demos: add imcorner demo (#144)

This adds a demo for corner detection.

Co-authored-by: Johnny Chen <[email protected]>
Co-authored-by: Tim Holy <[email protected]>

Author: 3 people

docs/examples/contours/detecting_corners.jl

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+# ---
+# cover: assets/corner.png
+# title: Detecting Corners
+# ---
+
+# Corner Detection, which is a subset of Interest Point Detection, tries to detect
+# points in the image which have a well-defined position and can be robustly detected
+# in multiple images of the same scene. Very often, these points lie along the corners
+# or edges of objects in the image - hence the name.
+
+# Corner detection is useful in several computer vision tasks - such as Image Registration,
+# Motion Detection and Panaroma Stitching. This is because if the locations of the same
+# points are known in two different images, it gives a reference to align those images. Corners,
+# with their well-defined positions serve as good candidates for such points.
+
+using Images, TestImages
+img = Gray.(testimage("house"))
+
+# We use this image of a house, with numerous edges and corners.
+
+# The `imcorner` function can be used to detect corners in the image - and it returns an
+# array of booleans, where a `true` value denotes that the corresponding pixel may be a
+# corner. We use this to mark those pixels in red on a copy of the image.
+
+corners = imcorner(img)
+img_copy = RGB.(img)
+img_copy[corners] .= RGB(1.0, 0.0, 0.0)
+img_copy
+
+# As you can see, several points which lie in the interior of the object (the house)
+# have also been detected as corners. We can fix this by specifying a higher threshold percentile.
+
+corners = imcorner(img, Percentile(98.5))
+img_copy2 = RGB.(img)
+img_copy2[corners] .= RGB(1.0, 0.0, 0.0)
+img_copy2
+save("assets/corner.png", img_copy2) #src
+
+# This seems much better. A detailed documentation of the function parameters can be found in
+# the documentation of [`imcorner`](@ref).
+
+# Internally, `imcorner` uses one of three algorithms: *Harris*, *Shi Tomasi* or
+# *Kitchen Rosenfield* to detect corners. Which one to use can be specified using
+# the `method` parameter to `imcorner`. Each algorithm also has a separate method.
+
+detection_methods = [harris, shi_tomasi, kitchen_rosenfeld]
+img_copies = [RGB.(img) for i in 1:length(detection_methods)]
+for i in 1:length(detection_methods)
+    corners = imcorner(img, Percentile(98.5); method=detection_methods[i])
+    img_copies[i] = RGB.(img)
+    img_copies[i][corners] .= RGB(1.0, 0.0, 0.0)
+end
+mosaicview(img_copies; nrow=1)
+
+# These algorithms use the gradient of the image to identify corners, because intensities
+# change abruptly at corner points, giving rise to large gradients. However, this makes them
+# computationally expensive.
+
+# The *FAST (Features from Accelarated Segment Test) Corner Detector* is a feature
+# detection algorithm which is designed to be computationally cheaper, and hence much faster.
+# It classifies a pixel $P$ as a corner if at least $n$ contiguous points out of the 16
+# points in a circle around it have intensities either higher (or lower) than that of $P$
+# by a certain threshold $t$.
+
+corners = fastcorners(img, 11, 0.1) # fastcorners(img, n, t) where n and t are optional
+img_copy3 = RGB.(img)
+img_copy3[corners] .= RGB(1.0, 0.0, 0.0)
+img_copy3