forked from mrq/DL-Art-School
72 lines
2.9 KiB
Python
72 lines
2.9 KiB
Python
import random
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# Performs image corruption on a list of images from a configurable set of corruption
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# options.
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class ImageCorruptor:
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def __init__(self, opt):
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self.num_corrupts = opt['num_corrupts_per_image'] if 'num_corrupts_per_image' in opt.keys() else 2
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self.corruptions_enabled = opt['corruptions']
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def corrupt_images(self, imgs):
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augmentations = random.choice(self.corruptions_enabled, k=self.num_corrupts)
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# Source of entropy, which should be used across all images.
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rand_int = random.randint(1, 999999)
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corrupted_imgs = []
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for img in imgs:
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for aug in augmentations:
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if 'color_quantization' in aug:
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# Color quantization
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quant_div = 2 ** random.randint(1, 4)
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augmentation_tensor[AUG_TENSOR_COLOR_QUANT] = float(quant_div) / 5.0
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pass
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elif 'gaussian_blur' in aug:
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# Gaussian Blur
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kernel = random.randint(1, 3) * 3
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image = cv2.GaussianBlur(image, (kernel, kernel), 3)
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augmentation_tensor[AUG_TENSOR_BLUR] = float(kernel) / 9
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elif 'median_blur' in aug:
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# Median Blur
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kernel = random.randint(1, 3) * 3
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image = cv2.medianBlur(image, kernel)
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augmentation_tensor[AUG_TENSOR_BLUR] = float(kernel) / 9
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elif 'motion_blur' in aug:
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# Motion blur
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intensity = random.randrange(1, 9)
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image = self.motion_blur(image, intensity, random.randint(0, 360))
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augmentation_tensor[AUG_TENSOR_BLUR] = intensity / 9
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elif 'smooth_blur' in aug:
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# Smooth blur
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kernel = random.randint(1, 3) * 3
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image = cv2.blur(image, ksize=kernel)
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augmentation_tensor[AUG_TENSOR_BLUR] = kernel / 9
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elif 'block_noise' in aug:
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# Block noise
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noise_intensity = random.randint(3, 10)
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image += np.random.randn()
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pass
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elif 'lq_resampling' in aug:
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# Bicubic LR->HR
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pass
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elif 'color_shift' in aug:
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# Color shift
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pass
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elif 'interlacing' in aug:
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# Interlacing distortion
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pass
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elif 'chromatic_aberration' in aug:
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# Chromatic aberration
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pass
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elif 'noise' in aug:
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# Noise
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pass
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elif 'jpeg' in aug:
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# JPEG compression
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pass
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elif 'saturation' in aug:
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# Lightening / saturation
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pass
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return corrupted_imgs |