def get_detections_from_im(cfg,
model,
im,
image_id,
featmap_blob_name,
feat_blob_name,
MIN_BOXES,
MAX_BOXES,
conf_thresh=0.2,
bboxes=None):
with c2_utils.NamedCudaScope(0):
scores, cls_boxes, im_scale = infer_engine.im_detect_bbox(
model, im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, boxes=bboxes)
region_feat = workspace.FetchBlob(feat_blob_name)
cls_prob = workspace.FetchBlob("gpu_0/cls_prob")
rois = workspace.FetchBlob("gpu_0/rois")
max_conf = np.zeros((rois.shape[0]))
# unscale back to raw image space
cls_boxes = rois[:, 1:5] / im_scale
for cls_ind in range(1, cls_prob.shape[1]):
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = np.array(nms(dets, cfg.TEST.NMS))
max_conf[keep] = np.where(cls_scores[keep] > max_conf[keep],
cls_scores[keep], max_conf[keep])
keep_boxes = np.where(max_conf >= conf_thresh)[0]
if len(keep_boxes) < MIN_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MIN_BOXES]
elif len(keep_boxes) > MAX_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MAX_BOXES]
objects = np.argmax(cls_prob[keep_boxes], axis=1)
obj_prob = np.amax(cls_prob[keep_boxes],
axis=1) # proposal not in order!
assert (np.sum(objects >= 1601) == 0)
return {
"image_id": image_id,
"image_h": np.size(im, 0),
"image_w": np.size(im, 1),
'num_boxes': len(keep_boxes),
'boxes': cls_boxes[keep_boxes],
'region_feat': region_feat[keep_boxes],
'object': objects,
'obj_prob': obj_prob
}
def get_detections_from_im(cfg,
model,
im,
image_id,
feat_blob_name,
MIN_BOXES,
MAX_BOXES,
conf_thresh=0.2,
bboxes=None):
with c2_utils.NamedCudaScope(0):
scores, cls_boxes, im_scale = infer_engine.im_detect_bbox(
model, im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, boxes=bboxes)
box_features = workspace.FetchBlob(feat_blob_name)
cls_prob = workspace.FetchBlob("gpu_0/cls_prob")
rois = workspace.FetchBlob("gpu_0/rois")
max_conf = np.zeros((rois.shape[0]))
# unscale back to raw image space
cls_boxes = rois[:, 1:5] / im_scale
for cls_ind in range(1, cls_prob.shape[1]):
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = np.array(nms(dets, cfg.TEST.NMS))
max_conf[keep] = np.where(cls_scores[keep] > max_conf[keep],
cls_scores[keep], max_conf[keep])
keep_boxes = np.where(max_conf >= conf_thresh)[0]
if len(keep_boxes) < MIN_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MIN_BOXES]
elif len(keep_boxes) > MAX_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MAX_BOXES]
objects = np.argmax(cls_prob[keep_boxes], axis=1)
return box_features[keep_boxes]
def get_detections_from_im(cfg,
model,
im,
image_id,
featmap_blob_name,
feat_blob_name,
MIN_BOXES,
MAX_BOXES,
conf_thresh=0.2,
bboxes=None):
assert conf_thresh >= 0.
with c2_utils.NamedCudaScope(0):
scores, cls_boxes, im_scale = infer_engine.im_detect_bbox(
model, im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, boxes=bboxes)
num_rpn = scores.shape[0]
region_feat = workspace.FetchBlob(feat_blob_name)
max_conf = np.zeros((num_rpn, ), dtype=np.float32)
max_cls = np.zeros((num_rpn, ), dtype=np.int32)
max_box = np.zeros((num_rpn, 4), dtype=np.float32)
for cls_ind in range(1, cfg.MODEL.NUM_CLASSES):
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes[:, (cls_ind * 4):(cls_ind * 4 + 4)],
cls_scores[:, np.newaxis])).astype(np.float32)
keep = np.array(nms(dets, cfg.TEST.NMS))
inds_update = np.where(cls_scores[keep] > max_conf[keep])
kinds = keep[inds_update]
max_conf[kinds] = cls_scores[kinds]
max_cls[kinds] = cls_ind
max_box[kinds] = dets[kinds][:, :4]
keep_boxes = np.where(max_conf > conf_thresh)[0]
if len(keep_boxes) < MIN_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MIN_BOXES]
elif len(keep_boxes) > MAX_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MAX_BOXES]
objects = max_cls[keep_boxes]
obj_prob = max_conf[keep_boxes]
obj_boxes = max_box[keep_boxes, :]
cls_prob = scores[keep_boxes, :]
# print('{} ({}x{}): {} boxes, box size {}, feature size {}, class size {}'.format(image_id,
# np.size(im, 0), np.size(im, 1), len(keep_boxes), cls_boxes[keep_boxes].shape,
# box_features[keep_boxes].shape, objects.shape))
# print(cls_boxes[keep_boxes][:10, :], objects[:10], obj_prob[:10])
assert (np.sum(objects >= cfg.MODEL.NUM_CLASSES) == 0)
# assert(np.min(obj_prob[:10])>=0.2)
# if np.min(obj_prob) < 0.2:
# print('confidence score too low!', np.min(obj_prob[:10]))
# if np.max(cls_boxes[keep_boxes]) > max(np.size(im, 0), np.size(im, 1)):
# print('box is offscreen!', np.max(cls_boxes[keep_boxes]), np.size(im, 0), np.size(im, 1))
return {
"image_id": image_id,
"image_h": np.size(im, 0),
"image_w": np.size(im, 1),
'num_boxes': len(keep_boxes),
'boxes': obj_boxes,
'region_feat': region_feat[keep_boxes, :],
'object': objects,
'obj_prob': obj_prob,
'cls_prob': cls_prob
}