fix_hsv.py

import cv2
import numpy as np
from pathlib import Path

def nothing(x):
    """滑块回调函数"""
    pass

def debug_hsv_color(image_paths):
    """
    交互式HSV调试工具，用于找到最佳的颜色检测范围
    支持多张图片同时显示对比

    使用方法：
    - 移动滑块调整颜色范围
    - 找到最佳范围后，记录下数值
    - 按 ESC 退出
    """
    # 读取所有图片
    images = []
    hsv_images = []
    for img_path in image_paths:
        img = cv2.imread(img_path)
        if img is None:
            print(f"无法读取图像: {img_path}")
            continue
        images.append((img_path, img))
        hsv_images.append(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))

    if not images:
        print("没有成功读取到任何图片")
        return

    # 创建窗口和滑块
    cv2.namedWindow('HSV Mask Debugger')

    cv2.createTrackbar('H_Low', 'HSV Mask Debugger', 90, 180, nothing)
    cv2.createTrackbar('H_High', 'HSV Mask Debugger', 130, 180, nothing)
    cv2.createTrackbar('S_Low', 'HSV Mask Debugger', 50, 255, nothing)
    cv2.createTrackbar('S_High', 'HSV Mask Debugger', 255, 255, nothing)
    cv2.createTrackbar('V_Low', 'HSV Mask Debugger', 100, 255, nothing)
    cv2.createTrackbar('V_High', 'HSV Mask Debugger', 255, 255, nothing)

    print("HSV调试工具已启动")
    print("调整滑块找到最佳的颜色范围")
    print("操作说明:")
    print("- 按 'S' 保存当前HSV范围配置")
    print("- 按 'Q' 退出程序")
    print(f"当前显示 {len(images)} 张图片")

    def resize_to_fit(image, num_images, max_total_width=1800, max_total_height=1200):
        """
        根据图片数量动态调整单张图片大小，确保1-6张图片都有良好显示效果

        参数:
            image: 输入图像
            num_images: 图片总数
            max_total_width: 总显示宽度上限
            max_total_height: 总显示高度上限
        """
        h, w = image.shape[:2]

        # 根据图片数量调整单张图片的最大尺寸
        if num_images <= 2:
            # 1-2张图片，较大尺寸
            max_width = 600
            max_height = 450
        elif num_images <= 4:
            # 3-4张图片，中等尺寸
            max_width = 500
            max_height = 375
        else:
            # 5-6张图片，较小尺寸
            max_width = 400
            max_height = 300

        # 计算缩放因子
        scale = min(max_width/w, max_height/h, 1.0)

        if scale < 1.0:
            new_w = int(w * scale)
            new_h = int(h * scale)
            return cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)
        return image

    def create_display_grid(images, masks):
        """
        创建1-6张图片的网格显示，优化布局效果
        """
        display_rows = []
        num_images = len(images)

        if num_images == 0:
            return np.zeros((100, 100, 3), dtype=np.uint8)

        # 为每张图片创建原图+掩码的水平组合
        combined_images = []
        for (img_path, img), mask in zip(images, masks):
            # 动态调整大小
            resized_img = resize_to_fit(img, num_images)
            resized_mask = resize_to_fit(cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR), num_images)

            # 添加图片名称和统计信息
            img_with_name = resized_img.copy()
            mask_pixels = cv2.countNonZero(mask)
            total_pixels = mask.shape[0] * mask.shape[1]
            coverage = f"{mask_pixels / total_pixels * 100:.1f}"

            cv2.putText(img_with_name, f"{Path(img_path).name}", (10, 25),
                       cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
            cv2.putText(img_with_name, f"覆盖率: {coverage}%", (10, 50),
                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)

            # 水平组合原图和掩码
            combined = np.hstack([img_with_name, resized_mask])
            combined_images.append(combined)

        # 根据图片数量优化网格布局
        if num_images <= 2:
            max_cols = 1  # 1-2张图片，每行1组
        else:
            max_cols = 2  # 3-6张图片，每行2组

        num_rows = (num_images + max_cols - 1) // max_cols

        # 为每行创建组合
        for i in range(num_rows):
            row_images = combined_images[i*max_cols : (i+1)*max_cols]

            # 确保所有行图像高度相同
            max_height = max(img.shape[0] for img in row_images)

            # 调整行内图像高度
            adjusted_row = []
            for img in row_images:
                h, w = img.shape[:2]
                if h < max_height:
                    # 垂直填充，保持居中
                    pad_top = (max_height - h) // 2
                    pad_bottom = max_height - h - pad_top
                    img = cv2.copyMakeBorder(img, pad_top, pad_bottom, 0, 0,
                                           cv2.BORDER_CONSTANT, value=(20, 20, 20))
                adjusted_row.append(img)

            # 水平组合行内图像
            row_combined = np.hstack(adjusted_row)
            display_rows.append(row_combined)

        # 确保所有行宽度相同，否则无法垂直堆叠
        if len(display_rows) > 1:
            max_row_width = max(row.shape[1] for row in display_rows)

            # 为每行添加左右填充，使宽度一致
            for i in range(len(display_rows)):
                row = display_rows[i]
                h, w = row.shape[:2]
                if w < max_row_width:
                    # 水平居中填充
                    pad_left = (max_row_width - w) // 2
                    pad_right = max_row_width - w - pad_left
                    row = cv2.copyMakeBorder(row, 0, 0, pad_left, pad_right,
                                           cv2.BORDER_CONSTANT, value=(20, 20, 20))
                    display_rows[i] = row

        # 垂直组合所有行
        final_display = np.vstack(display_rows)

        return final_display

    # 保存HSV配置的函数
    def save_hsv_config():
        """保存当前HSV范围配置"""
        # 读取当前滑块值
        h_current = cv2.getTrackbarPos('H_Low', 'HSV Mask Debugger')
        h_high_current = cv2.getTrackbarPos('H_High', 'HSV Mask Debugger')
        s_current = cv2.getTrackbarPos('S_Low', 'HSV Mask Debugger')
        s_high_current = cv2.getTrackbarPos('S_High', 'HSV Mask Debugger')
        v_current = cv2.getTrackbarPos('V_Low', 'HSV Mask Debugger')
        v_high_current = cv2.getTrackbarPos('V_High', 'HSV Mask Debugger')

        # 保存所有图片的最终掩码
        Path('output').mkdir(exist_ok=True)
        masks_current = []
        for hsv_img in hsv_images:
            mask = cv2.inRange(hsv_img, np.array([h_current, s_current, v_current]),
                             np.array([h_high_current, s_high_current, v_high_current]))
            masks_current.append(mask)

        for i, ((img_path, _), mask) in enumerate(zip(images, masks_current)):
            img_name = Path(img_path).stem
            mask_path = f'output/{img_name}_best_mask.jpg'
            cv2.imwrite(mask_path, mask)
            print(f"✓ {img_name} 的最佳掩码已保存到 {mask_path}")

        # 保存HSV范围到文件，供main.py使用
        hsv_config = {
            'lower_blue1': [h_current, s_current, v_current],
            'upper_blue1': [h_high_current, s_high_current, v_high_current],
            'lower_blue2': [h_current, s_current, v_current],  # 可根据需要调整第二个范围
            'upper_blue2': [h_high_current, s_high_current, v_high_current]   # 可根据需要调整第二个范围
        }

        # 直接保存JSON配置
        import json
        hsv_config_file = Path('output') / 'hsv_config.json'
        with open(hsv_config_file, 'w', encoding='utf-8') as f:
            json.dump(hsv_config, f, ensure_ascii=False, indent=2)
        print(f"✓ HSV范围已保存到 {hsv_config_file}")

    while True:
        # 读取滑块值
        h_low = cv2.getTrackbarPos('H_Low', 'HSV Mask Debugger')
        h_high = cv2.getTrackbarPos('H_High', 'HSV Mask Debugger')
        s_low = cv2.getTrackbarPos('S_Low', 'HSV Mask Debugger')
        s_high = cv2.getTrackbarPos('S_High', 'HSV Mask Debugger')
        v_low = cv2.getTrackbarPos('V_Low', 'HSV Mask Debugger')
        v_high = cv2.getTrackbarPos('V_High', 'HSV Mask Debugger')

        lower = np.array([h_low, s_low, v_low])
        upper = np.array([h_high, s_high, v_high])

        # 为所有图片创建掩码
        masks = []
        for hsv_img in hsv_images:
            mask = cv2.inRange(hsv_img, lower, upper)
            masks.append(mask)

        # 创建网格显示
        display = create_display_grid(images, masks)

        # 添加全局HSV参数信息
        text = f"Lower: [{h_low}, {s_low}, {v_low}]  Upper: [{h_high}, {s_high}, {v_high}]"
        cv2.putText(display, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)

        # 添加操作提示
        cv2.putText(display, "操作提示: Q-退出, S-保存配置", (10, display.shape[0]-20),
                   cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1)

        cv2.imshow('HSV Mask Debugger', display)

        key = cv2.waitKey(1) & 0xFF
        if key == ord('q') or key == ord('Q'):  # Q 退出
            break
        elif key == ord('s') or key == ord('S'):  # S 保存配置
            save_hsv_config()

    # 最终结果
    print(f"\n最佳范围找到:")
    print(f"lower_blue = np.array([{h_low}, {s_low}, {v_low}])")
    print(f"upper_blue = np.array([{h_high}, {s_high}, {v_high}])")

    # 自动保存最终配置
    save_hsv_config()

    cv2.destroyAllWindows()


if __name__ == "__main__":
    # 示例：支持1-6张图片的演示
    # 以下是不同数量图片的使用示例，根据需要取消注释相应行

    # 1张图片
    # image_paths = ['ticket.jpg']

    # 2张图片
    image_paths = ['ticket.jpg', 'ticket1.jpg']

    # 3张图片（当前使用）
    # image_paths = ['ticket.jpg', 'ticket1.jpg', 'ticket3.jpg']

    # 4张图片
    # image_paths = ['ticket.jpg', 'ticket1.jpg', 'ticket2.jpg', 'ticket3.jpg']

    # 5张图片
    # image_paths = ['ticket.jpg', 'ticket1.jpg', 'ticket3.jpg', 'ticket.jpg', 'ticket1.jpg']

    # 6张图片
    # image_paths = ['ticket.jpg', 'ticket1.jpg', 'ticket3.jpg', 'ticket.jpg', 'ticket1.jpg', 'ticket3.jpg']

    debug_hsv_color(image_paths)