feat: 特殊车牌生成管线 + C++/多进程/色温校正

CCPD_SPE_GEN/cv_pipeline/color_tuner.py

@@ -0,0 +1,295 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+车牌底板色手动调色 GUI
+======================
+用 HSV 滑块实时调整合成车牌底板色，预览效果，保存参数供管线调用。
+
+用法:
+  python color_tuner.py
+
+控件:
+  H 偏移    — 色相偏移量 (-30 ~ +30)
+  S 缩放%%   — 饱和度缩放 (10% ~ 150%)
+  V 缩放%%   — 明度缩放   (10% ~ 150%)
+  [1-9]     — 切换车牌类型
+  [R]       — 复位
+  [S]       — 保存当前参数到 plate_color_profile.py
+  [ESC]     — 退出
+"""
+
+import sys
+from pathlib import Path
+
+import cv2
+import numpy as np
+
+# ── 路径 ──
+_SCRIPT_DIR = Path(__file__).resolve().parent
+_PROJECT_DIR = _SCRIPT_DIR.parent.parent
+_GENERATOR_DIR = _PROJECT_DIR / 'chinese_license_plate_generator'
+sys.path.insert(0, str(_GENERATOR_DIR))
+
+from generate_multi_plate import MultiPlateGenerator
+from plate_number import (
+    generate_plate_number_white,
+    generate_plate_number_army,
+    generate_plate_number_black_gangao,
+    generate_plate_number_black_shi_old,
+    generate_plate_number_black_shi_new,
+    generate_plate_number_black_ling_old,
+    generate_plate_number_black_ling_new,
+    generate_plate_number_yellow_xue,
+    generate_plate_number_blue,
+)
+
+# ── 车牌类型 ──
+PLATE_TYPES = [
+    ('yellow',     '教练车',    generate_plate_number_yellow_xue),
+    ('green_truck','新能源',    lambda: generate_plate_number_blue(length=8)),
+    ('white',      '警车',      generate_plate_number_white),
+    ('white_army', '军车',      generate_plate_number_army),
+    ('black',      '港澳',      generate_plate_number_black_gangao),
+    ('black_shi',  '使馆旧',    generate_plate_number_black_shi_old),
+    ('black_shi',  '使馆新',    generate_plate_number_black_shi_new),
+    ('black',      '领馆旧',    generate_plate_number_black_ling_old),
+    ('black',      '领馆新',    generate_plate_number_black_ling_new),
+]
+
+# ── 参考图 ──
+REF_PATH = Path(r"C:\Users\Kirl\Downloads\R-C (3).jpg")
+
+# ── 全局状态 ──
+generator = None
+current_type_idx = 0
+synthetic_plate = None
+ref_image = None
+
+# 各类型的默认参数 (H偏移, S缩放%, V缩放%)
+params = {}
+
+
+def init_generator():
+    global generator
+    pm = str(_GENERATOR_DIR / 'plate_model')
+    fm = str(_GENERATOR_DIR / 'font_model')
+    generator = MultiPlateGenerator(pm, fm)
+
+
+def load_ref():
+    global ref_image
+    if REF_PATH.is_file():
+        ref = cv2.imread(str(REF_PATH))
+        if ref is not None:
+            h, w = ref.shape[:2]
+            ref = cv2.resize(ref, (int(w * 0.6), int(h * 0.6)))
+            ref_image = ref
+
+
+def generate_plate(type_idx):
+    global synthetic_plate
+    bg_color, label, gen_func = PLATE_TYPES[type_idx]
+    number = gen_func()
+    plate_w = 480 if len(number) == 8 else 440
+    plate = generator.generate_plate_special(number, bg_color, is_double=False, enhance=False)
+    synthetic_plate = plate
+    return plate, number, label
+
+
+def load_params(bg_color):
+    """从已保存的 profile 加载参数"""
+    key = bg_color
+    if key not in params:
+        params[key] = {'h_shift': 0, 's_scale': 100, 'v_scale': 100}
+    return params[key]
+
+
+def adjust_plate(plate_bgr, bg_color, h_shift, s_scale, v_scale):
+    """
+    在 HSV 空间调整底板色。
+    仅作用于底板区域（高S + 对应色相范围 + 高V），字符不受影响。
+    """
+    if h_shift == 0 and s_scale == 100 and v_scale == 100:
+        return plate_bgr.copy()
+
+    hue_ranges = {
+        'yellow': (15, 50), 'green_truck': (40, 85),
+        'white': (0, 180), 'white_army': (0, 180),
+        'black': (0, 180), 'black_shi': (0, 180),
+    }
+    h_lo, h_hi = hue_ranges.get(bg_color, (0, 180))
+
+    hsv = cv2.cvtColor(plate_bgr, cv2.COLOR_BGR2HSV).astype(np.float32)
+    h, s, v = hsv[:, :, 0], hsv[:, :, 1], hsv[:, :, 2]
+
+    # 底板区域 mask
+    hue_in = (h > h_lo) & (h < h_hi)
+    bg_mask = hue_in & (s > 80) & (v > 100)
+
+    if bg_mask.sum() < 200:
+        return plate_bgr.copy()
+
+    # 羽化
+    bg_u8 = bg_mask.astype(np.uint8) * 255
+    dist = cv2.distanceTransform(bg_u8, cv2.DIST_L2, cv2.DIST_MASK_PRECISE)
+    soft = np.clip(dist / 4.0, 0.0, 1.0)
+
+    h_new = h + h_shift * soft
+    s_new = s * (1.0 + (s_scale / 100.0 - 1.0) * soft)
+    v_new = v * (1.0 + (v_scale / 100.0 - 1.0) * soft)
+
+    hsv[:, :, 0] = np.clip(h_new, 0, 180)
+    hsv[:, :, 1] = np.clip(s_new, 0, 255)
+    hsv[:, :, 2] = np.clip(v_new, 0, 255)
+
+    return cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)
+
+
+def refresh():
+    global synthetic_plate
+    _, number, label = generate_plate(current_type_idx)
+    update_display()
+
+
+def update_display(*_args):
+    bg_color, label, _ = PLATE_TYPES[current_type_idx]
+    p = load_params(bg_color)
+
+    # trackbar 可能在创建过程中尚未就绪，容错读取
+    try:
+        h_shift = cv2.getTrackbarPos('H offset', 'Color Tuner') - 30
+        s_scale = cv2.getTrackbarPos('S scale', 'Color Tuner')
+        v_scale = cv2.getTrackbarPos('V scale', 'Color Tuner')
+    except cv2.error:
+        h_shift = p.get('h_shift', 0)
+        s_scale = p.get('s_scale', 100)
+        v_scale = p.get('v_scale', 100)
+
+    p['h_shift'] = h_shift
+    p['s_scale'] = s_scale
+    p['v_scale'] = v_scale
+
+    adjusted = adjust_plate(synthetic_plate, bg_color, h_shift, s_scale, v_scale)
+
+    # 并排显示：原始 | 调整后
+    h, w = synthetic_plate.shape[:2]
+    display = np.hstack([synthetic_plate, adjusted])
+
+    # 加标签
+    cv2.putText(display, f"Original ({bg_color})", (10, 25),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.55, (200, 200, 200), 1)
+    cv2.putText(display, f"Adjusted  H:{h_shift:+d}  S:{s_scale}  V:{v_scale}",
+                (w + 10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 200), 1)
+
+    # 类型标签
+    cv2.putText(display, f"[{current_type_idx+1}/9] {label}",
+                (10, display.shape[0] - 10),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (180, 180, 180), 1)
+
+    # 如果参考图存在，放在下方
+    if ref_image is not None:
+        rw = display.shape[1]
+        rh = ref_image.shape[0] * rw // ref_image.shape[1]
+        ref_rs = cv2.resize(ref_image, (rw, rh))
+        display = np.vstack([display, np.ones((4, rw, 3), dtype=np.uint8) * 30, ref_rs])
+        cv2.putText(display, "Reference", (10, display.shape[0] - rh + 25),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
+
+    cv2.imshow('Color Tuner', display)
+
+
+def save_params():
+    """保存当前所有类型的参数到 plate_color_profile.py"""
+    profile_path = _SCRIPT_DIR / 'plate_color_profile.py'
+    lines = ['# Auto-generated by color_tuner.py',
+             '# Plate background color correction profiles',
+             '# Format: (h_shift, s_scale_percent, v_scale_percent)',
+             '',
+             'PLATE_COLOR_PROFILE = {']
+    for bg_color in ['yellow', 'green_truck', 'white', 'white_army', 'black', 'black_shi']:
+        p = params.get(bg_color, {'h_shift': 0, 's_scale': 100, 'v_scale': 100})
+        lines.append(f"    '{bg_color}': ({p['h_shift']}, {p['s_scale']}, {p['v_scale']}),")
+    lines.append('}')
+    lines.append('')
+
+    profile_path.write_text('\n'.join(lines), encoding='utf-8')
+    print(f"\n[SAVED] {profile_path}")
+    print(f"  Params: { {k: v for k, v in params.items()} }")
+
+
+def on_key(key):
+    global current_type_idx, synthetic_plate
+    if ord('1') <= key <= ord('9'):
+        idx = key - ord('1')
+        if idx < len(PLATE_TYPES):
+            current_type_idx = idx
+            # 切换类型时复位 trackbar 到该类型已保存的参数
+            bg_color = PLATE_TYPES[current_type_idx][0]
+            p = load_params(bg_color)
+            cv2.setTrackbarPos('H offset', 'Color Tuner', p['h_shift'] + 30)
+            cv2.setTrackbarPos('S scale', 'Color Tuner', p['s_scale'])
+            cv2.setTrackbarPos('V scale', 'Color Tuner', p['v_scale'])
+            # 生成新车牌
+            synthetic_plate, _, _ = generate_plate(current_type_idx)
+            update_display()
+    elif key == ord('r') or key == ord('R'):
+        bg_color = PLATE_TYPES[current_type_idx][0]
+        params[bg_color] = {'h_shift': 0, 's_scale': 100, 'v_scale': 100}
+        # Reset trackbars
+        cv2.setTrackbarPos('H offset', 'Color Tuner', 30)
+        cv2.setTrackbarPos('S scale', 'Color Tuner', 100)
+        cv2.setTrackbarPos('V scale', 'Color Tuner', 100)
+        update_display()
+    elif key == ord('s') or key == ord('S'):
+        save_params()
+    elif key == ord('n') or key == ord('N'):
+        refresh()
+
+
+def main():
+    global ref_image
+    init_generator()
+    load_ref()
+
+    # 初始化第一张车牌
+    _, number, label = generate_plate(current_type_idx)
+
+    # 先建窗口和滑动条，再刷新显示
+    cv2.namedWindow('Color Tuner', cv2.WINDOW_NORMAL)
+    cv2.resizeWindow('Color Tuner', 1000, 500)
+
+    bg_color = PLATE_TYPES[current_type_idx][0]
+    p = load_params(bg_color)
+    cv2.createTrackbar('H offset', 'Color Tuner', p['h_shift'] + 30, 60, update_display)
+    cv2.createTrackbar('S scale', 'Color Tuner', p['s_scale'], 150, update_display)
+    cv2.createTrackbar('V scale', 'Color Tuner', p['v_scale'], 150, update_display)
+
+    update_display()
+
+    print("=" * 60)
+    print("  车牌底板色手动调色 GUI")
+    print("=" * 60)
+    print("  按键:")
+    print("    1-9   切换车牌类型")
+    print("    R     复位当前类型参数")
+    print("    N     生成新随机车牌")
+    print("    S     保存参数到 plate_color_profile.py")
+    print("    ESC   退出")
+    print("=" * 60)
+    print(f"  当前类型: {PLATE_TYPES[current_type_idx][1]}")
+    print("=" * 60)
+
+    while True:
+        key = cv2.waitKey(50) & 0xFF
+        if key == 27:  # ESC
+            break
+        if key != 255:
+            on_key(key)
+
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    import os
+    os.chdir(str(_PROJECT_DIR))
+    main()

CCPD_SPE_GEN/cv_pipeline/extract_real_plates.py

@@ -0,0 +1,145 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+CCPD 真实车牌 ROI 提取工具
+==========================
+从 CCPD 图片中透视校正并裁剪车牌区域，保存为统一尺寸的 ROI 图片。
+用于 CycleGAN/CUT 风格对齐训练的真实车牌域数据。
+
+用法:
+  python extract_real_plates.py --ccpd-dir ./CCPD/CCPD2019/ccpd_base --num 500 --output ./gan_data/real_plates
+
+输出:
+  440×140 的校正车牌 ROI (BGR .jpg)
+"""
+
+import os
+import sys
+import argparse
+import random
+from pathlib import Path
+from typing import Optional, Tuple
+
+import cv2
+import numpy as np
+from tqdm import tqdm
+
+
+def parse_ccpd_corners(filename: str) -> Optional[np.ndarray]:
+    """
+    从 CCPD 文件名解析4角点，几何排序为 [TL, TR, BR, BL]。
+    """
+    base = Path(filename).stem
+    parts = base.split('-')
+    if len(parts) < 5:
+        return None
+
+    try:
+        coords_str = parts[3]
+        pts = []
+        for pt in coords_str.split('_'):
+            x, y = pt.split('&')
+            pts.append((float(x), float(y)))
+
+        if len(pts) != 4:
+            return None
+
+        a = np.array(pts)
+        sums = a[:, 0] + a[:, 1]
+        diffs = a[:, 0] - a[:, 1]
+
+        tl = a[np.argmin(sums)]
+        tr = a[np.argmax(diffs)]
+        br = a[np.argmax(sums)]
+        bl = a[np.argmin(diffs)]
+
+        return np.float32([tl, tr, br, bl])
+    except (ValueError, IndexError):
+        return None
+
+
+def extract_plate_roi(image_bgr: np.ndarray,
+                       corners: np.ndarray,
+                       output_size: Tuple[int, int] = (440, 140)) -> Optional[np.ndarray]:
+    """
+    透视校正提取车牌 ROI。
+    """
+    h, w = image_bgr.shape[:2]
+    # 校验角点在图像范围内
+    if corners.min() < -10 or corners[:, 0].max() > w + 10 or corners[:, 1].max() > h + 10:
+        return None
+
+    dst = np.float32([
+        [0, 0],
+        [output_size[0] - 1, 0],
+        [output_size[0] - 1, output_size[1] - 1],
+        [0, output_size[1] - 1],
+    ])
+
+    M = cv2.getPerspectiveTransform(corners, dst)
+    roi = cv2.warpPerspective(image_bgr, M, output_size,
+                               flags=cv2.INTER_CUBIC,
+                               borderMode=cv2.BORDER_REPLICATE)
+    return roi
+
+
+def main():
+    parser = argparse.ArgumentParser(description='CCPD 真实车牌 ROI 提取')
+    parser.add_argument('--ccpd-dir', type=str, required=True,
+                        help='CCPD 图片目录（多个用逗号分隔）')
+    parser.add_argument('--num', type=int, default=500,
+                        help='提取数量 (默认: 500)')
+    parser.add_argument('--output', type=str, default='./gan_data/real_plates',
+                        help='输出目录')
+    parser.add_argument('--seed', type=int, default=42)
+
+    args = parser.parse_args()
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+
+    # 收集图片
+    ccpd_dirs = [d.strip() for d in args.ccpd_dir.split(',') if d.strip()]
+    all_images = []
+    for d in ccpd_dirs:
+        dd = Path(d)
+        if not dd.is_dir():
+            print(f"[WARN] 目录不存在: {d}")
+            continue
+        for ext in ('*.jpg', '*.jpeg', '*.png'):
+            all_images.extend(dd.glob(ext))
+
+    random.shuffle(all_images)
+    print(f"CCPD 图片总数: {len(all_images)}")
+    print(f"目标提取数:   {args.num}")
+    print(f"输出目录:     {args.output}")
+    print("-" * 50)
+
+    os.makedirs(args.output, exist_ok=True)
+
+    success = 0
+    for img_path in tqdm(all_images, desc='提取真实车牌ROI', unit='张'):
+        if success >= args.num:
+            break
+
+        corners = parse_ccpd_corners(img_path.name)
+        if corners is None:
+            continue
+
+        img = cv2.imread(str(img_path))
+        if img is None:
+            continue
+
+        roi = extract_plate_roi(img, corners)
+        if roi is None:
+            continue
+
+        out_name = f"real_{img_path.stem}.jpg"
+        cv2.imwrite(os.path.join(args.output, out_name), roi,
+                     [cv2.IMWRITE_JPEG_QUALITY, 95])
+        success += 1
+
+    print(f"\n完成! 提取: {success} 张")
+
+
+if __name__ == '__main__':
+    main()

CCPD_SPE_GEN/cv_pipeline/gen_pure_plates.py

@@ -0,0 +1,45 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+生成纯合成车牌（无场景背景）作为 CUT Domain A
+============================================
+独立于 CCPD 原图，直接调用车牌生成器渲染纯车牌图像。
+"""
+import sys
+import os
+import random
+from pathlib import Path
+
+import cv2
+import numpy as np
+
+# 路径解析：定位 chinese_license_plate_generator
+_SCRIPT_DIR = Path(__file__).resolve().parent  # .../CCPD_SPE_GEN/cv_pipeline/
+_PROJECT_DIR = _SCRIPT_DIR.parent.parent        # .../ICE_Dataset/
+_GENERATOR_DIR = _PROJECT_DIR / 'chinese_license_plate_generator'
+
+if str(_GENERATOR_DIR) not in sys.path:
+    sys.path.insert(0, str(_GENERATOR_DIR))
+
+from generate_multi_plate import MultiPlateGenerator
+
+random.seed(42)
+np.random.seed(42)
+
+g = MultiPlateGenerator(
+    str(_GENERATOR_DIR / 'plate_model'),
+    str(_GENERATOR_DIR / 'font_model')
+)
+
+out = 'gan_data/domain_A_synthetic/police_pure'
+os.makedirs(out, exist_ok=True)
+
+for i in range(500):
+    num, bg, _ = g.generate_plate_number()
+    # 强制police类型
+    if '警' not in num:
+        num = num[:6] + '警'
+    img = g.generate_plate_special(num, 'white', False)
+    cv2.imwrite(f'{out}/synth_{i:04d}.jpg', img)
+
+print(f'Done: {out} ({len(os.listdir(out))} images)')

CCPD_SPE_GEN/cv_pipeline/plate_color_profile.py

@@ -0,0 +1,12 @@
+# Auto-generated by color_tuner.py
+# Plate background color correction profiles
+# Format: (h_shift, s_scale_percent, v_scale_percent)
+
+PLATE_COLOR_PROFILE = {
+    'yellow': (-2, 150, 100),
+    'green_truck': (0, 100, 100),
+    'white': (0, 100, 100),
+    'white_army': (0, 100, 100),
+    'black': (0, 100, 100),
+    'black_shi': (0, 100, 100),
+}