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import math
from random import choice, choices, randint
import cv2
import numpy as np
from PIL import Image
from torch.utils.data.dataset import Dataset
from utils import USMSharp_npy, cvtColor, preprocess_input
from .degradations import (circular_lowpass_kernel, random_add_gaussian_noise,
random_add_poisson_noise, random_mixed_kernels)
from .transforms import augment, paired_random_crop
def cv_show(image):
image = np.array(image)
image = cv2.resize(image, (256, 128), interpolation=cv2.INTER_CUBIC)
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
def get_new_img_size(width, height, img_min_side=600):
if width <= height:
f = float(img_min_side) / width
resized_height = int(f * height)
resized_width = int(img_min_side)
else:
f = float(img_min_side) / height
resized_width = int(f * width)
resized_height = int(img_min_side)
return resized_width, resized_height
class SRGANDataset(Dataset):
def __init__(self, train_lines, lr_shape, hr_shape):
super(SRGANDataset, self).__init__()
self.train_lines = train_lines
self.train_batches = len(train_lines)
self.lr_shape = lr_shape
self.hr_shape = hr_shape
self.scale = int(hr_shape[0]/lr_shape[0])
self.usmsharp = USMSharp_npy()
# 第一次滤波的参数
self.blur_kernel_size = 21
self.kernel_list = ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
self.kernel_prob = [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
self.sinc_prob = 0.1
self.blur_sigma = [0.2, 3]
self.betag_range = [0.5, 4]
self.betap_range = [1, 2]
# 第二次滤波的参数
self.blur_kernel_size2 = 21
self.kernel_list2 = ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
self.kernel_prob2 = [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
self.sinc_prob2 = 0.1
self.blur_sigma2 = [0.2, 3]
self.betag_range2 = [0.5, 4]
self.betap_range2 = [1, 2]
# 最后的sinc滤波
self.final_sinc_prob = 0.8
# 卷积核大小从7到21分布
self.kernel_range = [2 * v + 1 for v in range(3, 11)]
# 使用脉冲张量进行卷积不会产生模糊效果
self.pulse_tensor = np.zeros(shape=[21, 21], dtype='float32')
self.pulse_tensor[10, 10] = 1
# 第一次退化的参数
self.resize_prob = [0.2, 0.7, 0.1] # up, down, keep
self.resize_range = [0.15, 1.5]
self.gaussian_noise_prob = 0.5
self.noise_range = [1, 30]
self.poisson_scale_range = [0.05, 3]
self.gray_noise_prob = 0.4
self.jpeg_range = [30, 95]
# 第二次退化的参数
self.second_blur_prob = 0.8
self.resize_prob2 = [0.3, 0.4, 0.3] # up, down, keep
self.resize_range2 = [0.3, 1.2]
self.gaussian_noise_prob2= 0.5
self.noise_range2 = [1, 25]
self.poisson_scale_range2= [0.05, 2.5]
self.gray_noise_prob2 = 0.4
self.jpeg_range2 = [30, 95]
def __len__(self):
return self.train_batches
def __getitem__(self, index):
index = index % self.train_batches
image_origin = Image.open(self.train_lines[index].split()[0])
lq, gt = self.get_random_data(image_origin, self.hr_shape)
gt = np.transpose(preprocess_input(np.array(gt, dtype=np.float32), [0.5,0.5,0.5], [0.5,0.5,0.5]), [2,0,1])
lq = np.transpose(preprocess_input(np.array(lq, dtype=np.float32), [0.5,0.5,0.5], [0.5,0.5,0.5]), [2,0,1])
return lq, gt
def rand(self, a=0, b=1):
return np.random.rand()*(b-a) + a
def get_random_data(self, image, input_shape):
#------------------------------#
# 读取图像并转换成RGB图像
# cvtColor将np转Image
#------------------------------#
image = cvtColor(image)
#------------------------------#
# 获得图像的高宽与目标高宽
#------------------------------#
iw, ih = image.size
h, w = input_shape
scale = min(w/iw, h/ih)
nw = int(iw*scale)
nh = int(ih*scale)
dx = (w-nw)//2
dy = (h-nh)//2
#---------------------------------#
# 将图像多余的部分加上灰条
#---------------------------------#
image = image.resize((nw,nh), Image.BICUBIC)
new_image = Image.new('RGB', (w,h), (128,128,128))
new_image.paste(image, (dx, dy))
image = np.array(new_image, np.float32)
rotate = self.rand()<.5
if rotate:
angle = np.random.randint(-15,15)
a,b = w/2,h/2
M = cv2.getRotationMatrix2D((a,b),angle,1)
image = cv2.warpAffine(np.array(image), M, (w,h), borderValue=[128,128,128])
# ------------------------ 生成卷积核以进行第一次退化处理 ------------------------ #
kernel_size = choice(self.kernel_range)
if np.random.uniform() < self.sinc_prob:
# 此sinc过滤器设置适用于[7,21]范围内的内核
if kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else:
kernel = random_mixed_kernels(
self.kernel_list,
self.kernel_prob,
kernel_size,
self.blur_sigma,
self.blur_sigma, [-math.pi, math.pi],
self.betag_range,
self.betap_range,
noise_range=None)
# pad kernel
pad_size = (21 - kernel_size) // 2
kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))
kernel = kernel.astype(np.float32)
# ------------------------ 生成卷积核以进行第二次退化处理 ------------------------ #
kernel_size = choice(self.kernel_range)
if np.random.uniform() < self.sinc_prob2:
if kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else:
kernel2 = random_mixed_kernels(
self.kernel_list2,
self.kernel_prob2,
kernel_size,
self.blur_sigma2,
self.blur_sigma2, [-math.pi, math.pi],
self.betag_range2,
self.betap_range2,
noise_range=None)
# pad kernel
pad_size = (21 - kernel_size) // 2
kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))
kernel2 = kernel2.astype(np.float32)
# ----------------------the final sinc kernel ------------------------- #
if np.random.uniform() < self.final_sinc_prob:
kernel_size = choice(self.kernel_range)
omega_c = np.random.uniform(np.pi / 3, np.pi)
sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
else:
sinc_kernel = self.pulse_tensor
sinc_kernel = sinc_kernel.astype(np.float32)
lq, gt = self.feed_data(image, kernel, kernel2, sinc_kernel)
return lq, gt
def feed_data(self, img_gt, kernel1, kernel2, sinc_kernel):
img_gt = np.array(img_gt, dtype=np.float32)
# 对gt进行锐化
img_gt = np.clip(img_gt / 255, 0, 1)
gt = self.usmsharp.filt(img_gt)
[ori_w, ori_h, _] = gt.shape
# ---------------------- 根据参数进行第一次退化 -------------------- #
# 模糊处理
out = cv2.filter2D(img_gt, -1, kernel1)
# 随机 resize
updown_type = choices(['up', 'down', 'keep'], self.resize_prob)[0]
if updown_type == 'up':
scale = np.random.uniform(1, self.resize_range[1])
elif updown_type == 'down':
scale = np.random.uniform(self.resize_range[0], 1)
else:
scale = 1
mode = choice(['area', 'bilinear', 'bicubic'])
if mode=='area':
out = cv2.resize(out, (int(ori_h * scale), int(ori_w * scale)), interpolation=cv2.INTER_AREA)
elif mode=='bilinear':
out = cv2.resize(out, (int(ori_h * scale), int(ori_w * scale)), interpolation=cv2.INTER_LINEAR)
else:
out = cv2.resize(out, (int(ori_h * scale), int(ori_w * scale)), interpolation=cv2.INTER_CUBIC)
# 灰度噪声
gray_noise_prob = self.gray_noise_prob
if np.random.uniform() < self.gaussian_noise_prob:
out = random_add_gaussian_noise(
out, sigma_range=self.noise_range, clip=True, rounds=False, gray_prob=gray_noise_prob)
else:
out = random_add_poisson_noise(
out,
scale_range=self.poisson_scale_range,
gray_prob=gray_noise_prob,
clip=True,
rounds=False)
# JPEG 压缩
jpeg_p = np.random.uniform(low=self.jpeg_range[0], high=self.jpeg_range[1])
jpeg_p = int(jpeg_p)
out = np.clip(out, 0, 1)
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_p]
_, encimg = cv2.imencode('.jpg', out * 255., encode_param)
out = np.float32(cv2.imdecode(encimg, 1))/255
# ---------------------- 根据参数进行第一次退化 -------------------- #
# 模糊
if np.random.uniform() < self.second_blur_prob:
out = cv2.filter2D(out, -1, kernel2)
# 随机 resize
updown_type = choices(['up', 'down', 'keep'], self.resize_prob2)[0]
if updown_type == 'up':
scale = np.random.uniform(1, self.resize_range2[1])
elif updown_type == 'down':
scale = np.random.uniform(self.resize_range2[0], 1)
else:
scale = 1
mode = choice(['area', 'bilinear', 'bicubic'])
if mode == 'area':
out = cv2.resize(out, (int(ori_h / self.scale * scale), int(ori_w / self.scale * scale)), interpolation=cv2.INTER_AREA)
elif mode == 'bilinear':
out = cv2.resize(out, (int(ori_h / self.scale * scale), int(ori_w / self.scale * scale)), interpolation=cv2.INTER_LINEAR)
else:
out = cv2.resize(out, (int(ori_h / self.scale * scale), int(ori_w / self.scale * scale)), interpolation=cv2.INTER_CUBIC)
# 灰度噪声
gray_noise_prob = self.gray_noise_prob2
if np.random.uniform() < self.gaussian_noise_prob2:
out = random_add_gaussian_noise(
out, sigma_range=self.noise_range2, clip=True, rounds=False, gray_prob=gray_noise_prob)
else:
out = random_add_poisson_noise(
out,
scale_range=self.poisson_scale_range2,
gray_prob=gray_noise_prob,
clip=True,
rounds=False)
# JPEG压缩+最后的sinc滤波器
# 我们还需要将图像的大小调整到所需的尺寸。我们把[调整大小+sinc过滤器]组合在一起
# 作为一个操作。
# 我们考虑两个顺序。
# 1. [调整大小+sinc filter] + JPEG压缩
# 2. 2. JPEG压缩+[调整大小+sinc过滤]。
# 根据经验,我们发现其他组合(sinc + JPEG + Resize)会引入扭曲的线条。
if np.random.uniform() < 0.5:
# resize back + the final sinc filter
mode = choice(['area', 'bilinear', 'bicubic'])
if mode == 'area':
out = cv2.resize(out, (ori_h // self.scale, ori_w // self.scale), interpolation=cv2.INTER_AREA)
elif mode == 'bilinear':
out = cv2.resize(out, (ori_h // self.scale, ori_w // self.scale), interpolation=cv2.INTER_LINEAR)
else:
out = cv2.resize(out, (ori_h // self.scale, ori_w // self.scale), interpolation=cv2.INTER_CUBIC)
out = cv2.filter2D(out, -1, sinc_kernel)
# JPEG 压缩
jpeg_p = np.random.uniform(low=self.jpeg_range[0], high=self.jpeg_range[1])
jpeg_p = jpeg_p
out = np.clip(out, 0, 1)
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_p]
_, encimg = cv2.imencode('.jpg', out * 255., encode_param)
out = np.float32(cv2.imdecode(encimg, 1)) / 255
else:
# JPEG 压缩
jpeg_p = np.random.uniform(low=self.jpeg_range[0], high=self.jpeg_range[1])
jpeg_p = jpeg_p
out = np.clip(out, 0, 1)
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_p]
_, encimg = cv2.imencode('.jpg', out * 255., encode_param)
out = np.float32(cv2.imdecode(encimg, 1)) / 255
# resize back + the final sinc filter
mode = choice(['area', 'bilinear', 'bicubic'])
if mode == 'area':
out = cv2.resize(out, (ori_h // self.scale, ori_w // self.scale),interpolation=cv2.INTER_AREA)
elif mode == 'bilinear':
out = cv2.resize(out, (ori_h // self.scale, ori_w // self.scale),interpolation=cv2.INTER_LINEAR)
else:
out = cv2.resize(out, (ori_h // self.scale, ori_w // self.scale),interpolation=cv2.INTER_CUBIC)
lq = np.clip((out * 255.0), 0, 255)
gt = np.clip((gt * 255.0), 0, 255)
return Image.fromarray(np.uint8(lq)), Image.fromarray(np.uint8(gt))
def SRGAN_dataset_collate(batch):
images_l = []
images_h = []
for img_l, img_h in batch:
images_l.append(img_l)
images_h.append(img_h)
return np.array(images_l), np.array(images_h)
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