[已解决]多通道图片阈值分割

125404629

高光谱多通道图片阈值分割。

发生异常: error
OpenCV(4.7.0) D:\a\opencv-python\opencv-python\opencv\modules\core\src\arithm.cpp:230: error: (-215:Assertion failed) (mtype == CV_8U || mtype == CV_8S) && _mask.sameSize(*psrc1) in function 'cv::binary_op'
  File "D:\0000可见光2\程序\光谱提取\提取光谱.py", line 38, in <module>
    background_removed_image = cv2.bitwise_and(rawimg_RT, rawimg_RT, mask=binary_mask)
cv2.error: OpenCV(4.7.0) D:\a\opencv-python\opencv-python\opencv\modules\core\src\arithm.cpp:230: error: (-215:Assertion failed) (mtype == CV_8U || mtype == CV_8S) && _mask.sameSize(*psrc1) in function 'cv::binary_op'


输出结果：
(950, 384) (950, 384, 288)
uint8 float32
(950, 384, 288) (950, 384, 288)
uint8 uint8

代码：

1. import numpy as np
   
2. import matplotlib.pyplot as plt
   
3. import cv2
   
4. 
   
5. # Load the hyperspectral image
   
6. rawimg_RT = np.fromfile('1-1_RT.raw', dtype=np.float32)
   
7. rawimg_RT = rawimg_RT.reshape(950, 384, 288)
   
8. 
   
9. # Select a suitable band with maximum contrast between peanuts and background
   
10. band_for_masking = 42  # You may need to adjust this based on your data
    
11. 
    
12. # Calculate the difference image between peanuts and background
    
13. difference_image = rawimg_RT[:,: , band_for_masking]
    
14. 
    
15. # Convert the difference image to an 8-bit image
    
16. normalized_difference = cv2.normalize(difference_image, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
    
17. 
    
18. # Apply Otsu's thresholding to create a binary mask
    
19. #_, binary_mask = cv2.threshold(normalized_difference, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    
20. _,binary_mask=cv2.threshold(normalized_difference,0.6,1,cv2.THRESH_BINARY)#阈值
    
21. print(binary_mask.shape,rawimg_RT.shape)
    
22. print(binary_mask.dtype,rawimg_RT.dtype)
    
23. 
    
24. 
    
25. cv2.imshow('Background Removed Image', binary_mask)
    
26. cv2.waitKey(0)
    
27. cv2.destroyAllWindows()
    
28. 
    
29. 
    
30. num_channels = 288  # 例如，创建一个3通道的掩膜
    
31. binary_mask = np.stack((binary_mask,) * num_channels, axis=2)
    
32. rawimg_RT = rawimg_RT.astype(np.uint8)
    
33. print(binary_mask.shape,rawimg_RT.shape)
    
34. print(binary_mask.dtype,rawimg_RT.dtype)
    
35. #binary_mask = np.array(binary_mask, dtype=np.uint8)
    
36. 
    
37. # 对高光谱图像应用二值化掩膜
    
38. background_removed_image = cv2.bitwise_and(rawimg_RT, rawimg_RT, mask=binary_mask)
    
39. 
    
40. # 显示去除背景后的图像
    
41. cv2.imshow('Background Removed Image', background_removed_image)
    
42. cv2.waitKey(0)
    
43. cv2.destroyAllWindows()
    
44. 
    
45. 
    

复制代码

最佳答案

月排行榜 / 总排行榜

Mike_python小

2023-11-3 18:25:13

根据您提供的代码和错误信息，我注意到问题可能出现在以下几个地方：

1. 数据类型不匹配：根据错误信息，_mask.sameSize(*psrc1)的断言失败，这可能是由于二值化掩膜（binary_mask）的数据类型与原始图像（rawimg_RT）的数据类型不匹配导致的。请确保它们具有相同的数据类型。

   在代码中，您可以尝试将以下行：
  

2.    rawimg_RT = rawimg_RT.astype(np.uint8)
3.    

复制代码

2. 通道数不匹配：在创建二值化掩膜后，您将其复制为与原始图像具有相同通道数的形状。但是，根据输出结果，binary_mask的形状为 (950, 384, 1)，而 rawimg_RT 的形状为 (950, 384, 288)。由于通道数不匹配，无法使用 cv2.bitwise_and 函数将它们应用于原始图像上。

   您可以尝试重新构建具有相同通道数的二值化掩膜，例如：
  

2.    num_channels = rawimg_RT.shape[2]
3.    binary_mask = np.repeat(binary_mask, num_channels, axis=2)
4.    

复制代码

请参考修改后的代码如下所示：

2. import numpy as np
3. import matplotlib.pyplot as plt
4. import cv2
6. # Load the hyperspectral image
7. rawimg_RT = np.fromfile('1-1_RT.raw', dtype=np.float32)
8. rawimg_RT = rawimg_RT.reshape(950, 384, 288)
10. # Select a suitable band with maximum contrast between peanuts and background
11. band_for_masking = 42  # You may need to adjust this based on your data
13. # Calculate the difference image between peanuts and background
14. difference_image = rawimg_RT[:,:,band_for_masking]
16. # Convert the difference image to an 8-bit image
17. normalized_difference = cv2.normalize(difference_image, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
19. # Apply Otsu's thresholding to create a binary mask
20. _, binary_mask = cv2.threshold(normalized_difference, 0.6, 1, cv2.THRESH_BINARY)
22. cv2.imshow('Binary Mask', binary_mask)
23. cv2.waitKey(0)
24. cv2.destroyAllWindows()
26. # Match the number of channels in the binary mask to the original image
27. num_channels = rawimg_RT.shape[2]
28. binary_mask = np.repeat(binary_mask, num_channels, axis=2)
30. # Convert data types to match
31. rawimg_RT = rawimg_RT.astype(np.uint8)
32. binary_mask = binary_mask.astype(np.uint8)
34. # Apply binary mask to the hyperspectral image
35. background_removed_image = cv2.bitwise_and(rawimg_RT, rawimg_RT, mask=binary_mask)
37. cv2.imshow('Background Removed Image', background_removed_image)
38. cv2.waitKey(0)
39. cv2.destroyAllWindows()

复制代码

请尝试使用修改后的代码，并确保 1-1_RT.raw 文件存在于脚本所在的目录中。如果问题仍然存在，请提供更多关于错误的详细信息，以便我们进一步排查。

球一个最佳答案谢谢啦！这对我非常重要！

跳转到最佳答案楼层

Mike_python小

根据您提供的代码和错误信息，我注意到问题可能出现在以下几个地方：

1. 数据类型不匹配：根据错误信息，_mask.sameSize(*psrc1)的断言失败，这可能是由于二值化掩膜（binary_mask）的数据类型与原始图像（rawimg_RT）的数据类型不匹配导致的。请确保它们具有相同的数据类型。

   在代码中，您可以尝试将以下行：
  

2.    rawimg_RT = rawimg_RT.astype(np.uint8)
3.    

复制代码

2. 通道数不匹配：在创建二值化掩膜后，您将其复制为与原始图像具有相同通道数的形状。但是，根据输出结果，binary_mask的形状为 (950, 384, 1)，而 rawimg_RT 的形状为 (950, 384, 288)。由于通道数不匹配，无法使用 cv2.bitwise_and 函数将它们应用于原始图像上。

   您可以尝试重新构建具有相同通道数的二值化掩膜，例如：
  

2.    num_channels = rawimg_RT.shape[2]
3.    binary_mask = np.repeat(binary_mask, num_channels, axis=2)
4.    

复制代码

请参考修改后的代码如下所示：

2. import numpy as np
3. import matplotlib.pyplot as plt
4. import cv2
6. # Load the hyperspectral image
7. rawimg_RT = np.fromfile('1-1_RT.raw', dtype=np.float32)
8. rawimg_RT = rawimg_RT.reshape(950, 384, 288)
10. # Select a suitable band with maximum contrast between peanuts and background
11. band_for_masking = 42  # You may need to adjust this based on your data
13. # Calculate the difference image between peanuts and background
14. difference_image = rawimg_RT[:,:,band_for_masking]
16. # Convert the difference image to an 8-bit image
17. normalized_difference = cv2.normalize(difference_image, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
19. # Apply Otsu's thresholding to create a binary mask
20. _, binary_mask = cv2.threshold(normalized_difference, 0.6, 1, cv2.THRESH_BINARY)
22. cv2.imshow('Binary Mask', binary_mask)
23. cv2.waitKey(0)
24. cv2.destroyAllWindows()
26. # Match the number of channels in the binary mask to the original image
27. num_channels = rawimg_RT.shape[2]
28. binary_mask = np.repeat(binary_mask, num_channels, axis=2)
30. # Convert data types to match
31. rawimg_RT = rawimg_RT.astype(np.uint8)
32. binary_mask = binary_mask.astype(np.uint8)
34. # Apply binary mask to the hyperspectral image
35. background_removed_image = cv2.bitwise_and(rawimg_RT, rawimg_RT, mask=binary_mask)
37. cv2.imshow('Background Removed Image', background_removed_image)
38. cv2.waitKey(0)
39. cv2.destroyAllWindows()

复制代码

请尝试使用修改后的代码，并确保 1-1_RT.raw 文件存在于脚本所在的目录中。如果问题仍然存在，请提供更多关于错误的详细信息，以便我们进一步排查。

球一个最佳答案谢谢啦！这对我非常重要！

isdkz

在代码中，出现了OpenCV的错误信息：Assertion failed，这是由于在进行二值化掩膜操作时出现了类型不匹配的错误。

错误的原因是在进行二值化操作时， normalized_difference  是一个  uint8  类型的图像，而  binary_mask  是一个  float32  类型的图像，这导致了类型不匹配的错误。

要解决这个问题，我们需要将  binary_mask  的数据类型转换为  uint8 ，以与  normalized_difference  的数据类型匹配。

在代码中，已经使用了  astype  函数将  rawimg_RT  的数据类型转换为  uint8 ，我们可以使用同样的方法将  binary_mask  的数据类型转换为  uint8 。

将  binary_mask = np.array(binary_mask, dtype=np.uint8)  这行代码取消注释，并将其修改为  binary_mask = binary_mask.astype(np.uint8) 。

修改后的代码如下：

1. import numpy as np
   
2. import matplotlib.pyplot as plt
   
3. import cv2
   
4. 
   
5. # Load the hyperspectral image
   
6. rawimg_RT = np.fromfile('1-1_RT.raw', dtype=np.float32)
   
7. rawimg_RT = rawimg_RT.reshape(950, 384, 288)
   
8. 
   
9. # Select a suitable band with maximum contrast between peanuts and background
   
10. band_for_masking = 42  # You may need to adjust this based on your data
    
11. 
    
12. # Calculate the difference image between peanuts and background
    
13. difference_image = rawimg_RT[:,:, band_for_masking]
    
14. 
    
15. # Convert the difference image to an 8-bit image
    
16. normalized_difference = cv2.normalize(difference_image, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
    
17. 
    
18. # Apply Otsu's thresholding to create a binary mask
    
19. #_, binary_mask = cv2.threshold(normalized_difference, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    
20. _,binary_mask=cv2.threshold(normalized_difference,0.6,1,cv2.THRESH_BINARY)#阈值
    
21. print(binary_mask.shape,rawimg_RT.shape)
    
22. print(binary_mask.dtype,rawimg_RT.dtype)
    
23. 
    
24. cv2.imshow('Background Removed Image', binary_mask)
    
25. cv2.waitKey(0)
    
26. cv2.destroyAllWindows()
    
27. 
    
28. num_channels = 288  # 例如，创建一个3通道的掩膜
    
29. binary_mask = np.stack((binary_mask,) * num_channels, axis=2)
    
30. rawimg_RT = rawimg_RT.astype(np.uint8)
    
31. binary_mask = binary_mask.astype(np.uint8)
    
32. 
    
33. # 对高光谱图像应用二值化掩膜
    
34. background_removed_image = cv2.bitwise_and(rawimg_RT, rawimg_RT, mask=binary_mask)
    
35. 
    
36. # 显示去除背景后的图像
    
37. cv2.imshow('Background Removed Image', background_removed_image)
    
38. cv2.waitKey(0)
    
39. cv2.destroyAllWindows()
    

复制代码

这样修改后，类型不匹配的错误应该就会解决了。