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live_cam_predict.py
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live_cam_predict.py
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import cv2
import numpy as np
from keras.models import model_from_json
from keras.preprocessing import image
# Load model from JSON file
json_file = open('top_models\\fer.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# Load weights and them to model
model.load_weights('top_models\\fer.h5')
face_haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
while True:
ret, img = cap.read()
if not ret:
break
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces_detected = face_haar_cascade.detectMultiScale(gray_img, 1.1, 6, minSize=(150, 150))
for (x, y, w, h) in faces_detected:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), thickness=2)
roi_gray = gray_img[y:y + w, x:x + h]
roi_gray = cv2.resize(roi_gray, (48, 48))
img_pixels = image.img_to_array(roi_gray)
img_pixels = np.expand_dims(img_pixels, axis=0)
img_pixels /= 255.0
predictions = model.predict(img_pixels)
max_index = int(np.argmax(predictions))
emotions = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear']
predicted_emotion = emotions[max_index]
cv2.putText(img, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), 2)
resized_img = cv2.resize(img, (1000, 700))
cv2.imshow('Facial Emotion Recognition', resized_img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()