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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) | |