import numpy as np
import cv2

def create_depth_difference_mask(depth1: np.ndarray, depth2: np.ndarray, 
                               threshold: float = 0.1,
                               gaussian_kernel: int = 5,
                               min_region_size: int = 50) -> tuple[np.ndarray, np.ndarray]:
    """
    创建两个深度图之间差异的掩码图像，返回原始掩码和滤波后的掩码
    
    Args:
        depth1: 第一个深度图
        depth2: 第二个深度图
        threshold: 判定为不一致的深度差异阈值
        gaussian_kernel: 高斯滤波核大小
        min_region_size: 最小连通区域大小，小于该值的区域将被过滤掉
        
    Returns:
        tuple[np.ndarray, np.ndarray]: 
            - raw_mask: 原始差异掩码，未经滤波处理
            - filtered_mask: 经过滤波处理后的差异掩码
    """
    # 创建有效区域的掩码（两个深度图都有有效值的区域）
    valid_mask = (depth1 > 0) & (depth2 > 0)
    
    # 计算深度差异
    diff = np.abs(depth1 - depth2)
    diff[~valid_mask] = 0
    
    # 创建原始差异掩码
    raw_mask = np.zeros_like(depth1, dtype=np.uint8)
    raw_mask[valid_mask & (diff > threshold)] = 255
    
    # 对原始掩码进行高斯滤波
    filtered_mask = cv2.GaussianBlur(raw_mask, (gaussian_kernel, gaussian_kernel), 0)
    
    # 重新二值化
    _, filtered_mask = cv2.threshold(filtered_mask, 127, 255, cv2.THRESH_BINARY)
    
    # 应用形态学操作
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
    filtered_mask = cv2.morphologyEx(filtered_mask, cv2.MORPH_OPEN, kernel)  # 开运算去除小噪点
    filtered_mask = cv2.morphologyEx(filtered_mask, cv2.MORPH_CLOSE, kernel)  # 闭运算填充小孔
    
    # 连通区域分析和过滤
    if min_region_size > 0:
        num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(filtered_mask, connectivity=8)
        for i in range(1, num_labels):  # 从1开始，跳过背景
            if stats[i, cv2.CC_STAT_AREA] < min_region_size:
                filtered_mask[labels == i] = 0
    
    return raw_mask, filtered_mask