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https://github.com/ruby-opencv/ruby-opencv
synced 2023-03-27 23:22:12 -04:00
add CvMat#adaptive_threshold
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ser1zw
parent
86b5d1918b
commit
71b2743c76
4 changed files with 135 additions and 0 deletions
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@ -338,6 +338,7 @@ void define_ruby_class()
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rb_define_method(rb_klass, "filter2d", RUBY_METHOD_FUNC(rb_filter2d), -1);
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rb_define_method(rb_klass, "integral", RUBY_METHOD_FUNC(rb_integral), -1);
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rb_define_method(rb_klass, "threshold", RUBY_METHOD_FUNC(rb_threshold), -1);
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rb_define_method(rb_klass, "adaptive_threshold", RUBY_METHOD_FUNC(rb_adaptive_threshold), -1);
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rb_define_method(rb_klass, "pyr_down", RUBY_METHOD_FUNC(rb_pyr_down), -1);
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rb_define_method(rb_klass, "pyr_up", RUBY_METHOD_FUNC(rb_pyr_up), -1);
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@ -4418,6 +4419,60 @@ rb_threshold(int argc, VALUE *argv, VALUE self)
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return rb_threshold_internal(type, threshold, max_value, use_otsu, self);
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}
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/*
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* call-seq:
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* adaptive_threshold(max_value[, options]) -> cvmat
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*
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* Applies an adaptive threshold to an array.
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*
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* ==== params:
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*
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* * <b>max_value (Number)</b> - Maximum value that is used with +CV_THRESH_BINARY+ and +CV_THRESH_BINARY_INV+
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* * <b>options (Hash)</b> - threshold option
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* * <b>:threshold_type (Integer or Symbol)</b> - Thresholding type; must be one of +CV_THRESH_BINARY+ or +:binary+, +CV_THRESH_BINARY_INV+ or +:binary_inv+ (default: +CV_THRESH_BINARY+)
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* * <b>:adaptive_method (Integer or Symbol)</b> - Adaptive thresholding algorithm to use: +CV_ADAPTIVE_THRESH_MEAN_C+ or +:mean_c+, +CV_ADAPTIVE_THRESH_GAUSSIAN_C+ or +:gaussian_c+ (default: +CV_ADAPTIVE_THRESH_MEAN_C+)
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* * <b>:block_size (Integer)</b> - The size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on (default: 3)
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* * <b>:param1 (Number)</b> - The method-dependent parameter. For the methods +CV_ADAPTIVE_THRESH_MEAN_C+ and +CV_ADAPTIVE_THRESH_GAUSSIAN_C+ it is a constant subtracted from the mean or weighted mean, though it may be negative (default: 5)
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*/
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VALUE
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rb_adaptive_threshold(int argc, VALUE *argv, VALUE self)
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{
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VALUE max_value, options;
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rb_scan_args(argc, argv, "11", &max_value, &options);
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int threshold_type = CV_THRESH_BINARY;
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int adaptive_method = CV_ADAPTIVE_THRESH_MEAN_C;
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int block_size = 3;
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double param1 = 5;
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if (!NIL_P(options)) {
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Check_Type(options, T_HASH);
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threshold_type = CVMETHOD("THRESHOLD_TYPE", LOOKUP_CVMETHOD(options, "threshold_type"),
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CV_THRESH_BINARY);
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adaptive_method = CVMETHOD("ADAPTIVE_METHOD", LOOKUP_CVMETHOD(options, "adaptive_method"),
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CV_ADAPTIVE_THRESH_MEAN_C);
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VALUE _block_size = LOOKUP_CVMETHOD(options, "block_size");
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if (!NIL_P(_block_size)) {
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block_size = NUM2INT(_block_size);
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}
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VALUE _param1 = LOOKUP_CVMETHOD(options, "param1");
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if (!NIL_P(_param1)) {
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param1 = NUM2INT(_param1);
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}
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}
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CvArr* self_ptr = CVARR(self);
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VALUE dst = new_mat_kind_object(cvGetSize(self_ptr), self);
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try {
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cvAdaptiveThreshold(self_ptr, CVARR(dst), NUM2DBL(max_value), adaptive_method, threshold_type,
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block_size, param1);
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}
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catch (cv::Exception& e) {
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raise_cverror(e);
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}
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return dst;
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}
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/*
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* call-seq:
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* pyr_down(<i>[filter = :gaussian_5x5]</i>) -> cvmat
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@ -216,6 +216,10 @@ define_ruby_module()
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rb_define_const(rb_module, "CV_THRESH_TOZERO_INV", INT2FIX(CV_THRESH_TOZERO_INV));
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rb_define_const(rb_module, "CV_THRESH_OTSU", INT2FIX(CV_THRESH_OTSU));
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/* Adaptive methods */
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rb_define_const(rb_module, "CV_ADAPTIVE_THRESH_MEAN_C", INT2FIX(CV_ADAPTIVE_THRESH_MEAN_C));
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rb_define_const(rb_module, "CV_ADAPTIVE_THRESH_GAUSSIAN_C", INT2FIX(CV_ADAPTIVE_THRESH_GAUSSIAN_C));
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/* Border type */
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rb_define_const(rb_module, "IPL_BORDER_CONSTANT", INT2FIX(IPL_BORDER_CONSTANT));
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rb_define_const(rb_module, "IPL_BORDER_REPLICATE", INT2FIX(IPL_BORDER_REPLICATE));
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@ -413,6 +417,12 @@ define_ruby_module()
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REGISTER_CVMETHOD(smoothing_type, "median", CV_MEDIAN);
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REGISTER_CVMETHOD(smoothing_type, "bilateral", CV_BILATERAL);
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VALUE adaptive_method = rb_hash_new();
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/* {:mean_c, :gaussian_c}: Adaptive thresholding algorithm */
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rb_define_const(rb_module, "ADAPTIVE_METHOD", adaptive_method);
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REGISTER_CVMETHOD(adaptive_method, "mean_c", CV_ADAPTIVE_THRESH_MEAN_C);
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REGISTER_CVMETHOD(adaptive_method, "gaussian_c", CV_ADAPTIVE_THRESH_GAUSSIAN_C);
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VALUE threshold_type = rb_hash_new();
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/* {:binary, :binary_inv, :trunc, :tozero, :tozero_inv, :otsu} : Thresholding types */
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rb_define_const(rb_module, "THRESHOLD_TYPE", threshold_type);
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@ -983,6 +983,72 @@ class TestCvMat_imageprocessing < OpenCVTestCase
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mat0.threshold(1, 2, :binary, DUMMY_OBJ)
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end
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def test_adaptive_threshold
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mat0 = create_cvmat(5, 5, :cv8u, 1) { |j, i, c| (c + 1) * 10 }
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mat1 = mat0.adaptive_threshold(128)
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expected1 = [0, 0, 0, 0, 0, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128]
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expected1.each_with_index { |expected, i|
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assert_equal(expected, mat1[i][0])
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}
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mat2a = mat0.adaptive_threshold(255, :adaptive_method => :mean_c,
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:threshold_type => :binary, :block_size => 5,
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:param1 => 10)
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mat2b = mat0.adaptive_threshold(255, :adaptive_method => CV_THRESH_BINARY,
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:threshold_type => CV_ADAPTIVE_THRESH_MEAN_C, :block_size => 5,
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:param1 => 10)
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expected2 = [0, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255]
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[mat2a, mat2b].each { |mat2|
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assert_equal(CvMat, mat2.class)
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assert_equal(mat0.rows, mat2.rows)
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assert_equal(mat0.cols, mat2.cols)
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assert_equal(mat0.depth, mat2.depth)
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assert_equal(mat0.channel, mat2.channel)
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expected2.each_with_index { |expected, i|
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assert_equal(expected, mat2[i][0])
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}
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}
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mat3a = mat0.adaptive_threshold(255, :adaptive_method => :gaussian_c,
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:threshold_type => :binary_inv, :block_size => 5,
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:param1 => 10)
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mat3b = mat0.adaptive_threshold(255, :adaptive_method => CV_ADAPTIVE_THRESH_GAUSSIAN_C,
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:threshold_type => CV_THRESH_BINARY_INV, :block_size => 5,
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:param1 => 10)
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expected3 = [255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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[mat3a, mat3b].each { |mat3|
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assert_equal(CvMat, mat3.class)
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assert_equal(mat0.rows, mat3.rows)
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assert_equal(mat0.cols, mat3.cols)
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assert_equal(mat0.depth, mat3.depth)
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assert_equal(mat0.channel, mat3.channel)
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expected3.each_with_index { |expected, i|
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assert_equal(expected, mat3[i][0])
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}
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}
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assert_raise(TypeError) {
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mat0.adaptive_threshold(DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.adaptive_threshold(0, DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.adaptive_threshold(0, :adaptive_method => DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.adaptive_threshold(0, :threshold_type => DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.adaptive_threshold(0, :block_size => DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.adaptive_threshold(0, :param1 => DUMMY_OBJ)
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}
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end
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def test_pyr_down
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
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mat1 = mat0.pyr_down
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@ -62,6 +62,10 @@ class TestOpenCV < OpenCVTestCase
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assert_equal(4, CV_THRESH_TOZERO_INV)
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assert_equal(8, CV_THRESH_OTSU)
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# Adaptive methods
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assert_equal(0, CV_ADAPTIVE_THRESH_MEAN_C)
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assert_equal(1, CV_ADAPTIVE_THRESH_GAUSSIAN_C)
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# Retrieval mode
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assert_equal(0, CV_RETR_EXTERNAL)
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assert_equal(1, CV_RETR_LIST)
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