System Specifications:
• Hardware Platform (Jetson / GPU) = Jetson
• DeepStream Version = 6.0.1
• JetPack Version (valid for Jetson only) = 4.6
Hello.
I wanted to run the deepstream pipeline on 2 CSI cameras, it works fine on single CSI input. Please can you provide the sample code for multiple CSI cameras or i have a sample code you can modify that as well
import sys
sys.path.append('../')
import gi
import configparser
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from ctypes import *
import time
import sys
import math
import platform
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
from common.FPS import PERF_DATA
import numpy as np
import pyds
import cv2
import os
import os.path
from os import path
from common import config
import pika
import logger
import json
perf_data = None
global PGIE_CLASS_ID_CIGAR
PGIE_CLASS_ID_CIGAR = 0
MAX_DISPLAY_LEN = 64
MUXER_OUTPUT_WIDTH = 1920
MUXER_OUTPUT_HEIGHT = 1080
MUXER_BATCH_TIMEOUT_USEC = 4000000
TILED_OUTPUT_WIDTH = 1920
TILED_OUTPUT_HEIGHT = 1080
GST_CAPS_FEATURES_NVMM = "memory:NVMM"
pgie_classes_str= ["Cigar"]
Queue = config.Queue
def initializeChannel():
credentials = pika.PlainCredentials(config.pika_name,config.pika_name)
parameters = pika.ConnectionParameters('localhost', 5672, '/', credentials, heartbeat=0, blocked_connection_timeout=3000)
connection = pika.BlockingConnection(parameters)
channel = connection.channel()
channel.queue_declare(queue=Queue,durable = True)
channel.queue_purge(queue=Queue)
print("connection established")
return channel, connection
def overlay(frame, n_detections, tray_no, text):
position = (10, 50)
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 2
font_color = (0, 255, 0)
font_thickness = 2
cv2.putText(frame, str(n_detections), position, font, font_scale, font_color, font_thickness)
# Overlay before /after
position = (10, 100) # (x, y) coordinates
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 2
font_color = (0, 255, 0) # (B, G, R) color
font_thickness = 2
cv2.putText(frame, str(text), position, font, font_scale, font_color, font_thickness)
# Overlay tray number
position = (10, 150) # (x, y) coordinates
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 1
font_color = (0, 255, 0) # (B, G, R) color
font_thickness = 2
cv2.putText(frame, "Tray No. " + str(tray_no), position, font, font_scale, font_color, font_thickness)
return frame
def write_file(file_path, value):
with open(file_path, 'w') as file:
file.write(str(value))
def read_file(file_path):
with open(file_path, 'r') as file:
value = file.read()
return value.strip()
def tiler_sink_pad_buffer_probe(pad, info, u_data):
frame_number = 0
num_rects = 0
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return
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:
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
is_first_obj = True
save_image = False
last_frame = False
if frame_number == 0:
global door_opened, recv, transid
door_opened = False
#recv = {'cmd':'DoorOpened', 'parm1':'123:True'}
if frame_number == 83:
pass
#recv = {"cmd": "OrderSettled","transaction_id": "123"}
method_frame, _ ,recv = channel.basic_get(Queue)
#print(recv)
count_cigars = {'Cigar':0}
while l_obj is not None:
try:
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
count_cigars['Cigar']+=1
if is_first_obj:
if recv != None:
#if 'cmd' in recv:
print(recv)
#print(frame_meta.source_id)
#print(frame_number)
recv = str(recv,'utf-8')
#logger.info(recv) #Debug
recv =json.loads(recv)
if recv["cmd"] == 'DoorOpened':
transid = recv["parm1"].split(":")[0]
door_info = recv["parm1"].split(":")[1]
#logger.info('\n')
#logger.info(" RECV: {} / cvCigar".format(recv["cmd"]))
#logger.info(" TRANSID: {}".format(transid))
if door_info == 'True':
door_opened = True
n_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
frame_copy = np.array(n_frame, copy=True, order='C')
frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_RGBA2BGRA)
#if is_aarch64(): # If Jetson, since the buffer is mapped to CPU for retrieval, it must also be unmapped
#pyds.unmap_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
save_image = True
#print("Entered Executed")
#recv = {'cmd':''}
if recv['cmd'] == 'OrderSettled':
door_opened = False
last_n_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
last_frame_copy = np.array(last_n_frame, copy=True, order='C')
last_frame_copy = cv2.cvtColor(last_frame_copy, cv2.COLOR_RGBA2BGRA)
#if is_aarch64(): # If Jetson, since the buffer is mapped to CPU for retrieval, it must also be unmapped
#pyds.unmap_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
last_frame = True
save_image = True
#recv = {'cmd':''}
if door_opened:
os.makedirs(os.path.join(config.base_path, 'archive', transid, 'frames_1'),exist_ok = True)
os.makedirs(os.path.join(config.base_path, 'archive', transid, 'frames_2'),exist_ok = True)
new_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
new_frame_copy = np.array(new_frame, copy=True, order='C')
new_frame_copy = cv2.cvtColor(new_frame_copy, cv2.COLOR_RGBA2BGRA)
#if is_aarch64(): # If Jetson, since the buffer is mapped to CPU for retrieval, it must also be unmapped
#pyds.unmap_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
img_path = os.path.join(config.base_path, 'archive', transid, 'frames_'+str(frame_meta.source_id+1), str(frame_number)+'.jpg')
cv2.imwrite(img_path, new_frame_copy)
is_first_obj = False
try:
l_obj = l_obj.next
except StopIteration:
break
print("Source ID=" , frame_meta.source_id, " Frame Number=", frame_number, "Number of Objects=", num_rects)# update frame rate through this probe
stream_index = "stream{0}".format(frame_meta.pad_index)
global perf_data
perf_data.update_fps(stream_index)
if save_image:
os.makedirs(os.path.join(config.base_path, 'archive', transid),exist_ok = True)
if frame_meta.source_id == 0:
tray_no = '23'
file_name = 'Cam1.txt'
else:
tray_no = '25'
file_name = 'Cam2.txt'
if last_frame:
last_frame_copy = overlay(last_frame_copy, count_cigars['Cigar'], tray_no, 'After')
cv2.imwrite("{}archive/{}/last_frame{}.jpg".format(config.base_path, transid, frame_meta.pad_index), last_frame_copy)
initial_cigar_count = int(read_file(os.path.join(config.base_path, 'archive', transid, file_name)))
if frame_meta.source_id == 0:
print("Cam1 Before",initial_cigar_count)
print("Cam1 After", count_cigars)
print("Cam2 Cigars Sold", initial_cigar_count-count_cigars["Cigar"])
else:
print("Cam2 Before",initial_cigar_count)
print("Cam2 After", count_cigars)
print("Cam2 Cigars Sold", initial_cigar_count-count_cigars["Cigar"])
else:
frame_copy = overlay(frame_copy, count_cigars['Cigar'], tray_no, 'Before')
cv2.imwrite("{}archive/{}/first_frame{}.jpg".format(config.base_path, transid, frame_meta.pad_index), frame_copy)
write_file(os.path.join(config.base_path, 'archive', transid, file_name), count_cigars['Cigar'])
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 not is_aarch64() and name.find("nvv4l2decoder") != -1:
# Use CUDA unified memory in the pipeline so frames
# can be easily accessed on CPU in Python.
Object.set_property("cudadec-memtype", 2)
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):
# Check input arguments
if len(args) < 2:
sys.stderr.write("usage: %s <uri1> [uri2] ... [uriN] <folder to save frames>\n" % args[0])
sys.exit(1)
global perf_data
perf_data = PERF_DATA(len(args) - 1)
number_sources = len(args) - 1
global channel, connection
channel, connection = initializeChannel()
# 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 streamux \n ")
source = Gst.ElementFactory.make("nvarguscamerasrc", "src-elem")
if not source:
sys.stderr.write(" Unable to create Source \n")
# Converter to scale the image
nvvidconv_src = Gst.ElementFactory.make("nvvideoconvert", "convertor_src")
if not nvvidconv_src:
sys.stderr.write(" Unable to create nvvidconv_src \n")
# Caps for NVMM and resolution scaling
caps_nvvidconv_src = Gst.ElementFactory.make("capsfilter", "nvmm_caps")
if not caps_nvvidconv_src:
sys.stderr.write(" Unable to create capsfilter \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")
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 is_aarch64():
print("Creating nv3dsink \n")
sink = Gst.ElementFactory.make("fakesink", "fakesink")
if not sink:
sys.stderr.write(" Unable to create nv3dsink \n")
else:
print("Creating EGLSink \n")
sink = Gst.ElementFactory.make("fakesink", "fakesink")
if not sink:
sys.stderr.write(" Unable to create egl sink \n")
source.set_property('bufapi-version', True)
caps_nvvidconv_src.set_property('caps', Gst.Caps.from_string('video/x-raw(memory:NVMM), width=1280, height=720'))
streammux.set_property('width', 1280)
streammux.set_property('height', 720)
streammux.set_property('batch-size', 1)
streammux.set_property('batched-push-timeout', 4000000)
pgie.set_property('config-file-path', "configs/pgie_yolov8.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)
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(source)
pipeline.add(nvvidconv_src)
pipeline.add(caps_nvvidconv_src)
pipeline.add(streammux)
pipeline.add(pgie)
pipeline.add(tiler)
pipeline.add(nvvidconv)
pipeline.add(filter1)
pipeline.add(nvvidconv1)
pipeline.add(nvosd)
pipeline.add(sink)
source.link(nvvidconv_src)
nvvidconv_src.link(caps_nvvidconv_src)
sinkpad = streammux.get_request_pad("sink_0")
if not sinkpad:
sys.stderr.write(" Unable to get the sink pad of streammux \n")
srcpad = caps_nvvidconv_src.get_static_pad("src")
if not srcpad:
sys.stderr.write(" Unable to get source pad of source \n")
srcpad.link(sinkpad)
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:
#print("Hello World")
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[:-1]):
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
print("Exiting app\n")
pipeline.set_state(Gst.State.NULL)
if __name__ == '__main__':
sys.exit(main(sys.argv))