375 lines
13 KiB
C++
375 lines
13 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 "precomp.hpp"
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#include <string>
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namespace {
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//#define DUMP
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struct HOG : testing::TestWithParam<cv::gpu::DeviceInfo>, cv::gpu::HOGDescriptor
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{
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cv::gpu::DeviceInfo devInfo;
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#ifdef DUMP
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std::ofstream f;
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#else
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std::ifstream f;
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#endif
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int wins_per_img_x;
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int wins_per_img_y;
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int blocks_per_win_x;
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int blocks_per_win_y;
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int block_hist_size;
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virtual void SetUp()
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{
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devInfo = GetParam();
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cv::gpu::setDevice(devInfo.deviceID());
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}
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#ifdef DUMP
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void dump(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
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{
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f.write((char*)&blockHists.rows, sizeof(blockHists.rows));
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f.write((char*)&blockHists.cols, sizeof(blockHists.cols));
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for (int i = 0; i < blockHists.rows; ++i)
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{
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for (int j = 0; j < blockHists.cols; ++j)
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{
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float val = blockHists.at<float>(i, j);
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f.write((char*)&val, sizeof(val));
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}
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}
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int nlocations = locations.size();
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f.write((char*)&nlocations, sizeof(nlocations));
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for (int i = 0; i < locations.size(); ++i)
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f.write((char*)&locations[i], sizeof(locations[i]));
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}
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#else
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void compare(const cv::Mat& blockHists, const std::vector<cv::Point>& locations)
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{
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int rows, cols;
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f.read((char*)&rows, sizeof(rows));
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f.read((char*)&cols, sizeof(cols));
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ASSERT_EQ(rows, blockHists.rows);
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ASSERT_EQ(cols, blockHists.cols);
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for (int i = 0; i < blockHists.rows; ++i)
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{
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for (int j = 0; j < blockHists.cols; ++j)
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{
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float val;
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f.read((char*)&val, sizeof(val));
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ASSERT_NEAR(val, blockHists.at<float>(i, j), 1e-3);
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}
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}
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int nlocations;
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f.read((char*)&nlocations, sizeof(nlocations));
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ASSERT_EQ(nlocations, static_cast<int>(locations.size()));
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for (int i = 0; i < nlocations; ++i)
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{
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cv::Point location;
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f.read((char*)&location, sizeof(location));
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ASSERT_EQ(location, locations[i]);
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}
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}
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#endif
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void testDetect(const cv::Mat& img)
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{
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gamma_correction = false;
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setSVMDetector(cv::gpu::HOGDescriptor::getDefaultPeopleDetector());
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std::vector<cv::Point> locations;
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// Test detect
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detect(loadMat(img), locations, 0);
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#ifdef DUMP
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dump(cv::Mat(block_hists), locations);
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#else
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compare(cv::Mat(block_hists), locations);
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#endif
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// Test detect on smaller image
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cv::Mat img2;
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cv::resize(img, img2, cv::Size(img.cols / 2, img.rows / 2));
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detect(loadMat(img2), locations, 0);
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#ifdef DUMP
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dump(cv::Mat(block_hists), locations);
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#else
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compare(cv::Mat(block_hists), locations);
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#endif
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// Test detect on greater image
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cv::resize(img, img2, cv::Size(img.cols * 2, img.rows * 2));
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detect(loadMat(img2), locations, 0);
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#ifdef DUMP
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dump(cv::Mat(block_hists), locations);
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#else
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compare(cv::Mat(block_hists), locations);
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#endif
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}
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// Does not compare border value, as interpolation leads to delta
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void compare_inner_parts(cv::Mat d1, cv::Mat d2)
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{
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for (int i = 1; i < blocks_per_win_y - 1; ++i)
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for (int j = 1; j < blocks_per_win_x - 1; ++j)
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for (int k = 0; k < block_hist_size; ++k)
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{
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float a = d1.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
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float b = d2.at<float>(0, (i * blocks_per_win_x + j) * block_hist_size);
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ASSERT_FLOAT_EQ(a, b);
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}
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}
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};
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TEST_P(HOG, Detect)
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{
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cv::Mat img_rgb = readImage("hog/road.png");
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ASSERT_FALSE(img_rgb.empty());
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#ifdef DUMP
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f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
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ASSERT_TRUE(f.is_open());
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#else
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f.open((std::string(cvtest::TS::ptr()->get_data_path()) + "hog/expected_output.bin").c_str(), std::ios_base::binary);
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ASSERT_TRUE(f.is_open());
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#endif
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// Test on color image
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cv::Mat img;
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
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testDetect(img);
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// Test on gray image
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cv::cvtColor(img_rgb, img, CV_BGR2GRAY);
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testDetect(img);
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f.close();
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}
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TEST_P(HOG, GetDescriptors)
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{
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// Load image (e.g. train data, composed from windows)
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cv::Mat img_rgb = readImage("hog/train_data.png");
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ASSERT_FALSE(img_rgb.empty());
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// Convert to C4
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cv::Mat img;
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
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cv::gpu::GpuMat d_img(img);
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// Convert train images into feature vectors (train table)
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cv::gpu::GpuMat descriptors, descriptors_by_cols;
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getDescriptors(d_img, win_size, descriptors, DESCR_FORMAT_ROW_BY_ROW);
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getDescriptors(d_img, win_size, descriptors_by_cols, DESCR_FORMAT_COL_BY_COL);
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// Check size of the result train table
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wins_per_img_x = 3;
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wins_per_img_y = 2;
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blocks_per_win_x = 7;
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blocks_per_win_y = 15;
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block_hist_size = 36;
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cv::Size descr_size_expected = cv::Size(blocks_per_win_x * blocks_per_win_y * block_hist_size,
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wins_per_img_x * wins_per_img_y);
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ASSERT_EQ(descr_size_expected, descriptors.size());
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// Check both formats of output descriptors are handled correctly
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cv::Mat dr(descriptors);
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cv::Mat dc(descriptors_by_cols);
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for (int i = 0; i < wins_per_img_x * wins_per_img_y; ++i)
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{
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const float* l = dr.rowRange(i, i + 1).ptr<float>();
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const float* r = dc.rowRange(i, i + 1).ptr<float>();
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for (int y = 0; y < blocks_per_win_y; ++y)
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for (int x = 0; x < blocks_per_win_x; ++x)
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for (int k = 0; k < block_hist_size; ++k)
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ASSERT_EQ(l[(y * blocks_per_win_x + x) * block_hist_size + k],
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r[(x * blocks_per_win_y + y) * block_hist_size + k]);
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}
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/* Now we want to extract the same feature vectors, but from single images. NOTE: results will
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be defferent, due to border values interpolation. Using of many small images is slower, however we
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wont't call getDescriptors and will use computeBlockHistograms instead of. computeBlockHistograms
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works good, it can be checked in the gpu_hog sample */
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img_rgb = readImage("hog/positive1.png");
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ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
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computeBlockHistograms(cv::gpu::GpuMat(img));
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// Everything is fine with interpolation for left top subimage
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ASSERT_EQ(0.0, cv::norm((cv::Mat)block_hists, (cv::Mat)descriptors.rowRange(0, 1)));
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img_rgb = readImage("hog/positive2.png");
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ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
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computeBlockHistograms(cv::gpu::GpuMat(img));
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compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(1, 2)));
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img_rgb = readImage("hog/negative1.png");
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ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
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computeBlockHistograms(cv::gpu::GpuMat(img));
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compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(2, 3)));
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img_rgb = readImage("hog/negative2.png");
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ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
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computeBlockHistograms(cv::gpu::GpuMat(img));
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compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(3, 4)));
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img_rgb = readImage("hog/positive3.png");
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ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
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computeBlockHistograms(cv::gpu::GpuMat(img));
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compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(4, 5)));
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img_rgb = readImage("hog/negative3.png");
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ASSERT_TRUE(!img_rgb.empty());
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cv::cvtColor(img_rgb, img, CV_BGR2BGRA);
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computeBlockHistograms(cv::gpu::GpuMat(img));
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compare_inner_parts(cv::Mat(block_hists), cv::Mat(descriptors.rowRange(5, 6)));
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}
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INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, HOG, ALL_DEVICES);
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//////////////////////////////////////////////////////////////////////////////////////////
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/// LBP classifier
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PARAM_TEST_CASE(LBP_Read_classifier, cv::gpu::DeviceInfo, int)
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{
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cv::gpu::DeviceInfo devInfo;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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TEST_P(LBP_Read_classifier, Accuracy)
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{
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cv::gpu::CascadeClassifier_GPU_LBP classifier;
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std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";
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ASSERT_TRUE(classifier.load(classifierXmlPath));
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}
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INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, LBP_Read_classifier,
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testing::Combine(ALL_DEVICES, testing::Values<int>(0)));
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PARAM_TEST_CASE(LBP_classify, cv::gpu::DeviceInfo, int)
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{
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cv::gpu::DeviceInfo devInfo;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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TEST_P(LBP_classify, Accuracy)
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{
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std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";
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std::string imagePath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/er.png";
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cv::CascadeClassifier cpuClassifier(classifierXmlPath);
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ASSERT_FALSE(cpuClassifier.empty());
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cv::Mat image = cv::imread(imagePath);
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image = image.colRange(0, image.cols/2);
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cv::Mat grey;
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cvtColor(image, grey, CV_BGR2GRAY);
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ASSERT_FALSE(image.empty());
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std::vector<cv::Rect> rects;
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cpuClassifier.detectMultiScale(grey, rects);
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cv::Mat markedImage = image.clone();
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std::vector<cv::Rect>::iterator it = rects.begin();
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for (; it != rects.end(); ++it)
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cv::rectangle(markedImage, *it, CV_RGB(0, 0, 255));
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cv::gpu::CascadeClassifier_GPU_LBP gpuClassifier;
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ASSERT_TRUE(gpuClassifier.load(classifierXmlPath));
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cv::gpu::GpuMat gpu_rects;
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cv::gpu::GpuMat tested(grey);
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int count = gpuClassifier.detectMultiScale(tested, gpu_rects);
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cv::Mat downloaded(gpu_rects);
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const cv::Rect* faces = downloaded.ptr<cv::Rect>();
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for (int i = 0; i < count; i++)
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{
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cv::Rect r = faces[i];
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#if defined (LOG_CASCADE_STATISTIC)
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std::cout << r.x << " " << r.y << " " << r.width << " " << r.height << std::endl;
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#endif
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cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
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}
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#if defined (LOG_CASCADE_STATISTIC)
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cv::imshow("Res", markedImage); cv::waitKey();
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#endif
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}
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INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, LBP_classify,
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testing::Combine(ALL_DEVICES, testing::Values<int>(0)));
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} // namespace
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