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iperov 2018-06-04 17:12:43 +04:00
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models/TrainingDataGenerator.py Normal file
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from facelib import FaceType
from facelib import LandmarksProcessor
import cv2
import numpy as np
from models import TrainingDataGeneratorBase
from utils import image_utils
from utils import random_utils
from enum import IntEnum
from models import TrainingDataType
class TrainingDataGenerator(TrainingDataGeneratorBase):
class SampleTypeFlags(IntEnum):
SOURCE = 0x000001,
WARPED = 0x000002,
WARPED_TRANSFORMED = 0x000004,
TRANSFORMED = 0x000008,
HALF_FACE = 0x000010,
FULL_FACE = 0x000020,
HEAD_FACE = 0x000040,
AVATAR_FACE = 0x000080,
MARK_ONLY_FACE = 0x000100,
MODE_BGR = 0x001000, #BGR
MODE_G = 0x002000, #Grayscale
MODE_GGG = 0x004000, #3xGrayscale
MODE_M = 0x008000, #mask only
MODE_BGR_SHUFFLE = 0x010000, #BGR shuffle
MASK_FULL = 0x100000,
MASK_EYES = 0x200000,
#overrided
def onInitialize(self, random_flip=False, normalize_tanh=False, rotation_range=[-10,10], scale_range=[-0.05, 0.05], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], output_sample_types=[], **kwargs):
self.random_flip = random_flip
self.normalize_tanh = normalize_tanh
self.output_sample_types = output_sample_types
self.rotation_range = rotation_range
self.scale_range = scale_range
self.tx_range = tx_range
self.ty_range = ty_range
#overrided
def onProcessSample(self, sample, debug):
source = sample.load_bgr()
h,w,c = source.shape
is_face_sample = self.trainingdatatype >= TrainingDataType.FACE_BEGIN and self.trainingdatatype <= TrainingDataType.FACE_END
if debug and is_face_sample:
LandmarksProcessor.draw_landmarks (source, sample.landmarks, (0, 1, 0))
params = image_utils.gen_warp_params(source, self.random_flip, rotation_range=self.rotation_range, scale_range=self.scale_range, tx_range=self.tx_range, ty_range=self.ty_range )
images = [[None]*3 for _ in range(4)]
outputs = []
for t,size in self.output_sample_types:
if t & self.SampleTypeFlags.SOURCE != 0:
img_type = 0
elif t & self.SampleTypeFlags.WARPED != 0:
img_type = 1
elif t & self.SampleTypeFlags.WARPED_TRANSFORMED != 0:
img_type = 2
elif t & self.SampleTypeFlags.TRANSFORMED != 0:
img_type = 3
else:
raise ValueError ('expected SampleTypeFlags type')
mask_type = 0
if t & self.SampleTypeFlags.MASK_FULL != 0:
mask_type = 1
elif t & self.SampleTypeFlags.MASK_EYES != 0:
mask_type = 2
if images[img_type][mask_type] is None:
img = source
if is_face_sample:
if mask_type == 1:
img = np.concatenate( (img, LandmarksProcessor.get_image_hull_mask (source, sample.landmarks) ), -1 )
elif mask_type == 2:
mask = LandmarksProcessor.get_image_eye_mask (source, sample.landmarks)
mask = np.expand_dims (cv2.blur (mask, ( w // 32, w // 32 ) ), -1)
mask[mask > 0.0] = 1.0
img = np.concatenate( (img, mask ), -1 )
images[img_type][mask_type] = image_utils.warp_by_params (params, img, (img_type==1 or img_type==2), (img_type==2 or img_type==3), img_type != 0)
img = images[img_type][mask_type]
target_face_type = -1
if t & self.SampleTypeFlags.HALF_FACE != 0:
target_face_type = FaceType.HALF
elif t & self.SampleTypeFlags.FULL_FACE != 0:
target_face_type = FaceType.FULL
elif t & self.SampleTypeFlags.HEAD_FACE != 0:
target_face_type = FaceType.HEAD
elif t & self.SampleTypeFlags.AVATAR_FACE != 0:
target_face_type = FaceType.AVATAR
elif t & self.SampleTypeFlags.MARK_ONLY_FACE != 0:
target_face_type = FaceType.MARK_ONLY
if is_face_sample and target_face_type != -1 and target_face_type != FaceType.MARK_ONLY:
if target_face_type > sample.face_type:
raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, target_face_type) )
img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, size, target_face_type), (size,size), flags=cv2.INTER_LANCZOS4 )
else:
img = cv2.resize( img, (size,size), cv2.INTER_LANCZOS4 )
img_bgr = img[...,0:3]
img_mask = img[...,3:4]
if t & self.SampleTypeFlags.MODE_BGR != 0:
img = img
elif t & self.SampleTypeFlags.MODE_BGR_SHUFFLE != 0:
img_bgr = np.take (img_bgr, np.random.permutation(img_bgr.shape[-1]), axis=-1)
img = np.concatenate ( (img_bgr,img_mask) , -1 )
elif t & self.SampleTypeFlags.MODE_G != 0:
img = np.concatenate ( (np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1),img_mask) , -1 )
elif t & self.SampleTypeFlags.MODE_GGG != 0:
img = np.concatenate ( ( np.repeat ( np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1), (3,), -1), img_mask), -1)
elif is_face_sample and t & self.SampleTypeFlags.MODE_M != 0:
if mask_type== 0:
raise ValueError ('no mask mode defined')
img = img_mask
else:
raise ValueError ('expected SampleTypeFlags mode')
if not debug and self.normalize_tanh:
img = img * 2.0 - 1.0
outputs.append ( img )
if debug:
result = ()
for output in outputs:
if output.shape[2] < 4:
result += (output,)
elif output.shape[2] == 4:
result += (output[...,0:3]*output[...,3:4],)
return result
else:
return outputs