class PersonDetector:
def __init__(self, gpu_id=0, fastface=False):
self.fastface = fastface
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg_priv.PersonDet.DEPLOY, cfg_priv.PersonDet.WEIGHTS, caffe.TEST)
self.D = Detector(det_net, mean=cfg_priv.PersonDet.PIXEL_MEANS, std=cfg_priv.PersonDet.PIXEL_STDS,
scales=(480,), max_sizes=(800,), preN=500, postN=50, conf_thresh=0.7,
color_map={0: [192, 0, 192], 1: [64, 64, 255]}, class_map=V.PERSON_CLASS)
if self.fastface:
face_net = caffe.Net(cfg_priv.FastFace.DEPLOY, cfg_priv.FastFace.WEIGHTS, caffe.TEST)
self.FastFace = Detector(face_net, mean=cfg_priv.FastFace.PIXEL_MEANS, std=cfg_priv.FastFace.PIXEL_STDS,
scales=(480,), max_sizes=(800,), preN=500, postN=50, conf_thresh=0.5,
color_map={0: [192, 0, 192], 1: [255, 64, 64]}, class_map=V.FACE_CLASS)
def __call__(self, img):
objs = self.D.det_im(img)
if self.fastface:
face_objs = self.FastFace.det_im(img)
objs.extend(face_objs)
if objs is None:
return None
else:
return objs_sort_by_center(objs)
def __del__(self):
print self.__class__.__name__
def __init__(self, gpu_id=0):
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg.FastFace.DEPLOY, cfg.FastFace.WEIGHTS,
caffe.TEST)
self.FastFace = Detector(det_net,
mean=cfg.FastFace.PIXEL_MEANS,
std=cfg.FastFace.PIXEL_STDS,
scales=(300, ),
max_sizes=(400, ),
preN=500,
postN=50,
conf_thresh=0.5,
color_map={
0: [192, 0, 192],
1: [255, 64, 64]
},
class_map=V.FACE_CLASS)
id_net = caffe.Net(cfg.FaceID.DEPLOY, cfg.FaceID.WEIGHTS, caffe.TEST)
self.I = Identity(id_net,
mean=cfg.FaceID.PIXEL_MEANS,
std=cfg.FaceID.PIXEL_STDS,
base_size=256,
crop_size=224,
crop_type='center',
prob_layer='classifier',
gallery=np.load(cfg.FaceID.GALLERY))
self.name_list = open(cfg.FaceID.GALLERY_NAMES).readlines()
print self.name_list
def __init__(self, gpu_id=0):
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg_priv.Traffic.DEPLOY, cfg_priv.Traffic.WEIGHTS, caffe.TEST)
self.D = Detector(det_net, mean=cfg_priv.Traffic.PIXEL_MEANS, std=cfg_priv.Traffic.PIXEL_STDS,
scales=(540,), max_sizes=(960,), preN=6000, postN=300, conf_thresh=0.6,
color_map=V.COLORMAP21, class_map=V.SHANXI7_CLASS)
def __init__(self, gpu_id=0):
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg.ObjDet.DEPLOY, cfg.ObjDet.WEIGHTS, caffe.TEST)
self.D = Detector(det_net,
mean=cfg.ObjDet.PIXEL_MEANS,
std=cfg.ObjDet.PIXEL_STDS,
scales=(480, ),
max_sizes=(960, ),
preN=1000,
postN=100,
conf_thresh=0.7,
color_map=V.COLORMAP81,
class_map=V.COCO_CLASS81)
def __init__(self, gpu_id=0, fastface=False):
self.fastface = fastface
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg_priv.PersonDet.DEPLOY, cfg_priv.PersonDet.WEIGHTS, caffe.TEST)
self.D = Detector(det_net, mean=cfg_priv.PersonDet.PIXEL_MEANS, std=cfg_priv.PersonDet.PIXEL_STDS,
scales=(480,), max_sizes=(800,), preN=500, postN=50, conf_thresh=0.7,
color_map={0: [192, 0, 192], 1: [64, 64, 255]}, class_map=V.PERSON_CLASS)
if self.fastface:
face_net = caffe.Net(cfg_priv.FastFace.DEPLOY, cfg_priv.FastFace.WEIGHTS, caffe.TEST)
self.FastFace = Detector(face_net, mean=cfg_priv.FastFace.PIXEL_MEANS, std=cfg_priv.FastFace.PIXEL_STDS,
scales=(480,), max_sizes=(800,), preN=500, postN=50, conf_thresh=0.5,
color_map={0: [192, 0, 192], 1: [255, 64, 64]}, class_map=V.FACE_CLASS)
class TrafficDetector:
def __init__(self, gpu_id=0):
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg.Traffic.DEPLOY, cfg.Traffic.WEIGHTS,
caffe.TEST)
self.D = Detector(det_net,
mean=cfg.Traffic.PIXEL_MEANS,
std=cfg.Traffic.PIXEL_STDS,
scales=(540, ),
max_sizes=(960, ),
preN=6000,
postN=300,
conf_thresh=0.6,
color_map=V.COLORMAP21,
class_map=V.SHANXI7_CLASS)
def __call__(self, img):
objs = self.D.det_im(img)
if objs is None:
return None
else:
return objs_sort_by_center(objs)
def __del__(self):
print self.__class__.__name__
class ObjDetector:
def __init__(self, gpu_id=0):
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg_priv.ObjDet.DEPLOY, cfg_priv.ObjDet.WEIGHTS,
caffe.TEST)
self.D = Detector(det_net,
mean=cfg_priv.ObjDet.PIXEL_MEANS,
std=cfg_priv.ObjDet.PIXEL_STDS,
scales=(480, ),
max_sizes=(960, ),
preN=1000,
postN=100,
conf_thresh=0.7,
color_map=V.COLORMAP81,
class_map=V.COCO_CLASS81)
def __call__(self, img):
objs = self.D.det_im(img)
if objs is None:
return None
else:
return objs_sort_by_center(objs)
def __del__(self):
print self.__class__.__name__
class FaceDetector:
def __init__(self, gpu_id=0):
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg.FaceDet.DEPLOY, cfg.FaceDet.WEIGHTS,
caffe.TEST)
self.D = Detector(det_net,
mean=cfg.FaceDet.PIXEL_MEANS,
std=cfg.FaceDet.PIXEL_STDS,
scales=(480, ),
max_sizes=(800, ),
preN=500,
postN=50,
conf_thresh=0.7,
color_map={
0: [192, 0, 192],
1: [255, 64, 64]
},
class_map=V.FACE_CLASS)
def __call__(self, img):
objs = self.D.det_im(img)
if objs is None:
return None
else:
return objs_sort_by_center(objs)
def __del__(self):
print self.__class__.__name__
def __init__(self, gpu_id=0):
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg.FaceDet.DEPLOY, cfg.FaceDet.WEIGHTS,
caffe.TEST)
self.D = Detector(det_net,
mean=cfg.FaceDet.PIXEL_MEANS,
std=cfg.FaceDet.PIXEL_STDS,
scales=(480, ),
max_sizes=(800, ),
preN=500,
postN=50,
conf_thresh=0.7,
color_map={
0: [192, 0, 192],
1: [255, 64, 64]
},
class_map=V.FACE_CLASS)
class FaceRecog:
def __init__(self, gpu_id=0):
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
det_net = caffe.Net(cfg.FastFace.DEPLOY, cfg.FastFace.WEIGHTS,
caffe.TEST)
self.FastFace = Detector(det_net,
mean=cfg.FastFace.PIXEL_MEANS,
std=cfg.FastFace.PIXEL_STDS,
scales=(300, ),
max_sizes=(400, ),
preN=500,
postN=50,
conf_thresh=0.5,
color_map={
0: [192, 0, 192],
1: [255, 64, 64]
},
class_map=V.FACE_CLASS)
id_net = caffe.Net(cfg.FaceID.DEPLOY, cfg.FaceID.WEIGHTS, caffe.TEST)
self.I = Identity(id_net,
mean=cfg.FaceID.PIXEL_MEANS,
std=cfg.FaceID.PIXEL_STDS,
base_size=256,
crop_size=224,
crop_type='center',
prob_layer='classifier',
gallery=np.load(cfg.FaceID.GALLERY))
self.name_list = open(cfg.FaceID.GALLERY_NAMES).readlines()
print self.name_list
def __call__(self, img):
img_det = img.copy()
objs = self.FastFace.det_im(img_det)
if objs is None:
return None
else:
faces = []
for i in objs:
face_box = T.extend_bbox(img, i['bbox'])
crop_face = T.bbox_crop(img, face_box)
faces.append(crop_face)
if len(faces) == 0:
return None
ids = self.I.cls_batch(faces)
for _ in xrange(len(ids)):
sims = self.I.one2n_identity(ids[_])
idx = np.asarray(sims).argsort()[-1]
score = sims[idx]
if score >= 0.5:
objs[_]['attri'] = dict(name=self.name_list[idx].strip())
# score='{:.3f}'.format(score),
# age='unknown')
else:
objs[_]['attri'] = dict(
name='--------') # , score=str(0.0), age='unknown')
return objs
def __del__(self):
print self.__class__.__name__
