Category Archives: Jetson Nano

In this video we introduce key concepts in the area of Machine Learning, Deep Neural Networks, and Deep Learning. We use the code below to demonstrate a simple image recognition capability.

import jetson.inference
import jetson.utils
import time
import cv2
import numpy as np
width=1280
height=720
#cam=jetson.utils.gstCamera(width,height,'/dev/video1')
cam=jetson.utils.gstCamera(width,height,'0')
net=jetson.inference.imageNet('googlenet')
timeMark=time.time()
fpsFilter=0
timeMark=time.time()
font=cv2.FONT_HERSHEY_SIMPLEX
while True:
    frame, width, height = cam.CaptureRGBA(zeroCopy=1)
    classID, confidence = net.Classify(frame, width, height)
    item = net.GetClassDesc(classID)
    dt=time.time()-timeMark
    fps=1/dt
    fpsFilter=.95*fpsFilter+.05*fps
    timeMark=time.time()
    frame=jetson.utils.cudaToNumpy(frame,width,height,4)
    frame=cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR).astype(np.uint8)
    cv2.putText(frame,str(round(fpsFilter,1))+'      '+item,(0,30),font,1,(0,0,255),2)
    cv2.imshow('webCam',frame)
    cv2.moveWindow('webCam',0,0)
    if cv2.waitKey(1)==ord('q'):
        break
cam.release()
cv2.destroyAllWindows()

import jetson.inference

import jetson.utils

import time

import cv2

import numpy as np

width=1280

height=720

#cam=jetson.utils.gstCamera(width,height,'/dev/video1')

cam=jetson.utils.gstCamera(width,height,'0')

net=jetson.inference.imageNet('googlenet')

timeMark=time.time()

fpsFilter=0

timeMark=time.time()

font=cv2.FONT_HERSHEY_SIMPLEX

while True:

frame, width, height = cam.CaptureRGBA(zeroCopy=1)

classID, confidence = net.Classify(frame, width, height)

item = net.GetClassDesc(classID)

dt=time.time()-timeMark

fps=1/dt

fpsFilter=.95*fpsFilter+.05*fps

timeMark=time.time()

frame=jetson.utils.cudaToNumpy(frame,width,height,4)

frame=cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR).astype(np.uint8)

cv2.putText(frame,str(round(fpsFilter,1))+' '+item,(0,30),font,1,(0,0,255),2)

cv2.imshow('webCam',frame)

cv2.moveWindow('webCam',0,0)

if cv2.waitKey(1)==ord('q'):

break

cam.release()

cv2.destroyAllWindows()

Jetson Nano

AI on the Jetson Nano Lesson 46: Synchronizing Multiple Cameras with OpenCV

June 20, 2020 admin

In this lesson we learn how to use Python Classes, Methods and Threading to run two cameras in OpenCV without latency or synchronization problems. Below we include the code developed in the video above.

from threading import Thread
import cv2
import time
import numpy as np

class vStream:
    def __init__(self,src,width,height):
        self.width=width
        self.height=height
        self.capture=cv2.VideoCapture(src)
        self.thread=Thread(target=self.update,args=())
        self.thread.daemon=True
        self.thread.start()
    def update(self):
        while True:
            _,self.frame=self.capture.read()
            self.frame2=cv2.resize(self.frame,(self.width,self.height))
    def getFrame(self):
        return self.frame2
flip=2
dispW=640
dispH=480
camSet='nvarguscamerasrc !  video/x-raw(memory:NVMM), width=3264, height=2464, format=NV12, framerate=21/1 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'
cam1=vStream(1,dispW,dispH)
cam2=vStream(camSet,dispW,dispH)
font=cv2.FONT_HERSHEY_SIMPLEX
startTime=time.time()
dtav=0
while True:
    try:
        myFrame1=cam1.getFrame()
        myFrame2=cam2.getFrame()
        myFrame3=np.hstack((myFrame1,myFrame2))
        dt=time.time()-startTime
        startTime=time.time()
        dtav=.9*dtav+.1*dt
        fps=1/dtav
        cv2.rectangle(myFrame3,(0,0),(140,40),(0,0,255),-1)
        cv2.putText(myFrame3,str(round(fps,1))+' fps',(0,25),font,.75,(0,255,255),2)
        cv2.imshow('ComboCam',myFrame3)
        cv2.moveWindow('ComboCam',0,0)



    except:
        print('frame not available')
        
    if cv2.waitKey(1)==ord('q'):
        cam1.capture.release()
        cam2.capture.release()
        cv2.destroyAllWindows()
        exit(1)
        break

from threading import Thread

import cv2

import time

import numpy as np

class vStream:

def __init__(self,src,width,height):

self.width=width

self.height=height

self.capture=cv2.VideoCapture(src)

self.thread=Thread(target=self.update,args=())

self.thread.daemon=True

self.thread.start()

def update(self):

while True:

_,self.frame=self.capture.read()

self.frame2=cv2.resize(self.frame,(self.width,self.height))

def getFrame(self):

return self.frame2

flip=2

dispW=640

dispH=480

camSet='nvarguscamerasrc ! video/x-raw(memory:NVMM), width=3264, height=2464, format=NV12, framerate=21/1 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'

cam1=vStream(1,dispW,dispH)

cam2=vStream(camSet,dispW,dispH)

font=cv2.FONT_HERSHEY_SIMPLEX

startTime=time.time()

dtav=0

while True:

try:

myFrame1=cam1.getFrame()

myFrame2=cam2.getFrame()

myFrame3=np.hstack((myFrame1,myFrame2))

dt=time.time()-startTime

startTime=time.time()

dtav=.9*dtav+.1*dt

fps=1/dtav

cv2.rectangle(myFrame3,(0,0),(140,40),(0,0,255),-1)

cv2.putText(myFrame3,str(round(fps,1))+' fps',(0,25),font,.75,(0,255,255),2)

cv2.imshow('ComboCam',myFrame3)

cv2.moveWindow('ComboCam',0,0)

except:

print('frame not available')

if cv2.waitKey(1)==ord('q'):

cam1.capture.release()

cam2.capture.release()

cv2.destroyAllWindows()

exit(1)

break

Jetson Nano

AI on the Jetson Nano LESSON 42: Recognizing and Identifying Faces from Live Video on OpenCV

May 30, 2020 admin

In this video lesson we show how to recognize and identify faces in live video on the Jestson Nano using OpenCV. We have a separate program that trains the system based on known faces, and this work was described in an earlier lesson in this series. Below is a copy of the code we develop in the video above.

import face_recognition
import cv2
import os
import pickle
import time
print(cv2.__version__)

Encodings=[]
Names=[]

with open('train.pkl','rb') as f:
    Names=pickle.load(f)
    Encodings=pickle.load(f)
font=cv2.FONT_HERSHEY_SIMPLEX

cam= cv2.VideoCapture(1)

while True:

    _,frame=cam.read()
    frameSmall=cv2.resize(frame,(0,0),fx=.25,fy=.25)
    frameRGB=cv2.cvtColor(frameSmall,cv2.COLOR_BGR2RGB)
    facePositions=face_recognition.face_locations(frameRGB,model='cnn')
    allEncodings=face_recognition.face_encodings(frameRGB,facePositions)
    for (top,right,bottom,left),face_encoding in zip(facePositions,allEncodings):
        name='Unkown Person'
        matches=face_recognition.compare_faces(Encodings,face_encoding)
        if True in matches:
            first_match_index=matches.index(True)
            name=Names[first_match_index]
        top=top*4
        right=right*4
        bottom=bottom*4
        left=left*4
        cv2.rectangle(frame,(left,top),(right, bottom),(0,0,255),2)
        cv2.putText(frame,name,(left,top-6),font,.75,(0,0,255),2)
    cv2.imshow('Picture',frame)
    cv2.moveWindow('Picture',0,0)
    if cv2.waitKey(1)==ord('q'):
        break

cam.release()
cv2.destroyAllWindows()

import face_recognition

import cv2

import os

import pickle

import time

print(cv2.__version__)

Encodings=[]

Names=[]

with open('train.pkl','rb') as f:

Names=pickle.load(f)

Encodings=pickle.load(f)

font=cv2.FONT_HERSHEY_SIMPLEX

cam= cv2.VideoCapture(1)

while True:

_,frame=cam.read()

frameSmall=cv2.resize(frame,(0,0),fx=.25,fy=.25)

frameRGB=cv2.cvtColor(frameSmall,cv2.COLOR_BGR2RGB)

facePositions=face_recognition.face_locations(frameRGB,model='cnn')

allEncodings=face_recognition.face_encodings(frameRGB,facePositions)

for (top,right,bottom,left),face_encoding in zip(facePositions,allEncodings):

name='Unkown Person'

matches=face_recognition.compare_faces(Encodings,face_encoding)

if True in matches:

first_match_index=matches.index(True)

name=Names[first_match_index]

top=top*4

right=right*4

bottom=bottom*4

left=left*4

cv2.rectangle(frame,(left,top),(right, bottom),(0,0,255),2)

cv2.putText(frame,name,(left,top-6),font,.75,(0,0,255),2)

cv2.imshow('Picture',frame)

cv2.moveWindow('Picture',0,0)

if cv2.waitKey(1)==ord('q'):

break

cam.release()

cv2.destroyAllWindows()

Jetson Nano

AI on the Jetson Nano LESSON 40: Training Facial Recognition Models in OpenCV

May 16, 2020 admin

In this lesson we learn to make training our AI Facial Recognition system simpler and more automated.

import face_recognition
import cv2
import os
print(cv2.__version__)

Encodings=[]
Names=[]
j=0

image_dir='/home/pjm/Desktop/pyPro/faceRecognizer/demoImages/known'
for root, dirs, files in os.walk(image_dir):
    print(files)
    for file in files:
        path=os.path.join(root,file)
        print(path)
        name=os.path.splitext(file)[0]
        print(name)
        person=face_recognition.load_image_file(path)
        encoding=face_recognition.face_encodings(person)[0]
        Encodings.append(encoding)
        Names.append(name)
print(Names)

font=cv2.FONT_HERSHEY_SIMPLEX

image_dir='/home/pjm/Desktop/pyPro/faceRecognizer/demoImages/unknown'
for root,dirs, files in os.walk(image_dir):
    for file in files:
        print(root)
        print(file)
        testImagePath=os.path.join(root,file)
        testImage=face_recognition.load_image_file(testImagePath)
        facePositions=face_recognition.face_locations(testImage)
        allEncodings=face_recognition.face_encodings(testImage,facePositions)
        testImage=cv2.cvtColor(testImage,cv2.COLOR_RGB2BGR)

        for (top,right,bottom,left),face_encoding in zip(facePositions,allEncodings):
            name='Unknown Person'
            matches=face_recognition.compare_faces(Encodings,face_encoding)
            if True in matches:
                first_match_index=matches.index(True)
                name=Names[first_match_index]
            cv2.rectangle(testImage,(left,top),(right,bottom),(0,0,255),2)
            cv2.putText(testImage,name,(left,top-6),font,.75,(0,255,255),2)
        cv2.imshow('Picture', testImage)
        cv2.moveWindow('Picture',0,0)
        if cv2.waitKey(0)==ord('q'):
            cv2.destroyAllWindows()

import face_recognition

import cv2

import os

print(cv2.__version__)

Encodings=[]

Names=[]

j=0

image_dir='/home/pjm/Desktop/pyPro/faceRecognizer/demoImages/known'

for root, dirs, files in os.walk(image_dir):

print(files)

for file in files:

path=os.path.join(root,file)

print(path)

name=os.path.splitext(file)[0]

print(name)

person=face_recognition.load_image_file(path)

encoding=face_recognition.face_encodings(person)[0]

Encodings.append(encoding)

Names.append(name)

print(Names)

font=cv2.FONT_HERSHEY_SIMPLEX

image_dir='/home/pjm/Desktop/pyPro/faceRecognizer/demoImages/unknown'

for root,dirs, files in os.walk(image_dir):

for file in files:

print(root)

print(file)

testImagePath=os.path.join(root,file)

testImage=face_recognition.load_image_file(testImagePath)

facePositions=face_recognition.face_locations(testImage)

allEncodings=face_recognition.face_encodings(testImage,facePositions)

testImage=cv2.cvtColor(testImage,cv2.COLOR_RGB2BGR)

for (top,right,bottom,left),face_encoding in zip(facePositions,allEncodings):

name='Unknown Person'

matches=face_recognition.compare_faces(Encodings,face_encoding)

if True in matches:

first_match_index=matches.index(True)

name=Names[first_match_index]

cv2.rectangle(testImage,(left,top),(right,bottom),(0,0,255),2)

cv2.putText(testImage,name,(left,top-6),font,.75,(0,255,255),2)

cv2.imshow('Picture', testImage)

cv2.moveWindow('Picture',0,0)

if cv2.waitKey(0)==ord('q'):

cv2.destroyAllWindows()

Jetson Nano

AI on the Jetson Nano LESSON 32: Tracking an Object with Servos in OpenCV

March 28, 2020 admin

In this lesson we actively track an object using the Jetson Nano, pan/tilt servos, and OpenCV. In order for this code to work, you need to first set up your servos, as explained in Lesson 31 on youtube. Make sure to follow the instructions on that lesson before trying to do this one, as that will show you how to get your servos connected and set up.

import cv2
print(cv2.__version__)
import numpy as np
from adafruit_servokit import ServoKit
kit=ServoKit(channels=16)

pan=90
tilt=45
kit.servo[0].angle=pan
kit.servo[1].angle=tilt

def nothing(x):
    pass

cv2.namedWindow('Trackbars')
cv2.moveWindow('Trackbars',1320,0)

cv2.createTrackbar('hueLower', 'Trackbars',96,179,nothing)
cv2.createTrackbar('hueUpper', 'Trackbars',120,179,nothing)

cv2.createTrackbar('hue2Lower', 'Trackbars',50,179,nothing)
cv2.createTrackbar('hue2Upper', 'Trackbars',0,179,nothing)

cv2.createTrackbar('satLow', 'Trackbars',157,255,nothing)
cv2.createTrackbar('satHigh', 'Trackbars',255,255,nothing)
cv2.createTrackbar('valLow','Trackbars',100,255,nothing)
cv2.createTrackbar('valHigh','Trackbars',255,255,nothing)


dispW=640
dispH=480
flip=2
#Uncomment These next Two Line for Pi Camera
#camSet='nvarguscamerasrc !  video/x-raw(memory:NVMM), width=3264, height=2464, format=NV12, framerate=21/1 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'
#cam= cv2.VideoCapture(camSet)

#Or, if you have a WEB cam, uncomment the next line
#(If it does not work, try setting to '1' instead of '0')
cam=cv2.VideoCapture(1)
width=cam.get(cv2.CAP_PROP_FRAME_WIDTH)
height=cam.get(cv2.CAP_PROP_FRAME_HEIGHT)
print('width:',width,'height:',height)
while True:   ret, frame = cam.read()
    #frame=cv2.imread('smarties.png')

    hsv=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)

    hueLow=cv2.getTrackbarPos('hueLower', 'Trackbars')
    hueUp=cv2.getTrackbarPos('hueUpper', 'Trackbars')

    hue2Low=cv2.getTrackbarPos('hue2Lower', 'Trackbars')
    hue2Up=cv2.getTrackbarPos('hue2Upper', 'Trackbars')

    Ls=cv2.getTrackbarPos('satLow', 'Trackbars')
    Us=cv2.getTrackbarPos('satHigh', 'Trackbars')

    Lv=cv2.getTrackbarPos('valLow', 'Trackbars')
    Uv=cv2.getTrackbarPos('valHigh', 'Trackbars')

    l_b=np.array([hueLow,Ls,Lv])
    u_b=np.array([hueUp,Us,Uv])

    l_b2=np.array([hue2Low,Ls,Lv])
    u_b2=np.array([hue2Up,Us,Uv])

    FGmask=cv2.inRange(hsv,l_b,u_b)
    FGmask2=cv2.inRange(hsv,l_b2,u_b2)
    FGmaskComp=cv2.add(FGmask,FGmask2)
    cv2.imshow('FGmaskComp',FGmaskComp)
    cv2.moveWindow('FGmaskComp',0,530)

    _,contours,_=cv2.findContours(FGmaskComp,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    contours=sorted(contours,key=lambda x:cv2.contourArea(x),reverse=True)
    for cnt in contours:
        area=cv2.contourArea(cnt)
        (x,y,w,h)=cv2.boundingRect(cnt)
        if area>=50:
            #cv2.drawContours(frame,[cnt],0,(255,0,0),3)
            cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),3)
            objX=x+w/2
            objY=y+h/2
            errorPan=objX-width/2
            errorTilt=objY-height/2
            if abs(errorPan)>15:
                pan=pan-errorPan/75
            if abs(errorTilt)>15:
                tilt=tilt-errorTilt/75


            if pan>180:
                pan=180
                print("Pan Out of  Range")   
            if pan<0:
                pan=0
                print("Pan Out of  Range") 
            if tilt>180:
                tilt=180
                print("Tilt Out of  Range") 
            if tilt<0:
                tilt=0
                print("Tilt Out of  Range")                 

            kit.servo[0].angle=pan
            kit.servo[1].angle=tilt 
            break        

    cv2.imshow('nanoCam',frame)
    cv2.moveWindow('nanoCam',0,0)
    

    if cv2.waitKey(1)==ord('q'):
        break
cam.release()
cv2.destroyAllWindows()

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import cv2

print(cv2.__version__)

import numpy as np

from adafruit_servokit import ServoKit

kit=ServoKit(channels=16)

pan=90

tilt=45

kit.servo[0].angle=pan

kit.servo[1].angle=tilt

def nothing(x):

pass

cv2.namedWindow('Trackbars')

cv2.moveWindow('Trackbars',1320,0)

cv2.createTrackbar('hueLower', 'Trackbars',96,179,nothing)

cv2.createTrackbar('hueUpper', 'Trackbars',120,179,nothing)

cv2.createTrackbar('hue2Lower', 'Trackbars',50,179,nothing)

cv2.createTrackbar('hue2Upper', 'Trackbars',0,179,nothing)

cv2.createTrackbar('satLow', 'Trackbars',157,255,nothing)

cv2.createTrackbar('satHigh', 'Trackbars',255,255,nothing)

cv2.createTrackbar('valLow','Trackbars',100,255,nothing)

cv2.createTrackbar('valHigh','Trackbars',255,255,nothing)

dispW=640

dispH=480

flip=2

#Uncomment These next Two Line for Pi Camera

#camSet='nvarguscamerasrc ! video/x-raw(memory:NVMM), width=3264, height=2464, format=NV12, framerate=21/1 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'

#cam= cv2.VideoCapture(camSet)

#Or, if you have a WEB cam, uncomment the next line

#(If it does not work, try setting to '1' instead of '0')

cam=cv2.VideoCapture(1)

width=cam.get(cv2.CAP_PROP_FRAME_WIDTH)

height=cam.get(cv2.CAP_PROP_FRAME_HEIGHT)

print('width:',width,'height:',height)

while True: ret, frame = cam.read()

#frame=cv2.imread('smarties.png')

hsv=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)

hueLow=cv2.getTrackbarPos('hueLower', 'Trackbars')

hueUp=cv2.getTrackbarPos('hueUpper', 'Trackbars')

hue2Low=cv2.getTrackbarPos('hue2Lower', 'Trackbars')

hue2Up=cv2.getTrackbarPos('hue2Upper', 'Trackbars')

Ls=cv2.getTrackbarPos('satLow', 'Trackbars')

Us=cv2.getTrackbarPos('satHigh', 'Trackbars')

Lv=cv2.getTrackbarPos('valLow', 'Trackbars')

Uv=cv2.getTrackbarPos('valHigh', 'Trackbars')

l_b=np.array([hueLow,Ls,Lv])

u_b=np.array([hueUp,Us,Uv])

l_b2=np.array([hue2Low,Ls,Lv])

u_b2=np.array([hue2Up,Us,Uv])

FGmask=cv2.inRange(hsv,l_b,u_b)

FGmask2=cv2.inRange(hsv,l_b2,u_b2)

FGmaskComp=cv2.add(FGmask,FGmask2)

cv2.imshow('FGmaskComp',FGmaskComp)

cv2.moveWindow('FGmaskComp',0,530)

_,contours,_=cv2.findContours(FGmaskComp,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

contours=sorted(contours,key=lambda x:cv2.contourArea(x),reverse=True)

for cnt in contours:

area=cv2.contourArea(cnt)

(x,y,w,h)=cv2.boundingRect(cnt)

if area>=50:

#cv2.drawContours(frame,[cnt],0,(255,0,0),3)

cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),3)

objX=x+w/2

objY=y+h/2

errorPan=objX-width/2

errorTilt=objY-height/2

if abs(errorPan)>15:

pan=pan-errorPan/75

if abs(errorTilt)>15:

tilt=tilt-errorTilt/75

if pan>180:

pan=180

print("Pan Out of Range")

if pan<0:

pan=0

print("Pan Out of Range")

if tilt>180:

tilt=180

print("Tilt Out of Range")

if tilt<0:

tilt=0

print("Tilt Out of Range")

kit.servo[0].angle=pan

kit.servo[1].angle=tilt

break

cv2.imshow('nanoCam',frame)

cv2.moveWindow('nanoCam',0,0)

if cv2.waitKey(1)==ord('q'):

break

cam.release()

cv2.destroyAllWindows()

Technology Tutorials

Category Archives: Jetson Nano

AI on the Jetson Nano LESSON 50: Introduction to Deep Learning and Deep Neural Networks

AI on the Jetson Nano Lesson 46: Synchronizing Multiple Cameras with OpenCV

AI on the Jetson Nano LESSON 42: Recognizing and Identifying Faces from Live Video on OpenCV

AI on the Jetson Nano LESSON 40: Training Facial Recognition Models in OpenCV

AI on the Jetson Nano LESSON 32: Tracking an Object with Servos in OpenCV

Making The World a Better Place One High Tech Project at a Time. Enjoy!