Transforms

Transforms

class torchelie.transforms.ResizeNoCrop(size)

Resize a PIL image so that its longer border is of size size

Parameters:size (int) – max size of the image
class torchelie.transforms.ResizedCrop(size, scale=0.54, interpolation=2)

Crop the given PIL Image to size. A crop of size of the original size is made. This crop is finally resized to given size.

Parameters:
  • size – expected output size of each edge
  • scale – size of the origin size cropped
  • interpolation – Default: PIL.Image.BILINEAR
static get_params(img, scale)

Get parameters for crop. :param img: Image to be cropped. :type img: PIL Image :param scale: range of size of the origin size cropped :type scale: float

Returns:params (i, j, h, w) to be passed to crop.
Return type:tuple
class torchelie.transforms.AdaptPad(sz, padding_mode='constant')

Pad an input image so that it reaches size size

Parameters:
  • sz ((int, int)) – target size
  • padding_mode (str) – one of the modes of torchvision.transforms.pad
class torchelie.transforms.MultiBranch(transforms)

Transform an image with multiple transforms

Parameters:transforms (list of transforms) – the parallel set of transforms
class torchelie.transforms.Canny(thresh_low=100, thresh_high=200)

Run Canny edge detector over an image. Requires OpenCV to be installed

Parameters:
  • thresh_low (int) – lower threshold (default: 100)
  • thresh_high (int) – upper threshold (default: 200)

Differentiable

torchelie.transforms.differentiable.center_crop(batch, size)

Crop the center of a 4D images tensor

Parameters:
  • batch (4D images tensor) – the tensor to crop
  • size ((int, int)) – size of the resulting image as (height, width)
Returns:

The cropped image
torchelie.transforms.differentiable.crop(img, warped=True, sub_img_factor=2)

Randomly crop a sub_img_factor smaller part of img.

Parameters:
  • img (3D or 4D image(s) tensor) – input image(s)
  • warped (bool) – Whether the image should be considered warped (default: True)
  • sub_img_factor (float) – fraction of the image to take. For instance, 2 will crop a quarter of the image (half the width, half the height). (default: 2)
torchelie.transforms.differentiable.gblur(input)

Gaussian blur with kernel size 3

Parameters:input (3D or 4D image(s) tensor) – input image
Returns:the blurred tensor
torchelie.transforms.differentiable.mblur(input)

Mean (or average) blur with kernel size 3

Parameters:input (3D or 4D image(s) tensor) – input image
Returns:the blurred tensor
torchelie.transforms.differentiable.roll(img, x_roll, y_roll)

Wrap an image

Parameters:
  • img (3D or 4D image(s) tensor) – an image tensor
  • x_roll (int) – how many pixels to roll on the x axis
  • y_roll (int) – how many pixels to roll on the y axis
Returns:

The rolled tensor