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ImageDistances

Distances between N-dimensional images

Readme

ImageDistances.jl

Build Status codecov.io

ImageDistances.jl aims to:

  • follow the same API in Distances.jl
  • support image types
  • provide image-specific distances

Usage

ImageDistances.jl is shipped together with Images.jl, but you can still use it as a standlone package.

# both line includes this package
using Images
using ImageDistances

Here's a simple usage example

using ImageDistances, TestImages

d = Euclidean()
imgA = testimage("cameraman") # N0f8
imgB = testimage("lena_gray_512") # N0f8

# distance between two images
evaluate(d, imgA, imgB) # 142.59576f0
d(imgA, imgB) # 142.59576f0

Distances are calculated regardless of the color type and storage type.

using ImageCore

# For gray image, all of them equals
d(imgA, imgB) # 142.59576f0
d(float32.(imgA), float32.(imgB)) # 142.59576f0
d(Float32.(imgA), Float32.(imgB)) # 142.59576f0
d(imgA, float32.(imgB)) # 142.59576f0

However, for Color3 images such as RGB, it's noteworthy that the following results are different in general.

d = NCC()
imgA = testimage("lena_color_512")
imgB = testimage("fabio_color_512")
# distance of each pixel is calculated first, and then sum up all pixels
d(imgA, imgB) # 0.023451565f0
# distance of each slice is calculated first, and then sum up three channels
d(channelview(imgA), channelview(imgB)) # 0.21142173f0

That's said, to achieve the same results to other languages, you need to channelview the image first to get a raw numeric view.

Just like in Distances.jl, huge performance gains are obtained by calling the colwise and pairwise functions instead of naively looping over a collection of images and calling evaluate.

d = ModifiedHausdorff()

# two lists of images
imgsA = [imgA, imgB, ...]
imgsB = [imgB, imgA, ...]

# distance between the "columns"
colwise(d, imgsA, imgsB)

# distance between every pair of images
pairwise(d, imgsA, imgsB)
pairwise(d, imgsA)

Distances

General Distances

type name convenient syntax math definition
Euclidean euclidean(x, y) sqrt(sum((x - y) .^ 2))
SqEuclidean sqeuclidean(x, y) sum((x - y).^2)
Cityblock cityblock(x, y) sum(abs(x - y))
TotalVariation totalvariation(x, y) sum(abs(x - y)) / 2
Minkowski minkowski(x, y, p) sum(abs(x - y).^p) ^ (1/p)
Hamming hamming(x, y) sum(x .!= y)
SumAbsoluteDifference sad(x, y) sum(abs(x - y))
SumSquaredDifference ssd(x, y) sum((x - y).^2)
MeanAbsoluteError mae(x, y), sadn(x, y) sum(abs(x - y))/len(x)
MeanSquaredError mse(x, y), ssdn(x, y) sum((x - y).^2)/len(x)
RootMeanSquaredError rmse(x, y) sqrt(sum((x - y) .^ 2))
NCC ncc(x, y) dot(x,y)/(norm(x)*norm(y))

Image-specific Distances

Distance type Convenient syntax References
Hausdorff and ModifiedHausdorff hausdorff(imgA,imgB) and modified_hausdorff(imgA,imgB) Dubuisson, M-P et al. 1994. A Modified Hausdorff Distance for Object-Matching.
CIEDE2000 ciede2000(imgA,imgB) and ciede2000(imgA,imgB; metric=DE_2000()) Sharma, G., Wu, W., and Dalal, E. N., 2005. The CIEDE2000 color‐difference formula.

Contributing

Contributions are very welcome, as are feature requests and suggestions.

Please open an issue if you encounter any problems.

First Commit

12/20/2017

Last Touched

23 days ago

Commits

75 commits