Testing.py

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

start = time.time()

# Define Path
model_path = './models/model.h5'
test_path = 'data/test_image'

# Load the pre-trained models
model = load_model(model_path)

# Define image parameters
img_width, img_height = 150, 150


# Prediction Function
def predict(file):
    x = load_img(file, target_size=(img_width, img_height))
    x = img_to_array(x)
    x = np.expand_dims(x, axis=0)
    array = model.predict(x)
    result = array[0]
    # print(result)
    answer = np.argmax(result)
    if answer == 0:
        print("Sepertinya si kucing")
    elif answer == 1:
        print("Sepertinya si mobil")
    elif answer == 2:
        print("Sepertinya si motor")

    return answer


# Walk the directory for every image
for i, ret in enumerate(os.walk(test_path)):
    for i, filename in enumerate(ret[2]):
        if filename.startswith("."):
            continue

        print(ret[0] + '/' + filename)
        result = predict(ret[0] + '/' + filename)
        print(" ")

# Calculate execution time
end = time.time()
dur = end - start

if dur < 60:
    print("Execution Time:", dur, "seconds")
elif 60 < dur < 3600:
    dur = dur / 60
    print("Execution Time:", dur, "minutes")
else:
    dur = dur / (60 * 60)
    print("Execution Time:", dur, "hours")