#!/usr/bin/env python3
#
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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# Software is furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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import sys
import argparse

from jetson_inference import segNet
from jetson_utils import videoSource, videoOutput, cudaOverlay, cudaDeviceSynchronize, Log

from segnet_utils import *

# parse the command line
parser = argparse.ArgumentParser(description="Segment a live camera stream using an semantic segmentation DNN.", 
                                 formatter_class=argparse.RawTextHelpFormatter, 
                                 epilog=segNet.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="fcn-resnet18-voc", help="pre-trained model to load, see below for options")
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("--visualize", type=str, default="overlay,mask", help="Visualization options (can be 'overlay' 'mask' 'overlay,mask'")
parser.add_argument("--ignore-class", type=str, default="void", help="optional name of class to ignore in the visualization results (default: 'void')")
parser.add_argument("--alpha", type=float, default=150.0, help="alpha blending value to use during overlay, between 0.0 and 255.0 (default: 150.0)")
parser.add_argument("--stats", action="store_true", help="compute statistics about segmentation mask class output")

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

# load the segmentation network
net = segNet(args.network, sys.argv)

# note: to hard-code the paths to load a model, the following API can be used:
#
# net = segNet(model="model/fcn_resnet18.onnx", labels="model/labels.txt", colors="model/colors.txt",
#              input_blob="input_0", output_blob="output_0")

# set the alpha blending value
net.SetOverlayAlpha(args.alpha)

# create video output
output = videoOutput(args.output, argv=sys.argv)

# create buffer manager
buffers = segmentationBuffers(net, args)

# create video source
input = videoSource(args.input, argv=sys.argv)

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

    if img_input is None: # timeout
        continue
        
    # allocate buffers for this size image
    buffers.Alloc(img_input.shape, img_input.format)

    # process the segmentation network
    net.Process(img_input, ignore_class=args.ignore_class)

    # generate the overlay
    if buffers.overlay:
        net.Overlay(buffers.overlay, filter_mode=args.filter_mode)

    # generate the mask
    if buffers.mask:
        net.Mask(buffers.mask, filter_mode=args.filter_mode)

    # composite the images
    if buffers.composite:
        cudaOverlay(buffers.overlay, buffers.composite, 0, 0)
        cudaOverlay(buffers.mask, buffers.composite, buffers.overlay.width, 0)

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

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

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

    # compute segmentation class stats
    if args.stats:
        buffers.ComputeStats()

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