I am working with some lidar data images that when loading the same images using pytorch ImageFolder and Dataloader with the only transform being converting the images to tensors there seems to be some extreme thresholding and I can’t seem to locate the cause of this.
Below is how I’m displaying the first image:
dataset = gdal.Open(dir)
print(dataset.RasterCount)
img = dataset.GetRasterBand(1).ReadAsArray()
f = plt.figure()
plt.imshow(img)
print(img.shape)
plt.show()
and here is how I am using the data loader and displaying the thresholded image:
Note: This isn’t the same image as above, the thresholded images do correspond with their original counterpart and its not the images being edited completely.
data_transforms = {
'train': transforms.Compose([
transforms.ToTensor(),
]),
'val': transforms.Compose([
transforms.ToTensor(),
]),
}
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x]) for x in ['train', 'val']}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
dataloders = {x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=1,
shuffle=True,
num_workers=2) for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
for image in dataloders["train"]:
f = plt.figure()
print(image[0].shape)
plt.imshow(image[0].squeeze()[0,:,:])
plt.show()
break
Any help on an alternative way to display the images or any mistakes I am making would be greatly appreciated.
