samples/dnn/classification.cpp

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#include <fstream>
#include <sstream>
#include <iostream>
 
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
 
#include "common.hpp"
 
std::string keys =
    "{ help h | | Print help message. }"
    "{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
    "{ zoo | models.yml | An optional path to file with preprocessing parameters }"
    "{ input i | | 输入图像或视频文件的路径。跳过此参数可从相机捕获帧。}"
    "{ initial_width | 0 | 通过将初始图像缩放到特定宽度来预处理输入图像。}"
    "{ initial_height | 0 | 通过将初始图像缩放到特定高度来预处理输入图像。}"
    "{ std | 0.0 0.0 0.0 | 通过除以标准差来预处理输入图像。}"
    "{ crop | false | 通过中心裁剪预处理输入图像。}"
    "{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
    "{ needSoftmax | false | 使用 Softmax 后处理网络的输出。}"
    "{ classes | | 包含类名称的文本文件可选路径。 }"
    "{ backend | 0 | Choose one of computation backends: "
                            "0: automatically (by default), "
                            "1: Halide language (http://halide-lang.org/), "
                            "2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
                            "3: OpenCV implementation, "
                            "4: VKCOM, "
                            "5: CUDA, "
                            "6: WebNN }"
    "{ target | 0 | Choose one of target computation devices: "
                            "0: CPU target (by default), "
                            "1: OpenCL, "
                            "2: OpenCL fp16 (half-float precision), "
                            "3: VPU, "
                            "4: Vulkan, "
                            "6: CUDA, "
                            "7: CUDA fp16 (half-float preprocess) }";
 
using namespace cv;
using namespace dnn;
 
std::vector<std::string> classes;
 
int main(int argc, char** argv)
{
    CommandLineParser parser(argc, argv, keys);
 
    const std::string modelName = parser.get<String>("@alias");
    const std::string zooFile = parser.get<String>("zoo");
 
keys += genPreprocArguments(modelName, zooFile);
 
parser = CommandLineParser(argc, argv, keys);
parser.about("使用此脚本使用OpenCV运行分类深度学习网络。");
    if (argc == 1 || parser.has("help"))
    {
parser.printMessage();
        return 0;
    }
 
    int rszWidth = parser.get<int>("initial_width");
    int rszHeight = parser.get<int>("initial_height");
    float scale = parser.get<float>("scale");
    Scalar mean = parser.get<Scalar>("mean");
    Scalar std = parser.get<Scalar>("std");
    bool swapRB = parser.get<bool>("rgb");
    bool crop = parser.get<bool>("crop");
    int inpWidth = parser.get<int>("width");
    int inpHeight = parser.get<int>("height");
    String model = findFile(parser.get<String>("model"));
    String config = findFile(parser.get<String>("config"));
    String framework = parser.get<String>("framework");
    int backendId = parser.get<int>("backend");
    int targetId = parser.get<int>("target");
    bool needSoftmax = parser.get<bool>("needSoftmax");
std::cout<<"mean: "<<mean<<std::endl;
std::cout<<"std: "<<std<<std::endl;
 
    // Open file with classes names.
    if (parser.has("classes"))
    {
std::string file = parser.get<String>("classes");
std::ifstream ifs(file.c_str());
        if (!ifs.is_open())
            CV_Error(Error::StsError, "文件 " + file + " 未找到");
std::string line;
        while (std::getline(ifs, line))
        {
classes.push_back(line);
        }
    }
 
    if (!parser.check())
    {
parser.printErrors();
        return 1;
    }
    CV_Assert(!model.empty());
 
Net net = readNet(model, config, framework);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
 
    // Create a window
    static const std::string kWinName = "OpenCV中的深度学习图像分类";
namedWindow(kWinName, WINDOW_NORMAL);
 
    VideoCapture cap;
    if (parser.has("input"))
cap.open(parser.get<String>("input"));
    else
cap.open(0);
 
    // Process frames.
    Mat frame, blob;
    while (waitKey(1) < 0)
    {
cap >> frame;
        if (frame.empty())
        {
waitKey();
            break;
        }
 
        if (rszWidth != 0 && rszHeight != 0)
        {
resize(frame, frame, Size(rszWidth, rszHeight));
        }
 
blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, crop);
 
        // Check std values.
        if (std.val[0] != 0.0 && std.val[1] != 0.0 && std.val[2] != 0.0)
        {
            // Divide blob by std.
divide(blob, std, blob);
        }
 
net.setInput(blob);
        // double t_sum = 0.0;
        // double t;
        int classId;
        double confidence;
        cv::TickMeter timeRecorder;
timeRecorder.reset();
        Mat prob = net.forward();
        double t1;
timeRecorder.start();
prob = net.forward();
timeRecorder.stop();
t1 = timeRecorder.getTimeMilli();
 
timeRecorder.reset();
        for(int i = 0; i < 200; i++) {
timeRecorder.start();
prob = net.forward();
timeRecorder.stop();
 
            Point classIdPoint;
minMaxLoc(prob.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
classId = classIdPoint.x;
 
            // Put efficiency information.
            // std::vector<double> layersTimes;
            // double freq = getTickFrequency() / 1000;
            // t = net.getPerfProfile(layersTimes) / freq;
            // t_sum += t;
        }
        if (needSoftmax == true)
        {
            float maxProb = 0.0;
            float sum = 0.0;
            Mat softmaxProb;
 
maxProb = *std::max_element(prob.begin<float>(), prob.end<float>());
            cv::exp(prob-maxProb, softmaxProb);
sum = (float)cv::sum(softmaxProb)[0];
softmaxProb /= sum;
            Point classIdPoint;
minMaxLoc(softmaxProb.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
classId = classIdPoint.x;
        }
std::string label = format("单轮推理时间: %.2f ms", t1);
std::string label2 = format("200轮平均时间: %.2f ms", timeRecorder.getTimeMilli()/200);
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
putText(frame, label2, Point(0, 35), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
 
        // 打印预测类别。
label = format("%s: %.4f", (classes.empty() ? format("类别 #%d", classId).c_str()
classes[classId].c_str()),
confidence);
putText(frame, label, Point(0, 55), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
 
imshow(kWinName, frame);
    }
    return 0;
}