Yolo-FastestV2调用例子

yolo-fastestv2.h

#ifndef NCNN_H_
#define NCNN_H_

#include "net.h"

#include 
#include 

class TargetBox
{
private:
    float getWidth() { return (x2 - x1); };
    float getHeight() { return (y2 - y1); };

public:
    int x1;
    int y1;
    int x2;
    int y2;

    int cate;
    float score;

    float area() { return getWidth() * getHeight(); };
};

class yoloFastestv2
{
private:
    ncnn::Net net;
    std::vector anchor;

    char *inputName;
    char *outputName1;
    char *outputName2;

    int numAnchor;
    int numOutput;
    int numThreads;
    int numCategory;
    int inputWidth, inputHeight;

    float nmsThresh;

    int nmsHandle(std::vector &tmpBoxes, std::vector &dstBoxes);
    int getCategory(const float *values, int index, int &category, float &score);
    int predHandle(const ncnn::Mat *out, std::vector &dstBoxes, 
                   const float scaleW, const float scaleH, const float thresh);

public:
    yoloFastestv2();
    ~yoloFastestv2();

    int loadModel(const char* paramPath, const char* binPath);
    int detection(const cv::Mat srcImg, std::vector &dstBoxes, 
                  const float thresh = 0.3);
};
#endif

yolo-fastestv2.cpp

#include 
#include 
#include "yolo-fastestv2.h"

//模型的参数配置
yoloFastestv2::yoloFastestv2()
{   
    printf("Creat yoloFastestv2 Detector...\n");
    //输出节点数
    numOutput = 2;
    //推理线程数
    numThreads = 4;
    //anchor num
    numAnchor = 3;
    //类别数目
    numCategory = 80;
    //NMS阈值
    nmsThresh = 0.25;

    //模型输入尺寸大小
    inputWidth = 352;
    inputHeight = 352;

    //模型输入输出节点名称
    inputName = "input.1";
    outputName1 = "794"; //22x22
    outputName2 = "796"; //11x11

    //打印初始化相关信息
    printf("numThreads:%d\n", numThreads);
    printf("inputWidth:%d inputHeight:%d\n", inputWidth, inputHeight);

    //anchor box w h
    std::vector bias {12.64,19.39, 37.88,51.48, 55.71,138.31, 
                             126.91,78.23, 131.57,214.55, 279.92,258.87};

    anchor.assign(bias.begin(), bias.end());
}

yoloFastestv2::~yoloFastestv2()
{
    printf("Destroy yoloFastestv2 Detector...\n");
}

//ncnn 模型加载
int yoloFastestv2::loadModel(const char* paramPath, const char* binPath)
{
    printf("Ncnn mode init:\n%s\n%s\n", paramPath, binPath);

    net.load_param(paramPath);
    net.load_model(binPath);    

    printf("Ncnn model init sucess...\n");

    return 0;
}

float intersection_area(const TargetBox &a, const TargetBox &b)
{
    if (a.x1 > b.x2 || a.x2 < b.x1 || a.y1 > b.y2 || a.y2 < b.y1)
    {
        // no intersection
        return 0.f;
    }

    float inter_width = std::min(a.x2, b.x2) - std::max(a.x1, b.x1);
    float inter_height = std::min(a.y2, b.y2) - std::max(a.y1, b.y1);

    return inter_width * inter_height;
}

bool scoreSort(TargetBox a, TargetBox b) 
{ 
    return (a.score > b.score); 
}

//NMS处理
int yoloFastestv2::nmsHandle(std::vector &tmpBoxes, 
                             std::vector &dstBoxes)
{
    std::vector picked;
    
    sort(tmpBoxes.begin(), tmpBoxes.end(), scoreSort);

    for (int i = 0; i < tmpBoxes.size(); i++) {
        int keep = 1;
        for (int j = 0; j < picked.size(); j++) {
            //交集
            float inter_area = intersection_area(tmpBoxes[i], tmpBoxes[picked[j]]);
            //并集
            float union_area = tmpBoxes[i].area() + tmpBoxes[picked[j]].area() - inter_area;
            float IoU = inter_area / union_area;

            if(IoU > nmsThresh && tmpBoxes[i].cate == tmpBoxes[picked[j]].cate) {
                keep = 0;
                break;
            }
        }

        if (keep) {
            picked.push_back(i);
        }
    }
    
    for (int i = 0; i < picked.size(); i++) {
        dstBoxes.push_back(tmpBoxes[picked[i]]);
    }

    return 0;
}

//检测类别分数处理
int yoloFastestv2::getCategory(const float *values, int index, int &category, float &score)
{
    float tmp = 0;
    float objScore  = values[4 * numAnchor + index];

    for (int i = 0; i < numCategory; i++) {
        float clsScore = values[4 * numAnchor + numAnchor + i];
        clsScore *= objScore;

        if(clsScore > tmp) {
            score = clsScore;
            category = i;

            tmp = clsScore;
        }
    }
    
    return 0;
}

//特征图后处理
int yoloFastestv2::predHandle(const ncnn::Mat *out, std::vector &dstBoxes, 
                              const float scaleW, const float scaleH, const float thresh)
{    //do result
    for (int i = 0; i < numOutput; i++) {   
        int stride;
        int outW, outH, outC;

        outH = out[i].c;
        outW = out[i].h;
        outC = out[i].w;

        assert(inputHeight / outH == inputWidth / outW);
        stride = inputHeight / outH;

        for (int h = 0; h < outH; h++) {
            const float* values = out[i].channel(h);

            for (int w = 0; w < outW; w++) {
                for (int b = 0; b < numAnchor; b++) {                    
                    //float objScore = values[4 * numAnchor + b];
                    TargetBox tmpBox;
                    int category = -1;
                    float score = -1;

                    getCategory(values, b, category, score);

                    if (score > thresh) {
                        float bcx, bcy, bw, bh;

                        bcx = ((values[b * 4 + 0] * 2. - 0.5) + w) * stride;
                        bcy = ((values[b * 4 + 1] * 2. - 0.5) + h) * stride;
                        bw = pow((values[b * 4 + 2] * 2.), 2) * anchor[(i * numAnchor * 2) + b * 2 + 0];
                        bh = pow((values[b * 4 + 3] * 2.), 2) * anchor[(i * numAnchor * 2) + b * 2 + 1];
                        
                        tmpBox.x1 = (bcx - 0.5 * bw) * scaleW;
                        tmpBox.y1 = (bcy - 0.5 * bh) * scaleH;
                        tmpBox.x2 = (bcx + 0.5 * bw) * scaleW;
                        tmpBox.y2 = (bcy + 0.5 * bh) * scaleH;
                        tmpBox.score = score;
                        tmpBox.cate = category;

                        dstBoxes.push_back(tmpBox);
                    }
                }
                values += outC;
            } 
        } 
    }
    return 0;
}

int yoloFastestv2::detection(const cv::Mat srcImg, std::vector &dstBoxes, const float thresh)
{   
    dstBoxes.clear();

    float scaleW = (float)srcImg.cols / (float)inputWidth;
    float scaleH = (float)srcImg.rows / (float)inputHeight;
    
    //resize of input image data
    ncnn::Mat inputImg = ncnn::Mat::from_pixels_resize(srcImg.data, ncnn::Mat::PIXEL_BGR,\
                                                       srcImg.cols, srcImg.rows, inputWidth, inputHeight); 

    //Normalization of input image data
    const float mean_vals[3] = {0.f, 0.f, 0.f};
    const float norm_vals[3] = {1/255.f, 1/255.f, 1/255.f};
    inputImg.substract_mean_normalize(mean_vals, norm_vals);  

    //creat extractor
    ncnn::Extractor ex = net.create_extractor();
    ex.set_num_threads(numThreads);

    //set input tensor
    ex.input(inputName, inputImg);

    //forward
    ncnn::Mat out[2]; 
    ex.extract(outputName1, out[0]); //22x22
    ex.extract(outputName2, out[1]); //11x11

    std::vector tmpBoxes;
    //特征图后处理
    predHandle(out, tmpBoxes, scaleW, scaleH, thresh);

    //NMS
    nmsHandle(tmpBoxes, dstBoxes);
    
    return 0;
}

demo.cpp

#include "yolo-fastestv2.h"

int main()
{   
    static const char* class_names[] = {
        "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
        "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
        "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
        "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
        "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
        "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
        "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
        "hair drier", "toothbrush"
    };
    
    yoloFastestv2 api;

    api.loadModel("./model/yolo-fastestv2-opt.param",
                  "./model/yolo-fastestv2-opt.bin");

    cv::Mat cvImg = cv::imread("test.jpg");

    std::vector boxes;
    api.detection(cvImg, boxes);

    for (int i = 0; i < boxes.size(); i++) {
        std::cout< cvImg.cols)
            x = cvImg.cols - label_size.width;

        cv::rectangle(cvImg, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
                      cv::Scalar(255, 255, 255), -1);

        cv::putText(cvImg, text, cv::Point(x, y + label_size.height),
                    cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));

        cv::rectangle (cvImg, cv::Point(boxes[i].x1, boxes[i].y1), 
                       cv::Point(boxes[i].x2, boxes[i].y2), cv::Scalar(255, 255, 0), 2, 2, 0);
    }
    
    cv::imwrite("output.png", cvImg);

    return 0;
}