| """split_3fold.json 으로 fold k 의 학습/inner_val/시험 목록을 만들고, 사진 1장과 마스크를 tar 에서 바로 읽는 예시.
|
|
|
| 사용법: python load_example.py [k] (필요: numpy, pillow)
|
| """
|
| import io
|
| import json
|
| import sys
|
| import tarfile
|
|
|
| import numpy as np
|
| from PIL import Image
|
|
|
| k = int(sys.argv[1]) if len(sys.argv) > 1 else 0
|
| split = json.load(open("split_3fold.json", encoding="utf-8"))
|
| classes = [c["name"] for c in json.load(open("classes.json", encoding="utf-8"))["classes"]]
|
| inner = set(split["inner_val"][str(k)])
|
| test = sorted(n for n, v in split["images"].items() if v["fold"] == k)
|
| train = sorted(n for n, v in split["images"].items() if v["fold"] != k and n not in inner)
|
| print(f"fold {k}: train {len(train)}, inner_val {len(inner)}, test {len(test)}")
|
|
|
| name = test[0]
|
| with tarfile.open(f"masks/fold{k}.tar") as t:
|
| mask = np.load(io.BytesIO(t.extractfile(f"masks/{name}.npz").read()))["mask"]
|
| with tarfile.open(f"images/fold{k}.tar") as t:
|
| img = Image.open(io.BytesIO(t.extractfile(f"images/{name}.jpg").read()))
|
| print(f"{name}: image {img.size[0]}x{img.size[1]}, mask {mask.shape} {mask.dtype}")
|
| for i, c in enumerate(classes):
|
| if mask[i].any():
|
| print(f" {i:2d} {c:14s} {int(mask[i].sum()):>10d} px")
|
| print(f" multi-label pixels (2+ classes): {int((mask.sum(0) > 1).sum())}")
|
|
|
