| """Read coco/train/labels.json, print counts per class and save one box overlay (overlay.jpg). |
| Run from the dataset root: python scripts/load_example.py (needs Pillow)""" |
| import collections, json |
| from pathlib import Path |
| from PIL import Image, ImageDraw |
|
|
| root = Path(__file__).resolve().parent.parent |
| d = json.loads((root / "coco/train/labels.json").read_text()) |
| names = {c["id"]: c["name"] for c in d["categories"]} |
| count = collections.Counter(names[a["category_id"]] for a in d["annotations"]) |
| print(len(d["images"]), "tiles,", len(d["annotations"]), "boxes") |
| for n in names.values(): |
| print(f" {n:13s} {count[n]:6d} boxes") |
|
|
| by_img = collections.defaultdict(list) |
| for a in d["annotations"]: |
| by_img[a["image_id"]].append(a) |
| img = max(d["images"], key=lambda i: len(by_img[i["id"]])) |
| im = Image.open(root / "coco/train/images" / img["file_name"]).convert("RGB") |
| dr = ImageDraw.Draw(im) |
| for a in by_img[img["id"]]: |
| x, y, w, h = a["bbox"] |
| dr.rectangle([x, y, x + w, y + h], outline=(255, 0, 0), width=3) |
| im.save(root / "overlay.jpg", quality=90) |
| print("saved overlay.jpg for", img["file_name"], "with", len(by_img[img["id"]]), "boxes") |
|
|
