Tag Archives: AI

<span data-mce-type="bookmark" style="display: inline-block; width: 0px; overflow: hidden; line-height: 0;" class="mce_SELRES_start"></span> In this video lesson we should you a simple method to train our face recognizer on larger data sets. We use the python os.walk command to step through, and train automatically on all the training images in our folder. We then show how to store our training set to our SD card using the pickle utility. This allows us to train once, and use the trained model over and over.

For your convenience, the code below is what we developed to allow training our face recognition model.

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

Encodings=[]
Names=[]
j=0

image_dir='/home/pjm/Desktop/pyPro/demoimages/known'
for root, dirs, files in os.walk(image_dir):
    print(files)
    for file in files:
        fullPath=os.path.join(root,file)
        print(fullPath)
        name=os.path.splitext(file)[0]
        print(name)
        person=face_recognition.load_image_file(fullPath)
        encoding=face_recognition.face_encodings(person)[0]
        Encodings.append(encoding)
        Names.append(name)
print(Names)
with open('train.pkl','wb') as f:
        pickle.dump(Names,f)
        pickle.dump(Encodings,f)

import face_recognition

import cv2

import os

import pickle

print(cv2.__version__)

Encodings=[]

Names=[]

j=0

image_dir='/home/pjm/Desktop/pyPro/demoimages/known'

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

print(files)

for file in files:

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

print(fullPath)

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

print(name)

person=face_recognition.load_image_file(fullPath)

encoding=face_recognition.face_encodings(person)[0]

Encodings.append(encoding)

Names.append(name)

print(Names)

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

pickle.dump(Names,f)

pickle.dump(Encodings,f)

Then this is a simple program that loads the trained model, and uses it to recognize people in unknown images.

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

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

font=cv2.FONT_HERSHEY_SIMPLEX
testImage=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/unkno$

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 Life Form'
    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),(255,0,0),2)
    cv2.rectangle(testImage, (left,top),(left+200, top+30),(0,255,255),-1)
    cv2.putText(testImage,name,(left,top+20),font,.75,(255,0,0),2)
cv2.imshow('mywindow',testImage)
cv2.moveWindow('mywindow',0,0)
if cv2.waitKey(0)==ord('q'):
    cv2.destroyAllWindows()

import cv2

print(cv2.__version__)

import face_recognition

import pickle

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

Names=pickle.load(f)

Encodings=pickle.load(f)

font=cv2.FONT_HERSHEY_SIMPLEX

testImage=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/unkno$

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 Life Form'

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),(255,0,0),2)

cv2.rectangle(testImage, (left,top),(left+200, top+30),(0,255,255),-1)

cv2.putText(testImage,name,(left,top+20),font,.75,(255,0,0),2)

cv2.imshow('mywindow',testImage)

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

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

cv2.destroyAllWindows()

Jetson Xavier NX

Jetson Xavier NX Lesson 14: Face Recognition and Identification on NVIDIA Xavier NX

August 18, 2020 admin

In this video lesson we show you how to train the NVIDIA Jetson Xavier NX to recognize faces, and then demonstrate finding those faces in a new unknown image. We show step by step instruction in this easy to follow tutorial. We develop two python programs. The first one simply finds the faces in an unknown image, and the second program actually identifies the known faces. For your convenience, the code is included below.

Simple face detection Python code:

import face_recognition
import cv2
print(cv2.__version__)
image=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/unknown/u1.jpg')
face_locations=face_recognition.face_locations(image)
print(face_locations)
image=cv2.cvtColor(image,cv2.COLOR_RGB2BGR)
for (row1,col1,row2,col2) in face_locations:
    cv2.rectangle(image,(col1,row1),(col2,row2),(255,0,0),2)
cv2.imshow('myWindow',image)
cv2.moveWindow('myWindow',0,0)
if cv2.waitKey(0)==ord('q'):
    cv2.destroyAllWindows()

import face_recognition

import cv2

print(cv2.__version__)

image=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/unknown/u1.jpg')

face_locations=face_recognition.face_locations(image)

print(face_locations)

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

for (row1,col1,row2,col2) in face_locations:

cv2.rectangle(image,(col1,row1),(col2,row2),(255,0,0),2)

cv2.imshow('myWindow',image)

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

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

cv2.destroyAllWindows()

This next python program will learn faces, and then recognize them in new images.

import face_recognition
import cv2
print(cv2.__version__)

donFace=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/known/Donald Trump.jpg')
nancyFace=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/known/Nancy Pelosi.jpg')
mikeFace=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/known/Mike Pence.jpg')

donEncode=face_recognition.face_encodings(donFace)[0]
nancyEncode=face_recognition.face_encodings(nancyFace)[0]
mikeEncode=face_recognition.face_encodings(mikeFace)[0]

Encodings=[donEncode,nancyEncode,mikeEncode]
Names=['Donald Trump','Nancy Pelosi','Mike Pence']

font=cv2.FONT_HERSHEY_SIMPLEX

testImage=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/unknown/u11.jpg')

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 Life Form'
    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),(255,0,0),2)
    cv2.rectangle(testImage,(left,top),(left+200, top+30),(0,255,255),-1)
    cv2.putText(testImage,name,(left,top+20),font,.75,(255,0,0),2)
cv2.imshow('myWindow',testImage)
cv2.moveWindow('myWindow',0,0)
if cv2.waitKey(0)==ord('q'):
    cv2.destroyAllWindows()

import face_recognition

import cv2

print(cv2.__version__)

donFace=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/known/Donald Trump.jpg')

nancyFace=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/known/Nancy Pelosi.jpg')

mikeFace=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/known/Mike Pence.jpg')

donEncode=face_recognition.face_encodings(donFace)[0]

nancyEncode=face_recognition.face_encodings(nancyFace)[0]

mikeEncode=face_recognition.face_encodings(mikeFace)[0]

Encodings=[donEncode,nancyEncode,mikeEncode]

Names=['Donald Trump','Nancy Pelosi','Mike Pence']

font=cv2.FONT_HERSHEY_SIMPLEX

testImage=face_recognition.load_image_file('/home/pjm/Desktop/pyPro/demoimages/unknown/u11.jpg')

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 Life Form'

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),(255,0,0),2)

cv2.rectangle(testImage,(left,top),(left+200, top+30),(0,255,255),-1)

cv2.putText(testImage,name,(left,top+20),font,.75,(255,0,0),2)

cv2.imshow('myWindow',testImage)

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

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

cv2.destroyAllWindows()

Jetson Xavier NX

Jetson Xavier NX Lesson 12: Intelligent Scanning for Objects of Interest

August 5, 2020 admin

In this Video Tutorial we show how a camera on a pan/tilt control system can be programmed to search for an object of interest, and then track it when found. Our system has two independent camera systems, and each can track a separate item of interest independently. The code is written in python, using the OpenCV library. The video takes you through the lesson step-by-step, and then the code is included below for your convenience.

If you want to play along at home, we are using the Jetson Xavier NX, which you can pick up HERE. You will also need to of the bracket/servo kits, which you can get HERE, and then two Raspberry Pi Version two cameras, available HERE.

import cv2
import numpy as np
import time
from adafruit_servokit import ServoKit
print(cv2.__version__)
timeMark=time.time()
dtFIL=0

scanRight=True
scanLeft=True

def nothing(x):
    pass

cv2.namedWindow('TrackBars')
cv2.moveWindow('TrackBars',1320,0)
cv2.createTrackbar('hueLower', 'TrackBars',100,179,nothing)
cv2.createTrackbar('hueUpper', 'TrackBars',116,179,nothing)
cv2.createTrackbar('satLow', 'TrackBars',160,255,nothing)
cv2.createTrackbar('satHigh', 'TrackBars',255,255,nothing)
cv2.createTrackbar('valLow', 'TrackBars',150,255,nothing)
cv2.createTrackbar('valHigh', 'TrackBars',255,255,nothing)

cv2.namedWindow('TrackBars2')
cv2.moveWindow('TrackBars2',1100,0)
cv2.createTrackbar('hueLower2', 'TrackBars2',150,179,nothing)
cv2.createTrackbar('hueUpper2', 'TrackBars2',170,179,nothing)
cv2.createTrackbar('satLow2', 'TrackBars2',160,255,nothing)
cv2.createTrackbar('satHigh2', 'TrackBars2',255,255,nothing)
cv2.createTrackbar('valLow2', 'TrackBars2',150,255,nothing)
cv2.createTrackbar('valHigh2', 'TrackBars2',255,255,nothing)


kit=ServoKit(channels=16)

tilt1=90
pan1=90
tilt2=90
pan2=90

dPan1=1
dPan2=1
dTilt1=10
dTilt2=10

kit.servo[0].angle=pan1
kit.servo[1].angle=tilt1
kit.servo[2].angle=pan2
kit.servo[3].angle=tilt2

width=720
height=480
flip=2
font=cv2.FONT_HERSHEY_SIMPLEX
camSet1='nvarguscamerasrc sensor-id=0 ee-mode=1 ee-strength=0 tnr-mode=2 tnr-strength=1 wbmode=3 ! video/x-raw(memory:NVMM), width=3264, height=2464, framerate=21/1,format=NV12 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(width)+', height='+str(height)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! videobalance contrast=1.3 brightness=-.2 saturation=1.2 ! appsink drop=True'
camSet2='nvarguscamerasrc sensor-id=1 ee-mode=1 ee-strength=0 tnr-mode=2 tnr-strength=1 wbmode=3 ! video/x-raw(memory:NVMM), width=3264, height=2464, framerate=21/1,format=NV12 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(width)+', height='+str(height)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! videobalance contrast=1.3 brightness=-.2 saturation=1.2 ! appsink drop=True'

#camSet='nvarguscamerasrc sensor-id=0 ! video/x-raw(memory:NVMM), width=3264, height=2464, framerate=21/1,format=NV12 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(width)+', height='+str(height)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'
#camSet ='v4l2src device=/dev/video1 ! video/x-raw,width='+str(width)+',height='+str(height)+',framerate=20/1 ! videoconvert ! appsink'
cam1=cv2.VideoCapture(camSet1)
cam2=cv2.VideoCapture(camSet2)
while True:
    _, frame1 = cam1.read()
    _, frame2 = cam2.read()

    hsv1=cv2.cvtColor(frame1,cv2.COLOR_BGR2HSV)
    hsv2=cv2.cvtColor(frame2,cv2.COLOR_BGR2HSV)

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


    hueLow2=cv2.getTrackbarPos('hueLower2', 'TrackBars2')
    hueUp2=cv2.getTrackbarPos('hueUpper2', 'TrackBars2')

    Ls2=cv2.getTrackbarPos('satLow2', 'TrackBars2')
    Us2=cv2.getTrackbarPos('satHigh2', 'TrackBars2')

    Lv2=cv2.getTrackbarPos('valLow2', 'TrackBars2')
    Uv2=cv2.getTrackbarPos('valHigh2', 'TrackBars2')

    l_b2=np.array([hueLow2,Ls2,Lv2])
    u_b2=np.array([hueUp2,Us2,Uv2])

    FGmask1=cv2.inRange(hsv1,l_b,u_b)
    FGmask2=cv2.inRange(hsv2,l_b2,u_b2)

    cv2.imshow('FGmask1',FGmask1)
    cv2.moveWindow('FGmask1',0,0)

    cv2.imshow('FGmask2',FGmask2)
    cv2.moveWindow('FGmask2',350,0)

    contours1,_ = cv2.findContours(FGmask1,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    contours1=sorted(contours1,key=lambda x:cv2.contourArea(x),reverse=True)
    for cnt in contours1:
        area=cv2.contourArea(cnt)
        (x,y,w,h)=cv2.boundingRect(cnt)
        if area>=100:
            scanLeft=False
            cv2.rectangle(frame1,(x,y),(x+w,y+h),(0,255,255),3)
            objX=x+w/2
            objY=y+h/2
            errorPan1=objX-width/2
            errorTilt1=objY-height/2
            if abs(errorPan1)>15:
                pan1=pan1+errorPan1/40
            if abs(errorTilt1)>15:
                tilt1=tilt1-errorTilt1/40
            if pan1>180:
                pan1=180
                print('Pan Out of Range')
            if pan1<0:
                pan1=0
                print('Pan Out of Range')
            if tilt1>180:
                tilt1=180
                print('Tilt Out of Range')
            if tilt1<0:
                tilt1=0
            kit.servo[2].angle=pan1
            kit.servo[3].angle=tilt1
            break
    
    contours2,_ = cv2.findContours(FGmask2,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    contours2=sorted(contours2,key=lambda x:cv2.contourArea(x),reverse=True)
    for cnt in contours2:
        area=cv2.contourArea(cnt)
        (x,y,w,h)=cv2.boundingRect(cnt)
        if area>=100:
            scanRight=False
            cv2.rectangle(frame2,(x,y),(x+w,y+h),(0,255,255),3)
            objX=x+w/2
            objY=y+h/2
            errorPan2=objX-width/2
            errorTilt2=objY-height/2
            if abs(errorPan2)>15:
                pan2=pan2+errorPan2/40
            if abs(errorTilt2)>15:
                tilt2=tilt2-errorTilt2/40
            if pan2>180:
                pan2=180
                print('Pan Out of Range')
            if pan2<0:
                pan2=0
                print('Pan Out of Range')
            if tilt2>180:
                tilt2=180
                print('Tilt Out of Range')
            if tilt2<0:
                tilt2=0
            kit.servo[0].angle=pan2
            kit.servo[1].angle=tilt2            
            break
    if scanLeft==True:
        if pan1>=179:
            dPan1=abs(dPan1)*(-1)
        if pan1<=1:
            dPan1=abs(dPan1)
        if pan1>=179 or pan1<=1:
            if tilt1>=170:
                dTilt1=abs(dTilt1)*(-1)
            if tilt1<=10:
                dTilt1=abs(dTilt1)
            tilt1=tilt1+dTilt1
        pan1=pan1+dPan1
        kit.servo[2].angle=pan1
        kit.servo[3].angle=tilt1
    scanLeft=True

    if scanRight==True:
        if pan2>=179:
            dPan2=abs(dPan2)*(-1)
        if pan2<=1:
            dPan2=abs(dPan2)
        if pan2>=179 or pan2<=1:
            if tilt2>=170:
                dTilt2=abs(dTilt2)*(-1)
            if tilt2<=10:
                dTilt2=abs(dTilt2)
            tilt2=tilt2+dTilt2
        pan2=pan2+dPan2
        kit.servo[0].angle=pan2
        kit.servo[1].angle=tilt2
    scanRight=True
    
    frame3=np.hstack((frame1,frame2))
    dt=time.time()-timeMark
    timeMark=time.time()
    dtFIL=.9*dtFIL + .1*dt
    fps=1/dtFIL
    cv2.rectangle(frame3,(0,0),(150,40),(0,0,255),-1)
    cv2.putText(frame3,'fps: '+str(round(fps,1)),(0,30),font,1,(0,255,255),2)

    #cv2.imshow('myCam1',frame1)
    #cv2.imshow('myCam2',frame2)
    cv2.imshow('comboCam',frame3)
    cv2.moveWindow('comboCam',0,450)

    if cv2.waitKey(1)==ord('q'):
        break
cam1.release()
cam2.release()
cv2.destroyAllWindows()

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import cv2

import numpy as np

import time

from adafruit_servokit import ServoKit

print(cv2.__version__)

timeMark=time.time()

dtFIL=0

scanRight=True

scanLeft=True

def nothing(x):

pass

cv2.namedWindow('TrackBars')

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

cv2.createTrackbar('hueLower', 'TrackBars',100,179,nothing)

cv2.createTrackbar('hueUpper', 'TrackBars',116,179,nothing)

cv2.createTrackbar('satLow', 'TrackBars',160,255,nothing)

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

cv2.createTrackbar('valLow', 'TrackBars',150,255,nothing)

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

cv2.namedWindow('TrackBars2')

cv2.moveWindow('TrackBars2',1100,0)

cv2.createTrackbar('hueLower2', 'TrackBars2',150,179,nothing)

cv2.createTrackbar('hueUpper2', 'TrackBars2',170,179,nothing)

cv2.createTrackbar('satLow2', 'TrackBars2',160,255,nothing)

cv2.createTrackbar('satHigh2', 'TrackBars2',255,255,nothing)

cv2.createTrackbar('valLow2', 'TrackBars2',150,255,nothing)

cv2.createTrackbar('valHigh2', 'TrackBars2',255,255,nothing)

kit=ServoKit(channels=16)

tilt1=90

pan1=90

tilt2=90

pan2=90

dPan1=1

dPan2=1

dTilt1=10

dTilt2=10

kit.servo[0].angle=pan1

kit.servo[1].angle=tilt1

kit.servo[2].angle=pan2

kit.servo[3].angle=tilt2

width=720

height=480

flip=2

font=cv2.FONT_HERSHEY_SIMPLEX

camSet1='nvarguscamerasrc sensor-id=0 ee-mode=1 ee-strength=0 tnr-mode=2 tnr-strength=1 wbmode=3 ! video/x-raw(memory:NVMM), width=3264, height=2464, framerate=21/1,format=NV12 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(width)+', height='+str(height)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! videobalance contrast=1.3 brightness=-.2 saturation=1.2 ! appsink drop=True'

camSet2='nvarguscamerasrc sensor-id=1 ee-mode=1 ee-strength=0 tnr-mode=2 tnr-strength=1 wbmode=3 ! video/x-raw(memory:NVMM), width=3264, height=2464, framerate=21/1,format=NV12 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(width)+', height='+str(height)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! videobalance contrast=1.3 brightness=-.2 saturation=1.2 ! appsink drop=True'

#camSet='nvarguscamerasrc sensor-id=0 ! video/x-raw(memory:NVMM), width=3264, height=2464, framerate=21/1,format=NV12 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(width)+', height='+str(height)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'

#camSet ='v4l2src device=/dev/video1 ! video/x-raw,width='+str(width)+',height='+str(height)+',framerate=20/1 ! videoconvert ! appsink'

cam1=cv2.VideoCapture(camSet1)

cam2=cv2.VideoCapture(camSet2)

while True:

_, frame1 = cam1.read()

_, frame2 = cam2.read()

hsv1=cv2.cvtColor(frame1,cv2.COLOR_BGR2HSV)

hsv2=cv2.cvtColor(frame2,cv2.COLOR_BGR2HSV)

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

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

hueLow2=cv2.getTrackbarPos('hueLower2', 'TrackBars2')

hueUp2=cv2.getTrackbarPos('hueUpper2', 'TrackBars2')

Ls2=cv2.getTrackbarPos('satLow2', 'TrackBars2')

Us2=cv2.getTrackbarPos('satHigh2', 'TrackBars2')

Lv2=cv2.getTrackbarPos('valLow2', 'TrackBars2')

Uv2=cv2.getTrackbarPos('valHigh2', 'TrackBars2')

l_b2=np.array([hueLow2,Ls2,Lv2])

u_b2=np.array([hueUp2,Us2,Uv2])

FGmask1=cv2.inRange(hsv1,l_b,u_b)

FGmask2=cv2.inRange(hsv2,l_b2,u_b2)

cv2.imshow('FGmask1',FGmask1)

cv2.moveWindow('FGmask1',0,0)

cv2.imshow('FGmask2',FGmask2)

cv2.moveWindow('FGmask2',350,0)

contours1,_ = cv2.findContours(FGmask1,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

contours1=sorted(contours1,key=lambda x:cv2.contourArea(x),reverse=True)

for cnt in contours1:

area=cv2.contourArea(cnt)

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

if area>=100:

scanLeft=False

cv2.rectangle(frame1,(x,y),(x+w,y+h),(0,255,255),3)

objX=x+w/2

objY=y+h/2

errorPan1=objX-width/2

errorTilt1=objY-height/2

if abs(errorPan1)>15:

pan1=pan1+errorPan1/40

if abs(errorTilt1)>15:

tilt1=tilt1-errorTilt1/40

if pan1>180:

pan1=180

print('Pan Out of Range')

if pan1<0:

pan1=0

print('Pan Out of Range')

if tilt1>180:

tilt1=180

print('Tilt Out of Range')

if tilt1<0:

tilt1=0

kit.servo[2].angle=pan1

kit.servo[3].angle=tilt1

break

contours2,_ = cv2.findContours(FGmask2,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

contours2=sorted(contours2,key=lambda x:cv2.contourArea(x),reverse=True)

for cnt in contours2:

area=cv2.contourArea(cnt)

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

if area>=100:

scanRight=False

cv2.rectangle(frame2,(x,y),(x+w,y+h),(0,255,255),3)

objX=x+w/2

objY=y+h/2

errorPan2=objX-width/2

errorTilt2=objY-height/2

if abs(errorPan2)>15:

pan2=pan2+errorPan2/40

if abs(errorTilt2)>15:

tilt2=tilt2-errorTilt2/40

if pan2>180:

pan2=180

print('Pan Out of Range')

if pan2<0:

pan2=0

print('Pan Out of Range')

if tilt2>180:

tilt2=180

print('Tilt Out of Range')

if tilt2<0:

tilt2=0

kit.servo[0].angle=pan2

kit.servo[1].angle=tilt2

break

if scanLeft==True:

if pan1>=179:

dPan1=abs(dPan1)*(-1)

if pan1<=1:

dPan1=abs(dPan1)

if pan1>=179 or pan1<=1:

if tilt1>=170:

dTilt1=abs(dTilt1)*(-1)

if tilt1<=10:

dTilt1=abs(dTilt1)

tilt1=tilt1+dTilt1

pan1=pan1+dPan1

kit.servo[2].angle=pan1

kit.servo[3].angle=tilt1

scanLeft=True

if scanRight==True:

if pan2>=179:

dPan2=abs(dPan2)*(-1)

if pan2<=1:

dPan2=abs(dPan2)

if pan2>=179 or pan2<=1:

if tilt2>=170:

dTilt2=abs(dTilt2)*(-1)

if tilt2<=10:

dTilt2=abs(dTilt2)

tilt2=tilt2+dTilt2

pan2=pan2+dPan2

kit.servo[0].angle=pan2

kit.servo[1].angle=tilt2

scanRight=True

frame3=np.hstack((frame1,frame2))

dt=time.time()-timeMark

timeMark=time.time()

dtFIL=.9*dtFIL + .1*dt

fps=1/dtFIL

cv2.rectangle(frame3,(0,0),(150,40),(0,0,255),-1)

cv2.putText(frame3,'fps: '+str(round(fps,1)),(0,30),font,1,(0,255,255),2)

#cv2.imshow('myCam1',frame1)

#cv2.imshow('myCam2',frame2)

cv2.imshow('comboCam',frame3)

cv2.moveWindow('comboCam',0,450)

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

break

cam1.release()

cam2.release()

cv2.destroyAllWindows()

Jetson Xavier NX

Jetson Xavier NX Lesson 11: Independently Tracking Different Objects in Different Cameras

July 29, 2020 admin

In this video lesson we show how two Raspberry Pi cameras can independently track to different objects of interest. As a demonstration, we track on two different colors, with pan/tilt servo systems adjusting to keep the object of interest in the center of the field of view.

In this project we are using the Jetson Xavier NX, which you can pick up HERE. You will also need to of the bracket/servo kits, which you can get HERE, and then two Raspberry Pi Version two cameras, available HERE.

import cv2
import numpy as np
import time
from adafruit_servokit import ServoKit
print(cv2.__version__)
timeMark=time.time()
dtFIL=0

def nothing(x):
    pass

cv2.namedWindow('TrackBars')
cv2.moveWindow('TrackBars',1320,0)
cv2.createTrackbar('hueLower', 'TrackBars',100,179,nothing)
cv2.createTrackbar('hueUpper', 'TrackBars',116,179,nothing)
cv2.createTrackbar('satLow', 'TrackBars',160,255,nothing)
cv2.createTrackbar('satHigh', 'TrackBars',255,255,nothing)
cv2.createTrackbar('valLow', 'TrackBars',150,255,nothing)
cv2.createTrackbar('valHigh', 'TrackBars',255,255,nothing)

cv2.namedWindow('TrackBars2')
cv2.moveWindow('TrackBars2',1100,0)
cv2.createTrackbar('hueLower2', 'TrackBars2',150,179,nothing)
cv2.createTrackbar('hueUpper2', 'TrackBars2',170,179,nothing)
cv2.createTrackbar('satLow2', 'TrackBars2',160,255,nothing)
cv2.createTrackbar('satHigh2', 'TrackBars2',255,255,nothing)
cv2.createTrackbar('valLow2', 'TrackBars2',150,255,nothing)
cv2.createTrackbar('valHigh2', 'TrackBars2',255,255,nothing)


kit=ServoKit(channels=16)

tilt1=90
pan1=90
tilt2=90
pan2=90

kit.servo[0].angle=pan1
kit.servo[1].angle=tilt1
kit.servo[2].angle=pan2
kit.servo[3].angle=tilt2

width=720
height=480
flip=2
font=cv2.FONT_HERSHEY_SIMPLEX
camSet1='nvarguscamerasrc sensor-id=0 ee-mode=1 ee-strength=0 tnr-mode=2 tnr-strength=1 wbmode=3 ! video/x-raw(memory:NVMM), width=3264, height=2464, framerate=21/1,format=NV12 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(width)+', height='+str(height)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! videobalance contrast=1.3 brightness=-.2 saturation=1.2 ! appsink drop=True'
camSet2='nvarguscamerasrc sensor-id=1 ee-mode=1 ee-strength=0 tnr-mode=2 tnr-strength=1 wbmode=3 ! video/x-raw(memory:NVMM), width=3264, height=2464, framerate=21/1,format=NV12 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(width)+', height='+str(height)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! videobalance contrast=1.3 brightness=-.2 saturation=1.2 ! appsink drop=True'

#camSet='nvarguscamerasrc sensor-id=0 ! video/x-raw(memory:NVMM), width=3264, height=2464, framerate=21/1,format=NV12 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(width)+', height='+str(height)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'
#camSet ='v4l2src device=/dev/video1 ! video/x-raw,width='+str(width)+',height='+str(height)+',framerate=20/1 ! videoconvert ! appsink'
cam1=cv2.VideoCapture(camSet1)
cam2=cv2.VideoCapture(camSet2)
while True:
    _, frame1 = cam1.read()
    _, frame2 = cam2.read()

    hsv1=cv2.cvtColor(frame1,cv2.COLOR_BGR2HSV)
    hsv2=cv2.cvtColor(frame2,cv2.COLOR_BGR2HSV)

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


    hueLow2=cv2.getTrackbarPos('hueLower2', 'TrackBars2')
    hueUp2=cv2.getTrackbarPos('hueUpper2', 'TrackBars2')

    Ls2=cv2.getTrackbarPos('satLow2', 'TrackBars2')
    Us2=cv2.getTrackbarPos('satHigh2', 'TrackBars2')

    Lv2=cv2.getTrackbarPos('valLow2', 'TrackBars2')
    Uv2=cv2.getTrackbarPos('valHigh2', 'TrackBars2')

    l_b2=np.array([hueLow2,Ls2,Lv2])
    u_b2=np.array([hueUp2,Us2,Uv2])

    FGmask1=cv2.inRange(hsv1,l_b,u_b)
    FGmask2=cv2.inRange(hsv2,l_b2,u_b2)

    cv2.imshow('FGmask1',FGmask1)
    cv2.moveWindow('FGmask1',0,0)

    cv2.imshow('FGmask2',FGmask2)
    cv2.moveWindow('FGmask2',350,0)

    contours1,_ = cv2.findContours(FGmask1,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    contours1=sorted(contours1,key=lambda x:cv2.contourArea(x),reverse=True)
    for cnt in contours1:
        area=cv2.contourArea(cnt)
        (x,y,w,h)=cv2.boundingRect(cnt)
        if area>=100:
            cv2.rectangle(frame1,(x,y),(x+w,y+h),(0,255,255),3)
            objX=x+w/2
            objY=y+h/2
            errorPan1=objX-width/2
            errorTilt1=objY-height/2
            if abs(errorPan1)>15:
                pan1=pan1+errorPan1/40
            if abs(errorTilt1)>15:
                tilt1=tilt1-errorTilt1/40
            if pan1>180:
                pan1=180
                print('Pan Out of Range')
            if pan1<0:
                pan1=0
                print('Pan Out of Range')
            if tilt1>180:
                tilt1=180
                print('Tilt Out of Range')
            if tilt1<0:
                tilt1=0
            kit.servo[2].angle=pan1
            kit.servo[3].angle=tilt1
            break
    
    contours2,_ = cv2.findContours(FGmask2,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    contours2=sorted(contours2,key=lambda x:cv2.contourArea(x),reverse=True)
    for cnt in contours2:
        area=cv2.contourArea(cnt)
        (x,y,w,h)=cv2.boundingRect(cnt)
        if area>=100:
            cv2.rectangle(frame2,(x,y),(x+w,y+h),(0,255,255),3)
            objX=x+w/2
            objY=y+h/2
            errorPan2=objX-width/2
            errorTilt2=objY-height/2
            if abs(errorPan2)>15:
                pan2=pan2+errorPan2/40
            if abs(errorTilt2)>15:
                tilt2=tilt2-errorTilt2/40
            if pan2>180:
                pan2=180
                print('Pan Out of Range')
            if pan2<0:
                pan2=0
                print('Pan Out of Range')
            if tilt2>180:
                tilt2=180
                print('Tilt Out of Range')
            if tilt2<0:
                tilt2=0
            kit.servo[0].angle=pan2
            kit.servo[1].angle=tilt2            
            break

    frame3=np.hstack((frame1,frame2))
    dt=time.time()-timeMark
    timeMark=time.time()
    dtFIL=.9*dtFIL + .1*dt
    fps=1/dtFIL
    cv2.rectangle(frame3,(0,0),(150,40),(0,0,255),-1)
    cv2.putText(frame3,'fps: '+str(round(fps,1)),(0,30),font,1,(0,255,255),2)

    #cv2.imshow('myCam1',frame1)
    #cv2.imshow('myCam2',frame2)
    cv2.imshow('comboCam',frame3)
    cv2.moveWindow('comboCam',0,450)

    if cv2.waitKey(1)==ord('q'):
        break
cam1.release()
cam2.release()
cv2.destroyAllWindows()

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import cv2

import numpy as np

import time

from adafruit_servokit import ServoKit

print(cv2.__version__)

timeMark=time.time()

dtFIL=0

def nothing(x):

pass

cv2.namedWindow('TrackBars')

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

cv2.createTrackbar('hueLower', 'TrackBars',100,179,nothing)

cv2.createTrackbar('hueUpper', 'TrackBars',116,179,nothing)

cv2.createTrackbar('satLow', 'TrackBars',160,255,nothing)

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

cv2.createTrackbar('valLow', 'TrackBars',150,255,nothing)

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

cv2.namedWindow('TrackBars2')

cv2.moveWindow('TrackBars2',1100,0)

cv2.createTrackbar('hueLower2', 'TrackBars2',150,179,nothing)

cv2.createTrackbar('hueUpper2', 'TrackBars2',170,179,nothing)

cv2.createTrackbar('satLow2', 'TrackBars2',160,255,nothing)

cv2.createTrackbar('satHigh2', 'TrackBars2',255,255,nothing)

cv2.createTrackbar('valLow2', 'TrackBars2',150,255,nothing)

cv2.createTrackbar('valHigh2', 'TrackBars2',255,255,nothing)

kit=ServoKit(channels=16)

tilt1=90

pan1=90

tilt2=90

pan2=90

kit.servo[0].angle=pan1

kit.servo[1].angle=tilt1

kit.servo[2].angle=pan2

kit.servo[3].angle=tilt2

width=720

height=480

flip=2

font=cv2.FONT_HERSHEY_SIMPLEX

#camSet ='v4l2src device=/dev/video1 ! video/x-raw,width='+str(width)+',height='+str(height)+',framerate=20/1 ! videoconvert ! appsink'

cam1=cv2.VideoCapture(camSet1)

cam2=cv2.VideoCapture(camSet2)

while True:

_, frame1 = cam1.read()

_, frame2 = cam2.read()

hsv1=cv2.cvtColor(frame1,cv2.COLOR_BGR2HSV)

hsv2=cv2.cvtColor(frame2,cv2.COLOR_BGR2HSV)

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

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

hueLow2=cv2.getTrackbarPos('hueLower2', 'TrackBars2')

hueUp2=cv2.getTrackbarPos('hueUpper2', 'TrackBars2')

Ls2=cv2.getTrackbarPos('satLow2', 'TrackBars2')

Us2=cv2.getTrackbarPos('satHigh2', 'TrackBars2')

Lv2=cv2.getTrackbarPos('valLow2', 'TrackBars2')

Uv2=cv2.getTrackbarPos('valHigh2', 'TrackBars2')

l_b2=np.array([hueLow2,Ls2,Lv2])

u_b2=np.array([hueUp2,Us2,Uv2])

FGmask1=cv2.inRange(hsv1,l_b,u_b)

FGmask2=cv2.inRange(hsv2,l_b2,u_b2)

cv2.imshow('FGmask1',FGmask1)

cv2.moveWindow('FGmask1',0,0)

cv2.imshow('FGmask2',FGmask2)

cv2.moveWindow('FGmask2',350,0)

contours1,_ = cv2.findContours(FGmask1,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

contours1=sorted(contours1,key=lambda x:cv2.contourArea(x),reverse=True)

for cnt in contours1:

area=cv2.contourArea(cnt)

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

if area>=100:

cv2.rectangle(frame1,(x,y),(x+w,y+h),(0,255,255),3)

objX=x+w/2

objY=y+h/2

errorPan1=objX-width/2

errorTilt1=objY-height/2

if abs(errorPan1)>15:

pan1=pan1+errorPan1/40

if abs(errorTilt1)>15:

tilt1=tilt1-errorTilt1/40

if pan1>180:

pan1=180

print('Pan Out of Range')

if pan1<0:

pan1=0

print('Pan Out of Range')

if tilt1>180:

tilt1=180

print('Tilt Out of Range')

if tilt1<0:

tilt1=0

kit.servo[2].angle=pan1

kit.servo[3].angle=tilt1

break

contours2,_ = cv2.findContours(FGmask2,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

contours2=sorted(contours2,key=lambda x:cv2.contourArea(x),reverse=True)

for cnt in contours2:

area=cv2.contourArea(cnt)

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

if area>=100:

cv2.rectangle(frame2,(x,y),(x+w,y+h),(0,255,255),3)

objX=x+w/2

objY=y+h/2

errorPan2=objX-width/2

errorTilt2=objY-height/2

if abs(errorPan2)>15:

pan2=pan2+errorPan2/40

if abs(errorTilt2)>15:

tilt2=tilt2-errorTilt2/40

if pan2>180:

pan2=180

print('Pan Out of Range')

if pan2<0:

pan2=0

print('Pan Out of Range')

if tilt2>180:

tilt2=180

print('Tilt Out of Range')

if tilt2<0:

tilt2=0

kit.servo[0].angle=pan2

kit.servo[1].angle=tilt2

break

frame3=np.hstack((frame1,frame2))

dt=time.time()-timeMark

timeMark=time.time()

dtFIL=.9*dtFIL + .1*dt

fps=1/dtFIL

cv2.rectangle(frame3,(0,0),(150,40),(0,0,255),-1)

cv2.putText(frame3,'fps: '+str(round(fps,1)),(0,30),font,1,(0,255,255),2)

#cv2.imshow('myCam1',frame1)

#cv2.imshow('myCam2',frame2)

cv2.imshow('comboCam',frame3)

cv2.moveWindow('comboCam',0,450)

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

break

cam1.release()

cam2.release()

cv2.destroyAllWindows()

Jetson Nano

AI on the Jetson Nano LESSON 52: Improving Picture Quality of the Raspberry Pi Camera with Gstreamer

July 25, 2020 admin

In this lesson we want to pause and work on improving the image quality of the video stream coming from the Raspberry Pi camera. Right now, we are using a boilerplate Gstreamer string to launch the Raspberry Pi camera. In the video above we show how image quality can be drastically improved by tweaking the Gstreamer launch string.

Based on the Video above, we develop a greatly improved image quality by adjusting the Gstreamer launch string. Below, for you enjoyment is the code that will optimize picture quality.

First, this is the key line that results in excellent video quality:

# Gstreamer code for improvded Raspberry Pi Camera Quality
camSet='nvarguscamerasrc wbmode=3 tnr-mode=2 tnr-strength=1 ee-mode=2 ee-strength=1 ! 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 ! videobalance contrast=1.5 brightness=-.2 saturation=1.2 ! appsink'

# Gstreamer code for improvded Raspberry Pi Camera Quality

camSet='nvarguscamerasrc wbmode=3 tnr-mode=2 tnr-strength=1 ee-mode=2 ee-strength=1 ! 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 ! videobalance contrast=1.5 brightness=-.2 saturation=1.2 ! appsink'

And here is the overall code for running and displaying from the camera with the enhanced quality:

import cv2
print(cv2.__version__)
dispW=1280
dispH=720
flip=2
#Uncomment These next Two Line for Pi Camera
camSet='nvarguscamerasrc wbmode=3 tnr-mode=2 tnr-strength=1 ee-mode=2 ee-strength=1 !  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 ! videobalance contrast=1.5 brightness=-.2 saturation=1.2 !  appsink'
cam= cv2.VideoCapture(camSet)

#Or, if you have a WEB cam, uncomment the next line
#cam=cv2.VideoCapture('/dev/video1')
while True:
    ret, frame = cam.read()
    cv2.imshow('nanoCam',frame)
    cv2.moveWindow('nanoCam',0,0)
    if cv2.waitKey(1)==ord('q'):
        break
cam.release()
cv2.destroyAllWindows()

import cv2

print(cv2.__version__)

dispW=1280

dispH=720

flip=2

#Uncomment These next Two Line for Pi Camera

cam= cv2.VideoCapture(camSet)

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

#cam=cv2.VideoCapture('/dev/video1')

while True:

ret, frame = cam.read()

cv2.imshow('nanoCam',frame)

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

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

break

cam.release()

cv2.destroyAllWindows()

Now, once we have optimized the Gstreamer launch stream, we need to consider what path to move forward. In lesson #50 we saw that we could either control the camera using the NVIDIA Jetson Utilities, or we could control the camera normally from OpenCV. The advantage of our old OpenCV method is that it gives us more control of the camera. The advantage of the Jetson Utility method is that it appears to run faster, and for the rPi camera, have less latency. Below are two code examples for the two methods above. In the video lesson above, we will figure out the best strategy by tweaking the parameters in these two programas.

OPTION #1: Launch the cameras using OpenCV

import jetson.inference
import jetson.utils
import cv2
import numpy as np
import time
width=1280
height=720
dispW=width
dispH=height
flip=2
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 ! videobalance  contrast=1.5 brightness=-.3 saturation=1.2 ! appsink  '
cam1=cv2.VideoCapture(camSet)
#cam1=cv2.VideoCapture('/dev/video1')
#cam1.set(cv2.CAP_PROP_FRAME_WIDTH,dispW)
#cam1.set(cv2.CAP_PROP_FRAME_HEIGHT,dispH)
net=jetson.inference.imageNet('alexnet')
font=cv2.FONT_HERSHEY_SIMPLEX
timeMark=time.time()
fpsFilter=0
while True:
    _,frame=cam1.read()
    img=cv2.cvtColor(frame,cv2.COLOR_BGR2RGBA).astype(np.float32)
    img=jetson.utils.cudaFromNumpy(img)
    classID, confidence =net.Classify(img, width, height)
    item=''
    item =net.GetClassDesc(classID)
    dt=time.time()-timeMark
    fps=1/dt
    fpsFilter=.95*fpsFilter +.05*fps
    timeMark=time.time()
    cv2.putText(frame,str(round(fpsFilter,1))+' fps '+item,(0,30),font,1,(0,0,255),2)
    cv2.imshow('recCam',frame)
    cv2.moveWindow('recCam',0,0)
    if cv2.waitKey(1)==ord('q'):
        break
cam.releast()
cv2.destroyAllWindows()

import jetson.inference

import jetson.utils

import cv2

import numpy as np

import time

width=1280

height=720

dispW=width

dispH=height

flip=2

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 ! videobalance contrast=1.5 brightness=-.3 saturation=1.2 ! appsink '

cam1=cv2.VideoCapture(camSet)

#cam1=cv2.VideoCapture('/dev/video1')

#cam1.set(cv2.CAP_PROP_FRAME_WIDTH,dispW)

#cam1.set(cv2.CAP_PROP_FRAME_HEIGHT,dispH)

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

font=cv2.FONT_HERSHEY_SIMPLEX

timeMark=time.time()

fpsFilter=0

while True:

_,frame=cam1.read()

img=cv2.cvtColor(frame,cv2.COLOR_BGR2RGBA).astype(np.float32)

img=jetson.utils.cudaFromNumpy(img)

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

item=''

item =net.GetClassDesc(classID)

dt=time.time()-timeMark

fps=1/dt

fpsFilter=.95*fpsFilter +.05*fps

timeMark=time.time()

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

cv2.imshow('recCam',frame)

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

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

break

cam.releast()

cv2.destroyAllWindows()

OPTION # 2: Control Camera with NVIDIA Jetson Utilities Library

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()

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