Preprocessing the images

The images for the different classes will be stored in different folders, so it will be easy to label their classes. We will read the images using Opencv functions, and will resize them to different dimensions, such as 224 x 224 x 3. We'll subtract the mean pixel intensity channel-wise from each of the images, based on the ImageNet dataset. This means subtraction will bring the diabetic retinopathy images to the same intensity range as that of the processed ImageNet images, on which the pre-trained models are trained. Once each image has been prepossessed, they will be stored in a numpy array. The image preprocessing functions can be defined as follows:

def get_im_cv2(path,dim=224):
     img = cv2.imread(path)
     resized = cv2.resize(img, (dim,dim), cv2.INTER_LINEAR)
     return resized
 
 def pre_process(img):
     img[:,:,0] =     img[:,:,0] - 103...