#!/usr/local/bin/python3
#!-*- coding: utf-8 -*-

import os
import numpy as np
from keras.models import model_from_json
from keras.preprocessing.image import load_img, img_to_array

if __name__ == '__main__':

    #Laden Sie das Modell
    model_json = open('model.json').read()
    model = model_from_json(model_json)
    model.load_weights('model.h5')

    #Bild laden
    image = load_img('{Bilddateipfad}', target_size=(32, 32))

    #In ein Array konvertieren
    x = img_to_array(image)

    #Erhöhen Sie die Anzahl der Dimensionen
    x = np.expand_dims(x, axis=0)

    #Eingabedaten sind[0,1]Normalisiert auf den Bereich von
    x = x.astype('float32')
    x /= 255.0

    #Prognose
    preds = model.predict(x)

    #Index des Maximalwerts abrufen
    answer = np.argmax(preds)

    cifar_map = {
        0: "airplane",
        1: "automobile",
        2: "bird",
        3: "cat",
        4: "deer",
        5: "dog",
        6: "frog",
        7: "hourse",
        8: "ship",
        9: "truck"
    }

    print(cifar_map[answer])

automobile

frog

hourse

ship

hourse

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