deepstream_imagedata-multistream_cupy.py

#!/usr/bin/env python3

################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################

import sys

sys.path.append('../')
import gi

gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from ctypes import *
import sys
import math
from common.platform_info import PlatformInfo
from common.bus_call import bus_call
from common.FPS import PERF_DATA
import pyds
import argparse

import ctypes
import cupy as cp

perf_data = None

MAX_DISPLAY_LEN = 64
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_BICYCLE = 1
PGIE_CLASS_ID_PERSON = 2
PGIE_CLASS_ID_ROADSIGN = 3
MUXER_OUTPUT_WIDTH = 1920
MUXER_OUTPUT_HEIGHT = 1080
MUXER_BATCH_TIMEOUT_USEC = 33000
TILED_OUTPUT_WIDTH = 1920
TILED_OUTPUT_HEIGHT = 1080
GST_CAPS_FEATURES_NVMM = "memory:NVMM"
pgie_classes_str= ["Vehicle", "TwoWheeler", "Person","RoadSign"]



# tiler_sink_pad_buffer_probe  will extract metadata received on tiler src pad
# and modify the frame buffer using cupy
def tiler_sink_pad_buffer_probe(pad, info, u_data):
    frame_number = 0
    gst_buffer = info.get_buffer()
    if not gst_buffer:
        print("Unable to get GstBuffer ")
        return

    # Retrieve batch metadata from the gst_buffer
    # Note that pyds.gst_buffer_get_nvds_batch_meta() expects the
    # C address of gst_buffer as input, which is obtained with hash(gst_buffer)
    batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))

    l_frame = batch_meta.frame_meta_list
    while l_frame is not None:
        try:
            # Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
            # The casting is done by pyds.NvDsFrameMeta.cast()
            # The casting also keeps ownership of the underlying memory
            # in the C code, so the Python garbage collector will leave
            # it alone.
            frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
        except StopIteration:
            break
        frame_number = frame_meta.frame_num
        l_obj=frame_meta.obj_meta_list
        num_rects = frame_meta.num_obj_meta
        obj_counter = {
        PGIE_CLASS_ID_VEHICLE:0,
        PGIE_CLASS_ID_PERSON:0,
        PGIE_CLASS_ID_BICYCLE:0,
        PGIE_CLASS_ID_ROADSIGN:0
        }
        while l_obj is not None:
            try: 
                # Casting l_obj.data to pyds.NvDsObjectMeta
                obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
            except StopIteration:
                break
            obj_counter[obj_meta.class_id] += 1
            try: 
                l_obj=l_obj.next
            except StopIteration:
                break
        # Create dummy owner object to keep memory for the image array alive
        owner = None
        # Getting Image data using nvbufsurface
        # the input should be address of buffer and batch_id
        # Retrieve dtype, shape of the array, strides, pointer to the GPU buffer, and size of the allocated memory
        data_type, shape, strides, dataptr, size = pyds.get_nvds_buf_surface_gpu(hash(gst_buffer), frame_meta.batch_id)
        # dataptr is of type PyCapsule -> Use ctypes to retrieve the pointer as an int to pass into cupy
        ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p
        ctypes.pythonapi.PyCapsule_GetPointer.argtypes = [ctypes.py_object, ctypes.c_char_p]
        # Get pointer to buffer and create UnownedMemory object from the gpu buffer
        c_data_ptr = ctypes.pythonapi.PyCapsule_GetPointer(dataptr, None)
        unownedmem = cp.cuda.UnownedMemory(c_data_ptr, size, owner)
        # Create MemoryPointer object from unownedmem, at index 0
        memptr = cp.cuda.MemoryPointer(unownedmem, 0)
        # Create cupy array to access the image data. This array is in GPU buffer
        n_frame_gpu = cp.ndarray(shape=shape, dtype=data_type, memptr=memptr, strides=strides, order='C')
        # Initialize cuda.stream object for stream synchronization
        stream = cp.cuda.stream.Stream(null=True) # Use null stream to prevent other cuda applications from making illegal memory access of buffer
        # Modify the red channel to add blue tint to image
        with stream:
            n_frame_gpu[:, :, 0] = 0.5 * n_frame_gpu[:, :, 0] + 0.5
        stream.synchronize()

        print("Frame Number=", frame_number, "Number of Objects=",num_rects,"Vehicle_count=",obj_counter[PGIE_CLASS_ID_VEHICLE],"Person_count=",obj_counter[PGIE_CLASS_ID_PERSON])
        # Get frame rate through this probe
        stream_index = "stream{0}".format(frame_meta.pad_index)
        global perf_data
        perf_data.update_fps(stream_index)
        try:
            l_frame = l_frame.next
        except StopIteration:
            break

    return Gst.PadProbeReturn.OK


def cb_newpad(decodebin, decoder_src_pad, data):
    print("In cb_newpad\n")
    caps = decoder_src_pad.get_current_caps()
    gststruct = caps.get_structure(0)
    gstname = gststruct.get_name()
    source_bin = data
    features = caps.get_features(0)

    # Need to check if the pad created by the decodebin is for video and not
    # audio.
    if (gstname.find("video") != -1):
        # Link the decodebin pad only if decodebin has picked nvidia
        # decoder plugin nvdec_*. We do this by checking if the pad caps contain
        # NVMM memory features.
        if features.contains("memory:NVMM"):
            # Get the source bin ghost pad
            bin_ghost_pad = source_bin.get_static_pad("src")
            if not bin_ghost_pad.set_target(decoder_src_pad):
                sys.stderr.write("Failed to link decoder src pad to source bin ghost pad\n")
        else:
            sys.stderr.write(" Error: Decodebin did not pick nvidia decoder plugin.\n")


def decodebin_child_added(child_proxy, Object, name, user_data):
    print("Decodebin child added:", name, "\n")
    if name.find("decodebin") != -1:
        Object.connect("child-added", decodebin_child_added, user_data)

    if "source" in name:
        source_element = child_proxy.get_by_name("source")
        if source_element.find_property('drop-on-latency') != None:
            Object.set_property("drop-on-latency", True)

def create_source_bin(index, uri):
    print("Creating source bin")

    # Create a source GstBin to abstract this bin's content from the rest of the
    # pipeline
    bin_name = "source-bin-%02d" % index
    print(bin_name)
    nbin = Gst.Bin.new(bin_name)
    if not nbin:
        sys.stderr.write(" Unable to create source bin \n")

    # Source element for reading from the uri.
    # We will use decodebin and let it figure out the container format of the
    # stream and the codec and plug the appropriate demux and decode plugins.
    uri_decode_bin = Gst.ElementFactory.make("uridecodebin", "uri-decode-bin")
    if not uri_decode_bin:
        sys.stderr.write(" Unable to create uri decode bin \n")
    # We set the input uri to the source element
    uri_decode_bin.set_property("uri", uri)
    # Connect to the "pad-added" signal of the decodebin which generates a
    # callback once a new pad for raw data has beed created by the decodebin
    uri_decode_bin.connect("pad-added", cb_newpad, nbin)
    uri_decode_bin.connect("child-added", decodebin_child_added, nbin)

    # We need to create a ghost pad for the source bin which will act as a proxy
    # for the video decoder src pad. The ghost pad will not have a target right
    # now. Once the decode bin creates the video decoder and generates the
    # cb_newpad callback, we will set the ghost pad target to the video decoder
    # src pad.
    Gst.Bin.add(nbin, uri_decode_bin)
    bin_pad = nbin.add_pad(Gst.GhostPad.new_no_target("src", Gst.PadDirection.SRC))
    if not bin_pad:
        sys.stderr.write(" Failed to add ghost pad in source bin \n")
        return None
    return nbin

def main(args):
    global perf_data
    perf_data = PERF_DATA(len(args))
    number_sources = len(args)

    # Standard GStreamer initialization
    Gst.init(None)

    # Create gstreamer elements */
    # Create Pipeline element that will form a connection of other elements
    print("Creating Pipeline \n ")
    pipeline = Gst.Pipeline()
    is_live = False

    if not pipeline:
        sys.stderr.write(" Unable to create Pipeline \n")
    print("Creating streammux \n ")

    # Create nvstreammux instance to form batches from one or more sources.
    streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
    if not streammux:
        sys.stderr.write(" Unable to create NvStreamMux \n")

    pipeline.add(streammux)
    for i in range(number_sources):
        print("Creating source_bin ", i, " \n ")
        uri_name = args[i]
        if uri_name.find("rtsp://") == 0:
            is_live = True
        source_bin = create_source_bin(i, uri_name)
        if not source_bin:
            sys.stderr.write("Unable to create source bin \n")
        pipeline.add(source_bin)
        padname = "sink_%u" % i
        sinkpad = streammux.request_pad_simple(padname)
        if not sinkpad:
            sys.stderr.write("Unable to create sink pad bin \n")
        srcpad = source_bin.get_static_pad("src")
        if not srcpad:
            sys.stderr.write("Unable to create src pad bin \n")
        srcpad.link(sinkpad)
    print("Creating Pgie \n ")
    pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
    if not pgie:
        sys.stderr.write(" Unable to create pgie \n")
    # Add nvvidconv1 and filter1 to convert the frames to RGBA
    # which is easier to work with in Python.
    print("Creating nvvidconv1 \n ")
    nvvidconv1 = Gst.ElementFactory.make("nvvideoconvert", "convertor1")
    if not nvvidconv1:
        sys.stderr.write(" Unable to create nvvidconv1 \n")
    print("Creating filter1 \n ")
    caps1 = Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
    filter1 = Gst.ElementFactory.make("capsfilter", "filter1")
    if not filter1:
        sys.stderr.write(" Unable to get the caps filter1 \n")
    filter1.set_property("caps", caps1)
    print("Creating tiler \n ")
    tiler = Gst.ElementFactory.make("nvmultistreamtiler", "nvtiler")
    if not tiler:
        sys.stderr.write(" Unable to create tiler \n")
    print("Creating nvvidconv \n ")
    nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
    if not nvvidconv:
        sys.stderr.write(" Unable to create nvvidconv \n")
    print("Creating nvosd \n ")
    nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
    if not nvosd:
        sys.stderr.write(" Unable to create nvosd \n")

    
    if platform_info.is_platform_aarch64():
        print("Creating nv3dsink \n")
        sink = Gst.ElementFactory.make("nv3dsink", "nv3d-sink")
    else:
        print("Creating EGLSink \n")
        sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
    if not sink:
        sys.stderr.write(" Unable to create egl sink \n")

    if is_live:
        print("Atleast one of the sources is live")
        streammux.set_property('live-source', 1)

    streammux.set_property('width', 1920)
    streammux.set_property('height', 1080)
    streammux.set_property('batch-size', number_sources)
    streammux.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC)
    pgie.set_property('config-file-path', "dstest_imagedata_cupy_config.txt")
    pgie_batch_size = pgie.get_property("batch-size")
    if (pgie_batch_size != number_sources):
        print("WARNING: Overriding infer-config batch-size", pgie_batch_size, " with number of sources ",
              number_sources, " \n")
        pgie.set_property("batch-size", number_sources)
    tiler_rows = int(math.sqrt(number_sources))
    tiler_columns = int(math.ceil((1.0 * number_sources) / tiler_rows))
    tiler.set_property("rows", tiler_rows)
    tiler.set_property("columns", tiler_columns)
    tiler.set_property("width", TILED_OUTPUT_WIDTH)
    tiler.set_property("height", TILED_OUTPUT_HEIGHT)

    sink.set_property("sync", 0)
    sink.set_property("qos", 0)

    print("Adding elements to Pipeline \n")
    pipeline.add(pgie)
    pipeline.add(tiler)
    pipeline.add(nvvidconv)
    pipeline.add(filter1)
    pipeline.add(nvvidconv1)
    pipeline.add(nvosd)
    pipeline.add(sink)

    print("Linking elements in the Pipeline \n")
    streammux.link(pgie)
    pgie.link(nvvidconv1)
    nvvidconv1.link(filter1)
    filter1.link(tiler)
    tiler.link(nvvidconv)
    nvvidconv.link(nvosd)
    nvosd.link(sink)

    # create an event loop and feed gstreamer bus mesages to it
    loop = GLib.MainLoop()
    bus = pipeline.get_bus()
    bus.add_signal_watch()
    bus.connect("message", bus_call, loop)

    tiler_sink_pad = tiler.get_static_pad("sink")
    if not tiler_sink_pad:
        sys.stderr.write(" Unable to get src pad \n")
    else:
        tiler_sink_pad.add_probe(Gst.PadProbeType.BUFFER, tiler_sink_pad_buffer_probe, 0)
        # perf callback function to print fps every 5 sec
        GLib.timeout_add(5000, perf_data.perf_print_callback)

    # List the sources
    print("Now playing...")
    for i, source in enumerate(args):
        print(i, ": ", source)

    print("Starting pipeline \n")
    # start play back and listed to events		
    pipeline.set_state(Gst.State.PLAYING)
    try:
        loop.run()
    except:
        pass
    # cleanup
    print("Exiting app\n")
    pipeline.set_state(Gst.State.NULL)

def parse_args():
    parser = argparse.ArgumentParser(prog="deepstream_imagedata-multistream_cupy.py", 
                description="deepstream-imagedata-multistream-cupy takes multiple URI streams as input" \
                    " and retrieves the image buffer from GPU as a cupy array for in-place modification")
    parser.add_argument(
        "-i",
        "--input",
        help="Path to input streams",
        nargs="+",
        metavar="URIs",
        default=["a"],
        required=True,
    )

    args = parser.parse_args()
    stream_paths = args.input
    return stream_paths


if __name__ == '__main__':
    platform_info = PlatformInfo()
    if platform_info.is_integrated_gpu():
        sys.stderr.write ("\nThis app is not currently supported on integrated GPU. Exiting...\n\n\n\n")
        sys.exit(1)
    stream_paths = parse_args()
    sys.exit(main(stream_paths))