Merge branch 'develop' into fix/issues-33_opencv_gpulib_not_found

Manifest.txt

@@ -8,8 +8,6 @@ README.md
 Rakefile
 config.yml
 examples/alpha_blend.rb
-examples/box.png
-examples/box_in_scene.png
 examples/contours/bitmap-contours-with-labels.png
 examples/contours/bitmap-contours.png
 examples/contours/bounding-box-detect-canny.rb
@@ -24,12 +22,19 @@ examples/facerec/facerec_lbph.rb
 examples/facerec/readme.md
 examples/find_obj.rb
 examples/houghcircle.rb
-examples/inpaint.png
+examples/images/box.png
+examples/images/box_in_scene.png
+examples/images/inpaint.png
+examples/images/lena-256x256.jpg
+examples/images/lena-eyes.jpg
+examples/images/lenna-rotated.jpg
+examples/images/lenna.jpg
+examples/images/stuff.jpg
+examples/images/tiffany.jpg
 examples/inpaint.rb
-examples/lenna-rotated.jpg
-examples/lenna.jpg
 examples/match_kdtree.rb
-examples/matching_to_many_images.rb
+examples/match_template.rb
+examples/matching_to_many_images/matching_to_many_images.rb
 examples/matching_to_many_images/query.png
 examples/matching_to_many_images/train/1.png
 examples/matching_to_many_images/train/2.png
@@ -37,8 +42,6 @@ examples/matching_to_many_images/train/3.png
 examples/matching_to_many_images/train/trainImages.txt
 examples/paint.rb
 examples/snake.rb
-examples/stuff.jpg
-examples/tiffany.jpg
 ext/opencv/algorithm.cpp
 ext/opencv/algorithm.h
 ext/opencv/curve.cpp

README.md

@@ -3,7 +3,7 @@
 An OpenCV wrapper for Ruby.
 
 * Web site: <https://github.com/ruby-opencv/ruby-opencv>
-* Ruby 1.9.3, 2.0.0 and OpenCV 2.4.6.1 are supported.
+* Ruby 1.9.3, 2.0.0 and OpenCV 2.4.7 are supported.
 
 ## Requirement
 

examples/alpha_blend.rb

@@ -6,8 +6,8 @@
 require 'opencv'
 include OpenCV
 
-img1 = IplImage.load('lenna.jpg', CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
-img2 = IplImage.load('tiffany.jpg', CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
+img1 = IplImage.load('images/lenna.jpg', CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
+img2 = IplImage.load('images/tiffany.jpg', CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
 
 window = GUI::Window.new('Alpha blend')
 max = 100.0

examples/facerec/facerec_eigenfaces.rb

@@ -74,7 +74,7 @@ model = EigenFaces.new
 model.train(images, labels)
 
 # The following line predicts the label of a given test image:
-predicted_label = model.predict(test_sample)
+predicted_label, predicted_confidence = model.predict(test_sample)
 
 puts "Predicted class: #{predicted_label} / Actual class: #{test_label}"
 

examples/facerec/facerec_fisherfaces.rb

@@ -73,7 +73,7 @@ model = FisherFaces.new
 model.train(images, labels)
 
 # The following line predicts the label of a given test image:
-predicted_label = model.predict(test_sample)
+predicted_label, predicted_confidence = model.predict(test_sample)
 
 puts "Predicted class: #{predicted_label} / Actual class: #{test_label}"
 

examples/facerec/facerec_lbph.rb

@@ -75,7 +75,7 @@ model = LBPH.new
 model.train(images, labels)
 
 # The following line predicts the label of a given test image:
-predicted_label = model.predict(test_sample)
+predicted_label, predicted_confidence = model.predict(test_sample)
 
 # To get the confidence of a prediction call the model with:
 # 

examples/find_obj.rb

@@ -100,8 +100,8 @@ puts 'Usage:'
 puts "ruby #{__FILE__} <object_filename> <scene_filename>, default is box.png and box_in_scene.png"
 puts
 
-object_filename = (ARGV.size == 2) ? ARGV[0] : 'box.png'
-scene_filename = (ARGV.size == 2) ? ARGV[1] : 'box_in_scene.png'
+object_filename = (ARGV.size == 2) ? ARGV[0] : 'images/box.png'
+scene_filename = (ARGV.size == 2) ? ARGV[1] : 'images/box_in_scene.png'
 
 object, image = nil, nil
 begin

examples/houghcircle.rb

@@ -7,7 +7,7 @@ include OpenCV
 original_window = GUI::Window.new "original"
 hough_window = GUI::Window.new "hough circles"
 
-image = IplImage::load "stuff.jpg"
+image = IplImage::load "images/stuff.jpg"
 gray = image.BGR2GRAY
 
 result = image.clone

examples/inpaint.rb

@@ -20,7 +20,7 @@ owindow = GUI::Window.new('original')
 mwindow = GUI::Window.new('mask')
 iwindow = GUI::Window.new('inpaint')
 
-image = IplImage::load('inpaint.png')
+image = IplImage::load('images/inpaint.png')
 noimage = image.zero
 b, g, r = image.split
 original_mask = r.threshold(0x00, 0xFF, CV_THRESH_BINARY_INV) & b.threshold(0x00, 0xFF, CV_THRESH_BINARY_INV)

examples/match_kdtree.rb

@@ -11,8 +11,8 @@ USE_EXTENDED_DESCRIPTOR = true
 THRESHOLD = 1500
 DESCRIPTOR_SIZE = USE_EXTENDED_DESCRIPTOR ? 128 : 64
 
-img1 = CvMat.load('lenna.jpg', CV_LOAD_IMAGE_GRAYSCALE)
-img2 = CvMat.load('lenna-rotated.jpg', CV_LOAD_IMAGE_GRAYSCALE)
+img1 = CvMat.load('images/lenna.jpg', CV_LOAD_IMAGE_GRAYSCALE)
+img2 = CvMat.load('images/lenna-rotated.jpg', CV_LOAD_IMAGE_GRAYSCALE)
 
 puts 'Extracting features from img1 using SURF...'
 param = CvSURFParams.new(THRESHOLD, USE_EXTENDED_DESCRIPTOR)

examples/match_template.rb

@@ -0,0 +1,26 @@
+#!/usr/bin/env ruby
+
+# A demo of Ruby/OpenCV's match_template function
+
+require 'opencv'
+include OpenCV
+
+puts 'This program demonstrates the match_template function'
+puts 'Usage:'
+puts "ruby #{__FILE__} <template_filename> <match_filename>"
+puts
+
+template_filename = (ARGV.size == 2) ? ARGV[0] : File.expand_path(File.dirname(__FILE__) + '/images/lena-eyes.jpg')
+match_image_filename = (ARGV.size == 2) ? ARGV[1] : File.expand_path(File.dirname(__FILE__) + '/images/lena-256x256.jpg')
+
+template = CvMat.load(template_filename)
+match_image = CvMat.load(match_image_filename)
+result = match_image.match_template(template, :sqdiff_normed)
+
+pt1 = result.min_max_loc[2] # minimum location
+pt2 = CvPoint.new(pt1.x + template.width, pt1.y + template.height)
+match_image.rectangle!(pt1, pt2, :color => CvColor::Black, :thickness => 3)
+
+window = GUI::Window.new('Display window') # Create a window for display.
+window.show(match_image) # Show our image inside it.
+GUI::wait_key # Wait for a keystroke in the window.

examples/matching_to_many_images/matching_to_many_images.rb

@@ -2,11 +2,11 @@ require 'opencv'
 require 'benchmark'
 include OpenCV
 
-data = File.join(File.dirname(__FILE__), 'matching_to_many_images')
+data = File.dirname(__FILE__)
 
-query = IplImage.load File.join(data, 'query.png'), CV_LOAD_IMAGE_GRAYSCALE
+query = CvMat.load File.join(data, 'query.png'), CV_LOAD_IMAGE_GRAYSCALE
 image_files = ['1.png', '2.png', '3.png'].map{|f| File.join(data, 'train', f)}
-images = image_files.map{|f| IplImage.load f, CV_LOAD_IMAGE_GRAYSCALE}
+images = image_files.map{|f| CvMat.load f, CV_LOAD_IMAGE_GRAYSCALE}
 
 
 matchs = query.match_descriptors(images)

ext/opencv/cvmat.cpp

@@ -352,6 +352,7 @@ void define_ruby_class()
   rb_define_method(rb_klass, "resize", RUBY_METHOD_FUNC(rb_resize), -1);
   rb_define_method(rb_klass, "warp_affine", RUBY_METHOD_FUNC(rb_warp_affine), -1);
   rb_define_singleton_method(rb_klass, "rotation_matrix2D", RUBY_METHOD_FUNC(rb_rotation_matrix2D), 3);
+  rb_define_singleton_method(rb_klass, "get_perspective_transform", RUBY_METHOD_FUNC(rb_get_perspective_transform), 2);
   rb_define_method(rb_klass, "warp_perspective", RUBY_METHOD_FUNC(rb_warp_perspective), -1);
   rb_define_singleton_method(rb_klass, "find_homography", RUBY_METHOD_FUNC(rb_find_homograpy), -1);
   rb_define_method(rb_klass, "remap", RUBY_METHOD_FUNC(rb_remap), -1);
@@ -4053,6 +4054,40 @@ rb_rotation_matrix2D(VALUE self, VALUE center, VALUE angle, VALUE scale)
   return map_matrix;
 }
 
+/*
+ * call-seq:
+ *   CvMat.get_perspective_transform(<i>from_points,to_points</i>) -> cvmat
+ *
+ * Calculates a perspective transform from four pairs of the corresponding points.
+ * Returns a matrix suitable for use with warp_perspective
+ */
+VALUE
+rb_get_perspective_transform(VALUE self, VALUE source, VALUE dest)
+{
+  Check_Type(source, T_ARRAY);
+  Check_Type(dest, T_ARRAY);
+
+  int count = RARRAY_LEN(source);
+
+  CvPoint2D32f* source_buff = ALLOCA_N(CvPoint2D32f, count);
+  CvPoint2D32f* dest_buff = ALLOCA_N(CvPoint2D32f, count);
+
+  for (int i = 0; i < count; i++) {
+    source_buff[i] = *(CVPOINT2D32F(RARRAY_PTR(source)[i]));
+    dest_buff[i] = *(CVPOINT2D32F(RARRAY_PTR(dest)[i]));
+  }
+
+  VALUE map_matrix = new_object(cvSize(3, 3), CV_MAKETYPE(CV_32F, 1));
+
+  try {
+    cvGetPerspectiveTransform(source_buff, dest_buff, CVMAT(map_matrix));
+  }
+  catch (cv::Exception& e) {
+    raise_cverror(e);
+  }
+  return map_matrix;
+}
+
 /*
  * call-seq:
  *   warp_perspective(<i>map_matrix[,flags = CV_INTER_LINEAR | CV_WARP_FILL_OUTLIERS][,fillval=0])</i>) -> cvmat
@@ -5351,7 +5386,6 @@ rb_match_descriptors(int argc, VALUE *argv, VALUE self)
 {
   VALUE images, detector_type, descriptor_type, matcher_type;
   rb_scan_args(argc, argv, "13", &images, &detector_type, &descriptor_type, &matcher_type);
-
   if (RARRAY_LEN(images) == 0) {
     return rb_hash_new();
   }
@@ -5365,45 +5399,44 @@ rb_match_descriptors(int argc, VALUE *argv, VALUE self)
     matcher_type = rb_str_new2("FlannBased");
   }
 
-  cv::Mat queryImage = CVMAT(self);
+  VALUE _matches = rb_hash_new();
+  try {
+    cv::Mat queryImage(CVMAT(self));
     std::vector<cv::Mat> trainImages;
-  for(int i=0; i < RARRAY_LEN(images); i++) {
+    for(int i = 0, n = RARRAY_LEN(images); i < n; i++) {
       trainImages.push_back(CVMAT_WITH_CHECK(RARRAY_PTR(images)[i]));
     }
 
-  cv::Ptr<cv::FeatureDetector> featureDetector = cv::FeatureDetector::create(RSTRING_PTR(detector_type));
+    cv::Ptr<cv::FeatureDetector> featureDetector = cv::FeatureDetector::create(StringValueCStr(detector_type));
     if (featureDetector.empty()) {
-    rb_raise(rb_eArgError, "Could not create feature detector by given detector type: %s", RSTRING_PTR(detector_type));
+      rb_raise(rb_eArgError, "Could not create feature detector by given detector type: %s", StringValueCStr(detector_type));
     }
-  cv::Ptr<cv::DescriptorExtractor> descriptorExtractor = cv::DescriptorExtractor::create(RSTRING_PTR(descriptor_type));
+    cv::Ptr<cv::DescriptorExtractor> descriptorExtractor = cv::DescriptorExtractor::create(StringValueCStr(descriptor_type));
     if (descriptorExtractor.empty()) {
-    rb_raise(rb_eArgError, "Could not create descriptor extractor by given descriptor type: %s", RSTRING_PTR(descriptor_type));
+      rb_raise(rb_eArgError, "Could not create descriptor extractor by given descriptor type: %s", StringValueCStr(descriptor_type));
     }
     cv::Ptr<cv::DescriptorMatcher> descriptorMatcher;
     try {
-    descriptorMatcher = cv::DescriptorMatcher::create(RSTRING_PTR(matcher_type));
+      descriptorMatcher = cv::DescriptorMatcher::create(StringValueCStr(matcher_type));
     }
     catch(cv::Exception& e) {
-    rb_raise(rb_eArgError, "Could not create descriptor matcher by given matcher type: %s", RSTRING_PTR(matcher_type));
+      rb_raise(rb_eArgError, "Could not create descriptor matcher by given matcher type: %s", StringValueCStr(matcher_type));
     }
 
     std::vector<cv::KeyPoint> queryKeypoints;
     std::vector<std::vector<cv::KeyPoint> > trainKeypoints;
     featureDetector->detect(queryImage, queryKeypoints);
     featureDetector->detect(trainImages, trainKeypoints);
-
     cv::Mat queryDescriptors;
     std::vector<cv::Mat> trainDescriptors;
     descriptorExtractor->compute(queryImage, queryKeypoints, queryDescriptors);
     descriptorExtractor->compute(trainImages, trainKeypoints, trainDescriptors);
-
     std::vector<cv::DMatch> matches;
     descriptorMatcher->add(trainDescriptors);
     descriptorMatcher->train();
     descriptorMatcher->match(queryDescriptors, matches);
 
-  VALUE _matches = rb_hash_new();
-  for (size_t i=0; i<matches.size(); i++) {
+    for (size_t i = 0, n = matches.size(); i < n; i++) {
       VALUE match = INT2FIX(matches[i].imgIdx);
       VALUE count = rb_hash_lookup(_matches, match);
       if (NIL_P(count)) {
@@ -5413,6 +5446,11 @@ rb_match_descriptors(int argc, VALUE *argv, VALUE self)
       }
       rb_hash_aset(_matches, match, count);
     }
+  }
+  catch (cv::Exception& e) {
+    raise_cverror(e);
+  }
+
   return _matches;
 }
 

ext/opencv/cvmat.h

@@ -175,6 +175,7 @@ VALUE rb_quadrangle_sub_pix(int argc, VALUE *argv, VALUE self);
 VALUE rb_resize(int argc, VALUE *argv, VALUE self);
 VALUE rb_warp_affine(int argc, VALUE *argv, VALUE self);
 VALUE rb_rotation_matrix2D(VALUE self, VALUE center, VALUE angle, VALUE scale);
+VALUE rb_get_perspective_transform(VALUE self, VALUE source, VALUE dest);
 VALUE rb_warp_perspective(int argc, VALUE *argv, VALUE self);
 VALUE rb_find_homograpy(int argc, VALUE *argv, VALUE self);
 VALUE rb_remap(int argc, VALUE *argv, VALUE self);

ext/opencv/facerecognizer.cpp

@@ -96,16 +96,18 @@ rb_predict(VALUE self, VALUE src)
   cv::Mat mat = cv::Mat(CVMAT_WITH_CHECK(src));
   cv::FaceRecognizer *self_ptr = FACERECOGNIZER(self);
   int label;
+  double confidence;
   try {
-    label = self_ptr->predict(mat);
+    self_ptr->predict(mat, label, confidence);
   }
   catch (cv::Exception& e) {
     raise_cverror(e);
   }
 
-  return INT2NUM(label);
+  return rb_ary_new3(2, INT2NUM(label), DBL2NUM(confidence));
 }
 
+
 /*
  * call-seq:
  *   save(filename)

ext/opencv/facerecognizer.h

@@ -22,6 +22,7 @@ VALUE rb_class();
 
 void define_ruby_class();
 VALUE rb_train(VALUE self, VALUE src, VALUE labels);
+VALUE rb_predict(VALUE self, VALUE src);
 VALUE rb_save(VALUE self, VALUE filename);
 VALUE rb_load(VALUE self, VALUE filename);
 

test/test_cvmat_imageprocessing.rb

@@ -450,6 +450,40 @@ class TestCvMat_imageprocessing < OpenCVTestCase
     # snap mat0, mat1, mat2, mat3, mat4
   end
 
+  def test_get_perspective_transform
+    from = [
+        OpenCV::CvPoint2D32f.new(540, 382),
+        OpenCV::CvPoint2D32f.new(802, 400),
+        OpenCV::CvPoint2D32f.new(850, 731),
+        OpenCV::CvPoint2D32f.new(540, 731),
+    ]
+    to = [
+      OpenCV::CvPoint2D32f.new(0, 0),
+      OpenCV::CvPoint2D32f.new(233, 0),
+      OpenCV::CvPoint2D32f.new(233, 310),
+      OpenCV::CvPoint2D32f.new(0, 310),
+    ]
+    transform = OpenCV::CvMat.get_perspective_transform(from, to)
+    assert_equal 3, transform.rows
+    assert_equal 3, transform.columns
+    expected = [
+      0.923332154750824,
+      0.0,
+      0.0,
+      1.4432899320127035e-15,
+      0.0,
+      0.0,
+      -498.599365234375,
+      0.0,
+      0.0,
+    ]
+    3.times do |i|
+      3.times do |j|
+        assert_in_delta(expected.shift, transform[i][j], 0.001)
+      end
+    end
+  end
+
   def test_rotation_matrix2D
     mat1 = CvMat.rotation_matrix2D(CvPoint2D32f.new(10, 20), 60, 2.0)
     expected = [1.0, 1.73205, -34.64102,

test/test_cvmat_matching.rb

@@ -18,7 +18,7 @@ class TestCvMat_matching < OpenCVTestCase
   end
 
   def read_test_image(*path)
-    IplImage.load File.join(data_dir, *path), CV_LOAD_IMAGE_GRAYSCALE
+    CvMat.load File.join(data_dir, *path), CV_LOAD_IMAGE_GRAYSCALE
   end
 
   def test_match_descriptors

test/test_eigenfaces.rb

@@ -13,8 +13,9 @@ class TestEigenFaces < OpenCVTestCase
     @eigenfaces = EigenFaces.new
 
     @eigenfaces_trained = EigenFaces.new
-    img = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
-    @eigenfaces_trained.train([img], [1])
+    @images = [CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)] * 2
+    @labels = [1, 2]
+    @eigenfaces_trained.train(@images, @labels)
   end
 
   def test_initialize
@@ -32,51 +33,46 @@ class TestEigenFaces < OpenCVTestCase
   end
 
   def test_train
-    img = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
-    assert_nil(@eigenfaces.train([img], [1]))
+    assert_nil(@eigenfaces.train(@images, @labels))
 
     assert_raise(TypeError) {
-      @eigenfaces.train(DUMMY_OBJ, [1])
+      @eigenfaces.train(DUMMY_OBJ, @labels)
     }
 
     assert_raise(TypeError) {
-      @eigenfaces.train([img], DUMMY_OBJ)
+      @eigenfaces.train(@images, DUMMY_OBJ)
     }
   end
 
   def test_predict
-    img = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
-    label = 1
-    @eigenfaces.train([img], [label])
-    assert_equal(label, @eigenfaces.predict(img))
+    predicted_label, predicted_confidence = @eigenfaces_trained.predict(@images[0])
+    assert_equal(@labels[0], predicted_label)
+    assert_in_delta(0.0, predicted_confidence, 0.01)
 
     assert_raise(TypeError) {
-      @eigenfaces.predict(DUMMY_OBJ)
+      @eigenfaces_trained.predict(DUMMY_OBJ)
     }
   end
 
   def test_save
-    img = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
-    label = 1
-    @eigenfaces.train([img], [label])
     filename = "eigenfaces_save-#{DateTime.now.strftime('%Y%m%d%H%M%S')}.xml"
     begin
-      @eigenfaces.save(filename)
+      @eigenfaces_trained.save(filename)
       assert(File.exist? filename)
     ensure
       File.delete filename
     end
     assert_raise(TypeError) {
-      @eigenfaces.save(DUMMY_OBJ)
+      @eigenfaces_trained.save(DUMMY_OBJ)
     }
   end
 
   def test_load
     assert_nothing_raised {
-      @eigenfaces.load('eigenfaces_save.xml')
+      @eigenfaces_trained.load('eigenfaces_save.xml')
     }
     assert_raise(TypeError) {
-      @eigenfaces.load(DUMMY_OBJ)
+      @eigenfaces_trained.load(DUMMY_OBJ)
     }
   end
 

test/test_fisherfaces.rb

@@ -14,7 +14,8 @@ class TestFisherFaces < OpenCVTestCase
 
     @fisherfaces_trained = FisherFaces.new
     @images = [CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)] * 2
-    @fisherfaces_trained.train(@images, [1, 2])
+    @labels = [1, 2]
+    @fisherfaces_trained.train(@images, @labels)
   end
 
   def test_initialize
@@ -32,10 +33,10 @@ class TestFisherFaces < OpenCVTestCase
   end
 
   def test_train
-    assert_nil(@fisherfaces.train(@images, [1, 2]))
+    assert_nil(@fisherfaces.train(@images, @labels))
 
     assert_raise(TypeError) {
-      @fisherfaces.train(DUMMY_OBJ, [1])
+      @fisherfaces.train(DUMMY_OBJ, @labels)
     }
 
     assert_raise(TypeError) {
@@ -44,8 +45,9 @@ class TestFisherFaces < OpenCVTestCase
   end
 
   def test_predict
-    label = 1
-    assert_equal(1, @fisherfaces_trained.predict(@images[0]))
+    predicted_label, predicted_confidence = @fisherfaces_trained.predict(@images[0])
+    assert_equal(@labels[0], predicted_label)
+    assert_in_delta(0.0, predicted_confidence, 0.01)
 
     assert_raise(TypeError) {
       @fisherfaces_trained.predict(DUMMY_OBJ)

test/test_lbph.rb

@@ -14,7 +14,8 @@ class TestLBPH < OpenCVTestCase
 
     @lbph_trained = LBPH.new
     @images = [CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)] * 2
-    @lbph_trained.train(@images, [1, 1])
+    @labels = [1, 2]
+    @lbph_trained.train(@images, @labels)
   end
 
   def test_initialize
@@ -40,10 +41,10 @@ class TestLBPH < OpenCVTestCase
   end
 
   def test_train
-    assert_nil(@lbph.train(@images, [1, 1]))
+    assert_nil(@lbph.train(@images, @labels))
 
     assert_raise(TypeError) {
-      @lbph.train(DUMMY_OBJ, [1, 1])
+      @lbph.train(DUMMY_OBJ, @labels)
     }
 
     assert_raise(TypeError) {
@@ -52,7 +53,9 @@ class TestLBPH < OpenCVTestCase
   end
 
   def test_predict
-    assert_equal(1, @lbph_trained.predict(@images[0]))
+    predicted_label, predicted_confidence = @lbph_trained.predict(@images[0])
+    assert_equal(@labels[0], predicted_label)
+    assert_in_delta(0.0, predicted_confidence, 0.01)
 
     assert_raise(TypeError) {
       @lbph_trained.predict(DUMMY_OBJ)

test/test_opencv.rb

@@ -9,15 +9,15 @@ include OpenCV
 class TestOpenCV < OpenCVTestCase
   def test_constants
     # OpenCV version
-    assert_equal('2.4.6.1', CV_VERSION)
+    assert_equal('2.4.7', CV_VERSION)
     assert_equal(2, CV_MAJOR_VERSION)
     assert_equal(4, CV_MINOR_VERSION)
-    assert_equal(6, CV_SUBMINOR_VERSION)
+    assert_equal(7, CV_SUBMINOR_VERSION)
 
     assert_equal(2, CV_VERSION_EPOCH)
     assert_equal(4, CV_VERSION_MAJOR)
-    assert_equal(6, CV_VERSION_MINOR)
-    assert_equal(1, CV_VERSION_REVISION)
+    assert_equal(7, CV_VERSION_MINOR)
+    assert_equal(0, CV_VERSION_REVISION)
 
     # Depths
     assert_equal(0, CV_8U)