App.py

from flask import *
import os
from werkzeug.utils import secure_filename
from keras.models import load_model
import numpy as np
from PIL import Image

app = Flask(__name__)

# Classes of trafic signs
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('./model/TSR.h5')
    data=[]
    image = Image.open(img)
    image = image.resize((30,30))
    data.append(np.array(image))
    X_test=np.array(data)
    predict_x=model.predict(X_test)
    classes_x=np.argmax(predict_x,axis=1)
    #Y_pred = model.predict_classes(X_test)
    #return Y_pred
    return classes_x

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/predict', methods=['GET', 'POST'])
def upload():
    if request.method == 'POST':
        # Get the file from post request
        f = request.files['file']
        file_path = secure_filename(f.filename)
        f.save(file_path)

        #Get the car speed from user
        # speed =request.text('text')

        # Make prediction
        result = image_processing(file_path)
        s = [str(i) for i in result]
        a = int("".join(s))
        if classes[a] == "Speed limit (20km/h)":
            result = "Maintain or lower the speed"
        elif classes[a] == "Speed limit (30km/h)":
            result = "you can maintain or increase the speed up to 30km/h"
        elif classes[a] == "Speed limit (50km/h)":
            result = "You can maintain the speed or increase up to 50km/h"
        elif classes[a] == "Speed limit (60km/h)":
            result = "You can maintain the speed or increase up to 60km/h"
        elif classes[a] == "Speed limit (70km/h)":
            result = "You can maintain the speed or increase up to 70km/h"
        elif classes[a] == "Speed limit (80km/h)":
            result = "You can maintain the speed or increase up to 80km/h"
        elif classes[a] == "Speed limit (100km/h)":
            result = "You can maintain the speed or increase up to 100km/h"
        elif classes[a] == "Speed limit (120km/h)":
            result = "You can maintain the speed or increase up to 120km/h"
        else:
            result = "Your Traffic🚦Sign is: " +classes[a]
        print(result)
        return result
    return None

if __name__ == '__main__':
    app.run(debug=True)