Tag Archives: Artificial Intelligenge

In today’s lesson we will become familiar with the capabilities of the Fusion AI Hat+ for the Raspberry Pi. This hat will be a core part of our class moving forward. The hat makes it easy to get data from the outside world, and to control things in the outside would. We will get an understanding of the core capabilities of the board, and your homework will be to build the first circuit with the board. This schematic shows the various parts of the board:

Fusion AI Hat for Raspberry Pi Schematic

Then for the homework, we need you to go ahead and build this circuit. This circuit will allow us to learn how to make Digital Output commands, PWM commands, and how to read analog inputs.

Fusion Hat Circuit Diagram — This is the circuit we will use moving forward in the class

Artificial Intelligence, OpenCV, Raspberry Pi

Raspberry Pi LESSON 63: Object Detection on Raspberry Pi Using Tensorflow Lite

May 25, 2023 admin

In this lesson I show you how to do object detection on the Raspberry Pi using Tensorflow Lite. We will write our first program and by the end of the lesson you will have your Pi detecting objects, boxing them and labeling them in OpenCV.

The video demonstrates step-by-step how to install the tensorflow libraries.

For your convenience I have included the code below we develop in this lesson

import cv2
import time
from picamera2 import Picamera2

from tflite_support.task import core
from tflite_support.task import processor
from tflite_support.task import vision
import utils

model='efficientdet_lite0.tflite'
num_threads=4

dispW=1280
dispH=720

picam2=Picamera2()
picam2.preview_configuration.main.size=(dispW,dispH)
picam2.preview_configuration.main.format='RGB888'
picam2.preview_configuration.align()
picam2.configure("preview")
picam2.start()

webCam='/dev/video2'
cam=cv2.VideoCapture(webCam)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, dispW)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, dispH)
cam.set(cv2.CAP_PROP_FPS, 30)

pos=(20,60)
font=cv2.FONT_HERSHEY_SIMPLEX
height=1.5
weight=3
myColor=(255,0,0)

fps=0

base_options=core.BaseOptions(file_name=model,use_coral=False, num_threads=num_threads)
detection_options=processor.DetectionOptions(max_results=3, score_threshold=.3)
options=vision.ObjectDetectorOptions(base_options=base_options,detection_options=detection_options)
detector=vision.ObjectDetector.create_from_options(options)
tStart=time.time()
while True:
    ret, im = cam.read()
    #im=picam2.capture_array()
    #im=cv2.flip(im,-1)
    imRGB=cv2.cvtColor(im,cv2.COLOR_BGR2RGB)
    imTensor=vision.TensorImage.create_from_array(imRGB)
    detections=detector.detect(imTensor)
    image=utils.visualize(im, detections)
    cv2.putText(im,str(int(fps))+' FPS',pos,font,height,myColor,weight)
    cv2.imshow('Camera',im)
    if cv2.waitKey(1)==ord('q'):
        break
    tEnd=time.time()
    loopTime=tEnd-tStart
    fps= .9*fps +.1*1/loopTime
    tStart=time.time()
cv2.destroyAllWindows()

import cv2

import time

from picamera2 import Picamera2

from tflite_support.task import core

from tflite_support.task import processor

from tflite_support.task import vision

import utils

model='efficientdet_lite0.tflite'

num_threads=4

dispW=1280

dispH=720

picam2=Picamera2()

picam2.preview_configuration.main.size=(dispW,dispH)

picam2.preview_configuration.main.format='RGB888'

picam2.preview_configuration.align()

picam2.configure("preview")

picam2.start()

webCam='/dev/video2'

cam=cv2.VideoCapture(webCam)

cam.set(cv2.CAP_PROP_FRAME_WIDTH, dispW)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT, dispH)

cam.set(cv2.CAP_PROP_FPS, 30)

pos=(20,60)

font=cv2.FONT_HERSHEY_SIMPLEX

height=1.5

weight=3

myColor=(255,0,0)

fps=0

base_options=core.BaseOptions(file_name=model,use_coral=False, num_threads=num_threads)

detection_options=processor.DetectionOptions(max_results=3, score_threshold=.3)

options=vision.ObjectDetectorOptions(base_options=base_options,detection_options=detection_options)

detector=vision.ObjectDetector.create_from_options(options)

tStart=time.time()

while True:

ret, im = cam.read()

#im=picam2.capture_array()

#im=cv2.flip(im,-1)

imRGB=cv2.cvtColor(im,cv2.COLOR_BGR2RGB)

imTensor=vision.TensorImage.create_from_array(imRGB)

detections=detector.detect(imTensor)

image=utils.visualize(im, detections)

cv2.putText(im,str(int(fps))+' FPS',pos,font,height,myColor,weight)

cv2.imshow('Camera',im)

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

break

tEnd=time.time()

loopTime=tEnd-tStart

fps= .9*fps +.1*1/loopTime

tStart=time.time()

cv2.destroyAllWindows()

OpenCV, Python

AI for Everyone LESSON 15: Face Detection using OpenCV

October 12, 2021 admin

In this video lesson we show a simple method to detect faces in a WEB cam frame using openCV and Haar Cascades. We use pretrained models to find, box and track faces in a frame. Enjoy!

import cv2
print(cv2.__version__)
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'))

faceCascade=cv2.CascadeClassifier('C:\Users\Valued Customer\Documents\Python\haar\haarcascade_frontalface_default.xml')

while True:
    ignore,  frame = cam.read()
    frameGray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
    faces=faceCascade.detectMultiScale(frameGray,1.3,5)
    for face in faces:
        x,y,w,h=face
        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),3)
    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=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'))

faceCascade=cv2.CascadeClassifier('C:\Users\Valued Customer\Documents\Python\haar\haarcascade_frontalface_default.xml')

while True:

ignore, frame = cam.read()

frameGray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)

faces=faceCascade.detectMultiScale(frameGray,1.3,5)

for face in faces:

x,y,w,h=face

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

cv2.imshow('my WEBcam', frame)

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

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

break

cam.release()

OpenCV, Python

Tracking Two Colors in OpenCV

September 30, 2021 admin

In this video lesson we show how you can track two different colored objects at the same time in OpenCV. It is the homework solution to the homework we assigned in the last lesson. It will also serve as the starting code for future lessons

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)

    #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()

100

101

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)

#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()

Technology Tutorials

Tag Archives: Artificial Intelligenge

AI on the Edge LESSON 5: Understanding Fusion AI Hat+ For Raspberry Pi

Raspberry Pi LESSON 63: Object Detection on Raspberry Pi Using Tensorflow Lite

AI for Everyone LESSON 15: Face Detection using OpenCV

Tracking Two Colors in OpenCV

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