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e813cf8 263cdb7 e813cf8 0aacbaf e813cf8 22b8900 5720000 e813cf8 4d27746 e813cf8 f33e47e e813cf8 81caf3d e813cf8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | import numpy as np
import streamlit as st
import cv2 as cv
from PIL import Image
from keras.models import load_model
from keras.layers import TFSMLayer
# Label traffic signs
labels_dict = {
0: '274. Speed limit (20km/h)',
1: '274. Speed limit (30km/h)',
2: '274. Speed limit (50km/h)',
3: '274. Speed limit (60km/h)',
4: '274. Speed limit (70km/h)',
5: '274. Speed limit (80km/h)',
6: '278. End of speed limit (80km/h)',
7: '274. Speed limit (100km/h)',
8: '274. Speed limit (120km/h)',
9: '276. No passing',
10: '277. No passing vehicle over 3.5 tons',
11: '301. Right-of-way at intersection',
12: '306. Priority road',
13: '205. Yield',
14: '206. Stop',
15: '250. No vehicles',
16: '253. Vehicle > 3.5 tons prohibited',
17: '267. No entry',
18: '101. General caution',
19: '104. Dangerous curve left',
20: '103. Dangerous curve right',
21: '105-10. Double curve',
22: '112. Bumpy road',
23: '114. Slippery road',
24: '121. Road narrows on the right',
25: '123. Road work',
26: '131. Traffic signals',
27: '133. Pedestrians',
28: '136. Children crossing',
29: '138. Bicycles crossing',
30: '101-51. Beware of ice/snow',
31: '142. Wild animals crossing',
32: '282. End speed + passing limits',
33: '209-20. Turn right ahead',
34: '209-10. Turn left ahead',
35: '209-30. Ahead only',
36: '214-20. Go straight or right',
37: '214-10. Go straight or left',
38: '222-20. Keep right',
39: '222-10. Keep left',
40: '215. Roundabout mandatory',
41: '280. End of no passing',
42: '281. End no passing vehicle > 3.5 tons'
}
# model = tf.keras.models.load_model('keras_model/')
# # model = TFSMLayer('keras_model/', call_endpoint='serving_default')
# model = load_model('keras_model/')''
model = TFSMLayer('keras_model/', call_endpoint='serving_default')
@st.cache(allow_output_mutation=True)
def sign_predict(image):
image = np.array(image, dtype=np.float32)
image = image/255
image = np.reshape(image, (1, 32, 32))
x = image.astype(np.float32)
prediction = model.predict(x)
prediction_max = np.argmax(prediction)
prediction_label = labels_dict[prediction_max]
confidence = np.max(prediction)
return prediction_label, confidence
def main():
# Set page config and markdowns
st.set_page_config(page_title='Traffic Signs Classifier', page_icon=':car:')
st.title('Traffic Signs Classifier')
st.markdown("""
This application classifies traffic signs. Upload any photo of a traffic sign
and receive its name out of 43 present classes. For getting the correct prediction,
try to upload a square picture containing only the sign.
""")
with st.expander("See list of classes"):
st.write(list(labels_dict.values()))
st.image('./app_images/road_sign.jpg', use_column_width=True)
image_usr = st.file_uploader('Upload a photo of traffic sign here', type=['jpg', 'jpeg', 'png'])
if image_usr is not None:
col1, col2 = st.columns(2)
col1.markdown('#### Your picture')
col2.markdown('#### Your picture 32x32 gray')
image = Image.open(image_usr)
with col1:
st.image(image, use_column_width=True)
image_np = np.array(image.convert('RGB'))
image_col = cv.cvtColor(image_np, 1)
image_gray = cv.cvtColor(image_col, cv.COLOR_BGR2GRAY)
image_32 = cv.resize(image_gray, (32, 32))
with col2:
st.image(image_32, use_column_width=True)
# Make prediction
prediction_label, confidence = sign_predict(image_32)
st.write('##### Prediction:', prediction_label)
st.write('##### Confidence:', str(confidence))
st.markdown('***')
# Markdowns
st.subheader('About this app')
st.markdown("""
The app uses an implementation of LeNet-5 Convolutional Neural Network.
The model was trained and tested on about 40.000 real photos of 43 types of Indian traffic signs.
Data was taken from The Indian Traffic Sign Recognition :
https://huggingface.co/datasets/kannanwisen/Indian-Traffic-Sign-Classification
Author: Prajwal Thawari
""")
if __name__ == '__main__':
main()
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