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import streamlit as st
import cv2
import numpy as np
from PIL import Image
# Set the page config
st.set_page_config(page_title="Emotion Recognition App", layout="centered")
st.title("Emotion Recognition App")
# Upload an image
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
# Load OpenCV's face detection model
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
# Load ONNX emotion detection model
emotion_model_path = "emotion_recognition.onnx" # Replace with your model path
emotion_net = cv2.dnn.readNetFromONNX(emotion_model_path)
# Emotion labels (based on model documentation)
emotion_labels = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
# Resize image to reduce memory usage
def resize_image(image, max_size=(800, 800)):
"""
Resizes the image to the specified maximum size while maintaining aspect ratio.
"""
image.thumbnail(max_size, Image.Resampling.LANCZOS)
return image
# Process the uploaded image
if uploaded_file is not None:
# Check file size to prevent loading large images
if uploaded_file.size > 10 * 1024 * 1024: # 10 MB limit
st.error("File too large. Please upload an image smaller than 10 MB.")
else:
# Open and resize the image
image = Image.open(uploaded_file)
image = resize_image(image)
# Convert image to numpy array
image_np = np.array(image)
# Convert image to grayscale for face detection
gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
if len(faces) > 0:
for (x, y, w, h) in faces:
# Extract face ROI
face_roi = image_np[y:y+h, x:x+w]
face_blob = cv2.dnn.blobFromImage(face_roi, 1.0, (64, 64), (104, 117, 123), swapRB=True)
# Predict emotion
emotion_net.setInput(face_blob)
predictions = emotion_net.forward()
emotion_idx = np.argmax(predictions)
emotion = emotion_labels[emotion_idx]
# Draw rectangle around the face
cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2)
# Display emotion
cv2.putText(
image_np,
emotion,
(x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.9,
(255, 0, 0),
2,
)
# Display the processed image
st.image(image_np, caption="Processed Image", use_column_width=True)
else:
st.warning("No faces detected in the image.")
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