add new prediction method

app.py

@@ -1,14 +1,24 @@
 import streamlit as st
+from streamlit_option_menu import option_menu
+from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
+from prediction import predict_emotion
 import eda
 import model_result
-import prediction
-from streamlit_option_menu import option_menu
-
-
 
 st.sidebar.header("Emotion Classification")
 st.title("Facial Emotion Classification")
 
+class EmotionDetectionTransformer(VideoTransformerBase):
+    def transform(self, frame):
+        annotated_frame = predict_emotion(frame)
+        return annotated_frame
+
+def main():
+    st.title('Emotion Detection App')
+    st.write("Press Start")
+
+    webrtc_streamer(key="example", video_transformer_factory=EmotionDetectionTransformer)
+
 with st.sidebar:
     st.write("Ediashta Revindra - FTDS-020")
     selected = option_menu(
@@ -29,6 +39,6 @@ if selected == "Distribution":
 elif selected == "Image Sample":
     eda.samples()
 elif selected == "Model Result":
-    model_result.report()   
+    model_result.report()
 elif selected == "Classification":
-    prediction.predict()
+    main()  # Call the main function for emotion detection

prediction.py

@@ -1,45 +1,41 @@
-import streamlit as st
-import pandas as pd
+import cv2
 import numpy as np
-import pickle
-import tensorflow as tf
-import matplotlib.pyplot as plt
-from tensorflow.keras.layers import Dense, Input, concatenate
-from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image
-from PIL import Image
-from urllib import request
-from io import BytesIO
-
-
-def predict():
-    
-    emotion_classification_model = load_model('./model/model_fine_tune.h5')
-    
-    
-    url = "https://lh3.googleusercontent.com/0e8O0JPOp_ydq7wqv6kgYz6UpF9w_INfnOLIhnJZBEHFcWIygkuLa3SVghhGYgE0XWzQYBPb6wb1eQFN0pVIAYlzEeNojYuCWg=s0"
-    
-    def img_url(url):
-        res = request.urlopen(url).read()
-        img_ori = image.load_img(BytesIO(res))
-        img = image.load_img(BytesIO(res), target_size=(48, 48), keep_aspect_ratio=True)
-        show_predict(img, img_ori)
-        
-    def show_predict(img, img_ori):
-        col1, col2 = st.columns(2)
-        fig = plt.figure()
-        plt.imshow(img_ori)
-        plt.axis('off')
-        col1.pyplot(fig)
-        
-        img_array = image.img_to_array(img)
+from keras.models import load_model
+import pandas as pd
+
+# Load pre-trained emotion classification model
+emotion_classification_model = load_model('.\model\model_fine_tune.h5')  # Replace with actual path
+
+# Load Haarcascades face detection classifier
+face_cascade = cv2.CascadeClassifier('.\model\haarcascade_frontalface_default.xml')
+
+class_labels = ["angry", "disgusted", "fearful", "happy", "neutral", "sad", "surprised"]
+
+def predict_emotion(frame):
+    img = frame.to_ndarray(format="bgr24")
+
+    gray_frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces
+    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    # Process each detected face
+    for (x, y, w, h) in faces:
+        face_img = img[y:y+h, x:x+w]
+
+        # Preprocess the face image
+        resized_face_img = cv2.resize(face_img, (48, 48))
+        img_array = image.img_to_array(resized_face_img)
         img_array = np.expand_dims(img_array, axis=0)
         img_array /= (255*117)  # Normalize the image
 
+        # Perform emotion prediction using the loaded model
         inf_pred_single = emotion_classification_model.predict(img_array)
-        
-        data_inf_single = []
 
+        max_pred_single = np.argsort(inf_pred_single[0])[-2:][::-1]
+
+        data_inf_single = []
         rank = []
 
         for i in inf_pred_single[0]:
@@ -48,57 +44,11 @@ def predict():
             data_inf_single.append(f'{value.round(2)}%')
 
         rank = (-np.array(rank)).argsort()[:2]
-        
+
         pred_class_single = pd.DataFrame(class_labels).loc[rank][0].tolist()
 
-        prediction_result_single = pd.DataFrame(columns=["angry", "disgusted", "fearful", "happy", "neutral", "sad", "surprised"])
-        prediction_result_single.loc[len(prediction_result_single)] = data_inf_single
-
-        prediction_result_single
-        
-        st.markdown("""
-                    <style>
-                    .big-font {
-                        font-size:30px !important;
-                    }
-                    </style>
-                    """, unsafe_allow_html=True)
-        
-        col2.write('Prediction Class:')
-        col2.markdown(f'<p class="big-font">{pred_class_single[0].capitalize()}</p>', unsafe_allow_html=True)
-        
-        col2.dataframe(prediction_result_single.set_index(prediction_result_single.columns[0]), use_container_width=True)
-    
-    class_labels = ["angry", "disgusted", "fearful", "happy", "neutral", "sad", "surprised"]
-
-    st.write('Insert Image URL Below (Make sure face is centered and fitted)')
-    
-    st.markdown('[Example Image](https://cdn.idntimes.com/content-images/community/2021/12/whatsapp-image-2021-12-02-at-190446-8ecf63e1fa6b5c8c5e9ac43034bc86d3-c563813ea99f16a795ad4c53af10881a_600x400.jpeg)')
-    
-    col1, col2 = st.columns((9,1))
-    url_input = col1.text_input(label="Image Links")
-    
-    st.markdown(
-                """
-            <style>
-            button {
-                height: auto;
-                margin-top: 28px !important;
-                padding-left: 24px !important;
-                padding-right: 24px !important;
-            }
-            </style>
-            """,
-                unsafe_allow_html=True,
-            )
-    pred_button = col2.button(label="Predict")
-    
-    if pred_button:
-        img_url(url_input)
-    else:
-        img_url(url)
-    
-    
-
-if __name__ == "__main__":
-    predict()
+        # Draw bounding box and emotion label on the frame
+        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
+        cv2.putText(img, f'Emotion: {pred_class_single[0]}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
+
+    return img  # Return the annotated frame

requirements.txt

@@ -6,4 +6,5 @@ Pillow
 scikit-learn==1.2.2
 feature_engine
 streamlit_option_menu
+streamlit-webrtc
 tensorflow ==2.13.0