mirror of
https://github.com/kerberos-io/openalpr-base.git
synced 2025-10-06 07:16:56 +08:00
144 lines
4.2 KiB
C++
144 lines
4.2 KiB
C++
/*
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* Copyright (c) 2013 New Designs Unlimited, LLC
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* Opensource Automated License Plate Recognition [http://www.openalpr.com]
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*
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* This file is part of OpenAlpr.
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*
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* OpenAlpr is free software: you can redistribute it and/or modify
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* it under the terms of the GNU Affero General Public License
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* version 3 as published by the Free Software Foundation
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU Affero General Public License for more details.
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*
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* You should have received a copy of the GNU Affero General Public License
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* along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "detectorcuda.h"
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#ifdef COMPILE_GPU
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using namespace cv;
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using namespace std;
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namespace alpr
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{
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DetectorCUDA::DetectorCUDA(Config* config) : Detector(config) {
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if( this->cuda_cascade.load( config->getCascadeRuntimeDir() + config->country + ".xml" ) )
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{
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this->loaded = true;
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printf("--(!)Loaded CUDA classifier\n");
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}
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else
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{
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this->loaded = false;
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printf("--(!)Error loading CUDA classifier\n");
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}
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}
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DetectorCUDA::~DetectorCUDA() {
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}
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vector<PlateRegion> DetectorCUDA::detect(Mat frame, std::vector<cv::Rect> regionsOfInterest)
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{
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Mat frame_gray;
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cvtColor( frame, frame_gray, CV_BGR2GRAY );
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vector<PlateRegion> detectedRegions;
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for (int i = 0; i < regionsOfInterest.size(); i++)
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{
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Mat cropped = frame_gray(regionsOfInterest[i]);
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vector<PlateRegion> subRegions = doCascade(cropped, regionsOfInterest[i].x, regionsOfInterest[i].y);
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for (int j = 0; j < subRegions.size(); j++)
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detectedRegions.push_back(subRegions[j]);
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}
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return detectedRegions;
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}
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vector<PlateRegion> DetectorCUDA::doCascade(Mat frame, int offset_x, int offset_y)
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{
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if (frame.cols > config->maxDetectionInputWidth)
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{
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// The frame is too wide
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this->scale_factor = ((float) config->maxDetectionInputWidth) / ((float) frame.cols);
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if (config->debugDetector)
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std::cout << "Input detection image is too wide. Resizing with scale: " << this->scale_factor << endl;
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}
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else if (frame.rows > config->maxDetectionInputHeight)
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{
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// The frame is too tall
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this->scale_factor = ((float) config->maxDetectionInputHeight) / ((float) frame.rows);
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if (config->debugDetector)
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std::cout << "Input detection image is too tall. Resizing with scale: " << this->scale_factor << endl;
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}
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int w = frame.size().width;
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int h = frame.size().height;
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vector<Rect> plates;
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equalizeHist( frame, frame );
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resize(frame, frame, Size(w * this->scale_factor, h * this->scale_factor));
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//-- Detect plates
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timespec startTime;
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getTimeMonotonic(&startTime);
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float maxWidth = ((float) w) * (config->maxPlateWidthPercent / 100.0f) * this->scale_factor;
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float maxHeight = ((float) h) * (config->maxPlateHeightPercent / 100.0f) * this->scale_factor;
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Size minSize(config->minPlateSizeWidthPx * this->scale_factor, config->minPlateSizeHeightPx * this->scale_factor);
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gpu::GpuMat cudaFrame, plateregions_buffer;
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Mat plateregions_downloaded;
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cudaFrame.upload(frame);
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int numdetected = cuda_cascade.detectMultiScale(cudaFrame, plateregions_buffer, (double) config->detection_iteration_increase, config->detectionStrictness, minSize);
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plateregions_buffer.colRange(0, numdetected).download(plateregions_downloaded);
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for (int i = 0; i < numdetected; ++i)
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{
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plates.push_back(plateregions_downloaded.ptr<cv::Rect>()[i]);
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}
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if (config->debugTiming)
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{
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timespec endTime;
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getTimeMonotonic(&endTime);
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cout << "LBP Time: " << diffclock(startTime, endTime) << "ms." << endl;
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}
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for( unsigned int i = 0; i < plates.size(); i++ )
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{
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plates[i].x = (plates[i].x / scale_factor) + offset_x;
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plates[i].y = (plates[i].y / scale_factor) + offset_y;
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plates[i].width = plates[i].width / scale_factor;
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plates[i].height = plates[i].height / scale_factor;
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}
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vector<PlateRegion> orderedRegions = aggregateRegions(plates);
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return orderedRegions;
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}
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}
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#endif |