I want to test my pre-trained model on a video. when I set the num_workers>0, my loaded data is chaotic after read each video image. I need to crop the full resolution image into three small regions with fixed coordinate. when I load data with num_workers>0, the returned data is different position cropped. This makes me confused.
my specific dataset is :
> #coding:utf8
import os
from PIL import Image
import numpy as np
from torch.utils import data
from torchvision import transforms as T
import string
import imageio
from torch.utils.data import DataLoader
class videoDataset(data.Dataset):
def __init__(self,root='./',transform=None,DataType='train'):
super(videoDataset,self).__init__()
videopath='video2.avi'
self.vid=imageio.get_reader(videopath,'ffmpeg')
self.referenceimg="./myData/new_cam6d/c22/reference"
self.para={'1':(384,90,768,218),'2':(444,218,1212,474),'3':(384,568,1920,1080)}
if transform is None:
self.transforms = T.Compose(
[T.ToTensor(),
T.Normalize((0.5, 0.5, 0.5,0.5,0.5,0.5), (0.5, 0.5, 0.5,0.5,0.5,0.5))])
def __getitem__(self,index):
self.ref1=Image.open('C0022_1.jpg')
self.ref2=Image.open('C0022_2.jpg')
self.ref3=Image.open('C0022_3.jpg')
data=self.vid.get_data(index)
data=Image.fromarray(data)
img1=data.crop(self.para['1'])
size=img1.size
#print(np.array(img1).shape, np.array(self.ref1).shape)
arr1=np.concatenate((np.array(img1), np.array(self.ref1)), axis=2) #将
img2=data.crop(self.para['2'])
img2=img2.resize(size)
arr2=np.concatenate((np.array(img2), np.array(self.ref2)), axis=2)
img3=data.crop(self.para['3'])
img3=img3.resize(size)
arr3=np.concatenate((np.array(img3), np.array(self.ref3)), axis=2)
arr=np.concatenate((arr1, arr2, arr3), axis=1)
finalImg=self.transforms(arr)
return finalImg,index
def __len__(self):
img_nums=self.vid.get_length()
return img_nums