Upload emotion_app.py

emotion_app.py

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+import cv2
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import img_to_array
+
+# Load your pre-trained model
+model = tf.keras.models.load_model('Final_Resnet50_Best_model.keras')
+
+# Emotion labels
+emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
+
+# Initialize the face classifier
+face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+
+cap = cv2.VideoCapture(0)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_classifier.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),
+                                             flags=cv2.CASCADE_SCALE_IMAGE)
+
+    for (x, y, w, h) in faces:
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        face = gray[y:y + h, x:x + w]
+        face = cv2.resize(face, (224, 224))
+        face = face.astype("float") / 255.0
+        face = img_to_array(face)
+        face = np.expand_dims(face, axis=0)
+
+        prediction = model.predict(face)[0]
+        emotion = emotion_labels[np.argmax(prediction)]
+
+        cv2.putText(frame, emotion, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+
+    cv2.imshow("Emotion Detector", frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()