Azure or AWS maybe i dont know

calib/utils.py

@@ -1,18 +1,19 @@
 from __future__ import division
+import os, glob
+import shutil
 import matplotlib.pyplot as plt
 import cv2
-import os, glob
+
 import torch
-import shutil
+import scipy.ndimage as ndimage
+from torch.utils.data import DataLoader, Dataset
 from PIL import Image
 from imageio import imread
 
 import random
 import numpy as np
 import numbers
 import types
-import scipy.ndimage as ndimage
-from torch.utils.data import DataLoader, Dataset
 import argparse
 
 def openit(path, line):
@@ -141,6 +142,7 @@ def DATA_LOADER(root, split):
  w = open(target_num[0],'r')
  drive_img.append(w.read())
  drive_img = drive_img[0].split("\n")
+
  for i in range(len(input_img)-1):
  yaw, pitch = drive_img[i].split(" ")
  yaw = 0 if yaw == "nan" else yaw
@@ -207,6 +209,7 @@ def frame_by_frame(input):
  frames.append(torch.zeros(frames[0].shape))
  print("inner",len(frames))
  final_images = [torch.cat((torch.tensor(frames[i]), torch.tensor(frames[i+1])), 1) for i in range(len(frames))]
+
  print(len(final_image))
  return final_image
 
@@ -215,11 +218,13 @@ def frame_by_frame(input):
 def break_into_images():
  input_vid = sorted(glob.glob(os.path.join('../labeled') + '/*.HEVC'))
  folder = 0
+
  for i in input_vid:
  folder += 1
  vidcap = cv2.VideoCapture(i)
  success,image = vidcap.read()
  count = 0
+
  while success:
  cv2.imwrite(f"../data/{folder}/frame%d.jpg" % count, image) # save frame as JPEG file 
  success,image = vidcap.read()
@@ -240,6 +245,7 @@ def onehot_vector(item, classes):
  if item <= classes[i]:
  onehot[i] = 1
  break
+
  return onehot
 
 ## ---------------- For Global Motion Aggregation ---------------- ##