基于DBNet实现mnn框架的条形码检测

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已于 2023-08-22 13:36:16 修改 · 380 阅读

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#mnn #人工智能 #深度学习

于 2023-08-22 13:28:10 首次发布

文章介绍了如何在Ubuntu环境下，利用MNN深度学习模型和OpenCV库进行条形码区域识别，包括CMake配置、代码实现和主函数调用，展示了从文件结构到代码执行的全过程。

基本思想：项目需求，需要对条形码区域进行识别。

一、文件结构

ubuntu@ubuntu:~/find_barcode$ tree -L 2
.
├── best.mnn
├── cmake-build-debug
│   ├── CMakeCache.txt
│   ├── CMakeFiles
│   ├── cmake_install.cmake
│   ├── find_barcode
│   ├── find_barcode.cbp
│   └── Makefile
├── CMakeLists.txt
├── DBNet.onnx
├── include
│   └── MNN
├── lib
│   └── libMNN.so
├── main.cpp
└── testimgs
    ├── 0.jpg
    ├── 1.jpg
    ├── 2.jpg
    ├── 3.jpg
    └── 4.jpg

6 directories, 15 files

二、代码

1.1 cmakelist.txt

cmake_minimum_required(VERSION 3.16)
project(find_barcode)

set(CMAKE_CXX_STANDARD 14)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fopenmp")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp")

include_directories(${CMAKE_SOURCE_DIR})
include_directories(${CMAKE_SOURCE_DIR}/include)
include_directories(${CMAKE_SOURCE_DIR}/include/MNN)

find_package(OpenCV REQUIRED)
#message(STATUS ${OpenCV_INCLUDE_DIRS})
#添加头文件
include_directories(${OpenCV_INCLUDE_DIRS})
#链接Opencv库

add_library(libmnn SHARED IMPORTED)
set_target_properties(libmnn PROPERTIES IMPORTED_LOCATION ${CMAKE_SOURCE_DIR}/lib/libMNN.so)

add_executable(find_barcode main.cpp)

target_link_libraries(find_barcode ${OpenCV_LIBS} libmnn)

1.2 main.cpp

这里将DBNet类写到一起，后续可以拆分出来。

#define _CRT_SECURE_NO_WARNINGS

#include <iostream>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <MNN/Interpreter.hpp>
#include <MNN/ImageProcess.hpp>


using namespace cv;
using namespace std;

class DBNet {
public:
    DBNet(const float binaryThreshold = 0.5, const float polygonThreshold = 0.7, const float unclipRatio = 1.5,
          const int maxCandidates = 1000);

    void detect(Mat &srcimg);

private:
    float binaryThreshold;
    float polygonThreshold;
    float unclipRatio;
    int maxCandidates;
    const int inpWidth = 736;
    const int inpHeight = 736;
    const float meanValues[3] = {0.485, 0.456, 0.406};
    const float normValues[3] = {0.229, 0.224, 0.225};

    float contourScore(const Mat &binary, const vector<Point> &conto

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