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Runtime error
| import cv2 | |
| import streamlit as st | |
| import tensorflow as tf | |
| import numpy as np | |
| trained_model = tf.keras.models.load_model('facial_emotions_model.h5') | |
| classes=['Angry','Disgust','Fear','Happy','Neutral','Sad','Surprise'] | |
| st.title('Emotion Detection') | |
| # run = st.checkbox('Run') | |
| frame_window = st.image([]) | |
| frame_window2 = st.image([]) | |
| cam = cv2.VideoCapture(0) | |
| # pred = 'Detecting' | |
| a = st.empty() | |
| face_cascade = cv2.CascadeClassifier('haarcascade.xml') | |
| # a.text_area(value='detecting', label='') | |
| while True: | |
| ret, frame = cam.read() | |
| if frame: | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| faces = face_cascade.detectMultiScale(gray, 1.1, 4) | |
| frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| frame_window2.image(frame) | |
| for (x, y, w, h) in faces: | |
| cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2) | |
| frame = frame[y:y+h, x:x+h] | |
| if frame.any(): | |
| resized = cv2.resize(frame, (48, 48)) | |
| resized_gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY) | |
| reshaped_array = np.reshape(resized_gray, (1, 48, 48, 1)) | |
| result=trained_model.predict(reshaped_array) | |
| y_pred=np.argmax(result[0]) | |
| pred = classes[y_pred] | |
| # print('The person facial emotion is:',classes[y_pred]) | |
| a.write(pred) | |
| # a = st.empty() | |
| frame_window.image(resized_gray) | |