python libs/convert_tools/convert_torch_to_onnx.py

"""
This code is used to convert the pytorch model into an onnx format model.
"""
import os
import torch.onnx
from pose.inference import PoseEstimation
from basetrainer.utils.converter import pytorch2onnx
 
 
def load_model(config_file, model_file, device="cuda:0"):
    pose = PoseEstimation(config_file, model_file, device=device)
    model = pose.model
    config = pose.config
    return model, config
 
 
def convert2onnx(config_file, model_file, device="cuda:0", onnx_type="kp"):
    """
    :param model_file:
    :param input_size:
    :param device:
    :param onnx_type:
    :return:
    """
    model, config = load_model(config_file, model_file, device=device)
    model = model.to(device)
    model.eval()
    model_name = os.path.basename(model_file)[:-len(".pth")]
    onnx_file = os.path.join(os.path.dirname(model_file), model_name + ".onnx")
    # dummy_input = torch.randn(1, 3, 240, 320).to("cuda")
    input_size = tuple(config.MODEL.IMAGE_SIZE)  # w,h
    input_shape = (1, 3, input_size[1], input_size[0])
    pytorch2onnx.convert2onnx(model,
                              input_shape=input_shape,
                              input_names=['input'],
                              output_names=['output'],
                              onnx_file=onnx_file,
                              opset_version=11)
 
 
if __name__ == "__main__":
    model_file = "../../work_space/hand/mobilenet_v2_21_192_192_custom_coco_20230928_065444_0934/model/best_model_153_0.7574.pth"
    config_file = "../../work_space/hand/mobilenet_v2_21_192_192_custom_coco_20230928_065444_0934/mobilenetv2_hand_192_192.yaml"
    convert2onnx(config_file, model_file)

cmake_minimum_required(VERSION 3.5)
project(Detector)

add_compile_options(-fPIC) # fix Bug: can not be used when making a shared object
set(CMAKE_CXX_FLAGS "-Wall -std=c++11 -pthread")
#set(CMAKE_CXX_FLAGS_RELEASE "-O2 -DNDEBUG")
#set(CMAKE_CXX_FLAGS_DEBUG "-g")

if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
    # -DCMAKE_BUILD_TYPE=Debug
    # -DCMAKE_BUILD_TYPE=Release
    message(STATUS "No build type selected, default to Release")
    set(CMAKE_BUILD_TYPE "Release" CACHE STRING "Build type (default Debug)" FORCE)
endif ()

# opencv set
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS} ./src/)
#MESSAGE(STATUS "OpenCV_INCLUDE_DIRS = ${OpenCV_INCLUDE_DIRS}")

# base_utils
set(BASE_ROOT 3rdparty/base-utils) # 设置base-utils所在的根目录
add_subdirectory(${BASE_ROOT}/base_utils/ base_build) # 添加子目录到build中
include_directories(${BASE_ROOT}/base_utils/include)
include_directories(${BASE_ROOT}/base_utils/src)
MESSAGE(STATUS "BASE_ROOT = ${BASE_ROOT}")


# TNN set
# Creates and names a library, sets it as either STATIC
# or SHARED, and provides the relative paths to its source code.
# You can define multiple libraries, and CMake buil ds it for you.
# Gradle automatically packages shared libraries with your APK.
# build for platform
# set(TNN_BUILD_SHARED OFF CACHE BOOL "" FORCE)
if (CMAKE_SYSTEM_NAME MATCHES "Android")
    set(TNN_OPENCL_ENABLE ON CACHE BOOL "" FORCE)
    set(TNN_ARM_ENABLE ON CACHE BOOL "" FORCE)
    set(TNN_BUILD_SHARED OFF CACHE BOOL "" FORCE)
    set(TNN_OPENMP_ENABLE ON CACHE BOOL "" FORCE)  # Multi-Thread
    #set(TNN_HUAWEI_NPU_ENABLE OFF CACHE BOOL "" FORCE)
    add_definitions(-DTNN_OPENCL_ENABLE)           # for OpenCL GPU
    add_definitions(-DTNN_ARM_ENABLE)              # for Android CPU
    add_definitions(-DDEBUG_ANDROID_ON)            # for Android Log
    add_definitions(-DPLATFORM_ANDROID)
elseif (CMAKE_SYSTEM_NAME MATCHES "Linux")
    set(TNN_OPENCL_ENABLE ON CACHE BOOL "" FORCE)
    set(TNN_CPU_ENABLE ON CACHE BOOL "" FORCE)
    set(TNN_X86_ENABLE OFF CACHE BOOL "" FORCE)
    set(TNN_QUANTIZATION_ENABLE OFF CACHE BOOL "" FORCE)
    set(TNN_OPENMP_ENABLE ON CACHE BOOL "" FORCE)  # Multi-Thread
    add_definitions(-DTNN_OPENCL_ENABLE)           # for OpenCL GPU
    add_definitions(-DDEBUG_ON)                    # for WIN/Linux Log
    add_definitions(-DDEBUG_LOG_ON)                # for WIN/Linux Log
    add_definitions(-DDEBUG_IMSHOW_OFF)            # for OpenCV show
    add_definitions(-DPLATFORM_LINUX)
elseif (CMAKE_SYSTEM_NAME MATCHES "Windows")
    set(TNN_OPENCL_ENABLE ON CACHE BOOL "" FORCE)
    set(TNN_CPU_ENABLE ON CACHE BOOL "" FORCE)
    set(TNN_X86_ENABLE ON CACHE BOOL "" FORCE)
    set(TNN_QUANTIZATION_ENABLE OFF CACHE BOOL "" FORCE)
    set(TNN_OPENMP_ENABLE ON CACHE BOOL "" FORCE)  # Multi-Thread
    add_definitions(-DTNN_OPENCL_ENABLE)           # for OpenCL GPU
    add_definitions(-DDEBUG_ON)                    # for WIN/Linux Log
    add_definitions(-DDEBUG_LOG_ON)                # for WIN/Linux Log
    add_definitions(-DDEBUG_IMSHOW_OFF)            # for OpenCV show
    add_definitions(-DPLATFORM_WINDOWS)
endif ()
set(TNN_ROOT 3rdparty/TNN)
include_directories(${TNN_ROOT}/include)
include_directories(${TNN_ROOT}/third_party/opencl/include)
add_subdirectory(${TNN_ROOT}) # 添加外部项目文件夹
set(TNN -Wl,--whole-archive TNN -Wl,--no-whole-archive)# set TNN library
MESSAGE(STATUS "TNN_ROOT = ${TNN_ROOT}")

# Detector
include_directories(src)
set(SRC_LIST
        src/Interpreter.cpp
        src/pose_detector.cpp
        src/object_detection.cpp
        src/pose_filter.cpp
        src/yolov5.cpp
        )
add_library(dlcv SHARED ${SRC_LIST})
target_link_libraries(dlcv ${OpenCV_LIBS} base_utils)
MESSAGE(STATUS "DIR_SRCS = ${SRC_LIST}")
add_executable(Detector src/main.cpp)
target_link_libraries(Detector dlcv ${TNN} -lpthread)

//
// Created by 390737991@qq.com on 2020/6/3.
//

#include "object_detection.h"
#include "yolov5.h"
#include "Types.h"
#include <iostream>
#include <string>
#include <vector>
#include "file_utils.h"
#include "image_utils.h"

using namespace dl;
using namespace vision;
using namespace std;

const int num_thread = 1; // 开启CPU线程数目
DeviceType device = GPU;  // 选择运行设备CPU/GPU

// 目标检测SSD或者YOLOv5
const float scoreThresh = 0.5;
const float iouThresh = 0.3;
//const char *det_model_file = (char *) "../data/tnn/ssd/rfb1.0_person_320_320_sim.opt.tnnmodel";
//const char *det_proto_file = (char *) "../data/tnn/ssd/rfb1.0_person_320_320_sim.opt.tnnproto";
//ObjectDetectionParam model_param = PERSON_MODEL;//模型参数
//ObjectDetection *detector = new ObjectDetection(det_model_file, det_proto_file, model_param, num_thread, device);

const char *det_model_file = (char *) "../data/tnn/yolov5/yolov5s05_320.sim.tnnmodel";
const char *det_proto_file = (char *) "../data/tnn/yolov5/yolov5s05_320.sim.tnnproto";
YOLOv5Param dets_model_param = YOLOv5s05_320;//模型参数
YOLOv5 *detector = new YOLOv5(det_model_file,
                              det_proto_file,
                              dets_model_param,
                              num_thread,
                              device);
// 关键点检测
const float poseThresh = 0.3;
const char *pose_model_file = (char *) "../data/tnn/pose/litehrnet18_192_192.sim.tnnmodel";
const char *pose_proto_file = (char *) "../data/tnn/pose/litehrnet18_192_192.sim.tnnproto";
PoseParam pose_model_param = HAND_PARAM;//模型参数
PoseDetector *pose = new PoseDetector(pose_model_file, pose_proto_file, pose_model_param, num_thread, device);

void test_image_file() {
    //测试图片的目录
    string image_dir = "../data/test_image";
    std::vector<string> image_list = get_files_list(image_dir);
    for (string image_path:image_list) {
        cv::Mat bgr = cv::imread(image_path);
        if (bgr.empty()) continue;
        FrameInfo resultInfo;
        // 进行目标检测
        detector->detect(bgr, &resultInfo, scoreThresh, iouThresh);
        // 进行关键点检测
        pose->detect(bgr, &resultInfo, poseThresh);
        // 可视化代码
        pose->visualizeResult(bgr, resultInfo, pose_model_param.skeleton, false, 0);
    }

    delete detector;
    detector = nullptr;
    delete pose;
    pose = nullptr;
    printf("FINISHED.\n");
}


/***
 * 测试视频文件
 * @return
 */
int test_video_file() {
    //测试视频文件
    string video_file = "../data/video/video-test.mp4";
    cv::VideoCapture cap;
    bool ret = get_video_capture(video_file, cap);
    cv::Mat frame;
    while (ret) {
        cap >> frame;
        if (frame.empty()) break;
        FrameInfo resultInfo;
        // 进行目标检测
        detector->detect(frame, &resultInfo, scoreThresh, iouThresh);
        // 进行关键点检测
        pose->detect(frame, &resultInfo, poseThresh);
        // 可视化代码
        pose->visualizeResult(frame, resultInfo, pose_model_param.skeleton, false, 5);
    }
    cap.release();

    delete detector;
    detector = nullptr;
    delete pose;
    pose = nullptr;
    printf("FINISHED.\n");
    return 0;

}


/***
 * 测试摄像头
 * @return
 */
int test_camera() {
    int camera = 0; //摄像头ID号(请修改成自己摄像头ID号)
    cv::VideoCapture cap;
    bool ret = get_video_capture(camera, cap);
    cv::Mat frame;
    while (ret) {
        cap >> frame;
        if (frame.empty()) break;
        FrameInfo resultInfo;
        // 进行目标检测
        detector->detect(frame, &resultInfo, scoreThresh, iouThresh);
        // 进行关键点检测
        pose->detect(frame, &resultInfo, poseThresh);
        // 可视化代码
        pose->visualizeResult(frame, resultInfo, pose_model_param.skeleton, false, 5);
    }
    cap.release();
    delete detector;
    detector = nullptr;
    delete pose;
    pose = nullptr;
    printf("FINISHED.\n");
    return 0;

}



int main() {
    test_image_file();   // 测试图片文件
    test_video_file();   // 测试视频文件
    test_camera();       //测试摄像头
    return 0;
}

#!/usr/bin/env bash
if [ ! -d "build/" ];then
  mkdir "build"
else
  echo "exist build"
fi
cd build
cmake ..
make -j4
sleep 1
./Detector