main3d.py

#!/usr/bin/env python
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import argparse
# import numpy as np
# import sys
import rospy
import yaml

# import grpc
# import cv2
#
# from tritonclient.grpc import service_pb2, service_pb2_grpc
# import tritonclient.grpc.model_config_pb2 as mc
#
# from utils.preprocess import parse_model, model_dtype_to_np, requestGenerator, image_adjust
# from utils.postprocess import extract_boxes_triton, load_class_names
# from utils.ros_input import RealSenseNode

from communicator import RosInference3D
from communicator.channel import grpc_channel
from clients import Pointpillars_client


FLAGS = None

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('-v',
                        '--verbose',
                        action="store_true",
                        required=False,
                        default=False,
                        help='Enable verbose output')
    parser.add_argument('-a',
                        '--async',
                        dest="async_set",
                        action="store_true",
                        required=False,
                        default=False,
                        help='Use asynchronous inference API')
    parser.add_argument('--streaming',
                        action="store_true",
                        required=False,
                        default=False,
                        help='Use streaming inference API')
    parser.add_argument('-m',
                        '--model-name',
                        type=str,
                        required=False,
                        # choices=['YOLOv5n', 'FCOS', 'fcos_weed_detector'],
                        default="YOLOv5n",
                        help='Name of model')
    parser.add_argument(
        '-x',
        '--model-version',
        type=str,
        required=False,
        default="",
        help='Version of model. Default is to use latest version.')
    parser.add_argument('-b',
                        '--batch-size',
                        type=int,
                        required=False,
                        default=1,
                        help='Batch size. Default is 1.')
    parser.add_argument('-c',
                        '--classes',
                        type=int,
                        required=False,
                        default=80,
                        help='Number of class results to report. Default is 1.')
    parser.add_argument(
        '-s',
        '--scaling',
        type=str,
        choices=['NONE', 'INCEPTION', 'VGG', 'COCO'],
        required=False,
        default='COCO',
        help='Type of scaling to apply to image pixels. Default is NONE.')
    parser.add_argument(
        '-i',
        '--image-src',
        type=str,
        choices=['ros', 'local'],
        required=False,
        default='ros',
        help='Source of input images to run inference on. Default is ROS image topic')
    return parser.parse_args()


if __name__ == '__main__':
    FLAGS = parse_args()
    rospy.init_node('ros_infer_3d')
    param_file = rospy.get_param('client_parameter_file', './data/client_parameter.yaml')
    with open(param_file) as file:
        param = yaml.load(file, Loader=yaml.FullLoader)

    # define client
    client = Pointpillars_client()

    #define channel
    channel = grpc_channel.GRPCChannel(param, FLAGS)

    #define inference
    inference = RosInference3D(channel, client)
    inference.start_inference()