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