pc端ncnn搭建与测试

目录

一、本文系统配置

二、编译

参考：

三、测试

1、配置ncnn、protobuf、opencv

2、模型文件拷贝

3、代码测试

---

在PC使用NCNN框架推理加速模型，需要先获取ncnn编译后的动静态库。

一、本文系统配置

windows10

VS2019

CMake 3.18.4

二、编译

编译前需要先下载protobuf和ncnn源码。

参考：

Windows下ncnn环境配置（VS2019）_逮仔的博客-CSDN博客_ncnn vs2019

（一）ncnn | Windows（VS2019）编译_Silence_Zzz的博客-CSDN博客_vs2019编译ncnn

ncnn和opencv在vs2022上创建工程推理示例_三叔家的猫的博客-CSDN博客_ncnn

如果不想编译也可以使用官方编译好的文件：Releases · Tencent/ncnn · GitHub

三、测试

1、配置ncnn、protobuf、opencv

新建VS空项目工程。

以上编译是release版本，VS配置器选择Realease x64平台

VC++目录-包含目录：

VC++目录-库目录

链接器-输入-附加依赖项：

 

2、模型文件拷贝

将ncnn下的example文件加下的模型及标签文件拷贝到VS创建的工程下，找一张分类图片做测试

 

3、代码测试

// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2017 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 "net.h"

#include 
#if defined(USE_NCNN_SIMPLEOCV)
#include "simpleocv.h"
#else
#include 
#include 
#include
#endif
#include 
#include 

static int detect_squeezenet(const cv::Mat& bgr, std::vector& cls_scores)
{
    ncnn::Net squeezenet;

    squeezenet.opt.use_vulkan_compute = true;

    // the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
    squeezenet.load_param("squeezenet_v1.1.param");
    squeezenet.load_model("squeezenet_v1.1.bin");

    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, 227, 227);

    const float mean_vals[3] = {104.f, 117.f, 123.f};
    in.substract_mean_normalize(mean_vals, 0);

    ncnn::Extractor ex = squeezenet.create_extractor();

    ex.input("data", in);

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

    cls_scores.resize(out.w);
    for (int j = 0; j < out.w; j++)
    {
        cls_scores[j] = out[j];

    }

    return 0;
}

static int print_topk(const std::vector& cls_scores, int topk, std::vector& indexs, std::vector& scores)
{
    // partial sort topk with index
    int size = cls_scores.size();
    std::vector > vec;
    vec.resize(size);
    for (int i = 0; i < size; i++)
    {
        vec[i] = std::make_pair(cls_scores[i], i);
    }

    std::partial_sort(vec.begin(), vec.begin() + topk, vec.end(),
                      std::greater >());

    // print topk and score
    for (int i = 0; i < topk; i++)
    {
        float score = vec[i].first;
        int index = vec[i].second;
        fprintf(stderr, "%d = %f\n", index, score);
        indexs.push_back(index);
        scores.push_back(score);
    }

    return 0;
}

static int load_labels(std::string path, std::vector& labels)
{
    FILE* fp = fopen(path.c_str(), "r");

    while (!feof(fp))
    {
        char str[1024];
        fgets(str, 1024, fp);
        std::string str_s(str);

        if (str_s.length() > 0)
        {
            for (int i = 0; i < str_s.length(); i++)
            {
                if (str_s[i] == ' ')
                {
                    std::string strr = str_s.substr(i, str_s.length() - i - 1);
                    labels.push_back(strr);
                    i = str_s.length();
                }
            }
        }


    }
}



int main(int argc, char** argv)
{
    /*if (argc != 2)
    {
        fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
        return -1;
    }*/

    clock_t start = clock();

    std::vectorlabels;
    load_labels("synset_words.txt", labels);

    const char* imagepath = "rabbit.jpg";

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

    std::vectorindex;
    std::vectorscore;

    std::vector cls_scores;
    detect_squeezenet(m, cls_scores);

    print_topk(cls_scores, 3,index,score);

    for (int i = 0; i < index.size(); i++)
    {
        cv::putText(m, labels[index[i]], cv::Point(10, 10 + i * 30), 0, 0.5, cv::Scalar(255, 100, 100), 2, 2);
    }

    clock_t end = clock();
    std::cout << "运行时间：" << (double)(end - start) / CLOCKS_PER_SEC << std::endl;
    cv::imshow("m", m);
    //imwrite("dog_result.jpg", m);
    cv::waitKey(0);
    return 0;
}

运行结果：