initial freak descriptor

Author: ashwanirathee

docs/examples/image_features/freak.jl

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+# ---
+# cover: assets/freak.gif
+# title: FREAK Descriptor
+# description: This demo shows FREAK descriptor
+# author: Anchit Navelkar, Ashwani Rathee
+# date: 2021-07-12
+# ---
+
+# `FREAK` (Fast Retina Keypoint) has a defined sampling pattern like [BRISK](brisk.md).
+# It uses a retinal sampling grid with more density of points near the centre
+# with the density decreasing exponentially with distance from the centre.
+
+# FREAK’s measure of orientation is similar to [BRISK](brisk.md) but instead of using 
+# long pairs, it uses a set of predefined 45 symmetric sampling pairs. The set of 
+# sampling pairs is determined using a method similar to [ORB](orb.md), by finding 
+# sampling pairs over keypoints in standard datasets and then extracting the most 
+# discriminative pairs. The orientation weights over these pairs are summed and the 
+# sampling window is rotated by this orientation to some canonical orientation to 
+# achieve rotation invariance.
+
+# The descriptor is built using intensity comparisons of a predetermined set of 512 
+# sampling pairs. This set is also obtained using a method similar to the one described 
+# above. For each pair if the first point has greater intensity than the second, 
+# then 1 is written else 0 is written to the corresponding bit of the descriptor.
+
+# ## Example
+
+# Let us take a look at a simple example where the FREAK descriptor is used to 
+# match two images where one has been translated by `(50, 40)` pixels and then 
+# rotated by an angle of 75 degrees. We will use the `lighthouse` image from 
+# the [TestImages](https://github.com/timholy/TestImages.jl) package for this example.
+
+# First, let us create the two images we will match using FREAK.
+
+using ImageFeatures, TestImages, Images, ImageDraw, CoordinateTransformations, Rotations
+
+img = testimage("peppers_color")
+
+# Original
+
+img1 = Gray.(img)
+rot = recenter(RotMatrix(5pi/6), [size(img1)...] .÷ 2)  # a rotation around the center
+tform = rot ∘ Translation(-50, -40)
+img2 = warp(img1, tform, 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, 0.35))
+keypoints_2 = Keypoints(fastcorners(img2, 12, 0.35))
+
+
+# To create the FREAK descriptor, we first need to define the parameters 
+# by calling the [`FREAK`](@ref) constructor.
+
+freak_params = FREAK()
+
+# 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, freak_params)
+desc_2, ret_keypoints_2 = create_descriptor(img2, keypoints_2, freak_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[1], m[2] + offset)), matches)
+grid
+
+save("assets/freak.gif", cat(img, img2, grid[1:512,1:512], grid[1:512,513:1024]; dims=3); fps=2) #src