if(type(th_outlier[0])==list):
print("Individual outlier thresholds are applied for each object")
dynamic_th=False
th_outliers = np.squeeze(np.array(th_outlier))
th_inlier=cfg['inlier_th']
th_ransac=3
dummy_run=True
MODEL_DIR = os.path.join(bop_dir, "weight_detection")
if(detect_type=='rcnn'):
#Load mask r_cnn
'''
standard estimation parameter for Mask R-CNN (identical for all dataset)
'''
config = BopInferenceConfig(dataset=dataset,
num_classes=model_ids.shape[0]+1,#1+len(model_plys),
im_width=im_width,im_height=im_height)
config.display()
model = modellib.MaskRCNN(mode="inference", config=config,
model_dir=MODEL_DIR)
last_path = model.find_last()
#Load the last model you trained and continue training
model.load_weights(last_path, by_name=True)
elif(detect_type=='retinanet'):
print("Loading weights for keras-retinanet")
detection_weight_fn = cfg['detection_weight']
model = models.load_model(os.path.join(MODEL_DIR,detection_weight_fn), backbone_name='resnet50')
'''
Load pix2pose inference weights
def __init__(self, cfg):
self.cfg = cfg
self.rgb_topic = cfg['rgb_topic']
self.depth_topic = cfg['depth_topic']
self.camK = np.array(cfg['cam_K']).reshape(3, 3)
self.im_width = int(cfg['im_width'])
self.im_height = int(cfg['im_height'])
self.inlier_th = float(cfg['inlier_th'])
self.ransac_th = float(cfg['ransac_th'])
self.pub_before_icp = False
self.graph = tf_backend.Graph()
if (int(cfg['icp']) == 1):
self.icp = True
else:
self.icp = False
self.model_params = inout.load_json(cfg['norm_factor_fn'])
self.detection_labels = cfg[
'obj_labels'] #labels of corresponding detections
n_objs = int(cfg['n_objs'])
self.target_objs = cfg['target_obj_name']
self.colors = np.random.randint(0, 255, (n_objs, 3))
with self.graph.as_default():
if (detect_type == "rcnn"):
#Load mask r_cnn
'''
standard estimation parameter for Mask R-CNN (identical for all dataset)
'''
self.config = BopInferenceConfig(dataset="ros",
num_classes=n_objs + 1,
im_width=self.im_width,
im_height=self.im_height)
self.config.DETECTION_MIN_CONFIDENCE = 0.3
self.config.DETECTION_MAX_INSTANCES = 30
self.config.DETECTION_NMS_THRESHOLD = 0.5
self.detection_model = modellib.MaskRCNN(mode="inference",
config=self.config,
model_dir="/")
self.detection_model.load_weights(
cfg['path_to_detection_weights'], by_name=True)
self.obj_models = []
self.obj_bboxes = []
self.obj_pix2pose = []
pix2pose_dir = cfg['path_to_pix2pose_weights']
th_outlier = cfg['outlier_th']
self.model_scale = cfg['model_scale']
for t_id, target_obj in enumerate(self.target_objs):
weight_fn = os.path.join(
pix2pose_dir, "{:02d}/inference.hdf5".format(target_obj))
print("Load pix2pose weights from ", weight_fn)
model_param = self.model_params['{}'.format(target_obj)]
obj_param = bop_io.get_model_params(model_param)
recog_temp = recog.pix2pose(weight_fn,
camK=self.camK,
res_x=self.im_width,
res_y=self.im_height,
obj_param=obj_param,
th_ransac=self.ransac_th,
th_outlier=th_outlier,
th_inlier=self.inlier_th)
self.obj_pix2pose.append(recog_temp)
ply_fn = os.path.join(self.cfg['model_dir'],
self.cfg['ply_files'][t_id])
if (self.icp):
#for pyrender rendering
obj_model = trimesh.load_mesh(ply_fn)
obj_model.vertices = obj_model.vertices * self.model_scale
mesh = pyrender.Mesh.from_trimesh(obj_model)
self.obj_models.append(mesh)
self.obj_bboxes.append(
self.get_3d_box_points(obj_model.vertices))
else:
obj_model = inout.load_ply(ply_fn)
self.obj_bboxes.append(
self.get_3d_box_points(obj_model['pts']))
rospy.init_node('pix2pose', anonymous=True)
self.detect_pub = rospy.Publisher("/pix2pose/detected_object",
ros_image)
#self.pose_pub = rospy.Publisher("/pix2pose/object_pose", Pose)
self.pose_pub = rospy.Publisher("/pix2pose/object_pose",
ros_image)
self.have_depth = False
if (self.icp):
self.sub_depth = rospy.Subscriber(self.depth_topic,
ros_image,
self.callback_depth,
queue_size=1)
if (self.pub_before_icp):
self.pose_pub_noicp = rospy.Publisher(
"/pix2pose/object_pose_noicp", ros_image)
self.depth_img = np.zeros((self.im_height, self.im_width))
self.sub = rospy.Subscriber(self.rgb_topic,
ros_image,
self.callback,
queue_size=1)