import cv2 import numpy as np from PIL import Image import copy 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 gauss_filter(kernel_size, sigma): max_idx = kernel_size // 2 idx = np.linspace(-max_idx, max_idx, kernel_size) Y, X = np.meshgrid(idx, idx) gauss_filter = np.exp(-(X**2 + Y**2) / (2*sigma**2)) gauss_filter /= np.sum(np.sum(gauss_filter)) return gauss_filter 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 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' 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 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 = cv2.resize(painted_image, (height, width)) return painted_image if __name__ == '__main__': background_alpha = 0.7 # transparency of background 1: all black, 0: do nothing background_blur_radius = 35 # radius of background blur, must be odd number contour_width = 7 # 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 painted_image = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha) # save painted_image = Image.fromarray(painted_image) painted_image.save('./test_img/painter_output_image.png')