Method run_PNet

examples/caffe_wrapper/mtcnn/caffe_mtcnn.cpp:137–171

}

int caffe_mtcnn::run_PNet(const cv::Mat& img, scale_window& win, std::vector<face_box>& box_list)
{
    cv::Mat resized;
    int scale_h = win.h;
    int scale_w = win.w;
    float scale = win.scale;

    cv::resize(img, resized, cv::Size(scale_w, scale_h), 0, 0, cv::INTER_NEAREST);

    Blob<float>* input_blob = PNet_->input_blobs()[0];
    input_blob->Reshape(1, 3, scale_h, scale_w);
    PNet_->Reshape();

    std::vector<cv::Mat> input_channels;
    float* input_data = PNet_->input_blobs()[0]->mutable_cpu_data();
    set_input_buffer(input_channels, input_data, scale_h, scale_w);

    cv::split(resized, input_channels);

    PNet_->Forward();

    Blob<float>* reg = PNet_->output_blobs()[0];
    Blob<float>* confidence = PNet_->output_blobs()[1];

    int feature_h = reg->shape(2);
    int feature_w = reg->shape(3);
    std::vector<face_box> candidate_boxes;

    generate_bounding_box(confidence->cpu_data(), confidence->count(), reg->cpu_data(), scale, pnet_threshold_,
                          feature_h, feature_w, candidate_boxes, true);

    nms_boxes(candidate_boxes, 0.5, NMS_UNION, box_list);

    return 0;
}

void caffe_mtcnn::copy_one_patch(const cv::Mat& img, face_box& input_box, float* data_to, int width, int height)
{