def enhance_predict(lr_imgs, network=None): outs_list = [] for _, flip_axis in enumerate([0, 1, 2, -1]): for _, rotate_rg in enumerate([0, 90]): en_imgs = utils.enhance_imgs(lr_imgs, rotate_rg, flip_axis) outs = network.predict(en_imgs) anti_outs = utils.anti_enhance_imgs(outs, rotate_rg, flip_axis) outs_list.append(anti_outs) return np.mean(outs_list, axis=0)
def ensem_predict(input_imgs, network): # ensembling outs_list = [] for _, flip_axis in enumerate([0, 1, 2, -1]): for _, rotate_rg in enumerate([0, 90]): en_imgs = utils.enhance_imgs(input_imgs, rotate_rg, flip_axis) outs = network.predict(en_imgs) composed_img = utils.compose_dwt_images(outs, FLAGS.wavelet) anti_outs = utils.anti_enhance_imgs(composed_img, rotate_rg, flip_axis) outs_list.append(anti_outs[0]) output = np.mean(outs_list, axis=0) return [output]