def main():
parser = argparse.ArgumentParser()
parser.add_argument('files', nargs='*', help="list of files to recognize")
parser.add_argument('--mnist', help="count of random MNIST images", type=int)
args = parser.parse_args(sys.argv[1:])
images = [np.flipud(1 - bmp.load(k, floatize=True)) for k in args.files]
if args.mnist:
m_images, m_labels = MNIST.get_data()
random.shuffle(m_images)
images.extend(m_images[:args.mnist])
drg = DigitRecognizerGui(images=images)
drg.show()
def recognizer(paths, config_file='digit_recognizer.pkl'):
with open(config_file, 'rb') as fd:
config = pickle.load(fd)
print("config file loaded")
print("shape : {}".format(config['layers']))
network = Bpn.NeuralNetwork(config['layers'])
if 'weights' in config:
network.weights = config['weights']
print("weights restored")
for path in paths:
if isinstance(path, (np.ndarray)):
image = path
else:
print("Path: {}".format(path))
image = bmp.load(path)
image = np.flipud(1 - image / 255)
image = image.reshape((functools.reduce(mul, image.shape)))
# print(image)
return network.run(image[None, :])
def test_load_bmp(self):
image = bmp.load('test_image.bmp')
self.assertIsNotNone(image)
