# Calculate Color and Spatial Statistics from Blob Region Wrapper function (see details) that calculates various statistics on x,y data that corresponds to an image. ```r calc.blob.stats(img, xy.coords) ``` ## Arguments - `img`: Matrix whose values at `xy.coords` are statistically analyzed. - `xy.coords`: Index locations corresponding region of interest (e.g. blob region) in the `img` ## Details Function calls multiple statistical functions (e.g. `mean`, `sd`) and applies them to regions in the `img` according to the index locations given by `xy.coords`. In general, this function is commented to promote any modifications needed to fit the users needs. For example, adding or removing statistical analyses is straight forward. ## Returns Numeric vector giving the statistics of the blob region. ## Author(s) Alex J.C. Witsil ## See Also `mean` `sd` `sum` `colMeans` `rowMeans` `length` `skewness` `kurtosis` ## Examples ```r ############ ### EG 1 ### ############ ## example with synthetic data ## create an image that is a simple gaussian img <- build.gaus(100,100,sig.x=2,sig.y=10,x.mid=80,y.mid=60) ## find the where the maximum value is img.max <- which(img==max(img),arr.ind=TRUE) ## define a sigma for the low pass filter sig=5 ## define the low pass filter as another gaussian lp.filt <- build.gaus(nrow(img),ncol(img),sig.x=sig) ## define a window size for the connected component algorithm win.size=0.05 ## perform the blob detection blob <- blob.extract(img=img, blob.point=img.max,win.size=win.size,gaus=lp.filt) ################################# ### CALCULATE BLOB STATISTICS ### ################################# blob.stats <- calc.blob.stats(img, blob$xy.coords) print(blob.stats) ############ ### EG 2 ### ############ ## example with volcano image data. data(sakurajima) ###################### ### PRE PROCESSING ### ###################### ## crop accroding to these corner values xleft = 1 xright = 188 ybottom = 1 ytop = 396 ## crop the image using crop.image cropped <- crop.image(sakurajima, xleft, ybottom, xright, ytop) ## redefine the crop image img <- cropped$img.crop ## separate the image into red, green, and blue images r.img <- img[,,1] g.img <- img[,,2] b.img <- img[,,3] ## remove the mean r.img <- r.img-mean(r.img) g.img <- g.img-mean(g.img) b.img <- b.img-mean(b.img) ## calculate the the plane trend... r.img.trend <- fit3d(r.img) g.img.trend <- fit3d(g.img) b.img.trend <- fit3d(b.img) ## remove the trend r.img.dtrend <- r.img-r.img.trend g.img.dtrend <- g.img-g.img.trend b.img.dtrend <- b.img-b.img.trend ################################ ### SET UP SOME FILTER MASKS ### ################################ ## define a sigma for the LP Gaussian Filter gaus.sig=30 ## build the Gaussian filter gaus <- build.gaus(nrow(img),ncol(img),gaus.sig) ## find the maximum value of each RGB channel blob.r.point <- which(r.img.dtrend==max(r.img.dtrend),arr.ind=TRUE) blob.g.point <- which(g.img.dtrend==max(g.img.dtrend),arr.ind=TRUE) blob.b.point <- which(b.img.dtrend==max(b.img.dtrend),arr.ind=TRUE) ## set a window size to be used in the connected component algorithm win.size = 0.05 ## extract the blob xy locations blob.r <- blob.extract(r.img.dtrend,blob.r.point,win.size,gaus) blob.g <- blob.extract(g.img.dtrend,blob.r.point,win.size,gaus) blob.b <- blob.extract(b.img.dtrend,blob.r.point,win.size,gaus) ################################# ### CALCULATE BLOB STATISTICS ### ################################# r.blob.stats <- calc.blob.stats(r.img.dtrend, blob.r$xy.coords) g.blob.stats <- calc.blob.stats(g.img.dtrend, blob.g$xy.coords) b.blob.stats <- calc.blob.stats(b.img.dtrend, blob.b$xy.coords) print(r.blob.stats) print(g.blob.stats) print(b.blob.stats) ```

calc.blob.stats function