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app.py
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app.py
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from flask import Flask, render_template, request
from flask_uploads import UploadSet, configure_uploads,IMAGES
from scipy.misc import imsave, imread, imresize
from keras.preprocessing import image
import numpy as np
from keras.models import load_model
from werkzeug import secure_filename
#for regular expressions, saves time dealing with string data
import re
#system level operations (like loading files)
import sys
#for reading operating system data
import os
#initalize our flask app
app = Flask(__name__)
app.static_folder = 'static'
photos = UploadSet('photos', IMAGES)
app.config['UPLOADED_PHOTOS_DEST'] = 'static/img'
configure_uploads(app, photos)
model = load_model('my_model.h5')
model._make_predict_function()
@app.route('/')
def index():
#render out pre-built HTML file right on the index page
return render_template("index.html")
@app.route('/upload', methods=['GET', 'POST'])
def upload():
classes = { 1:'Speed limit (20km/h)',
2:'Speed limit (30km/h)',
3:'Speed limit (50km/h)',
4:'Speed limit (60km/h)',
5:'Speed limit (70km/h)',
6:'Speed limit (80km/h)',
7:'End of speed limit (80km/h)',
8:'Speed limit (100km/h)',
9:'Speed limit (120km/h)',
10:'No passing',
11:'No passing veh over 3.5 tons',
12:'Right-of-way at intersection',
13:'Priority road',
14:'Yield',
15:'Stop',
16:'No vehicles',
17:'Veh > 3.5 tons prohibited',
18:'No entry',
19:'General caution',
20:'Dangerous curve left',
21:'Dangerous curve right',
22:'Double curve',
23:'Bumpy road',
24:'Slippery road',
25:'Road narrows on the right',
26:'Road work',
27:'Traffic signals',
28:'Pedestrians',
29:'Children crossing',
30:'Bicycles crossing',
31:'Beware of ice/snow',
32:'Wild animals crossing',
33:'End speed + passing limits',
34:'Turn right ahead',
35:'Turn left ahead',
36:'Ahead only',
37:'Go straight or right',
38:'Go straight or left',
39:'Keep right',
40:'Keep left',
41:'Roundabout mandatory',
42:'End of no passing',
43:'End no passing veh > 3.5 tons' }
if request.method == 'POST' and 'photo' in request.files:
filename = photos.save(request.files['photo'])
# os.rename('static//img//'+filename,'output.png')
img = image.load_img("static//img//"+filename, target_size=(30, 30))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
pred = model.predict_classes([x])[0]
sign = classes[pred+1]
return render_template("index2.html",prediction = sign)
else:
return render_template("index.html")
if __name__ == "__main__":
#decide what port to run the app in
port = int(os.environ.get('PORT', 5000))
#run the app locally on the givn port
app.run(port=port, debug=True)