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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
@st.cache_resource
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")
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