Dataset: add READMEs, organize scripts, update .gitignore
- Add README.md to all 16 dataset directories with script references - Centralize generation scripts under scripts/ with full README - Update .gitignore to cover all data directories, keep only metadata - Remove redundant CCPD/.gitignore (parent covers same rules) - Remove stale scripts/test/ (254MB pipeline residue) - Fix label parser reference: scripts are authoritative source of truth
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#!/usr/bin/env python3
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"""
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CCPD_Random_Blue5K_Green5K_EXP_CUT → ICE-COR 角点推理 → 中心外扩 → 透视变换
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==================================================================================
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输出: CCPD_Random_Blue5K_Green5K_EXP_CUT_TRANS/ (192×64, 文件名追加 _trans)
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用法:
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python build_exp_cut_trans.py --full
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python build_exp_cut_trans.py --n 100
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python build_exp_cut_trans.py --dry-run
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"""
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import argparse
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import os
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import sys
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import time
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from pathlib import Path
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import cv2
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import numpy as np
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import torch
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SCRIPT_DIR = Path(__file__).resolve().parent
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PROJ_ROOT = SCRIPT_DIR.parents[1]
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ICE_COR_DIR = PROJ_ROOT / "ICE_NNv2" / "ICE-COR"
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MODEL_PATH = ICE_COR_DIR / "output" / "1_CCPD_19K_EXP_CUT" / "best.pth"
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SRC_DIR = PROJ_ROOT / "ICE_Dataset" / "CCPD_Random_Blue5K_Green5K_EXP_CUT"
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DST_DIR = PROJ_ROOT / "ICE_Dataset" / "CCPD_Random_Blue5K_Green5K_EXP_CUT_TRANS"
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OUT_W, OUT_H = 192, 64
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PAD_X = 0.08
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PAD_Y = 0.05
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if str(ICE_COR_DIR) not in sys.path:
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sys.path.insert(0, str(ICE_COR_DIR))
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from corner_rectify import load_corner_model, predict_plate_corners, order_corners
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# ---- Unicode-safe I/O ----
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def _imread(path: str):
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with open(path, 'rb') as f:
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data = f.read()
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return cv2.imdecode(np.frombuffer(data, np.uint8), cv2.IMREAD_COLOR)
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def _imwrite(path: str, img: np.ndarray, params=None):
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ext = os.path.splitext(path)[1] or '.jpg'
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ok, buf = cv2.imencode(ext, img, params or [])
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if ok:
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with open(path, 'wb') as f:
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f.write(buf.tobytes())
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return ok
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# ---- Single image ----
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def process_one(model, device, img_path: Path, out_img_path: Path, draw: bool = False):
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img = _imread(str(img_path))
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if img is None:
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return "skip"
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H, W = img.shape[:2]
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try:
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corners = predict_plate_corners(img, model, device)
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if corners is None:
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return "skip"
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except Exception:
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return "skip"
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try:
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ordered = order_corners(corners)
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except Exception:
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return "skip"
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# center expand
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cx = ordered.mean(axis=0)
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expanded = ordered + (ordered - cx) * np.array([PAD_X, PAD_Y])
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expanded[:, 0] = np.clip(expanded[:, 0], 0, W - 1)
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expanded[:, 1] = np.clip(expanded[:, 1], 0, H - 1)
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src = expanded.astype(np.float32)
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dst = np.float32([[0, 0], [OUT_W-1, 0], [OUT_W-1, OUT_H-1], [0, OUT_H-1]])
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M = cv2.getPerspectiveTransform(src, dst)
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warped = cv2.warpPerspective(img, M, (OUT_W, OUT_H),
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flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
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if draw:
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for k, (px, py) in enumerate(expanded):
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cv2.circle(warped, tuple(dst[k].astype(int)), 3, (0, 255, 0), -1)
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cv2.polylines(warped, [dst.astype(np.int32)], isClosed=True, color=(0, 255, 0), thickness=1)
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out_stem = img_path.stem + "_trans"
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out_img_path.with_stem(out_stem).parent.mkdir(parents=True, exist_ok=True)
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_imwrite(str(out_img_path.with_stem(out_stem)), warped, [cv2.IMWRITE_JPEG_QUALITY, 92])
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return "ok"
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# ---- Main ----
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def main():
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parser = argparse.ArgumentParser()
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g = parser.add_mutually_exclusive_group(required=True)
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g.add_argument('--full', action='store_true')
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g.add_argument('--n', type=int)
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g.add_argument('--dry-run', action='store_true')
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args = parser.parse_args()
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print(f"Model: {MODEL_PATH}")
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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model, device = load_corner_model(str(MODEL_PATH), device)
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model.eval()
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print(f"Device: {device}\n")
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classes = ['blue', 'green']
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draw = bool(args.dry_run)
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for cls in classes:
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src_cls = SRC_DIR / cls
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dst_cls = DST_DIR / cls
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dst_cls.mkdir(parents=True, exist_ok=True)
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images = sorted(src_cls.glob('*.jpg'))
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if not images:
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print(f"[WARN] {cls}: no images")
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continue
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n = 2 if args.dry_run else (args.n or len(images))
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images = images[:n]
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print(f"[{cls}] {len(images)} images...", flush=True)
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t0 = time.perf_counter()
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ok = skip = 0
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for img_path in images:
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out = dst_cls / img_path.name
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r = process_one(model, device, img_path, out, draw=draw)
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if r == "ok": ok += 1
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else: skip += 1
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elapsed = time.perf_counter() - t0
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total = ok + skip
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print(f" → ok:{ok}/{total} skip:{skip} ({elapsed:.1f}s, {total/elapsed:.1f} img/s)")
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print(f"\nDone. Output: {DST_DIR}")
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if __name__ == '__main__':
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main()
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