def detect_im(net, im, thresh=0.05):
im_scale = _compute_scaling_factor(im.shape,cfg.TEST.SCALES[0],cfg.TEST.MAX_SIZE)
im_blob = _get_image_blob(im,[im_scale])
probs, boxes = forward_net(net,im_blob[0],im_scale,False)
boxes = boxes[:, 0:4]
inds = np.where(probs[:, 0] > thresh)[0]
probs = probs[inds, 0]
boxes = boxes[inds, :]
dets = np.hstack((boxes, probs[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(dets, cfg.TEST.NMS_THRESH)
cls_dets = dets[keep, :]
return cls_dets
def detect_im(net, im, thresh=0.05):
im_scale = _compute_scaling_factor(im.shape, cfg.TEST.SCALES[0],
cfg.TEST.MAX_SIZE)
im_blob = _get_image_blob(im, [im_scale])
probs, boxes = forward_net(net, im_blob[0], im_scale, False)
boxes = boxes[:, 0:4]
inds = np.where(probs[:, 0] > thresh)[0]
probs = probs[inds, 0]
boxes = boxes[inds, :]
dets = np.hstack((boxes, probs[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(dets, cfg.TEST.NMS_THRESH)
cls_dets = dets[keep, :]
return cls_dets
def detect(net,
im_path,
thresh=0.05,
visualize=False,
timers=None,
pyramid=False,
dect_visualization_folder=None):
"""
Main module to detect faces
:param net: The trained network
:param im_path: The path to the image
:param thresh: Detection with a less score than thresh are ignored
:param visualize: Whether to visualize the detections
:param timers: Timers for calculating detect time (if None new timers would be created)
:param pyramid: Whether to use pyramid during inference
:param visualization_folder: If set the visualizations would be saved in this folder (if visualize=True)
:return: cls_dets (bounding boxes concatenated with scores) and the timers
"""
if not timers:
timers = {'detect': Timer(), 'misc': Timer()}
im = cv2.imread(im_path)
im_class__file = im_path.split('/')[-2]
imfname = os.path.basename(im_path)
sys.stdout.flush()
timers['detect'].tic()
if not pyramid:
im_scale = _compute_scaling_factor(im.shape, cfg.TEST.SCALES[0],
cfg.TEST.MAX_SIZE)
im_blob = _get_image_blob(im, [im_scale])
probs, boxes = forward_net(net, im_blob[0], im_scale, False)
boxes = boxes[:, 0:4]
else:
all_probs = []
all_boxes = []
# Compute the scaling coefficients for the pyramid
base_scale = _compute_scaling_factor(im.shape,
cfg.TEST.PYRAMID_BASE_SIZE[0],
cfg.TEST.PYRAMID_BASE_SIZE[1])
pyramid_scales = [
float(scale) / cfg.TEST.PYRAMID_BASE_SIZE[0] * base_scale
for scale in cfg.TEST.SCALES
]
im_blobs = _get_image_blob(im, pyramid_scales)
for i in range(len(pyramid_scales)):
probs, boxes = forward_net(net, im_blobs[i], pyramid_scales[i],
True)
for j in xrange(len(probs)):
# Do not apply M3 to the largest scale
if i < len(pyramid_scales) - 1 or j < len(probs) - 1:
all_boxes.append(boxes[j][:, 0:4])
all_probs.append(probs[j].copy())
probs = np.concatenate(all_probs)
boxes = np.concatenate(all_boxes)
timers['detect'].toc()
timers['misc'].tic()
inds = np.where(probs[:, 0] > thresh)[0]
probs = probs[inds, 0]
boxes = boxes[inds, :]
dets = np.hstack((boxes, probs[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(dets, cfg.TEST.NMS_THRESH)
cls_dets = dets[keep, :]
if visualize:
plt_name = os.path.splitext(imfname)[0] + '_detections_{}'.format(
net.name)
dect_visualization_folder = os.path.join(dect_visualization_folder,
im_class__file)
visusalize_detections(im,
cls_dets,
plt_name=plt_name,
visualization_folder=dect_visualization_folder)
timers['misc'].toc()
return cls_dets, timers
def detect(net, im_path, thresh=0.05, visualize=False, timers=None, pyramid=False, visualization_folder=None):
"""
Main module to detect faces
:param net: The trained network
:param im_path: The path to the image
:param thresh: Detection with a less score than thresh are ignored
:param visualize: Whether to visualize the detections
:param timers: Timers for calculating detect time (if None new timers would be created)
:param pyramid: Whether to use pyramid during inference
:param visualization_folder: If set the visualizations would be saved in this folder (if visualize=True)
:return: cls_dets (bounding boxes concatenated with scores) and the timers
"""
if not timers:
timers = {'detect': Timer(),
'misc': Timer()}
im = cv2.imread(im_path)
imfname = os.path.basename(im_path)
sys.stdout.flush()
timers['detect'].tic()
if not pyramid:
im_scale = _compute_scaling_factor(im.shape,cfg.TEST.SCALES[0],cfg.TEST.MAX_SIZE)
im_blob = _get_image_blob(im,[im_scale])
probs, boxes = forward_net(net,im_blob[0],im_scale,False)
boxes = boxes[:, 0:4]
else:
all_probs = []
all_boxes = []
# Compute the scaling coefficients for the pyramid
base_scale = _compute_scaling_factor(im.shape,cfg.TEST.PYRAMID_BASE_SIZE[0],cfg.TEST.PYRAMID_BASE_SIZE[1])
pyramid_scales = [float(scale)/cfg.TEST.PYRAMID_BASE_SIZE[0]*base_scale
for scale in cfg.TEST.SCALES]
im_blobs = _get_image_blob(im,pyramid_scales)
for i in range(len(pyramid_scales)):
probs,boxes = forward_net(net,im_blobs[i],pyramid_scales[i],True)
for j in xrange(len(probs)):
# Do not apply M3 to the largest scale
if i<len(pyramid_scales)-1 or j<len(probs)-1:
all_boxes.append(boxes[j][:,0:4])
all_probs.append(probs[j].copy())
probs = np.concatenate(all_probs)
boxes = np.concatenate(all_boxes)
timers['detect'].toc()
timers['misc'].tic()
inds = np.where(probs[:, 0] > thresh)[0]
probs = probs[inds, 0]
boxes = boxes[inds, :]
dets = np.hstack((boxes, probs[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(dets, cfg.TEST.NMS_THRESH)
cls_dets = dets[keep, :]
if visualize:
plt_name = os.path.splitext(imfname)[0] + '_detections_{}'.format(net.name)
visusalize_detections(im, cls_dets, plt_name=plt_name, visualization_folder=visualization_folder)
timers['misc'].toc()
return cls_dets,timers
def detect(net, im_path, thresh=0.05, timers=None, pyramid=False):
if not timers:
timers = {'detect': Timer(), 'misc': Timer()}
im = cv2.imread(im_path)
imfname = os.path.basename(im_path)
sys.stdout.flush()
timers['detect'].tic()
if not pyramid:
im_scale = _compute_scaling_factor(im.shape, cfg.TEST.SCALES[0],
cfg.TEST.MAX_SIZE)
im_blob = _get_image_blob(im, [im_scale])
probs, boxes = forward_net(net, im_blob[0], im_scale, pyramid=False)
if isinstance(probs, list):
probs = np.vstack(probs)
boxes = np.vstack(boxes)
boxes = boxes[:, 0:4]
else:
all_probs = []
all_boxes = []
# Compute the scaling coefficients for the pyramid
base_scale = _compute_scaling_factor(im.shape,
cfg.TEST.PYRAMID_BASE_SIZE[0],
cfg.TEST.PYRAMID_BASE_SIZE[1])
pyramid_scales = [
float(scale) / cfg.TEST.PYRAMID_BASE_SIZE[0] * base_scale
for scale in cfg.TEST.SCALES
]
im_blobs = _get_image_blob(im, pyramid_scales)
for i in range(len(pyramid_scales)):
probs, boxes = forward_net(net,
im_blobs[i],
pyramid_scales[i],
pyramid=True)
for j in xrange(len(probs)):
all_boxes.append(boxes[j][:, 0:4])
all_probs.append(probs[j].copy())
if cfg.TEST.FLIP:
probs, boxes = forward_net(
net, {'data': im_blobs[i]['data'][..., ::-1]},
pyramid_scales[i],
pyramid=True,
flip=True)
for j in xrange(len(probs)):
all_boxes.append(boxes[j][:, 0:4])
all_probs.append(probs[j].copy())
probs = np.concatenate(all_probs)
boxes = np.concatenate(all_boxes)
timers['detect'].toc()
timers['misc'].tic()
cls_dets = [None] * (probs.shape[1] - 1)
for class_i in range(1, probs.shape[1]):
inds = np.where(probs[:, class_i] > thresh)[0]
probs_i = probs[inds, class_i]
boxes_i = boxes[inds, :]
dets = np.hstack((boxes_i, probs_i[:, np.newaxis])) \
.astype(np.float32, copy=False)
if cfg.TEST.NMS_METHOD == "BBOX_VOTE":
cls_dets[class_i - 1] = bbox_vote(dets)
elif cfg.TEST.NMS_METHOD == "NMS":
keep = nms(dets, cfg.TEST.NMS_THRESH)
cls_dets[class_i - 1] = dets[keep, :]
else:
raise NotImplementedError("Unknown NMS method: {}".format(
cfg.TEST.NMS_METHOD))
assert all([_ is not None for _ in cls_dets]), 'None in detection results'
timers['misc'].toc()
return cls_dets, timers
