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DerainDataset.py
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DerainDataset.py
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import os
import os.path
import numpy as np
import random
import h5py
import torch
import cv2
import glob
import torch.utils.data as udata
from utils import *
def Im2Patch(img, win, stride=1):
k = 0
endc = img.shape[0]
endw = img.shape[1]
endh = img.shape[2]
patch = img[:, 0:endw - win + 0 + 1:stride, 0:endh - win + 0 + 1:stride]
TotalPatNum = patch.shape[1] * patch.shape[2]
Y = np.zeros([endc, win * win, TotalPatNum], np.float32)
for i in range(win):
for j in range(win):
patch = img[:, i:endw - win + i + 1:stride, j:endh - win + j + 1:stride]
Y[:, k, :] = np.array(patch[:]).reshape(endc, TotalPatNum)
k = k + 1
return Y.reshape([endc, win, win, TotalPatNum])
def prepare_data_Rain12600(data_path, patch_size, stride):
# train
print('process training data')
input_path = os.path.join(data_path, 'rainy_image')
target_path = os.path.join(data_path, 'ground_truth')
save_target_path = os.path.join(data_path, 'train_target.h5')
save_input_path = os.path.join(data_path, 'train_input.h5')
target_h5f = h5py.File(save_target_path, 'w')
input_h5f = h5py.File(save_input_path, 'w')
train_num = 0
for i in range(900):
target_file = "%d.jpg" % (i + 1)
target = cv2.imread(os.path.join(target_path,target_file))
b, g, r = cv2.split(target)
target = cv2.merge([r, g, b])
for j in range(14):
input_file = "%d_%d.jpg" % (i+1, j+1)
input_img = cv2.imread(os.path.join(input_path,input_file))
b, g, r = cv2.split(input_img)
input_img = cv2.merge([r, g, b])
target_img = target
target_img = np.float32(normalize(target_img))
target_patches = Im2Patch(target_img.transpose(2,0,1), win=patch_size, stride=stride)
input_img = np.float32(normalize(input_img))
input_patches = Im2Patch(input_img.transpose(2, 0, 1), win=patch_size, stride=stride)
print("target file: %s # samples: %d" % (input_file, target_patches.shape[3]))
for n in range(target_patches.shape[3]):
target_data = target_patches[:, :, :, n].copy()
target_h5f.create_dataset(str(train_num), data=target_data)
input_data = input_patches[:, :, :, n].copy()
input_h5f.create_dataset(str(train_num), data=input_data)
train_num += 1
target_h5f.close()
input_h5f.close()
print('training set, # samples %d\n' % train_num)
def prepare_data_RainTrainH(data_path, patch_size, stride):
# train
print('process training data')
input_path = os.path.join(data_path)
target_path = os.path.join(data_path)
save_target_path = os.path.join(data_path, 'train_target.h5')
save_input_path = os.path.join(data_path, 'train_input.h5')
target_h5f = h5py.File(save_target_path, 'w')
input_h5f = h5py.File(save_input_path, 'w')
train_num = 0
for i in range(1800):
target_file = "norain-%d.png" % (i + 1)
if os.path.exists(os.path.join(target_path,target_file)):
target = cv2.imread(os.path.join(target_path,target_file))
b, g, r = cv2.split(target)
target = cv2.merge([r, g, b])
input_file = "rain-%d.png" % (i + 1)
if os.path.exists(os.path.join(input_path,input_file)): # we delete 546 samples
input_img = cv2.imread(os.path.join(input_path,input_file))
b, g, r = cv2.split(input_img)
input_img = cv2.merge([r, g, b])
target_img = target
target_img = np.float32(normalize(target_img))
target_patches = Im2Patch(target_img.transpose(2,0,1), win=patch_size, stride=stride)
input_img = np.float32(normalize(input_img))
input_patches = Im2Patch(input_img.transpose(2, 0, 1), win=patch_size, stride=stride)
print("target file: %s # samples: %d" % (input_file, target_patches.shape[3]))
for n in range(target_patches.shape[3]):
target_data = target_patches[:, :, :, n].copy()
target_h5f.create_dataset(str(train_num), data=target_data)
input_data = input_patches[:, :, :, n].copy()
input_h5f.create_dataset(str(train_num), data=input_data)
train_num += 1
target_h5f.close()
input_h5f.close()
print('training set, # samples %d\n' % train_num)
def prepare_data_RainTrainL(data_path, patch_size, stride):
# train
print('process training data')
input_path = os.path.join(data_path)
target_path = os.path.join(data_path)
save_target_path = os.path.join(data_path, 'train_target.h5')
save_input_path = os.path.join(data_path, 'train_input.h5')
target_h5f = h5py.File(save_target_path, 'w')
input_h5f = h5py.File(save_input_path, 'w')
train_num = 0
for i in range(200):
target_file = "norain-%d.png" % (i + 1)
target = cv2.imread(os.path.join(target_path,target_file))
b, g, r = cv2.split(target)
target = cv2.merge([r, g, b])
for j in range(2):
input_file = "rain-%d.png" % (i + 1)
input_img = cv2.imread(os.path.join(input_path,input_file))
b, g, r = cv2.split(input_img)
input_img = cv2.merge([r, g, b])
target_img = target
if j == 1:
target_img = cv2.flip(target_img, 1)
input_img = cv2.flip(input_img, 1)
target_img = np.float32(normalize(target_img))
target_patches = Im2Patch(target_img.transpose(2,0,1), win=patch_size, stride=stride)
input_img = np.float32(normalize(input_img))
input_patches = Im2Patch(input_img.transpose(2, 0, 1), win=patch_size, stride=stride)
print("target file: %s # samples: %d" % (input_file, target_patches.shape[3]))
for n in range(target_patches.shape[3]):
target_data = target_patches[:, :, :, n].copy()
target_h5f.create_dataset(str(train_num), data=target_data)
input_data = input_patches[:, :, :, n].copy()
input_h5f.create_dataset(str(train_num), data=input_data)
train_num += 1
target_h5f.close()
input_h5f.close()
print('training set, # samples %d\n' % train_num)
class Dataset(udata.Dataset):
def __init__(self, data_path='.'):
super(Dataset, self).__init__()
self.data_path = data_path
target_path = os.path.join(self.data_path, 'train_target.h5')
input_path = os.path.join(self.data_path, 'train_input.h5')
target_h5f = h5py.File(target_path, 'r')
input_h5f = h5py.File(input_path, 'r')
self.keys = list(target_h5f.keys())
random.shuffle(self.keys)
target_h5f.close()
input_h5f.close()
def __len__(self):
return len(self.keys)
def __getitem__(self, index):
target_path = os.path.join(self.data_path, 'train_target.h5')
input_path = os.path.join(self.data_path, 'train_input.h5')
target_h5f = h5py.File(target_path, 'r')
input_h5f = h5py.File(input_path, 'r')
key = self.keys[index]
target = np.array(target_h5f[key])
input = np.array(input_h5f[key])
target_h5f.close()
input_h5f.close()
return torch.Tensor(input), torch.Tensor(target)