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| # METEHAN AYHAN | |
| import streamlit as st | |
| from PIL import Image | |
| import numpy as np | |
| import tensorflow as tf | |
| model = tf.keras.models.load_model('model.h5') | |
| classes = { 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('German Traffic Sign Recognition - Metehan Ayhan') | |
| st.write("Upload an image of a traffic sign to predict its class.") | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"]) | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption='Uploaded Traffic Sign.', use_column_width=True) | |
| st.write("") | |
| st.write("Classifying...") | |
| image = image.resize((32, 32)) | |
| image = np.array(image) | |
| image = np.expand_dims(image, axis=0) # Modelin beklediği şekil | |
| predictions = model.predict(image) | |
| predicted_class = np.argmax(predictions[0]) | |
| st.write(f"Prediction: {classes[predicted_class]}") | |