File size: 5,140 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 | """Automated Zero-Shot Auto-Annotation Pipeline using YOLO-World.
Auto-labels all images in `data/images/` using the updated taxonomy:
0: cpu_socket
1: gold_finger
2: ic_chip
3: tantalum_capacitor
4: forcibly_desoldered_pad
5: factory_unpopulated_pad
6: occluded_region
Generates YOLO format bounding box label files (.txt) and data.yaml in `data/annotated_auto/`.
"""
from __future__ import annotations
import json
import logging
import os
import shutil
from pathlib import Path
import yaml
from PIL import Image
from tqdm import tqdm
# Ensure cache directories reside within workspace
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",
]
# Prompts tuned for zero-shot YOLO-World detector
CLASS_PROMPTS = [
"CPU socket",
"gold finger edge connector",
"microchip",
"tantalum capacitor",
"desoldered solder pad",
"unpopulated solder pad",
"heatsink shield",
]
def run_auto_annotation(
data_dir: str | Path = "data",
output_dir: str | Path = "data/annotated_auto",
conf_threshold: float = 0.02,
max_images: int | None = None,
) -> dict:
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 zero-shot model...")
from ultralytics import YOLOWorld
model = YOLOWorld("yolov8s-worldv2.pt")
model.set_classes(CLASS_PROMPTS)
log.info("YOLO-World model configured with %d classes (conf threshold: %.3f).", len(CLASS_PROMPTS), conf_threshold)
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("Found %d images to auto-annotate.", len(image_files))
class_detection_counts = {name: 0 for name in CLASS_NAMES}
total_images_processed = 0
total_boxes_generated = 0
for img_path in tqdm(image_files, desc="Auto-annotating images", unit="img"):
img_name = img_path.name
stem = img_path.stem
# Copy image to output directory
target_img_path = images_out_dir / img_name
if not target_img_path.exists():
try:
shutil.copy2(img_path, target_img_path)
except Exception as e:
log.warning("Could not copy %s: %s", img_name, e)
# Run inference
results = model.predict(str(img_path), conf=conf_threshold, verbose=False)
label_file = labels_out_dir / f"{stem}.txt"
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
with open(label_file, "w", encoding="utf-8") as f:
f.write("\n".join(lines) + ("\n" if lines else ""))
total_images_processed += 1
# Create dataset yaml specification
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,
"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 0.02
run_auto_annotation(conf_threshold=conf_val, max_images=max_imgs)
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