Added rgl view in tree.detection

tree.detection.Rmd

@@ -12,11 +12,11 @@ bibliography: "./bib/bibliography.bib"
 knitr::opts_chunk$set(echo = TRUE)
 # Set so that long lines in R will be wrapped:
 knitr::opts_chunk$set(tidy.opts=list(width.cutoff=80),tidy=TRUE)
-knitr::opts_chunk$set(fig.align = "center") 
-# library(rgl)
-# knit_hooks$set(webgl = hook_webgl)
-# rgl::setupKnitr()
-library(lidR)
+knitr::opts_chunk$set(fig.align = "center")
+# for display of rgl in html
+knitr::knit_hooks$set(webgl = rgl::hook_webgl)
+# output to html
+html <- TRUE
 ```
 
 ---
@@ -73,6 +73,20 @@ lidaRtRee::plotTreeInventory(
   species = as.character(treeinventorychablais3$s)
 )
 ```
+The `ggplot2` package also provides nice outputs.
+
+```{r ggplot2, include = TRUE, out.width = '50%', fig.dim=c(5.5, 5.5)}
+# use table of species of package lidaRtRee to always use the same color for a given species
+plot.species <- lidaRtRee::speciesColor()[levels(treeinventorychablais3$s), "col"]
+library(ggplot2)
+ggplot(treeinventorychablais3, aes(x = x, y = y, group = s)) +
+  geom_point(aes(color = s, size = d)) +
+  coord_sf(datum = 2154) +
+  scale_color_manual(values = plot.species) +
+  scale_radius(name="Diameter") + 
+  geom_text(aes(label=n, size=20), hjust=0, vjust=1) +
+  labs(color = "Species") # titre de la légende
+```
 
 We define the region of interest (ROI) to crop ALS data to corresponding extent before further processing. ROI is set on the extent of tree inventory, plus a 10 meter buffer.
 
@@ -85,14 +99,17 @@ ROI.range <- data.frame(round(apply(treeinventorychablais3[,c("x","y")],2,range)
 
 ### Airborne Laser Scanning data
 
-ALS data is loaded with functions of package `lidR`. First a catalog of files containing ALS data is built. Then points located inside our ROI are loaded.
+In this tutorial, ALS data available in the `lidaRtRee`` package is used. 
 
-```{r loadALS, include = FALSE}
+```{r loadALS, include = TRUE, message = FALSE}
 # load data in package lidaRtRee (default)
 data(laschablais3, package="lidaRtRee")
+laschablais3
 ```
 
-```{r prepareALS, eval=FALSE}
+Otherwise, ALS data is loaded with functions of package `lidR`. First a catalog of files containing ALS data is built. Then points located inside our ROI are loaded.
+
+```{r prepareALS, eval=FALSE, message = FALSE}
 # directory for laz files
 lazdir <- "/directory_of_las_files/"
 # build catalog of files
@@ -111,10 +128,6 @@ laschablais3 <- lidR::clip_rectangle(cata,
 # save(laschablais3, file="laschablais3.rda", compress = "bzip2")
 ```
 
-```{r displayALS, echo=FALSE}
-laschablais3
-```
-
 ## Data preparation
 ### Digital Elevation Models
 
@@ -163,7 +176,7 @@ v.plotMask <- raster::rasterToPolygons(plotMask,function(x)(x==1),dissolve=T)
 ```
 Displaying inventoried trees on the CHM shows a pretty good correspondance of crowns visible in the CHM with trunk locations and sizes.
 
-```{r plotPlot, include = TRUE, out.width = '60%', fig.dim=c(6.5, 4.5), warnings=FALSE}
+```{r plotPlot, include = TRUE, out.width = '70%', fig.dim=c(6.5, 4.5), warnings=FALSE}
 # display CHM
 raster::plot(chm, col=gray(seq(0,1,1/255)),
              main ="Canopy Height Model and tree positions")
@@ -293,7 +306,10 @@ lasn@data$seg.id <- raster::extract(segms[["segments.id"]], lasn@data[,1:2])
 # split las object by segment id
 lasl <- split(lasn@data,lasn@data$seg.id)
 # convert list of data.frames to list of las objects
-lasl <- lapply(lasl, function(x){LAS(x,lasn@header)})
+lasl <- lapply(lasl, function(x){lidR::LAS(x,lasn@header)})
+# set coordinate system
+dummy <- sp::CRS(SRS_string = "EPSG:2154")
+for (i in 1:length(lasl)) {sp::proj4string(lasl[[i]]) <- dummy}
 ```
 
 ### Metrics computation
@@ -382,7 +398,8 @@ plot(afd)
 
 The point cloud can be displayed colored by segment, with poles at the location of inventoried trees.
 
-```{r displayPointCloud, eval=FALSE, rgl=TRUE, dev='png'}
+```{r displayPointCloud, include=TRUE, eval=html, webgl=TRUE, fig.width=6, fig.height=6, warning=FALSE}
+rgl::par3d(mouseMode = "trackball") # parameters for interaction with mouse
 # select segment points and offset them to avoid truncated coordinates in 3d plot
 points.seg <- lasn@data[which(lasn@data$seg.id!=0), c("X", "Y", "Z", "seg.id")]
 points.seg$X <- points.seg$X - 974300
@@ -680,7 +697,7 @@ raster::plot(chm.all,
 # display segments border
 sp::plot(v.trees, border = "white", add = T)
 # add trees
-plot(trees, cex = trees$h/40, add = TRUE)
+sp::plot(trees, cex = trees$h/40, add = TRUE)
 ```
 
 The following lines save outputs to files.