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data_loader.py
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data_loader.py
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import os.path
import numpy as np
from PIL import Image
import torch.utils.data as data
import local_path
'''
# 代码重构
这个文件应该重命名为 DatasetForAGW
# 解释
给每个数据集定义一个Dataset类
'''
class SYSUData(data.Dataset):
def __init__(self, data_dir, transform=None, colorIndex = None, thermalIndex = None):
# data_dir = '../Datasets/SYSU-MM01/'
# data_dir = local_dataset_path.data_SYSU_MM01
data_dir = local_path.my_test_SYSU_MM01
# Load training images (path) and labels
# train_color_image = np.load(data_dir + 'train_rgb_resized_img.npy')
# self.train_color_label = np.load(data_dir + 'train_rgb_resized_label.npy')
# train_color_image = np.load(data_dir + '\\train_rgb_resized_img.npy')
train_color_image = np.load(os.path.join(data_dir, 'train_rgb_resized_img.npy'))
# self.train_color_label = np.load(data_dir + '\\train_rgb_resized_label.npy')
self.train_color_label = np.load(os.path.join(data_dir, 'train_rgb_resized_label.npy'))
# train_thermal_image = np.load(data_dir + 'train_ir_resized_img.npy')
# self.train_thermal_label = np.load(data_dir + 'train_ir_resized_label.npy')
# train_thermal_image = np.load(data_dir + '\\train_ir_resized_img.npy')
train_thermal_image = np.load(os.path.join(data_dir, 'train_ir_resized_img.npy'))
# self.train_thermal_label = np.load(data_dir + '\\train_ir_resized_label.npy')
self.train_thermal_label = np.load(os.path.join(data_dir, 'train_ir_resized_label.npy'))
# BGR to RGB
self.train_color_image = train_color_image
self.train_thermal_image = train_thermal_image
self.transform = transform
self.cIndex = colorIndex
self.tIndex = thermalIndex
def __getitem__(self, index):
'''根据 index 取出 visible 和 infrared 照片及 label'''
img1, target1 = self.train_color_image[self.cIndex[index]], self.train_color_label[self.cIndex[index]]
img2, target2 = self.train_thermal_image[self.tIndex[index]], self.train_thermal_label[self.tIndex[index]]
img1 = self.transform(img1)
img2 = self.transform(img2)
# 返回：图片1， 图片2， label1， label2
return img1, img2, target1, target2
def __len__(self):
return len(self.train_color_label)
class RegDBData(data.Dataset):
def __init__(self, data_dir, trial, transform=None, colorIndex = None, thermalIndex = None):
# Load training images (path) and labels
data_dir = '../Datasets/RegDB/'
data_dir = local_path.data_RegDB
train_color_list = data_dir + 'idx/train_visible_{}'.format(trial)+ '.txt'
train_thermal_list = data_dir + 'idx/train_thermal_{}'.format(trial)+ '.txt'
color_img_file, train_color_label = load_data(train_color_list)
thermal_img_file, train_thermal_label = load_data(train_thermal_list)
train_color_image = []
for i in range(len(color_img_file)):
img = Image.open(data_dir+ color_img_file[i])
img = img.resize((144, 288), Image.ANTIALIAS)
pix_array = np.array(img)
train_color_image.append(pix_array)
train_color_image = np.array(train_color_image)
train_thermal_image = []
for i in range(len(thermal_img_file)):
img = Image.open(data_dir+ thermal_img_file[i])
img = img.resize((144, 288), Image.ANTIALIAS)
pix_array = np.array(img)
train_thermal_image.append(pix_array)
train_thermal_image = np.array(train_thermal_image)
# BGR to RGB
self.train_color_image = train_color_image
self.train_color_label = train_color_label
# BGR to RGB
self.train_thermal_image = train_thermal_image
self.train_thermal_label = train_thermal_label
self.transform = transform
self.cIndex = colorIndex
self.tIndex = thermalIndex
def __getitem__(self, index):
img1, target1 = self.train_color_image[self.cIndex[index]], self.train_color_label[self.cIndex[index]]
img2, target2 = self.train_thermal_image[self.tIndex[index]], self.train_thermal_label[self.tIndex[index]]
img1 = self.transform(img1)
img2 = self.transform(img2)
return img1, img2, target1, target2
def __len__(self):
return len(self.train_color_label)
class TestData(data.Dataset):
'''将测试集中的照片resize为相同分辨率，并转为RGB'''
def __init__(self, test_img_file, test_label, transform=None, img_size = (144,288)):
test_image = []
for i in range(len(test_img_file)):
img = Image.open(test_img_file[i])
img = img.resize((img_size[0], img_size[1]), Image.ANTIALIAS)
pix_array = np.array(img)
test_image.append(pix_array)
test_image = np.array(test_image)
self.test_image = test_image
self.test_label = test_label
self.transform = transform
def __getitem__(self, index):
img1, target1 = self.test_image[index], self.test_label[index]
img1 = self.transform(img1)
return img1, target1
def __len__(self):
return len(self.test_image)
class TestDataOld(data.Dataset):
def __init__(self, data_dir, test_img_file, test_label, transform=None, img_size = (144,288)):
test_image = []
for i in range(len(test_img_file)):
img = Image.open(data_dir + test_img_file[i])
img = img.resize((img_size[0], img_size[1]), Image.ANTIALIAS)
pix_array = np.array(img)
test_image.append(pix_array)
test_image = np.array(test_image)
self.test_image = test_image
self.test_label = test_label
self.transform = transform
def __getitem__(self, index):
img1, target1 = self.test_image[index], self.test_label[index]
img1 = self.transform(img1)
return img1, target1
def __len__(self):
return len(self.test_image)
def load_data(input_data_path ):
with open(input_data_path) as f:
data_file_list = open(input_data_path, 'rt').read().splitlines()
# Get full list of image and labels
file_image = [s.split(' ')[0] for s in data_file_list]
file_label = [int(s.split(' ')[1]) for s in data_file_list]
return file_image, file_label