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app.py

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+import cv2
+import numpy as np
+import io
+import PIL
+from base64 import b64decode, b64encode
+from keras.models import load_model
+import streamlit as st
+from streamlit_webrtc import webrtc_streamer, VideoProcessorBase
+
+# Initialize the Haar Cascade face detection model
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+model = load_model('emotion_model.h5',compile=False)
+emotion_dict = {0: "Angry", 1: "Disgust", 2: "Fear", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+# Define functions to convert between JavaScript image reply and OpenCV image
+def js_to_image(js_reply):
+    image_bytes = b64decode(js_reply.split(',')[1])
+    jpg_as_np = np.frombuffer(image_bytes, dtype=np.uint8)
+    img = cv2.imdecode(jpg_as_np, flags=1)
+    return img
+
+def bbox_to_bytes(bbox_array):
+    bbox_PIL = PIL.Image.fromarray(bbox_array, 'RGBA')
+    iobuf = io.BytesIO()
+    bbox_PIL.save(iobuf, format='png')
+    bbox_bytes = 'data:image/png;base64,{}'.format((str(b64encode(iobuf.getvalue()), 'utf-8')))
+    return bbox_bytes
+
+# Define function to process each frame from the video stream
+def process_frame(frame):
+    # Convert frame to grayscale
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Perform face detection
+    faces = face_cascade.detectMultiScale(gray)
+
+    emotions = []
+
+    # Process each detected face
+    for (x, y, w, h) in faces:
+        face_region = gray[y:y+h, x:x+w]
+        face_resized = cv2.resize(face_region, (48, 48))
+        img = np.expand_dims(face_resized, axis=0)
+        img = np.expand_dims(img, axis=-1)
+        predictions = model.predict(img)
+        emo = model.predict(img)[0]
+        emotions.append(emo)
+        predicted_class = np.argmax(predictions)
+        predicted_emotion = emotion_dict[predicted_class]
+        accuracy = predictions[0][predicted_class]
+
+        # Draw bounding box and emotion label on the frame
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+        cv2.putText(frame, f"{predicted_emotion} ({accuracy:.2f})", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 2)
+
+    return frame, emotions
+
+
+class VideoProcessor(VideoProcessorBase):
+    def recv(self, frame):
+        img = frame.to_ndarray(format="bgr24")
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray)
+        
+        for (x, y, w, h) in faces:
+            face_region = gray[y:y+h, x:x+w]
+            face_resized = cv2.resize(face_region, (48, 48))
+            img_array = np.expand_dims(face_resized, axis=0)
+            img_array = np.expand_dims(img_array, axis=-1)
+            predictions = model.predict(img_array)
+            predicted_class = np.argmax(predictions)
+            predicted_emotion = emotion_dict[predicted_class]
+            accuracy = predictions[0][predicted_class]
+            
+            cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
+            cv2.putText(img, f"{predicted_emotion} ({accuracy:.2f})", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 2)
+        
+        return frame.from_ndarray(img, format="bgr24")
+
+
+
+
+
+# Page Title and Description
+st.set_page_config(page_title="Facial Emotion Recognition", layout="wide")
+st.title("Facial Emotion Recognition")
+
+# Sidebar
+st.sidebar.title("Options")
+option = st.sidebar.radio("Select Option", ("Drag a File","Process Video"))
+
+# Main Content Area
+if option == "Drag a File" :
+    st.subheader("Photo Processing")
+    
+    # Process image or captured frame
+    if option == "Drag a File":
+        uploaded_file = st.file_uploader("Upload Photo", type=["jpg", "jpeg", "png"])
+        if uploaded_file is not None:
+            file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+            image = cv2.imdecode(file_bytes, 1)
+    
+    if 'image' in locals():
+        processed_frame, emotions = process_frame(image)
+        # Display processed frame and emotions
+        st.subheader("Processed Frame")
+        st.image(processed_frame, channels="BGR", use_column_width=False)
+        if not emotions:
+            st.warning("No faces detected in the image.")
+elif option == "Process Video":
+    webrtc_streamer(key="camera", video_processor_factory=VideoProcessor)
+
+
+    

emotion_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df5da9f5739b0cae3008d1c12e902ab55074f20bc60be0e7847bd34378bbadca
+size 48573144

requirements.txt

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+opencv-python-headless
+numpy
+pillow
+keras
+streamlit
+tensorflow
+streamlit-webrtc