Updated example files

jakewilliami · Oct 27, 2020 · 9ba461e · 9ba461e
1 parent ff690f4
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Showing 8 changed files with 63 additions and 233 deletions.
diff --git a/examples/alt_data.jl b/examples/alt_data.jl
diff --git a/examples/basic.jl b/examples/basic.jl
@@ -23,17 +23,11 @@ println("...done")
 
 function main(;
     smart_choose_feats::Bool=false,
-    alt::Bool=false,
 	scale::Bool=false,
 	scale_to::Tuple=(200, 200)
 )
-    include("constants.jl")
-
-    if ! alt
-        include("main_data.jl")
-    else
-        include("alt_data.jl")
-    end
+	include("constants.jl")
+	include("main_data.jl")
 
     min_size_img = (19, 19) # default for our test dataset
     if smart_choose_feats
@@ -74,4 +68,4 @@ function main(;
     @printf("%10.9s %10.15s %15s\n\n", "Non-faces:", non_faces_frac, non_faces_percent)
 end
 
-@time main(smart_choose_feats=true, alt=false, scale=true, scale_to=(19, 19))
+@time main(smart_choose_feats=true, scale=true, scale_to=(20, 20))
diff --git a/examples/constants.jl b/examples/constants.jl
@@ -11,5 +11,3 @@ main_image_path = joinpath(main_path, "data", "main")
 alt_image_path = joinpath(main_path, "data", "alt")
 
 num_classifiers = 10
-
-data_file = joinpath(dirname(@__FILE__), "data", "haar-like_features_c$(num_classifiers)")
diff --git a/examples/read.jl b/examples/read.jl
@@ -20,22 +20,20 @@ using Printf: @printf
 using Images: imresize
 using Serialization: deserialize
 
-println("...done")
+println("...done\n")
 
 function main(;
     smart_choose_feats::Bool=false,
-	alt::Bool=false,
 	scale::Bool=false,
 	scale_to::Tuple=(200, 200)
 )
 
 	include("constants.jl")
-
-	if ! alt
-		include("main_data.jl")
-	else
-		include("alt_data.jl")
-	end
+	include("main_data.jl")
+
+	max_feature_width, max_feature_height, min_feature_height, min_feature_width, min_size_img = determine_feature_size(pos_testing_path, neg_testing_path; scale = scale, scale_to = scale_to)
+	img_size = scale ? scale_to : min_size_img
+	data_file = joinpath(dirname(@__FILE__), "data", "haar-like_features_c$(num_classifiers)_$(img_size)")
 
 	if ! isfile(data_file)
 		error(throw("You do not have a data file.  Ensure you run \"write.jl\" to obtain your Haar-like features before running this script/"))
@@ -65,4 +63,4 @@ function main(;
     @printf("%10.9s %10.15s %15s\n\n", "Non-faces:", non_faces_frac, non_faces_percent)
 end
 
-@time main(smart_choose_feats=true, alt=false, scale=true, scale_to=(20, 20))
+@time main(smart_choose_feats=true, scale=true, scale_to=(20, 20))
diff --git a/examples/scores.jl b/examples/scores.jl
@@ -19,16 +19,9 @@ using Serialization: deserialize
 
 println("...done")
 
-function main(;
-    smart_choose_feats::Bool=false, alt::Bool=false
-)
-    include("constants.jl")
-
-    if ! alt
-        include("main_data.jl")
-    else
-        include("alt_data.jl")
-    end
+function main(smart_choose_feats::Bool=false)
+	include("constants.jl")
+	include("main_data.jl")
 
     # read classifiers from file
 	classifiers = deserialize(data_file)
@@ -103,4 +96,4 @@ function main(;
     println("...done.  Plot created at ", joinpath(dirname(dirname(@__FILE__)), "figs", "scores.pdf"), "\n")
 end
 
-@time main(smart_choose_feats=true, alt=false)
+@time main(smart_choose_feats=true)
diff --git a/examples/validation.jl b/examples/validation.jl
@@ -23,51 +23,54 @@ println("...done")
 
 function main(;
     smart_choose_feats::Bool=false,
-    alt::Bool=false,
     image_reconstruction::Bool=true,
-    feat_validation::Bool=true
+    feat_validation::Bool=true,
+	scale::Bool=false,
+	scale_to::Tuple=(200, 200)
 )
     include("constants.jl")
-
-    if ! alt
-        include("main_data.jl")
-    else
-        include("alt_data.jl")
-    end
+    include("main_data.jl")
+
+	max_feature_width, max_feature_height, min_feature_height, min_feature_width, min_size_img = determine_feature_size(pos_testing_path, neg_testing_path; scale = scale, scale_to = scale_to)
+	img_size = scale ? scale_to : min_size_img
+	data_file = joinpath(dirname(@__FILE__), "data", "haar-like_features_c$(num_classifiers)_$(img_size)")
 
     # read classifiers from file
 	classifiers = deserialize(data_file)
 
-    FD.notify_user("Loading test faces...")
-
-    faces_testing = FD.load_images(pos_testing_path)[1]
+    # FD.notify_user("Loading test faces...")
+	#
+    # faces_testing = FD.load_images(pos_testing_path)[1]
+    # # faces_ii_testing = map(FD.to_integral_image, faces_testing)
     # faces_ii_testing = map(FD.to_integral_image, faces_testing)
-    faces_ii_testing = map(FD.to_integral_image, faces_testing)
-    println("...done. ", length(faces_testing), " faces loaded.")
-
-    FD.notify_user("Loading test non-faces..")
-
-    non_faces_testing = FD.load_images(neg_testing_path)[1]
-    non_faces_ii_testing = map(FD.to_integral_image, non_faces_testing)
-    println("...done. ", length(non_faces_testing), " non-faces loaded.\n")
+    # println("...done. ", length(faces_testing), " faces loaded.")
+	#
+    # FD.notify_user("Loading test non-faces..")
+	#
+    # non_faces_testing = FD.load_images(neg_testing_path)[1]
+    # non_faces_ii_testing = map(FD.to_integral_image, non_faces_testing)
+    # println("...done. ", length(non_faces_testing), " non-faces loaded.\n")
+
+	random_image = get_random_image(pos_testing_path)
 
     if image_reconstruction
         # Just for fun: putting all Haar-like features over each other generates a face-like image
         FD.notify_user("Constructing an image of all Haar-like Features found...")
 
-        reconstructed_image = FD.reconstruct(classifiers, size(faces_testing[1]))
+        reconstructed_image = FD.reconstruct(classifiers, img_size)
         save(joinpath(dirname(dirname(@__FILE__)), "figs", "reconstruction.png"), Gray.(map(clamp01nan, reconstructed_image)))
 
         println("...done.  See ", joinpath(dirname(dirname(@__FILE__)), "figs", "reconstruction.png"), ".\n")
     end
 
     if feat_validation
         FD.notify_user("Constructing a validation image on a random image...")
-
-        FD.generate_validation_image(FD.get_random_image(joinpath(dirname(dirname(@__FILE__)), "figs", "validation.png")), classifiers)
+
+		validation_image = FD.generate_validation_image(random_image, classifiers)
+        save(joinpath(dirname(dirname(@__FILE__)), "figs", "validation.png"), validation_image)
 
         println("...done.  See ", joinpath(dirname(dirname(@__FILE__)), "figs", "validation.png"), ".\n")
     end
 end
 
-@time main(smart_choose_feats=true, alt=false, image_reconstruction=true, feat_validation=true)
+@time main(smart_choose_feats=true, image_reconstruction=true, feat_validation=false)
diff --git a/examples/write.jl b/examples/write.jl
@@ -19,21 +19,15 @@ const FD = FaceDetection
 using Printf: @printf
 using Serialization: serialize
 
-println("...done")
+println("...done\n")
 
 function main(;
     smart_choose_feats::Bool=false,
-	alt::Bool=false,
 	scale::Bool=false,
 	scale_to::Tuple=(200, 200)
 )
 	include("constants.jl")
-
-	if ! alt
-		include("main_data.jl")
-	else
-		include("alt_data.jl")
-	end
+	include("main_data.jl")
 
 	min_size_img = (19, 19) # default for our test dataset
 	if smart_choose_feats
@@ -54,8 +48,9 @@ function main(;
 	classifiers = FD.learn(pos_training_path, neg_training_path, num_classifiers, min_feature_height, max_feature_height, min_feature_width, max_feature_width; scale = scale, scale_to = scale_to)
 
 	# write classifiers to file
-	data_file = joinpath(dirname(@__FILE__), "data", "haar-like_features_c$(num_classifiers)")
+	img_size = scale ? scale_to : min_size_img
+	data_file = joinpath(dirname(@__FILE__), "data", "haar-like_features_c$(num_classifiers)_$(img_size)")
 	serialize(data_file, classifiers)
 end
 
-@time main(smart_choose_feats=true, alt=false, scale=true, scale_to=(20, 20))
+@time main(smart_choose_feats=true, scale=true, scale_to=(20, 20))
Original file line number	Diff line number	Diff line change
Expand Up		@@ -11,5 +11,3 @@ main_image_path = joinpath(main_path, "data", "main")
		alt_image_path = joinpath(main_path, "data", "alt")

		num_classifiers = 10

		data_file = joinpath(dirname(@__FILE__), "data", "haar-like_features_c$(num_classifiers)")