#!/usr/bin/env python3 # # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. # import sys import argparse from jetson_inference import poseNet from jetson_utils import videoSource, videoOutput, Log # parse the command line parser = argparse.ArgumentParser(description="Run pose estimation DNN on a video/image stream.", formatter_class=argparse.RawTextHelpFormatter, epilog=poseNet.Usage() + videoSource.Usage() + videoOutput.Usage() + Log.Usage()) parser.add_argument("input", type=str, default="", nargs='?', help="URI of the input stream") parser.add_argument("output", type=str, default="", nargs='?', help="URI of the output stream") parser.add_argument("--network", type=str, default="resnet18-body", help="pre-trained model to load (see below for options)") parser.add_argument("--overlay", type=str, default="links,keypoints", help="pose overlay flags (e.g. --overlay=links,keypoints)\nvalid combinations are: 'links', 'keypoints', 'boxes', 'none'") parser.add_argument("--threshold", type=float, default=0.15, help="minimum detection threshold to use") try: args = parser.parse_known_args()[0] except: print("") parser.print_help() sys.exit(0) # load the pose estimation model net = poseNet(args.network, sys.argv, args.threshold) # create video sources & outputs input = videoSource(args.input, argv=sys.argv) output = videoOutput(args.output, argv=sys.argv) # process frames until EOS or the user exits while True: # capture the next image img = input.Capture() if img is None: # timeout continue # perform pose estimation (with overlay) poses = net.Process(img, overlay=args.overlay) # print the pose results print("detected {:d} objects in image".format(len(poses))) for pose in poses: print(pose) print(pose.Keypoints) print('Links', pose.Links) # render the image output.Render(img) # update the title bar output.SetStatus("{:s} | Network {:.0f} FPS".format(args.network, net.GetNetworkFPS())) # print out performance info net.PrintProfilerTimes() # exit on input/output EOS if not input.IsStreaming() or not output.IsStreaming(): break