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')