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import logging
import os
import sys
import time
from multiprocessing import freeze_support
import argv
from config import Config as conf
from utils import setup_log, read_image, check_shape
from processing import SimpleTransform, FolderImageTransform, MultipleImageTransform
from transform.gan.mask import CorrectToMask, MaskrefToMaskdet, MaskfinToNude
from transform.opencv.resize import ImageToCrop, ImageToOverlay, ImageToRescale, ImageToResized, ImageToResizedCrop
from transform.opencv.correct import DressToCorrect
from transform.opencv.mask import MaskToMaskref, MaskdetToMaskfin
def main(_):
Main logic entry point
conf.log = setup_log(logging.DEBUG) if conf.args['debug'] else setup_log()
conf.log.debug("Args : {}".format(conf.args))"Welcome to DreamPower")
if conf.args['gpu_ids']:"GAN Processing Will Use GPU IDs: {}".format(conf.args['gpu_ids']))
else:"GAN Processing Will Use CPU")
# Processing
start = time.time()
select_processing().run()"Done! We have taken {} seconds".format(round(time.time() - start, 2)))
# Exit
def select_phases():
Select the transformation phases to use following args parameters
:return: <ImageTransform[]> list of image transformation
def shift_step(shift_starting=0, shift_ending=0):
if not conf.args['steps']:
conf.args['steps'] = (0, 5)
conf.args['steps'] = (
conf.args['steps'][0] + shift_starting,
conf.args['steps'][1] + shift_ending
def add_tail(phases, phase):
phases = [phase] + phases
if conf.args['steps'] and conf.args['steps'][0] != 0:
return phases
def add_head(phases, phase):
phases = phases + [phase]
if conf.args['steps'] and conf.args['steps'][0] == len(phases) - 1:
return phases
phases = [DressToCorrect(), CorrectToMask(), MaskToMaskref(),
MaskrefToMaskdet(), MaskdetToMaskfin(), MaskfinToNude()]
if conf.args['overlay']:
phases = add_tail(phases, ImageToResized())
phases = add_tail(phases, ImageToCrop())
phases = add_head(phases, ImageToOverlay())
elif conf.args['auto_resize']:
phases = add_tail(phases, ImageToResized())
elif conf.args['auto_resize_crop']:
phases = add_tail(phases, ImageToResizedCrop())
elif conf.args['auto_rescale']:
phases = add_tail(phases, ImageToRescale())
elif not conf.args['folder']:
return phases
def select_processing():
Select the processing to use following args parameters
phases = select_phases()
if conf.args['folder']:
process = processing_image_folder(phases)
elif conf.args['n_runs'] != 1:
process = multiple_image_processing(phases, conf.args['n_runs'])
process = simple_image_processing(phases)
conf.log.debug("Process to execute : {}".format(process))
return process
def simple_image_processing(phases):
Define a simple image process ready to run
:param phases: <ImageTransform[]> list of image transformation
:return: <SimpleTransform> a image process run ready
return SimpleTransform(conf.args['input'], phases, conf.args['output'])
def multiple_image_processing(phases, n):
Define a multiple image process ready to run
:param phases: <ImageTransform[]> list of image transformation
:param n: number of times to process
:return: <MultipleTransform> a multiple image process run ready
filename, extension = os.path.splitext(conf.args['output'])
return MultipleImageTransform(
[conf.args['input'] for _ in range(n)],
["{}{}{}".format(filename, i, extension) for i in range(n)]
def processing_image_folder(phases):
Define a folder image process ready to run
:param phases: <ImageTransform[]> list of image transformation
:return: <FolderImageTransform> a image process run ready
return FolderImageTransform(conf.args['input'], phases, conf.args['output'])
if __name__ == "__main__":
# start_rook()