C++ Net::getLayer方法代码示例

// https://github.com/richzhang/colorization
TEST(Reproducibility_Colorization, Accuracy)
{
    const float l1 = 1e-5;
    const float lInf = 3e-3;

    Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
    Mat ref = blobFromNPY(_tf("colorization_out.npy"));
    Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));

    const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
    const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
    Net net = readNetFromCaffe(proto, model);

    net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
    net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));

    net.setInput(inp);
    Mat out = net.forward();

    normAssert(out, ref, "", l1, lInf);
}

    void expectNoFallbacks(Net& net)
    {
        // Check if all the layers are supported with current backend and target.
        // Some layers might be fused so their timings equal to zero.
        std::vector<double> timings;
        net.getPerfProfile(timings);
        std::vector<String> names = net.getLayerNames();
        CV_Assert(names.size() == timings.size());

        for (int i = 0; i < names.size(); ++i)
        {
            Ptr<dnn::Layer> l = net.getLayer(net.getLayerId(names[i]));
            bool fused = !timings[i];
            if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused)
                CV_Error(Error::StsNotImplemented, "Layer [" + l->name + "] of type [" +
                         l->type + "] is expected to has backend implementation");
        }
    }

int main(int argc, char **argv)
{
    
    string imageFileName;
    // Take arguments from commmand line
    if (argc < 2)
    {
        cout << "Please input the greyscale image filename." << endl;
        cout << "Usage example: ./colorizeImage.out greyscaleImage.png" << endl;
        return 1;
    }
    
    imageFileName = argv[1];
    Mat img = imread(imageFileName);
    if (img.empty())
    {
        cout << "Can't read image from file: " << imageFileName << endl;
        return 2;
    }
    
    string protoFile = "./models/colorization_deploy_v2.prototxt";
    string weightsFile = "./models/colorization_release_v2.caffemodel";
    //string weightsFile = "./models/colorization_release_v2_norebal.caffemodel";

    double t = (double) cv::getTickCount();
    
    // fixed input size for the pretrained network
    const int W_in = 224;
    const int H_in = 224;
    Net net = dnn::readNetFromCaffe(protoFile, weightsFile);
    
    // setup additional layers:
    int sz[] = {2, 313, 1, 1};
    const Mat pts_in_hull(4, sz, CV_32F, hull_pts);
    Ptr<dnn::Layer> class8_ab = net.getLayer("class8_ab");
    class8_ab->blobs.push_back(pts_in_hull);
    Ptr<dnn::Layer> conv8_313_rh = net.getLayer("conv8_313_rh");
    conv8_313_rh->blobs.push_back(Mat(1, 313, CV_32F, Scalar(2.606)));
    
    // extract L channel and subtract mean
    Mat lab, L, input;
    img.convertTo(img, CV_32F, 1.0/255);
    cvtColor(img, lab, COLOR_BGR2Lab);
    extractChannel(lab, L, 0);
    resize(L, input, Size(W_in, H_in));
    input -= 50;
    
    // run the L channel through the network
    Mat inputBlob = blobFromImage(input);
    net.setInput(inputBlob);
    Mat result = net.forward();
    
    // retrieve the calculated a,b channels from the network output
    Size siz(result.size[2], result.size[3]);
    Mat a = Mat(siz, CV_32F, result.ptr(0,0));
    Mat b = Mat(siz, CV_32F, result.ptr(0,1));
    resize(a, a, img.size());
    resize(b, b, img.size());
    
    // merge, and convert back to BGR
    Mat color, chn[] = {L, a, b};
    merge(chn, 3, lab);
    cvtColor(lab, color, COLOR_Lab2BGR);

    t = ((double)cv::getTickCount() - t)/cv::getTickFrequency();
    cout << "Time taken : " << t << " secs" << endl;
    
    string str = imageFileName;
    str.replace(str.end()-4, str.end(), "");
    str = str+"_colorized.png";
    
    color = color*255;
    color.convertTo(color, CV_8U);
    imwrite(str, color);

    cout << "Colorized image saved as " << str << endl;
    
    return 0;
}

int main(int argc, char **argv)
{
    const string about =
        "This sample demonstrates recoloring grayscale images with dnn.\n"
        "This program is based on:\n"
        "  http://richzhang.github.io/colorization\n"
        "  https://github.com/richzhang/colorization\n"
        "Download caffemodel and prototxt files:\n"
        "  http://eecs.berkeley.edu/~rich.zhang/projects/2016_colorization/files/demo_v2/colorization_release_v2.caffemodel\n"
        "  https://raw.githubusercontent.com/richzhang/colorization/master/colorization/models/colorization_deploy_v2.prototxt\n";
    const string keys =
        "{ h help |                                    | print this help message }"
        "{ proto  | colorization_deploy_v2.prototxt    | model configuration }"
        "{ model  | colorization_release_v2.caffemodel | model weights }"
        "{ image  | space_shuttle.jpg                  | path to image file }"
        "{ opencl |                                    | enable OpenCL }";
    CommandLineParser parser(argc, argv, keys);
    parser.about(about);
    if (parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }
    string modelTxt = samples::findFile(parser.get<string>("proto"));
    string modelBin = samples::findFile(parser.get<string>("model"));
    string imageFile = samples::findFile(parser.get<string>("image"));
    bool useOpenCL = parser.has("opencl");
    if (!parser.check())
    {
        parser.printErrors();
        return 1;
    }

    Mat img = imread(imageFile);
    if (img.empty())
    {
        cout << "Can't read image from file: " << imageFile << endl;
        return 2;
    }

    // fixed input size for the pretrained network
    const int W_in = 224;
    const int H_in = 224;
    Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
    if (useOpenCL)
        net.setPreferableTarget(DNN_TARGET_OPENCL);

    // setup additional layers:
    int sz[] = {2, 313, 1, 1};
    const Mat pts_in_hull(4, sz, CV_32F, hull_pts);
    Ptr<dnn::Layer> class8_ab = net.getLayer("class8_ab");
    class8_ab->blobs.push_back(pts_in_hull);
    Ptr<dnn::Layer> conv8_313_rh = net.getLayer("conv8_313_rh");
    conv8_313_rh->blobs.push_back(Mat(1, 313, CV_32F, Scalar(2.606)));

    // extract L channel and subtract mean
    Mat lab, L, input;
    img.convertTo(img, CV_32F, 1.0/255);
    cvtColor(img, lab, COLOR_BGR2Lab);
    extractChannel(lab, L, 0);
    resize(L, input, Size(W_in, H_in));
    input -= 50;

    // run the L channel through the network
    Mat inputBlob = blobFromImage(input);
    net.setInput(inputBlob);
    Mat result = net.forward();

    // retrieve the calculated a,b channels from the network output
    Size siz(result.size[2], result.size[3]);
    Mat a = Mat(siz, CV_32F, result.ptr(0,0));
    Mat b = Mat(siz, CV_32F, result.ptr(0,1));
    resize(a, a, img.size());
    resize(b, b, img.size());

    // merge, and convert back to BGR
    Mat color, chn[] = {L, a, b};
    merge(chn, 3, lab);
    cvtColor(lab, color, COLOR_Lab2BGR);

    imshow("color", color);
    imshow("original", img);
    waitKey();
    return 0;
}

void ONNXImporter::populateNet(Net dstNet)
{
    CV_Assert(model_proto.has_graph());
    opencv_onnx::GraphProto graph_proto = model_proto.graph();
    std::map<std::string, Mat> constBlobs = getGraphTensors(graph_proto);
    // List of internal blobs shapes.
    std::map<std::string, MatShape> outShapes;
    // Add all the inputs shapes. It includes as constant blobs as network's inputs shapes.
    for (int i = 0; i < graph_proto.input_size(); ++i)
    {
        opencv_onnx::ValueInfoProto valueInfoProto = graph_proto.input(i);
        CV_Assert(valueInfoProto.has_type());
        opencv_onnx::TypeProto typeProto = valueInfoProto.type();
        CV_Assert(typeProto.has_tensor_type());
        opencv_onnx::TypeProto::Tensor tensor = typeProto.tensor_type();
        CV_Assert(tensor.has_shape());
        opencv_onnx::TensorShapeProto tensorShape = tensor.shape();

        MatShape inpShape(tensorShape.dim_size());
        for (int j = 0; j < inpShape.size(); ++j)
        {
            inpShape[j] = tensorShape.dim(j).dim_value();
        }
        outShapes[valueInfoProto.name()] = inpShape;
    }

    std::string framework_name;
    if (model_proto.has_producer_name()) {
        framework_name = model_proto.producer_name();
    }

    // create map with network inputs (without const blobs)
    std::map<std::string, LayerInfo> layer_id;
    std::map<std::string, LayerInfo>::iterator layerId;
    std::map<std::string, MatShape>::iterator shapeIt;
    // fill map: push layer name, layer id and output id
    std::vector<String> netInputs;
    for (int j = 0; j < graph_proto.input_size(); j++)
    {
        const std::string& name = graph_proto.input(j).name();
        if (constBlobs.find(name) == constBlobs.end()) {
            netInputs.push_back(name);
            layer_id.insert(std::make_pair(name, LayerInfo(0, netInputs.size() - 1)));
        }
    }
    dstNet.setInputsNames(netInputs);

    int layersSize = graph_proto.node_size();
    LayerParams layerParams;
    opencv_onnx::NodeProto node_proto;

    for(int li = 0; li < layersSize; li++)
    {
        node_proto = graph_proto.node(li);
        layerParams = getLayerParams(node_proto);
        CV_Assert(node_proto.output_size() >= 1);
        layerParams.name = node_proto.output(0);

        std::string layer_type = node_proto.op_type();
        layerParams.type = layer_type;


        if (layer_type == "MaxPool")
        {
            layerParams.type = "Pooling";
            layerParams.set("pool", "MAX");
            layerParams.set("ceil_mode", isCeilMode(layerParams));
        }
        else if (layer_type == "AveragePool")
        {
            layerParams.type = "Pooling";
            layerParams.set("pool", "AVE");
            layerParams.set("ceil_mode", isCeilMode(layerParams));
            layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
        }
        else if (layer_type == "GlobalAveragePool")
        {
            layerParams.type = "Pooling";
            layerParams.set("pool", "AVE");
            layerParams.set("global_pooling", true);
        }
        else if (layer_type == "Add" || layer_type == "Sum")
        {
            if (layer_id.find(node_proto.input(1)) == layer_id.end())
            {
                Mat blob = getBlob(node_proto, constBlobs, 1);
                blob = blob.reshape(1, 1);
                if (blob.total() == 1) {
                    layerParams.type = "Power";
                    layerParams.set("shift", blob.at<float>(0));
                }
                else {
                    layerParams.type = "Scale";
                    layerParams.set("bias_term", true);
                    layerParams.blobs.push_back(blob);
                }
            }
            else {
                layerParams.type = "Eltwise";
            }
//.........这里部分代码省略.........

int main(int argc, char** argv)
{
    CommandLineParser parser(argc, argv, keys);
    parser.about("Use this script to run object detection deep learning networks using OpenCV.");
    if (argc == 1 || parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }

    confThreshold = parser.get<float>("thr");
    float scale = parser.get<float>("scale");
    Scalar mean = parser.get<Scalar>("mean");
    bool swapRB = parser.get<bool>("rgb");
    int inpWidth = parser.get<int>("width");
    int inpHeight = parser.get<int>("height");

    // 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);
        }
    }

    // Load a model.
    CV_Assert(parser.has("model"));
    Net net = readNet(parser.get<String>("model"), parser.get<String>("config"), parser.get<String>("framework"));
    net.setPreferableBackend(parser.get<int>("backend"));
    net.setPreferableTarget(parser.get<int>("target"));

    // Create a window
    static const std::string kWinName = "Deep learning object detection in OpenCV";
    namedWindow(kWinName, WINDOW_NORMAL);
    int initialConf = (int)(confThreshold * 100);
    createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback);

    // Open a video file or an image file or a camera stream.
    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;
        }

        // Create a 4D blob from a frame.
        Size inpSize(inpWidth > 0 ? inpWidth : frame.cols,
                     inpHeight > 0 ? inpHeight : frame.rows);
        blobFromImage(frame, blob, scale, inpSize, mean, swapRB, false);

        // Run a model.
        net.setInput(blob);
        if (net.getLayer(0)->outputNameToIndex("im_info") != -1)  // Faster-RCNN or R-FCN
        {
            resize(frame, frame, inpSize);
            Mat imInfo = (Mat_<float>(1, 3) << inpSize.height, inpSize.width, 1.6f);
            net.setInput(imInfo, "im_info");
        }
        std::vector<Mat> outs;
        net.forward(outs, getOutputsNames(net));

        postprocess(frame, outs, net);

        // 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));

        imshow(kWinName, frame);
    }
    return 0;
}

void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net)
{
    static std::vector<int> outLayers = net.getUnconnectedOutLayers();
    static std::string outLayerType = net.getLayer(outLayers[0])->type;

    if (net.getLayer(0)->outputNameToIndex("im_info") != -1)  // Faster-RCNN or R-FCN
    {
        // Network produces output blob with a shape 1x1xNx7 where N is a number of
        // detections and an every detection is a vector of values
        // [batchId, classId, confidence, left, top, right, bottom]
        CV_Assert(outs.size() == 1);
        float* data = (float*)outs[0].data;
        for (size_t i = 0; i < outs[0].total(); i += 7)
        {
            float confidence = data[i + 2];
            if (confidence > confThreshold)
            {
                int left = (int)data[i + 3];
                int top = (int)data[i + 4];
                int right = (int)data[i + 5];
                int bottom = (int)data[i + 6];
                int classId = (int)(data[i + 1]) - 1;  // Skip 0th background class id.
                drawPred(classId, confidence, left, top, right, bottom, frame);
            }
        }
    }
    else if (outLayerType == "DetectionOutput")
    {
        // Network produces output blob with a shape 1x1xNx7 where N is a number of
        // detections and an every detection is a vector of values
        // [batchId, classId, confidence, left, top, right, bottom]
        CV_Assert(outs.size() == 1);
        float* data = (float*)outs[0].data;
        for (size_t i = 0; i < outs[0].total(); i += 7)
        {
            float confidence = data[i + 2];
            if (confidence > confThreshold)
            {
                int left = (int)(data[i + 3] * frame.cols);
                int top = (int)(data[i + 4] * frame.rows);
                int right = (int)(data[i + 5] * frame.cols);
                int bottom = (int)(data[i + 6] * frame.rows);
                int classId = (int)(data[i + 1]) - 1;  // Skip 0th background class id.
                drawPred(classId, confidence, left, top, right, bottom, frame);
            }
        }
    }
    else if (outLayerType == "Region")
    {
        std::vector<int> classIds;
        std::vector<float> confidences;
        std::vector<Rect> boxes;
        for (size_t i = 0; i < outs.size(); ++i)
        {
            // Network produces output blob with a shape NxC where N is a number of
            // detected objects and C is a number of classes + 4 where the first 4
            // numbers are [center_x, center_y, width, height]
            float* data = (float*)outs[i].data;
            for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
            {
                Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
                Point classIdPoint;
                double confidence;
                minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
                if (confidence > confThreshold)
                {
                    int centerX = (int)(data[0] * frame.cols);
                    int centerY = (int)(data[1] * frame.rows);
                    int width = (int)(data[2] * frame.cols);
                    int height = (int)(data[3] * frame.rows);
                    int left = centerX - width / 2;
                    int top = centerY - height / 2;

                    classIds.push_back(classIdPoint.x);
                    confidences.push_back((float)confidence);
                    boxes.push_back(Rect(left, top, width, height));
                }
            }
        }
        std::vector<int> indices;
        NMSBoxes(boxes, confidences, confThreshold, 0.4f, indices);
        for (size_t i = 0; i < indices.size(); ++i)
        {
            int idx = indices[i];
            Rect box = boxes[idx];
            drawPred(classIds[idx], confidences[idx], box.x, box.y,
                     box.x + box.width, box.y + box.height, frame);
        }
    }
    else
        CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
}

void postprocess(Mat& frame, const Mat& out, Net& net)
{
    static std::vector<int> outLayers = net.getUnconnectedOutLayers();
    static std::string outLayerType = net.getLayer(outLayers[0])->type;

    float* data = (float*)out.data;
    if (net.getLayer(0)->outputNameToIndex("im_info") != -1)  // Faster-RCNN or R-FCN
    {
        // Network produces output blob with a shape 1x1xNx7 where N is a number of
        // detections and an every detection is a vector of values
        // [batchId, classId, confidence, left, top, right, bottom]
        for (size_t i = 0; i < out.total(); i += 7)
        {
            float confidence = data[i + 2];
            if (confidence > confThreshold)
            {
                int left = (int)data[i + 3];
                int top = (int)data[i + 4];
                int right = (int)data[i + 5];
                int bottom = (int)data[i + 6];
                int classId = (int)(data[i + 1]) - 1;  // Skip 0th background class id.
                drawPred(classId, confidence, left, top, right, bottom, frame);
            }
        }
    }
    else if (outLayerType == "DetectionOutput")
    {
        // Network produces output blob with a shape 1x1xNx7 where N is a number of
        // detections and an every detection is a vector of values
        // [batchId, classId, confidence, left, top, right, bottom]
        for (size_t i = 0; i < out.total(); i += 7)
        {
            float confidence = data[i + 2];
            if (confidence > confThreshold)
            {
                int left = (int)(data[i + 3] * frame.cols);
                int top = (int)(data[i + 4] * frame.rows);
                int right = (int)(data[i + 5] * frame.cols);
                int bottom = (int)(data[i + 6] * frame.rows);
                int classId = (int)(data[i + 1]) - 1;  // Skip 0th background class id.
                drawPred(classId, confidence, left, top, right, bottom, frame);
            }
        }
    }
    else if (outLayerType == "Region")
    {
        // Network produces output blob with a shape NxC where N is a number of
        // detected objects and C is a number of classes + 4 where the first 4
        // numbers are [center_x, center_y, width, height]
        for (int i = 0; i < out.rows; ++i, data += out.cols)
        {
            Mat confidences = out.row(i).colRange(5, out.cols);
            Point classIdPoint;
            double confidence;
            minMaxLoc(confidences, 0, &confidence, 0, &classIdPoint);
            if (confidence > confThreshold)
            {
                int classId = classIdPoint.x;
                int centerX = (int)(data[0] * frame.cols);
                int centerY = (int)(data[1] * frame.rows);
                int width = (int)(data[2] * frame.cols);
                int height = (int)(data[3] * frame.rows);
                int left = centerX - width / 2;
                int top = centerY - height / 2;
                drawPred(classId, (float)confidence, left, top, left + width, top + height, frame);
            }
        }
    }
    else
        CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
}

int main(int argc, char **argv)
{
    CommandLineParser parser(argc, argv,
        "{ help           | false | print this help message }"
        "{ proto          | colorization_deploy_v2.prototxt | model configuration }"
        "{ model          | colorization_release_v2.caffemodel | model weights }"
        "{ image          | space_shuttle.jpg | path to image file }"
        "{ opencl         | false | enable OpenCL }"
    );

    String modelTxt = parser.get<string>("proto");
    String modelBin = parser.get<string>("model");
    String imageFile = parser.get<String>("image");
    if (parser.get<bool>("help") || modelTxt.empty() || modelBin.empty() || imageFile.empty())
    {
        cout << "A sample app to demonstrate recoloring grayscale images with dnn." << endl;
        parser.printMessage();
        return 0;
    }

    // fixed input size for the pretrained network
    int W_in = 224;
    int H_in = 224;

    Net net = dnn::readNetFromCaffe(modelTxt, modelBin);

    // setup additional layers:
    int sz[] = {2, 313, 1, 1};
    Mat pts_in_hull(4, sz, CV_32F, hull_pts);
    Ptr<dnn::Layer> class8_ab = net.getLayer("class8_ab");
    class8_ab->blobs.push_back(pts_in_hull);

    Ptr<dnn::Layer> conv8_313_rh = net.getLayer("conv8_313_rh");
    conv8_313_rh->blobs.push_back(Mat(1, 313, CV_32F, 2.606f));

    if (parser.get<bool>("opencl"))
    {
        net.setPreferableTarget(DNN_TARGET_OPENCL);
    }

    Mat img = imread(imageFile);
    if (img.empty())
    {
        std::cerr << "Can't read image from the file: " << imageFile << std::endl;
        exit(-1);
    }

    // extract L channel and subtract mean
    Mat lab, L, input;
    img.convertTo(img, CV_32F, 1.0/255);
    cvtColor(img, lab, COLOR_BGR2Lab);
    extractChannel(lab, L, 0);
    resize(L, input, Size(W_in, H_in));
    input -= 50;

    // run the L channel through the network
    Mat inputBlob = blobFromImage(input);
    net.setInput(inputBlob);
    Mat result = net.forward("class8_ab");

    // retrieve the calculated a,b channels from the network output
    Size siz(result.size[2], result.size[3]);
    Mat a = Mat(siz, CV_32F, result.ptr(0,0));
    Mat b = Mat(siz, CV_32F, result.ptr(0,1));
    resize(a, a, img.size());
    resize(b, b, img.size());

    // merge, and convert back to bgr
    Mat color, chn[] = {L, a, b};
    merge(chn, 3, lab);
    cvtColor(lab, color, COLOR_Lab2BGR);

    namedWindow("color", WINDOW_NORMAL);
    namedWindow("original", WINDOW_NORMAL);
    imshow("color", color);
    imshow("original", img);
    waitKey();
    return 0;
}