546 lines
16 KiB
C++
546 lines
16 KiB
C++
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/*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_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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Point seed_pt;
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Scalar new_val;
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Scalar 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 buff[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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Size sz = sizes[INPUT_OUTPUT][0];
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sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = Size(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, buff );
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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], cvPoint(seed_pt), cvScalar(new_val), cvScalar(l_diff), cvScalar(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.empty() )
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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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cvtest::convert(cvarrToMat(_img), cvarrToMat(tmp), -1);
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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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cvtest::convert(cvarrToMat(_mask), cvarrToMat(mask), -1);
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else
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{
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Mat m_mask = cvarrToMat(mask);
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cvtest::set( m_mask, Scalar::all(0), Mat() );
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cvRectangle( mask, cvPoint(0,0), cvPoint(mask->cols-1,mask->rows-1), cvScalar(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;
|
||
|
|
p.iofs = p0.iofs + id;
|
||
|
|
cvSeqPush( seq, &p );
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
float a0 = (float)-l_diff.val[0];
|
||
|
|
float a1 = (float)-l_diff.val[1];
|
||
|
|
float a2 = (float)-l_diff.val[2];
|
||
|
|
float b0 = (float)u_diff.val[0];
|
||
|
|
float b1 = (float)u_diff.val[1];
|
||
|
|
float b2 = (float)u_diff.val[2];
|
||
|
|
|
||
|
|
s0 = img[p0.iofs];
|
||
|
|
s1 = img[p0.iofs + 1];
|
||
|
|
s2 = img[p0.iofs + 2];
|
||
|
|
|
||
|
|
if( range_type < 2 )
|
||
|
|
{
|
||
|
|
a0 += (float)s0; b0 += (float)s0;
|
||
|
|
a1 += (float)s1; b1 += (float)s1;
|
||
|
|
a2 += (float)s2; b2 += (float)s2;
|
||
|
|
}
|
||
|
|
|
||
|
|
while( seq->total )
|
||
|
|
{
|
||
|
|
cvSeqPop( seq, &p0 );
|
||
|
|
float _a0 = a0, _a1 = a1, _a2 = a2;
|
||
|
|
float _b0 = b0, _b1 = b1, _b2 = b2;
|
||
|
|
float* ptr = img + p0.iofs;
|
||
|
|
ushort* mptr = m + p0.mofs;
|
||
|
|
|
||
|
|
if( range_type == 2 )
|
||
|
|
{
|
||
|
|
_a0 += ptr[0]; _b0 += ptr[0];
|
||
|
|
_a1 += ptr[1]; _b1 += ptr[1];
|
||
|
|
_a2 += ptr[2]; _b2 += ptr[2];
|
||
|
|
}
|
||
|
|
|
||
|
|
for( i = 0; i < ncount; i++ )
|
||
|
|
{
|
||
|
|
int md = mdelta[i], id = idelta[i];
|
||
|
|
float v;
|
||
|
|
if( !mptr[md] &&
|
||
|
|
_a0 <= (v = ptr[id]) && v <= _b0 &&
|
||
|
|
_a1 <= (v = ptr[id+1]) && v <= _b1 &&
|
||
|
|
_a2 <= (v = ptr[id+2]) && v <= _b2 )
|
||
|
|
{
|
||
|
|
mptr[md] = (ushort)new_mask_val;
|
||
|
|
p.mofs = p0.mofs + md;
|
||
|
|
p.iofs = p0.iofs + id;
|
||
|
|
cvSeqPush( seq, &p );
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
r.x = r.width = seed_pt.x;
|
||
|
|
r.y = r.height = seed_pt.y;
|
||
|
|
|
||
|
|
if( !mask_only )
|
||
|
|
{
|
||
|
|
s0 = new_val.val[0];
|
||
|
|
s1 = new_val.val[1];
|
||
|
|
s2 = new_val.val[2];
|
||
|
|
|
||
|
|
if( tmp != _img )
|
||
|
|
{
|
||
|
|
u0 = saturate_cast<uchar>(s0);
|
||
|
|
u1 = saturate_cast<uchar>(s1);
|
||
|
|
u2 = saturate_cast<uchar>(s2);
|
||
|
|
|
||
|
|
s0 = u0;
|
||
|
|
s1 = u1;
|
||
|
|
s2 = u2;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
s0 = s1 = s2 = 0;
|
||
|
|
|
||
|
|
new_mask_val >>= 8;
|
||
|
|
|
||
|
|
for( i = 0; i < rows; i++ )
|
||
|
|
{
|
||
|
|
float* ptr = img + i*step;
|
||
|
|
ushort* mptr = m + i*mstep;
|
||
|
|
uchar* dmptr = _mask ? _mask->data.ptr + (i+1)*_mask->step + 1 : 0;
|
||
|
|
double area0 = area;
|
||
|
|
|
||
|
|
for( j = 0; j < cols; j++ )
|
||
|
|
{
|
||
|
|
if( mptr[j] > 255 )
|
||
|
|
{
|
||
|
|
if( dmptr )
|
||
|
|
dmptr[j] = (uchar)new_mask_val;
|
||
|
|
if( !mask_only )
|
||
|
|
{
|
||
|
|
if( cn == 1 )
|
||
|
|
ptr[j] = (float)s0;
|
||
|
|
else
|
||
|
|
{
|
||
|
|
ptr[j*3] = (float)s0;
|
||
|
|
ptr[j*3+1] = (float)s1;
|
||
|
|
ptr[j*3+2] = (float)s2;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
if( cn == 1 )
|
||
|
|
s0 += ptr[j];
|
||
|
|
else
|
||
|
|
{
|
||
|
|
s0 += ptr[j*3];
|
||
|
|
s1 += ptr[j*3+1];
|
||
|
|
s2 += ptr[j*3+2];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
area++;
|
||
|
|
if( r.x > j )
|
||
|
|
r.x = j;
|
||
|
|
if( r.width < j )
|
||
|
|
r.width = j;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
if( area != area0 )
|
||
|
|
{
|
||
|
|
if( r.y > i )
|
||
|
|
r.y = i;
|
||
|
|
if( r.height < i )
|
||
|
|
r.height = i;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
_exit_:
|
||
|
|
cvReleaseMat( &mask );
|
||
|
|
if( tmp != _img )
|
||
|
|
{
|
||
|
|
if( !mask_only )
|
||
|
|
cvtest::convert(cvarrToMat(tmp), cvarrToMat(_img), -1);
|
||
|
|
cvReleaseMat( &tmp );
|
||
|
|
}
|
||
|
|
|
||
|
|
comp[0] = area;
|
||
|
|
comp[1] = r.x;
|
||
|
|
comp[2] = r.y;
|
||
|
|
comp[3] = r.width - r.x + 1;
|
||
|
|
comp[4] = r.height - r.y + 1;
|
||
|
|
#if 0
|
||
|
|
if( mask_only )
|
||
|
|
{
|
||
|
|
double t = area ? 1./area : 0;
|
||
|
|
s0 *= t;
|
||
|
|
s1 *= t;
|
||
|
|
s2 *= t;
|
||
|
|
}
|
||
|
|
comp[5] = s0;
|
||
|
|
comp[6] = s1;
|
||
|
|
comp[7] = s2;
|
||
|
|
#else
|
||
|
|
comp[5] = new_val.val[0];
|
||
|
|
comp[6] = new_val.val[1];
|
||
|
|
comp[7] = new_val.val[2];
|
||
|
|
#endif
|
||
|
|
comp[8] = 0;
|
||
|
|
|
||
|
|
cvReleaseMemStorage(&st);
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void CV_FloodFillTest::prepare_to_validation( int /*test_case_idx*/ )
|
||
|
|
{
|
||
|
|
double* comp = test_mat[REF_OUTPUT][0].ptr<double>();
|
||
|
|
CvMat _input = cvMat(test_mat[REF_INPUT_OUTPUT][0]);
|
||
|
|
CvMat _mask = cvMat(test_mat[REF_INPUT_OUTPUT][1]);
|
||
|
|
cvTsFloodFill( &_input, cvPoint(seed_pt), cvScalar(new_val), cvScalar(l_diff), cvScalar(u_diff),
|
||
|
|
_mask.data.ptr ? &_mask : 0,
|
||
|
|
comp, connectivity, range_type,
|
||
|
|
new_mask_val, mask_only );
|
||
|
|
if(test_cpp)
|
||
|
|
comp[5] = comp[6] = comp[7] = comp[8] = 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(Imgproc_FloodFill, accuracy) { CV_FloodFillTest test; test.safe_run(); }
|
||
|
|
|
||
|
|
TEST(Imgproc_FloodFill, maskValue)
|
||
|
|
{
|
||
|
|
const int n = 50;
|
||
|
|
Mat img = Mat::zeros(n, n, CV_8U);
|
||
|
|
Mat mask;
|
||
|
|
|
||
|
|
circle(img, Point(n/2, n/2), 20, Scalar(100), 4);
|
||
|
|
|
||
|
|
int flags = 4 + FLOODFILL_MASK_ONLY;
|
||
|
|
floodFill(img, mask, Point(n/2 + 13, n/2), Scalar(100), NULL, Scalar(), Scalar(), flags);
|
||
|
|
|
||
|
|
ASSERT_EQ(1, cvtest::norm(mask.rowRange(1, n-1).colRange(1, n-1), NORM_INF));
|
||
|
|
}
|
||
|
|
|
||
|
|
}} // namespace
|
||
|
|
/* End of file. */
|