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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.")
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