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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| import time | |
| # Larger title | |
| st.markdown("<h1 style='text-align: center;'>Emotion Detection</h1>", unsafe_allow_html=True) | |
| # Smaller subtitle | |
| st.markdown("<h3 style='text-align: center;'>angry, fear, happy, neutral, sad, surprise</h3>", unsafe_allow_html=True) | |
| start = time.time() | |
| from keras.models import load_model | |
| import tempfile | |
| from PIL import Image | |
| def load_emotion_model(): | |
| model = load_model('CNN_Model_acc_75.h5') | |
| return model | |
| # Load the model | |
| model = load_emotion_model() | |
| print("time taken to load model : " , time.time() - start) | |
| img_shape = 48 | |
| emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise'] | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| def process_frame(frame): | |
| gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) | |
| for (x, y, w, h) in faces: | |
| roi_gray = gray_frame[y:y+h, x:x+w] | |
| roi_color = frame[y:y+h, x:x+w] | |
| face_roi = cv2.resize(roi_color, (img_shape, img_shape)) | |
| face_roi = np.expand_dims(face_roi, axis=0) | |
| face_roi = face_roi / float(img_shape) | |
| predictions = model.predict(face_roi) | |
| emotion = emotion_labels[np.argmax(predictions[0])] | |
| cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) | |
| cv2.putText(frame, emotion, (x, y+h), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) | |
| return frame | |
| # def video_feed(video_source): | |
| # # Read and process video frames | |
| # while True: | |
| # ret, frame = video_source.read() | |
| # if not ret: | |
| # break | |
| # frame = process_frame(frame) | |
| # st.image(frame, channels="BGR") | |
| def video_feed(video_source): | |
| # Create a placeholder to display the frames | |
| frame_placeholder = st.empty() # This placeholder will be used to replace frames in-place | |
| while True: | |
| ret, frame = video_source.read() | |
| if not ret: | |
| break | |
| frame = process_frame(frame) | |
| # Display the frame in the placeholder | |
| frame_placeholder.image(frame, channels="BGR", use_column_width=True) | |
| # Sidebar for video or image upload | |
| upload_choice = st.sidebar.radio("Choose input source", [ "Upload Video", "Upload Image" ,"Camera"]) | |
| if upload_choice == "Camera": | |
| # Access camera | |
| video_source = cv2.VideoCapture(0) | |
| video_feed(video_source) | |
| elif upload_choice == "Upload Video": | |
| uploaded_video = st.file_uploader("Upload Video", type=["mp4", "mov", "avi", "mkv", "webm"]) | |
| if uploaded_video: | |
| # Temporarily save the video to disk | |
| with tempfile.NamedTemporaryFile(delete=False) as tfile: | |
| tfile.write(uploaded_video.read()) | |
| video_source = cv2.VideoCapture(tfile.name) | |
| video_feed(video_source) | |
| elif upload_choice == "Upload Image": | |
| uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg", "gif"]) | |
| if uploaded_image: | |
| image = Image.open(uploaded_image) | |
| frame = np.array(image) | |
| frame = process_frame(frame) | |
| st.image(frame, caption='Processed Image', use_column_width=True) | |
| st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise") | |