Category Archives: Raspberry Pi

OK guys, you spoke, and I listened. You all are asking for a lesson on how to do object detection on a Pi 5 using YOLO and an IP Camera. Well, you are about to get what you asked for. We will make this work, or we will DIE TRYING. Never fear, once you watch the video you will both understand and be able to do it on your own. First, I am assuming you watched our previous lesson where I showed you how to do the basic install and setup of YOLO. If not, never fear, I have the commands below. NOTE: This tutorial is geared towards bookworm OS. I strongly suggest you start with a fresh bookworm SC card, as there are many dependencies, and it is most likely to work if you start exactly where I am starting . . . with a fresh OS. Thes these are the commands I shared last week to get YOLO up and working: (just open a terminal, and paste these commands one at a time)

# 1. Configure X11 (manual steps required)
sudo raspi-config
# → Go to Advanced Options → X11 → Enable X11
# → Finish and reboot when prompted

# 5. Update system and install OpenCV
sudo apt update
sudo apt full-upgrade -y
sudo apt install python3-opencv -y

# Verify OpenCV
python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"
# Expected output: something like "OpenCV version: 4.6.0" or higher

# 6. Install MediaPipe
pip install mediapipe --break

# Verify MediaPipe
python3 -c "import mediapipe as mp; print('MediaPipe version:', mp.__version__)"

# 7. Create and activate virtual environment for YOLO11 (Ultralytics)
python3 -m venv --system-site-packages YOLO
source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment
# Install Ultralytics YOLO11 inside it
pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model
yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation
python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:
# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you 
#just created. Open tools- options, select 'interpreter' tab, then click they Python
#executable, selecting ... and navigate from home directory, 
#to YOLO, to bin, and then select python

# 1. Configure X11 (manual steps required)

sudo raspi-config

# → Go to Advanced Options → X11 → Enable X11

# → Finish and reboot when prompted

# 5. Update system and install OpenCV

sudo apt update

sudo apt full-upgrade -y

sudo apt install python3-opencv -y

# Verify OpenCV

python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"

# Expected output: something like "OpenCV version: 4.6.0" or higher

# 6. Install MediaPipe

pip install mediapipe --break

# Verify MediaPipe

python3 -c "import mediapipe as mp; print('MediaPipe version:', mp.__version__)"

# 7. Create and activate virtual environment for YOLO11 (Ultralytics)

python3 -m venv --system-site-packages YOLO

source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment

# Install Ultralytics YOLO11 inside it

pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model

yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation

python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:

# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you

#just created. Open tools- options, select 'interpreter' tab, then click they Python

#executable, selecting ... and navigate from home directory,

#to YOLO, to bin, and then select python

Now, I will explain this code, and will help you configure it for your cameras, but you will need to open up thonny, and paste in the following code as a start. IMPORTANT, as mentioned above, you need to set interpreter in thonny to the virtual environment set up in the process above. If this is not familiar to you, go back and watch last weeks lesson (click previous at the bottom of this post). Without further adue, here is the code we will work with today:

import cv2
from ultralytics import YOLO
#import secret
import threading
import time

W=1280
H=720
RTSP_URL = "rtsp://user:password@192.168.88.44:554/cam/realmonitor?channel=1&subtype=0"
#RTSP_URL = secret.RTSP_URL1
# Load the exported NCNN model (replace with your model path)
model = YOLO("/home/pjm/yolo11n_ncnn_model/", task="detect")
lock = threading.Lock()
running = True
def frameGrabber(url):
    global ipFrame, running
    cap = cv2.VideoCapture(url, cv2.CAP_FFMPEG)
    cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)  # Reset frame position
    while running:
        ret, frame = cap.read()
        if ret:
            with lock:
                #frame = cv2.resize(frame, (W, H))
                ipFrame = frame.copy()
    cap.release()
    print("Thread Terminated")
thread = threading.Thread(target=frameGrabber, args=(RTSP_URL,), daemon=True)
thread.start()
tStart=time.time()
time.sleep(2)
fps=0
cnt=0
# Set resolution for faster processing (optional, adjust based on your needs)
while True:
    deltaT=time.time()-tStart
    fps= fps*.9 + .1/deltaT
    tStart=time.time()
    with lock:
        ipFrameShow=ipFrame.copy()
    results = model(ipFrameShow, conf=0.25, verbose=False)[0]  # conf: confidence threshold; adjust as needed

    # Annotate the frame with detections (boxes, labels, scores)
    annotatedFrame = results.plot()
    annotatedFrame=cv2.resize(annotatedFrame, (W,H))
    cv2.putText(annotatedFrame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)), 
            cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 3)
    cv2.imshow("IP Camera", annotatedFrame)
    #cv2.moveWindow("IP Camera",100,100)
    if cv2.waitKey(1)==ord('q'):
        break
running=False
thread.join()  # Wait for thread to fully exit
time.sleep(1)
cv2.destroyAllWindows()
import gc
gc.collect()  # Force garbage collection to reclaim memory/connections
print("Program Terminated")

import cv2

from ultralytics import YOLO

#import secret

import threading

import time

W=1280

H=720

RTSP_URL = "rtsp://user:password@192.168.88.44:554/cam/realmonitor?channel=1&subtype=0"

#RTSP_URL = secret.RTSP_URL1

# Load the exported NCNN model (replace with your model path)

model = YOLO("/home/pjm/yolo11n_ncnn_model/", task="detect")

lock = threading.Lock()

running = True

def frameGrabber(url):

global ipFrame, running

cap = cv2.VideoCapture(url, cv2.CAP_FFMPEG)

cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)

cap.set(cv2.CAP_PROP_POS_FRAMES, 0) # Reset frame position

while running:

ret, frame = cap.read()

if ret:

with lock:

#frame = cv2.resize(frame, (W, H))

ipFrame = frame.copy()

cap.release()

print("Thread Terminated")

thread = threading.Thread(target=frameGrabber, args=(RTSP_URL,), daemon=True)

thread.start()

tStart=time.time()

time.sleep(2)

fps=0

cnt=0

# Set resolution for faster processing (optional, adjust based on your needs)

while True:

deltaT=time.time()-tStart

fps= fps*.9 + .1/deltaT

tStart=time.time()

with lock:

ipFrameShow=ipFrame.copy()

results = model(ipFrameShow, conf=0.25, verbose=False)[0] # conf: confidence threshold; adjust as needed

# Annotate the frame with detections (boxes, labels, scores)

annotatedFrame = results.plot()

annotatedFrame=cv2.resize(annotatedFrame, (W,H))

cv2.putText(annotatedFrame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)),

cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 3)

cv2.imshow("IP Camera", annotatedFrame)

#cv2.moveWindow("IP Camera",100,100)

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

break

running=False

thread.join() # Wait for thread to fully exit

time.sleep(1)

cv2.destroyAllWindows()

import gc

gc.collect() # Force garbage collection to reclaim memory/connections

print("Program Terminated")

The video explains everything, please watch it!

MicroPython, Raspberry Pi, Raspberry Pi Pico

LCD1602 Display Library for Micropython and the Raspberry Pi Pico W

June 12, 2023 admin

This is some demonstration sample code showing use of the LCD1602 as an LCD display for the Raspberry Pi Pico W. The code is explained in the video above. It will prompt a user for his name, and then display a greeting on the LCD.

from lcd1602 import LCD
import utime as time
lcd=LCD()
while True:
    
    myName=input('What is Your Name? ')
    lcd.clear()
    greeting1='Hello '+myName
    greeting2='Welcome to My Pi'
    lcd.write(0,0,greeting1)
    lcd.write(0,1,greeting2)

from lcd1602 import LCD

import utime as time

lcd=LCD()

while True:

myName=input('What is Your Name? ')

lcd.clear()

greeting1='Hello '+myName

greeting2='Welcome to My Pi'

lcd.write(0,0,greeting1)

lcd.write(0,1,greeting2)

Below is the library for the Sunfounder Kepler Kit LCD1602 display. It allows the LCD display to operate with the Raspberry Pi Pico W. The code should be copied and pasted into Thonny, and then saved to your Raspberry Pi Pico W, to the same folder that contains you Python code. It MUST be saved with file name lcd1602.py

import machine
import time

class LCD():
    def __init__(self, addr=None, blen=1):
        sda = machine.Pin(6)
        scl = machine.Pin(7)
        self.bus = machine.I2C(1,sda=sda, scl=scl, freq=400000)
        #print(self.bus.scan())
        self.addr = self.scanAddress(addr)
        self.blen = blen
        self.send_command(0x33) # Must initialize to 8-line mode at first
        time.sleep(0.005)
        self.send_command(0x32) # Then initialize to 4-line mode
        time.sleep(0.005)
        self.send_command(0x28) # 2 Lines & 5*7 dots
        time.sleep(0.005)
        self.send_command(0x0C) # Enable display without cursor
        time.sleep(0.005)
        self.send_command(0x01) # Clear Screen
        self.bus.writeto(self.addr, bytearray([0x08]))

    def scanAddress(self, addr):
        devices = self.bus.scan()
        if len(devices) == 0:
            raise Exception("No LCD found")
        if addr is not None:
            if addr in devices:
                return addr
            else:
                raise Exception(f"LCD at 0x{addr:2X} not found")
        elif 0x27 in devices:
            return 0x27
        elif 0x3F in devices:
            return 0x3F
        else:
            raise Exception("No LCD found")

    def write_word(self, data):
        temp = data
        if self.blen == 1:
            temp |= 0x08
        else:
            temp &= 0xF7
        self.bus.writeto(self.addr, bytearray([temp]))
    
    def send_command(self, cmd):
        # Send bit7-4 firstly
        buf = cmd & 0xF0
        buf |= 0x04               # RS = 0, RW = 0, EN = 1
        self.write_word(buf)
        time.sleep(0.002)
        buf &= 0xFB               # Make EN = 0
        self.write_word(buf)

        # Send bit3-0 secondly
        buf = (cmd & 0x0F) << 4
        buf |= 0x04               # RS = 0, RW = 0, EN = 1
        self.write_word(buf)
        time.sleep(0.002)
        buf &= 0xFB               # Make EN = 0
        self.write_word(buf)
    
    def send_data(self, data):
        # Send bit7-4 firstly
        buf = data & 0xF0
        buf |= 0x05               # RS = 1, RW = 0, EN = 1
        self.write_word(buf)
        time.sleep(0.002)
        buf &= 0xFB               # Make EN = 0
        self.write_word(buf)

        # Send bit3-0 secondly
        buf = (data & 0x0F) << 4
        buf |= 0x05               # RS = 1, RW = 0, EN = 1
        self.write_word(buf)
        time.sleep(0.002)
        buf &= 0xFB               # Make EN = 0
        self.write_word(buf)
    
    def clear(self):
        self.send_command(0x01) # Clear Screen
        
    def openlight(self):  # Enable the backlight
        self.bus.writeto(self.addr,bytearray([0x08]))
        # self.bus.close()
    
    def write(self, x, y, str):
        if x < 0:
            x = 0
        if x > 15:
            x = 15
        if y < 0:
            y = 0
        if y > 1:
            y = 1

        # Move cursor
        addr = 0x80 + 0x40 * y + x
        self.send_command(addr)

        for chr in str:
            self.send_data(ord(chr))
    
    def message(self, text):
        #print("message: %s"%text)
        for char in text:
            if char == '\n':
                self.send_command(0xC0) # next line
            else:
                self.send_data(ord(char))

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import machine

import time

class LCD():

def __init__(self, addr=None, blen=1):

sda = machine.Pin(6)

scl = machine.Pin(7)

self.bus = machine.I2C(1,sda=sda, scl=scl, freq=400000)

#print(self.bus.scan())

self.addr = self.scanAddress(addr)

self.blen = blen

self.send_command(0x33) # Must initialize to 8-line mode at first

time.sleep(0.005)

self.send_command(0x32) # Then initialize to 4-line mode

time.sleep(0.005)

self.send_command(0x28) # 2 Lines & 5*7 dots

time.sleep(0.005)

self.send_command(0x0C) # Enable display without cursor

time.sleep(0.005)

self.send_command(0x01) # Clear Screen

self.bus.writeto(self.addr, bytearray([0x08]))

def scanAddress(self, addr):

devices = self.bus.scan()

if len(devices) == 0:

raise Exception("No LCD found")

if addr is not None:

if addr in devices:

return addr

else:

raise Exception(f"LCD at 0x{addr:2X} not found")

elif 0x27 in devices:

return 0x27

elif 0x3F in devices:

return 0x3F

else:

raise Exception("No LCD found")

def write_word(self, data):

temp = data

if self.blen == 1:

temp |= 0x08

else:

temp &= 0xF7

self.bus.writeto(self.addr, bytearray([temp]))

def send_command(self, cmd):

# Send bit7-4 firstly

buf = cmd & 0xF0

buf |= 0x04 # RS = 0, RW = 0, EN = 1

self.write_word(buf)

time.sleep(0.002)

buf &= 0xFB # Make EN = 0

self.write_word(buf)

# Send bit3-0 secondly

buf = (cmd & 0x0F) << 4

buf |= 0x04 # RS = 0, RW = 0, EN = 1

self.write_word(buf)

time.sleep(0.002)

buf &= 0xFB # Make EN = 0

self.write_word(buf)

def send_data(self, data):

# Send bit7-4 firstly

buf = data & 0xF0

buf |= 0x05 # RS = 1, RW = 0, EN = 1

self.write_word(buf)

time.sleep(0.002)

buf &= 0xFB # Make EN = 0

self.write_word(buf)

# Send bit3-0 secondly

buf = (data & 0x0F) << 4

buf |= 0x05 # RS = 1, RW = 0, EN = 1

self.write_word(buf)

time.sleep(0.002)

buf &= 0xFB # Make EN = 0

self.write_word(buf)

def clear(self):

self.send_command(0x01) # Clear Screen

def openlight(self): # Enable the backlight

self.bus.writeto(self.addr,bytearray([0x08]))

# self.bus.close()

def write(self, x, y, str):

if x < 0:

x = 0

if x > 15:

x = 15

if y < 0:

y = 0

if y > 1:

y = 1

# Move cursor

addr = 0x80 + 0x40 * y + x

self.send_command(addr)

for chr in str:

self.send_data(ord(chr))

def message(self, text):

#print("message: %s"%text)

for char in text:

if char == '\n':

self.send_command(0xC0) # next line

else:

self.send_data(ord(char))

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, Raspberry Pi

Raspberry Pi LESSON 61: Finding and Tracking Faces and Eyes In OpenCV

May 11, 2023 admin

In this video lesson we show how to use Haar Cascades in OpenCV on the Raspberry Pi to find and track faces and eyes. We show the intelligent way to find eyes, such that CPU resources are not wasted. Below we show the code for your convenience.

import cv2
from picamera2 import Picamera2
import time
picam2 = Picamera2()
dispW=1280
dispH=720
picam2.preview_configuration.main.size = (dispW,dispH)
picam2.preview_configuration.main.format = "RGB888"
picam2.preview_configuration.controls.FrameRate=30
picam2.preview_configuration.align()
picam2.configure("preview")
picam2.start()
fps=0
pos=(30,60)
font=cv2.FONT_HERSHEY_SIMPLEX
height=1.5
weight=3
myColor=(0,0,255)

faceCascade=cv2.CascadeClassifier('./haar/haarcascade_frontalface_default.xml')
eyeCascade=cv2.CascadeClassifier('./haar/haarcascade_eye.xml')


while True:
    tStart=time.time()
    frame= picam2.capture_array()
    frame=cv2.flip(frame,-1)
    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)
        roiGray=frameGray[y:y+h,x:x+w]
        roiColor=frame[y:y+h,x:x+w]
        eyes=eyeCascade.detectMultiScale(roiGray,1.3,5)
        for eye in eyes:
            x,y,w,h=eye
            cv2.rectangle(roiColor,(x,y),(x+w,y+h),(255,0,0),3)
    cv2.putText(frame,str(int(fps))+' FPS',pos,font,height,myColor,weight)
    cv2.imshow("Camera", frame)

    if cv2.waitKey(1)==ord('q'):
        break
    tEnd=time.time()
    loopTime=tEnd-tStart
    fps=.9*fps + .1*(1/loopTime)
cv2.destroyAllWindows()

import cv2

from picamera2 import Picamera2

import time

picam2 = Picamera2()

dispW=1280

dispH=720

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

picam2.preview_configuration.main.format = "RGB888"

picam2.preview_configuration.controls.FrameRate=30

picam2.preview_configuration.align()

picam2.configure("preview")

picam2.start()

fps=0

pos=(30,60)

font=cv2.FONT_HERSHEY_SIMPLEX

height=1.5

weight=3

myColor=(0,0,255)

faceCascade=cv2.CascadeClassifier('./haar/haarcascade_frontalface_default.xml')

eyeCascade=cv2.CascadeClassifier('./haar/haarcascade_eye.xml')

while True:

tStart=time.time()

frame= picam2.capture_array()

frame=cv2.flip(frame,-1)

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)

roiGray=frameGray[y:y+h,x:x+w]

roiColor=frame[y:y+h,x:x+w]

eyes=eyeCascade.detectMultiScale(roiGray,1.3,5)

for eye in eyes:

x,y,w,h=eye

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

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

cv2.imshow("Camera", frame)

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

break

tEnd=time.time()

loopTime=tEnd-tStart

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

cv2.destroyAllWindows()

OpenCV, Python, Raspberry Pi

Raspberry Pi LESSON 59: Improved Pan/Tilt Tracking Control Algorithm

April 27, 2023 admin

In this Video Lesson we show an improved control algorithm for tracking an Object of Interest in OpenCV. We develop a simple example of Proportional control, where the correction signal is proportional to the error signal. We show this is a much improved algorithm over our earlier one, which simply applied 1 degree corrections independent of the size of the error. The code we develop in this lesson is included below for your convenience.

import cv2
from picamera2 import Picamera2
import time
import numpy as np
from servo import Servo
picam2 = Picamera2()

pan=Servo(pin=13)
tilt=Servo(pin=12)

panAngle=0
tiltAngle=0

pan.set_angle(panAngle)
tilt.set_angle(tiltAngle)

dispW=1280
dispH=720
picam2.preview_configuration.main.size = (dispW,dispH)
picam2.preview_configuration.main.format = "RGB888"
picam2.preview_configuration.controls.FrameRate=30
picam2.preview_configuration.align()
picam2.configure("preview")
picam2.start()
fps=0
pos=(30,60)
font=cv2.FONT_HERSHEY_SIMPLEX
height=1.5
weight=3
myColor=(0,0,255)
track=0

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)
def onTrack7(val):
    global track
    track=val
    print('Track Value',track)

cv2.namedWindow('myTracker')

cv2.createTrackbar('Hue Low','myTracker',10,179,onTrack1)
cv2.createTrackbar('Hue High','myTracker',20,179,onTrack2)
cv2.createTrackbar('Sat Low','myTracker',100,255,onTrack3)
cv2.createTrackbar('Sat High','myTracker',255,255,onTrack4)
cv2.createTrackbar('Val Low','myTracker',100,255,onTrack5)
cv2.createTrackbar('Val High','myTracker',255,255,onTrack6)
cv2.createTrackbar('Train-0 Track-1','myTracker',0,1,onTrack7)


while True:
    tStart=time.time()
    frame= picam2.capture_array()
    frame=cv2.flip(frame,-1)
    frameHSV=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
    cv2.putText(frame,str(int(fps))+' FPS',pos,font,height,myColor,weight)
    lowerBound=np.array([hueLow,satLow,valLow])
    upperBound=np.array([hueHigh,satHigh,valHigh])
    myMask=cv2.inRange(frameHSV,lowerBound,upperBound)
    myMaskSmall=cv2.resize(myMask,(int(dispW/2),int(dispH/2)))
    myObject=cv2.bitwise_and(frame,frame, mask=myMask)
    myObjectSmall=cv2.resize(myObject,(int(dispW/2),int(dispH/2)))
    
    contours,junk=cv2.findContours(myMask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    if len(contours)>0:
        contours=sorted(contours,key=lambda x:cv2.contourArea(x),reverse=True)
        #cv2.drawContours(frame,contours,-1,(255,0,0),3)
        contour=contours[0]
        x,y,w,h=cv2.boundingRect(contour)
        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),3)
        if track==1:
            error=(x+w/2)-dispW/2
            panAngle=panAngle-error/75
            if panAngle>90:
                panAngle=90
            if panAngle<-90:
                panAngle=-90
            if abs(error)>35:
                pan.set_angle(panAngle)
            tiltError=(y+h/2)-dispH/2
            tiltAngle=tiltAngle+tiltError/75
            if tiltAngle>40:
                tiltAngle=40
            if tiltAngle<-90:
                tiltAngle=-90
            if abs(tiltError)>35:
                tilt.set_angle(tiltAngle)
        
    cv2.imshow('Camera',frame)
    cv2.imshow('Mask',myMaskSmall)
    cv2.imshow('My Object',myObjectSmall)
    if cv2.waitKey(1)==ord('q'):
        break
    tEnd=time.time()
    loopTime=tEnd-tStart
    fps=.9*fps + .1*(1/loopTime)
cv2.destroyAllWindows()

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import cv2

from picamera2 import Picamera2

import time

import numpy as np

from servo import Servo

picam2 = Picamera2()

pan=Servo(pin=13)

tilt=Servo(pin=12)

panAngle=0

tiltAngle=0

pan.set_angle(panAngle)

tilt.set_angle(tiltAngle)

dispW=1280

dispH=720

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

picam2.preview_configuration.main.format = "RGB888"

picam2.preview_configuration.controls.FrameRate=30

picam2.preview_configuration.align()

picam2.configure("preview")

picam2.start()

fps=0

pos=(30,60)

font=cv2.FONT_HERSHEY_SIMPLEX

height=1.5

weight=3

myColor=(0,0,255)

track=0

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)

def onTrack7(val):

global track

track=val

print('Track Value',track)

cv2.namedWindow('myTracker')

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

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

cv2.createTrackbar('Sat Low','myTracker',100,255,onTrack3)

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

cv2.createTrackbar('Val Low','myTracker',100,255,onTrack5)

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

cv2.createTrackbar('Train-0 Track-1','myTracker',0,1,onTrack7)

while True:

tStart=time.time()

frame= picam2.capture_array()

frame=cv2.flip(frame,-1)

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

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

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

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

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

myMaskSmall=cv2.resize(myMask,(int(dispW/2),int(dispH/2)))

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

myObjectSmall=cv2.resize(myObject,(int(dispW/2),int(dispH/2)))

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

if len(contours)>0:

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

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

contour=contours[0]

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

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

if track==1:

error=(x+w/2)-dispW/2

panAngle=panAngle-error/75

if panAngle>90:

panAngle=90

if panAngle<-90:

panAngle=-90

if abs(error)>35:

pan.set_angle(panAngle)

tiltError=(y+h/2)-dispH/2

tiltAngle=tiltAngle+tiltError/75

if tiltAngle>40:

tiltAngle=40

if tiltAngle<-90:

tiltAngle=-90

if abs(tiltError)>35:

tilt.set_angle(tiltAngle)

cv2.imshow('Camera',frame)

cv2.imshow('Mask',myMaskSmall)

cv2.imshow('My Object',myObjectSmall)

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

break

tEnd=time.time()

loopTime=tEnd-tStart

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

cv2.destroyAllWindows()

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