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https://github.com/ruby-opencv/ruby-opencv
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fixed a bug of CvMat.find_fundamental_mat_ransac (size of status matrix), and tested it
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ser1zw
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e56749c70d
commit
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5 changed files with 87 additions and 39 deletions
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@ -5039,7 +5039,7 @@ rb_find_fundamental_mat_8point(VALUE klass, VALUE points1, VALUE points2)
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* CvMat.find_fundamental_mat_ransac(<i>points1, points2[,options = {}]</i>) -> fundamental_matrix(cvmat) or nil
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*
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* Calculates fundamental matrix from corresponding points, use for RANSAC algorism.
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* <i>points1</i> and <i>points2</i> should be 2x7 or 3x7 single-channel, or 1x7 multi-channel matrix.
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* <i>points1</i> and <i>points2</i> should be 2xN, Nx2, 3xN or Nx3 1-channel, or 1xN or Nx1 multi-channel matrix (N >= 8).
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* <i>option</i> should be Hash include these keys.
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* :with_status (true or false)
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* If set true, return fundamental_matrix and status. [fundamental_matrix, status]
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@ -5059,13 +5059,17 @@ rb_find_fundamental_mat_ransac(int argc, VALUE *argv, VALUE klass)
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int num = 0;
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rb_scan_args(argc, argv, "21", &points1, &points2, &option);
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option = FIND_FUNDAMENTAL_MAT_OPTION(option);
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fundamental_matrix = cCvMat::new_object(3, 3, CV_32FC1);
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fundamental_matrix = cCvMat::new_object(3, 3, CVMAT(points1)->type);
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if(FFM_WITH_STATUS(option)){
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status = cCvMat::new_object(cvGetSize(CVARR(points1)), CV_8UC1);
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num = cvFindFundamentalMat(CVMAT(points1), CVMAT(points2), CVMAT(fundamental_matrix), CV_FM_RANSAC, FFM_MAXIMUM_DISTANCE(option), FFM_DESIRABLE_LEVEL(option), CVMAT(status));
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CvMat *points1_ptr = CVMAT(points1);
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int status_len = (points1_ptr->rows > points1_ptr->cols) ? points1_ptr->rows : points1_ptr->cols;
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status = cCvMat::new_object(1, status_len, CV_8UC1);
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num = cvFindFundamentalMat(CVMAT(points1), CVMAT(points2), CVMAT(fundamental_matrix), CV_FM_RANSAC,
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FFM_MAXIMUM_DISTANCE(option), FFM_DESIRABLE_LEVEL(option), CVMAT(status));
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return num == 0 ? Qnil : rb_ary_new3(2, fundamental_matrix, status);
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}else{
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num = cvFindFundamentalMat(CVMAT(points1), CVMAT(points2), CVMAT(fundamental_matrix), CV_FM_RANSAC, FFM_MAXIMUM_DISTANCE(option), FFM_DESIRABLE_LEVEL(option), NULL);
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num = cvFindFundamentalMat(CVMAT(points1), CVMAT(points2), CVMAT(fundamental_matrix), CV_FM_RANSAC,
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FFM_MAXIMUM_DISTANCE(option), FFM_DESIRABLE_LEVEL(option), NULL);
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return num == 0 ? Qnil : fundamental_matrix;
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}
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}
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@ -1936,6 +1936,84 @@ class TestCvMat < OpenCVTestCase
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assert_in_delta(val, f_mat[i][0], 1.0e-5)
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}
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end
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def test_find_fundamental_mat_ransac
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points1 = [[488.362, 169.911],
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[449.488, 174.44],
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[408.565, 179.669],
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[364.512, 184.56],
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[491.483, 122.366],
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[451.512, 126.56],
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[409.502, 130.342],
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[365.5, 134.0],
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[494.335, 74.544],
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[453.5, 76.5],
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[411.646, 79.5901],
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[366.498, 81.6577],
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[453.5, 76.5],
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[411.646, 79.5901],
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[366.498, 81.6577]]
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points2 = [[526.605, 213.332],
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[470.485, 207.632],
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[417.5, 201.0],
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[367.485, 195.632],
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[530.673, 156.417],
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[473.749, 151.39],
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[419.503, 146.656],
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[368.669, 142.565],
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[534.632, 97.5152],
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[475.84, 94.6777],
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[421.16, 90.3223],
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[368.5, 87.5],
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[475.84, 94.6777],
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[421.16, 90.3223],
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[368.5, 87.5]]
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mat1 = CvMat.new(points1.size, 2, :cv64f, 1)
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mat2 = CvMat.new(points2.size, 2, :cv64f, 1)
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points1.each_with_index { |pt, i|
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mat1[i, 0] = CvScalar.new(pt[0])
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mat1[i, 1] = CvScalar.new(pt[1])
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}
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points2.each_with_index { |pt, i|
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mat2[i, 0] = CvScalar.new(pt[0])
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mat2[i, 1] = CvScalar.new(pt[1])
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}
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# default
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[CvMat.find_fundamental_mat_ransac(mat1, mat2, :with_status => false,
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:maximum_distance => 1.0, :desirable_level => 0.99),
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CvMat.find_fundamental_mat_ransac(mat1, mat2)].each { |f_mat|
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assert_equal(3, f_mat.rows)
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assert_equal(3, f_mat.cols)
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expected = [0.000010, 0.000039, -0.011141,
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-0.000045, -0.000001, 0.019631,
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0.004873, -0.017604, 1.000000]
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expected.each_with_index { |val, i|
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assert_in_delta(val, f_mat[i][0], 1.0e-5)
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}
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}
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# with options
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f_mat, status = CvMat.find_fundamental_mat_ransac(mat1, mat2, :with_status => true,
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:maximum_distance => 2.0, :desirable_level => 0.8)
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assert_equal(3, f_mat.rows)
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assert_equal(3, f_mat.cols)
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assert_equal(1, status.rows)
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assert_equal(points1.size, status.cols)
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expected_f_mat = [0.000009, 0.000030, -0.010692,
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-0.000039, 0.000000, 0.020567,
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0.004779, -0.018064, 1.000000]
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expected_f_mat.each_with_index { |val, i|
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assert_in_delta(val, f_mat[i][0], 1.0e-5)
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}
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expected_status = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
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expected_status.each_with_index { |val, i|
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assert_in_delta(val, status[i][0], 1.0e-5)
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}
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end
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end
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@ -1,34 +0,0 @@
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#!/usr/bin/env ruby
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# -*- mode: ruby; coding: utf-8-unix -*-
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require 'test/unit'
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require 'opencv'
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require File.expand_path(File.dirname(__FILE__)) + '/helper'
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include OpenCV
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# Tests for OpenCV::IplImage
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class TestIplImage < OpenCVTestCase
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def should_classify_images_as(filename, classification)
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assert_equal(OpenCV::IplImage::load(filename, OpenCV::CV_LOAD_IMAGE_GRAYSCALE).smoothness[0], classification)
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end
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def test_smoothness
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asset_path = File.join(File.dirname(__FILE__), 'samples')
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for image in Array.new(9) { |e| e = File.join(asset_path, "smooth%d.jpg") % e } do
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should_classify_images_as image, :smooth
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end
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for image in Array.new(2) { |e| e = File.join(asset_path, "messy%d.jpg") % e } do
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should_classify_images_as image, :messy
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end
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for image in Array.new(10) { |e| e = File.join(asset_path, "blank%d.jpg") % e } do
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should_classify_images_as image, :blank
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end
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for image in Array.new(2) { |e| e = File.join(asset_path, "partially_blank%d.jpg") % e } do
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should_classify_images_as image, :blank
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end
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end
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end
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