【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows NCNN部署

发布时间:2023-05-04 08:00

目录

  • 前言
  • 一、Pytorch2ONNX
    • 1.1、具体操作
    • 1.2、代码
  • 二、ONNX2NCNN
    • 2.1、下载、编译protobuf
    • 2.2、下载编译ncnn
    • 2.3、生成ncnn模型
    • 2.4、优化ncnn
  • 三、VS2019编译NCNN
    • 3.1、VS2019环境配置
    • 3.2、使用VS2019编译ncnn权重模型
  • 四、结果比较
  • 四、v5lites.cpp源码:
  • Reference

前言

以YOLOv5为例子,在Windows下将权重文件进行这一整套的转换。

进行转换之前,首先得先安装以下环境:

  1. Visual Studio 2019 这个b站很多下载安装介绍,就不说了。
  2. OpenCV 注意它的安装配置:VS中opencv的配置.
  3. cmake 注意它的安装配置:windows下载安装cmake.

简单说说他们几个的关系:VS是美国微软公司的开发工具包系列产品,在该项目中有提供GCC、编译运行ncnn模型的C++程序的作用;CMake 是一个跨平台的,开源的构建系统,CMake可以通过CMakeLists.txt文件来产生特定平台的标准的构建文件,例如:为Unix平台生成makefiles文件(使用GCC编译),为Windows MSVC生成 projects/workspaces(使用VS IDE编译)或Makefile文件(使用nmake编译);OpenCV是一个基于BSD许可(开源)发行的跨平台计算机视觉和机器学习软件库,学习计算机视觉基本上离不开OpenCV。

一、Pytorch2ONNX

1.1、具体操作

首先,我们对数据集进行训练,得到best的Pytorch权重文件:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

在pycharm当前虚拟环境中执行

python models/export.py

会在同一个目录下生成onnx权重文件:

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

注意这里如果在pycharm里执行报如下错:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
官网也有人提到过这个问题,应该是pycharm版本的问题,直接在 mini-conda 中执行这个文件即可:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

注意export.py中的参数weights、img-size等还需要设置。

再简化ONNX,在当前环境下:

pip install onnx-simplifier

再执行

python -m onnxsim weights/v5lites-hive-best.onnx weights/v5lites-hive-best-sim.onnx

生成简化ONNX权重文件:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

这一步如果不做,后面ONNX转NCNN可能会报错。

1.2、代码

export.py主要由两部分代码组成:加载模型、模型前传forward + ONNX Export

1、加载模型+forward:

...
model = attempt_load(opt.weights, map_location=device)  # load FP32 model
...
y = model(img)  # dry run forward

2、ONNX Export:

# 2、ONNX export
    try:
        import onnx
        print(\'\\nStarting ONNX export with onnx %s...\' % onnx.__version__)
        f = opt.weights.replace(\'.pt\', \'.onnx\')  # filename

        # model: 由pt文件中读取的模型
        # args: 模型的输入 这里只需要输入图片即可,其他全部为默认值
        # f: onnx保存的文件名(地址)
        # verbose:  如果指定True,我们将打印出转换的一些信息
        # opset_version: ONNX的op(算子)版本
        # input_names: 定义输入层名
        # output_names: 定义输出层名
        # dynamic_axes: 一般可以不用关这三个动态输入输出变量
        torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=[\'images\'],
                          output_names=[\'classes\', \'boxes\'] if y is None else [\'output\'],
                          dynamic_axes={\'images\': {0: \'batch\', 2: \'height\', 3: \'width\'},  # size(1,3,640,640)
                                        \'output\': {0: \'batch\', 2: \'y\', 3: \'x\'}} if opt.dynamic else None)

        # Checks onnx weight file
        onnx_model = onnx.load(f)  # load onnx model
        onnx.checker.check_model(onnx_model)  # check onnx model
        # print(onnx.helper.printable_graph(onnx_model.graph))  # print a human readable model
        print(\'ONNX export success, saved as %s\' % f)
    except Exception as e:
        print(\'ONNX export failure: %s\' % e)

主要是调用了torch.onnx.export函数。这里注意要先在当前虚拟环境中 pip install onnx。

二、ONNX2NCNN

先在windows下搭建ncnn环境:

2.1、下载、编译protobuf

(1)下载 protobuf. 解压后最好和ncnn放在同一个目录。protobuf用于转换模型,protobuf(Google Protocol Buffers)是Google提供一个具有高效的协议数据交换格式工具库(类似Json),Protobuf 提供了C++、java、python语言的支持,提供了windows(proto.exe)和linux平台动态编译生成proto文件对应的源文件。

(2)在 vs2019 的本地工具命令提示符下编译 protobuf
\"在这里插入图片描述\"

指令:

> cd <protobuf-root-dir>                   #是指protobuf文件夹的根目录
> mkdir build_vs2019
> cd build_vs2019
> cmake -G\"NMake Makefiles\" -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=%cd%/install -Dprotobuf_BUILD_TESTS=OFF -Dprotobuf_MSVC_STATIC_RUNTIME=OFF ../cmake
> nmake                                   #编译cmake生成的Makefile文件
> nmake install                           #安装操作,把生成的文件复制到对应的目录中,并修改环境变量等。

cmake成功
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
nmake成功:

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

nmake install成功:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

2.2、下载编译ncnn

(1)下载 ncnn ,Git Bash指令如下(不能直接下载,那样就不是git格式的文件了)且这里下载的位置最好和上面的 protobuf 位置一样。

$ git clone https://github.com/Tencent/ncnn.git 或 git clone git://github.com/Tencent/ncnn.git 或 git clone https://gitee.com/Tencent/ncnn.git
# 更换代码版本 注意这里要看你的.cpp需要什么版本的ncnn 版本不对可能检测框会混乱
# cd ncnn
# git reset --hard f6c49523d2359ee598a8ba1793a8e958b52c20ca  
$ cd ncnn
$ git submodule update --init    # 这里最好是开启执行  很容易报错

(2)在 vs2019 的本地工具命令提示符下编译 ncnn

> cd <ncnn-root-dir>                   #是指ncnn的根目录
> mkdir build
> cd build
> cmake -G\"NMake Makefiles\" -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=%cd%/install -DProtobuf_INCLUDE_DIR=G:\\model_compression_quantization\\protobuf-3.4.0\\build_vs2019\\install\\include -DProtobuf_LIBRARIES=G:\\model_compression_quantization\\protobuf-3.4.0\\build_vs2019\\install\\lib\\libprotobuf.lib -DProtobuf_PROTOC_EXECUTABLE=G:\\model_compression_quantization\\protobuf-3.4.0\\build_vs2019\\install\\bin\\protoc.exe -DNCNN_VULKAN=OFF ..
> nmake
> nmake install

步骤和上面的编译 protobuf 步骤完全一样,就是cmake的命令下所有的DProtobuf开头的参数的值(路径)都要改为自己的 protobuf 路径(include、lib、bin三个)。

2.3、生成ncnn模型

将 v5lites-hive-best-sim.onnx 模型复制粘贴到 【ncnn-root-dir】\\ build \\ tools \\ onnx 文件夹下面,如下图:\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
打开cmd,执行指令:

onnx2ncnn v5lites-hive-best-sim.onnx v5lites-hive-best.param v5lites-hive-best.bin

则生成ncnn权重文件,其中 .param 保存是模型的配置结构,.bin 文件保存模型的参数,如下图:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

2.4、优化ncnn

将上一步生成的ncnn模型(.param和.bin)一起从 ncnn/build/tools/onnx 复制到 ncnn/build/tools,并执行指令:

ncnnoptimize v5lites-hive-best.param v5lites-hive-best.bin v5lites-hive-best-fp16.param v5lites-hive-best-fp16.bin 65536

65536生成的是fp16模型。也可以用0、1指令,0指的是fp32 , 1指的是fp16。
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
生成fp16格式的NCNN模型:

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

三、VS2019编译NCNN

3.1、VS2019环境配置

(1)打开VS2019 -> 创建新项目 -> 控制台应用 -> 配置新项目 -> 创建,如图:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
(2)模式选择 Release 和 x64,如图:

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
(3)打开属性管理器(没有就视图->其他窗口->属性管理器),找到Release|X64下的Microsoft.CPP.X64.user,如图:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
(4)双击Release|X64下的Microsoft.CPP.X64.user打开属性,选择VC++目录,配置包含目录(Include),配置如下属性:

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

(5)打开库目录(lib),配置如下属性:

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

(6)打开window运行库目录,配置如下属性:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

(7)打开链接器->输入->附加依赖库,配置如下属性:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
至此VS环境配置完毕!

3.2、使用VS2019编译ncnn权重模型

复制cpp_demo/ncnn/v5lite-s.cpp到新建的cpp上,因为源码是linux编程,所以还需要修改一些东西:

(1)修改class label
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

(2) 修改权重文件地址(名)
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

(3)记事本打开.param文件,3个Reshape都改为-1,如图:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

(4)还是.param文件,3个permute的输出层ID也要和代码中的对齐,如图:

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
(5)还有就是如果训练改了anchor,需要在.cpp中3个输出层(stride=8、16、32)中改掉anchor,如图:

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

好了,然后直接按本地Windows调试器即可完成编译(注意上面环境配置了什么就要选什么样的调试器):

\"在这里插入图片描述\"
运行结果在项目/x64/Release生成.exe可执行文件:

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

再将exe文件复制到项目/v5lites下,如图有这些文件:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows
其实只需要上图画线的4个文件,就可以完成部署。

打开cmd,指令

v5lites hive1.jpg

即可执行成功:
\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

四、结果比较

ID Computing backend System Input Size Framework speed(per img)
01 @i5-10500 Windows 320x320 pytorch 33.5ms
02 @i5-10500 Windows 320x320 ncnn fp16 29.5ms

性能也会损失一点点(左边pytorch 右边ncnn fp16):

\"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows \"【Pytorch->ONNX->NCNN->NCNNfp16->vs编译】Windows

四、v5lites.cpp源码:

// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2020 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 \"layer.h\"
#include \"net.h\"

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

// 0 : FP16
// 1 : INT8
#define USE_INT8 0

// 0 : Image
// 1 : Camera
#define USE_CAMERA 0
clock_t time_start, time_end;
double time_sum;

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

static inline float intersection_area(const Object& a, const Object& b)
{
    cv::Rect_<float> inter = a.rect & b.rect;
    return inter.area();
}

static void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right)
{
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;

    while (i <= j)
    {
        while (faceobjects[i].prob > p)
            i++;

        while (faceobjects[j].prob < p)
            j--;

        if (i <= j)
        {
            // swap
            std::swap(faceobjects[i], faceobjects[j]);

            i++;
            j--;
        }
    }

#pragma omp parallel sections
    {
#pragma omp section
        {
            if (left < j) qsort_descent_inplace(faceobjects, left, j);
        }
#pragma omp section
        {
            if (i < right) qsort_descent_inplace(faceobjects, i, right);
        }
    }
}

static void qsort_descent_inplace(std::vector<Object>& faceobjects)
{
    if (faceobjects.empty())
        return;

    qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1);
}

static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold)
{
    picked.clear();

    const int n = faceobjects.size();

    std::vector<float> areas(n);
    for (int i = 0; i < n; i++)
    {
        areas[i] = faceobjects[i].rect.area();
    }

    for (int i = 0; i < n; i++)
    {
        const Object& a = faceobjects[i];

        int keep = 1;
        for (int j = 0; j < (int)picked.size(); j++)
        {
            const Object& b = faceobjects[picked[j]];

            // intersection over union
            float inter_area = intersection_area(a, b);
            float union_area = areas[i] + areas[picked[j]] - inter_area;
            // float IoU = inter_area / union_area
            if (inter_area / union_area > nms_threshold)
                keep = 0;
        }

        if (keep)
            picked.push_back(i);
    }
}

static inline float sigmoid(float x)
{
    return static_cast<float>(1.f / (1.f + exp(-x)));
}

static void generate_proposals(const ncnn::Mat& anchors, int stride, const ncnn::Mat& in_pad, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects)
{
    const int num_grid = feat_blob.h;

    int num_grid_x;
    int num_grid_y;
    if (in_pad.w > in_pad.h)
    {
        num_grid_x = in_pad.w / stride;
        num_grid_y = num_grid / num_grid_x;
    }
    else
    {
        num_grid_y = in_pad.h / stride;
        num_grid_x = num_grid / num_grid_y;
    }

    const int num_class = feat_blob.w - 5;

    const int num_anchors = anchors.w / 2;

    for (int q = 0; q < num_anchors; q++)
    {
        const float anchor_w = anchors[q * 2];
        const float anchor_h = anchors[q * 2 + 1];

        const ncnn::Mat feat = feat_blob.channel(q);

        for (int i = 0; i < num_grid_y; i++)
        {
            for (int j = 0; j < num_grid_x; j++)
            {
                const float* featptr = feat.row(i * num_grid_x + j);

                // find class index with max class score
                int class_index = 0;
                float class_score = -FLT_MAX;
                for (int k = 0; k < num_class; k++)
                {
                    float score = featptr[5 + k];
                    if (score > class_score)
                    {
                        class_index = k;
                        class_score = score;
                    }
                }

                float box_score = featptr[4];

                float confidence = sigmoid(box_score) * sigmoid(class_score);

                if (confidence >= prob_threshold)
                {
                    // yolov5/models/yolo.py Detect forward
                    // y = x[i].sigmoid()
                    // y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
                    // y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh

                    float dx = sigmoid(featptr[0]);
                    float dy = sigmoid(featptr[1]);
                    float dw = sigmoid(featptr[2]);
                    float dh = sigmoid(featptr[3]);

                    float pb_cx = (dx * 2.f - 0.5f + j) * stride;
                    float pb_cy = (dy * 2.f - 0.5f + i) * stride;

                    float pb_w = pow(dw * 2.f, 2) * anchor_w;
                    float pb_h = pow(dh * 2.f, 2) * anchor_h;

                    float x0 = pb_cx - pb_w * 0.5f;
                    float y0 = pb_cy - pb_h * 0.5f;
                    float x1 = pb_cx + pb_w * 0.5f;
                    float y1 = pb_cy + pb_h * 0.5f;

                    Object obj;
                    obj.rect.x = x0;
                    obj.rect.y = y0;
                    obj.rect.width = x1 - x0;
                    obj.rect.height = y1 - y0;
                    obj.label = class_index;
                    obj.prob = confidence;

                    objects.push_back(obj);
                }
            }
        }
    }
}

static int detect_yolov5(const cv::Mat& bgr, std::vector<Object>& objects)
{
    ncnn::Net yolov5;

#if USE_INT8
    yolov5.opt.use_int8_inference = true;
#else
    yolov5.opt.use_vulkan_compute = true;
    yolov5.opt.use_bf16_storage = true;
#endif

    // original pretrained model from https://github.com/ultralytics/yolov5
    // the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models

#if USE_INT8
    yolov5.load_param(\"yolov5-lite-int8.param\");
    yolov5.load_model(\"yolov5-lite-int8.bin\");
#else
    yolov5.load_param(\"v5lites-hive-best-fp16.param\");
    yolov5.load_model(\"v5lites-hive-best-fp16.bin\");
#endif

    const int target_size = 320;
    const float prob_threshold = 0.45f;
    const float nms_threshold = 0.5f;

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

    // letterbox pad to multiple of 32
    int w = img_w;
    int h = img_h;
    float scale = 1.f;
    if (w > h)
    {
        scale = (float)target_size / w;
        w = target_size;
        h = h * scale;
    }
    else
    {
        scale = (float)target_size / h;
        h = target_size;
        w = w * scale;
    }

    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h);

    // pad to target_size rectangle
    // yolov5/utils/datasets.py letterbox
    int wpad = (w + 31) / 32 * 32 - w;
    int hpad = (h + 31) / 32 * 32 - h;
    ncnn::Mat in_pad;
    ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);

    const float norm_vals[3] = { 1 / 255.f, 1 / 255.f, 1 / 255.f };
    in_pad.substract_mean_normalize(0, norm_vals);

    ncnn::Extractor ex = yolov5.create_extractor();

    ex.input(\"images\", in_pad);

    std::vector<Object> proposals;

    // stride 8
    {
        ncnn::Mat out;
        ex.extract(\"output\", out);

        ncnn::Mat anchors(6);
        anchors[0] = 10.f;
        anchors[1] = 13.f;
        anchors[2] = 16.f;
        anchors[3] = 30.f;
        anchors[4] = 33.f;
        anchors[5] = 23.f;
        /*anchors[0] = 40.f;
        anchors[1] = 39.f;
        anchors[2] = 72.f;
        anchors[3] = 72.f;
        anchors[4] = 98.f;
        anchors[5] = 100.f;*/
        std::vector<Object> objects8;
        generate_proposals(anchors, 8, in_pad, out, prob_threshold, objects8);

        proposals.insert(proposals.end(), objects8.begin(), objects8.end());
    }
    // stride 16
    {
        ncnn::Mat out;
#if USE_INT8
        ex.extract(\"917\", out);
#else
        ex.extract(\"671\", out);
#endif
        ncnn::Mat anchors(6);
        anchors[0] = 30.f;
        anchors[1] = 61.f;
        anchors[2] = 62.f;
        anchors[3] = 45.f;
        anchors[4] = 59.f;
        anchors[5] = 119.f;
        /*anchors[0] = 121.f;
        anchors[1] = 121.f;
        anchors[2] = 143.f;
        anchors[3] = 152.f;
        anchors[4] = 169.f;
        anchors[5] = 172.f;*/
        std::vector<Object> objects16;
        generate_proposals(anchors, 16, in_pad, out, prob_threshold, objects16);
        proposals.insert(proposals.end(), objects16.begin(), objects16.end());
    }
    // stride 32
    {
        ncnn::Mat out;
#if USE_INT8
        ex.extract(\"937\", out);
#else
        ex.extract(\"691\", out);
#endif

        ncnn::Mat anchors(6);
        anchors[0] = 116.f;
        anchors[1] = 90.f;
        anchors[2] = 156.f;
        anchors[3] = 198.f;
        anchors[4] = 373.f;
        anchors[5] = 326.f;
        
      /*anchors[0] = 194.f;
        anchors[1] = 211.f;
        anchors[2] = 247.f;
        anchors[3] = 186.f;
        anchors[4] = 252.f;
        anchors[5] = 254.f;*/

        std::vector<Object> objects32;
        generate_proposals(anchors, 32, in_pad, out, prob_threshold, objects32);

        proposals.insert(proposals.end(), objects32.begin(), objects32.end());
    }

    // sort all proposals by score from highest to lowest
    qsort_descent_inplace(proposals);

    // apply nms with nms_threshold
    std::vector<int> picked;
    nms_sorted_bboxes(proposals, picked, nms_threshold);

    int count = picked.size();

    objects.resize(count);
    for (int i = 0; i < count; i++)
    {
        objects[i] = proposals[picked[i]];

        // adjust offset to original unpadded
        float x0 = (objects[i].rect.x - (wpad / 2)) / scale;
        float y0 = (objects[i].rect.y - (hpad / 2)) / scale;
        float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale;
        float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale;

        // clip
        x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
        y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
        x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
        y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);

        objects[i].rect.x = x0;
        objects[i].rect.y = y0;
        objects[i].rect.width = x1 - x0;
        objects[i].rect.height = y1 - y0;
    }

    return 0;
}

static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
{
    static const char* class_names[] = {
        \"hive\"
    };

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

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

        printf(\"%d label=%s prob=%.3f%% at %.2f %.2f %.2f x %.2f\\n\", i, class_names[obj.label], obj.prob * 100,
            obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);

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

        char text[256];
        /*strcpy_s(text, class_names[obj.label]);
        strcat(text, obj.prob * 100);*/
        sprintf_s(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), -1);
        
        cv::putText(image, text, cv::Point(x, y + label_size.height),
            cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
    }
#if USE_CAMERA
    imshow(\"外接摄像头\", image);
    cv::waitKey(1);
#else
    cv::imwrite(\"result.jpg\", image);
#endif
}

#if USE_CAMERA
int main(int argc, char** argv)
{
    cv::VideoCapture capture;
    capture.open(0);  //修改这个参数可以选择打开想要用的摄像头

    cv::Mat frame;
    while (true)
    {
        capture >> frame;
        cv::Mat m = frame;

        std::vector<Object> objects;
        detect_yolov5(frame, objects);

        draw_objects(m, objects);
        if (cv::waitKey(30) >= 0)
            break;
    }
}
#else
int main(int argc, char** argv)
{
    if (argc != 2)
    {
        fprintf(stderr, \"Usage: %s [imagepath]\\n\", argv[0]);
        return -1;
    }

    const char* imagepath = argv[1];
    std::vector<Object> objects;
    cv::Mat m = cv::imread(imagepath, 1);
    if (m.empty())
    {
        fprintf(stderr, \"cv::imread %s failed\\n\", imagepath);
        return -1;
    }

    time_start = clock();

    // 检测模型推理速度
    /*for (int i = 0; i < 1000; i++)
        detect_yolov5(m, objects);*/
    

    // 单张图片推理
    detect_yolov5(m, objects);

    time_end = clock();
    time_sum = (double)(time_end - time_start) / CLOCKS_PER_SEC * 1000;
    printf(\"per img speed : %f ms\\n\", time_sum);

    draw_objects(m, objects);
    return 0;
}
#endif

Reference

CSDN: Windows系统下把PyTorch模型转为ncnn模型流程.
zhihu nihui巨佬: 详细记录u版YOLOv5目标检测ncnn实现.
zhihu pogg大佬: ncnn+opencv+yolov5调用摄像头进行检测.
zhihu pogg大佬: NCNN+Int8+YOLOv4量化模型和实时推理.
CSDN pogg大佬: YOLOv5-Lite:NCNN+Int8部署和量化,树莓派也可实时.
Github pogg大佬: ONNX导出NCNN模型的问题解决+完整int8量化步骤 #53.

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