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add adaptive_threshold

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This commit is contained in:
ser1zw 2016-08-13 02:08:13 +09:00
parent 361fee6123
commit bc71727a34
5 changed files with 76 additions and 0 deletions
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1
ext/opencv/mat.cpp
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@ -1247,6 +1247,7 @@ namespace rubyopencv {
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, "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
rb_define_method(rb_klass, "save", RUBY_METHOD_FUNC(rb_save), -1);

32
ext/opencv/mat_imgproc.cpp
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@ -311,5 +311,37 @@ namespace rubyopencv {
}
return ret;
}
/*
* Applies an adaptive threshold to an array.
*
* @overload adaptive_threshold(max_value, adaptive_method, threshold_type, block_size, delta)
* @param max_value [Number] Non-zero value assigned to the pixels for which the condition is satisfied.
* @param adaptive_method [Integer] Adaptive thresholding algorithm to use.
* @param threshold_type [Integer] Thresholding type that must be either <tt>THRESH_BINARY</tt>
* or <tt>THRESH_BINARY_INV</tt>.
* @param block_size [Integer] Size of a pixel neighborhood that is used to calculate a threshold value
* for the pixel: 3, 5, 7, and so on.
* @param delta [Number] Constant subtracted from the mean or weighted mean.
* Normally, it is positive but may be zero or negative as well.
* @return [Mat] Destination image of the same size and the same type as <tt>self</tt>.
* @opencv_func cv::adaptiveThreshold
*/
VALUE rb_adaptive_threshold(VALUE self, VALUE max_value, VALUE adaptive_method, VALUE threshold_type,
VALUE block_size, VALUE delta) {
cv::Mat* selfptr = obj2mat(self);
cv::Mat* dstptr = NULL;
try {
dstptr = new cv::Mat();
cv::adaptiveThreshold(*selfptr, *dstptr, NUM2DBL(max_value), NUM2INT(adaptive_method),
NUM2INT(threshold_type), NUM2INT(block_size), NUM2DBL(delta));
}
catch (cv::Exception& e) {
delete dstptr;
Error::raise(e);
}
return mat2obj(dstptr, CLASS_OF(self));
}
}
}

2
ext/opencv/mat_imgproc.hpp
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@ -15,6 +15,8 @@ namespace rubyopencv {
VALUE rb_gaussian_blur(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,
VALUE threshold_type, VALUE block_size, VALUE delta);
}
}

3
ext/opencv/opencv_const.cpp
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@ -386,5 +386,8 @@ namespace rubyopencv {
rb_define_const(rb_module, "THRESH_MASK", INT2FIX(cv::THRESH_MASK));
rb_define_const(rb_module, "THRESH_OTSU", INT2FIX(cv::THRESH_OTSU));
rb_define_const(rb_module, "THRESH_TRIANGLE", INT2FIX(cv::THRESH_TRIANGLE));
rb_define_const(rb_module, "ADAPTIVE_THRESH_MEAN_C", INT2FIX(cv::ADAPTIVE_THRESH_MEAN_C));
rb_define_const(rb_module, "ADAPTIVE_THRESH_GAUSSIAN_C", INT2FIX(cv::ADAPTIVE_THRESH_GAUSSIAN_C));
}
}

38
test/test_mat_imgproc.rb
View file

@ -284,10 +284,48 @@ class TestCvMat < OpenCVTestCase
assert_raise(TypeError) {
m0.threshold(25, 255, DUMMY_OBJ)
}
# m0 = Cv::imread(FILENAME_LENA256x256, 0)
# m = m0.threshold(127, 255, Cv::THRESH_BINARY)
# w = Window.new('Original | Binary')
# w.show(Cv::hconcat([m0, m]))
# Cv::wait_key
end
def test_adaptive_threshold
m0 = Cv::Mat.new(2, 2, Cv::CV_8U)
m0[0, 0] = Cv::Scalar.new(10)
m0[0, 1] = Cv::Scalar.new(20)
m0[1, 0] = Cv::Scalar.new(30)
m0[1, 1] = Cv::Scalar.new(40)
expected = "<Cv::Mat:2x2,depth=0,channels=1,\n[ 0, 0;\n 255, 255]>"
m = m0.adaptive_threshold(255, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, 3, 0)
assert_equal(expected, m.to_s)
m = m0.adaptive_threshold(255, Cv::ADAPTIVE_THRESH_GAUSSIAN_C, Cv::THRESH_BINARY, 3, 0)
assert_equal(expected, m.to_s)
assert_raise(TypeError) {
m0.adaptive_threshold(DUMMY_OBJ, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, 3, 0)
}
assert_raise(TypeError) {
m0.adaptive_threshold(255, DUMMY_OBJ, Cv::THRESH_BINARY, 3, 0)
}
assert_raise(TypeError) {
m0.adaptive_threshold(255, Cv::ADAPTIVE_THRESH_MEAN_C, DUMMY_OBJ, 3, 0)
}
assert_raise(TypeError) {
m0.adaptive_threshold(DUMMY_OBJ, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, DUMMY_OBJ, 0)
}
assert_raise(TypeError) {
m0.adaptive_threshold(DUMMY_OBJ, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, 3, DUMMY_OBJ)
}
# m0 = Cv::imread(FILENAME_LENA256x256, 0)
# m = m0.adaptive_threshold(255, Cv::ADAPTIVE_THRESH_MEAN_C, Cv::THRESH_BINARY, 25, 0)
# w = Window.new('Original | Binary')
# w.show(Cv::hconcat([m0, m]))
# Cv::wait_key
end
end