app.py

# import numpy as np
# import cv2
# import streamlit as st
# from tensorflow import keras
# from keras.models import model_from_json
# from tensorflow.keras.utils import img_to_array
# from streamlit_webrtc import webrtc_streamer, VideoTransformerBase, RTCConfiguration, VideoProcessorBase, WebRtcMode
import numpy as np
import tensorflow as tf
from PIL import Image
import cv2
import streamlit as st
from tensorflow import keras
from keras.models import model_from_json
from tensorflow.keras.utils import  img_to_array
from streamlit_webrtc import webrtc_streamer, VideoTransformerBase, RTCConfiguration, VideoProcessorBase, WebRtcMode





# load model

emotion_dict = {0:'angry', 1 :'happy', 2: 'neutral', 3:'sad', 4: 'surprise'}
# load json and create model
json_file = open('emotion_model1.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
classifier = model_from_json(loaded_model_json)
# load weights into new model
classifier.load_weights("emotion_model1.h5")

#load face
try:
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
except Exception:
    st.write("Error loading cascade classifiers")

RTC_CONFIGURATION = RTCConfiguration({"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]})

class Faceemotion(VideoTransformerBase):
    def transform(self, frame):
        img = frame.to_ndarray(format="bgr24")

        #image gray
        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=(255, 0, 0), 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_dict[maxindex]
                output = str(finalout)
            label_position = (x, y)
            cv2.putText(img, 'i', label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

        return img


def generate_prediction(input_image):
    # img = frame.to_ndarray(format="bgr24")

        #image gray
    img = input_image
    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=(255, 0, 0), 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_dict[maxindex]
            output = str(finalout)
        label_position = (x, y)
        cv2.putText(img, output, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    return img

def main():
    # Face Analysis Application #
    st.title(" Face Emotion Detection Application")
    activiteis = ["Home", "Webcam Face Detection", "By Images","About"]
    choice = st.sidebar.selectbox("Select Activity", activiteis)
        
    if choice == "Home":
        html_temp_home1 = """<div style="background-color:#6D7B8D;padding:10px">
                                            <h3 style="color:yellow;text-align:center;"> Welcome to world of AI with Prince </h3>
                                            <h4 style="color:white;text-align:center;">
                                            Face Emotion detection application using OpenCV, Custom CNN model and Streamlit.</h4>
                                            </div>
                                            </br>"""
        st.markdown(html_temp_home1, unsafe_allow_html=True)
        st.write("""
                  Real time face emotion recognization just by one click.

                 """)
    elif choice == "Webcam Face Detection":
        st.header("Webcam Live Feed")
        st.write("Click on start to use webcam and detect your face emotion")
        webrtc_streamer(key="example", mode=WebRtcMode.SENDRECV, rtc_configuration=RTC_CONFIGURATION,
                        video_processor_factory=Faceemotion)
        # st.video('https://www.youtube.com/watch?v=wyWmWaXapmI')
        
    elif choice == "By Images":
        st.header("Image Prediction App")
        uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
        if uploaded_file is not None:
            image = np.array(Image.open(uploaded_file))
            
            prediction = generate_prediction(image)
            st.image(prediction, use_column_width=True)

    elif choice == "About":
        st.subheader("About this app")
        html_temp_about1= """<div style="background-color:#6D7B8D;padding:10px">
                                    <h4 style="color:white;text-align:center;">
                                    Real time face emotion detection application using OpenCV, Custom Trained CNN model and Streamlit.</h4>
                                    </div>
                                    </br>"""
        st.markdown(html_temp_about1, unsafe_allow_html=True)

        html_temp4 = """
                             		<div style="background-color:#98AFC7;padding:10px">
                             		<h4 style="color:white;text-align:center;">Thanks for Visiting</h4>
                             		</div>
                             		<br></br>
                             		<br></br>"""

        st.markdown(html_temp4, unsafe_allow_html=True)

    else:
        pass


if __name__ == "__main__":
    main()