def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input_dir', action='store', type=str, default=INPUT_DIR,
help=f'Specify name of input image directory --in_path=path. Default: {INPUT_DIR}',
dest='input_dir')
parser.add_argument('-od', '--output_dir', action='store', type=str, default=OUTPUT_DIR,
help=f'Specify name of output directory. Default: {OUTPUT_DIR}',
dest='output_dir')
parser.add_argument('-o', '--output_file', action='store', type=str, default=OUTPUT_FILENAME,
help=f'Specify name of output file. Default: {OUTPUT_FILENAME}',
dest='output_file')
parser.add_argument('-d', '--img_dim', action='store', type=int, default=IMG_DIMS,
help=f'Specify dimensions of square image to be created. Default: {IMG_DIMS}',
dest='img_dims')
args = parser.parse_args()
sequence_length = args.img_dims ** 2
# current working directory
cwd = os.getcwd()
# fix problem from running from different directory
if "scripts" in cwd:
cwd = cwd.replace("scripts", "")
dog_names = DOG_NAMES
# probability of the first pixel (upper left corner) is each dog
prior = PRIOR
# transition matrix
transition = [ROSIE_TRANSITION, CALLIE_TRANSITION, VENUS_TRANSITION, BEAR_TRANSITION, JAMIE_TRANSITION,
COOPER_TRANSITION, WINSTON_TRANSITION, BRUNO_TRANSITION, MAISY_TRANSITION, SPEEDY_TRANSITION,
BELLA_TRANSITION, BOOMER_TRANSITION, SASHA_TRANSITION]
all_states = []
# create State object for each dog in dog names
for i, dog in enumerate(dog_names):
new_state = State(id=i, name=dog)
all_states.append(new_state)
# run markov chain using states, prior probability vector, and transition matrix
m = MarkovChain(states=all_states, prior=prior, transition=transition)
sequence = m.run(sequence_length=sequence_length)
# create output_path variable
output_path = os.path.join(cwd, args.output_dir)
output_path = os.path.join(output_path, args.output_file)
# generate image from markov chain, input images, and output_path
i = ImgGenerator(order=sequence, input_dir=os.path.join(cwd, args.input_dir), output_path=output_path,
num_rows=args.img_dims,
num_cols=args.img_dims, all_states=all_states)
i.generate_img()
