Merge pull request #6 from MatlabCompat/dev

Dev

src/MatlabCompat/imagetools.jl

@@ -180,7 +180,7 @@ function bwlabel(inputImage,connectivity)
     error("Invalid pixel connectivity. Pixel connectivity can be either 4 or 8");
   end
 
-  labeledImage = label_components(inputImage,connectivityMatrix)'; #transposing to be consistent with original input Image
+  labeledImage = label_components(inputImage,connectivityMatrix);
 
 
   return labeledImage;

test/benchmark.jl

@@ -29,17 +29,81 @@ using MatlabCompat.MathTools
 using Images,FixedPointNumbers
 
 
-numberOfIterations = 1000;
 
-innerExecutionTimes = Float64[0];
-outerExecutionTimes = Float64[0];
-img = imread("C:\\Users\\Vardan\\Documents\\Julia\\matlabcompat.github.io\\img\\example.tif");
+function graythresh2(img)
+
+
+  #Check whether the input image is empty
+  if isempty(img)
+    error("Image is empty");
+  end
+  #Check whether the input image is gray
+
+  if Images.colorspace(img) != "Gray"
+    error("Input Image should be grayscale")
+  end
+
+  if isa(raw(img),Array{Uint16,2})
+    #Convert image to 8bit and return it's raw values to compute the histogram
+    image_array = map(Images.BitShift(Uint8,8),raw(img));
+  elseif isa(raw(img),Array{Uint8,2})
+    image_array = raw(img);
+  else
+    warn("Input Image is neither Uint8 or Uint16");
+    image_array = raw(img);
+  end
+  #image_array = raw(img)
+
+
+
+  expected_number_of_bins = 2^8;
+
+
+ (range, counts) = hist(reshape(image_array,length(image_array)),expected_number_of_bins);
+  methods(hist)
+
+  #get real number of bins to be used below
+  number_of_bins = size(counts)[1];
+  #Next the algorithm implementation according to the original paper
+  p = counts / sum(counts);
+  ω = cumsum(p);
+  μ = cumsum(p .* (1:number_of_bins));
+  μ_t = μ[end];
+
+
+  sigma_b_squared = (μ_t * ω - μ).^2 ./ (ω .* (1 - ω));
+
+  # Find the location of the maximum value of sigma_b_squared.
+  # The maximum may extend over several bins, so average together the
+  # locations.  If maxval is NaN, meaning that sigma_b_squared is all NaN,
+  # then return 0.
+  maxvalue = maximum(sigma_b_squared);
+  if isfinite(maxvalue)
+    index = findfirst(sigma_b_squared, maxvalue);
+
+    # Normalize the threshold to the range [0, 1].
+    threshold = (index - 1) / (expected_number_of_bins - 1);
+  else
+    threshold = 0.0;
+  end
+
+  return threshold
+end
+
+
+
+
+numberOfIterations = 10000;
+
+innerExecutionTimes = float64(zeros(1,numberOfIterations));
+outerExecutionTimes = float64(zeros(1,numberOfIterations));
+img = MatlabCompat.ImageTools.imread("http://matlabcompat.github.io/img/example.tif");
 
 function simpleJanus()
   threshold = graythresh(img); # compute graysacale threshold using Otsu algorithm
-  imgbw = im2bw(img, threshold); # create a binary image based on the graysacel image
+  imgbw =im2bw(img, threshold); # create a binary image based on the graysacel image
   labeledbw = bwlabel(imgbw, 4); # lable each connected object in the image
-  numberOfCells = max(reshape(labeledbw, 1,numel(labeledbw))); # count cells
+#   numberOfCells = max(reshape(labeledbw, 1,numel(labeledbw))); # count cells
   #disp(strcat("number of objects:", num2str(numberOfCells)));
 end
 
@@ -51,22 +115,22 @@ function forSimpleJanus()
   for j=1:numberOfIterations
     simpleJanus();
   end
-end
-
 
+end
 
+  methods(hist)
 
 for i=1:numberOfIterations
   inner = @elapsed(simpleJanus());
-  push!(innerExecutionTimes, inner);
+  innerExecutionTimes[i] = inner;
 end
 
 
-for i=1:numberOfIterations
-  outer =  @elapsed(forSimpleJanus());
-  push!(outerExecutionTimes,outer);
-end
+# for i=1:numberOfIterations
+#   outer =  @elapsed(forSimpleJanus());
+#   outerExecutionTimes[i] = outer;
+# end
 
 println([median(innerExecutionTimes) std(innerExecutionTimes)])
-print([median(outerExecutionTimes)  std(outerExecutionTimes)])
+# print([median(outerExecutionTimes)  std(outerExecutionTimes)])
 

test/benchmark.m

@@ -20,26 +20,28 @@
 % OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 % SOFTWARE.
 
-numberOfIterations = 1000;
+numberOfOuterIterations = 10;
+numberInnerOfIterations =10000;
 outerExecutionTimes=0;
 innerExecutionTimes=0;
 img = imread('http://matlabcompat.github.io/img/example.tif');
 
 i=1;
 
-for i = 1:numberOfIterations
-    outer = tic();
-for j = 1:numberOfIterations
+% for i = 1:numberOfOuterIterations
+%     outer = tic();
+for j = 1:numberInnerOfIterations
      inner = tic();
 threshold = graythresh(img); % compute graysacale threshold using Otsu algorithm
 imgbw = im2bw(img, threshold); % create a binary image based on the graysacel image
 labeledbw = bwlabel(imgbw, 4); % lable each connected object in the image
-numberOfCells = max(reshape(labeledbw, 1,numel(labeledbw))); % count cells
- innerExecutionTimes(j) =toc(inner);
-end
-outerExecutionTimes(i)=toc(outer);
+% numberOfCells = max(reshape(labeledbw, 1,numel(labeledbw))); % count cells
+  innerExecutionTimes(j) =toc(inner);
 end
+% outerExecutionTimes(i)=toc(outer);
+% 
+% end
 
 
 disp([median(innerExecutionTimes) std(innerExecutionTimes)]);
-disp([median(outerExecutionTimes) std(outerExecutionTimes)]);
+% disp([median(outerExecutionTimes) std(outerExecutionTimes)]);

test/janus.jl

@@ -32,13 +32,12 @@ importall MatlabCompat.MathTools
 
 tic()
 img = imread("http://matlabcompat.github.io/img/example.tif"); # read the remote image
-#img = imread("D:\github\matlabcompat.github.io\img\example.tif");
 threshold = graythresh(img); # compute graysacale threshold using Otsu algorithm
 imgbw = im2bw(img, threshold); # create a binary image based on the grayscale image
 imshow(imgbw); # display the resulting binary image
-labledbw = bwlabel(imgbw, 4); # lable each connected object in the image
-numberOfCells = max(reshape(labledbw, 1,numel(labledbw))); # count cells
-disp(strcat("number of objects:", num2str(numberOfCells)));
-imshow(label2rgb(labeledbw,"jet",[1 1 1],"shuffle")# display the number of objects
+labeledbw = bwlabel(imgbw, 4); # label each connected object in the image
+numberOfCells = max(reshape(labeledbw, 1,numel(labeledbw))); # count cells
+disp(strcat("number of objects:", num2str(numberOfCells)));# display the number of objects
+imshow(label2rgb(labeledbw,"jet",[1 1 1],"shuffle"));
 toc()
 

test/janus.m

@@ -25,11 +25,10 @@
 % SOFTWARE.
 tic()
 img = imread('http://matlabcompat.github.io/img/example.tif'); % read the remote image
-%img = imread('D:\github\matlabcompat.github.io\img\example.tif');
 threshold = graythresh(img); % compute graysacale threshold using Otsu algorithm
 imgbw = im2bw(img, threshold); % create a binary image based on the grayscale image
 imshow(imgbw); % display the resulting binary image
-labeledbw = bwlabel(imgbw, 4); % lable each connected object in the image
+labeledbw = bwlabel(imgbw, 4); % label each connected object in the image
 numberOfCells = max(reshape(labeledbw, 1,numel(labeledbw))); % count cells
 disp(strcat('number of objects:', num2str(numberOfCells)));% display the number of objects
 imshow(label2rgb(labeledbw,'jet',[0 0 0],'shuffle'));