43

# function converting images from opencv format to torch format
def preprocess_image(img, inp_dim):
    """
    Prepare image for inputting to the neural network. 
    
    Returns processed image, original image and original image dimension  
    """

    orig_im = cv2.imread(img)
    dim = orig_im.shape[1], orig_im.shape[0]
    img = (canvas_image(orig_im, (inp_dim, inp_dim)))
    img = img[:,:,::-1]
    img_ = img.transpose((2,0,1)).copy()
    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
    return img_, orig_im, dim

#function letterbox_image that resizes our image, keeping the 
# aspect ratio consistent, and padding the left out areas with the color (128,128,128)
def canvas_image(img, conf_inp_dim):
    '''resize image with unchanged aspect ratio using padding'''
    img_w, img_h = img.shape[1], img.shape[0]
    w, h = conf_inp_dim  # dimension from configuration file
    new_w = int(img_w * min(w/img_w, h/img_h))
    new_h = int(img_h * min(w/img_w, h/img_h))
    resized_image = cv2.resize(img, (new_w,new_h), interpolation = cv2.INTER_CUBIC)
    
    # we fill the extra pixels with 128
    canvas = np.full((conf_inp_dim[1], conf_inp_dim[0], 3), 128)

    canvas[(h-new_h)//2:(h-new_h)//2 + new_h,(w-new_w)//2:(w-new_w)//2 + new_w,:] = resized_image
    
    return canvas