| import os | |
| import skimage | |
| import numpy as np | |
| from skimage.transform import resize | |
| from skimage.io import imread, imsave | |
| from skimage.color import rgb2lab, lab2rgb, rgb2gray | |
| def resize_image(img, image_size=(320, 320)): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| img : ndarray | |
| ndarray of shape (H, W, 3) or (H, W) i.e. RGB and grayscale respectively | |
| image_size : tuple of ints | |
| image size to be used for resizing | |
| ------- | |
| Returns | |
| ------- | |
| resized image ndarray of shape (H_resized, W_resized, 3) or (H_resized, W_resized) | |
| if RGB returns resized image ndarray in range [0, 255] | |
| if grasycale returns resized image ndarray in range [0, 1] | |
| """ | |
| img_resized = resize(img, image_size) | |
| return img_resized | |
| def convert_rgb2gray(img_rgb): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| img_rgb : ndarray | |
| ndarray of shape (H, W, 3) i.e. RGB | |
| ------- | |
| Returns | |
| ------- | |
| grayscale image ndarray of shape (H, W) | |
| """ | |
| img_gray = rgb2gray(img_rgb) | |
| return img_gray | |
| def convert_lab2rgb(img_lab): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| img_lab : ndarray | |
| ndarray of shape (H, W, 3) i.e. Lab | |
| ------- | |
| Returns | |
| ------- | |
| RGB image ndarray of shape (H, W, 3) i.e. RGB space | |
| """ | |
| img_rgb = lab2rgb(img_lab) | |
| return img_rgb | |
| def convert_rgb2lab(img_rgb): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| img_rgb : ndarray | |
| ndarray of shape (H, W, 3) i.e. RGB | |
| ------- | |
| Returns | |
| ------- | |
| Lab image ndarray of shape (H, W, 3) i.e. Lab space | |
| """ | |
| img_lab = rgb2lab(img_rgb) | |
| return img_lab | |
| def apply_image_ab_post_processing(img_ab): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| img_ab : ndarray | |
| pre-processed ndarray of shape (H, W, 2) i.e. ab channels in Lab space in range [-1, 1] | |
| ------- | |
| Returns | |
| ------- | |
| post-processed ab channels ndarray of shape (H, W, 2) in range [-110, 110] | |
| """ | |
| img_ab = img_ab * 110.0 | |
| return img_ab | |
| def apply_image_l_pre_processing(img_l): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| img_l : ndarray | |
| ndarray of shape (H, W) i.e. L channel in Lab space in range [0, 100] | |
| ------- | |
| Returns | |
| ------- | |
| pre-processed L channel ndarray of shape (H, W) in range [-1, 1] | |
| """ | |
| img_l = (img_l / 50.0) - 1 | |
| return img_l | |
| def apply_image_ab_pre_processing(img_ab): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| img_ab : ndarray | |
| ndarray of shape (H, W, 2) i.e. ab channels in Lab space in range [-110, 110] | |
| ------- | |
| Returns | |
| ------- | |
| pre-processed ab channels ndarray of shape (H, W, 2) in range [-1, 1] | |
| """ | |
| img_ab = (img_ab) / 110.0 | |
| return img_ab | |
| def concat_images_l_ab(img_l, img_ab): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| img_l : ndarray | |
| ndarray of shape (H, W, 1) i.e. L channel | |
| img_ab : ndarray | |
| ndarray of shape (H, W, 2) i.e. ab channels | |
| ------- | |
| Returns | |
| ------- | |
| Lab space ndarray of shape (H, W, 3) | |
| """ | |
| img_lab = np.concatenate((img_l, img_ab), axis=-1) | |
| return img_lab | |
| def read_image(file_img): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| file_img : str | |
| full path of the image | |
| ------- | |
| Returns | |
| ------- | |
| ndarray of shape (H, W, 3) for RGB or (H, W) for grayscale | |
| """ | |
| img = imread(file_img) | |
| return img | |
| def save_image_rgb(file_img, img_arr): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| file_img : str | |
| full path of the image | |
| img_arr : ndarray | |
| image ndarray to be saved, of shape (H, W, 3) for RGB or (H, W) for grasycale | |
| """ | |
| imsave(file_img, img_arr) | |
| return | |
| def rescale_grayscale_image_l_channel(img_gray): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| img_gray : ndarray | |
| grayscale image of shape (H, W) in range [0, 1] | |
| ------- | |
| Returns | |
| ------- | |
| L channel ndarray of shape (H, W) in range [0, 100] | |
| """ | |
| img_l_rescaled = (img_gray) * 100.0 | |
| return img_l_rescaled | |