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worker, the SAM 3 audit and the mask export. The command line segments a folder of images per class.
"""
import argparse
import json
import os
import random
import re
import tempfile
from pathlib import Path
from typing import Optional
def _atomic_write_json(path: Path, data) -> None:
"""Write JSON to ``path`` atomically.
On some shared filesystems, ``open('w')`` doesn't truncate cleanly —
if the new content is shorter than the old, NULL bytes can appear
between new EOF and old EOF, leaving a corrupt mixed file. This
helper writes to a sibling tempfile then ``os.replace``s it onto
the target path; ``os.replace`` is atomic, so readers always see
either the old complete file or the new complete file.
"""
fd, tmp = tempfile.mkstemp(prefix=path.name + '.', suffix='.tmp',
dir=str(path.parent))
try:
with os.fdopen(fd, 'w') as f:
json.dump(data, f, indent=2)
os.replace(tmp, path)
except Exception:
try:
os.unlink(tmp)
except OSError:
pass
raise
import cv2
import numpy as np
import torch
from PIL import Image
def load_sam3_processor(bpe_path: str,
confidence_threshold: float = 0.5,
device: str = 'cuda',
checkpoint_path: Optional[str] = None):
"""Build SAM3 image model + processor."""
from sam3 import build_sam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor
if checkpoint_path is not None:
model = build_sam3_image_model(
bpe_path=bpe_path,
checkpoint_path=checkpoint_path,
load_from_HF=False,
)
else:
model = build_sam3_image_model(bpe_path=bpe_path)
model = model.to(device).eval()
processor = Sam3Processor(
model, confidence_threshold=confidence_threshold)
return processor
def load_clip_scorer(device: str = 'cuda', model_name: str = 'ViT-L/14'):
"""OpenAI CLIP for the optional white-bg re-score (paper metric)."""
import clip
model, preprocess = clip.load(model_name, device=device)
model = model.eval()
return model, preprocess, clip.tokenize
def category_name_for_prompt(cat_dir_name: str) -> str:
"""Reverse-clean directory name into a natural phrase.
Mirrors ``segment_sam.py`` so dir-name conventions stay shared.
"""
name = cat_dir_name.replace('_', ' ')
name = re.sub(r'\s+', ' ', name).strip()
return name
def get_largest_connect_component(mask: np.ndarray) -> np.ndarray:
"""Keep only the largest connected blob of a binary mask."""
contours, _ = cv2.findContours(
mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
return mask
areas = [cv2.contourArea(c) for c in contours]
max_idx = int(np.argmax(areas))
out = np.zeros_like(mask)
cv2.fillPoly(out, [contours[max_idx]], 1)
return out
@torch.no_grad()
def clip_rescore(
image_rgb: np.ndarray,
mask: np.ndarray,
category_phrase: str,
clip_model,
clip_preprocess,
clip_tokenizer,
device: str = 'cuda',
) -> float:
mask_bin = (mask > 128).astype(np.float32)[:, :, None]
white = np.full_like(image_rgb, 255)
composite = (image_rgb * mask_bin + white * (1.0 - mask_bin)).astype(
np.uint8)
img_tensor = clip_preprocess(Image.fromarray(composite)).unsqueeze(0)
img_tensor = img_tensor.to(device)
text_tokens = clip_tokenizer(
[f"a photo of a {category_phrase}"]).to(device)
image_features = clip_model.encode_image(img_tensor)
text_features = clip_model.encode_text(text_tokens)
image_features = image_features / image_features.norm(
dim=-1, keepdim=True)
text_features = text_features / text_features.norm(
dim=-1, keepdim=True)
similarity = (100.0 * text_features @ image_features.T).squeeze()
return float(similarity.cpu())
def _to_numpy_mask(m) -> np.ndarray:
"""Coerce a SAM3 mask output (torch/np, bool/uint8/float) to HxW uint8."""
if isinstance(m, torch.Tensor):
if m.dtype not in (torch.bool, torch.uint8, torch.float32):
m = m.float()
m = m.detach().cpu().numpy()
m = np.asarray(m)
while m.ndim > 2:
m = m[0]
if m.dtype == np.bool_:
return m.astype(np.uint8) * 255
if np.issubdtype(m.dtype, np.floating):
return ((m > 0.5).astype(np.uint8)) * 255
return ((m > 0).astype(np.uint8)) * 255
def segment_with_text(processor, image_rgb: np.ndarray,
prompt: str, top_k: int = 1) -> Optional[tuple]:
"""Run SAM3 with a text prompt; return (mask, score) or None."""
pil = Image.fromarray(image_rgb)
with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
state = processor.set_image(pil)
out = processor.set_text_prompt(state=state, prompt=prompt)
masks = out.get('masks')
scores = out.get('scores')
if masks is None or scores is None:
return None
if isinstance(scores, torch.Tensor):
scores_np = scores.detach().float().cpu().numpy()
else:
scores_np = np.asarray(scores)
if scores_np.size == 0:
return None
order = np.argsort(-scores_np)[:max(1, int(top_k))]
union_u8 = None
for idx in order:
m_raw = masks[idx] if isinstance(masks, (list, tuple)) else masks[idx]
m_u8 = _to_numpy_mask(m_raw)
union_u8 = m_u8 if union_u8 is None else np.maximum(union_u8, m_u8)
best_score = float(scores_np[order[0]])
return union_u8, best_score
def crop_to_mask_bbox(image: np.ndarray, mask: np.ndarray,
padding: int = 5) -> tuple:
"""Crop image+mask to mask bbox and stack to RGBA. Same as segment_sam."""
coords = np.where(mask > 0)
if len(coords[0]) == 0:
return None, None
y1, y2 = coords[0].min(), coords[0].max()
x1, x2 = coords[1].min(), coords[1].max()
h, w = image.shape[:2]
y1 = max(0, y1 - padding)
x1 = max(0, x1 - padding)
y2 = min(h, y2 + padding + 1)
x2 = min(w, x2 + padding + 1)
cropped_img = image[y1:y2, x1:x2]
cropped_mask = mask[y1:y2, x1:x2]
if cropped_img.shape[2] == 3:
rgba = np.concatenate(
[cropped_img, cropped_mask[:, :, None]], axis=2)
else:
rgba = cropped_img.copy()
rgba[:, :, 3] = cropped_mask
return rgba, (x1, y1, x2, y2)
def process_category_dir(
processor,
cat_dir: Path,
output_dir: Path,
crop: bool = True,
clip_components: tuple = None,
device: str = 'cuda',
max_per_class: int = None,
min_area_ratio: float = 0.02,
max_area_ratio: float = 0.85,
prompt_template: str = 'a {category}',
min_score: float = 0.2,
per_class_min_score: Optional[dict] = None,
per_class_prompt: Optional[dict] = None,
per_class_confidence_threshold: Optional[dict] = None,
per_class_top_k: Optional[dict] = None,
sample_seed: Optional[int] = 42,
) -> list:
"""Segment every img_*.png in cat_dir using SAM3 text prompts."""
cat_name = cat_dir.name
category_phrase = category_name_for_prompt(cat_name)
cls_template = (per_class_prompt or {}).get(cat_name, prompt_template)
prompt = cls_template.format(category=category_phrase)
out_cat_dir = output_dir / cat_name
out_cat_dir.mkdir(parents=True, exist_ok=True)
cls_min_score = (per_class_min_score or {}).get(cat_name, min_score)
pcct = per_class_confidence_threshold or {}
saved_proc_thr = None
if cat_name in pcct:
saved_proc_thr = getattr(processor, 'confidence_threshold', None)
processor.confidence_threshold = float(pcct[cat_name])
cls_top_k = int((per_class_top_k or {}).get(cat_name, 1))
meta_path = out_cat_dir / 'metadata.json'
if meta_path.exists():
with open(meta_path) as f:
existing = json.load(f)
existing_names = {m['image'] for m in existing}
else:
existing = []
existing_names = set()
src_meta_path = cat_dir / 'metadata.json'
src_meta = {}
if src_meta_path.exists():
with open(src_meta_path) as f:
src_meta_list = json.load(f)
src_meta = {m['image']: m for m in src_meta_list}
image_files = sorted(cat_dir.glob('img_*.png'))
if max_per_class is not None:
if sample_seed is None:
image_files = image_files[:max_per_class]
else:
rng = random.Random(f"{sample_seed}:{cat_name}")
k = min(max_per_class, len(image_files))
image_files = rng.sample(image_files, k)
image_files.sort()
results = list(existing)
flush_every = 500
n_processed_this_run = 0
for img_path in image_files:
out_name = img_path.stem + '.png'
if out_name in existing_names:
continue
try:
image = np.array(Image.open(img_path).convert('RGB'))
except Exception as e:
print(f" [WARN] skipping {img_path.name}: {type(e).__name__}: {e}")
continue
try:
seg = segment_with_text(processor, image, prompt, top_k=cls_top_k)
except Exception as e:
print(f" [WARN] segment failed on {img_path.name}: "
f"{type(e).__name__}: {e}")
continue
if seg is None:
continue
mask, sam3_score = seg
if sam3_score < cls_min_score:
continue
if cls_top_k <= 1:
mask01 = (mask > 0).astype(np.uint8)
mask01 = get_largest_connect_component(mask01)
mask = mask01 * 255
mask_area = mask.sum() / 255.0
total_area = mask.shape[0] * mask.shape[1]
area_ratio = mask_area / total_area
if area_ratio < min_area_ratio or area_ratio > max_area_ratio:
continue
seg_clip_score = None
if clip_components is not None:
cm, cp, ct = clip_components
seg_clip_score = clip_rescore(
image, mask, category_phrase, cm, cp, ct, device=device)
if crop:
rgba, bbox = crop_to_mask_bbox(image, mask)
if rgba is None:
continue
else:
rgba = np.concatenate([image, mask[:, :, None]], axis=2)
bbox = (0, 0, image.shape[1], image.shape[0])
out_path = out_cat_dir / out_name
Image.fromarray(rgba).save(out_path)
meta = {
'image': out_name,
'source_image': img_path.name,
'width': int(rgba.shape[1]),
'height': int(rgba.shape[0]),
'mask_area_ratio': float(area_ratio),
'bbox': [int(x) for x in bbox],
'sam3_score': float(sam3_score),
'prompt': prompt,
}
raw_score = src_meta.get(img_path.name, {}).get('clip_score')
if raw_score is not None:
meta['raw_clip_score'] = raw_score
if seg_clip_score is not None:
meta['clip_score'] = seg_clip_score
elif raw_score is not None:
meta['clip_score'] = raw_score
results.append(meta)
n_processed_this_run += 1
if n_processed_this_run % flush_every == 0:
_atomic_write_json(meta_path, results)
_atomic_write_json(meta_path, results)
if saved_proc_thr is not None:
processor.confidence_threshold = saved_proc_thr
return results
def main():
parser = argparse.ArgumentParser(
description='SAM 3 text-prompt segmentation of a folder of images, per class.')
parser.add_argument('--input_dir', required=True)
parser.add_argument('--output_dir', required=True)
parser.add_argument('--sam3_bpe_path', required=True,
help='Path to assets/bpe_simple_vocab_16e6.txt.gz '
'from the cloned sam3 repo.')
parser.add_argument('--sam3_checkpoint_path', type=str, default=None,
help='Optional local path to sam3.pt; when set, the Hugging Face Hub is not used.')
parser.add_argument('--confidence_threshold', type=float, default=0.05,
help='Score threshold inside the SAM 3 processor; --min_score makes the final decision.')
parser.add_argument('--min_score', type=float, default=0.2,
help='Minimum SAM 3 score of the kept mask.')
parser.add_argument('--per_class_min_score_json', type=str, default=None,
help="Optional JSON file with per-class min_score "
"overrides, e.g. {\"person\": 0.1, "
"\"banana\": 0.1}. Falls back to --min_score "
"for any class not in the file.")
parser.add_argument('--prompt_template', type=str,
default='a {category}',
help="Global text-prompt template; {category} is "
"replaced with the cleaned class name. "
"Default 'a {category}' — empirically the "
"old 'a photo of a {category}' wrapper "
"tanks SAM3 grounding for many classes "
"(e.g. bed/donut: 0.04 / 0.01 vs. 0.90 / "
"0.97 with 'a {category}').")
parser.add_argument('--per_class_prompt_json', type=str, default=None,
help="Optional JSON file with per-class prompt "
"templates, e.g. "
'{"bed": "a picture of a {category}"}. '
"Falls back to --prompt_template otherwise.")
parser.add_argument('--per_class_confidence_threshold_json',
type=str, default=None,
help='Optional JSON file with per-class processor score thresholds, e.g. {"bed": 0.0}.')
parser.add_argument('--per_class_top_k_json', type=str, default=None,
help='Optional JSON file with per-class top-K: the top-K masks of a class are merged (for plural-noun classes).')
parser.add_argument('--no_crop', action='store_true')
parser.add_argument('--no_clip_rescore', action='store_true',
help='Skip optional white-bg CLIP re-score. Faster; '
'clean_pool.py will fall back to text2im.py '
'raw-image CLIP scores.')
parser.add_argument('--clip_model', type=str, default='ViT-L/14')
parser.add_argument('--min_area_ratio', type=float, default=0.02)
parser.add_argument('--max_area_ratio', type=float, default=0.85)
parser.add_argument('--classes', type=str, default=None,
help='Optional comma-separated class allowlist '
'(e.g. "person,banana,broccoli"). When set, '
'only those subdirs of --input_dir are '
'processed; useful for threshold sweeps over '
'a subset of problem classes.')
parser.add_argument('--shard_id', type=int, default=0)
parser.add_argument('--num_shards', type=int, default=1)
parser.add_argument('--max_per_class', type=int, default=None,
help='Cap to N images per class (smoke / sweep). '
'Sampling is seeded random (see --sample_seed) '
'so the same N are picked across threshold '
'sweeps, but the sample is representative '
'instead of head-take img_0000..img_000N-1.')
parser.add_argument('--sample_seed', type=int, default=42,
help='Seed for per-class random sampling under '
'--max_per_class. Set to -1 to fall back to '
'deterministic head-take.')
parser.add_argument('--device', type=str, default='cuda')
args = parser.parse_args()
input_dir = Path(args.input_dir)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
cat_dirs = sorted([d for d in input_dir.iterdir() if d.is_dir()])
print(f"Found {len(cat_dirs)} category directories")
if args.classes:
wanted = {c.strip() for c in args.classes.split(',') if c.strip()}
cat_dirs = [d for d in cat_dirs if d.name in wanted]
missing = wanted - {d.name for d in cat_dirs}
if missing:
print(f" WARNING: requested classes not found: "
f"{sorted(missing)}")
print(f"Class allowlist applied: {len(cat_dirs)} categories")
cat_dirs = [d for i, d in enumerate(cat_dirs)
if i % args.num_shards == args.shard_id]
print(f"This shard: {len(cat_dirs)} categories")
print(f"Loading SAM3 (bpe={args.sam3_bpe_path}, "
f"conf_thr={args.confidence_threshold})...")
processor = load_sam3_processor(
args.sam3_bpe_path,
confidence_threshold=args.confidence_threshold,
device=args.device,
checkpoint_path=args.sam3_checkpoint_path,
)
clip_components = None
if not args.no_clip_rescore:
print(f"Loading CLIP {args.clip_model} for white-bg re-score...")
clip_components = load_clip_scorer(
device=args.device, model_name=args.clip_model)
per_class_min_score = None
if args.per_class_min_score_json:
with open(args.per_class_min_score_json) as f:
per_class_min_score = json.load(f)
print(f"Loaded per-class min_score overrides for "
f"{len(per_class_min_score)} classes from "
f"{args.per_class_min_score_json}")
per_class_prompt = None
if args.per_class_prompt_json:
with open(args.per_class_prompt_json) as f:
per_class_prompt = json.load(f)
print(f"Loaded per-class prompt overrides for "
f"{len(per_class_prompt)} classes from "
f"{args.per_class_prompt_json}")
per_class_conf_thr = None
if args.per_class_confidence_threshold_json:
with open(args.per_class_confidence_threshold_json) as f:
per_class_conf_thr = json.load(f)
print(f"Loaded per-class confidence_threshold overrides for "
f"{len(per_class_conf_thr)} classes from "
f"{args.per_class_confidence_threshold_json}")
per_class_top_k = None
if args.per_class_top_k_json:
with open(args.per_class_top_k_json) as f:
per_class_top_k = json.load(f)
print(f"Loaded per-class top-K overrides for "
f"{len(per_class_top_k)} classes from "
f"{args.per_class_top_k_json}")
total_instances = 0
for idx, cat_dir in enumerate(cat_dirs):
print(f"[{idx+1}/{len(cat_dirs)}] {cat_dir.name}...")
results = process_category_dir(
processor, cat_dir, output_dir,
crop=not args.no_crop,
clip_components=clip_components,
device=args.device,
max_per_class=args.max_per_class,
min_area_ratio=args.min_area_ratio,
max_area_ratio=args.max_area_ratio,
prompt_template=args.prompt_template,
min_score=args.min_score,
per_class_min_score=per_class_min_score,
per_class_prompt=per_class_prompt,
per_class_confidence_threshold=per_class_conf_thr,
per_class_top_k=per_class_top_k,
sample_seed=(None if args.sample_seed == -1 else args.sample_seed),
)
total_instances += len(results)
print(f" {len(results)} instances segmented")
print(f"\nDone. Total instances: {total_instances}")
if __name__ == '__main__':
main() |