ncnn之yolov2

ncnn框架是一个非常好的深度学习部署框架，基于ncnn，我们可以很方便的对训练好的深度学习模型部署到手机（Android或Ios）、linux和PC端上，而且ncnn还提供了各个框架模型转换成ncnn可读形式模型的工具，如图1所示。

图1 ncnn提供的模型转换工具

分别对应caffe、mxnet、pytorch、tensorflow，以及onnx转换，将这些框架训练好的深度学习模型，转换成ncnn框架可读的模型形式（.param和.bin文件），然后，基于ncnn框架，将模型进行部署。

在examples文件夹中，也提供了一些实例，像squeezenet、mobilenet-v1、mobilenet-v2、mobilefacenet，github上已经有资源了，这里，我做一个yolov2的笔记。

代码如下：

// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include <stdio.h>
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include "ncnn/net.h"

struct Object
{
    cv::Rect_<float> rect;
    int label;
    float prob;
};

static int detect_yolov2(const cv::Mat& bgr, std::vector<Object>& objects)
{
    ncnn::Net yolov2;

    // original pretrained model from https://github.com/eric612/Caffe-YOLOv2-Windows
    // yolov2_deploy.prototxt
    // yolov2_deploy_iter_30000.caffemodel
    // https://drive.google.com/file/d/17w7oZBbTHPI5TMuD9DKQzkPhSVDaTlC9/view?usp=sharing
    yolov2.load_param("../model/mobilenet_yolo.param");
    yolov2.load_model("../model/mobilenet_yolo.bin");

    // https://github.com/eric612/MobileNet-YOLO
    // https://github.com/eric612/MobileNet-YOLO/blob/master/models/yolov2/mobilenet_yolo_deploy%20.prototxt
    // https://github.com/eric612/MobileNet-YOLO/blob/master/models/yolov2/mobilenet_yolo_deploy_iter_57000.caffemodel
//     yolov2.load_param("mobilenet_yolo.param");
//     yolov2.load_model("mobilenet_yolo.bin");

    const int target_size = 416;

    int img_w = bgr.cols;
    int img_h = bgr.rows;

    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, target_size, target_size);

    // the Caffe-YOLOv2-Windows style
    // X' = X * scale - mean
    const float mean_vals[3] = {0.5f, 0.5f, 0.5f};
    const float norm_vals[3] = {0.007843f, 0.007843f, 0.007843f};
    in.substract_mean_normalize(0, norm_vals);
    in.substract_mean_normalize(mean_vals, 0);

    ncnn::Extractor ex = yolov2.create_extractor();
    ex.set_num_threads(4);

    ex.input("data", in);

    ncnn::Mat out;
    ex.extract("detection_out", out);

//     printf("%d %d %d\n", out.w, out.h, out.c);
    objects.clear();
    for (int i=0; i<out.h; i++)
    {
        const float* values = out.row(i);

        Object object;
        object.label = values[0];
        object.prob = values[1];
        object.rect.x = values[2] * img_w;
        object.rect.y = values[3] * img_h;
        object.rect.width = values[4] * img_w - object.rect.x;
        object.rect.height = values[5] * img_h - object.rect.y;

        objects.push_back(object);
    }

    return 0;
}

static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
{
    static const char* class_names[] = {"background",
        "aeroplane", "bicycle", "bird", "boat",
        "bottle", "bus", "car", "cat", "chair",
        "cow", "diningtable", "dog", "horse",
        "motorbike", "person", "pottedplant",
        "sheep", "sofa", "train", "tvmonitor"};

    cv::Mat image = bgr.clone();

    for (size_t i = 0; i < objects.size(); i++)
    {
        const Object& obj = objects[i];

        fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
                obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);

        cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0));

        char text[256];
        sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);

        int baseLine = 0;
        cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

        int x = obj.rect.x;
        int y = obj.rect.y - label_size.height - baseLine;
        if (y < 0)
            y = 0;
        if (x + label_size.width > image.cols)
            x = image.cols - label_size.width;

        cv::rectangle(image, cv::Rect(cv::Point(x, y),
                                      cv::Size(label_size.width, label_size.height + baseLine)),
                      cv::Scalar(255, 255, 255), CV_FILLED);

        cv::putText(image, text, cv::Point(x, y + label_size.height),
                    cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
    }

    cv::imshow("image", image);
    cv::waitKey(0);
}

int main(int argc, char** argv)
{
    const char* imagepath = "test.jpg";

    cv::Mat m = cv::imread(imagepath, CV_LOAD_IMAGE_COLOR);
    if (m.empty())
    {
        fprintf(stderr, "cv::imread %s failed\n", imagepath);
        return -1;
    }

    std::vector<Object> objects;
    detect_yolov2(m, objects);

    draw_objects(m, objects);

    return 0;
}

效果如图2所示。

图2 yolo v2效果图

最后，安利一波模型，这里用到的模型可以从这里下载：https://download.csdn.net/download/sinat_31425585/10737783

参考资料：

[1] https://github.com/Tencent/ncnn

[2] https://blog.csdn.net/CVAIDL/article/details/83030873

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