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S3FD : filtering faces < 40pix.

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iperov 2019-03-14 01:23:39 +04:00
parent 8da47fec13
commit 62af1b6f16
3 changed files with 27 additions and 12 deletions
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2
facelib/MTCExtractor.py
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@ -90,7 +90,7 @@ class MTCExtractor(object):
input_image = input_image[:,:,::-1].copy() input_image = input_image[:,:,::-1].copy()
(h, w, ch) = input_image.shape (h, w, ch) = input_image.shape
input_scale = self.scale_to / (w if w > h else h) input_scale = self.scale_to / max(w,h)
input_image = cv2.resize (input_image, ( int(w*input_scale), int(h*input_scale) ), interpolation=cv2.INTER_LINEAR) input_image = cv2.resize (input_image, ( int(w*input_scale), int(h*input_scale) ), interpolation=cv2.INTER_LINEAR)
detected_faces, pnts = detect_face ( input_image, self.min_face_size, self.pnet_fun, self.rnet_fun, self.onet_fun, [ self.thresh1, self.thresh2, self.thresh3 ], self.scale_factor ) detected_faces, pnts = detect_face ( input_image, self.min_face_size, self.pnet_fun, self.rnet_fun, self.onet_fun, [ self.thresh1, self.thresh2, self.thresh3 ], self.scale_factor )

24
facelib/S3FDExtractor.py
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@ -5,8 +5,6 @@ from nnlib import nnlib
class S3FDExtractor(object): class S3FDExtractor(object):
def __init__(self): def __init__(self):
self.scale_factor = 3
exec( nnlib.import_all(), locals(), globals() ) exec( nnlib.import_all(), locals(), globals() )
model_path = Path(__file__).parent / "S3FD.h5" model_path = Path(__file__).parent / "S3FD.h5"
@ -25,24 +23,30 @@ class S3FDExtractor(object):
input_image = input_image[:,:,::-1].copy() input_image = input_image[:,:,::-1].copy()
(h, w, ch) = input_image.shape (h, w, ch) = input_image.shape
d = w if w > h else h d = max(w, h)
input_scale = d / ( (d // self.scale_factor) - (d % self.scale_factor) ) scale_to = 640 if d >= 1280 else d / 2
scale_to = max(64, scale_to)
input_scale = d / scale_to
input_image = cv2.resize (input_image, ( int(w/input_scale), int(h/input_scale) ), interpolation=cv2.INTER_LINEAR) input_image = cv2.resize (input_image, ( int(w/input_scale), int(h/input_scale) ), interpolation=cv2.INTER_LINEAR)
olist = self.model.predict( np.expand_dims(input_image,0) ) olist = self.model.predict( np.expand_dims(input_image,0) )
detected_faces = self.refine (olist) detected_faces = self.refine (olist)
#filtering faces < 40pix by any side
#enlarging bottom line a bit for 2DFAN-4, because default is not enough covering a chin #enlarging bottom line a bit for 2DFAN-4, because default is not enough covering a chin
for face in detected_faces: new_detected_faces = []
l,t,r,b = face for l,t,r,b in detected_faces:
tb = (b-t) * 0.1 bt = b-t
face[3] += tb if min(r-l,bt) < 40:
continue
new_detected_faces.append ((l,t,r,b+bt*0.1))
return [ (int(face[0]*input_scale), return [ (int(face[0]*input_scale),
int(face[1]*input_scale), int(face[1]*input_scale),
int(face[2]*input_scale), int(face[2]*input_scale),
int(face[3]*input_scale)) for face in detected_faces ] int(face[3]*input_scale)) for face in new_detected_faces ]
def refine(self, olist): def refine(self, olist):
bboxlist = [] bboxlist = []

13
mainscripts/Extractor.py
View file

@ -79,13 +79,24 @@ class ExtractSubprocessor(Subprocessor):
image = self.cached_image[1] image = self.cached_image[1]
else: else:
image = cv2_imread( filename_path_str ) image = cv2_imread( filename_path_str )
h, w, ch = image.shape
wm = w % 2
hm = h % 2
if wm + hm != 0: #fix odd image
image = image[0:h-hm,0:w-wm,:]
self.cached_image = ( filename_path_str, image ) self.cached_image = ( filename_path_str, image )
if image is None: if image is None:
self.log_err ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) ) self.log_err ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) )
else: else:
if self.type == 'rects': if self.type == 'rects':
rects = self.e.extract_from_bgr (image) h, w, ch = image.shape
if min(w,h) < 128:
self.log_err ( 'Image is too small %s : [%d, %d]' % ( str(filename_path), w, h ) )
rects = []
else:
rects = self.e.extract_from_bgr (image)
return [str(filename_path), rects] return [str(filename_path), rects]
elif self.type == 'landmarks': elif self.type == 'landmarks':