imdb.evaluate_detections(dets)
result = []
if 1:
classes = imdb.classes
idx_image = 0
for j, index in enumerate(imdb.image_index):
curr_boxes = []
for i, cls in enumerate(classes):
if i == 0:
continue
box_info = dets[i][j]
if type(box_info) is list and len(box_info) == 0:
continue
assert type(box_info) == np.ndarray, (box_info, type(box_info))
im = cv2.imread(imdb.image_path_at(j))
vis_detections(im, str(cls), box_info, thresh= 0.5)
plt.show()
ind = box_info[:, -1] > 0.5
selected = box_info[ind, :]
for k in range(selected.shape[0]):
s = selected[k, :]
curr_boxes.append({'x1y1x2y2': '{} {} {} {}'.format(int(s[0]), int(s[1]), int(s[2]), int(s[3])), \
'label': cls, \
'confidence': float(s[4])})
curr_result = {'name': imdb.image_path_at(j), \
'boxes': curr_boxes}
result.append(curr_result)
result = dict(images = result)
pprint(result)
output_yaml = args.output_yaml
if output_yaml == None:
imdb.evaluate_detections(dets)
result = []
if 1:
classes = imdb.classes
idx_image = 0
for j, index in enumerate(imdb.image_index):
curr_boxes = []
for i, cls in enumerate(classes):
if i == 0:
continue
box_info = dets[i][j]
if type(box_info) is list and len(box_info) == 0:
continue
assert type(box_info) == np.ndarray, (box_info, type(box_info))
im = cv2.imread(imdb.image_path_at(j))
vis_detections(im, str(cls), box_info, thresh=0.5)
plt.show()
ind = box_info[:, -1] > 0.5
selected = box_info[ind, :]
for k in range(selected.shape[0]):
s = selected[k, :]
curr_boxes.append({'x1y1x2y2': '{} {} {} {}'.format(int(s[0]), int(s[1]), int(s[2]), int(s[3])), \
'label': cls, \
'confidence': float(s[4])})
curr_result = {'name': imdb.image_path_at(j), \
'boxes': curr_boxes}
result.append(curr_result)
result = dict(images=result)
pprint(result)
output_yaml = args.output_yaml
if output_yaml == None:
]
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for data/demo/{}'.format(im_name)
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, 'demo', im_name)
timer = Timer()
timer.tic()
im = pvanet.read_img(im_file)
dets = pvanet.process_img(im)
timer.toc()
print('Detection took {:.3f}s'.format(timer.total_time))
# Visualize detections for each class
CONF_THRESH = 0.7
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
det_this_cls = dets[np.where(dets[:, 0] == cls_ind)[0]]
boxes_and_scores = det_this_cls[:, 1:]
if len(boxes_and_scores) > 0:
vis_detections(im,
cls,
np.array(boxes_and_scores),
thresh=CONF_THRESH)
plt.show()
import cv2
if __name__ == '__main__':
imdb_name = 'imagenet_2015_val'
#test_name = 'imagenet_bvlc_fast_rcnn_stage1_iter_125000'
test_name = 'vgg16_fast_rcnn_iter_800000'
det_name = '../output/default/' + imdb_name + '/' + test_name + '/detections.pkl'
cfg.TRAIN.PROPOSAL_METHOD = 'gt'
cfg.TEST.HAS_RPN = True # Use RPN for proposals
imdb = get_imdb(imdb_name)
all_boxes = cPickle.load(open(det_name,'rb'))
for i in xrange(len(imdb._image_index)) :
im_path = imdb.image_path_at(i)
im = cv2.imread( im_path )
cls_set = []
det_set = []
for cls_ind in xrange(1,len(imdb.classes)):
dets = all_boxes[cls_ind][i]
keep = nms(dets, 0.3)
dets = dets[keep, :]
cls = imdb._class_name[cls_ind]
cls_set.append(cls)
det_set.append(dets)
vis_detections(im, cls_set, det_set, 0.7)
plt.show()
cls_stack = []
boxes_and_scores = np.array([])
# Visualize detections for each class
CONF_THRESH = 0.7
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
det_this_cls = dets[np.where(dets[:, 0] == cls_ind)[0]]
if (boxes_and_scores.size == 0):
boxes_and_scores = det_this_cls[:, 1:]
else:
boxes_and_scores = np.vstack(
(boxes_and_scores, det_this_cls[:, 1:]))
cls_stack.extend(cls for i in range(len(det_this_cls)))
if len(boxes_and_scores) > 0:
vis_detections(pvanet,
im,
im_file_o,
cls_stack,
np.array(boxes_and_scores),
thresh=CONF_THRESH,
save=True)
print('..........................................')
print('\tTESTING REPORT')
print('..........................................')
print('REPORTS:\tTotal\t{:.3f}s'.format(total_time))
print('------------------------------------------')