Inspirit AI Scholars Day 8

We spent our day playing around with a pre-trained deep learning model and trained the model to perform emotion detection. We used the Haar Cascade, which is the pre-trained deep-learning model and changed it to perform emotion detection.

Here is our code:

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import cv2

from deepface import DeepFace

# Load the pre-trained model

model = DeepFace.build_model('Emotion')

# Load the Haar Cascade for face detection

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Initialize the video capture

cap = cv2.VideoCapture(0)

# Main program loop

while True:

ret, frame = cap.read()

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

# Perform face detection using the Haar Cascade

faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

# Iterate through the detected faces (still in the main program loop!)

for (x, y, w, h) in faces:

# Extract the face region of interest (ROI)

roi = gray[y:y+h, x:x+w]

# Perform emotion detection using the pre-trained model (in the for loop)

emotion = DeepFace.analyze(img_path=None, img=roi, actions=['emotion'], enforce_detection=False)

emotion_label = emotion['emotion']

# Draw a rectangle around the face and display the detected emotion

cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

cv2.putText(frame, emotion_label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

# Display the resulting frame (after exiting the for loop)

cv2.imshow('Emotion Detection', frame)

# Break the loop when 'q' is pressed

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

break

cap.release()

cv2.destroyAllWindows()

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We plan on using this model in our presentation as well! See you next time!