image Package Reference Manual

image is the Torch7 distribution package for processing images. It contains a wide variety of functions divided into the following categories:

Saving and loading images as JPEG, PNG, PPM and PGM;
Simple transformations like translation, scaling and rotation;
Parameterized transformations like convolutions and warping;
Graphical user interfaces like display and window;
Color Space Conversions from and to RGB, YUV, Lab, and HSL;
Tensor Constructors for creating Lenna, Fabio and Gaussian and Laplacian kernels;

Note that unless speficied otherwise, this package deals with images of size nChannel x height x width.

## Saving and Loading ## This sections includes functions for saving and loading different types of images to and from disk. ### [res] image.load(filename, [depth, tensortype]) ### Loads an image located at path `filename` having `depth` channels (1 or 3) into a [Tensor](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor) of type `tensortype` (*float*, *double* or *byte*). The last two arguments are optional.

The image format is determined from the filename's extension suffix. Supported formats are JPEG, PNG, PPM and PGM.

The returned res Tensor has size nChannel x height x width where nChannel is 1 (greyscale) or 3 (usually RGB or YUV.

### image.save(filename, tensor) ### Saves Tensor `tensor` to disk at path `filename`. The format to which the image is saved is extrapolated from the `filename`'s extension suffix. The `tensor` should be of size `nChannel x height x width`. ## 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)`. 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](https://en.wikipedia.org/wiki/Bilinear_interpolation) (the default) 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, it specifies the height x width, maximum, or minimum height or width of the output, 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) ### Rotates image `src` by `theta` radians. If `dst` is specified it is used to store the results of the rotation. ### [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.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 : ```lua 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 [image.saturate](#image.saturate) 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](https://en.wikipedia.org/wiki/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.

## Parameterized transformations ## This section includes functions for performing transformations on images requiring parameter Tensors like a warp `field` or a convolution `kernel`. ### [res] image.warp([dst,]src,field,[mode,offset,clamp]) ### Warps image `src` (of size`KxHxW`) according to flow field `field`. The latter has size `2xHxW` where the first dimension is for the `(y,x)` flow field. String `mode` can take on values [lanczos](https://en.wikipedia.org/wiki/Lanczos_resampling), [bicubic](https://en.wikipedia.org/wiki/Bicubic_interpolation), [bilinear](https://en.wikipedia.org/wiki/Bilinear_interpolation) (the default), or *simple*. When `offset` is true (the default), `(x,y)` is added to the flow field. The `clamp` variable specifies how to handle the interpolation of samples off the input image. Permitted values are strings *clamp* (the default) or *pad*. If `dst` is specified, it is used to store the result of the warp. Otherwise, returns a new `res` Tensor. ### [res] image.convolve([dst,] src, kernel, [mode]) ### Convolves Tensor `kernel` over image `src`. Valid string values for argument `mode` are : * *full* : the `src` image is effectively zero-padded such that the `res` of the convolution has the same size as `src`; * *valid* (the default) : the `res` image will have `math.ceil(kernel/2)` less columns and rows on each side; * *same* : performs a *full* convolution, but crops out the portion fitting the output size of *valid*; Note that this function internally uses [torch.conv2](https://github.com/torch/torch7/blob/master/doc/maths.md#torch.conv.dok). If `dst` is provided, it is used to store the output image. Otherwise, returns a new `res` Tensor. ### [res] image.lcn(src, [kernel]) ### Local contrast normalization (LCN) on a given `src` image using kernel `kernel`. If `kernel` is not given, then a default `9x9` Gaussian is used (see [image.gaussian](#image.gaussian)).

To prevent border effects, the image is first global contrast normalized (GCN) by substracting the global mean and dividing by the global standard deviation.

Then the image is locally contrast normalized using the following equation:

res = (src - lm(src)) / sqrt( lm(src) - lm(src*src) )

where lm(x) is the local mean of each pixel in the image (i.e. image.convolve(x,kernel)) and sqrt(x) is the element-wise square root of x. In other words, LCN performs local substractive and divisive normalization.

Note that this implementation is different than the LCN Layer defined on page 3 of What is the Best Multi-Stage Architecture for Object Recognition?.

### [res] image.erode(src, [kernel, pad]) ### Performs a [morphological erosion](https://en.wikipedia.org/wiki/Erosion_(morphology)) on binary (zeros and ones) image `src` using odd dimensioned morphological binary kernel `kernel`. The default is a kernel consisting of ones of size `3x3`. Number `pad` is the value to assume outside the image boundary when performing the convolution. The default is 1. ### [res] image.dilate(src, [kernel, pad]) ### Performs a [morphological dilation](https://en.wikipedia.org/wiki/Dilation_(morphology)) on binary (zeros and ones) image `src` using odd dimensioned morphological binary kernel `kernel`. The default is a kernel consisting of ones of size `3x3`. Number `pad` is the value to assume outside the image boundary when performing the convolution. The default is 0. ## Graphical User Interfaces ## The following functions, except for [image.toDisplayTensor](#image.toDisplayTensor), require package [qtlua](https://github.com/torch/qtlua) and can only be accessed via the `qlua` Lua interpreter (as opposed to the [th](https://github.com/torch/trepl) or luajit interpreter). ### [res] image.toDisplayTensor(input, [...]) ### Optional arguments `[...]` expand to `padding`, `nrow`, `scaleeach`, `min`, `max`, `symmetric`, `saturate`. Returns a single `res` Tensor that contains a grid of all in the images in `input`. The latter can either be a table of image Tensors of size `height x width` (greyscale) or `nChannel x height x width` (color), or a single Tensor of size `batchSize x nChannel x height x width` or `nChannel x height x width` where `nChannel=[3,1]`, `batchSize x height x width` or `height x width`.

When scaleeach=false (the default), all detected images are compressed with successive calls to image.minmax:

image.minmax{tensor=input[i], min=min, max=max, symm=symmetric, saturate=saturate}

padding specifies the number of padding pixels between images. The default is 0. nrow specifies the number of images per row. The default is 6.

Note that arguments can also be specified as key-value arguments (in a table).

The default value of height and width is size, where the latter has a default value of 3. The amplitude of the Gaussian (its maximum value) is amplitude. The default is 1. When normalize=true, the kernel is normalized to have a sum of 1. This overrides the amplitude argument. The default is false. The default value of the horizontal and vertical standard deviation sigma_horz and sigma_vert of the Gaussian kernel is sigma, where the latter has a default value of 0.25. The default values for the corresponding means mean_horz and mean_vert are 0.5. Both the standard deviations and means are relative to kernels of unit width and height where the top-left corner is the origin. In other works, a mean of 0.5 is the center of the kernel size, while a standard deviation of 0.25 is a quarter of it.

Note that arguments can also be specified as key-value arguments (in a table).

### [res] image.gaussian1D([size, sigma, amplitude, normalize, mean]) ### Returns a 1D Gaussian kernel of size `size`, mean `mean` and standard deviation `sigma`. Respectively, these arguments have default values of 3, 0.25 and 0.5. The amplitude of the Gaussian (its maximum value) is `amplitude`. The default is 1. When `normalize=true`, the kernel is normalized to have a sum of 1. This overrides the `amplitude` argument. The default is `false`. Both the standard deviation and mean are relative to a kernel of unit size. In other works, a mean of 0.5 is the center of the kernel size, while a standard deviation of 0.25 is a quarter of it.

Note that arguments can also be specified as key-value arguments (in a table).

### [res] image.laplacian([size, sigma, amplitude, normalize, [...]]) ### Returns a 2D [Laplacian](https://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian) kernel of size `height x width`. When used in a 2D convolution, the Laplacian of an image highlights regions of rapid intensity change and is therefore often used for edge detection (ref.: [Laplacian/Laplacian of Gaussian](http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm)). Optional arguments `[...]` expand to `width`, `height`, `sigma_horz`, `sigma_vert`, `mean_horz`, `mean_vert`.

The default value of height and width is size, where the latter has a default value of 3. The amplitude of the Laplacian (its maximum value) is amplitude. The default is 1. When normalize=true, the kernel is normalized to have a sum of 1. This overrides the amplitude argument. The default is false. The default value of the horizontal and vertical standard deviation sigma_horz and sigma_vert of the Laplacian kernel is sigma, where the latter has a default value of 0.25. The default values for the corresponding means mean_horz and mean_vert are 0.5. Both the standard deviations and means are relative to kernels of unit width and height where the top-left corner is the origin. In other works, a mean of 0.5 is the center of the kernel size, while a standard deviation of 0.25 is a quarter of it.

### [res] image.colormap(nColor) ### Creates an optimally-spaced RGB color mapping of `nColor` colors. Note that the mapping is obtained by generating the colors around the HSV wheel, varying the Hue component. The returned `res` Tensor has size `nColor x 3`. ### [res] image.jetColormap(nColor) ### Creates a jet (blue to red) RGB color mapping of `nColor` colors. The returned `res` Tensor has size `nColor x 3`.

Dependencies:

Torch7

Install:

$ luarocks install image

Use:

> require 'image'
> l = image.lena()
> image.display(l)
> f = image.fabio()
> image.display(f)

Name		Name	Last commit message	Last commit date
Latest commit History 212 Commits
generic		generic
test		test
.gitignore		.gitignore
CMakeLists.txt		CMakeLists.txt
README.md		README.md
fabio.jpg		fabio.jpg
fabio.png		fabio.png
image-1.1.alpha-0.rockspec		image-1.1.alpha-0.rockspec
image.c		image.c
init.lua		init.lua
jpeg.c		jpeg.c
lena.jpg		lena.jpg
lena.png		lena.png
png.c		png.c
ppm.c		ppm.c
win.ui		win.ui

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image Package Reference Manual

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

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

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