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.gitattributes

@@ -32,3 +32,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+video/4.mp4 filter=lfs diff=lfs merge=lfs -text
+video/5.mp4 filter=lfs diff=lfs merge=lfs -text
+video/9.mp4 filter=lfs diff=lfs merge=lfs -text
+video/bicyclecity.mp4 filter=lfs diff=lfs merge=lfs -text
+video/car.mp4 filter=lfs diff=lfs merge=lfs -text

.github/ISSUE_TEMPLATE/bug.yml

@@ -0,0 +1,65 @@
+name: Bug
+# title: " "
+description: Report a Yolov5_StrongSORT_OSNet bug
+labels: [bug]
+body:
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/issues) and [discussions](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/discussions) to see if a similar question already exists.
+      options:
+        - label: >
+            I have searched the Yolov5_StrongSORT_OSNet [issues](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/issues) and [discussions](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/discussions) and found no similar questions.
+          required: true
+
+  - type: dropdown
+    attributes:
+      label: Yolov5_StrongSORT_OSNet Component
+      description: |
+        Please select the part of Yolov5_StrongSORT_OSNet where you found the bug.
+      multiple: true
+      options:
+        - "Tracking"
+        - "Evaluation"
+        - "Integrations"
+        - "Other"
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Bug
+      description: Provide console output with error messages and/or screenshots of the bug.
+      placeholder: |
+        💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Environment
+      description: Please specify the software and hardware you used to produce the bug.
+      placeholder: |
+        - YOLO: YOLOv5 🚀 v6.0-67-g60e42e1 torch 1.9.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)
+        - StrongSORT: osnet_x0_25
+        - OS: Ubuntu 20.04
+        - Python: 3.9.0
+        - ...
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Minimal Reproducible Example
+      description: >
+        When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem.
+        This is referred to by community members as creating a [minimal reproducible example](https://stackoverflow.com/help/minimal-reproducible-example).
+      placeholder: |
+        ```
+        # Code to reproduce your issue here
+        ```
+    validations:
+      required: false
+

.github/ISSUE_TEMPLATE/enhancement.yml

@@ -0,0 +1,32 @@
+name: Enhancement
+description: Suggest a Yolov5_StrongSORT_OSNet enhancement
+# title: " "
+labels: [enhancement]
+body:
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/issues) to see if a similar enhancement request already exists.
+      options:
+        - label: >
+            I have searched the YOLOv5 [issues](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/issues) and found no similar enhancement requests.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Description
+      description: A short description of your enhancement.
+      placeholder: |
+        What new enhancement would you like to see in Yolov5_StrongSORT_OSNet?
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Use case
+      description: |
+        Describe the use case of your feature request. It will help us understand and prioritize the feature request.
+      placeholder: |
+        How would this feature be used, and who would use it?

.github/ISSUE_TEMPLATE/question.yml

@@ -0,0 +1,24 @@
+name: Question
+description: Ask a Yolov5_StrongSORT_OSNet question
+# title: " "
+labels: [question]
+body:
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/issues) to see if a similar question already exists.
+      options:
+        - label: >
+            I have searched the Yolov5_StrongSORT_OSNet [issues](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/issues) and found no similar bug report.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Question
+      description: What is your question?
+      placeholder: |
+        💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
+    validations:
+      required: true

.github/workflows/ci-testing.yml

@@ -0,0 +1,71 @@
+name: CI CPU testing
+
+on:  # https://help.github.com/en/actions/reference/events-that-trigger-workflows
+  push:
+    branches: [master, CIdebug]
+  pull_request:
+    # The branches below must be a subset of the branches above
+    branches: [master, CIdebug]
+
+jobs:
+  cpu-tests:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, windows-latest]  # Error: Container action is only supported on Linux
+        python-version: [3.9]
+        model: ['yolov5s']  # models to test
+
+    # Timeout: https://stackoverflow.com/a/59076067/4521646
+    timeout-minutes: 50
+    steps:
+      - name: Set up Repository
+        uses: actions/checkout@v2
+        with:
+          submodules: recursive
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      # Note: This uses an internal pip API and may not always work
+      # https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
+      - name: Get pip cache
+        id: pip-cache
+        run: |
+          python -c "from pip._internal.locations import USER_CACHE_DIR; print('::set-output name=dir::' + USER_CACHE_DIR)"
+      - name: Cache pip
+        uses: actions/cache@v1
+        with:
+          path: ${{ steps.pip-cache.outputs.dir }}
+          key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-${{ matrix.python-version }}-pip-  
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip setuptools wheel
+          # ImportError: lap requires numpy, please "pip install numpy".
+          # ImportError: lap requires Cython, please "pip install Cython".
+          pip install numpy Cython
+          pip install -qr requirements.txt -f https://download.pytorch.org/whl/cpu/torch_stable.html
+          pip install requests
+          python --version
+          pip --version
+          pip list
+          cd
+        shell: bash
+      - name: Tests workflow
+        run: |
+          # STRONGSORT
+          python track.py --tracking-method strongsort --yolo-weights ./weights/yolov5n.pt --source yolov5/data/images/bus.jpg
+          # OCSORT
+          python track.py --tracking-method ocsort --yolo-weights ./weights/yolov5n.pt --source yolov5/data/images/bus.jpg
+          # BYTETRACK
+          python track.py --tracking-method bytetrack --yolo-weights ./weights/yolov5n.pt --source yolov5/data/images/bus.jpg
+          # EXPORT
+          python reid_export.py --weights ./weights/osnet_x0_25_msmt17.pt  # export deafults to torchscript
+          # STRONGSORT w. EXPORTED REID MODEL
+          python track.py --reid-weights ./weights/osnet_x0_25_msmt17.torchscript --source yolov5/data/images/bus.jpg
+        shell: bash

.github/workflows/stale.yml

@@ -0,0 +1,20 @@
+name: Close stale issues
+on:
+  schedule:
+    - cron: "0 0 * * *"  # At the end of every day
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v3
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          stale-issue-message: |
+            👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
+            Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!
+          stale-pr-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs.'
+          days-before-stale: 30
+          days-before-close: 5
+          exempt-issue-labels: 'documentation,tutorial'
+          operations-per-run: 100  # The maximum number of operations per run, used to control rate limiting.

.gitignore

@@ -0,0 +1,11 @@
+.vscode/
+
+# interpreter bytecode
+__pycache__/
+
+# exports
+*_openvino_model
+*.torchscript
+*.pt
+*.onnx
+*.engine

.gitmodules

@@ -0,0 +1,6 @@
+[submodule "yolov5"]
+	path = yolov5
+	url = https://github.com/ultralytics/yolov5.git
+[submodule "trackers/strong_sort/deep/reid"]
+	path = trackers/strong_sort/deep/reid
+	url = https://github.com/KaiyangZhou/deep-person-reid

.idea/.gitignore

@@ -0,0 +1,3 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,26 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="6">
+            <item index="0" class="java.lang.String" itemvalue="filterpy" />
+            <item index="1" class="java.lang.String" itemvalue="motmetrics" />
+            <item index="2" class="java.lang.String" itemvalue="lap" />
+            <item index="3" class="java.lang.String" itemvalue="yolov5" />
+            <item index="4" class="java.lang.String" itemvalue="torchreid" />
+            <item index="5" class="java.lang.String" itemvalue="strongsort" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N806" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (yolov5deepsort)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/yolov5_tracking-8.0.iml" filepath="$PROJECT_DIR$/.idea/yolov5_tracking-8.0.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/val_utils" vcs="Git" />
+  </component>
+</project>

.idea/workspace.xml

@@ -0,0 +1,181 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ChangeListManager">
+    <list default="true" id="22cd1d58-ba55-443f-bf91-c1fd073300cb" name="变更" comment="" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="FileTemplateManagerImpl">
+    <option name="RECENT_TEMPLATES">
+      <list>
+        <option value="Python Script" />
+      </list>
+    </option>
+  </component>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$/val_utils" />
+  </component>
+  <component name="GitSEFilterConfiguration">
+    <file-type-list>
+      <filtered-out-file-type name="LOCAL_BRANCH" />
+      <filtered-out-file-type name="REMOTE_BRANCH" />
+      <filtered-out-file-type name="TAG" />
+      <filtered-out-file-type name="COMMIT_BY_MESSAGE" />
+    </file-type-list>
+  </component>
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+  <component name="ProjectId" id="2Q0TDjaIWsZllNAxRhm630GOlwY" />
+  <component name="ProjectLevelVcsManager" settingsEditedManually="true" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent">
+    <property name="RunOnceActivity.OpenProjectViewOnStart" value="true" />
+    <property name="RunOnceActivity.ShowReadmeOnStart" value="true" />
+    <property name="last_opened_file_path" value="$PROJECT_DIR$/weights" />
+    <property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
+  </component>
+  <component name="RecentsManager">
+    <key name="CopyFile.RECENT_KEYS">
+      <recent name="E:\github\tracker\yolov5_tracking-8.0\weights" />
+      <recent name="E:\github\tracker\yolov5_tracking-8.0\yolov5" />
+      <recent name="E:\github\tracker\yolov5_tracking-8.0\video" />
+      <recent name="E:\github\tracker\yolov5_tracking-8.0" />
+    </key>
+    <key name="MoveFile.RECENT_KEYS">
+      <recent name="E:\github\tracker\yolov5_tracking-8.0" />
+      <recent name="E:\github\tracker\yolov5_tracking-8.0\wieghts" />
+    </key>
+  </component>
+  <component name="RunManager" selected="Python.demo">
+    <configuration name="demo" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+      <module name="yolov5_tracking-8.0" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/demo.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <configuration name="demo2" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+      <module name="yolov5_tracking-8.0" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/demo2.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <configuration name="demo" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+      <module name="yolov5_tracking-8.0" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <option name="SCRIPT_NAME" value="E:\github\tracker\yolov5_tracking-8.0\demo.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <configuration name="test" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+      <module name="yolov5_tracking-8.0" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <configuration name="test1" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+      <module name="yolov5_tracking-8.0" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test1.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <recent_temporary>
+      <list>
+        <item itemvalue="Python.demo2" />
+        <item itemvalue="Python.demo" />
+        <item itemvalue="Python.test1" />
+        <item itemvalue="Python.test" />
+      </list>
+    </recent_temporary>
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="应用程序级" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="默认任务">
+      <changelist id="22cd1d58-ba55-443f-bf91-c1fd073300cb" name="变更" comment="" />
+      <created>1684489383896</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1684489383896</updated>
+    </task>
+    <servers />
+  </component>
+</project>

.idea/yolov5_tracking-8.0.iml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (yolov5deepsort)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>

LICENSE

@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
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+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

demo.py

@@ -0,0 +1,98 @@
+import gradio as gr
+import tempfile
+import os
+import track
+import shutil
+from pathlib import Path
+from yolov5 import detect
+from PIL import Image
+
+# 目标检测
+def Detect(image):
+    # 创建临时文件夹
+    temp_path = tempfile.TemporaryDirectory(dir="./")
+    temp_dir = temp_path.name
+    # 临时图片的路径
+    temp_image_path = os.path.join(temp_dir, f"temp.jpg")
+    # 存储临时图片
+    img = Image.fromarray(image)
+    img.save(temp_image_path)
+    # 结果图片的存储目录
+    temp_result_path = os.path.join(temp_dir, "tempresult")
+    # 对临时图片进行检测
+    detect.run(source=temp_image_path, data="test_image/FLIR.yaml", weights="weights/best.pt", project=f'./{temp_dir}',name = 'tempresult', hide_conf=False,  conf_thres=0.35)
+    # 结果图片的路径
+    temp_result_path = os.path.join(temp_result_path, os.listdir(temp_result_path)[0])
+    # 读取结果图片
+    result_image = Image.open(temp_result_path).copy()
+    # 删除临时文件夹
+    temp_path.cleanup()
+    return result_image
+
+# 候选图片
+example_image= [
+    "./test_image/video-2SReBn5LtAkL5HMj2-frame-005072-MA7NCLQGoqq9aHaiL.jpg",
+    "./test_image/video-2rsjnZFyGQGeynfbv-frame-003708-6fPQbB7jtibwaYAE7.jpg",
+    "./test_image/video-2SReBn5LtAkL5HMj2-frame-000317-HTgPBFgZyPdwQnNvE.jpg",
+    "./test_image/video-jNQtRj6NGycZDEXpe-frame-002515-J3YntG8ntvZheKK3P.jpg",
+    "./test_image/video-kDDWXrnLSoSdHCZ7S-frame-003063-eaKjPvPskDPjenZ8S.jpg",
+    "./test_image/video-r68Yr9RPWEp5fW2ZF-frame-000333-X6K5iopqbmjKEsSqN.jpg"
+    ]
+
+# 目标追踪
+def Track(video, tracking_method):
+    # 存储临时视频的文件夹
+    temp_dir = "./temp"
+    # 先清空temp文件夹
+    shutil.rmtree("./temp")
+    os.mkdir("./temp")
+    # 获取视频的名字
+    video_name = os.path.basename(video)
+    # 对视频进行检测
+    track.run(source=video, yolo_weights=Path("weights/best2.pt"),reid_weights=Path("weights/osnet_x0_25_msmt17.pt") , project=Path(f'./{temp_dir}'),name = 'tempresult', tracking_method=tracking_method)
+    # 结果视频的路径
+    temp_result_path = os.path.join(f'./{temp_dir}', "tempresult", video_name)
+    # 返回结果视频的路径
+    return temp_result_path
+
+# 候选视频
+example_video= [
+    ["./video/5.mp4", None],
+    ["./video/bicyclecity.mp4", None],
+    ["./video/9.mp4", None],
+    ["./video/8.mp4", None],
+    ["./video/4.mp4", None],
+    ["./video/car.mp4", None],
+    ]
+
+iface_Image = gr.Interface(fn=Detect,
+                           inputs=gr.Image(label="上传一张红外图像,仅支持jpg格式"),
+                           outputs=gr.Image(label="检测结果"),
+                           examples=example_image)
+
+iface_video = gr.Interface(fn=Track,
+                           inputs=[gr.Video(label="上传段红外视频,仅支持mp4格式"), gr.Radio(["bytetrack", "strongsort"], label="track methond", info="选择追踪器", value="bytetrack")],
+                           outputs=gr.Video(label="追踪结果"),
+                           examples=example_video)
+
+demo = gr.TabbedInterface([iface_video, iface_Image], tab_names=["目标追踪", "目标检测"], title="红外目标检测追踪")
+
+demo.launch(share=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

flagged/log.csv

@@ -0,0 +1,2 @@
+text,output,flag,username,timestamp
+sfsda 东风东,SFSDA 东风东,,,2023-05-21 21:03:22.616101

reid_export.py

@@ -0,0 +1,314 @@
+import argparse
+
+import os
+# limit the number of cpus used by high performance libraries
+os.environ["OMP_NUM_THREADS"] = "1"
+os.environ["OPENBLAS_NUM_THREADS"] = "1"
+os.environ["MKL_NUM_THREADS"] = "1"
+os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
+os.environ["NUMEXPR_NUM_THREADS"] = "1"
+
+import sys
+import numpy as np
+from pathlib import Path
+import torch
+import time
+import platform
+import pandas as pd
+import subprocess
+import torch.backends.cudnn as cudnn
+from torch.utils.mobile_optimizer import optimize_for_mobile
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # yolov5 strongsort root directory
+WEIGHTS = ROOT / 'weights'
+
+print(ROOT)
+
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+if str(ROOT / 'yolov5') not in sys.path:
+    sys.path.append(str(ROOT / 'yolov5'))  # add yolov5 ROOT to PATH
+
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+import logging
+from yolov5.utils.torch_utils import select_device
+from yolov5.models.common import DetectMultiBackend
+from yolov5.utils.general import LOGGER, colorstr, check_requirements, check_version
+from trackers.strong_sort.deep.models import build_model
+from trackers.strong_sort.deep.reid_model_factory import get_model_name, load_pretrained_weights
+
+
+def file_size(path):
+    # Return file/dir size (MB)
+    path = Path(path)
+    if path.is_file():
+        return path.stat().st_size / 1E6
+    elif path.is_dir():
+        return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / 1E6
+    else:
+        return 0.0
+
+
+def export_formats():
+    # YOLOv5 export formats
+    x = [
+        ['PyTorch', '-', '.pt', True, True],
+        ['TorchScript', 'torchscript', '.torchscript', True, True],
+        ['ONNX', 'onnx', '.onnx', True, True],
+        ['OpenVINO', 'openvino', '_openvino_model', True, False],
+        ['TensorRT', 'engine', '.engine', False, True],
+        ['TensorFlow Lite', 'tflite', '.tflite', True, False],
+    ]
+    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
+
+
+def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
+    # YOLOv5 TorchScript model export
+    try:
+        LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
+        f = file.with_suffix('.torchscript')
+
+        ts = torch.jit.trace(model, im, strict=False)
+        if optimize:  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
+            optimize_for_mobile(ts)._save_for_lite_interpreter(str(f))
+        else:
+            ts.save(str(f))
+
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        return f
+    except Exception as e:
+        LOGGER.info(f'{prefix} export failure: {e}')
+
+
+def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
+    # ONNX export
+    try:
+        check_requirements(('onnx',))
+        import onnx
+
+        f = file.with_suffix('.onnx')
+        LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
+        
+        if dynamic:
+            dynamic = {'images': {0: 'batch'}}  # shape(1,3,640,640)
+            dynamic['output'] = {0: 'batch'}  # shape(1,25200,85)
+
+        torch.onnx.export(
+            model.cpu() if dynamic else model,  # --dynamic only compatible with cpu
+            im.cpu() if dynamic else im,
+            f,
+            verbose=False,
+            opset_version=opset,
+            do_constant_folding=True,
+            input_names=['images'],
+            output_names=['output'],
+            dynamic_axes=dynamic or None
+        )
+        # Checks
+        model_onnx = onnx.load(f)  # load onnx model
+        onnx.checker.check_model(model_onnx)  # check onnx model
+        onnx.save(model_onnx, f)
+
+        # Simplify
+        if simplify:
+            try:
+                cuda = torch.cuda.is_available()
+                check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
+                import onnxsim
+
+                LOGGER.info(f'simplifying with onnx-simplifier {onnxsim.__version__}...')
+                model_onnx, check = onnxsim.simplify(model_onnx)
+                assert check, 'assert check failed'
+                onnx.save(model_onnx, f)
+            except Exception as e:
+                LOGGER.info(f'simplifier failure: {e}')
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        return f
+    except Exception as e:
+        LOGGER.info(f'export failure: {e}')
+    
+        
+        
+def export_openvino(file, half, prefix=colorstr('OpenVINO:')):
+    # YOLOv5 OpenVINO export
+    check_requirements(('openvino-dev',))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+    import openvino.inference_engine as ie
+    try:
+        LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
+        f = str(file).replace('.pt', f'_openvino_model{os.sep}')
+
+        cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
+        subprocess.check_output(cmd.split())  # export
+    except Exception as e:
+        LOGGER.info(f'export failure: {e}')
+    LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+    return f
+        
+
+def export_tflite(file, half, prefix=colorstr('TFLite:')):
+    # YOLOv5 OpenVINO export
+    try:
+        check_requirements(('openvino2tensorflow', 'tensorflow', 'tensorflow_datasets'))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+        import openvino.inference_engine as ie
+        LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
+        output = Path(str(file).replace(f'_openvino_model{os.sep}', f'_tflite_model{os.sep}'))
+        modelxml = list(Path(file).glob('*.xml'))[0]
+        cmd = f"openvino2tensorflow \
+            --model_path {modelxml} \
+            --model_output_path {output} \
+            --output_pb \
+            --output_saved_model \
+            --output_no_quant_float32_tflite \
+            --output_dynamic_range_quant_tflite"
+        subprocess.check_output(cmd.split())  # export
+
+        LOGGER.info(f'{prefix} export success, results saved in {output} ({file_size(f):.1f} MB)')
+        return f
+    except Exception as e:
+        LOGGER.info(f'\n{prefix} export failure: {e}')
+        
+        
+def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
+    # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
+    try:
+        assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
+        try:
+            import tensorrt as trt
+        except Exception:
+            if platform.system() == 'Linux':
+                check_requirements(('nvidia-tensorrt',), cmds=('-U --index-url https://pypi.ngc.nvidia.com',))
+            import tensorrt as trt
+
+        if trt.__version__[0] == '7':  # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
+            grid = model.model[-1].anchor_grid
+            model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
+            export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
+            model.model[-1].anchor_grid = grid
+        else:  # TensorRT >= 8
+            check_version(trt.__version__, '8.0.0', hard=True)  # require tensorrt>=8.0.0
+            export_onnx(model, im, file, 12, dynamic, simplify)  # opset 13
+        onnx = file.with_suffix('.onnx')
+
+        LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
+        assert onnx.exists(), f'failed to export ONNX file: {onnx}'
+        f = file.with_suffix('.engine')  # TensorRT engine file
+        logger = trt.Logger(trt.Logger.INFO)
+        if verbose:
+            logger.min_severity = trt.Logger.Severity.VERBOSE
+
+        builder = trt.Builder(logger)
+        config = builder.create_builder_config()
+        config.max_workspace_size = workspace * 1 << 30
+        # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice
+
+        flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+        network = builder.create_network(flag)
+        parser = trt.OnnxParser(network, logger)
+        if not parser.parse_from_file(str(onnx)):
+            raise RuntimeError(f'failed to load ONNX file: {onnx}')
+
+        inputs = [network.get_input(i) for i in range(network.num_inputs)]
+        outputs = [network.get_output(i) for i in range(network.num_outputs)]
+        LOGGER.info(f'{prefix} Network Description:')
+        for inp in inputs:
+            LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
+        for out in outputs:
+            LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')
+
+        if dynamic:
+            if im.shape[0] <= 1:
+                LOGGER.warning(f"{prefix}WARNING: --dynamic model requires maximum --batch-size argument")
+            profile = builder.create_optimization_profile()
+            for inp in inputs:
+                profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
+            config.add_optimization_profile(profile)
+
+        LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine in {f}')
+        if builder.platform_has_fast_fp16 and half:
+            config.set_flag(trt.BuilderFlag.FP16)
+        with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
+            t.write(engine.serialize())
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        return f
+    except Exception as e:
+        LOGGER.info(f'\n{prefix} export failure: {e}')
+        
+        
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description="ReID export")
+    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
+    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[256, 128], help='image (h, w)')
+    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
+    parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
+    parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
+    parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
+    parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
+    parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
+    parser.add_argument('--weights', nargs='+', type=str, default=WEIGHTS / 'osnet_x0_25_msmt17.pt', help='model.pt path(s)')
+    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
+    parser.add_argument('--include',
+                        nargs='+',
+                        default=['torchscript'],
+                        help='torchscript, onnx, openvino, engine')
+    args = parser.parse_args()
+
+    t = time.time()
+
+    include = [x.lower() for x in args.include]  # to lowercase
+    fmts = tuple(export_formats()['Argument'][1:])  # --include arguments
+    flags = [x in include for x in fmts]
+    assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
+    jit, onnx, openvino, engine, tflite = flags  # export booleans
+
+    args.device = select_device(args.device)
+    if args.half:
+        assert args.device.type != 'cpu', '--half only compatible with GPU export, i.e. use --device 0'
+        assert not args.dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'
+    
+    if type(args.weights) is list:
+        args.weights = Path(args.weights[0])
+
+    model = build_model(
+        get_model_name(args.weights),
+        num_classes=1,
+        pretrained=not (args.weights and args.weights.is_file() and args.weights.suffix == '.pt'),
+        use_gpu=args.device
+    ).to(args.device)
+    load_pretrained_weights(model, args.weights)
+    model.eval()
+
+    if args.optimize:
+        assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
+    
+    im = torch.zeros(args.batch_size, 3, args.imgsz[0], args.imgsz[1]).to(args.device)  # image size(1,3,640,480) BCHW iDetection
+    for _ in range(2):
+        y = model(im)  # dry runs
+    if args.half:
+        im, model = im.half(), model.half()  # to FP16
+    shape = tuple((y[0] if isinstance(y, tuple) else y).shape)  # model output shape
+    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {args.weights} with output shape {shape} ({file_size(args.weights):.1f} MB)")
+    
+    # Exports
+    f = [''] * len(fmts)  # exported filenames
+    if jit:
+        f[0] = export_torchscript(model, im, args.weights, args.optimize)  # opset 12
+    if engine:  # TensorRT required before ONNX
+        f[1] = export_engine(model, im, args.weights, args.half, args.dynamic, args.simplify, args.workspace, args.verbose)
+    if onnx:  # OpenVINO requires ONNX
+        f[2] = export_onnx(model, im, args.weights, args.opset, args.dynamic, args.simplify)  # opset 12
+    if openvino:
+        f[3] = export_openvino(args.weights, args.half)
+    if tflite:
+        export_tflite(f, False)
+
+    # Finish
+    f = [str(x) for x in f if x]  # filter out '' and None
+    if any(f):
+        LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
+                    f"\nResults saved to {colorstr('bold', args.weights.parent.resolve())}"
+                    f"\nVisualize:       https://netron.app")
+

test.py

@@ -0,0 +1,17 @@
+import track
+import os
+from pathlib import Path
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # yolov5 strongsort root directory
+WEIGHTS = ROOT / 'weights'
+trackers =  "strongsort" # ["strongsort", "bytetrack"]
+path = "./video"
+v = [x for x in os.listdir(path) if x.endswith(".mp4")]
+
+for i in v:
+    # , classes=[0, 1, 2, 3, 5, 7, 9, 11, 10]
+    track.run(source=ROOT / f"video/{i}", tracking_method=trackers, yolo_weights=WEIGHTS / "best2.pt", project=ROOT / f"runs/{trackers}3", name=f"video{i.split('.')[0]}", iou_thres=0.45, conf_thres=0.35, hide_conf=True)
+    print(f"{i}检测完毕")
+
+

test_image/FLIR.yaml

@@ -0,0 +1,29 @@
+train: VOCdevkit\images\train # 8493 images 训练集
+val: VOCdevkit\images\val # 2249 images 验证集
+
+# number of classes 类别数
+nc: 15
+
+
+# Classes
+names:
+  0: person
+  1: bike
+  2: car
+  3: motor
+  4: bus
+  5: truck  #6-5
+  6: light  #7-6
+  7: hydrant  #8-7
+  8: sign  # 9-8
+
+
+#  5: train
+#  10: dog
+#  11: skateboard
+#  12: stroller
+#  13: scooter
+#  14: other vehicle
+
+
+

track.py

@@ -0,0 +1,308 @@
+import argparse
+
+import os
+# limit the number of cpus used by high performance libraries
+os.environ["OMP_NUM_THREADS"] = "1"
+os.environ["OPENBLAS_NUM_THREADS"] = "1"
+os.environ["MKL_NUM_THREADS"] = "1"
+os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
+os.environ["NUMEXPR_NUM_THREADS"] = "1"
+
+import sys
+import numpy as np
+from pathlib import Path
+import torch
+import torch.backends.cudnn as cudnn
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # yolov5 strongsort root directory
+WEIGHTS = ROOT / 'weights'
+
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+if str(ROOT / 'yolov5') not in sys.path:
+    sys.path.append(str(ROOT / 'yolov5'))  # add yolov5 ROOT to PATH
+if str(ROOT / 'trackers' / 'strong_sort') not in sys.path:
+    sys.path.append(str(ROOT / 'trackers' / 'strong_sort'))  # add strong_sort ROOT to PATH
+
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+import logging
+from yolov5.models.common import DetectMultiBackend
+from yolov5.utils.dataloaders import VID_FORMATS, LoadImages, LoadStreams
+from yolov5.utils.general import (LOGGER, check_img_size, non_max_suppression, scale_boxes, check_requirements, cv2,
+                                  check_imshow, xyxy2xywh, increment_path, strip_optimizer, colorstr, print_args, check_file)
+from yolov5.utils.torch_utils import select_device, time_sync
+from yolov5.utils.plots import Annotator, colors, save_one_box
+from trackers.multi_tracker_zoo import create_tracker
+
+# remove duplicated stream handler to avoid duplicated logging
+#logging.getLogger().removeHandler(logging.getLogger().handlers[0])
+
+@torch.no_grad()
+def run(
+        source='0',
+        yolo_weights=WEIGHTS / 'yolov5m.pt',  # model.pt path(s),
+        reid_weights=WEIGHTS / 'osnet_x0_25_msmt17.pt',  # model.pt path,
+        tracking_method='strongsort',
+        imgsz=(640, 640),  # inference size (height, width)
+        conf_thres=0.25,  # confidence threshold
+        iou_thres=0.45,  # NMS IOU threshold
+        max_det=1000,  # maximum detections per image
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        show_vid=False,  # show results
+        save_txt=False,  # save results to *.txt
+        save_conf=False,  # save confidences in --save-txt labels
+        save_crop=False,  # save cropped prediction boxes
+        save_vid=True,  # save confidences in --save-txt labels
+        nosave=False,  # do not save images/videos
+        classes=None,  # filter by class: --class 0, or --class 0 2 3
+        agnostic_nms=False,  # class-agnostic NMS
+        augment=False,  # augmented inference
+        visualize=False,  # visualize features
+        update=False,  # update all models
+        project=ROOT / 'runs/track',  # save results to project/name
+        name='exp',  # save results to project/name
+        exist_ok=False,  # existing project/name ok, do not increment
+        line_thickness=1,  # bounding box thickness (pixels)
+        hide_labels=False,  # hide labels
+        hide_conf=False,  # hide confidences
+        hide_class=False,  # hide IDs
+        half=False,  # use FP16 half-precision inference
+        dnn=False,  # use OpenCV DNN for ONNX inference
+        vid_stride=1,  # video frame-rate stride
+):
+    save_txt = True
+    source = str(source)
+    save_img = not nosave and not source.endswith('.txt')  # save inference images
+    is_file = Path(source).suffix[1:] in (VID_FORMATS)
+    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
+    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    if not isinstance(yolo_weights, list):  # single yolo model
+        exp_name = yolo_weights.stem
+    elif type(yolo_weights) is list and len(yolo_weights) == 1:  # single models after --yolo_weights
+        exp_name = Path(yolo_weights[0]).stem
+    else:  # multiple models after --yolo_weights
+        exp_name = 'ensemble'
+    exp_name = name if name else exp_name + "_" + reid_weights.stem
+    save_dir = increment_path(Path(project) / exp_name, exist_ok=exist_ok)  # increment run
+    (save_dir / 'tracks' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(yolo_weights, device=device, dnn=dnn, data=None, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    if webcam:
+        show_vid = check_imshow()
+        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
+        nr_sources = len(dataset)
+    else:
+        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
+        nr_sources = 1
+    vid_path, vid_writer, txt_path = [None] * nr_sources, [None] * nr_sources, [None] * nr_sources
+
+    # Create as many strong sort instances as there are video sources
+    tracker_list = []
+    for i in range(nr_sources):
+        tracker = create_tracker(tracking_method, reid_weights, device, half)
+        tracker_list.append(tracker, )
+        if hasattr(tracker_list[i], 'model'):
+            if hasattr(tracker_list[i].model, 'warmup'):
+                tracker_list[i].model.warmup()
+    outputs = [None] * nr_sources
+
+    # Run tracking
+    #model.warmup(imgsz=(1 if pt else nr_sources, 3, *imgsz))  # warmup
+    dt, seen = [0.0, 0.0, 0.0, 0.0], 0
+    curr_frames, prev_frames = [None] * nr_sources, [None] * nr_sources
+    for frame_idx, (path, im, im0s, vid_cap, s) in enumerate(dataset):
+        t1 = time_sync()
+        im = torch.from_numpy(im).to(device)
+        im = im.half() if half else im.float()  # uint8 to fp16/32
+        im /= 255.0  # 0 - 255 to 0.0 - 1.0
+        if len(im.shape) == 3:
+            im = im[None]  # expand for batch dim
+        t2 = time_sync()
+        dt[0] += t2 - t1
+
+        # Inference
+        visualize = increment_path(save_dir / Path(path[0]).stem, mkdir=True) if visualize else False
+        pred = model(im, augment=augment, visualize=visualize)
+        t3 = time_sync()
+        dt[1] += t3 - t2
+
+        # Apply NMS
+        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+        dt[2] += time_sync() - t3
+
+        # Process detections
+        for i, det in enumerate(pred):  # detections per image
+            seen += 1
+            if webcam:  # nr_sources >= 1
+                p, im0, _ = path[i], im0s[i].copy(), dataset.count
+                p = Path(p)  # to Path
+                s += f'{i}: '
+                txt_file_name = p.name
+                save_path = str(save_dir / p.name)  # im.jpg, vid.mp4, ...
+            else:
+                p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)
+                p = Path(p)  # to Path
+                # video file
+                if source.endswith(VID_FORMATS):
+                    txt_file_name = p.stem
+                    save_path = str(save_dir / p.name)  # im.jpg, vid.mp4, ...
+                # folder with imgs
+                else:
+                    txt_file_name = p.parent.name  # get folder name containing current img
+                    save_path = str(save_dir / p.parent.name)  # im.jpg, vid.mp4, ...
+            curr_frames[i] = im0
+
+            txt_path = str(save_dir / 'tracks' / txt_file_name)  # im.txt
+            s += '%gx%g ' % im.shape[2:]  # print string
+            imc = im0.copy() if save_crop else im0  # for save_crop
+
+            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
+            
+            if hasattr(tracker_list[i], 'tracker') and hasattr(tracker_list[i].tracker, 'camera_update'):
+                if prev_frames[i] is not None and curr_frames[i] is not None:  # camera motion compensation
+                    tracker_list[i].tracker.camera_update(prev_frames[i], curr_frames[i])
+
+            if det is not None and len(det):
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()  # xyxy
+
+                # Print results
+                for c in det[:, -1].unique():
+                    n = (det[:, -1] == c).sum()  # detections per class
+                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
+
+                # pass detections to strongsort
+                t4 = time_sync()
+                outputs[i] = tracker_list[i].update(det.cpu(), im0)
+                t5 = time_sync()
+                dt[3] += t5 - t4
+
+                # draw boxes for visualization
+                if len(outputs[i]) > 0:
+                    for j, (output, conf) in enumerate(zip(outputs[i], det[:, 4])):
+    
+                        bboxes = output[0:4]
+                        id = output[4]
+                        cls = output[5]
+
+                        if save_txt:
+                            # to MOT format
+                            bbox_left = output[0]
+                            bbox_top = output[1]
+                            bbox_w = output[2] - output[0]
+                            bbox_h = output[3] - output[1]
+                            # Write MOT compliant results to file
+                            with open(txt_path + '.txt', 'a') as f:
+                                f.write(('%g ' * 10 + '\n') % (frame_idx + 1, id, bbox_left,  # MOT format
+                                                               bbox_top, bbox_w, bbox_h, -1, -1, -1, i))
+                        save_vid=True
+
+                        if save_vid or save_crop or show_vid:  # Add bbox to image
+                            c = int(cls)  # integer class
+                            id = int(id)  # integer id
+                            label = None if hide_labels else (f'{id} {names[c]}' if hide_conf else \
+                                (f'{id} {conf:.2f}' if hide_class else f'{id} {names[c]} {conf:.2f}'))
+                            annotator.box_label(bboxes, label, color=colors(c, True))
+                            if save_crop:
+                                txt_file_name = txt_file_name if (isinstance(path, list) and len(path) > 1) else ''
+                                save_one_box(bboxes, imc, file=save_dir / 'crops' / txt_file_name / names[c] / f'{id}' / f'{p.stem}.jpg', BGR=True)
+
+                LOGGER.info(f'{s}Done. yolo:({t3 - t2:.3f}s), {tracking_method}:({t5 - t4:.3f}s)')
+
+            else:
+                #strongsort_list[i].increment_ages()
+                LOGGER.info('No detections')
+
+            # Stream results
+            im0 = annotator.result()
+            if show_vid:
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_vid:
+                if vid_path[i] != save_path:  # new video
+                    vid_path[i] = save_path
+                    if isinstance(vid_writer[i], cv2.VideoWriter):
+                        vid_writer[i].release()  # release previous video writer
+                    if vid_cap:  # video
+                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                    else:  # stream
+                        fps, w, h = 30, im0.shape[1], im0.shape[0]
+                    save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
+                    vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                vid_writer[i].write(im0)
+
+            prev_frames[i] = curr_frames[i]
+
+    # Print results
+    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
+    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS, %.1fms {tracking_method} update per image at shape {(1, 3, *imgsz)}' % t)
+    if save_txt or save_vid:
+        s = f"\n{len(list(save_dir.glob('tracks/*.txt')))} tracks saved to {save_dir / 'tracks'}" if save_txt else ''
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(yolo_weights)  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--yolo-weights', nargs='+', type=Path, default=WEIGHTS / 'best2.pt', help='model.pt path(s)')
+    parser.add_argument('--reid-weights', type=Path, default=WEIGHTS / 'osnet_x0_25_msmt17.pt')
+    parser.add_argument('--tracking-method', type=str, default='bytetrack', help='strongsort, ocsort, bytetrack')
+    parser.add_argument('--source', type=str, default=r'video', help='file/dir/URL/glob, 0 for webcam')
+    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
+    parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.5, help='NMS IoU threshold') #0.5
+    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--show-vid', action='store_true', help='display tracking video results')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
+    parser.add_argument('--save-vid', action='store_true', help='save video tracking results')
+    parser.add_argument('--nosave', action='store_false', help='do not save images/videos')
+    # class 0 is person, 1 is bycicle, 2 is car... 79 is oven  0 1 2 3  5 7 9 11 10
+    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
+    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
+    parser.add_argument('--augment', action='store_true', help='augmented inference')
+    parser.add_argument('--visualize', action='store_true', help='visualize features')
+    parser.add_argument('--update', action='store_true', help='update all models')
+    parser.add_argument('--project', default=ROOT / 'runs/track', help='save results to project/name')
+    parser.add_argument('--name', default='exp', help='save results to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--line-thickness', default=1, type=int, help='bounding box thickness (pixels)')
+    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
+    parser.add_argument('--hide-conf', default=True, action='store_true', help='hide confidences')
+    parser.add_argument('--hide-class', default=False, action='store_true', help='hide IDs')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
+    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

trackers/bytetrack/basetrack.py

@@ -0,0 +1,52 @@
+import numpy as np
+from collections import OrderedDict
+
+
+class TrackState(object):
+    New = 0
+    Tracked = 1
+    Lost = 2
+    Removed = 3
+
+
+class BaseTrack(object):
+    _count = 0
+
+    track_id = 0
+    is_activated = False
+    state = TrackState.New
+
+    history = OrderedDict()
+    features = []
+    curr_feature = None
+    score = 0
+    start_frame = 0
+    frame_id = 0
+    time_since_update = 0
+
+    # multi-camera
+    location = (np.inf, np.inf)
+
+    @property
+    def end_frame(self):
+        return self.frame_id
+
+    @staticmethod
+    def next_id():
+        BaseTrack._count += 1
+        return BaseTrack._count
+
+    def activate(self, *args):
+        raise NotImplementedError
+
+    def predict(self):
+        raise NotImplementedError
+
+    def update(self, *args, **kwargs):
+        raise NotImplementedError
+
+    def mark_lost(self):
+        self.state = TrackState.Lost
+
+    def mark_removed(self):
+        self.state = TrackState.Removed

trackers/bytetrack/byte_tracker.py

@@ -0,0 +1,353 @@
+import numpy as np
+from collections import deque
+import os
+import os.path as osp
+import copy
+import torch
+import torch.nn.functional as F
+
+from yolov5.utils.general import xywh2xyxy, xyxy2xywh
+
+
+from trackers.bytetrack.kalman_filter import KalmanFilter
+from trackers.bytetrack import matching
+from trackers.bytetrack.basetrack import BaseTrack, TrackState
+
+class STrack(BaseTrack):
+    shared_kalman = KalmanFilter()
+    def __init__(self, tlwh, score, cls):
+
+        # wait activate
+        self._tlwh = np.asarray(tlwh, dtype=np.float)
+        self.kalman_filter = None
+        self.mean, self.covariance = None, None
+        self.is_activated = False
+
+        self.score = score
+        self.tracklet_len = 0
+        self.cls = cls
+
+    def predict(self):
+        mean_state = self.mean.copy()
+        if self.state != TrackState.Tracked:
+            mean_state[7] = 0
+        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
+
+    @staticmethod
+    def multi_predict(stracks):
+        if len(stracks) > 0:
+            multi_mean = np.asarray([st.mean.copy() for st in stracks])
+            multi_covariance = np.asarray([st.covariance for st in stracks])
+            for i, st in enumerate(stracks):
+                if st.state != TrackState.Tracked:
+                    multi_mean[i][7] = 0
+            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
+            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
+                stracks[i].mean = mean
+                stracks[i].covariance = cov
+
+    def activate(self, kalman_filter, frame_id):
+        """Start a new tracklet"""
+        self.kalman_filter = kalman_filter
+        self.track_id = self.next_id()
+        self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
+
+        self.tracklet_len = 0
+        self.state = TrackState.Tracked
+        if frame_id == 1:
+            self.is_activated = True
+        # self.is_activated = True
+        self.frame_id = frame_id
+        self.start_frame = frame_id
+
+    def re_activate(self, new_track, frame_id, new_id=False):
+        self.mean, self.covariance = self.kalman_filter.update(
+            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
+        )
+        self.tracklet_len = 0
+        self.state = TrackState.Tracked
+        self.is_activated = True
+        self.frame_id = frame_id
+        if new_id:
+            self.track_id = self.next_id()
+        self.score = new_track.score
+        self.cls = new_track.cls
+
+    def update(self, new_track, frame_id):
+        """
+        Update a matched track
+        :type new_track: STrack
+        :type frame_id: int
+        :type update_feature: bool
+        :return:
+        """
+        self.frame_id = frame_id
+        self.tracklet_len += 1
+        # self.cls = cls
+
+        new_tlwh = new_track.tlwh
+        self.mean, self.covariance = self.kalman_filter.update(
+            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
+        self.state = TrackState.Tracked
+        self.is_activated = True
+
+        self.score = new_track.score
+
+    @property
+    # @jit(nopython=True)
+    def tlwh(self):
+        """Get current position in bounding box format `(top left x, top left y,
+                width, height)`.
+        """
+        if self.mean is None:
+            return self._tlwh.copy()
+        ret = self.mean[:4].copy()
+        ret[2] *= ret[3]
+        ret[:2] -= ret[2:] / 2
+        return ret
+
+    @property
+    # @jit(nopython=True)
+    def tlbr(self):
+        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
+        `(top left, bottom right)`.
+        """
+        ret = self.tlwh.copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    @staticmethod
+    # @jit(nopython=True)
+    def tlwh_to_xyah(tlwh):
+        """Convert bounding box to format `(center x, center y, aspect ratio,
+        height)`, where the aspect ratio is `width / height`.
+        """
+        ret = np.asarray(tlwh).copy()
+        ret[:2] += ret[2:] / 2
+        ret[2] /= ret[3]
+        return ret
+
+    def to_xyah(self):
+        return self.tlwh_to_xyah(self.tlwh)
+
+    @staticmethod
+    # @jit(nopython=True)
+    def tlbr_to_tlwh(tlbr):
+        ret = np.asarray(tlbr).copy()
+        ret[2:] -= ret[:2]
+        return ret
+
+    @staticmethod
+    # @jit(nopython=True)
+    def tlwh_to_tlbr(tlwh):
+        ret = np.asarray(tlwh).copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    def __repr__(self):
+        return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
+
+
+class BYTETracker(object):
+    def __init__(self, track_thresh=0.45, track_buffer=25, match_thresh=0.8, frame_rate=30):
+        self.tracked_stracks = []  # type: list[STrack]
+        self.lost_stracks = []  # type: list[STrack]
+        self.removed_stracks = []  # type: list[STrack]
+
+        self.frame_id = 0
+        self.track_buffer=track_buffer
+        
+        self.track_thresh = track_thresh
+        self.match_thresh = match_thresh
+        # self.det_thresh = track_thresh
+        self.det_thresh = track_thresh + 0.1
+        self.buffer_size = int(frame_rate / 30.0 * track_buffer)
+        self.max_time_lost = self.buffer_size
+        self.kalman_filter = KalmanFilter()
+
+    def update(self, dets, _):
+        self.frame_id += 1
+        activated_starcks = []
+        refind_stracks = []
+        lost_stracks = []
+        removed_stracks = []
+
+        xyxys = dets[:, 0:4]
+        xywh = xyxy2xywh(xyxys)
+        confs = dets[:, 4]
+        clss = dets[:, 5]
+        
+        classes = clss.numpy()
+        xyxys = xyxys.numpy()
+        confs = confs.numpy()
+
+        remain_inds = confs > self.track_thresh
+        inds_low = confs > 0.1
+        inds_high = confs < self.track_thresh
+
+        inds_second = np.logical_and(inds_low, inds_high)
+        
+        dets_second = xywh[inds_second]
+        dets = xywh[remain_inds]
+        
+        scores_keep = confs[remain_inds]
+        scores_second = confs[inds_second]
+        
+        clss_keep = classes[remain_inds]
+        clss_second = classes[remain_inds]
+        
+
+        if len(dets) > 0:
+            '''Detections'''
+            detections = [STrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores_keep, clss_keep)]
+        else:
+            detections = []
+
+        ''' Add newly detected tracklets to tracked_stracks'''
+        unconfirmed = []
+        tracked_stracks = []  # type: list[STrack]
+        for track in self.tracked_stracks:
+            if not track.is_activated:
+                unconfirmed.append(track)
+            else:
+                tracked_stracks.append(track)
+
+        ''' Step 2: First association, with high score detection boxes'''
+        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
+        # Predict the current location with KF
+        STrack.multi_predict(strack_pool)
+        dists = matching.iou_distance(strack_pool, detections)
+        #if not self.args.mot20:
+        dists = matching.fuse_score(dists, detections)
+        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.match_thresh)
+
+        for itracked, idet in matches:
+            track = strack_pool[itracked]
+            det = detections[idet]
+            if track.state == TrackState.Tracked:
+                track.update(detections[idet], self.frame_id)
+                activated_starcks.append(track)
+            else:
+                track.re_activate(det, self.frame_id, new_id=False)
+                refind_stracks.append(track)
+
+        ''' Step 3: Second association, with low score detection boxes'''
+        # association the untrack to the low score detections
+        if len(dets_second) > 0:
+            '''Detections'''
+            detections_second = [STrack(xywh, s, c) for (xywh, s, c) in zip(dets_second, scores_second, clss_second)]
+        else:
+            detections_second = []
+        r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
+        dists = matching.iou_distance(r_tracked_stracks, detections_second)
+        matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)
+        for itracked, idet in matches:
+            track = r_tracked_stracks[itracked]
+            det = detections_second[idet]
+            if track.state == TrackState.Tracked:
+                track.update(det, self.frame_id)
+                activated_starcks.append(track)
+            else:
+                track.re_activate(det, self.frame_id, new_id=False)
+                refind_stracks.append(track)
+
+        for it in u_track:
+            track = r_tracked_stracks[it]
+            if not track.state == TrackState.Lost:
+                track.mark_lost()
+                lost_stracks.append(track)
+
+        '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
+        detections = [detections[i] for i in u_detection]
+        dists = matching.iou_distance(unconfirmed, detections)
+        #if not self.args.mot20:
+        dists = matching.fuse_score(dists, detections)
+        matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
+        for itracked, idet in matches:
+            unconfirmed[itracked].update(detections[idet], self.frame_id)
+            activated_starcks.append(unconfirmed[itracked])
+        for it in u_unconfirmed:
+            track = unconfirmed[it]
+            track.mark_removed()
+            removed_stracks.append(track)
+
+        """ Step 4: Init new stracks"""
+        for inew in u_detection:
+            track = detections[inew]
+            if track.score < self.det_thresh:
+                continue
+            track.activate(self.kalman_filter, self.frame_id)
+            activated_starcks.append(track)
+        """ Step 5: Update state"""
+        for track in self.lost_stracks:
+            if self.frame_id - track.end_frame > self.max_time_lost:
+                track.mark_removed()
+                removed_stracks.append(track)
+
+        # print('Ramained match {} s'.format(t4-t3))
+
+        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
+        self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
+        self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
+        self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
+        self.lost_stracks.extend(lost_stracks)
+        self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
+        self.removed_stracks.extend(removed_stracks)
+        self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
+        # get scores of lost tracks
+        output_stracks = [track for track in self.tracked_stracks if track.is_activated]
+        outputs = []
+        for t in output_stracks:
+            output= []
+            tlwh = t.tlwh
+            tid = t.track_id
+            tlwh = np.expand_dims(tlwh, axis=0)
+            xyxy = xywh2xyxy(tlwh)
+            xyxy = np.squeeze(xyxy, axis=0)
+            output.extend(xyxy)
+            output.append(tid)
+            output.append(t.cls)
+            outputs.append(output)
+
+        return outputs
+
+
+def joint_stracks(tlista, tlistb):
+    exists = {}
+    res = []
+    for t in tlista:
+        exists[t.track_id] = 1
+        res.append(t)
+    for t in tlistb:
+        tid = t.track_id
+        if not exists.get(tid, 0):
+            exists[tid] = 1
+            res.append(t)
+    return res
+
+
+def sub_stracks(tlista, tlistb):
+    stracks = {}
+    for t in tlista:
+        stracks[t.track_id] = t
+    for t in tlistb:
+        tid = t.track_id
+        if stracks.get(tid, 0):
+            del stracks[tid]
+    return list(stracks.values())
+
+
+def remove_duplicate_stracks(stracksa, stracksb):
+    pdist = matching.iou_distance(stracksa, stracksb)
+    pairs = np.where(pdist < 0.15)
+    dupa, dupb = list(), list()
+    for p, q in zip(*pairs):
+        timep = stracksa[p].frame_id - stracksa[p].start_frame
+        timeq = stracksb[q].frame_id - stracksb[q].start_frame
+        if timep > timeq:
+            dupb.append(q)
+        else:
+            dupa.append(p)
+    resa = [t for i, t in enumerate(stracksa) if not i in dupa]
+    resb = [t for i, t in enumerate(stracksb) if not i in dupb]
+    return resa, resb

trackers/bytetrack/kalman_filter.py

@@ -0,0 +1,270 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import scipy.linalg
+
+
+"""
+Table for the 0.95 quantile of the chi-square distribution with N degrees of
+freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
+function and used as Mahalanobis gating threshold.
+"""
+chi2inv95 = {
+    1: 3.8415,
+    2: 5.9915,
+    3: 7.8147,
+    4: 9.4877,
+    5: 11.070,
+    6: 12.592,
+    7: 14.067,
+    8: 15.507,
+    9: 16.919}
+
+
+class KalmanFilter(object):
+    """
+    A simple Kalman filter for tracking bounding boxes in image space.
+
+    The 8-dimensional state space
+
+        x, y, a, h, vx, vy, va, vh
+
+    contains the bounding box center position (x, y), aspect ratio a, height h,
+    and their respective velocities.
+
+    Object motion follows a constant velocity model. The bounding box location
+    (x, y, a, h) is taken as direct observation of the state space (linear
+    observation model).
+
+    """
+
+    def __init__(self):
+        ndim, dt = 4, 1.
+
+        # Create Kalman filter model matrices.
+        self._motion_mat = np.eye(2 * ndim, 2 * ndim)
+        for i in range(ndim):
+            self._motion_mat[i, ndim + i] = dt
+        self._update_mat = np.eye(ndim, 2 * ndim)
+
+        # Motion and observation uncertainty are chosen relative to the current
+        # state estimate. These weights control the amount of uncertainty in
+        # the model. This is a bit hacky.
+        self._std_weight_position = 1. / 20
+        self._std_weight_velocity = 1. / 160
+
+    def initiate(self, measurement):
+        """Create track from unassociated measurement.
+
+        Parameters
+        ----------
+        measurement : ndarray
+            Bounding box coordinates (x, y, a, h) with center position (x, y),
+            aspect ratio a, and height h.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector (8 dimensional) and covariance matrix (8x8
+            dimensional) of the new track. Unobserved velocities are initialized
+            to 0 mean.
+
+        """
+        mean_pos = measurement
+        mean_vel = np.zeros_like(mean_pos)
+        mean = np.r_[mean_pos, mean_vel]
+
+        std = [
+            2 * self._std_weight_position * measurement[3],
+            2 * self._std_weight_position * measurement[3],
+            1e-2,
+            2 * self._std_weight_position * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            1e-5,
+            10 * self._std_weight_velocity * measurement[3]]
+        covariance = np.diag(np.square(std))
+        return mean, covariance
+
+    def predict(self, mean, covariance):
+        """Run Kalman filter prediction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The 8 dimensional mean vector of the object state at the previous
+            time step.
+        covariance : ndarray
+            The 8x8 dimensional covariance matrix of the object state at the
+            previous time step.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector and covariance matrix of the predicted
+            state. Unobserved velocities are initialized to 0 mean.
+
+        """
+        std_pos = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-2,
+            self._std_weight_position * mean[3]]
+        std_vel = [
+            self._std_weight_velocity * mean[3],
+            self._std_weight_velocity * mean[3],
+            1e-5,
+            self._std_weight_velocity * mean[3]]
+        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
+
+        #mean = np.dot(self._motion_mat, mean)
+        mean = np.dot(mean, self._motion_mat.T)
+        covariance = np.linalg.multi_dot((
+            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
+
+        return mean, covariance
+
+    def project(self, mean, covariance):
+        """Project state distribution to measurement space.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The state's mean vector (8 dimensional array).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the projected mean and covariance matrix of the given state
+            estimate.
+
+        """
+        std = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-1,
+            self._std_weight_position * mean[3]]
+        innovation_cov = np.diag(np.square(std))
+
+        mean = np.dot(self._update_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._update_mat, covariance, self._update_mat.T))
+        return mean, covariance + innovation_cov
+
+    def multi_predict(self, mean, covariance):
+        """Run Kalman filter prediction step (Vectorized version).
+        Parameters
+        ----------
+        mean : ndarray
+            The Nx8 dimensional mean matrix of the object states at the previous
+            time step.
+        covariance : ndarray
+            The Nx8x8 dimensional covariance matrics of the object states at the
+            previous time step.
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector and covariance matrix of the predicted
+            state. Unobserved velocities are initialized to 0 mean.
+        """
+        std_pos = [
+            self._std_weight_position * mean[:, 3],
+            self._std_weight_position * mean[:, 3],
+            1e-2 * np.ones_like(mean[:, 3]),
+            self._std_weight_position * mean[:, 3]]
+        std_vel = [
+            self._std_weight_velocity * mean[:, 3],
+            self._std_weight_velocity * mean[:, 3],
+            1e-5 * np.ones_like(mean[:, 3]),
+            self._std_weight_velocity * mean[:, 3]]
+        sqr = np.square(np.r_[std_pos, std_vel]).T
+
+        motion_cov = []
+        for i in range(len(mean)):
+            motion_cov.append(np.diag(sqr[i]))
+        motion_cov = np.asarray(motion_cov)
+
+        mean = np.dot(mean, self._motion_mat.T)
+        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))
+        covariance = np.dot(left, self._motion_mat.T) + motion_cov
+
+        return mean, covariance
+
+    def update(self, mean, covariance, measurement):
+        """Run Kalman filter correction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The predicted state's mean vector (8 dimensional).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+        measurement : ndarray
+            The 4 dimensional measurement vector (x, y, a, h), where (x, y)
+            is the center position, a the aspect ratio, and h the height of the
+            bounding box.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the measurement-corrected state distribution.
+
+        """
+        projected_mean, projected_cov = self.project(mean, covariance)
+
+        chol_factor, lower = scipy.linalg.cho_factor(
+            projected_cov, lower=True, check_finite=False)
+        kalman_gain = scipy.linalg.cho_solve(
+            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
+            check_finite=False).T
+        innovation = measurement - projected_mean
+
+        new_mean = mean + np.dot(innovation, kalman_gain.T)
+        new_covariance = covariance - np.linalg.multi_dot((
+            kalman_gain, projected_cov, kalman_gain.T))
+        return new_mean, new_covariance
+
+    def gating_distance(self, mean, covariance, measurements,
+                        only_position=False, metric='maha'):
+        """Compute gating distance between state distribution and measurements.
+        A suitable distance threshold can be obtained from `chi2inv95`. If
+        `only_position` is False, the chi-square distribution has 4 degrees of
+        freedom, otherwise 2.
+        Parameters
+        ----------
+        mean : ndarray
+            Mean vector over the state distribution (8 dimensional).
+        covariance : ndarray
+            Covariance of the state distribution (8x8 dimensional).
+        measurements : ndarray
+            An Nx4 dimensional matrix of N measurements, each in
+            format (x, y, a, h) where (x, y) is the bounding box center
+            position, a the aspect ratio, and h the height.
+        only_position : Optional[bool]
+            If True, distance computation is done with respect to the bounding
+            box center position only.
+        Returns
+        -------
+        ndarray
+            Returns an array of length N, where the i-th element contains the
+            squared Mahalanobis distance between (mean, covariance) and
+            `measurements[i]`.
+        """
+        mean, covariance = self.project(mean, covariance)
+        if only_position:
+            mean, covariance = mean[:2], covariance[:2, :2]
+            measurements = measurements[:, :2]
+
+        d = measurements - mean
+        if metric == 'gaussian':
+            return np.sum(d * d, axis=1)
+        elif metric == 'maha':
+            cholesky_factor = np.linalg.cholesky(covariance)
+            z = scipy.linalg.solve_triangular(
+                cholesky_factor, d.T, lower=True, check_finite=False,
+                overwrite_b=True)
+            squared_maha = np.sum(z * z, axis=0)
+            return squared_maha
+        else:
+            raise ValueError('invalid distance metric')

trackers/bytetrack/matching.py

@@ -0,0 +1,180 @@
+import cv2
+import numpy as np
+import scipy
+import lap
+from scipy.spatial.distance import cdist
+from cython_bbox import bbox_overlaps as bbox_ious
+from trackers.bytetrack import kalman_filter
+import time
+
+def merge_matches(m1, m2, shape):
+    O,P,Q = shape
+    m1 = np.asarray(m1)
+    m2 = np.asarray(m2)
+
+    M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
+    M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
+
+    mask = M1*M2
+    match = mask.nonzero()
+    match = list(zip(match[0], match[1]))
+    unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
+    unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
+
+    return match, unmatched_O, unmatched_Q
+
+
+def _indices_to_matches(cost_matrix, indices, thresh):
+    matched_cost = cost_matrix[tuple(zip(*indices))]
+    matched_mask = (matched_cost <= thresh)
+
+    matches = indices[matched_mask]
+    unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
+    unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
+
+    return matches, unmatched_a, unmatched_b
+
+
+def linear_assignment(cost_matrix, thresh):
+    if cost_matrix.size == 0:
+        return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
+    matches, unmatched_a, unmatched_b = [], [], []
+    cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
+    for ix, mx in enumerate(x):
+        if mx >= 0:
+            matches.append([ix, mx])
+    unmatched_a = np.where(x < 0)[0]
+    unmatched_b = np.where(y < 0)[0]
+    matches = np.asarray(matches)
+    return matches, unmatched_a, unmatched_b
+
+
+def ious(atlbrs, btlbrs):
+    """
+    Compute cost based on IoU
+    :type atlbrs: list[tlbr] | np.ndarray
+    :type atlbrs: list[tlbr] | np.ndarray
+
+    :rtype ious np.ndarray
+    """
+    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float64)
+    if ious.size == 0:
+        return ious
+
+    ious = bbox_ious(
+        np.ascontiguousarray(atlbrs, dtype=np.float64),
+        np.ascontiguousarray(btlbrs, dtype=np.float64)
+    )
+
+    return ious
+
+
+def iou_distance(atracks, btracks):
+    """
+    Compute cost based on IoU
+    :type atracks: list[STrack]
+    :type btracks: list[STrack]
+
+    :rtype cost_matrix np.ndarray
+    """
+
+    if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
+        atlbrs = atracks
+        btlbrs = btracks
+    else:
+        atlbrs = [track.tlbr for track in atracks]
+        btlbrs = [track.tlbr for track in btracks]
+    _ious = ious(atlbrs, btlbrs)
+    cost_matrix = 1 - _ious
+
+    return cost_matrix
+
+def v_iou_distance(atracks, btracks):
+    """
+    Compute cost based on IoU
+    :type atracks: list[STrack]
+    :type btracks: list[STrack]
+
+    :rtype cost_matrix np.ndarray
+    """
+
+    if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
+        atlbrs = atracks
+        btlbrs = btracks
+    else:
+        atlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in atracks]
+        btlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in btracks]
+    _ious = ious(atlbrs, btlbrs)
+    cost_matrix = 1 - _ious
+
+    return cost_matrix
+
+def embedding_distance(tracks, detections, metric='cosine'):
+    """
+    :param tracks: list[STrack]
+    :param detections: list[BaseTrack]
+    :param metric:
+    :return: cost_matrix np.ndarray
+    """
+
+    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float64)
+    if cost_matrix.size == 0:
+        return cost_matrix
+    det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float64)
+    #for i, track in enumerate(tracks):
+        #cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
+    track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float64)
+    cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric))  # Nomalized features
+    return cost_matrix
+
+
+def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
+    if cost_matrix.size == 0:
+        return cost_matrix
+    gating_dim = 2 if only_position else 4
+    gating_threshold = kalman_filter.chi2inv95[gating_dim]
+    measurements = np.asarray([det.to_xyah() for det in detections])
+    for row, track in enumerate(tracks):
+        gating_distance = kf.gating_distance(
+            track.mean, track.covariance, measurements, only_position)
+        cost_matrix[row, gating_distance > gating_threshold] = np.inf
+    return cost_matrix
+
+
+def fuse_motion(kf, cost_matrix, tracks, detections, only_position=False, lambda_=0.98):
+    if cost_matrix.size == 0:
+        return cost_matrix
+    gating_dim = 2 if only_position else 4
+    gating_threshold = kalman_filter.chi2inv95[gating_dim]
+    measurements = np.asarray([det.to_xyah() for det in detections])
+    for row, track in enumerate(tracks):
+        gating_distance = kf.gating_distance(
+            track.mean, track.covariance, measurements, only_position, metric='maha')
+        cost_matrix[row, gating_distance > gating_threshold] = np.inf
+        cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_) * gating_distance
+    return cost_matrix
+
+
+def fuse_iou(cost_matrix, tracks, detections):
+    if cost_matrix.size == 0:
+        return cost_matrix
+    reid_sim = 1 - cost_matrix
+    iou_dist = iou_distance(tracks, detections)
+    iou_sim = 1 - iou_dist
+    fuse_sim = reid_sim * (1 + iou_sim) / 2
+    det_scores = np.array([det.score for det in detections])
+    det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
+    #fuse_sim = fuse_sim * (1 + det_scores) / 2
+    fuse_cost = 1 - fuse_sim
+    return fuse_cost
+
+
+def fuse_score(cost_matrix, detections):
+    if cost_matrix.size == 0:
+        return cost_matrix
+    iou_sim = 1 - cost_matrix
+    det_scores = np.array([det.score for det in detections])
+    det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
+    fuse_sim = iou_sim * det_scores
+    fuse_cost = 1 - fuse_sim
+    return fuse_cost

trackers/multi_tracker_zoo.py

@@ -0,0 +1,44 @@
+from trackers.strong_sort.utils.parser import get_config
+from trackers.strong_sort.strong_sort import StrongSORT
+from trackers.ocsort.ocsort import OCSort
+from trackers.bytetrack.byte_tracker import BYTETracker
+
+
+def create_tracker(tracker_type, appearance_descriptor_weights, device, half):
+    if tracker_type == 'strongsort':
+        # initialize StrongSORT
+        cfg = get_config()
+        cfg.merge_from_file('trackers/strong_sort/configs/strong_sort.yaml')
+
+        strongsort = StrongSORT(
+            appearance_descriptor_weights,
+            device,
+            half,
+            max_dist=cfg.STRONGSORT.MAX_DIST,
+            max_iou_distance=cfg.STRONGSORT.MAX_IOU_DISTANCE,
+            max_age=cfg.STRONGSORT.MAX_AGE,
+            n_init=cfg.STRONGSORT.N_INIT,
+            nn_budget=cfg.STRONGSORT.NN_BUDGET,
+            mc_lambda=cfg.STRONGSORT.MC_LAMBDA,
+            ema_alpha=cfg.STRONGSORT.EMA_ALPHA,
+
+        )
+        return strongsort
+    elif tracker_type == 'ocsort':
+        ocsort = OCSort(
+            det_thresh=0.45,
+            iou_threshold=0.2,
+            use_byte=False 
+        )
+        return ocsort
+    elif tracker_type == 'bytetrack':
+        bytetracker = BYTETracker(
+            track_thresh=0.6,
+            track_buffer=30,
+            match_thresh=0.8,
+            frame_rate=30
+        )
+        return bytetracker
+    else:
+        print('No such tracker')
+        exit()