kirl
a64690477a
- 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
127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
"""
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CCPD原始真图 全量 TRANS (GT顶点外接框 + 12%/18%外扩)
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输出: ICE_Dataset/CCPD_REAL_TRANS/train/
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全量 ~338K, 文件名 _mask_real_ 标记 (训练时屏蔽省位+城市位)
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"""
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import os, sys, cv2, shutil
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import numpy as np
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from pathlib import Path
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ICEv2 = Path(r"C:\Users\Kirl\Desktop\ICEv2")
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sys.path.insert(0, str(ICEv2 / "ICE_NNv2/ICE-OCR/script/tools"))
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from dataset import parse_ccpd_filename, imread_unicode
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CCPD_DIRS = [
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ICEv2 / "ICE_Dataset/CCPD/CCPD2019",
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ICEv2 / "ICE_Dataset/CCPD/CCPD2020",
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]
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OUT_DIR = ICEv2 / "ICE_Dataset/CCPD_REAL_TRANS/train"
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EXPAND_NORMAL = 1.12
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EXPAND_SMALL = 1.18
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SMALL_BOX_W = SMALL_BOX_H = 16
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MIN_PAD = 2
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def process_one(full_path, stem):
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"""GT顶点→外接框→12/18%外扩→裁切→顶点重映射→warp"""
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img = imread_unicode(str(full_path))
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if img is None:
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return None
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h, w = img.shape[:2]
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parts = stem.split("-")
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if len(parts) < 4:
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return None
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# GT顶点 (CCPD: br, bl, tl, tr)
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verts = []
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for v in parts[3].split("_"):
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x, y = v.split("&")
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verts.append([float(x), float(y)])
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verts = np.float32(verts)
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# 外接框
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x1, y1 = verts[:, 0].min(), verts[:, 1].min()
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x2, y2 = verts[:, 0].max(), verts[:, 1].max()
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bw, bh = x2 - x1, y2 - y1
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# 外扩
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ef = EXPAND_SMALL if (bw < SMALL_BOX_W or bh < SMALL_BOX_H) else EXPAND_NORMAL
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px = max(MIN_PAD, int(round(bw * (ef - 1.0) / 2.0)))
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py = max(MIN_PAD, int(round(bh * (ef - 1.0) / 2.0)))
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cx1 = max(0, int(x1) - px); cy1 = max(0, int(y1) - py)
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cx2 = min(w, int(x2) + px); cy2 = min(h, int(y2) + py)
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if cx2 <= cx1 or cy2 <= cy1:
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return None
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cropped = img[cy1:cy2, cx1:cx2]
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# 顶点重映射到裁切坐标 + OpenCV顺序 (CCPD: br,bl,tl,tr → tl,tr,br,bl)
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verts_crop = verts[[2, 3, 0, 1], :] - np.array([cx1, cy1])
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verts_crop = verts_crop.astype(np.float32)
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OW, OH = 192, 64
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dst = np.float32([[0, 0], [OW - 1, 0], [OW - 1, OH - 1], [0, OH - 1]])
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M = cv2.getPerspectiveTransform(verts_crop, dst)
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return cv2.warpPerspective(cropped, M, (OW, OH))
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if __name__ == "__main__":
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# Step 0: 清空
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if OUT_DIR.exists():
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shutil.rmtree(str(OUT_DIR))
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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# Step 1: 收集
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print("[1/2] Scanning all CCPD...")
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all_files = []
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total = skipped = 0
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for d in CCPD_DIRS:
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for root, dirs, files in os.walk(str(d)):
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for f in files:
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if not f.lower().endswith(".jpg"):
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continue
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indices, plate = parse_ccpd_filename(f)
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if indices is None:
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skipped += 1
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continue
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total += 1
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all_files.append((os.path.join(root, f), Path(f).stem, plate))
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print(f" Valid: {total}, Skipped: {skipped}")
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for prov in sorted(set(p[2][0] for p in all_files),
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key=lambda p: -sum(1 for x in all_files if x[2][0] == p)):
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cnt = sum(1 for x in all_files if x[2][0] == prov)
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print(f" {prov}: {cnt}")
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# Step 2: GT顶点 warp
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print(f"\n[2/2] GT-vertex warp {len(all_files)} -> {OUT_DIR}...")
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done = err = 0
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for i, (full_path, stem, plate) in enumerate(all_files):
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try:
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warped = process_one(full_path, stem)
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if warped is None:
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err += 1
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continue
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# _mask_real_ 标记: 训练时屏蔽 pos0(省)+pos1(城市码)
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out_name = f"{stem}_mask_real_{plate}_trans.jpg"
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out_path = OUT_DIR / out_name
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ret, enc = cv2.imencode(".jpg", warped, [cv2.IMWRITE_JPEG_QUALITY, 95])
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if ret:
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with open(str(out_path), "wb") as wf:
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wf.write(enc.tobytes())
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done += 1
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else:
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err += 1
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except Exception as e:
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err += 1
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if err <= 3:
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print(f" [!] {Path(full_path).name}: {e}")
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if (i + 1) % 10000 == 0:
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print(f" [{i+1}/{len(all_files)}] done={done}, err={err}")
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print(f"\nDone! Saved: {done}, Errors: {err}")
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print(f"Output: {OUT_DIR} ({len(list(OUT_DIR.glob('*.jpg')))} files)")
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