Tag Archives: Artificial intelligence

AI FOR EVERYONE HOMEWORK SOLUTION: Face Recognition with Live Video in OpenCV

October 23, 2021 admin

In this video lesson we show you how to recognize faces in live video from your WEBcam. It is the homework solution for lesson #16. This builds on the earlier video lessons in this series, so if any of this does not make sense, go back and check out some of the earlier lessons.

import cv2
import face_recognition as FR
font=cv2.FONT_HERSHEY_SIMPLEX
width=640
height=360
cam=cv2.VideoCapture(0,cv2.CAP_DSHOW)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)
cam.set(cv2.CAP_PROP_FPS, 30)
cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

paulFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Paul McWhorter.jpg')
faceLoc=FR.face_locations(paulFace)[0]
paulFaceEncode=FR.face_encodings(paulFace)[0]

gavFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Gavriella Joy.jpg')
faceLoc=FR.face_locations(gavFace)[0]
gavFaceEncode=FR.face_encodings(gavFace)[0]

knownEncodings=[paulFaceEncode,gavFaceEncode]
names=['Paul McWhorter','Gavriella Joy']

while True:
    ignore,  unknownFace = cam.read()

    unknownFaceRGB=cv2.cvtColor(unknownFace,cv2.COLOR_RGB2BGR)
    faceLocations=FR.face_locations(unknownFaceRGB)
    unknownEncodings=FR.face_encodings(unknownFaceRGB,faceLocations)

    for faceLocation,unknownEncoding in zip(faceLocations,unknownEncodings):
        top,right,bottom,left=faceLocation
        print(faceLocation)
        cv2.rectangle(unknownFace,(left,top),(right,bottom),(255,0,0),3)
        name='Unknown Person'
        matches=FR.compare_faces(knownEncodings,unknownEncoding)
        print(matches)
        if True in matches:
            matchIndex=matches.index(True)
            print(matchIndex)
            print(names[matchIndex])
            name=names[matchIndex]
        cv2.putText(unknownFace,name,(left,top),font,.75,(0,0,255),2)

    cv2.imshow('My Faces',unknownFace)
    if cv2.waitKey(1) & 0xff ==ord('q'):
        break
cam.release()
cv2.destroyAllWindows()

import cv2

import face_recognition as FR

font=cv2.FONT_HERSHEY_SIMPLEX

width=640

height=360

cam=cv2.VideoCapture(0,cv2.CAP_DSHOW)

cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)

cam.set(cv2.CAP_PROP_FPS, 30)

cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

paulFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Paul McWhorter.jpg')

faceLoc=FR.face_locations(paulFace)[0]

paulFaceEncode=FR.face_encodings(paulFace)[0]

gavFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Gavriella Joy.jpg')

faceLoc=FR.face_locations(gavFace)[0]

gavFaceEncode=FR.face_encodings(gavFace)[0]

knownEncodings=[paulFaceEncode,gavFaceEncode]

names=['Paul McWhorter','Gavriella Joy']

while True:

ignore, unknownFace = cam.read()

unknownFaceRGB=cv2.cvtColor(unknownFace,cv2.COLOR_RGB2BGR)

faceLocations=FR.face_locations(unknownFaceRGB)

unknownEncodings=FR.face_encodings(unknownFaceRGB,faceLocations)

for faceLocation,unknownEncoding in zip(faceLocations,unknownEncodings):

top,right,bottom,left=faceLocation

print(faceLocation)

cv2.rectangle(unknownFace,(left,top),(right,bottom),(255,0,0),3)

name='Unknown Person'

matches=FR.compare_faces(knownEncodings,unknownEncoding)

print(matches)

if True in matches:

matchIndex=matches.index(True)

print(matchIndex)

print(names[matchIndex])

name=names[matchIndex]

cv2.putText(unknownFace,name,(left,top),font,.75,(0,0,255),2)

cv2.imshow('My Faces',unknownFace)

if cv2.waitKey(1) & 0xff ==ord('q'):

break

cam.release()

cv2.destroyAllWindows()

OpenCV, Python

AI for Everyone LESSON 16: Face Recognition with OpenCV

October 19, 2021 admin

Guys in this lesson we learn how to not only detect faces in an image, but also recognize who the person is. We start by training our model with known faces, and then find and identify those faces in other pictures. For your convenience, I include the code developed in this lesson below.

import cv2
import face_recognition as FR
font=cv2.FONT_HERSHEY_SIMPLEX

donFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Donald Trump.jpg')
faceLoc=FR.face_locations(donFace)[0]
donFaceEncode=FR.face_encodings(donFace)[0]


nancyFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Nancy Pelosi.jpg')
faceLoc=FR.face_locations(nancyFace)[0]
nancyFaceEncode=FR.face_encodings(nancyFace)[0]

penceFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Mike Pence.jpg')
faceLoc=FR.face_locations(penceFace)[0]
penceFaceEncode=FR.face_encodings(penceFace)[0]

knownEncodings=[donFaceEncode,nancyFaceEncode,penceFaceEncode]
names=['Donald Trump','Nancy Pelosi','Mike Pence']

unknownFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/unknown/u1.jpg')
unknownFaceBGR=cv2.cvtColor(unknownFace,cv2.COLOR_RGB2BGR)
faceLocations=FR.face_locations(unknownFace)
unknownEncodings=FR.face_encodings(unknownFace,faceLocations)

for faceLocation,unknownEncoding in zip(faceLocations,unknownEncodings):
    top,right,bottom,left=faceLocation
    print(faceLocation)
    cv2.rectangle(unknownFaceBGR,(left,top),(right,bottom),(255,0,0),3)
    name='Unknown Person'
    matches=FR.compare_faces(knownEncodings,unknownEncoding)
    print(matches)
    if True in matches:
        matchIndex=matches.index(True)
        print(matchIndex)
        print(names[matchIndex])
        name=names[matchIndex]
    cv2.putText(unknownFaceBGR,name,(left,top),font,.75,(0,0,255),2)

cv2.imshow('My Faces',unknownFaceBGR)

cv2.waitKey(10000)

import cv2

import face_recognition as FR

font=cv2.FONT_HERSHEY_SIMPLEX

donFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Donald Trump.jpg')

faceLoc=FR.face_locations(donFace)[0]

donFaceEncode=FR.face_encodings(donFace)[0]

nancyFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Nancy Pelosi.jpg')

faceLoc=FR.face_locations(nancyFace)[0]

nancyFaceEncode=FR.face_encodings(nancyFace)[0]

penceFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/known/Mike Pence.jpg')

faceLoc=FR.face_locations(penceFace)[0]

penceFaceEncode=FR.face_encodings(penceFace)[0]

knownEncodings=[donFaceEncode,nancyFaceEncode,penceFaceEncode]

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

unknownFace=FR.load_image_file('C:/Users/Valued Customer/Documents/Python/demoImages/unknown/u1.jpg')

unknownFaceBGR=cv2.cvtColor(unknownFace,cv2.COLOR_RGB2BGR)

faceLocations=FR.face_locations(unknownFace)

unknownEncodings=FR.face_encodings(unknownFace,faceLocations)

for faceLocation,unknownEncoding in zip(faceLocations,unknownEncodings):

top,right,bottom,left=faceLocation

print(faceLocation)

cv2.rectangle(unknownFaceBGR,(left,top),(right,bottom),(255,0,0),3)

name='Unknown Person'

matches=FR.compare_faces(knownEncodings,unknownEncoding)

print(matches)

if True in matches:

matchIndex=matches.index(True)

print(matchIndex)

print(names[matchIndex])

name=names[matchIndex]

cv2.putText(unknownFaceBGR,name,(left,top),font,.75,(0,0,255),2)

cv2.imshow('My Faces',unknownFaceBGR)

cv2.waitKey(10000)

OpenCV, Python

Using Contours in OpenCV to Track an Object of Interest

October 5, 2021 admin

In this lesson we showed how to track on object in openCV based on color, create contours for the object of interests, and then track the object of interest by drawing a bounding box around it. For your convenience, the code is included below.

import numpy as np
import cv2
print(cv2.__version__)
hueLow=90
hueHigh=100

hueLow2=90
hueHigh2=100

satLow=20
satHigh=200
valLow=20
valHigh=200
def onTrack1(val):
    global hueLow
    hueLow=val
    print('Low Hue: ',val)
def onTrack2(val):
    global hueHigh
    hueHigh=val
    print('High Hue: ',val)
def onTrack3(val):
    global satLow
    satLow=val
    print('Low Sat: ',val)
def onTrack4(val):
    global satHigh
    satHigh=val
    print('High Sat: ',val)
def onTrack5(val):
    global valLow
    valLow=val
    print('Low Val: ',val)
def onTrack6(val):
    global valHigh
    valHigh=val
    print('High Val: ',val)


def onTrack7(val):
    global hueLow2
    hueLow2=val
    print('Low Hue2: ',val)
def onTrack8(val):
    global hueHigh2
    hueHigh2=val
    print('High Hue2: ',val)


width=960
height=540
cam=cv2.VideoCapture(0)
cam.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG')) 
cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
cv2.namedWindow('myTracker')
cv2.moveWindow('myTracker',width,0)
cv2.resizeWindow('myTracker',400,500)
cv2.createTrackbar('Hue Low','myTracker',15,180,onTrack1)
cv2.createTrackbar('Hue High','myTracker',30,180,onTrack2)

cv2.createTrackbar('Hue Low2','myTracker',50,180,onTrack7)
cv2.createTrackbar('Hue High2','myTracker',60,180,onTrack8)

cv2.createTrackbar('Sat Low','myTracker',10,255,onTrack3)
cv2.createTrackbar('Sat High','myTracker',255,255,onTrack4)
cv2.createTrackbar('Val Low','myTracker',10,255,onTrack5)
cv2.createTrackbar('Val High','myTracker',255,255,onTrack6)


while True:
    ignore,  frame = cam.read()
    frameHSV=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)

    lowerBound=np.array([hueLow,satLow,valLow])
    upperBound=np.array([hueHigh,satHigh,valHigh])

    lowerBound2=np.array([hueLow2,satLow,valLow])
    upperBound2=np.array([hueHigh2,satHigh,valHigh])

    myMask=cv2.inRange(frameHSV,lowerBound,upperBound)
    myMask2=cv2.inRange(frameHSV,lowerBound2,upperBound2)

    myMask=myMask | myMask2
    #myMask=cv2.add(myMask,myMask2)
    #myMask=np.logical_or(myMask,myMask2)
    contours,junk=cv2.findContours(myMask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    #cv2.drawContours(frame,contours,-1,(255,0,0),3)
    
    for contour in contours:
        area=cv2.contourArea(contour)
        if area>=200:
            #cv2.drawContours(frame,[contour],0,(255,0,0),3)
            x,y,w,h=cv2.boundingRect(contour)
            cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),3)
    
    #myMask=cv2.bitwise_not(myMask)
    myMaskSmall=cv2.resize(myMask,(int(width/2),int(height/2)))
    mySelection=cv2.bitwise_and(frame,frame, mask=myMask)
    mySelection=cv2.resize(mySelection,(int(width/2),int(height/2)))
    cv2.imshow('My Selection', mySelection)
    cv2.moveWindow('My Selection',int(width/2),height)

    cv2.imshow('My Mask', myMaskSmall)
    cv2.moveWindow('My Mask',0,height)
    cv2.imshow('my WEBcam',frame)
    cv2.moveWindow('my WEBcam',0,0)
    if cv2.waitKey(1) & 0xff ==ord('q'):
        break
cam.release()

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import numpy as np

import cv2

print(cv2.__version__)

hueLow=90

hueHigh=100

hueLow2=90

hueHigh2=100

satLow=20

satHigh=200

valLow=20

valHigh=200

def onTrack1(val):

global hueLow

hueLow=val

print('Low Hue: ',val)

def onTrack2(val):

global hueHigh

hueHigh=val

print('High Hue: ',val)

def onTrack3(val):

global satLow

satLow=val

print('Low Sat: ',val)

def onTrack4(val):

global satHigh

satHigh=val

print('High Sat: ',val)

def onTrack5(val):

global valLow

valLow=val

print('Low Val: ',val)

def onTrack6(val):

global valHigh

valHigh=val

print('High Val: ',val)

def onTrack7(val):

global hueLow2

hueLow2=val

print('Low Hue2: ',val)

def onTrack8(val):

global hueHigh2

hueHigh2=val

print('High Hue2: ',val)

width=960

height=540

cam=cv2.VideoCapture(0)

cam.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))

cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT, height)

cv2.namedWindow('myTracker')

cv2.moveWindow('myTracker',width,0)

cv2.resizeWindow('myTracker',400,500)

cv2.createTrackbar('Hue Low','myTracker',15,180,onTrack1)

cv2.createTrackbar('Hue High','myTracker',30,180,onTrack2)

cv2.createTrackbar('Hue Low2','myTracker',50,180,onTrack7)

cv2.createTrackbar('Hue High2','myTracker',60,180,onTrack8)

cv2.createTrackbar('Sat Low','myTracker',10,255,onTrack3)

cv2.createTrackbar('Sat High','myTracker',255,255,onTrack4)

cv2.createTrackbar('Val Low','myTracker',10,255,onTrack5)

cv2.createTrackbar('Val High','myTracker',255,255,onTrack6)

while True:

ignore, frame = cam.read()

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

lowerBound=np.array([hueLow,satLow,valLow])

upperBound=np.array([hueHigh,satHigh,valHigh])

lowerBound2=np.array([hueLow2,satLow,valLow])

upperBound2=np.array([hueHigh2,satHigh,valHigh])

myMask=cv2.inRange(frameHSV,lowerBound,upperBound)

myMask2=cv2.inRange(frameHSV,lowerBound2,upperBound2)

myMask=myMask | myMask2

#myMask=cv2.add(myMask,myMask2)

#myMask=np.logical_or(myMask,myMask2)

contours,junk=cv2.findContours(myMask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

#cv2.drawContours(frame,contours,-1,(255,0,0),3)

for contour in contours:

area=cv2.contourArea(contour)

if area>=200:

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

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

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

#myMask=cv2.bitwise_not(myMask)

myMaskSmall=cv2.resize(myMask,(int(width/2),int(height/2)))

mySelection=cv2.bitwise_and(frame,frame, mask=myMask)

mySelection=cv2.resize(mySelection,(int(width/2),int(height/2)))

cv2.imshow('My Selection', mySelection)

cv2.moveWindow('My Selection',int(width/2),height)

cv2.imshow('My Mask', myMaskSmall)

cv2.moveWindow('My Mask',0,height)

cv2.imshow('my WEBcam',frame)

cv2.moveWindow('my WEBcam',0,0)

if cv2.waitKey(1) & 0xff ==ord('q'):

break

cam.release()

Python

Tracking an Object Based on Color in OpenCV

September 28, 2021 admin

In this lesson I describe how to track an object in OpenCV based on color. We use the HSV color space to make it simpler to train the model. Below, for your convenience, we include the code we develop in this lesson.

import numpy as np
import cv2
print(cv2.__version__)

def onTrack1(val):
    global hueLow
    hueLow=val
    print('Hue Low',hueLow)
def onTrack2(val):
    global hueHigh
    hueHigh=val
    print('Hue High',hueHigh)
def onTrack3(val):
    global satLow
    satLow=val
    print('Sat Low',satLow)
def onTrack4(val):
    global satHigh
    satHigh=val
    print('Sat High',satHigh)
def onTrack5(val):
    global valLow
    valLow=val
    print('Val Low',valLow)
def onTrack6(val):
    global valHigh
    valHigh=val
    print('Val High',valHigh)

width=640
height=360
cam=cv2.VideoCapture(0,cv2.CAP_DSHOW)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)
cam.set(cv2.CAP_PROP_FPS, 30)
cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

cv2.namedWindow('myTracker')
cv2.moveWindow('myTracker',width,0)

hueLow=10
hueHigh=20
satLow=10
satHigh=250
valLow=10
valHigh=250

cv2.createTrackbar('Hue Low','myTracker',10,179,onTrack1)
cv2.createTrackbar('Hue High','myTracker',20,179,onTrack2)
cv2.createTrackbar('Sat Low','myTracker',10,255,onTrack3)
cv2.createTrackbar('Sat High','myTracker',250,255,onTrack4)
cv2.createTrackbar('Val Low','myTracker',10,255,onTrack5)
cv2.createTrackbar('Val High','myTracker',250,255,onTrack6)

while True:
    ignore,  frame = cam.read()
    frameHSV=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
    lowerBound=np.array([hueLow,satLow,valLow])
    upperBound=np.array([hueHigh,satHigh,valHigh])
    myMask=cv2.inRange(frameHSV,lowerBound,upperBound)
    #myMask=cv2.bitwise_not(myMask)
    myObject=cv2.bitwise_and(frame,frame,mask=myMask)
    myObjectSmall=cv2.resize(myObject,(int(width/2),int(height/2)))
    cv2.imshow('My Object',myObjectSmall)
    cv2.moveWindow('My Object',int(width/2),int(height))
    myMaskSmall=cv2.resize(myMask,(int(width/2),int(height/2)))
    cv2.imshow('My Mask',myMaskSmall)
    cv2.moveWindow('My Mask',0,height)
    cv2.imshow('my WEBcam', frame)
    cv2.moveWindow('my WEBcam',0,0)
    if cv2.waitKey(1) & 0xff ==ord('q'):
        break
cam.release()

import numpy as np

import cv2

print(cv2.__version__)

def onTrack1(val):

global hueLow

hueLow=val

print('Hue Low',hueLow)

def onTrack2(val):

global hueHigh

hueHigh=val

print('Hue High',hueHigh)

def onTrack3(val):

global satLow

satLow=val

print('Sat Low',satLow)

def onTrack4(val):

global satHigh

satHigh=val

print('Sat High',satHigh)

def onTrack5(val):

global valLow

valLow=val

print('Val Low',valLow)

def onTrack6(val):

global valHigh

valHigh=val

print('Val High',valHigh)

width=640

height=360

cam=cv2.VideoCapture(0,cv2.CAP_DSHOW)

cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)

cam.set(cv2.CAP_PROP_FPS, 30)

cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

cv2.namedWindow('myTracker')

cv2.moveWindow('myTracker',width,0)

hueLow=10

hueHigh=20

satLow=10

satHigh=250

valLow=10

valHigh=250

cv2.createTrackbar('Hue Low','myTracker',10,179,onTrack1)

cv2.createTrackbar('Hue High','myTracker',20,179,onTrack2)

cv2.createTrackbar('Sat Low','myTracker',10,255,onTrack3)

cv2.createTrackbar('Sat High','myTracker',250,255,onTrack4)

cv2.createTrackbar('Val Low','myTracker',10,255,onTrack5)

cv2.createTrackbar('Val High','myTracker',250,255,onTrack6)

while True:

ignore, frame = cam.read()

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

lowerBound=np.array([hueLow,satLow,valLow])

upperBound=np.array([hueHigh,satHigh,valHigh])

myMask=cv2.inRange(frameHSV,lowerBound,upperBound)

#myMask=cv2.bitwise_not(myMask)

myObject=cv2.bitwise_and(frame,frame,mask=myMask)

myObjectSmall=cv2.resize(myObject,(int(width/2),int(height/2)))

cv2.imshow('My Object',myObjectSmall)

cv2.moveWindow('My Object',int(width/2),int(height))

myMaskSmall=cv2.resize(myMask,(int(width/2),int(height/2)))

cv2.imshow('My Mask',myMaskSmall)

cv2.moveWindow('My Mask',0,height)

cv2.imshow('my WEBcam', frame)

cv2.moveWindow('my WEBcam',0,0)

if cv2.waitKey(1) & 0xff ==ord('q'):

break

cam.release()

Python

Faster Launch of WEBcam and Smoother Video in OpenCV on Windows

August 12, 2021 admin

This is the code we developed in Video Lesson #6 showing how to launch a webcam quicker and with less delay in OpenCV running on windows. The video above explains the code step by step, but for your convenience, I include the code below.

import cv2
print(cv2.__version__)
width=1280
height=720
cam=cv2.VideoCapture(1,cv2.CAP_DSHOW)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)
cam.set(cv2.CAP_PROP_FPS, 30)
cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))
while True:
    ignore,  frame = cam.read()
    cv2.imshow('my WEBcam', frame)
    cv2.moveWindow('my WEBcam',0,0)
    if cv2.waitKey(1) & 0xff ==ord('q'):
        break
cam.release()

import cv2

print(cv2.__version__)

width=1280

height=720

cam=cv2.VideoCapture(1,cv2.CAP_DSHOW)

cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)

cam.set(cv2.CAP_PROP_FPS, 30)

cam.set(cv2.CAP_PROP_FOURCC,cv2.VideoWriter_fourcc(*'MJPG'))

while True:

ignore, frame = cam.read()

cv2.imshow('my WEBcam', frame)

cv2.moveWindow('my WEBcam',0,0)

if cv2.waitKey(1) & 0xff ==ord('q'):

break

cam.release()

Technology Tutorials

Tag Archives: Artificial intelligence

AI FOR EVERYONE HOMEWORK SOLUTION: Face Recognition with Live Video in OpenCV

AI for Everyone LESSON 16: Face Recognition with OpenCV

Using Contours in OpenCV to Track an Object of Interest

Tracking an Object Based on Color in OpenCV

Faster Launch of WEBcam and Smoother Video in OpenCV on Windows

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