Peter
998fa79f81
Preprocesses a folder of video files into UUID-named clips suitable as target inputs for roop-unleashed-style face-swap. Counterpart to the faceset (source-side) tooling. work/video_target_pipeline.py — orchestration with subcommands scan / scenes / stage / merge / track / score / cut / report. Quality gates default to face-sets-can-handle-side-profile values (yaw<=75°, pitch<=45°, face_short>=80px, det>=0.5). Cross-track segment merge fuses adjacent-in-time tracks within the same scene up to 2s gap. Output organized into <output_dir>/<source_stem>/<uuid>.mp4 + <uuid>.json sidecar with full provenance. work/video_face_worker.py — Windows DML face detect+embed worker. Uses JSONL append-only for results.jsonl: a critical perf fix (re- serializing the monolithic 245MB results.json on every flush was the dominant cost in the first attempt, dropping throughput to 0.5 fps). Append-only got it to 13+ fps, ~7.5 fps cumulative across the first 6.18h batch. Also uses seek-once-per-video + sequential cap.grab() between samples to dodge cv2 per-sample seek pathology on long H.264. Legacy results.json is auto-migrated to .jsonl on first load. work/run_video_pipeline.sh — generic chain driver, parameterized via WORK / INPUT_DIR / OUTPUT_DIR / FILTER_FROM / SKIP_PATTERN / MAX_DUR / IDENTITY env vars. work/status_video_pipeline.sh — generic status helper. First production batch (ct_src_00050..00062, 13 files, 6.18h input): 600 emitted segments, 239.5min accepted content (64.6% of input), 254 segments built from >=2 tracks (cross-track merge), 1h43m wall clock. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
124 lines
4.3 KiB
Bash
Executable File
124 lines
4.3 KiB
Bash
Executable File
#!/bin/bash
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# Generic chain driver for the video target preprocessing pipeline.
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#
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# Usage:
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# WORK=/path/to/workdir SKIP_PATTERN='ct_src_(0001[015]|005[0-9]|006[0-9])\.mp4' \
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# bash run_video_pipeline.sh > /opt/face-sets/work/logs/<name>.log 2>&1
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#
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# Required env vars:
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# WORK per-batch workdir (will hold scenes/, queue.json, results.jsonl, plan.json, review/)
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#
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# Optional env vars:
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# INPUT_DIR default /mnt/x/src/vd
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# OUTPUT_DIR default /mnt/x/src/vd/ct
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# FILTER_FROM basename floor; only files with name >= this go in (e.g. ct_src_00050.mp4)
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# SKIP_PATTERN regex of basenames to exclude (Python `re` syntax). Applied AFTER FILTER_FROM.
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# MAX_DUR score --max-dur (default 120)
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# IDENTITY "yes" to enable identity tagging; default "no"
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set -e
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: ${WORK:?WORK env var must point at a workdir}
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: ${INPUT_DIR:=/mnt/x/src/vd}
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: ${OUTPUT_DIR:=/mnt/x/src/vd/ct}
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: ${MAX_DUR:=120}
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: ${IDENTITY:=no}
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mkdir -p "$WORK" "$WORK/scenes"
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PY_WSL=/home/peter/face_sort_env/bin/python
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PY_WIN="/mnt/c/face_embed_venv/Scripts/python.exe"
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PIPELINE=/opt/face-sets/work/video_target_pipeline.py
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WORKER=/opt/face-sets/work/video_face_worker.py
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INVENTORY_FULL=/opt/face-sets/work/video_preprocess/inventory_full.json
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ts() { date +"%Y-%m-%d %H:%M:%S"; }
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log() { echo "[$(ts)] [$PHASE] $*"; }
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PHASE="setup"
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log "STARTED — host=$(hostname) pid=$$ work=$WORK"
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log "config: input=$INPUT_DIR output=$OUTPUT_DIR filter_from=${FILTER_FROM:-<none>} skip_pattern=${SKIP_PATTERN:-<none>} max_dur=$MAX_DUR identity=$IDENTITY"
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PHASE="inventory"
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log "building subset inventory"
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T0=$(date +%s)
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# rebuild full inventory if missing
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if [ ! -f "$INVENTORY_FULL" ]; then
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log "(no full inventory cached — running fresh scan)"
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$PY_WSL $PIPELINE scan --input "$INPUT_DIR" --output-dir "$OUTPUT_DIR" --out "$INVENTORY_FULL"
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fi
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$PY_WSL <<EOF
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import json, re
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from pathlib import Path
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inv = json.load(open('$INVENTORY_FULL'))
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subset = list(inv['videos'])
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filter_from = '${FILTER_FROM}'
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skip_pat = '${SKIP_PATTERN}'
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if filter_from:
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subset = [v for v in subset if Path(v['path']).name >= filter_from]
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if skip_pat:
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pat = re.compile(skip_pat)
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subset = [v for v in subset if not pat.search(Path(v['path']).name)]
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subset.sort(key=lambda v: v['path'])
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inv['videos'] = subset
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json.dump(inv, open('$WORK/inventory.json','w'), indent=2)
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total_dur = sum(v.get('duration_s', 0) for v in inv['videos'] if 'error' not in v)
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print(f' {len(inv["videos"])} videos, total {total_dur/3600:.2f}h input')
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EOF
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log "done in $(($(date +%s)-T0))s"
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PHASE="scenes"
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log "PySceneDetect AdaptiveDetector across all videos (cached entries skipped)"
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T0=$(date +%s)
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$PY_WSL $PIPELINE scenes --inventory "$WORK/inventory.json" --out-dir "$WORK/scenes"
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log "done in $(($(date +%s)-T0))s"
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PHASE="stage"
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log "building frame queue @ 2 fps within scenes"
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T0=$(date +%s)
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$PY_WSL $PIPELINE stage --inventory "$WORK/inventory.json" --scenes-dir "$WORK/scenes" --out "$WORK/queue.json"
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log "done in $(($(date +%s)-T0))s"
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PHASE="worker"
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log "Windows DML face detect+embed (resumable; the slow one)"
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T0=$(date +%s)
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$PY_WIN $WORKER "$WORK/queue.json" "$WORK/results.json"
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log "done in $(($(date +%s)-T0))s"
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PHASE="merge"
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log "ingesting worker output (jsonl)"
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T0=$(date +%s)
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$PY_WSL $PIPELINE merge --results "$WORK/results.json" --out "$WORK/frames.json"
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log "done in $(($(date +%s)-T0))s"
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PHASE="track"
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log "stitching detections into tracks"
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T0=$(date +%s)
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$PY_WSL $PIPELINE track --frames "$WORK/frames.json" --scenes-dir "$WORK/scenes" \
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--inventory "$WORK/inventory.json" --out "$WORK/tracks.json"
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log "done in $(($(date +%s)-T0))s"
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PHASE="score"
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log "scoring with relaxed gates + max-dur=$MAX_DUR identity=$IDENTITY"
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T0=$(date +%s)
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ID_FLAG=""
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if [ "$IDENTITY" != "yes" ]; then ID_FLAG="--no-identity"; fi
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$PY_WSL $PIPELINE score --tracks "$WORK/tracks.json" --inventory "$WORK/inventory.json" \
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--out "$WORK/plan.json" --max-dur "$MAX_DUR" $ID_FLAG
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log "done in $(($(date +%s)-T0))s"
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PHASE="cut"
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log "ffmpeg stream-copy into per-source subfolders (no --clean)"
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T0=$(date +%s)
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$PY_WSL $PIPELINE cut --plan "$WORK/plan.json" --output-dir "$OUTPUT_DIR"
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log "done in $(($(date +%s)-T0))s"
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PHASE="report"
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log "rendering HTML"
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T0=$(date +%s)
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$PY_WSL $PIPELINE report --plan "$WORK/plan.json" --output-dir "$OUTPUT_DIR" --out "$WORK/review"
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log "done in $(($(date +%s)-T0))s"
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PHASE="done"
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log "PIPELINE COMPLETE — review at file://$WORK/review/index.html"
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