Merge pull request #33 from cserteGT3/cst-updates

StaticArrays.jl compatibility has been updated to the 1.0 releases
progress is shown with the help of ProgressMeter.jl
documentation is updated to use the new RANSACVisualizer package with Makie recipes
handling the negative sqrt error

Project.toml

@@ -8,17 +8,19 @@ ExtractMacro = "f86d3d12-fd5b-522c-99e9-61577282a1e9"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RegionTrees = "dee08c22-ab7f-5625-9660-a9af2021b33f"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
 
 [compat]
+ExtractMacro = "^1"
 JSON = "0.20, 0.21"
+ProgressMeter = "1.7"
 RegionTrees = "^0.3"
-StaticArrays = "0.11, 0.12"
+StaticArrays = "0.11,0.12,1.0,1.1,1.2"
 YAML = "0.4"
-ExtractMacro = "^1"
 julia = "^1"
 
 [extras]

README.md

@@ -52,3 +52,9 @@ Follow the [detailed example](https://csertegt3.github.io/RANSAC.jl/stable/examp
 
 Here's an example with a point cloud and the detected primitives colored according to their type.
 ![RANSAC example](img/ransac_example.png)
+
+## Related projects
+
+* The [RANSACVisualizer.jl](https://github.com/cserteGT3/RANSACVisualizer.jl) package implements Makie.jl recipes to visualize results from this package.
+* There are other RANSAC implementations in Julia, for example:
+  * [ImageProjectiveGeometry.jl](https://github.com/peterkovesi/ImageProjectiveGeometry.jl/blob/master/doc/ransac.md): fits planes and lines to 3d points using RANSAC.

docs/Project.toml

@@ -2,4 +2,4 @@
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 
 [compat]
-Documenter = "0.24,0.25"
+Documenter = "0.24,0.25,0.25,0.26,0.27"

docs/src/example.md

@@ -69,34 +69,29 @@ julia> extr, _ = ransac(pc, p, true, reset_rand=true);
 
 ## See the results
 
-The [RANSACVisualizer](https://csertegt3.github.io/RANSACVisualizer.jl/stable/) package provides a few utility functions to check the results (check the docs for function signatures).
-
-### Check the input
-
-The `showgeometry()` function shows a mesh and its normal vectors:
+The [RANSACVisualizer](https://csertegt3.github.io/RANSACVisualizer.jl/stable/) package provides Makie recipes to check the results (check the docs for function signatures).
+To use them, install the package, and a Makie backend as well, for example:
 
 ```julia
-julia> showgeometry(m, arrow=0.3; show_axis = false)
+]add GLMakie
+add https://github.com/cserteGT3/RANSACVisualizer.jl.git
+using RANSACVisualizer, GLMakie
 ```
 
-![](img/showgeometry.png)
-
-### Results colored randomly
+### Check the input
 
-The `showshapes()` function provides this functionality.
+The `meshschatter`, `mesh` or `wireframe` functions (from Makie.jl) can be used to inspect a mesh:
 
 ```julia
-julia> showshapes(pc, extr; show_axis = false)
+julia> mesh(m)
 ```
 
-![](img/showshapes.png)
-
-### Results colored according to their type
+### See the coloured result
 
-The `showbytype()` function provides this functionality.
+The `visualize()` function (from RANSACVisualizer.jl) plots one `ExtractedShape`.
+It can be coloured by type or randomly.
+(See the [docs](https://csertegt3.github.io/RANSACVisualizer.jl/stable/) for details.)
 
 ```julia
-julia> showbytype(pc, extr; show_axis = false)
+julia> visualize(extr[1],pc, pointsize=0.5, coloringtype=:bytype)
 ```
-
-![](img/bytype.png)

src/RANSAC.jl

@@ -10,6 +10,7 @@ using RegionTrees
 using ExtractMacro
 import JSON
 import YAML
+using ProgressMeter
 
 import RegionTrees: AbstractRefinery, needs_refinement, refine_data
 import Base: show, length, deleteat!
@@ -81,6 +82,10 @@ include("iterations.jl")
 include("logging.jl")
 include("orientedbox_.jl")
 include("json-yaml.jl")
+# functions and tools unrelated to RANSAC,
+# but using the same methods
+# these are not exported
+include("unrelated.jl")
 
 """
 `const DEFAULT_PARAMETERS = 

src/confidenceintervals.jl

@@ -51,7 +51,10 @@ Standard deviation of the hypergeometric distribution.
 Just copied from the article (Efficient RANSAC).
 """
 function hypergeomdev(N, x, n)
-    sq = sqrt((x*n*(N-x)*(N-n))/(N-1))
+    sq_ = (x*n*(N-x)*(N-n))/(N-1)
+    # handle floating point errors:
+    # small negative numbers, that should be zero
+    sq = sq_ < 0 ? zero(sq_) : sqrt(sq_)
     (min=(x*n+sq)/N, max=(x*n-sq)/N)
 end
 

src/iterations.jl

@@ -70,6 +70,7 @@ function ransac(pc, params; reset_rand = false)
     countcandidates = [0, 0, 0]
 
     # iterate begin
+    progbar = Progress(itermax; desc = "Running iteration...", dt=1, barglyphs=BarGlyphs("[=> ]"))
     for k in 1:itermax
         if count(pc.isenabled) < τ
             @logmsg IterInf "Break at $k iteration, because left less points, then τ"
@@ -153,6 +154,7 @@ function ransac(pc, params; reset_rand = false)
             @logmsg IterInf "Break, at this point all shapes should be extracted: $k. iteration."
             break
         end
+        next!(progbar)
     end # iterate end
     fint = trunc((time_ns() - start_time)/1_000_000_000, digits=2)
     @logmsg IterInf "Iteration finished in $fint seconds with $(length(extracted)) extracted and $(length(scoredshapes)) scored shapes."

src/unrelated.jl

@@ -0,0 +1,5 @@
+function testshape(shape, vertices, normals, epsilon, alpha)
+    # compatiblesCylinder(cylinder, points, normals, params)
+    rpars = ransacparameters([FittedCylinder], cylinder=(ϵ=epsilon, α=alpha,))
+    compatiblesCylinder(shape, vertices, normals, rpars)
+end