Spaces:
Running
Running
| import cv2 | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.keras.models import model_from_json | |
| import streamlit as st | |
| from streamlit_webrtc import VideoTransformerBase, webrtc_streamer | |
| from PIL import Image | |
| # Load model | |
| with open("jsn_model.json", "r") as json_file: | |
| loaded_model_json = json_file.read() | |
| model = model_from_json(loaded_model_json) | |
| model.load_weights('weights_model1.h5') | |
| # Loading the classifier from the file. | |
| face_haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| UPLOAD_FOLDER = 'static' | |
| ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'} | |
| def allowed_file(filename): | |
| """Checks the file format when file is uploaded""" | |
| return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS | |
| def Emotion_Analysis(image): | |
| """It does prediction of Emotions found in the Image provided, saves as Images and returns them""" | |
| gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| faces = face_haar_cascade.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5) | |
| if len(faces) == 0: | |
| return image, None | |
| for (x, y, w, h) in faces: | |
| roi = gray_frame[y:y + h, x:x + w] | |
| roi = cv2.resize(roi, (48, 48)) | |
| roi = roi.astype("float") / 255.0 | |
| roi = tf.expand_dims(roi, axis=-1) # Adding channel dimension | |
| roi = np.expand_dims(roi, axis=0) # Adding batch dimension | |
| prediction = model.predict(roi) | |
| EMOTIONS_LIST = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"] | |
| rec_col = {"Happy": (0, 255, 0), "Sad": (255, 0, 0), "Surprise": (255, 204, 55), | |
| "Angry": (0, 0, 255), "Disgust": (230, 159, 0), "Neutral": (0, 255, 255), "Fear": (128, 0, 128)} | |
| pred_emotion = EMOTIONS_LIST[np.argmax(prediction)] | |
| Text = str(pred_emotion) | |
| cv2.rectangle(image, (x, y), (x + w, y + h), rec_col[str(pred_emotion)], 2) | |
| cv2.rectangle(image, (x, y - 40), (x + w, y), rec_col[str(pred_emotion)], -1) | |
| cv2.putText(image, Text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) | |
| return image, pred_emotion | |
| class EmotionDetector(VideoTransformerBase): | |
| def transform(self, frame): | |
| image = frame.to_ndarray(format="bgr24") | |
| result_image, _ = Emotion_Analysis(image) | |
| return result_image | |
| st.title('Emotion Detection App') | |
| st.sidebar.title("Options") | |
| # Options for manual upload or webcam capture | |
| upload_option = st.sidebar.selectbox("Choose Upload Option", ["Image Upload", "Webcam"]) | |
| if upload_option == "Image Upload": | |
| uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["png", "jpg", "jpeg", "gif"]) | |
| if uploaded_file is not None and allowed_file(uploaded_file.name): | |
| image = Image.open(uploaded_file) | |
| image = np.array(image.convert('RGB')) # Ensure image is in RGB format | |
| result = Emotion_Analysis(image) | |
| if result is None: | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| st.error("No face detected") | |
| else: | |
| processed_image, pred_emotion = result | |
| st.image(processed_image, caption=f"Predicted Emotion: {pred_emotion}", use_column_width=True) | |
| elif upload_option == "Webcam": | |
| st.sidebar.write("Webcam Capture") | |
| webrtc_streamer(key="example", video_transformer_factory=EmotionDetector) | |
| else: | |
| st.write("Please select an option to start.") | |