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import streamlit as st
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from tensorflow.keras.models import load_model
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from PIL import Image
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import numpy as np
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model=load_model('Capstone_model.h5')
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def process_image(img):
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img=img.resize((100,100))
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img=np.array(img)
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img=img/255.0
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img=np.expand_dims(img,axis=0)
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return img
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st.title('Alman Trafik Isiklarini Tahmin Etme')
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st.write('Resim seç ve hangi trafik işareti olduğunu tahmin etsin')
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file=st.file_uploader('Bir Resim Sec', type=['jpg','jpeg','png'])
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if file is not None:
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img=Image.open(file)
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st.image(img,caption='yuklenen resim')
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image=process_image(img)
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prediction=model.predict(image)
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predicted_class=np.argmax(prediction)
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class_names=['Speed limit (20km/h)','Speed limit (30km/h)', 'Speed limit (50km/h)', 'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)',
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'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)', 'No passing', 'No passing veh over 3.5 tons',
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'Right-of-way at intersection', 'Priority road', 'Yield', 'Stop', 'No vehicles', 'Veh > 3.5 tons prohibited', 'No entry',
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'General caution', 'Dangerous curve left', 'Dangerous curve right', 'Double curve', 'Bumpy road', 'Slippery road', 'Road narrows on the right',
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'Road work', 'Traffic signals', 'Pedestrians', 'Children crossing', 'Bicycles crossing', 'Beware of ice/snow','Wild animals crossing',
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'End speed + passing limits', 'Turn right ahead', 'Turn left ahead', 'Ahead only', 'Go straight or right', 'Go straight or left',
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'Keep right', 'Keep left', 'Roundabout mandatory', 'End of no passing', 'End no passing veh > 3.5 tons']
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st.write(class_names[predicted_class]) |
