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Simple Transformations

This section includes simple but very common image transformations like cropping, translation, scaling and rotation.

[res] image.crop([dst,] src, x1, y1, [x2, y2])

Crops image src at coordinate (x1, y1) up to coordinate (x2, y2). The coordinate indexing is zero-based and (x2, y2) is non-inclusive. If dst is provided, it is used to store the output image. Otherwise, returns a new res Tensor.

-- The indexing starts with 0 and 2 is non-inclusive coordinate.
> require('image')
> image.crop(torch.Tensor(3, 2, 2), 0, 0 , 2, 2) -- crop is a correct crop and the result is 3x2x2 tensor. 
(1,.,.) = 
  0  0
  0  0

(2,.,.) = 
  0  0
  0  0

(3,.,.) = 
  0  0
  0  0
[torch.DoubleTensor of size 3x2x2]

[res] image.crop([dst,] src, format, width, height)

Crops a width x height section of source image src. The argument format is a string specifying where to crop: it can be "c", "tl", "tr", "bl" or "br" for center, top left, top right, bottom left and bottom right, respectively. If dst is provided, it is used to store the output image. Otherwise, returns a new res Tensor.

[res] image.translate([dst,] src, x, y)

Translates image src by x pixels horizontally and y pixels vertically. If dst is provided, it is used to store the output image. Otherwise, returns a new res Tensor.

[res] image.scale(src, width, height, [mode])

Rescale the height and width of image src to have width width and height height. Variable mode specifies type of interpolation to be used. Valid values include bilinear (the default), bicubic, or simple interpolation. Returns a new res Tensor.

[res] image.scale(src, size, [mode])

Rescale the height and width of image src. Variable size is a number or a string specifying the size of the result image. When size is a number, it specifies the maximum height or width of the output. When it is a string like WxH or MAX or ^MIN, *SC or *SCn/SCd it specifies the height x width, maximum height or width of the output, minimum height or width of the output, scaling factor (number), or fractional scaling factor (int/int), respectively.

[res] image.scale(dst, src, [mode])

Rescale the height and width of image src to fit the dimensions of Tensor dst.

[res] image.rotate([dst,], src, theta, [mode])

Rotates image src by theta radians. If dst is specified it is used to store the results of the rotation. Variable mode specifies type of interpolation to be used. Valid values include simple (the default) or bilinear interpolation.

[res] image.polar([dst,], src, [interpolation], [mode])

Converts image src to polar coordinates. In the polar image, angular information is in the vertical direction and radius information in the horizontal direction. If dst is specified it is used to store the polar image. If dst is not specified, its size is automatically determined. Variable interpolation specifies type of interpolation to be used. Valid values include simple (the default) or bilinear interpolation. Variable mode determines whether the full image is converted to the polar space (implying empty regions in the polar image), or whether only the valid central part of the polar transform is returned (the default).

[res] image.logpolar([dst,], src, [interpolation], [mode])

Converts image src to log-polar coordinates. In the log-polar image, angular information is in the vertical direction and log-radius information in the horizontal direction. If dst is specified it is used to store the polar image. If dst is not specified, its size is automatically determined. Variable interpolation specifies type of interpolation to be used. Valid values include simple (the default) or bilinear interpolation. Variable mode determines whether the full image is converted to the log-polar space (implying empty regions in the log-polar image), or whether only the valid central part of the log-polar transform is returned (the default).

[res] image.hflip([dst,] src)

Flips image src horizontally (left<->right). If dst is provided, it is used to store the output image. Otherwise, returns a new res Tensor.

[res] image.vflip([dst,], src)

Flips image src vertically (upsize<->down). If dst is provided, it is used to store the output image. Otherwise, returns a new res Tensor.

[res] image.flip([dst,] src, flip_dim)

Flips image src along the specified dimension. If dst is provided, it is used to store the output image. Otherwise, returns a new res Tensor.

[res] image.minmax{tensor, [min, max, ...]}

Compresses image tensor between min and max. When omitted, min and max are infered from tensor:min() and tensor:max(), respectively. The tensor is normalized using min and max by performing :

tensor:add(-min):div(max-min)

Other optional arguments (...) include symm, inplace, saturate, and tensorOut. When symm=true and min and max are both omitted, max = min*2 in the above equation. This results in a symmetric dynamic range that is particularly useful for drawing filters. The default is false. When inplace=true, the result of the compression is stored in tensor. The default is false. When saturate=true, the result of the compression is passed through a function that clips the values between 0 and 1 (i.e. anything below 0 is set to 0, anything above 1 is set to 1). When provided, Tensor tensorOut is used to store results. Note that arguments should be provided as key-value pairs (in a table).

[res] image.gaussianpyramid([dst,] src, scales)

Constructs a Gaussian pyramid of scales scales from a 2D or 3D src image or size [nChannel x] width x height. Each Tensor at index i in the returned list of Tensors has size [nChannel x] width*scales[i] x height*scales[i].

If list dst is provided, with or without Tensors, it is used to store the output images. Otherwise, returns a new res list of Tensors.

Internally, this function makes use of functions image.gaussian, image.scale and image.convolve.