DL-Art-School/codes/data/image_corruptor.py
2020-09-28 14:26:15 -06:00

108 lines
4.2 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.fixed_corruptions = opt['fixed_corruptions']
self.num_corrupts = opt['num_corrupts_per_image'] if 'num_corrupts_per_image' in opt.keys() else 2
if self.num_corrupts == 0:
return
self.random_corruptions = opt['random_corruptions']
self.blur_scale = opt['corruption_blur_scale'] if 'corruption_blur_scale' in opt.keys() else 1
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 = []
for img in imgs:
for aug in augmentations:
img = self.apply_corruption(img, aug, rand_int_a)
for aug in self.fixed_corruptions:
img = self.apply_corruption(img, aug, rand_int_f)
corrupted_imgs.append(img)
return corrupted_imgs
def apply_corruption(self, img, aug, rand_int):
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
kernel = 2 * self.blur_scale * (rand_int % 3) + 1
img = cv2.GaussianBlur(img, (kernel, kernel), 3)
elif 'motion_blur' in aug:
# Motion blur
intensity = 2 * 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:
# Bicubic LR->HR
pass
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:
# Block noise
noise_intensity = (rand_int % 4 + 2) / 255.0 # Between 1-4
img += np.random.randn(*img.shape) * noise_intensity
elif 'jpeg' in aug:
if aug == 'jpeg':
lo=10
range=20
elif aug == 'jpeg-medium':
lo=23
range=25
# 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, optimice=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)
return img