import cv2 import numpy as np import torch from skimage.segmentation import slic from skimage.util import img_as_float # 读取A图像和B图像 img_a = cv2.imread(r'D:\Users\Administrator\PycharmProjects\pythonProject\my tools\super_pixel\1.png') img_b = cv2.imread(r'D:\Users\Administrator\PycharmProjects\pythonProject\my tools\super_pixel\2.jpg') # 转换为浮点数 img_a = img_as_float(img_a) img_b = img_as_float(img_b) # 使用SLIC算法进行超像素分割 segments_a = slic(img_a, n_segments=1000, compactness=10) segments_b = slic(img_b, n_segments=1000, compactness=10) # 计算A图像的超像素范围 segment_ids = np.unique(segments_a) segment_ranges = [] for segment_id in segment_ids: y, x = np.where(segments_a == segment_id) min_x, max_x = np.min(x), np.max(x) min_y, max_y = np.min(y), np.max(y) segment_ranges.append((min_x, min_y, max_x, max_y)) # 创建A图像的超像素范围图 segment_map_a = np.zeros_like(segments_a, dtype=np.int32) for i, segment_range in enumerate(segment_ranges): min_x, min_y, max_x, max_y = segment_range segment_map_a[min_y:max_y+1, min_x:max_x+1] = i # 使用A图像的超像素范围索引对B图像进行分割 segment_map_b = np.zeros_like(segments_b, dtype=np.int32) for i, segment_range in enumerate(segment_ranges): min_x, min_y, max_x, max_y = segment_range segment_id = segments_a[min_y, min_x] y, x = np.where(segments_b == segment_id) segment_map_b[y, x] = i # 转换为PyTorch张量 segment_map_b = torch.Tensor(segment_map_b).long() # 显示B图像的超像素范围图 cv2.imshow('Segment Map', segment_map_b.numpy()) cv2.waitKey(0) cv2.destroyAllWindows()。上述代码出现错误: cv2.imshow('Segment Map', segment_map_b.numpy()) cv2.error: OpenCV(4.7.0) D:/a/opencv-python/opencv-python/opencv/modules/highgui/src/precomp.hpp:155: error: (-215:Assertion failed) src_depth != CV_16F && src_depth != CV_32S in function 'convertToShow'
时间: 2024-03-09 17:49:46 浏览: 74
这个错误是因为在使用cv2.imshow()函数时,需要传入的图像是8位无符号整数类型,而segment_map_b是一个PyTorch张量,需要将其转换为NumPy数组并将其数据类型转换为np.uint8类型。你可以使用以下代码将张量转换为NumPy数组并将其数据类型转换为np.uint8类型:
```
cv2.imshow('Segment Map', segment_map_b.numpy().astype(np.uint8))
```
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