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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("model_traffic_sign.h5") |
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def process_image(img): |
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img = img.convert('RGB') |
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img = img.resize((30,30)) |
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img = np.array(img) |
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if img.ndim == 2: |
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img = np.stack((img,)*3, axis=-1) |
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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("TRAFFIC SIGN CLASSIFICATION:small_red_triangle:") |
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st.header("Identify what each traffic sign means!") |
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st.write("Upload your image and see the results") |
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file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png", "webp"]) |
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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="Downloaded image") |
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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 = { 0:'Speed limit (20km/h)', |
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1:'Speed limit (30km/h)', |
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2:'Speed limit (50km/h)', |
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3:'Speed limit (60km/h)', |
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4:'Speed limit (70km/h)', |
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5:'Speed limit (80km/h)', |
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6:'End of speed limit (80km/h)', |
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7:'Speed limit (100km/h)', |
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8:'Speed limit (120km/h)', |
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9:'No passing', |
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10:'No passing veh over 3.5 tons', |
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11:'Right-of-way at intersection', |
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12:'Priority road', |
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13:'Yield', |
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14:'Stop', |
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15:'No vehicles', |
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16:'Veh > 3.5 tons prohibited', |
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17:'No entry', |
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18:'General caution', |
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19:'Dangerous curve left', |
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20:'Dangerous curve right', |
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21:'Double curve', |
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22:'Bumpy road', |
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23:'Slippery road', |
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24:'Road narrows on the right', |
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25:'Road work', |
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26:'Traffic signals', |
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27:'Pedestrians', |
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28:'Children crossing', |
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29:'Bicycles crossing', |
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30:'Beware of ice/snow', |
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31:'Wild animals crossing', |
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32:'End speed + passing limits', |
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33:'Turn right ahead', |
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34:'Turn left ahead', |
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35:'Ahead only', |
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36:'Go straight or right', |
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37:'Go straight or left', |
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38:'Keep right', |
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39:'Keep left', |
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40:'Roundabout mandatory', |
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41:'End of no passing', |
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42:'End no passing veh > 3.5 tons' } |
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st.write(f"Predicted Traffic Sign: {class_names[predicted_class]}") |
