Update app.py

app.py

@@ -1,151 +1,175 @@
+# # 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
-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)
+
+
+
+
+
+# # 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)
+
+#         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>"""
 
-    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)
+#         st.markdown(html_temp4, 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>"""
+#     else:
+#         pass
 
-        st.markdown(html_temp4, unsafe_allow_html=True)
 
-    else:
-        pass
+# if __name__ == "__main__":
+#     main()
 
 
-if __name__ == "__main__":
-    main()
+
+# import tensorflow as tf
+import cv2
+import numpy as np
+# from glob import glob
+# from models import Yolov4
+import gradio as gr
+# model = Yolov4(weight_path="yolov4.weights", class_name_path='coco_classes.txt')
+def gradio_wrapper(img):
+    global model
+    #print(np.shape(img))
+    # results = model.predict(img)
+    # return results[0]
+demo = gr.Interface(
+    gradio_wrapper,
+    #gr.Image(source="webcam", streaming=True, flip=True),
+    gr.Image(source="webcam", streaming=True),
+    "image",
+    live=True
+)
+
+demo.launch()