yolo image using keras

yolo_keras.py

# import necessary packages
from numpy import expand_dims
from keras.models import load_model
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
import time
import pdb

# load and prepare an image
def load_image_pixels(filename, shape):
    image = load_img(filename, target_size=shape)
    width, height = image.size
    # convert to numpy array
    image = img_to_array(image)
    # scale pixel values to [0, 1]
    image = image.astype('float32')
    image /= 255.0
    # add a dimension so that we have one sample
    image = expand_dims(image, 0)
    return image, width, height

if __name__ == "__main__":
    model = load_model('yolov3.h5')
    input_w, input_h = 608, 608
    photo_filename = 'image_sample.png'

    image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))
    
    while(1):
        # make prediction
        start = time.time()
        yhat = model.predict(image)
        end = time.time()
        # show timing information on YOLO
        print("[INFO] YOLO took {:.6f} seconds".format(end - start))