| """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 |
|
|
| |
| 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", |
| "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 |
|
|
| |
| 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) |
|
|
| |
| 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 |
|
|
| |
| 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) |
|
|
|
|
