if success[0] != -1:
templateInfo[success[0]] = aTemplateInfo
inout.save_info(tempInfo_saved_to.format(obj_id, radius), templateInfo)
detector.writeClasses(template_saved_to)
# clear to save RAM
detector.clear_classes()
fbo.deactivate()
window.close()
elapsed_time = time.time() - start_time
print('train time: {}\n'.format(elapsed_time))
if mode == 'test':
poseRefine = linemodLevelup_pybind.poseRefine()
im_size = dp['test_im_size']
shape = (im_size[1], im_size[0])
print('test img size: {}'.format(shape))
# Frame buffer object, bind here to avoid memory leak, maybe?
window = renderer.app.Window(visible=False)
color_buf = np.zeros((shape[0], shape[1], 4), np.float32).view(renderer.gloo.TextureFloat2D)
depth_buf = np.zeros((shape[0], shape[1]), np.float32).view(renderer.gloo.DepthTexture)
fbo = renderer.gloo.FrameBuffer(color=color_buf, depth=depth_buf)
fbo.activate()
use_image_subset = True
if use_image_subset:
im_ids_sets = inout.load_yaml(dp['test_set_fpath'])
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
objIds = []
rgb = None
depth = None
lock = False
bridge = CvBridge()
readTemplFrom = './yaml/%s_templ.yaml'
readInfoFrom = './yaml/{}_info.yaml'
readModelFrom = './models/{0}/{0}.fly'
K_cam = None
detector = linemodLevelup_pybind.Detector()
poseRefine = linemodLevelup_pybind.poseRefine()
detector.readClasses(objIds, readTemplFrom)
infos = {}
models = {}
for id in objIds:
model = inout.load_ply(readModelFrom.format(id))
models[id] = model
templateInfo = inout.load_info(readInfoFrom.format(id))
infos[id] = templateInfo
def nms_norms(ts, scores, thresh):
order = scores.argsort()[::-1]
keep = []
while order.size > 0: # magic: order[[]] = []