def process_image()

in services/read-gauge-ml.py [0:0]

• 21 lines of code
• 4 McCabe index (conditional complexity)

def process_image(img):
    
    # Get the dimensions of the image
    width, height = img.size
    
    
    #test RGB conversion
    img = img.convert('RGB')
    
    # Resize by keeping the aspect ratio, but changing the dimension
    # so the shortest size is 255px
    img = img.resize((255, int(255*(height/width))) if width < height else (int(255*(width/height)), 255))
    
    # Get the dimensions of the new image size
    width, height = img.size
    
    # Set the coordinates to do a center crop of 224 x 224
    left = (width - 224)/2
    top = (height - 224)/2
    right = (width + 224)/2
    bottom = (height + 224)/2
    img = img.crop((left, top, right, bottom))
    
    # Turn image into numpy array
    img = np.array(img)
    
    # Make the color channel dimension first instead of last
    img = img.transpose((2, 0, 1))
    
    # Make all values between 0 and 1
    img = img/255
    
    # Normalize based on the preset mean and standard deviation
    img[0] = (img[0] - 0.485)/0.229
    img[1] = (img[1] - 0.456)/0.224
    img[2] = (img[2] - 0.406)/0.225
    
    # Add a fourth dimension to the beginning to indicate batch size
    img = img[np.newaxis,:]
    
    # Turn into a torch tensor
    image = torch.from_numpy(img)
    image = image.float()
    print("image processed")
    return image