Update app.py

app.py

@@ -1,154 +1,154 @@
-# # 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 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)
+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')
+    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))
+    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)
+            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)
+    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>"""
+        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)
+        st.markdown(html_temp4, unsafe_allow_html=True)
 
-#     else:
-#         pass
+    else:
+        pass
 
 
-# if __name__ == "__main__":
-#     main()
+if __name__ == "__main__":
+    main()