Ok, I start deepstream_lpr_app and it’s working ok. But I want to use it with python.
Everything works as long as I use the standard model to recognize US license plate.
But if I change it to my custom model the result is “MCM4M9M6MAMJM5M8” instead of “C496AT58”.
I just changed the LPR module to my custom and no more.
My script:
import sys
sys.path.append('/opt/nvidia/deepstream/deepstream-6.1/sources/deepstream_python_apps/apps/')
import gi
import configparser
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
import sys
import math
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
from common.FPS import PERF_DATA
import pyds
fps_streams = {}
OSD_PROCESS_MODE = 0
OSD_DISPLAY_TEXT = 1
TILED_OUTPUT_WIDTH = 1920
TILED_OUTPUT_HEIGHT = 1080
perf_data = None
pgie_classes_str = ["lpd"]
def osd_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
lp_dict = {}
# 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
# no ROI
l_class = obj_meta.classifier_meta_list
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
while l_label is not None:
try:
label_info = pyds.NvDsLabelInfo.cast(l_label.data)
except StopIteration:
break
print(label_info.result_label)
try:
l_label = l_label.next
except StopIteration:
break
try:
l_class = l_class.next
except StopIteration:
break
try:
l_obj = l_obj.next
except StopIteration:
break
# 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
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.
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 perf_data
perf_data = PERF_DATA(len(args))
# Check input arguments
if len(args) < 2:
sys.stderr.write("usage: %s <uri1> [uri2] ... [uriN]\n" % args[0])
sys.exit(1)
number_sources = len(args) - 1
# 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 ")
# 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 + 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 ")
sgie1 = Gst.ElementFactory.make("nvinfer", "secondary1-nvinference-engine")
if not sgie1:
sys.stderr.write(" Unable to create sgie1 \n")
sgie2 = Gst.ElementFactory.make("nvinfer", "secondary2-nvinference-engine")
if not sgie2:
sys.stderr.write(" Unable to make sgie2 \n")
print("Creating nvtracker \n ")
tracker = Gst.ElementFactory.make("nvtracker", "tracker")
if not tracker:
sys.stderr.write(" Unable to create tracker \n")
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")
sink.set_property('sync', 0)
sink.set_property('async', 1)
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('live-source', 1)
streammux.set_property('width', TILED_OUTPUT_WIDTH)
streammux.set_property('height', TILED_OUTPUT_HEIGHT)
streammux.set_property('batch-size', number_sources)
streammux.set_property('batched-push-timeout', 4000000)
pgie.set_property('config-file-path', "trafficamnet_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)
config = configparser.ConfigParser()
config.read('./config/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)
if key == 'enable-past-frame':
tracker_enable_past_frame = config.getint('tracker', key)
tracker.set_property('enable_past_frame', tracker_enable_past_frame)
sgie1.set_property('config-file-path', "lpd_us_config.txt")
sgie1.set_property('process-mode', 2)
sgie2.set_property('config-file-path', "lpr_config_sgie_us.txt")
sgie2.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)
print("Adding elements to Pipeline \n")
pipeline.add(pgie)
pipeline.add(tracker)
pipeline.add(sgie1)
pipeline.add(sgie2)
pipeline.add(tiler)
pipeline.add(nvvidconv)
pipeline.add(nvosd)
if is_aarch64():
pipeline.add(transform)
pipeline.add(sink)
print("Linking elements in the Pipeline \n")
streammux.link(queue1)
queue1.link(pgie)
pgie.link(queue2)
queue2.link(tracker)
tracker.link(queue3)
queue3.link(sgie1)
sgie1.link(queue4)
queue4.link(sgie2)
sgie2.link(queue9)
queue9.link(tiler)
tiler.link(queue5)
queue5.link(nvvidconv)
nvvidconv.link(queue6)
queue6.link(nvosd)
if is_aarch64():
nvosd.link(queue7)
queue7.link(transform)
transform.link(sink)
else:
nvosd.link(queue7)
queue7.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)
GLib.timeout_add(5000, perf_data.perf_print_callback)
# List the sources
print("Now playing...")
for i, source in enumerate(args):
if i != 0:
print(i, ": ", source)
# Lets add probe to get informed of the meta data generated, we add probe to
# the sink pad of the osd element, since by that time, the buffer would have
# had got all the metadata.
osdsinkpad = nvosd.get_static_pad("sink")
if not osdsinkpad:
sys.stderr.write(" Unable to get sink pad of nvosd \n")
osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe, 0)
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))
LPR config:
[property]
gpu-id=0
model-engine-file=/home/codeinside/test_num/LPR/lprnet_epoch-15.etlt_b16_gpu0_fp16.engine
labelfile-path=/home/codeinside/test_num/LPR/labels_ru.txt
tlt-encoded-model=/home/codeinside/test_num/LPR/lprnet_epoch-15.etlt
tlt-model-key=nvidia_tlt
batch-size=16
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=3
gie-unique-id=2
output-blob-names=output_bbox/BiasAdd;output_cov/Sigmoid
#0=Detection 1=Classifier 2=Segmentation
network-type=1
parse-classifier-func-name=NvDsInferParseCustomNVPlate
custom-lib-path=/home/codeinside/deepstream_lpr_app_/nvinfer_custom_lpr_parser/libnvdsinfer_custom_impl_lpr.so
process-mode=2
operate-on-gie-id=2
operate-on-class-ids=0
net-scale-factor=0.00392156862745098
#net-scale-factor=1.0
#0=RGB 1=BGR 2=GRAY
model-color-format=0
[class-attrs-all]
threshold=0.5