#!/usr/bin/env python3

################################################################################
# Copyright (c) 2020, 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
sys.path.append('../')
import gi
import configparser
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst
from gi.repository import GLib
from ctypes import *
import time
import sys
import numpy as np
import cv2
import math
import platform
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
from common.FPS import GETFPS
from common.utils import long_to_int
import pyds
from threading import Thread
from queue import Queue
import os
import datetime

fps_streams={}

MUXER_OUTPUT_WIDTH=640
MUXER_OUTPUT_HEIGHT=480
MUXER_BATCH_TIMEOUT_USEC=4000000
TILED_OUTPUT_WIDTH=640
TILED_OUTPUT_HEIGHT=480
GST_CAPS_FEATURES_NVMM="memory:NVMM"
OSD_PROCESS_MODE= 0
OSD_DISPLAY_TEXT= 0
pgie_classes_str= ["lpd"]

init = time.time()
id_list = Queue(maxsize=20)
path1 = "data"
if not os.path.exists(path1):
    os.mkdir(path1)
number_sources = 0
# tiler_sink_pad_buffer_probe  will extract metadata received on OSD sink pad
# and update params for drawing rectangle, object information etc.
def tiler_src_pad_buffer_probe(pad,info,u_data):

    global number_sources

    frame_number=0
    num_rects=0
    gst_buffer = info.get_buffer()
    if not gst_buffer:
        print("Unable to get GstBuffer ")
        return

    lp_dict = {i: {} for i in range(number_sources)}
    # 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

        '''
        print("Frame Number is ", frame_meta.frame_num)
        print("Source id is ", frame_meta.source_id)
        print("Batch id is ", frame_meta.batch_id)
        print("Source Frame Width ", frame_meta.source_frame_width)
        print("Source Frame Height ", frame_meta.source_frame_height)
        print("Num object meta ", frame_meta.num_obj_meta)
        '''
        frame_number=frame_meta.frame_num
        l_obj=frame_meta.obj_meta_list
        num_rects = frame_meta.num_obj_meta

        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
            # print(obj_meta.confidence)

            l_user_meta = obj_meta.obj_user_meta_list
            while l_user_meta:
                try:
                    user_meta = pyds.NvDsUserMeta.cast(l_user_meta.data)
                    if user_meta.base_meta.meta_type == pyds.nvds_get_user_meta_type("NVIDIA.DSANALYTICSOBJ.USER_META"):             
                        user_meta_data = pyds.NvDsAnalyticsObjInfo.cast(user_meta.user_meta_data)
                        print("Object {0} line crossing status: {1}".format(obj_meta.object_id, user_meta_data.lcStatus))
                        # print("Object {0} roi status: {1}".format(obj_meta.object_id, user_meta_data.roiStatus))
                        # if user_meta_data.dirStatus: print("Object {0} moving in direction: {1}".format(obj_meta.object_id, user_meta_data.dirStatus))                    
                        # if user_meta_data.lcStatus: print("Object {0} line crossing status: {1}".format(obj_meta.object_id, user_meta_data.lcStatus))
                        # if user_meta_data.ocStatus: print("Object {0} overcrowding status: {1}".format(obj_meta.object_id, user_meta_data.ocStatus))
                        # if user_meta_data.roiStatus: print("Object {0} roi status: {1}".format(obj_meta.object_id, user_meta_data.roiStatus))

                        l_class = obj_meta.classifier_meta_list
                        # print(l_class)

                        if user_meta_data.lcStatus:
                            while l_class is not None:
                                try:
                                    class_meta = pyds.NvDsClassifierMeta.cast(l_class.data)
                                except StopIteration:
                                    break

                                l_label = class_meta.label_info_list
                                # print(class_meta.num_labels)
                                while l_label is not None:
                                    try:
                                        label_info = pyds.NvDsLabelInfo.cast(l_label.data)
                                    except StopIteration:
                                        break

                                    print(label_info.result_label)
                                    # print(long_to_int(obj_meta.object_id))
                                    x = obj_meta.rect_params.left
                                    y = obj_meta.rect_params.top
                                    w = obj_meta.rect_params.width
                                    h = obj_meta.rect_params.height
                                    lp_dict[frame_meta.pad_index][long_to_int(obj_meta.object_id)] = [label_info.result_label, obj_meta.confidence, int(x), int(y), int(x+w), int(y+h)]
                                    try:
                                        l_label=l_label.next
                                    except StopIteration:
                                        break
                                try:
                                    l_class=l_class.next
                                except StopIteration:
                                    break

                except StopIteration:
                    break

                try:
                    l_user_meta = l_user_meta.next
                except StopIteration:
                    break

            try: 
                l_obj=l_obj.next
            except StopIteration:
                break

        #write image when object detected in "positive" folder
        for source_id, data in lp_dict.items():
            for tracking_id, bbox_info in data.items():
                if tracking_id not in list(id_list.queue):
                    if id_list.full():
                        id_list.get()
                    id_list.put(tracking_id)
                    try:
                        frame = get_frame(gst_buffer, frame_meta.batch_id)
                        name= "img_"+str(tracking_id)+"_"+datetime.datetime.now().strftime("%Y%m%d_%H%M%S")+".jpg"
                        if bbox_info[1] > 0.3:
                            cv2.rectangle(frame, (bbox_info[2], bbox_info[3]), (bbox_info[4], bbox_info[5]), (255, 0, 255), 1)
                            cv2.rectangle(frame, (bbox_info[2], bbox_info[3]-20), (bbox_info[4], bbox_info[3]), (255, 0, 255), -1)
                            cv2.putText(frame, bbox_info[0], (bbox_info[2], bbox_info[3]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1, cv2.LINE_AA)
                            cv2.imwrite(os.path.join(os.path.join(path1,"stream_"+str(source_id)), name), frame)
                            print(bbox_info)
                    except cv2.error as e:
                        print(e)

        print(lp_dict)

        # Get meta data from NvDsAnalyticsFrameMeta
        l_user = frame_meta.frame_user_meta_list
        while l_user:
            try:
                user_meta = pyds.NvDsUserMeta.cast(l_user.data)
                if user_meta.base_meta.meta_type == pyds.nvds_get_user_meta_type("NVIDIA.DSANALYTICSFRAME.USER_META"):
                    user_meta_data = pyds.NvDsAnalyticsFrameMeta.cast(user_meta.user_meta_data)
                    # if user_meta_data.objInROIcnt: print("Objs in ROI: {0}".format(user_meta_data.objInROIcnt))                    
                    if user_meta_data.objLCCumCnt: print("Linecrossing Cumulative: {0}".format(user_meta_data.objLCCumCnt))
                    # if user_meta_data.objLCCurrCnt: print("Linecrossing Current Frame: {0}".format(user_meta_data.objLCCurrCnt))
                    # if user_meta_data.ocStatus: print("Overcrowding status: {0}".format(user_meta_data.ocStatus))
            except StopIteration:
                break
            try:
                l_user = l_user.next
            except StopIteration:
                break

        # Get frame rate through this probe
        fps_streams["stream{0}".format(frame_meta.pad_index)].get_fps()
        try:
            l_frame=l_frame.next
        except StopIteration:
            break

    return Gst.PadProbeReturn.OK

def get_frame(gst_buffer, batch_id):
    n_frame=pyds.get_nvds_buf_surface(hash(gst_buffer),batch_id)
    #convert python array into numy array format.
    frame_image=np.array(n_frame,copy=True,order='C')
    #covert the array into cv2 default color format
    frame_image=cv2.cvtColor(frame_image,cv2.COLOR_RGBA2BGRA)
    return frame_image

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.
    print("gstname=",gstname)
    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.
        print("features=",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 name.find("nvv4l2decoder") != -1:
        if is_aarch64():
            print("Seting bufapi_version\n")
            Object.set_property("bufapi-version",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 number_sources

    # Check input arguments
    if len(args) < 2:
        sys.stderr.write("usage: %s <uri1> [uri2] ... [uriN]\n" % args[0])
        sys.exit(1)

    for i in range(0,len(args)-1):
        fps_streams["stream{0}".format(i)]=GETFPS(i)
    number_sources=len(args)-1

    # Standard GStreamer initialization
    GObject.threads_init()
    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 streamux \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):
        if not os.path.exists(os.path.join(path1,"stream_"+str(i))):
            os.mkdir(os.path.join(path1,"stream_"+str(i)))
        print("Creating source_bin ",i," \n ")
        uri_name=args[i+1]
        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.get_request_pad(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)

    queue1=Gst.ElementFactory.make("queue","queue1")
    queue2=Gst.ElementFactory.make("queue","queue2")
    queue3=Gst.ElementFactory.make("queue","queue3")
    queue4=Gst.ElementFactory.make("queue","queue4")
    queue5=Gst.ElementFactory.make("queue","queue5")
    queue6=Gst.ElementFactory.make("queue","queue6")
    queue7=Gst.ElementFactory.make("queue","queue7")
    queue8=Gst.ElementFactory.make("queue","queue8")
    queue9=Gst.ElementFactory.make("queue","queue9")
    pipeline.add(queue1)
    pipeline.add(queue2)
    pipeline.add(queue3)
    pipeline.add(queue4)
    pipeline.add(queue5)
    pipeline.add(queue6)
    pipeline.add(queue7)
    pipeline.add(queue8)
    pipeline.add(queue9)

    print("Creating Pgie \n ")
    pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
    if not pgie:
        sys.stderr.write(" Unable to create pgie \n")
    print("Creating tiler \n ")

    tracker = Gst.ElementFactory.make("nvtracker", "tracker")
    if not tracker:
        sys.stderr.write(" Unable to create tracker \n")

    print("Creating nvdsanalytics \n ")
    nvanalytics = Gst.ElementFactory.make("nvdsanalytics", "analytics")
    if not nvanalytics:
        sys.stderr.write(" Unable to create nvanalytics \n")
    nvanalytics.set_property("config-file", "config_nvdsanalytics.txt")

    sgie = Gst.ElementFactory.make("nvinfer", "secondary-nvinference-engine")
    if not sgie:
        sys.stderr.write(" Unable to make sgie \n")

    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")
    # nvosd.set_property('process-mode',OSD_PROCESS_MODE)
    # nvosd.set_property('display-text',OSD_DISPLAY_TEXT)
    if(is_aarch64()):
        print("Creating transform \n ")
        transform=Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
        if not transform:
            sys.stderr.write(" Unable to create transform \n")

    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', MUXER_OUTPUT_WIDTH)
    streammux.set_property('height', MUXER_OUTPUT_HEIGHT)
    streammux.set_property('batch-size', number_sources)
    streammux.set_property('batched-push-timeout', 4000000)

    pgie.set_property('config-file-path', "lpd_us_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)

    # Set properties of tracker
    config = configparser.ConfigParser()
    config.read('lpr_sample_tracker_config.txt')
    config.sections()

    for key in config['tracker']:
        if key == 'tracker-width':
            tracker_width = config.getint('tracker', key)
            tracker.set_property('tracker-width', tracker_width)
        if key == 'tracker-height':
            tracker_height = config.getint('tracker', key)
            tracker.set_property('tracker-height', tracker_height)
        if key == 'gpu-id':
            tracker_gpu_id = config.getint('tracker', key)
            tracker.set_property('gpu_id', tracker_gpu_id)
        if key == 'll-lib-file':
            tracker_ll_lib_file = config.get('tracker', key)
            tracker.set_property('ll-lib-file', tracker_ll_lib_file)
        if key == 'll-config-file':
            tracker_ll_config_file = config.get('tracker', key)
            tracker.set_property('ll-config-file', tracker_ll_config_file)
        if key == 'enable-batch-process':
            tracker_enable_batch_process = config.getint('tracker', key)
            tracker.set_property('enable_batch_process',
                                 tracker_enable_batch_process)

    sgie.set_property('config-file-path', "lpr_config_sgie_us.txt")
    sgie.set_property('process-mode', 2)

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

    if not is_aarch64():
        # Use CUDA unified memory in the pipeline so frames
        # can be easily accessed on CPU in Python.
        mem_type = int(pyds.NVBUF_MEM_CUDA_UNIFIED)
        streammux.set_property("nvbuf-memory-type", mem_type)
        nvvidconv.set_property("nvbuf-memory-type", mem_type)
        nvvidconv1.set_property("nvbuf-memory-type", mem_type)
        tiler.set_property("nvbuf-memory-type", mem_type)

    print("Adding elements to Pipeline \n")
    
    pipeline.add(pgie)
    pipeline.add(tracker)
    pipeline.add(nvanalytics)
    pipeline.add(sgie)
    pipeline.add(tiler)
    pipeline.add(nvvidconv)
    pipeline.add(filter1)
    pipeline.add(nvvidconv1)
    pipeline.add(nvosd)
    if is_aarch64():
        pipeline.add(transform)
    pipeline.add(sink)

    print("Linking elements in the Pipeline \n")

    streammux.link(pgie)
    pgie.link(queue1)
    queue1.link(tracker)
    tracker.link(queue2)
    queue2.link(nvanalytics)
    nvanalytics.link(queue3)
    queue3.link(sgie)
    sgie.link(queue4)
    queue4.link(nvvidconv1)
    nvvidconv1.link(queue5)
    queue5.link(filter1)
    filter1.link(queue6)
    queue6.link(tiler)
    tiler.link(queue7)
    queue7.link(nvvidconv)
    nvvidconv.link(queue8)
    queue8.link(nvosd)
    if is_aarch64():
        nvosd.link(queue9)
        queue9.link(transform)
        transform.link(sink)
    else:
        nvosd.link(queue9)
        queue9.link(sink)  

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

    tiler_src_pad=tiler.get_static_pad("sink")
    if not tiler_src_pad:
        sys.stderr.write(" Unable to get sink pad \n")
    else:
        tiler_src_pad.add_probe(Gst.PadProbeType.BUFFER, tiler_src_pad_buffer_probe, 0)

    # List the sources
    print("Now playing...")
    for i, source in enumerate(args):
        if (i != 0):
            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
    thread_stop = True
    print("Exiting app\n")
    pipeline.set_state(Gst.State.NULL)

if __name__ == '__main__':
    sys.exit(main(sys.argv))


#!/usr/bin/env python3

################################################################################
# Copyright (c) 2020, 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
sys.path.append('../')
import gi
import configparser
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst
from gi.repository import GLib
from ctypes import *
import time
import sys
import numpy as np
import cv2
import math
import platform
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
from common.FPS import GETFPS
from common.utils import long_to_int
import pyds
from threading import Thread
from queue import Queue
import os
import datetime

fps_streams={}

MUXER_OUTPUT_WIDTH=640
MUXER_OUTPUT_HEIGHT=480
MUXER_BATCH_TIMEOUT_USEC=4000000
TILED_OUTPUT_WIDTH=640
TILED_OUTPUT_HEIGHT=480
GST_CAPS_FEATURES_NVMM="memory:NVMM"
OSD_PROCESS_MODE= 0
OSD_DISPLAY_TEXT= 0
pgie_classes_str= ["lpd"]

init = time.time()
id_list = Queue(maxsize=20)
path1 = "data"
if not os.path.exists(path1):
    os.mkdir(path1)
number_sources = 0
# tiler_sink_pad_buffer_probe  will extract metadata received on OSD sink pad
# and update params for drawing rectangle, object information etc.
def tiler_src_pad_buffer_probe(pad,info,u_data):

    global number_sources

    frame_number=0
    num_rects=0
    gst_buffer = info.get_buffer()
    if not gst_buffer:
        print("Unable to get GstBuffer ")
        return

    lp_dict = {i: {} for i in range(number_sources)}
    # 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

        '''
        print("Frame Number is ", frame_meta.frame_num)
        print("Source id is ", frame_meta.source_id)
        print("Batch id is ", frame_meta.batch_id)
        print("Source Frame Width ", frame_meta.source_frame_width)
        print("Source Frame Height ", frame_meta.source_frame_height)
        print("Num object meta ", frame_meta.num_obj_meta)
        '''
        frame_number=frame_meta.frame_num
        l_obj=frame_meta.obj_meta_list
        num_rects = frame_meta.num_obj_meta

        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
            # print(obj_meta.confidence)

            l_user_meta = obj_meta.obj_user_meta_list
            while l_user_meta:
                try:
                    user_meta = pyds.NvDsUserMeta.cast(l_user_meta.data)
                    if user_meta.base_meta.meta_type == pyds.nvds_get_user_meta_type("NVIDIA.DSANALYTICSOBJ.USER_META"):             
                        user_meta_data = pyds.NvDsAnalyticsObjInfo.cast(user_meta.user_meta_data)
                        print("Object {0} line crossing status: {1}".format(obj_meta.object_id, user_meta_data.lcStatus))
                        # print("Object {0} roi status: {1}".format(obj_meta.object_id, user_meta_data.roiStatus))
                        # if user_meta_data.dirStatus: print("Object {0} moving in direction: {1}".format(obj_meta.object_id, user_meta_data.dirStatus))                    
                        # if user_meta_data.lcStatus: print("Object {0} line crossing status: {1}".format(obj_meta.object_id, user_meta_data.lcStatus))
                        # if user_meta_data.ocStatus: print("Object {0} overcrowding status: {1}".format(obj_meta.object_id, user_meta_data.ocStatus))
                        # if user_meta_data.roiStatus: print("Object {0} roi status: {1}".format(obj_meta.object_id, user_meta_data.roiStatus))

                        l_class = obj_meta.classifier_meta_list
                        # print(l_class)

                        if user_meta_data.lcStatus:
                            while l_class is not None:
                                try:
                                    class_meta = pyds.NvDsClassifierMeta.cast(l_class.data)
                                except StopIteration:
                                    break

                                l_label = class_meta.label_info_list
                                # print(class_meta.num_labels)
                                while l_label is not None:
                                    try:
                                        label_info = pyds.NvDsLabelInfo.cast(l_label.data)
                                    except StopIteration:
                                        break

                                    print(label_info.result_label)
                                    # print(long_to_int(obj_meta.object_id))
                                    x = obj_meta.rect_params.left
                                    y = obj_meta.rect_params.top
                                    w = obj_meta.rect_params.width
                                    h = obj_meta.rect_params.height
                                    lp_dict[frame_meta.pad_index][long_to_int(obj_meta.object_id)] = [label_info.result_label, obj_meta.confidence, int(x), int(y), int(x+w), int(y+h)]
                                    try:
                                        l_label=l_label.next
                                    except StopIteration:
                                        break
                                try:
                                    l_class=l_class.next
                                except StopIteration:
                                    break

                except StopIteration:
                    break

                try:
                    l_user_meta = l_user_meta.next
                except StopIteration:
                    break

            try: 
                l_obj=l_obj.next
            except StopIteration:
                break

        #write image when object detected in "positive" folder
        for source_id, data in lp_dict.items():
            for tracking_id, bbox_info in data.items():
                if tracking_id not in list(id_list.queue):
                    if id_list.full():
                        id_list.get()
                    id_list.put(tracking_id)
                    try:
                        frame = get_frame(gst_buffer, frame_meta.batch_id)
                        name= "img_"+str(tracking_id)+"_"+datetime.datetime.now().strftime("%Y%m%d_%H%M%S")+".jpg"
                        if bbox_info[1] > 0.3:
                            cv2.rectangle(frame, (bbox_info[2], bbox_info[3]), (bbox_info[4], bbox_info[5]), (255, 0, 255), 1)
                            cv2.rectangle(frame, (bbox_info[2], bbox_info[3]-20), (bbox_info[4], bbox_info[3]), (255, 0, 255), -1)
                            cv2.putText(frame, bbox_info[0], (bbox_info[2], bbox_info[3]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1, cv2.LINE_AA)
                            cv2.imwrite(os.path.join(os.path.join(path1,"stream_"+str(source_id)), name), frame)
                            print(bbox_info)
                    except cv2.error as e:
                        print(e)

        print(lp_dict)

        # Get meta data from NvDsAnalyticsFrameMeta
        l_user = frame_meta.frame_user_meta_list
        while l_user:
            try:
                user_meta = pyds.NvDsUserMeta.cast(l_user.data)
                if user_meta.base_meta.meta_type == pyds.nvds_get_user_meta_type("NVIDIA.DSANALYTICSFRAME.USER_META"):
                    user_meta_data = pyds.NvDsAnalyticsFrameMeta.cast(user_meta.user_meta_data)
                    # if user_meta_data.objInROIcnt: print("Objs in ROI: {0}".format(user_meta_data.objInROIcnt))                    
                    if user_meta_data.objLCCumCnt: print("Linecrossing Cumulative: {0}".format(user_meta_data.objLCCumCnt))
                    # if user_meta_data.objLCCurrCnt: print("Linecrossing Current Frame: {0}".format(user_meta_data.objLCCurrCnt))
                    # if user_meta_data.ocStatus: print("Overcrowding status: {0}".format(user_meta_data.ocStatus))
            except StopIteration:
                break
            try:
                l_user = l_user.next
            except StopIteration:
                break

        # Get frame rate through this probe
        fps_streams["stream{0}".format(frame_meta.pad_index)].get_fps()
        try:
            l_frame=l_frame.next
        except StopIteration:
            break

    return Gst.PadProbeReturn.OK

def get_frame(gst_buffer, batch_id):
    n_frame=pyds.get_nvds_buf_surface(hash(gst_buffer),batch_id)
    #convert python array into numy array format.
    frame_image=np.array(n_frame,copy=True,order='C')
    #covert the array into cv2 default color format
    frame_image=cv2.cvtColor(frame_image,cv2.COLOR_RGBA2BGRA)
    return frame_image

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.
    print("gstname=",gstname)
    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.
        print("features=",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 name.find("nvv4l2decoder") != -1:
        if is_aarch64():
            print("Seting bufapi_version\n")
            Object.set_property("bufapi-version",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 number_sources

    # Check input arguments
    if len(args) < 2:
        sys.stderr.write("usage: %s <uri1> [uri2] ... [uriN]\n" % args[0])
        sys.exit(1)

    for i in range(0,len(args)-1):
        fps_streams["stream{0}".format(i)]=GETFPS(i)
    number_sources=len(args)-1

    # Standard GStreamer initialization
    GObject.threads_init()
    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 streamux \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):
        if not os.path.exists(os.path.join(path1,"stream_"+str(i))):
            os.mkdir(os.path.join(path1,"stream_"+str(i)))
        print("Creating source_bin ",i," \n ")
        uri_name=args[i+1]
        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.get_request_pad(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)

    queue1=Gst.ElementFactory.make("queue","queue1")
    queue2=Gst.ElementFactory.make("queue","queue2")
    queue3=Gst.ElementFactory.make("queue","queue3")
    queue4=Gst.ElementFactory.make("queue","queue4")
    queue5=Gst.ElementFactory.make("queue","queue5")
    queue6=Gst.ElementFactory.make("queue","queue6")
    queue7=Gst.ElementFactory.make("queue","queue7")
    queue8=Gst.ElementFactory.make("queue","queue8")
    queue9=Gst.ElementFactory.make("queue","queue9")
    pipeline.add(queue1)
    pipeline.add(queue2)
    pipeline.add(queue3)
    pipeline.add(queue4)
    pipeline.add(queue5)
    pipeline.add(queue6)
    pipeline.add(queue7)
    pipeline.add(queue8)
    pipeline.add(queue9)

    print("Creating Pgie \n ")
    pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
    if not pgie:
        sys.stderr.write(" Unable to create pgie \n")
    print("Creating tiler \n ")

    tracker = Gst.ElementFactory.make("nvtracker", "tracker")
    if not tracker:
        sys.stderr.write(" Unable to create tracker \n")

    print("Creating nvdsanalytics \n ")
    nvanalytics = Gst.ElementFactory.make("nvdsanalytics", "analytics")
    if not nvanalytics:
        sys.stderr.write(" Unable to create nvanalytics \n")
    nvanalytics.set_property("config-file", "config_nvdsanalytics.txt")

    sgie = Gst.ElementFactory.make("nvinfer", "secondary-nvinference-engine")
    if not sgie:
        sys.stderr.write(" Unable to make sgie \n")

    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")
    # nvosd.set_property('process-mode',OSD_PROCESS_MODE)
    # nvosd.set_property('display-text',OSD_DISPLAY_TEXT)
    if(is_aarch64()):
        print("Creating transform \n ")
        transform=Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
        if not transform:
            sys.stderr.write(" Unable to create transform \n")

    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', MUXER_OUTPUT_WIDTH)
    streammux.set_property('height', MUXER_OUTPUT_HEIGHT)
    streammux.set_property('batch-size', number_sources)
    streammux.set_property('batched-push-timeout', 4000000)

    pgie.set_property('config-file-path', "lpd_us_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)

    # Set properties of tracker
    config = configparser.ConfigParser()
    config.read('lpr_sample_tracker_config.txt')
    config.sections()

    for key in config['tracker']:
        if key == 'tracker-width':
            tracker_width = config.getint('tracker', key)
            tracker.set_property('tracker-width', tracker_width)
        if key == 'tracker-height':
            tracker_height = config.getint('tracker', key)
            tracker.set_property('tracker-height', tracker_height)
        if key == 'gpu-id':
            tracker_gpu_id = config.getint('tracker', key)
            tracker.set_property('gpu_id', tracker_gpu_id)
        if key == 'll-lib-file':
            tracker_ll_lib_file = config.get('tracker', key)
            tracker.set_property('ll-lib-file', tracker_ll_lib_file)
        if key == 'll-config-file':
            tracker_ll_config_file = config.get('tracker', key)
            tracker.set_property('ll-config-file', tracker_ll_config_file)
        if key == 'enable-batch-process':
            tracker_enable_batch_process = config.getint('tracker', key)
            tracker.set_property('enable_batch_process',
                                 tracker_enable_batch_process)

    sgie.set_property('config-file-path', "lpr_config_sgie_us.txt")
    sgie.set_property('process-mode', 2)

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

    if not is_aarch64():
        # Use CUDA unified memory in the pipeline so frames
        # can be easily accessed on CPU in Python.
        mem_type = int(pyds.NVBUF_MEM_CUDA_UNIFIED)
        streammux.set_property("nvbuf-memory-type", mem_type)
        nvvidconv.set_property("nvbuf-memory-type", mem_type)
        nvvidconv1.set_property("nvbuf-memory-type", mem_type)
        tiler.set_property("nvbuf-memory-type", mem_type)

    print("Adding elements to Pipeline \n")
    
    pipeline.add(pgie)
    pipeline.add(tracker)
    pipeline.add(nvanalytics)
    pipeline.add(sgie)
    pipeline.add(tiler)
    pipeline.add(nvvidconv)
    pipeline.add(filter1)
    pipeline.add(nvvidconv1)
    pipeline.add(nvosd)
    if is_aarch64():
        pipeline.add(transform)
    pipeline.add(sink)

    print("Linking elements in the Pipeline \n")

    streammux.link(pgie)
    pgie.link(queue1)
    queue1.link(tracker)
    tracker.link(queue2)
    queue2.link(nvanalytics)
    nvanalytics.link(queue3)
    queue3.link(sgie)
    sgie.link(queue4)
    queue4.link(nvvidconv1)
    nvvidconv1.link(queue5)
    queue5.link(filter1)
    filter1.link(queue6)
    queue6.link(tiler)
    tiler.link(queue7)
    queue7.link(nvvidconv)
    nvvidconv.link(queue8)
    queue8.link(nvosd)
    if is_aarch64():
        nvosd.link(queue9)
        queue9.link(transform)
        transform.link(sink)
    else:
        nvosd.link(queue9)
        queue9.link(sink)  

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

    tiler_src_pad=tiler.get_static_pad("sink")
    if not tiler_src_pad:
        sys.stderr.write(" Unable to get sink pad \n")
    else:
        tiler_src_pad.add_probe(Gst.PadProbeType.BUFFER, tiler_src_pad_buffer_probe, 0)

    # List the sources
    print("Now playing...")
    for i, source in enumerate(args):
        if (i != 0):
            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
    thread_stop = True
    print("Exiting app\n")
    pipeline.set_state(Gst.State.NULL)

if __name__ == '__main__':
    sys.exit(main(sys.argv))


#!/usr/bin/env python3 ################################################################################ # Copyright (c) 2020, 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. ################################################################################ # tiler_sink_pad_buffer_probe  will extract metadata received on OSD sink pad # and update params for drawing rectangle, object information etc. # 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) # 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. print("Frame Number is ", frame_meta.frame_num)         print("Source id is ", frame_meta.source_id)         print("Batch id is ", frame_meta.batch_id)         print("Source Frame Width ", frame_meta.source_frame_width)         print("Source Frame Height ", frame_meta.source_frame_height)         print("Num object meta ", frame_meta.num_obj_meta) # Casting l_obj.data to pyds.NvDsObjectMeta # print(obj_meta.confidence) # print("Object {0} roi status: {1}".format(obj_meta.object_id, user_meta_data.roiStatus)) # if user_meta_data.dirStatus: print("Object {0} moving in direction: {1}".format(obj_meta.object_id, user_meta_data.dirStatus)) # if user_meta_data.lcStatus: print("Object {0} line crossing status: {1}".format(obj_meta.object_id, user_meta_data.lcStatus)) # if user_meta_data.ocStatus: print("Object {0} overcrowding status: {1}".format(obj_meta.object_id, user_meta_data.ocStatus)) # if user_meta_data.roiStatus: print("Object {0} roi status: {1}".format(obj_meta.object_id, user_meta_data.roiStatus)) # print(l_class) # print(class_meta.num_labels) # print(long_to_int(obj_meta.object_id)) #write image when object detected in "positive" folder # Get meta data from NvDsAnalyticsFrameMeta # if user_meta_data.objInROIcnt: print("Objs in ROI: {0}".format(user_meta_data.objInROIcnt)) # if user_meta_data.objLCCurrCnt: print("Linecrossing Current Frame: {0}".format(user_meta_data.objLCCurrCnt)) # if user_meta_data.ocStatus: print("Overcrowding status: {0}".format(user_meta_data.ocStatus)) # Get frame rate through this probe #convert python array into numy array format. #covert the array into cv2 default color format # Need to check if the pad created by the decodebin is for video and not # audio. # 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. # Get the source bin ghost pad # Create a source GstBin to abstract this bin's content from the rest of the # pipeline # 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. # We set the input uri to the source element # 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 # 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. # Check input arguments # Standard GStreamer initialization # Create gstreamer elements */ # Create Pipeline element that will form a connection of other elements # Create nvstreammux instance to form batches from one or more sources. # nvosd.set_property('process-mode',OSD_PROCESS_MODE) # nvosd.set_property('display-text',OSD_DISPLAY_TEXT) # Set properties of tracker # Use CUDA unified memory in the pipeline so frames # can be easily accessed on CPU in Python. # create an event loop and feed gstreamer bus mesages to it # List the sources # start play back and listed to events # cleanup