image classification using pre-trained caffe-model

Question

I'm doing a project on image classification using pre trained caffe model, in visual studio, openCV3.4.0, C++.

I'm facing with some errors:

1. readNet:Identifier not found
2. blobFromImage:function does not take 7 arguments

I copied the code from this link

please help me, as i'm new to this.Thanks in advance.

Code:

const char* keys =
"{ help  h     | | Print help message. }"
"{ input i     | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
"{ model m     | | Path to a binary file of model contains trained weights. "
"It could be a file with extensions .caffemodel (Caffe), "
".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet) }"
"{ config c    | | Path to a text file of model contains network configuration. "
"It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet) }"
"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
"{ classes     | | Optional path to a text file with names of classes. }"
"{ mean        | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
"{ scale       | 1 | Preprocess input image by multiplying on a scale factor. }"
"{ width       |   | Preprocess input image by resizing to a specific width. }"
"{ height      |   | Preprocess input image by resizing to a specific height. }"
"{ rgb         |   | Indicate that model works with RGB input images instead BGR ones. }"
"{ backend     | 0 | Choose one of computation backends: "
"0: default C++ backend, "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
"{ target      | 0 | Choose one of target computation devices: "
"0: CPU target (by default),"
"1: OpenCL }";


using namespace cv;
using namespace dnn;
std::vector<std::string> classes;


int main(int argc, char** argv)
{
    CommandLineParser parser(argc, argv, keys);
    parser.about("Use this script to run classification deep learning networks using OpenCV.");
    if (argc == 1 || parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }
    float scale = parser.get<float>("scale");
    Scalar mean = parser.get<Scalar>("mean");
    bool swapRB = parser.get<bool>("rgb");
    CV_Assert(parser.has("width"), parser.has("height"));
    int inpWidth = parser.get<int>("width");
    int inpHeight = parser.get<int>("height");
    String model = parser.get<String>("model");
    String config = parser.get<String>("config");
    String framework = parser.get<String>("framework");
    int backendId = parser.get<int>("backend");
    int targetId = parser.get<int>("target");
    // 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, "File " + file + " not found");
        std::string line;
        while (std::getline(ifs, line))
        {
            classes.push_back(line);
        }
    }
    CV_Assert(parser.has("model"));
    Net net = readNet(model, config, framework);
    net.setPreferableBackend(backendId);
    net.setPreferableTarget(targetId);
    // Create a window
    static const std::string kWinName = "Deep learning image classification in OpenCV";
    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;
        }
        blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false);
        net.setInput(blob);
        Mat prob = net.forward();
        Point classIdPoint;
        double confidence;
        minMaxLoc(prob.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
        int classId = classIdPoint.x;
        // Put efficiency information.
        std::vector<double> layersTimes;
        double freq = getTickFrequency() / 1000;
        double t = net.getPerfProfile(layersTimes) / freq;
        std::string label = format("Inference time: %.2f ms", t);
        putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
        // Print predicted class.
        label = format("%s: %.4f", (classes.empty() ? format("Class #%d", classId).c_str() :
            classes[classId].c_str()),
            confidence);
        putText(frame, label, Point(0, 40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
        imshow(kWinName, frame);
    }
    return 0;
}

Answer 1

The code you copied refers to the development branch 3.4.1-dev which has quite some differences compared to the version you are using (3.4.0).

For once, according to the documentation here the method readNet is not available (hence, the error).

Either upgrade to branch 3.4.1-dev or use the example provided for your version here.