Datasets:
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b01d916 503ce4e b01d916 503ce4e b01d916 503ce4e b01d916 503ce4e b01d916 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | """Legacy CLI: sketch + fabric for EquiFashion_DB train/test layout. Core logic: equifashion_pipeline.sketch_fabric."""
from __future__ import annotations
import argparse
import json
import os
import sys
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
from typing import Optional, Tuple
from tqdm import tqdm
from EquiFashionDB_pipeline import sketch_fabric as sf
def _default_workers() -> int:
return max(1, (os.cpu_count() or 4) - 1)
def process_one(
gt_name: str,
image_root: Path,
out_sketch: Path,
out_fabric: Path,
fabric_patch: int,
) -> Tuple[str, Optional[str]]:
stem = Path(gt_name).stem
img_path = image_root / gt_name
if not img_path.is_file():
return gt_name, f"missing image: {img_path}"
return sf.process_one_sample(img_path, out_sketch, out_fabric, stem, fabric_patch)
def _worker(
job: Tuple[str, Path, Path, Path, int],
) -> Tuple[str, Optional[str]]:
return process_one(*job)
def load_gt_list(manifest: Path) -> list[str]:
with open(manifest, encoding="utf-8") as f:
data = json.load(f)
out = []
for row in data:
if isinstance(row, dict) and "gt" in row:
out.append(row["gt"])
elif isinstance(row, str):
out.append(row)
return out
def main() -> None:
p = argparse.ArgumentParser(description="Sketch + fabric enrichment for EquiFashion-DB")
p.add_argument(
"--db-root",
type=Path,
default=Path(__file__).resolve().parent / "EquiFashion_DB",
help="Folder containing train/, test/, train_pose/",
)
p.add_argument("--split", choices=("train", "test", "all"), default="train")
p.add_argument("--workers", type=int, default=_default_workers())
p.add_argument("--fabric-patch", type=int, default=128, help="Fabric patch side length (pixels)")
p.add_argument("--limit", type=int, default=0, help="If >0, only process first N items per split (debug)")
args = p.parse_args()
root: Path = args.db_root
jobs: list[Tuple[str, Path, Path, Path, int]] = []
def enqueue(split: str) -> None:
manifest = root / f"{split}.json"
image_root = root / split
out_sketch = root / f"{split}_sketch"
out_fabric = root / f"{split}_fabric"
gts = load_gt_list(manifest)
for i, gt in enumerate(gts):
if args.limit and i >= args.limit:
break
jobs.append((gt, image_root, out_sketch, out_fabric, args.fabric_patch))
if args.split in ("train", "all"):
enqueue("train")
if args.split in ("test", "all"):
enqueue("test")
if not jobs:
print("No jobs.", file=sys.stderr)
sys.exit(1)
errs: list[Tuple[str, str]] = []
if args.workers <= 1:
for job in tqdm(jobs, desc="enrich"):
name, err = process_one(*job)
if err:
errs.append((name, err))
else:
with ProcessPoolExecutor(max_workers=args.workers) as ex:
futs = {ex.submit(_worker, job): job[0] for job in jobs}
for fut in tqdm(as_completed(futs), total=len(futs), desc="enrich"):
name, err = fut.result()
if err:
errs.append((name, err))
if errs:
err_path = root / "enrich_sketch_fabric_errors.txt"
with open(err_path, "w", encoding="utf-8") as f:
for name, msg in errs:
f.write(f"{name}\t{msg}\n")
print(f"Done with {len(errs)} errors logged to {err_path}")
else:
print("Done. No errors.")
if __name__ == "__main__":
main()
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