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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.imagelib import sd
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, SampleLoader, SampleProcessor, SampleType)
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 SampleGeneratorFaceSkinSegDataset(SampleGeneratorBase):
def __init__ (self, root_path, debug=False, batch_size=1, resolution=256, face_type=None,
generators_count=4, data_format="NHWC",
**kwargs):
super().__init__(debug, batch_size)
self.initialized = False
aligned_path = root_path /'aligned'
if not aligned_path.exists():
raise ValueError(f'Unable to find {aligned_path}')
obstructions_path = root_path / 'obstructions'
obstructions_images_paths = pathex.get_image_paths(obstructions_path, image_extensions=['.png'], subdirs=True)
samples = SampleLoader.load (SampleType.FACE, aligned_path, subdirs=True)
self.samples_len = len(samples)
pickled_samples = pickle.dumps(samples, 4)
if self.debug:
self.generators_count = 1
else:
self.generators_count = max(1, generators_count)
if self.debug:
self.generators = [ThisThreadGenerator ( self.batch_func, (pickled_samples, obstructions_images_paths, resolution, face_type, data_format) )]
else:
self.generators = [SubprocessGenerator ( self.batch_func, (pickled_samples, obstructions_images_paths, resolution, face_type, 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 ):
pickled_samples, obstructions_images_paths, resolution, face_type, data_format = param
samples = pickle.loads(pickled_samples)
obstructions_images_paths_len = len(obstructions_images_paths)
shuffle_o_idxs = []
o_idxs = [*range(obstructions_images_paths_len)]
shuffle_idxs = []
idxs = [*range(len(samples))]
random_flip = True
rotation_range=[-10,10]
scale_range=[-0.05, 0.05]
tx_range=[-0.05, 0.05]
ty_range=[-0.05, 0.05]
o_random_flip = True
o_rotation_range=[-180,180]
o_scale_range=[-0.5, 0.05]
o_tx_range=[-0.5, 0.5]
o_ty_range=[-0.5, 0.5]
random_bilinear_resize_chance, random_bilinear_resize_max_size_per = 25,75
motion_blur_chance, motion_blur_mb_max_size = 25, 5
gaussian_blur_chance, gaussian_blur_kernel_max_size = 25, 5
bs = self.batch_size
while True:
batches = [ [], [] ]
n_batch = 0
while n_batch < bs:
try:
if len(shuffle_idxs) == 0:
shuffle_idxs = idxs.copy()
np.random.shuffle(shuffle_idxs)
idx = shuffle_idxs.pop()
sample = samples[idx]
img = sample.load_bgr()
h,w,c = img.shape
mask = np.zeros ((h,w,1), dtype=np.float32)
sample.ie_polys.overlay_mask(mask)
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 )
if face_type == sample.face_type:
if w != resolution:
img = cv2.resize( img, (resolution, resolution), cv2.INTER_LANCZOS4 )
mask = cv2.resize( mask, (resolution, resolution), cv2.INTER_LANCZOS4 )
else:
mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 )
mask = cv2.warpAffine( mask, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 )
if len(mask.shape) == 2:
mask = mask[...,None]
if obstructions_images_paths_len != 0:
# apply obstruction
if len(shuffle_o_idxs) == 0:
shuffle_o_idxs = o_idxs.copy()
np.random.shuffle(shuffle_o_idxs)
o_idx = shuffle_o_idxs.pop()
o_img = cv2_imread (obstructions_images_paths[o_idx]).astype(np.float32) / 255.0
oh,ow,oc = o_img.shape
if oc == 4:
ohw = max(oh,ow)
scale = resolution / ohw
#o_img = cv2.resize (o_img, ( int(ow*rate), int(oh*rate), ), cv2.INTER_CUBIC)
mat = cv2.getRotationMatrix2D( (ow/2,oh/2),
np.random.uniform( o_rotation_range[0], o_rotation_range[1] ),
1.0 )
mat += np.float32( [[0,0, -ow/2 ],
[0,0, -oh/2 ]])
mat *= scale * np.random.uniform(1 +o_scale_range[0], 1 +o_scale_range[1])
mat += np.float32( [[0, 0, resolution/2 + resolution*np.random.uniform( o_tx_range[0], o_tx_range[1] ) ],
[0, 0, resolution/2 + resolution*np.random.uniform( o_ty_range[0], o_ty_range[1] ) ] ])
o_img = cv2.warpAffine( o_img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 )
if o_random_flip and np.random.randint(10) < 4:
o_img = o_img[:,::-1,...]
o_mask = o_img[...,3:4]
o_mask[o_mask>0] = 1.0
o_mask = cv2.erode (o_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)), iterations = 1 )
o_mask = cv2.GaussianBlur(o_mask, (5, 5) , 0)[...,None]
img = img*(1-o_mask) + o_img[...,0:3]*o_mask
o_mask[o_mask<0.5] = 0.0
#import code
#code.interact(local=dict(globals(), **locals()))
mask *= (1-o_mask)
#cv2.imshow ("", np.clip(o_img*255, 0,255).astype(np.uint8) )
#cv2.waitKey(0)
img = imagelib.warp_by_params (warp_params, img, can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
mask = imagelib.warp_by_params (warp_params, mask, can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
img = np.clip(img.astype(np.float32), 0, 1)
mask[mask < 0.5] = 0.0
mask[mask >= 0.5] = 1.0
mask = np.clip(mask, 0, 1)
img = imagelib.apply_random_hsv_shift(img, mask=sd.random_circle_faded ([resolution,resolution]))
img = imagelib.apply_random_motion_blur( img, motion_blur_chance, motion_blur_mb_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
img = imagelib.apply_random_gaussian_blur( img, gaussian_blur_chance, gaussian_blur_kernel_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
img = imagelib.apply_random_bilinear_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
if data_format == "NCHW":
img = np.transpose(img, (2,0,1) )
mask = np.transpose(mask, (2,0,1) )
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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