add Mat#gaussian_blur!

ext/opencv/mat.cpp

@@ -1254,6 +1254,7 @@ namespace rubyopencv {
       rb_define_method(rb_klass, "blur", RUBY_METHOD_FUNC(rb_blur), -1); // in ext/opencv/mat_imgproc.cpp
       rb_define_method(rb_klass, "blur!", RUBY_METHOD_FUNC(rb_blur_bang), -1); // in ext/opencv/mat_imgproc.cpp
       rb_define_method(rb_klass, "gaussian_blur", RUBY_METHOD_FUNC(rb_gaussian_blur), -1); // in ext/opencv/mat_imgproc.cpp
+      rb_define_method(rb_klass, "gaussian_blur!", RUBY_METHOD_FUNC(rb_gaussian_blur_bang), -1); // in ext/opencv/mat_imgproc.cpp
       rb_define_method(rb_klass, "median_blur", RUBY_METHOD_FUNC(rb_median_blur), 1); // in ext/opencv/mat_imgproc.cpp
       rb_define_method(rb_klass, "threshold", RUBY_METHOD_FUNC(rb_threshold), 3); // in ext/opencv/mat_imgproc.cpp
       rb_define_method(rb_klass, "adaptive_threshold", RUBY_METHOD_FUNC(rb_adaptive_threshold), 5); // in ext/opencv/mat_imgproc.cpp

ext/opencv/mat_imgproc.cpp

@@ -351,6 +351,19 @@ namespace rubyopencv {
       return self;
     }
 
+    cv::Mat* rb_gaussian_blur_internal(int argc, VALUE *argv, VALUE self, cv::Mat* destptr) {
+      VALUE ksize, sigma_x, sigma_y, border_type;
+      rb_scan_args(argc, argv, "22", &ksize, &sigma_x, &sigma_y, &border_type);
+      double sigma_y_value = NIL_P(sigma_y) ? 0 : NUM2DBL(sigma_y);
+      int border_type_value = NIL_P(border_type) ? cv::BORDER_DEFAULT : NUM2INT(border_type);
+
+      cv::Mat* selfptr = obj2mat(self);
+      cv::GaussianBlur(*selfptr, *destptr, *(Size::obj2size(ksize)), NUM2DBL(sigma_x),
+		       sigma_y_value, border_type_value);
+
+      return destptr;
+    }
+
     /*
      * Blurs an image using a Gaussian filter.
      *
@@ -366,23 +379,32 @@ namespace rubyopencv {
      *   @opencv_func cv::GaussianBlur
      */
     VALUE rb_gaussian_blur(int argc, VALUE *argv, VALUE self) {
-      VALUE ksize, sigma_x, sigma_y, border_type;
+      cv::Mat* destptr = new cv::Mat();
-      rb_scan_args(argc, argv, "22", &ksize, &sigma_x, &sigma_y, &border_type);
-      double sigma_y_value = NIL_P(sigma_y) ? 0 : NUM2DBL(sigma_y);
-      int border_type_value = NIL_P(border_type) ? cv::BORDER_DEFAULT : NUM2INT(border_type);
-
-      cv::Mat* selfptr = obj2mat(self);
-      cv::Mat* dstptr = new cv::Mat();
       try {
-	cv::GaussianBlur(*selfptr, *dstptr, *(Size::obj2size(ksize)), NUM2DBL(sigma_x),
+	rb_gaussian_blur_internal(argc, argv, self, destptr);
-			 sigma_y_value, border_type_value);
       }
       catch (cv::Exception& e) {
-	delete dstptr;
+	delete destptr;
 	Error::raise(e);
       }
 
-      return mat2obj(dstptr, CLASS_OF(self));
+      return mat2obj(destptr, CLASS_OF(self));
+    }
+
+    /*
+     * @overload gaussian_blur!(ksize, sigma_x, sigma_y = 0, border_type = BORDER_DEFAULT)
+     * @see #gaussian_blur
+     */
+    VALUE rb_gaussian_blur_bang(int argc, VALUE *argv, VALUE self) {
+      cv::Mat* destptr = obj2mat(self);
+      try {
+	rb_gaussian_blur_internal(argc, argv, self, destptr);
+      }
+      catch (cv::Exception& e) {
+	Error::raise(e);
+      }
+
+      return self;
     }
 
     /*

ext/opencv/mat_imgproc.hpp

@@ -19,6 +19,7 @@ namespace rubyopencv {
     VALUE rb_blur(int argc, VALUE *argv, VALUE self);
     VALUE rb_blur_bang(int argc, VALUE *argv, VALUE self);
     VALUE rb_gaussian_blur(int argc, VALUE *argv, VALUE self);
+    VALUE rb_gaussian_blur_bang(int argc, VALUE *argv, VALUE self);
     VALUE rb_median_blur(VALUE self, VALUE ksize);
     VALUE rb_threshold(VALUE self, VALUE threshold, VALUE max_value, VALUE threshold_type);
     VALUE rb_adaptive_threshold(VALUE self, VALUE max_value, VALUE adaptive_method,

test/test_mat_imgproc.rb

@@ -459,6 +459,44 @@ class TestCvMat < OpenCVTestCase
     # Cv::wait_key
   end
 
+  def test_gaussian_blur_bang
+    m0 = Cv::imread(FILENAME_LENA256x256, -1)
+    m1 = m0.clone
+    m2 = m0.clone
+    m3 = m0.clone
+
+    ksize = Cv::Size.new(3, 3)
+    m1.gaussian_blur!(ksize, 3)
+    m2.gaussian_blur!(ksize, 3, 5)
+    m3.gaussian_blur!(ksize, 3, 5, Cv::BORDER_REPLICATE)
+
+    gaussian_blurs = [m1, m2, m3]
+    gaussian_blurs.each { |m|
+      assert_equal(m0.rows, m.rows)
+      assert_equal(m0.cols, m.cols)
+      assert_equal(m0.depth, m.depth)
+      assert_equal(m0.dims, m.dims)
+      assert_equal(m0.channels, m.channels)
+    }
+
+    assert_raise(TypeError) {
+      m0.gaussian_blur!(ksize, DUMMY_OBJ)
+    }
+    assert_raise(TypeError) {
+      m0.gaussian_blur!(ksize, 3, DUMMY_OBJ)
+    }
+    assert_raise(TypeError) {
+      m0.gaussian_blur!(ksize, 3, 5, DUMMY_OBJ)
+    }
+
+    # w = Window.new('Gaussian blur')
+    # m0 = Cv::imread(FILENAME_LENA256x256, -1)
+    # ksize = Cv::Size.new(13, 13)
+    # m0.gaussian_blur!(ksize, 3, 5, Cv::BORDER_REPLICATE)
+    # w.show(m0)
+    # Cv::wait_key
+  end
+
   def test_median_blur
     m0 = Cv::Mat.ones(3, 3, Cv::CV_8U)
     m0[1, 1] = Cv::Scalar.new(0)