Adds demonstration of fundamental matrix estimation

demo/example_fundamental_matrix.jl

@@ -0,0 +1,58 @@
+using MultipleViewGeometry, Images, ImageFeatures, ImageView
+using BenchmarkTools, Compat
+using StaticArrays, MAT, Plots, PyPlot
+
+file = matopen("./data/sene-adelaideRMF.mat")
+# img1 = read(file,"img1")
+# img2 = read(file,"img2")
+img1 = colorview(RGB, normedview(permutedims(read(file,"img1"),[3,1,2])))
+img2 = colorview(RGB, normedview(permutedims(read(file,"img2"),[3,1,2])))
+inlier_pts1 = read(file,"inlierPts1")
+inlier_pts2 = read(file,"inlierPts2")
+close(file)
+
+img1g = Gray.(img1)
+img2g = Gray.(img2)
+
+x = 1:10; y = rand(10); # These are the plotting data
+plot(x,y)
+#pyplot()
+plotly()
+
+p = Plots.plot(img1g)
+Plots.plot!(inlier_pts1[1,:],inlier_pts1[2,:],seriestype=:scatter,w=5)
+
+p2 = Plots.plot(img2g)
+Plots.plot!(inlier_pts2[1,:],inlier_pts2[2,:],seriestype=:scatter,w=5)
+
+#Plots.plot(img1g)
+#Plots.plot!(x->200sin(.05x)+300, 0, 700, seriestype=:scatter,w=5)
+
+#img = reinterpret(N0f8, img1)
+# test = permutedims(img1,[3,1,2])
+# img_l = colorview(RGB, normedview(test))
+# img_r = colorview(RGB, normedview(test))
+# img1 = Gray.(img1)
+#
+# img = colorview(Gray,img1)
+#
+# colorview(RGB,img1)
+#
+# img = reinterpret(N0f8, img1)
+# test = colorview(RGB,img,size(img))
+#
+# test = channelview(img)
+#
+# Gray.(test)
+#
+# test = permutedims(img,[3,1,2])
+# Gray.(channelview(test))
+# Gray.(test)
+#
+# colorview(RGB, normedview(test))
+
+
+# using PyPlot
+# x = linspace(0,2*pi,1000); y = sin.(3 * x + 4 * cos.(2 * x));
+# plot(x, y, color="red", linewidth=2.0, linestyle="--")
+# title("A sinusoidally modulated sinusoid")