||
add_header Cache-Control "no-cache, no-store, must-revalidate";
|
||||
add_header Pragma "no-cache";
|
||||
expires 0;
|
||||
}
|
||||
|
||||
# SPA fallback
|
||||
location / {
|
||||
try_files $uri /index.html;
|
||||
}
|
||||
|
||||
# API proxy — Staging 环境代理到 staging API 容器
|
||||
resolver 127.0.0.11 valid=10s;
|
||||
resolver_timeout 5s;
|
||||
location /api/ {
|
||||
proxy_pass http://xiaoxia-api-staging:8000/api/;
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
proxy_read_timeout 300s;
|
||||
proxy_send_timeout 300s;
|
||||
proxy_request_buffering off;
|
||||
}
|
||||
|
||||
# Generated files — 通过 alias 映射容器内 /app/generated/ 目录
|
||||
location /generated-files/ {
|
||||
alias /app/generated/;
|
||||
}
|
||||
|
||||
# 静态资源长缓存
|
||||
location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
|
||||
expires 1y;
|
||||
add_header Cache-Control "public, immutable";
|
||||
}
|
||||
}
|
||||
@@ -30,14 +30,6 @@ RUN apt-get update \
|
||||
&& apt-get install -y --no-install-recommends docker-ce-cli docker-buildx-plugin \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Pre-bake ffmpeg: unit-tests run in fresh containers each time; installing ffmpeg
|
||||
# on every job cost ~24 min (apt update + hundreds of codec deps). Bake it into the
|
||||
# image so step_install_ffmpeg.sh detects it and exits instantly.
|
||||
RUN apt-get update \
|
||||
&& apt-get install -y --no-install-recommends ffmpeg \
|
||||
&& ffmpeg -version | head -1 \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Pre-install base deps (layer cache)
|
||||
COPY requirements-base.txt ./
|
||||
RUN python -m venv "$VIRTUAL_ENV" \
|
||||
|
||||
@@ -175,14 +175,9 @@ services:
|
||||
- xiaoxia-net
|
||||
|
||||
# =========================================
|
||||
# Nginx 配置运行时覆盖
|
||||
# 确保容器使用正确环境的 nginx 配置，即使镜像构建时使用了默认配置
|
||||
# 注意: 只覆盖 /etc/nginx/conf.d/default.conf，不挂载 /usr/share/nginx/html
|
||||
# 重要: 生产环境不要添加任何 volume 挂载到 /usr/share/nginx/html
|
||||
# 这会导致静态文件被覆盖，返回 403 错误
|
||||
# =========================================
|
||||
environment:
|
||||
- NGINX_ENV=${ENV:-staging}
|
||||
volumes:
|
||||
- ./nginx-${ENV:-staging}.conf:/etc/nginx/conf.d/default.conf:ro
|
||||
|
||||
healthcheck:
|
||||
test: ["CMD", "wget", "--spider", "-q", "http://127.0.0.1:80"]
|
||||
@@ -213,7 +208,7 @@ volumes:
|
||||
# 重要: 确保主机目录存在且有正确权限
|
||||
# Staging: /var/lib/xiaoxia-saas-staging/generated
|
||||
# Production: /var/lib/xiaoxia-saas-production/generated
|
||||
device: ${GENERATED_FILES_HOST_DIR:?GENERATED_FILES_HOST_DIR must be set in .env}
|
||||
device: ${GENERATED_FILES_HOST_DIR:-/var/lib/xiaoxia-saas-staging/generated}
|
||||
|
||||
# ===========================================
|
||||
# 网络配置
|
||||
|
||||
@@ -127,13 +127,6 @@ class InMemoryAssetRepository:
|
||||
items = [a for a in self._assets.values() if tag_set.issubset(set(a.tag_ids))]
|
||||
return items[skip : skip + limit]
|
||||
|
||||
def find_by_storage_key(self, storage_key: str) -> Asset | None:
|
||||
"""按 storage_key 查找素材。"""
|
||||
for asset in self._assets.values():
|
||||
if asset.storage_key == storage_key:
|
||||
return asset
|
||||
return None
|
||||
|
||||
def find_by_library_and_file_hash(
|
||||
self,
|
||||
library_id: str,
|
||||
|
||||
@@ -99,8 +99,8 @@ class SessionStore(SessionStorePort):
|
||||
session_id: str,
|
||||
user_id: str,
|
||||
refresh_token: str,
|
||||
device_info: str = "",
|
||||
ip_address: str = "",
|
||||
device_info: str,
|
||||
ip_address: str,
|
||||
expires_in_seconds: int = 30 * 24 * 60 * 60, # 30 天
|
||||
) -> bool:
|
||||
"""
|
||||
|
||||
@@ -96,7 +96,7 @@ class EmailService(EmailServicePort):
|
||||
except Exception as e:
|
||||
return False, str(e)
|
||||
|
||||
def send_verification_email( # type: ignore[override]
|
||||
def send_verification_email(
|
||||
self,
|
||||
to_email: str,
|
||||
username: str,
|
||||
@@ -165,7 +165,7 @@ class EmailService(EmailServicePort):
|
||||
|
||||
return self.send_email(to_email, subject, html_body, text_body)
|
||||
|
||||
def send_password_reset_email( # type: ignore[override]
|
||||
def send_password_reset_email(
|
||||
self,
|
||||
to_email: str,
|
||||
username: str,
|
||||
|
||||
@@ -426,13 +426,6 @@ class SQLAlchemyAssetRepository:
|
||||
models = self.session.query(AssetModel).filter(AssetModel.id.in_(ids)).offset(skip).limit(limit).all()
|
||||
return [self._to_domain(m) for m in models]
|
||||
|
||||
def find_by_storage_key(self, storage_key: str) -> Asset | None:
|
||||
"""按 storage_key（对应 DB 中的 file_url）查找素材。"""
|
||||
model = self.session.query(AssetModel).filter(AssetModel.file_url == storage_key).first()
|
||||
if model is None:
|
||||
return None
|
||||
return self._to_domain(model)
|
||||
|
||||
def find_by_library_and_file_hash(
|
||||
self,
|
||||
library_id: str,
|
||||
|
||||
@@ -131,65 +131,3 @@ class SQLAlchemyEditPlanClipRepository:
|
||||
created_at=model.created_at,
|
||||
updated_at=model.updated_at,
|
||||
)
|
||||
|
||||
def list_used_segments_by_user(
|
||||
self,
|
||||
user_id: str,
|
||||
*,
|
||||
limit_recent: int = 50,
|
||||
) -> dict[str, list[tuple[float, float]]]:
|
||||
"""查询用户已有视频中已使用的素材区间（跨视频避让）.
|
||||
|
||||
JOIN edit_plans 表，按 created_by_user_id 过滤，只查 status='completed'
|
||||
的 plan 下 status='rendered' 且 asset_id 非空的 clips。按 plan 的
|
||||
created_at DESC 取最近 limit_recent 个 plan。
|
||||
|
||||
Returns:
|
||||
{asset_id: [(start_time, start_time + duration), ...]}
|
||||
空结果返回空 dict。
|
||||
"""
|
||||
from packages.adapters.sqlalchemy_impl.models import EditPlanModel
|
||||
|
||||
if not user_id:
|
||||
return {}
|
||||
|
||||
# 1. 查出最近 limit_recent 个已完成 plan 的 ID
|
||||
recent_plan_ids = [
|
||||
row[0]
|
||||
for row in self.session.query(EditPlanModel.id)
|
||||
.filter(
|
||||
EditPlanModel.created_by_user_id == user_id,
|
||||
EditPlanModel.status == "completed",
|
||||
)
|
||||
.order_by(EditPlanModel.created_at.desc())
|
||||
.limit(limit_recent)
|
||||
.all()
|
||||
]
|
||||
|
||||
if not recent_plan_ids:
|
||||
return {}
|
||||
|
||||
# 2. 查这些 plan 下已渲染、有素材的 clips
|
||||
clips = (
|
||||
self.session.query(
|
||||
EditPlanClipModel.asset_id,
|
||||
EditPlanClipModel.start_time,
|
||||
EditPlanClipModel.duration,
|
||||
)
|
||||
.filter(
|
||||
EditPlanClipModel.plan_id.in_(recent_plan_ids),
|
||||
EditPlanClipModel.status == "rendered",
|
||||
EditPlanClipModel.asset_id != "",
|
||||
EditPlanClipModel.asset_id.isnot(None),
|
||||
)
|
||||
.all()
|
||||
)
|
||||
|
||||
# 3. 聚合为 {asset_id: [(start, start+duration), ...]}
|
||||
result: dict[str, list[tuple[float, float]]] = {}
|
||||
for asset_id, start_time, duration in clips:
|
||||
if asset_id not in result:
|
||||
result[asset_id] = []
|
||||
result[asset_id].append((start_time or 0.0, (start_time or 0.0) + (duration or 0.0)))
|
||||
|
||||
return result
|
||||
|
||||
@@ -289,7 +289,6 @@ class GenerationTaskModel(Base):
|
||||
completed_at = Column(DateTime, nullable=True)
|
||||
created_by_user_id = Column(String(36), nullable=False, default="", index=True)
|
||||
source_edit_plan_id = Column(String(36), nullable=True, index=True)
|
||||
edit_plan_id = Column(String(36), nullable=True, index=True)
|
||||
asset_select_mode = Column(String(20), nullable=False, default="")
|
||||
batch_id = Column(String(36), nullable=False, default="", index=True)
|
||||
video_title = Column(String(255), nullable=False, default="")
|
||||
@@ -498,7 +497,6 @@ class TemplateCategoryModel(Base):
|
||||
id = Column(String(36), primary_key=True)
|
||||
user_id = Column(String(36), nullable=False, index=True)
|
||||
name = Column(String(100), nullable=False)
|
||||
sort_order = Column(Integer, nullable=False, default=0)
|
||||
created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(timezone.utc))
|
||||
|
||||
|
||||
@@ -620,20 +618,3 @@ class CoverTemplateModel(Base):
|
||||
config = Column(JSON, nullable=False, default=dict)
|
||||
created_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(timezone.utc))
|
||||
updated_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(timezone.utc))
|
||||
|
||||
|
||||
class VideoFingerprintChunkModel(Base):
|
||||
"""分片视频指纹 — 每个视频按时间分片存储 pHash + color_histogram."""
|
||||
|
||||
__tablename__ = "video_fingerprint_chunks"
|
||||
|
||||
id = Column(String(36), primary_key=True)
|
||||
video_id = Column(String(36), nullable=False, index=True)
|
||||
project_id = Column(String(36), nullable=False, index=True)
|
||||
user_id = Column(String(36), nullable=False, index=True, default="")
|
||||
start_time_ms = Column(Integer, nullable=False)
|
||||
end_time_ms = Column(Integer, nullable=False)
|
||||
phash_binary = Column(String(16), nullable=False)
|
||||
color_histogram = Column(JSON, nullable=False)
|
||||
frame_count = Column(Integer, nullable=False, default=1)
|
||||
created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(timezone.utc))
|
||||
|
||||
@@ -51,7 +51,7 @@ def parse_titles_from_response(content: str) -> list[str]:
|
||||
pass
|
||||
|
||||
# 尝试按行解析
|
||||
titles = []
|
||||
titles: list[str] = []
|
||||
for line in content.strip().split("\n"):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
|
||||
@@ -256,42 +256,6 @@ def _wrap_title_text(
|
||||
return "\\N".join(wrapped_segments)
|
||||
|
||||
|
||||
def _parse_title_position(
|
||||
title_config: dict[str, Any],
|
||||
video_width: int,
|
||||
video_height: int,
|
||||
) -> tuple[int, int] | None:
|
||||
"""解析标题自由拖拽坐标 pos_x/pos_y（PlayRes 像素坐标系）。
|
||||
|
||||
要求两个字段同时存在、可转 int，且落在 [0, video_width] × [0, video_height]
|
||||
闭区间内。任一条件不满足返回 None，调用方回退 position 三档逻辑。
|
||||
|
||||
Args:
|
||||
title_config: 标题配置 dict
|
||||
video_width: PlayResX（视频宽度像素）
|
||||
video_height: PlayResY（视频高度像素）
|
||||
|
||||
Returns:
|
||||
(x, y) 整数坐标，或 None 表示不使用自由位置
|
||||
"""
|
||||
if "pos_x" not in title_config or "pos_y" not in title_config:
|
||||
return None
|
||||
raw_x = title_config["pos_x"]
|
||||
raw_y = title_config["pos_y"]
|
||||
# 坐标必须是 PlayRes 像素整数：bool 是 int 子类（isinstance(True,int)=True）
|
||||
# 但 True/False 作坐标无意义；float 静默截断会造成拖拽位置偏差，一律按非法回退
|
||||
if isinstance(raw_x, bool) or isinstance(raw_y, bool):
|
||||
return None
|
||||
if not isinstance(raw_x, int) or not isinstance(raw_y, int):
|
||||
return None
|
||||
x, y = raw_x, raw_y
|
||||
if video_width <= 0 or video_height <= 0:
|
||||
return None
|
||||
if not (0 <= x <= video_width and 0 <= y <= video_height):
|
||||
return None
|
||||
return (x, y)
|
||||
|
||||
|
||||
def build_ass_content(
|
||||
*,
|
||||
video_width: int,
|
||||
@@ -379,16 +343,7 @@ def build_ass_content(
|
||||
title_shadow.get("offset_y", 2) if title_shadow.get("enabled", False) else 0,
|
||||
)
|
||||
|
||||
# ── 自由位置拖拽（工单 #1405 方案 B）────────────────────────────
|
||||
# pos_x/pos_y 为 PlayRes 坐标系像素整数（PlayResX/Y = video_width/height）。
|
||||
# 合法时：TitleStyle Alignment 固定 5（\an5 中对齐，使 \pos 锚点为文本块中心），
|
||||
# Dialogue 文本前注入 {\pos(x,y)}。字段缺失/非法/越界时一律回退
|
||||
# position → alignment 三档逻辑，现有输出保持一字节不变。
|
||||
title_pos = _parse_title_position(title_config, video_width, video_height)
|
||||
|
||||
title_alignment = 5 if title_pos is not None else position_to_ass_alignment(
|
||||
title_config.get("position", "top")
|
||||
)
|
||||
title_alignment = position_to_ass_alignment(title_config.get("position", "top"))
|
||||
|
||||
styles.append(
|
||||
build_ass_style(
|
||||
@@ -415,10 +370,6 @@ def build_ass_content(
|
||||
safe_title_text_raw = escape_ass_text(title_text)
|
||||
safe_title_text = _wrap_title_text(safe_title_text_raw, video_width, title_font_size)
|
||||
|
||||
# 自由位置：在文本前注入 \pos override tag（锚点为文本块中心，配合 \an5）
|
||||
if title_pos is not None:
|
||||
safe_title_text = f"{{\\pos({title_pos[0]},{title_pos[1]})}}{safe_title_text}"
|
||||
|
||||
events.append(
|
||||
"Dialogue: 0,0:00:00.00," f"{format_ass_time(video_duration)}," "TitleStyle,,0,0,0,," f"{safe_title_text}"
|
||||
)
|
||||
|
||||
@@ -27,137 +27,6 @@ DEFAULT_INTRO_DURATION = 3.0
|
||||
DEFAULT_OUTRO_DURATION = 3.0
|
||||
|
||||
|
||||
# ── SceneChange 镜头段工具 ────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def build_scene_segments(
|
||||
scene_changes: list[float],
|
||||
asset_duration: float,
|
||||
) -> list[tuple[float, float]]:
|
||||
"""根据场景切换点构建镜头段列表.
|
||||
|
||||
Args:
|
||||
scene_changes: 场景切换点时间戳列表（已排序，首位为 0.0）
|
||||
asset_duration: 素材总时长
|
||||
|
||||
Returns:
|
||||
镜头段列表 [(start, end), ...]，仅保留长度 >= 0.5s 的段
|
||||
"""
|
||||
segments: list[tuple[float, float]] = []
|
||||
for i, ts in enumerate(scene_changes):
|
||||
end = scene_changes[i + 1] if i + 1 < len(scene_changes) else asset_duration
|
||||
# 只保留有效长度的镜头段（至少 0.5 秒）
|
||||
if end - ts >= 0.5:
|
||||
segments.append((ts, end))
|
||||
return segments
|
||||
|
||||
|
||||
def pick_start_in_scene_segment(
|
||||
seg_start: float,
|
||||
seg_end: float,
|
||||
clip_duration: float,
|
||||
) -> float | None:
|
||||
"""在镜头段内随机选取一个起始时间点.
|
||||
|
||||
确保 start + clip_duration <= seg_end。
|
||||
若镜头段长度不足以容纳片段，返回 None。
|
||||
"""
|
||||
available = seg_end - seg_start - clip_duration
|
||||
if available < 0:
|
||||
return None
|
||||
max_start = seg_start + available
|
||||
return random.uniform(seg_start, max_start)
|
||||
|
||||
|
||||
def _segments_overlap(
|
||||
start: float,
|
||||
duration: float,
|
||||
used: list[tuple[float, float]],
|
||||
edge_gap: float = 0.0,
|
||||
) -> bool:
|
||||
"""候选区间 [start, start+duration] 是否与已用区间冲突（含边缘间隙扩边）。"""
|
||||
end = start + duration
|
||||
for used_start, used_end in used:
|
||||
if start < used_end + edge_gap and end > used_start - edge_gap:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def pick_scene_aware_start(
|
||||
asset_id: str,
|
||||
clip_duration: float,
|
||||
asset_durations: dict[str, float],
|
||||
asset_scene_points: dict[str, list[float]] | None,
|
||||
used_segments: dict[str, list[tuple[float, float]]],
|
||||
*,
|
||||
edge_gap: float = 0.0,
|
||||
) -> float | None:
|
||||
"""基于缓存的场景切换点，从随机镜头段中选取不冲突的起始时间.
|
||||
|
||||
流程：
|
||||
1. 读取 asset_scene_points 中该素材的场景切换点缓存 → 构建镜头段
|
||||
2. random.shuffle 镜头段（保证同一素材多次生成选不同镜头，而非固定第N段）
|
||||
3. 依次尝试：段内随机取点 → 越界检查 → 与 used_segments 冲突检查
|
||||
4. 全部冲突/无缓存 → 返回 None，由调用方回退 _calc_random_start_time
|
||||
|
||||
Args:
|
||||
asset_id: 素材 ID
|
||||
clip_duration: 片段时长（秒）
|
||||
asset_durations: 素材 ID -> 总时长
|
||||
asset_scene_points: 素材 ID -> 场景切换点列表（metadata 缓存）
|
||||
used_segments: 素材 ID -> 已用区间列表（冲突避让）
|
||||
edge_gap: 冲突判定的边缘间隙（秒），已用区间按 [s-gap, e+gap] 扩边
|
||||
"""
|
||||
asset_total = (asset_durations or {}).get(asset_id)
|
||||
if not asset_total or asset_total <= 0:
|
||||
return None
|
||||
scene_points = (asset_scene_points or {}).get(asset_id)
|
||||
if not scene_points:
|
||||
return None
|
||||
used = used_segments.get(asset_id, []) if used_segments else []
|
||||
|
||||
scene_segments = build_scene_segments(scene_points, asset_total)
|
||||
if not scene_segments:
|
||||
return None
|
||||
random.shuffle(scene_segments)
|
||||
|
||||
for seg_start, seg_end in scene_segments:
|
||||
candidate = pick_start_in_scene_segment(seg_start, seg_end, clip_duration)
|
||||
if candidate is None:
|
||||
continue
|
||||
# 越界检查（防御：场景点末尾段理论上不越界，metadata 脏数据兜底）
|
||||
if candidate + clip_duration > asset_total:
|
||||
continue
|
||||
# 与已用区间冲突检查
|
||||
if _segments_overlap(candidate, clip_duration, used, edge_gap):
|
||||
continue
|
||||
return candidate
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def extract_scene_points_from_metadata(metadata: object) -> list[float] | None:
|
||||
"""从素材 metadata 中提取并校验场景切换点缓存.
|
||||
|
||||
合法缓存：list 类型、至少 2 个数值点、单调非负；否则返回 None（按未缓存处理）。
|
||||
"""
|
||||
if not isinstance(metadata, dict):
|
||||
return None
|
||||
points = metadata.get("scene_change_points")
|
||||
if not isinstance(points, list) or len(points) < 2:
|
||||
return None
|
||||
try:
|
||||
cleaned = [float(p) for p in points]
|
||||
except (TypeError, ValueError):
|
||||
return None
|
||||
if any(p < 0 for p in cleaned):
|
||||
return None
|
||||
cleaned = sorted(cleaned)
|
||||
if cleaned[0] != 0.0:
|
||||
cleaned.insert(0, 0.0)
|
||||
return cleaned
|
||||
|
||||
|
||||
# ── 素材分配 ────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@@ -168,8 +37,6 @@ def distribute_assets(
|
||||
*,
|
||||
random_selection: bool = False,
|
||||
asset_durations: dict[str, float] | None = None,
|
||||
asset_scene_points: dict[str, list[float]] | None = None,
|
||||
external_used_segments: dict[str, list[tuple[float, float]]] | None = None,
|
||||
) -> None:
|
||||
"""按 editing_mode 将素材分配到 clips（就地修改）.
|
||||
|
||||
@@ -179,17 +46,12 @@ def distribute_assets(
|
||||
- VOICE_OVER: 素材→main clips (B-roll)
|
||||
- VOICE_PIP: 第1个→background, 第2个→corner_voice, 其余→b_roll
|
||||
|
||||
start_time 选取：素材 metadata 中有场景切换点缓存时，优先从随机镜头段
|
||||
取起点（不同片段来自不同镜头）；无缓存或镜头段都冲突时回退随机起点。
|
||||
|
||||
Args:
|
||||
clips: 剪辑片段列表（就地修改 asset_id）
|
||||
asset_ids: 素材 ID 列表
|
||||
editing_mode: 剪辑模式字符串
|
||||
random_selection: 是否随机选择素材（用于预览生成）
|
||||
asset_durations: 素材 ID -> 时长（秒）映射，用于设置 start_time
|
||||
asset_scene_points: 素材 ID -> 场景切换点列表（metadata 缓存）
|
||||
external_used_segments: 跨视频已用区间（来自其他视频的 clips），注入到分配逻辑中避让
|
||||
asset_durations: 素材 ID -> 时长（秒）映射，用于设置随机 start_time
|
||||
"""
|
||||
if not asset_ids or not clips:
|
||||
return
|
||||
@@ -200,69 +62,30 @@ def distribute_assets(
|
||||
random.shuffle(asset_ids)
|
||||
|
||||
if editing_mode == EditingMode.ONE_TAKE.value:
|
||||
_distribute_one_take(clips, asset_ids, asset_durations, asset_scene_points, external_used_segments)
|
||||
_distribute_one_take(clips, asset_ids, asset_durations)
|
||||
elif editing_mode == EditingMode.PIP.value:
|
||||
_distribute_pip(clips, asset_ids, asset_durations, asset_scene_points, external_used_segments)
|
||||
_distribute_pip(clips, asset_ids, asset_durations)
|
||||
elif editing_mode == EditingMode.VOICE_OVER.value:
|
||||
_distribute_voice_over(clips, asset_ids, asset_durations, asset_scene_points, external_used_segments)
|
||||
_distribute_voice_over(clips, asset_ids, asset_durations)
|
||||
elif editing_mode == EditingMode.VOICE_PIP.value:
|
||||
_distribute_voice_pip(clips, asset_ids, asset_durations, asset_scene_points, external_used_segments)
|
||||
_distribute_voice_pip(clips, asset_ids, asset_durations)
|
||||
else:
|
||||
# 未知模式，退化为 one_take
|
||||
_distribute_one_take(clips, asset_ids, asset_durations, asset_scene_points, external_used_segments)
|
||||
|
||||
|
||||
def _resolve_start_time(
|
||||
asset_id: str,
|
||||
clip_duration: float,
|
||||
asset_durations: dict[str, float] | None,
|
||||
used_segments: dict[str, list[tuple[float, float]]],
|
||||
asset_scene_points: dict[str, list[float]] | None = None,
|
||||
on_exhausted: Callable[[str, float], tuple[float, float] | None] | None = None,
|
||||
) -> float | None:
|
||||
"""选取片段起点：场景缓存优先（随机镜头段），无缓存/全冲突回退随机起点.
|
||||
|
||||
场景路径与随机路径共享 used_segments 冲突避让；场景路径返回 None 时
|
||||
（无缓存、镜头段全冲突）回退 _calc_random_start_time，其受控复用逻辑
|
||||
（on_exhausted）不受影响。
|
||||
"""
|
||||
if asset_scene_points and asset_scene_points.get(asset_id):
|
||||
scene_start = pick_scene_aware_start(
|
||||
asset_id,
|
||||
clip_duration,
|
||||
asset_durations or {},
|
||||
asset_scene_points,
|
||||
used_segments,
|
||||
)
|
||||
if scene_start is not None:
|
||||
return scene_start
|
||||
return _calc_random_start_time(
|
||||
asset_id,
|
||||
clip_duration,
|
||||
asset_durations,
|
||||
used_segments,
|
||||
on_exhausted=on_exhausted,
|
||||
)
|
||||
_distribute_one_take(clips, asset_ids, asset_durations)
|
||||
|
||||
|
||||
def _distribute_one_take(
|
||||
clips: List[EditPlanClip],
|
||||
asset_ids: List[str],
|
||||
asset_durations: dict[str, float] | None = None,
|
||||
asset_scene_points: dict[str, list[float]] | None = None,
|
||||
external_used_segments: dict[str, list[tuple[float, float]]] | None = None,
|
||||
) -> None:
|
||||
"""ONE_TAKE: 素材按顺序依次分配给 main 类型 clips."""
|
||||
used_segments: dict[str, list[tuple[float, float]]] = (
|
||||
{k: list(v) for k, v in external_used_segments.items()} if external_used_segments else {}
|
||||
)
|
||||
used_segments: dict[str, list[tuple[float, float]]] = {}
|
||||
main_clips = [c for c in clips if c.clip_type == ClipType.MAIN.value]
|
||||
for i, clip in enumerate(main_clips):
|
||||
if i < len(asset_ids):
|
||||
asset_id = asset_ids[i]
|
||||
start_time = _resolve_start_time(
|
||||
asset_id, clip.duration, asset_durations, used_segments, asset_scene_points
|
||||
)
|
||||
start_time = _calc_random_start_time(asset_id, clip.duration, asset_durations, used_segments)
|
||||
clip.assign_asset(asset_id, start_time=start_time)
|
||||
# Record used segment
|
||||
if start_time is not None and asset_durations is not None:
|
||||
@@ -275,20 +98,14 @@ def _distribute_pip(
|
||||
clips: List[EditPlanClip],
|
||||
asset_ids: List[str],
|
||||
asset_durations: dict[str, float] | None = None,
|
||||
asset_scene_points: dict[str, list[float]] | None = None,
|
||||
external_used_segments: dict[str, list[tuple[float, float]]] | None = None,
|
||||
) -> None:
|
||||
"""PIP: 第1个素材→main(全屏背景)，其余→overlay clips."""
|
||||
used_segments: dict[str, list[tuple[float, float]]] = (
|
||||
{k: list(v) for k, v in external_used_segments.items()} if external_used_segments else {}
|
||||
)
|
||||
used_segments: dict[str, list[tuple[float, float]]] = {}
|
||||
# 第1个素材 → main clip
|
||||
main_clips = [c for c in clips if c.clip_type == ClipType.MAIN.value]
|
||||
if main_clips and asset_ids:
|
||||
asset_id = asset_ids[0]
|
||||
start_time = _resolve_start_time(
|
||||
asset_id, main_clips[0].duration, asset_durations, used_segments, asset_scene_points
|
||||
)
|
||||
start_time = _calc_random_start_time(asset_id, main_clips[0].duration, asset_durations, used_segments)
|
||||
main_clips[0].assign_asset(asset_id, start_time=start_time)
|
||||
# Record used segment
|
||||
if start_time is not None and asset_durations is not None:
|
||||
@@ -302,9 +119,7 @@ def _distribute_pip(
|
||||
for i, clip in enumerate(overlay_clips):
|
||||
if i < len(remaining):
|
||||
asset_id = remaining[i]
|
||||
start_time = _resolve_start_time(
|
||||
asset_id, clip.duration, asset_durations, used_segments, asset_scene_points
|
||||
)
|
||||
start_time = _calc_random_start_time(asset_id, clip.duration, asset_durations, used_segments)
|
||||
clip.assign_asset(asset_id, start_time=start_time)
|
||||
# Record used segment
|
||||
if start_time is not None and asset_durations is not None:
|
||||
@@ -317,20 +132,14 @@ def _distribute_voice_over(
|
||||
clips: List[EditPlanClip],
|
||||
asset_ids: List[str],
|
||||
asset_durations: dict[str, float] | None = None,
|
||||
asset_scene_points: dict[str, list[float]] | None = None,
|
||||
external_used_segments: dict[str, list[tuple[float, float]]] | None = None,
|
||||
) -> None:
|
||||
"""VOICE_OVER: 素材→main clips (B-roll)."""
|
||||
used_segments: dict[str, list[tuple[float, float]]] = (
|
||||
{k: list(v) for k, v in external_used_segments.items()} if external_used_segments else {}
|
||||
)
|
||||
used_segments: dict[str, list[tuple[float, float]]] = {}
|
||||
main_clips = [c for c in clips if c.clip_type == ClipType.MAIN.value]
|
||||
for i, clip in enumerate(main_clips):
|
||||
if i < len(asset_ids):
|
||||
asset_id = asset_ids[i]
|
||||
start_time = _resolve_start_time(
|
||||
asset_id, clip.duration, asset_durations, used_segments, asset_scene_points
|
||||
)
|
||||
start_time = _calc_random_start_time(asset_id, clip.duration, asset_durations, used_segments)
|
||||
clip.assign_asset(asset_id, start_time=start_time)
|
||||
# Record used segment
|
||||
if start_time is not None and asset_durations is not None:
|
||||
@@ -343,13 +152,9 @@ def _distribute_voice_pip(
|
||||
clips: List[EditPlanClip],
|
||||
asset_ids: List[str],
|
||||
asset_durations: dict[str, float] | None = None,
|
||||
asset_scene_points: dict[str, list[float]] | None = None,
|
||||
external_used_segments: dict[str, list[tuple[float, float]]] | None = None,
|
||||
) -> None:
|
||||
"""VOICE_PIP: 第1个→background, 第2个→corner_voice, 其余→b_roll."""
|
||||
used_segments: dict[str, list[tuple[float, float]]] = (
|
||||
{k: list(v) for k, v in external_used_segments.items()} if external_used_segments else {}
|
||||
)
|
||||
used_segments: dict[str, list[tuple[float, float]]] = {}
|
||||
bg_clips = [c for c in clips if c.clip_type == "background"]
|
||||
voice_clips = [c for c in clips if c.clip_type == "corner_voice"]
|
||||
broll_clips = [c for c in clips if c.clip_type == "b_roll"]
|
||||
@@ -359,9 +164,7 @@ def _distribute_voice_pip(
|
||||
# 第1个 → background
|
||||
if idx < len(asset_ids) and bg_clips:
|
||||
asset_id = asset_ids[idx]
|
||||
start_time = _resolve_start_time(
|
||||
asset_id, bg_clips[0].duration, asset_durations, used_segments, asset_scene_points
|
||||
)
|
||||
start_time = _calc_random_start_time(asset_id, bg_clips[0].duration, asset_durations, used_segments)
|
||||
bg_clips[0].assign_asset(asset_id, start_time=start_time)
|
||||
# Record used segment
|
||||
if start_time is not None and asset_durations is not None:
|
||||
@@ -373,9 +176,7 @@ def _distribute_voice_pip(
|
||||
# 第2个 → corner_voice
|
||||
if idx < len(asset_ids) and voice_clips:
|
||||
asset_id = asset_ids[idx]
|
||||
start_time = _resolve_start_time(
|
||||
asset_id, voice_clips[0].duration, asset_durations, used_segments, asset_scene_points
|
||||
)
|
||||
start_time = _calc_random_start_time(asset_id, voice_clips[0].duration, asset_durations, used_segments)
|
||||
voice_clips[0].assign_asset(asset_id, start_time=start_time)
|
||||
# Record used segment
|
||||
if start_time is not None and asset_durations is not None:
|
||||
@@ -389,9 +190,7 @@ def _distribute_voice_pip(
|
||||
for i, clip in enumerate(broll_clips):
|
||||
if i < len(remaining):
|
||||
asset_id = remaining[i]
|
||||
start_time = _resolve_start_time(
|
||||
asset_id, clip.duration, asset_durations, used_segments, asset_scene_points
|
||||
)
|
||||
start_time = _calc_random_start_time(asset_id, clip.duration, asset_durations, used_segments)
|
||||
clip.assign_asset(asset_id, start_time=start_time)
|
||||
# Record used segment
|
||||
if start_time is not None and asset_durations is not None:
|
||||
|
||||
@@ -14,13 +14,6 @@ from dataclasses import dataclass, field
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any
|
||||
|
||||
# 素材选取排序时注入的随机噪声上限（分）。
|
||||
# score_asset 综合得分范围为 0-100，噪声 0~20 意味着：
|
||||
# - 素材间得分差距 > 20 分时，排名不受影响（质量差异显著的素材保持稳定优先级）
|
||||
# - 得分接近（差距 <= 20 分）的素材排名会随机浮动，使每次生成选出的素材组合不同，
|
||||
# 从素材组合层面降低成片重复率；排名靠后的低分素材也有机会入选。
|
||||
SCORE_RANDOM_NOISE_MAX = 20.0
|
||||
|
||||
|
||||
@dataclass
|
||||
class SmartMatchResult:
|
||||
|
||||
@@ -150,11 +150,8 @@ def build_xfade_filter_chain(
|
||||
else:
|
||||
first_input_dur = cumulative - total_transition
|
||||
|
||||
# 正确的 offset 计算：offset 应相对于累积输出时长
|
||||
# offset = 累积输出中，转场开始的时间点
|
||||
# = first_input_dur - transition_duration
|
||||
# 这样每个转场之间的"纯内容"时长等于原始 clip 时长
|
||||
offset = max(0.0, first_input_dur - transition_duration)
|
||||
# 原始 offset 计算
|
||||
offset = max(0.0, cumulative - transition_duration * i)
|
||||
|
||||
# 安全钳制：offset + td 不能超过第一个输入的时长
|
||||
available = max(0.0, first_input_dur - offset)
|
||||
|
||||
@@ -112,11 +112,6 @@ class AssetRepository(ABC):
|
||||
"""查找包含所有指定标签的素材。"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def find_by_storage_key(self, storage_key: str) -> Asset | None:
|
||||
"""按 storage_key 查找素材（用于异步处理时更新已创建的记录）。"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def find_by_library_and_file_hash(
|
||||
self,
|
||||
|
||||
@@ -119,70 +119,6 @@ class MediaKitClient:
|
||||
|
||||
return None
|
||||
|
||||
def detect_scene_changes(
|
||||
self,
|
||||
video_url: str,
|
||||
max_frames: int = 20,
|
||||
poll_interval: float = 2.0,
|
||||
max_poll_attempts: int = 30,
|
||||
) -> Optional[List[float]]:
|
||||
"""检测视频场景切换点，返回时间戳列表.
|
||||
|
||||
降级策略：
|
||||
1. 先尝试 SceneChange 策略
|
||||
2. SceneChange 失败（OOM等）→ 退回 TimeInterval（5秒间隔）
|
||||
3. MediaKit 不可用 → 返回 None
|
||||
|
||||
Returns:
|
||||
场景切换点时间戳列表，如 [0.0, 3.2, 7.8, 12.5]
|
||||
失败返回 None
|
||||
"""
|
||||
if not self.is_available:
|
||||
logger.warning("MediaKit 未配置，跳过场景检测")
|
||||
return None
|
||||
|
||||
# 策略1：尝试 SceneChange
|
||||
frames = self.extract_frames(
|
||||
video_url=video_url,
|
||||
strategy="SceneChange",
|
||||
max_frames=max_frames,
|
||||
poll_interval=poll_interval,
|
||||
max_poll_attempts=max_poll_attempts,
|
||||
)
|
||||
|
||||
# 策略2：SceneChange 失败 → 退回 TimeInterval（5秒间隔）
|
||||
if frames is None:
|
||||
logger.info("SceneChange 策略失败，降级为 TimeInterval（5秒间隔）")
|
||||
# 估算帧数：假设视频最长60秒，每5秒一帧
|
||||
ti_max_frames = max(max_frames, 12)
|
||||
frames = self.extract_frames(
|
||||
video_url=video_url,
|
||||
strategy="TimeInterval",
|
||||
max_frames=ti_max_frames,
|
||||
poll_interval=poll_interval,
|
||||
max_poll_attempts=max_poll_attempts,
|
||||
)
|
||||
|
||||
if frames is None:
|
||||
return None
|
||||
|
||||
# 从帧列表中提取 timestamp，排序
|
||||
timestamps = sorted({float(f.get("timestamp", 0.0)) for f in frames if "timestamp" in f})
|
||||
|
||||
if not timestamps:
|
||||
return None
|
||||
|
||||
# 始终在列表开头加 0.0（素材起始点）
|
||||
if timestamps[0] != 0.0:
|
||||
timestamps.insert(0, 0.0)
|
||||
|
||||
logger.info(
|
||||
"场景检测完成: video_url=%s scene_changes=%s",
|
||||
video_url[:80],
|
||||
timestamps,
|
||||
)
|
||||
return timestamps
|
||||
|
||||
def _submit_extract_task(
|
||||
self,
|
||||
video_url: str,
|
||||
|
||||
@@ -100,7 +100,7 @@ def _check_ssrf_domain(hostname: str) -> None:
|
||||
raise UrlSecurityError(f"域名解析失败: {hostname}")
|
||||
|
||||
for info in infos:
|
||||
ip_str = str(info[4][0])
|
||||
ip_str = info[4][0]
|
||||
try:
|
||||
_check_ssrf_ip_base(ip_str)
|
||||
except ValueError:
|
||||
|
||||
@@ -24,10 +24,8 @@ celery==5.4.0
|
||||
# 对象存储
|
||||
oss2==2.18.4
|
||||
|
||||
# HTTP 客户端（pin 间接依赖防止版本漂移）
|
||||
# HTTP 客户端
|
||||
httpx==0.27.2
|
||||
httpcore==1.0.7
|
||||
h2==4.1.0
|
||||
|
||||
# Prometheus monitoring
|
||||
prometheus-client==0.21.1
|
||||
|
||||
@@ -15,6 +15,5 @@ pytest-xdist==3.6.1
|
||||
diff-cover==8.0.3
|
||||
|
||||
# 资产质量评分依赖（与 requirements-worker.txt 保持一致）
|
||||
numpy==1.26.4
|
||||
scipy==1.13.1
|
||||
Pillow==10.4.0
|
||||
|
||||
@@ -28,12 +28,15 @@ if [ "$SKIP_BACKEND" = "true" ]; then
|
||||
CONTEXTS=("CI/CD Pipeline / Frontend Lint (pull_request)")
|
||||
else
|
||||
CONTEXTS=(
|
||||
# CI 优化后 job 名称（2026-08）：Code Quality 拆分为 Style+Security，Type Check+Migration 合并为 Python
|
||||
# 与 pr_auto_scan.py 的 REQUIRED_CONTEXTS_APPROVE 保持一致
|
||||
"CI/CD Pipeline / Validate - Style (pull_request)"
|
||||
"CI/CD Pipeline / Validate - Security (pull_request)"
|
||||
"CI/CD Pipeline / Validate - Python (mypy + alembic) (pull_request)"
|
||||
"CI/CD Pipeline / Validate - Code Quality (pull_request)"
|
||||
"CI/CD Pipeline / Validate - Type Check (mypy) (pull_request)"
|
||||
"CI/CD Pipeline / Validate - Migration (alembic) (pull_request)"
|
||||
"CI/CD Pipeline / Frontend Lint (pull_request)"
|
||||
"CI/CD Pipeline / Unit Tests (pull_request)"
|
||||
"CI/CD Pipeline / Frontend Unit Tests (pull_request)"
|
||||
"CI/CD Pipeline / PR Build API Image (pull_request)"
|
||||
"CI/CD Pipeline / PR Build Web Image (pull_request)"
|
||||
"CI/CD Pipeline / PR Build Worker Image (pull_request)"
|
||||
)
|
||||
fi
|
||||
|
||||
@@ -150,4 +153,4 @@ done
|
||||
|
||||
echo
|
||||
echo "⏰ 快速检查超时（2分钟），CI尚未完成，退出等待下次触发（workflow_run事件或5分钟定时扫描）"
|
||||
exit 0
|
||||
exit 0
|
||||
|
||||
@@ -32,9 +32,9 @@ CONTEXTS=(
|
||||
echo "检查CI Gate统一门禁"
|
||||
echo
|
||||
|
||||
# 等待30秒后开始轮询，最多10分钟
|
||||
echo "等待30秒让CI启动..."
|
||||
sleep 30
|
||||
# 等待60秒，给CI启动写status的时间
|
||||
echo "等待60秒让CI启动..."
|
||||
sleep 60
|
||||
|
||||
# 405计数器（单次运行内重试）
|
||||
MERGE_405_COUNT=0
|
||||
@@ -72,9 +72,9 @@ check_and_merge() {
|
||||
# CI未全绿（pending中）→ 退出，等下次触发
|
||||
if [ "$ALL_SUCCESS" != "true" ]; then
|
||||
echo
|
||||
echo "⏳ CI尚未全绿（仍有pending），等待重试..."
|
||||
echo " （当前第${attempt}次轮询，最多${MAX_ATTEMPTS}次）"
|
||||
return 1
|
||||
echo "⏳ CI尚未全绿（仍有pending），退出等待下次触发"
|
||||
echo " （pr-auto-scan每5分钟扫描一次，CI通过后会自动合并）"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# CI全绿 → 合并
|
||||
@@ -136,28 +136,13 @@ check_and_merge() {
|
||||
fi
|
||||
}
|
||||
|
||||
# 轮询等待CI就绪+审批完成，最多10分钟（60次x10秒）
|
||||
MAX_ATTEMPTS=60
|
||||
for attempt in $(seq 1 $MAX_ATTEMPTS); do
|
||||
# 最多重试3次（用于405重试，非CI轮询）
|
||||
for i in 1 2 3; do
|
||||
if check_and_merge; then
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# 检查PR是否还open（可能已被手动合并或关闭）
|
||||
PR_STATE=$(curl -s -H "Authorization: token ${MERGE_TOKEN}" \
|
||||
"${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}" \
|
||||
| python3 -c "import sys,json; print(json.load(sys.stdin).get('state',''))" 2>/dev/null || echo "?")
|
||||
|
||||
if [ "$PR_STATE" != "open" ]; then
|
||||
echo "PR状态为 ${PR_STATE}，无需继续等待"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
if [ $attempt -lt $MAX_ATTEMPTS ]; then
|
||||
sleep 10
|
||||
fi
|
||||
done
|
||||
|
||||
echo
|
||||
echo "⏰ 等待10分钟后仍未满足合并条件，退出。pr-auto-scan定时扫描会继续重试。"
|
||||
echo "本次检查未满足合并条件，退出。pr-auto-scan每5分钟会继续扫描。"
|
||||
exit 0
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
# - docker-container driver, host 网络
|
||||
# - 层缓存保存在 buildkit 容器及其 _state 命名卷中，job 结束不清理
|
||||
# - 宿主机 ci-docker-cleanup.sh 已豁免该 builder
|
||||
# - 每次执行自动同步宿主机 docker config 到 BuildKit 容器（确保 registry 认证）
|
||||
# 用法: bash scripts/ci/ensure_persistent_builder.sh
|
||||
set -eu
|
||||
|
||||
@@ -35,26 +34,3 @@ docker buildx use "$BUILDER"
|
||||
docker buildx inspect "$BUILDER" --bootstrap
|
||||
echo "✅ builder ready"
|
||||
docker buildx ls | head -5
|
||||
|
||||
# === 同步宿主机 docker config 到 BuildKit 容器（确保 registry 认证） ===
|
||||
# BuildKit 容器名遵循 docker buildx 命名规则: buildx_buildkit_<builder-name>_0
|
||||
BUILDKIT_CONTAINER="buildx_buildkit_${BUILDER}_0"
|
||||
|
||||
if docker inspect "$BUILDKIT_CONTAINER" >/dev/null 2>&1; then
|
||||
# 宿主机 docker config 路径
|
||||
HOST_DOCKER_CONFIG="/root/.docker/config.json"
|
||||
|
||||
if [ -f "$HOST_DOCKER_CONFIG" ]; then
|
||||
echo "=== 同步 docker config 到 BuildKit 容器 ==="
|
||||
# 确保容器内 .docker 目录存在
|
||||
docker exec "$BUILDKIT_CONTAINER" mkdir -p /root/.docker
|
||||
# 拷贝 config.json
|
||||
docker cp "$HOST_DOCKER_CONFIG" "$BUILDKIT_CONTAINER:/root/.docker/config.json"
|
||||
echo "✅ docker config 已同步到 BuildKit 容器"
|
||||
else
|
||||
echo "⚠️ 宿主机 docker config 不存在: $HOST_DOCKER_CONFIG（跳过同步）"
|
||||
fi
|
||||
else
|
||||
echo "⚠️ BuildKit 容器不存在: $BUILDKIT_CONTAINER（跳过 config 同步）"
|
||||
fi
|
||||
|
||||
|
||||
@@ -303,10 +303,9 @@ def main():
|
||||
"CI/CD Pipeline / CI Gate (pull_request)",
|
||||
]
|
||||
REQUIRED_CONTEXTS_APPROVE = [
|
||||
# CI 优化后 job 名称（2026-08）：Code Quality 拆分为 Style+Security，Type Check+Migration 合并为 Python
|
||||
"CI/CD Pipeline / Validate - Style (pull_request)",
|
||||
"CI/CD Pipeline / Validate - Security (pull_request)",
|
||||
"CI/CD Pipeline / Validate - Python (mypy + alembic) (pull_request)",
|
||||
"CI/CD Pipeline / Validate - Code Quality (pull_request)",
|
||||
"CI/CD Pipeline / Validate - Type Check (mypy) (pull_request)",
|
||||
"CI/CD Pipeline / Validate - Migration (alembic) (pull_request)",
|
||||
"CI/CD Pipeline / Frontend Lint (pull_request)",
|
||||
]
|
||||
FRONTEND_ONLY_CONTEXT = [
|
||||
|
||||
@@ -52,45 +52,70 @@ bash scripts/ci/step_install_ffmpeg.sh
|
||||
# 需要用宿主机IP访问映射端口
|
||||
# 检测策略：host.docker.internal -> docker0桥接IP -> 容器IP直连 -> 默认网关 -> 127.0.0.1
|
||||
detect_docker_host() {
|
||||
# 目标：找到宿主机IP（DooD模式下CI容器访问宿主机上其他容器用）
|
||||
# 不依赖特定端口TCP探测，直接用网络拓扑信息
|
||||
local test_port="${1:-${CI_LOCAL_PG_PORT}}"
|
||||
|
||||
# 候选IP列表
|
||||
local candidates=()
|
||||
|
||||
# 1. host.docker.internal（runner配置了--add-host时可用）
|
||||
if python3 -c "import socket; socket.gethostbyname('host.docker.internal')" 2>/dev/null; then
|
||||
echo "host.docker.internal"
|
||||
return 0
|
||||
candidates+=("host.docker.internal")
|
||||
fi
|
||||
|
||||
# 2. 默认网关（Docker bridge模式下网关即宿主机）
|
||||
# 2. docker0 桥接网关 (172.17.0.1)
|
||||
candidates+=("172.17.0.1")
|
||||
|
||||
# 3. 默认网关（容器网络的网关即宿主机）
|
||||
local gw=""
|
||||
gw=$(ip route 2>/dev/null | grep default | awk '{print $3}' | head -1)
|
||||
if [ -n "$gw" ] && [ "$gw" != "127.0.0.1" ]; then
|
||||
echo "$gw"
|
||||
return 0
|
||||
candidates+=("$gw")
|
||||
fi
|
||||
|
||||
# 3. docker0 桥接网关
|
||||
if [ -n "$(ip addr show docker0 2>/dev/null)" ]; then
|
||||
echo "172.17.0.1"
|
||||
return 0
|
||||
# 4. 宿主机可能的IP：容器同网段的.1或.254
|
||||
local my_ip=""
|
||||
my_ip=$(hostname -I 2>/dev/null | awk '{print $1}')
|
||||
if [ -n "$my_ip" ]; then
|
||||
# 尝试同网段的常见宿主机IP
|
||||
local subnet=$(echo "$my_ip" | cut -d. -f1-3)
|
||||
candidates+=("${subnet}.1")
|
||||
candidates+=("${subnet}.254")
|
||||
fi
|
||||
|
||||
# 4. 通过 git server hostname 反查（runner 配置了 ExtraHosts host-gateway）
|
||||
local git_host_ip=""
|
||||
git_host_ip=$(python3 -c "import socket; print(socket.gethostbyname('git.xiaoxiajianji.com'))" 2>/dev/null || true)
|
||||
if [ -n "$git_host_ip" ] && [ "$git_host_ip" != "127.0.0.1" ]; then
|
||||
echo "$git_host_ip"
|
||||
return 0
|
||||
fi
|
||||
# 5. 127.0.0.1 最后尝试
|
||||
candidates+=("127.0.0.1")
|
||||
|
||||
# 5. 最终 fallback
|
||||
# 测试每个候选IP
|
||||
for candidate in "${candidates[@]}"; do
|
||||
if python3 -c "
|
||||
import socket
|
||||
s = socket.socket()
|
||||
s.settimeout(2)
|
||||
try:
|
||||
s.connect(('$candidate', $test_port))
|
||||
s.close()
|
||||
print('ok')
|
||||
except:
|
||||
pass
|
||||
" 2>/dev/null | grep -q ok; then
|
||||
echo "$candidate"
|
||||
return 0
|
||||
fi
|
||||
done
|
||||
|
||||
# 都失败则返回127.0.0.1
|
||||
echo "127.0.0.1"
|
||||
return 0
|
||||
return 1
|
||||
}
|
||||
|
||||
# 获取宿主机IP（先尝试用共享PG端口5433测试，再回退到其他端口）
|
||||
if [ -S /var/run/docker.sock ]; then
|
||||
DOCKER_HOST_IP=$(detect_docker_host)
|
||||
# 先用共享PG端口5433探测
|
||||
DOCKER_HOST_IP=$(detect_docker_host "${CI_SHARED_PG_PORT}")
|
||||
if [ "$DOCKER_HOST_IP" = "127.0.0.1" ]; then
|
||||
# 如果共享PG端口探测失败，说明不在DooD或共享PG不可用，再试其他端口
|
||||
DOCKER_HOST_IP=$(detect_docker_host 22)
|
||||
fi
|
||||
echo "检测到DooD模式（/var/run/docker.sock已挂载），宿主机地址: $DOCKER_HOST_IP"
|
||||
else
|
||||
DOCKER_HOST_IP="127.0.0.1"
|
||||
@@ -172,8 +197,8 @@ if [ "$USE_SHARED_PG" = "true" ]; then
|
||||
# 创建主数据库（xdist 模式下各 worker 会创建自己的数据库，主库作为 fallback）
|
||||
echo "创建主测试数据库: $CI_DB_NAME"
|
||||
PGPASSWORD="$SHARED_PG_PASSWORD" python3 -c "
|
||||
import psycopg
|
||||
conn = psycopg.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
import psycopg2
|
||||
conn = psycopg2.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
conn.autocommit = True
|
||||
cur = conn.cursor()
|
||||
cur.execute(f'DROP DATABASE IF EXISTS \"$CI_DB_NAME\" WITH (FORCE)')
|
||||
@@ -202,7 +227,7 @@ else
|
||||
postgres:16
|
||||
PG_PORT=$(docker port "$PG_CONTAINER" ${CI_LOCAL_PG_PORT}/tcp | cut -d: -f2)
|
||||
echo "PostgreSQL port: $PG_PORT"
|
||||
export DATABASE_URL="postgresql+psycopg://postgres:postgres@${PG_HOST}:${PG_PORT}/${CI_DEFAULT_DB}"
|
||||
export DATABASE_URL="postgresql+psycopg://${CI_SHARED_PG_USER}:${CI_SHARED_PG_PASSWORD}@${PG_HOST}:${PG_PORT}/${CI_DEFAULT_DB}"
|
||||
|
||||
# 等待容器健康
|
||||
for i in $(seq 1 30); do
|
||||
@@ -279,8 +304,8 @@ if [ "$USE_SHARED_PG" = "true" ]; then
|
||||
|
||||
# 清理所有以 CI_DB_NAME 开头的数据库（主库 + worker 库）
|
||||
PGPASSWORD="${SHARED_PG_PASSWORD}" python3 -c "
|
||||
import psycopg
|
||||
conn = psycopg.connect(host='${SHARED_PG_HOST}', port=${SHARED_PG_PORT}, user='${SHARED_PG_USER}', password='${SHARED_PG_PASSWORD}', dbname='postgres')
|
||||
import psycopg2
|
||||
conn = psycopg2.connect(host='${SHARED_PG_HOST}', port=${SHARED_PG_PORT}, user='${SHARED_PG_USER}', password='${SHARED_PG_PASSWORD}', dbname='postgres')
|
||||
conn.autocommit = True
|
||||
cur = conn.cursor()
|
||||
|
||||
@@ -319,4 +344,4 @@ python3 scripts/ci_coverage_summary.py
|
||||
set -e
|
||||
|
||||
echo ""
|
||||
echo "=== CI Integration Tests 全部通过 ✅ ==="
|
||||
echo "=== CI Integration Tests 全部通过 ✅ ==="
|
||||
|
||||
+47
-106
@@ -1,136 +1,77 @@
|
||||
#!/bin/bash
|
||||
# CI Unit Tests Job 主脚本
|
||||
# 包含：依赖缓存、增量测试选择、覆盖率测试、diff覆盖率门禁
|
||||
# 包含：依赖安装、增量测试选择、覆盖率测试、diff覆盖率门禁
|
||||
set -eu
|
||||
|
||||
JOB_NAME="${1:-Unit Tests}"
|
||||
|
||||
echo "=== CI Unit Tests 开始 ==="
|
||||
|
||||
# --- 依赖缓存检查 ---
|
||||
# 如果 requirements 文件未变化且依赖已安装，跳过 pip install（持久 runner 优化）
|
||||
REQ_HASH_FILE="/tmp/.ci_unit_tests_req_hash"
|
||||
CURRENT_REQ_HASH=""
|
||||
if [ -f requirements-base.txt ] && [ -f requirements.txt ] && [ -f requirements-dev.txt ]; then
|
||||
CURRENT_REQ_HASH=$(cat requirements-base.txt requirements.txt requirements-dev.txt | md5sum | cut -d' ' -f1)
|
||||
fi
|
||||
|
||||
SKIP_PIP_INSTALL=false
|
||||
if [ -n "$CURRENT_REQ_HASH" ] && [ -f "$REQ_HASH_FILE" ]; then
|
||||
CACHED_HASH=$(cat "$REQ_HASH_FILE")
|
||||
if [ "$CACHED_HASH" = "$CURRENT_REQ_HASH" ]; then
|
||||
# 验证关键包是否还在
|
||||
if python3 -c "import pytest; import celery" 2>/dev/null; then
|
||||
echo "✅ 依赖无变化 (hash=$CURRENT_REQ_HASH)，跳过 pip install"
|
||||
SKIP_PIP_INSTALL=true
|
||||
else
|
||||
echo "⚠️ 依赖 hash 匹配但关键包缺失，重新安装"
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
# --- 安装依赖 ---
|
||||
if [ "$SKIP_PIP_INSTALL" = "false" ]; then
|
||||
echo ""
|
||||
echo "=== 安装 Python 依赖 ==="
|
||||
# pip install 带重试（网络不稳定时自动重试），合并为一次调用减少开销
|
||||
for i in 1 2 3; do
|
||||
python3 -m pip install -q -r requirements-base.txt -r requirements.txt -r requirements-dev.txt && break
|
||||
echo "pip install 失败，重试 $i/3..."
|
||||
[ $i -eq 3 ] && exit 1
|
||||
sleep 5
|
||||
done
|
||||
# 保存 hash 标记
|
||||
if [ -n "$CURRENT_REQ_HASH" ]; then
|
||||
echo "$CURRENT_REQ_HASH" > "$REQ_HASH_FILE"
|
||||
fi
|
||||
fi
|
||||
echo ""
|
||||
echo "=== 安装 Python 依赖 ==="
|
||||
# pip install 带重试（网络不稳定时自动重试）
|
||||
for i in 1 2 3; do
|
||||
python3 -m pip install -q -r requirements-base.txt && break
|
||||
echo "pip install requirements-base.txt 失败，重试 $i/3..."
|
||||
[ $i -eq 3 ] && exit 1
|
||||
sleep 5
|
||||
done
|
||||
for i in 1 2 3; do
|
||||
python3 -m pip install -q -r requirements.txt && break
|
||||
echo "pip install requirements.txt 失败，重试 $i/3..."
|
||||
[ $i -eq 3 ] && exit 1
|
||||
sleep 5
|
||||
done
|
||||
for i in 1 2 3; do
|
||||
python3 -m pip install -q -r requirements-dev.txt && break
|
||||
echo "pip install requirements-dev.txt 失败，重试 $i/3..."
|
||||
[ $i -eq 3 ] && exit 1
|
||||
sleep 5
|
||||
done
|
||||
pytest --version
|
||||
|
||||
# 双保险：确保numpy已安装
|
||||
echo "=== 验证 numpy 安装 ==="
|
||||
SKIP_NUMPY_TESTS=0
|
||||
if python3 -c "import numpy; assert numpy.__version__ == '1.26.4'" 2>/dev/null; then
|
||||
echo "✅ numpy 1.26.4 已就绪（缓存命中）"
|
||||
else
|
||||
echo "需要安装 numpy 1.26.4..."
|
||||
python3 -m pip install numpy==1.26.4 || {
|
||||
echo "❌ numpy 首次安装失败，尝试不使用缓存重新安装..."
|
||||
python3 -m pip install --no-cache-dir numpy==1.26.4 || {
|
||||
echo "⚠️ numpy 安装失败，跳过需要 numpy 的测试"
|
||||
SKIP_NUMPY_TESTS=1
|
||||
}
|
||||
python3 -m pip install numpy==1.26.4 || {
|
||||
echo "❌ numpy 首次安装失败，尝试不使用缓存重新安装..."
|
||||
python3 -m pip install --no-cache-dir numpy==1.26.4 || {
|
||||
echo "⚠️ numpy 安装失败，跳过需要 numpy 的测试"
|
||||
SKIP_NUMPY_TESTS=1
|
||||
}
|
||||
fi
|
||||
}
|
||||
if [ "$SKIP_NUMPY_TESTS" = "0" ]; then
|
||||
python3 -c "import numpy; print(f'✅ numpy {numpy.__version__} 就绪')" || {
|
||||
python3 -c "import numpy; print(f'✅ numpy {numpy.__version__} 安装成功')" || {
|
||||
echo "⚠️ numpy 导入失败，跳过需要 numpy 的测试"
|
||||
SKIP_NUMPY_TESTS=1
|
||||
}
|
||||
fi
|
||||
|
||||
# --- 增量测试选择（PR + push 均支持） ---
|
||||
# --- 增量测试选择（仅PR） ---
|
||||
UNIT_TEST_MODE="full"
|
||||
SELECTED_TEST_FILES="tests/unit"
|
||||
|
||||
IS_PULL_REQUEST=false
|
||||
IS_PUSH=false
|
||||
[ "${GITHUB_EVENT_NAME:-}" = "pull_request" ] && IS_PULL_REQUEST=true
|
||||
[ "${GITHUB_EVENT_NAME:-}" = "push" ] && IS_PUSH=true
|
||||
|
||||
if ($IS_PULL_REQUEST || $IS_PUSH) && [ -n "${GITHUB_TOKEN:-}" ]; then
|
||||
if [ "${GITHUB_EVENT_NAME:-}" = "pull_request" ] && [ -n "${GITHUB_TOKEN:-}" ]; then
|
||||
echo ""
|
||||
echo "=== 增量测试选择 ==="
|
||||
|
||||
CHANGED_FILES=""
|
||||
|
||||
if $IS_PULL_REQUEST; then
|
||||
PR_NUMBER=$(echo "$GITHUB_REF" | sed 's|refs/pull/||; s|/.*||')
|
||||
API_URL="${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}/files?limit=300"
|
||||
CHANGED_FILES=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" "$API_URL" \
|
||||
| python3 -c "import sys,json; [print(f['filename']) for f in json.load(sys.stdin) if f['status'] != 'removed']")
|
||||
elif $IS_PUSH && [ -n "${GITHUB_SHA:-}" ]; then
|
||||
# Push 事件：通过 GitHub API 获取本次 push 改动的文件
|
||||
API_URL="${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/commits/${GITHUB_SHA}"
|
||||
RESPONSE=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" \
|
||||
-H "Accept: application/vnd.github.v3.diff" "$API_URL" 2>/dev/null || echo "")
|
||||
|
||||
if [ -n "$RESPONSE" ]; then
|
||||
CHANGED_FILES=$(echo "$RESPONSE" | grep '^diff --git' | sed 's|diff --git a/\(.*\) b/.*|\1|' || echo "")
|
||||
fi
|
||||
|
||||
# 备用方案：获取 previous commit SHA 再查 API
|
||||
if [ -z "$CHANGED_FILES" ]; then
|
||||
PREV_SHA=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" \
|
||||
"${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/commits?sha=${GITHUB_SHA}&per_page=2" \
|
||||
| python3 -c "import sys,json; commits=json.load(sys.stdin); print(commits[1]['sha'] if len(commits)>1 else '')" 2>/dev/null || echo "")
|
||||
if [ -n "$PREV_SHA" ]; then
|
||||
COMPARE_URL="${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/compare/${PREV_SHA}...${GITHUB_SHA}"
|
||||
CHANGED_FILES=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" "$COMPARE_URL" \
|
||||
| python3 -c "import sys,json; data=json.load(sys.stdin); [print(f['filename']) for f in data.get('files',[]) if f['status'] != 'removed']" 2>/dev/null || echo "")
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
PR_NUMBER=$(echo "$GITHUB_REF" | sed 's|refs/pull/||; s|/.*||')
|
||||
API_URL="${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}/files?limit=300"
|
||||
CHANGED_FILES=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" "$API_URL" | python3 -c "import sys,json; [print(f['filename']) for f in json.load(sys.stdin) if f['status'] != 'removed']")
|
||||
echo "改动文件数: $(echo "$CHANGED_FILES" | grep -c . || echo 0)"
|
||||
|
||||
if [ -n "$CHANGED_FILES" ]; then
|
||||
set +e
|
||||
CHANGED_FILES="$CHANGED_FILES" \
|
||||
SELECTED_TESTS_OUTPUT=/tmp/selected_tests.txt \
|
||||
python3 scripts/ci/select_unit_tests.py
|
||||
SELECT_EXIT=$?
|
||||
set -e
|
||||
if [ $SELECT_EXIT -eq 0 ]; then
|
||||
UNIT_TEST_MODE="incremental"
|
||||
TEST_FILES=$(cat /tmp/selected_tests.txt | tr '\n' ' ')
|
||||
SELECTED_TEST_FILES="$TEST_FILES"
|
||||
echo "增量模式: $(cat /tmp/selected_tests.txt | wc -l) 个测试文件"
|
||||
else
|
||||
echo "全量模式（增量选择失败）"
|
||||
fi
|
||||
set +e
|
||||
CHANGED_FILES="$CHANGED_FILES" \
|
||||
SELECTED_TESTS_OUTPUT=/tmp/selected_tests.txt \
|
||||
python3 scripts/ci/select_unit_tests.py
|
||||
SELECT_EXIT=$?
|
||||
set -e
|
||||
if [ $SELECT_EXIT -eq 0 ]; then
|
||||
UNIT_TEST_MODE="incremental"
|
||||
TEST_FILES=$(cat /tmp/selected_tests.txt | tr '\n' ' ')
|
||||
SELECTED_TEST_FILES="$TEST_FILES"
|
||||
echo "增量模式: $(cat /tmp/selected_tests.txt | wc -l) 个测试文件"
|
||||
else
|
||||
echo "无法获取改动文件列表，使用全量模式"
|
||||
echo "全量模式"
|
||||
fi
|
||||
fi
|
||||
|
||||
@@ -175,7 +116,7 @@ if [ "${GITHUB_EVENT_NAME:-}" = "pull_request" ] && [ -n "${GITHUB_TOKEN:-}" ];
|
||||
|
||||
PR_CODE_DIR="/tmp/pr-code-$$"
|
||||
mkdir -p "$PR_CODE_DIR"
|
||||
# 备份PR代码（含coverage.xml，diff-cover需要用到）
|
||||
# 备份PR代码（含coverage.xml，diff-cover需要用到
|
||||
find . -maxdepth 1 -mindepth 1 ! -name 'diff_coverage.html' -exec cp -r {} "$PR_CODE_DIR/" \;
|
||||
rm -rf .git
|
||||
git init > /dev/null 2>&1
|
||||
|
||||
@@ -409,8 +409,8 @@ except:
|
||||
|
||||
echo "创建测试数据库: $CI_DB_NAME"
|
||||
PGPASSWORD="$SHARED_PG_PASSWORD" python3 -c "
|
||||
import psycopg
|
||||
conn = psycopg.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
import psycopg2
|
||||
conn = psycopg2.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
conn.autocommit = True
|
||||
cur = conn.cursor()
|
||||
cur.execute(f'DROP DATABASE IF EXISTS \"$CI_DB_NAME\" WITH (FORCE)')
|
||||
@@ -431,8 +431,8 @@ conn.close()
|
||||
# 清理数据库
|
||||
echo "清理测试数据库: $CI_DB_NAME"
|
||||
PGPASSWORD="$SHARED_PG_PASSWORD" python3 -c "
|
||||
import psycopg
|
||||
conn = psycopg.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
import psycopg2
|
||||
conn = psycopg2.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
conn.autocommit = True
|
||||
cur = conn.cursor()
|
||||
cur.execute(f'DROP DATABASE IF EXISTS \"$CI_DB_NAME\" WITH (FORCE)')
|
||||
|
||||
@@ -59,40 +59,56 @@ echo ""
|
||||
# ============================================================
|
||||
|
||||
detect_docker_host() {
|
||||
# 目标：找到宿主机IP（DooD模式下CI容器访问宿主机上其他容器用）
|
||||
# 不依赖特定端口TCP探测，直接用网络拓扑信息
|
||||
local test_port="${1:-${CI_LOCAL_PG_PORT}}"
|
||||
|
||||
# 1. host.docker.internal（runner配置了--add-host时可用）
|
||||
local candidates=()
|
||||
|
||||
# 1. host.docker.internal
|
||||
if python3 -c "import socket; socket.gethostbyname('host.docker.internal')" 2>/dev/null; then
|
||||
echo "host.docker.internal"
|
||||
return 0
|
||||
candidates+=("host.docker.internal")
|
||||
fi
|
||||
|
||||
# 2. 默认网关（Docker bridge模式下网关即宿主机）
|
||||
# 2. docker0 桥接网关
|
||||
candidates+=("172.17.0.1")
|
||||
|
||||
# 3. 默认网关
|
||||
local gw=""
|
||||
gw=$(ip route 2>/dev/null | grep default | awk '{print $3}' | head -1)
|
||||
if [ -n "$gw" ] && [ "$gw" != "127.0.0.1" ]; then
|
||||
echo "$gw"
|
||||
return 0
|
||||
candidates+=("$gw")
|
||||
fi
|
||||
|
||||
# 3. docker0 桥接网关
|
||||
if [ -n "$(ip addr show docker0 2>/dev/null)" ]; then
|
||||
echo "172.17.0.1"
|
||||
return 0
|
||||
# 4. 宿主机同网段的.1或.254
|
||||
local my_ip=""
|
||||
my_ip=$(hostname -I 2>/dev/null | awk '{print $1}')
|
||||
if [ -n "$my_ip" ]; then
|
||||
local subnet=$(echo "$my_ip" | cut -d. -f1-3)
|
||||
candidates+=("${subnet}.1")
|
||||
candidates+=("${subnet}.254")
|
||||
fi
|
||||
|
||||
# 4. 通过 git server hostname 反查（runner 配置了 ExtraHosts host-gateway）
|
||||
local git_host_ip=""
|
||||
git_host_ip=$(python3 -c "import socket; print(socket.gethostbyname('git.xiaoxiajianji.com'))" 2>/dev/null || true)
|
||||
if [ -n "$git_host_ip" ] && [ "$git_host_ip" != "127.0.0.1" ]; then
|
||||
echo "$git_host_ip"
|
||||
return 0
|
||||
fi
|
||||
# 5. 127.0.0.1 最后尝试
|
||||
candidates+=("127.0.0.1")
|
||||
|
||||
for candidate in "${candidates[@]}"; do
|
||||
if python3 -c "
|
||||
import socket
|
||||
s = socket.socket()
|
||||
s.settimeout(2)
|
||||
try:
|
||||
s.connect(('$candidate', $test_port))
|
||||
s.close()
|
||||
print('ok')
|
||||
except:
|
||||
pass
|
||||
" 2>/dev/null | grep -q ok; then
|
||||
echo "$candidate"
|
||||
return 0
|
||||
fi
|
||||
done
|
||||
|
||||
# 5. 最终 fallback
|
||||
echo "127.0.0.1"
|
||||
return 0
|
||||
return 1
|
||||
}
|
||||
|
||||
# 指数退避TCP连接检查
|
||||
@@ -116,7 +132,10 @@ wait_tcp_ready() {
|
||||
|
||||
# 获取宿主机IP
|
||||
if [ -S /var/run/docker.sock ]; then
|
||||
DOCKER_HOST_IP=$(detect_docker_host)
|
||||
DOCKER_HOST_IP=$(detect_docker_host "${CI_SHARED_PG_PORT}")
|
||||
if [ "$DOCKER_HOST_IP" = "127.0.0.1" ]; then
|
||||
DOCKER_HOST_IP=$(detect_docker_host 22)
|
||||
fi
|
||||
echo "检测到DooD模式，宿主机地址: $DOCKER_HOST_IP"
|
||||
else
|
||||
DOCKER_HOST_IP="127.0.0.1"
|
||||
@@ -150,8 +169,8 @@ if [ "$USE_SHARED_PG" = "true" ]; then
|
||||
|
||||
echo "创建测试数据库: $CI_DB_NAME"
|
||||
PGPASSWORD="$SHARED_PG_PASSWORD" python3 -c "
|
||||
import psycopg
|
||||
conn = psycopg.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
import psycopg2
|
||||
conn = psycopg2.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
conn.autocommit = True
|
||||
cur = conn.cursor()
|
||||
cur.execute(f'DROP DATABASE IF EXISTS \"$CI_DB_NAME\" WITH (FORCE)')
|
||||
@@ -166,8 +185,8 @@ conn.close()
|
||||
echo ""
|
||||
echo "清理测试数据库: $CI_DB_NAME"
|
||||
PGPASSWORD="$SHARED_PG_PASSWORD" python3 -c "
|
||||
import psycopg
|
||||
conn = psycopg.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
import psycopg2
|
||||
conn = psycopg2.connect(host='$SHARED_PG_HOST', port=$SHARED_PG_PORT, user='$SHARED_PG_USER', password='$SHARED_PG_PASSWORD', dbname='postgres')
|
||||
conn.autocommit = True
|
||||
cur = conn.cursor()
|
||||
cur.execute(f'DROP DATABASE IF EXISTS \"$CI_DB_NAME\" WITH (FORCE)')
|
||||
@@ -194,7 +213,7 @@ else
|
||||
postgres:16-alpine
|
||||
PG_PORT=$(docker port "$PG_CONTAINER" ${CI_LOCAL_PG_PORT}/tcp | cut -d: -f2)
|
||||
echo "PostgreSQL port: $PG_PORT"
|
||||
export DATABASE_URL="postgresql+psycopg://postgres:postgres@${PG_HOST}:${PG_PORT}/${CI_DEFAULT_DB}"
|
||||
export DATABASE_URL="postgresql+psycopg://${CI_SHARED_PG_USER}:${CI_SHARED_PG_PASSWORD}@${PG_HOST}:${PG_PORT}/${CI_DEFAULT_DB}"
|
||||
|
||||
# 等待容器健康
|
||||
for i in $(seq 1 30); do
|
||||
|
||||
@@ -59,7 +59,6 @@ REGISTRY_TOKEN="${ACR_PASSWORD:-${REGISTRY_TOKEN:-}}"
|
||||
ENV_FILE="${ENV_FILE:-/var/lib/xiaoxia-saas-production/.env}"
|
||||
GENERATED_DIR="${GENERATED_DIR:-/var/lib/xiaoxia-saas-production/generated}"
|
||||
LEGACY_ASSETS_DIR="${LEGACY_ASSETS_DIR:-/var/lib/xiaoxia-saas-production/legacy-assets}"
|
||||
NGINX_CONF_FILE="${NGINX_CONF_FILE:-/var/lib/xiaoxia-saas-production/nginx-production.conf}"
|
||||
|
||||
SKIP_MIGRATION="${SKIP_MIGRATION:-false}"
|
||||
SKIP_ROLLBACK="${SKIP_ROLLBACK:-false}"
|
||||
@@ -73,52 +72,6 @@ test -f "$ENV_FILE"
|
||||
mkdir -p "$GENERATED_DIR"
|
||||
mkdir -p "$LEGACY_ASSETS_DIR"
|
||||
|
||||
# ── 写入 Production Nginx 配置 ──
|
||||
echo "Writing production nginx config..."
|
||||
cat > "$NGINX_CONF_FILE" << 'NGINX_EOF'
|
||||
server {
|
||||
listen 80;
|
||||
server_name _;
|
||||
root /usr/share/nginx/html;
|
||||
index index.html;
|
||||
|
||||
gzip on;
|
||||
gzip_vary on;
|
||||
gzip_min_length 1024;
|
||||
gzip_types text/plain text/css text/xml text/javascript application/javascript application/json application/xml+rss;
|
||||
|
||||
client_max_body_size 800m;
|
||||
|
||||
location / {
|
||||
try_files $uri /index.html;
|
||||
}
|
||||
|
||||
resolver 127.0.0.11 valid=10s;
|
||||
resolver_timeout 5s;
|
||||
|
||||
location /api/ {
|
||||
proxy_pass http://xiaoxia-api-production:8000/api/;
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
proxy_read_timeout 300s;
|
||||
proxy_send_timeout 300s;
|
||||
proxy_request_buffering off;
|
||||
}
|
||||
|
||||
location /generated-files/ {
|
||||
alias /app/generated/;
|
||||
}
|
||||
|
||||
location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
|
||||
expires 1y;
|
||||
add_header Cache-Control "public, immutable";
|
||||
}
|
||||
}
|
||||
NGINX_EOF
|
||||
echo "✅ Nginx config written: $NGINX_CONF_FILE"
|
||||
|
||||
echo "==========================================="
|
||||
echo " Production 部署 - $IMAGE_TAG"
|
||||
echo "==========================================="
|
||||
@@ -235,7 +188,6 @@ rollback() {
|
||||
--cpus 0.5 \
|
||||
--memory 512m \
|
||||
$LEGACY_VOLUME \
|
||||
-v "$NGINX_CONF_FILE:/etc/nginx/conf.d/default.conf:ro" \
|
||||
--health-cmd "wget --spider -q http://127.0.0.1:80" \
|
||||
--health-interval 30s \
|
||||
--health-timeout 5s \
|
||||
@@ -433,7 +385,6 @@ docker run -d \
|
||||
--restart unless-stopped \
|
||||
--cpus 0.5 \
|
||||
--memory 512m \
|
||||
-v "$NGINX_CONF_FILE:/etc/nginx/conf.d/default.conf:ro" \
|
||||
$LEGACY_VOLUME \
|
||||
--health-cmd "wget --spider -q http://127.0.0.1:80" \
|
||||
--health-interval 30s \
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
# 环境变量:
|
||||
# PROD_API_URL - Production API 公网地址 (默认 https://api.xiaoxiajianji.com)
|
||||
# PROD_WEB_URL - Production Web 公网地址 (默认 https://saas.xiaoxiajianji.com)
|
||||
# HEALTH_CHECK_TIMEOUT - 健康检查总超时秒数 (默认 300)
|
||||
# HEALTH_CHECK_TIMEOUT - 健康检查总超时秒数 (默认 180)
|
||||
# SKIP_ROLLBACK - 失败时不自动回滚 (true/false, 默认 false)
|
||||
# SKIP_NOTIFY - 跳过通知 (true/false, 默认 false)
|
||||
# CI_NOTIFY_WEBHOOK - 通知 Webhook URL
|
||||
@@ -36,7 +36,7 @@ SCRIPT_DIR="$(CDPATH= cd -- "$(dirname -- "$0")" && pwd)"
|
||||
# 配置
|
||||
PROD_API_URL="${PROD_API_URL:-https://api.xiaoxiajianji.com}"
|
||||
PROD_WEB_URL="${PROD_WEB_URL:-https://saas.xiaoxiajianji.com}"
|
||||
HEALTH_CHECK_TIMEOUT="${HEALTH_CHECK_TIMEOUT:-300}"
|
||||
HEALTH_CHECK_TIMEOUT="${HEALTH_CHECK_TIMEOUT:-180}"
|
||||
SKIP_ROLLBACK="${SKIP_ROLLBACK:-false}"
|
||||
SKIP_NOTIFY="${SKIP_NOTIFY:-false}"
|
||||
|
||||
@@ -44,7 +44,7 @@ PRODUCTION_SSH_HOST="${PRODUCTION_SSH_HOST:-47.98.113.167}"
|
||||
PRODUCTION_SSH_USER="${PRODUCTION_SSH_USER:-root}"
|
||||
PRODUCTION_SSH_PORT="${PRODUCTION_SSH_PORT:-22222}"
|
||||
|
||||
REGISTRY="${REGISTRY:-xiaoxia-registry.cn-hangzhou.cr.aliyuncs.com/xiaoxiakeji}"
|
||||
REGISTRY="${REGISTRY:-git.xiaoxiajianji.com/xiaoxia/xiaoxia-saas}"
|
||||
REGISTRY_USER="${REGISTRY_USER:-xiaoxia}"
|
||||
|
||||
# 颜色
|
||||
@@ -160,11 +160,11 @@ health_check() {
|
||||
web_ok=true
|
||||
fi
|
||||
|
||||
# 检查 API docs（生产环境禁用 /docs，404 表示 API 在正常响应，视为健康）
|
||||
# 检查 API docs
|
||||
if [ "$api_docs_ok" = false ]; then
|
||||
HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" --max-time 10 "${PROD_API_URL}/docs" 2>/dev/null || echo "000")
|
||||
if [ "$HTTP_CODE" = "200" ] || [ "$HTTP_CODE" = "404" ]; then
|
||||
log_info "✅ API Docs 检查通过（HTTP $HTTP_CODE）"
|
||||
if [ "$HTTP_CODE" = "200" ]; then
|
||||
log_info "✅ API Docs 检查通过"
|
||||
api_docs_ok=true
|
||||
fi
|
||||
fi
|
||||
@@ -232,7 +232,7 @@ set -eu
|
||||
|
||||
IMAGE_TAG="$1"
|
||||
REGISTRY_TOKEN="$2"
|
||||
REGISTRY="${REGISTRY:-xiaoxia-registry.cn-hangzhou.cr.aliyuncs.com/xiaoxiakeji}"
|
||||
REGISTRY="${REGISTRY:-git.xiaoxiajianji.com/xiaoxia/xiaoxia-saas}"
|
||||
REGISTRY_USER="${REGISTRY_USER:-xiaoxia}"
|
||||
|
||||
ENV_FILE="${ENV_FILE:-/var/lib/xiaoxia-saas-production/.env}"
|
||||
|
||||
@@ -46,7 +46,6 @@ REGISTRY_TOKEN="${ACR_PASSWORD:-${REGISTRY_TOKEN:-}}"
|
||||
ENV_FILE="${ENV_FILE:-/var/lib/xiaoxia-saas-staging/.env}"
|
||||
GENERATED_DIR="${GENERATED_DIR:-/var/lib/xiaoxia-saas-staging/generated}"
|
||||
LEGACY_ASSETS_DIR="${LEGACY_ASSETS_DIR:-/var/lib/xiaoxia-saas-staging/legacy-assets}"
|
||||
NGINX_CONF_FILE="${NGINX_CONF_FILE:-/var/lib/xiaoxia-saas-staging/nginx-staging.conf}"
|
||||
|
||||
SKIP_MIGRATION="${SKIP_MIGRATION:-false}"
|
||||
SKIP_ROLLBACK="${SKIP_ROLLBACK:-false}"
|
||||
@@ -56,63 +55,10 @@ if [ -z "$IMAGE_TAG" ]; then
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# .env 文件由 CI 从模板 + Secrets 渲染后通过 SCP 上传到服务器
|
||||
# 如果文件不存在，说明 CI 渲染步骤失败或未执行
|
||||
if [ ! -f "$ENV_FILE" ]; then
|
||||
echo "ERROR: $ENV_FILE 不存在。CI 应先在 render_env 步骤渲染并上传此文件"
|
||||
exit 1
|
||||
fi
|
||||
echo "✅ .env file found: $ENV_FILE ($(wc -l < "$ENV_FILE") lines)"
|
||||
test -f "$ENV_FILE"
|
||||
mkdir -p "$GENERATED_DIR"
|
||||
mkdir -p "$LEGACY_ASSETS_DIR"
|
||||
|
||||
# ── 写入 Staging Nginx 配置 ──
|
||||
# 运行时覆盖 nginx 配置，确保 upstream 指向正确的 staging 网络
|
||||
echo "Writing staging nginx config..."
|
||||
cat > "$NGINX_CONF_FILE" << 'NGINX_EOF'
|
||||
server {
|
||||
listen 80;
|
||||
server_name _;
|
||||
root /usr/share/nginx/html;
|
||||
index index.html;
|
||||
|
||||
gzip on;
|
||||
gzip_vary on;
|
||||
gzip_min_length 1024;
|
||||
gzip_types text/plain text/css text/xml text/javascript application/javascript application/json application/xml+rss;
|
||||
|
||||
client_max_body_size 800m;
|
||||
|
||||
location / {
|
||||
try_files $uri /index.html;
|
||||
}
|
||||
|
||||
resolver 127.0.0.11 valid=10s;
|
||||
resolver_timeout 5s;
|
||||
|
||||
location /api/ {
|
||||
proxy_pass http://xiaoxia-api-staging:8000/api/;
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
proxy_read_timeout 300s;
|
||||
proxy_send_timeout 300s;
|
||||
proxy_request_buffering off;
|
||||
}
|
||||
|
||||
location /generated-files/ {
|
||||
alias /app/generated/;
|
||||
}
|
||||
|
||||
location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ {
|
||||
expires 1y;
|
||||
add_header Cache-Control "public, immutable";
|
||||
}
|
||||
}
|
||||
NGINX_EOF
|
||||
echo "✅ Nginx config written: $NGINX_CONF_FILE"
|
||||
|
||||
echo "==========================================="
|
||||
echo " Staging 部署 - $IMAGE_TAG (并行优化版)"
|
||||
echo "==========================================="
|
||||
@@ -213,7 +159,6 @@ rollback() {
|
||||
-p 127.0.0.1:3001:80 \
|
||||
--restart unless-stopped \
|
||||
$LEGACY_VOLUME \
|
||||
-v "$NGINX_CONF_FILE:/etc/nginx/conf.d/default.conf:ro" \
|
||||
--health-cmd "wget --spider -q http://127.0.0.1:80" \
|
||||
--health-interval 30s \
|
||||
--health-timeout 5s \
|
||||
@@ -452,7 +397,7 @@ fi
|
||||
echo "Stopping old containers..."
|
||||
# 优雅关闭：先 stop（发 SIGTERM，等待），再 rm
|
||||
# Worker 需要更长时间（视频任务最长可能5分钟）
|
||||
docker stop -t 120 xiaoxia-worker-staging 2>/dev/null || true
|
||||
docker stop -t 300 xiaoxia-worker-staging 2>/dev/null || true
|
||||
docker stop -t 30 xiaoxia-api-staging 2>/dev/null || true
|
||||
docker stop -t 10 xiaoxia-web-staging 2>/dev/null || true
|
||||
docker rm xiaoxia-worker-staging xiaoxia-api-staging xiaoxia-web-staging 2>/dev/null || true
|
||||
@@ -516,7 +461,6 @@ docker run -d \
|
||||
-p 127.0.0.1:3001:80 \
|
||||
--restart unless-stopped \
|
||||
$LEGACY_VOLUME \
|
||||
-v "$NGINX_CONF_FILE:/etc/nginx/conf.d/default.conf:ro" \
|
||||
--health-cmd "wget --spider -q http://127.0.0.1:80" \
|
||||
--health-interval 30s \
|
||||
--health-timeout 5s \
|
||||
|
||||
@@ -187,7 +187,7 @@ health_check() {
|
||||
return 0
|
||||
fi
|
||||
|
||||
sleep 3
|
||||
sleep 5
|
||||
done
|
||||
|
||||
# 超时了
|
||||
|
||||
@@ -1,177 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
# ===========================================================
|
||||
# config_diff_check.sh — 对比渲染 .env 与服务器当前 .env
|
||||
# ===========================================================
|
||||
# 用法: scripts/config_diff_check.sh <rendered_file> <current_file>
|
||||
#
|
||||
# 输出:
|
||||
# + ADDED 渲染文件有、当前文件没有（新增配置）
|
||||
# - REMOVED 当前文件有、渲染文件没有（将被删除）
|
||||
# ~ CHANGED 两边都有但值不同（将被覆盖）
|
||||
#
|
||||
# 敏感值脱敏：KEY/SECRET/PASSWORD/TOKEN/URL 类变量只显示前4字符+***
|
||||
# 退出码: 始终返回 0（仅告警，不阻塞部署）
|
||||
# ===========================================================
|
||||
set -u
|
||||
|
||||
RENDERED_FILE="${1:-}"
|
||||
CURRENT_FILE="${2:-}"
|
||||
|
||||
if [ -z "$RENDERED_FILE" ] || [ -z "$CURRENT_FILE" ]; then
|
||||
echo "ERROR: 用法: $0 <rendered_file> <current_file>" >&2
|
||||
exit 0
|
||||
fi
|
||||
|
||||
if [ ! -f "$RENDERED_FILE" ]; then
|
||||
echo "ERROR: 渲染文件不存在: $RENDERED_FILE" >&2
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# 判断是否为敏感变量（键名包含以下关键词）
|
||||
is_sensitive() {
|
||||
local key="$1"
|
||||
case "$key" in
|
||||
*KEY*|*SECRET*|*PASSWORD*|*TOKEN*|*URL*|*BROKER*|*BACKEND*) return 0 ;;
|
||||
*) return 1 ;;
|
||||
esac
|
||||
}
|
||||
|
||||
# 脱敏：敏感值只显示前4字符+***
|
||||
mask_value() {
|
||||
local key="$1"
|
||||
local value="$2"
|
||||
if is_sensitive "$key"; then
|
||||
if [ ${#value} -le 4 ]; then
|
||||
echo "****"
|
||||
else
|
||||
echo "${value:0:4}***"
|
||||
fi
|
||||
else
|
||||
echo "$value"
|
||||
fi
|
||||
}
|
||||
|
||||
# 解析文件为 KEY=VALUE（忽略注释和空行）
|
||||
parse_env() {
|
||||
local file="$1"
|
||||
grep -vE '^\s*#|^\s*$' "$file" 2>/dev/null | while IFS= read -r line; do
|
||||
# 只取第一个 = 之前的部分作为 key
|
||||
key="${line%%=*}"
|
||||
value="${line#*=}"
|
||||
# 跳过无效行
|
||||
if [ -n "$key" ] && [ "$key" != "$line" ]; then
|
||||
echo "${key}=${value}"
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
echo "=========================================="
|
||||
echo " 配置 Diff 检查（检测配置漂移）"
|
||||
echo "=========================================="
|
||||
echo "渲染文件: $RENDERED_FILE"
|
||||
echo "当前文件: $CURRENT_FILE"
|
||||
echo ""
|
||||
|
||||
# 解析两个文件
|
||||
if [ ! -f "$CURRENT_FILE" ] || [ ! -s "$CURRENT_FILE" ]; then
|
||||
# 服务器 .env 不存在或为空（首次部署）
|
||||
echo "⚠️ 服务器 .env 不存在或为空（可能是首次部署）"
|
||||
echo " 所有配置项将标记为 ADDED"
|
||||
echo ""
|
||||
|
||||
added=0
|
||||
while IFS='=' read -r key value; do
|
||||
[ -z "$key" ] && continue
|
||||
masked=$(mask_value "$key" "$value")
|
||||
echo " + ADDED ${key}=${masked}"
|
||||
added=$((added + 1))
|
||||
done < <(parse_env "$RENDERED_FILE")
|
||||
|
||||
echo ""
|
||||
echo "=========================================="
|
||||
echo " 汇总: 新增 ${added} 项 | 删除 0 项 | 变更 0 项 | 无变化 0 项"
|
||||
echo "=========================================="
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# 用临时文件存储解析结果
|
||||
tmp_rendered=$(mktemp)
|
||||
tmp_current=$(mktemp)
|
||||
trap "rm -f $tmp_rendered $tmp_current" EXIT
|
||||
|
||||
parse_env "$RENDERED_FILE" | sort > "$tmp_rendered"
|
||||
parse_env "$CURRENT_FILE" | sort > "$tmp_current"
|
||||
|
||||
added=0
|
||||
removed=0
|
||||
changed=0
|
||||
unchanged=0
|
||||
|
||||
echo "--- 新增配置（渲染文件有、当前文件无）---"
|
||||
# 找 ADDED：渲染文件有但当前文件没有的 key
|
||||
while IFS='=' read -r key value; do
|
||||
[ -z "$key" ] && continue
|
||||
current_line=$(grep -m1 "^${key}=" "$tmp_current" 2>/dev/null || true)
|
||||
if [ -z "$current_line" ]; then
|
||||
masked=$(mask_value "$key" "$value")
|
||||
echo " + ADDED ${key}=${masked}"
|
||||
added=$((added + 1))
|
||||
fi
|
||||
done < "$tmp_rendered"
|
||||
|
||||
if [ "$added" -eq 0 ]; then
|
||||
echo " (无)"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "--- 删除配置（当前文件有、渲染文件无）---"
|
||||
# 找 REMOVED：当前文件有但渲染文件没有的 key
|
||||
while IFS='=' read -r key value; do
|
||||
[ -z "$key" ] && continue
|
||||
rendered_line=$(grep -m1 "^${key}=" "$tmp_rendered" 2>/dev/null || true)
|
||||
if [ -z "$rendered_line" ]; then
|
||||
masked=$(mask_value "$key" "$value")
|
||||
echo " - REMOVED ${key}=${masked}"
|
||||
removed=$((removed + 1))
|
||||
fi
|
||||
done < "$tmp_current"
|
||||
|
||||
if [ "$removed" -eq 0 ]; then
|
||||
echo " (无)"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "--- 变更配置（两边都有但值不同）---"
|
||||
# 找 CHANGED：两边都有但值不同
|
||||
while IFS='=' read -r key value; do
|
||||
[ -z "$key" ] && continue
|
||||
current_line=$(grep -m1 "^${key}=" "$tmp_current" 2>/dev/null || true)
|
||||
if [ -n "$current_line" ]; then
|
||||
current_value="${current_line#*=}"
|
||||
if [ "$value" != "$current_value" ]; then
|
||||
masked_new=$(mask_value "$key" "$value")
|
||||
masked_old=$(mask_value "$key" "$current_value")
|
||||
echo " ~ CHANGED ${key}: ${masked_old} → ${masked_new}"
|
||||
changed=$((changed + 1))
|
||||
else
|
||||
unchanged=$((unchanged + 1))
|
||||
fi
|
||||
fi
|
||||
done < "$tmp_rendered"
|
||||
|
||||
if [ "$changed" -eq 0 ]; then
|
||||
echo " (无)"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "=========================================="
|
||||
echo " 汇总: 新增 ${added} 项 | 删除 ${removed} 项 | 变更 ${changed} 项 | 无变化 ${unchanged} 项"
|
||||
echo "=========================================="
|
||||
|
||||
if [ "$added" -gt 0 ] || [ "$removed" -gt 0 ] || [ "$changed" -gt 0 ]; then
|
||||
echo "⚠️ 检测到配置漂移，请确认以上变更是否符合预期"
|
||||
else
|
||||
echo "✅ 配置无漂移，与服务器当前配置一致"
|
||||
fi
|
||||
|
||||
exit 0
|
||||
@@ -1,142 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
# ===========================================================
|
||||
# render_env.sh — 从模板 + Secrets 渲染 .env 文件
|
||||
# ===========================================================
|
||||
# 用法: scripts/render_env.sh <staging|production>
|
||||
#
|
||||
# 输入: deploy/configs/.env.staging 或 .env.production 模板
|
||||
# 输出: .env.rendered（包含真实密钥，切勿提交或打印）
|
||||
#
|
||||
# 环境变量映射规则:
|
||||
# STAGING_xxx / PRODUCTION_xxx → xxx（去掉环境前缀）
|
||||
# 共用 secrets 直接使用（如 OSS_ACCESS_KEY_ID）
|
||||
# ===========================================================
|
||||
set -eu
|
||||
|
||||
TARGET_ENV="${1:-}"
|
||||
|
||||
if [ -z "$TARGET_ENV" ] || { [ "$TARGET_ENV" != "staging" ] && [ "$TARGET_ENV" != "production" ]; }; then
|
||||
echo "ERROR: 用法: $0 <staging|production>" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
TEMPLATE_FILE="deploy/configs/.env.${TARGET_ENV}"
|
||||
OUTPUT_FILE=".env.rendered"
|
||||
|
||||
if [ ! -f "$TEMPLATE_FILE" ]; then
|
||||
echo "ERROR: 模板文件不存在: $TEMPLATE_FILE" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# 构建环境变量映射（带环境前缀的 secrets → 模板变量名）
|
||||
ENV_PREFIX=$(echo "$TARGET_ENV" | tr '[:lower:]' '[:upper:]')
|
||||
|
||||
# 需要映射的带环境前缀变量
|
||||
MAPPED_VARS="DATABASE_URL REDIS_URL CELERY_BROKER_URL CELERY_RESULT_BACKEND JWT_SECRET_KEY"
|
||||
|
||||
# Staging 独有的 MinIO 变量
|
||||
if [ "$TARGET_ENV" = "staging" ]; then
|
||||
MAPPED_VARS="$MAPPED_VARS MINIO_ENDPOINT MINIO_ACCESS_KEY MINIO_SECRET_KEY"
|
||||
fi
|
||||
|
||||
# 将带前缀的 secrets 导出为无前缀的环境变量
|
||||
for var in $MAPPED_VARS; do
|
||||
prefixed_var="${ENV_PREFIX}_${var}"
|
||||
value="${!prefixed_var:-}"
|
||||
if [ -n "$value" ]; then
|
||||
export "$var=$value"
|
||||
fi
|
||||
done
|
||||
|
||||
# 特殊映射：CI secret 名称与模板占位符不一致的变量
|
||||
# STAGING_MINIO_BUCKET → MINIO_BUCKET_NAME
|
||||
if [ "$TARGET_ENV" = "staging" ]; then
|
||||
if [ -n "${STAGING_MINIO_BUCKET:-}" ]; then
|
||||
export "MINIO_BUCKET_NAME=$STAGING_MINIO_BUCKET"
|
||||
fi
|
||||
fi
|
||||
|
||||
# 共用 secrets 直接导出（如果存在）
|
||||
SHARED_SECRETS="OSS_ACCESS_KEY_ID OSS_ACCESS_KEY_SECRET COSYVOICE_API_KEY DASHSCOPE_API_KEY MEDIAKIT_API_KEY"
|
||||
for var in $SHARED_SECRETS; do
|
||||
value="${!var:-}"
|
||||
# 已经在环境中了，无需额外操作
|
||||
done
|
||||
|
||||
# 使用 Python 进行变量替换（Python 在 CI runner 中一定存在）
|
||||
python3 - "$TEMPLATE_FILE" "$OUTPUT_FILE" "$ENV_PREFIX" "$MAPPED_VARS" "$SHARED_SECRETS" <<'PYTHON_SCRIPT'
|
||||
import sys
|
||||
import os
|
||||
import re
|
||||
|
||||
template_file = sys.argv[1]
|
||||
output_file = sys.argv[2]
|
||||
env_prefix = sys.argv[3]
|
||||
mapped_vars_str = sys.argv[4]
|
||||
shared_secrets_str = sys.argv[5]
|
||||
|
||||
# 收集所有可用的替换变量
|
||||
all_vars = set()
|
||||
for v in mapped_vars_str.split():
|
||||
all_vars.add(v)
|
||||
for v in shared_secrets_str.split():
|
||||
all_vars.add(v)
|
||||
|
||||
# 读取模板
|
||||
with open(template_file, 'r') as f:
|
||||
template = f.read()
|
||||
|
||||
# 找出模板中所有的 ${VAR} 占位符（仅检查非注释行）
|
||||
pattern = re.compile(r'\$\{(\w+)\}')
|
||||
placeholders = set()
|
||||
for line in template.splitlines():
|
||||
stripped = line.strip()
|
||||
if stripped.startswith('#'):
|
||||
continue
|
||||
placeholders.update(pattern.findall(line))
|
||||
|
||||
# 检查必需变量是否已设置
|
||||
missing = []
|
||||
for var in placeholders:
|
||||
value = os.environ.get(var, '')
|
||||
if not value:
|
||||
missing.append(var)
|
||||
|
||||
if missing:
|
||||
print(f"ERROR: 以下变量未设置或为空: {', '.join(sorted(missing))}", file=sys.stderr)
|
||||
print(f"请确认对应的 {env_prefix}_xxx 或共用 secrets 已在 Gitea Secrets 中配置", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
# 执行替换
|
||||
def replace_var(match):
|
||||
var_name = match.group(1)
|
||||
return os.environ.get(var_name, match.group(0))
|
||||
|
||||
rendered = pattern.sub(replace_var, template)
|
||||
|
||||
# 写入输出文件
|
||||
with open(output_file, 'w') as f:
|
||||
f.write(rendered)
|
||||
|
||||
# 设置文件权限为仅 owner 可读写
|
||||
os.chmod(output_file, 0o600)
|
||||
|
||||
print(f"✅ .env 渲染完成: {template_file} → {output_file}")
|
||||
print(f" 替换了 {len(placeholders)} 个变量")
|
||||
PYTHON_SCRIPT
|
||||
|
||||
# 验证输出文件
|
||||
if [ ! -f "$OUTPUT_FILE" ]; then
|
||||
echo "ERROR: 渲染失败，输出文件不存在" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# 检查输出文件中是否还有未替换的占位符（仅检查非注释行）
|
||||
if grep -vE '^\s*#' "$OUTPUT_FILE" | grep -qE '\$\{[A-Z_]+\}'; then
|
||||
echo "ERROR: 输出文件中仍有未替换的占位符:" >&2
|
||||
grep -nE '\$\{[A-Z_]+\}' "$OUTPUT_FILE" | grep -v '^\s*#' >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "✅ 渲染文件校验通过，无残留占位符"
|
||||
echo "⚠️ $OUTPUT_FILE 包含敏感信息，请勿提交或打印到日志"
|
||||
@@ -602,148 +602,3 @@ class TestFontsizeCompensation:
|
||||
result = build_ass_style("S", font_size=0)
|
||||
parts = result.split(",")
|
||||
assert int(parts[2]) >= 1
|
||||
|
||||
|
||||
# ── 标题自由位置拖拽（工单 #1405 方案 B）──────────────────────────────────────
|
||||
|
||||
|
||||
def _title_style_line(content: str) -> str:
|
||||
return [line for line in content.splitlines() if line.startswith("Style: TitleStyle")][0]
|
||||
|
||||
|
||||
def _title_dialogue_line(content: str) -> str:
|
||||
return [line for line in content.splitlines() if line.startswith("Dialogue:") and "TitleStyle" in line][0]
|
||||
|
||||
|
||||
class TestTitleFreePosition:
|
||||
"""pos_x/pos_y 合法时注入 \\pos 且 Alignment=5；非法/缺失时回退原逻辑。"""
|
||||
|
||||
def _base_kwargs(self):
|
||||
return dict(
|
||||
video_width=1080,
|
||||
video_height=1920,
|
||||
video_duration=8.0,
|
||||
title_text="测试标题",
|
||||
)
|
||||
|
||||
def test_valid_position_injects_pos_tag_and_alignment_5(self):
|
||||
content = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": "top", "size": 36, "pos_x": 540, "pos_y": 300},
|
||||
)
|
||||
# Dialogue 文本前注入 {\pos(540,300)}
|
||||
dialogue = _title_dialogue_line(content)
|
||||
assert "{\\pos(540,300)}" in dialogue
|
||||
# TitleStyle Alignment 固定 5（\an5 中对齐，\pos 锚点为文本块中心）
|
||||
fields = [f.strip() for f in _title_style_line(content).split(",")]
|
||||
assert fields[18] == "5"
|
||||
|
||||
def test_boundary_coordinates_zero_and_max_accepted(self):
|
||||
"""边界值 0 和 video_width/video_height 合法（闭区间）。"""
|
||||
content = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"pos_x": 0, "pos_y": 1920},
|
||||
)
|
||||
assert "{\\pos(0,1920)}" in _title_dialogue_line(content)
|
||||
|
||||
content2 = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"pos_x": 1080, "pos_y": 0},
|
||||
)
|
||||
assert "{\\pos(1080,0)}" in _title_dialogue_line(content2)
|
||||
|
||||
def test_no_coords_output_identical_to_before(self):
|
||||
"""不传坐标 → 输出与现有断言完全一致（回归保护）。"""
|
||||
content = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": "top", "size": 36},
|
||||
)
|
||||
# 无 \pos 注入
|
||||
assert "\\pos(" not in content
|
||||
# Alignment 走 position 映射（top → 8）
|
||||
fields = [f.strip() for f in _title_style_line(content).split(",")]
|
||||
assert fields[18] == "8"
|
||||
|
||||
def test_out_of_bounds_falls_back(self):
|
||||
"""越界坐标 → 回退 position 三档逻辑，输出与无坐标一致。"""
|
||||
base = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": "top", "size": 36},
|
||||
)
|
||||
for pos_x, pos_y in [(-1, 300), (540, -1), (1081, 300), (540, 1921), (99999, 99999)]:
|
||||
content = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": "top", "size": 36, "pos_x": pos_x, "pos_y": pos_y},
|
||||
)
|
||||
assert "\\pos(" not in content, f"({pos_x},{pos_y}) should be rejected"
|
||||
assert content == base, f"({pos_x},{pos_y}) output differs from fallback"
|
||||
|
||||
def test_invalid_coords_falls_back(self):
|
||||
"""非法类型坐标 → 回退原逻辑。"""
|
||||
base = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": "top", "size": 36},
|
||||
)
|
||||
for pos_x, pos_y in [("abc", 300), (540, None), (None, None), (True, 300), (540, False), (540.5, 300.9)]:
|
||||
content = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": "top", "size": 36, "pos_x": pos_x, "pos_y": pos_y},
|
||||
)
|
||||
assert "\\pos(" not in content, f"({pos_x!r},{pos_y!r}) should be rejected"
|
||||
assert content == base, f"({pos_x!r},{pos_y!r}) output differs from fallback"
|
||||
|
||||
def test_only_one_coord_falls_back(self):
|
||||
"""只传 pos_x 或 pos_y → 回退原逻辑。"""
|
||||
base = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": "center", "size": 36},
|
||||
)
|
||||
content_x = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": "center", "size": 36, "pos_x": 540},
|
||||
)
|
||||
content_y = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": "center", "size": 36, "pos_y": 300},
|
||||
)
|
||||
assert content_x == base
|
||||
assert content_y == base
|
||||
assert "\\pos(" not in content_x
|
||||
assert "\\pos(" not in content_y
|
||||
|
||||
def test_position_three_levels_unchanged_without_coords(self):
|
||||
"""无坐标时 top/center/bottom 三档 Alignment 输出不变。"""
|
||||
for position, expected_align in [("top", "8"), ("center", "5"), ("bottom", "2")]:
|
||||
content = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": position, "size": 36},
|
||||
)
|
||||
fields = [f.strip() for f in _title_style_line(content).split(",")]
|
||||
assert fields[18] == expected_align
|
||||
|
||||
def test_pos_overrides_position_alignment(self):
|
||||
"""有合法坐标时，无论 position 是什么，Alignment 都固定为 5。"""
|
||||
for position in ["top", "center", "bottom"]:
|
||||
content = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"position": position, "size": 36, "pos_x": 100, "pos_y": 200},
|
||||
)
|
||||
fields = [f.strip() for f in _title_style_line(content).split(",")]
|
||||
assert fields[18] == "5"
|
||||
assert "{\\pos(100,200)}" in _title_dialogue_line(content)
|
||||
|
||||
def test_subtitle_not_affected_by_pos(self):
|
||||
"""pos_x/pos_y 只影响 Title，Subtitle 输出不变。"""
|
||||
content = build_ass_content(
|
||||
**self._base_kwargs(),
|
||||
title_config={"pos_x": 540, "pos_y": 300},
|
||||
subtitle_text="配音字幕",
|
||||
subtitle_config={"position": "bottom", "size": 24},
|
||||
)
|
||||
sub_style = [line for line in content.splitlines() if line.startswith("Style: SubtitleStyle")][0]
|
||||
sub_fields = [f.strip() for f in sub_style.split(",")]
|
||||
assert sub_fields[18] == "2" # bottom
|
||||
sub_dialogue = [
|
||||
line for line in content.splitlines() if line.startswith("Dialogue:") and "SubtitleStyle" in line
|
||||
][0]
|
||||
assert "\\pos(" not in sub_dialogue
|
||||
|
||||
@@ -197,14 +197,14 @@ class TestComputeAssetAvailability:
|
||||
|
||||
def test_large_gap_remains_usable(self):
|
||||
"""区间之间留有 ≥3s 空闲段（扩边后仍 ≥3s）→ usable=True。"""
|
||||
# [0,2] 扩边到 [0,3.5]，[9,10] 扩边到 [7.5,10]；空闲 [3.5,7.5]=4.0s >=3
|
||||
# 使用 [0,2] 与 [9,10]，扩边后空闲 [3.5,7.5]=4.0s ≥3 → usable
|
||||
# [0,2] 扩边到 [0,2.3]，[5.3,10] 扩边前为 [5,10] 扩边起 4.7；空闲 [2.3,4.7]=2.4s <3
|
||||
# 改用更大间隙：[0,2] 与 [6,10]，扩边后空闲 [2.3,5.7]=3.4s ≥3
|
||||
info = compute_asset_availability(
|
||||
_make_asset(
|
||||
duration=10.0,
|
||||
ranges=[
|
||||
_range(0.0, 2.0, use_count=MAX_RANGE_USE_COUNT),
|
||||
_range(9.0, 10.0, use_count=MAX_RANGE_USE_COUNT),
|
||||
_range(6.0, 10.0, use_count=MAX_RANGE_USE_COUNT),
|
||||
],
|
||||
)
|
||||
)
|
||||
@@ -236,8 +236,8 @@ class TestComputeAssetAvailability:
|
||||
assert info["usable"] is True
|
||||
|
||||
def test_segment_edge_gap_constant(self):
|
||||
"""边缘间隙常量为 1.5s（与 MediaKit 冲突检测同口径）。"""
|
||||
assert SEGMENT_EDGE_GAP == 1.5
|
||||
"""边缘间隙常量为 0.3s（与 MediaKit 冲突检测同口径）。"""
|
||||
assert SEGMENT_EDGE_GAP == 0.3
|
||||
|
||||
def test_domain_entity_metadata_dict_form(self):
|
||||
"""领域实体形态（metadata 为 dict，无 classification_result）也能读到区间。
|
||||
@@ -395,13 +395,11 @@ class TestSmartMatchFiltersExhausted:
|
||||
# 返回的素材全部 usable=True
|
||||
assert all(item.usable for item in resp.items)
|
||||
|
||||
def test_all_exhausted_falls_back_to_all(self):
|
||||
"""全部素材已用尽时回退保留全部（不返回空——空结果会让前端回退全选，
|
||||
反而绕过评分排序；耗尽素材仍可走复用区间）。"""
|
||||
def test_all_exhausted_returns_empty(self):
|
||||
"""全部素材已用尽时返回空列表（不报错，前端显示空结果）。"""
|
||||
assets = [_exhausted_asset("a-ex-1"), _exhausted_asset("a-ex-2")]
|
||||
resp = self._call(assets)
|
||||
returned_ids = {item.id for item in resp.items}
|
||||
assert returned_ids == {"a-ex-1", "a-ex-2"}
|
||||
assert resp.items == []
|
||||
assert resp.total_candidates == 2
|
||||
|
||||
def test_fresh_assets_all_returned(self):
|
||||
|
||||
@@ -124,9 +124,9 @@ def _ranges(db, aid="a1"):
|
||||
|
||||
|
||||
def test_config_constants():
|
||||
assert MAX_RANGE_USE_COUNT == 2
|
||||
assert REUSE_RATIO_LIMIT == 0.10
|
||||
assert SEGMENT_EDGE_GAP == 1.5
|
||||
assert MAX_RANGE_USE_COUNT == 3
|
||||
assert REUSE_RATIO_LIMIT == 0.15
|
||||
assert SEGMENT_EDGE_GAP == 0.3
|
||||
|
||||
|
||||
# ── get_used_segments ─────────────────────────────────────────────────────────
|
||||
@@ -320,13 +320,13 @@ def test_find_reusable_prefers_oldest_unused(patched_model):
|
||||
|
||||
|
||||
def test_find_reusable_excludes_max_use_count(patched_model):
|
||||
"""use_count 达到上限（2）的区间不再参与复用；全部达上限返回 None。"""
|
||||
"""use_count 达到上限（3）的区间不再参与复用；全部达上限返回 None。"""
|
||||
models = {
|
||||
"a1": FakeModel(
|
||||
"a1",
|
||||
{
|
||||
"used_time_ranges": [
|
||||
{"start": 0.0, "end": 10.0, "use_count": 2, "last_used_at": "2026-01-01T00:00:00"},
|
||||
{"start": 0.0, "end": 10.0, "use_count": 3, "last_used_at": "2026-01-01T00:00:00"},
|
||||
]
|
||||
},
|
||||
)
|
||||
@@ -335,22 +335,22 @@ def test_find_reusable_excludes_max_use_count(patched_model):
|
||||
assert find_reusable_range(db, "a1", 5.0, 30.0) is None
|
||||
|
||||
|
||||
def test_find_reusable_third_use_rejected(patched_model):
|
||||
"""同区间复用第 3 次被拒绝：use_count=1 的可复用，use_count=2 的不可复用。"""
|
||||
def test_find_reusable_fourth_use_rejected(patched_model):
|
||||
"""同区间复用第 4 次被拒绝：use_count=2 的可复用，use_count=3 的不可复用。"""
|
||||
models = {
|
||||
"a1": FakeModel(
|
||||
"a1",
|
||||
{
|
||||
"used_time_ranges": [
|
||||
{"start": 0.0, "end": 10.0, "use_count": 1, "last_used_at": "2026-03-01T00:00:00"},
|
||||
{"start": 10.0, "end": 20.0, "use_count": 2, "last_used_at": "2026-01-01T00:00:00"},
|
||||
{"start": 0.0, "end": 10.0, "use_count": 2, "last_used_at": "2026-03-01T00:00:00"},
|
||||
{"start": 10.0, "end": 20.0, "use_count": 3, "last_used_at": "2026-01-01T00:00:00"},
|
||||
]
|
||||
},
|
||||
)
|
||||
}
|
||||
db = _db(models)
|
||||
result = find_reusable_range(db, "a1", 5.0, 30.0)
|
||||
# 只能选 use_count=1 的区间（start=0），不能选 use_count=2 的（虽然它更老）
|
||||
# 只能选 use_count=2 的区间（start=0），不能选 use_count=3 的（虽然它更老）
|
||||
assert result is not None and result[0] == 0.0
|
||||
|
||||
|
||||
|
||||
@@ -1,334 +0,0 @@
|
||||
"""Tests for Issue #1670 — 跨视频片段避让（生成前注入已用区间）."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime, timezone
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.edit_plan_clip_repository import (
|
||||
SQLAlchemyEditPlanClipRepository,
|
||||
)
|
||||
from packages.domain.edit_plan_clip import EditPlanClip, EditPlanClipStatus
|
||||
from packages.domain.plan_generator_utils import (
|
||||
_distribute_one_take,
|
||||
distribute_assets,
|
||||
)
|
||||
|
||||
# ── Repository 层测试 ─────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class TestListUsedSegmentsByUser:
|
||||
"""测试 list_used_segments_by_user 方法."""
|
||||
|
||||
def _make_repo(self, session_mock):
|
||||
return SQLAlchemyEditPlanClipRepository(session_mock)
|
||||
|
||||
def test_empty_user_id_returns_empty_dict(self):
|
||||
"""空 user_id 直接返回空 dict，不查 DB."""
|
||||
session = MagicMock()
|
||||
repo = self._make_repo(session)
|
||||
result = repo.list_used_segments_by_user("")
|
||||
assert result == {}
|
||||
session.query.assert_not_called()
|
||||
|
||||
def test_no_completed_plans_returns_empty_dict(self):
|
||||
"""用户没有已完成的 plan 时返回空 dict."""
|
||||
session = MagicMock()
|
||||
# Mock plan query returns empty
|
||||
plan_query = MagicMock()
|
||||
plan_query.filter.return_value = plan_query
|
||||
plan_query.order_by.return_value = plan_query
|
||||
plan_query.limit.return_value = plan_query
|
||||
plan_query.all.return_value = []
|
||||
session.query.return_value = plan_query
|
||||
|
||||
repo = self._make_repo(session)
|
||||
result = repo.list_used_segments_by_user("user_123")
|
||||
assert result == {}
|
||||
|
||||
def test_aggregates_clips_from_multiple_plans(self):
|
||||
"""从多个已完成 plan 的 clips 聚合已用区间."""
|
||||
session = MagicMock()
|
||||
|
||||
# Mock plan query: 2 completed plans
|
||||
plan_query = MagicMock()
|
||||
plan_query.filter.return_value = plan_query
|
||||
plan_query.order_by.return_value = plan_query
|
||||
plan_query.limit.return_value = plan_query
|
||||
plan_query.all.return_value = [("plan_1",), ("plan_2",)]
|
||||
session.query.return_value = plan_query
|
||||
|
||||
# Mock clip query: clips from both plans
|
||||
clip_query = MagicMock()
|
||||
clip_query.filter.return_value = clip_query
|
||||
clip_query.all.return_value = [
|
||||
("asset_A", 0.0, 5.0), # plan_1, asset A: 0~5s
|
||||
("asset_A", 10.0, 3.0), # plan_1, asset A: 10~13s
|
||||
("asset_B", 2.0, 4.0), # plan_2, asset B: 2~6s
|
||||
]
|
||||
# Second session.query call is for clips
|
||||
session.query.side_effect = [plan_query, clip_query]
|
||||
|
||||
repo = self._make_repo(session)
|
||||
result = repo.list_used_segments_by_user("user_123")
|
||||
|
||||
assert "asset_A" in result
|
||||
assert len(result["asset_A"]) == 2
|
||||
assert result["asset_A"][0] == (0.0, 5.0)
|
||||
assert result["asset_A"][1] == (10.0, 13.0)
|
||||
assert "asset_B" in result
|
||||
assert result["asset_B"][0] == (2.0, 6.0)
|
||||
|
||||
def test_respects_limit_recent_parameter(self):
|
||||
"""limit_recent 参数限制查询的 plan 数量."""
|
||||
session = MagicMock()
|
||||
|
||||
plan_query = MagicMock()
|
||||
plan_query.filter.return_value = plan_query
|
||||
plan_query.order_by.return_value = plan_query
|
||||
plan_query.limit.return_value = plan_query
|
||||
plan_query.all.return_value = [("plan_1",)]
|
||||
session.query.return_value = plan_query
|
||||
|
||||
clip_query = MagicMock()
|
||||
clip_query.filter.return_value = clip_query
|
||||
clip_query.all.return_value = [("asset_X", 1.0, 2.0)]
|
||||
session.query.side_effect = [plan_query, clip_query]
|
||||
|
||||
repo = self._make_repo(session)
|
||||
result = repo.list_used_segments_by_user("user_123", limit_recent=10)
|
||||
|
||||
# Verify limit was called with the parameter
|
||||
plan_query.limit.assert_called_once_with(10)
|
||||
assert "asset_X" in result
|
||||
|
||||
|
||||
# ── Domain 层测试 ─────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class TestDistributeAssetsWithExternalSegments:
|
||||
"""测试 distribute_assets 传入 external_used_segments 的行为."""
|
||||
|
||||
def _make_clips(self, count: int, duration: float = 3.0) -> list[EditPlanClip]:
|
||||
"""创建指定数量的 MAIN 类型 clips."""
|
||||
return [
|
||||
EditPlanClip(
|
||||
id=f"clip_{i}",
|
||||
plan_id="plan_1",
|
||||
clip_type="main",
|
||||
order=i,
|
||||
template_clip_config_id="",
|
||||
asset_id="",
|
||||
text_content="",
|
||||
start_time=0.0,
|
||||
duration=duration,
|
||||
status=EditPlanClipStatus.PENDING,
|
||||
)
|
||||
for i in range(count)
|
||||
]
|
||||
|
||||
def test_external_used_segments_none_backward_compatible(self):
|
||||
"""external_used_segments=None 时行为不变（向后兼容）."""
|
||||
clips = self._make_clips(3)
|
||||
asset_ids = ["asset_1", "asset_2", "asset_3"]
|
||||
asset_durations = {aid: 30.0 for aid in asset_ids}
|
||||
|
||||
# Should not raise
|
||||
distribute_assets(
|
||||
clips,
|
||||
asset_ids,
|
||||
"one_take",
|
||||
asset_durations=asset_durations,
|
||||
external_used_segments=None,
|
||||
)
|
||||
|
||||
# All clips should have assets assigned
|
||||
for clip in clips:
|
||||
assert clip.asset_id != ""
|
||||
|
||||
def test_external_used_segments_avoids_existing_ranges(self):
|
||||
"""传入 external_used_segments 后，新分配的 start_time 避开已有区间."""
|
||||
clips = self._make_clips(2, duration=3.0)
|
||||
asset_ids = ["asset_1"]
|
||||
asset_durations = {"asset_1": 30.0}
|
||||
|
||||
# Pretend asset_1 0~10s is already used by another video
|
||||
external = {"asset_1": [(0.0, 10.0)]}
|
||||
|
||||
# Run multiple times to check that start_time always avoids 0~10s
|
||||
# (with some randomness, but the avoidance should be consistent)
|
||||
for _ in range(10):
|
||||
test_clips = self._make_clips(1, duration=3.0)
|
||||
distribute_assets(
|
||||
test_clips,
|
||||
asset_ids,
|
||||
"one_take",
|
||||
asset_durations=asset_durations,
|
||||
external_used_segments=external,
|
||||
)
|
||||
start = test_clips[0].start_time
|
||||
# Start time + duration (3s) should not overlap with 0~10
|
||||
# i.e., start >= 10.0 or start + 3 <= 0.0 (impossible since start >= 0)
|
||||
assert (
|
||||
start >= 10.0 or start + 3.0 <= 0.0 or start >= 10.0
|
||||
), f"start_time {start} overlaps with existing segment 0~10"
|
||||
|
||||
def test_external_used_segments_deep_copy(self):
|
||||
"""external_used_segments 会被深拷贝，不会修改外部数据."""
|
||||
external = {"asset_1": [(0.0, 5.0)]}
|
||||
original = {"asset_1": [(0.0, 5.0)]}
|
||||
|
||||
clips = self._make_clips(1, duration=2.0)
|
||||
asset_ids = ["asset_1"]
|
||||
asset_durations = {"asset_1": 20.0}
|
||||
|
||||
distribute_assets(
|
||||
clips,
|
||||
asset_ids,
|
||||
"one_take",
|
||||
asset_durations=asset_durations,
|
||||
external_used_segments=external,
|
||||
)
|
||||
|
||||
# External dict should be unchanged
|
||||
assert external == original
|
||||
|
||||
def test_empty_external_used_segments_same_as_none(self):
|
||||
"""空 dict 的 external_used_segments 行为与 None 相同."""
|
||||
clips = self._make_clips(2, duration=3.0)
|
||||
asset_ids = ["asset_1", "asset_2"]
|
||||
asset_durations = {aid: 30.0 for aid in asset_ids}
|
||||
|
||||
# Should not raise and should assign assets normally
|
||||
distribute_assets(
|
||||
clips,
|
||||
asset_ids,
|
||||
"one_take",
|
||||
asset_durations=asset_durations,
|
||||
external_used_segments={},
|
||||
)
|
||||
for clip in clips:
|
||||
assert clip.asset_id != ""
|
||||
|
||||
|
||||
# ── Service 层测试 ────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class TestServiceLayerIntegration:
|
||||
"""测试 _distribute_assets 在 service 层的查询逻辑."""
|
||||
|
||||
def _make_service(self, clip_repo_mock, asset_repo_mock=None):
|
||||
"""创建 PlanGeneratorService 并注入 mock repos."""
|
||||
|
||||
from apps.api.app.services.plan_generator_service import PlanGeneratorService
|
||||
|
||||
with (
|
||||
patch("apps.api.app.services.plan_generator_service.SQLAlchemyEditPlanRepository"),
|
||||
patch(
|
||||
"apps.api.app.services.plan_generator_service.SQLAlchemyEditPlanClipRepository",
|
||||
return_value=clip_repo_mock,
|
||||
),
|
||||
):
|
||||
db = MagicMock()
|
||||
svc = PlanGeneratorService(db, asset_repo=asset_repo_mock)
|
||||
svc._clip_repo = clip_repo_mock
|
||||
return svc
|
||||
|
||||
def _make_clip(self):
|
||||
return EditPlanClip(
|
||||
id="clip_1",
|
||||
plan_id="plan_1",
|
||||
clip_type="main",
|
||||
order=0,
|
||||
template_clip_config_id="",
|
||||
asset_id="",
|
||||
text_content="",
|
||||
start_time=0.0,
|
||||
duration=3.0,
|
||||
status=EditPlanClipStatus.PENDING,
|
||||
)
|
||||
|
||||
def test_query_called_with_user_id(self):
|
||||
"""有 user_id 时调用 list_used_segments_by_user."""
|
||||
clip_repo = MagicMock()
|
||||
clip_repo.list_used_segments_by_user.return_value = {"asset_A": [(0.0, 5.0)]}
|
||||
asset_repo = MagicMock()
|
||||
asset_repo.get.return_value = None # smart_match fallback
|
||||
|
||||
svc = self._make_service(clip_repo, asset_repo)
|
||||
clips = [self._make_clip()]
|
||||
|
||||
svc._distribute_assets(
|
||||
clips,
|
||||
["asset_A"],
|
||||
"one_take",
|
||||
asset_durations={"asset_A": 30.0},
|
||||
user_id="user_123",
|
||||
)
|
||||
|
||||
clip_repo.list_used_segments_by_user.assert_called_once_with("user_123", limit_recent=50)
|
||||
|
||||
def test_query_not_called_without_user_id(self):
|
||||
"""无 user_id 时不调用查询."""
|
||||
clip_repo = MagicMock()
|
||||
asset_repo = MagicMock()
|
||||
asset_repo.get.return_value = None
|
||||
|
||||
svc = self._make_service(clip_repo, asset_repo)
|
||||
clips = [self._make_clip()]
|
||||
|
||||
svc._distribute_assets(
|
||||
clips,
|
||||
["asset_A"],
|
||||
"one_take",
|
||||
asset_durations={"asset_A": 30.0},
|
||||
user_id="",
|
||||
)
|
||||
|
||||
clip_repo.list_used_segments_by_user.assert_not_called()
|
||||
|
||||
def test_query_failure_does_not_block_generation(self):
|
||||
"""查询失败时不阻塞生成，回退到纯随机."""
|
||||
clip_repo = MagicMock()
|
||||
clip_repo.list_used_segments_by_user.side_effect = Exception("DB error")
|
||||
asset_repo = MagicMock()
|
||||
asset_repo.get.return_value = None
|
||||
|
||||
svc = self._make_service(clip_repo, asset_repo)
|
||||
clips = [self._make_clip()]
|
||||
|
||||
# Should not raise
|
||||
svc._distribute_assets(
|
||||
clips,
|
||||
["asset_A"],
|
||||
"one_take",
|
||||
asset_durations={"asset_A": 30.0},
|
||||
user_id="user_123",
|
||||
)
|
||||
|
||||
# Clip should still get an asset assigned (fallback to random)
|
||||
assert clips[0].asset_id == "asset_A"
|
||||
|
||||
def test_preview_and_final_both_query(self):
|
||||
"""预览和正式生成都触发查询."""
|
||||
for random_selection in [True, False]:
|
||||
clip_repo = MagicMock()
|
||||
clip_repo.list_used_segments_by_user.return_value = {}
|
||||
asset_repo = MagicMock()
|
||||
asset_repo.get.return_value = None
|
||||
|
||||
svc = self._make_service(clip_repo, asset_repo)
|
||||
clips = [self._make_clip()]
|
||||
|
||||
svc._distribute_assets(
|
||||
clips,
|
||||
["asset_A"],
|
||||
"one_take",
|
||||
random_selection=random_selection,
|
||||
asset_durations={"asset_A": 30.0},
|
||||
user_id="user_123",
|
||||
)
|
||||
|
||||
clip_repo.list_used_segments_by_user.assert_called_once()
|
||||
@@ -285,10 +285,8 @@ class TestVideoDeduplicatorCheckDuplicate:
|
||||
result = deduplicator.check_duplicate(fingerprint, "proj-1", mock_session)
|
||||
assert result is not None
|
||||
assert result["duplicate"] is True
|
||||
assert result["similarity"] == pytest.approx(
|
||||
0.85, abs=0.01
|
||||
) # combined: 0.7*1.0 + 0.3*0.5 (no hist fallback)
|
||||
assert result["reason"] == "phash_histogram_fusion"
|
||||
assert result["similarity"] == 1.0 # distance=0 → 1.0
|
||||
assert result["reason"] == "phash_similar"
|
||||
finally:
|
||||
self._restore_repo(mod, orig)
|
||||
|
||||
@@ -427,11 +425,8 @@ class TestVideoDeduplicatorCheckDuplicate:
|
||||
result = deduplicator.check_duplicate(fingerprint, "proj-1", mock_session)
|
||||
assert result is not None
|
||||
assert result["duplicate"] is True
|
||||
# 新算法: median_distance=1, phash_sim=1-1/64=0.984375
|
||||
# 无直方图 → hist_sim=0.5(fallback)
|
||||
# combined = 0.7*0.984375 + 0.3*0.5 = 0.839062
|
||||
expected_sim = 0.7 * (1.0 - 1.0 / 64) + 0.3 * 0.5
|
||||
assert abs(result["similarity"] - expected_sim) < 1e-6
|
||||
# similarity = 1.0 - (1 / 64) = 0.984375
|
||||
assert abs(result["similarity"] - (1.0 - 1.0 / 64)) < 1e-6
|
||||
finally:
|
||||
self._restore_repo(mod, orig)
|
||||
|
||||
@@ -461,9 +456,7 @@ class TestVideoDeduplicatorCheckDuplicate:
|
||||
result = deduplicator.check_duplicate(fingerprint, "proj-1", mock_session)
|
||||
assert result is not None
|
||||
assert result["duplicate"] is True
|
||||
# 新算法: median_distance=0, phash_sim=1.0, hist_sim=0.5(fallback)
|
||||
# combined = 0.7*1.0 + 0.3*0.5 = 0.85
|
||||
assert result["similarity"] == pytest.approx(0.85, abs=0.01)
|
||||
assert result["similarity"] == 1.0 # avg_distance = 0
|
||||
finally:
|
||||
self._restore_repo(mod, orig)
|
||||
|
||||
@@ -546,7 +539,7 @@ class TestVideoDeduplicatorCheckBatchDuplicate:
|
||||
result = deduplicator.check_batch_duplicate(fingerprint, "batch-1", "vid-self", mock_session)
|
||||
assert result is not None
|
||||
assert result["duplicate"] is True
|
||||
assert result["reason"] == "batch_phash_histogram_fusion"
|
||||
assert result["reason"] == "batch_phash_similar"
|
||||
finally:
|
||||
self._restore_repo(mod, orig)
|
||||
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user