Medium Articulo: Parte 5

prediction.py

import argparse, keras, os
from keras.models import model_from_json
parser = argparse.ArgumentParser()
parser.add_argument("image", help="Image or folder of images to predict",
                    type=str)
args = parser.parse_args()

# Cargamos el modelo
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights("model.h5")

# Cargamos la imagen/imagenes
if os.path.isdir(args.image):
	path_images = []
	for ext in ['jpg', 'jpeg', 'tiff', 'png', 'gif']:
		path_images += glob.glob(os.path.join(args.image, "*." + ext))
else:
	path_images = [args.image]

images = []
for path_im in path_images:
	images.append(np.float32(cv2.resize(cv2.imread(path_im), (32, 32))[..., ::-1]/255.0))

raw_results = model.predict(images)

results = {}
for i in range(results.shape[0]):
	if(results[i] > 0.5):
		results[path_images[i]] = 'dog'
	else:
		results[path_images[i]] = 'cat'