Tag Archives: Artificial intelligence

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()

Python

Launch a Windows WEB Camera in Ten Lines of Code with OpenCV

August 4, 2021 admin

In this lesson we show you step-by-step how to launch a WEB camera using OpenCV running on windows. In the video we show how to install OpenCV and how to write your first simple programs. Below is the code we developed in the video to launch the WEB cam.

import cv2
print(cv2.__version__)
cam=cv2.VideoCapture(1)
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__)

cam=cv2.VideoCapture(1)

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()

Jetson Nano

AI on the Jetson Nano LESSON 60: Make Your Nano Talk With Text to Speech

September 19, 2020 admin

In this lesson we show you how to teach the NVIDIA Jetson Nano to talk. We show two different ways. In the first, we use the espeak TTS engine. We then show how to do it with gTTS. We compare and contrast the pros and cons of each technique.

Jetson Xavier NX, NVIDIA Jetson Xavier NX

Jetson Xavier NX Lesson 15: Training the Face Recognition Program to Recognize People

August 25, 2020 admin

<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()

Technology Tutorials

Tag Archives: Artificial intelligence

Faster Launch of WEBcam and Smoother Video in OpenCV on Windows

Launch a Windows WEB Camera in Ten Lines of Code with OpenCV

AI on the Jetson Nano LESSON 60: Make Your Nano Talk With Text to Speech

Jetson Xavier NX Lesson 15: Training the Face Recognition Program to Recognize People

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

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