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makedataset.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Feb 12 20:00:46 2020
@author: Administrator
"""
import os
import os.path
import random
import numpy as np
import cv2
import h5py
import torch
import torch.utils.data as udata
import argparse
from PIL import Image
class Dataset(udata.Dataset):
r"""Implements torch.utils.data.Dataset
"""
def __init__(self, trainrgb=True,trainsyn = True, shuffle=False):
super(Dataset, self).__init__()
self.trainrgb = trainrgb
self.trainsyn = trainsyn
self.train_haze = 'dataset.h5'
h5f = h5py.File(self.train_haze, 'r')
self.keys = list(h5f.keys())
if shuffle:
random.shuffle(self.keys)
h5f.close()
def __len__(self):
return len(self.keys)
def __getitem__(self, index):
h5f = h5py.File(self.train_haze, 'r')
key = self.keys[index]
data = np.array(h5f[key])
h5f.close()
return torch.Tensor(data)
def filter_high_f(fshift,img, radius_ratio):
"""
过滤掉除了中心区域外的高频信息
"""
# 1, 生成圆形过滤器, 圆内值1, 其他部分为0的过滤器, 过滤
template = np.zeros(fshift.shape, np.uint8)
crow, ccol = int(fshift.shape[0] / 2), int(fshift.shape[1] / 2) # 圆心
radius = int(radius_ratio * img.shape[0] / 2)
if len(img.shape) == 3:
cv2.circle(template, (crow, ccol), radius, (1, 1, 1), -1)
else:
cv2.circle(template, (crow, ccol), radius, 1, -1)
# 2, 过滤掉除了中心区域外的高频信息
return template * fshift
def filter_low_f(fshift,img, radius_ratio):
"""
去除中心区域低频信息
"""
# 1 生成圆形过滤器, 圆内值0, 其他部分为1的过滤器, 过滤
filter_img = np.ones(fshift.shape, np.uint8)
crow, col = int(fshift.shape[0] / 2), int(fshift.shape[1] / 2)
radius = int(radius_ratio * img.shape[0] / 2)
if len(img.shape) == 3:
cv2.circle(filter_img, (crow, col), radius, (0, 0, 0), -1)
else:
cv2.circle(filter_img, (crow, col), radius, 0, -1)
# 2 过滤中心低频部分的信息
return filter_img * fshift
def ifft(fshift):
"""
傅里叶逆变换
"""
ishift = np.fft.ifftshift(fshift) # 把低频部分sift回左上角
iimg = np.fft.ifftn(ishift) # 出来的是复数，无法显示
iimg = np.abs(iimg) # 返回复数的模
return iimg
def get_low_high_f(img, radius_ratio):
"""
获取低频和高频部分图像
"""
# 傅里叶变换
# np.fft.fftn
f = np.fft.fftn(img) # Compute the N-dimensional discrete Fourier Transform. 零频率分量位于频谱图像的左上角
fshift = np.fft.fftshift(f) # 零频率分量会被移到频域图像的中心位置，即低频
# 获取低频和高频部分
hight_parts_fshift = filter_low_f(fshift.copy(),img, radius_ratio=radius_ratio) # 过滤掉中心低频
low_parts_fshift = filter_high_f(fshift.copy(),img, radius_ratio=radius_ratio)
low_parts_img = ifft(low_parts_fshift) # 先sift回来，再反傅里叶变换
high_parts_img = ifft(hight_parts_fshift)
# 显示原始图像和高通滤波处理图像
img_new_low = (low_parts_img - np.amin(low_parts_img)) / (np.amax(low_parts_img) - np.amin(low_parts_img) + 0.00001)
img_new_high = (high_parts_img - np.amin(high_parts_img) + 0.00001) / (np.amax(high_parts_img) - np.amin(high_parts_img) + 0.00001)
# uint8
img_new_low = np.array(img_new_low*255, np.uint8)
img_new_high = np.array(img_new_high * 255, np.uint8)
return img_new_low, img_new_high
def fft2torch(image):
image = 255*image
low = np.zeros(image.shape)
high = np.zeros(image.shape)
for i in range(image.shape[0]):
low[i,:,:],high[i,:,:] = get_low_high_f(image[i,:,:], radius_ratio=0.5)
low = low/255#(low-low.min())/(low.max()-low.min())
high = high/255#(low-low.min())/(low.max()-low.min())
return low,high
def data_augmentation(clear ,haze, mode):
r"""Performs dat augmentation of the input image
Args:
image: a cv2 (OpenCV) image
mode: int. Choice of transformation to apply to the image
0 - no transformation
1 - flip up and down
2 - rotate counterwise 90 degree
3 - rotate 90 degree and flip up and down
4 - rotate 180 degree
5 - rotate 180 degree and flip
6 - rotate 270 degree
7 - rotate 270 degree and flip
"""
clear = np.transpose(clear, (1, 2, 0))
haze = np.transpose(haze, (1, 2, 0))
if mode == 0:
# original
clear = clear
haze = haze
elif mode == 1:
# flip up and down
clear = np.flipud(clear)
haze = np.flipud(haze)
elif mode == 2:
# rotate counterwise 90 degree
clear = np.rot90(clear)
haze = np.rot90(haze)
elif mode == 3:
# rotate 90 degree and flip up and down
clear = np.rot90(clear)
clear = np.flipud(clear)
haze = np.rot90(haze)
haze = np.flipud(haze)
elif mode == 4:
# rotate 180 degree
clear = np.rot90(clear, k=2)
haze = np.rot90(haze, k=2)
elif mode == 5:
# rotate 180 degree and flip
clear = np.rot90(clear, k=2)
clear = np.flipud(clear)
haze = np.rot90(haze, k=2)
haze = np.flipud(haze)
elif mode == 6:
# rotate 270 degree
clear = np.rot90(clear, k=3)
haze = np.rot90(haze, k=3)
elif mode == 7:
# rotate 270 degree and flip
clear = np.rot90(clear, k=3)
clear = np.flipud(clear)
haze = np.rot90(haze, k=3)
haze = np.flipud(haze)
else:
raise Exception('Invalid choice of image transformation')
return np.transpose(clear, (2, 0, 1)),np.transpose(haze, (2, 0, 1))
def img_to_patches(img,win,stride,Syn=True):
chl,raw,col = img.shape
chl = int(chl)
num_raw = np.ceil((raw-win)/stride+1).astype(np.uint8)
num_col = np.ceil((col-win)/stride+1).astype(np.uint8)
count = 0
total_process = int(num_col)*int(num_raw)
img_patches = np.zeros([chl,win,win,total_process])
if Syn:
for i in range(num_raw):
for j in range(num_col):
if stride * i + win <= raw and stride * j + win <=col:
img_patches[:,:,:,count] = img[:,stride*i : stride*i + win, stride*j : stride*j + win]
elif stride * i + win > raw and stride * j + win<=col:
img_patches[:,:,:,count] = img[:,raw-win : raw,stride * j : stride * j + win]
elif stride * i + win <= raw and stride*j + win>col:
img_patches[:,:,:,count] = img[:,stride*i : stride*i + win, col-win : col]
else:
img_patches[:,:,:,count] = img[:,raw-win : raw,col-win : col]
count +=1
return img_patches
def normalize(data):
return np.float32(data/255.)
def samesize(img,size):
img = cv2.resize(img,size)
return img
def concatenate2imgs(img,depth):
c,w,h = img.shape
conimg = np.zeros((c+1,w,h))
conimg[0:c,:,:] = img
conimg[c,:,:] = depth
return conimg
def Train_data():
'''synthetic Haze images'''
train_data = 'dataset.h5'
files1_haze = os.listdir('./input/train/haze1/')
files1_clear = os.listdir('./input/train/clear1/')
files2_haze = os.listdir('./input/train/haze2/')
files2_clear = os.listdir('./input/train/clear2/')
with h5py.File(train_data, 'w') as h5f:
count1 = 0
for i in range(len(files1_haze)):
haze1 = np.array(Image.open('./input/train/haze1/' + files1_haze[i]))/255
clear1 = np.array(Image.open('./input/train/clear1/' + files1_clear[i]))/255
haze1 = haze1.transpose(2, 0, 1)
clear1 = clear1.transpose(2, 0, 1)
haze1 = img_to_patches(haze1,256,200)
clear1 = img_to_patches(clear1,256,200)
for nx in range(clear1.shape[3]):
haze,clear = data_augmentation(haze1[:, :, :, nx].copy(),clear1[:, :, :, nx].copy(), np.random.randint(0, 7))
clear_high,clear_low = fft2torch(clear)
# print(clear_high.shape,clear_low.shape)
# cv2.imwrite('./1.png',np.transpose(np.concatenate([haze,clear,clear_high,clear_low],2), (1, 2, 0))*255)
data1 = np.concatenate([haze,clear_high,clear_low,clear],0)
h5f.create_dataset(str(count1), data=data1)
count1 += 1
print(count1,clear1.shape[3],data1.shape)
count2 = 0
for i in range(len(files2_haze)):
haze2 = np.array(Image.open('./input/train/haze2/' + files2_haze[i]))/255
clear2 = np.array(Image.open('./input/train/clear2/' + files2_clear[i]))/255
haze2 = haze2.transpose(2, 0, 1)
clear2 = clear2.transpose(2, 0, 1)
haze2 = img_to_patches(haze2,256,256)
clear2 = img_to_patches(clear2,256,256)
for nx in range(clear2.shape[3]):
haze,clear = data_augmentation(haze2[:, :, :, nx].copy(),clear2[:, :, :, nx].copy(), np.random.randint(0, 7))
clear_high,clear_low = fft2torch(clear)
data2 = np.concatenate([haze,clear_high,clear_low,clear],0)
h5f.create_dataset(str(count1), data=data2)
count1 += 1
count2 += 1
print(count2,clear2.shape[3],data2.shape)
print(count1-count2,count2)
h5f.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description = "Building the training patch database")
parser.add_argument("--patch_size", "--p", type = int, default=128, help="Patch size")
parser.add_argument("--stride", "--s", type = int, default=64, help="Size of stride")
args = parser.parse_args()
Train_data()