depthnet.py

#!/usr/bin/python3
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import jetson.inference
import jetson.utils

import argparse
import sys

from depthnet_utils import depthBuffers

# parse the command line
parser = argparse.ArgumentParser(description="Mono depth estimation on a video/image stream using depthNet DNN.", 
                                 formatter_class=argparse.RawTextHelpFormatter, epilog=jetson.inference.depthNet.Usage() +
                                 jetson.utils.videoSource.Usage() + jetson.utils.videoOutput.Usage() + jetson.utils.logUsage())

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="fcn-mobilenet", help="pre-trained model to load, see below for options")
parser.add_argument("--visualize", type=str, default="input,depth", help="visualization options (can be 'input' 'depth' 'input,depth'")
parser.add_argument("--depth-size", type=float, default=1.0, help="scales the size of the depth map visualization, as a percentage of the input size (default is 1.0)")
parser.add_argument("--filter-mode", type=str, default="linear", choices=["point", "linear"], help="filtering mode used during visualization, options are:\n  'point' or 'linear' (default: 'linear')")
parser.add_argument("--colormap", type=str, default="viridis-inverted", help="colormap to use for visualization (default is 'viridis-inverted')",
                                  choices=["inferno", "inferno-inverted", "magma", "magma-inverted", "parula", "parula-inverted", 
                                           "plasma", "plasma-inverted", "turbo", "turbo-inverted", "viridis", "viridis-inverted"])

try:
	opt = parser.parse_known_args()[0]
except:
	print("")
	parser.print_help()
	sys.exit(0)

# load the segmentation network
net = jetson.inference.depthNet(opt.network, sys.argv)

# create buffer manager
buffers = depthBuffers(opt)

# create video sources & outputs
input = jetson.utils.videoSource(opt.input_URI, argv=sys.argv)
output = jetson.utils.videoOutput(opt.output_URI, argv=sys.argv)

# process frames until user exits
while True:
    # capture the next image
    img_input = input.Capture()

    # allocate buffers for this size image
    buffers.Alloc(img_input.shape, img_input.format)

    # process the mono depth and visualize
    net.Process(img_input, buffers.depth, opt.colormap, opt.filter_mode)

    # composite the images
    if buffers.use_input:
        jetson.utils.cudaOverlay(img_input, buffers.composite, 0, 0)
        
    if buffers.use_depth:
        jetson.utils.cudaOverlay(buffers.depth, buffers.composite, img_input.width if buffers.use_input else 0, 0)

    # render the output image
    output.Render(buffers.composite)

    # update the title bar
    output.SetStatus("{:s} | {:s} | Network {:.0f} FPS".format(opt.network, net.GetNetworkName(), net.GetNetworkFPS()))

    # print out performance info
    jetson.utils.cudaDeviceSynchronize()
    net.PrintProfilerTimes()

    # exit on input/output EOS
    if not input.IsStreaming() or not output.IsStreaming():
        break