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tools.py
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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')