ucf101Loader.py 文件源码

def hookTrainData(self, sampleIdxs):
        assert len(sampleIdxs) > 0, 'we need a non-empty batch list'
        source_list, target_list, label_list = [], [], []
        for idx in sampleIdxs:
            classList = self.trainDict[idx]
            img_pair = classList[np.random.choice(self.trainLenClass[idx], 1)]
            prev_img = img_pair[0]
            next_img = img_pair[1]
            label = idx
            # print prev_img, next_img, label
            source = cv2.imread(os.path.join(self.img_path, prev_img), cv2.IMREAD_COLOR)
            target = cv2.imread(os.path.join(self.img_path, next_img), cv2.IMREAD_COLOR)
            source_list.append(np.expand_dims(cv2.resize(source, (self.image_size[1], self.image_size[0])), 0))
            target_list.append(np.expand_dims(cv2.resize(target, (self.image_size[1], self.image_size[0])) ,0))
            label_list.append(np.expand_dims(label, 0))
        return np.concatenate(source_list, axis=0), np.concatenate(target_list, axis=0), np.concatenate(label_list, axis=0)
        # Adding the channel dimension if images are read in grayscale
        # return np.expand_dims(source_list, axis = 3), np.expand_dims(target_list, axis = 3)