169 lines
6.5 KiB
Python
169 lines
6.5 KiB
Python
import random
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from math import cos, pi
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import cv2
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import numpy as np
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from data.util import read_img
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from PIL import Image
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from io import BytesIO
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# Get a rough visualization of the above distribution. (Y-axis is meaningless, just spreads data)
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from utils.util import opt_get
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'''
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if __name__ == '__main__':
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import numpy as np
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import matplotlib.pyplot as plt
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data = np.asarray([get_rand() for _ in range(5000)])
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plt.plot(data, np.random.uniform(size=(5000,)), 'x')
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plt.show()
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'''
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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.opt = opt
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self.reset_random()
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self.blur_scale = opt['corruption_blur_scale'] if 'corruption_blur_scale' in opt.keys() else 1
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self.fixed_corruptions = opt['fixed_corruptions'] if 'fixed_corruptions' in opt.keys() else []
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self.num_corrupts = opt['num_corrupts_per_image'] if 'num_corrupts_per_image' in opt.keys() else 0
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self.cosine_bias = opt_get(opt, ['cosine_bias'], True)
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if self.num_corrupts == 0:
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return
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else:
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self.random_corruptions = opt['random_corruptions'] if 'random_corruptions' in opt.keys() else []
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def reset_random(self):
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if 'random_seed' in self.opt.keys():
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self.rand = random.Random(self.opt['random_seed'])
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else:
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self.rand = random.Random()
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# Feeds a random uniform through a cosine distribution to slightly bias corruptions towards "uncorrupted".
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# Return is on [0,1] with a bias towards 0.
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def get_rand(self):
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r = self.rand.random()
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if self.cosine_bias:
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return 1 - cos(r * pi / 2)
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else:
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return r
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def corrupt_images(self, imgs, return_entropy=False):
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if self.num_corrupts == 0 and not self.fixed_corruptions:
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if return_entropy:
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return imgs, []
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else:
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return imgs
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if self.num_corrupts == 0:
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augmentations = []
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else:
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augmentations = random.choices(self.random_corruptions, k=self.num_corrupts)
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# Sources of entropy
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corrupted_imgs = []
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entropy = []
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applied_augs = augmentations + self.fixed_corruptions
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for img in imgs:
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for aug in augmentations:
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r = self.get_rand()
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img = self.apply_corruption(img, aug, r, applied_augs)
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for aug in self.fixed_corruptions:
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r = self.get_rand()
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img = self.apply_corruption(img, aug, r, applied_augs)
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entropy.append(r)
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corrupted_imgs.append(img)
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if return_entropy:
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return corrupted_imgs, entropy
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else:
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return corrupted_imgs
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def apply_corruption(self, img, aug, rand_val, applied_augmentations):
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if 'color_quantization' in aug:
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# Color quantization
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quant_div = 2 ** (int(rand_val * 10 / 3) + 2)
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img = img * 255
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img = (img // quant_div) * quant_div
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img = img / 255
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elif 'gaussian_blur' in aug:
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img = cv2.GaussianBlur(img, (0,0), self.blur_scale*rand_val*1.5)
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elif 'motion_blur' in aug:
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# Motion blur
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intensity = self.blur_scale*rand_val * 3 + 1
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angle = random.randint(0,360)
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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 = cv2.warpAffine(k, cv2.getRotationMatrix2D((intensity / 2 - 0.5, intensity / 2 - 0.5), angle, 1.0),
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(intensity, intensity))
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k = k * (1.0 / np.sum(k))
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img = cv2.filter2D(img, -1, k)
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elif 'block_noise' in aug:
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# Large distortion blocks in part of an img, such as is used to mask out a face.
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pass
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elif 'lq_resampling' in aug:
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# Random mode interpolation HR->LR->HR
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scale = 2
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if 'lq_resampling4x' == aug:
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scale = 4
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interpolation_modes = [cv2.INTER_NEAREST, cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_LANCZOS4]
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mode = random.randint(0,4) % len(interpolation_modes)
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# Downsample first, then upsample using the random mode.
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img = cv2.resize(img, dsize=(img.shape[1]//scale, img.shape[0]//scale), interpolation=cv2.INTER_NEAREST)
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img = cv2.resize(img, dsize=(img.shape[1]*scale, img.shape[0]*scale), interpolation=mode)
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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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# Random noise
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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_val*4 + 2) / 255.0
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img += np.random.randn(*img.shape) * noise_intensity
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elif 'jpeg' in aug:
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if 'noise' not in applied_augmentations and 'noise-5' not in applied_augmentations:
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if aug == 'jpeg':
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lo=10
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range=20
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elif aug == 'jpeg-low':
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lo=15
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range=10
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elif aug == 'jpeg-medium':
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lo=23
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range=25
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elif aug == 'jpeg-broad':
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lo=15
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range=60
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elif aug == 'jpeg-normal':
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lo=47
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range=35
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else:
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raise NotImplementedError("specified jpeg corruption doesn't exist")
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# JPEG compression
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qf = (int((1-rand_val)*range) + lo)
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# Use PIL to perform a mock compression to a data buffer, then swap back to cv2.
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img = (img * 255).astype(np.uint8)
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img = Image.fromarray(img)
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buffer = BytesIO()
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img.save(buffer, "JPEG", quality=qf, optimize=True)
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buffer.seek(0)
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jpeg_img_bytes = np.asarray(bytearray(buffer.read()), dtype="uint8")
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img = read_img("buffer", jpeg_img_bytes, rgb=True)
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elif 'saturation' in aug:
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# Lightening / saturation
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saturation = rand_val * .3
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img = np.clip(img + saturation, a_max=1, a_min=0)
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elif 'none' not in aug:
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raise NotImplementedError("Augmentation doesn't exist")
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return img
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