How to match cv2.imread to the keras image.img_load output

Question

I'm studying deep learning. Trained an image classification algorithm. The problem is, however, that to train images I used:

test_image = image.load_img('some.png', target_size = (64, 64))
test_image = image.img_to_array(test_image)

While for actual application I use:

test_image = cv2.imread('trick.png')
test_image = cv2.resize(test_image, (64, 64))

But I found that those give a different ndarray (different data):

Last entries from load_image:

  [ 64.  71.  66.]
  [ 64.  71.  66.]
  [ 62.  69.  67.]]]

Last entries from cv2.imread:

  [ 15  23  27]
  [ 16  24  28]
  [ 14  24  28]]]

, so the system is not working. Is there a way to match results of one to another?

Answer 1

OpenCV reads images in BGR format whereas in keras, it is represented in RGB. To get the OpenCV version to correspond to the order we expect (RGB), simply reverse the channels:

test_image = cv2.imread('trick.png')
test_image = cv2.resize(test_image, (64, 64))
test_image = test_image[...,::-1] # Added

The last line reverses the channels to be in RGB order. You can then feed this into your keras model.

Another point I'd like to add is that cv2.imread usually reads in images in uint8 precision. Examining the output of your keras loaded image, you can see that the data is in floating point precision so you may also want to convert to a floating-point representation, such as float32:

import numpy as np
# ...
# ...
test_image = test_image[...,::-1].astype(np.float32)

As a final point, depending on how you trained your model it's usually customary to normalize the image pixel values to a [0,1] range. If you did this with your keras model, make sure you divide your values by 255 in your image read in through OpenCV:

import numpy as np
# ...
# ...
test_image = (test_image[...,::-1].astype(np.float32)) / 255.0