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import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
model = load_model("model_traffic_sign.h5")
def process_image(img):
img = img.convert('RGB')
img = img.resize((30,30))
img = np.array(img)
if img.ndim == 2:
img = np.stack((img,)*3, axis=-1) # Convert grayscale to RGB if needed
img = img/255.0
img = np.expand_dims(img, axis=0)
return img
st.title("TRAFFIC SIGN CLASSIFICATION:small_red_triangle:")
st.header("Discover what each traffic sign represents!")
st.write("Upload your image and see the results")
file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png", "webp"])
if file is not None:
img = Image.open(file)
st.image(img, caption="Downloaded image")
image = process_image(img)
prediction = model.predict(image)
predicted_class = np.argmax(prediction)
class_names = { 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.write(f"Predicted Traffic Sign: {class_names[predicted_class]}") |