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| import cv2 | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import copy | |
| import time | |
| def colormap(rgb=True): | |
| color_list = np.array( | |
| [ | |
| 0.000, 0.000, 0.000, | |
| 1.000, 1.000, 1.000, | |
| 1.000, 0.498, 0.313, | |
| 0.392, 0.581, 0.929, | |
| 0.000, 0.447, 0.741, | |
| 0.850, 0.325, 0.098, | |
| 0.929, 0.694, 0.125, | |
| 0.494, 0.184, 0.556, | |
| 0.466, 0.674, 0.188, | |
| 0.301, 0.745, 0.933, | |
| 0.635, 0.078, 0.184, | |
| 0.300, 0.300, 0.300, | |
| 0.600, 0.600, 0.600, | |
| 1.000, 0.000, 0.000, | |
| 1.000, 0.500, 0.000, | |
| 0.749, 0.749, 0.000, | |
| 0.000, 1.000, 0.000, | |
| 0.000, 0.000, 1.000, | |
| 0.667, 0.000, 1.000, | |
| 0.333, 0.333, 0.000, | |
| 0.333, 0.667, 0.000, | |
| 0.333, 1.000, 0.000, | |
| 0.667, 0.333, 0.000, | |
| 0.667, 0.667, 0.000, | |
| 0.667, 1.000, 0.000, | |
| 1.000, 0.333, 0.000, | |
| 1.000, 0.667, 0.000, | |
| 1.000, 1.000, 0.000, | |
| 0.000, 0.333, 0.500, | |
| 0.000, 0.667, 0.500, | |
| 0.000, 1.000, 0.500, | |
| 0.333, 0.000, 0.500, | |
| 0.333, 0.333, 0.500, | |
| 0.333, 0.667, 0.500, | |
| 0.333, 1.000, 0.500, | |
| 0.667, 0.000, 0.500, | |
| 0.667, 0.333, 0.500, | |
| 0.667, 0.667, 0.500, | |
| 0.667, 1.000, 0.500, | |
| 1.000, 0.000, 0.500, | |
| 1.000, 0.333, 0.500, | |
| 1.000, 0.667, 0.500, | |
| 1.000, 1.000, 0.500, | |
| 0.000, 0.333, 1.000, | |
| 0.000, 0.667, 1.000, | |
| 0.000, 1.000, 1.000, | |
| 0.333, 0.000, 1.000, | |
| 0.333, 0.333, 1.000, | |
| 0.333, 0.667, 1.000, | |
| 0.333, 1.000, 1.000, | |
| 0.667, 0.000, 1.000, | |
| 0.667, 0.333, 1.000, | |
| 0.667, 0.667, 1.000, | |
| 0.667, 1.000, 1.000, | |
| 1.000, 0.000, 1.000, | |
| 1.000, 0.333, 1.000, | |
| 1.000, 0.667, 1.000, | |
| 0.167, 0.000, 0.000, | |
| 0.333, 0.000, 0.000, | |
| 0.500, 0.000, 0.000, | |
| 0.667, 0.000, 0.000, | |
| 0.833, 0.000, 0.000, | |
| 1.000, 0.000, 0.000, | |
| 0.000, 0.167, 0.000, | |
| 0.000, 0.333, 0.000, | |
| 0.000, 0.500, 0.000, | |
| 0.000, 0.667, 0.000, | |
| 0.000, 0.833, 0.000, | |
| 0.000, 1.000, 0.000, | |
| 0.000, 0.000, 0.167, | |
| 0.000, 0.000, 0.333, | |
| 0.000, 0.000, 0.500, | |
| 0.000, 0.000, 0.667, | |
| 0.000, 0.000, 0.833, | |
| 0.000, 0.000, 1.000, | |
| 0.143, 0.143, 0.143, | |
| 0.286, 0.286, 0.286, | |
| 0.429, 0.429, 0.429, | |
| 0.571, 0.571, 0.571, | |
| 0.714, 0.714, 0.714, | |
| 0.857, 0.857, 0.857 | |
| ] | |
| ).astype(np.float32) | |
| color_list = color_list.reshape((-1, 3)) * 255 | |
| if not rgb: | |
| color_list = color_list[:, ::-1] | |
| return color_list | |
| color_list = colormap() | |
| color_list = color_list.astype('uint8').tolist() | |
| def vis_add_mask(image, mask, color, alpha, kernel_size): | |
| color = np.array(color) | |
| mask = mask.astype('float').copy() | |
| mask = (cv2.GaussianBlur(mask, (kernel_size, kernel_size), kernel_size) / 255.) * (alpha) | |
| for i in range(3): | |
| image[:, :, i] = image[:, :, i] * (1-alpha+mask) + color[i] * (alpha-mask) | |
| return image | |
| def vis_add_mask_wo_blur(image, mask, color, alpha): | |
| color = np.array(color) | |
| mask = mask.astype('float').copy() | |
| for i in range(3): | |
| image[:, :, i] = image[:, :, i] * (1-alpha+mask) + color[i] * (alpha-mask) | |
| return image | |
| def vis_add_mask_wo_gaussian(image, background_mask, contour_mask, background_color, contour_color, background_alpha, contour_alpha): | |
| background_color = np.array(background_color) | |
| contour_color = np.array(contour_color) | |
| # background_mask = 1 - background_mask | |
| # contour_mask = 1 - contour_mask | |
| for i in range(3): | |
| image[:, :, i] = image[:, :, i] * (1-background_alpha+background_mask*background_alpha) \ | |
| + background_color[i] * (background_alpha-background_mask*background_alpha) | |
| image[:, :, i] = image[:, :, i] * (1-contour_alpha+contour_mask*contour_alpha) \ | |
| + contour_color[i] * (contour_alpha-contour_mask*contour_alpha) | |
| return image.astype('uint8') | |
| def mask_painter(input_image, input_mask, background_alpha=0.7, background_blur_radius=7, contour_width=3, contour_color=3, contour_alpha=1): | |
| """ | |
| Input: | |
| input_image: numpy array | |
| input_mask: numpy array | |
| background_alpha: transparency of background, [0, 1], 1: all black, 0: do nothing | |
| background_blur_radius: radius of background blur, must be odd number | |
| contour_width: width of mask contour, must be odd number | |
| contour_color: color index (in color map) of mask contour, 0: black, 1: white, >1: others | |
| contour_alpha: transparency of mask contour, [0, 1], if 0: no contour highlighted | |
| Output: | |
| painted_image: numpy array | |
| """ | |
| assert input_image.shape[:2] == input_mask.shape, 'different shape' | |
| assert background_blur_radius % 2 * contour_width % 2 > 0, 'background_blur_radius and contour_width must be ODD' | |
| # 0: background, 1: foreground | |
| input_mask[input_mask>0] = 255 | |
| # mask background | |
| painted_image = vis_add_mask(input_image, input_mask, color_list[0], background_alpha, background_blur_radius) # black for background | |
| # mask contour | |
| contour_mask = input_mask.copy() | |
| contour_mask = cv2.Canny(contour_mask, 100, 200) # contour extraction | |
| # widden contour | |
| kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (contour_width, contour_width)) | |
| contour_mask = cv2.dilate(contour_mask, kernel) | |
| painted_image = vis_add_mask(painted_image, 255-contour_mask, color_list[contour_color], contour_alpha, contour_width) | |
| # painted_image = background_dist_map | |
| return painted_image | |
| def mask_generator_00(mask, background_radius, contour_radius): | |
| # no background width when '00' | |
| # distance map | |
| dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3) | |
| dist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3) | |
| dist_map = dist_transform_fore - dist_transform_back | |
| # ...:::!!!:::... | |
| contour_radius += 2 | |
| contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius)) | |
| contour_mask = contour_mask / np.max(contour_mask) | |
| contour_mask[contour_mask>0.5] = 1. | |
| return mask, contour_mask | |
| def mask_generator_01(mask, background_radius, contour_radius): | |
| # no background width when '00' | |
| # distance map | |
| dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3) | |
| dist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3) | |
| dist_map = dist_transform_fore - dist_transform_back | |
| # ...:::!!!:::... | |
| contour_radius += 2 | |
| contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius)) | |
| contour_mask = contour_mask / np.max(contour_mask) | |
| return mask, contour_mask | |
| def mask_generator_10(mask, background_radius, contour_radius): | |
| # distance map | |
| dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3) | |
| dist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3) | |
| dist_map = dist_transform_fore - dist_transform_back | |
| # .....:::::!!!!! | |
| background_mask = np.clip(dist_map, -background_radius, background_radius) | |
| background_mask = (background_mask - np.min(background_mask)) | |
| background_mask = background_mask / np.max(background_mask) | |
| # ...:::!!!:::... | |
| contour_radius += 2 | |
| contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius)) | |
| contour_mask = contour_mask / np.max(contour_mask) | |
| contour_mask[contour_mask>0.5] = 1. | |
| return background_mask, contour_mask | |
| def mask_generator_11(mask, background_radius, contour_radius): | |
| # distance map | |
| dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3) | |
| dist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3) | |
| dist_map = dist_transform_fore - dist_transform_back | |
| # .....:::::!!!!! | |
| background_mask = np.clip(dist_map, -background_radius, background_radius) | |
| background_mask = (background_mask - np.min(background_mask)) | |
| background_mask = background_mask / np.max(background_mask) | |
| # ...:::!!!:::... | |
| contour_radius += 2 | |
| contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius)) | |
| contour_mask = contour_mask / np.max(contour_mask) | |
| return background_mask, contour_mask | |
| def mask_painter_wo_gaussian(input_image, input_mask, background_alpha=0.5, background_blur_radius=7, contour_width=3, contour_color=3, contour_alpha=1, mode='11'): | |
| """ | |
| Input: | |
| input_image: numpy array | |
| input_mask: numpy array | |
| background_alpha: transparency of background, [0, 1], 1: all black, 0: do nothing | |
| background_blur_radius: radius of background blur, must be odd number | |
| contour_width: width of mask contour, must be odd number | |
| contour_color: color index (in color map) of mask contour, 0: black, 1: white, >1: others | |
| contour_alpha: transparency of mask contour, [0, 1], if 0: no contour highlighted | |
| mode: painting mode, '00', no blur, '01' only blur contour, '10' only blur background, '11' blur both | |
| Output: | |
| painted_image: numpy array | |
| """ | |
| assert input_image.shape[:2] == input_mask.shape, 'different shape' | |
| assert background_blur_radius % 2 * contour_width % 2 > 0, 'background_blur_radius and contour_width must be ODD' | |
| assert mode in ['00', '01', '10', '11'], 'mode should be 00, 01, 10, or 11' | |
| # downsample input image and mask | |
| width, height = input_image.shape[0], input_image.shape[1] | |
| res = 1024 | |
| ratio = min(1.0 * res / max(width, height), 1.0) | |
| input_image = cv2.resize(input_image, (int(height*ratio), int(width*ratio))) | |
| input_mask = cv2.resize(input_mask, (int(height*ratio), int(width*ratio))) | |
| # 0: background, 1: foreground | |
| msk = np.clip(input_mask, 0, 1) | |
| # generate masks for background and contour pixels | |
| background_radius = (background_blur_radius - 1) // 2 | |
| contour_radius = (contour_width - 1) // 2 | |
| generator_dict = {'00':mask_generator_00, '01':mask_generator_01, '10':mask_generator_10, '11':mask_generator_11} | |
| background_mask, contour_mask = generator_dict[mode](msk, background_radius, contour_radius) | |
| # paint | |
| painted_image = vis_add_mask_wo_gaussian\ | |
| (input_image, background_mask, contour_mask, color_list[0], color_list[contour_color], background_alpha, contour_alpha) # black for background | |
| return painted_image | |
| if __name__ == '__main__': | |
| background_alpha = 0.7 # transparency of background 1: all black, 0: do nothing | |
| background_blur_radius = 31 # radius of background blur, must be odd number | |
| contour_width = 11 # contour width, must be odd number | |
| contour_color = 3 # id in color map, 0: black, 1: white, >1: others | |
| contour_alpha = 1 # transparency of background, 0: no contour highlighted | |
| # load input image and mask | |
| input_image = np.array(Image.open('./test_img/painter_input_image.jpg').convert('RGB')) | |
| input_mask = np.array(Image.open('./test_img/painter_input_mask.jpg').convert('P')) | |
| # paint | |
| overall_time_1 = 0 | |
| overall_time_2 = 0 | |
| overall_time_3 = 0 | |
| overall_time_4 = 0 | |
| overall_time_5 = 0 | |
| for i in range(50): | |
| t2 = time.time() | |
| painted_image_00 = mask_painter_wo_gaussian(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='00') | |
| e2 = time.time() | |
| t3 = time.time() | |
| painted_image_10 = mask_painter_wo_gaussian(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='10') | |
| e3 = time.time() | |
| t1 = time.time() | |
| painted_image = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha) | |
| e1 = time.time() | |
| t4 = time.time() | |
| painted_image_01 = mask_painter_wo_gaussian(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='01') | |
| e4 = time.time() | |
| t5 = time.time() | |
| painted_image_11 = mask_painter_wo_gaussian(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='11') | |
| e5 = time.time() | |
| overall_time_1 += (e1 - t1) | |
| overall_time_2 += (e2 - t2) | |
| overall_time_3 += (e3 - t3) | |
| overall_time_4 += (e4 - t4) | |
| overall_time_5 += (e5 - t5) | |
| print(f'average time w gaussian: {overall_time_1/50}') | |
| print(f'average time w/o gaussian00: {overall_time_2/50}') | |
| print(f'average time w/o gaussian10: {overall_time_3/50}') | |
| print(f'average time w/o gaussian01: {overall_time_4/50}') | |
| print(f'average time w/o gaussian11: {overall_time_5/50}') | |
| # save | |
| painted_image_00 = Image.fromarray(painted_image_00) | |
| painted_image_00.save('./test_img/painter_output_image_00.png') | |
| painted_image_10 = Image.fromarray(painted_image_10) | |
| painted_image_10.save('./test_img/painter_output_image_10.png') | |
| painted_image_01 = Image.fromarray(painted_image_01) | |
| painted_image_01.save('./test_img/painter_output_image_01.png') | |
| painted_image_11 = Image.fromarray(painted_image_11) | |
| painted_image_11.save('./test_img/painter_output_image_11.png') | |