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Huperflow_image.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b730ee80bf2d27a42f73f529a1fb9dbd6015161c4f36396a46f6b311f9d4f64
+size 2035884283

human_response/example_to_load_data.py

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+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"]
+# csvprename={'KITTI', 'vkitti','MPI','VIPER','Spring','Monkaa','MHOF','Driving','FT3D' ,'TartanAir'};
+
+
+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):
+    # map flow to rgb image
+    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):  # ten dataset in total
+        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_)
+
+                # load all images
+                image_list = [load_image(img) for img in image_list_]
+                # resize the image to that divisible by 8
+                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()