mirror of
https://github.com/opencv/opencv.git
synced 2024-11-27 12:40:05 +08:00
1606 lines
50 KiB
C++
1606 lines
50 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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class CV_ImgWarpBaseTest : public cvtest::ArrayTest
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{
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public:
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CV_ImgWarpBaseTest( bool warp_matrix );
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protected:
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int read_params( const cv::FileStorage& fs );
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int prepare_test_case( int test_case_idx );
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void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
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void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
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void fill_array( int test_case_idx, int i, int j, Mat& arr );
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int interpolation;
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int max_interpolation;
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double spatial_scale_zoom, spatial_scale_decimate;
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};
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CV_ImgWarpBaseTest::CV_ImgWarpBaseTest( bool warp_matrix )
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{
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test_array[INPUT].push_back(NULL);
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if( warp_matrix )
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test_array[INPUT].push_back(NULL);
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test_array[INPUT_OUTPUT].push_back(NULL);
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test_array[REF_INPUT_OUTPUT].push_back(NULL);
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max_interpolation = 5;
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interpolation = 0;
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element_wise_relative_error = false;
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spatial_scale_zoom = 0.01;
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spatial_scale_decimate = 0.005;
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}
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int CV_ImgWarpBaseTest::read_params( const cv::FileStorage& fs )
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{
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int code = cvtest::ArrayTest::read_params( fs );
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return code;
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}
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void CV_ImgWarpBaseTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
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{
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cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
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if( CV_MAT_DEPTH(type) == CV_32F )
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{
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low = Scalar::all(-10.);
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high = Scalar::all(10);
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}
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}
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void CV_ImgWarpBaseTest::get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes, vector<vector<int> >& types )
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{
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RNG& rng = ts->get_rng();
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int depth = cvtest::randInt(rng) % 3;
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int cn = cvtest::randInt(rng) % 3 + 1;
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cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : CV_32F;
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cn += cn == 2;
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types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(depth, cn);
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if( test_array[INPUT].size() > 1 )
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types[INPUT][1] = cvtest::randInt(rng) & 1 ? CV_32FC1 : CV_64FC1;
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interpolation = cvtest::randInt(rng) % max_interpolation;
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}
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void CV_ImgWarpBaseTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
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{
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if( i != INPUT || j != 0 )
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cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
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}
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int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx )
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{
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int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
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Mat& img = test_mat[INPUT][0];
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int i, j, cols = img.cols;
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int type = img.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
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double scale = depth == CV_16U ? 1000. : 255.*0.5;
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double space_scale = spatial_scale_decimate;
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vector<float> buffer(img.cols*cn);
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if( code <= 0 )
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return code;
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if( test_mat[INPUT_OUTPUT][0].cols >= img.cols &&
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test_mat[INPUT_OUTPUT][0].rows >= img.rows )
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space_scale = spatial_scale_zoom;
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for( i = 0; i < img.rows; i++ )
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{
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uchar* ptr = img.ptr(i);
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switch( cn )
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{
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case 1:
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for( j = 0; j < cols; j++ )
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buffer[j] = (float)((sin((i+1)*space_scale)*sin((j+1)*space_scale)+1.)*scale);
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break;
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case 2:
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for( j = 0; j < cols; j++ )
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{
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buffer[j*2] = (float)((sin((i+1)*space_scale)+1.)*scale);
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buffer[j*2+1] = (float)((sin((i+j)*space_scale)+1.)*scale);
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}
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break;
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case 3:
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for( j = 0; j < cols; j++ )
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{
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buffer[j*3] = (float)((sin((i+1)*space_scale)+1.)*scale);
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buffer[j*3+1] = (float)((sin(j*space_scale)+1.)*scale);
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buffer[j*3+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
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}
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break;
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case 4:
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for( j = 0; j < cols; j++ )
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{
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buffer[j*4] = (float)((sin((i+1)*space_scale)+1.)*scale);
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buffer[j*4+1] = (float)((sin(j*space_scale)+1.)*scale);
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buffer[j*4+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
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buffer[j*4+3] = (float)((sin((i-j)*space_scale)+1.)*scale);
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}
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break;
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default:
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assert(0);
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}
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/*switch( depth )
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{
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case CV_8U:
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for( j = 0; j < cols*cn; j++ )
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ptr[j] = (uchar)cvRound(buffer[j]);
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break;
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case CV_16U:
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for( j = 0; j < cols*cn; j++ )
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((ushort*)ptr)[j] = (ushort)cvRound(buffer[j]);
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break;
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case CV_32F:
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for( j = 0; j < cols*cn; j++ )
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((float*)ptr)[j] = (float)buffer[j];
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break;
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default:
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assert(0);
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}*/
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cv::Mat src(1, cols*cn, CV_32F, &buffer[0]);
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cv::Mat dst(1, cols*cn, depth, ptr);
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src.convertTo(dst, dst.type());
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}
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return code;
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}
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/////////////////////////
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class CV_ResizeTest : public CV_ImgWarpBaseTest
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{
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public:
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CV_ResizeTest();
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protected:
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void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
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void run_func();
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void prepare_to_validation( int /*test_case_idx*/ );
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double get_success_error_level( int test_case_idx, int i, int j );
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};
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CV_ResizeTest::CV_ResizeTest() : CV_ImgWarpBaseTest( false )
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{
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}
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void CV_ResizeTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
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{
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RNG& rng = ts->get_rng();
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CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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Size sz;
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sz.width = (cvtest::randInt(rng) % sizes[INPUT][0].width) + 1;
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sz.height = (cvtest::randInt(rng) % sizes[INPUT][0].height) + 1;
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if( cvtest::randInt(rng) & 1 )
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{
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int xfactor = cvtest::randInt(rng) % 10 + 1;
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int yfactor = cvtest::randInt(rng) % 10 + 1;
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if( cvtest::randInt(rng) & 1 )
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yfactor = xfactor;
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sz.width = sizes[INPUT][0].width / xfactor;
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sz.width = MAX(sz.width,1);
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sz.height = sizes[INPUT][0].height / yfactor;
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sz.height = MAX(sz.height,1);
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sizes[INPUT][0].width = sz.width * xfactor;
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sizes[INPUT][0].height = sz.height * yfactor;
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}
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if( cvtest::randInt(rng) & 1 )
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sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = sz;
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else
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{
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sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = sizes[INPUT][0];
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sizes[INPUT][0] = sz;
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}
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if( interpolation == 4 &&
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(MIN(sizes[INPUT][0].width,sizes[INPUT_OUTPUT][0].width) < 4 ||
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MIN(sizes[INPUT][0].height,sizes[INPUT_OUTPUT][0].height) < 4))
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interpolation = 2;
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}
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void CV_ResizeTest::run_func()
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{
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cvResize( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], interpolation );
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}
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double CV_ResizeTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
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{
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int depth = test_mat[INPUT][0].depth();
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return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 1e-1;
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}
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void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ )
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{
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CvMat _src = cvMat(test_mat[INPUT][0]), _dst = cvMat(test_mat[REF_INPUT_OUTPUT][0]);
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CvMat *src = &_src, *dst = &_dst;
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int i, j, k;
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CvMat* x_idx = cvCreateMat( 1, dst->cols, CV_32SC1 );
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CvMat* y_idx = cvCreateMat( 1, dst->rows, CV_32SC1 );
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int* x_tab = x_idx->data.i;
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int elem_size = CV_ELEM_SIZE(src->type);
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int drows = dst->rows, dcols = dst->cols;
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if( interpolation == CV_INTER_NN )
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{
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for( j = 0; j < dcols; j++ )
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{
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int t = (j*src->cols*2 + MIN(src->cols,dcols) - 1)/(dcols*2);
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t -= t >= src->cols;
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x_idx->data.i[j] = t*elem_size;
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}
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for( j = 0; j < drows; j++ )
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{
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int t = (j*src->rows*2 + MIN(src->rows,drows) - 1)/(drows*2);
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t -= t >= src->rows;
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y_idx->data.i[j] = t;
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}
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}
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else
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{
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double scale_x = (double)src->cols/dcols;
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double scale_y = (double)src->rows/drows;
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for( j = 0; j < dcols; j++ )
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{
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double f = ((j+0.5)*scale_x - 0.5);
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i = cvRound(f);
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x_idx->data.i[j] = (i < 0 ? 0 : i >= src->cols ? src->cols - 1 : i)*elem_size;
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}
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for( j = 0; j < drows; j++ )
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{
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double f = ((j+0.5)*scale_y - 0.5);
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i = cvRound(f);
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y_idx->data.i[j] = i < 0 ? 0 : i >= src->rows ? src->rows - 1 : i;
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}
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}
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for( i = 0; i < drows; i++ )
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{
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uchar* dptr = dst->data.ptr + dst->step*i;
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const uchar* sptr0 = src->data.ptr + src->step*y_idx->data.i[i];
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for( j = 0; j < dcols; j++, dptr += elem_size )
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{
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const uchar* sptr = sptr0 + x_tab[j];
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for( k = 0; k < elem_size; k++ )
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dptr[k] = sptr[k];
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}
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}
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cvReleaseMat( &x_idx );
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cvReleaseMat( &y_idx );
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}
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class CV_ResizeExactTest : public CV_ResizeTest
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{
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public:
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CV_ResizeExactTest();
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protected:
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void get_test_array_types_and_sizes(int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types);
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};
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CV_ResizeExactTest::CV_ResizeExactTest() : CV_ResizeTest()
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{
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max_interpolation = 1;
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}
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void CV_ResizeExactTest::get_test_array_types_and_sizes(int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types)
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{
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CV_ResizeTest::get_test_array_types_and_sizes(test_case_idx, sizes, types);
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interpolation = INTER_LINEAR_EXACT;
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if (CV_MAT_DEPTH(types[INPUT][0]) == CV_32F ||
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CV_MAT_DEPTH(types[INPUT][0]) == CV_64F)
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types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(CV_8U, CV_MAT_CN(types[INPUT][0]));
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}
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/////////////////////////
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static void test_remap( const Mat& src, Mat& dst, const Mat& mapx, const Mat& mapy,
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Mat* mask=0, int interpolation=CV_INTER_LINEAR )
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{
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int x, y, k;
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int drows = dst.rows, dcols = dst.cols;
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int srows = src.rows, scols = src.cols;
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const uchar* sptr0 = src.ptr();
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int depth = src.depth(), cn = src.channels();
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int elem_size = (int)src.elemSize();
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int step = (int)(src.step / CV_ELEM_SIZE(depth));
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int delta;
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if( interpolation != CV_INTER_CUBIC )
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{
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delta = 0;
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scols -= 1; srows -= 1;
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}
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else
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{
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delta = 1;
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scols = MAX(scols - 3, 0);
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srows = MAX(srows - 3, 0);
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}
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int scols1 = MAX(scols - 2, 0);
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int srows1 = MAX(srows - 2, 0);
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if( mask )
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*mask = Scalar::all(0);
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for( y = 0; y < drows; y++ )
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{
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uchar* dptr = dst.ptr(y);
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const float* mx = mapx.ptr<float>(y);
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const float* my = mapy.ptr<float>(y);
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uchar* m = mask ? mask->ptr(y) : 0;
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for( x = 0; x < dcols; x++, dptr += elem_size )
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{
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float xs = mx[x];
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float ys = my[x];
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int ixs = cvFloor(xs);
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int iys = cvFloor(ys);
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if( (unsigned)(ixs - delta - 1) >= (unsigned)scols1 ||
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(unsigned)(iys - delta - 1) >= (unsigned)srows1 )
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{
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if( m )
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m[x] = 1;
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if( (unsigned)(ixs - delta) >= (unsigned)scols ||
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(unsigned)(iys - delta) >= (unsigned)srows )
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continue;
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}
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xs -= ixs;
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ys -= iys;
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switch( depth )
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{
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case CV_8U:
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{
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const uchar* sptr = sptr0 + iys*step + ixs*cn;
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for( k = 0; k < cn; k++ )
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{
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float v00 = sptr[k];
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float v01 = sptr[cn + k];
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float v10 = sptr[step + k];
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float v11 = sptr[step + cn + k];
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v00 = v00 + xs*(v01 - v00);
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v10 = v10 + xs*(v11 - v10);
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v00 = v00 + ys*(v10 - v00);
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dptr[k] = (uchar)cvRound(v00);
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}
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}
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break;
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case CV_16U:
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{
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const ushort* sptr = (const ushort*)sptr0 + iys*step + ixs*cn;
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for( k = 0; k < cn; k++ )
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{
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float v00 = sptr[k];
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float v01 = sptr[cn + k];
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float v10 = sptr[step + k];
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float v11 = sptr[step + cn + k];
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v00 = v00 + xs*(v01 - v00);
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v10 = v10 + xs*(v11 - v10);
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v00 = v00 + ys*(v10 - v00);
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((ushort*)dptr)[k] = (ushort)cvRound(v00);
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}
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}
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break;
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case CV_32F:
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{
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const float* sptr = (const float*)sptr0 + iys*step + ixs*cn;
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for( k = 0; k < cn; k++ )
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{
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float v00 = sptr[k];
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float v01 = sptr[cn + k];
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float v10 = sptr[step + k];
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float v11 = sptr[step + cn + k];
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v00 = v00 + xs*(v01 - v00);
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v10 = v10 + xs*(v11 - v10);
|
|
v00 = v00 + ys*(v10 - v00);
|
|
((float*)dptr)[k] = (float)v00;
|
|
}
|
|
}
|
|
break;
|
|
default:
|
|
assert(0);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/////////////////////////
|
|
|
|
class CV_WarpAffineTest : public CV_ImgWarpBaseTest
|
|
{
|
|
public:
|
|
CV_WarpAffineTest();
|
|
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
void run_func();
|
|
int prepare_test_case( int test_case_idx );
|
|
void prepare_to_validation( int /*test_case_idx*/ );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
};
|
|
|
|
|
|
CV_WarpAffineTest::CV_WarpAffineTest() : CV_ImgWarpBaseTest( true )
|
|
{
|
|
//spatial_scale_zoom = spatial_scale_decimate;
|
|
spatial_scale_decimate = spatial_scale_zoom;
|
|
}
|
|
|
|
|
|
void CV_WarpAffineTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
Size sz = sizes[INPUT][0];
|
|
// run for the second time to get output of a different size
|
|
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
sizes[INPUT][0] = sz;
|
|
sizes[INPUT][1] = Size( 3, 2 );
|
|
}
|
|
|
|
|
|
void CV_WarpAffineTest::run_func()
|
|
{
|
|
CvMat mtx = cvMat(test_mat[INPUT][1]);
|
|
cvWarpAffine( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &mtx, interpolation );
|
|
}
|
|
|
|
|
|
double CV_WarpAffineTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
|
{
|
|
int depth = test_mat[INPUT][0].depth();
|
|
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
|
|
}
|
|
|
|
|
|
int CV_WarpAffineTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
|
|
const Mat& src = test_mat[INPUT][0];
|
|
const Mat& dst = test_mat[INPUT_OUTPUT][0];
|
|
Mat& mat = test_mat[INPUT][1];
|
|
Point2f center;
|
|
double scale, angle;
|
|
|
|
if( code <= 0 )
|
|
return code;
|
|
|
|
double buffer[6];
|
|
Mat tmp( 2, 3, mat.type(), buffer );
|
|
|
|
center.x = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.cols);
|
|
center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows);
|
|
angle = cvtest::randReal(rng)*360;
|
|
scale = ((double)dst.rows/src.rows + (double)dst.cols/src.cols)*0.5;
|
|
getRotationMatrix2D(center, angle, scale).convertTo(mat, mat.depth());
|
|
rng.fill( tmp, CV_RAND_NORMAL, Scalar::all(1.), Scalar::all(0.01) );
|
|
cv::max(tmp, 0.9, tmp);
|
|
cv::min(tmp, 1.1, tmp);
|
|
cv::multiply(tmp, mat, mat, 1.);
|
|
|
|
return code;
|
|
}
|
|
|
|
|
|
void CV_WarpAffineTest::prepare_to_validation( int /*test_case_idx*/ )
|
|
{
|
|
const Mat& src = test_mat[INPUT][0];
|
|
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
|
|
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
|
|
Mat mapx(dst.size(), CV_32F), mapy(dst.size(), CV_32F);
|
|
double m[6];
|
|
Mat srcAb, dstAb( 2, 3, CV_64FC1, m );
|
|
|
|
//cvInvert( &tM, &M, CV_LU );
|
|
// [R|t] -> [R^-1 | -(R^-1)*t]
|
|
test_mat[INPUT][1].convertTo( srcAb, CV_64F );
|
|
Mat A = srcAb.colRange(0, 2);
|
|
Mat b = srcAb.col(2);
|
|
Mat invA = dstAb.colRange(0, 2);
|
|
Mat invAb = dstAb.col(2);
|
|
cv::invert(A, invA, CV_SVD);
|
|
cv::gemm(invA, b, -1, Mat(), 0, invAb);
|
|
|
|
for( int y = 0; y < dst.rows; y++ )
|
|
for( int x = 0; x < dst.cols; x++ )
|
|
{
|
|
mapx.at<float>(y, x) = (float)(x*m[0] + y*m[1] + m[2]);
|
|
mapy.at<float>(y, x) = (float)(x*m[3] + y*m[4] + m[5]);
|
|
}
|
|
|
|
Mat mask( dst.size(), CV_8U );
|
|
test_remap( src, dst, mapx, mapy, &mask );
|
|
dst.setTo(Scalar::all(0), mask);
|
|
dst0.setTo(Scalar::all(0), mask);
|
|
}
|
|
|
|
|
|
/////////////////////////
|
|
|
|
class CV_WarpPerspectiveTest : public CV_ImgWarpBaseTest
|
|
{
|
|
public:
|
|
CV_WarpPerspectiveTest();
|
|
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
void run_func();
|
|
int prepare_test_case( int test_case_idx );
|
|
void prepare_to_validation( int /*test_case_idx*/ );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
};
|
|
|
|
|
|
CV_WarpPerspectiveTest::CV_WarpPerspectiveTest() : CV_ImgWarpBaseTest( true )
|
|
{
|
|
//spatial_scale_zoom = spatial_scale_decimate;
|
|
spatial_scale_decimate = spatial_scale_zoom;
|
|
}
|
|
|
|
|
|
void CV_WarpPerspectiveTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
Size sz = sizes[INPUT][0];
|
|
// run for the second time to get output of a different size
|
|
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
sizes[INPUT][0] = sz;
|
|
sizes[INPUT][1] = Size( 3, 3 );
|
|
}
|
|
|
|
|
|
void CV_WarpPerspectiveTest::run_func()
|
|
{
|
|
CvMat mtx = cvMat(test_mat[INPUT][1]);
|
|
cvWarpPerspective( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &mtx, interpolation );
|
|
}
|
|
|
|
|
|
double CV_WarpPerspectiveTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
|
{
|
|
int depth = test_mat[INPUT][0].depth();
|
|
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
|
|
}
|
|
|
|
|
|
int CV_WarpPerspectiveTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
|
|
const CvMat src = cvMat(test_mat[INPUT][0]);
|
|
const CvMat dst = cvMat(test_mat[INPUT_OUTPUT][0]);
|
|
Mat& mat = test_mat[INPUT][1];
|
|
Point2f s[4], d[4];
|
|
int i;
|
|
|
|
if( code <= 0 )
|
|
return code;
|
|
|
|
s[0] = Point2f(0,0);
|
|
d[0] = Point2f(0,0);
|
|
s[1] = Point2f(src.cols-1.f,0);
|
|
d[1] = Point2f(dst.cols-1.f,0);
|
|
s[2] = Point2f(src.cols-1.f,src.rows-1.f);
|
|
d[2] = Point2f(dst.cols-1.f,dst.rows-1.f);
|
|
s[3] = Point2f(0,src.rows-1.f);
|
|
d[3] = Point2f(0,dst.rows-1.f);
|
|
|
|
float bufer[16];
|
|
Mat tmp( 1, 16, CV_32FC1, bufer );
|
|
|
|
rng.fill( tmp, CV_RAND_NORMAL, Scalar::all(0.), Scalar::all(0.1) );
|
|
|
|
for( i = 0; i < 4; i++ )
|
|
{
|
|
s[i].x += bufer[i*4]*src.cols/2;
|
|
s[i].y += bufer[i*4+1]*src.rows/2;
|
|
d[i].x += bufer[i*4+2]*dst.cols/2;
|
|
d[i].y += bufer[i*4+3]*dst.rows/2;
|
|
}
|
|
|
|
cv::getPerspectiveTransform( s, d ).convertTo( mat, mat.depth() );
|
|
return code;
|
|
}
|
|
|
|
|
|
void CV_WarpPerspectiveTest::prepare_to_validation( int /*test_case_idx*/ )
|
|
{
|
|
Mat& src = test_mat[INPUT][0];
|
|
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
|
|
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
|
|
Mat mapx(dst.size(), CV_32F), mapy(dst.size(), CV_32F);
|
|
double m[9];
|
|
Mat srcM, dstM(3, 3, CV_64F, m);
|
|
|
|
//cvInvert( &tM, &M, CV_LU );
|
|
// [R|t] -> [R^-1 | -(R^-1)*t]
|
|
test_mat[INPUT][1].convertTo( srcM, CV_64F );
|
|
cv::invert(srcM, dstM, CV_SVD);
|
|
|
|
for( int y = 0; y < dst.rows; y++ )
|
|
{
|
|
for( int x = 0; x < dst.cols; x++ )
|
|
{
|
|
double xs = x*m[0] + y*m[1] + m[2];
|
|
double ys = x*m[3] + y*m[4] + m[5];
|
|
double ds = x*m[6] + y*m[7] + m[8];
|
|
|
|
ds = ds ? 1./ds : 0;
|
|
xs *= ds;
|
|
ys *= ds;
|
|
|
|
mapx.at<float>(y, x) = (float)xs;
|
|
mapy.at<float>(y, x) = (float)ys;
|
|
}
|
|
}
|
|
|
|
Mat mask( dst.size(), CV_8U );
|
|
test_remap( src, dst, mapx, mapy, &mask );
|
|
dst.setTo(Scalar::all(0), mask);
|
|
dst0.setTo(Scalar::all(0), mask);
|
|
}
|
|
|
|
|
|
/////////////////////////
|
|
|
|
class CV_RemapTest : public CV_ImgWarpBaseTest
|
|
{
|
|
public:
|
|
CV_RemapTest();
|
|
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
void run_func();
|
|
int prepare_test_case( int test_case_idx );
|
|
void prepare_to_validation( int /*test_case_idx*/ );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
void fill_array( int test_case_idx, int i, int j, Mat& arr );
|
|
};
|
|
|
|
|
|
CV_RemapTest::CV_RemapTest() : CV_ImgWarpBaseTest( false )
|
|
{
|
|
//spatial_scale_zoom = spatial_scale_decimate;
|
|
test_array[INPUT].push_back(NULL);
|
|
test_array[INPUT].push_back(NULL);
|
|
|
|
spatial_scale_decimate = spatial_scale_zoom;
|
|
}
|
|
|
|
|
|
void CV_RemapTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
types[INPUT][1] = types[INPUT][2] = CV_32FC1;
|
|
interpolation = CV_INTER_LINEAR;
|
|
}
|
|
|
|
|
|
void CV_RemapTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
|
|
{
|
|
if( i != INPUT )
|
|
CV_ImgWarpBaseTest::fill_array( test_case_idx, i, j, arr );
|
|
}
|
|
|
|
|
|
void CV_RemapTest::run_func()
|
|
{
|
|
cvRemap( test_array[INPUT][0], test_array[INPUT_OUTPUT][0],
|
|
test_array[INPUT][1], test_array[INPUT][2], interpolation );
|
|
}
|
|
|
|
|
|
double CV_RemapTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
|
{
|
|
int depth = test_mat[INPUT][0].depth();
|
|
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
|
|
}
|
|
|
|
|
|
int CV_RemapTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
|
|
const Mat& src = test_mat[INPUT][0];
|
|
double a[9] = {0,0,0,0,0,0,0,0,1}, k[4];
|
|
Mat _a( 3, 3, CV_64F, a );
|
|
Mat _k( 4, 1, CV_64F, k );
|
|
double sz = MAX(src.rows, src.cols);
|
|
|
|
if( code <= 0 )
|
|
return code;
|
|
|
|
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
|
|
a[2] = (src.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
|
|
a[5] = (src.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
|
|
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
|
|
a[4] = aspect_ratio*a[0];
|
|
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
|
|
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
|
|
if( k[0]*k[1] > 0 )
|
|
k[1] = -k[1];
|
|
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
|
|
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
|
|
|
|
cvtest::initUndistortMap( _a, _k, Mat(), Mat(), test_mat[INPUT][1].size(), test_mat[INPUT][1], test_mat[INPUT][2], CV_32F );
|
|
return code;
|
|
}
|
|
|
|
|
|
void CV_RemapTest::prepare_to_validation( int /*test_case_idx*/ )
|
|
{
|
|
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
|
|
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
|
|
Mat mask( dst.size(), CV_8U );
|
|
test_remap(test_mat[INPUT][0], dst, test_mat[INPUT][1],
|
|
test_mat[INPUT][2], &mask, interpolation );
|
|
dst.setTo(Scalar::all(0), mask);
|
|
dst0.setTo(Scalar::all(0), mask);
|
|
}
|
|
|
|
////////////////////////////// GetRectSubPix /////////////////////////////////
|
|
|
|
static void
|
|
test_getQuadrangeSubPix( const Mat& src, Mat& dst, double* a )
|
|
{
|
|
int sstep = (int)(src.step / sizeof(float));
|
|
int scols = src.cols, srows = src.rows;
|
|
|
|
CV_Assert( src.depth() == CV_32F && src.type() == dst.type() );
|
|
|
|
int cn = dst.channels();
|
|
|
|
for( int y = 0; y < dst.rows; y++ )
|
|
for( int x = 0; x < dst.cols; x++ )
|
|
{
|
|
float* d = dst.ptr<float>(y) + x*cn;
|
|
float sx = (float)(a[0]*x + a[1]*y + a[2]);
|
|
float sy = (float)(a[3]*x + a[4]*y + a[5]);
|
|
int ix = cvFloor(sx), iy = cvFloor(sy);
|
|
int dx = cn, dy = sstep;
|
|
const float* s;
|
|
sx -= ix; sy -= iy;
|
|
|
|
if( (unsigned)ix >= (unsigned)(scols-1) )
|
|
ix = ix < 0 ? 0 : scols - 1, sx = 0, dx = 0;
|
|
if( (unsigned)iy >= (unsigned)(srows-1) )
|
|
iy = iy < 0 ? 0 : srows - 1, sy = 0, dy = 0;
|
|
|
|
s = src.ptr<float>(iy) + ix*cn;
|
|
for( int k = 0; k < cn; k++, s++ )
|
|
{
|
|
float t0 = s[0] + sx*(s[dx] - s[0]);
|
|
float t1 = s[dy] + sx*(s[dy + dx] - s[dy]);
|
|
d[k] = t0 + sy*(t1 - t0);
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
class CV_GetRectSubPixTest : public CV_ImgWarpBaseTest
|
|
{
|
|
public:
|
|
CV_GetRectSubPixTest();
|
|
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
void run_func();
|
|
int prepare_test_case( int test_case_idx );
|
|
void prepare_to_validation( int /*test_case_idx*/ );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
void fill_array( int test_case_idx, int i, int j, Mat& arr );
|
|
|
|
CvPoint2D32f center;
|
|
bool test_cpp;
|
|
};
|
|
|
|
|
|
CV_GetRectSubPixTest::CV_GetRectSubPixTest() : CV_ImgWarpBaseTest( false )
|
|
{
|
|
//spatial_scale_zoom = spatial_scale_decimate;
|
|
spatial_scale_decimate = spatial_scale_zoom;
|
|
test_cpp = false;
|
|
}
|
|
|
|
|
|
void CV_GetRectSubPixTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
int src_depth = cvtest::randInt(rng) % 2, dst_depth;
|
|
int cn = cvtest::randInt(rng) % 2 ? 3 : 1;
|
|
Size src_size, dst_size;
|
|
|
|
dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F;
|
|
if( src_depth < CV_32F && cvtest::randInt(rng) % 2 )
|
|
dst_depth = CV_32F;
|
|
|
|
types[INPUT][0] = CV_MAKETYPE(src_depth,cn);
|
|
types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn);
|
|
|
|
src_size = sizes[INPUT][0];
|
|
dst_size.width = cvRound(sqrt(cvtest::randReal(rng)*src_size.width) + 1);
|
|
dst_size.height = cvRound(sqrt(cvtest::randReal(rng)*src_size.height) + 1);
|
|
dst_size.width = MIN(dst_size.width,src_size.width);
|
|
dst_size.height = MIN(dst_size.width,src_size.height);
|
|
sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = dst_size;
|
|
|
|
center.x = (float)(cvtest::randReal(rng)*src_size.width);
|
|
center.y = (float)(cvtest::randReal(rng)*src_size.height);
|
|
interpolation = CV_INTER_LINEAR;
|
|
|
|
test_cpp = (cvtest::randInt(rng) & 256) == 0;
|
|
}
|
|
|
|
|
|
void CV_GetRectSubPixTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
|
|
{
|
|
if( i != INPUT )
|
|
CV_ImgWarpBaseTest::fill_array( test_case_idx, i, j, arr );
|
|
}
|
|
|
|
|
|
void CV_GetRectSubPixTest::run_func()
|
|
{
|
|
if(!test_cpp)
|
|
cvGetRectSubPix( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], center );
|
|
else
|
|
{
|
|
cv::Mat _out = cv::cvarrToMat(test_array[INPUT_OUTPUT][0]);
|
|
cv::getRectSubPix( cv::cvarrToMat(test_array[INPUT][0]), _out.size(), center, _out, _out.type());
|
|
}
|
|
}
|
|
|
|
|
|
double CV_GetRectSubPixTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
|
{
|
|
int in_depth = test_mat[INPUT][0].depth();
|
|
int out_depth = test_mat[INPUT_OUTPUT][0].depth();
|
|
|
|
return in_depth >= CV_32F ? 1e-3 : out_depth >= CV_32F ? 1e-2 : 1;
|
|
}
|
|
|
|
|
|
int CV_GetRectSubPixTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
return CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
|
|
}
|
|
|
|
|
|
void CV_GetRectSubPixTest::prepare_to_validation( int /*test_case_idx*/ )
|
|
{
|
|
Mat& src0 = test_mat[INPUT][0];
|
|
Mat& dst0 = test_mat[REF_INPUT_OUTPUT][0];
|
|
Mat src = src0, dst = dst0;
|
|
int ftype = CV_MAKETYPE(CV_32F,src0.channels());
|
|
double a[] = { 1, 0, center.x - dst.cols*0.5 + 0.5,
|
|
0, 1, center.y - dst.rows*0.5 + 0.5 };
|
|
if( src.depth() != CV_32F )
|
|
src0.convertTo(src, CV_32F);
|
|
|
|
if( dst.depth() != CV_32F )
|
|
dst.create(dst0.size(), ftype);
|
|
|
|
test_getQuadrangeSubPix( src, dst, a );
|
|
|
|
if( dst.data != dst0.data )
|
|
dst.convertTo(dst0, dst0.depth());
|
|
}
|
|
|
|
|
|
class CV_GetQuadSubPixTest : public CV_ImgWarpBaseTest
|
|
{
|
|
public:
|
|
CV_GetQuadSubPixTest();
|
|
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
void run_func();
|
|
int prepare_test_case( int test_case_idx );
|
|
void prepare_to_validation( int /*test_case_idx*/ );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
};
|
|
|
|
|
|
CV_GetQuadSubPixTest::CV_GetQuadSubPixTest() : CV_ImgWarpBaseTest( true )
|
|
{
|
|
//spatial_scale_zoom = spatial_scale_decimate;
|
|
spatial_scale_decimate = spatial_scale_zoom;
|
|
}
|
|
|
|
|
|
void CV_GetQuadSubPixTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
int min_size = 4;
|
|
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
Size sz = sizes[INPUT][0], dsz;
|
|
RNG& rng = ts->get_rng();
|
|
int msz, src_depth = cvtest::randInt(rng) % 2, dst_depth;
|
|
int cn = cvtest::randInt(rng) % 2 ? 3 : 1;
|
|
|
|
dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F;
|
|
if( src_depth < CV_32F && cvtest::randInt(rng) % 2 )
|
|
dst_depth = CV_32F;
|
|
|
|
types[INPUT][0] = CV_MAKETYPE(src_depth,cn);
|
|
types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn);
|
|
|
|
sz.width = MAX(sz.width,min_size);
|
|
sz.height = MAX(sz.height,min_size);
|
|
sizes[INPUT][0] = sz;
|
|
msz = MIN( sz.width, sz.height );
|
|
|
|
dsz.width = cvRound(sqrt(cvtest::randReal(rng)*msz) + 1);
|
|
dsz.height = cvRound(sqrt(cvtest::randReal(rng)*msz) + 1);
|
|
dsz.width = MIN(dsz.width,msz);
|
|
dsz.height = MIN(dsz.width,msz);
|
|
dsz.width = MAX(dsz.width,min_size);
|
|
dsz.height = MAX(dsz.height,min_size);
|
|
sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = dsz;
|
|
sizes[INPUT][1] = cvSize( 3, 2 );
|
|
}
|
|
|
|
|
|
void CV_GetQuadSubPixTest::run_func()
|
|
{
|
|
CvMat mtx = cvMat(test_mat[INPUT][1]);
|
|
cvGetQuadrangleSubPix( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &mtx );
|
|
}
|
|
|
|
|
|
double CV_GetQuadSubPixTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
|
{
|
|
int in_depth = test_mat[INPUT][0].depth();
|
|
//int out_depth = test_mat[INPUT_OUTPUT][0].depth();
|
|
|
|
return in_depth >= CV_32F ? 1e-2 : 4;
|
|
}
|
|
|
|
|
|
int CV_GetQuadSubPixTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
|
|
const Mat& src = test_mat[INPUT][0];
|
|
Mat& mat = test_mat[INPUT][1];
|
|
Point2f center;
|
|
double scale, angle;
|
|
|
|
if( code <= 0 )
|
|
return code;
|
|
|
|
double a[6];
|
|
Mat A( 2, 3, CV_64FC1, a );
|
|
|
|
center.x = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.cols);
|
|
center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows);
|
|
angle = cvtest::randReal(rng)*360;
|
|
scale = cvtest::randReal(rng)*0.2 + 0.9;
|
|
|
|
// y = Ax + b -> x = A^-1(y - b) = A^-1*y - A^-1*b
|
|
scale = 1./scale;
|
|
angle = angle*(CV_PI/180.);
|
|
a[0] = a[4] = cos(angle)*scale;
|
|
a[1] = sin(angle)*scale;
|
|
a[3] = -a[1];
|
|
a[2] = center.x - a[0]*center.x - a[1]*center.y;
|
|
a[5] = center.y - a[3]*center.x - a[4]*center.y;
|
|
A.convertTo( mat, mat.depth() );
|
|
|
|
return code;
|
|
}
|
|
|
|
|
|
void CV_GetQuadSubPixTest::prepare_to_validation( int /*test_case_idx*/ )
|
|
{
|
|
Mat& src0 = test_mat[INPUT][0];
|
|
Mat& dst0 = test_mat[REF_INPUT_OUTPUT][0];
|
|
Mat src = src0, dst = dst0;
|
|
int ftype = CV_MAKETYPE(CV_32F,src0.channels());
|
|
double a[6], dx = (dst0.cols - 1)*0.5, dy = (dst0.rows - 1)*0.5;
|
|
Mat A( 2, 3, CV_64F, a );
|
|
|
|
if( src.depth() != CV_32F )
|
|
src0.convertTo(src, CV_32F);
|
|
|
|
if( dst.depth() != CV_32F )
|
|
dst.create(dst0.size(), ftype);
|
|
|
|
test_mat[INPUT][1].convertTo( A, CV_64F );
|
|
a[2] -= a[0]*dx + a[1]*dy;
|
|
a[5] -= a[3]*dx + a[4]*dy;
|
|
test_getQuadrangeSubPix( src, dst, a );
|
|
|
|
if( dst.data != dst0.data )
|
|
dst.convertTo(dst0, dst0.depth());
|
|
}
|
|
|
|
////////////////////////////// resizeArea /////////////////////////////////
|
|
|
|
template <typename T>
|
|
static void check_resize_area(const Mat& expected, const Mat& actual, double tolerance = 1.0)
|
|
{
|
|
ASSERT_EQ(actual.type(), expected.type());
|
|
ASSERT_EQ(actual.size(), expected.size());
|
|
|
|
Mat diff;
|
|
absdiff(actual, expected, diff);
|
|
|
|
Mat one_channel_diff = diff; //.reshape(1);
|
|
|
|
Size dsize = actual.size();
|
|
bool next = true;
|
|
for (int dy = 0; dy < dsize.height && next; ++dy)
|
|
{
|
|
const T* eD = expected.ptr<T>(dy);
|
|
const T* aD = actual.ptr<T>(dy);
|
|
|
|
for (int dx = 0; dx < dsize.width && next; ++dx)
|
|
if (fabs(static_cast<double>(aD[dx] - eD[dx])) > tolerance)
|
|
{
|
|
cvtest::TS::ptr()->printf(cvtest::TS::SUMMARY, "Inf norm: %f\n", static_cast<float>(cvtest::norm(actual, expected, NORM_INF)));
|
|
cvtest::TS::ptr()->printf(cvtest::TS::SUMMARY, "Error in : (%d, %d)\n", dx, dy);
|
|
|
|
const int radius = 3;
|
|
int rmin = MAX(dy - radius, 0), rmax = MIN(dy + radius, dsize.height);
|
|
int cmin = MAX(dx - radius, 0), cmax = MIN(dx + radius, dsize.width);
|
|
|
|
std::cout << "Abs diff:" << std::endl << diff << std::endl;
|
|
std::cout << "actual result:\n" << actual(Range(rmin, rmax), Range(cmin, cmax)) << std::endl;
|
|
std::cout << "expected result:\n" << expected(Range(rmin, rmax), Range(cmin, cmax)) << std::endl;
|
|
|
|
next = false;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, cvtest::norm(one_channel_diff, cv::NORM_INF));
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(Imgproc_cvWarpAffine, regression)
|
|
{
|
|
IplImage* src = cvCreateImage(cvSize(100, 100), IPL_DEPTH_8U, 1);
|
|
IplImage* dst = cvCreateImage(cvSize(100, 100), IPL_DEPTH_8U, 1);
|
|
|
|
cvZero(src);
|
|
|
|
float m[6];
|
|
CvMat M = cvMat( 2, 3, CV_32F, m );
|
|
int w = src->width;
|
|
int h = src->height;
|
|
cv2DRotationMatrix(cvPoint2D32f(w*0.5f, h*0.5f), 45.0, 1.0, &M);
|
|
cvWarpAffine(src, dst, &M);
|
|
|
|
cvReleaseImage(&src);
|
|
cvReleaseImage(&dst);
|
|
}
|
|
|
|
TEST(Imgproc_fitLine_vector_3d, regression)
|
|
{
|
|
std::vector<Point3f> points_vector;
|
|
|
|
Point3f p21(4,4,4);
|
|
Point3f p22(8,8,8);
|
|
|
|
points_vector.push_back(p21);
|
|
points_vector.push_back(p22);
|
|
|
|
std::vector<float> line;
|
|
|
|
cv::fitLine(points_vector, line, CV_DIST_L2, 0 ,0 ,0);
|
|
|
|
ASSERT_EQ(line.size(), (size_t)6);
|
|
|
|
}
|
|
|
|
TEST(Imgproc_fitLine_vector_2d, regression)
|
|
{
|
|
std::vector<Point2f> points_vector;
|
|
|
|
Point2f p21(4,4);
|
|
Point2f p22(8,8);
|
|
Point2f p23(16,16);
|
|
|
|
points_vector.push_back(p21);
|
|
points_vector.push_back(p22);
|
|
points_vector.push_back(p23);
|
|
|
|
std::vector<float> line;
|
|
|
|
cv::fitLine(points_vector, line, CV_DIST_L2, 0 ,0 ,0);
|
|
|
|
ASSERT_EQ(line.size(), (size_t)4);
|
|
}
|
|
|
|
TEST(Imgproc_fitLine_Mat_2dC2, regression)
|
|
{
|
|
cv::Mat mat1 = Mat::zeros(3, 1, CV_32SC2);
|
|
std::vector<float> line1;
|
|
|
|
cv::fitLine(mat1, line1, CV_DIST_L2, 0 ,0 ,0);
|
|
|
|
ASSERT_EQ(line1.size(), (size_t)4);
|
|
}
|
|
|
|
TEST(Imgproc_fitLine_Mat_2dC1, regression)
|
|
{
|
|
cv::Matx<int, 3, 2> mat2;
|
|
std::vector<float> line2;
|
|
|
|
cv::fitLine(mat2, line2, CV_DIST_L2, 0 ,0 ,0);
|
|
|
|
ASSERT_EQ(line2.size(), (size_t)4);
|
|
}
|
|
|
|
TEST(Imgproc_fitLine_Mat_3dC3, regression)
|
|
{
|
|
cv::Mat mat1 = Mat::zeros(2, 1, CV_32SC3);
|
|
std::vector<float> line1;
|
|
|
|
cv::fitLine(mat1, line1, CV_DIST_L2, 0 ,0 ,0);
|
|
|
|
ASSERT_EQ(line1.size(), (size_t)6);
|
|
}
|
|
|
|
TEST(Imgproc_fitLine_Mat_3dC1, regression)
|
|
{
|
|
cv::Mat mat2 = Mat::zeros(2, 3, CV_32SC1);
|
|
std::vector<float> line2;
|
|
|
|
cv::fitLine(mat2, line2, CV_DIST_L2, 0 ,0 ,0);
|
|
|
|
ASSERT_EQ(line2.size(), (size_t)6);
|
|
}
|
|
|
|
TEST(Imgproc_resize_area, regression)
|
|
{
|
|
static ushort input_data[16 * 16] = {
|
|
90, 94, 80, 3, 231, 2, 186, 245, 188, 165, 10, 19, 201, 169, 8, 228,
|
|
86, 5, 203, 120, 136, 185, 24, 94, 81, 150, 163, 137, 88, 105, 132, 132,
|
|
236, 48, 250, 218, 19, 52, 54, 221, 159, 112, 45, 11, 152, 153, 112, 134,
|
|
78, 133, 136, 83, 65, 76, 82, 250, 9, 235, 148, 26, 236, 179, 200, 50,
|
|
99, 51, 103, 142, 201, 65, 176, 33, 49, 226, 177, 109, 46, 21, 67, 130,
|
|
54, 125, 107, 154, 145, 51, 199, 189, 161, 142, 231, 240, 139, 162, 240, 22,
|
|
231, 86, 79, 106, 92, 47, 146, 156, 36, 207, 71, 33, 2, 244, 221, 71,
|
|
44, 127, 71, 177, 75, 126, 68, 119, 200, 129, 191, 251, 6, 236, 247, 6,
|
|
133, 175, 56, 239, 147, 221, 243, 154, 242, 82, 106, 99, 77, 158, 60, 229,
|
|
2, 42, 24, 174, 27, 198, 14, 204, 246, 251, 141, 31, 114, 163, 29, 147,
|
|
121, 53, 74, 31, 147, 189, 42, 98, 202, 17, 228, 123, 209, 40, 77, 49,
|
|
112, 203, 30, 12, 205, 25, 19, 106, 145, 185, 163, 201, 237, 223, 247, 38,
|
|
33, 105, 243, 117, 92, 179, 204, 248, 160, 90, 73, 126, 2, 41, 213, 204,
|
|
6, 124, 195, 201, 230, 187, 210, 167, 48, 79, 123, 159, 145, 218, 105, 209,
|
|
240, 152, 136, 235, 235, 164, 157, 9, 152, 38, 27, 209, 120, 77, 238, 196,
|
|
240, 233, 10, 241, 90, 67, 12, 79, 0, 43, 58, 27, 83, 199, 190, 182};
|
|
|
|
static ushort expected_data[5 * 5] = {
|
|
120, 100, 151, 101, 130,
|
|
106, 115, 141, 130, 127,
|
|
91, 136, 170, 114, 140,
|
|
104, 122, 131, 147, 133,
|
|
161, 163, 70, 107, 182
|
|
};
|
|
|
|
cv::Mat src(16, 16, CV_16UC1, input_data);
|
|
cv::Mat expected(5, 5, CV_16UC1, expected_data);
|
|
cv::Mat actual(expected.size(), expected.type());
|
|
|
|
cv::resize(src, actual, cv::Size(), 0.3, 0.3, INTER_AREA);
|
|
|
|
check_resize_area<ushort>(expected, actual, 1.0);
|
|
}
|
|
|
|
TEST(Imgproc_resize_area, regression_half_round)
|
|
{
|
|
static uchar input_data[32 * 32];
|
|
for(int i = 0; i < 32 * 32; ++i)
|
|
input_data[i] = (uchar)(i % 2 + 253 + i / (16 * 32));
|
|
|
|
static uchar expected_data[16 * 16];
|
|
for(int i = 0; i < 16 * 16; ++i)
|
|
expected_data[i] = (uchar)(254 + i / (16 * 8));
|
|
|
|
cv::Mat src(32, 32, CV_8UC1, input_data);
|
|
cv::Mat expected(16, 16, CV_8UC1, expected_data);
|
|
cv::Mat actual(expected.size(), expected.type());
|
|
|
|
cv::resize(src, actual, cv::Size(), 0.5, 0.5, INTER_AREA);
|
|
|
|
check_resize_area<uchar>(expected, actual, 0.5);
|
|
}
|
|
|
|
TEST(Imgproc_resize_area, regression_quarter_round)
|
|
{
|
|
static uchar input_data[32 * 32];
|
|
for(int i = 0; i < 32 * 32; ++i)
|
|
input_data[i] = (uchar)(i % 2 + 253 + i / (16 * 32));
|
|
|
|
static uchar expected_data[8 * 8];
|
|
for(int i = 0; i < 8 * 8; ++i)
|
|
expected_data[i] = 254;
|
|
|
|
cv::Mat src(32, 32, CV_8UC1, input_data);
|
|
cv::Mat expected(8, 8, CV_8UC1, expected_data);
|
|
cv::Mat actual(expected.size(), expected.type());
|
|
|
|
cv::resize(src, actual, cv::Size(), 0.25, 0.25, INTER_AREA);
|
|
|
|
check_resize_area<uchar>(expected, actual, 0.5);
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(Imgproc_Resize, accuracy) { CV_ResizeTest test; test.safe_run(); }
|
|
TEST(Imgproc_ResizeExact, accuracy) { CV_ResizeExactTest test; test.safe_run(); }
|
|
TEST(Imgproc_WarpAffine, accuracy) { CV_WarpAffineTest test; test.safe_run(); }
|
|
TEST(Imgproc_WarpPerspective, accuracy) { CV_WarpPerspectiveTest test; test.safe_run(); }
|
|
TEST(Imgproc_Remap, accuracy) { CV_RemapTest test; test.safe_run(); }
|
|
TEST(Imgproc_GetRectSubPix, accuracy) { CV_GetRectSubPixTest test; test.safe_run(); }
|
|
TEST(Imgproc_GetQuadSubPix, accuracy) { CV_GetQuadSubPixTest test; test.safe_run(); }
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T, typename WT>
|
|
struct IntCast
|
|
{
|
|
T operator() (WT val) const
|
|
{
|
|
return cv::saturate_cast<T>(val >> 2);
|
|
}
|
|
};
|
|
|
|
template <typename T, typename WT>
|
|
struct FltCast
|
|
{
|
|
T operator() (WT val) const
|
|
{
|
|
return cv::saturate_cast<T>(val * 0.25);
|
|
}
|
|
};
|
|
|
|
template <typename T, typename WT, int one, typename CastOp>
|
|
void resizeArea(const cv::Mat & src, cv::Mat & dst)
|
|
{
|
|
int cn = src.channels();
|
|
CastOp castOp;
|
|
|
|
for (int y = 0; y < dst.rows; ++y)
|
|
{
|
|
const T * sptr0 = src.ptr<T>(y << 1);
|
|
const T * sptr1 = src.ptr<T>((y << 1) + 1);
|
|
T * dptr = dst.ptr<T>(y);
|
|
|
|
for (int x = 0; x < dst.cols * cn; x += cn)
|
|
{
|
|
int x1 = x << 1;
|
|
|
|
for (int c = 0; c < cn; ++c)
|
|
{
|
|
WT sum = WT(sptr0[x1 + c]) + WT(sptr0[x1 + c + cn]);
|
|
sum += WT(sptr1[x1 + c]) + WT(sptr1[x1 + c + cn]) + (WT)(one);
|
|
|
|
dptr[x + c] = castOp(sum);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST(Resize, Area_half)
|
|
{
|
|
const int size = 1000;
|
|
int types[] = { CV_8UC1, CV_8UC4,
|
|
CV_16UC1, CV_16UC4,
|
|
CV_16SC1, CV_16SC3, CV_16SC4,
|
|
CV_32FC1, CV_32FC4 };
|
|
|
|
cv::RNG rng(17);
|
|
|
|
for (int i = 0, _size = sizeof(types) / sizeof(types[0]); i < _size; ++i)
|
|
{
|
|
int type = types[i], depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
|
|
const float eps = depth <= CV_32S ? 0 : 7e-5f;
|
|
|
|
SCOPED_TRACE(depth);
|
|
SCOPED_TRACE(cn);
|
|
|
|
cv::Mat src(size, size, type), dst_actual(size >> 1, size >> 1, type),
|
|
dst_reference(size >> 1, size >> 1, type);
|
|
|
|
rng.fill(src, cv::RNG::UNIFORM, -1000, 1000, true);
|
|
|
|
if (depth == CV_8U)
|
|
resizeArea<uchar, ushort, 2, IntCast<uchar, ushort> >(src, dst_reference);
|
|
else if (depth == CV_16U)
|
|
resizeArea<ushort, uint, 2, IntCast<ushort, uint> >(src, dst_reference);
|
|
else if (depth == CV_16S)
|
|
resizeArea<short, int, 2, IntCast<short, int> >(src, dst_reference);
|
|
else if (depth == CV_32F)
|
|
resizeArea<float, float, 0, FltCast<float, float> >(src, dst_reference);
|
|
else
|
|
CV_Assert(0);
|
|
|
|
cv::resize(src, dst_actual, dst_actual.size(), 0, 0, cv::INTER_AREA);
|
|
|
|
ASSERT_GE(eps, cvtest::norm(dst_reference, dst_actual, cv::NORM_INF));
|
|
}
|
|
}
|
|
|
|
TEST(Imgproc_Warp, multichannel)
|
|
{
|
|
static const int inter_types[] = {INTER_NEAREST, INTER_AREA, INTER_CUBIC,
|
|
INTER_LANCZOS4, INTER_LINEAR};
|
|
static const int inter_n = sizeof(inter_types) / sizeof(int);
|
|
|
|
static const int border_types[] = {BORDER_CONSTANT, BORDER_DEFAULT,
|
|
BORDER_REFLECT, BORDER_REPLICATE,
|
|
BORDER_WRAP, BORDER_WRAP};
|
|
static const int border_n = sizeof(border_types) / sizeof(int);
|
|
|
|
RNG& rng = theRNG();
|
|
for( int iter = 0; iter < 100; iter++ )
|
|
{
|
|
int inter = inter_types[rng.uniform(0, inter_n)];
|
|
int border = border_types[rng.uniform(0, border_n)];
|
|
int width = rng.uniform(3, 333);
|
|
int height = rng.uniform(3, 333);
|
|
int cn = rng.uniform(1, 15);
|
|
if(inter == INTER_CUBIC || inter == INTER_LANCZOS4)
|
|
cn = rng.uniform(1, 5);
|
|
Mat src(height, width, CV_8UC(cn)), dst;
|
|
//randu(src, 0, 256);
|
|
src.setTo(0.);
|
|
|
|
Mat rot = getRotationMatrix2D(Point2f(0.f, 0.f), 1.0, 1.0);
|
|
warpAffine(src, dst, rot, src.size(), inter, border);
|
|
ASSERT_EQ(0.0, cvtest::norm(dst, NORM_INF));
|
|
Mat rot2 = Mat::eye(3, 3, rot.type());
|
|
rot.copyTo(rot2.rowRange(0, 2));
|
|
warpPerspective(src, dst, rot2, src.size(), inter, border);
|
|
ASSERT_EQ(0.0, cvtest::norm(dst, NORM_INF));
|
|
}
|
|
}
|
|
|
|
TEST(Imgproc_GetAffineTransform, singularity)
|
|
{
|
|
Point2f A_sample[3];
|
|
A_sample[0] = Point2f(8.f, 9.f);
|
|
A_sample[1] = Point2f(40.f, 41.f);
|
|
A_sample[2] = Point2f(47.f, 48.f);
|
|
Point2f B_sample[3];
|
|
B_sample[0] = Point2f(7.37465f, 11.8295f);
|
|
B_sample[1] = Point2f(15.0113f, 12.8994f);
|
|
B_sample[2] = Point2f(38.9943f, 9.56297f);
|
|
Mat trans = getAffineTransform(A_sample, B_sample);
|
|
ASSERT_EQ(0.0, cvtest::norm(trans, NORM_INF));
|
|
}
|
|
|
|
TEST(Imgproc_Remap, DISABLED_memleak)
|
|
{
|
|
Mat src;
|
|
const int N = 400;
|
|
src.create(N, N, CV_8U);
|
|
randu(src, 0, 256);
|
|
Mat map_x, map_y, dst;
|
|
dst.create( src.size(), src.type() );
|
|
map_x.create( src.size(), CV_32FC1 );
|
|
map_y.create( src.size(), CV_32FC1 );
|
|
randu(map_x, 0., N+0.);
|
|
randu(map_y, 0., N+0.);
|
|
|
|
for( int iter = 0; iter < 10000; iter++ )
|
|
{
|
|
if(iter % 100 == 0)
|
|
{
|
|
putchar('.');
|
|
fflush(stdout);
|
|
}
|
|
remap(src, dst, map_x, map_y, CV_INTER_LINEAR);
|
|
}
|
|
}
|
|
|
|
//** @deprecated */
|
|
TEST(Imgproc_linearPolar, identity)
|
|
{
|
|
const int N = 33;
|
|
Mat in(N, N, CV_8UC3, Scalar(255, 0, 0));
|
|
in(cv::Rect(N/3, N/3, N/3, N/3)).setTo(Scalar::all(255));
|
|
cv::blur(in, in, Size(5, 5));
|
|
cv::blur(in, in, Size(5, 5));
|
|
|
|
Mat src = in.clone();
|
|
Mat dst;
|
|
|
|
Rect roi = Rect(0, 0, in.cols - ((N+19)/20), in.rows);
|
|
|
|
for (int i = 1; i <= 5; i++)
|
|
{
|
|
linearPolar(src, dst,
|
|
Point2f((N-1) * 0.5f, (N-1) * 0.5f), N * 0.5f,
|
|
CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR | CV_WARP_INVERSE_MAP);
|
|
|
|
linearPolar(dst, src,
|
|
Point2f((N-1) * 0.5f, (N-1) * 0.5f), N * 0.5f,
|
|
CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR);
|
|
|
|
double psnr = cvtest::PSNR(in(roi), src(roi));
|
|
EXPECT_LE(25, psnr) << "iteration=" << i;
|
|
}
|
|
|
|
#if 0
|
|
Mat all(N*2+2,N*2+2, src.type(), Scalar(0,0,255));
|
|
in.copyTo(all(Rect(0,0,N,N)));
|
|
src.copyTo(all(Rect(0,N+1,N,N)));
|
|
src.copyTo(all(Rect(N+1,0,N,N)));
|
|
dst.copyTo(all(Rect(N+1,N+1,N,N)));
|
|
imwrite("linearPolar.png", all);
|
|
imshow("input", in); imshow("result", dst); imshow("restore", src); imshow("all", all);
|
|
cv::waitKey();
|
|
#endif
|
|
}
|
|
|
|
//** @deprecated */
|
|
TEST(Imgproc_logPolar, identity)
|
|
{
|
|
const int N = 33;
|
|
Mat in(N, N, CV_8UC3, Scalar(255, 0, 0));
|
|
in(cv::Rect(N/3, N/3, N/3, N/3)).setTo(Scalar::all(255));
|
|
cv::blur(in, in, Size(5, 5));
|
|
cv::blur(in, in, Size(5, 5));
|
|
|
|
Mat src = in.clone();
|
|
Mat dst;
|
|
|
|
Rect roi = Rect(0, 0, in.cols - ((N+19)/20), in.rows);
|
|
|
|
double M = N/log(N * 0.5f);
|
|
for (int i = 1; i <= 5; i++)
|
|
{
|
|
logPolar(src, dst,
|
|
Point2f((N-1) * 0.5f, (N-1) * 0.5f), M,
|
|
CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR | CV_WARP_INVERSE_MAP);
|
|
|
|
logPolar(dst, src,
|
|
Point2f((N-1) * 0.5f, (N-1) * 0.5f), M,
|
|
CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR);
|
|
|
|
double psnr = cvtest::PSNR(in(roi), src(roi));
|
|
EXPECT_LE(25, psnr) << "iteration=" << i;
|
|
}
|
|
|
|
#if 0
|
|
Mat all(N*2+2,N*2+2, src.type(), Scalar(0,0,255));
|
|
in.copyTo(all(Rect(0,0,N,N)));
|
|
src.copyTo(all(Rect(0,N+1,N,N)));
|
|
src.copyTo(all(Rect(N+1,0,N,N)));
|
|
dst.copyTo(all(Rect(N+1,N+1,N,N)));
|
|
imwrite("logPolar.png", all);
|
|
imshow("input", in); imshow("result", dst); imshow("restore", src); imshow("all", all);
|
|
cv::waitKey();
|
|
#endif
|
|
}
|
|
|
|
TEST(Imgproc_warpPolar, identity)
|
|
{
|
|
const int N = 33;
|
|
Mat in(N, N, CV_8UC3, Scalar(255, 0, 0));
|
|
in(cv::Rect(N / 3, N / 3, N / 3, N / 3)).setTo(Scalar::all(255));
|
|
cv::blur(in, in, Size(5, 5));
|
|
cv::blur(in, in, Size(5, 5));
|
|
|
|
Mat src = in.clone();
|
|
Mat dst;
|
|
|
|
Rect roi = Rect(0, 0, in.cols - ((N + 19) / 20), in.rows);
|
|
Point2f center = Point2f((N - 1) * 0.5f, (N - 1) * 0.5f);
|
|
double radius = N * 0.5;
|
|
int flags = CV_WARP_FILL_OUTLIERS | CV_INTER_LINEAR;
|
|
// test linearPolar
|
|
for (int ki = 1; ki <= 5; ki++)
|
|
{
|
|
warpPolar(src, dst, src.size(), center, radius, flags + WARP_POLAR_LINEAR + CV_WARP_INVERSE_MAP);
|
|
warpPolar(dst, src, src.size(), center, radius, flags + WARP_POLAR_LINEAR);
|
|
|
|
double psnr = cv::PSNR(in(roi), src(roi));
|
|
EXPECT_LE(25, psnr) << "iteration=" << ki;
|
|
}
|
|
// test logPolar
|
|
src = in.clone();
|
|
for (int ki = 1; ki <= 5; ki++)
|
|
{
|
|
warpPolar(src, dst, src.size(),center, radius, flags + WARP_POLAR_LOG + CV_WARP_INVERSE_MAP );
|
|
warpPolar(dst, src, src.size(),center, radius, flags + WARP_POLAR_LOG);
|
|
|
|
double psnr = cv::PSNR(in(roi), src(roi));
|
|
EXPECT_LE(25, psnr) << "iteration=" << ki;
|
|
}
|
|
|
|
#if 0
|
|
Mat all(N*2+2,N*2+2, src.type(), Scalar(0,0,255));
|
|
in.copyTo(all(Rect(0,0,N,N)));
|
|
src.copyTo(all(Rect(0,N+1,N,N)));
|
|
src.copyTo(all(Rect(N+1,0,N,N)));
|
|
dst.copyTo(all(Rect(N+1,N+1,N,N)));
|
|
imwrite("linearPolar.png", all);
|
|
imshow("input", in); imshow("result", dst); imshow("restore", src); imshow("all", all);
|
|
cv::waitKey();
|
|
#endif
|
|
}
|
|
|
|
}} // namespace
|
|
/* End of file. */
|