Tag Archives: AI

Welcome back, everyone! In this lesson, we take edge AI interactivity to the next level. Up until now, we have learned how to track hand landmarks, extract pixel coordinates, and calculate distances using MediaPipe. Today, we put all those building blocks together into a self-contained, real-time hand gesture recognition system running on the Raspberry Pi 5.

We are not relying on a black-box pre-trained gesture model. Instead, we write our own mathematical feature extractor, train our custom hand poses on the fly, save those gesture profiles directly to disk, and map each gesture to a non-blocking lighting animation on a NeoPixel LED ring driven over SPI.

System Architecture Overview

The system operates in three distinct phases running simultaneously inside our main loop:

Normalized Feature Extraction: MediaPipe extracts 3D hand landmark coordinates. To make gesture detection scale-invariant and distance-invariant, we normalize the distances between the fingertips and the wrist against a reference palm dimension.
Nearest-Neighbor Gesture Matching: Live distance vectors are compared against stored gesture profiles using a mean absolute error algorithm. If the error falls below our maximum threshold, the gesture is identified; otherwise, it defaults to “Unknown.”
Non-Blocking Hardware Animation Dispatch: The recognized gesture string triggers corresponding pattern functions on the 12-LED NeoPixel ring via neopixel_spi. Animations use non-blocking time delta checks to preserve high camera preview frame rates.

Gesture-to-Hardware Animation Mapping

Our script contains predefined animation routines mapped to recognized gesture strings. Here is how each pose controls the LED ring:

Recognized Gesture	LED Animation Pattern	Visual Effect
`fist`	Red Pulse	Sinusoidal breathing effect fading in and out
`palm`	Solid Beacon	Full brightness white ring
`peace`	Dual Spinner	Dual green and blue dots rotating over a dim red background
`thumbsup`	Solid Success	Constant green feedback
`point`	Radar Sweep	A green trailing dot chasing around a red ring
`rock`	Cyan Flame	Sinusoidal green-cyan intensity modulation
`ok`	Rainbow Wheel	Rotating HSV spectrum cycle
`Unknown` / Unmatched	Clear	LEDs turn completely off

Interactive Keyboard Controls

While the camera loop is active, you can interact with the system using your keyboard in the preview window:

Spacebar: Captures the current hand pose’s normalized distance vector, assigns it to the active target gesture name, and saves the updated dataset to gestureNEOPIXEL.pkl.
t: Prompts the terminal to add a new custom gesture name to your training queue dynamically.
d: Prompts the terminal to delete a specific gesture profile from the saved dataset.
q: Clears the NeoPixel ring, closes the camera pipeline, saves training data, and terminates the program cleanly.

Hardware Wiring Reference

Ensure your Raspberry Pi 5 SPI interface is enabled before running the code (sudo raspi-config -> Interface Options -> SPI).

NeoPixel Data Input (DIN): Connect to GPIO 10 (SPI0 MOSI / Pin 19).
NeoPixel Ground: Connect to any GND pin on the Pi expansion header.
NeoPixel Power: Connect to an external 5V power supply (ensure a shared ground with the Raspberry Pi).

Homework Challenge

Now that you have a working gesture recognition system linked to hardware animations, try adding your own custom gestures! Train a brand-new gesture pose (like a “three-finger count”), write a corresponding non-blocking animation function in updateNeoPixels(), and share your video results.

Here is the code we developed in the video lesson:

# ====================================================================
# DISCLAIMER:
# This code is provided as-is for educational and experimental 
# purposes only. The author makes no representations or warranties of 
# any kind concerning the safety, suitability, or accuracy of this 
# code. Use at your own risk. The author assumes no liability for any 
# damages, system failures, security breaches, or network issues 
# resulting from the use or implementation of this script.
# ====================================================================
import cv2
import time
from picamera2 import Picamera2
import mediapipe as mp
import math
import pickle
import os
import colorsys

# ====================================================================
# NEOPIXEL CONTROL INITIALIZATION (Adafruit neopixel_spi via SPI)
# ====================================================================

import board
import neopixel_spi as neopixel

NUM_LEDS = 12  # Set to match your NeoPixel ring size (e.g., 12)
PO = neopixel.GRB

# Initialize NeoPixel ring using SPI
spi = board.SPI()
strip = neopixel.NeoPixel_SPI(
    spi, 
    NUM_LEDS, 
    pixel_order=PO, 
    auto_write=False
)

# Turn off all LEDs on startup
strip.fill(0)
strip.show()

NEOPIXEL_ENABLED = True
print("[INFO] NeoPixel SPI initialized successfully.")

# Animation state variables (non-blocking updates)
animation_step = 0
lastAnimUpdate = time.time()
# ====================================================================

DATA_FILE = "gestureNEOPIXEL.pkl"

# Function to save the trained gesture dictionary to a file using pickle
def saveTrainedData(filename, data):
    with open(filename, 'wb') as f:
        pickle.dump(data, f)
    print("[INFO] Saved gesture data to " + str(filename))

# Function to load previously saved gesture data from a file
def loadTrainedData(filename):
    if os.path.exists(filename):
        with open(filename, 'rb') as f:
            data = pickle.load(f)
            print("[INFO] Loaded " + str(len(data)) + " gesture(s) from " + str(filename))
        return data
    return {}

# --- SETUP & INITIAL TRAINING ---
trainedData = loadTrainedData(DATA_FILE)
gestureNames = []

if not trainedData:
    print("HAND GESTURE TRAINING SETUP")
    numGestures = int(input("Enter Number of Gestures to Train On: "))
    for idx in range(numGestures):
        name = input("Enter Name for Gesture #" + str(idx + 1) + ": ")
        gestureNames.append(name)

def findDistance(pt1, pt2):
    dX = pt1.x - pt2.x
    dY = pt1.y - pt2.y
    dZ = pt1.z - pt2.z
    return math.sqrt(dX*dX + dY*dY + dZ*dZ)

def getTipDistances(handLm):
    lm = handLm.landmark
    wrist = lm[0]
    indexBase = lm[5]  
    scaleFactor = findDistance(wrist, indexBase)
    tips = [lm[4], lm[8], lm[12], lm[16], lm[20]]
    tipDistances = []
    for tip in tips:
        dist = findDistance(wrist, tip) / scaleFactor
        tipDistances.append(dist)
    for startTip in range(len(tips)):
        for endTip in range(startTip + 1, len(tips)):
            dist = findDistance(tips[startTip], tips[endTip]) / scaleFactor
            tipDistances.append(dist)
    return tipDistances

def calculateError(tipDist, trainDist):
    totalError = 0
    for idx in range(len(tipDist)):
        diff = tipDist[idx] - trainDist[idx]
        totalError += abs(diff)
    return totalError

def findGesture(tipLive, trainedData, maxAllowedError):
    bestMatch = "Unknown"
    lowestError = 999999999
    for gestureName, trainedDist in trainedData.items():
        error = calculateError(tipLive, trainedDist)
        if error < lowestError:
            lowestError = error
            bestMatch = gestureName
    if lowestError > maxAllowedError:
        return "Unknown"
    return bestMatch

# ====================================================================
# NEOPIXEL CONTROL (COLOR HELPER & ANIMATION DISPATCHER)
# ====================================================================
def hsv2rgb(h):
    """ Converts HSV float (0.0-1.0) to RGB integer tuple (0-255) """
    r, g, b = colorsys.hsv_to_rgb(h, 1, 1)
    return (int(r * 255), int(g * 255), int(b * 255))

def updateNeoPixels(gesture):
    """ Non-blocking LED pattern update based on the current detected gesture """
    global animation_step, lastAnimUpdate
    if not NEOPIXEL_ENABLED:
        return

    now = time.time()
    gestureClean = gesture.lower()
    pattern_matched = False

    # 1. FIST / CHARGE (Red pulse)
    if gestureClean == "fist":
        pulse = int(127 + 127 * math.sin(now * 5.0))
        strip.fill((pulse, 0, 0))
        strip.show()
        pattern_matched = True

    # 2. OPEN PALM / BEACON (Solid Bright White)
    if gestureClean == "palm":
        strip.fill((255, 255, 255))
        strip.show()
        pattern_matched = True

    # 3. PEACE SIGN / DUAL SPINNER (Rotating Blue/Cyan dots)
    if gestureClean == "peace":
        if now - lastAnimUpdate > 0.08:
            animation_step = (animation_step + 1) % NUM_LEDS
            lastAnimUpdate = now
        strip.fill((40, 0, 0))  # Background red
        pos1 = animation_step
        pos2 = (animation_step + (NUM_LEDS // 2)) % NUM_LEDS
        strip[pos1] = (0, 255, 0)   # Green spinner dot
        strip[pos2] = (0, 0, 255)   # Blue spinner dot
        strip.show()
        pattern_matched = True

    # 4. THUMBS UP / SUCCESS (Solid Green)
    if gestureClean == "thumbsup":
        strip.fill((0, 255, 0))
        strip.show()
        pattern_matched = True

    # 5. POINTING / CHASER (Radar sweep)
    if gestureClean in "point":
        if now - lastAnimUpdate > 0.06:
            animation_step = (animation_step + 1) % NUM_LEDS
            lastAnimUpdate = now
        strip.fill((255, 0, 0))
        strip[animation_step] = (0, 255, 0)
        strip.show()
        pattern_matched = True

    # 6. ROCK / FIRE (Sinusoidal Green-Cyan pulse)
    if gestureClean == "rock":
        step = int((now * 100) % 360)
        brightness = (math.sin(step * math.pi / 180) + 1) / 2
        g = int(255 * brightness)
        b = int(50 * brightness)
        strip.fill((0, g, b))
        strip.show()
        pattern_matched = True

    # 7. OK SIGN / RAINBOW WHEEL (Rotating RGB spectrum)
    if gestureClean in "ok":
        if now - lastAnimUpdate > 0.03:
            animation_step = (animation_step + 1) % 100
            lastAnimUpdate = now
        hue = animation_step / 100.0
        strip.fill(hsv2rgb(hue))
        strip.show()
        pattern_matched = True

    # DEFAULT / UNKNOWN (LEDs OFF)
    if not pattern_matched:
        strip.fill((0, 0, 0))
        strip.show()
# ====================================================================

W, H = 1280, 720
tStart = time.time()
fps = 15
RES = (W, H)

piCam = Picamera2(1)
piCam.preview_configuration.main.size = RES
piCam.preview_configuration.main.format = "RGB888"
piCam.preview_configuration.controls.FrameRate = 60
piCam.preview_configuration.align()
piCam.configure("preview")
piCam.start()

textLowerLeft = (int(W * .01), int(H * .07))
fontFace = cv2.FONT_HERSHEY_SIMPLEX
fontThickness = int(W / 425)
fontScale = H * .002
fontColor = (0, 0, 255)

textFps = (int(W * .01), int(H * .07))
textGesture = (int(W * .01), int(H * .14))
textGesturePrompt = (int(W * .01), int(H * .21))

maxErrorThreshold = 3.5 

hands = mp.solutions.hands.Hands(
    model_complexity=0,
    min_detection_confidence=.5,
    min_tracking_confidence=.5,
    max_num_hands=2
)

trainIdx = 0

while True:
    deltaT = time.time() - tStart
    tStart = time.time()
    fps = fps * .95 + (1 / deltaT) * .05
    
    frame = piCam.capture_array()
    frame = cv2.flip(frame, -1)
    rgbFrame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    results = hands.process(rgbFrame)
    
    if results.multi_hand_landmarks:
        frameHeight, frameWidth, _ = frame.shape        
        for handLm in results.multi_hand_landmarks:
            for idx, landmark in enumerate(handLm.landmark):
                pixelX = int(landmark.x * frameWidth)
                pixelY = int(landmark.y * frameHeight)
                cv2.putText(
                    frame, str(idx), (pixelX, pixelY),
                    cv2.FONT_HERSHEY_SIMPLEX, .6 * fontScale, (255, 0, 0), fontThickness
                )
                
    if trainIdx < len(gestureNames):
        targetName = gestureNames[trainIdx]
        promptText = "show " + targetName + " & Press Space"
        cv2.putText(frame, promptText, textGesturePrompt, fontFace, fontScale, (0, 255, 255), fontThickness)

    detectGesture = "Unknown"
    if results.multi_hand_landmarks:
        liveDistances = getTipDistances(results.multi_hand_landmarks[0])
        detectGesture = findGesture(liveDistances, trainedData, maxErrorThreshold)

    # ====================================================================
    # EXECUTE HARDWARE PATTERN UPDATE
    # ====================================================================
    updateNeoPixels(detectGesture)
    # ====================================================================

    labelText = "Gesture: " + detectGesture
    cv2.putText(frame, labelText, textGesture, fontFace, fontScale, (0, 255, 0), fontThickness)
    myText = "FPS: " + str(round(fps, 1))
    cv2.putText(frame, myText, textLowerLeft, fontFace, fontScale, fontColor, fontThickness)
    
    cv2.imshow("Camera", frame)
    #cv2.moveWindow("Camera", 0, 60)
    
    key = cv2.waitKey(1)
    
    # Save landmark measurements when Space is pressed
    if key == ord(' '):
        if results.multi_hand_landmarks:
            if trainIdx < len(gestureNames):
                targetHand = results.multi_hand_landmarks[0]
                targetName = gestureNames[trainIdx]
                trainedData[targetName] = getTipDistances(targetHand)
                saveTrainedData(DATA_FILE, trainedData)
                trainIdx += 1

    # 't' key allows adding a NEW gesture dynamically during runtime
    if key == ord('t'):
        newGestureName = input("Enter name for new gesture to train: ").strip()
        if newGestureName:
            gestureNames.append(newGestureName)

    # 'd' key deletes a gesture
    if key == ord('d'):
        delName = input("Enter name of gesture to remove: ").strip()
        if delName in trainedData:
            del trainedData[delName]
            saveTrainedData(DATA_FILE, trainedData)
            print("[INFO] Removed " + delName + " from trained data.")
            if delName in gestureNames:
                gestureNames.remove(delName)
        if delName not in trainedData:
            print("[WARNING] Gesture: " + delName + " not found in trained data.")
        trainIdx=min(trainIdx,len(gestureNames))
    if key == ord('q'):
        break

# Clean up hardware and exit
# ====================================================================
# CLEAR LEDS ON SHUTDOWN
# ====================================================================
if NEOPIXEL_ENABLED:
    strip.fill((0, 0, 0))
    strip.show()
# ====================================================================

cv2.destroyAllWindows()
saveTrainedData(DATA_FILE, trainedData)
print('Program Terminated')

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# ====================================================================

# DISCLAIMER:

# This code is provided as-is for educational and experimental

# purposes only. The author makes no representations or warranties of

# any kind concerning the safety, suitability, or accuracy of this

# code. Use at your own risk. The author assumes no liability for any

# damages, system failures, security breaches, or network issues

# resulting from the use or implementation of this script.

# ====================================================================

import cv2

import time

from picamera2 import Picamera2

import mediapipe as mp

import math

import pickle

import os

import colorsys

# ====================================================================

# NEOPIXEL CONTROL INITIALIZATION (Adafruit neopixel_spi via SPI)

# ====================================================================

import board

import neopixel_spi as neopixel

NUM_LEDS = 12 # Set to match your NeoPixel ring size (e.g., 12)

PO = neopixel.GRB

# Initialize NeoPixel ring using SPI

spi = board.SPI()

strip = neopixel.NeoPixel_SPI(

spi,

NUM_LEDS,

pixel_order=PO,

auto_write=False

)

# Turn off all LEDs on startup

strip.fill(0)

strip.show()

NEOPIXEL_ENABLED = True

print("[INFO] NeoPixel SPI initialized successfully.")

# Animation state variables (non-blocking updates)

animation_step = 0

lastAnimUpdate = time.time()

# ====================================================================

DATA_FILE = "gestureNEOPIXEL.pkl"

# Function to save the trained gesture dictionary to a file using pickle

def saveTrainedData(filename, data):

with open(filename, 'wb') as f:

pickle.dump(data, f)

print("[INFO] Saved gesture data to " + str(filename))

# Function to load previously saved gesture data from a file

def loadTrainedData(filename):

if os.path.exists(filename):

with open(filename, 'rb') as f:

data = pickle.load(f)

print("[INFO] Loaded " + str(len(data)) + " gesture(s) from " + str(filename))

return data

return {}

# --- SETUP & INITIAL TRAINING ---

trainedData = loadTrainedData(DATA_FILE)

gestureNames = []

if not trainedData:

print("HAND GESTURE TRAINING SETUP")

numGestures = int(input("Enter Number of Gestures to Train On: "))

for idx in range(numGestures):

name = input("Enter Name for Gesture #" + str(idx + 1) + ": ")

gestureNames.append(name)

def findDistance(pt1, pt2):

dX = pt1.x - pt2.x

dY = pt1.y - pt2.y

dZ = pt1.z - pt2.z

return math.sqrt(dX*dX + dY*dY + dZ*dZ)

def getTipDistances(handLm):

lm = handLm.landmark

wrist = lm[0]

indexBase = lm[5]

scaleFactor = findDistance(wrist, indexBase)

tips = [lm[4], lm[8], lm[12], lm[16], lm[20]]

tipDistances = []

for tip in tips:

dist = findDistance(wrist, tip) / scaleFactor

tipDistances.append(dist)

for startTip in range(len(tips)):

for endTip in range(startTip + 1, len(tips)):

dist = findDistance(tips[startTip], tips[endTip]) / scaleFactor

tipDistances.append(dist)

return tipDistances

def calculateError(tipDist, trainDist):

totalError = 0

for idx in range(len(tipDist)):

diff = tipDist[idx] - trainDist[idx]

totalError += abs(diff)

return totalError

def findGesture(tipLive, trainedData, maxAllowedError):

bestMatch = "Unknown"

lowestError = 999999999

for gestureName, trainedDist in trainedData.items():

error = calculateError(tipLive, trainedDist)

if error < lowestError:

lowestError = error

bestMatch = gestureName

if lowestError > maxAllowedError:

return "Unknown"

return bestMatch

# ====================================================================

# NEOPIXEL CONTROL (COLOR HELPER & ANIMATION DISPATCHER)

# ====================================================================

def hsv2rgb(h):

""" Converts HSV float (0.0-1.0) to RGB integer tuple (0-255) """

r, g, b = colorsys.hsv_to_rgb(h, 1, 1)

return (int(r * 255), int(g * 255), int(b * 255))

def updateNeoPixels(gesture):

""" Non-blocking LED pattern update based on the current detected gesture """

global animation_step, lastAnimUpdate

if not NEOPIXEL_ENABLED:

return

now = time.time()

gestureClean = gesture.lower()

pattern_matched = False

# 1. FIST / CHARGE (Red pulse)

if gestureClean == "fist":

pulse = int(127 + 127 * math.sin(now * 5.0))

strip.fill((pulse, 0, 0))

strip.show()

pattern_matched = True

# 2. OPEN PALM / BEACON (Solid Bright White)

if gestureClean == "palm":

strip.fill((255, 255, 255))

strip.show()

pattern_matched = True

# 3. PEACE SIGN / DUAL SPINNER (Rotating Blue/Cyan dots)

if gestureClean == "peace":

if now - lastAnimUpdate > 0.08:

animation_step = (animation_step + 1) % NUM_LEDS

lastAnimUpdate = now

strip.fill((40, 0, 0)) # Background red

pos1 = animation_step

pos2 = (animation_step + (NUM_LEDS // 2)) % NUM_LEDS

strip[pos1] = (0, 255, 0) # Green spinner dot

strip[pos2] = (0, 0, 255) # Blue spinner dot

strip.show()

pattern_matched = True

# 4. THUMBS UP / SUCCESS (Solid Green)

if gestureClean == "thumbsup":

strip.fill((0, 255, 0))

strip.show()

pattern_matched = True

# 5. POINTING / CHASER (Radar sweep)

if gestureClean in "point":

if now - lastAnimUpdate > 0.06:

animation_step = (animation_step + 1) % NUM_LEDS

lastAnimUpdate = now

strip.fill((255, 0, 0))

strip[animation_step] = (0, 255, 0)

strip.show()

pattern_matched = True

# 6. ROCK / FIRE (Sinusoidal Green-Cyan pulse)

if gestureClean == "rock":

step = int((now * 100) % 360)

brightness = (math.sin(step * math.pi / 180) + 1) / 2

g = int(255 * brightness)

b = int(50 * brightness)

strip.fill((0, g, b))

strip.show()

pattern_matched = True

# 7. OK SIGN / RAINBOW WHEEL (Rotating RGB spectrum)

if gestureClean in "ok":

if now - lastAnimUpdate > 0.03:

animation_step = (animation_step + 1) % 100

lastAnimUpdate = now

hue = animation_step / 100.0

strip.fill(hsv2rgb(hue))

strip.show()

pattern_matched = True

# DEFAULT / UNKNOWN (LEDs OFF)

if not pattern_matched:

strip.fill((0, 0, 0))

strip.show()

# ====================================================================

W, H = 1280, 720

tStart = time.time()

fps = 15

RES = (W, H)

piCam = Picamera2(1)

piCam.preview_configuration.main.size = RES

piCam.preview_configuration.main.format = "RGB888"

piCam.preview_configuration.controls.FrameRate = 60

piCam.preview_configuration.align()

piCam.configure("preview")

piCam.start()

textLowerLeft = (int(W * .01), int(H * .07))

fontFace = cv2.FONT_HERSHEY_SIMPLEX

fontThickness = int(W / 425)

fontScale = H * .002

fontColor = (0, 0, 255)

textFps = (int(W * .01), int(H * .07))

textGesture = (int(W * .01), int(H * .14))

textGesturePrompt = (int(W * .01), int(H * .21))

maxErrorThreshold = 3.5

hands = mp.solutions.hands.Hands(

model_complexity=0,

min_detection_confidence=.5,

min_tracking_confidence=.5,

max_num_hands=2

)

trainIdx = 0

while True:

deltaT = time.time() - tStart

tStart = time.time()

fps = fps * .95 + (1 / deltaT) * .05

frame = piCam.capture_array()

frame = cv2.flip(frame, -1)

rgbFrame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

results = hands.process(rgbFrame)

if results.multi_hand_landmarks:

frameHeight, frameWidth, _ = frame.shape

for handLm in results.multi_hand_landmarks:

for idx, landmark in enumerate(handLm.landmark):

pixelX = int(landmark.x * frameWidth)

pixelY = int(landmark.y * frameHeight)

cv2.putText(

frame, str(idx), (pixelX, pixelY),

cv2.FONT_HERSHEY_SIMPLEX, .6 * fontScale, (255, 0, 0), fontThickness

)

if trainIdx < len(gestureNames):

targetName = gestureNames[trainIdx]

promptText = "show " + targetName + " & Press Space"

cv2.putText(frame, promptText, textGesturePrompt, fontFace, fontScale, (0, 255, 255), fontThickness)

detectGesture = "Unknown"

if results.multi_hand_landmarks:

liveDistances = getTipDistances(results.multi_hand_landmarks[0])

detectGesture = findGesture(liveDistances, trainedData, maxErrorThreshold)

# ====================================================================

# EXECUTE HARDWARE PATTERN UPDATE

# ====================================================================

updateNeoPixels(detectGesture)

# ====================================================================

labelText = "Gesture: " + detectGesture

cv2.putText(frame, labelText, textGesture, fontFace, fontScale, (0, 255, 0), fontThickness)

myText = "FPS: " + str(round(fps, 1))

cv2.putText(frame, myText, textLowerLeft, fontFace, fontScale, fontColor, fontThickness)

cv2.imshow("Camera", frame)

#cv2.moveWindow("Camera", 0, 60)

key = cv2.waitKey(1)

# Save landmark measurements when Space is pressed

if key == ord(' '):

if results.multi_hand_landmarks:

if trainIdx < len(gestureNames):

targetHand = results.multi_hand_landmarks[0]

targetName = gestureNames[trainIdx]

trainedData[targetName] = getTipDistances(targetHand)

saveTrainedData(DATA_FILE, trainedData)

trainIdx += 1

# 't' key allows adding a NEW gesture dynamically during runtime

if key == ord('t'):

newGestureName = input("Enter name for new gesture to train: ").strip()

if newGestureName:

gestureNames.append(newGestureName)

# 'd' key deletes a gesture

if key == ord('d'):

delName = input("Enter name of gesture to remove: ").strip()

if delName in trainedData:

del trainedData[delName]

saveTrainedData(DATA_FILE, trainedData)

print("[INFO] Removed " + delName + " from trained data.")

if delName in gestureNames:

gestureNames.remove(delName)

if delName not in trainedData:

print("[WARNING] Gesture: " + delName + " not found in trained data.")

trainIdx=min(trainIdx,len(gestureNames))

if key == ord('q'):

break

# Clean up hardware and exit

# ====================================================================

# CLEAR LEDS ON SHUTDOWN

# ====================================================================

if NEOPIXEL_ENABLED:

strip.fill((0, 0, 0))

strip.show()

# ====================================================================

cv2.destroyAllWindows()

saveTrainedData(DATA_FILE, trainedData)

print('Program Terminated')

Homework & Next Steps

Try setting up the circuit and running the code on your Raspberry Pi 5. Observe how the frame rate behaves when driving both the camera preview, the hand-tracking model, and the I2C OLED display simultaneously. For your homework, extend this script to include a dynamic color slider or gesture-based brightness control!

AI On the Edge

AI on the Edge LESSON 51: Gesture Control of NeoPixel Ring

September 18, 2026 admin

Welcome back, everyone! In today’s class, AI on the Edge LESSON 51: Gesture Control of NeoPixel Ring, we are bridging the gap between computer vision and physical hardware in a big way. Up until now, we have spent a lot of time tracking hands, plotting 3D landmarks, and rendering graphical user interfaces directly onto our camera feeds. But today, we take those virtual interactions and project them out into the real physical world using a 12-LED NeoPixel ring driven over SPI by our Raspberry Pi 5.

The core concept of this lesson is creating an intuitive, spatial gesture interface. Imagine looking at your monitor display where a graphical representation of a circular LED ring is overlaid onto your live camera feed. By extending your hand and placing your index finger tip over any of the twelve virtual LED nodes on screen, the system detects your hover position. When you pinch your thumb and index finger together, the program registers a toggle event, turning the corresponding physical LED on or off on the actual hardware ring sitting on your workbench.

To make this work reliably without wild bouncing or rapid flickering, we dive deep into the mechanics of gesture latching and debouncing. When a student first attempts spatial tracking, holding a pinch gesture over a target often causes the state to flip back and forth dozens of times per second. In this lesson, we implement a stateful boolean latch flag that locks down the trigger state the moment a pinch is registered. The algorithm strictly requires you to release the pinch—breaking contact between your thumb and index finger—before it will accept another command. This simple state machine logic is essential for turning noisy computer vision inputs into rock-solid physical control interfaces.

Beyond individual pixel control, we also construct a dynamic UI Mode Selection Panel on the right side of the screen. By hovering over the panel buttons and executing the same pinch gesture, you can cycle through five distinct lighting animation profiles in real time:

Static Mode: Displays fixed, custom-assigned palette colors for each active LED node.
Rainbow Mode: Distributes a static 360-degree color wheel spectrum across all twelve positions using HSV-to-RGB conversion algorithms.
Pulsate Mode: Modulates the physical brightness using a squared sine wave math function to guarantee a true black-floor fade on turned-on pixels.
Blink Mode: Synchronizes an on/off strobe animation derived from live system timestamps.
Running Rainbow Mode: Dynamically rotates the active hue spectrum around the ring by stepping the color offset frame-by-frame.

We handle all of this frame rate processing seamlessly using high-speed SPI bus communications through neopixel_spi and direct frame acquisition via Picamera2. By keeping auto-write disabled until all mathematical animation steps and hand-tracking calculations are completed for a given frame, we push smooth, flicker-free updates out to both the physical hardware and the OpenCV display overlay simultaneously.

Work through the complete Python script provided on this page, load it onto your Pi, and assemble your hardware circuit. Pay close attention to how the spatial math maps coordinates from normalized MediaPipe landmark spaces directly into circular screen coordinates using polar angle formulas. Grab your components, fire up your IDE, and let’s get building!

# ====================================================================
# DISCLAIMER:
# This code is provided as-is for educational and experimental 
# purposes only. The author makes no representations or warranties of 
# any kind concerning the safety, suitability, or accuracy of this 
# code. Use at your own risk. The author assumes no liability for any 
# damages, system failures, security breaches, or network issues 
# resulting from the use or implementation of this script.
# ====================================================================

# Import required computer vision, system, math, and hardware libraries
import cv2
import time
import numpy as np
import colorsys
import board
import neopixel_spi as neopixel
from picamera2 import Picamera2
import mediapipe as mp

# ==============================================================================
# HARDWARE & NEOPIXEL INITIALIZATION
# ==============================================================================

# Initialize SPI communication bus for high-speed LED control
spiBus = board.SPI()

# Total number of physical LEDs in the circular NeoPixel ring
ledCount = 12

# Color order setting required by NeoPixel hardware (Green-Red-Blue)
pixelOrder = neopixel.GRB

# Create the SPI NeoPixel strip object, disabling auto-write for manual updates
ledStrip = neopixel.NeoPixel_SPI(spiBus, ledCount, pixel_order=pixelOrder, auto_write=False)

# Clear any previous LED states by turning all physical pixels off initially
ledStrip.fill(0)
ledStrip.show()

print("✅ NeoPixel initialized!")

# Boolean array tracking the active power state (True = On / False = Off) for each LED
ledStates = [False] * 12

# Array tracking the active RGB color for each physical LED to sync with screen drawing
currentLedRgb = [(0, 0, 0)] * 12

# Static base RGB color mapping assigned to each individual LED index
ledColors = [
    (255, 0, 0),    # LED 0: Red
    (0, 255, 0),    # LED 1: Green
    (0, 0, 255),    # LED 2: Blue
    (0, 255, 255),  # LED 3: Cyan
    (255, 0, 255),  # LED 4: Magenta
    (255, 255, 0),  # LED 5: Yellow
    (255, 100, 0),  # LED 6: Orange
    (128, 0, 255),  # LED 7: Purple
    (0, 255, 128),  # LED 8: Spring Green
    (255, 0, 128),  # LED 9: Pink
    (128, 255, 0),  # LED 10: Chartreuse
    (0, 128, 255)   # LED 11: Light Blue
]

# ==============================================================================
# LIGHTING MODES & STATE TRACKING
# ==============================================================================

# Available animation profiles selectable from the UI panel
modes = ["Static", "Rainbow", "Pulsate", "Blink", "Running Rainbow"]

# Index tracker for the active lighting mode
currentMode = 0

# Math phase tracker used for calculating brightness modulation in Pulsate mode
pulsePhase = 0

# Degree offset tracker used for rotating hue spectrums in Rainbow modes
rainbowOffset = 0

# Degree offset for frame-by-frame 1-degree hue increment
runningHueOffset = 0

# ==============================================================================
# DEBOUNCE & GESTURE LATCHING CONTROL FLAGS
# ==============================================================================

# Stateful latch flag to require full gesture release before accepting new toggles.
# This prevents rapid re-triggering (blinking) when holding a pinch over a node.
isGestureActive = False

# ==============================================================================
# PICAMERA2 & MEDIAPIPE HAND TRACKING SETUP
# ==============================================================================

# Instantiate PiCamera2 object targeting camera device 1
piCam = Picamera2(1)

# Screen resolution settings matching camera stream aspect ratio
frameWidth = 1280
frameHeight = 720

# Configure preview window resolution and color stream formatting
piCam.preview_configuration.main.size = (frameWidth, frameHeight)
piCam.preview_configuration.main.format = "RGB888"
piCam.preview_configuration.controls.FrameRate = 60
piCam.preview_configuration.align()
piCam.configure("preview")
piCam.start()

# Load MediaPipe hand detection module components
mpHands = mp.solutions.hands
handsDetector = mpHands.Hands(
    model_complexity=1, 
    min_detection_confidence=0.7,
    min_tracking_confidence=0.7, 
    max_num_hands=1
)
mpDraw = mp.solutions.drawing_utils

# Visual overlay center coordinate and radius for rendering the circular LED ring
centerX = frameWidth // 2 
centerY = frameHeight // 2
ringRadius = 220
cv2.namedWindow("NeoPixel Ring Controller",cv2.WINDOW_GUI_NORMAL)
cv2.moveWindow("NeoPixel Ring Controller",0,65)
cv2.resizeWindow("NeoPixel Ring Controller",frameWidth,frameHeight)
# ==============================================================================
# MAIN APPLICATION LOOP
# ==============================================================================
while True:
    # Capture raw frame matrix from PiCamera stream
    frameData = piCam.capture_array()
    
    # Mirror preview display horizontally and vertically to match natural orientation
    frameData = cv2.flip(frameData, -1)
    frameData = cv2.flip(frameData, 1)
    
    # Convert image format from BGR to RGB required by MediaPipe
    rgbFrame = cv2.cvtColor(frameData, cv2.COLOR_BGR2RGB)

    # Execute hand keypoint extraction on the converted frame
    trackingResults = handsDetector.process(rgbFrame)

    # Create image copy dedicated for drawing visual UI overlays
    displayFrame = frameData.copy()

    # Capture current UNIX epoch timestamp for time-based animation calculations
    currentTime = time.time()

    # Flag tracking whether a valid action gesture occurred in the current frame
    gestureTriggeredThisFrame = False

    # Process hand gesture logic if hand landmarks are detected
    if trackingResults.multi_hand_landmarks:
        for handLandmarks in trackingResults.multi_hand_landmarks:
            # Draw skeletal connections over the detected hand on display image
            mpDraw.draw_landmarks(displayFrame, handLandmarks, mpHands.HAND_CONNECTIONS)

            # Extract full list of 3D spatial points for all key joints
            landmarksList = handLandmarks.landmark
            indexTipPoint = landmarksList[8]  # Joint landmark index 8 (Index finger tip)
            thumbTipPoint = landmarksList[4]  # Joint landmark index 4 (Thumb tip)

            # Map coordinates from normalized values (0-1) to pixel dimensions
            indexPixelX = int(indexTipPoint.x * frameWidth)
            indexPixelY = int(indexTipPoint.y * frameHeight)
            thumbPixelX = int(thumbTipPoint.x * frameWidth)
            thumbPixelY = int(thumbTipPoint.y * frameHeight)

            # Calculate Euclidean distance between index tip and thumb tip in pixels
            pinchDistance = np.hypot(indexPixelX - thumbPixelX, indexPixelY - thumbPixelY)

            # Check pointing interaction against every LED position along the circle
            for ledIdx in range(12):
                # Angle formula: Starts at +pi/2 (bottom) and rotates clockwise (- direction)
                polarAngle = (np.pi / 2) - ledIdx * (2 * np.pi / 12)
                
                # Derive 2D screen positions matching physical ring layout
                ledPixelX = int(centerX + ringRadius * np.cos(polarAngle))
                ledPixelY = int(centerY + ringRadius * np.sin(polarAngle))
                
                # Compute straight-line Euclidean distance between index finger and target LED
                pointDistance = np.hypot(indexPixelX - ledPixelX, indexPixelY - ledPixelY)

                # Determine if fingertip is hovering within interaction radius of an LED point (Relaxed to 60px)
                if pointDistance < 60:
                    # Highlight active target LED with concentric indicator rings
                    cv2.circle(displayFrame, (ledPixelX, ledPixelY), 38, (0, 255, 255), 6)
                    cv2.circle(displayFrame, (ledPixelX, ledPixelY), 22, (255, 255, 100), -1)

                    # Verify pinch condition (Relaxed to 55px threshold for easier detection)
                    if pinchDistance < 55:
                        gestureTriggeredThisFrame = True
                        
                        # Draw pinch visual feedback ring connecting index and thumb
                        cv2.circle(displayFrame, (indexPixelX, indexPixelY), 15, (0, 255, 0), -1)
                        
                        # Execute state change only if the gesture was not already latched
                        if not isGestureActive:
                            # Invert boolean state for selected LED
                            ledStates[ledIdx] = not ledStates[ledIdx]
                            
                            # Lock gesture activation state until user unpinches
                            isGestureActive = True

            # Process UI mode panel interaction if pointing inside designated top-right screen area
            if indexPixelX > frameWidth - 280:
                if indexPixelY < 420:
                    # Map vertical pixel coordinate directly to corresponding mode list index
                    modeIndexChoice = (indexPixelY - 80) // 70
                    
                    # Verify target index falls within valid bounds of the mode selection list
                    if 0 <= modeIndexChoice < len(modes):
                        # Verify pinch condition to confirm mode selection (Relaxed to 55px)
                        if pinchDistance < 55:
                            gestureTriggeredThisFrame = True
                            
                            # Draw pinch visual feedback ring connecting index and thumb
                            cv2.circle(displayFrame, (indexPixelX, indexPixelY), 15, (0, 255, 0), -1)
                            
                            # Execute mode switch only if the gesture was not already latched
                            if not isGestureActive:
                                currentMode = modeIndexChoice
                                
                                # Lock gesture activation state until user unpinches
                                isGestureActive = True

    # Reset latch flag when hand gesture is unpinched or moved off targets
    if not gestureTriggeredThisFrame:
        isGestureActive = False

    # ==========================================================================
    # DYNAMIC LIGHTING ANIMATION EFFECTS & HARDWARE COLOR UPDATES
    # ==========================================================================
    
    # MODE 0: Static colors assigned directly from preset ledColors palette
    if currentMode == 0:
        for ledIdx in range(12):
            if ledStates[ledIdx]:
                currentLedRgb[ledIdx] = ledColors[ledIdx]
            if not ledStates[ledIdx]:
                currentLedRgb[ledIdx] = (0, 0, 0)

# MODE 1: Fixed rainbow distribution (Red at LED 0, stepping by 30 degrees, static)
    if currentMode == 1:
        for ledIdx in range(12):
            if ledStates[ledIdx]:
                calculatedHue = ledIdx * 30  # 360 degrees / 12 LEDs = 30 degrees per step
                redVal, greenVal, blueVal = [int(channel * 255) for channel in colorsys.hsv_to_rgb(calculatedHue / 360.0, 1, 1)]
                currentLedRgb[ledIdx] = (redVal, greenVal, blueVal)
            if not ledStates[ledIdx]:
                currentLedRgb[ledIdx] = (0, 0, 0)

    # MODE 2: Pulsating brightness modulation (squared sine wave for true black floor fade)
    if currentMode == 2:
        pulsePhase += 0.15
        calculatedBrightness = ((np.sin(pulsePhase) + 1) / 2) ** 2
        for ledIdx in range(12):
            if ledStates[ledIdx]:
                baseRed, baseGreen, baseBlue = ledColors[ledIdx]
                currentLedRgb[ledIdx] = (
                    int(baseRed * calculatedBrightness),
                    int(baseGreen * calculatedBrightness),
                    int(baseBlue * calculatedBrightness)
                )
            if not ledStates[ledIdx]:
                currentLedRgb[ledIdx] = (0, 0, 0)

    # MODE 3: Blinking on/off light strobe animation
    if currentMode == 3:
        blinkToggleState = int(currentTime * 3) % 2
        for ledIdx in range(12):
            if ledStates[ledIdx]:
                if blinkToggleState:
                    currentLedRgb[ledIdx] = ledColors[ledIdx]
                if not blinkToggleState:
                    currentLedRgb[ledIdx] = (0, 0, 0)
            if not ledStates[ledIdx]:
                currentLedRgb[ledIdx] = (0, 0, 0)

# MODE 4: Evenly-spaced rainbow incrementing 1 degree per cycle
    if currentMode == 4:
        for ledIdx in range(12):
            if ledStates[ledIdx]:
                # Start at evenly spaced base angle (30 deg apart) and add 1-degree step offset
                calculatedHue = (ledIdx * 30 + runningHueOffset) % 360

                # Convert normalized float hue (0.0 to 1.0) to RGB
                rFloat, gFloat, bFloat = colorsys.hsv_to_rgb(calculatedHue / 360.0, 1.0, 1.0)
                currentLedRgb[ledIdx] = (
                    int(round(rFloat * 255)),
                    int(round(gFloat * 255)),
                    int(round(bFloat * 255))
                )
            if not ledStates[ledIdx]:
                currentLedRgb[ledIdx] = (0, 0, 0)

        # Increment offset by exactly 1 degree each frame cycle, wrapping 360 back to 0
        runningHueOffset = (runningHueOffset + 2) % 360
    # Push all updated mode colors out to physical NeoPixel hardware strip
    for ledIdx in range(12):
        ledStrip[ledIdx] = currentLedRgb[ledIdx]
    ledStrip.show()

    # ==========================================================================
    # ON-SCREEN GRAPHICAL RENDERING (LED RING MATCHING PHYSICAL HARDWARE)
    # ==========================================================================
    for ledIdx in range(12):
        # Angle formula: Starts at +pi/2 (bottom) and rotates clockwise (- direction)
        polarAngle = (np.pi / 2) - ledIdx * (2 * np.pi / 12)
        targetX = int(centerX + ringRadius * np.cos(polarAngle))
        targetY = int(centerY + ringRadius * np.sin(polarAngle))

        # Render illuminated state representation using exact active hardware color
        if ledStates[ledIdx]:
            # Extract current RGB hardware values
            redVal, greenVal, blueVal = currentLedRgb[ledIdx]
            
            # Convert RGB tuple to BGR tuple format required by OpenCV rendering
            openCvBgrColor = (blueVal, greenVal, redVal)
            
            # Draw illuminated circle matching active physical LED color
            cv2.circle(displayFrame, (targetX, targetY), 24, openCvBgrColor, -1)
            cv2.circle(displayFrame, (targetX, targetY), 31, (255, 255, 255), 4)

        # Render dimmed inactive state representation if turned off
        if not ledStates[ledIdx]:
            cv2.circle(displayFrame, (targetX, targetY), 20, (50, 50, 50), -1)
            cv2.circle(displayFrame, (targetX, targetY), 27, (90, 90, 90), 3)

        # Draw LED index numerical labels inside each visual circle node
        cv2.putText(
            displayFrame, str(ledIdx), (targetX - 9, targetY + 7),
            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2
        )

    # ==========================================================================
    # ON-SCREEN GRAPHICAL RENDERING (MODE SELECTION PANEL)
    # ==========================================================================
    panelPositionX = frameWidth - 260
    for modeIdx, modeName in enumerate(modes):
        panelPositionY = 80 + modeIdx * 70
        
        # Default unselected button background color in BGR (Dark Gray)
        highlightColor = (100, 100, 100)
        
        # Override background color with vibrant Blue in BGR format when mode is selected
        if modeIdx == currentMode:
            highlightColor = (255, 100, 0)

        # Draw background menu card rectangle
        cv2.rectangle(displayFrame, (panelPositionX, panelPositionY), (panelPositionX + 240, panelPositionY + 60), highlightColor, -1)
        
        # Draw outer border line around menu card rectangle
        cv2.rectangle(displayFrame, (panelPositionX, panelPositionY), (panelPositionX + 240, panelPositionY + 60), (255, 255, 255), 3)
        
        # Render string name for lighting mode into menu card
        cv2.putText(
            displayFrame, modeName, (panelPositionX + 20, panelPositionY + 40),
            cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), 2
        )

    # ==========================================================================
    # INSTRUCTION OVERLAYS & WINDOW MANAGEMENT
    # ==========================================================================
    cv2.putText(
        displayFrame, "Point + Pinch = Toggle LED", (30, frameHeight - 40),
        cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2
    )
    cv2.putText(
        displayFrame, "Point Right Panel + Pinch = Select Mode", (30, frameHeight - 80),
        cv2.FONT_HERSHEY_SIMPLEX, 0.65, (200, 200, 200), 2
    )

    # Render completed frame to OpenCV GUI window
    cv2.imshow("NeoPixel Ring Controller", displayFrame)
    # Read user keypress events (Wait for 1 millisecond)
    activeKey = cv2.waitKey(1) & 0xFF
    
    # Process exit program trigger if 'q' key is pressed
    if activeKey == ord('q'):
        break
        
    # Process reset/clear trigger if 'c' key is pressed
    if activeKey == ord('c'):
        ledStates = [False] * 12
        currentLedRgb = [(0, 0, 0)] * 12
        ledStrip.fill(0)
        ledStrip.show()

# ==============================================================================
# CLEANUP & SHUTDOWN PROCEDURES
# ==============================================================================
print('Program Terminated')

# Close open OpenCV GUI windows
cv2.destroyAllWindows()

# Ensure all physical NeoPixels are safely powered off on exit
ledStrip.fill(0)
ledStrip.show()

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# ====================================================================

# DISCLAIMER:

# This code is provided as-is for educational and experimental

# purposes only. The author makes no representations or warranties of

# any kind concerning the safety, suitability, or accuracy of this

# code. Use at your own risk. The author assumes no liability for any

# damages, system failures, security breaches, or network issues

# resulting from the use or implementation of this script.

# ====================================================================

# Import required computer vision, system, math, and hardware libraries

import cv2

import time

import numpy as np

import colorsys

import board

import neopixel_spi as neopixel

from picamera2 import Picamera2

import mediapipe as mp

# ==============================================================================

# HARDWARE & NEOPIXEL INITIALIZATION

# ==============================================================================

# Initialize SPI communication bus for high-speed LED control

spiBus = board.SPI()

# Total number of physical LEDs in the circular NeoPixel ring

ledCount = 12

# Color order setting required by NeoPixel hardware (Green-Red-Blue)

pixelOrder = neopixel.GRB

# Create the SPI NeoPixel strip object, disabling auto-write for manual updates

ledStrip = neopixel.NeoPixel_SPI(spiBus, ledCount, pixel_order=pixelOrder, auto_write=False)

# Clear any previous LED states by turning all physical pixels off initially

ledStrip.fill(0)

ledStrip.show()

print("✅ NeoPixel initialized!")

# Boolean array tracking the active power state (True = On / False = Off) for each LED

ledStates = [False] * 12

# Array tracking the active RGB color for each physical LED to sync with screen drawing

currentLedRgb = [(0, 0, 0)] * 12

# Static base RGB color mapping assigned to each individual LED index

ledColors = [

(255, 0, 0), # LED 0: Red

(0, 255, 0), # LED 1: Green

(0, 0, 255), # LED 2: Blue

(0, 255, 255), # LED 3: Cyan

(255, 0, 255), # LED 4: Magenta

(255, 255, 0), # LED 5: Yellow

(255, 100, 0), # LED 6: Orange

(128, 0, 255), # LED 7: Purple

(0, 255, 128), # LED 8: Spring Green

(255, 0, 128), # LED 9: Pink

(128, 255, 0), # LED 10: Chartreuse

(0, 128, 255) # LED 11: Light Blue

]

# ==============================================================================

# LIGHTING MODES & STATE TRACKING

# ==============================================================================

# Available animation profiles selectable from the UI panel

modes = ["Static", "Rainbow", "Pulsate", "Blink", "Running Rainbow"]

# Index tracker for the active lighting mode

currentMode = 0

# Math phase tracker used for calculating brightness modulation in Pulsate mode

pulsePhase = 0

# Degree offset tracker used for rotating hue spectrums in Rainbow modes

rainbowOffset = 0

# Degree offset for frame-by-frame 1-degree hue increment

runningHueOffset = 0

# ==============================================================================

# DEBOUNCE & GESTURE LATCHING CONTROL FLAGS

# ==============================================================================

# Stateful latch flag to require full gesture release before accepting new toggles.

# This prevents rapid re-triggering (blinking) when holding a pinch over a node.

isGestureActive = False

# ==============================================================================

# PICAMERA2 & MEDIAPIPE HAND TRACKING SETUP

# ==============================================================================

# Instantiate PiCamera2 object targeting camera device 1

piCam = Picamera2(1)

# Screen resolution settings matching camera stream aspect ratio

frameWidth = 1280

frameHeight = 720

# Configure preview window resolution and color stream formatting

piCam.preview_configuration.main.size = (frameWidth, frameHeight)

piCam.preview_configuration.main.format = "RGB888"

piCam.preview_configuration.controls.FrameRate = 60

piCam.preview_configuration.align()

piCam.configure("preview")

piCam.start()

# Load MediaPipe hand detection module components

mpHands = mp.solutions.hands

handsDetector = mpHands.Hands(

model_complexity=1,

min_detection_confidence=0.7,

min_tracking_confidence=0.7,

max_num_hands=1

)

mpDraw = mp.solutions.drawing_utils

# Visual overlay center coordinate and radius for rendering the circular LED ring

centerX = frameWidth // 2

centerY = frameHeight // 2

ringRadius = 220

cv2.namedWindow("NeoPixel Ring Controller",cv2.WINDOW_GUI_NORMAL)

cv2.moveWindow("NeoPixel Ring Controller",0,65)

cv2.resizeWindow("NeoPixel Ring Controller",frameWidth,frameHeight)

# ==============================================================================

# MAIN APPLICATION LOOP

# ==============================================================================

while True:

# Capture raw frame matrix from PiCamera stream

frameData = piCam.capture_array()

# Mirror preview display horizontally and vertically to match natural orientation

frameData = cv2.flip(frameData, -1)

frameData = cv2.flip(frameData, 1)

# Convert image format from BGR to RGB required by MediaPipe

rgbFrame = cv2.cvtColor(frameData, cv2.COLOR_BGR2RGB)

# Execute hand keypoint extraction on the converted frame

trackingResults = handsDetector.process(rgbFrame)

# Create image copy dedicated for drawing visual UI overlays

displayFrame = frameData.copy()

# Capture current UNIX epoch timestamp for time-based animation calculations

currentTime = time.time()

# Flag tracking whether a valid action gesture occurred in the current frame

gestureTriggeredThisFrame = False

# Process hand gesture logic if hand landmarks are detected

if trackingResults.multi_hand_landmarks:

for handLandmarks in trackingResults.multi_hand_landmarks:

# Draw skeletal connections over the detected hand on display image

mpDraw.draw_landmarks(displayFrame, handLandmarks, mpHands.HAND_CONNECTIONS)

# Extract full list of 3D spatial points for all key joints

landmarksList = handLandmarks.landmark

indexTipPoint = landmarksList[8] # Joint landmark index 8 (Index finger tip)

thumbTipPoint = landmarksList[4] # Joint landmark index 4 (Thumb tip)

# Map coordinates from normalized values (0-1) to pixel dimensions

indexPixelX = int(indexTipPoint.x * frameWidth)

indexPixelY = int(indexTipPoint.y * frameHeight)

thumbPixelX = int(thumbTipPoint.x * frameWidth)

thumbPixelY = int(thumbTipPoint.y * frameHeight)

# Calculate Euclidean distance between index tip and thumb tip in pixels

pinchDistance = np.hypot(indexPixelX - thumbPixelX, indexPixelY - thumbPixelY)

# Check pointing interaction against every LED position along the circle

for ledIdx in range(12):

# Angle formula: Starts at +pi/2 (bottom) and rotates clockwise (- direction)

polarAngle = (np.pi / 2) - ledIdx * (2 * np.pi / 12)

# Derive 2D screen positions matching physical ring layout

ledPixelX = int(centerX + ringRadius * np.cos(polarAngle))

ledPixelY = int(centerY + ringRadius * np.sin(polarAngle))

# Compute straight-line Euclidean distance between index finger and target LED

pointDistance = np.hypot(indexPixelX - ledPixelX, indexPixelY - ledPixelY)

# Determine if fingertip is hovering within interaction radius of an LED point (Relaxed to 60px)

if pointDistance < 60:

# Highlight active target LED with concentric indicator rings

cv2.circle(displayFrame, (ledPixelX, ledPixelY), 38, (0, 255, 255), 6)

cv2.circle(displayFrame, (ledPixelX, ledPixelY), 22, (255, 255, 100), -1)

# Verify pinch condition (Relaxed to 55px threshold for easier detection)

if pinchDistance < 55:

gestureTriggeredThisFrame = True

# Draw pinch visual feedback ring connecting index and thumb

cv2.circle(displayFrame, (indexPixelX, indexPixelY), 15, (0, 255, 0), -1)

# Execute state change only if the gesture was not already latched

if not isGestureActive:

# Invert boolean state for selected LED

ledStates[ledIdx] = not ledStates[ledIdx]

# Lock gesture activation state until user unpinches

isGestureActive = True

# Process UI mode panel interaction if pointing inside designated top-right screen area

if indexPixelX > frameWidth - 280:

if indexPixelY < 420:

# Map vertical pixel coordinate directly to corresponding mode list index

modeIndexChoice = (indexPixelY - 80) // 70

# Verify target index falls within valid bounds of the mode selection list

if 0 <= modeIndexChoice < len(modes):

# Verify pinch condition to confirm mode selection (Relaxed to 55px)

if pinchDistance < 55:

gestureTriggeredThisFrame = True

# Draw pinch visual feedback ring connecting index and thumb

cv2.circle(displayFrame, (indexPixelX, indexPixelY), 15, (0, 255, 0), -1)

# Execute mode switch only if the gesture was not already latched

if not isGestureActive:

currentMode = modeIndexChoice

# Lock gesture activation state until user unpinches

isGestureActive = True

# Reset latch flag when hand gesture is unpinched or moved off targets

if not gestureTriggeredThisFrame:

isGestureActive = False

# ==========================================================================

# DYNAMIC LIGHTING ANIMATION EFFECTS & HARDWARE COLOR UPDATES

# ==========================================================================

# MODE 0: Static colors assigned directly from preset ledColors palette

if currentMode == 0:

for ledIdx in range(12):

if ledStates[ledIdx]:

currentLedRgb[ledIdx] = ledColors[ledIdx]

if not ledStates[ledIdx]:

currentLedRgb[ledIdx] = (0, 0, 0)

# MODE 1: Fixed rainbow distribution (Red at LED 0, stepping by 30 degrees, static)

if currentMode == 1:

for ledIdx in range(12):

if ledStates[ledIdx]:

calculatedHue = ledIdx * 30 # 360 degrees / 12 LEDs = 30 degrees per step

redVal, greenVal, blueVal = [int(channel * 255) for channel in colorsys.hsv_to_rgb(calculatedHue / 360.0, 1, 1)]

currentLedRgb[ledIdx] = (redVal, greenVal, blueVal)

if not ledStates[ledIdx]:

currentLedRgb[ledIdx] = (0, 0, 0)

# MODE 2: Pulsating brightness modulation (squared sine wave for true black floor fade)

if currentMode == 2:

pulsePhase += 0.15

calculatedBrightness = ((np.sin(pulsePhase) + 1) / 2) ** 2

for ledIdx in range(12):

if ledStates[ledIdx]:

baseRed, baseGreen, baseBlue = ledColors[ledIdx]

currentLedRgb[ledIdx] = (

int(baseRed * calculatedBrightness),

int(baseGreen * calculatedBrightness),

int(baseBlue * calculatedBrightness)

)

if not ledStates[ledIdx]:

currentLedRgb[ledIdx] = (0, 0, 0)

# MODE 3: Blinking on/off light strobe animation

if currentMode == 3:

blinkToggleState = int(currentTime * 3) % 2

for ledIdx in range(12):

if ledStates[ledIdx]:

if blinkToggleState:

currentLedRgb[ledIdx] = ledColors[ledIdx]

if not blinkToggleState:

currentLedRgb[ledIdx] = (0, 0, 0)

if not ledStates[ledIdx]:

currentLedRgb[ledIdx] = (0, 0, 0)

# MODE 4: Evenly-spaced rainbow incrementing 1 degree per cycle

if currentMode == 4:

for ledIdx in range(12):

if ledStates[ledIdx]:

# Start at evenly spaced base angle (30 deg apart) and add 1-degree step offset

calculatedHue = (ledIdx * 30 + runningHueOffset) % 360

# Convert normalized float hue (0.0 to 1.0) to RGB

rFloat, gFloat, bFloat = colorsys.hsv_to_rgb(calculatedHue / 360.0, 1.0, 1.0)

currentLedRgb[ledIdx] = (

int(round(rFloat * 255)),

int(round(gFloat * 255)),

int(round(bFloat * 255))

)

if not ledStates[ledIdx]:

currentLedRgb[ledIdx] = (0, 0, 0)

# Increment offset by exactly 1 degree each frame cycle, wrapping 360 back to 0

runningHueOffset = (runningHueOffset + 2) % 360

# Push all updated mode colors out to physical NeoPixel hardware strip

for ledIdx in range(12):

ledStrip[ledIdx] = currentLedRgb[ledIdx]

ledStrip.show()

# ==========================================================================

# ON-SCREEN GRAPHICAL RENDERING (LED RING MATCHING PHYSICAL HARDWARE)

# ==========================================================================

for ledIdx in range(12):

# Angle formula: Starts at +pi/2 (bottom) and rotates clockwise (- direction)

polarAngle = (np.pi / 2) - ledIdx * (2 * np.pi / 12)

targetX = int(centerX + ringRadius * np.cos(polarAngle))

targetY = int(centerY + ringRadius * np.sin(polarAngle))

# Render illuminated state representation using exact active hardware color

if ledStates[ledIdx]:

# Extract current RGB hardware values

redVal, greenVal, blueVal = currentLedRgb[ledIdx]

# Convert RGB tuple to BGR tuple format required by OpenCV rendering

openCvBgrColor = (blueVal, greenVal, redVal)

# Draw illuminated circle matching active physical LED color

cv2.circle(displayFrame, (targetX, targetY), 24, openCvBgrColor, -1)

cv2.circle(displayFrame, (targetX, targetY), 31, (255, 255, 255), 4)

# Render dimmed inactive state representation if turned off

if not ledStates[ledIdx]:

cv2.circle(displayFrame, (targetX, targetY), 20, (50, 50, 50), -1)

cv2.circle(displayFrame, (targetX, targetY), 27, (90, 90, 90), 3)

# Draw LED index numerical labels inside each visual circle node

cv2.putText(

displayFrame, str(ledIdx), (targetX - 9, targetY + 7),

cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2

)

# ==========================================================================

# ON-SCREEN GRAPHICAL RENDERING (MODE SELECTION PANEL)

# ==========================================================================

panelPositionX = frameWidth - 260

for modeIdx, modeName in enumerate(modes):

panelPositionY = 80 + modeIdx * 70

# Default unselected button background color in BGR (Dark Gray)

highlightColor = (100, 100, 100)

# Override background color with vibrant Blue in BGR format when mode is selected

if modeIdx == currentMode:

highlightColor = (255, 100, 0)

# Draw background menu card rectangle

cv2.rectangle(displayFrame, (panelPositionX, panelPositionY), (panelPositionX + 240, panelPositionY + 60), highlightColor, -1)

# Draw outer border line around menu card rectangle

cv2.rectangle(displayFrame, (panelPositionX, panelPositionY), (panelPositionX + 240, panelPositionY + 60), (255, 255, 255), 3)

# Render string name for lighting mode into menu card

cv2.putText(

displayFrame, modeName, (panelPositionX + 20, panelPositionY + 40),

cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), 2

)

# ==========================================================================

# INSTRUCTION OVERLAYS & WINDOW MANAGEMENT

# ==========================================================================

cv2.putText(

displayFrame, "Point + Pinch = Toggle LED", (30, frameHeight - 40),

cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2

)

cv2.putText(

displayFrame, "Point Right Panel + Pinch = Select Mode", (30, frameHeight - 80),

cv2.FONT_HERSHEY_SIMPLEX, 0.65, (200, 200, 200), 2

)

# Render completed frame to OpenCV GUI window

cv2.imshow("NeoPixel Ring Controller", displayFrame)

# Read user keypress events (Wait for 1 millisecond)

activeKey = cv2.waitKey(1) & 0xFF

# Process exit program trigger if 'q' key is pressed

if activeKey == ord('q'):

break

# Process reset/clear trigger if 'c' key is pressed

if activeKey == ord('c'):

ledStates = [False] * 12

currentLedRgb = [(0, 0, 0)] * 12

ledStrip.fill(0)

ledStrip.show()

# ==============================================================================

# CLEANUP & SHUTDOWN PROCEDURES

# ==============================================================================

print('Program Terminated')

# Close open OpenCV GUI windows

cv2.destroyAllWindows()

# Ensure all physical NeoPixels are safely powered off on exit

ledStrip.fill(0)

ledStrip.show()

AI On the Edge, Raspberry Pi

AI on the Edge LESSON 50: Control NeoPixel Ring With Hand Gestures and MediaPipe

September 13, 2026 admin

Hey everybody, welcome back to AI on the Edge! In Lesson 50 we’re taking things to the next level — we’re combining real-time hand tracking with a physical NeoPixel ring so you can change colors just by pointing at the screen! Using MediaPipe on the Raspberry Pi, we track your index finger tip and turn the big camera view into an interactive color picker. Hover over any of the seven big colored circles (Red, Green, Blue, Cyan, Magenta, Yellow, or Black) and — boom — the NeoPixel ring instantly lights up with that color!

This lesson is super fun because it finally brings together everything we’ve been building: smooth hand detection, coordinate mapping between camera and OLED, visual feedback, and real-world hardware control. You’ll see your finger tracked with a big blue dot, watch the selected color update live on both the screen and the OLED, and control actual RGB lights with nothing but a gesture.

By the end of this lesson you’ll have a working gesture-controlled color mixer that feels like real interactive magic. This is the kind of project that makes people say “Wow, you built that on a Raspberry Pi?!”

This is also a perfect foundation for even cooler future projects — gesture-controlled lighting, touchless interfaces, interactive games, or even a full gesture-controlled robot or light show.

So grab your Raspberry Pi, hook up that NeoPixel ring, fire up the camera, and let’s start waving our hands around like we’re casting spells! As always, the complete code is right below the video. Watch along, run the code, then start tweaking it — maybe add more colors, make the ring react differently to gestures, or combine it with the hand connection drawing from earlier lessons.I’m really excited to see what you create with this one!Now go make something awesome!

# ====================================================================
# DISCLAIMER:
# This code is provided as-is for educational and experimental 
# purposes only. The author makes no representations or warranties of 
# any kind concerning the safety, suitability, or accuracy of this 
# code. Use at your own risk. The author assumes no liability for any 
# damages, system failures, security breaches, or network issues 
# resulting from the use or implementation of this script.
# ====================================================================
 
import cv2
import time
from picamera2 import Picamera2
import mediapipe as mp
import board
import adafruit_ssd1306
from PIL import Image,ImageDraw,ImageFont

wOLED = 128
hOLED = 64
I2C_ADDRESS = 0x3C
i2c = board.I2C()
oled = adafruit_ssd1306.SSD1306_I2C(wOLED, hOLED, i2c, addr = I2C_ADDRESS)

W=1280
H=720
tStart = time.time()
fps = 0

RES = (W,H)
piCam = Picamera2(1)
piCam.preview_configuration.main.size = RES
piCam.preview_configuration.main.format = "RGB888"
piCam.preview_configuration.controls.FrameRate=60
piCam.preview_configuration.align()
piCam.configure("preview")
piCam.start()

textLowerLeft = (int(W*.01),int(H*.05))
fontFace = cv2.FONT_HERSHEY_SIMPLEX
fontThickness = int(W/425)
fontScale = H*.0015
fontColor = (0,0,255)

hands = mp.solutions.hands.Hands(
    model_complexity =  0,
    min_detection_confidence = .5,
    min_tracking_confidence = .5,
    max_num_hands =2)
mpDraw = mp.solutions.drawing_utils

cv2.namedWindow('Camera',cv2.WINDOW_GUI_NORMAL)
cv2.moveWindow('Camera',0,65)
cv2.resizeWindow('Camera',W,H)

import neopixel_spi as neopixel
spi = board.SPI()
strip = neopixel.NeoPixel_SPI(spi, 12,
        pixel_order=neopixel.GRB,auto_write=False)
strip.fill(0)
strip.show()

xStart=int(W/2)
yStart=int(H/10)
margin=100
radius=25
radiusOutline=30
radiusFlash=35
boxW = 4*radius
boxH =int(.75*boxW)
xSpace= int((W-2*margin)/2/3)
colorDot= [(xStart-3*xSpace,yStart),(xStart-2*xSpace,yStart),(xStart-xSpace,yStart),(xStart,yStart),(xStart+xSpace,yStart),(xStart+2*xSpace,yStart),(xStart+3*xSpace,yStart)]
oledPOS=[]
for dot in colorDot:
    oledPOS.append((int(dot[0]/W*wOLED),int(dot[1]/H*hOLED*.4)))
oledText = ['R','G','B','C','M','Y','B']
colorLabel = 'B'
colors =[(0,0,255),(0,255,0),(255,0,0),(255,255,0),(255,0,255),(0,255,255),(0,0,0)]
xBox = xStart-3*xSpace
yBox = int(yStart*1.5)
boxUpperLeft = (int(xBox-radius-boxW/4),yBox)
boxLowerRight = (int(xBox-radius-boxW/4+boxW),yBox+boxH)
colorActive =(0,0,0)
inCircle = False
                 
while True:
    deltaT = time.time() - tStart
    tStart=time.time()
    fps = fps*.95 + (1/deltaT)*.05
    frame= piCam.capture_array()
    frame=cv2.flip(frame,-1)
    frame=cv2.flip(frame,1)
    image = Image.new('1',(wOLED,hOLED))
    draw = ImageDraw.Draw(image)
    for i in range(7):
        if inCircle:
            cv2.circle(frame,colorDot[i],radiusFlash,(255,255,255),-1)
        cv2.circle(frame,colorDot[i],radiusOutline,(200,200,200),-1)
        cv2.circle(frame,colorDot[i],radius,colors[i],-1)
        draw.text(oledPOS[i],oledText[i],fill=255)
    cv2.rectangle(frame,boxUpperLeft,boxLowerRight,colorActive,-1)
    rgbFrame= cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    handResults = hands.process(rgbFrame)
    if handResults.multi_hand_landmarks:
        for myHands in handResults.multi_hand_landmarks:
            landMarks = myHands.landmark
            x = int(landMarks[8].x*W)
            y = int(landMarks[8].y*H)
            xOLED = int(landMarks[8].x*wOLED)
            yOLED = int(landMarks[8].y*hOLED)
            cv2.circle(frame,(x,y),10,(255,0,0), -1)
            draw.ellipse((xOLED-3,yOLED-4,xOLED+3,yOLED+4),fill=255)
            cnt = 0
            for pos in colorDot:
                if abs(x-pos[0])<radius and abs(y-pos[1])<radius:
                    colorActive=colors[cnt]
                    colorLabel =oledText[cnt]
                    #draw.text((int(wOLED/2),int(hOLED/2)),oledText[cnt],fill=255)
                    strip.fill((colorActive[2],colorActive[1],colorActive[0]))
                    strip.show()
                               
                cnt=cnt+1
    draw.text((int(wOLED/2),int(hOLED/2)),colorLabel,fill=255)                    
    oled.image(image)
    oled.show()
    myText = "FPS: "+str(round(fps,1))
    cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)
    cv2.imshow("Camera", frame)
    cv2.moveWindow("Camera",0,60)
    if cv2.waitKey(1)==ord('q'):
        break
cv2.destroyAllWindows()
print('Program Terminated')

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# ====================================================================

# DISCLAIMER:

# This code is provided as-is for educational and experimental

# purposes only. The author makes no representations or warranties of

# any kind concerning the safety, suitability, or accuracy of this

# code. Use at your own risk. The author assumes no liability for any

# damages, system failures, security breaches, or network issues

# resulting from the use or implementation of this script.

# ====================================================================

import cv2

import time

from picamera2 import Picamera2

import mediapipe as mp

import board

import adafruit_ssd1306

from PIL import Image,ImageDraw,ImageFont

wOLED = 128

hOLED = 64

I2C_ADDRESS = 0x3C

i2c = board.I2C()

oled = adafruit_ssd1306.SSD1306_I2C(wOLED, hOLED, i2c, addr = I2C_ADDRESS)

W=1280

H=720

tStart = time.time()

fps = 0

RES = (W,H)

piCam = Picamera2(1)

piCam.preview_configuration.main.size = RES

piCam.preview_configuration.main.format = "RGB888"

piCam.preview_configuration.controls.FrameRate=60

piCam.preview_configuration.align()

piCam.configure("preview")

piCam.start()

textLowerLeft = (int(W*.01),int(H*.05))

fontFace = cv2.FONT_HERSHEY_SIMPLEX

fontThickness = int(W/425)

fontScale = H*.0015

fontColor = (0,0,255)

hands = mp.solutions.hands.Hands(

model_complexity = 0,

min_detection_confidence = .5,

min_tracking_confidence = .5,

max_num_hands =2)

mpDraw = mp.solutions.drawing_utils

cv2.namedWindow('Camera',cv2.WINDOW_GUI_NORMAL)

cv2.moveWindow('Camera',0,65)

cv2.resizeWindow('Camera',W,H)

import neopixel_spi as neopixel

spi = board.SPI()

strip = neopixel.NeoPixel_SPI(spi, 12,

pixel_order=neopixel.GRB,auto_write=False)

strip.fill(0)

strip.show()

xStart=int(W/2)

yStart=int(H/10)

margin=100

radius=25

radiusOutline=30

radiusFlash=35

boxW = 4*radius

boxH =int(.75*boxW)

xSpace= int((W-2*margin)/2/3)

colorDot= [(xStart-3*xSpace,yStart),(xStart-2*xSpace,yStart),(xStart-xSpace,yStart),(xStart,yStart),(xStart+xSpace,yStart),(xStart+2*xSpace,yStart),(xStart+3*xSpace,yStart)]

oledPOS=[]

for dot in colorDot:

oledPOS.append((int(dot[0]/W*wOLED),int(dot[1]/H*hOLED*.4)))

oledText = ['R','G','B','C','M','Y','B']

colorLabel = 'B'

colors =[(0,0,255),(0,255,0),(255,0,0),(255,255,0),(255,0,255),(0,255,255),(0,0,0)]

xBox = xStart-3*xSpace

yBox = int(yStart*1.5)

boxUpperLeft = (int(xBox-radius-boxW/4),yBox)

boxLowerRight = (int(xBox-radius-boxW/4+boxW),yBox+boxH)

colorActive =(0,0,0)

inCircle = False

while True:

deltaT = time.time() - tStart

tStart=time.time()

fps = fps*.95 + (1/deltaT)*.05

frame= piCam.capture_array()

frame=cv2.flip(frame,-1)

frame=cv2.flip(frame,1)

image = Image.new('1',(wOLED,hOLED))

draw = ImageDraw.Draw(image)

for i in range(7):

if inCircle:

cv2.circle(frame,colorDot[i],radiusFlash,(255,255,255),-1)

cv2.circle(frame,colorDot[i],radiusOutline,(200,200,200),-1)

cv2.circle(frame,colorDot[i],radius,colors[i],-1)

draw.text(oledPOS[i],oledText[i],fill=255)

cv2.rectangle(frame,boxUpperLeft,boxLowerRight,colorActive,-1)

rgbFrame= cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

handResults = hands.process(rgbFrame)

if handResults.multi_hand_landmarks:

for myHands in handResults.multi_hand_landmarks:

landMarks = myHands.landmark

x = int(landMarks[8].x*W)

y = int(landMarks[8].y*H)

xOLED = int(landMarks[8].x*wOLED)

yOLED = int(landMarks[8].y*hOLED)

cv2.circle(frame,(x,y),10,(255,0,0), -1)

draw.ellipse((xOLED-3,yOLED-4,xOLED+3,yOLED+4),fill=255)

cnt = 0

for pos in colorDot:

if abs(x-pos[0])<radius and abs(y-pos[1])<radius:

colorActive=colors[cnt]

colorLabel =oledText[cnt]

#draw.text((int(wOLED/2),int(hOLED/2)),oledText[cnt],fill=255)

strip.fill((colorActive[2],colorActive[1],colorActive[0]))

strip.show()

cnt=cnt+1

draw.text((int(wOLED/2),int(hOLED/2)),colorLabel,fill=255)

oled.image(image)

oled.show()

myText = "FPS: "+str(round(fps,1))

cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)

cv2.imshow("Camera", frame)

cv2.moveWindow("Camera",0,60)

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

break

cv2.destroyAllWindows()

print('Program Terminated')

This is the core circuit we use for the class:

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

This is the schematic to add the OLED:

Then this is the schematic to add the NeoPixel

AI On the Edge, Raspberry Pi

AI on the Edge LESSON 49: Live Animated Avatars on the SSD1306 OLED With OpenCV

September 10, 2026 admin

Hey everybody! Welcome back to the AI on the Edge series!

In Lesson 49, we’re doing something that’s just plain cool — we’re turning that tiny little SSD1306 OLED into a live animated avatar that follows your face and hands in real time!

That’s right — no more static images, and no more being tethered to your desktop and monitor. This time your OLED is alive! Move your head, smile, raise your eyebrows, wave your hands… and the little display instantly mirrors your movements like a miniature digital twin. It’s like having a tiny version of yourself living on that 128×64 screen!

What You’ll Learn in This Lesson:

How to use MediaPipe Face Mesh to track 468 facial landmarks
How to combine it with Hand Tracking at the same time
The trick to projecting those landmarks onto a tiny OLED in real time
How to draw both points and connecting lines to create smooth, animated avatars
How to keep everything running fast on the Raspberry Pi

This is one of those projects that makes people stop and say “Wait… how is that even possible on that little screen?!”

By the end of this lesson, you’ll have a foundation for building all kinds of fun real-time avatar projects — everything from simple face mirrors to gesture-controlled characters and beyond.

Why This Is Awesome:

We’re not just displaying video on the big screen anymore — we’re compressing all that AI power down onto a cheap little OLED. This is real AI on the Edge stuff, and it looks incredibly impressive for how simple the hardware is.

Whether you want to build interactive displays, robot faces, wearable tech, or just blow your friends’ minds, this lesson gives you the core technique to make it happen.

Ready to make your OLED come alive?

Fire up the code, run it, and watch your tiny digital self start moving with you. Then start experimenting! Try drawing just the eyes and mouth, or just the hands, or maybe even turn it into a little stick figure avatar.

As always, I want to see what you create with this! Drop your versions in the comments or tag me — I love seeing the creative stuff you guys come up with. Don’t just copy and paste my code. Show you really understand the video by making the code yout own.

Enough talk, lets get this party started!

# ====================================================================
# DISCLAIMER:
# This code is provided as-is for educational and experimental 
# purposes only. The author makes no representations or warranties of 
# any kind concerning the safety, suitability, or accuracy of this 
# code. Use at your own risk. The author assumes no liability for any 
# damages, system failures, security breaches, or network issues 
# resulting from the use or implementation of this script.
# ====================================================================

import cv2
import time
from picamera2 import Picamera2
import mediapipe as mp
import numpy as np
import board
import adafruit_ssd1306
from PIL import Image, ImageDraw, ImageFont
import time

wOLED = 128
hOLED = 64
I2C_ADDRESS = 0x3C
i2c = board.I2C()
oled = adafruit_ssd1306.SSD1306_I2C(wOLED, hOLED, i2c, addr = I2C_ADDRESS)

W=1280
H=720
tStart = time.time()
fps = 0

piCam = Picamera2(1)
piCam.preview_configuration.main.size = (W, H)
piCam.preview_configuration.main.format = "RGB888"
piCam.preview_configuration.controls.FrameRate = 60
piCam.preview_configuration.align()
piCam.configure("preview")
piCam.start()

textLowerLeft = (int(W*.01),int(H*.05))
fontFace = cv2.FONT_HERSHEY_SIMPLEX
fontThickness = int(W/425)
fontScale = H*.0015
fontColor = (0,0,255)

faceMesh = mp.solutions.face_mesh.FaceMesh(
    
    max_num_faces=1,
    refine_landmarks=True,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5)

hands = mp.solutions.hands.Hands(
    model_complexity = 0,
    min_detection_confidence = .5,
    min_tracking_confidence = .5,
    max_num_hands=2 )

cv2.namedWindow('Camera',cv2.WINDOW_GUI_NORMAL)
cv2.moveWindow('Camera',0,65)
cv2.resizeWindow('Camera',W,H)
while True:
    deltaT = time.time() - tStart
    tStart=time.time()
    fps = fps*.95 + (1/deltaT)*.05
    frame = piCam.capture_array()
    frame = cv2.flip(frame, -1)

    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    faceResults = faceMesh.process(rgb)
    handResults = hands.process(rgb)
    
    image = Image.new("1",(wOLED,hOLED))
    draw = ImageDraw.Draw(image)
    
    if handResults.multi_hand_landmarks:
        for myHands in handResults.multi_hand_landmarks:
            landMarks = myHands.landmark
            lmAll =[]
            for lm in landMarks:
                x = int(lm.x*W)
                y = int(lm.y*H)
                cv2.circle(frame,(x,y),10,(255,0,0),-1)
                xOLED = int(lm.x*wOLED)
                yOLED = int(lm.y*hOLED)
                draw.point((xOLED,yOLED), fill =255)
                lmAll.append((xOLED,yOLED))
            #Uncomment out these lines if you want
            #Lines between the finger joints
#             for idx in mp.solutions.hands.HAND_CONNECTIONS:
#                 lineStart = idx[0]
#                 lineEnd = idx[1]
#                 pt1 = lmAll[lineStart]
#                 pt2 = lmAll[lineEnd]
#                 draw.line((pt1[0],pt1[1],pt2[0],pt2[1]),width=1,fill=255)
#                              
    if faceResults.multi_face_landmarks:
        for faceLandMarks in faceResults.multi_face_landmarks:
            #print(faceLandmarks)
            lmAll = []
            for lm in faceLandMarks.landmark:
                xOLED = int(lm.x*wOLED)
                yOLED = int(lm.y*hOLED)
                draw.point((xOLED,yOLED), fill = 255)
                #print(xOLED,yOLED)
                lmAll.append((xOLED,yOLED))
            for idx in mp.solutions.face_mesh.FACEMESH_CONTOURS:
                #print(idx)
                lineStart = idx[0]
                lineEnd = idx[1]
                pt1 = lmAll[lineStart]
                pt2 = lmAll[lineEnd]
                draw.line((pt1[0],pt1[1],pt2[0],pt2[1]), width=1, fill = 255)



    oled.image(image)
    oled.show()


    myText = "FPS: "+str(round(fps,1))
    cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)

    cv2.imshow("Camera", frame)

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

cv2.destroyAllWindows()
piCam.stop()
oled.fill(0)
oled.show()

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# ====================================================================

# DISCLAIMER:

# This code is provided as-is for educational and experimental

# purposes only. The author makes no representations or warranties of

# any kind concerning the safety, suitability, or accuracy of this

# code. Use at your own risk. The author assumes no liability for any

# damages, system failures, security breaches, or network issues

# resulting from the use or implementation of this script.

# ====================================================================

import cv2

import time

from picamera2 import Picamera2

import mediapipe as mp

import numpy as np

import board

import adafruit_ssd1306

from PIL import Image, ImageDraw, ImageFont

import time

wOLED = 128

hOLED = 64

I2C_ADDRESS = 0x3C

i2c = board.I2C()

oled = adafruit_ssd1306.SSD1306_I2C(wOLED, hOLED, i2c, addr = I2C_ADDRESS)

W=1280

H=720

tStart = time.time()

fps = 0

piCam = Picamera2(1)

piCam.preview_configuration.main.size = (W, H)

piCam.preview_configuration.main.format = "RGB888"

piCam.preview_configuration.controls.FrameRate = 60

piCam.preview_configuration.align()

piCam.configure("preview")

piCam.start()

textLowerLeft = (int(W*.01),int(H*.05))

fontFace = cv2.FONT_HERSHEY_SIMPLEX

fontThickness = int(W/425)

fontScale = H*.0015

fontColor = (0,0,255)

faceMesh = mp.solutions.face_mesh.FaceMesh(

max_num_faces=1,

refine_landmarks=True,

min_detection_confidence=0.5,

min_tracking_confidence=0.5)

hands = mp.solutions.hands.Hands(

model_complexity = 0,

min_detection_confidence = .5,

min_tracking_confidence = .5,

max_num_hands=2 )

cv2.namedWindow('Camera',cv2.WINDOW_GUI_NORMAL)

cv2.moveWindow('Camera',0,65)

cv2.resizeWindow('Camera',W,H)

while True:

deltaT = time.time() - tStart

tStart=time.time()

fps = fps*.95 + (1/deltaT)*.05

frame = piCam.capture_array()

frame = cv2.flip(frame, -1)

rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

faceResults = faceMesh.process(rgb)

handResults = hands.process(rgb)

image = Image.new("1",(wOLED,hOLED))

draw = ImageDraw.Draw(image)

if handResults.multi_hand_landmarks:

for myHands in handResults.multi_hand_landmarks:

landMarks = myHands.landmark

lmAll =[]

for lm in landMarks:

x = int(lm.x*W)

y = int(lm.y*H)

cv2.circle(frame,(x,y),10,(255,0,0),-1)

xOLED = int(lm.x*wOLED)

yOLED = int(lm.y*hOLED)

draw.point((xOLED,yOLED), fill =255)

lmAll.append((xOLED,yOLED))

#Uncomment out these lines if you want

#Lines between the finger joints

# for idx in mp.solutions.hands.HAND_CONNECTIONS:

# lineStart = idx[0]

# lineEnd = idx[1]

# pt1 = lmAll[lineStart]

# pt2 = lmAll[lineEnd]

# draw.line((pt1[0],pt1[1],pt2[0],pt2[1]),width=1,fill=255)

if faceResults.multi_face_landmarks:

for faceLandMarks in faceResults.multi_face_landmarks:

#print(faceLandmarks)

lmAll = []

for lm in faceLandMarks.landmark:

xOLED = int(lm.x*wOLED)

yOLED = int(lm.y*hOLED)

draw.point((xOLED,yOLED), fill = 255)

#print(xOLED,yOLED)

lmAll.append((xOLED,yOLED))

for idx in mp.solutions.face_mesh.FACEMESH_CONTOURS:

#print(idx)

lineStart = idx[0]

lineEnd = idx[1]

pt1 = lmAll[lineStart]

pt2 = lmAll[lineEnd]

draw.line((pt1[0],pt1[1],pt2[0],pt2[1]), width=1, fill = 255)

oled.image(image)

oled.show()

myText = "FPS: "+str(round(fps,1))

cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)

cv2.imshow("Camera", frame)

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

break

cv2.destroyAllWindows()

piCam.stop()

oled.fill(0)

oled.show()

AI On the Edge, Raspberry Pi

AI on the Edge LESSON 48: Hand Detection in OpenCV and MediaPipe on the Raspberry Pi

September 6, 2026 admin

In Lesson 48 we’re taking a huge step forward — we’re bringing real-time hand detection to the Raspberry Pi using the incredible power of MediaPipe combined with OpenCV and the Pi Camera. This is one of those projects that feels like pure magic when you see it working: the camera picks up your hands instantly, tracks all 21 landmarks on each hand, draws beautiful connections in real time, and does it all right on the edge with no cloud required.

You’ll learn how to set up MediaPipe’s Hands solution for reliable multi-hand tracking, how to efficiently process frames from the Picamera2 library, flip and convert images for proper display, and draw professional-looking landmarks and connections with custom styling. We also keep a smooth FPS counter running so you can see exactly how well your Pi is performing.

This lesson is exciting because hand tracking is the foundation for so many advanced gesture-control projects — think touchless interfaces, sign language recognition, robotic control, virtual instruments, and interactive art installations. Once you have solid hand detection running smoothly on your Raspberry Pi, the creative possibilities are almost endless.

So grab your Raspberry Pi, fire up the camera, and let’s get those hands dancing on the screen! As always, the full code is waiting for you below. Watch the video, follow along, and then start experimenting — I can’t wait to see what you build with this!

Let’s make something awesome!

# ====================================================================
# DISCLAIMER:
# This code is provided as-is for educational and experimental 
# purposes only. The author makes no representations or warranties of 
# any kind concerning the safety, suitability, or accuracy of this 
# code. Use at your own risk. The author assumes no liability for any 
# damages, system failures, security breaches, or network issues 
# resulting from the use or implementation of this script.
# ====================================================================

import cv2
import time
from picamera2 import Picamera2
import mediapipe as mp
piCam = Picamera2(1)
W=1280
H=720
tStart = time.time()
fps = 0
RES = (W,H)
piCam.preview_configuration.main.size = RES
piCam.preview_configuration.main.format = "RGB888"
piCam.preview_configuration.controls.FrameRate=60
piCam.preview_configuration.align()
piCam.configure("preview")
piCam.start()

textLowerLeft = (int(W*.01),int(H*.05))
fontFace = cv2.FONT_HERSHEY_SIMPLEX
fontThickness = int(W/425)
fontScale = H*.0015
fontColor = (0,0,255)

hands = mp.solutions.hands.Hands(
    model_complexity =  0,
    min_detection_confidence = .5,
    min_tracking_confidence = .5,
    max_num_hands =2)
mpDraw = mp.solutions.drawing_utils

handStyle = mpDraw.DrawingSpec(
    color = (100, 0, 255),
    thickness = 6,
    circle_radius = 8)
connectionStyle = mpDraw.DrawingSpec(
    color = (0,255,0),
    thickness = 5)

while True:
    deltaT = time.time() - tStart
    tStart=time.time()
    fps = fps*.95 + (1/deltaT)*.05
    frame= piCam.capture_array()
    frame=cv2.flip(frame,-1)
    rgbFrame= cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    results = hands.process(rgbFrame)
    if results.multi_hand_landmarks:
        for handLM in results.multi_hand_landmarks:
            mpDraw.draw_landmarks(
                frame,
                handLM,
                mp.solutions.hands.HAND_CONNECTIONS,
                handStyle,
                connectionStyle)
        
    myText = "FPS: "+str(round(fps,1))
    cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)
    cv2.imshow("Camera", frame)
    cv2.moveWindow("Camera",0,60)
    if cv2.waitKey(1)==ord('q'):
        break
cv2.destroyAllWindows()
print('Program Terminated')

# ====================================================================

# DISCLAIMER:

# This code is provided as-is for educational and experimental

# purposes only. The author makes no representations or warranties of

# any kind concerning the safety, suitability, or accuracy of this

# code. Use at your own risk. The author assumes no liability for any

# damages, system failures, security breaches, or network issues

# resulting from the use or implementation of this script.

# ====================================================================

import cv2

import time

from picamera2 import Picamera2

import mediapipe as mp

piCam = Picamera2(1)

W=1280

H=720

tStart = time.time()

fps = 0

RES = (W,H)

piCam.preview_configuration.main.size = RES

piCam.preview_configuration.main.format = "RGB888"

piCam.preview_configuration.controls.FrameRate=60

piCam.preview_configuration.align()

piCam.configure("preview")

piCam.start()

textLowerLeft = (int(W*.01),int(H*.05))

fontFace = cv2.FONT_HERSHEY_SIMPLEX

fontThickness = int(W/425)

fontScale = H*.0015

fontColor = (0,0,255)

hands = mp.solutions.hands.Hands(

model_complexity = 0,

min_detection_confidence = .5,

min_tracking_confidence = .5,

max_num_hands =2)

mpDraw = mp.solutions.drawing_utils

handStyle = mpDraw.DrawingSpec(

color = (100, 0, 255),

thickness = 6,

circle_radius = 8)

connectionStyle = mpDraw.DrawingSpec(

color = (0,255,0),

thickness = 5)

while True:

deltaT = time.time() - tStart

tStart=time.time()

fps = fps*.95 + (1/deltaT)*.05

frame= piCam.capture_array()

frame=cv2.flip(frame,-1)

rgbFrame= cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

results = hands.process(rgbFrame)

if results.multi_hand_landmarks:

for handLM in results.multi_hand_landmarks:

mpDraw.draw_landmarks(

frame,

handLM,

mp.solutions.hands.HAND_CONNECTIONS,

handStyle,

connectionStyle)

myText = "FPS: "+str(round(fps,1))

cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)

cv2.imshow("Camera", frame)

cv2.moveWindow("Camera",0,60)

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

break

cv2.destroyAllWindows()

print('Program Terminated')

Technology Tutorials

Tag Archives: AI

AI on the Edge LESSON 54: Custom Hand Gesture Recognition & Dynamic NeoPixel Control

System Architecture Overview

Gesture-to-Hardware Animation Mapping

Interactive Keyboard Controls

Hardware Wiring Reference

Homework Challenge

Homework & Next Steps

AI on the Edge LESSON 51: Gesture Control of NeoPixel Ring

AI on the Edge LESSON 50: Control NeoPixel Ring With Hand Gestures and MediaPipe

AI on the Edge LESSON 49: Live Animated Avatars on the SSD1306 OLED With OpenCV

What You’ll Learn in This Lesson:

Why This Is Awesome:

AI on the Edge LESSON 48: Hand Detection in OpenCV and MediaPipe on the Raspberry Pi

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