16、JETSON NANO 对特定目标进行识别和gpio输出高低电平报警

基本思想：需要对特定目标进行识别和报警，TensorRT做的的效果会极其好，但是没时间，先使用ncnn的yolo-fastest做个例子吧，后期修改为TensorRT框架识别吧，该博客主要学习如何控制gpio的输入输出电平

首先JETSON NANO装opencv和基本的配置库；

创建一个工程，然后引入NCNN的基本库，

ubuntu@ubuntu:~/yolo5$ tree -L 2
.
├── build
│   ├── CMakeCache.txt
│   ├── CMakeFiles
│   ├── cmake_install.cmake
│   ├── main
│   └── Makefile
├── CMakeLists.txt
├── include
│   └── ncnn
├── lib
│   ├── cmake
│   └── libncnn.a
├── main.cpp
└── model├── yolo-fastest.bin└── yolo-fastest.param7 directories, 9 files

CMakelists.txt的设置


cmake_minimum_required(VERSION 3.10)
project(untitled3)set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fopenmp")include_directories(${CMAKE_SOURCE_DIR}/include/ncnn/)
#导入ncnn
add_library(libncnn STATIC IMPORTED)
set_target_properties(libncnn PROPERTIES IMPORTED_LOCATION ${CMAKE_SOURCE_DIR}/lib/libncnn.a)
find_package(OpenCV REQUIRED)
set(CMAKE_CXX_STANDARD 11)add_executable(main main.cpp)
target_link_libraries(main libncnn ${OpenCV_LIBS})

JETSON NANO 的源码

#include <string>
#include <opencv4/opencv2/opencv.hpp>
#include <opencv4/opencv2/core.hpp>
#include <opencv4/opencv2/highgui.hpp>
#include <opencv4/opencv2/imgproc.hpp>
#include <opencv4/opencv2/objdetect.hpp>
#include <opencv4/opencv2/imgproc/types_c.h>
#include <opencv4/opencv2/videoio.hpp>
#include "benchmark.h"
#include "cpu.h"
#include "datareader.h"
#include "net.h"
#include "gpu.h"#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <stdio.h>
#include <vector>
#include <algorithm>
using namespace std;
using namespace cv;
using namespace ncnn;
int demo(cv::Mat& image, ncnn::Net &detector, int detector_size_width, int detector_size_height)
{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 bgr = image.clone();int img_w = bgr.cols;int img_h = bgr.rows;ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB,\bgr.cols, bgr.rows, detector_size_width, detector_size_height);//数据预处理const float mean_vals[3] = {0.f, 0.f, 0.f};const float norm_vals[3] = {1/255.f, 1/255.f, 1/255.f};in.substract_mean_normalize(mean_vals, norm_vals);ncnn::Extractor ex = detector.create_extractor();ex.set_num_threads(8);ex.input("data", in);ncnn::Mat out;ex.extract("output", out);for (int i = 0; i < out.h; i++){int label;float x1, y1, x2, y2, score;float pw,ph,cx,cy;const float* values = out.row(i);x1 = values[2] * img_w;y1 = values[3] * img_h;x2 = values[4] * img_w;y2 = values[5] * img_h;score = values[1];label = values[0];//处理坐标越界问题if(x1<0) x1=0;if(y1<0) y1=0;if(x2<0) x2=0;if(y2<0) y2=0;if(x1>img_w) x1=img_w;if(y1>img_h) y1=img_h;if(x2>img_w) x2=img_w;if(y2>img_h) y2=img_h;cv::rectangle (image, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(255, 255, 0), 1, 1, 0);char text[256];sprintf(text, "%s %.1f%%", class_names[label], score * 100);int baseLine = 0;cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);cv::putText(image, text, cv::Point(x1, y1 + label_size.height),cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));std::cout<<class_names[label]<<endl;if( class_names[label]=="dog") //识别到狗就点亮 检测不到狗 就不点亮{std::cout<<class_names[label]<<endl;system("echo 1 > /sys/class/gpio/gpio79/value");system("echo 0 > /sys/class/gpio/gpio79/value");}}return 0;
}string gstreamer_pipeline (int capture_width, int capture_height, int display_width, int display_height, int framerate, int flip_method)
{return "nvarguscamerasrc ! video/x-raw(memory:NVMM), width=(int)" + to_string(capture_width) + ", height=(int)" +to_string(capture_height) + ", format=(string)NV12, framerate=(fraction)" + to_string(framerate) +"/1 ! nvvidconv flip-method=" + to_string(flip_method) + " ! video/x-raw, width=(int)" + to_string(display_width) + ", height=(int)" +to_string(display_height) + ", format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink";
}//摄像头测试
int test_cam(){//定义yolo-fastest VOC检测器ncnn::Net detector;detector.load_param("/home/li/yolo5/model/yolo-fastest.param");detector.load_model("/home/li/yolo5/model/yolo-fastest.bin");int detector_size_width  = 320;int detector_size_height = 320;cv::Mat frame;int capture_width = 1280 ;int capture_height = 720 ;int display_width = 1280 ;int display_height = 720 ;int framerate = 60 ;int flip_method = 0 ;string pipeline = gstreamer_pipeline(capture_width,capture_height,display_width,display_height,framerate,flip_method);std::cout << "使用gstreamer管道: \n\t" << pipeline << "\n";//管道与视频流绑定cv::VideoCapture cap(pipeline, CAP_GSTREAMER); // 该操作是外接 CSI 普通摄像头system("echo 79 > /sys/class/gpio/export");system("echo out > /sys/class/gpio/gpio79/direction");// 对应NVIDIA Jetson Nano 对应pin脚12cv::VideoCapture cap(1); // 该操作是外接 usb 普通摄像头while (true){cap >> frame;double start = ncnn::get_current_time();demo(frame, detector, detector_size_width, detector_size_height);double end = ncnn::get_current_time();double time = end - start;printf("Time:%7.2f \n",time);cv::imshow("demo", frame);//system("echo 1 > /sys/class/gpio/gpio79/value");//system("echo 0 > /sys/class/gpio/gpio79/value");cv::waitKey(1);}return 0;
}
int main()
{test_cam();return 0;
}

后期改TensorRT，，，，原谅我~~ 主要想引入一个例子如何在nano上控制gpio口

附录接口图

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