Datasets:
Formats:
text
Size:
< 1K
Tags:
synthetic-data
computer-vision
transparent-objects
glass
transparent-packaging
object-detection
License:
| import cv2 | |
| import numpy as np | |
| from pathlib import Path | |
| # Paths to your folders | |
| DATA_DIR = Path("data") | |
| IMG_DIR = DATA_DIR / "images" | |
| BOX_DIR = DATA_DIR / "labels_box" | |
| SEG_DIR = DATA_DIR / "labels_segment" | |
| OUTPUT_DIR = Path("preview_output") | |
| OUTPUT_DIR.mkdir(exist_ok=True) | |
| # Define label colors (BGR format) | |
| COLOR_BOX = (0, 255, 0) # Green for Bounding Box | |
| COLOR_SEG = (255, 0, 0) # Blue for Segmentation Polygon | |
| images = list(IMG_DIR.glob("*.*")) | |
| if not images: | |
| print(f"❌ No images found in {IMG_DIR}") | |
| exit(1) | |
| print(f"Found {len(images)} sample images. Generating preview overlays...") | |
| for img_path in images: | |
| img = cv2.imread(str(img_path)) | |
| if img is None: | |
| continue | |
| h, w, _ = img.shape | |
| base_name = img_path.stem | |
| # 1. Overlay Bounding Boxes (if present) | |
| box_path = BOX_DIR / f"{base_name}.txt" | |
| if box_path.exists(): | |
| with open(box_path, "r") as f: | |
| for line in f: | |
| parts = line.strip().split() | |
| if len(parts) < 5: | |
| continue | |
| # YOLO format: class x_center y_center width height (normalized) | |
| cls, xc, yc, bw, bh = map(float, parts[:5]) | |
| x1 = int((xc - bw / 2) * w) | |
| y1 = int((yc - bh / 2) * h) | |
| x2 = int((xc + bw / 2) * w) | |
| y2 = int((yc + bh / 2) * h) | |
| cv2.rectangle(img, (x1, y1), (x2, y2), COLOR_BOX, 2) | |
| cv2.putText(img, f"Box: {int(cls)}", (x1, max(y1 - 5, 15)), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_BOX, 2) | |
| # 2. Overlay Segment Polygons (if present) | |
| seg_path = SEG_DIR / f"{base_name}.txt" | |
| if seg_path.exists(): | |
| with open(seg_path, "r") as f: | |
| for line in f: | |
| parts = line.strip().split() | |
| if len(parts) < 3: | |
| continue | |
| cls = int(parts[0]) | |
| coords = list(map(float, parts[1:])) | |
| # Reconstruct (x, y) pixel coordinates | |
| pts = [] | |
| for i in range(0, len(coords), 2): | |
| px = int(coords[i] * w) | |
| py = int(coords[i + 1] * h) | |
| pts.append([px, py]) | |
| pts = np.array(pts, dtype=np.int32).reshape((-1, 1, 2)) | |
| cv2.polylines(img, [pts], isClosed=True, color=COLOR_SEG, thickness=2) | |
| # Save output preview image | |
| out_file = OUTPUT_DIR / f"{base_name}_preview.jpg" | |
| cv2.imwrite(str(out_file), img) | |
| print(f"✅ Saved overlay preview: {out_file}") | |
| print(f"\n🚀 Visualization complete! Check the '{OUTPUT_DIR}' folder.") |