New image corruptor gradations
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@ -47,11 +47,14 @@ class ImageCorruptor:
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img = img / 255
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img = img / 255
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elif 'gaussian_blur' in aug:
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elif 'gaussian_blur' in aug:
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# Gaussian Blur
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# Gaussian Blur
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kernel = 2 * self.blur_scale * (rand_int % 3) + 1
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if aug == 'gaussian_blur_3':
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kernel = 3
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else:
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kernel = 2 * self.blur_scale * (rand_int % 3) + 1
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img = cv2.GaussianBlur(img, (kernel, kernel), 3)
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img = cv2.GaussianBlur(img, (kernel, kernel), 3)
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elif 'motion_blur' in aug:
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elif 'motion_blur' in aug:
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# Motion blur
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# Motion blur
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intensity = 2 * self.blur_scale * (rand_int % 3) + 1
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intensity = self.blur_scale * (rand_int % 3) + 1
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angle = (rand_int // 3) % 360
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angle = (rand_int // 3) % 360
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k = np.zeros((intensity, intensity), dtype=np.float32)
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k = np.zeros((intensity, intensity), dtype=np.float32)
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k[(intensity - 1) // 2, :] = np.ones(intensity, dtype=np.float32)
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k[(intensity - 1) // 2, :] = np.ones(intensity, dtype=np.float32)
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@ -79,8 +82,11 @@ class ImageCorruptor:
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# Chromatic aberration
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# Chromatic aberration
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pass
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pass
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elif 'noise' in aug:
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elif 'noise' in aug:
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# Block noise
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# Random noise
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noise_intensity = (rand_int % 4 + 2) / 255.0 # Between 1-4
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if 'noise-5' == aug:
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noise_intensity = 5 / 255.0
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else:
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noise_intensity = (rand_int % 4 + 2) / 255.0 # Between 1-4
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img += np.random.randn(*img.shape) * noise_intensity
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img += np.random.randn(*img.shape) * noise_intensity
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elif 'jpeg' in aug:
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elif 'jpeg' in aug:
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if aug == 'jpeg':
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if aug == 'jpeg':
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@ -89,6 +95,9 @@ class ImageCorruptor:
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elif aug == 'jpeg-medium':
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elif aug == 'jpeg-medium':
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lo=23
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lo=23
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range=25
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range=25
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elif aug == 'jpeg-broad':
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lo=10
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range=60
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# JPEG compression
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# JPEG compression
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qf = (rand_int % range + lo)
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qf = (rand_int % range + lo)
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# cv2's jpeg compression is "odd". It introduces artifacts. Use PIL instead.
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# cv2's jpeg compression is "odd". It introduces artifacts. Use PIL instead.
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