mirror of
https://github.com/ruby-opencv/ruby-opencv
synced 2023-03-27 23:22:12 -04:00
2025 lines
68 KiB
Ruby
Executable file
2025 lines
68 KiB
Ruby
Executable file
#!/usr/bin/env ruby
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# -*- mode: ruby; coding: utf-8 -*-
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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 image processing functions of OpenCV::CvMat
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class TestCvMat_imageprocessing < OpenCVTestCase
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FILENAME_LENA256x256 = File.expand_path(File.dirname(__FILE__)) + '/samples/lena-256x256.jpg'
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FILENAME_LENA_INPAINT = File.expand_path(File.dirname(__FILE__)) + '/samples/lena-inpaint.jpg'
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FILENAME_INPAINT_MASK = File.expand_path(File.dirname(__FILE__)) + '/samples/inpaint-mask.bmp'
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FILENAME_LENA32x32 = File.expand_path(File.dirname(__FILE__)) + '/samples/lena-32x32.jpg'
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FILENAME_LINES = File.expand_path(File.dirname(__FILE__)) + '/samples/lines.jpg'
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FILENAME_LENA_EYES = File.expand_path(File.dirname(__FILE__)) + '/samples/lena-eyes.jpg'
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FILENAME_STR_CV = File.expand_path(File.dirname(__FILE__)) + '/samples/str-cv.jpg'
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FILENAME_STR_OV = File.expand_path(File.dirname(__FILE__)) + '/samples/str-ov.jpg'
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FILENAME_STR_CV_ROTATED = File.expand_path(File.dirname(__FILE__)) + '/samples/str-cv-rotated.jpg'
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def test_sobel
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
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mat1 = mat0.sobel(1, 0).convert_scale_abs(:scale => 1, :shift => 0)
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mat2 = mat0.sobel(0, 1).convert_scale_abs(:scale => 1, :shift => 0)
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mat3 = mat0.sobel(1, 1).convert_scale_abs(:scale => 1, :shift => 0)
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mat4 = mat0.sobel(1, 1, 3).convert_scale_abs(:scale => 1, :shift => 0)
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mat5 = mat0.sobel(1, 1, 5).convert_scale_abs(:scale => 1, :shift => 0)
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assert_equal('30a26b7287fac75bb697bc7eef6bb53a', hash_img(mat1))
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assert_equal('b740afb13b556d55280fa785190ac902', hash_img(mat2))
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assert_equal('36c29ca64a599e0f5633f4f3948ed858', hash_img(mat3))
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assert_equal('36c29ca64a599e0f5633f4f3948ed858', hash_img(mat4))
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assert_equal('30b9e8fd64e7f86c50fb67d8703628e3', hash_img(mat5))
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assert_equal(:cv16s, CvMat.new(16, 16, :cv8u, 1).sobel(1, 1).depth)
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assert_equal(:cv32f, CvMat.new(16, 16, :cv32f, 1).sobel(1, 1).depth)
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(DEPTH.keys - [:cv8u, :cv32f]).each { |depth|
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assert_raise(ArgumentError) {
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CvMat.new(3, 3, depth).sobel(1, 1)
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}
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}
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# Uncomment the following lines to view the images
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# snap(['original', mat0], ['sobel(1,0)', mat1], ['sobel(0,1)', mat2],
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# ['sobel(1,1)', mat3], ['sobel(1,1,3)', mat4], ['sobel(1,1,5)', mat5])
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assert_raise(TypeError) {
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mat0.sobel(DUMMY_OBJ, 0)
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}
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assert_raise(TypeError) {
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mat0.sobel(1, DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.sobel(1, 0, DUMMY_OBJ)
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}
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end
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def test_laplace
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
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mat1 = mat0.laplace.convert_scale_abs(:scale => 1, :shift => 0)
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mat2 = mat0.laplace(3).convert_scale_abs(:scale => 1, :shift => 0)
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mat3 = mat0.laplace(5).convert_scale_abs(:scale => 1, :shift => 0)
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assert_equal('824f8de75bfead5d83c4226f3948ce69', hash_img(mat1))
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assert_equal('824f8de75bfead5d83c4226f3948ce69', hash_img(mat2))
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assert_equal('23850bb8cfe9fd1b82cd73b7b4659369', hash_img(mat3))
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assert_equal(:cv16s, CvMat.new(16, 16, :cv8u, 1).laplace.depth)
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assert_equal(:cv32f, CvMat.new(16, 16, :cv32f, 1).laplace.depth)
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(DEPTH.keys - [:cv8u, :cv32f]).each { |depth|
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assert_raise(ArgumentError) {
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CvMat.new(3, 3, depth).laplace
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}
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}
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# Uncomment the following line to view the images
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# snap(['original', mat0], ['laplace', mat1], ['laplace(3)', mat2], ['laplace(5)', mat3])
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assert_raise(TypeError) {
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mat0.laplace(DUMMY_OBJ)
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}
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end
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def test_canny
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
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mat1 = mat0.canny(50, 200)
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mat2 = mat0.canny(50, 200, 3)
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mat3 = mat0.canny(50, 200, 5)
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assert_equal('ec3e88035bb98b5c5f1a08c8e07ab0a8', hash_img(mat1))
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assert_equal('ec3e88035bb98b5c5f1a08c8e07ab0a8', hash_img(mat2))
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assert_equal('1983a6d325d11eea3261462103b0dae1', hash_img(mat3))
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# Uncomment the following line to view the images
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# snap(['canny(50,200)', mat1], ['canny(50,200,3)', mat2], ['canny(50,200,5)', mat3])
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assert_raise(TypeError) {
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mat0.canny(DUMMY_OBJ, 200)
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}
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assert_raise(TypeError) {
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mat0.canny(50, DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.canny(50, 200, DUMMY_OBJ)
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}
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end
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def test_pre_corner_detect
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
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mat1 = mat0.pre_corner_detect
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mat2 = mat0.pre_corner_detect(3)
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mat3 = mat0.pre_corner_detect(5)
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assert_in_delta(0, count_threshold(mat1, 0.1), 30)
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assert_in_delta(0, count_threshold(mat2, 0.1), 30)
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assert_in_delta(380, count_threshold(mat3, 0.1), 30)
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# Uncomment the following lines to show the images
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# snap(['original', mat0], ['pre_coner_detect', mat1],
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# ['pre_coner_detect(3)', mat2], ['pre_coner_detect(5)', mat3])
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assert_raise(TypeError) {
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mat0.pre_corner_detect(DUMMY_OBJ)
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}
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end
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def test_corner_eigenvv
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
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mat1 = mat0.corner_eigenvv(3)
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mat2 = mat0.corner_eigenvv(3, 3)
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assert_raise(TypeError) {
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mat0.corner_eigenvv(DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.corner_eigenvv(3, DUMMY_OBJ)
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}
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flunk('FIXME: CvMat#corner_eigenvv is not tested yet.')
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end
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def test_corner_min_eigen_val
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
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mat1 = mat0.corner_min_eigen_val(3)
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mat2 = mat0.corner_min_eigen_val(3, 3)
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assert_raise(TypeError) {
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mat0.corner_min_eigen_val(DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.corner_min_eigen_val(3, DUMMY_OBJ)
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}
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flunk('FIXME: CvMat#corner_min_eigen_val is not tested yet.')
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end
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def test_corner_harris
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
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mat1 = mat0.corner_harris(3)
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mat2 = mat0.corner_harris(3, 3)
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mat3 = mat0.corner_harris(3, 3, 0.04)
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mat4 = mat0.corner_harris(3, 7, 0.01)
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[mat1, mat2, mat3].each { |mat|
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assert_equal(mat0.rows, mat.rows)
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assert_equal(mat0.cols, mat.cols)
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assert_in_delta(0, count_threshold(mat, 10), 10)
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}
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assert_equal(mat0.rows, mat4.rows)
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assert_equal(mat0.cols, mat4.cols)
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assert_in_delta(90, count_threshold(mat4, 10), 10)
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# Uncomment the following lines to show the images
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# snap(['original', mat0], ['corner_harris(3)', mat1], ['corner_harris(3,3)', mat2],
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# ['corner_harris(3,3,0.04)', mat3], ['corner_harris(3,7,0.01)', mat4])
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assert_raise(TypeError) {
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mat0.corner_harris(DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.corner_harris(3, DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.corner_harris(3, 3, DUMMY_OBJ)
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}
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end
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def test_find_chessboard_corners
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mat = CvMat.load(FILENAME_CHESSBOARD, CV_LOAD_IMAGE_GRAYSCALE)
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pattern_size = CvSize.new(4, 4)
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corners1, found1 = mat.find_chessboard_corners(pattern_size)
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corners2, found2 = mat.find_chessboard_corners(pattern_size, CV_CALIB_CB_ADAPTIVE_THRESH)
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corners3, found3 = mat.find_chessboard_corners(pattern_size, CV_CALIB_CB_NORMALIZE_IMAGE)
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corners4, found4 = mat.find_chessboard_corners(pattern_size, CV_CALIB_CB_FILTER_QUADS)
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corners5, found5 = mat.find_chessboard_corners(pattern_size, CV_CALIB_CB_FAST_CHECK)
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expected = [[39, 39], [79, 39], [119, 39], [159, 39], [39, 79], [79, 79],
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[119, 79], [159, 78], [38, 119], [79, 119], [119, 119], [158, 118],
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[39, 159], [79, 159], [119, 159], [159, 159]]
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[corners1, corners2, corners3, corners4, corners5].each { |corners|
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assert_equal(expected.size, corners.size)
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expected.zip(corners).each { |e, a|
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assert_in_delta(e[0], a.x, 3.0)
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assert_in_delta(e[1], a.y, 3.0)
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}
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}
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[found1, found2, found3, found4, found5].each { |found|
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assert(found)
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}
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assert_raise(TypeError) {
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mat.find_chessboard_corners(DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat.find_chessboard_corners(pattern_size, DUMMY_OBJ)
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}
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end
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def test_find_corner_sub_pix
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mat = CvMat.load(FILENAME_CHESSBOARD, CV_LOAD_IMAGE_GRAYSCALE)
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pattern_size = CvSize.new(4, 4)
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corners, found = mat.find_chessboard_corners(pattern_size)
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expected = [[39, 39], [79, 39], [119, 39], [159, 39], [39, 79], [79, 79],
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[119, 79], [159, 78], [38, 119], [79, 119], [119, 119], [158, 118],
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[39, 159], [79, 159], [119, 159], [159, 159]]
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refined_corners = mat.find_corner_sub_pix(corners, CvSize.new(3, 3), CvSize.new(-1, -1),
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CvTermCriteria.new(20, 0.03));
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assert_equal(expected.size, refined_corners.size)
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assert(found)
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expected.zip(refined_corners).each { |e, a|
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assert_in_delta(e[0], a.x, 3.0)
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assert_in_delta(e[1], a.y, 3.0)
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}
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assert_raise(TypeError) {
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mat.find_corner_sub_pix(DUMMY_OBJ, CvSize.new(3, 3), CvSize.new(-1, -1),
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CvTermCriteria.new(20, 0.03));
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}
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assert_raise(TypeError) {
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mat.find_corner_sub_pix(corners, DUMMY_OBJ, CvSize.new(-1, -1),
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CvTermCriteria.new(20, 0.03));
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}
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assert_raise(TypeError) {
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mat.find_corner_sub_pix(corners, CvSize.new(3, 3), DUMMY_OBJ,
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CvTermCriteria.new(20, 0.03));
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}
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assert_raise(TypeError) {
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mat.find_corner_sub_pix(corners, CvSize.new(3, 3), CvSize.new(-1, -1), DUMMY_OBJ);
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}
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end
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def test_good_features_to_track
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mat0 = CvMat.load(FILENAME_LENA32x32, CV_LOAD_IMAGE_GRAYSCALE)
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mask = create_cvmat(mat0.rows, mat0.cols, :cv8u, 1) { |j, i, c|
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if (i > 8 and i < 18) and (j > 8 and j < 18)
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CvScalar.new(1)
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else
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CvScalar.new(0)
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end
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}
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corners1 = mat0.good_features_to_track(0.2, 5)
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corners2 = mat0.good_features_to_track(0.2, 5, :mask => mask)
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corners3 = mat0.good_features_to_track(0.2, 5, :block_size => 7)
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corners4 = mat0.good_features_to_track(0.2, 5, :use_harris => true)
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corners5 = mat0.good_features_to_track(0.2, 5, :k => 0.01)
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corners6 = mat0.good_features_to_track(0.2, 5, :max => 1)
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expected1 = [[24, 7], [20, 23], [17, 11], [26, 29], [30, 24],
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[19, 16], [28, 2], [13, 18], [14, 4]]
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assert_equal(expected1.size, corners1.size)
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expected1.each_with_index { |e, i|
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assert_equal(e[0], corners1[i].x.to_i)
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assert_equal(e[1], corners1[i].y.to_i)
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}
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expected2 = [[17, 11], [17, 16]]
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assert_equal(expected2.size, corners2.size)
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expected2.each_with_index { |e, i|
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assert_equal(e[0], corners2[i].x.to_i)
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assert_equal(e[1], corners2[i].y.to_i)
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}
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expected3 = [[21, 7], [22, 23], [18, 12], [28, 4], [28, 26],
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[17, 27], [13, 20], [10, 11], [14, 5]]
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assert_equal(expected3.size, corners3.size)
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expected3.each_with_index { |e, i|
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assert_equal(e[0], corners3[i].x.to_i)
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assert_equal(e[1], corners3[i].y.to_i)
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}
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expected4 = [[24, 8], [20, 23], [16, 11],
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[20, 16],[27, 28], [28, 2]]
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assert_equal(expected4.size, corners4.size)
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expected4.each_with_index { |e, i|
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assert_equal(e[0], corners4[i].x.to_i)
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assert_equal(e[1], corners4[i].y.to_i)
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}
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expected5 = [[24, 7], [20, 23], [17, 11], [26, 29], [30, 24],
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[19, 16], [28, 2], [13, 18], [14, 4]]
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assert_equal(expected5.size, corners5.size)
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expected5.each_with_index { |e, i|
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assert_equal(e[0], corners5[i].x.to_i)
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assert_equal(e[1], corners5[i].y.to_i)
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}
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assert_equal(1, corners6.size)
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assert_equal(24, corners6[0].x.to_i)
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assert_equal(7, corners6[0].y.to_i)
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assert_raise(ArgumentError) {
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mat0.good_features_to_track(0.2, 5, :max => 0)
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}
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assert_raise(TypeError) {
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mat0.good_features_to_track(DUMMY_OBJ, 5)
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}
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assert_raise(TypeError) {
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mat0.good_features_to_track(0.2, DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.good_features_to_track(0.2, 5, :mask => DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.good_features_to_track(0.2, 5, :block_size => DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.good_features_to_track(0.2, 5, :k => DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.good_features_to_track(0.2, 5, :max => DUMMY_OBJ)
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}
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mat0.good_features_to_track(0.2, 5, :use_harris => DUMMY_OBJ)
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end
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def test_rect_sub_pix
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
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center = CvPoint2D32f.new(mat0.width / 2, mat0.height / 2)
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mat1 = mat0.rect_sub_pix(center)
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mat2 = mat0.rect_sub_pix(center, mat0.size)
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mat3 = mat0.rect_sub_pix(center, CvSize.new(512, 512))
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assert_equal('b3dc0e31260dd42b5341471e23e825d3', hash_img(mat1))
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assert_equal('b3dc0e31260dd42b5341471e23e825d3', hash_img(mat2))
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assert_equal('cc27ce8f4068efedcd31c4c782c3825c', hash_img(mat3))
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assert_raise(TypeError) {
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mat0.rect_sub_pix(DUMMY_OBJ)
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}
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assert_raise(TypeError) {
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mat0.rect_sub_pix(center, DUMMY_OBJ)
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}
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end
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def test_quadrangle_sub_pix
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mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
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angle = 60 * Math::PI / 180
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map_matrix = CvMat.new(2, 3, :cv32f, 1)
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map_matrix[0] = CvScalar.new(Math.cos(angle))
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map_matrix[1] = CvScalar.new(-Math.sin(angle))
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map_matrix[2] = CvScalar.new(mat0.width * 0.5)
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map_matrix[3] = CvScalar.new(-map_matrix[1][0])
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map_matrix[4] = map_matrix[0]
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map_matrix[5] = CvScalar.new(mat0.height * 0.5)
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mat1 = mat0.quadrangle_sub_pix(map_matrix)
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mat2 = mat0.quadrangle_sub_pix(map_matrix, mat0.size)
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mat3 = mat0.quadrangle_sub_pix(map_matrix, CvSize.new(512, 512))
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assert_equal('f170c05fa50c3ac2a762d7b3f5c4ae2f', hash_img(mat1))
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assert_equal('f170c05fa50c3ac2a762d7b3f5c4ae2f', hash_img(mat2))
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|
assert_equal('4d949d5083405381ad9ea09dcd95e5a2', hash_img(mat3))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.quadrangle_sub_pix(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.quadrangle_sub_pix(map_matrix, DUMMY_OBJ)
|
|
}
|
|
# assert_raise(CvError) {
|
|
# mat0.quadrangle_sub_pix(CvMat.new(3, 3))
|
|
# }
|
|
end
|
|
|
|
def test_resize
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
size = CvSize.new(384, 384)
|
|
mat1 = mat0.resize(size)
|
|
mat2 = mat0.resize(size, CV_INTER_LINEAR)
|
|
mat3 = mat0.resize(size, CV_INTER_NN)
|
|
mat4 = mat0.resize(size, CV_INTER_AREA)
|
|
mat5 = mat0.resize(size, CV_INTER_CUBIC)
|
|
mat6 = mat0.resize(size, CV_INTER_LANCZOS4)
|
|
|
|
[mat1, mat2, mat3, mat4, mat5, mat6].each { |m|
|
|
assert_equal(size.width, m.cols)
|
|
assert_equal(size.height, m.rows)
|
|
assert_equal(mat0.depth, m.depth)
|
|
assert_equal(mat0.channel, m.channel)
|
|
}
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.resize(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.resize(size, DUMMY_OBJ)
|
|
}
|
|
|
|
# Uncomment the following lines to show the results
|
|
# snap(['original', mat0], ['default(linear)', mat1], ['linear', mat2],
|
|
# ['nn', mat3], ['area', mat4], ['cubic', mat5] , ['lanczos4', mat6])
|
|
end
|
|
|
|
def test_warp_affine
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
map_matrix = CvMat.new(2, 3, :cv32f, 1)
|
|
# center: (128, 128), angle: 25 deg., scale: 1.0
|
|
map_matrix[0] = CvScalar.new(0.90631)
|
|
map_matrix[1] = CvScalar.new(0.42262)
|
|
map_matrix[2] = CvScalar.new(-42.10254)
|
|
map_matrix[3] = CvScalar.new(-0.42262)
|
|
map_matrix[4] = CvScalar.new(0.90631)
|
|
map_matrix[5] = CvScalar.new(66.08774)
|
|
|
|
mat1 = mat0.warp_affine(map_matrix)
|
|
mat2 = mat0.warp_affine(map_matrix, CV_INTER_NN | CV_WARP_FILL_OUTLIERS)
|
|
mat3 = mat0.warp_affine(map_matrix, CV_INTER_LINEAR | CV_WARP_FILL_OUTLIERS, CvColor::Yellow)
|
|
mat4 = mat0.warp_affine(map_matrix, CV_INTER_LINEAR | CV_WARP_FILL_OUTLIERS | CV_WARP_INVERSE_MAP)
|
|
|
|
[mat1, mat2, mat3, mat4].each { |m|
|
|
assert_equal(mat0.cols, m.cols)
|
|
assert_equal(mat0.rows, m.rows)
|
|
assert_equal(mat0.depth, m.depth)
|
|
assert_equal(mat0.channel, m.channel)
|
|
}
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.warp_affine(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.warp_affine(map_matrix, DUMMY_OBJ)
|
|
}
|
|
|
|
# Uncomment the following lines to show the results
|
|
# snap mat0, mat1, mat2, mat3, mat4
|
|
end
|
|
|
|
def test_rotation_matrix2D
|
|
mat1 = CvMat.rotation_matrix2D(CvPoint2D32f.new(10, 20), 60, 2.0)
|
|
expected = [1.0, 1.73205, -34.64102,
|
|
-1.73205, 1.0, 17.32051]
|
|
assert_equal(2, mat1.rows)
|
|
assert_equal(3, mat1.cols)
|
|
assert_equal(:cv32f, mat1.depth)
|
|
assert_equal(1, mat1.channel)
|
|
expected.each_with_index { |x, i|
|
|
assert_in_delta(x, mat1[i][0], 0.001)
|
|
}
|
|
|
|
assert_raise(TypeError) {
|
|
CvMat.rotation_matrix2D(DUMMY_OBJ, 60, 2.0)
|
|
}
|
|
assert_raise(TypeError) {
|
|
CvMat.rotation_matrix2D(CvPoint2D32f.new(10, 20), DUMMY_OBJ, 2.0)
|
|
}
|
|
assert_raise(TypeError) {
|
|
CvMat.rotation_matrix2D(CvPoint2D32f.new(10, 20), 60, DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_warp_perspective
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
# Homography
|
|
# <src> => <dst>
|
|
# (0, 0) => (50, 0)
|
|
# (255, 0) => (205, 0)
|
|
# (255, 255) => (255, 220)
|
|
# (0, 255) => (0, 275)
|
|
map_matrix = CvMat.new(3, 3, :cv32f, 1)
|
|
map_matrix[0] = CvScalar.new(0.72430)
|
|
map_matrix[1] = CvScalar.new(-0.19608)
|
|
map_matrix[2] = CvScalar.new(50.00000)
|
|
map_matrix[3] = CvScalar.new(0.0)
|
|
map_matrix[4] = CvScalar.new(0.62489)
|
|
map_matrix[5] = CvScalar.new(0.0)
|
|
map_matrix[6] = CvScalar.new(0.00057)
|
|
map_matrix[7] = CvScalar.new(-0.00165)
|
|
map_matrix[8] = CvScalar.new(1.00000)
|
|
|
|
mat1 = mat0.warp_perspective(map_matrix)
|
|
mat2 = mat0.warp_perspective(map_matrix, CV_INTER_NN)
|
|
mat3 = mat0.warp_perspective(map_matrix, CV_INTER_LINEAR | CV_WARP_INVERSE_MAP)
|
|
mat4 = mat0.warp_perspective(map_matrix, CV_INTER_LINEAR | CV_WARP_FILL_OUTLIERS, CvColor::Yellow)
|
|
|
|
[mat1, mat2, mat3, mat4].each { |m|
|
|
assert_equal(mat0.cols, m.cols)
|
|
assert_equal(mat0.rows, m.rows)
|
|
assert_equal(mat0.depth, m.depth)
|
|
assert_equal(mat0.channel, m.channel)
|
|
}
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.warp_perspective(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.warp_perspective(map_matrix, DUMMY_OBJ)
|
|
}
|
|
|
|
# Uncomment the following line to show the results
|
|
# snap mat0, mat1, mat2, mat3, mat4
|
|
end
|
|
|
|
def test_remap
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
matx = CvMat.new(mat0.height, mat0.width, :cv32f, 1).clear
|
|
maty = CvMat.new(mat0.height, mat0.width, :cv32f, 1).clear
|
|
|
|
cos30, sin30 = Math.cos(30 * Math::PI / 180), Math.sin(30 * Math::PI / 180)
|
|
half_width, half_height = mat0.width / 2, mat0.height / 2
|
|
mat0.height.times { |j|
|
|
mat0.width.times { |i|
|
|
x0 = i - half_width
|
|
y0 = j - half_height
|
|
x = x0 * cos30 - y0 * sin30 + half_width
|
|
y = x0 * sin30 + y0 * cos30 + half_height
|
|
matx[j, i] = CvScalar.new(x)
|
|
maty[j, i] = CvScalar.new(y)
|
|
}
|
|
}
|
|
|
|
mat1 = mat0.remap(matx, maty)
|
|
mat2 = mat0.remap(matx, maty, CV_INTER_NN)
|
|
mat3 = mat0.remap(matx, maty, CV_INTER_LINEAR | CV_WARP_FILL_OUTLIERS, CvColor::Yellow)
|
|
|
|
[mat1, mat2, mat3].each { |m|
|
|
assert_equal(mat0.cols, m.cols)
|
|
assert_equal(mat0.rows, m.rows)
|
|
assert_equal(mat0.depth, m.depth)
|
|
assert_equal(mat0.channel, m.channel)
|
|
}
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.remap(DUMMY_OBJ, maty)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.remap(matx, DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.remap(matx, maty, DUMMY_OBJ)
|
|
}
|
|
|
|
# Uncomment the following line to show the results
|
|
# snap mat0, mat1, mat2, mat3
|
|
end
|
|
|
|
def test_log_polar
|
|
mat0 = CvMat.load(FILENAME_FRUITS, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
|
|
mat1 = mat0.log_polar(CvSize.new(255, 255), CvPoint2D32f.new(mat0.width / 2, mat0.height / 2), 40)
|
|
mat2 = mat0.log_polar(CvSize.new(255, 255), CvPoint2D32f.new(mat0.width / 2, mat0.height / 2), 40,
|
|
CV_INTER_LINEAR | CV_WARP_FILL_OUTLIERS)
|
|
mat3 = mat1.log_polar(mat0.size, CvPoint2D32f.new(mat0.width / 2, mat0.height / 2), 40,
|
|
CV_INTER_LINEAR | CV_WARP_FILL_OUTLIERS | CV_WARP_INVERSE_MAP)
|
|
|
|
[mat1, mat2].each { |mat|
|
|
assert_equal(mat0.depth, mat.depth)
|
|
assert_equal(mat0.channel, mat.channel)
|
|
b, g, r = color_hists(mat)
|
|
assert_in_delta(4000000, b, 100000)
|
|
assert_in_delta(5860000, g, 100000)
|
|
assert_in_delta(7700000, r, 100000)
|
|
}
|
|
|
|
b, g, r = color_hists(mat3)
|
|
assert_equal(mat0.depth, mat3.depth)
|
|
assert_equal(mat0.channel, mat3.channel)
|
|
assert_in_delta(11200000, b, 1000000)
|
|
assert_in_delta(20800000, g, 1000000)
|
|
assert_in_delta(26900000, r, 1000000)
|
|
|
|
# Uncomment the following line to show the results
|
|
# snap mat0, mat1, mat2
|
|
end
|
|
|
|
def test_erode
|
|
mat0 = create_cvmat(9, 9, :cv8u, 1) { |j, i, c|
|
|
if i >= 3 and i < 6 and j >= 3 and j < 6
|
|
CvScalar.new(255)
|
|
else
|
|
CvScalar.new(0)
|
|
end
|
|
}
|
|
|
|
mat1 = create_cvmat(9, 9, :cv8u, 1) { |j, i, c|
|
|
if i >= 1 and i < 8 and j >= 1 and j < 8
|
|
CvScalar.new(255)
|
|
else
|
|
CvScalar.new(0)
|
|
end
|
|
}
|
|
|
|
mat2 = create_cvmat(5, 5, :cv8u, 1) { |j, i, c|
|
|
if i == 2 or j == 2
|
|
CvScalar.new(255)
|
|
else
|
|
CvScalar.new(0)
|
|
end
|
|
}
|
|
|
|
mat3 = mat0.erode
|
|
mat4 = mat0.erode(nil, 1)
|
|
mat5 = mat1.erode(nil, 2)
|
|
mat6 = mat1.erode(IplConvKernel.new(5, 5, 2, 2, :cross))
|
|
mat7 = mat0.clone
|
|
mat7.erode!
|
|
|
|
assert_equal('075eb0e281328f768eb862735d16979d', hash_img(mat3))
|
|
assert_equal('075eb0e281328f768eb862735d16979d', hash_img(mat4))
|
|
assert_equal('9f02fc4438b1d69fea75a10dfd2b66b0', hash_img(mat5))
|
|
assert_equal('9f02fc4438b1d69fea75a10dfd2b66b0', hash_img(mat6))
|
|
assert_equal('075eb0e281328f768eb862735d16979d', hash_img(mat7))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.erode(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.erode(nil, DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_dilate
|
|
mat0 = create_cvmat(9, 9, :cv8u, 1) { |j, i, c|
|
|
if i == 4 and j == 4
|
|
CvScalar.new(255)
|
|
else
|
|
CvScalar.new(0)
|
|
end
|
|
}
|
|
|
|
mat1 = create_cvmat(5, 5, :cv8u, 1) { |j, i, c|
|
|
if i == 2 or j == 2
|
|
CvScalar.new(255)
|
|
else
|
|
CvScalar.new(0)
|
|
end
|
|
}
|
|
|
|
mat2 = mat0.dilate
|
|
mat3 = mat0.dilate(nil, 1)
|
|
mat4 = mat0.dilate(nil, 2)
|
|
mat5 = mat1.dilate(IplConvKernel.new(5, 5, 2, 2, :cross))
|
|
mat6 = mat0.clone
|
|
mat6.dilate!
|
|
|
|
assert_equal('9f02fc4438b1d69fea75a10dfd2b66b0', hash_img(mat2))
|
|
assert_equal('9f02fc4438b1d69fea75a10dfd2b66b0', hash_img(mat3))
|
|
assert_equal('ebf07f2a0edd2fd0fe26ff5921c6871b', hash_img(mat4))
|
|
assert_equal('2841937c35c311e947bee49864b9d295', hash_img(mat5))
|
|
assert_equal('9f02fc4438b1d69fea75a10dfd2b66b0', hash_img(mat6))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.dilate(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.dilate(nil, DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_morphology
|
|
mat0 = create_cvmat(64, 64, :cv8u, 1) { |j, i, c|
|
|
if i >= 8 and i < 56 and j >= 8 and j < 56 and (i + j) % 15 != 0
|
|
CvScalar.new(255)
|
|
else
|
|
CvScalar.new(0)
|
|
end
|
|
}
|
|
|
|
# Open
|
|
kernel = IplConvKernel.new(5, 5, 2, 2, :cross)
|
|
mat1 = mat0.morphology(CV_MOP_OPEN, kernel)
|
|
mat2 = mat0.morphology(:open, kernel)
|
|
assert_equal('63ccb07cb93efb1563657f51e3d89252', hash_img(mat1))
|
|
assert_equal('63ccb07cb93efb1563657f51e3d89252', hash_img(mat2))
|
|
|
|
# Close
|
|
mat1 = mat0.morphology(CV_MOP_CLOSE, kernel)
|
|
mat2 = mat0.morphology(:close, kernel)
|
|
assert_equal('831c513d6ed86bce3f15c697de4a72f8', hash_img(mat1))
|
|
assert_equal('831c513d6ed86bce3f15c697de4a72f8', hash_img(mat2))
|
|
|
|
# Gradient
|
|
mat1 = mat0.morphology(CV_MOP_GRADIENT, kernel)
|
|
mat2 = mat0.morphology(:gradient, kernel)
|
|
assert_equal('1e8007c211d6f464cf8584e8e83b3c35', hash_img(mat1))
|
|
assert_equal('1e8007c211d6f464cf8584e8e83b3c35', hash_img(mat2))
|
|
|
|
# Top hat
|
|
mat1 = mat0.morphology(CV_MOP_TOPHAT, kernel)
|
|
mat2 = mat0.morphology(:tophat, kernel)
|
|
assert_equal('1760c5b63a52df37069164fe3e901aa4', hash_img(mat1))
|
|
assert_equal('1760c5b63a52df37069164fe3e901aa4', hash_img(mat2))
|
|
|
|
# Black hat
|
|
mat1 = mat0.morphology(CV_MOP_BLACKHAT, kernel)
|
|
mat2 = mat0.morphology(:blackhat, kernel)
|
|
assert_equal('18b1d51637b912a38133341ee006c6ff', hash_img(mat1))
|
|
assert_equal('18b1d51637b912a38133341ee006c6ff', hash_img(mat2))
|
|
|
|
[:open, :close, :gradient, :tophat, :blackhat].each { |type|
|
|
assert_raise(TypeError) {
|
|
mat0.morphology(type, DUMMY_OBJ)
|
|
}
|
|
}
|
|
end
|
|
|
|
def test_smooth
|
|
mat0 = CvMat.load(FILENAME_LENA32x32, CV_LOAD_IMAGE_GRAYSCALE)
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(DUMMY_OBJ)
|
|
}
|
|
|
|
# Blur no scale
|
|
mat1 = mat0.smooth(CV_BLUR_NO_SCALE)
|
|
mat2 = mat0.smooth(:blur_no_scale, 3, 3)
|
|
mat3 = mat0.smooth(CV_BLUR_NO_SCALE, 7, 7)
|
|
mat4 = CvMat.new(32, 32, :cv32f, 1).smooth(:blur_no_scale)
|
|
|
|
[mat1, mat2, mat3].each { |m|
|
|
assert_equal(1, m.channel)
|
|
assert_equal(:cv16u, m.depth)
|
|
}
|
|
assert_equal(1, mat4.channel)
|
|
assert_equal(:cv32f, mat4.depth)
|
|
|
|
assert_equal('3c9074c87b65117798f48e41a17b2f30', hash_img(mat1))
|
|
assert_equal('3c9074c87b65117798f48e41a17b2f30', hash_img(mat2))
|
|
assert_equal('9c549aa406a425a65b036c2f9a2689e0', hash_img(mat3))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_BLUR_NO_SCALE, DUMMY_OBJ, 0, 0, 0)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_BLUR_NO_SCALE, 3, DUMMY_OBJ, 0, 0)
|
|
}
|
|
|
|
# Blur
|
|
mat1 = mat0.smooth(CV_BLUR)
|
|
mat2 = mat0.smooth(:blur, 3, 3)
|
|
mat3 = mat0.smooth(CV_BLUR, 7, 7)
|
|
mat4 = CvMat.new(32, 32, :cv16u, 1).smooth(:blur)
|
|
mat5 = CvMat.new(32, 32, :cv32f, 1).smooth(CV_BLUR)
|
|
mat6 = CvMat.new(32, 32, :cv8u, 3).smooth(:blur)
|
|
|
|
[mat1, mat2, mat3].each { |m|
|
|
assert_equal(1, m.channel)
|
|
assert_equal(:cv8u, m.depth)
|
|
}
|
|
assert_equal(1, mat4.channel)
|
|
assert_equal(:cv16u, mat4.depth)
|
|
assert_equal(1, mat5.channel)
|
|
assert_equal(:cv32f, mat5.depth)
|
|
assert_equal(3, mat6.channel)
|
|
assert_equal(:cv8u, mat6.depth)
|
|
|
|
assert_equal('f2473b5b964ae8950f6a7fa5cde4c67a', hash_img(mat1))
|
|
assert_equal('f2473b5b964ae8950f6a7fa5cde4c67a', hash_img(mat2))
|
|
assert_equal('d7bb344fc0f6ec0da4b9754d319e4e4a', hash_img(mat3))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_BLUR, DUMMY_OBJ, 0, 0, 0)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_BLUR, 3, DUMMY_OBJ, 0, 0)
|
|
}
|
|
|
|
# Gaussian
|
|
mat1 = mat0.smooth(CV_GAUSSIAN)
|
|
mat2 = mat0.smooth(:gaussian, 3, 3)
|
|
mat3 = mat0.smooth(CV_GAUSSIAN, 3, 3, 3)
|
|
mat4 = mat0.smooth(:gaussian, 3, 3, 3, 3)
|
|
mat5 = mat0.smooth(CV_GAUSSIAN, 7, 7, 5, 3)
|
|
|
|
mat6 = CvMat.new(32, 32, :cv16u, 1).smooth(CV_GAUSSIAN)
|
|
mat7 = CvMat.new(32, 32, :cv32f, 1).smooth(CV_GAUSSIAN)
|
|
mat8 = CvMat.new(32, 32, :cv8u, 3).smooth(CV_GAUSSIAN)
|
|
|
|
[mat1, mat2, mat3, mat4, mat5].each { |m|
|
|
assert_equal(1, m.channel)
|
|
assert_equal(:cv8u, m.depth)
|
|
}
|
|
assert_equal(1, mat6.channel)
|
|
assert_equal(:cv16u, mat6.depth)
|
|
assert_equal(1, mat7.channel)
|
|
assert_equal(:cv32f, mat7.depth)
|
|
assert_equal(3, mat8.channel)
|
|
assert_equal(:cv8u, mat8.depth)
|
|
|
|
assert_equal('580c88f3e0e317a5770be3f28f31eda2', hash_img(mat1))
|
|
assert_equal('580c88f3e0e317a5770be3f28f31eda2', hash_img(mat2))
|
|
assert_equal('a1ffaa14522719e37d75eec18ff8b309', hash_img(mat3))
|
|
assert_equal('a1ffaa14522719e37d75eec18ff8b309', hash_img(mat4))
|
|
assert_equal('f7f8b4eff3240ffc8f259ce975936d92', hash_img(mat5))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_GAUSSIAN, DUMMY_OBJ, 0, 0, 0)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_GAUSSIAN, 3, DUMMY_OBJ, 0, 0)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_GAUSSIAN, 3, 0, DUMMY_OBJ, 0)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_GAUSSIAN, 3, 0, 0, DUMMY_OBJ)
|
|
}
|
|
|
|
# Median
|
|
mat0 = create_cvmat(64, 64, :cv8u, 1) { |j, i, c|
|
|
if (i + j) % 15 != 0
|
|
CvScalar.new(255)
|
|
else
|
|
CvScalar.new(0)
|
|
end
|
|
}
|
|
(-1..1).each { |dy|
|
|
(-1..1).each { |dx|
|
|
mat0[32 + dy, 32 + dx] = CvScalar.new(0)
|
|
}
|
|
}
|
|
|
|
mat1 = mat0.smooth(CV_MEDIAN)
|
|
mat2 = mat0.smooth(:median, 3)
|
|
mat3 = mat0.smooth(CV_MEDIAN, 7)
|
|
mat4 = CvMat.new(64, 64, :cv8u, 3).smooth(CV_MEDIAN)
|
|
|
|
assert_equal('7343a41c542e034db356636c06134961', hash_img(mat1))
|
|
assert_equal('7343a41c542e034db356636c06134961', hash_img(mat2))
|
|
assert_equal('6ae59e64850377ee5470c854761551ea', hash_img(mat3))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_MEDIAN, DUMMY_OBJ, 0, 0, 0)
|
|
}
|
|
|
|
# Bilateral
|
|
mat0 = create_cvmat(64, 64, :cv8u, 1) { |j, i, c|
|
|
if i > 32
|
|
(i + j) % 15 != 0 ? CvScalar.new(32) : CvScalar.new(224)
|
|
else
|
|
(i + j) % 15 != 0 ? CvScalar.new(224) : CvScalar.new(32)
|
|
end
|
|
}
|
|
|
|
mat1 = mat0.smooth(CV_BILATERAL)
|
|
mat2 = mat0.smooth(:bilateral, 3, 3)
|
|
mat3 = mat0.smooth(CV_BILATERAL, 7, 7)
|
|
mat4 = CvMat.new(64, 64, :cv8u, 3).smooth(CV_BILATERAL)
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_BILATERAL, DUMMY_OBJ, 0, 0, 0)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.smooth(CV_BILATERAL, 3, DUMMY_OBJ, 0, 0)
|
|
}
|
|
|
|
flunk('FIXME: Cases of CvMat#smooth(CV_BILATERAL) are not tested yet.')
|
|
end
|
|
|
|
def test_filter2d
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
|
|
kernel = CvMat.new(3, 3, :cv32f, 1)
|
|
|
|
# Laplacian filter kernel
|
|
laplace4 = [0, 1, 0,
|
|
1, -4, 1,
|
|
0, 1, 0]
|
|
laplace4.each_with_index { |x, i| kernel[i] = CvScalar.new(x) }
|
|
|
|
mat1 = mat0.filter2d(kernel)
|
|
mat2 = mat0.filter2d(kernel, CvPoint.new(-1, -1))
|
|
mat3 = mat0.filter2d(kernel, CvPoint.new(0, 0))
|
|
|
|
assert_equal('14a01cc47078e8f8fe4f0fd510d5521b', hash_img(mat1))
|
|
assert_equal('14a01cc47078e8f8fe4f0fd510d5521b', hash_img(mat2))
|
|
assert_equal('30e04de43f9240df6aadbaea6467b8fe', hash_img(mat3))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.filter2d(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.filter2d(kernel, DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_copy_make_border
|
|
mat0 = create_cvmat(32, 32, :cv8u, 1) { CvScalar.new(128) }
|
|
|
|
[IPL_BORDER_CONSTANT, :constant].each { |type|
|
|
mat1 = mat0.copy_make_border(type, CvSize.new(64, 48), CvPoint.new(16, 8), 255)
|
|
assert_equal('5e231f8ca051b8f93e4aaa42d193d095', hash_img(mat1))
|
|
}
|
|
|
|
[IPL_BORDER_REPLICATE, :replicate].each { |type|
|
|
mat2 = mat0.copy_make_border(type, CvSize.new(300, 300), CvPoint.new(30, 30))
|
|
assert_equal('96940dc9e3abb6e2556ea51af1468031', hash_img(mat2))
|
|
}
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.copy_make_border(DUMMY_OBJ, CvSize.new(64, 48), CvPoint.new(16, 8))
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.copy_make_border(IPL_BORDER_CONSTANT, CvSize.new(64, 48), DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.copy_make_border(IPL_BORDER_CONSTANT, CvSize.new(64, 48), CvPoint.new(16, 8), DUMMY_OBJ)
|
|
}
|
|
assert_raise(ArgumentError) {
|
|
mat0.copy_make_border(:dummy, CvSize.new(64, 48), CvPoint.new(16, 8), DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_integral
|
|
mat0 = create_cvmat(3, 3, :cv8u, 1) { |j, i, n| CvScalar.new(n) }
|
|
|
|
result_sum = []
|
|
result_sqsum = []
|
|
result_tiled_sum = []
|
|
|
|
result1 = mat0.integral
|
|
assert_equal(CvMat, result1.class)
|
|
result_sum << result1
|
|
|
|
result2 = mat0.integral(true)
|
|
assert_equal(Array, result2.class)
|
|
assert_equal(2, result2.size)
|
|
assert(result2.all? {|a| a.class == CvMat})
|
|
result_sum << result2[0]
|
|
result_sqsum << result2[1]
|
|
|
|
result3 = mat0.integral(true, true)
|
|
assert_equal(Array, result3.class)
|
|
assert_equal(3, result3.size)
|
|
assert(result3.all? {|a| a.class == CvMat})
|
|
result_sum << result3[0]
|
|
result_sqsum << result3[1]
|
|
result_tiled_sum << result3[2]
|
|
|
|
result4 = mat0.integral(true, false)
|
|
assert_equal(Array, result4.class)
|
|
assert_equal(2, result4.size)
|
|
assert(result4.all? {|a| a.class == CvMat})
|
|
result_sum << result4[0]
|
|
result_sqsum << result4[1]
|
|
|
|
result5 = mat0.integral(false, true)
|
|
assert_equal(Array, result5.class)
|
|
assert_equal(2, result5.size)
|
|
assert(result5.all? {|a| a.class == CvMat})
|
|
result_sum << result5[0]
|
|
result_tiled_sum << result5[1]
|
|
|
|
(result_sum + result_sqsum + result_tiled_sum).each { |s|
|
|
assert_equal(mat0.height + 1, s.height)
|
|
assert_equal(mat0.width + 1, s.width)
|
|
assert_equal(:cv64f, s.depth)
|
|
assert_equal(1, s.channel)
|
|
}
|
|
|
|
expected_sum = [0, 0, 0, 0,
|
|
0, 0, 1, 3,
|
|
0, 3, 8, 15,
|
|
0, 9, 21, 36]
|
|
result_sum.each { |sum|
|
|
expected_sum.each_with_index { |x, i|
|
|
assert_in_delta(x, sum[i][0], 0.001)
|
|
}
|
|
}
|
|
|
|
expected_sqsum = [0, 0, 0, 0,
|
|
0, 0, 1, 5,
|
|
0, 9, 26, 55,
|
|
0, 45, 111, 204]
|
|
result_sqsum.each { |sqsum|
|
|
expected_sqsum.each_with_index { |x, i|
|
|
assert_in_delta(x, sqsum[i][0], 0.001)
|
|
}
|
|
}
|
|
|
|
expected_tilted_sum = [0, 0, 0, 0,
|
|
0, 0, 1, 2,
|
|
0, 4, 7, 8,
|
|
4, 16, 22, 20]
|
|
result_tiled_sum.each { |tiled_sum|
|
|
expected_tilted_sum.each_with_index { |x, i|
|
|
assert_in_delta(x, tiled_sum[i][0], 0.001)
|
|
}
|
|
}
|
|
|
|
mat0.integral(DUMMY_OBJ, DUMMY_OBJ)
|
|
end
|
|
|
|
def test_threshold
|
|
mat0 = create_cvmat(3, 3, :cv8u, 1) { |j, i, n| CvScalar.new(n) }
|
|
test_proc = lambda { |type, type_sym, expected_mat, expected_threshold|
|
|
mat1 = mat0.threshold(expected_threshold, 7, type)
|
|
mat2 = mat0.threshold(expected_threshold, 7, type_sym)
|
|
mat3, th3 = mat0.threshold(5, 7, type | CV_THRESH_OTSU)
|
|
mat4, th4 = mat0.threshold(3, 7, type_sym, true)
|
|
mat5, th5 = mat0.threshold(5, 7, type | CV_THRESH_OTSU, true)
|
|
[mat1, mat2, mat3, mat4, mat5].each { |m|
|
|
expected_mat.each_with_index { |x, i|
|
|
assert_equal(x, m[i][0])
|
|
}
|
|
}
|
|
[th3, th4, th5].each { |th|
|
|
assert_in_delta(expected_threshold, th, 0.001)
|
|
}
|
|
}
|
|
# Binary
|
|
expected = [0, 0, 0,
|
|
0, 0, 7,
|
|
7, 7, 7]
|
|
test_proc.call(CV_THRESH_BINARY, :binary, expected, 4)
|
|
|
|
# Binary inverse
|
|
expected = [7, 7, 7,
|
|
7, 7, 0,
|
|
0, 0, 0]
|
|
test_proc.call(CV_THRESH_BINARY_INV, :binary_inv, expected, 4)
|
|
|
|
# Trunc
|
|
expected = [0, 1, 2,
|
|
3, 4, 4,
|
|
4, 4, 4]
|
|
test_proc.call(CV_THRESH_TRUNC, :trunc, expected, 4)
|
|
|
|
# To zero
|
|
expected = [0, 0, 0,
|
|
0, 0, 5,
|
|
6, 7, 8]
|
|
test_proc.call(CV_THRESH_TOZERO, :tozero, expected, 4)
|
|
|
|
# To zero inverse
|
|
expected = [0, 1, 2,
|
|
3, 4, 0,
|
|
0, 0, 0]
|
|
test_proc.call(CV_THRESH_TOZERO_INV, :tozero_inv, expected, 4)
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.threshold(DUMMY_OBJ, 2, :binary)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.threshold(1, DUMMY_OBJ, :binary)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.threshold(1, 2, DUMMY_OBJ)
|
|
}
|
|
assert_raise(ArgumentError) {
|
|
mat0.threshold(1, 2, :dummy)
|
|
}
|
|
mat0.threshold(1, 2, :binary, DUMMY_OBJ)
|
|
end
|
|
|
|
def test_adaptive_threshold
|
|
mat0 = create_cvmat(5, 5, :cv8u, 1) { |j, i, c| (c + 1) * 10 }
|
|
|
|
mat1 = mat0.adaptive_threshold(128)
|
|
expected1 = [0, 0, 0, 0, 0, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128]
|
|
expected1.each_with_index { |expected, i|
|
|
assert_equal(expected, mat1[i][0])
|
|
}
|
|
|
|
mat2a = mat0.adaptive_threshold(255, :adaptive_method => :mean_c,
|
|
:threshold_type => :binary, :block_size => 5,
|
|
:param1 => 10)
|
|
mat2b = mat0.adaptive_threshold(255, :adaptive_method => CV_THRESH_BINARY,
|
|
:threshold_type => CV_ADAPTIVE_THRESH_MEAN_C, :block_size => 5,
|
|
:param1 => 10)
|
|
expected2 = [0, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255]
|
|
[mat2a, mat2b].each { |mat2|
|
|
assert_equal(CvMat, mat2.class)
|
|
assert_equal(mat0.rows, mat2.rows)
|
|
assert_equal(mat0.cols, mat2.cols)
|
|
assert_equal(mat0.depth, mat2.depth)
|
|
assert_equal(mat0.channel, mat2.channel)
|
|
expected2.each_with_index { |expected, i|
|
|
assert_equal(expected, mat2[i][0])
|
|
}
|
|
}
|
|
|
|
|
|
mat3a = mat0.adaptive_threshold(255, :adaptive_method => :gaussian_c,
|
|
:threshold_type => :binary_inv, :block_size => 5,
|
|
:param1 => 10)
|
|
mat3b = mat0.adaptive_threshold(255, :adaptive_method => CV_ADAPTIVE_THRESH_GAUSSIAN_C,
|
|
:threshold_type => CV_THRESH_BINARY_INV, :block_size => 5,
|
|
:param1 => 10)
|
|
expected3 = [255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
|
[mat3a, mat3b].each { |mat3|
|
|
assert_equal(CvMat, mat3.class)
|
|
assert_equal(mat0.rows, mat3.rows)
|
|
assert_equal(mat0.cols, mat3.cols)
|
|
assert_equal(mat0.depth, mat3.depth)
|
|
assert_equal(mat0.channel, mat3.channel)
|
|
expected3.each_with_index { |expected, i|
|
|
assert_equal(expected, mat3[i][0])
|
|
}
|
|
}
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.adaptive_threshold(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.adaptive_threshold(0, DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.adaptive_threshold(0, :adaptive_method => DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.adaptive_threshold(0, :threshold_type => DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.adaptive_threshold(0, :block_size => DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.adaptive_threshold(0, :param1 => DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_pyr_down
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
mat1 = mat0.pyr_down
|
|
mat2 = mat0.pyr_down(:gaussian_5x5)
|
|
|
|
assert_equal('de9ff2ffcf8e43f28564a201cf90b7f4', hash_img(mat1))
|
|
assert_equal('de9ff2ffcf8e43f28564a201cf90b7f4', hash_img(mat2))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.pyr_down(DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_pyr_up
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
mat1 = mat0.pyr_up
|
|
mat2 = mat0.pyr_up(:gaussian_5x5)
|
|
|
|
[mat1, mat2].each { |mat|
|
|
assert_equal(mat0.cols * 2, mat.cols)
|
|
assert_equal(mat0.rows * 2, mat.rows)
|
|
assert_equal(mat0.depth, mat.depth)
|
|
assert_equal(mat0.channel, mat.channel)
|
|
b, g, r = color_hists(mat)
|
|
assert_in_delta(27500000, b, 1000000)
|
|
assert_in_delta(26000000, g, 1000000)
|
|
assert_in_delta(47000000, r, 1000000)
|
|
}
|
|
# Uncomment the following lines to show the result
|
|
# snap mat0, mat1, mat2
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.pyr_up(DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_flood_fill
|
|
mat0 = create_cvmat(128, 128, :cv8u, 1) { |j, i, c|
|
|
if (i >= 32 and i < 96) and (j >= 32 and j < 96)
|
|
CvScalar.new(255)
|
|
elsif (i >= 16 and i < 112) and (j >= 16 and j < 112)
|
|
CvScalar.new(192)
|
|
else
|
|
CvScalar.new(128)
|
|
end
|
|
}
|
|
|
|
point = CvPoint.new(20, 20)
|
|
mat1, comp1, mask1 = mat0.flood_fill(point, 0)
|
|
mat2, comp2, mask2 = mat0.flood_fill(point, 0, CvScalar.new(64))
|
|
mat3, comp3, mask3 = mat0.flood_fill(point, 0, CvScalar.new(0), CvScalar.new(64))
|
|
mat4, comp4, mask4 = mat0.flood_fill(point, 0, CvScalar.new(0), CvScalar.new(64),
|
|
{:connectivity => 8, :fixed_range => true, :mask_only => true})
|
|
mat05 = mat0.clone
|
|
mat5, comp5, mask5 = mat05.flood_fill!(point, 0, CvScalar.new(0), CvScalar.new(64),
|
|
{:connectivity => 8, :fixed_range => true, :mask_only => true})
|
|
|
|
assert_equal('8c6a235fdf4c9c4f6822a45daac5b1af', hash_img(mat1))
|
|
assert_equal(5120.0, comp1.area)
|
|
assert_equal(16, comp1.rect.x)
|
|
assert_equal(16, comp1.rect.y)
|
|
assert_equal(96, comp1.rect.width)
|
|
assert_equal(96, comp1.rect.height)
|
|
assert_cvscalar_equal(CvScalar.new(0, 0, 0, 0), comp1.value)
|
|
assert_equal('1fd2537966283987b39c8b2c9d778383', hash_img(mask1))
|
|
|
|
assert_equal('7456e5de74bb8b4e783d04bbf1904644', hash_img(mat2))
|
|
assert_equal(12288.0, comp2.area)
|
|
assert_equal(0, comp2.rect.x)
|
|
assert_equal(0, comp2.rect.y)
|
|
assert_equal(128, comp2.rect.width)
|
|
assert_equal(128, comp2.rect.height)
|
|
assert_cvscalar_equal(CvScalar.new(0, 0, 0, 0), comp2.value)
|
|
assert_equal('847934f5170e2072cdfd63e16a1e06ad', hash_img(mask2))
|
|
|
|
assert_equal('df720005423762ca1b68e06571f58b21', hash_img(mat3))
|
|
assert_equal(9216.0, comp3.area)
|
|
assert_equal(16, comp3.rect.x)
|
|
assert_equal(16, comp3.rect.y)
|
|
assert_equal(96, comp3.rect.width)
|
|
assert_equal(96, comp3.rect.height)
|
|
assert_cvscalar_equal(CvScalar.new(0, 0, 0, 0), comp3.value)
|
|
|
|
assert_equal('7833f4c85c77056db71e33ae8072a1b5', hash_img(mat4))
|
|
assert_equal(9216.0, comp4.area)
|
|
assert_equal(16, comp4.rect.x)
|
|
assert_equal(16, comp4.rect.y)
|
|
assert_equal(96, comp4.rect.width)
|
|
assert_equal(96, comp4.rect.height)
|
|
assert_cvscalar_equal(CvScalar.new(220, 0, 0, 0), comp4.value)
|
|
assert_equal('b34b0269872fe3acde0e0c73e5cdd23b', hash_img(mask4))
|
|
|
|
assert_equal('7833f4c85c77056db71e33ae8072a1b5', hash_img(mat5))
|
|
assert_equal('7833f4c85c77056db71e33ae8072a1b5', hash_img(mat05))
|
|
assert_equal(9216.0, comp5.area)
|
|
assert_equal(16, comp5.rect.x)
|
|
assert_equal(16, comp5.rect.y)
|
|
assert_equal(96, comp5.rect.width)
|
|
assert_equal(96, comp5.rect.height)
|
|
assert_cvscalar_equal(CvScalar.new(220, 0, 0, 0), comp5.value)
|
|
assert_equal('b34b0269872fe3acde0e0c73e5cdd23b', hash_img(mask5))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.flood_fill(DUMMY_OBJ, 0)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.flood_fill(point, DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.flood_fill(point, 0, DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.flood_fill(point, 0, CvScalar.new(0), DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.flood_fill(point, 0, CvScalar.new(0), CvScalar.new(64), DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_find_contours
|
|
mat0 = CvMat.load(FILENAME_CONTOURS, CV_LOAD_IMAGE_GRAYSCALE)
|
|
|
|
# Make binary image
|
|
mat0.height.times { |j|
|
|
mat0.width.times { |i|
|
|
mat0[j, i] = (mat0[j, i][0] < 128) ? CvColor::Black : CvColor::White
|
|
}
|
|
}
|
|
|
|
[mat0.find_contours, mat0.find_contours(:mode => CV_RETR_LIST),
|
|
mat0.find_contours(:method => CV_CHAIN_APPROX_SIMPLE),
|
|
mat0.find_contours(:mode => CV_RETR_LIST, :method => CV_CHAIN_APPROX_SIMPLE)].each { |contours|
|
|
assert_not_nil(contours)
|
|
assert_equal(8, contours.total)
|
|
assert_not_nil(contours.h_next)
|
|
assert_equal(4, contours.h_next.total)
|
|
assert_not_nil(contours.h_next.h_next)
|
|
assert_equal(8, contours.h_next.h_next.total)
|
|
assert_not_nil(contours.h_next.h_next.h_next)
|
|
assert_equal(4, contours.h_next.h_next.h_next.total)
|
|
assert_nil(contours.v_next)
|
|
assert_nil(contours.h_next.v_next)
|
|
assert_nil(contours.h_next.h_next.v_next)
|
|
assert_nil(contours.h_next.h_next.h_next.v_next)
|
|
}
|
|
|
|
contours = mat0.find_contours(:mode => CV_RETR_TREE)
|
|
assert_not_nil(contours)
|
|
assert_equal(4, contours.total)
|
|
assert_not_nil(contours.v_next)
|
|
assert_equal(8, contours.v_next.total)
|
|
assert_nil(contours.v_next.v_next)
|
|
assert_not_nil(contours.h_next)
|
|
assert_equal(4, contours.h_next.total)
|
|
assert_not_nil(contours.h_next.v_next)
|
|
assert_equal(8, contours.h_next.v_next.total)
|
|
assert_nil(contours.h_next.v_next.v_next)
|
|
|
|
contours = mat0.find_contours(:mode => CV_RETR_CCOMP)
|
|
assert_not_nil(contours)
|
|
assert_equal(4, contours.total)
|
|
assert_not_nil(contours.v_next)
|
|
assert_equal(8, contours.v_next.total)
|
|
assert_nil(contours.v_next.v_next)
|
|
assert_not_nil(contours.h_next)
|
|
assert_equal(4, contours.h_next.total)
|
|
assert_not_nil(contours.h_next.v_next)
|
|
assert_equal(8, contours.h_next.v_next.total)
|
|
assert_nil(contours.h_next.v_next.v_next)
|
|
|
|
contours = mat0.find_contours(:mode => CV_RETR_EXTERNAL)
|
|
assert_not_nil(contours)
|
|
assert_equal(4, contours.total)
|
|
assert_nil(contours.v_next)
|
|
assert_not_nil(contours.h_next)
|
|
assert_equal(4, contours.h_next.total)
|
|
assert_nil(contours.h_next.v_next)
|
|
|
|
contours = mat0.find_contours(:mode => CV_RETR_TREE, :method => CV_CHAIN_APPROX_NONE)
|
|
assert_not_nil(contours)
|
|
assert_equal(474, contours.total)
|
|
assert_not_nil(contours.v_next)
|
|
assert_equal(318, contours.v_next.total)
|
|
assert_nil(contours.v_next.v_next)
|
|
assert_not_nil(contours.h_next)
|
|
assert_equal(396, contours.h_next.total)
|
|
assert_not_nil(contours.h_next.v_next)
|
|
assert_equal(240, contours.h_next.v_next.total)
|
|
assert_nil(contours.h_next.v_next.v_next)
|
|
|
|
contours = mat0.find_contours(:mode => CV_RETR_EXTERNAL, :method => CV_CHAIN_CODE)
|
|
assert_equal(474, contours.total)
|
|
assert_equal(396, contours.h_next.total)
|
|
|
|
contours = mat0.find_contours(:mode => CV_RETR_EXTERNAL, :method => CV_CHAIN_APPROX_TC89_L1)
|
|
assert_equal(4, contours.total)
|
|
assert_equal(4, contours.h_next.total)
|
|
|
|
contours = mat0.find_contours(:mode => CV_RETR_EXTERNAL, :method => CV_CHAIN_APPROX_TC89_KCOS)
|
|
assert_equal(4, contours.total)
|
|
assert_equal(4, contours.h_next.total)
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.find_contours(DUMMY_OBJ)
|
|
}
|
|
assert_raise(CvStsUnsupportedFormat) {
|
|
CvMat.new(10, 10, :cv32f, 3).find_contours
|
|
}
|
|
end
|
|
|
|
def test_pyr_mean_shift_filtering
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
mat1 = mat0.pyr_mean_shift_filtering(30, 30)
|
|
mat2 = mat0.pyr_mean_shift_filtering(30, 30, 2)
|
|
mat3 = mat0.pyr_mean_shift_filtering(30, 30, nil, CvTermCriteria.new(3, 0.01))
|
|
|
|
[mat1, mat2, mat3].each { |mat|
|
|
b, g, r = color_hists(mat)
|
|
assert_in_delta(6900000, b, 100000)
|
|
assert_in_delta(6500000, g, 100000)
|
|
assert_in_delta(11800000, r, 100000)
|
|
}
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.pyr_mean_shift_filtering(DUMMY_OBJ, 30)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.pyr_mean_shift_filtering(30, DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.pyr_mean_shift_filtering(30, 30, 2, DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_watershed
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
marker = CvMat.new(mat0.cols, mat0.rows, :cv32s, 1).set_zero
|
|
marker[150, 150] = CvScalar.new(1, 1, 1, 1)
|
|
marker[210, 210] = CvScalar.new(2, 2, 2, 2)
|
|
marker[40, 90] = CvScalar.new(3, 3, 3, 3)
|
|
|
|
mat1 = mat0.watershed(marker)
|
|
assert_equal('ee6bec03296039c8df1899d3edc4684e', hash_img(mat1))
|
|
|
|
assert_raise(TypeError) {
|
|
mat0.watershed(DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_moments
|
|
mat = create_cvmat(128, 128, :cv8u, 1) { |j, i|
|
|
if j >= 32 and j < 96 and i >= 16 and i < 112
|
|
CvScalar.new(0)
|
|
elsif j >= 16 and j < 112 and i >= 16 and i < 112
|
|
CvScalar.new(128)
|
|
else
|
|
CvScalar.new(255)
|
|
end
|
|
}
|
|
|
|
moments1 = mat.moments
|
|
moments2 = mat.moments(false)
|
|
moments3 = mat.moments(true)
|
|
|
|
[moments1, moments2].each { |m|
|
|
assert_in_delta(2221056, m.spatial(0, 0), 0.1)
|
|
assert_in_delta(2221056, m.central(0, 0), 0.1)
|
|
assert_in_delta(1, m.normalized_central(0, 0), 0.1)
|
|
|
|
hu_moments = m.hu
|
|
assert_equal(CvHuMoments, hu_moments.class)
|
|
assert_in_delta(0.001771, hu_moments.hu1, 0.000001)
|
|
hu_moments.to_a[1..7].each { |hu|
|
|
assert_in_delta(0.0, hu, 0.000001)
|
|
}
|
|
|
|
center = m.gravity_center
|
|
assert_equal(CvPoint2D32f, center.class)
|
|
assert_in_delta(63.5, center.x, 0.001)
|
|
assert_in_delta(63.5, center.y, 0.001)
|
|
|
|
assert_in_delta(0, m.angle, 0.001)
|
|
|
|
assert_in_delta(2221056, m.m00, 0.001)
|
|
assert_in_delta(141037056, m.m10, 0.001)
|
|
assert_in_delta(141037056, m.m01, 0.001)
|
|
assert_in_delta(13157049856, m.m20, 0.001)
|
|
assert_in_delta(8955853056, m.m11, 0.001)
|
|
assert_in_delta(13492594176, m.m02, 0.001)
|
|
assert_in_delta(1369024659456, m.m30, 0.001)
|
|
assert_in_delta(835472665856, m.m21, 0.001)
|
|
assert_in_delta(856779730176, m.m12, 0.001)
|
|
assert_in_delta(1432945852416, m.m03, 0.001)
|
|
assert_in_delta(4201196800, m.mu20, 0.001)
|
|
assert_in_delta(0, m.mu11, 0.001)
|
|
assert_in_delta(4536741120, m.mu02, 0.001)
|
|
assert_in_delta(0, m.mu30, 0.001)
|
|
assert_in_delta(0, m.mu21, 0.001)
|
|
assert_in_delta(0, m.mu12, 0.001)
|
|
assert_in_delta(0, m.mu03, 0.001)
|
|
assert_in_delta(0.000671, m.inv_sqrt_m00, 0.000001)
|
|
}
|
|
|
|
m = moments3
|
|
assert_in_delta(10240, m.spatial(0, 0), 0.1)
|
|
assert_in_delta(10240, m.central(0, 0), 0.1)
|
|
assert_in_delta(1, m.normalized_central(0, 0), 0.1)
|
|
|
|
hu_moments = m.hu
|
|
assert_equal(CvHuMoments, hu_moments.class)
|
|
assert_in_delta(0.361650, hu_moments.hu1, 0.000001)
|
|
assert_in_delta(0.000625, hu_moments.hu2, 0.000001)
|
|
hu_moments.to_a[2..7].each { |hu|
|
|
assert_in_delta(0.0, hu, 0.000001)
|
|
}
|
|
|
|
center = m.gravity_center
|
|
assert_equal(CvPoint2D32f, center.class)
|
|
assert_in_delta(63.5, center.x, 0.001)
|
|
assert_in_delta(63.5, center.y, 0.001)
|
|
|
|
assert_in_delta(0, m.angle, 0.001)
|
|
|
|
assert_in_delta(10240, m.m00, 0.001)
|
|
assert_in_delta(650240, m.m10, 0.001)
|
|
assert_in_delta(650240, m.m01, 0.001)
|
|
assert_in_delta(58940416, m.m20, 0.001)
|
|
assert_in_delta(41290240, m.m11, 0.001)
|
|
assert_in_delta(61561856, m.m02, 0.001)
|
|
assert_in_delta(5984288768, m.m30, 0.001)
|
|
assert_in_delta(3742716416, m.m21, 0.001)
|
|
assert_in_delta(3909177856, m.m12, 0.001)
|
|
assert_in_delta(6483673088, m.m03, 0.001)
|
|
assert_in_delta(17650176, m.mu20, 0.001)
|
|
assert_in_delta(0, m.mu11, 0.001)
|
|
assert_in_delta(20271616, m.mu02, 0.001)
|
|
assert_in_delta(0, m.mu30, 0.001)
|
|
assert_in_delta(0, m.mu21, 0.001)
|
|
assert_in_delta(0, m.mu12, 0.001)
|
|
assert_in_delta(0, m.mu03, 0.001)
|
|
assert_in_delta(0.009882, m.inv_sqrt_m00, 0.000001)
|
|
end
|
|
|
|
def test_hough_lines
|
|
mat0 = CvMat.load(FILENAME_LINES, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
# make a binary image
|
|
mat = CvMat.new(mat0.rows, mat0.cols, :cv8u, 1)
|
|
(mat0.rows * mat0.cols).times { |i|
|
|
mat[i] = (mat0[i][0] <= 100) ? CvScalar.new(0) : CvScalar.new(255);
|
|
}
|
|
|
|
[CV_HOUGH_STANDARD, :standard].each { |method|
|
|
seq = mat.hough_lines(method, 1, Math::PI / 180, 65)
|
|
assert_equal(4, seq.size)
|
|
}
|
|
|
|
[CV_HOUGH_PROBABILISTIC, :probabilistic].each { |method|
|
|
seq = mat.hough_lines(method, 1, Math::PI / 180, 40, 30, 10)
|
|
assert_equal(4, seq.size)
|
|
}
|
|
|
|
# [CV_HOUGH_MULTI_SCALE, :multi_scale].each { |method|
|
|
# seq = mat.hough_lines(method, 1, Math::PI / 180, 40, 2, 3)
|
|
# assert_equal(9, seq.size)
|
|
# }
|
|
|
|
assert_raise(TypeError) {
|
|
mat.hough_lines(DUMMY_OBJ, 1, Math::PI / 180, 40, 2, 3)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_lines(CV_HOUGH_STANDARD, DUMMY_OBJ, Math::PI / 180, 40, 2, 3)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_lines(CV_HOUGH_STANDARD, 1, DUMMY_OBJ, 40, 2, 3)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_lines(CV_HOUGH_STANDARD, 1, Math::PI / 180, DUMMY_OBJ, 2, 3)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_lines(CV_HOUGH_STANDARD, 1, Math::PI / 180, 40, DUMMY_OBJ, 3)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_lines(CV_HOUGH_STANDARD, 1, Math::PI / 180, 40, 2, DUMMY_OBJ)
|
|
}
|
|
assert_raise(ArgumentError) {
|
|
mat.hough_lines(:dummy, 1, Math::PI / 180, 40, 2, DUMMY_OBJ)
|
|
}
|
|
assert_raise(CvStsBadArg) {
|
|
CvMat.new(10, 10, :cv32f, 3).hough_lines(:standard, 1, Math::PI / 180, 65)
|
|
}
|
|
end
|
|
|
|
def test_hough_circles
|
|
mat0 = CvMat.load(FILENAME_LINES, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
# make a binary image
|
|
mat = CvMat.new(mat0.rows, mat0.cols, :cv8u, 1)
|
|
(mat0.rows * mat0.cols).times { |i|
|
|
mat[i] = (mat0[i][0] <= 100) ? CvScalar.new(0) : CvScalar.new(255);
|
|
}
|
|
|
|
[mat.hough_circles(CV_HOUGH_GRADIENT, 1.5, 40, 100, 40, 10, 50),
|
|
mat.hough_circles(:gradient, 1.5, 40, 100, 40, 10, 50),
|
|
mat.hough_circles(CV_HOUGH_GRADIENT, 1.5, 40, 100, 40),
|
|
mat.hough_circles(:gradient, 1.5, 40, 100, 40)].each { |seq|
|
|
assert_equal(2, seq.size)
|
|
}
|
|
|
|
# Uncomment the following lines to show the result
|
|
# seq = mat.hough_circles(:gradient, 1.5, 40, 100, 40, 10, 50)
|
|
# seq.each { |circle|
|
|
# mat0.circle!(circle.center, circle.radius, :color => CvColor::Red, :thickness => 2)
|
|
# }
|
|
# snap mat0
|
|
|
|
assert_raise(TypeError) {
|
|
mat.hough_circles(DUMMY_OBJ, 1.5, 40, 100, 50, 10, 50)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_circles(CV_HOUGH_GRADIENT, DUMMY_OBJ, 40, 100, 50, 10, 50)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_circles(CV_HOUGH_GRADIENT, 1.5, DUMMY_OBJ, 100, 50, 10, 50)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_circles(CV_HOUGH_GRADIENT, 1.5, 40, DUMMY_OBJ, 50, 10, 50)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_circles(CV_HOUGH_GRADIENT, 1.5, 40, 100, DUMMY_OBJ, 10, 50)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_circles(CV_HOUGH_GRADIENT, 1.5, 40, 100, 50, DUMMY_OBJ, 50)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.hough_circles(CV_HOUGH_GRADIENT, 1.5, 40, 100, 50, 10, DUMMY_OBJ)
|
|
}
|
|
assert_raise(ArgumentError) {
|
|
mat.hough_circles(:dummy, 1.5, 40, 100, 50, 10, DUMMY_OBJ)
|
|
}
|
|
assert_raise(CvStsBadArg) {
|
|
CvMat.new(10, 10, :cv32f, 3).hough_circles(:gradient, 1.5, 40, 100, 50, 10, 50)
|
|
}
|
|
end
|
|
|
|
def test_inpaint
|
|
mat = CvMat.load(FILENAME_LENA_INPAINT, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
mask = CvMat.load(FILENAME_INPAINT_MASK, CV_LOAD_IMAGE_GRAYSCALE)
|
|
|
|
[CV_INPAINT_NS, :ns].each { |method|
|
|
result_ns = mat.inpaint(method, mask, 10)
|
|
assert_in_delta(14000, count_threshold(result_ns, 128), 1000)
|
|
}
|
|
[CV_INPAINT_TELEA, :telea].each { |method|
|
|
result_telea = mat.inpaint(method, mask, 10)
|
|
assert_in_delta(13500, count_threshold(result_telea, 128), 1000)
|
|
}
|
|
|
|
# Uncomment the following lines to show the results
|
|
# result_ns = mat.inpaint(:ns, mask, 10)
|
|
# result_telea = mat.inpaint(:telea, mask, 10)
|
|
# snap mat, result_ns, result_telea
|
|
|
|
assert_raise(TypeError) {
|
|
mat.inpaint(DUMMY_OBJ, mask, 10)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.inpaint(:ns, DUMMY_OBJ, 10)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.inpaint(:ns, mask, DUMMY_OBJ)
|
|
}
|
|
assert_raise(ArgumentError) {
|
|
mat.inpaint(:dummy, mask, 10)
|
|
}
|
|
assert_raise(CvStsUnsupportedFormat) {
|
|
CvMat.new(10, 10, :cv32f, 3).inpaint(:ns, CvMat.new(10, 10, :cv8u, 1), 10)
|
|
}
|
|
end
|
|
|
|
def test_equalize_hist
|
|
mat = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
|
|
result = mat.equalize_hist
|
|
assert_equal('de235065c746193d7f3de9359f63a7af', hash_img(result))
|
|
|
|
assert_raise(CvStsAssert) {
|
|
CvMat.new(10, 10, :cv32f, 3).equalize_hist
|
|
}
|
|
|
|
# Uncomment the following lines to show the result
|
|
# snap mat, result
|
|
end
|
|
|
|
def test_match_template
|
|
mat = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
templ = CvMat.load(FILENAME_LENA_EYES, CV_LOAD_IMAGE_ANYCOLOR | CV_LOAD_IMAGE_ANYDEPTH)
|
|
|
|
expected_pt = CvPoint.new(100, 120)
|
|
|
|
# sqdiff
|
|
result = mat.match_template(templ)
|
|
pt = result.min_max_loc[2]
|
|
assert_in_delta(expected_pt.x, pt.x, 20)
|
|
assert_in_delta(expected_pt.y, pt.y, 20)
|
|
|
|
[CV_TM_SQDIFF, :sqdiff].each { |method|
|
|
result = mat.match_template(templ, method)
|
|
assert_in_delta(expected_pt.x, pt.x, 20)
|
|
assert_in_delta(expected_pt.y, pt.y, 20)
|
|
}
|
|
|
|
# sqdiff_normed
|
|
[CV_TM_SQDIFF_NORMED, :sqdiff_normed].each { |method|
|
|
result = mat.match_template(templ, method)
|
|
pt = result.min_max_loc[2]
|
|
assert_in_delta(expected_pt.x, pt.x, 20)
|
|
assert_in_delta(expected_pt.y, pt.y, 20)
|
|
}
|
|
|
|
# ccorr
|
|
[CV_TM_CCORR, :ccorr].each { |method|
|
|
result = mat.match_template(templ, method)
|
|
pt = result.min_max_loc[3]
|
|
assert_in_delta(110, pt.x, 20)
|
|
assert_in_delta(60, pt.y, 20)
|
|
}
|
|
|
|
# ccorr_normed
|
|
[CV_TM_CCORR_NORMED, :ccorr_normed].each { |method|
|
|
result = mat.match_template(templ, method)
|
|
pt = result.min_max_loc[3]
|
|
assert_in_delta(expected_pt.x, pt.x, 20)
|
|
assert_in_delta(expected_pt.y, pt.y, 20)
|
|
}
|
|
|
|
# ccoeff
|
|
[CV_TM_CCOEFF, :ccoeff].each { |method|
|
|
result = mat.match_template(templ, method)
|
|
pt = result.min_max_loc[3]
|
|
assert_in_delta(expected_pt.x, pt.x, 20)
|
|
assert_in_delta(expected_pt.y, pt.y, 20)
|
|
}
|
|
|
|
# ccoeff_normed
|
|
[CV_TM_CCOEFF_NORMED, :ccoeff_normed].each { |method|
|
|
result = mat.match_template(templ, method)
|
|
pt = result.min_max_loc[3]
|
|
assert_in_delta(expected_pt.x, pt.x, 20)
|
|
assert_in_delta(expected_pt.y, pt.y, 20)
|
|
}
|
|
|
|
# Uncomment the following lines to show the result
|
|
# result = mat.match_template(templ)
|
|
# pt1 = result.min_max_loc[2] # minimum location
|
|
# pt2 = CvPoint.new(pt1.x + templ.width, pt1.y + templ.height)
|
|
# mat.rectangle!(pt1, pt2, :color => CvColor::Black, :thickness => 3)
|
|
# snap mat, templ, result
|
|
|
|
assert_raise(TypeError) {
|
|
mat.match_template(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.match_template(templ, DUMMY_OBJ)
|
|
}
|
|
end
|
|
|
|
def test_match_shapes
|
|
mat_cv = CvMat.load(FILENAME_STR_CV, CV_LOAD_IMAGE_GRAYSCALE)
|
|
mat_ov = CvMat.load(FILENAME_STR_OV, CV_LOAD_IMAGE_GRAYSCALE)
|
|
mat_cv_rotated = CvMat.load(FILENAME_STR_CV_ROTATED, CV_LOAD_IMAGE_GRAYSCALE)
|
|
|
|
[CV_CONTOURS_MATCH_I1, :i1].each { |method|
|
|
assert_in_delta(0, mat_cv.match_shapes(mat_cv_rotated, method), 0.00001)
|
|
assert_in_delta(0.0010649, mat_cv.match_shapes(mat_ov, method), 0.00001)
|
|
}
|
|
|
|
[CV_CONTOURS_MATCH_I2, :i2].each { |method|
|
|
assert_in_delta(0, mat_cv.match_shapes(mat_cv_rotated, method), 0.00001)
|
|
assert_in_delta(0.0104650, mat_cv.match_shapes(mat_ov, method), 0.00001)
|
|
}
|
|
|
|
[CV_CONTOURS_MATCH_I3, :i3].each { |method|
|
|
assert_in_delta(0, mat_cv.match_shapes(mat_cv_rotated, method), 0.00001)
|
|
assert_in_delta(0.0033327, mat_cv.match_shapes(mat_ov, method), 0.00001)
|
|
}
|
|
end
|
|
|
|
def test_mean_shift
|
|
flunk('FIXME: CvMat#mean_shift is not tested yet.')
|
|
end
|
|
|
|
def test_cam_shift
|
|
flunk('FIXME: CvMat#cam_shift is not tested yet.')
|
|
end
|
|
|
|
def test_snake_image
|
|
radius = 40
|
|
center = CvPoint.new(128, 128)
|
|
mat = CvMat.new(center.y * 2, center.x * 2, :cv8u, 1).zero!
|
|
mat.circle!(center, radius, :color => CvColor::White, :thickness => -1)
|
|
|
|
num_points = 10
|
|
alpha = 0.05
|
|
beta = 0.05
|
|
gamma = 0.9
|
|
|
|
arr_alpha = [alpha] * num_points
|
|
arr_beta = [beta] * num_points
|
|
arr_gamma = [gamma] * num_points
|
|
size = CvSize.new(3, 3)
|
|
term_criteria = CvTermCriteria.new(100, num_points / 2)
|
|
|
|
# initialize contours
|
|
points = []
|
|
num_points.times { |i|
|
|
x = center.x * Math.cos(2 * Math::PI * i / num_points) + center.x
|
|
y = center.y * Math.sin(2 * Math::PI * i / num_points) + center.y
|
|
points << CvPoint.new(x, y)
|
|
}
|
|
|
|
acceptable_error = 50
|
|
|
|
# test snake_image
|
|
# calc_gradient = true
|
|
[mat.snake_image(points, alpha, beta, gamma, size, term_criteria),
|
|
mat.snake_image(points, alpha, beta, gamma, size, term_criteria, true),
|
|
mat.snake_image(points, arr_alpha, arr_beta, arr_gamma, size, term_criteria),
|
|
mat.snake_image(points, arr_alpha, arr_beta, arr_gamma, size, term_criteria, true)].each { |result|
|
|
assert_equal(num_points, result.size)
|
|
result.each { |pt|
|
|
x = pt.x - center.x
|
|
y = pt.y - center.y
|
|
error = Math.sqrt((x * x + y * y - radius * radius).abs)
|
|
assert(error < acceptable_error)
|
|
}
|
|
}
|
|
|
|
# calc_gradient = false
|
|
[mat.snake_image(points, alpha, beta, gamma, size, term_criteria, false),
|
|
mat.snake_image(points, arr_alpha, arr_beta, arr_gamma, size, term_criteria, false)].each { |result|
|
|
expected_points = [[149, 102], [139, 144], [95, 144], [56, 124], [17, 105],
|
|
[25, 61], [63, 39], [101, 17], [145, 17], [158, 59]]
|
|
assert_equal(num_points, result.size)
|
|
result.each { |pt|
|
|
x = pt.x - center.x
|
|
y = pt.y - center.y
|
|
error = Math.sqrt((x * x + y * y - radius * radius).abs)
|
|
assert(error < acceptable_error)
|
|
}
|
|
}
|
|
|
|
# raise error
|
|
assert_raise(TypeError) {
|
|
mat.snake_image(DUMMY_OBJ, arr_alpha, arr_beta, arr_gamma, size, term_criteria)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.snake_image(points, DUMMY_OBJ, arr_beta, arr_gamma, size, term_criteria)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.snake_image(points, arr_alpha, DUMMY_OBJ, arr_gamma, size, term_criteria)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.snake_image(points, arr_alpha, arr_beta, DUMMY_OBJ, size, term_criteria)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.snake_image(points, arr_alpha, arr_beta, arr_gamma, DUMMY_OBJ, term_criteria)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat.snake_image(points, arr_alpha, arr_beta, arr_gamma, size, DUMMY_OBJ)
|
|
}
|
|
mat.snake_image(points, arr_alpha, arr_beta, arr_gamma, size, term_criteria, DUMMY_OBJ)
|
|
|
|
assert_raise(ArgumentError) {
|
|
mat.snake_image(points, arr_alpha[0 .. num_points / 2], arr_beta, arr_gamma, size, term_criteria)
|
|
}
|
|
assert_raise(CvBadNumChannels) {
|
|
CvMat.new(10, 10, :cv8u, 3).snake_image(points, alpha, beta, gamma, size, term_criteria)
|
|
}
|
|
|
|
# Uncomment the following lines to show the result
|
|
# result = mat.clone.GRAY2BGR
|
|
# pts = mat.snake_image(points, alpha, beta, gamma, size, term_criteria)
|
|
# w = GUI::Window.new('HoughCircle')
|
|
# result.poly_line!([pts], :color => CvColor::Red, :is_closed => true, :thickness => 2)
|
|
# result.poly_line!([points], :color => CvColor::Yellow, :is_closed => true, :thickness => 2)
|
|
# w.show result
|
|
# GUI::wait_key
|
|
end
|
|
|
|
def test_optical_flow_hs
|
|
size = 128
|
|
prev = create_cvmat(size, size, :cv8u, 1) { |j, i|
|
|
if ((i - (size / 2)) ** 2 ) + ((j - (size / 2)) ** 2 ) < size
|
|
CvColor::Black
|
|
else
|
|
CvColor::White
|
|
end
|
|
}
|
|
curr = create_cvmat(size, size, :cv8u, 1) { |j, i|
|
|
if ((i - (size / 2) - 10) ** 2) + ((j - (size / 2) - 7) ** 2 ) < size
|
|
CvColor::Black
|
|
else
|
|
CvColor::White
|
|
end
|
|
}
|
|
|
|
[curr.optical_flow_hs(prev, nil, nil, :lambda => 0.0005, :criteria => CvTermCriteria.new(1, 0.001)),
|
|
curr.optical_flow_hs(prev)].each { |velx, vely|
|
|
assert_in_delta(60, count_threshold(velx, 1), 20)
|
|
assert_in_delta(50, count_threshold(vely, 1), 20)
|
|
}
|
|
|
|
velx, vely = curr.optical_flow_hs(prev, nil, nil, :lambda => 0.001)
|
|
assert_in_delta(60, count_threshold(velx, 1), 20)
|
|
assert_in_delta(50, count_threshold(vely, 1), 20)
|
|
|
|
velx, vely = curr.optical_flow_hs(prev, nil, nil, :criteria => CvTermCriteria.new(10, 0.01))
|
|
assert_in_delta(130, count_threshold(velx, 1), 20)
|
|
assert_in_delta(110, count_threshold(vely, 1), 20)
|
|
|
|
prev_velx, prev_vely = curr.optical_flow_hs(prev)
|
|
velx, vely = curr.optical_flow_hs(prev, prev_velx, prev_vely)
|
|
assert_in_delta(70, count_threshold(velx, 1), 20)
|
|
assert_in_delta(60, count_threshold(vely, 1), 20)
|
|
|
|
velx, vely = curr.optical_flow_hs(prev, prev_velx, prev_vely, :lambda => 0.001)
|
|
assert_in_delta(80, count_threshold(velx, 1), 20)
|
|
assert_in_delta(70, count_threshold(vely, 1), 20)
|
|
|
|
velx, vely = curr.optical_flow_hs(prev, prev_velx, prev_vely, :criteria => CvTermCriteria.new(10, 0.01))
|
|
assert_in_delta(150, count_threshold(velx, 1), 20)
|
|
assert_in_delta(130, count_threshold(vely, 1), 20)
|
|
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_hs(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_hs(prev, DUMMY_OBJ, prev_vely)
|
|
}
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_hs(prev, prev_velx, DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_hs(prev, prev_velx, prev_vely, DUMMY_OBJ)
|
|
}
|
|
assert_raise(CvStsUnmatchedFormats) {
|
|
CvMat.new(10, 10, :cv8u, 3).optical_flow_hs(prev)
|
|
}
|
|
end
|
|
|
|
def test_optical_flow_lk
|
|
size = 128
|
|
prev = create_cvmat(size, size, :cv8u, 1) { |j, i|
|
|
if ((i - (size / 2)) ** 2 ) + ((j - (size / 2)) ** 2 ) < size
|
|
CvColor::Black
|
|
else
|
|
CvColor::White
|
|
end
|
|
}
|
|
curr = create_cvmat(size, size, :cv8u, 1) { |j, i|
|
|
if ((i - (size / 2) - 10) ** 2) + ((j - (size / 2) - 7) ** 2 ) < size
|
|
CvColor::Black
|
|
else
|
|
CvColor::White
|
|
end
|
|
}
|
|
|
|
velx, vely = curr.optical_flow_lk(prev, CvSize.new(3, 3))
|
|
assert_in_delta(100, count_threshold(velx, 1), 20)
|
|
assert_in_delta(90, count_threshold(vely, 1), 20)
|
|
|
|
velx, vely = curr.optical_flow_lk(prev, CvSize.new(5, 5))
|
|
assert_in_delta(180, count_threshold(velx, 1), 20)
|
|
assert_in_delta(150, count_threshold(vely, 1), 20)
|
|
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_lk(DUMMY_OBJ, CvSize.new(3, 3))
|
|
}
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_lk(prev, DUMMY_OBJ)
|
|
}
|
|
assert_raise(CvStsUnmatchedFormats) {
|
|
CvMat.new(10, 10, :cv8u, 3).optical_flow_lk(prev, CvSize.new(3, 3))
|
|
}
|
|
end
|
|
|
|
def test_optical_flow_bm
|
|
size = 128
|
|
prev = create_cvmat(size, size, :cv8u, 1) { |j, i|
|
|
if ((i - (size / 2)) ** 2 ) + ((j - (size / 2)) ** 2 ) < size
|
|
CvColor::Black
|
|
else
|
|
CvColor::White
|
|
end
|
|
}
|
|
curr = create_cvmat(size, size, :cv8u, 1) { |j, i|
|
|
if ((i - (size / 2) - 10) ** 2) + ((j - (size / 2) - 7) ** 2 ) < size
|
|
CvColor::Black
|
|
else
|
|
CvColor::White
|
|
end
|
|
}
|
|
|
|
[curr.optical_flow_bm(prev, nil, nil, :block_size => CvSize.new(4, 4),
|
|
:shift_size => CvSize.new(1, 1), :max_range => CvSize.new(4, 4)),
|
|
curr.optical_flow_bm(prev)].each { |velx, vely|
|
|
assert_in_delta(350, count_threshold(velx, 1), 30)
|
|
assert_in_delta(250, count_threshold(vely, 1), 30)
|
|
}
|
|
|
|
velx, vely = curr.optical_flow_bm(prev, nil, nil, :block_size => CvSize.new(3, 3))
|
|
assert_in_delta(280, count_threshold(velx, 1), 30)
|
|
assert_in_delta(200, count_threshold(vely, 1), 30)
|
|
|
|
velx, vely = curr.optical_flow_bm(prev, nil, nil, :shift_size => CvSize.new(2, 2))
|
|
assert_in_delta(80, count_threshold(velx, 1), 30)
|
|
assert_in_delta(60, count_threshold(vely, 1), 30)
|
|
|
|
velx, vely = curr.optical_flow_bm(prev, nil, nil, :max_range => CvSize.new(5, 5))
|
|
assert_in_delta(400, count_threshold(velx, 1), 30)
|
|
assert_in_delta(300, count_threshold(vely, 1), 30)
|
|
|
|
prev_velx, prev_vely = curr.optical_flow_bm(prev)
|
|
velx, vely = curr.optical_flow_bm(prev, prev_velx, prev_vely)
|
|
assert_in_delta(350, count_threshold(velx, 1), 30)
|
|
assert_in_delta(270, count_threshold(vely, 1), 30)
|
|
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_bm(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_bm(prev, DUMMY_OBJ, prev_vely)
|
|
}
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_bm(prev, prev_velx, DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
curr.optical_flow_bm(prev, prev_velx, prev_vely, DUMMY_OBJ)
|
|
}
|
|
assert_raise(CvStsUnmatchedFormats) {
|
|
CvMat.new(10, 10, :cv8u, 3).optical_flow_bm(prev)
|
|
}
|
|
end
|
|
|
|
def test_extract_surf
|
|
mat0 = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
|
|
|
|
# simple
|
|
keypoints1, descriptors1 = mat0.extract_surf(CvSURFParams.new(500, true, 2, 3))
|
|
assert_equal(CvSeq, keypoints1.class)
|
|
assert_equal(254, keypoints1.size)
|
|
assert_equal(Array, descriptors1.class)
|
|
assert_equal(254, descriptors1.size)
|
|
assert_equal(Array, descriptors1[0].class)
|
|
assert_equal(128, descriptors1[0].size)
|
|
|
|
# use mask
|
|
mask = create_cvmat(mat0.rows, mat0.cols, :cv8u, 1) { |j, i|
|
|
if i < mat0.cols / 2
|
|
CvScalar.new(1)
|
|
else
|
|
CvScalar.new(0)
|
|
end
|
|
}
|
|
keypoints2, descriptors2 = mat0.extract_surf(CvSURFParams.new(500, false), mask)
|
|
assert_equal(CvSeq, keypoints2.class)
|
|
assert_equal(170, keypoints2.size)
|
|
assert_equal(Array, descriptors2.class)
|
|
assert_equal(170, descriptors2.size)
|
|
assert_equal(Array, descriptors2[0].class)
|
|
assert_equal(64, descriptors2[0].size)
|
|
|
|
# raise exceptions because of invalid arguments
|
|
assert_raise(TypeError) {
|
|
mat0.extract_surf(DUMMY_OBJ)
|
|
}
|
|
assert_raise(TypeError) {
|
|
mat0.extract_surf(CvSURFParams.new(500), DUMMY_OBJ)
|
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}
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# Uncomment the following lines to show the result
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# results = []
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# [keypoints1, keypoints2].each { |kpts|
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# tmp = mat0.GRAY2BGR
|
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# kpts.each { |kp|
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# tmp.circle!(kp.pt, 3, :color => CvColor::Red, :thickness => 1, :line_type => :aa)
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# }
|
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# results << tmp
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# }
|
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# snap mat0, *results
|
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end
|
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end
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|