numpy.resize throws valueError despite image matrix product equaling the total image size-CodePudding

I am trying to resize a grayscale image into a numpy array like so:

return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)

and getting this error:

ValueError: cannot reshape array of size 1909760 into shape (1024,1865,3)

I've read that the product of an images columns and rows (1024 x 1865) is supposed to equal the size of the array being reshaped - (1909760) which it does. I've also tried the same code on images with three channels and it works.

CodePudding user response：

If you're using the PIL module for your image, you could try converting it to an RGB before getting the data. Something like this should work:

image = image.convert("RGB")
return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)

This works because when you convert from a grayscale to an RGB, PIL automatically sets each pixel to have three values, an R, G, and B.

CodePudding user response：

Do not use .getdata(). That's pointless and a waste of effort. What'll happen is that a python list of integers is constructed as an intermediate. Directly converting to a numpy array is much more efficient.

Just use this:

# image = Image.open(...)
image_array = np.array(image)

Secondly you need to handle the conversion from grayscale to RGB, which you seem to want. Your PIL image appears to be grayscale, yet you want a numpy array with three channels (third dimension sized 3). You can either use PIL to convert, or you can use OpenCV.

PIL: image = image.convert("RGB") before converting to numpy (thanks Timmy Diehl, I don't use PIL that often)

OpenCV: image_array = cv.cvtColor(image_array, cv.COLOR_GRAY2BGR) after converting to numpy

Also note the order of color channels. PIL prefers RGB. OpenCV prefers BGR. What you need depends on what you'll do with the numpy array.