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