classifier-imageprocess

# process image

def process_image(image, width, height):
    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns an torchNumpy array

    Args:
        image (nn_model.Neural_Network): The Neural_Network instance to use for the prediction.
        width (int): The path to the image we want to test
        height (int): The label map with the class names

    Raises:
        TODO: Add exceptions

    Returns:
        t_image (torch.Tensor): 
    '''

    # open and resize
    img = Image.open(image)
    img = img.resize((width,height))

    # crop
    current_width, current_height = img.size
    left = (current_width - width)/2
    top = (current_height - height)/2
    right = left + width
    bottom = top + height
    img = img.crop((left, top, right, bottom))

    # normalize the values
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    np_img = np.array(img) / 255
    np_img = (np_img - mean) / std

    # swap color channel position
    np_img = np.transpose(np_img, (2,0,1))

    # conver to tensor from numpy ndarray
    t_image = torch.from_numpy(np_img)

    return t_image