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Running
on
Zero
Running
on
Zero
File size: 2,928 Bytes
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from PIL import Image, ImageEnhance
import random
import numpy as np
import random
def preproc(image, label, preproc_methods=['flip']):
if 'flip' in preproc_methods:
image, label = cv_random_flip(image, label)
if 'crop' in preproc_methods:
image, label = random_crop(image, label)
if 'rotate' in preproc_methods:
image, label = random_rotate(image, label)
if 'enhance' in preproc_methods:
image = color_enhance(image)
if 'pepper' in preproc_methods:
label = random_pepper(label)
return image, label
def cv_random_flip(img, label):
if random.random() > 0.5:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
label = label.transpose(Image.FLIP_LEFT_RIGHT)
return img, label
def random_crop(image, label):
border = 30
image_width = image.size[0]
image_height = image.size[1]
border = int(min(image_width, image_height) * 0.1)
crop_win_width = np.random.randint(image_width - border, image_width)
crop_win_height = np.random.randint(image_height - border, image_height)
random_region = (
(image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1,
(image_height + crop_win_height) >> 1)
return image.crop(random_region), label.crop(random_region)
def random_rotate(image, label, angle=15):
mode = Image.BICUBIC
if random.random() > 0.8:
random_angle = np.random.randint(-angle, angle)
image = image.rotate(random_angle, mode)
label = label.rotate(random_angle, mode)
return image, label
def color_enhance(image):
bright_intensity = random.randint(5, 15) / 10.0
image = ImageEnhance.Brightness(image).enhance(bright_intensity)
contrast_intensity = random.randint(5, 15) / 10.0
image = ImageEnhance.Contrast(image).enhance(contrast_intensity)
color_intensity = random.randint(0, 20) / 10.0
image = ImageEnhance.Color(image).enhance(color_intensity)
sharp_intensity = random.randint(0, 30) / 10.0
image = ImageEnhance.Sharpness(image).enhance(sharp_intensity)
return image
def random_gaussian(image, mean=0.1, sigma=0.35):
def gaussianNoisy(im, mean=mean, sigma=sigma):
for _i in range(len(im)):
im[_i] += random.gauss(mean, sigma)
return im
img = np.asarray(image)
width, height = img.shape
img = gaussianNoisy(img[:].flatten(), mean, sigma)
img = img.reshape([width, height])
return Image.fromarray(np.uint8(img))
def random_pepper(img, N=0.0015):
img = np.array(img)
noiseNum = int(N * img.shape[0] * img.shape[1])
for i in range(noiseNum):
randX = random.randint(0, img.shape[0] - 1)
randY = random.randint(0, img.shape[1] - 1)
if random.randint(0, 1) == 0:
img[randX, randY] = 0
else:
img[randX, randY] = 255
return Image.fromarray(img)
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