138 lines
5.8 KiB
Python
138 lines
5.8 KiB
Python
import random
|
|
import cv2
|
|
import numpy as np
|
|
from data.util import read_img
|
|
from PIL import Image
|
|
from io import BytesIO
|
|
|
|
# Performs image corruption on a list of images from a configurable set of corruption
|
|
# options.
|
|
class ImageCorruptor:
|
|
def __init__(self, opt):
|
|
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
|
|
if self.num_corrupts == 0:
|
|
return
|
|
self.random_corruptions = opt['random_corruptions'] if 'random_corruptions' in opt.keys() else []
|
|
|
|
def corrupt_images(self, imgs):
|
|
if self.num_corrupts == 0 and not self.fixed_corruptions:
|
|
return imgs
|
|
|
|
if self.num_corrupts == 0:
|
|
augmentations = []
|
|
else:
|
|
augmentations = random.choices(self.random_corruptions, k=self.num_corrupts)
|
|
# Source of entropy, which should be used across all images.
|
|
rand_int_f = random.randint(1, 999999)
|
|
rand_int_a = random.randint(1, 999999)
|
|
|
|
corrupted_imgs = []
|
|
applied_augs = augmentations + self.fixed_corruptions
|
|
for img in imgs:
|
|
for aug in augmentations:
|
|
img = self.apply_corruption(img, aug, rand_int_a, applied_augs)
|
|
for aug in self.fixed_corruptions:
|
|
img = self.apply_corruption(img, aug, rand_int_f, applied_augs)
|
|
corrupted_imgs.append(img)
|
|
|
|
return corrupted_imgs
|
|
|
|
def apply_corruption(self, img, aug, rand_int, applied_augmentations):
|
|
if 'color_quantization' in aug:
|
|
# Color quantization
|
|
quant_div = 2 ** ((rand_int % 3) + 2)
|
|
img = img * 255
|
|
img = (img // quant_div) * quant_div
|
|
img = img / 255
|
|
elif 'gaussian_blur' in aug:
|
|
# Gaussian Blur
|
|
if aug == 'gaussian_blur_3':
|
|
kernel = 3
|
|
elif aug == 'gaussian_blur_5':
|
|
kernel = 5
|
|
else:
|
|
kernel = 2 * self.blur_scale * (rand_int % 3) + 1
|
|
img = cv2.GaussianBlur(img, (kernel, kernel), 3)
|
|
elif 'motion_blur' in aug:
|
|
# Motion blur
|
|
intensity = self.blur_scale * (rand_int % 3) + 1
|
|
angle = (rand_int // 3) % 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 'smooth_blur' in aug:
|
|
# Smooth blur
|
|
kernel = 2 * self.blur_scale * (rand_int % 3) + 1
|
|
img = cv2.blur(img, ksize=(kernel, kernel))
|
|
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
|
|
scale = 2
|
|
if 'lq_resampling4x' == aug:
|
|
scale = 4
|
|
interpolation_modes = [cv2.INTER_NEAREST, cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_LANCZOS4]
|
|
mode = rand_int % 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=cv2.INTER_NEAREST)
|
|
img = cv2.resize(img, dsize=(img.shape[1]*scale, img.shape[0]*scale), interpolation=mode)
|
|
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_int % 4 + 2) / 255.0 # Between 1-4
|
|
img += np.random.randn(*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 = (rand_int % range + lo)
|
|
# cv2's jpeg compression is "odd". It introduces artifacts. Use PIL instead.
|
|
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 = float(rand_int % 10) * .03
|
|
img = np.clip(img + saturation, a_max=1, a_min=0)
|
|
elif 'none' not in aug:
|
|
raise NotImplementedError("Augmentation doesn't exist")
|
|
|
|
return img
|