removed tmp dir; fixed array mismatch; need to work on feature::??? e…

…rror

.gitignore

@@ -27,5 +27,8 @@ docs/site/
 # It records a fixed state of all packages used by the project. As such, it should not be
 # committed for packages, but should be committed for applications that require a static
 # environment.
-Manifest.tomltest/images/pos/
-test/images/neg
+Manifest.toml
+
+# 
+data/images/pos/
+data/images/neg

setup.sh

@@ -10,6 +10,11 @@ trap "exit" INT
 echo "Please ensure FaceDetection.jl is installed in your home directory..."
 sleep 5
 
+mkdir -p ~/FaceDetection.jl/test/images/testing/pos/
+mkdir -p ~/FaceDetection.jl/test/images/testing/neg/
+mkdir -p ~/FaceDetection.jl/test/images/testing/testing/pos/
+mkdir -p ~/FaceDetection.jl/test/images/testing/testing/neg/
+
 brew list > /tmp/brewlist
 if ! grep "^imagemagick$" /tmp/brewlist > /dev/null 2>&1
 then
@@ -39,11 +44,11 @@ cd ~/FaceDetection.jl/ && \
 	mv face-detection-data/pos/ test/images/ && \
 	mv face-detection-data/neg/ test/images/ && \
 	rm -rf face-detection-data/
-# echo "Pruning the positive training images to have the same number as the negative images, or else there will be an array mismatch when constructing the image array in src/Adaboost.jl"
-# for i in $(seq $(ls ~/FaceDetection.jl/test/images/neg/ | wc -l) $(ls ~/FaceDetection.jl/test/images/pos/ | wc -l)); do
-# 	rm ~/FaceDetection.jl/test/images/pos/${i}.pgm
-# 	# echo "${i}"
-# done
+echo "Pruning the positive training images to have the same number as the negative images, or else there will be an array mismatch when constructing the image array in src/Adaboost.jl"
+for i in $(seq $(ls ~/FaceDetection.jl/test/images/neg/ | wc -l) $(($(ls ~/FaceDetection.jl/test/images/pos/ | wc -l)-1)); do
+	rm ~/FaceDetection.jl/test/images/pos/${i}.pgm
+	# echo "${i}"
+done
 
 	
 #### The following step was only for python testing code.  Netpbm.jl is powerful and fixed this.

src/Adaboost.jl

@@ -7,7 +7,7 @@
 using ProgressMeter
 using Distributed # for parallel processing (namely, @everywhere)
 
-include("HaarFeatureSelection.jl")
+include("HaarLikeFeature.jl")
 
 
 
@@ -31,7 +31,7 @@ function learn(positiveIIs, negativeIIs, numClassifiers=-1, minFeatureWidth=1, m
     """
     numPos = length(positiveIIs)
     numNeg = length(negativeIIs)
-    numImgs = numPos + numNeg
+    global numImgs = numPos + numNeg
     imgHeight, imgWidth = size(positiveIIs[1])
 
     # Maximum feature width and height default to image width and height
@@ -48,8 +48,8 @@ function learn(positiveIIs, negativeIIs, numClassifiers=-1, minFeatureWidth=1, m
     weights = hcat((posWeights, negWeights))
     labels = hcat((ones(numPos), ones(numNeg) * -1))
 
-    # images = positiveIIs + negativeIIs
-    images = vcat(positiveIIs, negativeIIs)
+    global images = positiveIIs + negativeIIs
+    # global images = vcat(positiveIIs, negativeIIs)
 
     # Create features for all sizes and locations
     features = _create_features(imgHeight, imgWidth, minFeatureWidth, maxFeatureWidth, minFeatureHeight, maxFeatureHeight)
@@ -63,12 +63,14 @@ function learn(positiveIIs, negativeIIs, numClassifiers=-1, minFeatureWidth=1, m
 
     votes = zeros((numImgs, numFeatures))
     # bar = progressbar.ProgressBar()
+    # @everywhere numImgs begin
     @everywhere begin
         n = numImgs
+        processes = length(numImgs)
         p = Progress(n, 1)   # minimum update interval: 1 second
-        for i in processes
-            # votes[i, :] = np.array(pool.map(partial(_get_feature_vote, image=images[i]), features))
-            votes[i, :] = Array(_get_feature_vote, image=images[i]), features
+        for i in processes # bar(range(num_imgs)):
+            # votes[i, :] = np.array(partial(_get_feature_vote, image=images[i]), features)
+            votes[i, :] = Array(map(partial(_get_feature_vote)(image=images[i])), features)
             next!(p)
         end
     end # end everywhere (end parallel processing)
@@ -131,28 +133,32 @@ end
 
 function _create_features(imgHeight::Int64, imgWidth::Int64, minFeatureWidth::Int64, maxFeatureWidth::Int64, minFeatureHeight::Int64, maxFeatureHeight::Int64)
     println("Creating Haar-like features...")
-    features = Array()
+    # features = Array()
+    features = []
 
-    for feature in FeatureTypes
+    for feature in FeatureTypes # from HaarLikeFeature.jl
+        # FeatureTypes are just tuples
+        println(typeof(feature), " " , feature)
         featureStartWidth = max(minFeatureWidth, feature[1])
         for featureWidth in range(featureStartWidth, stop=maxFeatureWidth, step=feature[1])
-            for x in 1:(imgWidth - featureWidth)
-                for y in 1:(imgHeight - featureHeight)
-                    #=features.append(HaarLikeFeature(feature, (x, y), feature_width, feature_height, 0, 1))
-                    features.append(HaarLikeFeature(feature, (x, y), feature_width, feature_height, 0, -1))=#
-                end # end for y
-            end # end for x
+            featureStartHeight = max(minFeatureHeight, feature[2])
+            for featureHeight in range(featureStartHeight, stop=maxFeatureHeight, step=feature[2])
+                for x in 1:(imgWidth - featureWidth)
+                    for y in 1:(imgHeight - featureHeight)
+                        feature = vcat(feature, HaarLikeFeature(feature, (x, y), featureWidth, featureHeight, 0, 1))
+                        feature = vcat(feature, HaarLikeFeature(feature, (x, y), featureWidth, featureHeight, 0, -1))
+                    end # end for y
+                end # end for x
+            end # end for feature height
         end # end for feature width
     end # end for feature in feature types
 
-    println("...finished processing; ", str(len(features)) + " features created.")
+    println("...finished processing; ", length(features), " features created.")
 
     return features
 end
 
 
-
-
 export learn
 export _get_feature_vote
 export _create_features

src/FDA.jl

@@ -9,17 +9,17 @@
 Adapted from https://github.com/Simon-Hohberg/Viola-Jones/
 """
 
-println("Loading required libraries (it will take a moment to precompile is this is your first time doing this)...")
+println("Loading required libraries (it will take a moment to precompile if it is your first time doing this)...")
 
 include("IntegralImage.jl")
 include("AdaBoost.jl")
 include("Utils.jl")
 
 
 function main()
-      posTrainingPath = "/Users/jakeireland/FaceDetection.jl/test/images/pos/"
-      negTrainingPath = "/Users/jakeireland/FaceDetection.jl/test/images/neg/"
-      posTestingPath = "/Users/jakeireland/FaceDetection.jl/test/images/testing/pos/"
+      posTrainingPath = "/Users/jakeireland/FaceDetection.jl/data/images/pos/"
+      negTrainingPath = "/Users/jakeireland/FaceDetection.jl/data/images/neg/"
+      posTestingPath = "/Users/jakeireland/FaceDetection.jl/data/images/testing/pos/"
       negTestingPath = "/Users/jakeireland/Desktop/Assorted Personal Documents/Wallpapers copy/"
 
       numClassifiers = 2
@@ -38,6 +38,16 @@ function main()
       nonFacesTraining = loadImages(negTrainingPath)
       nonFacesIITraining = map(toIntegralImage, nonFacesTraining) # list(map(...))
       println("...done. ", length(nonFacesTraining), " non-faces loaded.\n")
+
+      # println("Writing arrays to files for testing...")
+      # rm(joinpath(homedir(), "Desktop", "facesIITraining.txt"))
+      # rm(joinpath(homedir(), "Desktop", "nonFacesIITraining.txt"))
+      # open(joinpath(homedir(), "Desktop", "facesIITraining.txt"), "w") do io
+      #      write(io, string(facesIITraining))
+      # end
+      # open(joinpath(homedir(), "Desktop", "nonFacesIITraining.txt"), "w") do io
+      #      write(io, string(nonFacesIITraining))
+      # end
 
       # classifiers are haar like features
       println("Determining classifiers; this will take a while...")