蚕茧视频识别AI程序关键代码（不包含资源、模型、转换库）
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BBIT-Kai
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%YAML:1.0
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BaseConfig:
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minDetectorConfidence: 0.6 # If the confidence of a detector bbox is lower than this, then it won't be considered for tracking
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TargetManagement:
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enableBboxUnClipping: 1 # In case the bbox is likely to be clipped by image border, unclip bbox
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maxTargetsPerStream: 150 # Max number of targets to track per stream. Recommended to set >10. Note: this value should account for the targets being tracked in shadow mode as well. Max value depends on the GPU memory capacity
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# [Creation & Termination Policy]
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minIouDiff4NewTarget: 0.55 # If the IOU between the newly detected object and any of the existing targets is higher than this threshold, this newly detected object will be discarded.
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minTrackerConfidence: 0.2 # If the confidence of an object tracker is lower than this on the fly, then it will be tracked in shadow mode. Valid Range: [0.0, 1.0]
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probationAge: 5 # If the target's age exceeds this, the target will be considered to be valid.
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maxShadowTrackingAge: 30 # Max length of shadow tracking. If the shadowTrackingAge exceeds this limit, the tracker will be terminated.
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earlyTerminationAge: 1 # If the shadowTrackingAge reaches this threshold while in TENTATIVE period, the target will be terminated prematurely.
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TrajectoryManagement:
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useUniqueID: 0 # Use 64-bit long Unique ID when assignining tracker ID. Default is [true]
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DataAssociator:
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dataAssociatorType: 0 # the type of data associator among { DEFAULT= 0 }
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associationMatcherType: 0 # the type of matching algorithm among { GREEDY=0, GLOBAL=1 }
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checkClassMatch: 1 # If checked, only the same-class objects are associated with each other. Default: true
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# [Association Metric: Thresholds for valid candidates]
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minMatchingScore4Overall: 0.0 # Min total score
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minMatchingScore4SizeSimilarity: 0.6 # Min bbox size similarity score
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minMatchingScore4Iou: 0.0 # Min IOU score
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minMatchingScore4VisualSimilarity: 0.7 # Min visual similarity score
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# [Association Metric: Weights]
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matchingScoreWeight4VisualSimilarity: 0.6 # Weight for the visual similarity (in terms of correlation response ratio)
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matchingScoreWeight4SizeSimilarity: 0.0 # Weight for the Size-similarity score
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matchingScoreWeight4Iou: 0.4 # Weight for the IOU score
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StateEstimator:
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stateEstimatorType: 1 # the type of state estimator among { DUMMY=0, SIMPLE=1, REGULAR=2 }
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# [Dynamics Modeling]
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processNoiseVar4Loc: 2.0 # Process noise variance for bbox center
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processNoiseVar4Size: 1.0 # Process noise variance for bbox size
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processNoiseVar4Vel: 0.1 # Process noise variance for velocity
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measurementNoiseVar4Detector: 4.0 # Measurement noise variance for detector's detection
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measurementNoiseVar4Tracker: 16.0 # Measurement noise variance for tracker's localization
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VisualTracker:
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visualTrackerType: 1 # the type of visual tracker among { DUMMY=0, NvDCF=1 }
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# [NvDCF: Feature Extraction]
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useColorNames: 1 # Use ColorNames feature
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useHog: 0 # Use Histogram-of-Oriented-Gradient (HOG) feature
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featureImgSizeLevel: 2 # Size of a feature image. Valid range: {1, 2, 3, 4, 5}, from the smallest to the largest
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featureFocusOffsetFactor_y: -0.2 # The offset for the center of hanning window relative to the feature height. The center of hanning window would move by (featureFocusOffsetFactor_y*featureMatSize.height) in vertical direction
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# [NvDCF: Correlation Filter]
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filterLr: 0.075 # learning rate for DCF filter in exponential moving average. Valid Range: [0.0, 1.0]
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filterChannelWeightsLr: 0.1 # learning rate for the channel weights among feature channels. Valid Range: [0.0, 1.0]
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gaussianSigma: 0.75 # Standard deviation for Gaussian for desired response when creating DCF filter [pixels]
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################################################################################
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# SPDX-FileCopyrightText: Copyright (c) 2019-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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)
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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=0.00392156862745098
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onnx-file=../../models/best.pt.onnx
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model-engine-file=../../models/best.pt.onnx_b1_gpu0_fp16.engine
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labelfile-path=../../models/labels.txt
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batch-size=1
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process-mode=1
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model-color-format=0
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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=5
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interval=0
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gie-unique-id=1
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## 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)
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cluster-mode=2
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custom-lib-path=../lib/libnvdsinfer_custom_impl_Yolo.so
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parse-bbox-func-name=NvDsInferParseYolo
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engine-create-func-name=NvDsInferYoloCudaEngineGet
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network-type=0
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maintain-aspect-ratio=1
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symmetric-padding=1
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[class-attrs-all]
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topk=20
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nms-iou-threshold=0.45
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pre-cluster-threshold=0.2
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