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import streamlit as st
import numpy as np
from keras.models import load_model
from PIL import Image
import tensorflow as tf
import cv2
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:'Vehicle > 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 vehicle > 3.5 tons' }
def image_processing(img):
model = load_model('TSR.h5')
image = Image.open(img)
image = image.resize((30,30))
image = np.expand_dims(image, axis=0)
image = np.array(image)
predict_x=model.predict(image)
classes_x=np.argmax(predict_x,axis=1)
sign = classes[int(classes_x)]
return sign
st.title('Traffic Sign Recognition')
st.write('This is a simple image classification web app to predict traffic signs.')
st.write('Please upload an image file to classify.')
file = st.file_uploader("Please upload an image file", type=["jpg", "png"])
if file is None:
st.text("You haven't uploaded an image file")
else:
image = Image.open(file)
st.image(image, caption='Uploaded Image.', use_column_width=True)
st.write("")
label = image_processing(file)
st.success('This image most likely belongs to {}.'.format(label, 100))
st.write('Done!')
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