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import onnxruntime as ort
import numpy as np
import cv2
from PIL import Image
import streamlit as st
# Load ONNX model
onnx_model = ort.InferenceSession("onnx_model.onnx")
# Preprocess image function
def preprocess_image(image):
"""Preprocess image to match model input requirements"""
# Convert the image to grayscale
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
# Resize image to 48x48 (model's expected input size)
image_resized = cv2.resize(image, (48, 48))
# Add batch dimension and channels (for grayscale: 1 channel)
image_input = np.expand_dims(image_resized, axis=0) # Add batch dimension (1, 48, 48)
image_input = np.expand_dims(image_input, axis=1) # Add channel dimension (1, 1, 48, 48)
# Normalize the image
image_input = image_input.astype(np.float32) / 255.0
return image_input
# Predict emotion using the ONNX model
def predict_emotion_onnx(onnx_model, image_input):
input_name = onnx_model.get_inputs()[0].name
output_name = onnx_model.get_outputs()[0].name
prediction = onnx_model.run([output_name], {input_name: image_input})
return prediction
# Streamlit interface
st.title("Emotion Recognition with ONNX")
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Preprocess the image
image_input = preprocess_image(image)
# Predict the emotion
emotion_prediction = predict_emotion_onnx(onnx_model, image_input)
# Display the prediction
st.write(f"Predicted Emotion: {emotion_prediction[0]}")
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