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Copy pathSampleGeneratorFaceXSeg.py
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297 lines (236 loc) · 12.6 KB
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import multiprocessing
import pickle
import time
import traceback
from enum import IntEnum
import cv2
import numpy as np
from pathlib import Path
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 Subprocessor, SubprocessGenerator, ThisThreadGenerator
from facelib import LandmarksProcessor
from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor, SampleType)
class SampleGeneratorFaceXSeg(SampleGeneratorBase):
def __init__ (self, paths, 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
samples = sum([ SampleLoader.load (SampleType.FACE, path) for path in paths ] )
seg_sample_idxs = SegmentedSampleFilterSubprocessor(samples).run()
if len(seg_sample_idxs) == 0:
seg_sample_idxs = SegmentedSampleFilterSubprocessor(samples, count_xseg_mask=True).run()
if len(seg_sample_idxs) == 0:
raise Exception(f"No segmented faces found.")
else:
io.log_info(f"Using {len(seg_sample_idxs)} xseg labeled samples.")
else:
io.log_info(f"Using {len(seg_sample_idxs)} segmented samples.")
if self.debug:
self.generators_count = 1
else:
self.generators_count = max(1, generators_count)
args = (samples, seg_sample_idxs, resolution, face_type, data_format)
if self.debug:
self.generators = [ThisThreadGenerator ( self.batch_func, args )]
else:
self.generators = [SubprocessGenerator ( self.batch_func, args, 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 ):
samples, seg_sample_idxs, resolution, face_type, data_format = param
shuffle_idxs = []
bg_shuffle_idxs = []
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]
random_bilinear_resize_chance, random_bilinear_resize_max_size_per = 25,75
sharpen_chance, sharpen_kernel_max_size = 25, 5
motion_blur_chance, motion_blur_mb_max_size = 25, 5
gaussian_blur_chance, gaussian_blur_kernel_max_size = 25, 5
random_jpeg_compress_chance = 25
def gen_img_mask(sample):
img = sample.load_bgr()
h,w,c = img.shape
if sample.seg_ie_polys.has_polys():
mask = np.zeros ((h,w,1), dtype=np.float32)
sample.seg_ie_polys.overlay_mask(mask)
elif sample.has_xseg_mask():
mask = sample.get_xseg_mask()
mask[mask < 0.5] = 0.0
mask[mask >= 0.5] = 1.0
else:
raise Exception(f'no mask in sample {sample.filename}')
if face_type == sample.face_type:
if w != resolution:
img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_LANCZOS4 )
mask = cv2.resize( mask, (resolution, resolution), interpolation=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]
return img, mask
bs = self.batch_size
while True:
batches = [ [], [] ]
n_batch = 0
while n_batch < bs:
try:
if len(shuffle_idxs) == 0:
shuffle_idxs = seg_sample_idxs.copy()
np.random.shuffle(shuffle_idxs)
sample = samples[shuffle_idxs.pop()]
img, mask = gen_img_mask(sample)
if np.random.randint(2) == 0:
if len(bg_shuffle_idxs) == 0:
bg_shuffle_idxs = seg_sample_idxs.copy()
np.random.shuffle(bg_shuffle_idxs)
bg_sample = samples[bg_shuffle_idxs.pop()]
bg_img, bg_mask = gen_img_mask(bg_sample)
bg_wp = imagelib.gen_warp_params(resolution, True, rotation_range=[-180,180], scale_range=[-0.10, 0.10], tx_range=[-0.10, 0.10], ty_range=[-0.10, 0.10] )
bg_img = imagelib.warp_by_params (bg_wp, bg_img, can_warp=False, can_transform=True, can_flip=True, border_replicate=True)
bg_mask = imagelib.warp_by_params (bg_wp, bg_mask, can_warp=False, can_transform=True, can_flip=True, border_replicate=False)
bg_img = bg_img*(1-bg_mask)
if np.random.randint(2) == 0:
bg_img = imagelib.apply_random_hsv_shift(bg_img)
else:
bg_img = imagelib.apply_random_rgb_levels(bg_img)
c_mask = 1.0 - (1-bg_mask) * (1-mask)
rnd = 0.15 + np.random.uniform()*0.85
img = img*(c_mask) + img*(1-c_mask)*rnd + bg_img*(1-c_mask)*(1-rnd)
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 = imagelib.warp_by_params (warp_params, img, can_warp=True, can_transform=True, can_flip=True, border_replicate=True)
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)
if np.random.randint(2) == 0:
# random face flare
krn = np.random.randint( resolution//4, resolution )
krn = krn - krn % 2 + 1
img = img + cv2.GaussianBlur(img*mask, (krn,krn), 0)
if np.random.randint(2) == 0:
# random bg flare
krn = np.random.randint( resolution//4, resolution )
krn = krn - krn % 2 + 1
img = img + cv2.GaussianBlur(img*(1-mask), (krn,krn), 0)
if np.random.randint(2) == 0:
img = imagelib.apply_random_hsv_shift(img, mask=sd.random_circle_faded ([resolution,resolution]))
else:
img = imagelib.apply_random_rgb_levels(img, mask=sd.random_circle_faded ([resolution,resolution]))
if np.random.randint(2) == 0:
img = imagelib.apply_random_sharpen( img, sharpen_chance, sharpen_kernel_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
else:
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]))
if np.random.randint(2) == 0:
img = imagelib.apply_random_nearest_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
else:
img = imagelib.apply_random_bilinear_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
img = np.clip(img, 0, 1)
img = imagelib.apply_random_jpeg_compress( img, random_jpeg_compress_chance, 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]
class SegmentedSampleFilterSubprocessor(Subprocessor):
#override
def __init__(self, samples, count_xseg_mask=False ):
self.samples = samples
self.samples_len = len(self.samples)
self.count_xseg_mask = count_xseg_mask
self.idxs = [*range(self.samples_len)]
self.result = []
super().__init__('SegmentedSampleFilterSubprocessor', SegmentedSampleFilterSubprocessor.Cli, 60)
#override
def process_info_generator(self):
for i in range(multiprocessing.cpu_count()):
yield 'CPU%d' % (i), {}, {'samples':self.samples, 'count_xseg_mask':self.count_xseg_mask}
#override
def on_clients_initialized(self):
io.progress_bar ("Filtering", self.samples_len)
#override
def on_clients_finalized(self):
io.progress_bar_close()
#override
def get_data(self, host_dict):
if len (self.idxs) > 0:
return self.idxs.pop(0)
return None
#override
def on_data_return (self, host_dict, data):
self.idxs.insert(0, data)
#override
def on_result (self, host_dict, data, result):
idx, is_ok = result
if is_ok:
self.result.append(idx)
io.progress_bar_inc(1)
def get_result(self):
return self.result
class Cli(Subprocessor.Cli):
#overridable optional
def on_initialize(self, client_dict):
self.samples = client_dict['samples']
self.count_xseg_mask = client_dict['count_xseg_mask']
def process_data(self, idx):
if self.count_xseg_mask:
return idx, self.samples[idx].has_xseg_mask()
else:
return idx, self.samples[idx].seg_ie_polys.get_pts_count() != 0
"""
bg_path = None
for path in paths:
bg_path = Path(path) / 'backgrounds'
if bg_path.exists():
break
if bg_path is None:
io.log_info(f'Random backgrounds will not be used. Place no face jpg images to aligned\backgrounds folder. ')
bg_pathes = None
else:
bg_pathes = pathex.get_image_paths(bg_path, image_extensions=['.jpg'], return_Path_class=True)
io.log_info(f'Using {len(bg_pathes)} random backgrounds from {bg_path}')
if bg_pathes is not None:
bg_path = bg_pathes[ np.random.randint(len(bg_pathes)) ]
bg_img = cv2_imread(bg_path)
if bg_img is not None:
bg_img = bg_img.astype(np.float32) / 255.0
bg_img = imagelib.normalize_channels(bg_img, 3)
bg_img = imagelib.random_crop(bg_img, resolution, resolution)
bg_img = cv2.resize(bg_img, (resolution, resolution), interpolation=cv2.INTER_LINEAR)
if np.random.randint(2) == 0:
bg_img = imagelib.apply_random_hsv_shift(bg_img)
else:
bg_img = imagelib.apply_random_rgb_levels(bg_img)
bg_wp = imagelib.gen_warp_params(resolution, True, rotation_range=[-180,180], scale_range=[0,0], tx_range=[0,0], ty_range=[0,0])
bg_img = imagelib.warp_by_params (bg_wp, bg_img, can_warp=False, can_transform=True, can_flip=True, border_replicate=True)
bg = img*(1-mask)
fg = img*mask
c_mask = sd.random_circle_faded ([resolution,resolution])
bg = ( bg_img*c_mask + bg*(1-c_mask) )*(1-mask)
img = fg+bg
else:
"""
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