def demo(sess, net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, im)
timer.toc()
print('Detection took {:.3f}s for {:d} object proposals'.format(
timer.total_time, boxes.shape[0]))
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
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, :]
vis_detections(im, cls, dets, thresh=CONF_THRESH)
def camdemo(sess, net, im, resize=(500, 375)):
#Resize iamge so it fullfills the requirements needed by Faster-RCNN
im = cv2.resize(im, resize)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, im)
timer.toc()
# print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))
# return scores,boxes
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
boxess = []
classes = []
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
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(cls,dets)
# Threshing(cls,dets,thresh=CONF_THRESH)
Img, box, CLASS = vis_detectionOnCam(im, cls, dets, thresh=CONF_THRESH)
boxess.append(box),
classes.append(CLASS)
return Img, boxess, classes
def demo(sess, net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, image_name)
im = cv2.imread(im_file)
print('bg img', im.shape)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, im)
timer.toc()
print('Detection took {:.3f}s for {:d} object proposals'.format(
timer.total_time, boxes.shape[0]))
N = scores.shape[0] # num of proposals
C = scores.shape[1] - 1 # num of real classes
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
all_boxes_scores = np.zeros((N * C, 5), dtype=np.float32)
print(len(CLASSES), all_boxes_scores.shape)
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
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)
all_boxes_scores[N * (cls_ind - 1):N * cls_ind, :4] = cls_boxes
all_boxes_scores[N * (cls_ind - 1):N * cls_ind, 4] = cls_scores
# keep = nms(dets, NMS_THRESH)
# dets = dets[keep, :]
# print('for class %d(%s) remain %d bboxes' % (cls_ind, cls, len(keep)))
# vis_detections(im, cls, dets, thresh=CONF_THRESH)
keep = nms(all_boxes_scores, NMS_THRESH)
dets = all_boxes_scores[keep, :]
idx_cls = [i // N + 1 for i in keep]
print('it remains %d bboxes, total %d proposals' %
(len(dets), len(all_boxes_scores)))
vis_detections_all(im, dets, idx_cls, thresh=CONF_THRESH)
def recognize(sess, net, im):
"""Detect object classes in an image using pre-computed object proposals."""
print('img size:', im.shape)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, im)
timer.toc()
print('Detection took {:.3f}s for {:d} object proposals'.format(
timer.total_time, boxes.shape[0]))
N = scores.shape[0] # num of proposals
C = scores.shape[1] - 1 # num of real classes
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
all_boxes_scores = np.zeros((N * C, 5), dtype=np.float32)
print(len(CLASSES), all_boxes_scores.shape)
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
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)
all_boxes_scores[N * (cls_ind - 1):N * cls_ind, :4] = cls_boxes
all_boxes_scores[N * (cls_ind - 1):N * cls_ind, 4] = cls_scores
keep = nms(all_boxes_scores, NMS_THRESH)
dets = all_boxes_scores[keep, :]
idx_cls = [i // N + 1 for i in keep]
print('for class %d(%s) remain %d bboxes, total %d proposals' %
(cls_ind, cls, len(dets), len(all_boxes_scores)))
return dets, idx_cls
