class FaceSwapInterface:
def __init__(self):
self._parser = FullHelpArgumentParser()
self._subparser = self._parser.add_subparsers()
def extract(self, input_dir, output_dir, filter_path, processes):
extract = ExtractTrainingData(
self._subparser, "extract", "Extract the faces from a pictures.")
args_str = "extract --input-dir {} --output-dir {} --processes {} --detector cnn --filter {}"
args_str = args_str.format(input_dir, output_dir, processes, filter_path)
self._run_script(args_str)
def train(self, input_a_dir, input_b_dir, model_dir, gan = False):
model_type = "Original"
if gan:
model_type = "GAN"
train = TrainingProcessor(
self._subparser, "train", "This command trains the model for the two faces A and B.")
args_str = "train --input-A {} --input-B {} --model-dir {} --trainer {} --batch-size {} --write-image"
args_str = args_str.format(input_a_dir, input_b_dir, model_dir, model_type, 512)
self._run_script(args_str)
def _run_script(self, args_str):
args = self._parser.parse_args(args_str.split(' '))
args.func(args)
class FaceSwapInterface:
def __init__(self):
self._parser = FullHelpArgumentParser()
self._subparser = self._parser.add_subparsers()
def extract(self, input_dir, output_dir, filter_path):
extract = ExtractTrainingData(self._subparser, "extract",
"Extract the faces from a pictures.")
args_str = "extract --input-dir {} --output-dir {} --processes 1 --detector cnn --filter {}"
args_str = args_str.format(input_dir, output_dir, filter_path)
self._run_script(args_str)
def train(self,
input_a_dir,
input_b_dir,
model_dir,
gpus,
gan=False,
preview=True,
stop_threshold=0,
stop_iternum=float('inf')):
# --------------------------
# ORIGINAL IMPLEMENTATION:
# --------------------------
# model_type = "Original"
# if gan:
# model_type = "GAN"
# train = TrainingProcessor(
# self._subparser, "train", "This command trains the model for the two faces A and B.")
# args_str = "train --input-A {} --input-B {} --model-dir {} --trainer {} --batch-size {} --gpus {} --write-image"
# if preview:
# args_str += " -p"
# args_str = args_str.format(input_a_dir, input_b_dir, model_dir, model_type, 512, gpus)
# self._run_script(args_str)
myswap = MyFaceSwap()
myswap.train(input_A=input_a_dir,
input_B=input_b_dir,
model_dir=model_dir,
gpus=gpus,
preview=preview,
stop_threshold=stop_threshold,
stop_iternum=stop_iternum)
def _run_script(self, args_str):
args = self._parser.parse_args(args_str.split(' '))
# print('\n\nARGS: {}\n\n'.format(args))
args.func(args)
def __init__(self): self._parser = FullHelpArgumentParser() self._subparser = self._parser.add_subparsers()
import sys
if sys.version_info[0] < 3:
raise Exception("This program requires at least python3.2")
if sys.version_info[0] == 3 and sys.version_info[1] < 2:
raise Exception("This program requires at least python3.2")
from lib.utils import FullHelpArgumentParser
from scripts.extract import ExtractTrainingData
from scripts.train import TrainingProcessor
from scripts.convert import ConvertImage
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
if __name__ == "__main__":
parser = FullHelpArgumentParser()
subparser = parser.add_subparsers()
extract = ExtractTrainingData(
subparser, "extract", "Extract the faces from a pictures.")
train = TrainingProcessor(
subparser, "train", "This command trains the model for the two faces A and B.")
convert = ConvertImage(
subparser, "convert", "Convert a source image to a new one with the face swapped.")
arguments = parser.parse_args()
arguments.func(arguments)
raise Exception("This Program requires atleast Python3.2")
def bad_args(args):
parser.print_help()
exit(0)
from lib.utils import FullHelpArgumentParser
from script.extract import ExtractTrainingData
from script.train import TrainingProcessor
from script.convert import ConvertImage
if __name__ == '__main__':
#parser
parser = FullHelpArgumentParser()
subparser = parser.add_subparsers()
#to extract the face from the images
extract = ExtractTrainingData(subparser, "extract",
"Extract the faces from the picture")
#to train the whole autoencoder network
train = TrainingProcessor(subparser, "train",
"To Train a model for two faces A and B")
#convert src image to new one with face swapped
convert = ConvertImage(subparser, "convert",
"Convert a src image into Target Image")
parser.set_defaults(func=bad_args)
#parse the arguments
arguments = parser.parse_args()
arguments.func(arguments)
#!/usr/bin/env python3
import sys
if sys.version_info[0] < 3:
raise Exception("This program requires at least python3.2")
if sys.version_info[0] == 3 and sys.version_info[1] < 2:
raise Exception("This program requires at least python3.2")
from lib.utils import FullHelpArgumentParser
from scripts.extract import ExtractTrainingData
from scripts.train import TrainingProcessor
from scripts.convert import ConvertImage
def bad_args(args):
parser.print_help()
exit(0)
if __name__ == "__main__":
parser = FullHelpArgumentParser()
subparser = parser.add_subparsers()
extract = ExtractTrainingData(
subparser, "extract", "Extract the faces from a pictures.")
train = TrainingProcessor(
subparser, "train", "This command trains the model for the two faces A and B.")
convert = ConvertImage(
subparser, "convert", "Convert a source image to a new one with the face swapped.")
parser.set_defaults(func=bad_args)
arguments = parser.parse_args()
arguments.func(arguments)
