initial brief descriptor

Author: ashwanirathee

docs/examples/image_features/brief.jl

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+# ---
+# cover: assets/brief.gif
+# title: BRIEF Descriptor
+# description: This demo shows BRIEF descriptor
+# author: Anchit Navelkar, Ashwani Rathee
+# date: 2021-07-12
+# ---
+
+# `BRIEF` (Binary Robust Independent Elementary Features) is an efficient feature point descriptor.
+# It is highly discriminative even when using relatively few bits and is computed using simple 
+# intensity difference tests. BRIEF does not have a sampling pattern thus pairs can be chosen 
+# at any point on the `SxS` patch.
+
+# To build a BRIEF descriptor of length `n`, we need to determine `n` pairs `(Xi,Yi)`. 
+# Denote by `X` and `Y` the vectors of point `Xi` and `Yi`, respectively.
+
+# In ImageFeatures.jl we have five methods to determine the vectors `X` and `Y` :
+
+# - [`random_uniform`](@ref) : `X` and `Y` are randomly uniformly sampled
+# - [`gaussian`](@ref) : `X` and `Y` are randomly sampled using a Gaussian distribution, meaning that locations that are closer to the center of the patch are preferred
+# - [`gaussian_local`](@ref) : `X` and `Y` are randomly sampled using a Gaussian distribution where first `X` is sampled with a standard deviation of `0.04*S^2` and then the `Yi’s` are sampled using a Gaussian distribution – Each `Yi` is sampled with mean `Xi` and standard deviation of `0.01 * S^2`
+# - [`random_coarse`](@ref) : `X` and `Y` are randomly sampled from discrete location of a coarse polar grid
+# - [`center_sample`](@ref) : For each `i`, `Xi` is `(0, 0)` and `Yi` takes all possible values on a coarse polar grid
+
+# As with all the binary descriptors, BRIEF’s distance measure is the number of 
+# different bits between two binary strings which can also be computed as the sum
+# of the XOR operation between the strings.
+
+# BRIEF is a very simple feature descriptor and does not provide scale or rotation 
+# invariance (only translation invariance). To achieve those, see [ORB](orb.md), 
+# [BRISK](brisk.md) and [FREAK](freak.md).
+
+# ## Example
+
+# Let us take a look at a simple example where the BRIEF descriptor is used to match 
+# two images where one has been translated by `(100, 200)` pixels. We will use the 
+# `lighthouse` image from the [TestImages](https://github.com/timholy/TestImages.jl) 
+# package for this example.
+
+
+# Now, let us create the two images we will match using BRIEF.
+
+using ImageFeatures, TestImages, Images, ImageDraw, CoordinateTransformations
+
+img = testimage("sudoku")
+img1 = Gray.(img)
+trans = Translation(-50, -50)
+img2 = warp(img1, trans, axes(img1))
+
+# To calculate the descriptors, we first need to get the keypoints. For this tutorial,
+# we will use the FAST corners to generate keypoints (see [`fastcorners`](@ref)).
+
+keypoints_1 = Keypoints(fastcorners(img1, 12, 0.4))
+keypoints_2 = Keypoints(fastcorners(img2, 12, 0.4))
+
+
+# To create the BRIEF descriptor, we first need to define the parameters by calling
+# the [`BRIEF`](@ref) constructor.
+
+brief_params = BRIEF(size = 256, window = 10, seed = 123)
+
+# Now pass the image with the keypoints and the parameters to the 
+# [`create_descriptor`](@ref) function.
+
+desc_1, ret_keypoints_1 = create_descriptor(img1, keypoints_1, brief_params)
+desc_2, ret_keypoints_2 = create_descriptor(img2, keypoints_2, brief_params)
+
+# The obtained descriptors can be used to find the matches between the two images 
+# using the [`match_keypoints`](@ref) function.
+
+matches = match_keypoints(ret_keypoints_1, ret_keypoints_2, desc_1, desc_2, 0.1)
+
+# We can use the [ImageDraw.jl](https://github.com/JuliaImages/ImageDraw.jl) package
+# to view the results.
+
+grid = hcat(img1, img2)
+offset = CartesianIndex(0, size(img1, 2))
+map(m -> draw!(grid, LineSegment(m[2] + offset,m[1] )), matches)
+grid
+
+
+# `grid` shows the results
+
+save("assets/brief.gif", cat(img1, img2, grid[1:512,1:512], grid[1:512,513:1024]; dims=3); fps=2) #src