| """Crop detected product regions from shelf images.""" |
|
|
| import cv2 |
| import numpy as np |
| from pathlib import Path |
|
|
|
|
| def crop_detections( |
| image_path: str, |
| detections: list[dict], |
| output_dir: str = None, |
| padding: int = 5, |
| ) -> list[dict]: |
| """ |
| Crop each detected bounding box from the shelf image. |
| |
| Args: |
| image_path: path to the shelf image |
| detections: list of dicts with 'bbox' key ([x1, y1, x2, y2]) |
| output_dir: if set, save crops as JPEGs |
| padding: pixels to add around each box (clamped to image bounds) |
| |
| Returns: |
| Same detections list, each dict augmented with: |
| - 'crop': np.ndarray of the cropped region (BGR) |
| - 'crop_path': str path if output_dir was set |
| """ |
| img = cv2.imread(image_path) |
| h, w = img.shape[:2] |
| image_name = Path(image_path).stem |
|
|
| if output_dir: |
| Path(output_dir).mkdir(parents=True, exist_ok=True) |
|
|
| for i, det in enumerate(detections): |
| x1, y1, x2, y2 = [int(c) for c in det["bbox"]] |
|
|
| x1 = max(0, x1 - padding) |
| y1 = max(0, y1 - padding) |
| x2 = min(w, x2 + padding) |
| y2 = min(h, y2 + padding) |
|
|
| crop = img[y1:y2, x1:x2] |
| det["crop"] = crop |
|
|
| if output_dir: |
| crop_path = str(Path(output_dir) / f"{image_name}_crop_{i:03d}.jpg") |
| cv2.imwrite(crop_path, crop) |
| det["crop_path"] = crop_path |
|
|
| print(f"[cropper] Cropped {len(detections)} regions from {image_path}") |
| return detections |
|
|
|
|
| if __name__ == "__main__": |
| import sys |
| from detector import detect_products |
|
|
| path = sys.argv[1] if len(sys.argv) > 1 else "shelf_images/shelf_01.jpg" |
| out_dir = sys.argv[2] if len(sys.argv) > 2 else "outputs/crops" |
|
|
| dets = detect_products(path) |
| dets = crop_detections(path, dets, output_dir=out_dir) |
| print(f"Saved {len(dets)} crops to {out_dir}") |
|
|
