Method run_PNet

examples/mtcnn/mtcnn.cpp:92–146

}

int mtcnn::run_PNet(image img, scale_window& win, std::vector<face_box>& box_list)
{
    int scale_h = win.h;

    int scale_w = win.w;
    float scale = win.scale;
    static bool first_run = true;
    image resImg = resize_image(img, scale_w, scale_h);

    /* input */
    tensor_t input_tensor = get_graph_tensor(PNet_graph, "data");
    int dims[] = {1, 3, scale_h, scale_w};
    set_tensor_shape(input_tensor, dims, 4);
    int in_mem = sizeof(float) * scale_h * scale_w * 3;
    // std::cout<<"mem "<<in_mem<<"\n";
    float* input_data = ( float* )malloc(in_mem);

    memcpy(input_data, resImg.data, sizeof(float) * 3 * scale_h * scale_w);

    set_tensor_buffer(input_tensor, input_data, in_mem);

    if(first_run)
    {
        if(prerun_graph(PNet_graph) != 0)
        {
            std::cout << "Prerun PNet graph failed, errno: " << get_tengine_errno() << "\n";
            return 1;
        }
        first_run = false;
    }

    if(run_graph(PNet_graph, 1) != 0)
    {
        std::cout << "Run PNet graph failed, errno: " << get_tengine_errno() << "\n";
        return 1;
    }
    free(input_data);
    /* output */
    tensor_t tensor = get_graph_tensor(PNet_graph, "conv4-2");
    get_tensor_shape(tensor, dims, 4);
    float* reg_data = ( float* )get_tensor_buffer(tensor);
    int feature_h = dims[2];
    int feature_w = dims[3];

    tensor = get_graph_tensor(PNet_graph, "prob1");
    float* prob_data = ( float* )get_tensor_buffer(tensor);
    std::vector<face_box> candidate_boxes;
    generate_bounding_box(prob_data, reg_data, scale, conf_p_threshold_, feature_h, feature_w, candidate_boxes, true);

    release_graph_tensor(input_tensor);
    release_graph_tensor(tensor);

    nms_boxes(candidate_boxes, nms_p_threshold_, NMS_UNION, box_list);
    return 0;
}

int mtcnn::run_RNet(image img, std::vector<face_box>& pnet_boxes, std::vector<face_box>& output_boxes)
{