Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,803 Bytes
af44a4b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import cv2
import numpy as np
from skimage.filters import gaussian
def rgb2gray(rgb):
return np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140])
def spatter(x, severity=1):
c = [(0.65, 0.3, 4, 0.69, 0.6, 0),
(0.65, 0.3, 3, 0.68, 0.6, 0),
(0.65, 0.3, 2, 0.68, 0.5, 0),
(0.65, 0.3, 1, 0.65, 1.5, 1),
(0.67, 0.4, 1, 0.65, 1.5, 1)][severity - 1]
x_PIL = x
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
if len(x.shape) < 3 or x.shape[2] < 3:
add_spatter_color = cv2.cvtColor(np.clip(m * color, 0, 1),
cv2.COLOR_BGRA2BGR)
add_spatter_gray = rgb2gray(add_spatter_color)
return np.clip(x + add_spatter_gray, 0, 1) * 255
else:
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1),
cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
x_rgb = np.array(x_PIL.convert('RGB'))
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x_rgb[..., :1]),
42 / 255. * np.ones_like(x_rgb[..., :1]),
20 / 255. * np.ones_like(x_rgb[..., :1])),
axis=2)
color *= m[..., np.newaxis]
if len(x.shape) < 3 or x.shape[2] < 3:
x *= (1 - m)
return np.clip(x + rgb2gray(color), 0, 1) * 255
else:
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255 |