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import collections
import math
from enum import IntEnum
import cv2
import numpy as np
from core import imagelib
from core.cv2ex import *
from core.imagelib import sd
from facelib import FaceType, LandmarksProcessor
class SampleProcessor(object):
class SampleType(IntEnum):
NONE = 0
IMAGE = 1
FACE_IMAGE = 2
FACE_MASK = 3
LANDMARKS_ARRAY = 4
PITCH_YAW_ROLL = 5
PITCH_YAW_ROLL_SIGMOID = 6
class ChannelType(IntEnum):
NONE = 0
BGR = 1 #BGR
G = 2 #Grayscale
GGG = 3 #3xGrayscale
class FaceMaskType(IntEnum):
NONE = 0
FULL_FACE = 1 # mask all hull as grayscale
EYES = 2 # mask eyes hull as grayscale
EYES_MOUTH = 3 # eyes and mouse
class Options(object):
def __init__(self, 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] ):
self.random_flip = random_flip
self.rotation_range = rotation_range
self.scale_range = scale_range
self.tx_range = tx_range
self.ty_range = ty_range
@staticmethod
def process (samples, sample_process_options, output_sample_types, debug, ct_sample=None):
SPST = SampleProcessor.SampleType
SPCT = SampleProcessor.ChannelType
SPFMT = SampleProcessor.FaceMaskType
outputs = []
for sample in samples:
sample_rnd_seed = np.random.randint(0x80000000)
sample_face_type = sample.face_type
sample_bgr = sample.load_bgr()
sample_landmarks = sample.landmarks
ct_sample_bgr = None
h,w,c = sample_bgr.shape
def get_full_face_mask():
xseg_mask = sample.get_xseg_mask()
if xseg_mask is not None:
if xseg_mask.shape[0] != h or xseg_mask.shape[1] != w:
xseg_mask = cv2.resize(xseg_mask, (w,h), interpolation=cv2.INTER_CUBIC)
xseg_mask = imagelib.normalize_channels(xseg_mask, 1)
return np.clip(xseg_mask, 0, 1)
else:
full_face_mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample_landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
return np.clip(full_face_mask, 0, 1)
def get_eyes_mask():
eyes_mask = LandmarksProcessor.get_image_eye_mask (sample_bgr.shape, sample_landmarks)
return np.clip(eyes_mask, 0, 1)
def get_eyes_mouth_mask():
eyes_mask = LandmarksProcessor.get_image_eye_mask (sample_bgr.shape, sample_landmarks)
mouth_mask = LandmarksProcessor.get_image_mouth_mask (sample_bgr.shape, sample_landmarks)
mask = eyes_mask + mouth_mask
return np.clip(mask, 0, 1)
is_face_sample = sample_landmarks is not None
if debug and is_face_sample:
LandmarksProcessor.draw_landmarks (sample_bgr, sample_landmarks, (0, 1, 0))
outputs_sample = []
for opts in output_sample_types:
resolution = opts.get('resolution', 0)
sample_type = opts.get('sample_type', SPST.NONE)
channel_type = opts.get('channel_type', SPCT.NONE)
nearest_resize_to = opts.get('nearest_resize_to', None)
warp = opts.get('warp', False)
transform = opts.get('transform', False)
random_hsv_shift_amount = opts.get('random_hsv_shift_amount', 0)
normalize_tanh = opts.get('normalize_tanh', False)
ct_mode = opts.get('ct_mode', None)
data_format = opts.get('data_format', 'NHWC')
rnd_seed_shift = opts.get('rnd_seed_shift', 0)
warp_rnd_seed_shift = opts.get('warp_rnd_seed_shift', rnd_seed_shift)
rnd_state = np.random.RandomState (sample_rnd_seed+rnd_seed_shift)
warp_rnd_state = np.random.RandomState (sample_rnd_seed+warp_rnd_seed_shift)
warp_params = imagelib.gen_warp_params(resolution,
sample_process_options.random_flip,
rotation_range=sample_process_options.rotation_range,
scale_range=sample_process_options.scale_range,
tx_range=sample_process_options.tx_range,
ty_range=sample_process_options.ty_range,
rnd_state=rnd_state,
warp_rnd_state=warp_rnd_state,
)
if sample_type == SPST.FACE_MASK or sample_type == SPST.IMAGE:
border_replicate = False
elif sample_type == SPST.FACE_IMAGE:
border_replicate = True
border_replicate = opts.get('border_replicate', border_replicate)
borderMode = cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT
if sample_type == SPST.FACE_IMAGE or sample_type == SPST.FACE_MASK:
if not is_face_sample:
raise ValueError("face_samples should be provided for sample_type FACE_*")
if sample_type == SPST.FACE_IMAGE or sample_type == SPST.FACE_MASK:
face_type = opts.get('face_type', None)
face_mask_type = opts.get('face_mask_type', SPFMT.NONE)
if face_type is None:
raise ValueError("face_type must be defined for face samples")
if sample_type == SPST.FACE_MASK:
if face_mask_type == SPFMT.FULL_FACE:
img = get_full_face_mask()
elif face_mask_type == SPFMT.EYES:
img = get_eyes_mask()
elif face_mask_type == SPFMT.EYES_MOUTH:
mask = get_full_face_mask().copy()
mask[mask != 0.0] = 1.0
img = get_eyes_mouth_mask()*mask
else:
img = np.zeros ( sample_bgr.shape[0:2]+(1,), dtype=np.float32)
if sample_face_type == FaceType.MARK_ONLY:
raise NotImplementedError()
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
img = cv2.warpAffine( img, mat, (warp_resolution, warp_resolution), flags=cv2.INTER_LINEAR )
img = imagelib.warp_by_params (warp_params, img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
img = cv2.resize( img, (resolution,resolution), interpolation=cv2.INTER_LINEAR )
else:
if face_type != sample_face_type:
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_LINEAR )
else:
if w != resolution:
img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_LINEAR )
img = imagelib.warp_by_params (warp_params, img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
if face_mask_type == SPFMT.EYES_MOUTH:
div = img.max()
if div != 0.0:
img = img / div # normalize to 1.0 after warp
if len(img.shape) == 2:
img = img[...,None]
if channel_type == SPCT.G:
out_sample = img.astype(np.float32)
else:
raise ValueError("only channel_type.G supported for the mask")
elif sample_type == SPST.FACE_IMAGE:
img = sample_bgr
if face_type != sample_face_type:
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC )
else:
if w != resolution:
img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_CUBIC )
# Apply random color transfer
if ct_mode is not None and ct_sample is not None:
if ct_sample_bgr is None:
ct_sample_bgr = ct_sample.load_bgr()
img = imagelib.color_transfer (ct_mode, img, cv2.resize( ct_sample_bgr, (resolution,resolution), interpolation=cv2.INTER_LINEAR ) )
if random_hsv_shift_amount != 0:
a = random_hsv_shift_amount
h_amount = max(1, int(360*a*0.5))
img_h, img_s, img_v = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
img_h = (img_h + rnd_state.randint(-h_amount, h_amount+1) ) % 360
img_s = np.clip (img_s + (rnd_state.random()-0.5)*a, 0, 1 )
img_v = np.clip (img_v + (rnd_state.random()-0.5)*a, 0, 1 )
img = np.clip( cv2.cvtColor(cv2.merge([img_h, img_s, img_v]), cv2.COLOR_HSV2BGR) , 0, 1 )
img = imagelib.warp_by_params (warp_params, img, warp, transform, can_flip=True, border_replicate=border_replicate)
img = np.clip(img.astype(np.float32), 0, 1)
# Transform from BGR to desired channel_type
if channel_type == SPCT.BGR:
out_sample = img
elif channel_type == SPCT.G:
out_sample = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[...,None]
elif channel_type == SPCT.GGG:
out_sample = np.repeat ( np.expand_dims(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),-1), (3,), -1)
# Final transformations
if nearest_resize_to is not None:
out_sample = cv2_resize(out_sample, (nearest_resize_to,nearest_resize_to), interpolation=cv2.INTER_NEAREST)
if not debug:
if normalize_tanh:
out_sample = np.clip (out_sample * 2.0 - 1.0, -1.0, 1.0)
if data_format == "NCHW":
out_sample = np.transpose(out_sample, (2,0,1) )
elif sample_type == SPST.IMAGE:
img = sample_bgr
img = imagelib.warp_by_params (warp_params, img, warp, transform, can_flip=True, border_replicate=True)
img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_CUBIC )
out_sample = img
if data_format == "NCHW":
out_sample = np.transpose(out_sample, (2,0,1) )
elif sample_type == SPST.LANDMARKS_ARRAY:
l = sample_landmarks
l = np.concatenate ( [ np.expand_dims(l[:,0] / w,-1), np.expand_dims(l[:,1] / h,-1) ], -1 )
l = np.clip(l, 0.0, 1.0)
out_sample = l
elif sample_type == SPST.PITCH_YAW_ROLL or sample_type == SPST.PITCH_YAW_ROLL_SIGMOID:
pitch,yaw,roll = sample.get_pitch_yaw_roll()
if warp_params['flip']:
yaw = -yaw
if sample_type == SPST.PITCH_YAW_ROLL_SIGMOID:
pitch = np.clip( (pitch / math.pi) / 2.0 + 0.5, 0, 1)
yaw = np.clip( (yaw / math.pi) / 2.0 + 0.5, 0, 1)
roll = np.clip( (roll / math.pi) / 2.0 + 0.5, 0, 1)
out_sample = (pitch, yaw)
else:
raise ValueError ('expected sample_type')
outputs_sample.append ( out_sample )
outputs += [outputs_sample]
return outputs
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