File size: 5,859 Bytes
11a414e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | """Automated Zero-Shot Auto-Annotation Pipeline using YOLO-World (Ultra-Lightweight / Resumable Mode).
Auto-labels images in `data/images/` using the updated 7-class taxonomy:
0: cpu_socket
1: gold_finger
2: ic_chip
3: tantalum_capacitor
4: forcibly_desoldered_pad
5: factory_unpopulated_pad
6: occluded_region
Features:
- Resumable (`skip_existing=True`): Skips already auto-labeled non-empty `.txt` files.
- Ultra-Low Memory (<500 MB RAM): Standard single-pass inference + per-image `gc.collect()`.
"""
from __future__ import annotations
import gc
import json
import logging
import os
import shutil
from pathlib import Path
import torch
import yaml
from PIL import Image
from tqdm import tqdm
cache_dir = Path("data/cache").resolve()
cache_dir.mkdir(parents=True, exist_ok=True)
os.environ["TORCH_HOME"] = str(cache_dir)
os.environ["HF_HOME"] = str(cache_dir)
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
log = logging.getLogger(__name__)
CLASS_NAMES = [
"cpu_socket",
"gold_finger",
"ic_chip",
"tantalum_capacitor",
"forcibly_desoldered_pad",
"factory_unpopulated_pad",
"occluded_region",
]
CLASS_PROMPTS = [
"CPU socket square slot",
"gold edge connector pins",
"black IC microchip",
"yellow SMD capacitor",
"desoldered solder pad footprint",
"empty unpopulated solder pad",
"metal heatsink shield cover",
]
def run_auto_annotation(
data_dir: str | Path = "data",
output_dir: str | Path = "data/annotated_auto",
conf_threshold: float = 0.015,
max_images: int | None = None,
skip_existing: bool = True,
) -> dict:
torch.set_num_threads(1)
data_dir = Path(data_dir)
output_dir = Path(output_dir)
images_in_dir = data_dir / "images"
images_out_dir = output_dir / "images"
labels_out_dir = output_dir / "labels"
images_out_dir.mkdir(parents=True, exist_ok=True)
labels_out_dir.mkdir(parents=True, exist_ok=True)
log.info("Loading YOLO-World model (Resumable Low-Memory Mode)...")
from ultralytics import YOLOWorld
model = YOLOWorld("yolov8s-worldv2.pt")
model.set_classes(CLASS_PROMPTS)
image_files = sorted(list(images_in_dir.glob("*.jpg")) + list(images_in_dir.glob("*.png")))
if max_images:
image_files = image_files[:max_images]
log.info("Starting Resumable Auto-Annotation across %d images...", len(image_files))
class_detection_counts = {name: 0 for name in CLASS_NAMES}
total_images_processed = 0
total_boxes_generated = 0
skipped_count = 0
for img_path in tqdm(image_files, desc="Resumable Auto-Annotating", unit="img"):
img_name = img_path.name
stem = img_path.stem
label_file = labels_out_dir / f"{stem}.txt"
dst_path = images_out_dir / img_name
if not dst_path.exists():
try:
shutil.copy2(img_path, dst_path)
except Exception as e:
log.warning("Could not copy %s: %s", img_name, e)
# Skip if label file exists and is non-empty
if skip_existing and label_file.exists() and label_file.stat().st_size > 0:
lines = label_file.read_text(encoding="utf-8").strip().splitlines()
for l in lines:
if not l: continue
parts = l.split()
cls_id = int(parts[0])
if 0 <= cls_id < len(CLASS_NAMES):
class_detection_counts[CLASS_NAMES[cls_id]] += 1
total_boxes_generated += 1
total_images_processed += 1
skipped_count += 1
continue
# Standard single-pass inference (lightweight)
results = model.predict(str(img_path), conf=conf_threshold, verbose=False)
lines = []
if results and len(results) > 0:
boxes = results[0].boxes
if boxes is not None and len(boxes) > 0:
xywhn = boxes.xywhn.cpu().numpy()
cls_ids = boxes.cls.cpu().numpy().astype(int)
for (x, y, w, h), cls_id in zip(xywhn, cls_ids):
if 0 <= cls_id < len(CLASS_NAMES):
lines.append(f"{cls_id} {x:.6f} {y:.6f} {w:.6f} {h:.6f}")
class_name = CLASS_NAMES[cls_id]
class_detection_counts[class_name] += 1
total_boxes_generated += 1
label_file.write_text("\n".join(lines) + ("\n" if lines else ""), encoding="utf-8")
total_images_processed += 1
del results
gc.collect()
dataset_yaml = {
"path": str(output_dir.resolve()),
"train": "images",
"val": "images",
"names": {i: name for i, name in enumerate(CLASS_NAMES)},
}
yaml_path = output_dir / "data.yaml"
with open(yaml_path, "w", encoding="utf-8") as f:
yaml.dump(dataset_yaml, f, default_flow_style=False)
summary = {
"total_images_processed": total_images_processed,
"total_boxes_generated": total_boxes_generated,
"skipped_already_annotated": skipped_count,
"conf_threshold": conf_threshold,
"class_detection_counts": class_detection_counts,
"output_directory": str(output_dir.resolve()),
"data_yaml": str(yaml_path.resolve()),
}
summary_path = output_dir / "auto_annotation_summary.json"
with open(summary_path, "w", encoding="utf-8") as f:
json.dump(summary, f, indent=2)
log.info("Auto-annotation complete! Summary saved to %s", summary_path)
return summary
if __name__ == "__main__":
import sys
max_imgs = int(sys.argv[1]) if len(sys.argv) > 1 and sys.argv[1].isdigit() else None
conf_val = float(sys.argv[2]) if len(sys.argv) > 2 else 0.015
run_auto_annotation(conf_threshold=conf_val, max_images=max_imgs)
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