Cherokee
d6d208081b
- Add SelectActorPreview node to load per-actor preview images as ComfyUI IMAGE tensors
- Fix SelectActorPreview return value: must return (tensor,) tuple, not bare tensor,
to prevent ComfyUI's merge_result_data from misinterpreting batch dimension as
output count (root cause of 1920-image bug)
- Change VideoActorExtractor third output from IMAGE tensor to INT actor_count
- Save top-5 preview JPGs per actor to {output_dir}/previews/ instead of
building a large preview tensor in memory
- Unify InsightFace model path to ComfyUI/models/video-actor-extract/buffalo_l/
- Add temporal gap penalty with grace gap to identity clustering
- Add temporal boost with Gaussian decay and spatial blocker check
- Add dual-signal track splitting (centroid displacement + bbox area ratio)
- Add spatial centroid proximity tiebreaker for identity merging
- Add debris mask filtering (min_mask_area=20000) in MaskTracker
- Clean up unused imports across modified files
- Add AGENTS.md project guidelines and test infrastructure
91 lines
3.0 KiB
Python
91 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""Diagnose MaskTracker frame assignments for frames 85-115."""
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import os, sys, cv2, numpy as np
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PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if PROJECT_ROOT not in sys.path:
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sys.path.insert(0, PROJECT_ROOT)
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from pipeline.segmenter import PersonSegmenter
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from pipeline.mask_tracker import MaskTracker
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from core.config import DEFAULT_MAX_LOST_FRAMES
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# Load video
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cap = cv2.VideoCapture(os.path.join(PROJECT_ROOT, "tests", "sample1.mp4"))
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all_frames = []
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while True:
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ret, f = cap.read()
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if not ret:
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break
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all_frames.append(f) # BGR
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cap.release()
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print(f"Loaded {len(all_frames)} frames")
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# Init
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seg = PersonSegmenter(
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model_path="/Users/cherokee/App/ComfyUI/models/video-actor-extract/yolov8n-seg.pt"
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)
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tracker = MaskTracker(max_lost_frames=DEFAULT_MAX_LOST_FRAMES, match_threshold_px=150.0)
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# Process frames 190-289 (Track 3 creation zone)
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for fi in range(190, 290):
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frame_bgr = all_frames[fi]
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masks = seg.detect_masks(frame_bgr)
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# Compute mask info
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mask_info = []
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for mid, mask in masks:
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ys, xs = np.where(mask)
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if len(ys) > 0:
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cx, cy = float(xs.mean()), float(ys.mean())
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area = int(mask.sum())
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mask_info.append((mid, cx, cy, area))
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# Snapshot before update
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actors_before = {}
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for aid, actor in tracker.get_active_actors().items():
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if not actor.closed:
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actors_before[aid] = (actor.last_centroid, actor.last_frame_idx)
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tracker.update(fi, masks, frame_bgr, seg)
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# Snapshot after update
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actors_after = {}
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for aid, actor in tracker.get_active_actors().items():
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actors_after[aid] = (
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actor.last_centroid,
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actor.last_frame_idx,
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actor.closed,
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len(actor.frames),
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)
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if fi >= 190:
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print(f"\nFrame {fi}: {len(masks)} masks")
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for mid, cx, cy, area in mask_info:
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assigned = "UNMATCHED"
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for aid, (cent, lfi, closed, cnt) in sorted(actors_after.items()):
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if lfi == fi and not closed:
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dist = ((cx - cent[0]) ** 2 + (cy - cent[1]) ** 2) ** 0.5
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assigned = f"-> actor_{aid} (dist={dist:.0f}px)"
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break
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print(f" mask_{mid}: ({cx:.0f},{cy:.0f}) area={area:>7d} {assigned}")
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for aid, (cent, lfi) in sorted(actors_before.items()):
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still_active = aid in actors_after
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matched_this_frame = still_active and actors_after[aid][1] == fi
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if (
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not matched_this_frame
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and not actors_after.get(aid, (None, None, True, 0))[2]
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):
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print(
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f" actor_{aid}: UNMATCHED (centroid=({cent[0]:.0f},{cent[1]:.0f}), last={lfi})"
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)
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# Final summary
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print("\n=== FINAL TRACK SUMMARY ===")
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final = tracker.finish()
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for aid, actor in sorted(final.items()):
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fidx = [f[0] for f in actor.frames]
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print(f" actor_{aid}: frames {min(fidx)}-{max(fidx)}, count={len(fidx)}")
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