使用 OpenCV Python 的 2D 图像中的深度错误
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Depth error in 2D image with OpenCV Python
提问by jmerkow
I am trying to compute the Canny Edges in an image (ndarray) using OpenCV with Python.
我正在尝试使用 OpenCV 和 Python 计算图像（ndarray）中的 Canny Edge。
slice1 = slices[15,:,:]
slice1 = slice1[40:80,60:100]
print slice1.shape
print slice1.dtype
slicecanny = cv2.Canny(slice1, 1, 100)
Output:
输出：
(40, 40)
float64
...
error: /Users/jmerkow/code/opencv-2.4.6.1/modules/imgproc/src/canny.cpp:49:
error: (-215) src.depth() == CV_8U in function Canny
For some reason I get the above error. Any ideas why?
出于某种原因，我收到了上述错误。任何想法为什么？
回答by rifkinni
You can work around this error by saving slice1 to a file and then reading it
您可以通过将 slice1 保存到文件然后读取它来解决此错误
from scipy import ndimage, misc
misc.imsave('fileName.jpg', slice1)
image = ndimage.imread('fileName.jpg',0)
slicecanny = cv2.Canny(image,1,100)
This is not the most elegant solution, but it solved the problem for me
这不是最优雅的解决方案，但它为我解决了问题
回答by Ross
Slice1 will need to be casted or created as a uint8. CV_8U is just an alias for the datatype uint8.
Slice1 需要转换或创建为 uint8。CV_8U 只是数据类型 uint8 的别名。
import numpy as np
slice1Copy = np.uint8(slice1)
slicecanny = cv2.Canny(slice1Copy,1,100)
回答by Toda
In order to avoid losing precision while changing the data type to uint8, you can first adapt the scale to the 255 format just doing:
为了避免在将数据类型更改为 uint8 时丢失精度，您可以先将比例调整为 255 格式，只需执行以下操作：
(image*255).astype(np.uint8)
Here I'm considering that image is a numpy array and np stand for numpy. I hope it can help someone!
在这里，我认为该图像是一个 numpy 数组，而 np 代表 numpy。我希望它可以帮助某人！
