Applying a simple transformation to get a binary image using pytorch
I'd like to binarize image before passing it to the dataloader, I have created a dataset class which works well. but in the __getitem__() method  I'd like to threshold the image:
    def __getitem__(self, idx):
        # Open image, apply transforms and return with label
        img_path = os.path.join(self.dir, self.filelist[filename&quot;])
        image = Image.open(img_path)
        label = self.x_data.iloc[idx][&quot;label&quot;]

        # Applying transformation to the image
        if self.transforms is not None:
           image = self.transforms(image)

        # applying threshold here:
        my_threshold = 240
        image = image.point(lambda p: p &lt; my_threshold and 255)
        image = torch.tensor(image)

        return image, label

And then I tried to invoke the dataset:
    data_transformer = transforms.Compose([
        transforms.Resize((10, 10)),
        transforms.Grayscale()
        //transforms.ToTensor()
    ])

train_set = MyNewDataset(data_path, data_transformer, rows_train)

Since I have applied the threshold on a PIL object I need to apply afterwards a conversion to a tensor object , but for some reason it crashes. can somebody please assist me?

Why not apply the binarization after the conversion from PIL.Image to torch.Tensor?
class ThresholdTransform(object):
  def __init__(self, thr_255):
    self.thr = thr_255 / 255.  # input threshold for [0..255] gray level, convert to [0..1]

  def __call__(self, x):
    return (x &gt; self.thr).to(x.dtype)  # do not change the data type

Once you have this transformation, you simply add it:
data_transformer = transforms.Compose([
        transforms.Resize((10, 10)),
        transforms.Grayscale(),
        transforms.ToTensor(),
        ThresholdTransform(thr_255=240)
    ])

