| |
| |
| """ |
| Sample N real images per class per dataset and pack into one zip for inspection. |
| Pools images across train/val/test, picks N with a fixed seed, copies into |
| dataset_samples/<Disease_Dataset>/<label>_<class_name>/<original_name> |
| then zips to dataset_samples.zip. |
| """ |
| import os, csv, random, shutil |
| from collections import defaultdict |
|
|
| PROJ = "/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image" |
| DS = f"{PROJ}/Dataset" |
| OUT = f"{PROJ}/dataset_samples" |
| N, SEED = 20, 42 |
|
|
| DSETS = { |
| "mmac": ("Myopia/Classification_of_Myopic_Maculopathy", "Myopia_MMAC"), |
| "adam": ("AMD/adamdataset", "AMD_ADAM"), |
| "airogs": ("Glaucoma/eyepacs-airogs-light", "Glaucoma_AIROGS"), |
| "papila": ("Glaucoma/papila-retinal-fundus-images", "Glaucoma_PAPILA"), |
| "idrid": ("DR/idrid-dataset", "DR_IDRiD"), |
| "aptos": ("DR/aptos2019", "DR_APTOS"), |
| "deepdrid": ("DR/deepdrid", "DR_DeepDRiD"), |
| } |
|
|
|
|
| def main(): |
| rnd = random.Random(SEED) |
| shutil.rmtree(OUT, ignore_errors=True) |
| total = 0 |
| for k, (rel, prefix) in DSETS.items(): |
| base = os.path.join(DS, rel) |
| rows = list(csv.DictReader(open(os.path.join(base, "labels.csv")))) |
| by = defaultdict(list) |
| for r in rows: |
| by[(r["label"], r.get("class_name", ""))].append(r["filepath"]) |
| print(f"### {prefix}") |
| for (lab, cn), files in sorted(by.items(), key=lambda x: int(x[0][0])): |
| pick = sorted(files) |
| rnd.shuffle(pick) |
| pick = pick[:N] |
| dst = os.path.join(OUT, prefix, f"{lab}_{cn}") |
| os.makedirs(dst, exist_ok=True) |
| for fp in pick: |
| shutil.copy2(os.path.join(base, fp), os.path.join(dst, os.path.basename(fp))) |
| total += len(pick) |
| print(f" {lab}_{cn:10} picked {len(pick):2d} / {len(files)} available") |
| zip_path = shutil.make_archive(OUT, "zip", OUT) |
| mb = os.path.getsize(zip_path) / (1024 * 1024) |
| print(f"\nTOTAL images: {total}") |
| print(f"ZIP: {zip_path} ({mb:.1f} MB)") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
