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data/CDDataset.py

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+"""
+CD Dataset
+"""
+import os
+from PIL import Image
+import numpy as np
+from torch.utils import data
+import data.util as Util
+from torch.utils.data import Dataset
+import torchvision
+import torch
+
+totensor = torchvision.transforms.ToTensor()
+
+"""
+CD Dataset 
+├─image
+├─image_post
+├─label
+└─list
+"""
+
+IMG_FOLDER_NAME = 'A'
+IMG_POST_FOLDER_NAME = 'B'
+LABEL_FOLDER_NAME = 'label'
+LABEL1_FOLDER_NAME = 'label1'
+LABEL2_FOLDER_NAME = 'label2'
+LIST_FOLDER_NAME = 'list'
+
+label_suffix = ".png"
+
+#list内存放image_name 构建读取图片名字函数
+def load_img_name_list(dataset_path):
+    img_name_list = np.loadtxt(dataset_path, dtype=np.str_)
+    if img_name_list.ndim == 2:
+        return img_name_list[:, 0]
+    return img_name_list
+
+#获取各个文件夹的路径
+def get_img_path(root_dir, img_name):
+    return os.path.join(root_dir, IMG_FOLDER_NAME, img_name)
+
+def get_img_post_path(root_dir, img_name):
+    return os.path.join(root_dir, IMG_POST_FOLDER_NAME, img_name)
+
+def get_label_path(root_dir, img_name):
+    return os.path.join(root_dir, LABEL_FOLDER_NAME, img_name)
+
+def get_label1_path(root_dir, img_name):
+    return os.path.join(root_dir, LABEL1_FOLDER_NAME, img_name)
+
+def get_label2_path(root_dir, img_name):
+    return os.path.join(root_dir, LABEL2_FOLDER_NAME, img_name)
+
+class CDDataset(Dataset):
+    def __init__(self, root_dir, resolution=256, split='train', data_len=-1, label_transform=None):
+
+        self.root_dir = root_dir
+        self.resolution = resolution
+        self.data_len = data_len
+        self.split = split #train / val / test
+        self.label_transform = label_transform
+
+        self.list_path = os.path.join(self.root_dir, LIST_FOLDER_NAME, self.split + '.txt')
+
+        self.img_name_list = load_img_name_list(self.list_path)
+
+        self.dataset_len = len(self.img_name_list)
+
+        if self.data_len <= 0:
+            self.data_len = self.dataset_len
+        else:
+            self.data_len = min(self.dataset_len, self.data_len)
+
+    def __len__(self):
+        return self.data_len
+
+    def __getitem__(self, index):
+        A_path = get_img_path(self.root_dir, self.img_name_list[index % self.data_len])
+        B_path = get_img_post_path(self.root_dir, self.img_name_list[index % self.data_len])
+
+        img_A = Image.open(A_path).convert('RGB')
+        img_B = Image.open(B_path).convert('RGB')
+
+        L_path = get_label_path(self.root_dir, self.img_name_list[index % self.data_len])
+        img_label = Image.open(L_path).convert("RGB")
+
+        img_A = Util.transform_augment_cd(img_A, min_max=(-1, 1))
+        img_B = Util.transform_augment_cd(img_B, min_max=(-1, 1))
+        img_label = Util.transform_augment_cd(img_label, min_max=(0, 1))
+        if img_label.dim() > 2:
+            img_label = img_label[0]
+
+        return {'A':img_A, 'B':img_B, 'L':img_label, 'Index':index}
+
+
+
+class SCDDataset(Dataset):
+    def __init__(self, root_dir, resolution=512, split='train', data_len=-1, label_transform=None):
+
+        self.root_dir = root_dir
+        self.resolution = resolution
+        self.data_len = data_len
+        self.split = split #train / val / test
+        self.label_transform = label_transform
+
+        self.list_path = os.path.join(self.root_dir, LIST_FOLDER_NAME, self.split + '.txt')
+
+        self.img_name_list = load_img_name_list(self.list_path)
+
+        self.dataset_len = len(self.img_name_list)
+
+        if self.data_len <= 0:
+            self.data_len = self.dataset_len
+        else:
+            self.data_len = min(self.dataset_len, self.data_len)
+
+    def __len__(self):
+        return self.data_len
+
+    def __getitem__(self, index):
+        A_path = get_img_path(self.root_dir, self.img_name_list[index % self.data_len])
+        B_path = get_img_post_path(self.root_dir, self.img_name_list[index % self.data_len])
+        name = A_path.split('\\')[-1].split('.')[0]
+        img_A = Image.open(A_path).convert('RGB')
+        img_B = Image.open(B_path).convert('RGB')
+
+        L_path = get_label_path(self.root_dir, self.img_name_list[index % self.data_len])
+        L1_path = get_label1_path(self.root_dir, self.img_name_list[index % self.data_len])
+        L2_path = get_label2_path(self.root_dir, self.img_name_list[index % self.data_len])
+        img_label = np.array(Image.open(L_path), dtype=np.uint8)
+        img_label1 = np.array(Image.open(L1_path), dtype=np.uint8)
+        img_label2 = np.array(Image.open(L2_path), dtype=np.uint8)
+
+        img_A = Util.transform_augment_cd(img_A, min_max=(-1, 1))
+        img_B = Util.transform_augment_cd(img_B, min_max=(-1, 1))
+        img_label = torch.from_numpy(img_label)
+        img_label1 = torch.from_numpy(img_label1)
+        # add cls label on label1
+        cls_category1 = torch.unique(img_label1)
+        cls_label1 = torch.zeros(7, dtype = int)
+        for index in cls_category1:
+            cls_label1[int(index)] = 1
+
+        img_label2 = torch.from_numpy(img_label2)
+        # add cls label on label2
+        cls_category2 = torch.unique(img_label2)
+        cls_label2 = torch.zeros(7, dtype=int)
+        for index in cls_category2:
+            cls_label2[int(index)] = 1
+
+        if img_label.dim() > 2:
+            img_label = img_label[0]
+            img_label1 = img_label1[0]
+            img_label2 = img_label2[0]
+
+        return {'A':img_A, 'B':img_B, 'L':img_label, 'L1':img_label1, 'L2':img_label2,
+                'Index':index, 'name':name, 'cls1':cls_label1, 'cls2':cls_label2}
+
+if __name__ == '__main__':
+    root_dir = r'E:\cddataset\mmcd\Second_my'
+    cddata = SCDDataset(root_dir=root_dir)
+    list_path = os.path.join(root_dir, 'list', 'val', '.txt')
+    for i in range(593):
+        cls_labe1 = cddata.__getitem__(i)['cls1']
+        print(cls_labe1)
+        cls_labe2 = cddata.__getitem__(i)['cls2']
+        print(cls_labe2)

data/__init__.py

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+import argparse
+import logging
+import core.logger as Logger
+import data as Data
+
+#Create chaneg detection dataset
+import logging
+import torch.utils.data
+
+def create_cd_dataloader(dataset, dataset_opt, phase):
+    if phase == 'train' or 'val' or 'test':
+        return torch.utils.data.DataLoader(
+            dataset,
+            batch_size=dataset_opt['batch_size'],
+            shuffle=dataset_opt['use_shuffle'],
+            num_workers=dataset_opt['num_workers'],
+            pin_memory=True)
+    else:
+        raise NotImplementedError(
+            'Dataloader [{:s}] is not found'.format(phase)
+        )
+
+def create_cd_dataset(dataset_opt, phase):
+    from data.CDDataset import CDDataset
+    print(dataset_opt["datasetroot"])
+    dataset = CDDataset(root_dir=dataset_opt["datasetroot"],
+                        resolution=dataset_opt["resolution"],
+                        split=phase,
+                        data_len=dataset_opt["data_len"]
+                        )
+    logger = logging.getLogger('base')
+    logger.info('Dataset [{:s} - {:s} - {:s}] is created'.format(dataset.__class__.__name__,
+                                                                 dataset_opt['name'],
+                                                                 phase))
+    return dataset
+
+def create_scd_dataset(dataset_opt, phase):
+    from data.CDDataset import SCDDataset
+    print(dataset_opt["datasetroot"])
+    dataset = SCDDataset(root_dir=dataset_opt["datasetroot"],
+                        resolution=dataset_opt["resolution"],
+                        split=phase,
+                        data_len=dataset_opt["data_len"]
+                        )
+    logger = logging.getLogger('base')
+    logger.info('Dataset [{:s} - {:s} - {:s}] is created'.format(dataset.__class__.__name__,
+                                                                 dataset_opt['name'],
+                                                                 phase))
+    return dataset
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', type=str, default='../config/levir.json')
+    parser.add_argument('-p', '--phase', type=str, choices=['train', 'test'], default='train')
+    parser.add_argument('-gpu', '--gpu_ids', type=str, default=None)
+
+    args = parser.parse_args()
+    opt = Logger.parse(args)
+    opt = Logger.dict_to_nonedict(opt)
+    print(opt)
+
+    for phase, dataset_opt in opt['datasets'].items():
+        if phase == 'train' and args.phase != 'test':
+            print("Creating [train] change-detection dataloader.")
+            train_set  = Data.create_cd_dataset(dataset_opt, phase)
+            train_loader = Data.create_cd_dataloader(train_set, dataset_opt, phase)
+

data/colormap.py

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+
+
+second_colormap = [[255, 255, 255], [0, 0, 255], [128, 128, 128], [0, 128, 0], [0, 255, 0], [128, 0, 0], [255, 0, 0]]

data/generate_list.py

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+import os
+
+def genreate_list(root, split):
+
+    list_path = os.path.join(root, split+'.txt')
+    with open(list_path, 'w') as f:
+        for img_name in os.listdir(os.path.join(root)):
+            f.write(img_name + '\n')
+
+if __name__ == "__main__":
+    root = r'E:\cddataset\mmcd\Second_my\val\im1'
+    genreate_list(root, 'val')

data/util.py

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+import torchvision
+
+totensor = torchvision.transforms.ToTensor()
+
+def transform_augment_cd(img, min_max=(0, 1)):
+    img = totensor(img)
+    ret_img = img * (min_max[1] - min_max[0]) + min_max[0]
+    return ret_img