def demo(net, image_name, anchor_file):
# Load the demo image
im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', image_name)
if not os.path.exists(im_file):
print 'Image `{:s}` not found!'.format(image_name)
return
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im, anchor_file)
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
cls = 'obj'
dets = np.hstack((boxes, scores)).astype(np.float32, copy=False)
dets = boxes_filter(
dets,
2000, # NMS_CONFIG['PRE_NMS_TOPN'],
0.3, # NMS_CONFIG['NMS_THRESH'],
10 #NMS_CONFIG['POST_NMS_TOPN']
)
CONF_THRESH = 0.99
print 'All {} detections with p({} | box) >= {:.1f}'.format(
cls, cls, CONF_THRESH)
# vis_detections(im, cls, dets, thresh=CONF_THRESH)
res_im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo',
'res_' + image_name)
save_detection_res(im, res_im_file, dets, CONF_THRESH)
def demo(net, image_name, anchor_file):
# Load the demo image
im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', image_name)
if not os.path.exists(im_file):
print 'Image `{:s}` not found!'.format(image_name)
return
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im, anchor_file)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
cls = 'obj'
dets = np.hstack((boxes, scores)).astype(np.float32, copy=False)
dets = boxes_filter(dets, 2000, # NMS_CONFIG['PRE_NMS_TOPN'],
0.3, # NMS_CONFIG['NMS_THRESH'],
10 #NMS_CONFIG['POST_NMS_TOPN']
)
CONF_THRESH = 0.99
print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls,
CONF_THRESH)
# vis_detections(im, cls, dets, thresh=CONF_THRESH)
res_im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', 'res_'+image_name)
save_detection_res(im, res_im_file, dets, CONF_THRESH)
def demo(net, im_path, anchor_file, des_dir='demo'):
# Load the demo image
if not os.path.exists(im_path):
print 'Image `{:s}` not found!'.format(im_path)
return
#im = cv2.imread(im_path)
im = exif.load_exif_jpg(im_path)
#wdth = 1280
#eight = 720
#ulti = img.shape[0]*img.shape[1]*1.0/(width*height)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im, anchor_file)
#print scores
#print boxes
#print len(scores)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
cls = 'obj'
dets = np.hstack((boxes, scores)).astype(np.float32, copy=False)
dets = boxes_filter(dets, NMS_CONFIG['PRE_NMS_TOPN'],
NMS_CONFIG['NMS_THRESH'],
NMS_CONFIG['POST_NMS_TOPN'],
CONF_THRESH
)
print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls, CONF_THRESH)
#vis_detections(im, cls, dets, thresh=CONF_THRESH)
#print dets
# save result images
output_dir = os.path.join(cfg.ROOT_DIR, 'data', des_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# image_name = os.path.splitext(os.path.basename(im_path))[0]
# res_im_file = os.path.join(output_dir, 'res_'+image_name+'.jpg')
image_name = '_'.join(im_path.split('/')[-3:])
res_im_file = os.path.join(output_dir, image_name)
#save_detection_res(im, res_im_file, dets)
return (dets)
def demo(net, im_path, anchor_file, des_dir='demo'):
# Load the demo image
if not os.path.exists(im_path):
print 'Image `{:s}` not found!'.format(im_path)
return
#im = cv2.imread(im_path)
im = exif.load_exif_jpg(im_path)
#wdth = 1280
#eight = 720
#ulti = img.shape[0]*img.shape[1]*1.0/(width*height)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im, anchor_file)
#print scores
#print boxes
#print len(scores)
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
cls = 'obj'
dets = np.hstack((boxes, scores)).astype(np.float32, copy=False)
dets = boxes_filter(dets, NMS_CONFIG['PRE_NMS_TOPN'],
NMS_CONFIG['NMS_THRESH'], NMS_CONFIG['POST_NMS_TOPN'],
CONF_THRESH)
print 'All {} detections with p({} | box) >= {:.1f}'.format(
cls, cls, CONF_THRESH)
#vis_detections(im, cls, dets, thresh=CONF_THRESH)
#print dets
# save result images
output_dir = os.path.join(cfg.ROOT_DIR, 'data', des_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# image_name = os.path.splitext(os.path.basename(im_path))[0]
# res_im_file = os.path.join(output_dir, 'res_'+image_name+'.jpg')
image_name = '_'.join(im_path.split('/')[-3:])
res_im_file = os.path.join(output_dir, image_name)
#save_detection_res(im, res_im_file, dets)
return (dets)
def demo(rpn_net, fast_rcnn_net, anchor_file, image_name, classes=CLASSES,
share_conv=False, last_shared_blob_name=None):
"""
Detect object classes in an image.
The proposals are generating by region proposal network.
"""
# Load the demo image
im_file = os.path.join(rpn_config.cfg.ROOT_DIR, 'data', 'demo', image_name)
im = cv2.imread(im_file)
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
# ####### RPN
# Generate the proposal boxes
_t['im_detect'].tic()
scores, boxes = rpn_test.im_detect(rpn_net, im, anchor_file)
_t['im_detect'].toc()
print 'image: {:s} num proposal: {:d}'.format(image_name, boxes.shape[0])
# Filter the proposal boxes
_t['misc'].tic()
obj_proposals = np.hstack((boxes, scores)).astype(np.float32, copy=False)
obj_proposals = test_rpn_net.boxes_filter(obj_proposals, 6000, 0.7, 300)
_t['misc'].toc()
print 'image: {:s} num proposal filtered: {:d}'.format(image_name,
obj_proposals.shape[0])
print ('Actions took {:.3f}s for generating'
'{:d} proposal boxes, {:.3f}s for '
'filtering proposals.').format(_t['im_detect'].total_time,
boxes.shape[0], _t['misc'].total_time)
# ###### Fast-RCNN
# Detect all object classes and regress object bounds
_t['im_detect'].tic()
if share_conv:
# conv_feat_blob = rpn_net.blobs['conv5'].data
scores, boxes = fast_rcnn_test.im_detect(fast_rcnn_net, im, obj_proposals[:, 0:4],
rpn_net.blobs[last_shared_blob_name].data)
else:
scores, boxes = fast_rcnn_test.im_detect(fast_rcnn_net, im, obj_proposals[:, 0:4])
_t['im_detect'].toc()
print 'image: {:s} num obj boxes: {:d}'.format(image_name, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.6
NMS_THRESH = 0.3
_t['misc'].tic()
plt.figure()
for cls in classes:
cls_ind = CLASSES.index(cls)
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls, CONF_THRESH)
vis_detections(im, cls, dets, thresh=CONF_THRESH)
_t['misc'].toc()
print 'Actions took {:.3f}s for detecton, {:.3f}s for nms.' \
.format( _t['im_detect'].total_time, _t['misc'].total_time)
def demo(rpn_net,
fast_rcnn_net,
anchor_file,
image_name,
classes=CLASSES,
share_conv=False,
last_shared_blob_name=None):
"""
Detect object classes in an image.
The proposals are generating by region proposal network.
"""
# Load the demo image
im_file = os.path.join(rpn_config.cfg.ROOT_DIR, 'data', 'demo', image_name)
im = cv2.imread(im_file)
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
# ####### RPN
# Generate the proposal boxes
_t['im_detect'].tic()
scores, boxes = rpn_test.im_detect(rpn_net, im, anchor_file)
_t['im_detect'].toc()
print 'image: {:s} num proposal: {:d}'.format(image_name, boxes.shape[0])
# Filter the proposal boxes
_t['misc'].tic()
obj_proposals = np.hstack((boxes, scores)).astype(np.float32, copy=False)
obj_proposals = test_rpn_net.boxes_filter(obj_proposals, 6000, 0.7, 300)
_t['misc'].toc()
print 'image: {:s} num proposal filtered: {:d}'.format(
image_name, obj_proposals.shape[0])
print(
'Actions took {:.3f}s for generating'
'{:d} proposal boxes, {:.3f}s for '
'filtering proposals.').format(_t['im_detect'].total_time,
boxes.shape[0], _t['misc'].total_time)
# ###### Fast-RCNN
# Detect all object classes and regress object bounds
_t['im_detect'].tic()
if share_conv:
# conv_feat_blob = rpn_net.blobs['conv5'].data
scores, boxes = fast_rcnn_test.im_detect(
fast_rcnn_net, im, obj_proposals[:, 0:4],
rpn_net.blobs[last_shared_blob_name].data)
else:
scores, boxes = fast_rcnn_test.im_detect(fast_rcnn_net, im,
obj_proposals[:, 0:4])
_t['im_detect'].toc()
print 'image: {:s} num obj boxes: {:d}'.format(image_name, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.6
NMS_THRESH = 0.3
_t['misc'].tic()
plt.figure()
for cls in classes:
cls_ind = CLASSES.index(cls)
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
print 'All {} detections with p({} | box) >= {:.1f}'.format(
cls, cls, CONF_THRESH)
vis_detections(im, cls, dets, thresh=CONF_THRESH)
_t['misc'].toc()
print 'Actions took {:.3f}s for detecton, {:.3f}s for nms.' \
.format( _t['im_detect'].total_time, _t['misc'].total_time)
