zhipuzi_pos_windows/opencv-test/test/YoloFeatureExtractor.cpp


#include "YoloFeatureExtractor.h"
#include <fstream>
#include <algorithm>
#include <iostream>

#include <opencv2/core/ocl.hpp>

YoloFeatureExtractor::YoloFeatureExtractor(const std::string & modelPath, const std::string & classesPath)
    : inputWidth(448), inputHeight(448)
{
	try
	{
		net = cv::dnn::readNetFromONNX(modelPath);

		//net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
		//net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);  // 或DNN_TARGET_MYRIAD等

		loadClassNames(classesPath);
	}
	catch (const std::exception& e)
	{
		std::string aa = std::string(e.what());

		std::cout << "Error loading model: " << aa << std::endl;
	}

    	
}

YoloFeatureExtractor::YoloFeatureExtractor(const std::string& modelPath, const std::string& configPath, const std::string& classesPath)
{
	try
	{
		net = cv::dnn::readNetFromModelOptimizer(modelPath, configPath);

		// 设置目标设备 (可选: CPU, GPU, MYRIAD等)
		net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
		net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);  // 或DNN_TARGET_MYRIAD等
		loadClassNames(classesPath);
	}
	catch (const std::exception& e)
	{
		std::string aa = std::string(e.what());
		
		std::cout << "Error loading model: " << aa << std::endl;
	}
	
}

void YoloFeatureExtractor::initOpenCL()
{
    if (cv::ocl::haveOpenCL())
    {
        std::cout << "OpenCL is available. Enabling OpenCL for DNN module." << std::endl;
        net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
        net.setPreferableTarget(cv::dnn::DNN_TARGET_OPENCL);
    }
    else
    {
        std::cout << "OpenCL is not available. Using default backend and target." << std::endl;
        net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
        net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
    }
}

void YoloFeatureExtractor::loadClassNames(const std::string & file)
{
    std::ifstream ifs(file);
    std::string line;
    while (std::getline(ifs, line))
    {
        classNames.push_back(line);
    }
}

std::vector<float> YoloFeatureExtractor::extractFeatures(const std::string & imagePath)
{
    auto start_time = std::chrono::high_resolution_clock::now();

    cv::Mat image = cv::imread(imagePath);
    if (image.empty())
    {
        throw std::runtime_error("Could not load image: " + imagePath);
    }

    cv::Mat blob;
    cv::dnn::blobFromImage(image, blob, 1.0 / 255.0, cv::Size(inputWidth, inputHeight), cv::Scalar(0, 0, 0), true, false);
    net.setInput(blob);

    auto start_time2 = std::chrono::high_resolution_clock::now();

    std::vector<cv::Mat> outputs;
    net.forward(outputs, net.getUnconnectedOutLayersNames());

    auto start_time3 = std::chrono::high_resolution_clock::now();



    std::vector<float> features;
    for (size_t i = 0; i < outputs.size(); ++i)
    {
        float * data = (float *)outputs[i].data;
        int totalElements = outputs[i].total();
        for (int idx = 0; idx < totalElements; ++idx)
        {
            features.push_back(data[idx]);
        }
    }

    

    auto duration1 = std::chrono::duration_cast<std::chrono::milliseconds>(start_time2 - start_time);

    auto duration2 = std::chrono::duration_cast<std::chrono::milliseconds>(start_time3 - start_time2);

    auto totalDuration = std::chrono::duration_cast<std::chrono::milliseconds>(start_time3 - start_time);

    std::cout << "图片前期处理耗时: " << duration1.count() << " 毫秒" << std::endl;
    std::cout << "模型推理耗时: " << duration2.count() << " 毫秒" << std::endl;
    std::cout << "总耗时: " << totalDuration.count() << " 毫秒" << std::endl;

    return features;
}

std::vector<float> YoloFeatureExtractor::extractBackboneFeatures(const std::string & imagePath)
{
    cv::Mat image = cv::imread(imagePath);
    if (image.empty())
    {
        throw std::runtime_error("Could not load image: " + imagePath);
    }

    cv::Mat blob;
    cv::dnn::blobFromImage(image, blob, 1.0 / 255.0, cv::Size(inputWidth, inputHeight), cv::Scalar(0, 0, 0), true, false);
    net.setInput(blob);

    std::vector<cv::String> layerNames = net.getLayerNames();
    std::vector<cv::String> backboneLayers;

    for (const auto & name : layerNames)
    {
        if (name.find("backbone") != std::string::npos ||
            name.find("conv") != std::string::npos ||
            name.find("stage") != std::string::npos)
        {
            backboneLayers.push_back(name);
        }
    }

    if (backboneLayers.empty())
    {
        backboneLayers.push_back(layerNames[layerNames.size() / 2]);
    }

    std::vector<cv::Mat> outputs;
    net.forward(outputs, backboneLayers);

    std::vector<float> features;
    for (size_t i = 0; i < outputs.size(); ++i)
    {
        cv::Mat output = outputs[i];
        features.reserve(features.size() + output.total());
        for (int j = 0; j < output.total(); ++j)
        {
            features.push_back(output.at<float>(j));
        }
    }

    return features;
}

std::vector<std::vector<float>> YoloFeatureExtractor::extractROIFeatures(const std::string & imagePath)
{
    cv::Mat image = cv::imread(imagePath);
    if (image.empty())
    {
        throw std::runtime_error("Could not load image: " + imagePath);
    }

    cv::Mat blob;
    cv::dnn::blobFromImage(image, blob, 1.0 / 255.0, cv::Size(inputWidth, inputHeight), cv::Scalar(0, 0, 0), true, false);
    net.setInput(blob);

    std::vector<cv::Mat> outputs;
    net.forward(outputs, net.getUnconnectedOutLayersNames());

    const float CONFIDENCE_THRESHOLD = 0.5;
    const float NMS_THRESHOLD = 0.4;

    std::vector<int> classIds;
    std::vector<float> confidences;
    std::vector<cv::Rect> boxes;

    float x_factor = static_cast<float>(image.cols) / inputWidth;
    float y_factor = static_cast<float>(image.rows) / inputHeight;

    for (size_t outputIdx = 0; outputIdx < outputs.size(); ++outputIdx)
    {
        float * data = (float *)outputs[outputIdx].data;
        int rows = outputs[outputIdx].rows;
        int dimensions = outputs[outputIdx].cols;

        for (int i = 0; i < rows; ++i)
        {
            float objectness = data[4];
            if (objectness >= CONFIDENCE_THRESHOLD)
            {
                std::vector<float> probs;
                for (int c = 5; c < dimensions; ++c)
                {
                    probs.push_back(data[c]);
                }

                int maxClassId = 0;
                float maxScore = probs[0];
                for (size_t p = 1; p < probs.size(); ++p)
                {
                    if (probs[p] > maxScore)
                    {
                        maxScore = probs[p];
                        maxClassId = static_cast<int>(p);
                    }
                }

                if (maxScore > CONFIDENCE_THRESHOLD)
                {
                    confidences.push_back(objectness * maxScore);
                    classIds.push_back(maxClassId);

                    float x = data[0];
                    float y = data[1];
                    float w = data[2];
                    float h = data[3];

                    int left = static_cast<int>((x - 0.5 * w) * x_factor);
                    int top = static_cast<int>((y - 0.5 * h) * y_factor);
                    int width = static_cast<int>(w * x_factor);
                    int height = static_cast<int>(h * y_factor);

                    boxes.push_back(cv::Rect(left, top, width, height));
                }
            }
            data += dimensions;
        }
    }

    std::vector<int> nms_result;
    cv::dnn::NMSBoxes(boxes, confidences, CONFIDENCE_THRESHOLD, NMS_THRESHOLD, nms_result);

    std::vector<std::vector<float>> roiFeatures;
    for (size_t i = 0; i < nms_result.size(); ++i)
    {
        int idx = nms_result[i];
        cv::Rect box = boxes[idx];

        box.x = std::max(0, std::min(box.x, image.cols - 1));
        box.y = std::max(0, std::min(box.y, image.rows - 1));
        box.width = std::max(0, std::min(box.width, image.cols - box.x));
        box.height = std::max(0, std::min(box.height, image.rows - box.y));

        std::vector<float> roiFeature;
        roiFeature.push_back(static_cast<float>(box.x) / image.cols);
        roiFeature.push_back(static_cast<float>(box.y) / image.rows);
        roiFeature.push_back(static_cast<float>(box.width) / image.cols);
        roiFeature.push_back(static_cast<float>(box.height) / image.rows);
        roiFeature.push_back(confidences[idx]);
        roiFeature.push_back(static_cast<float>(classIds[idx]));

        roiFeatures.push_back(roiFeature);
    }

    return roiFeatures;
}