dcnum.segm.segm_torch.torch_preproc
Functions
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Transform image data to something torch models expect |
Module Contents
- dcnum.segm.segm_torch.torch_preproc.preprocess_images(images: numpy.ndarray, norm_mean: float | None, norm_std: float | None, image_shape: tuple[int, int] | None = None)[source]
Transform image data to something torch models expect
The transformation includes:
normalization (division by 255, subtraction of mean, division by std)
cropping and padding of the input images to image_shape. For padding, the median of each individual image is used.
casting the input images to four dimensions (batch_size, 1, height, width) where the second axis is “channels”
- Parameters:
images – Input image array (batch_size, height_in, width_in). If this is a 2D image, it will be reshaped to a 3D image with a batch_size of 1.
norm_mean – Mean value used for standard score data normalization, i.e. normalized = `(images / 255 - norm_mean) / norm_std; Set to None to disable normalization.
norm_std – Standard deviation used for standard score data normalization; Set to None to disable normalization (see above).
image_shape – Image shape for which the model was created (height, width). If the image shape does not match the input image shape, then the input images are padded/cropped to fit the image shape of the model.
- Returns:
3D array with preprocessed image data of shape (batch_size, 1, height, width)
- Return type: