def get_feature(buffer):
embedding = np.zeros( (len(buffer), emb_size), dtype=np.float32 )
if use_flip:
input_blob = np.zeros( (len(buffer)*2, 3, image_shape[1], image_shape[2] ) )
else:
input_blob = np.zeros( (len(buffer), 3, image_shape[1], image_shape[2] ) )
idx = 0
for item in buffer:
img = face_preprocess.read_image(item[0], mode='rgb')
img = face_preprocess.preprocess(img, bbox=None, landmark=item[1], image_size='%d,%d'%(image_shape[1], image_shape[2]))
img = np.transpose( img, (2,0,1) )
attempts = [0,1] if use_flip else [0]
for flipid in attempts:
_img = np.copy(img)
if flipid==1:
do_flip(_img)
input_blob[idx] = _img
idx+=1
data = mx.nd.array(input_blob)
db = mx.io.DataBatch(data=(data,))
net.model.forward(db, is_train=False)
_embedding = net.model.get_outputs()[0].asnumpy()
if use_flip:
embedding1 = _embedding[0::2]
embedding2 = _embedding[1::2]
embedding = embedding1+embedding2
else:
embedding = _embedding
embedding = sklearn.preprocessing.normalize(embedding)
return embedding
def get_feature(image_path, bbox, landmark, nets, image_shape, use_align,
aligned, use_mean):
img = face_preprocess.read_image(image_path, mode='rgb')
# print(img.shape)
if img is None:
print('parse image', image_path, 'error')
return None
if not aligned:
_landmark = landmark
if not use_align:
_landmark = None
# cv2.imwrite("./align/origin_%s"%image_path.split('/')[-1], img)
img = face_preprocess.preprocess(img,
bbox=bbox,
landmark=_landmark,
image_size='%d,%d' %
(image_shape[1], image_shape[2]))
else:
assert img.shape == (image_shape[1], image_shape[2], image_shape[0])
# print('already aligned', image_path, img.shape)
# img = cv2.resize(img, (image_shape[2], image_shape[1]))
# cv2.imwrite("./align/%s"%image_path.split('/')[-1], img)
if use_mean > 0:
v_mean = np.array([127.5, 127.5, 127.5], dtype=np.float32).reshape(
(1, 1, 3))
img = img.astype(np.float32) - v_mean
img *= 0.0078125
img = np.transpose(img, (2, 0, 1))
F = None
for net in nets:
embedding = None
# ppatch = net.patch
for flipid in [0, 1]:
_img = np.copy(img)
if flipid == 1:
do_flip(_img)
# nimg = np.zeros(_img.shape, dtype=np.float32)
# nimg[:,ppatch[1]:ppatch[3],ppatch[0]:ppatch[2]] = _img[:, ppatch[1]:ppatch[3], ppatch[0]:ppatch[2]]
# _img = nimg
input_blob = np.expand_dims(_img, axis=0)
data = mx.nd.array(input_blob)
db = mx.io.DataBatch(data=(data, ))
net.model.forward(db, is_train=False)
_embedding = net.model.get_outputs()[0].asnumpy().flatten()
# print(_embedding.shape)
if embedding is None:
embedding = _embedding
else:
embedding += _embedding
_norm = np.linalg.norm(embedding)
embedding /= _norm
if F is None:
F = embedding
else:
# F += embedding
F = np.concatenate((F, embedding), axis=0)
_norm = np.linalg.norm(F)
F /= _norm
# print(F.shape)
return F
def get_feature(image_path, bbox, landmark, nets, image_shape, use_align, aligned, use_mean):
img = face_preprocess.read_image(image_path, mode='rgb')
#print(img.shape)
if img is None:
print('parse image',image_path,'error')
return None
if not aligned:
_landmark = landmark
if not use_align:
_landmark = None
#cv2.imwrite("./align/origin_%s"%image_path.split('/')[-1], img)
img = face_preprocess.preprocess(img, bbox=bbox, landmark=_landmark, image_size='%d,%d'%(image_shape[1], image_shape[2]))
else:
assert img.shape==(image_shape[1],image_shape[2],image_shape[0])
#print('already aligned', image_path, img.shape)
#img = cv2.resize(img, (image_shape[2], image_shape[1]))
#cv2.imwrite("./align/%s"%image_path.split('/')[-1], img)
if use_mean>0:
v_mean = np.array([127.5,127.5,127.5], dtype=np.float32).reshape( (1,1,3) )
img = img.astype(np.float32) - v_mean
img *= 0.0078125
img = np.transpose( img, (2,0,1) )
F = None
for net in nets:
embedding = None
ppatch = net.patch
for flipid in [0,1]:
_img = np.copy(img)
if flipid==1:
do_flip(_img)
nimg = np.zeros(_img.shape, dtype=np.float32)
nimg[:,ppatch[1]:ppatch[3],ppatch[0]:ppatch[2]] = _img[:, ppatch[1]:ppatch[3], ppatch[0]:ppatch[2]]
_img = nimg
input_blob = np.expand_dims(_img, axis=0)
net.arg_params["data"] = mx.nd.array(input_blob, net.ctx)
net.arg_params["softmax_label"] = mx.nd.empty((1,), net.ctx)
exe = net.sym.bind(net.ctx, net.arg_params ,args_grad=None, grad_req="null", aux_states=net.aux_params)
exe.forward(is_train=False)
_embedding = exe.outputs[0].asnumpy().flatten()
#print(_embedding.shape)
if embedding is None:
embedding = _embedding
else:
embedding += _embedding
_norm=np.linalg.norm(embedding)
embedding /= _norm
if F is None:
F = embedding
else:
#F += embedding
F = np.concatenate((F,embedding), axis=0)
_norm=np.linalg.norm(F)
F /= _norm
#print(F.shape)
return F
def get_feature(image_path, bbox, landmark, nets, image_shape, use_align, aligned, use_mean):
img = face_preprocess.read_image(image_path, mode='rgb')
#print(img.shape)
if img is None:
print('parse image',image_path,'error')
return None
if not aligned:
_landmark = landmark
if not use_align:
_landmark = None
#cv2.imwrite("./align/origin_%s"%image_path.split('/')[-1], img)
img = face_preprocess.preprocess(img, bbox=bbox, landmark=_landmark, image_size='%d,%d'%(image_shape[1], image_shape[2]))
else:
assert img.shape==(image_shape[1],image_shape[2],image_shape[0])
#print('already aligned', image_path, img.shape)
#img = cv2.resize(img, (image_shape[2], image_shape[1]))
#cv2.imwrite("./align/%s"%image_path.split('/')[-1], img)
if use_mean>0:
v_mean = np.array([127.5,127.5,127.5], dtype=np.float32).reshape( (1,1,3) )
img = img.astype(np.float32) - v_mean
img *= 0.0078125
img = np.transpose( img, (2,0,1) )
F = None
for net in nets:
embedding = None
#ppatch = net.patch
for flipid in [0,1]:
_img = np.copy(img)
if flipid==1:
do_flip(_img)
#nimg = np.zeros(_img.shape, dtype=np.float32)
#nimg[:,ppatch[1]:ppatch[3],ppatch[0]:ppatch[2]] = _img[:, ppatch[1]:ppatch[3], ppatch[0]:ppatch[2]]
#_img = nimg
input_blob = np.expand_dims(_img, axis=0)
data = mx.nd.array(input_blob)
db = mx.io.DataBatch(data=(data,))
net.model.forward(db, is_train=False)
_embedding = net.model.get_outputs()[0].asnumpy().flatten()
#print(_embedding.shape)
if embedding is None:
embedding = _embedding
else:
embedding += _embedding
_norm=np.linalg.norm(embedding)
embedding /= _norm
if F is None:
F = embedding
else:
#F += embedding
F = np.concatenate((F,embedding), axis=0)
_norm=np.linalg.norm(F)
F /= _norm
#print(F.shape)
return F
