DeepStream Pipeline#
This notebook is continuation of the intro 👈🏼👈🏼… Just wanted to avoid infinite scroll, so thought to split this section into its own cohesion… Here we will start with a simple DeepStream pipeline and then move towards a multi DNN pipeline…
We begin by using the ffprobe command line utility to understand the raw input video’s format (see documentation if needed). When using the ffprobe command, the -i option lets us read an input URL and the -hide_banner option suppresses printing the banner to reduce the output.
import os
# Set the input video path to an environment variable
os.environ['TARGET_VIDEO_PATH']='data/sample_30.h264'
os.environ['TARGET_VIDEO_PATH_MP4']='sample_30.mp4'
target_video_path=os.environ['TARGET_VIDEO_PATH']
target_video_path_mp4=os.environ['TARGET_VIDEO_PATH_MP4']
# Analyze video
!ffprobe -i $TARGET_VIDEO_PATH \
-hide_banner
Input #0, h264, from 'data/sample_30.h264':
Duration: N/A, bitrate: N/A
Stream #0:0: Video: h264 (High), yuv420p(progressive), 882x692, 30 fps, 30 tbr, 1200k tbn, 60 tbc
TrafficCamNet Object Detection Model#
The TrafficCamNet Object Detection model, according to its model card, detects one or more physical objects from four categories (car, persons, road signs, and two-wheelers) within an image and returns a box around each object, as well as a category label for each object.
For example, given an input image/frame, the inference engine will generate the bounding box coordinates as well as the category labels:
We can then optionally perform post-processing and draw the bounding boxes and text labels on top of the original frame.
Installing NGC CLI#
Pre-trained models can be downloaded from NGC using the NGC CLI.
To download a model from the registry, use the ngc registry model download-version <org-name>/<model-name:version> command and specify the model name as well as version. The model will be downloaded to a folder that corresponds to the model name in the current directory but can also be specified using the -d or --dest option.
!ngc registry model download-version nvidia/tao/trafficcamnet:pruned_v1.0 --dest $NGC_DIR
{
"download_end": "2025-08-29 03:22:17",
"download_start": "2025-08-29 03:22:15",
"download_time": "1s",
"files_downloaded": 3,
"local_path": "/dli/task/ngc_assets/trafficcamnet_vpruned_v1.0",
"size_downloaded": "5.2 MB",
"status": "COMPLETED"
}
Simple DeepStream Pipeline#
This is the pipeline architecture of the application. We’ve selected each plugin based on their functionality.
The filesrc plugin reads data from a local file. There are other plugins available for reading data from various types of sources like camera, RTSP streams, and disk.
The h264parse plugin is used to parse the input elementary H.264 stream into frame-size bytes of data.
The nvv4l2decoder plugin decodes the input data using the appropriate codec, which is H.264 in this case.
The nvstreammux plugin is a required stream aggregator. This video aggregator helps in accepting
ninput streams and converts them into sequential batched frames. Even though our simple application only consumes one input stream, this plugin is required asnvinferaccepts batched buffers with theNvDsBatchMetastructure already attached.This plugin takes
width,height, andbatch-sizeas parameters.
The nvinfer plugin performs transformation (format conversion and scaling) on the input frame based on network requirements and passes the transformed data to the low-level library. This is the plugin that we will use to define the deep learning task(s) associated with our application. The low-level library pre-processes the transformed frames (performs normalization and mean subtraction) and produces final float RGB/BGR/GRAY planar data which are passed to the TensorRT engine for inferencing. The output type generated by the low-level library depends on the network type. We will configure the
nvinferplugin for ourTrafficCamNetobject detection model. It attaches the inference results such as object class(s), bounding box coordinates, etc., to the metadata.
The fakesink plugin acts as the end of the pipeline where the data flow ends.
There is a synchronization-against-the-clock mechanism in GStreamer. For local video file, it is recommended to set the
syncproperty to1to get smooth video playback, vs.0for live-sources such as IP camera since they give frames at a static rate.
More information about the plugins can be found in the DeepStream Plugin Guide and GStreamer Plugin Guide.
Additionally, we will add a callback function on the source pad of the nvinfer plugin to access the metadata in the application. The application can then use this metadata to solve the given problem (in this case counting the number of cars, persons, road signs, and two-wheelers).
Initializing GStreamer and Pipeline#
We initialize GStreamer with Gst.init(list=None) and instantiate a Gst.Pipeline object as pipeline to contain all the elements shown in the pipeline diagram.
# Import necessary GStreamer libraries and DeepStream python bindings
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst, GLib
import pyds
# Initialize GStreamer
Gst.init(None)
# Create Pipeline element that will form a connection of other elements
pipeline=Gst.Pipeline()
print('Created pipeline')
Created pipeline
Creating Pipeline Elements#
We create each element in the pipeline using Gst.ElementFactory.make(factoryname, name) (see documentation). We can configure elements using set_property(property_name, value) (see documentation) with the required parameters as needed. In this step, we also add the elements to the pipeline with Gst.Pipeline.add(element).
# Create Source element for reading from a file and set the location property
source=Gst.ElementFactory.make("filesrc", "file-source")
source.set_property('location', target_video_path)
# Create H264 Parser with h264parse as the input file is an elementary h264 stream
h264parser=Gst.ElementFactory.make("h264parse", "h264-parser")
# Create Decoder with nvv4l2decoder for accelerated decoding on GPU
decoder=Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
# Create Streamux with nvstreammux to form batches for one or more sources and set properties
streammux=Gst.ElementFactory.make("nvstreammux", "stream-muxer")
streammux.set_property('width', 888)
streammux.set_property('height', 696)
streammux.set_property('batch-size', 1)
# Create Primary GStreamer Inference Element with nvinfer to run inference on the decoder's output after batching
pgie=Gst.ElementFactory.make("nvinfer", "primary-inference")
# Create Sink with fakesink as the end point of the pipeline
fakesink=Gst.ElementFactory.make('fakesink', 'fakesink')
fakesink.set_property('sync', 1)
print('Created elements')
Created elements
# Add elements to pipeline
pipeline.add(source)
pipeline.add(h264parser)
pipeline.add(decoder)
pipeline.add(streammux)
pipeline.add(pgie)
pipeline.add(fakesink)
print('Added elements to pipeline')
Added elements to pipeline
Explore Gst-nvinfer Configuration File#
The nvinfer plugin needs to be configured through a config file
These properties are important to understand:
Properties related to models downloaded from NGC or trained with the TAO Toolkit:
tlt-encoded-model- pathname of the TAO Toolkit encoded modeltlt-model-key- model load key for the TAO Toolkit encoded modellabelfile-path- pathname of a text file containing the labels for the model. The labels must be new line delimited for object detection modelsuff-input-blob-name- name of the input blob in the UFF fileoutput-blob-names- array of output layer namesinfer-dims- dimensions of the model input as [channel; height; width]net-scale-factor- pixel normalization factor (default=1)
Recommended properties:
batch-size- number of frames to be inferred together in a batch (default=1)
Mandatory properties for detectors:
num-detected-classes- number of classes detected by the network
Optional properties for detectors:
cluster-mode- clustering algorithm to use (default=0 i.e. Group Rectangles). Refer to the section on clustering algorithms supported by nvinfer in the documentation for more information.nms-iou-thresholds: maximum IOU score between two proposals after which the proposal with the lower confidence will be rejectedpre-cluster-thresholds: detection threshold to be applied prior to clusteringtopk: keep only top K objects with highest detection scores
Other optional properties:
network-mode- data format to be used for inference (0=FP32, 1=INT8, 2=FP16 mode | default=0 i.e. FP32)process-mode- mode (primary or secondary) in which the plugin is to operate on (default=1 i.e. primary)model-color-format- color format required by the model (default=0 i.e. RGB)interval- number of consecutive batches to be skipped for inference (primary mode only | default=0)gie-unique-id- unique ID to be assigned to the GIE to enable the application and other elements to identify detected bounding boxes and labels (default=0)gpu-id- device ID of GPU to use for pre-processing/inference (dGPU only)
You can find most of the information needed on the model card:
!cat spec_files/pgie_config_trafficcamnet_03.txt
[property]
gpu-id=0
net-scale-factor=0.00392156862745098
tlt-model-key=tlt_encode
tlt-encoded-model=/dli/task/ngc_assets/trafficcamnet_vpruned_v1.0/resnet18_trafficcamnet_pruned.etlt
labelfile-path=/dli/task/ngc_assets/trafficcamnet_vpruned_v1.0/labels.txt
infer-dims=3;544;960
uff-input-blob-name=input_1
batch-size=1
process-mode=1
model-color-format=0
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
num-detected-classes=4
interval=0
gie-unique-id=1
output-blob-names=output_bbox/BiasAdd;output_cov/Sigmoid
cluster-mode=2
# Use the config params below for NMS clustering mode
[class-attrs-all]
topk=20
nms-iou-threshold=0.5
pre-cluster-threshold=0.2
Note: The values in the config file can be overridden by values set through GObject properties. Another important thing to remember is that the properties recommended are specific to a primary detector. You may need to configure other properties for secondary and/or classifier.
After the configuration file has been created, we set the configuration file path of the nvinfer plugin using set_property(property_name, value).
# Set the configuration-file-path property for nvinfer
pgie.set_property('config-file-path', '/dli/task/spec_files/pgie_config_trafficcamnet_03.txt')
Linking Pipeline Elements#
Finally, we link all these elements in the order that data flow through the pipeline with Gst.Element.link(Gst.Element).
In GStreamer, elements are connected through pads:
Static pads: Always available.
Request pads: Created on-demand; you need to “request” one explicitly.
You can find the inforamtion of the pad using
gst-inspect-1.0 <element-name>
Pad Templates:
SRC template: 'src'
Availability: Always
...
SINK template: 'sink_%u'
Availability: Request
...
In DeepStream pipelines, nvstreammux (streammux) requires a separate input pad per input stream, like sink_0, sink_1 etc… Or in other words– when connecting a source to nvstreammux (the muxer), the input’s source pad, obtained from get_static_pad(name='src'), must be manually linked to a newly requested sink pad from the muxer using get_request_pad(name) and the pad template sink_%u. This enables the ability to have multiple sources feeding into the nvstreammux plugin.
# Finding pad information using python
from gi.repository import Gst
factory = Gst.ElementFactory.find("nvstreammux")
pad_templates = factory.get_static_pad_templates()
for pad_template in pad_templates:
direction = "SRC" if pad_template.direction == Gst.PadDirection.SRC else "SINK"
availability = pad_template.presence.value_nick
name_template = pad_template.name_template
print(f"{direction} pad: {name_template} ({availability})")
SINK pad: sink_%u (request)
SRC pad: src (always)
Linking the pipeline#
# Link elements in the pipeline
source.link(h264parser)
h264parser.link(decoder)
# Link decoder source pad to streammux sink pad
decoder_srcpad=decoder.get_static_pad("src")
streammux_sinkpad=streammux.get_request_pad("sink_0")
decoder_srcpad.link(streammux_sinkpad)
# Link the rest of the elements in the pipeline
streammux.link(pgie)
pgie.link(fakesink)
print('Linked elements in pipeline')
Linked elements in pipeline
Probe to Metadata Access#
Recall that we use probes to access metadata, which are callback functions that interact with the pads of elements. To access the metadata, we can use the DeepStream Python bindings, pyds. DeepStream uses an extensible standard structure for metadata. The basic metadata structure NvDsBatchMeta starts with batch-level metadata, created inside the Gst-nvstreammux plugin (see below). The object-level metadata we are looking for is accessible through NvDsBatchMeta > NvDsFrameMeta > NvDsObjectMeta. Some metadata instances are stored in GList form, which requires the data to be casted to the appropriate structure using pyds.NvDsFrameMeta.cast(data) or pyds.NvDsObjectMeta.cast(data). The NvDsObjectMeta contains the inference results from the deep learning neural networks, depending on what the configurations are.
We write the user-defined callback function pgie_source_pad_buffer_probe. Inside the function, we first get the batch metadata from the buffer with pyds.gst_buffer_get_nvds_batch_meta. From there, we can iterate through all the metadata types that are attached to the buffer. The DeepStream plugins attach metadata of type NVDS_META_FRAME_INFO to the buffer. To achieve our goal, we access the object metadata to count the number of objects in each frame and print the bounding box coordindates. In this example we are using a 4-class-detectors (vehicle, person, two-wheeler, and road sign). The return value of the probe function is programmed to Gst.PadProbeReturn.OK, which is the normal probe return value and leaves the probe in place. There are other options for the return value that can be considered for more complex cases.
# Declare list to hold count data
obj_counts=[]
# Define the Probe Function
def pgie_src_pad_buffer_probe(pad, info):
gst_buffer=info.get_buffer()
# 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
# Iterate through each frame in the batch metadata until the end
while l_frame is not None:
try:
frame_meta=pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
frame_num=frame_meta.frame_num
num_obj=frame_meta.num_obj_meta
l_obj=frame_meta.obj_meta_list
print("Frame Number={} Number of Objects={}".format(frame_num, num_obj))
# Append number of objects a list
obj_counts.append(num_obj)
# Iterate through each object in the frame metadata until the end
while l_obj is not None:
try:
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
print('\t Object: {} - Top: {}, Left: {}, Width: {}, Height: {}'.format(obj_meta.obj_label, \
round(obj_meta.rect_params.top), \
round(obj_meta.rect_params.left), \
round(obj_meta.rect_params.width), \
round(obj_meta.rect_params.height)))
except StopIteration:
break
try:
l_obj=l_obj.next
except StopIteration:
break
try:
l_frame=l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
With the pipeline defined and the elements linked, we add the callback function on the source pad of the nvinfer plugin using Gst.Pad.add_probe(mask, callback). While attached, the probe notifies when there are data passing on a pad. We can use GST_PAD_PROBE_TYPE_BUFFER or GST_PAD_PROBE_TYPE_BUFFER_LIST for mask when creating the probe. We designed the callback function to work with a single buffer so we’re using Gst.PadProbeType.BUFFER.
# Add probe to inference plugin's source
pgie_src_pad=pgie.get_static_pad('src')
probe_id=pgie_src_pad.add_probe(Gst.PadProbeType.BUFFER, pgie_src_pad_buffer_probe)
print('Attached probe')
Attached probe
Starting the Pipeline#
The pipeline has a bus that we will use to monitor messages. We run a GLib/Gtk+ MainLoop (or iterate the default GLib main context regularly) and attach a watch/message handler to the bus with Gst.Bus.add_signal_watch(). This way the GLib.Mainloop will check the bus for new messages and notify. The message handler is also achieved through a callback function, which we define as bus_call. This handler will be called whenever the pipeline emits a message to the bus. The return value of the callback function should be True to keep it attached to the bus. With the message handler in place, we put the pipeline in the PLAYING state and run the MainLoop. Finally, when the pipeline is finished, we put the pipeline into the NULL state to clean up.
import gi
import sys
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst
def bus_call(bus, message, loop):
t = message.type
if t == Gst.MessageType.EOS:
sys.stdout.write("End-of-stream")
loop.quit()
elif t==Gst.MessageType.WARNING:
err, debug = message.parse_warning()
sys.stderr.write("Warning: %s: %s\n" % (err, debug))
elif t == Gst.MessageType.ERROR:
err, debug = message.parse_error()
sys.stderr.write("Error: %s: %s\n" % (err, debug))
loop.quit()
return True
# Create an event loop
loop=GLib.MainLoop()
# Feed GStreamer bus messages to loop
bus=pipeline.get_bus()
bus.add_signal_watch()
bus.connect("message", bus_call, loop)
print('Added bus message handler')
Added bus message handler
# Start play back and listen to events
print("Starting pipeline")
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except:
pass
# Cleaning up as the pipeline comes to an end
pipeline.set_state(Gst.State.NULL)
Starting pipeline
0:28:08.735014002 436 0x2ac5920 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2002> [UID = 1]: Trying to create engine from model files
ERROR: [TRT]: 3: [builder.cpp::~Builder::307] Error Code 3: API Usage Error (Parameter check failed at: optimizer/api/builder.cpp::~Builder::307, condition: mObjectCounter.use_count() == 1. Destroying a builder object before destroying objects it created leads to undefined behavior.
)
0:28:31.241559380 436 0x2ac5920 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2034> [UID = 1]: serialize cuda engine to file: /dli/task/ngc_assets/trafficcamnet_vpruned_v1.0/resnet18_trafficcamnet_pruned.etlt_b1_gpu0_fp32.engine successfully
0:28:31.309896234 436 0x2ac5920 INFO nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:/dli/task/spec_files/pgie_config_trafficcamnet_03.txt sucessfully
Frame Number=0 Number of Objects=1
Object: car - Top: 26, Left: 366, Width: 59, Height: 37
Frame Number=1 Number of Objects=1
Object: car - Top: 26, Left: 366, Width: 60, Height: 37
Frame Number=2 Number of Objects=1
Object: car - Top: 26, Left: 366, Width: 60, Height: 37
Frame Number=3 Number of Objects=1
Object: car - Top: 26, Left: 367, Width: 62, Height: 41
Frame Number=4 Number of Objects=1
Object: car - Top: 26, Left: 367, Width: 62, Height: 41
Frame Number=5 Number of Objects=1
Object: car - Top: 26, Left: 367, Width: 63, Height: 41
Frame Number=6 Number of Objects=1
Object: car - Top: 28, Left: 369, Width: 65, Height: 40
Frame Number=7 Number of Objects=1
Object: car - Top: 28, Left: 368, Width: 66, Height: 41
Frame Number=8 Number of Objects=1
Object: car - Top: 28, Left: 369, Width: 66, Height: 40
Frame Number=9 Number of Objects=1
Object: car - Top: 29, Left: 369, Width: 74, Height: 50
Frame Number=10 Number of Objects=1
Object: car - Top: 29, Left: 369, Width: 74, Height: 50
Frame Number=11 Number of Objects=1
Object: car - Top: 29, Left: 369, Width: 74, Height: 50
Frame Number=12 Number of Objects=1
Object: car - Top: 29, Left: 370, Width: 84, Height: 55
Frame Number=13 Number of Objects=1
Object: car - Top: 29, Left: 370, Width: 84, Height: 55
Frame Number=14 Number of Objects=1
Object: car - Top: 29, Left: 370, Width: 84, Height: 55
Frame Number=15 Number of Objects=1
Object: car - Top: 31, Left: 373, Width: 89, Height: 56
Frame Number=16 Number of Objects=1
Object: car - Top: 31, Left: 371, Width: 89, Height: 56
Frame Number=17 Number of Objects=1
Object: car - Top: 31, Left: 373, Width: 89, Height: 56
Frame Number=18 Number of Objects=1
Object: car - Top: 33, Left: 375, Width: 93, Height: 59
Frame Number=19 Number of Objects=1
Object: car - Top: 33, Left: 375, Width: 93, Height: 59
Frame Number=20 Number of Objects=1
Object: car - Top: 33, Left: 375, Width: 93, Height: 59
Frame Number=21 Number of Objects=1
Object: car - Top: 34, Left: 376, Width: 99, Height: 64
Frame Number=22 Number of Objects=1
Object: car - Top: 34, Left: 376, Width: 99, Height: 64
Frame Number=23 Number of Objects=1
Object: car - Top: 34, Left: 376, Width: 99, Height: 64
Frame Number=24 Number of Objects=1
Object: car - Top: 36, Left: 380, Width: 103, Height: 68
Frame Number=25 Number of Objects=1
Object: car - Top: 36, Left: 380, Width: 103, Height: 68
Frame Number=26 Number of Objects=1
Object: car - Top: 36, Left: 380, Width: 103, Height: 68
Frame Number=27 Number of Objects=1
Object: car - Top: 39, Left: 382, Width: 119, Height: 84
Frame Number=28 Number of Objects=1
Object: car - Top: 39, Left: 382, Width: 119, Height: 84
Frame Number=29 Number of Objects=1
Object: car - Top: 39, Left: 382, Width: 119, Height: 84
Frame Number=30 Number of Objects=1
Object: car - Top: 42, Left: 381, Width: 127, Height: 93
Frame Number=31 Number of Objects=1
Object: car - Top: 42, Left: 381, Width: 127, Height: 93
Frame Number=32 Number of Objects=1
Object: car - Top: 42, Left: 381, Width: 127, Height: 93
Frame Number=33 Number of Objects=1
Object: car - Top: 44, Left: 383, Width: 133, Height: 104
Frame Number=34 Number of Objects=1
Object: car - Top: 44, Left: 383, Width: 133, Height: 104
Frame Number=35 Number of Objects=1
Object: car - Top: 44, Left: 383, Width: 133, Height: 104
Frame Number=36 Number of Objects=1
Object: car - Top: 46, Left: 380, Width: 145, Height: 119
Frame Number=37 Number of Objects=1
Object: car - Top: 46, Left: 380, Width: 145, Height: 119
Frame Number=38 Number of Objects=1
Object: car - Top: 46, Left: 380, Width: 145, Height: 119
Frame Number=39 Number of Objects=1
Object: car - Top: 51, Left: 372, Width: 160, Height: 133
Frame Number=40 Number of Objects=1
Object: car - Top: 51, Left: 372, Width: 160, Height: 133
Frame Number=41 Number of Objects=1
Object: car - Top: 51, Left: 372, Width: 160, Height: 133
Frame Number=42 Number of Objects=2
Object: car - Top: 25, Left: 372, Width: 47, Height: 37
Object: car - Top: 52, Left: 360, Width: 178, Height: 156
Frame Number=43 Number of Objects=2
Object: car - Top: 25, Left: 372, Width: 47, Height: 37
Object: car - Top: 51, Left: 360, Width: 178, Height: 156
Frame Number=44 Number of Objects=2
Object: car - Top: 25, Left: 372, Width: 48, Height: 37
Object: car - Top: 52, Left: 360, Width: 177, Height: 156
Frame Number=45 Number of Objects=2
Object: car - Top: 29, Left: 375, Width: 54, Height: 37
Object: car - Top: 59, Left: 325, Width: 223, Height: 209
Frame Number=46 Number of Objects=2
Object: car - Top: 28, Left: 375, Width: 54, Height: 39
Object: car - Top: 59, Left: 325, Width: 223, Height: 209
Frame Number=47 Number of Objects=2
Object: car - Top: 28, Left: 375, Width: 54, Height: 39
Object: car - Top: 59, Left: 325, Width: 223, Height: 209
Frame Number=48 Number of Objects=1
Object: car - Top: 68, Left: 299, Width: 255, Height: 250
Frame Number=49 Number of Objects=1
Object: car - Top: 68, Left: 299, Width: 255, Height: 251
Frame Number=50 Number of Objects=1
Object: car - Top: 68, Left: 299, Width: 255, Height: 250
Frame Number=51 Number of Objects=1
Object: car - Top: 78, Left: 276, Width: 267, Height: 317
Frame Number=52 Number of Objects=1
Object: car - Top: 78, Left: 276, Width: 267, Height: 317
Frame Number=53 Number of Objects=1
Object: car - Top: 78, Left: 276, Width: 267, Height: 317
Frame Number=54 Number of Objects=1
Object: car - Top: 77, Left: 232, Width: 328, Height: 375
Frame Number=55 Number of Objects=1
Object: car - Top: 77, Left: 232, Width: 328, Height: 375
Frame Number=56 Number of Objects=2
Object: car - Top: 32, Left: 376, Width: 70, Height: 47
Object: car - Top: 77, Left: 232, Width: 328, Height: 375
Frame Number=57 Number of Objects=0
Frame Number=58 Number of Objects=0
Frame Number=59 Number of Objects=0
Frame Number=60 Number of Objects=1
Object: car - Top: 112, Left: 227, Width: 320, Height: 259
Frame Number=61 Number of Objects=1
Object: car - Top: 112, Left: 227, Width: 320, Height: 260
Frame Number=62 Number of Objects=1
Object: car - Top: 112, Left: 227, Width: 320, Height: 261
Frame Number=63 Number of Objects=1
Object: car - Top: 35, Left: 383, Width: 94, Height: 64
Frame Number=64 Number of Objects=1
Object: car - Top: 35, Left: 383, Width: 94, Height: 64
Frame Number=65 Number of Objects=1
Object: car - Top: 35, Left: 383, Width: 94, Height: 64
Frame Number=66 Number of Objects=1
Object: car - Top: 35, Left: 385, Width: 99, Height: 69
Frame Number=67 Number of Objects=1
Object: car - Top: 35, Left: 384, Width: 99, Height: 69
Frame Number=68 Number of Objects=1
Object: car - Top: 35, Left: 385, Width: 99, Height: 69
Frame Number=69 Number of Objects=1
Object: car - Top: 38, Left: 384, Width: 106, Height: 71
Frame Number=70 Number of Objects=1
Object: car - Top: 38, Left: 384, Width: 106, Height: 71
Frame Number=71 Number of Objects=1
Object: car - Top: 38, Left: 384, Width: 106, Height: 71
Frame Number=72 Number of Objects=1
Object: car - Top: 41, Left: 386, Width: 110, Height: 74
Frame Number=73 Number of Objects=1
Object: car - Top: 41, Left: 386, Width: 110, Height: 74
Frame Number=74 Number of Objects=1
Object: car - Top: 41, Left: 386, Width: 110, Height: 74
Frame Number=75 Number of Objects=1
Object: car - Top: 43, Left: 388, Width: 114, Height: 79
Frame Number=76 Number of Objects=1
Object: car - Top: 43, Left: 388, Width: 114, Height: 79
Frame Number=77 Number of Objects=1
Object: car - Top: 43, Left: 387, Width: 114, Height: 79
Frame Number=78 Number of Objects=1
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Object: car - Top: 3, Left: 577, Width: 100, Height: 91
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Object: car - Top: 3, Left: 577, Width: 100, Height: 91
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Object: car - Top: 47, Left: 387, Width: 51, Height: 57
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Object: car - Top: 53, Left: 401, Width: 166, Height: 127
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Object: car - Top: 57, Left: 398, Width: 188, Height: 142
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Object: car - Top: 57, Left: 398, Width: 188, Height: 142
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Object: car - Top: 61, Left: 402, Width: 204, Height: 150
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Object: car - Top: 61, Left: 402, Width: 204, Height: 150
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Object: car - Top: 22, Left: 367, Width: 37, Height: 26
Object: car - Top: 66, Left: 428, Width: 218, Height: 180
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Object: car - Top: 22, Left: 367, Width: 37, Height: 26
Object: car - Top: 66, Left: 428, Width: 218, Height: 180
Frame Number=539 Number of Objects=2
Object: car - Top: 22, Left: 367, Width: 37, Height: 26
Object: car - Top: 66, Left: 428, Width: 218, Height: 180
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Object: car - Top: 24, Left: 369, Width: 38, Height: 26
Object: car - Top: 68, Left: 436, Width: 224, Height: 203
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Object: car - Top: 24, Left: 369, Width: 38, Height: 26
Object: car - Top: 68, Left: 436, Width: 224, Height: 203
Frame Number=542 Number of Objects=2
Object: car - Top: 24, Left: 369, Width: 38, Height: 26
Object: car - Top: 68, Left: 436, Width: 224, Height: 203
Frame Number=543 Number of Objects=2
Object: car - Top: 76, Left: 430, Width: 264, Height: 230
Object: car - Top: 25, Left: 373, Width: 37, Height: 27
Frame Number=544 Number of Objects=2
Object: car - Top: 76, Left: 430, Width: 264, Height: 230
Object: car - Top: 25, Left: 373, Width: 37, Height: 27
Frame Number=545 Number of Objects=2
Object: car - Top: 76, Left: 430, Width: 264, Height: 230
Object: car - Top: 25, Left: 374, Width: 37, Height: 27
Frame Number=546 Number of Objects=2
Object: car - Top: 75, Left: 417, Width: 289, Height: 283
Object: car - Top: 25, Left: 373, Width: 42, Height: 29
Frame Number=547 Number of Objects=2
Object: car - Top: 75, Left: 417, Width: 289, Height: 283
Object: car - Top: 25, Left: 373, Width: 42, Height: 29
Frame Number=548 Number of Objects=2
Object: car - Top: 75, Left: 416, Width: 289, Height: 283
Object: car - Top: 25, Left: 373, Width: 42, Height: 29
Frame Number=549 Number of Objects=3
Object: bicycle - Top: 249, Left: 378, Width: 144, Height: 217
Object: car - Top: 84, Left: 359, Width: 314, Height: 362
Object: car - Top: 25, Left: 372, Width: 46, Height: 31
Frame Number=550 Number of Objects=3
Object: bicycle - Top: 249, Left: 377, Width: 144, Height: 217
Object: car - Top: 84, Left: 360, Width: 313, Height: 362
Object: car - Top: 25, Left: 372, Width: 46, Height: 31
Frame Number=551 Number of Objects=3
Object: bicycle - Top: 249, Left: 378, Width: 144, Height: 217
Object: car - Top: 84, Left: 360, Width: 314, Height: 362
Object: car - Top: 25, Left: 372, Width: 46, Height: 31
Frame Number=552 Number of Objects=2
Object: car - Top: 87, Left: 335, Width: 340, Height: 465
Object: car - Top: 25, Left: 372, Width: 51, Height: 35
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Object: car - Top: 87, Left: 336, Width: 339, Height: 465
Object: car - Top: 25, Left: 372, Width: 51, Height: 35
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Object: car - Top: 87, Left: 336, Width: 339, Height: 465
Object: car - Top: 25, Left: 372, Width: 51, Height: 35
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Object: car - Top: 27, Left: 380, Width: 52, Height: 38
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Object: car - Top: 27, Left: 380, Width: 52, Height: 38
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Object: car - Top: 27, Left: 376, Width: 62, Height: 41
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Object: car - Top: 27, Left: 376, Width: 62, Height: 41
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Object: car - Top: 28, Left: 379, Width: 65, Height: 46
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Object: car - Top: 28, Left: 379, Width: 65, Height: 46
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Object: car - Top: 35, Left: 390, Width: 98, Height: 70
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Object: car - Top: 35, Left: 390, Width: 98, Height: 70
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Object: car - Top: 38, Left: 392, Width: 107, Height: 78
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Object: car - Top: 38, Left: 392, Width: 107, Height: 78
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Object: car - Top: 41, Left: 395, Width: 114, Height: 88
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Object: car - Top: 41, Left: 395, Width: 114, Height: 88
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Object: car - Top: 44, Left: 395, Width: 124, Height: 101
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Object: car - Top: 44, Left: 395, Width: 124, Height: 101
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Object: car - Top: 49, Left: 388, Width: 137, Height: 119
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Object: car - Top: 49, Left: 388, Width: 137, Height: 118
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Object: car - Top: 56, Left: 372, Width: 160, Height: 146
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Object: car - Top: 56, Left: 372, Width: 160, Height: 146
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Object: car - Top: 67, Left: 305, Width: 229, Height: 242
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Object: car - Top: 67, Left: 305, Width: 229, Height: 242
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Object: car - Top: 67, Left: 305, Width: 229, Height: 242
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Object: car - Top: 80, Left: 205, Width: 323, Height: 350
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Object: car - Top: 80, Left: 205, Width: 323, Height: 350
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Object: car - Top: 80, Left: 205, Width: 323, Height: 350
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Object: car - Top: 91, Left: 209, Width: 309, Height: 290
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Object: car - Top: 91, Left: 209, Width: 309, Height: 290
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Object: car - Top: 92, Left: 209, Width: 309, Height: 289
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Object: person - Top: 34, Left: 309, Width: 306, Height: 661
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Object: car - Top: 27, Left: 487, Width: 42, Height: 35
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Object: car - Top: 27, Left: 487, Width: 42, Height: 35
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Object: car - Top: 27, Left: 487, Width: 42, Height: 35
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Object: car - Top: 16, Left: 575, Width: 104, Height: 87
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Object: car - Top: 16, Left: 575, Width: 104, Height: 87
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Object: car - Top: 4, Left: 576, Width: 176, Height: 118
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Object: car - Top: 4, Left: 576, Width: 176, Height: 118
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Object: car - Top: 14, Left: 576, Width: 246, Height: 139
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Object: car - Top: 13, Left: 576, Width: 245, Height: 139
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Object: car - Top: 29, Left: 462, Width: 51, Height: 22
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Object: car - Top: 29, Left: 462, Width: 51, Height: 22
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Object: car - Top: 23, Left: 480, Width: 48, Height: 33
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Object: car - Top: 23, Left: 480, Width: 48, Height: 33
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Object: car - Top: 26, Left: 484, Width: 44, Height: 34
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Object: car - Top: 26, Left: 484, Width: 44, Height: 34
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Object: car - Top: 26, Left: 484, Width: 44, Height: 34
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Object: car - Top: 26, Left: 477, Width: 51, Height: 38
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Object: car - Top: 26, Left: 478, Width: 51, Height: 38
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Object: car - Top: 26, Left: 478, Width: 51, Height: 38
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Object: car - Top: 35, Left: 482, Width: 47, Height: 34
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Object: car - Top: 35, Left: 482, Width: 47, Height: 34
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Object: car - Top: 3, Left: 575, Width: 110, Height: 98
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Object: car - Top: 3, Left: 575, Width: 110, Height: 97
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Object: car - Top: 27, Left: 575, Width: 152, Height: 86
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Object: car - Top: 27, Left: 575, Width: 152, Height: 86
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Object: car - Top: 27, Left: 575, Width: 152, Height: 86
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Object: car - Top: 31, Left: 577, Width: 202, Height: 98
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Object: car - Top: 32, Left: 577, Width: 202, Height: 98
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Object: car - Top: 31, Left: 577, Width: 203, Height: 99
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Object: car - Top: 33, Left: 579, Width: 233, Height: 117
Frame Number=715 Number of Objects=1
Object: car - Top: 33, Left: 579, Width: 234, Height: 117
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Object: car - Top: 33, Left: 579, Width: 235, Height: 117
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Frame Number=726 Number of Objects=0
Frame Number=727 Number of Objects=0
Frame Number=728 Number of Objects=0
Frame Number=729 Number of Objects=0
WARNING: [TRT]: The implicit batch dimension mode has been deprecated. Please create the network with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag whenever possible.
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x544x960
1 OUTPUT kFLOAT output_bbox/BiasAdd 16x34x60
2 OUTPUT kFLOAT output_cov/Sigmoid 4x34x60
nvstreammux: Successfully handled EOS for source_id=0
Frame Number=730 Number of Objects=0
Frame Number=731 Number of Objects=0
End-of-stream
<enum GST_STATE_CHANGE_SUCCESS of type Gst.StateChangeReturn>
Analyzing Inference Results#
After the DeepStream pipeline completes the video processing, we summarize the inference results.
import pandas as pd
# Export data to a Pandas Series
obj_count_df=pd.Series(obj_counts, name='Object Count')
obj_count_df.index.name='Frame Number'
# Plot the Series
obj_count_df.plot(
linestyle='none',
marker='.',
figsize=(15, 5),
ylim=[-.1, 3.1],
title='Object Count Over Time'
)
<AxesSubplot: title={'center': 'Object Count Over Time'}, xlabel='Frame Number'>
Extending the Pipeline#
We will add additional functionalities to the DeepStream pipeline to also create a composite video and write the output to a file. This is the updated pipeline architecture. We’ve adding new plugins based on their functionality.
The filesrc plugin will read data from a local file. There are other plugins available for reading data from various types of sources like camera, RTSP streams, and disk.
The h264parse plugin is used to parse the input elementary H.264 stream into frame-size bytes of data.
The nvv4l2decoder plugin will decode the input data using the appropriate codec, which is H.264 in this case.
The nvstreammux plugin is a required stream aggregator. This video aggregator helps in accepting
ninput streams and converts them into sequential batched frames. Even though our simple application only consumes one input stream, this plugin is required asnvinferaccepts batched buffers with theNvDsBatchMetastructure already attached.This plugin takes
width,height, andbatch-sizeas parameters.
The nvinfer plugin performs transformation (format conversion and scaling) on the input frame based on network requirements and passes the transformed data to the low-level library. This is the plugin that we will use to define the deep learning task(s) associated with our application. The low-level library pre-processes the transformed frames (performs normalization and mean subtraction) and produces final float RGB/BGR/GRAY planar data which are passed to the TensorRT engine for inferencing. The output type generated by the low-level library depends on the network type. We will configure the
nvinferplugin for ourTrafficCamNetobject detection model. It attaches the inference results such as object class(s), bounding box coordinates, etc., to the metadata.Note: Behaviour of inference is set through the config file. You can use the files provided below as a template or start the text file from scratch.
The nvvideoconvert plugin converts frames from NV12 (YUV) to RGBA as required by
nvdsosd. It is also capable of performing scaling, cropping, and rotating on the frames.The nvdsosd plugin draws bounding boxes and texts based on the metadata. It requires RGBA buffer as well as
NvDsBatchMeta.The nvvideoconvert plugin converts frames from RGBA to I420 (YUV) as required by
avenc_mpeg4.The capsfilter plugin does not modify data as such, but can enforce limitations on the data format. We use it to enforce the video conversion by
nvvideoconvertto I420 (YUV) format.The avenc_mpeg4 plugin encodes the I420 formatted frames using the MPEG4 codec.
The filesink plugin writes incoming data to a file in the local file system.
More information about the plugins can be found in the DeepStream Plugin Guide and GStreamer Plugin Guide.
Additionally, we will add a callback function on the sink pad of the nvdsosd plugin to access the metadata in the application. The application can then use this metadata to solve the given problem (in this case draw the bounding boxes and texts).
Initializing GStreamer and Pipeline#
To begin, we initialize GStreamer with Gst.init(list=None) and build a Gst.Pipeline object as pipeline using build_simple_pipeline(input_source). We will modify the elements included in this pipeline.
# Initialize GStreamer
Gst.init(None)
# Build pipeline
pipeline=build_simple_pipeline('data/sample_30.h264')
print('Successfully created a {} object'.format(type(pipeline)))
Created Pipeline
Created elements
Added elements to pipeline
Linked elements in pipeline
Successfully created a <class 'gi.repository.Gst.Pipeline'> object
Modifying Pipeline Elements#
The simple pipeline created already includes filesink > h264parse > nvv4l2decoder > nvstreammux > nvinfer > fakesink. We modify this pipeline to suit our new use case. First, we remove fakesink from the existing pipeline with Gst.Pipeline.remove(element). Next, we create the nvvideoconvert, nvdsosd, another nvvideoconvert, capsfilter, avenc_mpeg4, and filesink elements using Gst.ElementFactory.make(factoryname, name) (see documentation). We can configure elements using set_property(property_name, value) (see documentation) with the required parameters as needed. Finally, we add the elements to the pipeline with Gst.Pipeline.add(element).
# Remove Fakesink
fakesink=pipeline.get_by_name('fakesink')
pipeline.remove(fakesink)
# Create Convertor to convert from YUV to RGBA as required by nvdsosd
nvvidconv1=Gst.ElementFactory.make("nvvideoconvert", "convertor1")
# Create OSD with nvdsosd to draw on the converted RGBA buffer
nvosd=Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
# Create Convertor to convert from RGBA to I420 as required by encoder
nvvidconv2=Gst.ElementFactory.make("nvvideoconvert", "convertor2")
# Create Capsfilter to enforce frame image format
capsfilter=Gst.ElementFactory.make("capsfilter", "capsfilter")
caps=Gst.Caps.from_string("video/x-raw, format=I420")
capsfilter.set_property("caps", caps)
# Create Encoder to encode I420 formatted frames using the MPEG4 codec
encoder = Gst.ElementFactory.make("avenc_mpeg4", "encoder")
encoder.set_property("bitrate", 2000000)
# Create Sink and set the location for the output file
filesink=Gst.ElementFactory.make('filesink', 'filesink')
filesink.set_property('location', 'output_03_encoded.mpeg4')
filesink.set_property("sync", 1)
print('Created elements')
Created elements
# Add elements to pipeline
pipeline.add(nvvidconv1)
pipeline.add(nvosd)
pipeline.add(nvvidconv2)
pipeline.add(capsfilter)
pipeline.add(encoder)
pipeline.add(filesink)
print('Added elements to pipeline')
Added elements to pipeline
Linking Pipeline Elements#
Finally, we link all these elements in the order that data flow through the pipeline with Gst.Element.link(Gst.Element).
# Get the nvinfer plugin by name
pgie=pipeline.get_by_name('primary-inference')
# Link elements together
pgie.link(nvvidconv1)
nvvidconv1.link(nvosd)
nvosd.link(nvvidconv2)
nvvidconv2.link(capsfilter)
capsfilter.link(encoder)
encoder.link(filesink)
print('Linked elements in pipeline')
Linked elements in pipeline
Modify Probe to Draw on Frames#
By the time the buffers reach the nvdsosd plugin, it will have the metadata it needs to draw bounding boxes and text labels for the detected objects on the frames. This means that a probe will not be needed if the default settings are sufficient. Based on specific use cases, we can use a probe to access and modify the metadata used by nvdsosd to draw, but it would have to be inserted at or before the sink pad of the nvdsosd plugin. To access the metadata, we can use the DeepStream Python bindings, pyds. There are generally two types of metadata we are interested in for drawing:
NvDsObjectMeta.rect_paramsandNvDsObjectMeta.text_paramsrelated to the objects detected orNvDsBatchMeta>NvDsFrameMeta>NvDsDisplayMetarelated to overlays we want to add to each frame
There are a few similar parameters we can change for each but adding overlays to each frame requires some additional work. In order to acquire this display metadata, we use
pyds.nvds_acquire_display_meta_from_pool(batch_meta)
and set num_labels, num_rects, and num_lines. We can then assign values via text_params, rect_params, and line_params. The display metadata have to be added to the frame metadata with
pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)
for drawing. We write the user-defined callback function osd_sink_pad_buffer_probe. Inside the function, we iterate through all the metadata types that are attached to the buffer. We want to add a text label at the top of each frame and modify the border color of the bounding boxes.
# DO NOT CHANGE THIS CELL
from random import random
# Define the Probe Function
def osd_sink_pad_buffer_probe(pad, info):
gst_buffer=info.get_buffer()
# 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
# Iterate through each frame in the batch metadata until the end
while l_frame is not None:
try:
frame_meta=pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
frame_number=frame_meta.frame_num
num_rects=frame_meta.num_obj_meta
l_obj=frame_meta.obj_meta_list
# Iterate through each object in the frame metadata until the end
while l_obj is not None:
try:
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
# Set border color (red, green, blue, alpha) to random values
obj_meta.rect_params.border_color.set(random(), random(), random(), random())
try:
l_obj=l_obj.next
except StopIteration:
break
# Acquire display metadata from pool and set number of labels to 1
display_meta=pyds.nvds_acquire_display_meta_from_pool(batch_meta)
display_meta.num_labels=1
# Set text_params of the display metadata to local variable
py_nvosd_text_params=display_meta.text_params[0]
# Setting display text to be shown on screen
py_nvosd_text_params.display_text="Frame Number={} Number of Objects={}".format(frame_number, num_rects)
# Use pyds.get_string() to get display_text as string
# Reading the display_text field here will return the C address of the
# allocated string. Use pyds.get_string() to get the string content.
print(pyds.get_string(py_nvosd_text_params.display_text))
# Set the offsets where the string should appear
py_nvosd_text_params.x_offset=10
py_nvosd_text_params.y_offset=10
# Set font, font-color (red, green, blue, alpha), and font-size
py_nvosd_text_params.font_params.font_name="Serif"
py_nvosd_text_params.font_params.font_size=15
py_nvosd_text_params.font_params.font_color.set(1.0, 1.0, 1.0, 1.0)
# Set text background color (red, green, blue, alpha)
py_nvosd_text_params.set_bg_clr=1
py_nvosd_text_params.text_bg_clr.set(0.0, 0.0, 0.0, 1.0)
# Add to frame metadata
pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)
try:
l_frame=l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
With the pipeline defined and the elements linked, we add the callback function on the sink pad of the nvdsosd plugin using Gst.Pad.add_probe(mask, callback). While attached, the probe notifies when there are data passing on a pad. We can use GST_PAD_PROBE_TYPE_BUFFER or GST_PAD_PROBE_TYPE_BUFFER_LIST for mask when creating the probe. We designed the callback function to work with a single buffer so we’re using Gst.PadProbeType.BUFFER.
# Add probe to nvdsosd plugin's sink
osdsinkpad=nvosd.get_static_pad("sink")
probe_id=osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe)
print('Attached probe')
Attached probe
Starting the Pipeline#
# Create an event loop
loop=GLib.MainLoop()
# Feed GStreamer bus messages to loop
bus=pipeline.get_bus()
bus.add_signal_watch()
bus.connect("message", bus_call, loop)
print('Added bus message handler')
Added bus message handler
# Start play back and listen to events - this will generate the output_03_raw.mpeg4 file
print("Starting pipeline")
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except:
pass
# Cleaning up as the pipeline comes to an end
pipeline.set_state(Gst.State.NULL)
Starting pipeline
0:47:00.746298571 436 0x2ac5920 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2002> [UID = 1]: Trying to create engine from model files
ERROR: [TRT]: 3: [builder.cpp::~Builder::307] Error Code 3: API Usage Error (Parameter check failed at: optimizer/api/builder.cpp::~Builder::307, condition: mObjectCounter.use_count() == 1. Destroying a builder object before destroying objects it created leads to undefined behavior.
)
0:47:11.455634246 436 0x2ac5920 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2034> [UID = 1]: serialize cuda engine to file: /dli/task/ngc_assets/trafficcamnet_vpruned_v1.0/resnet18_trafficcamnet_pruned.etlt_b1_gpu0_fp32.engine successfully
0:47:11.518760321 436 0x2ac5920 INFO nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:/dli/task/spec_files/pgie_config_trafficcamnet_03.txt sucessfully
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WARNING: [TRT]: The implicit batch dimension mode has been deprecated. Please create the network with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag whenever possible.
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x544x960
1 OUTPUT kFLOAT output_bbox/BiasAdd 16x34x60
2 OUTPUT kFLOAT output_cov/Sigmoid 4x34x60
nvstreammux: Successfully handled EOS for source_id=0
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End-of-stream
<enum GST_STATE_CHANGE_SUCCESS of type Gst.StateChangeReturn>
Viewing the Inference#
In the next step, we convert the video file into a container file before playing it since the MPEG4 encoded video file can’t be played directly in Jupyter Notebook. The FFmpeg tool is a very fast video and audio converter with the general syntax:
ffmpeg [global_options] {[input_file_options] -i input_url} ... {[output_file_options] output_url} ...
# Convert MPEG4 video file to MP4 container file
!ffmpeg -i /dli/task/output_03_encoded.mpeg4 /dli/task/output_03.mp4 -y -loglevel quiet
# View the output video
Video("output_03.mp4", width=720)
Multi-DNN DeepStream Pipeline#
DeepStream pipelines can be constructed to perform complex analytics that involve multiple neural networks. One common use case for this would be to use a detector as a primary inference engine to localize an object and a classifier as a secondary inference engine. This is useful since classification models can often perform better on single objects within the frame.
Preparing the Deep Learning Models#
We’ll be using two purpose-built models from NGC - the TrafficCamNet object detection model and the VehicleTypeNet classification model.
# Download the purpose-built TrafficCamNet model from NGC
!ngc registry model download-version nvidia/tao/trafficcamnet:pruned_v1.0 --dest $NGC_DIR
# Download the purpose-built VehicleTypeNet model from NGC
!ngc registry model download-version nvidia/tao/vehicletypenet:pruned_v1.0.2 --dest $NGC_DIR
CLI_VERSION: Latest - 3.169.4 available (current: 3.60.0). Please update by using the command 'ngc version upgrade'
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Pipeline Components#
This is the pipeline architecture of the application. We’ll be using an object detection network to identify and localize the cars in the frames, followed by a secondary inference to classify vehicle types.
Initializing GStreamer and Pipeline#
# Import necessary GStreamer libraries and DeepStream python bindings
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst, GLib
from common.bus_call import bus_call
import pyds
# Initialize GStreamer
Gst.init(None)
# Create Pipeline
pipeline=Gst.Pipeline()
print('Created pipeline')
Created pipeline
Creating Elements#
# Create Source element for reading from a file and set the location property
source = Gst.ElementFactory.make("filesrc", "file-source")
source.set_property('location', "data/sample_30.h264")
# Create H264 Parser with h264parse as the input file is an elementary h264 stream
h264parser = Gst.ElementFactory.make("h264parse", "h264-parser")
# Create Decoder with nvv4l2decoder for accelerating decoding on GPU
decoder = Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
# Create Streamux with nvstreammux to form batches for one or more sources and set properties
streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
streammux.set_property('width', 888)
streammux.set_property('height', 696)
streammux.set_property('batch-size', 1)
# Create Primary GStreamer Inference Element with nvinfer to run inference on the decoder's output after batching
pgie=Gst.ElementFactory.make("nvinfer", "primary-inference")
# Create Secondary Inference Element with nvinfer to run inference on the pgie's output
sgie=Gst.ElementFactory.make("nvinfer", "secondary-inference")
# Create Convertor to convert from YUV to RGBA as required by nvdsosd
nvvidconv1=Gst.ElementFactory.make("nvvideoconvert", "convertor1")
# Create OSD with nvdsosd to draw on the converted RGBA buffer
nvosd=Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
# Create Convertor to convert from RGBA to I420 as required by encoder
nvvidconv2=Gst.ElementFactory.make("nvvideoconvert", "convertor2")
# Create Capsfilter to enforce frame image format
capsfilter=Gst.ElementFactory.make("capsfilter", "capsfilter")
caps=Gst.Caps.from_string("video/x-raw, format=I420")
capsfilter.set_property("caps", caps)
# Create Encoder to encode I420 formatted frames using the MPEG4 codec
encoder=Gst.ElementFactory.make("avenc_mpeg4", "encoder")
encoder.set_property("bitrate", 2000000)
# Create Sink with fakesink as the end point of the pipeline
sink=Gst.ElementFactory.make('filesink', 'filesink')
sink.set_property('location', 'output_04_raw.mpeg4')
sink.set_property("sync", 1)
print('Created elements')
Created elements
# Add elements to pipeline
pipeline.add(source)
pipeline.add(h264parser)
pipeline.add(decoder)
pipeline.add(streammux)
pipeline.add(pgie)
pipeline.add(sgie)
pipeline.add(nvvidconv1)
pipeline.add(nvosd)
pipeline.add(nvvidconv2)
pipeline.add(capsfilter)
pipeline.add(encoder)
pipeline.add(sink)
print('Added elements to pipeline')
Added elements to pipeline
Review the Configuration File(s)#
!cat spec_files/pgie_config_trafficcamnet_04.txt
[property]
gpu-id=0
net-scale-factor=0.00392156862745098
tlt-model-key=tlt_encode
tlt-encoded-model=/dli/task/ngc_assets/trafficcamnet_vpruned_v1.0/resnet18_trafficcamnet_pruned.etlt
labelfile-path=/dli/task/ngc_assets/trafficcamnet_vpruned_v1.0/labels.txt
infer-dims=3;544;960
uff-input-blob-name=input_1
batch-size=1
process-mode=1
model-color-format=0
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
num-detected-classes=4
interval=0
gie-unique-id=1
output-blob-names=output_bbox/BiasAdd;output_cov/Sigmoid
cluster-mode=2
# Use the config params below for NMS clustering mode
[class-attrs-all]
topk=20
nms-iou-threshold=0.5
pre-cluster-threshold=0.2
!cat spec_files/sgie_config_vehicletypenet_04.txt
[property]
gpu-id=0
net-scale-factor=1
tlt-model-key=tlt_encode
tlt-encoded-model=/dli/task/ngc_assets/vehicletypenet_vpruned_v1.0.2/resnet18_vehicletypenet_pruned.etlt
labelfile-path=/dli/task/ngc_assets/vehicletypenet_vpruned_v1.0.2/labels.txt
infer-dims=3;224;224
uff-input-blob-name=input_1
batch-size=1
process-mode=2
model-color-format=0
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
network-type=1
num-detected-classes=6
gie-unique-id=2
operate-on-gie-id=1
operate-on-class-ids=0
output-blob-names=predictions/Softmax
verbose=1
# Set the location of the config file
pgie.set_property('config-file-path', 'spec_files/pgie_config_trafficcamnet_04.txt')
sgie.set_property('config-file-path', 'spec_files/sgie_config_vehicletypenet_04.txt')
Labels for the vehicletypenet model (classification model)
# Review the labels
!cat ngc_assets/vehicletypenet_vpruned_v1.0.2/labels.txt
coupe;largevehicle;sedan;suv;truck;van
Link Elements#
# Link elements together
source.link(h264parser)
h264parser.link(decoder)
# Link decoder source pad to streammux sink pad
decoder_srcpad=decoder.get_static_pad("src")
streammux_sinkpad=streammux.get_request_pad("sink_0")
decoder_srcpad.link(streammux_sinkpad)
# Link the rest of the elements in the pipeline
streammux.link(pgie)
pgie.link(sgie)
sgie.link(nvvidconv1)
nvvidconv1.link(nvosd)
nvosd.link(nvvidconv2)
nvvidconv2.link(capsfilter)
capsfilter.link(encoder)
encoder.link(sink)
print('Linked elements in pipeline')
Linked elements in pipeline
Add Probe to OSD Sink Pad#
We can use a similar probe function to access the metadata. However, in this case we also traverse the metadata generated from the secondary inference plugin. In this example our secondary inference was a classifier performed on the car class from the primary inference. We can access the metadata generated in classifier_meta_list after we cast it with NvDsClassifierMeta.cast(). Depending on how many secondary inferences there are, the NvDsObjectMeta object may have one or more NvDsClassifierMeta objects. We will also need to cast to NvDsLabelInfo class to get the resulting classification of the secondary inference(s).
# Define the Probe Function
def osd_sink_pad_buffer_probe(pad, info):
gst_buffer = info.get_buffer()
# 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
# Iterate through each frame in the batch metadata until the end
while l_frame is not None:
try:
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
frame_num=frame_meta.frame_num
num_obj = frame_meta.num_obj_meta
l_obj=frame_meta.obj_meta_list
print("Frame Number={} Number of Objects={}".format(frame_num, num_obj))
# Iterate through each object in the frame metadata until the end
while l_obj is not None:
try:
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
# Define an analyze_meta function to manipulate metadata
analyze_meta(obj_meta)
except StopIteration:
break
try:
l_obj=l_obj.next
except StopIteration:
break
try:
l_frame=l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
PGIE_CLASS_ID_CAR=0
# Define helper function
def analyze_meta(obj_meta):
# Only car supports secondary inference
if obj_meta.class_id == PGIE_CLASS_ID_CAR:
cls_meta=obj_meta.classifier_meta_list
# Iterate through each class meta until the end
while cls_meta is not None:
cls=pyds.NvDsClassifierMeta.cast(cls_meta.data)
# Get label info
label_info=cls.label_info_list
# Iterate through each label info meta until the end
while label_info is not None:
# Cast data type of label from pyds.GList
label_meta=pyds.glist_get_nvds_label_info(label_info.data)
if cls.unique_component_id==2:
print('\t Type & Probability = {}% {}'.format(round(label_meta.result_prob*100), label_meta.result_label))
try:
label_info=label_info.next
except StopIteration:
break
try:
cls_meta=cls_meta.next
except StopIteration:
break
return None
# Add probe to nvdsosd plugin's sink
osdsinkpad = nvosd.get_static_pad("sink")
osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe)
print('Attached probe')
Attached probe
Start the Pipeline#
# Create an event loop
loop=GLib.MainLoop()
# Feed GStreamer bus messages to loop
bus=pipeline.get_bus()
bus.add_signal_watch()
bus.connect ("message", bus_call, loop)
print('Added bus message handler')
Added bus message handler
# Start play back and listen to events - this will generate the output_04_raw.mpeg4 file
print("Starting pipeline \n")
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except:
pass
# Cleaning up as the pipeline comes to an end
pipeline.set_state(Gst.State.NULL)
Starting pipeline
0:03:08.820121737 65 0x4208580 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<secondary-inference> NvDsInferContext[UID 2]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2002> [UID = 2]: Trying to create engine from model files
ERROR: [TRT]: 3: [builder.cpp::~Builder::307] Error Code 3: API Usage Error (Parameter check failed at: optimizer/api/builder.cpp::~Builder::307, condition: mObjectCounter.use_count() == 1. Destroying a builder object before destroying objects it created leads to undefined behavior.
)
0:03:22.282364400 65 0x4208580 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<secondary-inference> NvDsInferContext[UID 2]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2034> [UID = 2]: serialize cuda engine to file: /dli/task/ngc_assets/vehicletypenet_vpruned_v1.0.2/resnet18_vehicletypenet_pruned.etlt_b1_gpu0_fp32.engine successfully
0:03:22.345215790 65 0x4208580 INFO nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<secondary-inference> [UID 2]: Load new model:spec_files/sgie_config_vehicletypenet_04.txt sucessfully
0:03:22.345279039 65 0x4208580 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2002> [UID = 1]: Trying to create engine from model files
ERROR: [TRT]: 3: [builder.cpp::~Builder::307] Error Code 3: API Usage Error (Parameter check failed at: optimizer/api/builder.cpp::~Builder::307, condition: mObjectCounter.use_count() == 1. Destroying a builder object before destroying objects it created leads to undefined behavior.
)
0:03:44.875901407 65 0x4208580 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2034> [UID = 1]: serialize cuda engine to file: /dli/task/ngc_assets/trafficcamnet_vpruned_v1.0/resnet18_trafficcamnet_pruned.etlt_b1_gpu0_fp32.engine successfully
0:03:44.946473488 65 0x4208580 INFO nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:spec_files/pgie_config_trafficcamnet_04.txt sucessfully
Frame Number=0 Number of Objects=1
Type & Probability = 65% suv
Frame Number=1 Number of Objects=1
Type & Probability = 65% suv
Frame Number=2 Number of Objects=1
Type & Probability = 65% suv
Frame Number=3 Number of Objects=1
Type & Probability = 100% suv
Frame Number=4 Number of Objects=1
Type & Probability = 100% suv
Frame Number=5 Number of Objects=1
Type & Probability = 100% suv
Frame Number=6 Number of Objects=1
Type & Probability = 100% suv
Frame Number=7 Number of Objects=1
Type & Probability = 100% suv
Frame Number=8 Number of Objects=1
Type & Probability = 100% suv
Frame Number=9 Number of Objects=1
Type & Probability = 100% suv
Frame Number=10 Number of Objects=1
Type & Probability = 100% suv
Frame Number=11 Number of Objects=1
Type & Probability = 100% suv
Frame Number=12 Number of Objects=1
Type & Probability = 100% suv
Frame Number=13 Number of Objects=1
Type & Probability = 100% suv
Frame Number=14 Number of Objects=1
Type & Probability = 100% suv
Frame Number=15 Number of Objects=1
Type & Probability = 100% suv
Frame Number=16 Number of Objects=1
Type & Probability = 100% suv
Frame Number=17 Number of Objects=1
Type & Probability = 100% suv
Frame Number=18 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=19 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=20 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=21 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=22 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=23 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=24 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=25 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=26 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=27 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=28 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=29 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=30 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=31 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=32 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=33 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=34 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=35 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=36 Number of Objects=1
Type & Probability = 100% suv
Frame Number=37 Number of Objects=1
Type & Probability = 100% suv
Frame Number=38 Number of Objects=1
Type & Probability = 100% suv
Frame Number=39 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=40 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=41 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=42 Number of Objects=2
Type & Probability = 49% largevehicle
Type & Probability = 99% sedan
Frame Number=43 Number of Objects=2
Type & Probability = 49% largevehicle
Type & Probability = 99% sedan
Frame Number=44 Number of Objects=2
Type & Probability = 49% largevehicle
Type & Probability = 99% sedan
Frame Number=45 Number of Objects=2
Type & Probability = 51% suv
Type & Probability = 54% coupe
Frame Number=46 Number of Objects=2
Type & Probability = 74% largevehicle
Type & Probability = 54% coupe
Frame Number=47 Number of Objects=2
Type & Probability = 75% largevehicle
Type & Probability = 53% coupe
Frame Number=48 Number of Objects=1
Type & Probability = 85% sedan
Frame Number=49 Number of Objects=1
Type & Probability = 84% sedan
Frame Number=50 Number of Objects=1
Type & Probability = 85% sedan
Frame Number=51 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=52 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=53 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=54 Number of Objects=1
Type & Probability = 54% sedan
Frame Number=55 Number of Objects=1
Type & Probability = 53% sedan
Frame Number=56 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 52% coupe
Frame Number=57 Number of Objects=0
Frame Number=58 Number of Objects=0
Frame Number=59 Number of Objects=0
Frame Number=60 Number of Objects=1
Type & Probability = 95% largevehicle
Frame Number=61 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=62 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=63 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=64 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=65 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=66 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=67 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=68 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=69 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=70 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=71 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=72 Number of Objects=1
Type & Probability = 87% largevehicle
Frame Number=73 Number of Objects=1
Type & Probability = 87% largevehicle
Frame Number=74 Number of Objects=1
Type & Probability = 87% largevehicle
Frame Number=75 Number of Objects=1
Type & Probability = 54% largevehicle
Frame Number=76 Number of Objects=1
Type & Probability = 82% largevehicle
Frame Number=77 Number of Objects=1
Type & Probability = 73% sedan
Frame Number=78 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=79 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=80 Number of Objects=1
Type & Probability = 75% largevehicle
Frame Number=81 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=82 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=83 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=84 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=85 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=86 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=87 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=88 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=89 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=90 Number of Objects=1
Type & Probability = 50% sedan
Frame Number=91 Number of Objects=1
Type & Probability = 52% sedan
Frame Number=92 Number of Objects=1
Type & Probability = 50% sedan
Frame Number=93 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=94 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=95 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=96 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=97 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=98 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=99 Number of Objects=1
Type & Probability = 81% truck
Frame Number=100 Number of Objects=1
Type & Probability = 85% truck
Frame Number=101 Number of Objects=1
Type & Probability = 84% truck
Frame Number=102 Number of Objects=1
Type & Probability = 93% truck
Frame Number=103 Number of Objects=1
Type & Probability = 93% truck
Frame Number=104 Number of Objects=1
Type & Probability = 92% truck
Frame Number=105 Number of Objects=1
Type & Probability = 88% sedan
Frame Number=106 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=107 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=108 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=109 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=110 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=111 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=112 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=113 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=114 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=115 Number of Objects=2
Type & Probability = 67% sedan
Type & Probability = 98% sedan
Frame Number=116 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=117 Number of Objects=0
Frame Number=118 Number of Objects=0
Frame Number=119 Number of Objects=0
Frame Number=120 Number of Objects=0
Frame Number=121 Number of Objects=0
Frame Number=122 Number of Objects=0
Frame Number=123 Number of Objects=0
Frame Number=124 Number of Objects=0
Frame Number=125 Number of Objects=0
Frame Number=126 Number of Objects=0
Frame Number=127 Number of Objects=0
Frame Number=128 Number of Objects=0
Frame Number=129 Number of Objects=0
Frame Number=130 Number of Objects=0
Frame Number=131 Number of Objects=0
Frame Number=132 Number of Objects=0
Frame Number=133 Number of Objects=0
Frame Number=134 Number of Objects=0
Frame Number=135 Number of Objects=0
Frame Number=136 Number of Objects=0
Frame Number=137 Number of Objects=0
Frame Number=138 Number of Objects=1
Type & Probability = 99% coupe
Frame Number=139 Number of Objects=1
Type & Probability = 99% coupe
Frame Number=140 Number of Objects=1
Type & Probability = 99% coupe
Frame Number=141 Number of Objects=0
Frame Number=142 Number of Objects=0
Frame Number=143 Number of Objects=0
Frame Number=144 Number of Objects=0
Frame Number=145 Number of Objects=0
Frame Number=146 Number of Objects=0
Frame Number=147 Number of Objects=0
Frame Number=148 Number of Objects=0
Frame Number=149 Number of Objects=0
Frame Number=150 Number of Objects=0
Frame Number=151 Number of Objects=0
Frame Number=152 Number of Objects=0
Frame Number=153 Number of Objects=0
Frame Number=154 Number of Objects=0
Frame Number=155 Number of Objects=0
Frame Number=156 Number of Objects=0
Frame Number=157 Number of Objects=0
Frame Number=158 Number of Objects=0
Frame Number=159 Number of Objects=1
Type & Probability = 93% largevehicle
Frame Number=160 Number of Objects=1
Type & Probability = 93% largevehicle
Frame Number=161 Number of Objects=1
Type & Probability = 93% largevehicle
Frame Number=162 Number of Objects=1
Type & Probability = 90% largevehicle
Frame Number=163 Number of Objects=1
Type & Probability = 90% largevehicle
Frame Number=164 Number of Objects=1
Type & Probability = 90% largevehicle
Frame Number=165 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=166 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=167 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=168 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=169 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=170 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=171 Number of Objects=1
Type & Probability = 86% suv
Frame Number=172 Number of Objects=1
Type & Probability = 86% suv
Frame Number=173 Number of Objects=1
Type & Probability = 86% suv
Frame Number=174 Number of Objects=1
Type & Probability = 88% sedan
Frame Number=175 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=176 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=177 Number of Objects=1
Type & Probability = 82% largevehicle
Frame Number=178 Number of Objects=1
Type & Probability = 82% largevehicle
Frame Number=179 Number of Objects=1
Type & Probability = 82% largevehicle
Frame Number=180 Number of Objects=1
Type & Probability = 69% sedan
Frame Number=181 Number of Objects=1
Type & Probability = 69% sedan
Frame Number=182 Number of Objects=1
Type & Probability = 54% sedan
Frame Number=183 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=184 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=185 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=186 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=187 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=188 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=189 Number of Objects=1
Type & Probability = 68% sedan
Frame Number=190 Number of Objects=1
Type & Probability = 68% sedan
Frame Number=191 Number of Objects=1
Type & Probability = 68% sedan
Frame Number=192 Number of Objects=1
Type & Probability = 83% suv
Frame Number=193 Number of Objects=1
Type & Probability = 40% suv
Frame Number=194 Number of Objects=1
Type & Probability = 95% suv
Frame Number=195 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=196 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=197 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=198 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=199 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=200 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=201 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=202 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=203 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=204 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=205 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=206 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=207 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=208 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=209 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=210 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=211 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=212 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=213 Number of Objects=1
Type & Probability = 87% sedan
Frame Number=214 Number of Objects=1
Type & Probability = 87% sedan
Frame Number=215 Number of Objects=1
Type & Probability = 87% sedan
Frame Number=216 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=217 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=218 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=219 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=220 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=221 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=222 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=223 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=224 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=225 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=226 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=227 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=228 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=229 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=230 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=231 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=232 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=233 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=234 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=235 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=236 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=237 Number of Objects=1
Type & Probability = 73% largevehicle
Frame Number=238 Number of Objects=1
Type & Probability = 72% largevehicle
Frame Number=239 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=240 Number of Objects=0
Frame Number=241 Number of Objects=0
Frame Number=242 Number of Objects=0
Frame Number=243 Number of Objects=0
Frame Number=244 Number of Objects=0
Frame Number=245 Number of Objects=0
Frame Number=246 Number of Objects=0
Frame Number=247 Number of Objects=0
Frame Number=248 Number of Objects=0
Frame Number=249 Number of Objects=0
Frame Number=250 Number of Objects=0
Frame Number=251 Number of Objects=0
Frame Number=252 Number of Objects=0
Frame Number=253 Number of Objects=0
Frame Number=254 Number of Objects=0
Frame Number=255 Number of Objects=1
Type & Probability = 99% suv
Frame Number=256 Number of Objects=1
Type & Probability = 98% suv
Frame Number=257 Number of Objects=1
Type & Probability = 98% suv
Frame Number=258 Number of Objects=1
Type & Probability = 84% largevehicle
Frame Number=259 Number of Objects=1
Type & Probability = 87% largevehicle
Frame Number=260 Number of Objects=1
Type & Probability = 85% largevehicle
Frame Number=261 Number of Objects=0
Frame Number=262 Number of Objects=0
Frame Number=263 Number of Objects=0
Frame Number=264 Number of Objects=0
Frame Number=265 Number of Objects=0
Frame Number=266 Number of Objects=0
Frame Number=267 Number of Objects=0
Frame Number=268 Number of Objects=0
Frame Number=269 Number of Objects=0
Frame Number=270 Number of Objects=0
Frame Number=271 Number of Objects=0
Frame Number=272 Number of Objects=0
Frame Number=273 Number of Objects=0
Frame Number=274 Number of Objects=0
Frame Number=275 Number of Objects=0
Frame Number=276 Number of Objects=0
Frame Number=277 Number of Objects=0
Frame Number=278 Number of Objects=0
Frame Number=279 Number of Objects=0
Frame Number=280 Number of Objects=0
Frame Number=281 Number of Objects=0
Frame Number=282 Number of Objects=0
Frame Number=283 Number of Objects=0
Frame Number=284 Number of Objects=0
Frame Number=285 Number of Objects=0
Frame Number=286 Number of Objects=0
Frame Number=287 Number of Objects=0
Frame Number=288 Number of Objects=1
Type & Probability = 65% largevehicle
Frame Number=289 Number of Objects=1
Type & Probability = 75% largevehicle
Frame Number=290 Number of Objects=1
Type & Probability = 74% largevehicle
Frame Number=291 Number of Objects=1
Type & Probability = 58% largevehicle
Frame Number=292 Number of Objects=1
Type & Probability = 53% largevehicle
Frame Number=293 Number of Objects=1
Type & Probability = 73% largevehicle
Frame Number=294 Number of Objects=0
Frame Number=295 Number of Objects=0
Frame Number=296 Number of Objects=0
Frame Number=297 Number of Objects=0
Frame Number=298 Number of Objects=0
Frame Number=299 Number of Objects=0
Frame Number=300 Number of Objects=0
Frame Number=301 Number of Objects=0
Frame Number=302 Number of Objects=0
Frame Number=303 Number of Objects=0
Frame Number=304 Number of Objects=0
Frame Number=305 Number of Objects=0
Frame Number=306 Number of Objects=0
Frame Number=307 Number of Objects=0
Frame Number=308 Number of Objects=0
Frame Number=309 Number of Objects=0
Frame Number=310 Number of Objects=0
Frame Number=311 Number of Objects=0
Frame Number=312 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=313 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=314 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=315 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=316 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=317 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=318 Number of Objects=1
Type & Probability = 88% suv
Frame Number=319 Number of Objects=1
Type & Probability = 88% suv
Frame Number=320 Number of Objects=1
Type & Probability = 88% suv
Frame Number=321 Number of Objects=1
Type & Probability = 55% suv
Frame Number=322 Number of Objects=1
Type & Probability = 55% suv
Frame Number=323 Number of Objects=1
Type & Probability = 52% suv
Frame Number=324 Number of Objects=1
Type & Probability = 54% largevehicle
Frame Number=325 Number of Objects=1
Type & Probability = 52% largevehicle
Frame Number=326 Number of Objects=1
Type & Probability = 56% largevehicle
Frame Number=327 Number of Objects=1
Type & Probability = 69% largevehicle
Frame Number=328 Number of Objects=1
Type & Probability = 69% largevehicle
Frame Number=329 Number of Objects=1
Type & Probability = 75% largevehicle
Frame Number=330 Number of Objects=1
Type & Probability = 100% suv
Frame Number=331 Number of Objects=1
Type & Probability = 100% suv
Frame Number=332 Number of Objects=1
Type & Probability = 100% suv
Frame Number=333 Number of Objects=1
Type & Probability = 45% largevehicle
Frame Number=334 Number of Objects=1
Type & Probability = 45% largevehicle
Frame Number=335 Number of Objects=0
Frame Number=336 Number of Objects=2
Type & Probability = 51% truck
Type & Probability = 100% largevehicle
Frame Number=337 Number of Objects=2
Type & Probability = 51% truck
Type & Probability = 100% largevehicle
Frame Number=338 Number of Objects=2
Type & Probability = 51% truck
Type & Probability = 100% largevehicle
Frame Number=339 Number of Objects=1
Type & Probability = 100% suv
Frame Number=340 Number of Objects=1
Type & Probability = 100% suv
Frame Number=341 Number of Objects=1
Type & Probability = 100% suv
Frame Number=342 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=343 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=344 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=345 Number of Objects=0
Frame Number=346 Number of Objects=0
Frame Number=347 Number of Objects=0
Frame Number=348 Number of Objects=2
Type & Probability = 78% largevehicle
Type & Probability = 100% largevehicle
Frame Number=349 Number of Objects=2
Type & Probability = 76% largevehicle
Type & Probability = 99% largevehicle
Frame Number=350 Number of Objects=2
Type & Probability = 72% largevehicle
Type & Probability = 100% largevehicle
Frame Number=351 Number of Objects=1
Type & Probability = 41% largevehicle
Frame Number=352 Number of Objects=1
Type & Probability = 45% suv
Frame Number=353 Number of Objects=1
Type & Probability = 43% suv
Frame Number=354 Number of Objects=1
Type & Probability = 60% sedan
Frame Number=355 Number of Objects=1
Type & Probability = 84% sedan
Frame Number=356 Number of Objects=1
Type & Probability = 84% sedan
Frame Number=357 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=358 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=359 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=360 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=361 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=362 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=363 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=364 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=365 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=366 Number of Objects=0
Frame Number=367 Number of Objects=0
Frame Number=368 Number of Objects=0
Frame Number=369 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=370 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=371 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=372 Number of Objects=0
Frame Number=373 Number of Objects=0
Frame Number=374 Number of Objects=0
Frame Number=375 Number of Objects=0
Frame Number=376 Number of Objects=0
Frame Number=377 Number of Objects=0
Frame Number=378 Number of Objects=0
Frame Number=379 Number of Objects=0
Frame Number=380 Number of Objects=0
Frame Number=381 Number of Objects=0
Frame Number=382 Number of Objects=0
Frame Number=383 Number of Objects=0
Frame Number=384 Number of Objects=0
Frame Number=385 Number of Objects=0
Frame Number=386 Number of Objects=0
Frame Number=387 Number of Objects=0
Frame Number=388 Number of Objects=0
Frame Number=389 Number of Objects=0
Frame Number=390 Number of Objects=0
Frame Number=391 Number of Objects=0
Frame Number=392 Number of Objects=0
Frame Number=393 Number of Objects=0
Frame Number=394 Number of Objects=0
Frame Number=395 Number of Objects=0
Frame Number=396 Number of Objects=0
Frame Number=397 Number of Objects=0
Frame Number=398 Number of Objects=0
Frame Number=399 Number of Objects=0
Frame Number=400 Number of Objects=0
Frame Number=401 Number of Objects=0
Frame Number=402 Number of Objects=0
Frame Number=403 Number of Objects=0
Frame Number=404 Number of Objects=0
Frame Number=405 Number of Objects=0
Frame Number=406 Number of Objects=0
Frame Number=407 Number of Objects=0
Frame Number=408 Number of Objects=0
Frame Number=409 Number of Objects=0
Frame Number=410 Number of Objects=0
Frame Number=411 Number of Objects=1
Type & Probability = 95% largevehicle
Frame Number=412 Number of Objects=1
Type & Probability = 95% largevehicle
Frame Number=413 Number of Objects=1
Type & Probability = 95% largevehicle
Frame Number=414 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=415 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=416 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=417 Number of Objects=0
Frame Number=418 Number of Objects=0
Frame Number=419 Number of Objects=0
Frame Number=420 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=421 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=422 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=423 Number of Objects=1
Type & Probability = 89% largevehicle
Frame Number=424 Number of Objects=1
Type & Probability = 89% largevehicle
Frame Number=425 Number of Objects=1
Type & Probability = 89% largevehicle
Frame Number=426 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=427 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=428 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=429 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=430 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=431 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=432 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=433 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=434 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=435 Number of Objects=0
Frame Number=436 Number of Objects=0
Frame Number=437 Number of Objects=0
Frame Number=438 Number of Objects=0
Frame Number=439 Number of Objects=0
Frame Number=440 Number of Objects=0
Frame Number=441 Number of Objects=0
Frame Number=442 Number of Objects=0
Frame Number=443 Number of Objects=0
Frame Number=444 Number of Objects=0
Frame Number=445 Number of Objects=0
Frame Number=446 Number of Objects=0
Frame Number=447 Number of Objects=1
Type & Probability = 88% suv
Frame Number=448 Number of Objects=1
Type & Probability = 88% suv
Frame Number=449 Number of Objects=1
Type & Probability = 89% suv
Frame Number=450 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=451 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=452 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=453 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=454 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=455 Number of Objects=1
Type & Probability = 88% largevehicle
Frame Number=456 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=457 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=458 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=459 Number of Objects=1
Type & Probability = 98% suv
Frame Number=460 Number of Objects=1
Type & Probability = 98% suv
Frame Number=461 Number of Objects=1
Type & Probability = 98% suv
Frame Number=462 Number of Objects=2
Type & Probability = 81% largevehicle
Type & Probability = 100% largevehicle
Frame Number=463 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 100% largevehicle
Frame Number=464 Number of Objects=2
Type & Probability = 99% largevehicle
Type & Probability = 100% largevehicle
Frame Number=465 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=466 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=467 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=468 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=469 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=470 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=471 Number of Objects=0
Frame Number=472 Number of Objects=0
Frame Number=473 Number of Objects=0
Frame Number=474 Number of Objects=0
Frame Number=475 Number of Objects=0
Frame Number=476 Number of Objects=0
Frame Number=477 Number of Objects=0
Frame Number=478 Number of Objects=0
Frame Number=479 Number of Objects=0
Frame Number=480 Number of Objects=0
Frame Number=481 Number of Objects=0
Frame Number=482 Number of Objects=0
Frame Number=483 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=484 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=485 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=486 Number of Objects=1
Type & Probability = 77% suv
Frame Number=487 Number of Objects=1
Type & Probability = 77% suv
Frame Number=488 Number of Objects=1
Type & Probability = 77% suv
Frame Number=489 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=490 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=491 Number of Objects=1
Type & Probability = 93% largevehicle
Frame Number=492 Number of Objects=1
Type & Probability = 62% largevehicle
Frame Number=493 Number of Objects=1
Type & Probability = 62% largevehicle
Frame Number=494 Number of Objects=1
Type & Probability = 62% largevehicle
Frame Number=495 Number of Objects=1
Type & Probability = 100% suv
Frame Number=496 Number of Objects=1
Type & Probability = 100% suv
Frame Number=497 Number of Objects=1
Type & Probability = 100% suv
Frame Number=498 Number of Objects=1
Type & Probability = 100% suv
Frame Number=499 Number of Objects=1
Type & Probability = 100% suv
Frame Number=500 Number of Objects=0
Frame Number=501 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=502 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=503 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=504 Number of Objects=0
Frame Number=505 Number of Objects=0
Frame Number=506 Number of Objects=0
Frame Number=507 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=508 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=509 Number of Objects=0
Frame Number=510 Number of Objects=1
Type & Probability = 100% suv
Frame Number=511 Number of Objects=1
Type & Probability = 100% suv
Frame Number=512 Number of Objects=1
Type & Probability = 100% suv
Frame Number=513 Number of Objects=1
Type & Probability = 98% suv
Frame Number=514 Number of Objects=1
Type & Probability = 98% suv
Frame Number=515 Number of Objects=1
Type & Probability = 98% suv
Frame Number=516 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=517 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=518 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=519 Number of Objects=2
Type & Probability = 99% largevehicle
Type & Probability = 88% truck
Frame Number=520 Number of Objects=2
Type & Probability = 99% largevehicle
Type & Probability = 88% truck
Frame Number=521 Number of Objects=2
Type & Probability = 99% largevehicle
Type & Probability = 88% truck
Frame Number=522 Number of Objects=1
Type & Probability = 79% truck
Frame Number=523 Number of Objects=1
Type & Probability = 79% truck
Frame Number=524 Number of Objects=1
Type & Probability = 79% truck
Frame Number=525 Number of Objects=1
Type & Probability = 48% sedan
Frame Number=526 Number of Objects=1
Type & Probability = 48% sedan
Frame Number=527 Number of Objects=1
Type & Probability = 48% sedan
Frame Number=528 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=529 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=530 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=531 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=532 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=533 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=534 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=535 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=536 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=537 Number of Objects=2
Type & Probability = 53% suv
Type & Probability = 60% coupe
Frame Number=538 Number of Objects=2
Type & Probability = 53% suv
Type & Probability = 60% coupe
Frame Number=539 Number of Objects=2
Type & Probability = 53% suv
Type & Probability = 61% coupe
Frame Number=540 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 99% sedan
Frame Number=541 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 99% sedan
Frame Number=542 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 99% sedan
Frame Number=543 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 66% largevehicle
Frame Number=544 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 63% largevehicle
Frame Number=545 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 66% largevehicle
Frame Number=546 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 85% largevehicle
Frame Number=547 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 88% sedan
Frame Number=548 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 85% largevehicle
Frame Number=549 Number of Objects=3
Type & Probability = 91% suv
Type & Probability = 70% largevehicle
Frame Number=550 Number of Objects=3
Type & Probability = 91% suv
Type & Probability = 70% largevehicle
Frame Number=551 Number of Objects=3
Type & Probability = 91% suv
Type & Probability = 70% largevehicle
Frame Number=552 Number of Objects=2
Type & Probability = 56% sedan
Type & Probability = 93% largevehicle
Frame Number=553 Number of Objects=2
Type & Probability = 55% sedan
Type & Probability = 93% largevehicle
Frame Number=554 Number of Objects=2
Type & Probability = 55% sedan
Type & Probability = 93% largevehicle
Frame Number=555 Number of Objects=1
Type & Probability = 72% largevehicle
Frame Number=556 Number of Objects=1
Type & Probability = 72% largevehicle
Frame Number=557 Number of Objects=1
Type & Probability = 72% largevehicle
Frame Number=558 Number of Objects=1
Type & Probability = 58% suv
Frame Number=559 Number of Objects=1
Type & Probability = 58% suv
Frame Number=560 Number of Objects=1
Type & Probability = 58% suv
Frame Number=561 Number of Objects=1
Type & Probability = 61% largevehicle
Frame Number=562 Number of Objects=1
Type & Probability = 61% largevehicle
Frame Number=563 Number of Objects=1
Type & Probability = 61% largevehicle
Frame Number=564 Number of Objects=1
Type & Probability = 98% suv
Frame Number=565 Number of Objects=1
Type & Probability = 98% suv
Frame Number=566 Number of Objects=1
Type & Probability = 98% suv
Frame Number=567 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=568 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=569 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=570 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=571 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=572 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=573 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=574 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=575 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=576 Number of Objects=1
Type & Probability = 99% suv
Frame Number=577 Number of Objects=1
Type & Probability = 99% suv
Frame Number=578 Number of Objects=1
Type & Probability = 99% suv
Frame Number=579 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=580 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=581 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=582 Number of Objects=1
Type & Probability = 81% suv
Frame Number=583 Number of Objects=1
Type & Probability = 81% suv
Frame Number=584 Number of Objects=1
Type & Probability = 81% suv
Frame Number=585 Number of Objects=1
Type & Probability = 83% suv
Frame Number=586 Number of Objects=1
Type & Probability = 83% suv
Frame Number=587 Number of Objects=1
Type & Probability = 84% suv
Frame Number=588 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=589 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=590 Number of Objects=1
Type & Probability = 92% sedan
Frame Number=591 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=592 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=593 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=594 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=595 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=596 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=597 Number of Objects=1
Type & Probability = 95% coupe
Frame Number=598 Number of Objects=1
Type & Probability = 95% coupe
Frame Number=599 Number of Objects=1
Type & Probability = 97% coupe
Frame Number=600 Number of Objects=1
Frame Number=601 Number of Objects=1
Frame Number=602 Number of Objects=0
Frame Number=603 Number of Objects=0
Frame Number=604 Number of Objects=0
Frame Number=605 Number of Objects=0
Frame Number=606 Number of Objects=0
Frame Number=607 Number of Objects=0
Frame Number=608 Number of Objects=0
Frame Number=609 Number of Objects=0
Frame Number=610 Number of Objects=0
Frame Number=611 Number of Objects=0
Frame Number=612 Number of Objects=0
Frame Number=613 Number of Objects=0
Frame Number=614 Number of Objects=0
Frame Number=615 Number of Objects=0
Frame Number=616 Number of Objects=0
Frame Number=617 Number of Objects=0
Frame Number=618 Number of Objects=0
Frame Number=619 Number of Objects=0
Frame Number=620 Number of Objects=0
Frame Number=621 Number of Objects=0
Frame Number=622 Number of Objects=0
Frame Number=623 Number of Objects=0
Frame Number=624 Number of Objects=0
Frame Number=625 Number of Objects=0
Frame Number=626 Number of Objects=0
Frame Number=627 Number of Objects=0
Frame Number=628 Number of Objects=0
Frame Number=629 Number of Objects=0
Frame Number=630 Number of Objects=0
Frame Number=631 Number of Objects=0
Frame Number=632 Number of Objects=0
Frame Number=633 Number of Objects=0
Frame Number=634 Number of Objects=0
Frame Number=635 Number of Objects=0
Frame Number=636 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=637 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=638 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=639 Number of Objects=0
Frame Number=640 Number of Objects=0
Frame Number=641 Number of Objects=0
Frame Number=642 Number of Objects=0
Frame Number=643 Number of Objects=0
Frame Number=644 Number of Objects=0
Frame Number=645 Number of Objects=1
Type & Probability = 98% suv
Frame Number=646 Number of Objects=1
Type & Probability = 98% suv
Frame Number=647 Number of Objects=1
Type & Probability = 98% suv
Frame Number=648 Number of Objects=1
Type & Probability = 90% sedan
Frame Number=649 Number of Objects=1
Type & Probability = 70% sedan
Frame Number=650 Number of Objects=1
Type & Probability = 73% largevehicle
Frame Number=651 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=652 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=653 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=654 Number of Objects=0
Frame Number=655 Number of Objects=0
Frame Number=656 Number of Objects=0
Frame Number=657 Number of Objects=0
Frame Number=658 Number of Objects=0
Frame Number=659 Number of Objects=0
Frame Number=660 Number of Objects=0
Frame Number=661 Number of Objects=0
Frame Number=662 Number of Objects=0
Frame Number=663 Number of Objects=0
Frame Number=664 Number of Objects=0
Frame Number=665 Number of Objects=0
Frame Number=666 Number of Objects=0
Frame Number=667 Number of Objects=0
Frame Number=668 Number of Objects=0
Frame Number=669 Number of Objects=0
Frame Number=670 Number of Objects=0
Frame Number=671 Number of Objects=0
Frame Number=672 Number of Objects=0
Frame Number=673 Number of Objects=0
Frame Number=674 Number of Objects=0
Frame Number=675 Number of Objects=0
Frame Number=676 Number of Objects=0
Frame Number=677 Number of Objects=0
Frame Number=678 Number of Objects=0
Frame Number=679 Number of Objects=0
Frame Number=680 Number of Objects=0
Frame Number=681 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=682 Number of Objects=1
Type & Probability = 55% largevehicle
Frame Number=683 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=684 Number of Objects=0
Frame Number=685 Number of Objects=0
Frame Number=686 Number of Objects=0
Frame Number=687 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=688 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=689 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=690 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=691 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=692 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=693 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=694 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=695 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=696 Number of Objects=1
Type & Probability = 38% largevehicle
Frame Number=697 Number of Objects=1
Type & Probability = 38% largevehicle
Frame Number=698 Number of Objects=1
Type & Probability = 38% largevehicle
Frame Number=699 Number of Objects=0
Frame Number=700 Number of Objects=0
Frame Number=701 Number of Objects=0
Frame Number=702 Number of Objects=0
Frame Number=703 Number of Objects=0
Frame Number=704 Number of Objects=0
Frame Number=705 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=706 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=707 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=708 Number of Objects=1
Type & Probability = 78% sedan
Frame Number=709 Number of Objects=1
Type & Probability = 78% sedan
Frame Number=710 Number of Objects=1
Type & Probability = 78% sedan
Frame Number=711 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=712 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=713 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=714 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=715 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=716 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=717 Number of Objects=0
Frame Number=718 Number of Objects=0
Frame Number=719 Number of Objects=0
Frame Number=720 Number of Objects=0
Frame Number=721 Number of Objects=0
Frame Number=722 Number of Objects=0
Frame Number=723 Number of Objects=0
Frame Number=724 Number of Objects=0
Frame Number=725 Number of Objects=0
Frame Number=726 Number of Objects=0
Frame Number=727 Number of Objects=0
WARNING: [TRT]: The implicit batch dimension mode has been deprecated. Please create the network with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag whenever possible.
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [Implicit Engine Info]: layers num: 2
0 INPUT kFLOAT input_1 3x224x224
1 OUTPUT kFLOAT predictions/Softmax 6x1x1
WARNING: [TRT]: The implicit batch dimension mode has been deprecated. Please create the network with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag whenever possible.
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x544x960
1 OUTPUT kFLOAT output_bbox/BiasAdd 16x34x60
2 OUTPUT kFLOAT output_cov/Sigmoid 4x34x60
nvstreammux: Successfully handled EOS for source_id=0
Frame Number=728 Number of Objects=0
Frame Number=729 Number of Objects=0
Frame Number=730 Number of Objects=0
Frame Number=731 Number of Objects=0
End-of-stream
<enum GST_STATE_CHANGE_SUCCESS of type Gst.StateChangeReturn>
Latency with Debug Log#
Latency is the time difference between when data enter one element and into the next in the pipeline. In general, low latency is desirable and particularly important for real-time pipelines that are time-critical. In the context of video analytics, latency is the amount of time it takes for the input streams to be processed. Latency in a DeepStream pipeline can be measured using GStreamer debugging capabilities. The debug log is very useful but can be verbose. We specify the amount of desired output based on the below table (take from GStreamer documentation). By setting the GST-DEBUG environment variable to GST_SCHEDULING:7 when running the DeepStream pipeline, we get a trace log that contains details on when the buffers are processed. GStreamer allows for custom debugging information handlers, but the default one is very useful. When using the default handler, the content of each line in the debug output looks like the below and includes various information.
In the next step, we run the pipeline using app_3_pt_1.py and preview the first few lines of the output trace.log file. In order to make the log file useful, we parse the text and export the data into a dataframe. For demonstration, we have included a simple script to accomplish this.
# DO NOT CHANGE THIS CELL
!GST_DEBUG='GST_SCHEDULING:7' GST_DEBUG_FILE='/dli/task/logs/trace.log' python final_app.py data/sample_30.h264
Created Pipeline
Created elements
Added elements to pipeline
Linked elements in pipeline
Attached probe
Added bus message handler
Starting pipeline
ERROR: [TRT]: 3: [builder.cpp::~Builder::307] Error Code 3: API Usage Error (Parameter check failed at: optimizer/api/builder.cpp::~Builder::307, condition: mObjectCounter.use_count() == 1. Destroying a builder object before destroying objects it created leads to undefined behavior.
)
WARNING: [TRT]: The implicit batch dimension mode has been deprecated. Please create the network with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag whenever possible.
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [Implicit Engine Info]: layers num: 2
0 INPUT kFLOAT input_1 3x224x224
1 OUTPUT kFLOAT predictions/Softmax 6x1x1
ERROR: [TRT]: 3: [builder.cpp::~Builder::307] Error Code 3: API Usage Error (Parameter check failed at: optimizer/api/builder.cpp::~Builder::307, condition: mObjectCounter.use_count() == 1. Destroying a builder object before destroying objects it created leads to undefined behavior.
)
WARNING: [TRT]: The implicit batch dimension mode has been deprecated. Please create the network with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag whenever possible.
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:610 [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x544x960
1 OUTPUT kFLOAT output_bbox/BiasAdd 16x34x60
2 OUTPUT kFLOAT output_cov/Sigmoid 4x34x60
(python:339): GStreamer-CRITICAL **: 07:06:03.727: gst_debug_log_valist: assertion 'category != NULL' failed
(python:339): GStreamer-CRITICAL **: 07:06:03.727: gst_debug_log_valist: assertion 'category != NULL' failed
(python:339): GStreamer-CRITICAL **: 07:06:03.727: gst_debug_log_valist: assertion 'category != NULL' failed
(python:339): GStreamer-CRITICAL **: 07:06:03.727: gst_debug_log_valist: assertion 'category != NULL' failed
Frame Number=0 Number of Objects=1
Type & Probability = 65% suv
Frame Number=1 Number of Objects=1
Type & Probability = 65% suv
Frame Number=2 Number of Objects=1
Type & Probability = 65% suv
Frame Number=3 Number of Objects=1
Type & Probability = 100% suv
Frame Number=4 Number of Objects=1
Type & Probability = 100% suv
Frame Number=5 Number of Objects=1
Type & Probability = 100% suv
Frame Number=6 Number of Objects=1
Type & Probability = 100% suv
Frame Number=7 Number of Objects=1
Type & Probability = 100% suv
Frame Number=8 Number of Objects=1
Type & Probability = 100% suv
Frame Number=9 Number of Objects=1
Type & Probability = 100% suv
Frame Number=10 Number of Objects=1
Type & Probability = 100% suv
Frame Number=11 Number of Objects=1
Type & Probability = 100% suv
Frame Number=12 Number of Objects=1
Type & Probability = 100% suv
Frame Number=13 Number of Objects=1
Type & Probability = 100% suv
Frame Number=14 Number of Objects=1
Type & Probability = 100% suv
Frame Number=15 Number of Objects=1
Type & Probability = 100% suv
Frame Number=16 Number of Objects=1
Type & Probability = 100% suv
Frame Number=17 Number of Objects=1
Type & Probability = 100% suv
Frame Number=18 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=19 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=20 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=21 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=22 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=23 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=24 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=25 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=26 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=27 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=28 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=29 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=30 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=31 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=32 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=33 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=34 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=35 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=36 Number of Objects=1
Type & Probability = 100% suv
Frame Number=37 Number of Objects=1
Type & Probability = 100% suv
Frame Number=38 Number of Objects=1
Type & Probability = 100% suv
Frame Number=39 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=40 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=41 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=42 Number of Objects=2
Type & Probability = 49% largevehicle
Type & Probability = 99% sedan
Frame Number=43 Number of Objects=2
Type & Probability = 49% largevehicle
Type & Probability = 99% sedan
Frame Number=44 Number of Objects=2
Type & Probability = 49% largevehicle
Type & Probability = 99% sedan
Frame Number=45 Number of Objects=2
Type & Probability = 51% suv
Type & Probability = 54% coupe
Frame Number=46 Number of Objects=2
Type & Probability = 74% largevehicle
Type & Probability = 54% coupe
Frame Number=47 Number of Objects=2
Type & Probability = 75% largevehicle
Type & Probability = 53% coupe
Frame Number=48 Number of Objects=1
Type & Probability = 85% sedan
Frame Number=49 Number of Objects=1
Type & Probability = 84% sedan
Frame Number=50 Number of Objects=1
Type & Probability = 85% sedan
Frame Number=51 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=52 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=53 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=54 Number of Objects=1
Type & Probability = 54% sedan
Frame Number=55 Number of Objects=1
Type & Probability = 53% sedan
Frame Number=56 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 52% coupe
Frame Number=57 Number of Objects=0
Frame Number=58 Number of Objects=0
Frame Number=59 Number of Objects=0
Frame Number=60 Number of Objects=1
Type & Probability = 95% largevehicle
Frame Number=61 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=62 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=63 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=64 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=65 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=66 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=67 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=68 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=69 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=70 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=71 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=72 Number of Objects=1
Type & Probability = 87% largevehicle
Frame Number=73 Number of Objects=1
Type & Probability = 87% largevehicle
Frame Number=74 Number of Objects=1
Type & Probability = 87% largevehicle
Frame Number=75 Number of Objects=1
Type & Probability = 54% largevehicle
Frame Number=76 Number of Objects=1
Type & Probability = 82% largevehicle
Frame Number=77 Number of Objects=1
Type & Probability = 73% sedan
Frame Number=78 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=79 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=80 Number of Objects=1
Type & Probability = 75% largevehicle
Frame Number=81 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=82 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=83 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=84 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=85 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=86 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=87 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=88 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=89 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=90 Number of Objects=1
Type & Probability = 50% sedan
Frame Number=91 Number of Objects=1
Type & Probability = 52% sedan
Frame Number=92 Number of Objects=1
Type & Probability = 50% sedan
Frame Number=93 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=94 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=95 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=96 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=97 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=98 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=99 Number of Objects=1
Type & Probability = 81% truck
Frame Number=100 Number of Objects=1
Type & Probability = 85% truck
Frame Number=101 Number of Objects=1
Type & Probability = 84% truck
Frame Number=102 Number of Objects=1
Type & Probability = 93% truck
Frame Number=103 Number of Objects=1
Type & Probability = 93% truck
Frame Number=104 Number of Objects=1
Type & Probability = 92% truck
Frame Number=105 Number of Objects=1
Type & Probability = 88% sedan
Frame Number=106 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=107 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=108 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=109 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=110 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=111 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=112 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=113 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=114 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=115 Number of Objects=2
Type & Probability = 67% sedan
Type & Probability = 98% sedan
Frame Number=116 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=117 Number of Objects=0
Frame Number=118 Number of Objects=0
Frame Number=119 Number of Objects=0
Frame Number=120 Number of Objects=0
Frame Number=121 Number of Objects=0
Frame Number=122 Number of Objects=0
Frame Number=123 Number of Objects=0
Frame Number=124 Number of Objects=0
Frame Number=125 Number of Objects=0
Frame Number=126 Number of Objects=0
Frame Number=127 Number of Objects=0
Frame Number=128 Number of Objects=0
Frame Number=129 Number of Objects=0
Frame Number=130 Number of Objects=0
Frame Number=131 Number of Objects=0
Frame Number=132 Number of Objects=0
Frame Number=133 Number of Objects=0
Frame Number=134 Number of Objects=0
Frame Number=135 Number of Objects=0
Frame Number=136 Number of Objects=0
Frame Number=137 Number of Objects=0
Frame Number=138 Number of Objects=1
Type & Probability = 99% coupe
Frame Number=139 Number of Objects=1
Type & Probability = 99% coupe
Frame Number=140 Number of Objects=1
Type & Probability = 99% coupe
Frame Number=141 Number of Objects=0
Frame Number=142 Number of Objects=0
Frame Number=143 Number of Objects=0
Frame Number=144 Number of Objects=0
Frame Number=145 Number of Objects=0
Frame Number=146 Number of Objects=0
Frame Number=147 Number of Objects=0
Frame Number=148 Number of Objects=0
Frame Number=149 Number of Objects=0
Frame Number=150 Number of Objects=0
Frame Number=151 Number of Objects=0
Frame Number=152 Number of Objects=0
Frame Number=153 Number of Objects=0
Frame Number=154 Number of Objects=0
Frame Number=155 Number of Objects=0
Frame Number=156 Number of Objects=0
Frame Number=157 Number of Objects=0
Frame Number=158 Number of Objects=0
Frame Number=159 Number of Objects=1
Type & Probability = 93% largevehicle
Frame Number=160 Number of Objects=1
Type & Probability = 93% largevehicle
Frame Number=161 Number of Objects=1
Type & Probability = 93% largevehicle
Frame Number=162 Number of Objects=1
Type & Probability = 90% largevehicle
Frame Number=163 Number of Objects=1
Type & Probability = 90% largevehicle
Frame Number=164 Number of Objects=1
Type & Probability = 90% largevehicle
Frame Number=165 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=166 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=167 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=168 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=169 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=170 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=171 Number of Objects=1
Type & Probability = 86% suv
Frame Number=172 Number of Objects=1
Type & Probability = 86% suv
Frame Number=173 Number of Objects=1
Type & Probability = 86% suv
Frame Number=174 Number of Objects=1
Type & Probability = 88% sedan
Frame Number=175 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=176 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=177 Number of Objects=1
Type & Probability = 82% largevehicle
Frame Number=178 Number of Objects=1
Type & Probability = 82% largevehicle
Frame Number=179 Number of Objects=1
Type & Probability = 82% largevehicle
Frame Number=180 Number of Objects=1
Type & Probability = 69% sedan
Frame Number=181 Number of Objects=1
Type & Probability = 69% sedan
Frame Number=182 Number of Objects=1
Type & Probability = 54% sedan
Frame Number=183 Number of Objects=1
Type & Probability = 95% sedan
Frame Number=184 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=185 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=186 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=187 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=188 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=189 Number of Objects=1
Type & Probability = 68% sedan
Frame Number=190 Number of Objects=1
Type & Probability = 68% sedan
Frame Number=191 Number of Objects=1
Type & Probability = 68% sedan
Frame Number=192 Number of Objects=1
Type & Probability = 83% suv
Frame Number=193 Number of Objects=1
Type & Probability = 40% suv
Frame Number=194 Number of Objects=1
Type & Probability = 95% suv
Frame Number=195 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=196 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=197 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=198 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=199 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=200 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=201 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=202 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=203 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=204 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=205 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=206 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=207 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=208 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=209 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=210 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=211 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=212 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=213 Number of Objects=1
Type & Probability = 87% sedan
Frame Number=214 Number of Objects=1
Type & Probability = 87% sedan
Frame Number=215 Number of Objects=1
Type & Probability = 87% sedan
Frame Number=216 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=217 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=218 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=219 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=220 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=221 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=222 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=223 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=224 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=225 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=226 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=227 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=228 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=229 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=230 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=231 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=232 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=233 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=234 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=235 Number of Objects=1
Type & Probability = 96% sedan
Frame Number=236 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=237 Number of Objects=1
Type & Probability = 73% largevehicle
Frame Number=238 Number of Objects=1
Type & Probability = 72% largevehicle
Frame Number=239 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=240 Number of Objects=0
Frame Number=241 Number of Objects=0
Frame Number=242 Number of Objects=0
Frame Number=243 Number of Objects=0
Frame Number=244 Number of Objects=0
Frame Number=245 Number of Objects=0
Frame Number=246 Number of Objects=0
Frame Number=247 Number of Objects=0
Frame Number=248 Number of Objects=0
Frame Number=249 Number of Objects=0
Frame Number=250 Number of Objects=0
Frame Number=251 Number of Objects=0
Frame Number=252 Number of Objects=0
Frame Number=253 Number of Objects=0
Frame Number=254 Number of Objects=0
Frame Number=255 Number of Objects=1
Type & Probability = 99% suv
Frame Number=256 Number of Objects=1
Type & Probability = 98% suv
Frame Number=257 Number of Objects=1
Type & Probability = 98% suv
Frame Number=258 Number of Objects=1
Type & Probability = 84% largevehicle
Frame Number=259 Number of Objects=1
Type & Probability = 87% largevehicle
Frame Number=260 Number of Objects=1
Type & Probability = 85% largevehicle
Frame Number=261 Number of Objects=0
Frame Number=262 Number of Objects=0
Frame Number=263 Number of Objects=0
Frame Number=264 Number of Objects=0
Frame Number=265 Number of Objects=0
Frame Number=266 Number of Objects=0
Frame Number=267 Number of Objects=0
Frame Number=268 Number of Objects=0
Frame Number=269 Number of Objects=0
Frame Number=270 Number of Objects=0
Frame Number=271 Number of Objects=0
Frame Number=272 Number of Objects=0
Frame Number=273 Number of Objects=0
Frame Number=274 Number of Objects=0
Frame Number=275 Number of Objects=0
Frame Number=276 Number of Objects=0
Frame Number=277 Number of Objects=0
Frame Number=278 Number of Objects=0
Frame Number=279 Number of Objects=0
Frame Number=280 Number of Objects=0
Frame Number=281 Number of Objects=0
Frame Number=282 Number of Objects=0
Frame Number=283 Number of Objects=0
Frame Number=284 Number of Objects=0
Frame Number=285 Number of Objects=0
Frame Number=286 Number of Objects=0
Frame Number=287 Number of Objects=0
Frame Number=288 Number of Objects=1
Type & Probability = 65% largevehicle
Frame Number=289 Number of Objects=1
Type & Probability = 75% largevehicle
Frame Number=290 Number of Objects=1
Type & Probability = 74% largevehicle
Frame Number=291 Number of Objects=1
Type & Probability = 58% largevehicle
Frame Number=292 Number of Objects=1
Type & Probability = 53% largevehicle
Frame Number=293 Number of Objects=1
Type & Probability = 73% largevehicle
Frame Number=294 Number of Objects=0
Frame Number=295 Number of Objects=0
Frame Number=296 Number of Objects=0
Frame Number=297 Number of Objects=0
Frame Number=298 Number of Objects=0
Frame Number=299 Number of Objects=0
Frame Number=300 Number of Objects=0
Frame Number=301 Number of Objects=0
Frame Number=302 Number of Objects=0
Frame Number=303 Number of Objects=0
Frame Number=304 Number of Objects=0
Frame Number=305 Number of Objects=0
Frame Number=306 Number of Objects=0
Frame Number=307 Number of Objects=0
Frame Number=308 Number of Objects=0
Frame Number=309 Number of Objects=0
Frame Number=310 Number of Objects=0
Frame Number=311 Number of Objects=0
Frame Number=312 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=313 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=314 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=315 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=316 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=317 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=318 Number of Objects=1
Type & Probability = 88% suv
Frame Number=319 Number of Objects=1
Type & Probability = 88% suv
Frame Number=320 Number of Objects=1
Type & Probability = 88% suv
Frame Number=321 Number of Objects=1
Type & Probability = 55% suv
Frame Number=322 Number of Objects=1
Type & Probability = 55% suv
Frame Number=323 Number of Objects=1
Type & Probability = 52% suv
Frame Number=324 Number of Objects=1
Type & Probability = 54% largevehicle
Frame Number=325 Number of Objects=1
Type & Probability = 52% largevehicle
Frame Number=326 Number of Objects=1
Type & Probability = 56% largevehicle
Frame Number=327 Number of Objects=1
Type & Probability = 69% largevehicle
Frame Number=328 Number of Objects=1
Type & Probability = 69% largevehicle
Frame Number=329 Number of Objects=1
Type & Probability = 75% largevehicle
Frame Number=330 Number of Objects=1
Type & Probability = 100% suv
Frame Number=331 Number of Objects=1
Type & Probability = 100% suv
Frame Number=332 Number of Objects=1
Type & Probability = 100% suv
Frame Number=333 Number of Objects=1
Type & Probability = 45% largevehicle
Frame Number=334 Number of Objects=1
Type & Probability = 45% largevehicle
Frame Number=335 Number of Objects=0
Frame Number=336 Number of Objects=2
Type & Probability = 51% truck
Type & Probability = 100% largevehicle
Frame Number=337 Number of Objects=2
Type & Probability = 51% truck
Type & Probability = 100% largevehicle
Frame Number=338 Number of Objects=2
Type & Probability = 51% truck
Type & Probability = 100% largevehicle
Frame Number=339 Number of Objects=1
Type & Probability = 100% suv
Frame Number=340 Number of Objects=1
Type & Probability = 100% suv
Frame Number=341 Number of Objects=1
Type & Probability = 100% suv
Frame Number=342 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=343 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=344 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=345 Number of Objects=0
Frame Number=346 Number of Objects=0
Frame Number=347 Number of Objects=0
Frame Number=348 Number of Objects=2
Type & Probability = 78% largevehicle
Type & Probability = 100% largevehicle
Frame Number=349 Number of Objects=2
Type & Probability = 76% largevehicle
Type & Probability = 99% largevehicle
Frame Number=350 Number of Objects=2
Type & Probability = 72% largevehicle
Type & Probability = 100% largevehicle
Frame Number=351 Number of Objects=1
Type & Probability = 41% largevehicle
Frame Number=352 Number of Objects=1
Type & Probability = 45% suv
Frame Number=353 Number of Objects=1
Type & Probability = 43% suv
Frame Number=354 Number of Objects=1
Type & Probability = 60% sedan
Frame Number=355 Number of Objects=1
Type & Probability = 84% sedan
Frame Number=356 Number of Objects=1
Type & Probability = 84% sedan
Frame Number=357 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=358 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=359 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=360 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=361 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=362 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=363 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=364 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=365 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=366 Number of Objects=0
Frame Number=367 Number of Objects=0
Frame Number=368 Number of Objects=0
Frame Number=369 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=370 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=371 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=372 Number of Objects=0
Frame Number=373 Number of Objects=0
Frame Number=374 Number of Objects=0
Frame Number=375 Number of Objects=0
Frame Number=376 Number of Objects=0
Frame Number=377 Number of Objects=0
Frame Number=378 Number of Objects=0
Frame Number=379 Number of Objects=0
Frame Number=380 Number of Objects=0
Frame Number=381 Number of Objects=0
Frame Number=382 Number of Objects=0
Frame Number=383 Number of Objects=0
Frame Number=384 Number of Objects=0
Frame Number=385 Number of Objects=0
Frame Number=386 Number of Objects=0
Frame Number=387 Number of Objects=0
Frame Number=388 Number of Objects=0
Frame Number=389 Number of Objects=0
Frame Number=390 Number of Objects=0
Frame Number=391 Number of Objects=0
Frame Number=392 Number of Objects=0
Frame Number=393 Number of Objects=0
Frame Number=394 Number of Objects=0
Frame Number=395 Number of Objects=0
Frame Number=396 Number of Objects=0
Frame Number=397 Number of Objects=0
Frame Number=398 Number of Objects=0
Frame Number=399 Number of Objects=0
Frame Number=400 Number of Objects=0
Frame Number=401 Number of Objects=0
Frame Number=402 Number of Objects=0
Frame Number=403 Number of Objects=0
Frame Number=404 Number of Objects=0
Frame Number=405 Number of Objects=0
Frame Number=406 Number of Objects=0
Frame Number=407 Number of Objects=0
Frame Number=408 Number of Objects=0
Frame Number=409 Number of Objects=0
Frame Number=410 Number of Objects=0
Frame Number=411 Number of Objects=1
Type & Probability = 95% largevehicle
Frame Number=412 Number of Objects=1
Type & Probability = 95% largevehicle
Frame Number=413 Number of Objects=1
Type & Probability = 95% largevehicle
Frame Number=414 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=415 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=416 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=417 Number of Objects=0
Frame Number=418 Number of Objects=0
Frame Number=419 Number of Objects=0
Frame Number=420 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=421 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=422 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=423 Number of Objects=1
Type & Probability = 89% largevehicle
Frame Number=424 Number of Objects=1
Type & Probability = 89% largevehicle
Frame Number=425 Number of Objects=1
Type & Probability = 89% largevehicle
Frame Number=426 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=427 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=428 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=429 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=430 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=431 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=432 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=433 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=434 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=435 Number of Objects=0
Frame Number=436 Number of Objects=0
Frame Number=437 Number of Objects=0
Frame Number=438 Number of Objects=0
Frame Number=439 Number of Objects=0
Frame Number=440 Number of Objects=0
Frame Number=441 Number of Objects=0
Frame Number=442 Number of Objects=0
Frame Number=443 Number of Objects=0
Frame Number=444 Number of Objects=0
Frame Number=445 Number of Objects=0
Frame Number=446 Number of Objects=0
Frame Number=447 Number of Objects=1
Type & Probability = 88% suv
Frame Number=448 Number of Objects=1
Type & Probability = 88% suv
Frame Number=449 Number of Objects=1
Type & Probability = 89% suv
Frame Number=450 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=451 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=452 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=453 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=454 Number of Objects=1
Type & Probability = 98% largevehicle
Frame Number=455 Number of Objects=1
Type & Probability = 88% largevehicle
Frame Number=456 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=457 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=458 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=459 Number of Objects=1
Type & Probability = 98% suv
Frame Number=460 Number of Objects=1
Type & Probability = 98% suv
Frame Number=461 Number of Objects=1
Type & Probability = 98% suv
Frame Number=462 Number of Objects=2
Type & Probability = 81% largevehicle
Type & Probability = 100% largevehicle
Frame Number=463 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 100% largevehicle
Frame Number=464 Number of Objects=2
Type & Probability = 99% largevehicle
Type & Probability = 100% largevehicle
Frame Number=465 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=466 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=467 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=468 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=469 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=470 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=471 Number of Objects=0
Frame Number=472 Number of Objects=0
Frame Number=473 Number of Objects=0
Frame Number=474 Number of Objects=0
Frame Number=475 Number of Objects=0
Frame Number=476 Number of Objects=0
Frame Number=477 Number of Objects=0
Frame Number=478 Number of Objects=0
Frame Number=479 Number of Objects=0
Frame Number=480 Number of Objects=0
Frame Number=481 Number of Objects=0
Frame Number=482 Number of Objects=0
Frame Number=483 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=484 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=485 Number of Objects=1
Type & Probability = 97% largevehicle
Frame Number=486 Number of Objects=1
Type & Probability = 77% suv
Frame Number=487 Number of Objects=1
Type & Probability = 77% suv
Frame Number=488 Number of Objects=1
Type & Probability = 77% suv
Frame Number=489 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=490 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=491 Number of Objects=1
Type & Probability = 93% largevehicle
Frame Number=492 Number of Objects=1
Type & Probability = 62% largevehicle
Frame Number=493 Number of Objects=1
Type & Probability = 62% largevehicle
Frame Number=494 Number of Objects=1
Type & Probability = 62% largevehicle
Frame Number=495 Number of Objects=1
Type & Probability = 100% suv
Frame Number=496 Number of Objects=1
Type & Probability = 100% suv
Frame Number=497 Number of Objects=1
Type & Probability = 100% suv
Frame Number=498 Number of Objects=1
Type & Probability = 100% suv
Frame Number=499 Number of Objects=1
Type & Probability = 100% suv
Frame Number=500 Number of Objects=0
Frame Number=501 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=502 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=503 Number of Objects=1
Type & Probability = 97% sedan
Frame Number=504 Number of Objects=0
Frame Number=505 Number of Objects=0
Frame Number=506 Number of Objects=0
Frame Number=507 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=508 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=509 Number of Objects=0
Frame Number=510 Number of Objects=1
Type & Probability = 100% suv
Frame Number=511 Number of Objects=1
Type & Probability = 100% suv
Frame Number=512 Number of Objects=1
Type & Probability = 100% suv
Frame Number=513 Number of Objects=1
Type & Probability = 98% suv
Frame Number=514 Number of Objects=1
Type & Probability = 98% suv
Frame Number=515 Number of Objects=1
Type & Probability = 98% suv
Frame Number=516 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=517 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=518 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=519 Number of Objects=2
Type & Probability = 99% largevehicle
Type & Probability = 88% truck
Frame Number=520 Number of Objects=2
Type & Probability = 99% largevehicle
Type & Probability = 88% truck
Frame Number=521 Number of Objects=2
Type & Probability = 99% largevehicle
Type & Probability = 88% truck
Frame Number=522 Number of Objects=1
Type & Probability = 79% truck
Frame Number=523 Number of Objects=1
Type & Probability = 79% truck
Frame Number=524 Number of Objects=1
Type & Probability = 79% truck
Frame Number=525 Number of Objects=1
Type & Probability = 48% sedan
Frame Number=526 Number of Objects=1
Type & Probability = 48% sedan
Frame Number=527 Number of Objects=1
Type & Probability = 48% sedan
Frame Number=528 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=529 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=530 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=531 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=532 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=533 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=534 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=535 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=536 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=537 Number of Objects=2
Type & Probability = 53% suv
Type & Probability = 60% coupe
Frame Number=538 Number of Objects=2
Type & Probability = 53% suv
Type & Probability = 60% coupe
Frame Number=539 Number of Objects=2
Type & Probability = 53% suv
Type & Probability = 61% coupe
Frame Number=540 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 99% sedan
Frame Number=541 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 99% sedan
Frame Number=542 Number of Objects=2
Type & Probability = 100% largevehicle
Type & Probability = 99% sedan
Frame Number=543 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 66% largevehicle
Frame Number=544 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 63% largevehicle
Frame Number=545 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 66% largevehicle
Frame Number=546 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 85% largevehicle
Frame Number=547 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 88% sedan
Frame Number=548 Number of Objects=2
Type & Probability = 99% sedan
Type & Probability = 85% largevehicle
Frame Number=549 Number of Objects=3
Type & Probability = 91% suv
Type & Probability = 70% largevehicle
Frame Number=550 Number of Objects=3
Type & Probability = 91% suv
Type & Probability = 70% largevehicle
Frame Number=551 Number of Objects=3
Type & Probability = 91% suv
Type & Probability = 70% largevehicle
Frame Number=552 Number of Objects=2
Type & Probability = 56% sedan
Type & Probability = 93% largevehicle
Frame Number=553 Number of Objects=2
Type & Probability = 55% sedan
Type & Probability = 93% largevehicle
Frame Number=554 Number of Objects=2
Type & Probability = 55% sedan
Type & Probability = 93% largevehicle
Frame Number=555 Number of Objects=1
Type & Probability = 72% largevehicle
Frame Number=556 Number of Objects=1
Type & Probability = 72% largevehicle
Frame Number=557 Number of Objects=1
Type & Probability = 72% largevehicle
Frame Number=558 Number of Objects=1
Type & Probability = 58% suv
Frame Number=559 Number of Objects=1
Type & Probability = 58% suv
Frame Number=560 Number of Objects=1
Type & Probability = 58% suv
Frame Number=561 Number of Objects=1
Type & Probability = 61% largevehicle
Frame Number=562 Number of Objects=1
Type & Probability = 61% largevehicle
Frame Number=563 Number of Objects=1
Type & Probability = 61% largevehicle
Frame Number=564 Number of Objects=1
Type & Probability = 98% suv
Frame Number=565 Number of Objects=1
Type & Probability = 98% suv
Frame Number=566 Number of Objects=1
Type & Probability = 98% suv
Frame Number=567 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=568 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=569 Number of Objects=1
Type & Probability = 99% largevehicle
Frame Number=570 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=571 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=572 Number of Objects=1
Type & Probability = 96% largevehicle
Frame Number=573 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=574 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=575 Number of Objects=1
Type & Probability = 77% largevehicle
Frame Number=576 Number of Objects=1
Type & Probability = 99% suv
Frame Number=577 Number of Objects=1
Type & Probability = 99% suv
Frame Number=578 Number of Objects=1
Type & Probability = 99% suv
Frame Number=579 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=580 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=581 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=582 Number of Objects=1
Type & Probability = 81% suv
Frame Number=583 Number of Objects=1
Type & Probability = 81% suv
Frame Number=584 Number of Objects=1
Type & Probability = 81% suv
Frame Number=585 Number of Objects=1
Type & Probability = 83% suv
Frame Number=586 Number of Objects=1
Type & Probability = 83% suv
Frame Number=587 Number of Objects=1
Type & Probability = 84% suv
Frame Number=588 Number of Objects=1
Type & Probability = 93% sedan
Frame Number=589 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=590 Number of Objects=1
Type & Probability = 92% sedan
Frame Number=591 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=592 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=593 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=594 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=595 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=596 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=597 Number of Objects=1
Type & Probability = 95% coupe
Frame Number=598 Number of Objects=1
Type & Probability = 95% coupe
Frame Number=599 Number of Objects=1
Type & Probability = 97% coupe
Frame Number=600 Number of Objects=1
Frame Number=601 Number of Objects=1
Frame Number=602 Number of Objects=0
Frame Number=603 Number of Objects=0
Frame Number=604 Number of Objects=0
Frame Number=605 Number of Objects=0
Frame Number=606 Number of Objects=0
Frame Number=607 Number of Objects=0
Frame Number=608 Number of Objects=0
Frame Number=609 Number of Objects=0
Frame Number=610 Number of Objects=0
Frame Number=611 Number of Objects=0
Frame Number=612 Number of Objects=0
Frame Number=613 Number of Objects=0
Frame Number=614 Number of Objects=0
Frame Number=615 Number of Objects=0
Frame Number=616 Number of Objects=0
Frame Number=617 Number of Objects=0
Frame Number=618 Number of Objects=0
Frame Number=619 Number of Objects=0
Frame Number=620 Number of Objects=0
Frame Number=621 Number of Objects=0
Frame Number=622 Number of Objects=0
Frame Number=623 Number of Objects=0
Frame Number=624 Number of Objects=0
Frame Number=625 Number of Objects=0
Frame Number=626 Number of Objects=0
Frame Number=627 Number of Objects=0
Frame Number=628 Number of Objects=0
Frame Number=629 Number of Objects=0
Frame Number=630 Number of Objects=0
Frame Number=631 Number of Objects=0
Frame Number=632 Number of Objects=0
Frame Number=633 Number of Objects=0
Frame Number=634 Number of Objects=0
Frame Number=635 Number of Objects=0
Frame Number=636 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=637 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=638 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=639 Number of Objects=0
Frame Number=640 Number of Objects=0
Frame Number=641 Number of Objects=0
Frame Number=642 Number of Objects=0
Frame Number=643 Number of Objects=0
Frame Number=644 Number of Objects=0
Frame Number=645 Number of Objects=1
Type & Probability = 98% suv
Frame Number=646 Number of Objects=1
Type & Probability = 98% suv
Frame Number=647 Number of Objects=1
Type & Probability = 98% suv
Frame Number=648 Number of Objects=1
Type & Probability = 90% sedan
Frame Number=649 Number of Objects=1
Type & Probability = 70% sedan
Frame Number=650 Number of Objects=1
Type & Probability = 73% largevehicle
Frame Number=651 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=652 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=653 Number of Objects=1
Type & Probability = 100% sedan
Frame Number=654 Number of Objects=0
Frame Number=655 Number of Objects=0
Frame Number=656 Number of Objects=0
Frame Number=657 Number of Objects=0
Frame Number=658 Number of Objects=0
Frame Number=659 Number of Objects=0
Frame Number=660 Number of Objects=0
Frame Number=661 Number of Objects=0
Frame Number=662 Number of Objects=0
Frame Number=663 Number of Objects=0
Frame Number=664 Number of Objects=0
Frame Number=665 Number of Objects=0
Frame Number=666 Number of Objects=0
Frame Number=667 Number of Objects=0
Frame Number=668 Number of Objects=0
Frame Number=669 Number of Objects=0
Frame Number=670 Number of Objects=0
Frame Number=671 Number of Objects=0
Frame Number=672 Number of Objects=0
Frame Number=673 Number of Objects=0
Frame Number=674 Number of Objects=0
Frame Number=675 Number of Objects=0
Frame Number=676 Number of Objects=0
Frame Number=677 Number of Objects=0
Frame Number=678 Number of Objects=0
Frame Number=679 Number of Objects=0
Frame Number=680 Number of Objects=0
Frame Number=681 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=682 Number of Objects=1
Type & Probability = 55% largevehicle
Frame Number=683 Number of Objects=1
Type & Probability = 51% largevehicle
Frame Number=684 Number of Objects=0
Frame Number=685 Number of Objects=0
Frame Number=686 Number of Objects=0
Frame Number=687 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=688 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=689 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=690 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=691 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=692 Number of Objects=1
Type & Probability = 89% sedan
Frame Number=693 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=694 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=695 Number of Objects=1
Type & Probability = 94% sedan
Frame Number=696 Number of Objects=1
Type & Probability = 38% largevehicle
Frame Number=697 Number of Objects=1
Type & Probability = 38% largevehicle
Frame Number=698 Number of Objects=1
Type & Probability = 38% largevehicle
Frame Number=699 Number of Objects=0
Frame Number=700 Number of Objects=0
Frame Number=701 Number of Objects=0
Frame Number=702 Number of Objects=0
Frame Number=703 Number of Objects=0
Frame Number=704 Number of Objects=0
Frame Number=705 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=706 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=707 Number of Objects=1
Type & Probability = 100% largevehicle
Frame Number=708 Number of Objects=1
Type & Probability = 78% sedan
Frame Number=709 Number of Objects=1
Type & Probability = 78% sedan
Frame Number=710 Number of Objects=1
Type & Probability = 78% sedan
Frame Number=711 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=712 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=713 Number of Objects=1
Type & Probability = 98% sedan
Frame Number=714 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=715 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=716 Number of Objects=1
Type & Probability = 99% sedan
Frame Number=717 Number of Objects=0
Frame Number=718 Number of Objects=0
Frame Number=719 Number of Objects=0
Frame Number=720 Number of Objects=0
Frame Number=721 Number of Objects=0
Frame Number=722 Number of Objects=0
Frame Number=723 Number of Objects=0
Frame Number=724 Number of Objects=0
Frame Number=725 Number of Objects=0
Frame Number=726 Number of Objects=0
Frame Number=727 Number of Objects=0
nvstreammux: Successfully handled EOS for source_id=0
Frame Number=728 Number of Objects=0
Frame Number=729 Number of Objects=0
Frame Number=730 Number of Objects=0
Frame Number=731 Number of Objects=0
End-of-stream
# DO NOT CHANGE THIS CELL
!head /dli/task/logs/trace.log
0:00:00.172938764 339 0x3a10690 LOG GST_SCHEDULING gstpad.c:1440:gst_pad_add_probe:<onscreendisplay:sink> adding probe for mask 0x00000010
0:00:00.172971657 339 0x3a10690 LOG GST_SCHEDULING gstpad.c:1466:gst_pad_add_probe:<onscreendisplay:sink> got probe id 1
0:00:00.406916273 339 0x3a10690 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<secondary-inference> NvDsInferContext[UID 2]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2002> [UID = 2]: Trying to create engine from model files
0:00:12.497221643 339 0x3a10690 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<secondary-inference> NvDsInferContext[UID 2]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2034> [UID = 2]: serialize cuda engine to file: /dli/task/ngc_assets/vehicletypenet_vpruned_v1.0.2/resnet18_vehicletypenet_pruned.etlt_b1_gpu0_fp32.engine successfully
0:00:12.558627894 339 0x3a10690 INFO nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<secondary-inference> [UID 2]: Load new model:/dli/task/spec_files/sgie_config_vehicletypenet_04.txt sucessfully
0:00:12.558691981 339 0x3a10690 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2002> [UID = 1]: Trying to create engine from model files
0:00:34.863342139 339 0x3a10690 INFO nvinfer gstnvinfer.cpp:682:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:2034> [UID = 1]: serialize cuda engine to file: /dli/task/ngc_assets/trafficcamnet_vpruned_v1.0/resnet18_trafficcamnet_pruned.etlt_b1_gpu0_fp32.engine successfully
0:00:34.932542529 339 0x3a10690 INFO nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-inference> [UID 1]: Load new model:/dli/task/spec_files/pgie_config_trafficcamnet_03.txt sucessfully
0:00:34.934497080 339 0x3a291e0 LOG GST_SCHEDULING gstpad.c:3725:do_probe_callbacks:<onscreendisplay:sink> do probes
0:00:34.934529293 339 0x3a291e0 LOG GST_SCHEDULING gstpad.c:3612:probe_hook_marshal:<onscreendisplay:sink> hook 1 with flags 0x00003010 does not match 00001042
# Import dependencies
import re
import pandas as pd
trace_log=[]
# Column headers per documentation
headers=['time_stamp', 'process_id', 'thread_id', 'level', 'category', 'src_file_line', 'function', 'object_name', 'message']
# Helper function to remove ANSI escape sequences
def escape_ansi(line):
ansi_escape = re.compile(r'(?:\x1B[@-_]|[\x80-\x9F])[0-?]*[ -/]*[@-~]')
return ansi_escape.sub('', line)
# Open trace.log
with open('/dli/task/logs/trace.log') as f:
# Read file
lines=f.readlines()
# Iterate through each line
for each_line in lines:
# Exclude the last character, which is a newline (\n) character
current_line=escape_ansi(each_line[:-1])
# Split based on white space(s), keeping in mind that src_file, line, function, and object are concatenated together
time_stamp, process_id, thread_id, level, category, src_file_line_function_object, message=re.split(' +', current_line, maxsplit=6)
# Split src_file, line, function, and object based on the semicolon character
src_file, line, function, object=src_file_line_function_object.split(':', maxsplit=3)
# Add all data to the trace_log list
trace_log.append([time_stamp, process_id, thread_id, level, category, f'{src_file}:{line}', function, object, message])
# Export data to a DataFrame
df=pd.DataFrame(trace_log, columns=headers)
# Preview the dataframe
df.head()
| time_stamp | process_id | thread_id | level | category | src_file_line | function | object_name | message | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0:00:00.172938764 | 339 | 0x3a10690 | LOG | GST_SCHEDULING | gstpad.c:1440 | gst_pad_add_probe | <onscreendisplay:sink> | adding probe for mask 0x00000010 |
| 1 | 0:00:00.172971657 | 339 | 0x3a10690 | LOG | GST_SCHEDULING | gstpad.c:1466 | gst_pad_add_probe | <onscreendisplay:sink> | got probe id 1 |
| 2 | 0:00:00.406916273 | 339 | 0x3a10690 | INFO | nvinfer | gstnvinfer.cpp:682 | gst_nvinfer_logger | <secondary-inference> | NvDsInferContext[UID 2]: Info from NvDsInferCo... |
| 3 | 0:00:12.497221643 | 339 | 0x3a10690 | INFO | nvinfer | gstnvinfer.cpp:682 | gst_nvinfer_logger | <secondary-inference> | NvDsInferContext[UID 2]: Info from NvDsInferCo... |
| 4 | 0:00:12.558627894 | 339 | 0x3a10690 | INFO | nvinfer | gstnvinfer_impl.cpp:328 | notifyLoadModelStatus | <secondary-inference> | [UID 2]: Load new model:/dli/task/spec_files/s... |
Each line captures the time stamp of the message as well as the time stamp of the frame in the message column. For demonstration, we’ve included the below script to parse out the time stamp of the frames being processed from the message column. By examining the time stamp of the messages related to the same frame, we can see when each frame enters different elements in the pipeline. We can therefore derive the approximate latency by analyzing the time differences. For example, we can calculate the time difference between getting a message from fakesink and nvinfer to determine the amount of time it took for nvinfer to process the frame.
# Iterate through rows backwards to get the time stamp
for idx, row in df[::-1].iterrows():
# Time stamp is pts if object is a sink
if row['object_name'] in ['<stream-muxer:sink_0>', '<primary-inference:sink>', '<fakesink:sink>']:
try:
df.loc[idx, 'frame_ts']=re.findall('pts \d+:\d+:\d+.\d+', row['message'])[0].split('pts ')[-1]
except:
pass
# Time stamp is dts if object is a decoder sink
elif row['object_name']=='<nvv4l2-decoder:sink>':
try:
ts=re.findall('dts \d+:\d+:\d+.\d+', row['message'])[0].split('dts ')[-1]
if ts:
df.loc[idx, 'frame_ts']=ts
decoder_offset=re.findall('offset \d+', row['message'])[0].split('offset ')[-1]
except:
pass
# Time stamp is same as dts of decoder with same offset for file source
elif row['object_name']=='<file-source:src>':
try:
src_offset=re.findall('offset \d+', row['message'])[0].split('offset ')[-1]
if src_offset==decoder_offset:
df.loc[idx, 'frame_ts']=ts
except:
pass
time_df=df[['time_stamp', 'object_name', 'frame_ts']].dropna().drop_duplicates(subset=['object_name', 'frame_ts'])
# Pivot dataframe
time_df=time_df.pivot(index='object_name', values='time_stamp', columns='frame_ts')
# Leave time stamp as only seconds
time_df.columns=[float(each_column.split(':')[2]) for each_column in time_df.columns]
# Clean up
time_df=time_df.dropna(axis=1)
# Display time_df
time_df=time_df.sort_values(0.0).applymap(lambda x: float(x.rsplit(':')[2]))
print('Time Stamp when Buffer Arrives (seconds)')
display(time_df)
Time Stamp when Buffer Arrives (seconds)
| 0.000000 | 0.433333 | 1.100000 | 1.766667 | 2.733333 | 3.633333 | 4.766667 | 5.400000 | 7.666667 | 8.333333 | ... | 24.066666 | 24.100000 | 24.133333 | 24.166666 | 24.200000 | 24.233333 | 24.266666 | 24.300000 | 24.333333 | 24.366666 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| object_name | |||||||||||||||||||||
| <file-source:src> | 34.934574 | 35.663063 | 36.302625 | 37.001356 | 37.935973 | 38.835896 | 39.969197 | 40.603594 | 42.869313 | 43.535860 | ... | 59.269717 | 59.302680 | 59.336046 | 59.369420 | 59.402713 | 59.435944 | 59.469314 | 59.502681 | 59.535807 | 59.569175 |
| <nvv4l2-decoder:sink> | 34.936760 | 35.663137 | 36.302768 | 37.001467 | 37.936045 | 38.836007 | 39.969273 | 40.603679 | 42.869385 | 43.536024 | ... | 59.269765 | 59.302720 | 59.336087 | 59.369460 | 59.402782 | 59.435986 | 59.469356 | 59.502720 | 59.535848 | 59.569206 |
| <stream-muxer:sink_0> | 35.048593 | 35.902513 | 36.601253 | 37.267906 | 38.202834 | 39.134591 | 40.235822 | 40.967915 | 43.135842 | 43.901223 | ... | 59.535682 | 59.569027 | 59.602390 | 59.635679 | 59.669107 | 59.702322 | 59.735592 | 59.769025 | 59.802324 | 59.835729 |
| <primary-inference:sink> | 35.619003 | 35.935681 | 36.602584 | 37.269316 | 38.235794 | 39.137380 | 40.269077 | 40.968153 | 43.169133 | 43.901988 | ... | 59.569023 | 59.602311 | 59.635604 | 59.669091 | 59.702336 | 59.735513 | 59.768942 | 59.802317 | 59.835571 | 59.869005 |
4 rows × 51 columns
# Calculate time difference as processed time
diff_df=-time_df.diff(-1).T
# Plot results
diff_df.iloc[:, :-1].plot(figsize=(15, 5)).legend(loc='upper right')
<matplotlib.legend.Legend at 0x7f78945625e0>
Viewing the Inference#
In the next step, we convert the video file into a container file before playing it since the MPEG4 encoded video file can’t be played directly in JupyterLab. The FFmpeg tool is a very fast video and audio converter with the general syntax:
ffmpeg [global_options] {[input_file_options] -i input_url} ... {[output_file_options] output_url} ...
# Convert MPEG4 video file to MP4 container file
!ffmpeg -i /dli/task/output_04_raw.mpeg4 /dli/task/output_04.mp4 -y -loglevel quiet
from IPython.display import Video
Video("output_04.mp4", width=720)