/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "test_precomp.hpp" using namespace cv; using namespace std; class CV_FloodFillTest : public cvtest::ArrayTest { public: CV_FloodFillTest(); protected: void get_test_array_types_and_sizes( int test_case_idx, vector >& sizes, vector >& types ); double get_success_error_level( int test_case_idx, int i, int j ); void run_func(); void prepare_to_validation( int ); void fill_array( int test_case_idx, int i, int j, Mat& arr ); /*int write_default_params(CvFileStorage* fs); void get_timing_test_array_types_and_sizes( int test_case_idx, vector >& sizes, vector >& types CvSize** whole_sizes, bool *are_images ); void print_timing_params( int test_case_idx, char* ptr, int params_left );*/ CvPoint seed_pt; CvScalar new_val; CvScalar l_diff, u_diff; int connectivity; bool use_mask, mask_only; int range_type; int new_mask_val; bool test_cpp; }; CV_FloodFillTest::CV_FloodFillTest() { test_array[INPUT_OUTPUT].push_back(NULL); test_array[INPUT_OUTPUT].push_back(NULL); test_array[REF_INPUT_OUTPUT].push_back(NULL); test_array[REF_INPUT_OUTPUT].push_back(NULL); test_array[OUTPUT].push_back(NULL); test_array[REF_OUTPUT].push_back(NULL); optional_mask = false; element_wise_relative_error = true; test_cpp = false; } void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, vector >& sizes, vector >& types ) { RNG& rng = ts->get_rng(); int depth, cn; int i; double buff[8]; cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); depth = cvtest::randInt(rng) % 3; depth = depth == 0 ? CV_8U : depth == 1 ? CV_32S : CV_32F; cn = cvtest::randInt(rng) & 1 ? 3 : 1; use_mask = (cvtest::randInt(rng) & 1) != 0; connectivity = (cvtest::randInt(rng) & 1) ? 4 : 8; mask_only = use_mask && (cvtest::randInt(rng) & 1) != 0; new_mask_val = cvtest::randInt(rng) & 255; range_type = cvtest::randInt(rng) % 3; types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(depth, cn); types[INPUT_OUTPUT][1] = types[REF_INPUT_OUTPUT][1] = CV_8UC1; types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1; sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(9,1); if( !use_mask ) sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(0,0); else { CvSize sz = sizes[INPUT_OUTPUT][0]; sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(sz.width+2,sz.height+2); } seed_pt.x = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].width; seed_pt.y = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].height; if( range_type == 0 ) l_diff = u_diff = Scalar::all(0.); else { Mat m( 1, 8, CV_16S, buff ); rng.fill( m, RNG::NORMAL, Scalar::all(0), Scalar::all(32) ); for( i = 0; i < 4; i++ ) { l_diff.val[i] = fabs(m.at(i)/16.); u_diff.val[i] = fabs(m.at(i+4)/16.); } } new_val = Scalar::all(0.); for( i = 0; i < cn; i++ ) new_val.val[i] = cvtest::randReal(rng)*255; test_cpp = (cvtest::randInt(rng) & 256) == 0; } double CV_FloodFillTest::get_success_error_level( int /*test_case_idx*/, int i, int j ) { return i == OUTPUT ? FLT_EPSILON : j == 0 ? FLT_EPSILON : 0; } void CV_FloodFillTest::fill_array( int test_case_idx, int i, int j, Mat& arr ) { RNG& rng = ts->get_rng(); if( i != INPUT && i != INPUT_OUTPUT ) { cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr ); return; } if( j == 0 ) { Mat tmp = arr; Scalar m = Scalar::all(128); Scalar s = Scalar::all(10); if( arr.depth() == CV_32FC1 ) tmp.create(arr.size(), CV_MAKETYPE(CV_8U, arr.channels())); if( range_type == 0 ) s = Scalar::all(2); rng.fill(tmp, RNG::NORMAL, m, s ); if( arr.data != tmp.data ) cvtest::convert(tmp, arr, arr.type()); } else { Scalar l = Scalar::all(-2); Scalar u = Scalar::all(2); cvtest::randUni(rng, arr, l, u ); rectangle( arr, Point(0,0), Point(arr.cols-1,arr.rows-1), Scalar::all(1), 1, 8, 0 ); } } void CV_FloodFillTest::run_func() { int flags = connectivity + (mask_only ? CV_FLOODFILL_MASK_ONLY : 0) + (range_type == 1 ? CV_FLOODFILL_FIXED_RANGE : 0) + (new_mask_val << 8); double* odata = test_mat[OUTPUT][0].ptr(); if(!test_cpp) { CvConnectedComp comp; cvFloodFill( test_array[INPUT_OUTPUT][0], seed_pt, new_val, l_diff, u_diff, &comp, flags, test_array[INPUT_OUTPUT][1] ); odata[0] = comp.area; odata[1] = comp.rect.x; odata[2] = comp.rect.y; odata[3] = comp.rect.width; odata[4] = comp.rect.height; odata[5] = comp.value.val[0]; odata[6] = comp.value.val[1]; odata[7] = comp.value.val[2]; odata[8] = comp.value.val[3]; } else { cv::Mat img = cv::cvarrToMat(test_array[INPUT_OUTPUT][0]), mask = test_array[INPUT_OUTPUT][1] ? cv::cvarrToMat(test_array[INPUT_OUTPUT][1]) : cv::Mat(); cv::Rect rect; int area; if( !mask.data ) area = cv::floodFill( img, seed_pt, new_val, &rect, l_diff, u_diff, flags ); else area = cv::floodFill( img, mask, seed_pt, new_val, &rect, l_diff, u_diff, flags ); odata[0] = area; odata[1] = rect.x; odata[2] = rect.y; odata[3] = rect.width; odata[4] = rect.height; odata[5] = odata[6] = odata[7] = odata[8] = 0; } } typedef struct ff_offset_pair_t { int mofs, iofs; } ff_offset_pair_t; static void cvTsFloodFill( CvMat* _img, CvPoint seed_pt, CvScalar new_val, CvScalar l_diff, CvScalar u_diff, CvMat* _mask, double* comp, int connectivity, int range_type, int new_mask_val, bool mask_only ) { CvMemStorage* st = cvCreateMemStorage(); ff_offset_pair_t p0, p; CvSeq* seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(p0), st ); CvMat* tmp = _img; CvMat* mask; CvRect r = cvRect( 0, 0, -1, -1 ); int area = 0; int i, j; ushort* m; float* img; int mstep, step; int cn = CV_MAT_CN(_img->type); int mdelta[8], idelta[8], ncount; int cols = _img->cols, rows = _img->rows; int u0 = 0, u1 = 0, u2 = 0; double s0 = 0, s1 = 0, s2 = 0; if( CV_MAT_DEPTH(_img->type) == CV_8U || CV_MAT_DEPTH(_img->type) == CV_32S ) { tmp = cvCreateMat( rows, cols, CV_MAKETYPE(CV_32F,CV_MAT_CN(_img->type)) ); cvTsConvert(_img, tmp); } mask = cvCreateMat( rows + 2, cols + 2, CV_16UC1 ); if( _mask ) cvTsConvert( _mask, mask ); else { cvTsZero( mask ); cvRectangle( mask, cvPoint(0,0), cvPoint(mask->cols-1,mask->rows-1), Scalar::all(1.), 1, 8, 0 ); } new_mask_val = (new_mask_val != 0 ? new_mask_val : 1) << 8; m = (ushort*)(mask->data.ptr + mask->step) + 1; mstep = mask->step / sizeof(m[0]); img = tmp->data.fl; step = tmp->step / sizeof(img[0]); p0.mofs = seed_pt.y*mstep + seed_pt.x; p0.iofs = seed_pt.y*step + seed_pt.x*cn; if( m[p0.mofs] ) goto _exit_; cvSeqPush( seq, &p0 ); m[p0.mofs] = (ushort)new_mask_val; if( connectivity == 4 ) { ncount = 4; mdelta[0] = -mstep; idelta[0] = -step; mdelta[1] = -1; idelta[1] = -cn; mdelta[2] = 1; idelta[2] = cn; mdelta[3] = mstep; idelta[3] = step; } else { ncount = 8; mdelta[0] = -mstep-1; mdelta[1] = -mstep; mdelta[2] = -mstep+1; idelta[0] = -step-cn; idelta[1] = -step; idelta[2] = -step+cn; mdelta[3] = -1; mdelta[4] = 1; idelta[3] = -cn; idelta[4] = cn; mdelta[5] = mstep-1; mdelta[6] = mstep; mdelta[7] = mstep+1; idelta[5] = step-cn; idelta[6] = step; idelta[7] = step+cn; } if( cn == 1 ) { float a0 = (float)-l_diff.val[0]; float b0 = (float)u_diff.val[0]; s0 = img[p0.iofs]; if( range_type < 2 ) { a0 += (float)s0; b0 += (float)s0; } while( seq->total ) { cvSeqPop( seq, &p0 ); float a = a0, b = b0; float* ptr = img + p0.iofs; ushort* mptr = m + p0.mofs; if( range_type == 2 ) a += ptr[0], b += ptr[0]; for( i = 0; i < ncount; i++ ) { int md = mdelta[i], id = idelta[i]; float v; if( !mptr[md] && a <= (v = ptr[id]) && v <= b ) { mptr[md] = (ushort)new_mask_val; 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(s0); u1 = saturate_cast(s1); u2 = saturate_cast(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 ) cvTsConvert(tmp, _img); 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( mask_only ) { double t = area ? 1./area : 0; s0 *= t; s1 *= t; s2 *= t; } comp[5] = s0; comp[6] = s1; comp[7] = s2; comp[8] = 0; cvReleaseMemStorage(&st); } void CV_FloodFillTest::prepare_to_validation( int /*test_case_idx*/ ) { double* comp = test_mat[REF_OUTPUT][0].ptr(); CvMat _input = test_mat[REF_INPUT_OUTPUT][0]; CvMat _mask = test_mat[REF_INPUT_OUTPUT][1]; cvTsFloodFill( &_input, seed_pt, new_val, l_diff, 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(); } /* End of file. */