Spaces:
Runtime error
Runtime error
| # import the rquired libraries. | |
| import numpy as np | |
| import cv2 | |
| from keras.models import load_model | |
| import streamlit as st | |
| from tensorflow import keras | |
| from tensorflow.keras.preprocessing.image import img_to_array | |
| from streamlit_webrtc import webrtc_streamer, VideoTransformerBase, RTCConfiguration, VideoProcessorBase, WebRtcMode | |
| emotion_labels = ['Angry','Disgust','Fear','Happy','Neutral', 'Sad', 'Surprise'] | |
| classifier =load_model('model_78.h5') | |
| classifier.load_weights("model_weights_78.h5") | |
| try: | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| except Exception: | |
| st.write("Error loading cascade classifiers") | |
| class VideoTransformer(VideoTransformerBase): | |
| def transform(self, frame): | |
| img = frame.to_ndarray(format="bgr24") | |
| img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| faces = face_cascade.detectMultiScale( | |
| image=img_gray, scaleFactor=1.3, minNeighbors=5) | |
| for (x, y, w, h) in faces: | |
| cv2.rectangle(img=img, pt1=(x, y), pt2=( | |
| x + w, y + h), color=(0, 255, 255), thickness=2) | |
| roi_gray = img_gray[y:y + h, x:x + w] | |
| roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA) | |
| if np.sum([roi_gray]) != 0: | |
| roi = roi_gray.astype('float') / 255.0 | |
| roi = img_to_array(roi) | |
| roi = np.expand_dims(roi, axis=0) | |
| prediction = classifier.predict(roi)[0] | |
| maxindex = int(np.argmax(prediction)) | |
| finalout = emotion_labels[maxindex] | |
| output = str(finalout) | |
| label_position = (x, y-10) | |
| cv2.putText(img, output, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) | |
| return img | |
| def main(): | |
| # Face Analysis Application # | |
| st.title("Real Time Face Emotion Detection Application 😠🤮😨😀😐😔😮") | |
| activiteis = ["Home", "Live Face Emotion Detection"] | |
| choice = st.sidebar.selectbox("Select Activity", activiteis) | |
| # Homepage. | |
| if choice == "Home": | |
| html_temp_home1 = """<div style="background-color:#FC4C02;padding:0.5px"> | |
| <h4 style="color:white;text-align:center;"> | |
| Start Real Time Face Emotion Detection. | |
| </h4> | |
| </div> | |
| </br>""" | |
| st.markdown(html_temp_home1, unsafe_allow_html=True) | |
| st.write(""" | |
| How to use... | |
| 1. Click the dropdown list in the top left corner and select Live Face Emotion Detection. | |
| 2. This takes you to a page which will tell if it recognizes your emotions. | |
| """) | |
| # Live Face Emotion Detection. | |
| elif choice == "Live Face Emotion Detection": | |
| st.header("Webcam Live Feed") | |
| st.subheader(''' | |
| Welcome to the other side of the SCREEN!!! | |
| * Get ready with all the emotions you can express. | |
| ''') | |
| st.write("1. Click Start to open your camera and give permission for prediction") | |
| st.write("2. This will predict your emotion.") | |
| st.write("3. When you done, click stop to end.") | |
| webrtc_streamer(key="example", video_processor_factory=VideoTransformer) | |
| else: | |
| pass | |
| if __name__ == "__main__": | |
| main() |