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f211247 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | #!/usr/bin/env python3
"""SAM 2.1 polygon annotation — GPU run.
Detection bbox'larını prompt olarak kullanıp RLE mask üretir.
Output: her kaynağın detection.coco.json dosyasına 'segmentation' alanı ekler.
"""
import json, os, argparse, time
from pathlib import Path
ROOT = Path("/arf/scratch/stakan/hitit-proje")
SOURCES = ROOT / "datasets" / "sources"
def main():
import torch
from PIL import Image
import numpy as np
ap = argparse.ArgumentParser()
ap.add_argument('--source', default='all', help='all|hitit_local|compvis|yeni_veri|deepscribe')
ap.add_argument('--limit', type=int, default=0)
args = ap.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Device: {device}", flush=True)
# SAM 2.1 import (ultralytics YOLO'dan sam kullanabiliriz; veya segment_anything_2 paketi)
try:
from ultralytics import SAM
sam = SAM("sam2.1_l.pt") # auto-download
model_name = "sam2.1_l (ultralytics)"
except Exception as e:
print(f"Ultralytics SAM yüklenmedi: {e}; alternatif deneniyor", flush=True)
try:
from segment_anything import SamPredictor, sam_model_registry
print("segment_anything varsa — manual checkpoint path gerekir")
return
except ImportError:
print("SAM kurulu değil. Install: pip install ultralytics", flush=True)
return
source_dirs = ['hitit_local', 'compvis', 'yeni_veri', 'deepscribe'] if args.source == 'all' else [args.source]
try:
from pycocotools import mask as mask_utils
except ImportError:
os.system("pip install --user --quiet pycocotools")
from pycocotools import mask as mask_utils
t0 = time.time()
total_done = 0
for src_name in source_dirs:
src = SOURCES / src_name
mp = src / "manifest_detection.jsonl"
if not mp.exists(): continue
records = []
with open(mp) as f:
for line in f:
records.append(json.loads(line))
if args.limit:
records = records[:args.limit]
print(f"\n{src_name}: {len(records)} record", flush=True)
out_json = src / "detection.coco.json"
coco = json.load(open(out_json)) if out_json.exists() else {
"info": {"description": f"{src_name} detection with SAM 2.1 masks"},
"images": [], "annotations": [], "categories": []
}
ann_id = 1 + (max([a['id'] for a in coco.get('annotations', [])], default=0))
cat_map = {c['name']: c['id'] for c in coco.get('categories', [])}
img_id = 1 + (max([i['id'] for i in coco.get('images', [])], default=0))
existing_img_paths = {i.get('file_name') for i in coco.get('images', [])}
for ri, r in enumerate(records):
p = r.get('path')
if not p or not os.path.exists(p): continue
if p in existing_img_paths: continue
# Bbox topla (YOLO label_path veya extra.bboxes)
bboxes_xyxy = []
labels = []
w = r.get('width') or 0
h = r.get('height') or 0
if not (w and h):
try:
with Image.open(p) as img:
w, h = img.size
except: continue
lp = r.get('label_path')
if lp and os.path.exists(lp):
try:
with open(lp) as f:
for ln in f:
parts = ln.strip().split()
if len(parts) == 5:
cls, cx, cy, bw, bh = parts
cx, cy, bw, bh = float(cx)*w, float(cy)*h, float(bw)*w, float(bh)*h
x1, y1, x2, y2 = cx-bw/2, cy-bh/2, cx+bw/2, cy+bh/2
bboxes_xyxy.append([x1, y1, x2, y2])
labels.append(f"class_{cls}")
except: pass
extra = r.get('extra') or {}
if isinstance(extra, dict):
for b in extra.get('bboxes', []):
if not isinstance(b, dict): continue
lbl = b.get('class_name') or b.get('mzl_label') or b.get('train_label') or 'unknown'
yolo = b.get('yolo_bbox')
xyxy = b.get('bbox')
if yolo and len(yolo) == 4:
cx, cy, bw, bh = yolo
cx, cy, bw, bh = cx*w, cy*h, bw*w, bh*h
bboxes_xyxy.append([cx-bw/2, cy-bh/2, cx+bw/2, cy+bh/2])
labels.append(str(lbl))
elif xyxy and len(xyxy) == 4:
bboxes_xyxy.append([float(x) for x in xyxy])
labels.append(str(lbl))
if not bboxes_xyxy: continue
# SAM predict
try:
results = sam(p, bboxes=bboxes_xyxy, verbose=False)
except Exception as e:
print(f" skip {p}: {e}", flush=True)
continue
# Image entry
coco['images'].append({
"id": img_id,
"file_name": p,
"width": w, "height": h,
"fold": r.get('fold'),
"tablet_id": r.get('tablet_id'),
})
# Annotations
if results and hasattr(results[0], 'masks') and results[0].masks is not None:
masks = results[0].masks.data.cpu().numpy() # (N, H, W) bool
for mi, (mask, lbl, bb) in enumerate(zip(masks, labels, bboxes_xyxy)):
if lbl not in cat_map:
cat_map[lbl] = len(cat_map) + 1
coco['categories'].append({"id": cat_map[lbl], "name": lbl, "supercategory": "sign"})
# RLE encode
mask_u8 = (mask > 0).astype(np.uint8)
rle = mask_utils.encode(np.asfortranarray(mask_u8))
rle['counts'] = rle['counts'].decode('utf-8')
x1, y1, x2, y2 = bb
coco['annotations'].append({
"id": ann_id,
"image_id": img_id,
"category_id": cat_map[lbl],
"bbox": [x1, y1, x2-x1, y2-y1],
"area": float(mask_u8.sum()),
"iscrowd": 0,
"segmentation": rle,
})
ann_id += 1
img_id += 1
total_done += 1
if (ri+1) % 100 == 0:
elapsed = time.time() - t0
rate = total_done / max(elapsed, 1)
print(f" [{src_name}] {ri+1}/{len(records)} ({rate:.1f} img/s)", flush=True)
with open(out_json, 'w') as f:
json.dump(coco, f, ensure_ascii=False)
print(f" {src_name}: yazıldı {out_json} — {len(coco['images'])} img, {len(coco['annotations'])} ann", flush=True)
if __name__ == '__main__':
main()
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