nvidia jetson nano 操作指南

硬件购买以及注意点

        购买硬件之后在网卡后面有个短路帽要加上，否则无法开机。

系统安装

nvidia jetson nano系统安装在SD卡中，请预先准备一个32G的SD卡

系统安装步骤请参考官网

Getting Started With Jetson Nano Developer Kit | NVIDIA Developer

注意：烧录完成会弹出好多个窗口，请求格式化，此处一定要点  取消 不要格式化，因为这是软件在为系统分盘。

开发前准备

检查opencv是否安装

命令行：opencv_version

输出：4.1.1

检查显卡驱动和cuda是否安装

命令行：nvcc -V

输出：bash: nvcc command not found

解决：驱动其实已经安装了，我们需要加入环境变量

sudo vim ~./bashrc

或者也可以直接到本地电脑的Home文件夹中找到.bashrc文件，直接打开文件，在文件的最后位置添加上三行命令。（若进到Home文件夹没看有.bashrc文件，则点击一下Ctrl + H就可以显示出.bashrc）

在文件最后添加

export PATH=/usr/local/cuda-10.2/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda

设置完成后可以正常看到nvcc -V 的信息

安装jtop以便查看设备信息

sudo apt-get install git cmake
sudo apt-get install python3-dev
sudo apt-get install libhdf5-serial-dev hdf5-tools
sudo apt-get install libatlas-base-dev gfortran
sudo apt-get install python3-pip
sudo -H pip3 install -U jetson-stats
sudo systemctl restart jetson_stats.service
reboot
jtop

 安装opencv

sys Update

sudo apt-get update
sudo apt-get full-upgrade

sudo add-apt-repository "deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial main multiverse restricted universe"

sudo apt update

set nvcc

sudo vim ~/.bashrc
export CUDA_HOME=/usr/local/cuda-10.2
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda-10.2/bin:$PATH
source ~/.bashrc
# test

nvcc -V

安装OpenCV with CUDA

sudo apt-get install build-essential

sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

sudo apt-get install python3-dev python3-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

sudo apt-get install pkg-config

sudo apt-get install opencv3-pip

unzip opencv4.6-contrib-xfture

build> cmake 
-D CMAKE_INSTALL_PREFIX=/usr/local 
-D CMAKE_BUILD_TYPE=Release 
-D OPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules 
-D CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.2 
-D CUDA_ARCH_BIN=5.3 
-D CUDA_ARCH_PTX="" 
-D BUILD_PNG=OFF 
-D BUILD_TIFF=OFF 
-D BUILD_TBB=OFF 
-D BUILD_JPEG=OFF 
-D BUILD_JASPER=OFF 
-D BUILD_ZLIB=OFF 
-D BUILD_EXAMPLES=ON 
-D BUILD_opencv_java=OFF 
-D BUILD_opencv_python2=OFF 
-D BUILD_opencv_python3=ON 
-D ENABLE_PRECOMPILED_HEADERS=OFF 
-D WITH_OPENCL=OFF 
-D WITH_OPENMP=OFF 
-D WITH_FFMPEG=ON 
-D WITH_GSTREAMER=OFF 
-D WITH_GSTREAMER_0_10=OFF 
-D WITH_CUDA=ON 
-D WITH_GTK=ON 
-D WITH_VTK=OFF 
-D WITH_TBB=ON 
-D PYTHON_DEFAULT_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") 
-D PYTHON3_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") 
-D PYTHON3_NUMPY_INCLUDE_DIRS=$(python3 -c "import numpy; print (numpy.get_include())") 
-D PYTHON3_PACKAGES_PATH=$(python3 -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())") 
-D WITH_1394=OFF 
-D WITH_OPENEXR=OFF 
-D INSTALL_C_EXAMPLES=ON -D INSTALL_TESTS=OFF 
..

build> make -j4
build> sudo make install
build> sudo python3 setup.py install

if "sudo python3 setup.py install " error :

pip3 install setuptools

pip3 install --upgrade setuptools

pip3 install --upgrade pip

do sudo python3 setup.py install again!

pip3 install Pillow
pip3 install pygame -i https://pypi.tuna.tsinghua.edu.cn/simple/

CameraCall.py
 
# encoding: utf-8
import cv2 as cv
import subprocess
 
 
class CameraCallTool(object):
    cap = None
    
    
    def __init__(self, width, height, CamIndex):
        print(f'Init Camera Tool!')
        if CamIndex == -1:
            self.CallCameraOnBoard(width, height)
        elif CamIndex == 0:
            self.CallCameraOnCSI(width, height)
        else:
            print('no usb camera!')
		
    def CallCameraOnCSI(self, width, height):
        self.cap = cv.VideoCapture(0) #设置摄像头 0是默认的摄像头 如果你有多个摄像头的话呢，可以设置1,2,3....
        self.cap.set(cv.CAP_PROP_FRAME_WIDTH, width)
        self.cap.set(cv.CAP_PROP_FRAME_HEIGHT, height)
        self.cap.set(cv.CAP_PROP_EXPOSURE, 0.1)
        
        
    def Test(self):
        print('CameraCallTool Test')
        
    
    def CameraStatus(self):
        return self.cap.isOpened()
        
        
    def CallCameraOnBoard(self, width, height):
        gst_elements = str(subprocess.check_output('gst-inspect-1.0'))
        if 'nvcamerasrc' in gst_elements:
                # On versions of L4T prior to 28.1, add 'flip-method=2' into gst_str
                gst_str = ('nvcamerasrc ! '
                                   'video/x-raw(memory:NVMM), '
                                   'width=(int)2592, height=(int)1458, '
                                   'format=(string)I420, framerate=(fraction)30/1 ! '
                                   'nvvidconv ! '
                                   'video/x-raw, width=(int){}, height=(int){}, '
                                   'format=(string)BGRx ! '
                                   'videoconvert ! appsink').format(width, height)
        elif 'nvarguscamerasrc' in gst_elements:
                gst_str = ('nvarguscamerasrc ! '
                                   'video/x-raw(memory:NVMM), '
                                   'width=(int)1920, height=(int)1080, '
                                   'format=(string)NV12, framerate=(fraction)30/1 ! '
                                   'nvvidconv flip-method=2 ! '
                                   'video/x-raw, width=(int){}, height=(int){}, '
                                   'format=(string)BGRx ! '
                                   'videoconvert ! appsink').format(width, height)
        else:
                raise RuntimeError('onboard camera source not found!')
        # return cv.VideoCapture(gst_str, cv.CAP_GSTREAMER)
        self.cap = cv.VideoCapture(gst_str, cv.CAP_GSTREAMER)
        
        
    def GetOneFrame(self):
        if self.cap.isOpened():
            ret, frame = self.cap.read()
            if ret is True:
                # 摄像头是和人对立的，将图像左右调换回来正常显示。
                frame = cv.flip(frame, 1)
                frame = cv.flip(frame, 0)
                return frame
            else:
                return None
        else:
            return None
        
        
    def __del__(self):
        if self.cap is not None:
            self.cap.release()
        print('CameraCallTool del')

DnnClass.py
 
# encoding: utf-8
import numpy as np
import cv2 as cv
import os
import time
 
 
class MyDnn(object):
    # YOLO文件路径 G:\vs2017Project\yolov2-tiny-voc
    yolo_dir = './yolov3-tiny'
    # 过滤弱检测的最小概率
    CONFIDENCE = 0.5  
    # 非最大值抑制阈值
    THRESHOLD = 0.4
    
    
    def __init__(self):
        print('import Dnn Tool Calss!')
        # YOLO文件路径
        self.yolo_dir_name = self.yolo_dir  
        # 权重文件
        self.weightsPath = os.path.join(self.yolo_dir, 'yolov3.weights')  
        # 配置文件
        self.configPath = os.path.join(self.yolo_dir, 'yolov3.cfg')  
        # label名称
        self.labelsPath = os.path.join(self.yolo_dir_name, 'coco.names')  
        # 加载网络、配置权重
        self.net = cv.dnn.readNetFromDarknet(self.configPath, self.weightsPath)
        # intel gpu
        # self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
        # self.net.setPreferableTarget(cv.dnn.DNN_TARGET_OPENCL)
        # cpu
        # self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
        # self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
        # nvidia gpu
        self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)
        self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA)
        print("[INFO] loading YOLO from disk...")  # # 可以打印下信息
        
    
    def importTest(self):
        print('dnn import test!')
        
        
    def DecodeImage(self, img):
        start = time.time()
        # 加载图片、转为blob格式、送入网络输入层
        # img = imagePath
        # net需要的输入是blob格式的，用blobFromImage这个函数来转格式
        blobImg = cv.dnn.blobFromImage(img, 1.0/255.0, (416, 416), None, True, False)
        # 调用setInput函数将图片送入输入层
        self.net.setInput(blobImg)
        # 前面的yolov3架构也讲了，yolo在每个scale都有输出，outInfo是每个scale的名字信息，供net.forward使用
        outInfo = self.net.getUnconnectedOutLayersNames()  
        # 得到各个输出层的、各个检测框等信息，是二维结构。
        layerOutputs = self.net.forward(outInfo)  
        end = time.time()
        print("[INFO] YOLO took {:.6f} seconds".format(end - start))
        # 拿到图片尺寸
        (H, W) = img.shape[:2]
        # 过滤layerOutputs
        # layerOutputs的第1维的元素内容: [center_x, center_y, width, height, objectness, N-class score data]
        # 过滤后的结果放入：
        # 所有边界框（各层结果放一起）
        boxes = []
        # 所有置信度
        confidences = []
        # 所有分类ID
        classIDs = []
        # # 1）过滤掉置信度低的框框
        # 各个输出层
        for out in layerOutputs:
            # 各个框框
            for detection in out:
                # 拿到置信度
                # 各个类别的置信度
                scores = detection[5:]
                # 最高置信度的id即为分类id
                classID = np.argmax(scores)
                # 拿到置信度
                confidence = scores[classID]
                # 根据置信度筛查
                if confidence > self.CONFIDENCE:
                    # 将边界框放会图片尺寸
                    box = detection[0:4] * np.array([W, H, W, H])
                    (centerX, centerY, width, height) = box.astype("int")
                    x = int(centerX - (width / 2))
                    y = int(centerY - (height / 2))
                    boxes.append([x, y, int(width), int(height)])   # 框
                    confidences.append(float(confidence))           # 置信度
                    classIDs.append(classID)                        # 分类ID
        # # 2）应用非最大值抑制(non-maxima suppression，nms)进一步筛掉
        # boxes中，保留的box的索引index存入idxs
        idxs = cv.dnn.NMSBoxes(boxes, confidences, self.CONFIDENCE, self.THRESHOLD)
        # 得到labels列表
        with open(self.labelsPath, 'rt') as f:
            labels = f.read().rstrip('\n').split('\n')
        np.random.seed(42)
        # 框框显示颜色，每一类有不同的颜色，每种颜色都是由RGB三个值组成的，所以size为(len(labels), 3)
        COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")  
        if len(idxs) > 0:
            # alarm pic
            for i in idxs.flatten():  # indxs是二维的，第0维是输出层，所以这里把它展平成1维
                (x, y) = (boxes[i][0], boxes[i][1])
                (w, h) = (boxes[i][2], boxes[i][3])
                color = [int(c) for c in COLORS[classIDs[i]]]
                # 线条粗细为2px
                cv.rectangle(img, (x, y), (x+w, y+h), color, 2)
                cv.rectangle(img, (x, y), (x+w, y+h), color, 2)
                # 线条粗细为2px
                text = "{}: {:.4f}".format(labels[classIDs[i]], confidences[i])
                # cv.FONT_HERSHEY_SIMPLEX字体风格、0.5字体大小、粗细2px
                cv.putText(img, text, (x, y-5), cv.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
            print('alarm pic!')
            cv.imshow('decode', img)
            return img
        else:
            # normal pic
            print('normal pic!')
            return None

DnnMain.py
 
# encoding: utf-8
import cv2 as cv
from DnnClass import MyDnn
from CameraCall import CameraCallTool
import pygame
from PIL import Image, ImageDraw, ImageFont
import threading
import subprocess
 
 
def LoadMusic():
    pygame.mixer.init()
    pygame.mixer.music.load('./audio/succ.wav')
    pygame.mixer.music.set_volume(0.5)
    
    
def PlaySuccess():
    pygame.mixer.music.play()
 
 
def DrawTextOnImage(img, text1, text2, left, top, textColor=(0,255,0), textSize=20):
    if(isinstance(img, np.ndarray)):
        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    # create a draw to draw text
    draw = ImageDraw.Draw(img)
    fontstyle = ImageFont.truetype('./font/simsun.ttc', textSize, encoding='utf-8')
    draw.text((left,top), text1, textColor, font=fontstyle)
    draw.text((left,int(top + textSize + 2)), text2, textColor, font=fontstyle)
    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
 
 
def CallCameraOnBoard(width, height):
    gst_elements = str(subprocess.check_output('gst-inspect-1.0'))
    if 'nvcamerasrc' in gst_elements:
        # On versions of L4T prior to 28.1, add 'flip-method=2' into gst_str
        gst_str = ('nvcamerasrc ! '
                   'video/x-raw(memory:NVMM), '
                   'width=(int)2592, height=(int)1458, '
                   'format=(string)I420, framerate=(fraction)30/1 ! '
                   'nvvidconv ! '
                   'video/x-raw, width=(int){}, height=(int){}, '
                   'format=(string)BGRx ! '
                   'videoconvert ! appsink').format(width, height)
    elif 'nvarguscamerasrc' in gst_elements:
        gst_str = ('nvarguscamerasrc ! '
                   'video/x-raw(memory:NVMM), '
                   'width=(int)1920, height=(int)1080, '
                   'format=(string)NV12, framerate=(fraction)30/1 ! '
                   'nvvidconv flip-method=2 ! '
                   'video/x-raw, width=(int){}, height=(int){}, '
                   'format=(string)BGRx ! '
                   'videoconvert ! appsink').format(width, height)
    else:
        raise RuntimeError('onboard camera source not found!')
    return cv.VideoCapture(gst_str, cv.CAP_GSTREAMER)
 
 
if __name__ == "__main__":
    print('camera onboard')
    DnnTool = MyDnn()
    cameraCallTool = CameraCallTool(1024, 768, 0)
    t = 0
    try:
        while cameraCallTool.CameraStatus():
            CameraFrame = cameraCallTool.GetOneFrame()
            if CameraFrame is not None:
                cv.imshow('Video', CameraFrame)
                if t == 0 or t.is_alive() is False:
                    # image_np = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
                    t = threading.Thread(target=DnnTool.DecodeImage, args=(CameraFrame, ))
                    t.start()
            #判断退出的条件 当按下'Q'键的时候呢，就退出
            c = cv.waitKey(1)
            # 如果按下q 就截图保存并退出
            if c == ord('q'):  
                saveFile = "Capture.jpg"  # 带有中文的保存文件路径
                img_write = cv.imencode(".jpg", CameraFrame)[1].tofile(saveFile)
                break
    except:
        print('Camera Show Exception!')
    finally:
        del cameraCallTool
        cv.destroyAllWindows()

lost : audo  font  yolov3-tiny