Upload emotion.py

emotion.py

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+import cv2
+from deepface import DeepFace
+
+# Load the pre-trained emotion detection model
+model = DeepFace.build_model("Emotion")
+
+# Define emotion labels
+emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
+
+# Load face cascade classifier
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# Start capturing video
+cap = cv2.VideoCapture(0)
+
+while True:
+    # Capture frame-by-frame
+    ret, frame = cap.read()
+
+    # Convert frame to grayscale
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the frame
+    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    for (x, y, w, h) in faces:
+        # Extract the face ROI (Region of Interest)
+        face_roi = gray_frame[y:y + h, x:x + w]
+
+        # Resize the face ROI to match the input shape of the model
+        resized_face = cv2.resize(face_roi, (48, 48), interpolation=cv2.INTER_AREA)
+
+        # Normalize the resized face image
+        normalized_face = resized_face / 255.0
+
+        # Reshape the image to match the input shape of the model
+        reshaped_face = normalized_face.reshape(1, 48, 48, 1)
+
+        # Predict emotions using the pre-trained model
+        preds = model.predict(reshaped_face)[0]
+        emotion_idx = preds.argmax()
+        emotion = emotion_labels[emotion_idx]
+
+        # Draw rectangle around face and label with predicted emotion
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
+        cv2.putText(frame, emotion, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
+
+    # Display the resulting frame
+    cv2.imshow('Real-time Emotion Detection', frame)
+
+    # Press 'q' to exit
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release the capture and close all windows
+cap.release()
+cv2.destroyAllWindows()