Function _validate_cv_rectangle

modules/basic_shape_processor.py:757–814

验证CV检测到的矩形是否有效 检查内容： 1. 内部颜色是否有足够变化（排除纯色背景误检） 2. 边框与内部是否有明显区别 :param cv2_image: BGR图像 :param bbox: [x1, y1, x2, y2] :param min_std: 最小颜色标准差 :return: True=有效, False=可能是误检

(cv2_image: np.ndarray, bbox: list, min_std: float = 8)

# ======================== CV结果验证 ========================
def _validate_cv_rectangle(cv2_image: np.ndarray, bbox: list, min_std: float = 8) -> bool:
    """
    验证CV检测到的矩形是否有效
    
    检查内容：
    1. 内部颜色是否有足够变化（排除纯色背景误检）
    2. 边框与内部是否有明显区别
    
    :param cv2_image: BGR图像
    :param bbox: [x1, y1, x2, y2]
    :param min_std: 最小颜色标准差
    :return: True=有效, False=可能是误检
    """
    x1, y1, x2, y2 = map(int, bbox)
    h, w = cv2_image.shape[:2]
    
    # 边界检查
    x1, y1 = max(0, x1), max(0, y1)
    x2, y2 = min(w, x2), min(h, y2)
    
    if x2 - x1 < 20 or y2 - y1 < 20:
        return False
    
    roi = cv2_image[y1:y2, x1:x2]
    gray_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    
    # 检查1：内部是否有足够的颜色变化
    roi_h, roi_w = gray_roi.shape
    margin = max(3, min(roi_w, roi_h) // 10)
    
    if roi_h > 2 * margin and roi_w > 2 * margin:
        inner = gray_roi[margin:-margin, margin:-margin]
        inner_std = np.std(inner)
        
        # 如果内部颜色太均匀，可能是误检的背景区域
        if inner_std < min_std:
            return False
    
    # 检查2：边框与内部是否有对比度
    border_size = max(2, min(roi_w, roi_h) // 20)
    
    if roi_h > 2 * border_size and roi_w > 2 * border_size:
        border_top = gray_roi[:border_size, :].mean()
        border_bottom = gray_roi[-border_size:, :].mean()
        border_left = gray_roi[:, :border_size].mean()
        border_right = gray_roi[:, -border_size:].mean()
        border_mean = np.mean([border_top, border_bottom, border_left, border_right])
        
        inner_region = gray_roi[border_size:-border_size, border_size:-border_size]
        inner_mean = inner_region.mean()
        
        contrast = abs(border_mean - inner_mean)
        
        # 边框和内部需要有一定对比度
        if contrast < 5:
            return False
    
    return True