add image_deblurring_dataset for REDS dataset

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11493284

modelscope/msdatasets/task_datasets/reds_image_deblurring_dataset.py

@@ -0,0 +1,60 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import cv2
+import numpy as np
+
+from modelscope.metainfo import Datasets
+from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.task_datasets.sidd_image_denoising.data_utils import (
+    img2tensor, padding)
+from modelscope.msdatasets.task_datasets.sidd_image_denoising.transforms import (
+    augment, paired_random_crop)
+from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    TorchTaskDataset
+from modelscope.utils.constant import Tasks
+
+
+def default_loader(path):
+    return cv2.imread(path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.0
+
+
+@TASK_DATASETS.register_module(
+    Tasks.image_deblurring, module_name=Datasets.PairedDataset)
+class RedsImageDeblurringDataset(TorchTaskDataset):
+    """Paired image dataset for image restoration.
+    """
+
+    def __init__(self, dataset, opt, is_train):
+        self.dataset = dataset
+        self.opt = opt
+        self.is_train = is_train
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, index):
+        item_dict = self.dataset[index]
+        hq_path = item_dict['LQ Frame:FILE']
+        img_hq = default_loader(hq_path)
+        lq_path = item_dict['HQ Frame:FILE']
+        img_lq = default_loader(lq_path)
+
+        # augmentation for training
+        if self.is_train:
+            gt_size = self.opt.gt_size
+            # padding
+            img_hq, img_lq = padding(img_hq, img_lq, gt_size)
+
+            # random crop
+            img_hq, img_lq = paired_random_crop(
+                img_hq, img_lq, gt_size, scale=1)
+
+            # flip, rotation
+            img_hq, img_lq = augment([img_hq, img_lq], self.opt.use_flip,
+                                     self.opt.use_rot)
+
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        img_hq, img_lq = img2tensor([img_hq, img_lq],
+                                    bgr2rgb=True,
+                                    float32=True)
+        return {'input': img_lq, 'target': img_hq}