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Debugging chunk boundaries with SAM2
This commit is contained in:
Jonathan Thomas
68
nodes.py
68
nodes.py
@@ -58,6 +58,19 @@ GROUNDING_DINO_MODEL_IDS = (
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"IDEA-Research/grounding-dino-base",
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)
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GROUNDING_DINO_CACHE = {}
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def _sam2_debug_enabled():
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v = str(os.environ.get("OPENSHOT_SAM2_DEBUG", "")).strip().lower()
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return v in ("1", "true", "yes", "on", "debug")
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def _sam2_debug(*parts):
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if _sam2_debug_enabled():
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try:
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print("[OpenShot-SAM2-DEBUG]", *parts)
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except Exception:
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pass
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SAM2_MODELS = {
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"sam2.1_hiera_tiny.safetensors": {
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"url": "https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt",
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@@ -1409,6 +1422,12 @@ class OpenShotSam2VideoSegmentationAddPoints:
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)
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if dino_boxes:
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_sam2_debug(
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"dino-seed",
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"prompt=", dino_prompt,
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"seed_frame_idx=", int(seed_frame_idx),
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"boxes=", len(dino_boxes),
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)
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seed_entry = dict(prompt_schedule.get(seed_frame_idx, {}) or {})
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seed_object_prompts = list(seed_entry.get("object_prompts") or [])
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next_obj_id = 0
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@@ -1462,6 +1481,14 @@ class OpenShotSam2VideoSegmentationAddPoints:
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if not has_any_positive and not allow_empty_schedule:
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raise ValueError("No positive points/rectangles provided")
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_sam2_debug(
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"add_points",
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"seed_frame_idx=", int(seed_frame_idx),
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"schedule_frames=", sorted([int(k) for k in prompt_schedule.keys()]),
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"windowed=", bool(windowed_mode),
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"has_prev_state=", bool(prev_inference_state is not None),
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)
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serial_schedule = []
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for fidx in sorted(prompt_schedule.keys()):
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entry = prompt_schedule.get(fidx, {}) or {}
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@@ -1839,6 +1866,14 @@ class OpenShotSam2VideoSegmentationChunked:
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labels = np.ones((len(centers),), dtype=np.int32)
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object_prompts = list(entry.get("object_prompts") or [])
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_sam2_debug(
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"apply_prompt_entry",
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"frame_idx=", int(frame_idx),
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"points=", len(points_list),
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"rects=", len(rects),
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"object_prompts=", len(object_prompts),
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"neg_points=", len(global_negative_points),
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)
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if object_prompts:
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for op in object_prompts:
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if not isinstance(op, dict):
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@@ -1875,6 +1910,12 @@ class OpenShotSam2VideoSegmentationChunked:
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def _seed_window_prompt(self, model, local_state, inference_state):
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carries = dict(inference_state.get("object_carries", {}) or {})
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_sam2_debug(
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"seed_window_prompt",
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"next_frame_idx=", int(max(0, inference_state.get("next_frame_idx", 0) or 0)),
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"carry_count=", len(carries),
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"seed_rects=", len(list(inference_state.get("seed_rects") or [])),
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)
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if carries:
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for raw_obj_id, point in carries.items():
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try:
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@@ -1918,6 +1959,12 @@ class OpenShotSam2VideoSegmentationChunked:
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return
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obj_id = int(inference_state.get("object_index", 0))
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_sam2_add_prompts(model, local_state, 0, obj_id, points, labels, rects)
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_sam2_debug(
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"seed_window_prompt-applied",
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"obj_id=", int(obj_id),
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"points=", int(points.shape[0]) if hasattr(points, "shape") else 0,
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"rects=", len(rects),
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)
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def _collect_range_masks(self, model, state_obj, frame_start, frame_count):
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frame_start = int(max(0, frame_start))
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@@ -1994,6 +2041,13 @@ class OpenShotSam2VideoSegmentationChunked:
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global_start = int(max(0, inference_state.get("next_frame_idx", 0) or 0))
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applied_frames = set(int(v) for v in (inference_state.get("prompt_frames_applied") or []))
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_sam2_debug(
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"segment_windowed-start",
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"global_start=", int(global_start),
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"bsz=", int(bsz),
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"applied_frames=", sorted(list(applied_frames))[:8],
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"carry_count=", len(dict(inference_state.get("object_carries", {}) or {})),
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)
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for entry in self._prompt_schedule(inference_state):
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gidx = int(entry.get("frame_idx", 0))
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if gidx < global_start or gidx >= (global_start + bsz):
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@@ -2001,6 +2055,7 @@ class OpenShotSam2VideoSegmentationChunked:
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if gidx in applied_frames:
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continue
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self._apply_prompt_entry(model, local_state, inference_state, int(gidx - global_start), entry)
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_sam2_debug("segment_windowed-prompt-applied", "global_frame=", int(gidx), "local_frame=", int(gidx - global_start))
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applied_frames.add(gidx)
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inference_state["prompt_frames_applied"] = sorted(list(applied_frames))
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@@ -2085,6 +2140,14 @@ class OpenShotSam2VideoSegmentationChunked:
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float(xs.float().mean().item()),
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float(ys.float().mean().item()),
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]
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else:
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_sam2_debug(
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"carry-drop-windowed",
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"obj_id=", int(obj_id_int),
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"area_ratio=", round(float(area_ratio), 6),
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"fill_ratio=", round(float(fill_ratio), 6),
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"touches_edge=", bool(touches_edge),
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)
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if combined is None:
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combined = torch.zeros((h, w), dtype=torch.bool, device=out_mask_logits.device)
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by_idx[idx] = combined.float().cpu()
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@@ -2095,6 +2158,11 @@ class OpenShotSam2VideoSegmentationChunked:
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for obj_id in sorted(seen_obj_ids)
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if str(obj_id) in carries
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}
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_sam2_debug(
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"segment_windowed-end",
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"kept_carries=", sorted([int(v) for v in seen_obj_ids]),
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"next_frame_idx=", int(inference_state.get("next_frame_idx", 0) or 0),
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)
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for i in range(bsz):
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out_chunks.append(by_idx.get(i, torch.zeros((h, w), dtype=torch.float32)))
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