Emotion Detector | Technology Tutorials

In this exciting lesson, we build a real-time Emotion Detector that can recognize basic human emotions using just a Raspberry Pi, a camera, and the power of MediaPipe. The system watches your face and identifies whether you look Neutral, Smiling, Surprised, or Angry — and then reacts instantly by changing the color of a NeoPixel ring and displaying the detected emotion on both the camera preview and a small OLED display.Using MediaPipe’s Face Mesh solution, the program tracks 468 facial landmarks in real time. From these points, we carefully calculate key facial ratios — such as eye openness, mouth width, and mouth height relative to head width. These measurements allow us to create simple but effective rules that distinguish between different emotional expressions. For example, a wide mouth combined with raised cheeks indicates a smile, while a small mouth opening and narrowed eyes suggests anger.The project beautifully integrates several powerful technologies:

Picamera2 for high-performance camera capture
OpenCV for image processing and on-screen text display
MediaPipe Face Mesh for accurate facial landmark detection
NeoPixel RGB ring that lights up in different colors depending on the detected emotion
SSD1306 OLED display that shows both the emotion name and a simplified wireframe of your face

One of the most satisfying parts of this project is seeing the NeoPixel ring instantly change color to match your emotion — green for happy, cyan for surprised, red for angry, and yellow for neutral. The OLED also mirrors the detected emotion, making the entire system feel alive and responsive.This lesson is a fantastic step forward in understanding how to combine computer vision with physical outputs on the edge. You will learn how to extract meaningful measurements from facial landmarks, build rule-based emotion logic, and synchronize visual feedback across multiple devices (camera preview, NeoPixel, and OLED).By the end of Lesson 47, you will have a working real-time emotion recognition system running entirely on a Raspberry Pi — a great foundation for more advanced projects like mood-reactive lights, interactive robots, or even assistive technology.

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

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

wOLED = 128
hOLED = 64
I2C_ADDRESS = 0x3C
i2c = board.I2C()
oled = adafruit_ssd1306.SSD1306_I2C(wOLED, hOLED, i2c, addr = I2C_ADDRESS)
fontLarge = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",14)
#################################
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)

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)
    results = faceMesh.process(rgb)
    
    if results.multi_face_landmarks:
        for faceLandMarks in results.multi_face_landmarks:
            #print(faceLandmarks)
            image = Image.new("1",(wOLED,hOLED))
            draw = ImageDraw.Draw(image)
            lmAll = []
            j=0
            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))
                x = int(lm.x*W)
                y = int(lm.y*H)
                #cv2.putText(frame,str(j),(x,y),fontFace, .3, (0,255,0),1)
                j = j+1
            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)

            keyLM = faceLandMarks.landmark
            
            headRight = 323
            headLeft = 93
            headWidth = keyLM[headRight].x -keyLM[headLeft].x
            cv2.putText(frame,str(headRight),(int(keyLM[headRight].x*W),int(keyLM[headRight].y*H)),fontFace, .3, (0,255,0),1)
            cv2.putText(frame,str(headLeft),(int(keyLM[headLeft].x*W),int(keyLM[headLeft].y*H)),fontFace, .3, (0,255,0),1)
            
            leftEyeTop = 159
            leftEyeBottom = 145
            leftEyeHeight =keyLM[leftEyeBottom].y -keyLM[leftEyeTop].y
            leftEyeLeft = 130
            leftEyeRight = 154
            leftEyeWidth = keyLM[leftEyeRight].x -keyLM[leftEyeLeft].x
            leftEyeRatio = leftEyeHeight/leftEyeWidth*100 
            
            rightEyeTop = 385
            rightEyeBottom = 253
            rightEyeHeight = keyLM[rightEyeBottom].y -keyLM[rightEyeTop].y
            rightEyeLeft = 465
            rightEyeRight = 249
            rightEyeWidth = keyLM[rightEyeRight].x -keyLM[rightEyeLeft].x
            rightEyeRatio = rightEyeHeight/rightEyeWidth*100
            
            eyeRatio = (rightEyeRatio +leftEyeRatio)/2
            
            mouthLeft = 61
            mouthRight = 291
            mouthTop = 13
            mouthBottom = 15
            mouthWidth = keyLM[mouthRight].x-keyLM[mouthLeft].x
            mouthHeight = keyLM[mouthBottom].y-keyLM[mouthTop].y
            mouthWidthRatio = mouthWidth/headWidth *100
            mouthHeightRatio = mouthHeight/headWidth*100
            
            print(round(mouthWidthRatio,1),round(mouthHeightRatio,1),round(eyeRatio,1))
            color = (255,255,0)
            emotion = 'NEUTRAL'
            if mouthWidthRatio >37 and mouthHeightRatio>4 and eyeRatio<74:
                emotion = 'SMILE'
                color = (0,255,0)
            if mouthWidthRatio <37 and mouthHeightRatio>10 and eyeRatio>70:
                emotion = 'SURPRISED'
                color = (0,255,255)
            if mouthWidthRatio <32 and mouthHeightRatio<4 and eyeRatio<57:
                emotion = 'ANGRY'
                color = (0,0,255)               
            cv2.putText(frame, emotion, (int(W*.1),int(H*.15)),
                    fontFace, 2.2, color, 4)
            strip.fill((color[2],color[1],color[0]))
            strip.show()
            #print('XXXXXXXXXXXXXXXXXXx')
            draw.text((0,0),emotion,font=fontLarge,fill=255)
            oled.image(image)
            oled.show()
        #print()

#     if results.multi_face_landmarks:
#         for faceLandmarks in results.multi_face_landmarks:
#             # Optional second pass for glow effect
#             mp.solutions.drawing_utils.draw_landmarks(
#                 image=frame,
#                 landmark_list=faceLandmarks,
#                 connections=mp.solutions.face_mesh.FACEMESH_TESSELATION,
#                 landmark_drawing_spec=None,
#                 connection_drawing_spec=mp.solutions.drawing_utils.DrawingSpec(
#                     color=(100, 100, 100),
#                     thickness=1
#                 )
#             )
#             # === Best looking translucent mesh ===
#             mp.solutions.drawing_utils.draw_landmarks(
#                 image=frame,
#                 landmark_list=faceLandmarks,
#                 connections=mp.solutions.face_mesh.FACEMESH_CONTOURS,
#                 landmark_drawing_spec=None,                    # Hide dots
#                 connection_drawing_spec=mp.solutions.drawing_utils.DrawingSpec(
#                     color=(0, 0, 180),      # Bright cyan/teal
#                     thickness=7
#                 )
#             )
# 
#             # Optional: Extra detail on irises (makes eyes look better)
#             mp.solutions.drawing_utils.draw_landmarks(
#                 image=frame,
#                 landmark_list=faceLandmarks,
#                 connections=mp.solutions.face_mesh.FACEMESH_IRISES,
#                 landmark_drawing_spec=None,
#                 connection_drawing_spec=mp.solutions.drawing_utils.DrawingSpec(
#                     color=(255, 0, 0),
#                     thickness=7
#                 )
#             )

    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()
strip.fill(0)
strip.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

import neopixel_spi as neopixel

import colorsys

spi=board.SPI()

LC = 12

PO = neopixel.GRB

strip = neopixel.NeoPixel_SPI(spi, LC,

pixel_order=PO,auto_write=False)

strip.fill(0)

strip.show()

wOLED = 128

hOLED = 64

I2C_ADDRESS = 0x3C

i2c = board.I2C()

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

fontLarge = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",14)

#################################

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)

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)

results = faceMesh.process(rgb)

if results.multi_face_landmarks:

for faceLandMarks in results.multi_face_landmarks:

#print(faceLandmarks)

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

draw = ImageDraw.Draw(image)

lmAll = []

j=0

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))

x = int(lm.x*W)

y = int(lm.y*H)

#cv2.putText(frame,str(j),(x,y),fontFace, .3, (0,255,0),1)

j = j+1

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)

keyLM = faceLandMarks.landmark

headRight = 323

headLeft = 93

headWidth = keyLM[headRight].x -keyLM[headLeft].x

cv2.putText(frame,str(headRight),(int(keyLM[headRight].x*W),int(keyLM[headRight].y*H)),fontFace, .3, (0,255,0),1)

cv2.putText(frame,str(headLeft),(int(keyLM[headLeft].x*W),int(keyLM[headLeft].y*H)),fontFace, .3, (0,255,0),1)

leftEyeTop = 159

leftEyeBottom = 145

leftEyeHeight =keyLM[leftEyeBottom].y -keyLM[leftEyeTop].y

leftEyeLeft = 130

leftEyeRight = 154

leftEyeWidth = keyLM[leftEyeRight].x -keyLM[leftEyeLeft].x

leftEyeRatio = leftEyeHeight/leftEyeWidth*100

rightEyeTop = 385

rightEyeBottom = 253

rightEyeHeight = keyLM[rightEyeBottom].y -keyLM[rightEyeTop].y

rightEyeLeft = 465

rightEyeRight = 249

rightEyeWidth = keyLM[rightEyeRight].x -keyLM[rightEyeLeft].x

rightEyeRatio = rightEyeHeight/rightEyeWidth*100

eyeRatio = (rightEyeRatio +leftEyeRatio)/2

mouthLeft = 61

mouthRight = 291

mouthTop = 13

mouthBottom = 15

mouthWidth = keyLM[mouthRight].x-keyLM[mouthLeft].x

mouthHeight = keyLM[mouthBottom].y-keyLM[mouthTop].y

mouthWidthRatio = mouthWidth/headWidth *100

mouthHeightRatio = mouthHeight/headWidth*100

print(round(mouthWidthRatio,1),round(mouthHeightRatio,1),round(eyeRatio,1))

color = (255,255,0)

emotion = 'NEUTRAL'

if mouthWidthRatio >37 and mouthHeightRatio>4 and eyeRatio<74:

emotion = 'SMILE'

color = (0,255,0)

if mouthWidthRatio <37 and mouthHeightRatio>10 and eyeRatio>70:

emotion = 'SURPRISED'

color = (0,255,255)

if mouthWidthRatio <32 and mouthHeightRatio<4 and eyeRatio<57:

emotion = 'ANGRY'

color = (0,0,255)

cv2.putText(frame, emotion, (int(W*.1),int(H*.15)),

fontFace, 2.2, color, 4)

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

strip.show()

#print('XXXXXXXXXXXXXXXXXXx')

draw.text((0,0),emotion,font=fontLarge,fill=255)

oled.image(image)

oled.show()

#print()

# if results.multi_face_landmarks:

# for faceLandmarks in results.multi_face_landmarks:

# # Optional second pass for glow effect

# mp.solutions.drawing_utils.draw_landmarks(

# image=frame,

# landmark_list=faceLandmarks,

# connections=mp.solutions.face_mesh.FACEMESH_TESSELATION,

# landmark_drawing_spec=None,

# connection_drawing_spec=mp.solutions.drawing_utils.DrawingSpec(

# color=(100, 100, 100),

# thickness=1

# )

# # === Best looking translucent mesh ===

# mp.solutions.drawing_utils.draw_landmarks(

# image=frame,

# landmark_list=faceLandmarks,

# connections=mp.solutions.face_mesh.FACEMESH_CONTOURS,

# landmark_drawing_spec=None, # Hide dots

# connection_drawing_spec=mp.solutions.drawing_utils.DrawingSpec(

# color=(0, 0, 180), # Bright cyan/teal

# thickness=7

# )

# # Optional: Extra detail on irises (makes eyes look better)

# mp.solutions.drawing_utils.draw_landmarks(

# image=frame,

# landmark_list=faceLandmarks,

# connections=mp.solutions.face_mesh.FACEMESH_IRISES,

# landmark_drawing_spec=None,

# connection_drawing_spec=mp.solutions.drawing_utils.DrawingSpec(

# color=(255, 0, 0),

# thickness=7

# )

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()

strip.fill(0)

strip.show()

These are the schematics we are using for todays project. If you are taking the class, you should already have these components connected:

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

SSD1306 OLED Connected to the Fusion AI Hat

Technology Tutorials

Tag Archives: Emotion Detector

AI on the Edge LESSON 47: Emotion Detector Using MediaPipe, OpenCV and Raspberry Pi

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