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| ''' | |
| This code is partially borrowed from IFRNet (https://github.com/ltkong218/IFRNet). | |
| In the consideration of the difficulty in flow supervision generation, we abort | |
| flow loss in the 8x case. | |
| ''' | |
| import os | |
| import cv2 | |
| import torch | |
| import random | |
| import numpy as np | |
| from torch.utils.data import Dataset | |
| from utils.utils import read, img2tensor | |
| def random_resize_woflow(img0, imgt, img1, p=0.1): | |
| if random.uniform(0, 1) < p: | |
| img0 = cv2.resize(img0, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR) | |
| imgt = cv2.resize(imgt, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR) | |
| img1 = cv2.resize(img1, dsize=None, fx=2.0, fy=2.0, interpolation=cv2.INTER_LINEAR) | |
| return img0, imgt, img1 | |
| def random_crop_woflow(img0, imgt, img1, crop_size=(224, 224)): | |
| h, w = crop_size[0], crop_size[1] | |
| ih, iw, _ = img0.shape | |
| x = np.random.randint(0, ih-h+1) | |
| y = np.random.randint(0, iw-w+1) | |
| img0 = img0[x: x + h, y : y + w, :] | |
| imgt = imgt[x: x + h, y : y + w, :] | |
| img1 = img1[x: x + h, y : y + w, :] | |
| return img0, imgt, img1 | |
| def center_crop_woflow(img0, imgt, img1, crop_size=(512, 512)): | |
| h, w = crop_size[0], crop_size[1] | |
| ih, iw, _ = img0.shape | |
| img0 = img0[ih // 2 - h // 2: ih // 2 + h // 2, iw // 2 - w // 2: iw // 2 + w // 2, :] | |
| imgt = imgt[ih // 2 - h // 2: ih // 2 + h // 2, iw // 2 - w // 2: iw // 2 + w // 2, :] | |
| img1 = img1[ih // 2 - h // 2: ih // 2 + h // 2, iw // 2 - w // 2: iw // 2 + w // 2, :] | |
| return img0, imgt, img1 | |
| def random_reverse_channel_woflow(img0, imgt, img1, p=0.5): | |
| if random.uniform(0, 1) < p: | |
| img0 = img0[:, :, ::-1] | |
| imgt = imgt[:, :, ::-1] | |
| img1 = img1[:, :, ::-1] | |
| return img0, imgt, img1 | |
| def random_vertical_flip_woflow(img0, imgt, img1, p=0.3): | |
| if random.uniform(0, 1) < p: | |
| img0 = img0[::-1] | |
| imgt = imgt[::-1] | |
| img1 = img1[::-1] | |
| return img0, imgt, img1 | |
| def random_horizontal_flip_woflow(img0, imgt, img1, p=0.5): | |
| if random.uniform(0, 1) < p: | |
| img0 = img0[:, ::-1] | |
| imgt = imgt[:, ::-1] | |
| img1 = img1[:, ::-1] | |
| return img0, imgt, img1 | |
| def random_rotate_woflow(img0, imgt, img1, p=0.05): | |
| if random.uniform(0, 1) < p: | |
| img0 = img0.transpose((1, 0, 2)) | |
| imgt = imgt.transpose((1, 0, 2)) | |
| img1 = img1.transpose((1, 0, 2)) | |
| return img0, imgt, img1 | |
| def random_reverse_time_woflow(img0, imgt, img1, embt, p=0.5): | |
| if random.uniform(0, 1) < p: | |
| tmp = img1 | |
| img1 = img0 | |
| img0 = tmp | |
| embt = 1 - embt | |
| return img0, imgt, img1, embt | |
| class GoPro_Train_Dataset(Dataset): | |
| def __init__(self, dataset_dir='data/GOPRO', interFrames=7, augment=True): | |
| self.dataset_dir = dataset_dir + '/train' | |
| self.interFrames = interFrames | |
| self.augment = augment | |
| self.setLength = interFrames + 2 | |
| video_list = [ | |
| 'GOPR0372_07_00', 'GOPR0374_11_01', 'GOPR0378_13_00', 'GOPR0384_11_01', | |
| 'GOPR0384_11_04', 'GOPR0477_11_00', 'GOPR0868_11_02', 'GOPR0884_11_00', | |
| 'GOPR0372_07_01', 'GOPR0374_11_02', 'GOPR0379_11_00', 'GOPR0384_11_02', | |
| 'GOPR0385_11_00', 'GOPR0857_11_00', 'GOPR0871_11_01', 'GOPR0374_11_00', | |
| 'GOPR0374_11_03', 'GOPR0380_11_00', 'GOPR0384_11_03', 'GOPR0386_11_00', | |
| 'GOPR0868_11_01', 'GOPR0881_11_00'] | |
| self.frames_list = [] | |
| self.file_list = [] | |
| for video in video_list: | |
| frames = sorted(os.listdir(os.path.join(self.dataset_dir, video))) | |
| n_sets = (len(frames) - self.setLength) // (interFrames+1) + 1 | |
| videoInputs = [frames[(interFrames + 1) * i: (interFrames + 1) * i + self.setLength | |
| ] for i in range(n_sets)] | |
| videoInputs = [[os.path.join(video, f) for f in group] for group in videoInputs] | |
| self.file_list.extend(videoInputs) | |
| def __len__(self): | |
| return len(self.file_list) * self.interFrames | |
| def __getitem__(self, idx): | |
| clip_idx = idx // self.interFrames | |
| embt_idx = idx % self.interFrames | |
| imgpaths = [os.path.join(self.dataset_dir, fp) for fp in self.file_list[clip_idx]] | |
| pick_idxs = list(range(0, self.setLength, self.interFrames + 1)) | |
| imgt_beg = self.setLength // 2 - self.interFrames // 2 | |
| imgt_end = self.setLength // 2 + self.interFrames // 2 + self.interFrames % 2 | |
| imgt_idx = list(range(imgt_beg, imgt_end)) | |
| input_paths = [imgpaths[idx] for idx in pick_idxs] | |
| imgt_paths = [imgpaths[idx] for idx in imgt_idx] | |
| embt = torch.from_numpy(np.array((embt_idx + 1) / (self.interFrames+1) | |
| ).reshape(1, 1, 1).astype(np.float32)) | |
| img0 = np.array(read(input_paths[0])) | |
| imgt = np.array(read(imgt_paths[embt_idx])) | |
| img1 = np.array(read(input_paths[1])) | |
| if self.augment == True: | |
| img0, imgt, img1 = random_resize_woflow(img0, imgt, img1, p=0.1) | |
| img0, imgt, img1 = random_crop_woflow(img0, imgt, img1, crop_size=(224, 224)) | |
| img0, imgt, img1 = random_reverse_channel_woflow(img0, imgt, img1, p=0.5) | |
| img0, imgt, img1 = random_vertical_flip_woflow(img0, imgt, img1, p=0.3) | |
| img0, imgt, img1 = random_horizontal_flip_woflow(img0, imgt, img1, p=0.5) | |
| img0, imgt, img1 = random_rotate_woflow(img0, imgt, img1, p=0.05) | |
| img0, imgt, img1, embt = random_reverse_time_woflow(img0, imgt, img1, | |
| embt=embt, p=0.5) | |
| else: | |
| img0, imgt, img1 = center_crop_woflow(img0, imgt, img1, crop_size=(512, 512)) | |
| img0 = img2tensor(img0.copy()).squeeze(0) | |
| imgt = img2tensor(imgt.copy()).squeeze(0) | |
| img1 = img2tensor(img1.copy()).squeeze(0) | |
| return {'img0': img0.float(), | |
| 'imgt': imgt.float(), | |
| 'img1': img1.float(), | |
| 'embt': embt} | |
| class GoPro_Test_Dataset(Dataset): | |
| def __init__(self, dataset_dir='data/GOPRO', interFrames=7): | |
| self.dataset_dir = dataset_dir + '/test' | |
| self.interFrames = interFrames | |
| self.setLength = interFrames + 2 | |
| video_list = [ | |
| 'GOPR0384_11_00', 'GOPR0385_11_01', 'GOPR0410_11_00', | |
| 'GOPR0862_11_00', 'GOPR0869_11_00', 'GOPR0881_11_01', | |
| 'GOPR0384_11_05', 'GOPR0396_11_00', 'GOPR0854_11_00', | |
| 'GOPR0868_11_00', 'GOPR0871_11_00'] | |
| self.frames_list = [] | |
| self.file_list = [] | |
| for video in video_list: | |
| frames = sorted(os.listdir(os.path.join(self.dataset_dir, video))) | |
| n_sets = (len(frames) - self.setLength)//(interFrames+1) + 1 | |
| videoInputs = [frames[(interFrames + 1) * i:(interFrames + 1) * i + self.setLength | |
| ] for i in range(n_sets)] | |
| videoInputs = [[os.path.join(video, f) for f in group] for group in videoInputs] | |
| self.file_list.extend(videoInputs) | |
| def __len__(self): | |
| return len(self.file_list) * self.interFrames | |
| def __getitem__(self, idx): | |
| clip_idx = idx // self.interFrames | |
| embt_idx = idx % self.interFrames | |
| imgpaths = [os.path.join(self.dataset_dir, fp) for fp in self.file_list[clip_idx]] | |
| pick_idxs = list(range(0, self.setLength, self.interFrames + 1)) | |
| imgt_beg = self.setLength // 2 - self.interFrames // 2 | |
| imgt_end = self.setLength // 2 + self.interFrames // 2 + self.interFrames % 2 | |
| imgt_idx = list(range(imgt_beg, imgt_end)) | |
| input_paths = [imgpaths[idx] for idx in pick_idxs] | |
| imgt_paths = [imgpaths[idx] for idx in imgt_idx] | |
| img0 = np.array(read(input_paths[0])) | |
| imgt = np.array(read(imgt_paths[embt_idx])) | |
| img1 = np.array(read(input_paths[1])) | |
| img0, imgt, img1 = center_crop_woflow(img0, imgt, img1, crop_size=(512, 512)) | |
| img0 = img2tensor(img0).squeeze(0) | |
| imgt = img2tensor(imgt).squeeze(0) | |
| img1 = img2tensor(img1).squeeze(0) | |
| embt = torch.from_numpy(np.array((embt_idx + 1) / (self.interFrames + 1) | |
| ).reshape(1, 1, 1).astype(np.float32)) | |
| return {'img0': img0.float(), | |
| 'imgt': imgt.float(), | |
| 'img1': img1.float(), | |
| 'embt': embt} |