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optimizations of nnlib and SampleGeneratorFace,

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refactorings
This commit is contained in:
iperov 2019-01-22 11:52:04 +04:00
parent 2de45083a4
commit b6c4171ea1
9 changed files with 175 additions and 79 deletions
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85
samples/SampleProcessor.py
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@ -14,10 +14,15 @@ class SampleProcessor(object):
TRANSFORMED = 0x00000008,
LANDMARKS_ARRAY = 0x00000010, #currently unused
RANDOM_CLOSE = 0x00000020,
MORPH_TO_RANDOM_CLOSE \
= 0x00000040,
FACE_ALIGN_HALF = 0x00000100,
FACE_ALIGN_FULL = 0x00000200,
FACE_ALIGN_HEAD = 0x00000400,
FACE_ALIGN_AVATAR = 0x00000800,
FACE_ALIGN_AVATAR = 0x00000800,
FACE_MASK_FULL = 0x00001000,
FACE_MASK_EYES = 0x00002000,
@ -38,18 +43,24 @@ class SampleProcessor(object):
@staticmethod
def process (sample, sample_process_options, output_sample_types, debug):
source = sample.load_bgr()
h,w,c = source.shape
sample_bgr = sample.load_bgr()
h,w,c = sample_bgr.shape
is_face_sample = sample.landmarks is not None
if debug and is_face_sample:
LandmarksProcessor.draw_landmarks (source, sample.landmarks, (0, 1, 0))
LandmarksProcessor.draw_landmarks (sample_bgr, sample.landmarks, (0, 1, 0))
close_sample = sample.close_target_list[ np.random.randint(0, len(sample.close_target_list)) ] if sample.close_target_list is not None else None
close_sample_bgr = close_sample.load_bgr() if close_sample is not None else None
if debug and close_sample_bgr is not None:
LandmarksProcessor.draw_landmarks (close_sample_bgr, close_sample.landmarks, (0, 1, 0))
params = image_utils.gen_warp_params(sample_bgr, 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 )
params = image_utils.gen_warp_params(source, 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 )
images = [[None]*3 for _ in range(5)]
images = [[None]*3 for _ in range(30)]
sample_rnd_seed = np.random.randint(0x80000000)
outputs = []
@ -71,6 +82,11 @@ class SampleProcessor(object):
else:
raise ValueError ('expected SampleTypeFlags type')
if f & SampleProcessor.TypeFlags.RANDOM_CLOSE != 0:
img_type += 10
elif f & SampleProcessor.TypeFlags.MORPH_TO_RANDOM_CLOSE != 0:
img_type += 20
face_mask_type = 0
if f & SampleProcessor.TypeFlags.FACE_MASK_FULL != 0:
face_mask_type = 1
@ -92,14 +108,54 @@ class SampleProcessor(object):
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)
img = l
else:
else:
if images[img_type][face_mask_type] is None:
img = source
if img_type >= 10 and img_type <= 19: #RANDOM_CLOSE
img_type -= 10
img = close_sample_bgr
cur_sample = close_sample
elif img_type >= 20 and img_type <= 29: #MORPH_TO_RANDOM_CLOSE
img_type -= 20
res = sample.shape[0]
s_landmarks = sample.landmarks.copy()
d_landmarks = close_sample.landmarks.copy()
idxs = list(range(len(s_landmarks)))
#remove landmarks near boundaries
for i in idxs[:]:
s_l = s_landmarks[i]
d_l = d_landmarks[i]
if s_l[0] < 5 or s_l[1] < 5 or s_l[0] >= res-5 or s_l[1] >= res-5 or \
d_l[0] < 5 or d_l[1] < 5 or d_l[0] >= res-5 or d_l[1] >= res-5:
idxs.remove(i)
#remove landmarks that close to each other in 5 dist
for landmarks in [s_landmarks, d_landmarks]:
for i in idxs[:]:
s_l = landmarks[i]
for j in idxs[:]:
if i == j:
continue
s_l_2 = landmarks[j]
diff_l = np.abs(s_l - s_l_2)
if np.sqrt(diff_l.dot(diff_l)) < 5:
idxs.remove(i)
break
s_landmarks = s_landmarks[idxs]
d_landmarks = d_landmarks[idxs]
s_landmarks = np.concatenate ( [s_landmarks, [ [0,0], [ res // 2, 0], [ res-1, 0], [0, res//2], [res-1, res//2] ,[0,res-1] ,[res//2, res-1] ,[res-1,res-1] ] ] )
d_landmarks = np.concatenate ( [d_landmarks, [ [0,0], [ res // 2, 0], [ res-1, 0], [0, res//2], [res-1, res//2] ,[0,res-1] ,[res//2, res-1] ,[res-1,res-1] ] ] )
img = image_utils.morph_by_points (sample_bgr, s_landmarks, d_landmarks)
cur_sample = close_sample
else:
img = sample_bgr
cur_sample = sample
if is_face_sample:
if face_mask_type == 1:
img = np.concatenate( (img, LandmarksProcessor.get_image_hull_mask (source, sample.landmarks) ), -1 )
img = np.concatenate( (img, LandmarksProcessor.get_image_hull_mask (img.shape, cur_sample.landmarks) ), -1 )
elif face_mask_type == 2:
mask = LandmarksProcessor.get_image_eye_mask (source, sample.landmarks)
mask = LandmarksProcessor.get_image_eye_mask (img.shape, cur_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 )
@ -107,11 +163,10 @@ class SampleProcessor(object):
images[img_type][face_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, face_mask_type == 0)
img = images[img_type][face_mask_type]
if is_face_sample and target_face_type != -1:
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 )