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added detection script for saving object snapshots
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| #!/usr/bin/python3 | ||
| # | ||
| # Copyright (c) 2022, 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. | ||
| # | ||
| # This script saves the detected objects to individual images in a directory. | ||
| # The directory these are stored in can be set using the --snapshots argument: | ||
| # | ||
| # python3 detectnet-snap.py --snapshots /path/to/snapshots <input_URI> <output_URI> | ||
| # | ||
| # The input and output streams are specified the same way as shown here: | ||
| # https://github.com/dusty-nv/jetson-inference/blob/master/docs/aux-streaming.md | ||
| # | ||
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| import jetson.inference | ||
| import jetson.utils | ||
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| import argparse | ||
| import datetime | ||
| import math | ||
| import sys | ||
| import os | ||
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| # parse the command line | ||
| parser = argparse.ArgumentParser(description="Locate objects in a live camera stream using an object detection DNN.", | ||
| formatter_class=argparse.RawTextHelpFormatter, epilog=jetson.inference.detectNet.Usage() + | ||
| jetson.utils.videoSource.Usage() + jetson.utils.videoOutput.Usage() + jetson.utils.logUsage()) | ||
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| parser.add_argument("input_URI", type=str, default="", nargs='?', help="URI of the input stream") | ||
| parser.add_argument("output_URI", type=str, default="", nargs='?', help="URI of the output stream") | ||
| parser.add_argument("--network", type=str, default="ssd-mobilenet-v2", help="pre-trained model to load (see below for options)") | ||
| parser.add_argument("--overlay", type=str, default="box,labels,conf", help="detection overlay flags (e.g. --overlay=box,labels,conf)\nvalid combinations are: 'box', 'labels', 'conf', 'none'") | ||
| parser.add_argument("--threshold", type=float, default=0.5, help="minimum detection threshold to use") | ||
| parser.add_argument("--snapshots", type=str, default="images/test/detections", help="output directory of detection snapshots") | ||
| parser.add_argument("--timestamp", type=str, default="%Y%m%d-%H%M%S-%f", help="timestamp format used in snapshot filenames") | ||
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| try: | ||
| opt = parser.parse_known_args()[0] | ||
| except: | ||
| print("") | ||
| parser.print_help() | ||
| sys.exit(0) | ||
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| # make sure the snapshots dir exists | ||
| os.makedirs(opt.snapshots, exist_ok=True) | ||
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| # create video output object | ||
| output = jetson.utils.videoOutput(opt.output_URI, argv=sys.argv) | ||
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| # load the object detection network | ||
| net = jetson.inference.detectNet(opt.network, sys.argv, opt.threshold) | ||
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| # create video sources | ||
| input = jetson.utils.videoSource(opt.input_URI, argv=sys.argv) | ||
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| # process frames until the user exits | ||
| while True: | ||
| # capture the next image | ||
| img = input.Capture() | ||
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| # detect objects in the image (with overlay) | ||
| detections = net.Detect(img, overlay=opt.overlay) | ||
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| # print the detections | ||
| print("detected {:d} objects in image".format(len(detections))) | ||
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| timestamp = datetime.datetime.now().strftime(opt.timestamp) | ||
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| for idx, detection in enumerate(detections): | ||
| print(detection) | ||
| roi = (int(detection.Left), int(detection.Top), int(detection.Right), int(detection.Bottom)) | ||
| snapshot = jetson.utils.cudaAllocMapped(width=roi[2]-roi[0], height=roi[3]-roi[1], format=img.format) | ||
| jetson.utils.cudaCrop(img, snapshot, roi) | ||
| jetson.utils.cudaDeviceSynchronize() | ||
| jetson.utils.saveImage(os.path.join(opt.snapshots, f"{timestamp}-{idx}.jpg"), snapshot) | ||
| del snapshot | ||
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| # render the image | ||
| output.Render(img) | ||
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| # update the title bar | ||
| output.SetStatus("{:s} | Network {:.0f} FPS".format(opt.network, net.GetNetworkFPS())) | ||
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| # print out performance info | ||
| net.PrintProfilerTimes() | ||
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| # exit on input/output EOS | ||
| if not input.IsStreaming() or not output.IsStreaming(): | ||
| break | ||
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