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522 lines
16 KiB
522 lines
16 KiB
/*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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using namespace cv; |
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using namespace std; |
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class CV_FloodFillTest : public cvtest::ArrayTest |
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{ |
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public: |
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CV_FloodFillTest(); |
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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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double get_success_error_level( int test_case_idx, int i, int j ); |
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void run_func(); |
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void prepare_to_validation( int ); |
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void fill_array( int test_case_idx, int i, int j, Mat& arr ); |
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/*int write_default_params(CvFileStorage* fs); |
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void get_timing_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types |
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CvSize** whole_sizes, bool *are_images ); |
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void print_timing_params( int test_case_idx, char* ptr, int params_left );*/ |
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CvPoint seed_pt; |
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CvScalar new_val; |
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CvScalar l_diff, u_diff; |
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int connectivity; |
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bool use_mask, mask_only; |
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int range_type; |
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int new_mask_val; |
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bool test_cpp; |
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}; |
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CV_FloodFillTest::CV_FloodFillTest() |
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{ |
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test_array[INPUT_OUTPUT].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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test_array[REF_INPUT_OUTPUT].push_back(NULL); |
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test_array[OUTPUT].push_back(NULL); |
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test_array[REF_OUTPUT].push_back(NULL); |
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optional_mask = false; |
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element_wise_relative_error = true; |
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test_cpp = false; |
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} |
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void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, |
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vector<vector<Size> >& sizes, |
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vector<vector<int> >& types ) |
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{ |
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RNG& rng = ts->get_rng(); |
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int depth, cn; |
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int i; |
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double buf[8]; |
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cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); |
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depth = cvtest::randInt(rng) % 3; |
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depth = depth == 0 ? CV_8U : depth == 1 ? CV_32S : CV_32F; |
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cn = cvtest::randInt(rng) & 1 ? 3 : 1; |
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use_mask = (cvtest::randInt(rng) & 1) != 0; |
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connectivity = (cvtest::randInt(rng) & 1) ? 4 : 8; |
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mask_only = use_mask && (cvtest::randInt(rng) & 1) != 0; |
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new_mask_val = cvtest::randInt(rng) & 255; |
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range_type = cvtest::randInt(rng) % 3; |
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types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(depth, cn); |
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types[INPUT_OUTPUT][1] = types[REF_INPUT_OUTPUT][1] = CV_8UC1; |
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types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1; |
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sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(9,1); |
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if( !use_mask ) |
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sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(0,0); |
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else |
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{ |
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CvSize sz = sizes[INPUT_OUTPUT][0]; |
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sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(sz.width+2,sz.height+2); |
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} |
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seed_pt.x = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].width; |
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seed_pt.y = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].height; |
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if( range_type == 0 ) |
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l_diff = u_diff = Scalar::all(0.); |
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else |
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{ |
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Mat m( 1, 8, CV_16S, buf ); |
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rng.fill( m, RNG::NORMAL, Scalar::all(0), Scalar::all(32) ); |
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for( i = 0; i < 4; i++ ) |
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{ |
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l_diff.val[i] = fabs(m.at<short>(i)/16.); |
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u_diff.val[i] = fabs(m.at<short>(i+4)/16.); |
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} |
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} |
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new_val = Scalar::all(0.); |
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for( i = 0; i < cn; i++ ) |
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new_val.val[i] = cvtest::randReal(rng)*255; |
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test_cpp = (cvtest::randInt(rng) & 256) == 0; |
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} |
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double CV_FloodFillTest::get_success_error_level( int /*test_case_idx*/, int i, int j ) |
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{ |
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return i == OUTPUT ? FLT_EPSILON : j == 0 ? FLT_EPSILON : 0; |
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} |
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void CV_FloodFillTest::fill_array( int test_case_idx, int i, int j, Mat& arr ) |
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{ |
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RNG& rng = ts->get_rng(); |
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if( i != INPUT && i != INPUT_OUTPUT ) |
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{ |
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cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr ); |
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return; |
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} |
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if( j == 0 ) |
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{ |
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Mat tmp = arr; |
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Scalar m = Scalar::all(128); |
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Scalar s = Scalar::all(10); |
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if( arr.depth() == CV_32FC1 ) |
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tmp.create(arr.size(), CV_MAKETYPE(CV_8U, arr.channels())); |
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if( range_type == 0 ) |
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s = Scalar::all(2); |
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rng.fill(tmp, RNG::NORMAL, m, s ); |
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if( arr.data != tmp.data ) |
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cvtest::convert(tmp, arr, arr.type()); |
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} |
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else |
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{ |
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Scalar l = Scalar::all(-2); |
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Scalar u = Scalar::all(2); |
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cvtest::randUni(rng, arr, l, u ); |
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rectangle( arr, Point(0,0), Point(arr.cols-1,arr.rows-1), Scalar::all(1), 1, 8, 0 ); |
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} |
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} |
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void CV_FloodFillTest::run_func() |
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{ |
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int flags = connectivity + (mask_only ? CV_FLOODFILL_MASK_ONLY : 0) + |
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(range_type == 1 ? CV_FLOODFILL_FIXED_RANGE : 0) + (new_mask_val << 8); |
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double* odata = test_mat[OUTPUT][0].ptr<double>(); |
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if(!test_cpp) |
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{ |
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CvConnectedComp comp; |
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cvFloodFill( test_array[INPUT_OUTPUT][0], seed_pt, new_val, l_diff, u_diff, &comp, |
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flags, test_array[INPUT_OUTPUT][1] ); |
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odata[0] = comp.area; |
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odata[1] = comp.rect.x; |
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odata[2] = comp.rect.y; |
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odata[3] = comp.rect.width; |
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odata[4] = comp.rect.height; |
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odata[5] = comp.value.val[0]; |
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odata[6] = comp.value.val[1]; |
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odata[7] = comp.value.val[2]; |
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odata[8] = comp.value.val[3]; |
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} |
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else |
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{ |
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cv::Mat img = cv::cvarrToMat(test_array[INPUT_OUTPUT][0]), |
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mask = test_array[INPUT_OUTPUT][1] ? cv::cvarrToMat(test_array[INPUT_OUTPUT][1]) : cv::Mat(); |
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cv::Rect rect; |
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int area; |
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if( !mask.data ) |
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area = cv::floodFill( img, seed_pt, new_val, &rect, l_diff, u_diff, flags ); |
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else |
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area = cv::floodFill( img, mask, seed_pt, new_val, &rect, l_diff, u_diff, flags ); |
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odata[0] = area; |
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odata[1] = rect.x; |
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odata[2] = rect.y; |
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odata[3] = rect.width; |
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odata[4] = rect.height; |
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odata[5] = odata[6] = odata[7] = odata[8] = 0; |
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} |
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} |
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typedef struct ff_offset_pair_t |
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{ |
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int mofs, iofs; |
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} |
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ff_offset_pair_t; |
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static void |
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cvTsFloodFill( CvMat* _img, CvPoint seed_pt, CvScalar new_val, |
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CvScalar l_diff, CvScalar u_diff, CvMat* _mask, |
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double* comp, int connectivity, int range_type, |
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int new_mask_val, bool mask_only ) |
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{ |
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CvMemStorage* st = cvCreateMemStorage(); |
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ff_offset_pair_t p0, p; |
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CvSeq* seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(p0), st ); |
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CvMat* tmp = _img; |
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CvMat* mask; |
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CvRect r = cvRect( 0, 0, -1, -1 ); |
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int area = 0; |
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int i, j; |
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ushort* m; |
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float* img; |
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int mstep, step; |
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int cn = CV_MAT_CN(_img->type); |
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int mdelta[8], idelta[8], ncount; |
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int cols = _img->cols, rows = _img->rows; |
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int u0 = 0, u1 = 0, u2 = 0; |
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double s0 = 0, s1 = 0, s2 = 0; |
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if( CV_MAT_DEPTH(_img->type) == CV_8U || CV_MAT_DEPTH(_img->type) == CV_32S ) |
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{ |
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tmp = cvCreateMat( rows, cols, CV_MAKETYPE(CV_32F,CV_MAT_CN(_img->type)) ); |
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cvTsConvert(_img, tmp); |
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} |
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mask = cvCreateMat( rows + 2, cols + 2, CV_16UC1 ); |
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if( _mask ) |
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cvTsConvert( _mask, mask ); |
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else |
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{ |
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cvTsZero( mask ); |
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cvRectangle( mask, cvPoint(0,0), cvPoint(mask->cols-1,mask->rows-1), Scalar::all(1.), 1, 8, 0 ); |
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} |
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new_mask_val = (new_mask_val != 0 ? new_mask_val : 1) << 8; |
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m = (ushort*)(mask->data.ptr + mask->step) + 1; |
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mstep = mask->step / sizeof(m[0]); |
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img = tmp->data.fl; |
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step = tmp->step / sizeof(img[0]); |
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p0.mofs = seed_pt.y*mstep + seed_pt.x; |
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p0.iofs = seed_pt.y*step + seed_pt.x*cn; |
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if( m[p0.mofs] ) |
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goto _exit_; |
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cvSeqPush( seq, &p0 ); |
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m[p0.mofs] = (ushort)new_mask_val; |
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if( connectivity == 4 ) |
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{ |
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ncount = 4; |
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mdelta[0] = -mstep; idelta[0] = -step; |
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mdelta[1] = -1; idelta[1] = -cn; |
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mdelta[2] = 1; idelta[2] = cn; |
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mdelta[3] = mstep; idelta[3] = step; |
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} |
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else |
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{ |
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ncount = 8; |
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mdelta[0] = -mstep-1; mdelta[1] = -mstep; mdelta[2] = -mstep+1; |
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idelta[0] = -step-cn; idelta[1] = -step; idelta[2] = -step+cn; |
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mdelta[3] = -1; mdelta[4] = 1; |
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idelta[3] = -cn; idelta[4] = cn; |
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mdelta[5] = mstep-1; mdelta[6] = mstep; mdelta[7] = mstep+1; |
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idelta[5] = step-cn; idelta[6] = step; idelta[7] = step+cn; |
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} |
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if( cn == 1 ) |
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{ |
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float a0 = (float)-l_diff.val[0]; |
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float b0 = (float)u_diff.val[0]; |
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s0 = img[p0.iofs]; |
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if( range_type < 2 ) |
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{ |
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a0 += (float)s0; b0 += (float)s0; |
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} |
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while( seq->total ) |
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{ |
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cvSeqPop( seq, &p0 ); |
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float a = a0, b = b0; |
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float* ptr = img + p0.iofs; |
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ushort* mptr = m + p0.mofs; |
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if( range_type == 2 ) |
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a += ptr[0], b += ptr[0]; |
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for( i = 0; i < ncount; i++ ) |
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{ |
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int md = mdelta[i], id = idelta[i]; |
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float v; |
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if( !mptr[md] && a <= (v = ptr[id]) && v <= b ) |
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{ |
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mptr[md] = (ushort)new_mask_val; |
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p.mofs = p0.mofs + md; |
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p.iofs = p0.iofs + id; |
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cvSeqPush( seq, &p ); |
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} |
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} |
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} |
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} |
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else |
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{ |
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float a0 = (float)-l_diff.val[0]; |
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float a1 = (float)-l_diff.val[1]; |
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float a2 = (float)-l_diff.val[2]; |
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float b0 = (float)u_diff.val[0]; |
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float b1 = (float)u_diff.val[1]; |
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float b2 = (float)u_diff.val[2]; |
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s0 = img[p0.iofs]; |
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s1 = img[p0.iofs + 1]; |
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s2 = img[p0.iofs + 2]; |
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if( range_type < 2 ) |
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{ |
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a0 += (float)s0; b0 += (float)s0; |
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a1 += (float)s1; b1 += (float)s1; |
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a2 += (float)s2; b2 += (float)s2; |
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} |
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while( seq->total ) |
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{ |
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cvSeqPop( seq, &p0 ); |
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float _a0 = a0, _a1 = a1, _a2 = a2; |
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float _b0 = b0, _b1 = b1, _b2 = b2; |
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float* ptr = img + p0.iofs; |
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ushort* mptr = m + p0.mofs; |
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if( range_type == 2 ) |
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{ |
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_a0 += ptr[0]; _b0 += ptr[0]; |
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_a1 += ptr[1]; _b1 += ptr[1]; |
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_a2 += ptr[2]; _b2 += ptr[2]; |
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} |
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for( i = 0; i < ncount; i++ ) |
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{ |
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int md = mdelta[i], id = idelta[i]; |
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float v; |
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if( !mptr[md] && |
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_a0 <= (v = ptr[id]) && v <= _b0 && |
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_a1 <= (v = ptr[id+1]) && v <= _b1 && |
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_a2 <= (v = ptr[id+2]) && v <= _b2 ) |
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{ |
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mptr[md] = (ushort)new_mask_val; |
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p.mofs = p0.mofs + md; |
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p.iofs = p0.iofs + id; |
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cvSeqPush( seq, &p ); |
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} |
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} |
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} |
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} |
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r.x = r.width = seed_pt.x; |
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r.y = r.height = seed_pt.y; |
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if( !mask_only ) |
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{ |
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s0 = new_val.val[0]; |
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s1 = new_val.val[1]; |
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s2 = new_val.val[2]; |
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if( tmp != _img ) |
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{ |
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u0 = saturate_cast<uchar>(s0); |
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u1 = saturate_cast<uchar>(s1); |
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u2 = saturate_cast<uchar>(s2); |
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s0 = u0; |
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s1 = u1; |
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s2 = u2; |
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} |
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} |
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else |
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s0 = s1 = s2 = 0; |
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new_mask_val >>= 8; |
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for( i = 0; i < rows; i++ ) |
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{ |
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float* ptr = img + i*step; |
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ushort* mptr = m + i*mstep; |
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uchar* dmptr = _mask ? _mask->data.ptr + (i+1)*_mask->step + 1 : 0; |
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double area0 = area; |
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for( j = 0; j < cols; j++ ) |
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{ |
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if( mptr[j] > 255 ) |
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{ |
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if( dmptr ) |
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dmptr[j] = (uchar)new_mask_val; |
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if( !mask_only ) |
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{ |
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if( cn == 1 ) |
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ptr[j] = (float)s0; |
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else |
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{ |
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ptr[j*3] = (float)s0; |
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ptr[j*3+1] = (float)s1; |
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ptr[j*3+2] = (float)s2; |
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} |
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} |
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else |
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{ |
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if( cn == 1 ) |
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s0 += ptr[j]; |
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else |
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{ |
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s0 += ptr[j*3]; |
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s1 += ptr[j*3+1]; |
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s2 += ptr[j*3+2]; |
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} |
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} |
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area++; |
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if( r.x > j ) |
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r.x = j; |
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if( r.width < j ) |
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r.width = j; |
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} |
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} |
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if( area != area0 ) |
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{ |
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if( r.y > i ) |
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r.y = i; |
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if( r.height < i ) |
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r.height = i; |
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} |
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} |
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_exit_: |
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cvReleaseMat( &mask ); |
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if( tmp != _img ) |
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{ |
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if( !mask_only ) |
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cvTsConvert(tmp, _img); |
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cvReleaseMat( &tmp ); |
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} |
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comp[0] = area; |
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comp[1] = r.x; |
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comp[2] = r.y; |
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comp[3] = r.width - r.x + 1; |
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comp[4] = r.height - r.y + 1; |
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if( mask_only ) |
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{ |
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double t = area ? 1./area : 0; |
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s0 *= t; |
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s1 *= t; |
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s2 *= t; |
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} |
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comp[5] = s0; |
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comp[6] = s1; |
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comp[7] = s2; |
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comp[8] = 0; |
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} |
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void CV_FloodFillTest::prepare_to_validation( int /*test_case_idx*/ ) |
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{ |
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double* comp = test_mat[REF_OUTPUT][0].ptr<double>(); |
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CvMat _input = test_mat[REF_INPUT_OUTPUT][0]; |
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CvMat _mask = test_mat[REF_INPUT_OUTPUT][1]; |
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cvTsFloodFill( &_input, seed_pt, new_val, l_diff, u_diff, |
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_mask.data.ptr ? &_mask : 0, |
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comp, connectivity, range_type, |
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new_mask_val, mask_only ); |
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if(test_cpp) |
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comp[5] = comp[6] = comp[7] = comp[8] = 0; |
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} |
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TEST(Imgproc_FloodFill, accuracy) { CV_FloodFillTest test; test.safe_run(); } |
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/* End of file. */
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