def test_imdb_comp(net, imdb, anchors):
# generate proposal result boxes
res_boxes = test_net(net, imdb, anchors)
num_image = len(imdb.image_index)
max_per_image = 100
max_per_set = 40 * num_image
thresh = -np.inf
top_scores = []
# get boxes with high score
for i in xrange(num_image):
dets = res_boxes[i]
dets = dets[:min(len(dets), max_per_image)]
res_boxes[i] = dets
for det in dets:
heapq.heappush(top_scores, det[-1])
if len(top_scores) > max_per_set:
while len(top_scores) > max_per_set:
heapq.heappop(top_scores)
thresh = top_scores[0]
print 'filter boxes (top {:d}): {:d}/{:d}'.format(
max_per_image, i, num_image)
# conf thresh and nms
for i in xrange(num_image):
inds = np.where(res_boxes[i][:, -1] > thresh)[0]
res_boxes[i] = res_boxes[i][inds, :]
res_boxes[i] = boxes_filter(res_boxes[i], -1, cfg.TEST.NMS, -1)
print 'filter boxes (conf thresh & nms): {:d}/{:d}'.format(
i, num_image)
return res_boxes
def test_imdb_comp(net, imdb, anchors):
# generate proposal result boxes
res_boxes = test_net(net, imdb, anchors)
num_image = len(imdb.image_index)
max_per_image = 100
max_per_set = 40 * num_image
thresh = -np.inf
top_scores = []
# get boxes with high score
for i in xrange(num_image):
dets = res_boxes[i]
dets = dets[: min(len(dets), max_per_image)]
res_boxes[i] = dets
for det in dets:
heapq.heappush(top_scores, det[-1])
if len(top_scores) > max_per_set:
while len(top_scores) > max_per_set:
heapq.heappop(top_scores)
thresh = top_scores[0]
print 'filter boxes (top {:d}): {:d}/{:d}'.format(max_per_image, i, num_image)
# conf thresh and nms
for i in xrange(num_image):
inds = np.where(res_boxes[i][:, -1] > thresh)[0]
res_boxes[i] = res_boxes[i][inds, :]
res_boxes[i] = boxes_filter(res_boxes[i], -1, cfg.TEST.NMS, -1)
print 'filter boxes (conf thresh & nms): {:d}/{:d}'.format(i, num_image)
return res_boxes
def test_imdb(net, imdb, anchors):
""" Test a region proposal network on a image dataset """
output_dir = get_output_dir(imdb, net)
cache_file = os.path.join(output_dir, 'res_boxes.pkl')
# load cache result boxes (filtered)
if os.path.exists(cache_file):
with open(cache_file, 'rb') as f:
proposal_boxes = cPickle.load(f)
print 'load res boxes from \'{}\''.format(cache_file)
return proposal_boxes
if not os.path.exists(output_dir):
os.makedirs(output_dir)
print 'Generating proposal boxes by rpn model...'
proposal_boxes = test_net(net, imdb, anchors)
print 'Get proposal boxes done!'
print 'Current NMS configuration:'
print NMS_CONFIG
expand_val = lambda boxes: np.array([
boxes[:, 0] - boxes[:, 2], boxes[:, 1] - boxes[:, 3], boxes[:, 2] -
boxes[:, 0], boxes[:, 3] - boxes[:, 1],
np.zeros(boxes.shape[0])
]).T * EXPAND_RATIO
# filter boxes
print 'Filtering proposal boxes...'
for i in xrange(len(proposal_boxes)):
proposal_boxes[i] = boxes_filter(
proposal_boxes[i],
PRE_NMS_TOPN=NMS_CONFIG['PRE_NMS_TOPN'],
NMS_THRESH=NMS_CONFIG['NMS_THRESH'],
POST_NMS_TOPN=NMS_CONFIG['POST_NMS_TOPN'],
CONF_THRESH=CONF_THRESH,
USE_GPU=NMS_CONFIG['USE_GPU'])
# expand bounding box
if len(proposal_boxes[i]) > 0:
proposal_boxes[i] = proposal_boxes[i] + expand_val(
proposal_boxes[i])
print 'filter proposal box: {:d}/{:d}'.format(i + 1,
len(proposal_boxes))
print 'Filter proposal boxes done!'
# save file
with open(cache_file, 'wb') as f:
cPickle.dump(proposal_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'save result boxes to `{:s}`'.format(cache_file)
return proposal_boxes
def test_imdb(net, imdb, anchors):
""" Test a region proposal network on a image dataset """
output_dir = get_output_dir(imdb, net)
cache_file = os.path.join(output_dir, 'res_boxes.pkl')
# load cache result boxes (filtered)
if os.path.exists(cache_file):
with open(cache_file, 'rb') as f:
proposal_boxes = cPickle.load(f)
print 'load res boxes from \'{}\''.format(cache_file)
return proposal_boxes
if not os.path.exists(output_dir):
os.makedirs(output_dir)
print 'Generating proposal boxes by rpn model...'
proposal_boxes = test_net(net, imdb, anchors)
print 'Get proposal boxes done!'
print 'Current NMS configuration:'
print NMS_CONFIG
expand_val = lambda boxes: np.array([boxes[:,0] - boxes[:,2], boxes[:,1] - boxes[:,3],
boxes[:,2] - boxes[:,0], boxes[:,3] - boxes[:,1],
np.zeros(boxes.shape[0])]).T * EXPAND_RATIO
# filter boxes
print 'Filtering proposal boxes...'
for i in xrange(len(proposal_boxes)):
proposal_boxes[i] = boxes_filter(proposal_boxes[i],
PRE_NMS_TOPN=NMS_CONFIG['PRE_NMS_TOPN'],
NMS_THRESH=NMS_CONFIG['NMS_THRESH'],
POST_NMS_TOPN=NMS_CONFIG['POST_NMS_TOPN'],
CONF_THRESH=CONF_THRESH,
USE_GPU=NMS_CONFIG['USE_GPU'])
# expand bounding box
if len(proposal_boxes[i]) > 0:
proposal_boxes[i] = proposal_boxes[i] + expand_val(proposal_boxes[i])
print 'filter proposal box: {:d}/{:d}'.format(i+1, len(proposal_boxes))
print 'Filter proposal boxes done!'
# save file
with open(cache_file, 'wb') as f:
cPickle.dump(proposal_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'save result boxes to `{:s}`'.format(cache_file)
return proposal_boxes
def test_imdb(net, imdb, anchors):
""" Test a region proposal network on a image dataset """
print 'Generating proposal boxes by rpn model...'
proposal_boxes = test_net(net, imdb, anchors)
print 'Get proposal boxes done!'
print 'Current NMS configuration:'
print NMS_CONFIG
# filter boxes
print 'Filtering proposal boxes...'
for i in xrange(len(proposal_boxes)):
proposal_boxes[i] = boxes_filter(proposal_boxes[i],
PRE_NMS_TOPN=NMS_CONFIG['PRE_NMS_TOPN'],
NMS_THRESH=NMS_CONFIG['NMS_THRESH'],
POST_NMS_TOPN=NMS_CONFIG['POST_NMS_TOPN'],
USE_GPU=NMS_CONFIG['USE_GPU'])
print 'filter proposal box: {:d}/{:d}'.format(i, len(proposal_boxes))
print 'Filter proposal boxes done!'
return proposal_boxes
def test_imdb(net, imdb, anchors):
""" Test a region proposal network on a image dataset """
print 'Generating proposal boxes by rpn model...'
proposal_boxes = test_net(net, imdb, anchors)
print 'Get proposal boxes done!'
print 'Current NMS configuration:'
print NMS_CONFIG
# filter boxes
print 'Filtering proposal boxes...'
for i in xrange(len(proposal_boxes)):
proposal_boxes[i] = boxes_filter(
proposal_boxes[i],
PRE_NMS_TOPN=NMS_CONFIG['PRE_NMS_TOPN'],
NMS_THRESH=NMS_CONFIG['NMS_THRESH'],
POST_NMS_TOPN=NMS_CONFIG['POST_NMS_TOPN'],
USE_GPU=NMS_CONFIG['USE_GPU'])
print 'filter proposal box: {:d}/{:d}'.format(i, len(proposal_boxes))
print 'Filter proposal boxes done!'
return proposal_boxes
