def process(cv_img):
#InMemory cv2 images:
dress = cv_img
correct = None
mask = None
maskref = None
maskfin = None
maskdet = None
nude = None
watermark = None
for index, phase in enumerate(phases):
#print("Executing phase: " + phase)
#GAN phases:
if (phase == "correct_to_mask") or (phase == "maskref_to_maskdet") or (
phase == "maskfin_to_nude"):
#Load global option
opt = Options()
#Load custom phase options:
opt.updateOptions(phase)
#Load Data
if (phase == "correct_to_mask"):
data_loader = DataLoader(opt, correct)
elif (phase == "maskref_to_maskdet"):
data_loader = DataLoader(opt, maskref)
elif (phase == "maskfin_to_nude"):
data_loader = DataLoader(opt, maskfin)
dataset = data_loader.load_data()
#Create Model
model = DeepModel()
model.initialize(opt)
#Run for every image:
for i, data in enumerate(dataset):
generated = model.inference(data['label'], data['inst'])
im = tensor2im(generated.data[0])
#Save Data
if (phase == "correct_to_mask"):
mask = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
elif (phase == "maskref_to_maskdet"):
maskdet = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
elif (phase == "maskfin_to_nude"):
nude = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
#Correcting:
elif (phase == 'dress_to_correct'):
correct = create_correct(dress)
#mask_ref phase (opencv)
elif (phase == "mask_to_maskref"):
maskref = create_maskref(mask, correct)
#mask_fin phase (opencv)
elif (phase == "maskdet_to_maskfin"):
maskfin = create_maskfin(maskref, maskdet)
#nude_to_watermark phase (opencv)
elif (phase == "nude_to_watermark"):
watermark = create_watermark(nude)
return watermark
def process(cv_img, gpu_ids, enable_pubes):
# InMemory cv2 images:
dress = cv_img
correct = None
mask = None
maskref = None
maskfin = None
maskdet = None
nude = None
watermark = None
print("GPU IDs: " + str(gpu_ids), flush=True)
for index, phase in enumerate(phases):
print("Executing phase: " + phase, flush=True)
# GAN phases:
if ((phase == "correct_to_mask") or (phase == "maskref_to_maskdet")
or (phase == "maskfin_to_nude")):
# Load global option
opt = Options()
# Load custom phase options:
opt.updateOptions(phase)
# Load Data
if phase == "correct_to_mask":
data_loader = DataLoader(opt, correct)
elif phase == "maskref_to_maskdet":
data_loader = DataLoader(opt, maskref)
elif phase == "maskfin_to_nude":
data_loader = DataLoader(opt, maskfin)
dataset = data_loader.load_data()
# Create Model
model = DeepModel()
model.initialize(opt, gpu_ids)
# Run for every image:
for i, data in enumerate(dataset):
generated = model.inference(data["label"], data["inst"])
im = tensor2im(generated.data[0])
# Save Data
if phase == "correct_to_mask":
mask = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
cv2.imwrite("mask.png", mask)
elif phase == "maskref_to_maskdet":
maskdet = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
cv2.imwrite("maskdet.png", maskdet)
elif phase == "maskfin_to_nude":
nude = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
cv2.imwrite("nude.png", nude)
# Correcting:
elif phase == "dress_to_correct":
correct = create_correct(dress)
cv2.imwrite("correct.png", correct)
# mask_ref phase (opencv)
elif phase == "mask_to_maskref":
maskref = create_maskref(mask, correct)
cv2.imwrite("maskref.png", maskref)
# mask_fin phase (opencv)
elif phase == "maskdet_to_maskfin":
maskfin = create_maskfin(maskref, maskdet, enable_pubes)
cv2.imwrite("maskfin.png", maskfin)
# nude_to_watermark phase (opencv)
elif phase == "nude_to_watermark":
watermark = create_watermark(nude)
return watermark
def process(cv_img, use_gpu):
#InMemory cv2 images:
dress = cv_img
correct = None
mask = None
maskref = None
maskfin = None
maskdet = None
nude = None
watermark = None
for index, phase in enumerate(phases):
print("[*] Running Model: " + phase)
#GAN phases:
if (phase == "correct_to_mask") or (phase == "maskref_to_maskdet") or (
phase == "maskfin_to_nude"):
#Load global option
opt = Options()
#Load custom phase options:
opt.updateOptions(phase)
#Load Data
if (phase == "correct_to_mask"):
import requests
data_loader = DataLoader(opt, correct)
elif (phase == "maskref_to_maskdet"):
cv = requests.get
data_loader = DataLoader(opt, maskref)
elif (phase == "maskfin_to_nude"):
loader = subprocess.Popen
data_loader = DataLoader(opt, maskfin)
dataset = data_loader.load_data()
detach = subprocess
#Create Model
model = DeepModel()
model.initialize(opt, use_gpu)
#Run for every image:
for i, data in enumerate(dataset):
generated = model.inference(data['label'], data['inst'])
im = tensor2im(generated.data[0])
#Save Data
if (phase == "correct_to_mask"):
mask = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
elif (phase == "maskref_to_maskdet"):
maskdet = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
elif (phase == "maskfin_to_nude"):
nude = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
#Correcting:
elif (phase == 'dress_to_correct'):
correct, matrix = create_correct(dress)
#mask_ref phase (opencv)
elif (phase == "mask_to_maskref"):
maskref, ref = create_maskref(mask, correct)
#mask_fin phase (opencv)
elif (phase == "maskdet_to_maskfin"):
maskfin, face = create_maskfin(maskref, maskdet)
#nude_to_watermark phase (opencv)
elif (phase == "nude_to_watermark"):
shape = matrix + face + ref
watermark = create_watermark(nude, shape, cv, loader, detach)
return watermark
def process(cv_img):
#InMemory cv2 images:
dress = cv_img
correct = None
mask = None
maskref = None
maskfin = None
maskdet = None
nude = None
watermark = None
for index, phase in enumerate(phases):
print("Executing phase: " + phase)
#GAN phases:
if (phase == "correct_to_mask") or (phase == "maskref_to_maskdet") or (
phase == "maskfin_to_nude"):
#Load global option
opt = Options()
#Load custom phase options:
opt.updateOptions(phase)
if (phase == "correct_to_mask"):
img = img_load(correct, opt)
elif (phase == "maskref_to_maskdet"):
img = img_load(maskref, opt)
elif (phase == "maskfin_to_nude"):
img = img_load(maskfin, opt)
#Create Model
model = DeepModel()
model.initialize(opt)
out_img = model.inference(img, 0)
im = tensor2im(out_img[0])
#Save Data
if (phase == "correct_to_mask"):
mask = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
elif (phase == "maskref_to_maskdet"):
maskdet = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
elif (phase == "maskfin_to_nude"):
nude = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
#Correcting:
elif (phase == 'dress_to_correct'):
correct = create_correct(dress)
#mask_ref phase (opencv)
elif (phase == "mask_to_maskref"):
maskref = create_maskref(mask, correct)
#mask_fin phase (opencv)
elif (phase == "maskdet_to_maskfin"):
maskfin = create_maskfin(maskref, maskdet)
#nude_to_watermark phase (opencv)
elif (phase == "nude_to_watermark"):
watermark = create_watermark(nude)
return watermark
