| from __future__ import print_function, division |
| import argparse |
| import os |
| import copy |
| import numpy as np |
| from PIL import Image |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import re |
| import glob |
| import torchvision |
| import cv2 |
|
|
|
|
| DEVICE = 'cuda' |
| maindir = 'path to the selected kitti 2015 dataset' |
| datasetName = ["1_KITTI"] |
| |
|
|
|
|
| datasetN = len(datasetName) |
| sessionN = 12 |
| movN = 2 |
| frameN = 15 |
|
|
|
|
| def load_image(imfile): |
| img = np.array(Image.open(imfile)).astype(np.uint8) |
| if len(img.shape) == 2: |
| img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) |
| cv2.imshow('image', img[:, :, [2, 1, 0]] / 255.0) |
| img = torch.from_numpy(img).permute(2, 0, 1).float() |
| return img[None].to(DEVICE) |
|
|
|
|
| def save_video(flo, img, writer): |
| |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
| flo = cv2.cvtColor(flo, cv2.COLOR_BGR2RGB) |
| print(flo.shape) |
| img_flo = np.concatenate([img, flo], axis=0).astype(np.uint8) |
| writer.write(img_flo) |
|
|
|
|
| def demo(args): |
| for dataset in range(datasetN): |
| for session in range(1, sessionN + 1): |
| destination_folder = os.path.join(maindir, datasetName[dataset], f'session{session:03d}') |
| video_file = os.path.join(destination_folder, f'session{session:03d}.mp4') |
|
|
| out = None |
| for file in glob.glob(os.path.join(destination_folder, 'flow_*.mat')): |
| os.remove(file) |
|
|
| for mov in range(1, movN + 1): |
| image_list_ = glob.glob(os.path.join(destination_folder, f'Mov{mov}_F*.jpg')) |
|
|
| if len(image_list_) == 0: |
| image_list_ = glob.glob(os.path.join(destination_folder, f'Mov{mov}_F*.png')) |
| image_list_.sort(key=lambda x: int(re.sub('\D', '', x))) |
|
|
| print(image_list_) |
|
|
| |
| image_list = [load_image(img) for img in image_list_] |
| |
| image_size_ori = image_list[0].shape[-2:] |
| image_size = [(image_size_ori[0] // 8 + 1) * 8, (image_size_ori[1] // 8 + 1) * 8] |
| image_list_resize = [F.interpolate(img, size=image_size, mode='bicubic', align_corners=True) for img in |
| image_list] |
|
|
|
|
| if __name__ == '__main__': |
| demo() |
|
|
