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import multiprocessing
import pickle
import time
import traceback
from enum import IntEnum
import cv2
import numpy as np
from core import imagelib, mplib, pathex
from core.cv2ex import *
from core.interact import interact as io
from core.joblib import SubprocessGenerator, ThisThreadGenerator
from facelib import LandmarksProcessor
from samplelib import SampleGeneratorBase
class MaskType(IntEnum):
none = 0,
cloth = 1,
ear_r = 2,
eye_g = 3,
hair = 4,
hat = 5,
l_brow = 6,
l_ear = 7,
l_eye = 8,
l_lip = 9,
mouth = 10,
neck = 11,
neck_l = 12,
nose = 13,
r_brow = 14,
r_ear = 15,
r_eye = 16,
skin = 17,
u_lip = 18
MaskType_to_name = {
int(MaskType.none ) : 'none',
int(MaskType.cloth ) : 'cloth',
int(MaskType.ear_r ) : 'ear_r',
int(MaskType.eye_g ) : 'eye_g',
int(MaskType.hair ) : 'hair',
int(MaskType.hat ) : 'hat',
int(MaskType.l_brow) : 'l_brow',
int(MaskType.l_ear ) : 'l_ear',
int(MaskType.l_eye ) : 'l_eye',
int(MaskType.l_lip ) : 'l_lip',
int(MaskType.mouth ) : 'mouth',
int(MaskType.neck ) : 'neck',
int(MaskType.neck_l) : 'neck_l',
int(MaskType.nose ) : 'nose',
int(MaskType.r_brow) : 'r_brow',
int(MaskType.r_ear ) : 'r_ear',
int(MaskType.r_eye ) : 'r_eye',
int(MaskType.skin ) : 'skin',
int(MaskType.u_lip ) : 'u_lip',
}
MaskType_from_name = { MaskType_to_name[k] : k for k in MaskType_to_name.keys() }
class SampleGeneratorFaceCelebAMaskHQ(SampleGeneratorBase):
def __init__ (self, root_path, debug=False, batch_size=1, resolution=256,
generators_count=4, data_format="NHWC",
**kwargs):
super().__init__(debug, batch_size)
self.initialized = False
dataset_path = root_path / 'CelebAMask-HQ'
if not dataset_path.exists():
raise ValueError(f'Unable to find {dataset_path}')
images_path = dataset_path /'CelebA-HQ-img'
if not images_path.exists():
raise ValueError(f'Unable to find {images_path}')
masks_path = dataset_path / 'CelebAMask-HQ-mask-anno'
if not masks_path.exists():
raise ValueError(f'Unable to find {masks_path}')
if self.debug:
self.generators_count = 1
else:
self.generators_count = max(1, generators_count)
source_images_paths = pathex.get_image_paths(images_path, return_Path_class=True)
source_images_paths_len = len(source_images_paths)
mask_images_paths = pathex.get_image_paths(masks_path, subdirs=True, return_Path_class=True)
if source_images_paths_len == 0 or len(mask_images_paths) == 0:
raise ValueError('No training data provided.')
mask_file_id_hash = {}
for filepath in io.progress_bar_generator(mask_images_paths, "Loading"):
stem = filepath.stem
file_id, mask_type = stem.split('_', 1)
file_id = int(file_id)
if file_id not in mask_file_id_hash:
mask_file_id_hash[file_id] = {}
mask_file_id_hash[file_id][ MaskType_from_name[mask_type] ] = str(filepath.relative_to(masks_path))
source_file_id_set = set()
for filepath in source_images_paths:
stem = filepath.stem
file_id = int(stem)
source_file_id_set.update ( {file_id} )
for k in mask_file_id_hash.keys():
if k not in source_file_id_set:
io.log_err (f"Corrupted dataset: {k} not in {images_path}")
if self.debug:
self.generators = [ThisThreadGenerator ( self.batch_func, (images_path, masks_path, mask_file_id_hash, data_format) )]
else:
self.generators = [SubprocessGenerator ( self.batch_func, (images_path, masks_path, mask_file_id_hash, data_format), start_now=False ) \
for i in range(self.generators_count) ]
SubprocessGenerator.start_in_parallel( self.generators )
self.generator_counter = -1
self.initialized = True
#overridable
def is_initialized(self):
return self.initialized
def __iter__(self):
return self
def __next__(self):
self.generator_counter += 1
generator = self.generators[self.generator_counter % len(self.generators) ]
return next(generator)
def batch_func(self, param ):
images_path, masks_path, mask_file_id_hash, data_format = param
file_ids = list(mask_file_id_hash.keys())
shuffle_file_ids = []
resolution = 256
random_flip = True
rotation_range=[-15,15]
scale_range=[-0.10, 0.95]
tx_range=[-0.3, 0.3]
ty_range=[-0.3, 0.3]
random_bilinear_resize = (25,75)
motion_blur = (25, 5)
gaussian_blur = (25, 5)
bs = self.batch_size
while True:
batches = None
n_batch = 0
while n_batch < bs:
try:
if len(shuffle_file_ids) == 0:
shuffle_file_ids = file_ids.copy()
np.random.shuffle(shuffle_file_ids)
file_id = shuffle_file_ids.pop()
masks = mask_file_id_hash[file_id]
image_path = images_path / f'{file_id}.jpg'
skin_path = masks.get(MaskType.skin, None)
hair_path = masks.get(MaskType.hair, None)
hat_path = masks.get(MaskType.hat, None)
#neck_path = masks.get(MaskType.neck, None)
img = cv2_imread(image_path).astype(np.float32) / 255.0
mask = cv2_imread(masks_path / skin_path)[...,0:1].astype(np.float32) / 255.0
if hair_path is not None:
hair_path = masks_path / hair_path
if hair_path.exists():
hair = cv2_imread(hair_path)[...,0:1].astype(np.float32) / 255.0
mask *= (1-hair)
if hat_path is not None:
hat_path = masks_path / hat_path
if hat_path.exists():
hat = cv2_imread(hat_path)[...,0:1].astype(np.float32) / 255.0
mask *= (1-hat)
#if neck_path is not None:
# neck_path = masks_path / neck_path
# if neck_path.exists():
# neck = cv2_imread(neck_path)[...,0:1].astype(np.float32) / 255.0
# mask = np.clip(mask+neck, 0, 1)
warp_params = imagelib.gen_warp_params(resolution, random_flip, rotation_range=rotation_range, scale_range=scale_range, tx_range=tx_range, ty_range=ty_range )
img = cv2.resize( img, (resolution,resolution), cv2.INTER_LANCZOS4 )
h, s, v = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
h = ( h + np.random.randint(360) ) % 360
s = np.clip ( s + np.random.random()-0.5, 0, 1 )
v = np.clip ( v + np.random.random()/2-0.25, 0, 1 )
img = np.clip( cv2.cvtColor(cv2.merge([h, s, v]), cv2.COLOR_HSV2BGR) , 0, 1 )
if motion_blur is not None:
chance, mb_max_size = motion_blur
chance = np.clip(chance, 0, 100)
mblur_rnd_chance = np.random.randint(100)
mblur_rnd_kernel = np.random.randint(mb_max_size)+1
mblur_rnd_deg = np.random.randint(360)
if mblur_rnd_chance < chance:
img = imagelib.LinearMotionBlur (img, mblur_rnd_kernel, mblur_rnd_deg )
img = imagelib.warp_by_params (warp_params, img, can_warp=True, can_transform=True, can_flip=True, border_replicate=False, cv2_inter=cv2.INTER_LANCZOS4)
if gaussian_blur is not None:
chance, kernel_max_size = gaussian_blur
chance = np.clip(chance, 0, 100)
gblur_rnd_chance = np.random.randint(100)
gblur_rnd_kernel = np.random.randint(kernel_max_size)*2+1
if gblur_rnd_chance < chance:
img = cv2.GaussianBlur(img, (gblur_rnd_kernel,) *2 , 0)
if random_bilinear_resize is not None:
chance, max_size_per = random_bilinear_resize
chance = np.clip(chance, 0, 100)
pick_chance = np.random.randint(100)
resize_to = resolution - int( np.random.rand()* int(resolution*(max_size_per/100.0)) )
img = cv2.resize (img, (resize_to,resize_to), cv2.INTER_LINEAR )
img = cv2.resize (img, (resolution,resolution), cv2.INTER_LINEAR )
mask = cv2.resize( mask, (resolution,resolution), cv2.INTER_LANCZOS4 )[...,None]
mask = imagelib.warp_by_params (warp_params, mask, can_warp=True, can_transform=True, can_flip=True, border_replicate=False, cv2_inter=cv2.INTER_LANCZOS4)
mask[mask < 0.5] = 0.0
mask[mask >= 0.5] = 1.0
mask = np.clip(mask, 0, 1)
if data_format == "NCHW":
img = np.transpose(img, (2,0,1) )
mask = np.transpose(mask, (2,0,1) )
if batches is None:
batches = [ [], [] ]
batches[0].append ( img )
batches[1].append ( mask )
n_batch += 1
except:
io.log_err ( traceback.format_exc() )
yield [ np.array(batch) for batch in batches]
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