Spaces:
Running
Running
File size: 1,689 Bytes
a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb |
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 |
import cv2
import numpy as np
import random
def random_brightness_np(image, max_abs_change=50):
delta = random.uniform(-max_abs_change, max_abs_change)
return np.clip(image + delta, 0, 255)
def random_contrast_np(image, strength_range=[0.3, 1.5]):
delta = random.uniform(*strength_range)
mean = image.mean()
return np.clip((image - mean) * delta + mean, 0, 255)
def motion_blur_np(img, max_kernel_size=3):
# Either vertial, hozirontal or diagonal blur
mode = np.random.choice(["h", "v", "diag_down", "diag_up"])
ksize = np.random.randint(0, (max_kernel_size + 1) / 2) * 2 + 1 # make sure is odd
center = int((ksize - 1) / 2)
kernel = np.zeros((ksize, ksize))
if mode == "h":
kernel[center, :] = 1.0
elif mode == "v":
kernel[:, center] = 1.0
elif mode == "diag_down":
kernel = np.eye(ksize)
elif mode == "diag_up":
kernel = np.flip(np.eye(ksize), 0)
var = ksize * ksize / 16.0
grid = np.repeat(np.arange(ksize)[:, np.newaxis], ksize, axis=-1)
gaussian = np.exp(
-(np.square(grid - center) + np.square(grid.T - center)) / (2.0 * var)
)
kernel *= gaussian
kernel /= np.sum(kernel)
img = cv2.filter2D(img, -1, kernel)
return np.clip(img, 0, 255)
def additive_gaussian_noise(image, stddev_range=[5, 95]):
stddev = random.uniform(*stddev_range)
noise = np.random.normal(size=image.shape, scale=stddev)
noisy_image = np.clip(image + noise, 0, 255)
return noisy_image
def photaug(img):
img = random_brightness_np(img)
img = random_contrast_np(img)
# img = additive_gaussian_noise(img)
img = motion_blur_np(img)
return img
|