import functools
import random
from math import cos, pi
import cv2
import kornia
import numpy as np
import torch
from kornia.augmentation import ColorJitter
from data.util import read_img
from PIL import Image
from io import BytesIO
# Get a rough visualization of the above distribution. (Y-axis is meaningless, just spreads data)
from utils.util import opt_get
'''
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
data = np.asarray([get_rand() for _ in range(5000)])
plt.plot(data, np.random.uniform(size=(5000,)), 'x')
plt.show()
'''
def kornia_color_jitter_numpy(img, setting):
if setting * 255 > 1:
# I'm using Kornia's ColorJitter, which requires pytorch arrays in b,c,h,w format.
img = torch.from_numpy(img).permute(2,0,1).unsqueeze(0)
img = ColorJitter(setting, setting, setting, setting)(img)
img = img.squeeze(0).permute(1,2,0).numpy()
return img
# Performs image corruption on a list of images from a configurable set of corruption
# options.
class ImageCorruptor:
def __init__(self, opt):
self.opt = opt
self.reset_random()
self.blur_scale = opt['corruption_blur_scale'] if 'corruption_blur_scale' in opt.keys() else 1
self.fixed_corruptions = opt['fixed_corruptions'] if 'fixed_corruptions' in opt.keys() else []
self.num_corrupts = opt['num_corrupts_per_image'] if 'num_corrupts_per_image' in opt.keys() else 0
self.cosine_bias = opt_get(opt, ['cosine_bias'], True)
if self.num_corrupts == 0:
return
else:
self.random_corruptions = opt['random_corruptions'] if 'random_corruptions' in opt.keys() else []
def reset_random(self):
if 'random_seed' in self.opt.keys():
self.rand = random.Random(self.opt['random_seed'])
else:
self.rand = random.Random()
# Feeds a random uniform through a cosine distribution to slightly bias corruptions towards "uncorrupted".
# Return is on [0,1] with a bias towards 0.
def get_rand(self):
r = self.rand.random()
if self.cosine_bias:
return 1 - cos(r * pi / 2)
else:
return r
def corrupt_images(self, imgs, return_entropy=False):
if self.num_corrupts == 0 and not self.fixed_corruptions:
if return_entropy:
return imgs, []
else:
return imgs
if self.num_corrupts == 0:
augmentations = []
else:
augmentations = random.choices(self.random_corruptions, k=self.num_corrupts)
# Sources of entropy
corrupted_imgs = []
entropy = []
undo_fns = []
applied_augs = augmentations + self.fixed_corruptions
for img in imgs:
for aug in augmentations:
r = self.get_rand()
img, undo_fn = self.apply_corruption(img, aug, r, applied_augs)
if undo_fn is not None:
undo_fns.append(undo_fn)
for aug in self.fixed_corruptions:
r = self.get_rand()
img, undo_fn = self.apply_corruption(img, aug, r, applied_augs)
entropy.append(r)
if undo_fn is not None:
undo_fns.append(undo_fn)
# Apply undo_fns after all corruptions are finished, in same order.
for ufn in undo_fns:
img = ufn(img)
corrupted_imgs.append(img)
if return_entropy:
return corrupted_imgs, entropy
else:
return corrupted_imgs
def apply_corruption(self, img, aug, rand_val, applied_augmentations):
undo_fn = None
if 'color_quantization' in aug:
# Color quantization
quant_div = 2 ** (int(rand_val * 10 / 3) + 2)
img = img * 255
img = (img // quant_div) * quant_div
img = img / 255
elif 'color_jitter' in aug:
lo_end = 0
hi_end = .2
setting = rand_val * (hi_end - lo_end) + lo_end
img = kornia_color_jitter_numpy(img, setting)
elif 'gaussian_blur' in aug:
img = cv2.GaussianBlur(img, (0,0), self.blur_scale*rand_val*1.5)
elif 'motion_blur' in aug:
# Motion blur
intensity = self.blur_scale*rand_val * 3 + 1
angle = random.randint(0,360)
k = np.zeros((intensity, intensity), dtype=np.float32)
k[(intensity - 1) // 2, :] = np.ones(intensity, dtype=np.float32)
k = cv2.warpAffine(k, cv2.getRotationMatrix2D((intensity / 2 - 0.5, intensity / 2 - 0.5), angle, 1.0),
(intensity, intensity))
k = k * (1.0 / np.sum(k))
img = cv2.filter2D(img, -1, k)
elif 'block_noise' in aug:
# Large distortion blocks in part of an img, such as is used to mask out a face.
pass
elif 'lq_resampling' in aug:
# Random mode interpolation HR->LR->HR
if 'lq_resampling4x' == aug:
scale = 4
else:
if rand_val < .3:
scale = 1
elif rand_val < .7:
scale = 2
else:
scale = 4
if scale > 1:
interpolation_modes = [cv2.INTER_NEAREST, cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_LANCZOS4]
mode = random.randint(0,4) % len(interpolation_modes)
# Downsample first, then upsample using the random mode.
img = cv2.resize(img, dsize=(img.shape[1]//scale, img.shape[0]//scale), interpolation=mode)
def lq_resampling_undo_fn(scale, img):
return cv2.resize(img, dsize=(img.shape[1]*scale, img.shape[0]*scale), interpolation=cv2.INTER_LINEAR)
undo_fn = functools.partial(lq_resampling_undo_fn, scale)
elif 'color_shift' in aug:
# Color shift
pass
elif 'interlacing' in aug:
# Interlacing distortion
pass
elif 'chromatic_aberration' in aug:
# Chromatic aberration
pass
elif 'noise' in aug:
# Random noise
if 'noise-5' == aug:
noise_intensity = 5 / 255.0
else:
noise_intensity = (rand_val*6) / 255.0
img += np.random.rand(*img.shape) * noise_intensity
elif 'jpeg' in aug:
if 'noise' not in applied_augmentations and 'noise-5' not in applied_augmentations:
if aug == 'jpeg':
lo=10
range=20
elif aug == 'jpeg-low':
lo=15
range=10
elif aug == 'jpeg-medium':
lo=23
range=25
elif aug == 'jpeg-broad':
lo=15
range=60
elif aug == 'jpeg-normal':
lo=47
range=35
else:
raise NotImplementedError("specified jpeg corruption doesn't exist")
# JPEG compression
qf = (int((1-rand_val)*range) + lo)
# Use PIL to perform a mock compression to a data buffer, then swap back to cv2.
img = (img * 255).astype(np.uint8)
img = Image.fromarray(img)
buffer = BytesIO()
img.save(buffer, "JPEG", quality=qf, optimize=True)
buffer.seek(0)
jpeg_img_bytes = np.asarray(bytearray(buffer.read()), dtype="uint8")
img = read_img("buffer", jpeg_img_bytes, rgb=True)
elif 'saturation' in aug:
# Lightening / saturation
saturation = rand_val * .3
img = np.clip(img + saturation, a_max=1, a_min=0)
elif 'greyscale' in aug:
img = np.tile(np.mean(img, axis=2, keepdims=True), [1,1,3])
elif 'none' not in aug:
raise NotImplementedError("Augmentation doesn't exist")
return img, undo_fn