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