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import multiprocessing
import time
import traceback
import cv2
import numpy as np
import numpy.linalg as npla
from core import mplib
from core import imagelib
from core.interact import interact as io
from core.joblib import SubprocessGenerator, ThisThreadGenerator
from core import mathlib
from facelib import LandmarksProcessor, FaceType
from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor,
SampleType)
class SampleGeneratorSAE(SampleGeneratorBase):
def __init__ (self, src_samples_path, dst_samples_path,
resolution,
face_type,
random_src_flip=False,
random_dst_flip=False,
ct_mode=None,
uniform_yaw_distribution=False,
data_format='NHWC',
debug=False, batch_size=1,
raise_on_no_data=True,
**kwargs):
super().__init__(debug, batch_size)
self.initialized = False
self.resolution = resolution
self.face_type = face_type
self.random_src_flip = random_src_flip
self.random_dst_flip = random_dst_flip
self.ct_mode = ct_mode
self.data_format = data_format
if self.debug:
self.generators_count = 1
else:
self.generators_count = 8
src_samples = SampleLoader.load (SampleType.FACE, src_samples_path)
src_samples_len = len(src_samples)
if src_samples_len == 0:
raise ValueError(f'No samples in {src_samples_path}')
dst_samples = SampleLoader.load (SampleType.FACE, dst_samples_path)
dst_samples_len = len(dst_samples)
if dst_samples_len == 0:
raise ValueError(f'No samples in {dst_samples_path}')
if uniform_yaw_distribution:
src_index_host = self._filter_uniform_yaw(src_samples)
dst_index_host = self._filter_uniform_yaw(dst_samples)
else:
src_index_host = mplib.IndexHost(src_samples_len)
dst_index_host = mplib.IndexHost(dst_samples_len)
ct_index_host = mplib.IndexHost(dst_samples_len) if ct_mode is not None else None
self.comm_qs = [ multiprocessing.Queue() for i in range(self.generators_count) ]
if self.debug:
self.generators = [ThisThreadGenerator ( self.batch_func, (self.comm_qs[0], src_samples, dst_samples, src_index_host.create_cli(), dst_index_host.create_cli(), ct_index_host.create_cli() if ct_index_host is not None else None) )]
else:
self.generators = [SubprocessGenerator ( self.batch_func, (self.comm_qs[i], src_samples, dst_samples, src_index_host.create_cli(), dst_index_host.create_cli(), ct_index_host.create_cli() if ct_index_host is not None else None), start_now=False ) \
for i in range(self.generators_count) ]
self.generator_counter = -1
self.initialized = True
def start(self):
if not self.debug:
SubprocessGenerator.start_in_parallel( self.generators )
def _filter_uniform_yaw(self, samples):
samples_pyr = [ ( idx, sample.get_pitch_yaw_roll() ) for idx, sample in enumerate(samples) ]
grads = 128
#instead of math.pi / 2, using -1.2,+1.2 because actually maximum yaw for 2DFAN landmarks are -1.2+1.2
grads_space = np.linspace (-1.2, 1.2,grads)
yaws_sample_list = [None]*grads
for g in io.progress_bar_generator ( range(grads), "Sort by yaw"):
yaw = grads_space[g]
next_yaw = grads_space[g+1] if g < grads-1 else yaw
yaw_samples = []
for idx, pyr in samples_pyr:
s_yaw = -pyr[1]
if (g == 0 and s_yaw < next_yaw) or \
(g < grads-1 and s_yaw >= yaw and s_yaw < next_yaw) or \
(g == grads-1 and s_yaw >= yaw):
yaw_samples += [ idx ]
if len(yaw_samples) > 0:
yaws_sample_list[g] = yaw_samples
yaws_sample_list = [ y for y in yaws_sample_list if y is not None ]
return mplib.Index2DHost( yaws_sample_list )
def set_face_scale(self, scale):
for comm_q in self.comm_qs:
comm_q.put( ('face_scale', scale) )
#overridable
def is_initialized(self):
return self.initialized
def __iter__(self):
return self
def __next__(self):
if not self.initialized:
return []
self.generator_counter += 1
generator = self.generators[self.generator_counter % len(self.generators) ]
return next(generator)
def batch_func(self, param ):
comm_q, src_samples, dst_samples, src_index_host, dst_index_host, ct_index_host = param
batch_size = self.batch_size
resolution = self.resolution
face_type = self.face_type
data_format = self.data_format
random_src_flip = self.random_src_flip
random_dst_flip = self.random_dst_flip
ct_mode = self.ct_mode
rotation_range=[-10,10]
scale_range=[-0.05, 0.05]
tx_range=[-0.05, 0.05]
ty_range=[-0.05, 0.05]
rnd_state = np.random
face_scale = 1.0
hi_res = 1024
def gen_sample(sample, target_face_type, resolution, allow_flip=False, scale=1.0, ct_mode=None, ct_sample=None):#:, tx, ty, rotation, scale):
tx = rnd_state.uniform( tx_range[0], tx_range[1] )
ty = rnd_state.uniform( ty_range[0], ty_range[1] )
rotation = rnd_state.uniform( rotation_range[0], rotation_range[1] )
scale = rnd_state.uniform(scale +scale_range[0], scale +scale_range[1])
flip = allow_flip and rnd_state.randint(10) < 4
face_type = sample.face_type
face_lmrks = sample.landmarks
face = sample.load_bgr()
h,w,c = face.shape
if face_type == FaceType.HEAD:
hi_mat = LandmarksProcessor.get_transform_mat (face_lmrks, hi_res, FaceType.HEAD)
else:
hi_mat = LandmarksProcessor.get_transform_mat (face_lmrks, hi_res, FaceType.HEAD_FACE)
hi_lmrks = LandmarksProcessor.transform_points(face_lmrks, hi_mat)
hi_warp_params = imagelib.gen_warp_params(hi_res)
face_warp_params = imagelib.gen_warp_params(resolution)
hi_to_target_mat = LandmarksProcessor.get_transform_mat (hi_lmrks, resolution, target_face_type)
hi_to_target_mat = mathlib.transform_mat(hi_to_target_mat, resolution, tx, ty, rotation, scale)
face_to_target_mat = LandmarksProcessor.get_transform_mat (face_lmrks, resolution, target_face_type)
face_to_target_mat = mathlib.transform_mat(face_to_target_mat, resolution, tx, ty, rotation, scale)
warped_face = face
if ct_mode is not None:
ct_bgr = ct_sample.load_bgr()
ct_bgr = cv2.resize(ct_bgr, (w,h), interpolation=cv2.INTER_LINEAR )
warped_face = imagelib.color_transfer (ct_mode, warped_face, ct_bgr)
warped_face = cv2.warpAffine(warped_face, hi_mat, (hi_res,hi_res), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
warped_face = np.clip( imagelib.warp_by_params (hi_warp_params, warped_face, can_warp=True, can_transform=False, can_flip=False, border_replicate=cv2.BORDER_REPLICATE), 0, 1)
warped_face = cv2.warpAffine(warped_face, hi_to_target_mat, (resolution,resolution), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
"""
if face_type != target_face_type:
...
else:
if w != resolution:
face = cv2.resize(face, (resolution, resolution), interpolation=cv2.INTER_CUBIC )
"""
# warped_face = cv2.warpAffine(warped_face, face_to_target_mat, (resolution,resolution), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
# warped_face = np.clip( imagelib.warp_by_params (face_warp_params, warped_face, can_warp=True, can_transform=False, can_flip=False, border_replicate=cv2.BORDER_REPLICATE), 0, 1)
target_face = face
if ct_mode is not None:
target_face = imagelib.color_transfer (ct_mode, target_face, ct_bgr)
target_face = cv2.warpAffine(target_face, face_to_target_mat, (resolution,resolution), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
face_mask = sample.get_xseg_mask()
if face_mask is not None:
if face_mask.shape[0] != h or face_mask.shape[1] != w:
face_mask = cv2.resize(face_mask, (w,h), interpolation=cv2.INTER_CUBIC)
face_mask = imagelib.normalize_channels(face_mask, 1)
else:
face_mask = LandmarksProcessor.get_image_hull_mask (face.shape, face_lmrks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
face_mask = np.clip(face_mask, 0, 1)
target_face_mask = cv2.warpAffine(face_mask, face_to_target_mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LINEAR )
target_face_mask = imagelib.normalize_channels(target_face_mask, 1)
target_face_mask = np.clip(target_face_mask, 0, 1)
em_mask = np.clip(LandmarksProcessor.get_image_eye_mask (face.shape, face_lmrks) + \
LandmarksProcessor.get_image_mouth_mask (face.shape, face_lmrks), 0, 1)
target_face_em = cv2.warpAffine(em_mask, face_to_target_mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LINEAR )
target_face_em = imagelib.normalize_channels(target_face_em, 1)
div = target_face_em.max()
if div != 0.0:
target_face_em = target_face_em / div
target_face_em = target_face_em * target_face_mask
# while True:
# cv2.imshow('', warped_face)
# cv2.waitKey(0)
# cv2.imshow('', target_face)
# cv2.waitKey(0)
# cv2.imshow('', target_face_mask)
# cv2.waitKey(0)
# cv2.imshow('', target_face_em)
# cv2.waitKey(0)
# import code
# code.interact(local=dict(globals(), **locals()))
if flip:
warped_face = warped_face[:,::-1,...]
target_face = target_face[:,::-1,...]
target_face_mask = target_face_mask[:,::-1,...]
target_face_em = target_face_em[:,::-1,...]
return warped_face, target_face, target_face_mask, target_face_em
while True:
while not comm_q.empty():
cmd, param = comm_q.get()
if cmd == 'face_scale':
face_scale = param
batches = [ [], [], [], [], [], [] ,[] ,[] ] #
src_indexes = src_index_host.multi_get(batch_size)
dst_indexes = dst_index_host.multi_get(batch_size)
for n_batch in range(batch_size):
src_sample = src_samples[src_indexes[n_batch]]
dst_sample = dst_samples[dst_indexes[n_batch]]
src_warped_face, src_target_face, src_target_face_mask, src_target_face_em = \
gen_sample(src_sample, face_type, resolution, allow_flip=random_src_flip, scale=face_scale, ct_mode=ct_mode, ct_sample=dst_sample)
dst_warped_face, dst_target_face, dst_target_face_mask, dst_target_face_em = \
gen_sample(dst_sample, face_type, resolution, allow_flip=random_dst_flip, scale=face_scale)
if data_format == "NCHW":
src_warped_face = np.transpose(src_warped_face, (2,0,1) )
src_target_face = np.transpose(src_target_face, (2,0,1) )
src_target_face_mask = np.transpose(src_target_face_mask, (2,0,1) )
src_target_face_em = np.transpose(src_target_face_em, (2,0,1) )
dst_warped_face = np.transpose(dst_warped_face, (2,0,1) )
dst_target_face = np.transpose(dst_target_face, (2,0,1) )
dst_target_face_mask = np.transpose(dst_target_face_mask, (2,0,1) )
dst_target_face_em = np.transpose(dst_target_face_em, (2,0,1) )
batches[0].append(src_warped_face)
batches[1].append(src_target_face)
batches[2].append(src_target_face_mask)
batches[3].append(src_target_face_em)
batches[4].append(dst_warped_face)
batches[5].append(dst_target_face)
batches[6].append(dst_target_face_mask)
batches[7].append(dst_target_face_em)
yield [ np.array(batch) for batch in batches]
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