| import streamlit as st |
| import tensorflow as tf |
| import numpy as np |
| from PIL import Image |
|
|
| |
| |
| model = tf.keras.models.load_model('src/traffic_sign_model.keras') |
|
|
| |
| sign_labels = {0:'Speed limit (20km/h)', 1:'Speed limit (30km/h)', 2:'Speed limit (50km/h)', |
| 3:'Speed limit (60km/h)', 4:'Speed limit (70km/h)', 5:'Speed limit (80km/h)', |
| 6:'End of speed limit (80km/h)', 7:'Speed limit (100km/h)', 8:'Speed limit (120km/h)', |
| 9:'No passing', 10:'No passing veh over 3.5 tons', 11:'Right-of-way at intersection', |
| 12:'Priority road', 13:'Yield', 14:'Stop', 15:'No vehicles', 16:'Veh > 3.5 tons prohibited', |
| 17:'No entry', 18:'General caution', 19:'Dangerous curve left', 20:'Dangerous curve right', |
| 21:'Double curve', 22:'Bumpy road', 23:'Slippery road', 24:'Road narrows on the right', |
| 25:'Road work', 26:'Traffic signals', 27:'Pedestrians', 28:'Children crossing', |
| 29:'Bicycles crossing', 30:'Beware of ice/snow', 31:'Wild animals crossing', |
| 32:'End speed + passing limits', 33:'Turn right ahead', 34:'Turn left ahead', 35:'Ahead only', |
| 36:'Go straight or right', 37:'Go straight or left', 38:'Keep right', 39:'Keep left', |
| 40:'Roundabout mandatory', 41:'End of no passing', 42:'End no passing veh > 3.5 tons'} |
|
|
| st.title("Traffic Sign Classifier") |
| uploaded_file = st.file_uploader("Choose a traffic sign image...", type=["jpg", "png"]) |
|
|
| if uploaded_file is not None: |
| image = Image.open(uploaded_file) |
| st.image(image, caption='Uploaded Image', use_column_width=True) |
| |
| |
| image = image.resize((224, 224)) |
| img_array = np.array(image) / 255.0 |
| img_array = np.expand_dims(img_array, axis=0) |
| |
| |
| predictions = model.predict(img_array) |
| predicted_class = np.argmax(predictions, axis=1)[0] |
| |
| st.write(f"Prediction: {sign_labels[predicted_class]}") |
