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Merge pull request #204 from KelvinCPChiu/master
Added the Required Nvinfer Plugin and DeepStream Config File
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‎DeepStream/Readme.md‎

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# This should be run in JetPack 4.4 / JetPack 4.4 G.A. with DeepStream 5.0 / DeepStream 5.0 GA .
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1. Compile the custom plugin for Yolo
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2. Convert the ONNX file to TRT with TRTEXEC / TensorRT
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3. Change the model-engine-file in config_infer_primary_yoloV4.txt
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4. In the deepstream_app_config_yoloV4.txt, change
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a) source0 : uri=file:<your file> directory.
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b) primary-gie : model-engine-file=<your_onnx_engine>
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# Note that for multi-batch, overhead is large owing to NMS is not used.

‎DeepStream/config_infer_primary_yoloV4.txt‎

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################################################################################
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# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a
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# copy of this software and associated documentation files (the "Software"),
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# to deal in the Software without restriction, including without limitation
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# the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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# DEALINGS IN THE SOFTWARE.
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################################################################################
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# Following properties are mandatory when engine files are not specified:
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# int8-calib-file(Only in INT8), model-file-format
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# Caffemodel mandatory properties: model-file, proto-file, output-blob-names
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# UFF: uff-file, input-dims, uff-input-blob-name, output-blob-names
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# ONNX: onnx-file
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#
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# Mandatory properties for detectors:
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# num-detected-classes
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#
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# Optional properties for detectors:
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# cluster-mode(Default=Group Rectangles), interval(Primary mode only, Default=0)
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# custom-lib-path
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# parse-bbox-func-name
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#
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# Mandatory properties for classifiers:
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# classifier-threshold, is-classifier
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#
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# Optional properties for classifiers:
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# classifier-async-mode(Secondary mode only, Default=false)
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#
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# Optional properties in secondary mode:
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# operate-on-gie-id(Default=0), operate-on-class-ids(Defaults to all classes),
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# input-object-min-width, input-object-min-height, input-object-max-width,
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# input-object-max-height
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#
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# Following properties are always recommended:
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# batch-size(Default=1)
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#
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# Other optional properties:
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# net-scale-factor(Default=1), network-mode(Default=0 i.e FP32),
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# model-color-format(Default=0 i.e. RGB) model-engine-file, labelfile-path,
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# mean-file, gie-unique-id(Default=0), offsets, process-mode (Default=1 i.e. primary),
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# custom-lib-path, network-mode(Default=0 i.e FP32)
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#
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# The values in the config file are overridden by values set through GObject
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# properties.
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[property]
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gpu-id=0
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net-scale-factor=0.0039215697906911373
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#0=RGB, 1=BGR
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model-color-format=0
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model-engine-file=<onnx_engine_file>
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labelfile-path=labels.txt
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## 0=FP32, 1=INT8, 2=FP16 mode
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network-mode=2
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num-detected-classes=80
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gie-unique-id=1
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network-type=0
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is-classifier=0
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## 0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)
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cluster-mode=2
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maintain-aspect-ratio=1
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parse-bbox-func-name=NvDsInferParseCustomYoloV4
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custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
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engine-create-func-name=NvDsInferYoloCudaEngineGet
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#scaling-filter=0
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#scaling-compute-hw=0
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#output-blob-names=2012
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[class-attrs-all]
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nms-iou-threshold=0.2
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pre-cluster-threshold=0.4

‎DeepStream/deepstream_app_config_yoloV4.txt‎

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################################################################################
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# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a
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# copy of this software and associated documentation files (the "Software"),
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# to deal in the Software without restriction, including without limitation
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# the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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# DEALINGS IN THE SOFTWARE.
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################################################################################
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[application]
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enable-perf-measurement=1
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perf-measurement-interval-sec=5
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#gie-kitti-output-dir=streamscl
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[tiled-display]
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enable=0
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rows=1
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columns=1
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width=1280
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height=720
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gpu-id=0
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#(0): nvbuf-mem-default - Default memory allocated, specific to particular platform
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#(1): nvbuf-mem-cuda-pinned - Allocate Pinned/Host cuda memory, applicable for Tesla
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#(2): nvbuf-mem-cuda-device - Allocate Device cuda memory, applicable for Tesla
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#(3): nvbuf-mem-cuda-unified - Allocate Unified cuda memory, applicable for Tesla
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#(4): nvbuf-mem-surface-array - Allocate Surface Array memory, applicable for Jetson
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nvbuf-memory-type=0
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[source0]
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enable=1
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#Type - 1=CameraV4L2 2=URI 3=MultiURI
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type=3
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uri=file:<Your_file_source>
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num-sources=1
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gpu-id=0
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# (0): memtype_device - Memory type Device
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# (1): memtype_pinned - Memory type Host Pinned
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# (2): memtype_unified - Memory type Unified
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cudadec-memtype=0
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[sink0]
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enable=1
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#Type - 1=FakeSink 2=EglSink 3=File
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type=2
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sync=1
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source-id=0
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gpu-id=0
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[osd]
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enable=1
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gpu-id=0
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border-width=1
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text-size=12
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text-color=1;1;1;1;
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text-bg-color=0.3;0.3;0.3;1
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font=Serif
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show-clock=0
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clock-x-offset=800
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clock-y-offset=820
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clock-text-size=12
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clock-color=1;0;0;0
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nvbuf-memory-type=0
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[streammux]
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gpu-id=0
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##Boolean property to inform muxer that sources are live
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live-source=0
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batch-size=1
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##time out in usec, to wait after the first buffer is available
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##to push the batch even if the complete batch is not formed
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batched-push-timeout=40000
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## Set muxer output width and height
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width=1280
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height=720
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##Enable to maintain aspect ratio wrt source, and allow black borders, works
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##along with width, height properties
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enable-padding=0
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nvbuf-memory-type=0
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# config-file property is mandatory for any gie section.
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# Other properties are optional and if set will override the properties set in
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# the infer config file.
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[primary-gie]
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enable=1
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gpu-id=0
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model-engine-file=<onnx_engine_file>
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labelfile-path=labels.txt
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#batch-size=1
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#Required by the app for OSD, not a plugin property
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bbox-border-color0=1;0;0;1
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bbox-border-color1=0;1;1;1
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bbox-border-color2=0;0;1;1
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bbox-border-color3=0;1;0;1
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interval=0
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gie-unique-id=1
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nvbuf-memory-type=0
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config-file=config_infer_primary_yoloV4.txt
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[sink1]
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enable=1
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type=3
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#1=mp4 2=mkv
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container=1
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#1=h264 2=h265 3=mpeg4
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codec=1
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#encoder type 0=Hardware 1=Software
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enc-type=0
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sync=0
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bitrate=4000000
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#H264 Profile - 0=Baseline 2=Main 4=High
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#H265 Profile - 0=Main 1=Main10
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profile=0
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output-file=fp16_clip1_cam1.mp4
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source-id=0
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[tracker]
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enable=1
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# For the case of NvDCF tracker, tracker-width and tracker-height must be a multiple of 32, respectively
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tracker-width=608
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tracker-height=608
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#ll-lib-file=/opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_mot_iou.so
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#ll-lib-file=/opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_nvdcf.so
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ll-lib-file=/opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_mot_klt.so
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#ll-config-file required for IOU only
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#ll-config-file=iou_config.txt
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gpu-id=0
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[tests]
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file-loop=0

‎DeepStream/labels.txt‎

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person
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bicycle
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car
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motorcycle
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airplane
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bus
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train
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truck
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boat
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traffic light
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fire hydrant
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stop sign
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parking meter
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bench
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bird
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cat
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dog
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horse
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sheep
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cow
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elephant
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bear
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zebra
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giraffe
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backpack
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umbrella
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handbag
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tie
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suitcase
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frisbee
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skis
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snowboard
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sports ball
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kite
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baseball bat
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baseball glove
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skateboard
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surfboard
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tennis racket
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bottle
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wine glass
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cup
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fork
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knife
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spoon
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bowl
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banana
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apple
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sandwich
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orange
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broccoli
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carrot
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hot dog
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pizza
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donut
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cake
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chair
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couch
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potted plant
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bed
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dining table
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toilet
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tv
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laptop
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mouse
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remote
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keyboard
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cell phone
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microwave
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oven
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toaster
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sink
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refrigerator
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book
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clock
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vase
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scissors
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teddy bear
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hair drier
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toothbrush

‎DeepStream/nvdsinfer_custom_impl_Yolo/Makefile‎

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################################################################################
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a
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# copy of this software and associated documentation files (the "Software"),
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# to deal in the Software without restriction, including without limitation
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# the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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# DEALINGS IN THE SOFTWARE.
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################################################################################
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CUDA_VER?=
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ifeq ($(CUDA_VER),)
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$(error "CUDA_VER is not set")
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endif
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CC:= g++
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NVCC:=/usr/local/cuda-$(CUDA_VER)/bin/nvcc
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CFLAGS:= -Wall -std=c++11 -shared -fPIC -Wno-error=deprecated-declarations
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CFLAGS+= -I../../includes -I/usr/local/cuda-$(CUDA_VER)/include
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LIBS:= -lnvinfer_plugin -lnvinfer -lnvparsers -L/usr/local/cuda-$(CUDA_VER)/lib64 -lcudart -lcublas -lstdc++fs
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LFLAGS:= -shared -Wl,--start-group $(LIBS) -Wl,--end-group
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INCS:= $(wildcard *.h)
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SRCFILES:= nvdsinfer_yolo_engine.cpp \
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nvdsparsebbox_Yolo.cpp \
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yoloPlugins.cpp \
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trt_utils.cpp \
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yolo.cpp \
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kernels.cu
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TARGET_LIB:= libnvdsinfer_custom_impl_Yolo.so
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TARGET_OBJS:= $(SRCFILES:.cpp=.o)
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TARGET_OBJS:= $(TARGET_OBJS:.cu=.o)
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all: $(TARGET_LIB)
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%.o: %.cpp $(INCS) Makefile
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$(CC) -c -o $@ $(CFLAGS) $<
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%.o: %.cu $(INCS) Makefile
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$(NVCC) -c -o $@ --compiler-options '-fPIC' $<
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$(TARGET_LIB) : $(TARGET_OBJS)
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$(CC) -o $@ $(TARGET_OBJS) $(LFLAGS)
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clean:
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rm -rf $(TARGET_LIB)

‎DeepStream/nvdsinfer_custom_impl_Yolo/Readme.md‎

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export CUDA_VER=X.Y
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make

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