更新

.idea/deployment.xml

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="PublishConfigData" serverName="th@192.168.11.8:32178" remoteFilesAllowedToDisappearOnAutoupload="false">
+  <component name="PublishConfigData" remoteFilesAllowedToDisappearOnAutoupload="false">
     <serverData>
       <paths name="10.21">
         <serverdata>
@@ -46,13 +46,6 @@
           </mappings>
         </serverdata>
       </paths>
-      <paths name="th@192.168.11.8:32178">
-        <serverdata>
-          <mappings>
-            <mapping deploy="/home/th/tuo_heng/dev/tuoheng_alg" local="$PROJECT_DIR$" />
-          </mappings>
-        </serverdata>
-      </paths>
     </serverData>
   </component>
 </project>

.idea/sshConfigs.xml

@@ -6,7 +6,6 @@
       <sshConfig authType="PASSWORD" host="192.168.10.21" id="adf5e1da-4910-4668-bfbb-432f4e2ae77c" port="22" nameFormat="DESCRIPTIVE" username="th" />
       <sshConfig authType="PASSWORD" host="192.168.10.22" id="ac18a75e-ff42-4875-a5da-ad98d2d695ea" port="22" nameFormat="DESCRIPTIVE" username="th" />
       <sshConfig authType="PASSWORD" connectionConfig="{&quot;serverAliveInterval&quot;:300}" host="192.168.10.66" id="dcf03076-1bc5-4ce3-a4e4-38f7f00ea74a" port="32782" nameFormat="DESCRIPTIVE" username="root" />
-      <sshConfig authType="PASSWORD" host="192.168.11.8" id="34e9c3c2-edbc-42f0-8c89-cb75bfdf55e1" port="32178" nameFormat="DESCRIPTIVE" username="th" />
       <sshConfig authType="PASSWORD" host="192.168.11.7" id="5bb44c10-4e9c-4059-a0c0-9f2596b74bc0" port="22" nameFormat="DESCRIPTIVE" username="th" />
     </configs>
   </component>

.idea/webServers.xml

@@ -31,9 +31,9 @@
         </fileTransfer>
       </webServer>
       <webServer id="b761b5c5-5f66-4c6a-ad49-4783ff5df619" name="192.168.11.8">
-        <fileTransfer accessType="SFTP" host="192.168.11.8" port="32178" sshConfigId="34e9c3c2-edbc-42f0-8c89-cb75bfdf55e1" sshConfig="th@192.168.11.8:32178 password">
+        <fileTransfer accessType="SFTP" host="192.168.11.8" port="32178" sshConfigId="080a8ea2-04ef-404c-8202-a30cad7668a2" sshConfig="th@192.168.11.8:32178 password">
           <advancedOptions>
-            <advancedOptions dataProtectionLevel="Private" keepAliveTimeout="0" passiveMode="true" shareSSLContext="true" />
+            <advancedOptions dataProtectionLevel="Private" passiveMode="true" shareSSLContext="true" />
           </advancedOptions>
         </fileTransfer>
       </webServer>

.idea/workspace.xml

@@ -5,15 +5,13 @@
   </component>
   <component name="ChangeListManager">
     <list default="true" id="4f7dccd9-8f92-4a6e-90cc-33890d102263" name="Changes" comment="Changes">
+      <change afterPath="$PROJECT_DIR$/test/__init__.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/.idea/deployment.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/deployment.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/.idea/misc.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/misc.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/sshConfigs.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/sshConfigs.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/webServers.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/webServers.xml" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/common/Constant.py" beforeDir="false" afterPath="$PROJECT_DIR$/common/Constant.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/concurrency/FeedbackThread.py" beforeDir="false" afterPath="$PROJECT_DIR$/concurrency/FeedbackThread.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/concurrency/FileUpdateThread.py" beforeDir="false" afterPath="$PROJECT_DIR$/concurrency/FileUploadThread.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/concurrency/HeartbeatThread.py" beforeDir="false" afterPath="$PROJECT_DIR$/concurrency/HeartbeatThread.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/concurrency/FileUploadThread.py" beforeDir="false" afterPath="$PROJECT_DIR$/concurrency/FileUploadThread.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/concurrency/IntelligentRecognitionProcess.py" beforeDir="false" afterPath="$PROJECT_DIR$/concurrency/IntelligentRecognitionProcess.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/concurrency/PullStreamThread.py" beforeDir="false" afterPath="$PROJECT_DIR$/concurrency/PullStreamThread.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/concurrency/PullVideoStreamProcess.py" beforeDir="false" afterPath="$PROJECT_DIR$/concurrency/PullVideoStreamProcess.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/dsp_application.yml" beforeDir="false" afterPath="$PROJECT_DIR$/dsp_application.yml" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/dsp_master.py" beforeDir="false" afterPath="$PROJECT_DIR$/dsp_master.py" afterDir="false" />
@@ -21,20 +19,14 @@
       <change beforePath="$PROJECT_DIR$/enums/BaiduSdkEnum.py" beforeDir="false" afterPath="$PROJECT_DIR$/enums/BaiduSdkEnum.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/enums/ExceptionEnum.py" beforeDir="false" afterPath="$PROJECT_DIR$/enums/ExceptionEnum.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/enums/ModelTypeEnum.py" beforeDir="false" afterPath="$PROJECT_DIR$/enums/ModelTypeEnum.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/exception/CustomerException.py" beforeDir="false" afterPath="$PROJECT_DIR$/exception/CustomerException.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/service/Dispatcher.py" beforeDir="false" afterPath="$PROJECT_DIR$/service/Dispatcher.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/test/ffmpeg11/ffmpeg2.py" beforeDir="false" afterPath="$PROJECT_DIR$/test/ffmpeg11/ffmpeg2.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/test/gpu/gputest.py" beforeDir="false" afterPath="$PROJECT_DIR$/test/gpu/gputest.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/test/路径/Test.py" beforeDir="false" afterPath="$PROJECT_DIR$/test/路径/Test.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/test/集合/test.py" beforeDir="false" afterPath="$PROJECT_DIR$/test/集合/test.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/util/AliyunSdk.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/AliyunSdk.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/util/Cv2Utils.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/Cv2Utils.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/util/FileUtils.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/FileUtils.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/util/GPUtils.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/GPUtils.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/util/ImageUtils.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/ImageUtils.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/util/ImgBaiduSdk.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/ImgBaiduSdk.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/util/KafkaUtils.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/KafkaUtils.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/util/LogUtils.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/LogUtils.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/util/ModelUtils.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/ModelUtils.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/util/OcrBaiduSdk.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/OcrBaiduSdk.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/util/YmlUtils.py" beforeDir="false" afterPath="$PROJECT_DIR$/util/YmlUtils.py" afterDir="false" />
     </list>
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
@@ -163,7 +155,7 @@
     "WebServerToolWindowPanel.toolwindow.show.date": "false",
     "WebServerToolWindowPanel.toolwindow.show.permissions": "false",
     "WebServerToolWindowPanel.toolwindow.show.size": "false",
-    "last_opened_file_path": "D:/tuoheng/codenew/tuoheng_alg",
+    "last_opened_file_path": "D:/tuoheng/codenew/tuoheng_dsp",
     "node.js.detected.package.eslint": "true",
     "node.js.detected.package.tslint": "true",
     "node.js.selected.package.eslint": "(autodetect)",
@@ -177,20 +169,21 @@
 }]]></component>
   <component name="RecentsManager">
     <key name="CopyFile.RECENT_KEYS">
+      <recent name="D:\tuoheng\codenew\tuoheng_alg\test\cuda" />
+      <recent name="D:\tuoheng\codenew\tuoheng_alg\util" />
+      <recent name="D:\tuoheng\codenew\tuoheng_alg\test" />
+      <recent name="D:\tuoheng\codenew\tuoheng_alg\test\aliyun" />
       <recent name="D:\tuoheng\codenew\tuoheng_alg\enums" />
-      <recent name="D:\tuoheng\codenew\tuoheng_alg\.idea" />
-      <recent name="D:\tuoheng\codenew\tuoheng_alg\test\demo" />
-      <recent name="D:\tuoheng\code\tuoheng_alg" />
-      <recent name="D:\tuoheng\code\tuoheng_alg\concurrency" />
     </key>
     <key name="MoveFile.RECENT_KEYS">
+      <recent name="D:\tuoheng\codenew\tuoheng_alg\font" />
       <recent name="D:\work\alg_new\tuoheng_alg\test\image" />
       <recent name="D:\work\alg\tuoheng_alg\test\水印" />
       <recent name="D:\work\alg\tuoheng_alg\image" />
     </key>
   </component>
-  <component name="RunManager" selected="Python.Test (2)">
-    <configuration name="协程笔记" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+  <component name="RunManager" selected="Python.test">
+    <configuration name="Test" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
       <module name="tuoheng_alg" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
@@ -198,12 +191,12 @@
         <env name="PYTHONUNBUFFERED" value="1" />
       </envs>
       <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/协程" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/序列化" />
       <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
       <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/协程/协程笔记.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/序列化/Test.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -212,20 +205,20 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="Test (1)" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+    <configuration name="editImage" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
       <module name="tuoheng_alg" />
       <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="$PROJECT_DIR$/test/线程" />
-      <option name="IS_MODULE_SDK" value="true" />
+      <option name="SDK_HOME" value="$PROJECT_DIR$/../../../software/anaconda/envs/test/python.exe" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/editimage" />
+      <option name="IS_MODULE_SDK" value="false" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
       <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/线程/Test.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/editimage/editImage.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -234,7 +227,7 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="Test (2)" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+    <configuration name="gputest" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
       <module name="tuoheng_alg" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
@@ -242,12 +235,12 @@
         <env name="PYTHONUNBUFFERED" value="1" />
       </envs>
       <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/路径" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/gpu" />
       <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
       <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/路径/Test.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/gpu/gputest.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -256,7 +249,7 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="Test" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+    <configuration name="mysqltest" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
       <module name="tuoheng_alg" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
@@ -264,12 +257,12 @@
         <env name="PYTHONUNBUFFERED" value="1" />
       </envs>
       <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/validate" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test" />
       <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
       <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/validate/Test.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/mysqltest.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -278,20 +271,20 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="editImage" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
+    <configuration name="test (1)" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
       <module name="tuoheng_alg" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
       <envs>
         <env name="PYTHONUNBUFFERED" value="1" />
       </envs>
-      <option name="SDK_HOME" value="$PROJECT_DIR$/../../../software/anaconda/envs/test/python.exe" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/editimage" />
-      <option name="IS_MODULE_SDK" value="false" />
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/cuda" />
+      <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
       <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/editimage/editImage.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/cuda/test.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -300,7 +293,7 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="mysqltest" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
+    <configuration name="test" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
       <module name="tuoheng_alg" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
@@ -308,12 +301,12 @@
         <env name="PYTHONUNBUFFERED" value="1" />
       </envs>
       <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/集合" />
       <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
       <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/mysqltest.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/集合/test.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -322,7 +315,7 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="test" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+    <configuration name="test1" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
       <module name="tuoheng_alg" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
@@ -330,12 +323,12 @@
         <env name="PYTHONUNBUFFERED" value="1" />
       </envs>
       <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/集合" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/test/cuda" />
       <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
       <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/集合/test.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test/cuda/test1.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -347,18 +340,18 @@
     <list>
       <item itemvalue="Python.editImage" />
       <item itemvalue="Python.mysqltest" />
-      <item itemvalue="Python.Test (2)" />
       <item itemvalue="Python.test" />
-      <item itemvalue="Python.Test (1)" />
-      <item itemvalue="Python.协程笔记" />
+      <item itemvalue="Python.gputest" />
+      <item itemvalue="Python.test1" />
+      <item itemvalue="Python.test (1)" />
       <item itemvalue="Python.Test" />
     </list>
     <recent_temporary>
       <list>
-        <item itemvalue="Python.Test (2)" />
         <item itemvalue="Python.test" />
-        <item itemvalue="Python.Test (1)" />
-        <item itemvalue="Python.协程笔记" />
+        <item itemvalue="Python.test1" />
+        <item itemvalue="Python.test (1)" />
+        <item itemvalue="Python.gputest" />
         <item itemvalue="Python.Test" />
       </list>
     </recent_temporary>
@@ -500,7 +493,15 @@
       <workItem from="1681301257655" duration="429000" />
       <workItem from="1681344786746" duration="5993000" />
       <workItem from="1681363389283" duration="5626000" />
-      <workItem from="1681431288218" duration="1010000" />
+      <workItem from="1681431288218" duration="25974000" />
+      <workItem from="1681690599771" duration="2894000" />
+      <workItem from="1681696465772" duration="30396000" />
+      <workItem from="1681826261843" duration="1474000" />
+      <workItem from="1681863254347" duration="13207000" />
+      <workItem from="1681950317514" duration="23460000" />
+      <workItem from="1682036333722" duration="651000" />
+      <workItem from="1682405963588" duration="37651000" />
+      <workItem from="1682554149580" duration="33819000" />
     </task>
     <servers />
   </component>
@@ -532,9 +533,14 @@
           <option name="timeStamp" value="2" />
         </line-breakpoint>
         <line-breakpoint enabled="true" suspend="THREAD" type="python-line">
-          <url>file://$PROJECT_DIR$/concurrency/IntelligentRecognitionProcess.py</url>
-          <line>341</line>
-          <option name="timeStamp" value="3" />
+          <url>file://$PROJECT_DIR$/test/aliyun/ossdemo.py</url>
+          <line>4</line>
+          <option name="timeStamp" value="4" />
+        </line-breakpoint>
+        <line-breakpoint enabled="true" suspend="THREAD" type="python-line">
+          <url>file://$PROJECT_DIR$/util/Cv2Utils.py</url>
+          <line>1</line>
+          <option name="timeStamp" value="5" />
         </line-breakpoint>
       </breakpoints>
     </breakpoint-manager>
@@ -544,6 +550,7 @@
     <select />
   </component>
   <component name="com.intellij.coverage.CoverageDataManagerImpl">
+    <SUITE FILE_PATH="coverage/tuoheng_alg$voddemo.coverage" NAME="voddemo 覆盖结果" MODIFIED="1681722102430" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/aliyun" />
     <SUITE FILE_PATH="coverage/tuoheng_alg___$producer_start.coverage" NAME="producer_start 覆盖结果" MODIFIED="1668522825199" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="/home/thsw/chenyukun/algSch" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$demo1.coverage" NAME="demo1 覆盖结果" MODIFIED="1680162882599" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/demo" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$ffmpeg33.coverage" NAME="ffmpeg33 覆盖结果" MODIFIED="1670489109246" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/ffmpeg11" />
@@ -554,10 +561,14 @@
     <SUITE FILE_PATH="coverage/tuoheng_alg___$ffmpeg11.coverage" NAME="ffmpeg11 覆盖结果" MODIFIED="1668410004435" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/ffmpeg11" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$ffmpeg12.coverage" NAME="ffmpeg12 覆盖结果" MODIFIED="1675391366890" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/ffmpeg11" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$producer_start.coverage" NAME="producer_start1 覆盖结果" MODIFIED="1670999187123" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/kafka" />
-    <SUITE FILE_PATH="coverage/tuoheng_alg$Test__2_.coverage" NAME="Test (2) 覆盖结果" MODIFIED="1681431899512" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/路径" />
+    <SUITE FILE_PATH="coverage/tuoheng_alg$Test__2_.coverage" NAME="Test (2) 覆盖结果" MODIFIED="1681796501563" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/路径" />
+    <SUITE FILE_PATH="coverage/tuoheng_alg$test1.coverage" NAME="test1 覆盖结果" MODIFIED="1681988279624" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/cuda" />
+    <SUITE FILE_PATH="coverage/tuoheng_alg$ossdemo.coverage" NAME="ossdemo 覆盖结果" MODIFIED="1681715255761" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/aliyun" />
+    <SUITE FILE_PATH="coverage/tuoheng_alg$test__1_.coverage" NAME="test (1) 覆盖结果" MODIFIED="1681969578447" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/cuda" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$aa1.coverage" NAME="aa1 覆盖结果" MODIFIED="1667351136888" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/ffmpeg11" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$.coverage" NAME="协程笔记 覆盖结果" MODIFIED="1680926972744" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/协程" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$4.coverage" NAME="视频添加图片水印4 Coverage Results" MODIFIED="1661874731395" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test" />
+    <SUITE FILE_PATH="coverage/tuoheng_alg$gputest.coverage" NAME="gputest 覆盖结果" MODIFIED="1681950938970" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/gpu" />
     <SUITE FILE_PATH="coverage/tuoheng_alg___$test.coverage" NAME="test 覆盖结果" MODIFIED="1668577200259" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/while" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$1.coverage" NAME="协程1 覆盖结果" MODIFIED="1667866542122" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/协程" />
     <SUITE FILE_PATH="coverage/tuoheng_alg___$3.coverage" NAME="协程3 覆盖结果" MODIFIED="1668147029048" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/协程" />
@@ -574,8 +585,8 @@
     <SUITE FILE_PATH="coverage/tuoheng_alg___$producer_stop.coverage" NAME="producer_stop 覆盖结果" MODIFIED="1668522920533" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="/home/thsw/chenyukun/algSch" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$dsp_master.coverage" NAME="dsp_master 覆盖结果" MODIFIED="1680503755624" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$Test__1_.coverage" NAME="Test (1) 覆盖结果" MODIFIED="1681199611277" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/线程" />
-    <SUITE FILE_PATH="coverage/tuoheng_alg$test.coverage" NAME="test 覆盖结果" MODIFIED="1681199625806" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/集合" />
-    <SUITE FILE_PATH="coverage/tuoheng_alg$Test.coverage" NAME="Test 覆盖结果" MODIFIED="1680847539455" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/validate" />
+    <SUITE FILE_PATH="coverage/tuoheng_alg$test.coverage" NAME="test 覆盖结果" MODIFIED="1682582986112" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/集合" />
+    <SUITE FILE_PATH="coverage/tuoheng_alg$Test.coverage" NAME="Test 覆盖结果" MODIFIED="1681810213173" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test/序列化" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$mysqltest.coverage" NAME="mysqltest Coverage Results" MODIFIED="1660868712851" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$asnyc__1_.coverage" NAME="asnyc (1) Coverage Results" MODIFIED="1663458917599" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/test" />
     <SUITE FILE_PATH="coverage/tuoheng_alg$cv2test1.coverage" NAME="cv2test1 覆盖结果" MODIFIED="1665738045603" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="/home/DATA/chenyukun/algSch/test/" />

concurrency/FileUploadThread.py

@@ -1,5 +1,5 @@
-import asyncio
-from concurrent.futures import ThreadPoolExecutor
+import copy
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from threading import Thread
 from loguru import logger
 import cv2
@@ -7,6 +7,7 @@ from util.AliyunSdk import AliyunOssSdk
 from util import TimeUtils, ImageUtils
 from entity import FeedBack
 from enums.AnalysisStatusEnum import AnalysisStatus
+from util.PlotsUtils import draw_painting_joint
 
 
 class FileUpload(Thread):
@@ -19,16 +20,19 @@ class FileUpload(Thread):
         self.msg = msg
         self.similarity = self.content["service"]["filter"]["similarity"]
         self.picture_similarity = self.content["service"]["filter"]["picture_similarity"]
-        self.frame_score = float(self.content["service"]["frame_score"])
         self.frame_step = int(self.content["service"]["filter"]["frame_step"])
 
     # 推送执行结果
     def sendResult(self, result):
         self.fbQueue.put(result)
 
-    def build_image_name(self, current_frame, last_frame, mode_type, image_type):
-        image_format = "{base_dir}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}-{base_dir}" \
-                       "-{requestId}_{image_type}.jpg"
+    '''
+        比如原图检测目标等信息，target用O表示
+    '''
+
+    def build_image_name(self, current_frame, last_frame, mode_type, image_type, target):
+        image_format = "{base_dir}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}" \
+                       "-{target}-{requestId}_{image_type}.jpg"
         random_num = TimeUtils.now_date_to_str(TimeUtils.YMDHMSF)
         time_now = TimeUtils.now_date_to_str("%Y-%m-%d-%H-%M-%S")
         image_name = image_format.format(
@@ -38,6 +42,7 @@ class FileUpload(Thread):
             last_frame=last_frame,
             random_num=random_num,
             mode_type=mode_type,
+            target=target,
             requestId=self.msg.get('request_id'),
             image_type=image_type)
         return image_name
@@ -64,38 +69,43 @@ class ImageFileUpload(FileUpload):
             if diff_frame_num < self.frame_step:
                 flag = False
         det_result = frame_all.get("det_xywh")
+        model_info = []
         if flag and det_result is not None and len(det_result) > 0:
-            model_info = []
-            # 封装当前图片信息
-            num = 0
-            for c in list(det_result.keys()):
-                mode_code_info = {"code": c, "target": []}
-                det_xywh = det_result.get(c)
-                det_num = 0
-                if det_xywh is not None and len(det_xywh) > 0:
-                    # [float(cls_c), xc,yc,w,h, float(conf_c),code]
-                    for d in det_xywh:
-                        score = d[5]  # 得分
-                        target = str(int(d[0]))  # 检测目标
-                        mode_code_info["target"].append(target)
-                        if score < self.frame_score:
-                            det_num += 1
-                    model_info.append(mode_code_info)
-                    if det_num != len(det_xywh):
-                        num += 1
-            if num == 0:
-                return None
+            '''
+            det_xywh:{
+                'code':{
+                    1: [[detect_targets_code, box, score, label_array, color]]
+                }
+            }
+            模型编号：modeCode
+            检测目标：detectTargetCode
+            '''
+            # 更加模型编码解析数据
+            for modelCode in list(det_result.keys()):
+                # 模型编号下面的检测目标对象
+                det_info = det_result.get(modelCode)
+                if det_info is not None and len(det_info) > 0:
+                    for detectTargetCode in list(det_info.keys()):
+                        target_list = det_info.get(detectTargetCode)
+                        if target_list is not None and len(target_list) > 0:
+                            orFrame = copy.deepcopy(frame_all.get("frame"))
+                            for target in target_list:
+                                draw_painting_joint(target[1], orFrame, target[3], target[2], target[4], "leftTop")
+                            model_info.append({
+                                "modelCode": str(modelCode),
+                                "detectTargetCode": str(detectTargetCode),
+                                "frame": orFrame
+                            })
             if len(model_info) > 0:
                 high_score_image["or_frame"] = frame_all.get("frame")
                 high_score_image["current_frame"] = frame_all.get("cct_frame")
                 image_result = {
                     "or_frame": frame_all.get("frame"),
-                    "ai_frame": frame_all.get("ai_frame"),
+                    "model_info": model_info,
                     "current_frame": frame_all.get("cct_frame"),
                     "last_frame": frame_all.get("cct_frame") + self.frame_step,
                     "progress": "",
                     "mode_service": self.analyse_type,
-                    "model_info": model_info
                 }
                 return image_result
         return None
@@ -106,7 +116,7 @@ class ImageFileUpload(FileUpload):
         aliyunOssSdk = AliyunOssSdk(self.content, logger, self.msg.get("request_id"))
         aliyunOssSdk.get_oss_bucket()
         high_score_image = {}
-        with ThreadPoolExecutor(max_workers=2) as t:
+        with ThreadPoolExecutor(max_workers=5) as t:
             try:
                 while True:
                     try:
@@ -118,40 +128,46 @@ class ImageFileUpload(FileUpload):
                             if command == 'stop':
                                 break
                             if image_dict is not None and len(image_dict) > 0:
-                                image_resut = self.handle_image(high_score_image, image_dict)
-                                if image_resut is not None:
+                                image_result = self.handle_image(high_score_image, image_dict)
+                                if image_result is not None:
                                     # 图片帧数编码
-                                    or_result, or_image = cv2.imencode(".jpg", image_resut.get("or_frame"))
-                                    ai_result, ai_image = cv2.imencode(".jpg", image_resut.get("ai_frame"))
-                                    # 图片名称待后期修改
-                                    or_image_name = self.build_image_name(str(image_resut.get("current_frame")),
-                                                                          str(image_resut.get("last_frame")),
-                                                                          image_resut.get("mode_service"),
-                                                                          "OR")
-                                    ai_image_name = self.build_image_name(str(image_resut.get("current_frame")),
-                                                                          str(image_resut.get("last_frame")),
-                                                                          image_resut.get("mode_service"),
-                                                                          "AI")
+                                    task = []
+                                    or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
+                                    or_image_name = self.build_image_name(str(image_result.get("current_frame")),
+                                                                          str(image_result.get("last_frame")),
+                                                                          image_result.get("mode_service"),
+                                                                          "OR", "O")
                                     or_future = t.submit(aliyunOssSdk.sync_upload_file, or_image_name,
                                                          or_image.tobytes())
-                                    ai_future = t.submit(aliyunOssSdk.sync_upload_file, ai_image_name,
-                                                         ai_image.tobytes())
-                                    or_future.result()
-                                    ai_future.result()
-                                    # 发送kafka消息
-                                    self.sendResult({"feedback": FeedBack.message_feedback(self.msg.get('request_id'),
-                                                                                           AnalysisStatus.RUNNING.value,
-                                                                                           self.analyse_type, "", "",
-                                                                                           image_resut.get("progress"),
-                                                                                           or_image_name,
-                                                                                           ai_image_name,
-                                                                                           image_resut.get(
-                                                                                               "model_info"),
-                                                                                           TimeUtils.now_date_to_str())})
+                                    task.append(or_future)
+                                    model_info_list = image_result.get("model_info")
+                                    msg_list = []
+                                    for model_info in model_info_list:
+                                        ai_result, ai_image = cv2.imencode(".jpg", model_info.get("frame"))
+                                        ai_image_name = self.build_image_name(str(image_result.get("current_frame")),
+                                                                              str(image_result.get("last_frame")),
+                                                                              image_result.get("mode_service"),
+                                                                              "AI", model_info.get("detectTargetCode"))
+                                        ai_future = t.submit(aliyunOssSdk.sync_upload_file, ai_image_name,
+                                                             ai_image.tobytes())
+                                        task.append(ai_future)
+                                        msg_list.append(
+                                            {"feedback": FeedBack.message_feedback(self.msg.get('request_id'),
+                                                                                   AnalysisStatus.RUNNING.value,
+                                                                                   self.analyse_type, "", "",
+                                                                                   image_result.get("progress"),
+                                                                                   or_image_name,
+                                                                                   ai_image_name,
+                                                                                   model_info.get('modelCode'),
+                                                                                   model_info.get('detectTargetCode'),
+                                                                                   TimeUtils.now_date_to_str())})
+                                    for thread_result in as_completed(task):
+                                        thread_result.result()
+                                    for msg in msg_list:
+                                        self.sendResult(msg)
                     except Exception as e:
                         logger.exception("图片上传异常：{}, requestId:{}", e, self.msg.get("request_id"))
             finally:
                 high_score_image.clear()
-                t.shutdown(wait=True)  # wait=True等待所有线程完成
 
         logger.info("停止图片上传线程, requestId:{}", self.msg.get("request_id"))

concurrency/PullVideoStreamProcess.py

@@ -118,9 +118,6 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
                 if stop_pull_stream_step:
                     time.sleep(1)
                     continue
-                if self.pullQueue.full():
-                    time.sleep(0.1)
-                    continue
                 # 检测视频信息是否存在或拉流对象是否存在
                 if cv2tool.checkconfig():
                     logger.info("开始重新获取视频信息: {}次, requestId: {}", cv2_init_num, self.msg.get("request_id"))
@@ -152,6 +149,9 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
                     continue
                 init_pull_num = 1
                 pull_stream_read_start_time = time.time()
+                if self.pullQueue.full():
+                    time.sleep(0.1)
+                    continue
                 self.sendPullQueue({"status": "4",
                                     "frame": frame,
                                     "cct_frame": concurrent_frame,

dsp_application.yml

@@ -132,7 +132,7 @@ service:
     picture_similarity: True
     # 相似度阀值
     similarity: 0.65
-    frame_step: 80
+    frame_step: 160
   timeout: 21600 # 一次识别任务超时时间，单位秒，默认6个小时
   cv2_pull_stream_timeout: 3600 # 直播开始视频未推流超时时间
   cv2_read_stream_timeout: 1800 # 直播读流中超时时间

dsp_master.py

@@ -3,7 +3,7 @@ import os
 import sys
 
 from service import Dispatcher
-# import torch
+import torch
 
 '''
     dsp主程序入口
@@ -12,5 +12,5 @@ if __name__ == '__main__':
     print("(♥◠‿◠)ﾉﾞ  DSP【算法调度服务】开始启动   ლ(´ڡ`ლ)ﾞ")
     # 获取主程序执行根路径
     base_dir = os.path.dirname(os.path.realpath(sys.argv[0]))
-    # torch.multiprocessing.set_start_method('spawn')
+    torch.multiprocessing.set_start_method('spawn')
     Dispatcher.DispatcherService(base_dir).start_service()

entity/FeedBack.py

@@ -1,5 +1,5 @@
 def message_feedback(requestId, status, type, error_code="", error_msg="", progress="", original_url="", sign_url="",
-                     model_info=[], analyse_time="", analyse_results=""):
+                     modelCode="", detectTargetCode="", analyse_time="", analyse_results=""):
     taskfb = {}
     results = []
     result_msg = {}
@@ -12,7 +12,8 @@ def message_feedback(requestId, status, type, error_code="", error_msg="", progr
     result_msg["original_url"] = original_url
     result_msg["sign_url"] = sign_url
     result_msg["analyse_results"] = analyse_results
-    result_msg["model_info"] = model_info
+    result_msg["model_code"] = modelCode
+    result_msg["detect_targets_code"] = detectTargetCode
     result_msg["analyse_time"] = analyse_time
     results.append(result_msg)
     taskfb["results"] = results

enums/BaiduSdkEnum.py

@@ -170,12 +170,12 @@ BAIDUERRORDATA = {
 
 @unique
 class VehicleEnum(Enum):
-    CAR = ("car", "car")
-    TRICYCLE = ("tricycle", "tricycle")
-    MOTORBIKE = ("motorbike", "motorbike")
-    CARPLATE = ("carplate", "carplate")
-    TRUCK = ("truck", "truck")
-    BUS = ("bus", "bus")
+    CAR = ("car", "小汽车", 0)
+    TRICYCLE = ("tricycle", "三轮车", 1)
+    MOTORBIKE = ("motorbike", "摩托车", 2)
+    CARPLATE = ("carplate", "车牌", 3)
+    TRUCK = ("truck", "卡车", 4)
+    BUS = ("bus", "巴士", 5)
 
 
 VehicleEnumVALUE={

enums/ExceptionEnum.py

@@ -13,7 +13,7 @@ class ExceptionType(Enum):
 
     READSTREAM_TIMEOUT_EXCEPTION = ("SP003", "原视频读取视频流超时!")
 
-    GET_VIDEO_URL_EXCEPTION = ("SP004", "获取原视频播放地址失败!")
+    GET_VIDEO_URL_EXCEPTION = ("SP004", "获取视频播放地址失败!")
 
     GET_VIDEO_URL_TIMEOUT_EXCEPTION = ("SP005", "获取原视频播放地址超时!")
 

enums/ModelTypeEnum.py

@@ -12,14 +12,13 @@ from enum import Enum, unique
     2: 只支持图片模型之间的组合
 '''
 
+
 @unique
 class ModelType(Enum):
     WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', 0)
 
     FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', 0)
 
-    # TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'road')
-
     TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', 0)
 
     EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, 2)
@@ -36,7 +35,7 @@ class ModelType(Enum):
 
     COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', 0)
 
-    SHIP_MODEL = ("11", "011", "船只模型", 'ship', 0)
+    SHIP_MODEL = ("11", "011", "船只模型", 'ship2', 0)
 
     BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, 2)
 
@@ -58,16 +57,17 @@ class ModelType(Enum):
 
 @unique
 class BaiduModelTarget(Enum):
-    VEHICLE_DETECTION = ("车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
+    VEHICLE_DETECTION = (
+    "车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
 
-    HUMAN_DETECTION = ("人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
+    HUMAN_DETECTION = (
+    "人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
 
     PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
 
+
 BAIDU_MODEL_TARGET_CONFIG = {
     BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
     BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
     BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
 }
-
-

service/Dispatcher.py

@@ -1,5 +1,7 @@
 # -*- coding: utf-8 -*-
 import time
+
+import torch
 from cerberus import Validator
 from concurrency.FeedbackThread import FeedbackThread
 from entity.FeedBack import message_feedback, recording_feedback
@@ -14,6 +16,7 @@ from multiprocessing import Queue
 from concurrency.IntelligentRecognitionProcess import OnlineIntelligentRecognitionProcess, \
     OfflineIntelligentRecognitionProcess, PhotosIntelligentRecognitionProcess, ScreenRecordingProcess
 from util import GPUtils
+from util.GPUtils import get_all_gpu_ids, get_first_gpu_name
 
 '''
    分发服务
@@ -21,20 +24,22 @@ from util import GPUtils
 
 
 class DispatcherService:
+    '''
+        初始化
+    '''
 
-    # 初始化
     def __init__(self, base_dir):
         ##################################### 初始化alg相关配置 #####################################
-        self.base_dir = base_dir
-        self.content = YmlUtils.getConfigs(base_dir)
-        self.content['base_dir'] = base_dir
-        self.feedbackThread = None
+        self.base_dir = base_dir  # 根路径
+        self.content = YmlUtils.getConfigs(base_dir)  # 获取alg需要使用的配置
+        self.content['base_dir'] = base_dir  # 将根路径设置到上下文中
+        self.feedbackThread = None  # 初始化反馈线程对象
 
         ###################################### 初始化日志框架 ######################################
-        LogUtils.init_log(self.content)
+        LogUtils.init_log(self.content)  # 初始化日志框架
 
         #################################### 初始化视频保存文件夹 ####################################
-        FileUtils.create_dir_not_exist(self.content["video"]["file_path"])
+        FileUtils.create_dir_not_exist(self.content["video"]["file_path"])  # 创建文件夹
 
         ###################################### 创建任务记录字典 ######################################
         self.onlineProcesses = {}  # 记录当前正在执行的实时流分析任务
@@ -55,21 +60,36 @@ class DispatcherService:
         self.topics = [self.online_topic, self.offline_topic, self.image_topic, self.recording_task_topic]
         self.analysisType = {
             self.online_topic: (AnalysisType.ONLINE.value,
-                                lambda x: self.online(x),
-                                lambda x, y, z, t: self.identify_method(x, y, z, t)),
+                                lambda x, y: self.online(x, y),
+                                lambda x, y, z: self.identify_method(x, y, z)),
             self.offline_topic: (AnalysisType.OFFLINE.value,
-                                 lambda x: self.offline(x),
-                                 lambda x, y, z, t: self.identify_method(x, y, z, t)),
+                                 lambda x, y: self.offline(x, y),
+                                 lambda x, y, z: self.identify_method(x, y, z)),
             self.image_topic: (AnalysisType.IMAGE.value,
-                               lambda x: self.image(x),
-                               lambda x, y, z, t: self.identify_method(x, y, z, t)),
+                               lambda x, y: self.image(x, y),
+                               lambda x, y, z: self.identify_method(x, y, z)),
             self.recording_task_topic: (AnalysisType.RECORDING.value,
-                                        lambda x: self.recording(x),
-                                        lambda x, y, z, t: self.recording_method(x, y, z, t))
+                                        lambda x, y: self.recording(x, y),
+                                        lambda x, y, z: self.recording_method(x, y, z))
         }
 
+        ####################################### 获取当前显卡设备名称 ######################################
+        gpu_name = get_first_gpu_name()
+        gpu_codes = ['3090', '2080', '4090', 'A10']
+        gpu_array = [g for g in gpu_codes if g in gpu_name]
+        if len(gpu_array) > 0:
+            self.content['gpu_name'] = gpu_array[0]
+            if gpu_array[0] == '2080':
+                self.content['gpu_name'] = '2080Ti'
+        else:
+            raise Exception("GPU资源不在提供的模型所支持的范围内！请先提供对应的GPU模型！")
+
+
+
     # 服务调用启动方法
     def start_service(self):
+        if not torch.cuda.is_available():
+            raise Exception("cuda不在活动状态, 请检测显卡驱动是否正常！！！！")
         # 初始化kafka监听者
         customerKafkaConsumer = KafkaUtils.CustomerKafkaConsumer(self.content, topics=self.topics)
         logger.info("(♥◠‿◠)ﾉﾞ  DSP【算法调度服务】启动成功   ლ(´ڡ`ლ)ﾞ")
@@ -84,69 +104,94 @@ class DispatcherService:
                     for k, v in msg.items():
                         for m in v:
                             message = m.value
-                            analysisType = self.analysisType.get(m.topic)[0]
-                            self.analysisType.get(m.topic)[2](customerKafkaConsumer, m, message, analysisType)
+                            # 提交offset
+                            customerKafkaConsumer.commit_offset(m)
+                            logger.info("当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
+                                        m.topic, m.offset, m.partition, message,
+                                        self.getRequestId(message.get("request_id")))
+                            topic_method = self.analysisType.get(m.topic)
+                            topic_method[2](m, message, topic_method[0])
                 else:
                     time.sleep(1)
             except Exception as e:
                 logger.exception("主线程异常：", e)
 
-    def identify_method(self, customerKafkaConsumer, m, message, analysisType):
+    '''
+        考虑到requestId为空的场景
+    '''
+
+    def getRequestId(self, request_id):
+        if request_id is None or len(request_id) == 0:
+            return ''
+        return request_id
+
+    '''
+        实时、离线、图片识别逻辑
+        1. m kafka消息体
+        2. 请求消息体
+        3. 分析类型：实时、离线、图片
+    '''
+
+    def identify_method(self, m, message, analysisType):
         try:
-            customerKafkaConsumer.commit_offset(m)
-            logger.info(
-                "当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
-                m.topic, m.offset, m.partition, message, message.get("request_id"))
-            self.analysisType.get(m.topic)[1](message)
+            # 校验参数
+            check_result = self.check_msg(message)
+            if not check_result:
+                return
+            self.analysisType.get(m.topic)[1](message, analysisType)
         except ServiceException as s:
-            logger.exception("消息监听异常：{}, requestId: {}", s.msg, message.get("request_id"))
-            self.fbQueue.put({
-                "feedback": message_feedback(message.get("request_id"),
-                                             AnalysisStatus.FAILED.value,
-                                             analysisType,
-                                             s.code,
-                                             s.msg,
-                                             analyse_time=TimeUtils.now_date_to_str())})
+            logger.exception("消息监听异常：{}, requestId: {}", s.msg, self.getRequestId(message.get("request_id")))
+            if message.get("request_id"):
+                self.fbQueue.put({
+                    "feedback": message_feedback(message.get("request_id"),
+                                                 AnalysisStatus.FAILED.value,
+                                                 analysisType,
+                                                 s.code,
+                                                 s.msg,
+                                                 analyse_time=TimeUtils.now_date_to_str())})
         except Exception as e:
-            logger.exception("消息监听异常：{}, requestId: {}", e, message.get("request_id"))
-            self.fbQueue.put({
-                "feedback": message_feedback(message.get("request_id"),
-                                             AnalysisStatus.FAILED.value,
-                                             analysisType,
-                                             ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
-                                             ExceptionType.SERVICE_INNER_EXCEPTION.value[1],
-                                             analyse_time=TimeUtils.now_date_to_str())})
-
-    def recording_method(self, customerKafkaConsumer, m, message, analysisType):
+            logger.exception("消息监听异常：{}, requestId: {}", e, self.getRequestId(message.get("request_id")))
+            if message.get("request_id"):
+                self.fbQueue.put({
+                    "feedback": message_feedback(message.get("request_id"),
+                                                 AnalysisStatus.FAILED.value,
+                                                 analysisType,
+                                                 ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
+                                                 ExceptionType.SERVICE_INNER_EXCEPTION.value[1],
+                                                 analyse_time=TimeUtils.now_date_to_str())})
+
+    def recording_method(self, m, message, analysisType):
         try:
-            customerKafkaConsumer.commit_offset(m)
-            logger.info(
-                "当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
-                m.topic, m.offset, m.partition, message, message.get("request_id"))
-            self.analysisType.get(m.topic)[1](message)
+            # 校验参数
+            check_result = self.check_msg(message)
+            if not check_result:
+                return
+            self.analysisType.get(m.topic)[1](message, analysisType)
         except ServiceException as s:
-            logger.exception("消息监听异常：{}, requestId: {}", s.msg, message.get("request_id"))
-            self.fbQueue.put({
-                "recording": recording_feedback(message.get("request_id"),
-                                                RecordingStatus.RECORDING_FAILED.value[0],
-                                                error_code=s.code,
-                                                error_msg=s.msg)})
+            logger.exception("消息监听异常：{}, requestId: {}", s.msg, self.getRequestId(message.get("request_id")))
+            if message.get("request_id"):
+                self.fbQueue.put({
+                    "recording": recording_feedback(message.get("request_id"),
+                                                    RecordingStatus.RECORDING_FAILED.value[0],
+                                                    error_code=s.code,
+                                                    error_msg=s.msg)})
         except Exception as e:
-            logger.exception("消息监听异常：{}, requestId: {}", e, message.get("request_id"))
-            self.fbQueue.put({
-                "recording": recording_feedback(message.get("request_id"),
-                                                RecordingStatus.RECORDING_FAILED.value[0],
-                                                ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
-                                                ExceptionType.SERVICE_INNER_EXCEPTION.value[1])})
+            logger.exception("消息监听异常：{}, requestId: {}", e, self.getRequestId(message.get("request_id")))
+            if message.get("request_id"):
+                self.fbQueue.put({
+                    "recording": recording_feedback(message.get("request_id"),
+                                                    RecordingStatus.RECORDING_FAILED.value[0],
+                                                    ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
+                                                    ExceptionType.SERVICE_INNER_EXCEPTION.value[1])})
 
     # 开启实时进程
-    def startOnlineProcess(self, msg, gpu_ids):
+    def startOnlineProcess(self, msg, gpu_ids, analysisType):
         # 相同的requestId不在执行
         if self.onlineProcesses.get(msg.get("request_id")):
             logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
             return
         cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids,
-               "analyse_type": AnalysisType.ONLINE.value}
+               "analyse_type": analysisType}
         # 创建在线识别进程并启动
         oirp = OnlineIntelligentRecognitionProcess(cfg)
         oirp.start()
@@ -161,7 +206,9 @@ class DispatcherService:
             return
         ps.sendEvent({'command': 'stop'})
 
-    # 检查实时、离线进程任务运行情况，去除不活动的任务
+    '''
+        检查实时、离线进程任务运行情况，去除不活动的任务
+    '''
     def check_process_task(self):
         for process in self.listeningProcesses:
             for requestId in list(process.keys()):
@@ -169,13 +216,13 @@ class DispatcherService:
                     del process[requestId]
 
     # 开启离线进程
-    def startOfflineProcess(self, msg, gpu_ids):
+    def startOfflineProcess(self, msg, gpu_ids, analysisType):
         # 相同的requestId不在执行
         if self.offlineProcesses.get(msg.get("request_id")):
             logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
             return
         cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids,
-               "analyse_type": AnalysisType.OFFLINE.value}
+               "analyse_type": analysisType}
         # 创建在线识别进程并启动
         ofirp = OfflineIntelligentRecognitionProcess(cfg)
         ofirp.start()
@@ -190,21 +237,21 @@ class DispatcherService:
         ps.sendEvent({'command': 'stop'})
 
     # 开启图片分析进程
-    def startImageProcess(self, msg, gpu_ids):
+    def startImageProcess(self, msg, gpu_ids, analysisType):
         # 相同的requestId不在执行
         pp = self.photoProcesses.get(msg.get("request_id"))
         if pp is not None:
             logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
             return
         cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg, "gpu_ids": gpu_ids,
-               "analyse_type": AnalysisType.IMAGE.value}
+               "analyse_type": analysisType}
         # 创建在线识别进程并启动
         imagep = PhotosIntelligentRecognitionProcess(cfg)
         imagep.start()
         self.photoProcesses[msg.get("request_id")] = imagep
 
     '''
-    校验kafka消息
+        校验kafka消息
     '''
 
     def check_msg(self, msg):
@@ -223,72 +270,62 @@ class DispatcherService:
             raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
                                    ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
 
+    '''
+        开启反馈线程，用于发送消息
+    '''
+
     def start_feedback_thread(self):
+        # 如果反馈线程为空，启动反馈线程，如果反馈线程意外停止，再次启动反馈线程
         if self.feedbackThread is None or not self.feedbackThread.is_alive():
             self.feedbackThread = FeedbackThread(self.fbQueue, self.content)
             self.feedbackThread.setDaemon(True)
             self.feedbackThread.start()
 
-
-
     '''
     在线分析逻辑
     '''
 
-    def online(self, message):
-        # 参数校验
-        check_result = self.check_msg(message)
-        if not check_result:
-            return
+    def online(self, message, analysisType):
         if 'start' == message.get("command"):
             gpu_ids = GPUtils.check_gpu_resource(self.content)
-            self.startOnlineProcess(message, gpu_ids)
+            self.startOnlineProcess(message, gpu_ids, analysisType)
         elif 'stop' == message.get("command"):
             self.stopOnlineProcess(message)
         else:
             pass
 
-    def offline(self, message):
-        check_result = self.check_msg(message)
-        if not check_result:
-            return
+    def offline(self, message, analysisType):
         if 'start' == message.get("command"):
             gpu_ids = GPUtils.check_gpu_resource(self.content)
-            self.startOfflineProcess(message, gpu_ids)
+            self.startOfflineProcess(message, gpu_ids, analysisType)
         elif 'stop' == message.get("command"):
             self.stopOfflineProcess(message)
         else:
             pass
 
-    def image(self, message):
-        check_result = self.check_msg(message)
-        if not check_result:
-            return
+    def image(self, message, analysisType):
         if 'start' == message.get("command"):
             gpu_ids = GPUtils.check_gpu_resource(self.content)
-            self.startImageProcess(message, gpu_ids)
+            self.startImageProcess(message, gpu_ids, analysisType)
         else:
             pass
 
-    def recording(self, message):
-        check_result = self.check_msg(message)
-        if not check_result:
-            return
+    def recording(self, message, analysisType):
         if 'start' == message.get("command"):
             logger.info("开始录屏")
-            self.startRecordingProcess(message)
+            self.startRecordingProcess(message, analysisType)
         elif 'stop' == message.get("command"):
             self.stopRecordingProcess(message)
         else:
             pass
 
     # 开启录屏进程
-    def startRecordingProcess(self, msg):
+    def startRecordingProcess(self, msg, analysisType):
         if self.recordingProcesses.get(msg.get("request_id")):
             logger.warning("重复任务，请稍后再试！requestId:{}", msg.get("request_id"))
             return
         cfg = {"fbQueue": self.fbQueue, "content": self.content, "msg": msg,
-               "analyse_type": AnalysisType.RECORDING.value}
+               "analyse_type": analysisType}
         srp = ScreenRecordingProcess(cfg)
         srp.start()
         self.recordingProcesses[msg.get("request_id")] = srp

test/aliyun/ossdemo.py

@@ -0,0 +1,119 @@
+# -*- coding: utf-8 -*-
+import datetime
+
+import cv2
+import oss2
+import time
+
+from loguru import logger
+
+'''
+ 图片上传使用OSS
+ 1. 阿里云对象存储OSS官网地址：https://help.aliyun.com/product/31815.html?spm=a2c4g.32006.0.0.8c546cf0BpkAQ2
+ 2. 阿里云对象存储OSS SDK示例地址：https://help.aliyun.com/document_detail/32006.html?spm=a2c4g.32006.0.0.66874b78q1pwLa
+ 3. python安装SDK地址： https://help.aliyun.com/document_detail/85288.html?spm=a2c4g.32026.0.0.3f24417coCphWj
+ 4. 安装SDK： pip install oss2
+ 5. 安装python-devel
+        安装python-devel
+        由于SDK需要crcmod库计算CRC校验码，而crcmod依赖Python.h文件，如果系统缺少这个头文件，安装SDK不会失败，但crcmod的C扩展模式安装会失败，因此导致上传、下载等操作效率非常低下。
+        如果python-devel包不存在，则首先要安装这个包。
+        对于Windows系统和Mac OS X系统，由于安装Python的时候会将Python依赖的头文件一并安装，因此您无需安装python-devel。
+        对于CentOS、RHEL、Fedora系统，请执行以下命令安装python-devel。
+        sudo yum install python-devel                 
+        对于Debian，Ubuntu系统，请执行以下命令安装python-devel。
+        sudo apt-get install python-dev  
+ 6、图片域名地址：https://image.t-aaron.com/ 
+'''
+
+
+class AliyunOssSdk:
+
+    def __init__(self):
+        self.__client = None
+        self.__access_key = 'LTAI5tMiefafZ6br4zmrQWv9'
+        self.__access_secret = 'JgzQjSCkwZ7lefZO6egOArw38YH1Tk'
+        self.__endpoint = 'http://oss-cn-shanghai.aliyuncs.com'
+        self.__bucket = 'ta-tech-image'
+
+    def get_oss_bucket(self):
+        if not self.__client:
+            auth = oss2.Auth(self.__access_key, self.__access_secret)
+            self.__client = oss2.Bucket(auth, self.__endpoint, self.__bucket, connect_timeout=30)
+
+    def upload_file(self, updatePath, fileByte):
+        logger.info("开始上传文件到oss!")
+        MAX_RETRIES = 3
+        retry_count = 0
+        while True:
+            try:
+                self.get_oss_bucket()
+                result = self.__client.put_object(updatePath, fileByte)
+                return result
+                logger.info("上传文件到oss成功!")
+                break
+            except Exception as e:
+                self.__client = None
+                retry_count += 1
+                time.sleep(1)
+                logger.info("上传文件到oss失败, 重试次数：{}", retry_count)
+                if retry_count > MAX_RETRIES:
+                    logger.exception("上传文件到oss重试失败:{}", e)
+                    raise e
+
+
+YY_MM_DD_HH_MM_SS = "%Y-%m-%d %H:%M:%S"
+YMDHMSF = "%Y%m%d%H%M%S%f"
+
+def generate_timestamp():
+    """根据当前时间获取时间戳，返回整数"""
+    return int(time.time())
+
+def now_date_to_str(fmt=None):
+    if fmt is None:
+        fmt = YY_MM_DD_HH_MM_SS
+    return datetime.datetime.now().strftime(fmt)
+
+if __name__ == "__main__":
+    # 初始化oss对象
+    ossClient = AliyunOssSdk()
+    # 读取本地图片
+    image_frame = cv2.imread('aaa.jpeg')
+    or_result, or_image = cv2.imencode(".jpg", image_frame)
+    # 图片名称命名规则
+    # 1、base_dir 基本文件夹名称，由拓恒公司传参
+    # 2、time_now 现在的时间
+    # 3、current_frame 当前视频的帧数
+    # 4、last_frame 如果有跳帧操作, 填写跳帧的步长，如果没有，和current_frame参数保持一致
+    # 5、random_num 随机时间字符串
+    # 6、mode_type 类型：实时视频直播的方式用（online） 离线视频直播（填写视频地址识别）用（offline）
+    # 7、requestId 请求id, 拓恒公司传参
+    # 8、image_type 原图用（OR） AI识别后的图片用（AI）
+    random_num = now_date_to_str(YMDHMSF)
+    time_now = now_date_to_str("%Y-%m-%d-%H-%M-%S")
+    image_format = "{base_dir}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}-{base_dir}" \
+                   "-{requestId}_{image_type}.jpg"
+    image_name = image_format.format(
+        base_dir='PWL202304141639429276',
+        time_now=time_now,
+        current_frame='0',
+        last_frame='0',
+        random_num=random_num,
+        mode_type='offline',
+        requestId='111111111111111111',
+        image_type='OR')
+    result = ossClient.upload_file(image_name, or_image.tobytes())
+    # print('http status: {0}'.format(result.status))
+    # # 请求ID。请求ID是本次请求的唯一标识，强烈建议在程序日志中添加此参数。
+    # print('request_id: {0}'.format(result.request_id))
+    # # ETag是put_object方法返回值特有的属性，用于标识一个Object的内容。
+    # print('ETag: {0}'.format(result.etag))
+    # # HTTP响应头部。
+    # print('date: {0}'.format(result.headers['date']))
+    # print(result.__reduce__())
+    # 对于图片上传, 上传成功后，直接将image_name给拓恒公司就可以了
+    # 如果测试查看图片是否上传成功
+    # 可以使用域名拼接
+    image_url = 'https://image.t-aaron.com/' + image_name
+    print(image_url)
+    # 拓恒公司只需要image_name
+

test/aliyun/voddemo.py

@@ -0,0 +1,130 @@
+# -*- coding: utf-8 -*-
+
+import time
+
+import json
+
+from aliyunsdkcore.client import AcsClient
+from aliyunsdkvod.request.v20170321 import GetPlayInfoRequest
+from voduploadsdk.AliyunVodUtils import *
+from voduploadsdk.AliyunVodUploader import AliyunVodUploader
+from voduploadsdk.UploadVideoRequest import UploadVideoRequest
+
+'''
+ 视频上传使用vod
+ 1. 阿里云VOD文档地址：https://help.aliyun.com/product/29932.html?spm=5176.8413026.J_3895079540.5.1b4a1029mXvncc
+ 2. 阿里云对象存储OSS SDK示例地址：https://help.aliyun.com/document_detail/64148.html?spm=a2c4g.64148.0.0.5ae54150jUecEU
+ 4. 安装SDK： 
+    python -m pip install aliyun-python-sdk-core -i https://pypi.tuna.tsinghua.edu.cn/simple 
+    python -m pip install aliyun-python-sdk-live -i https://pypi.tuna.tsinghua.edu.cn/simple 
+    python -m pip install aliyun-python-sdk-core-v3  -i https://pypi.tuna.tsinghua.edu.cn/simple 
+    python -m pip install aliyun-python-sdk-vod  -i https://pypi.tuna.tsinghua.edu.cn/simple 
+    python -m pip install alibabacloud_vod20170321 -i https://pypi.tuna.tsinghua.edu.cn/simple
+    python -m pip install oss2 -i https://pypi.tuna.tsinghua.edu.cn/simple
+    python -m pip install voduploadsdk -i https://pypi.tuna.tsinghua.edu.cn/simple
+ 5. 视频域名地址：https://vod.play.t-aaron.com/
+'''
+
+
+class AliyunVodSdk:
+
+    def __init__(self):
+        self.__client = None
+        self.__access_key = 'LTAI5tMiefafZ6br4zmrQWv9'
+        self.__access_secret = 'JgzQjSCkwZ7lefZO6egOArw38YH1Tk'
+        self.__regionId = "cn-shanghai"
+        self.__cateId = '1000468340'
+
+    def init_vod_client(self):
+        return AcsClient(self.__access_key, self.__access_secret, self.__regionId, auto_retry=True, max_retry_time=3,
+                         timeout=5)
+
+    '''
+        根据videoId获取视频地址
+    '''
+
+    def get_play_info(self, videoId):
+        logger.info("开始获取视频地址，videoId:{}", videoId)
+        start = time.time()
+        while True:
+            try:
+                clt = self.init_vod_client()
+                request = GetPlayInfoRequest.GetPlayInfoRequest()
+                request.set_accept_format('JSON')
+                request.set_VideoId(videoId)
+                request.set_AuthTimeout(3600 * 5)
+                response = json.loads(clt.do_action_with_exception(request))
+                play_url = response["PlayInfoList"]["PlayInfo"][0]["PlayURL"]
+                logger.info("获取视频地址成功，视频地址: {}", play_url)
+                return play_url
+            except Exception as e:
+                logger.error("获取视频地址失败，5秒后重试, requestId: {}")
+                time.sleep(5)
+                current_time = time.time()
+                if "HTTP Status: 403" not in str(e):
+                    logger.exception("获取视频地址失败: {}", e)
+                    raise e
+                if "HTTP Status: 403" in str(e) and ("UploadFail" in str(e) or "TranscodeFail" in str(e)):
+                    self.logger.exception("获取视频地址失败: {}", e)
+                    raise e
+                diff_time = current_time - start
+                if diff_time > 60 * 60 * 2:
+                    logger.exception("获取视频地址失败超时异常: {}，超时时间：{}", e, diff_time)
+                    raise e
+
+    def upload_local_video(self, filePath, file_title, storageLocation=None):
+        logger.info("开始执行vod视频上传, filePath: {}", filePath)
+        uploader = AliyunVodUploader(self.__access_key, self.__access_secret)
+        uploadVideoRequest = UploadVideoRequest(filePath, file_title)
+        uploadVideoRequest.setCateId(self.__cateId)
+        if storageLocation:
+            uploadVideoRequest.setStorageLocation(storageLocation)
+        MAX_RETRIES = 3
+        retry_count = 0
+        while True:
+            try:
+                result = uploader.uploadLocalVideo(uploadVideoRequest)
+                logger.info("vod视频上传成功, videoId:{}", result.get("VideoId"))
+                return result.get("VideoId")
+            except AliyunVodException as e:
+                retry_count += 1
+                time.sleep(3)
+                logger.error("vod视频上传失败，重试次数：{}", retry_count)
+                if retry_count >= MAX_RETRIES:
+                    self.logger.exception("vod视频上传重试失败: {}", e)
+                    raise e
+
+
+YY_MM_DD_HH_MM_SS = "%Y-%m-%d %H:%M:%S"
+YMDHMSF = "%Y%m%d%H%M%S%f"
+
+
+def generate_timestamp():
+    """根据当前时间获取时间戳，返回整数"""
+    return int(time.time())
+
+
+def now_date_to_str(fmt=None):
+    if fmt is None:
+        fmt = YY_MM_DD_HH_MM_SS
+    return datetime.datetime.now().strftime(fmt)
+
+
+if __name__ == "__main__":
+    # 本地原视频命名
+    random_time = now_date_to_str(YMDHMSF)
+    # # 如果是离线视频，将 _on_or_ 替换为 _off_or_
+    # orFilePath = "%s%s%s%s%s" % ('本地路径', random_time, "_on_or_", 'requestId', ".mp4")
+    # # 本地AI识别后的视频命名
+    # # 如果是离线视频，将 _on_ai_ 替换为 _off_ai_
+    # aiFilePath = "%s%s%s%s%s" % ('本地路径', random_time, "_on_ai_", 'requestId', ".mp4")
+    # filePath = "%s%s%s%s%s" % ('D:\\shipin\\', random_time, "_on_ai_", '11111111', ".mp4")
+    filePath = 'D:\\shipin\\777.mp4'
+    codClinet = AliyunVodSdk()
+    result = codClinet.upload_local_video(filePath, 'aiOnLineVideo1')
+    print(result)
+    url = codClinet.get_play_info(result)
+    print(url)
+
+
+

test/cuda/test.py

@@ -0,0 +1,88 @@
+import time
+from pathlib import Path
+
+import GPUtil
+import cv2
+import numpy as np
+import torch
+from PIL import ImageFont, Image, ImageDraw
+
+
+# print(Path(__file__))  # 表示当前脚本文件的路径
+# print(Path(__file__).parent)  # 表示当前路径的父级目录
+
+
+# import time
+# from contextlib import contextmanager
+#
+# @contextmanager
+# def timer():
+#     start_time = time.time()
+#     yield
+#     end_time = time.time()
+#     print('Time elapsed:', end_time - start_time)
+# # 使用上下文管理器
+# with timer():
+#     time.sleep(1)
+
+# print(torch.cuda.is_available())
+# print(GPUtil.getGPUs()[0].name)
+
+# def get_first_gpu_name():
+#     gps = GPUtil.getGPUs()
+#     if gps is None or len(gps) == 0:
+#         raise Exception("未获取到gpu资源, 先检测服务器是否已经配置GPU资源！")
+#     return gps[0].name
+# gpu_name = get_first_gpu_name()
+# aa = [g for g in ['3090', '2080', '4090', 'A10'] if g in gpu_name]
+# print(aa)
+#
+# import tensorrt as trt
+# # 定义反序列化引擎文件的函数
+# def deserialize_engine_from_file(engine_file_path):
+#     runtime = trt.Runtime(trt.Logger())
+#     engine = None
+#     with open(engine_file_path, "rb") as f:
+#         while True:
+#             data = f.read(1024 * 1024)
+#             if not data:
+#                 break
+#             tmp_engine = runtime.deserialize_cuda_engine(data)
+#             if engine is None:
+#                 engine = tmp_engine
+#             else:
+#                 for i in range(tmp_engine.num_bindings):
+#                     engine.set_binding_shape(i, tmp_engine.get_binding_shape(i))
+#     return engine
+# engine_file_path = "/path/to/engine_file.engine"
+# s = time.time()
+# engine = deserialize_engine_from_file(engine_file_path)
+# print("1 加载trt文件时间", time.time() - s)
+# s1 = time.time()
+# with open(engine_file_path, "rb") as f1, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
+#     model = runtime.deserialize_cuda_engine(f1.read())
+# print("2 加载trt文件时间", time.time() - s1)
+
+def get_label_array(color=None, label=None, outfontsize=None, fontpath="conf/platech.ttf"):
+    # Plots one bounding box on image 'im' using PIL
+    fontsize = outfontsize
+    font = ImageFont.truetype(fontpath, fontsize, encoding='utf-8')
+    x,y,txt_width, txt_height = font.getbbox(label)
+    print(x,y,txt_width, txt_height)
+    im = np.zeros((txt_height, txt_width, 3), dtype=np.uint8)
+    im = Image.fromarray(im)
+    draw = ImageDraw.Draw(im)
+    draw.rectangle([0, 0, txt_width, txt_height], fill=tuple(color))
+    draw.text((0, -3), label, fill=(255, 255, 255), font=font)
+    im_array = np.asarray(im)
+    # if outfontsize:
+    #     scaley = outfontsize / txt_height
+    #     im_array = cv2.resize(im_array, (0, 0), fx=scaley, fy=scaley)
+    return im_array
+
+
+aaa = time.time()
+im_array = get_label_array(color=(0, 255, 0), label="排口", outfontsize=40, fontpath="platech.ttf")
+print(time.time() - aaa)
+cv2.imshow("frame", im_array)
+cv2.waitKey(0)

test/cuda/test1.py

@@ -0,0 +1,27 @@
+
+
+from sklearn import linear_model
+
+# x = [[20, 3],
+#      [23, 7],
+#      [31, 10],
+#      [42, 13],
+#      [50, 7],
+#      [60, 5]]
+# y = [0, 1, 1, 1, 0, 0]
+# lr = linear_model.LogisticRegression()
+# lr.fit(x, y)
+# testX = [[28, 8]]
+# label = lr.predict(testX)
+# print("predicted label = ", label)
+#
+# prob = lr.predict_proba(testX)
+# print("probability = ", prob)
+
+import tensorflow as tf
+tf.compat.v1.disable_eager_execution()
+hello = tf.constant("hello, world!")
+sess = tf.compat.v1.Session()
+result = sess.run(hello)
+sess.close()
+print(result)

test/dsp/logs/dsp.log

@@ -0,0 +1,7 @@
+2023-04-18 13:41:42.066 [ERROR][MainProcess-25160-MainThread-30168][16] Test-<module> - 异常信息：division by zero
+Traceback (most recent call last):
+
+> File "D:\tuoheng\codenew\tuoheng_alg\test\路径\Test.py", line 14, in <module>
+    2/0
+
+ZeroDivisionError: division by zero

test/gpu/gputest.py

@@ -1,34 +1,7 @@
-import os
-import pynvml
-pynvml.nvmlInit()
 
-
-# 安装 pip install nvidia-ml-py3
-def usegpu(need_gpu_count=1):
-
-    nouse=[]
-    for index in range(pynvml.nvmlDeviceGetCount()):
-        # 这里的0是GPU id
-        handle = pynvml.nvmlDeviceGetHandleByIndex(index)
-        meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
-        used= meminfo.used/meminfo.total
-        print(meminfo.used)
-        print(meminfo.total)
-        if used < 0.8:
-            nouse.append(index)
-    if len(nouse) >= need_gpu_count:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(map(str, nouse[:need_gpu_count]))
-        return nouse[:need_gpu_count]
-    elif len(nouse)>0:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(map(str, nouse))
-        return len(nouse)
-    else:
-        return 0
+import tensorflow as tf
+import torch
 
 if __name__ == '__main__':
-    gpus=usegpu(need_gpu_count=2)
-    print(gpus)
-    if gpus:
-        print("use gpu ok")
-    else:
-        print("no gpu is valid")
+    gpu_name = torch.cuda.get_device_name(0)
+    print(gpu_name)

test/序列化/Test.py

@@ -0,0 +1,21 @@
+from loguru import logger
+import pickle
+
+# 定义一个类
+class Person:
+    def __init__(self, name, age):
+        self.name = name
+        self.age = age
+
+# 创建一个Person实例
+person = Person("Alice", 25)
+
+# 使用Loguru的serialize方法将Person实例序列化为字节字符串
+serialized_person = logger.serialize(person)
+print(serialized_person)
+# 使用pickle库将字节字符串反序列化为Python对象
+deserialized_person = pickle.loads(serialized_person)
+
+# 输出反序列化后的对象属性
+print(deserialized_person.name) # Alice
+print(deserialized_person.age)  # 25

test/路径/Test.py

@@ -1,5 +1,16 @@
 import os
+import sys
 
+from util import YmlUtils, LogUtils
+from loguru import logger
 print(os.getcwd())
 
-print(os.path.relpath(__file__))
+print(os.path.relpath(__file__))
+base_dir = os.path.dirname(os.path.realpath(sys.argv[0]))
+content = YmlUtils.getConfigs(base_dir + "/../../")
+LogUtils.init_log(content)
+
+try:
+    2/0
+except Exception as e:
+    logger.exception("异常信息：{}", e)

test/集合/test.py

@@ -1,7 +1,23 @@
-list1 = [1, 2, 3, 4]
-list2 = [1,2,4]
-if set(list2) == set(list1):
-    print("1111111")
-else:
-    print("222222")
+# list1 = [1, 2, 3, 4]
+# list2 = [1,2,4]
+# if set(list2) == set(list1):
+#     print("1111111")
+# else:
+#     print("222222")
 
+import numpy as np
+
+# list1 = [1, 2, 3, 4]
+# tl = np.asarray([1, 2], np.float32)
+# box = np.asarray([tl], np.int32)
+# print(tl)c
+# print(box[0][1])
+import cv2
+ai_video_file = cv2.VideoWriter(r"C:\Users\chenyukun\Desktop\fsdownload\aa.mp4", cv2.VideoWriter_fourcc(*'mp4v'), 25,
+                                (1920,1080))
+# ai_video_file.set(cv2.VIDEOWRITER_PROP_BITRATE, 4000)
+ai_video_file.set(cv2.CAP_PROP_BITRATE, 4000)
+ai_video_file.set(cv2.VIDEOWRITER_PROP_QUALITY, 80)
+print(help(cv2.VideoWriter.set))
+print(dir(cv2))
+print(help(cv2))

util/AliyunSdk.py

@@ -55,7 +55,7 @@ class ThAliyunVodSdk():
 
     def init_vod_client(self, accessKeyId, accessKeySecret):
         regionId = self.content["aliyun"]["vod"]["ecsRegionId"]
-        return AcsClient(accessKeyId, accessKeySecret, regionId, auto_retry=True, max_retry_time=3, timeout=5)
+        return AcsClient(accessKeyId, accessKeySecret, regionId, auto_retry=True, max_retry_time=3, timeout=30)
 
     def get_play_info(self, videoId):
         self.logger.info("开始获取视频地址，videoId:{}, requestId:{}", videoId, self.requestId)
@@ -116,14 +116,13 @@ class ThAliyunVodSdk():
                 time.sleep(3)
                 self.logger.error("vod视频上传失败，重试次数：{}, requestId:{}", retry_count, self.requestId)
                 if retry_count >= MAX_RETRIES:
-                    self.logger.exception("vod视频上传重试失败: {}, requestId:{}", e, self.requestId)
+                    self.logger.exception("vod视频上传重试失败: {}, requestId:{}", e.message, self.requestId)
                     raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
                                            ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
 
+    def get_play_url(self, filePath, file_title):
+        videoId = self.upload_local_video(filePath, file_title)
+        if videoId is None or len(videoId) == 0:
+            return None
+        return self.get_play_info(videoId)
 
-def get_play_url(args):
-    thAliyunVodSdk = ThAliyunVodSdk(args[2], args[3], args[4])
-    videoId = thAliyunVodSdk.upload_local_video(args[0], args[1])
-    if videoId is None or len(videoId) == 0:
-        return None
-    return thAliyunVodSdk.get_play_info(videoId)

util/Cv2Utils.py

@@ -113,7 +113,7 @@ class Cv2Util():
             self.clear_video_info()
             raise s
         except Exception as e:
-            logger.exception("获取视频信息异常：{}, requestId:{}", e, self.requestId)
+            logger.error("获取视频信息异常：{}, requestId:{}", e, self.requestId)
             self.clear_video_info()
 
     '''
@@ -285,7 +285,7 @@ class Cv2Util():
             in_bytes = self.pull_p.stdout.read(self.wh)
             if in_bytes is not None and len(in_bytes) > 0:
                 try:
-                    result = (np.frombuffer(in_bytes, np.uint8).reshape([int(self.height), int(self.width), 3]))
+                    result = (np.frombuffer(in_bytes, np.uint8).reshape([self.height, self.width, 3]))
                     # img = (np.frombuffer(in_bytes, np.uint8)).reshape((self.h, self.w))
                 except Exception as ei:
                     logger.exception("视频格式异常：{}, requestId:{}", ei, self.requestId)
@@ -294,7 +294,7 @@ class Cv2Util():
                 # result = cv2.cvtColor(img, cv2.COLOR_YUV2BGR_NV12)
                 # result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
                 if self.width > Constant.width:
-                    result = cv2.resize(result, self.w, self.h, interpolation=cv2.INTER_LINEAR)
+                    result = cv2.resize(result, (self.w, self.h), interpolation=cv2.INTER_LINEAR)
         except ServiceException as s:
             raise s
         except Exception as e:
@@ -420,7 +420,8 @@ class Cv2Util():
                        "-an",
                        # '-flvflags', 'no_duration_filesize',
                        # '-preset', 'fast',  # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
-                       '-preset', 'llhq',  # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
+                       '-preset', 'p6',  # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
+                       '-tune', 'll',
                        '-f', 'flv',
                        self.pushUrl]
             logger.info("fps:{}|height:{}|width:{}|requestId:{}", self.fps, self.height, self.width, self.requestId)
@@ -479,13 +480,9 @@ class Cv2Util():
                     raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
                                            ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
         except ServiceException as s:
-            if self.or_video_file is not None:
-                self.or_video_file.release()
             logger.exception("构建OR文件写对象异常: {}, requestId:{}", s.msg, self.requestId)
             raise s
         except Exception as e:
-            if self.or_video_file is not None:
-                self.or_video_file.release()
             logger.exception("构建OR文件写对象异常: {}, requestId:{}", e, self.requestId)
             raise e
 
@@ -499,13 +496,9 @@ class Cv2Util():
                     raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
                                            ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
         except ServiceException as s:
-            if self.ai_video_file is not None:
-                self.ai_video_file.release()
             logger.exception("构建AI文件写对象异常: {}, requestId:{}", s.msg, self.requestId)
             raise s
         except Exception as e:
-            if self.ai_video_file is not None:
-                self.ai_video_file.release()
             logger.exception("构建AI文件写对象异常: {}, requestId:{}", e, self.requestId)
             raise e
 
@@ -522,12 +515,13 @@ class Cv2Util():
                 try:
                     ai_retry_num += 1
                     if ai_retry_num > 3:
-                        logger.exception("重新写入原视频视频到本地,重试失败：{}, requestId: {}", e, self.requestId)
+                        logger.exception("重新写入原视频视频到本地,重试失败, requestId: {}", self.requestId)
                         raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
                                                ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
                     self.or_video_file.write(frame)
                     logger.info("重新写入原视频视到本地, 当前重试次数: {}, requestId: {}", ai_retry_num,
                                 self.requestId)
+                    break
                 except Exception as e:
                     logger.exception("重新写入原视频视到本地：{}, 开始重试, 当前重试次数：{}, requestId: {}", e,
                                      ai_retry_num, self.requestId)
@@ -545,12 +539,13 @@ class Cv2Util():
                 try:
                     ai_retry_num += 1
                     if ai_retry_num > 3:
-                        logger.exception("重新写入分析后的视频到本地,重试失败：{}, requestId: {}", e, self.requestId)
+                        logger.exception("重新写入分析后的视频到本地,重试失败, requestId: {}", self.requestId)
                         raise ServiceException(ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
                                                ExceptionType.SERVICE_INNER_EXCEPTION.value[1])
                     self.ai_video_file.write(frame)
                     logger.info("重新写入分析后的视频到本地, 当前重试次数: {}, requestId: {}", ai_retry_num,
                                 self.requestId)
+                    break
                 except Exception as e:
                     logger.exception("重新写入分析后的视频到本地：{}, 开始重试, 当前重试次数：{}, requestId: {}", e,
                                      ai_retry_num, self.requestId)

util/GPUtils.py

@@ -33,6 +33,13 @@ def get_all_gpu_ids():
     return GPUtil.getGPUs()
 
 
+def get_first_gpu_name():
+    gps = GPUtil.getGPUs()
+    if gps is None or len(gps) == 0:
+        raise Exception("未获取到gpu资源, 先检测服务器是否已经配置GPU资源！")
+    return gps[0].name
+
+
 def check_gpu_resource(content):
     gpu_ids = get_gpu_ids(content)
     if gpu_ids is None or len(gpu_ids) == 0 or (0 not in gpu_ids and str(0) not in gpu_ids):

util/PlotsUtils.py

@@ -0,0 +1,224 @@
+
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+from PIL.Image import Image
+
+
+def get_label_array(color=None, label=None, font=None, fontSize=None):
+    x, y, width, height = font.getbbox(label)
+    text_image = np.zeros((height, width, 3), dtype=np.uint8)
+    text_image = Image.fromarray(text_image)
+    draw = ImageDraw.Draw(text_image)
+    draw.rectangle((0, 0, width, height), fill=tuple(color))
+    draw.text((0, -3), label, fill=(255, 255, 255), font=font)
+    im_array = np.asarray(text_image)
+    scale = fontSize / height
+    im_array = cv2.resize(im_array, (0, 0), fx=scale, fy=scale)
+    return im_array
+
+
+def get_label_arrays(labelNames, colors, fontSize=40, fontPath="platech.ttf"):
+    font = ImageFont.truetype(fontPath, fontSize, encoding='utf-8')
+    label_arraylist = []
+    for i, label_name in enumerate(labelNames):
+        color = colors[i % 20]
+        label_arraylist.append(get_label_array(color, label_name, font, fontSize))
+    return label_arraylist
+
+
+def draw_painting_joint(box, img, label_array, score=0.5, color=None, score_location="leftTop"):
+    # 识别问题描述图片的高、宽
+    lh, lw = label_array.shape[0:2]
+    # 图片的长度和宽度
+    imh, imw = img.shape[0:2]
+    if not isinstance(box[0],  (list, tuple, np.ndarray)):
+        xc = int(box[0])
+        yc = int(box[1])
+        w = int(box[2])
+        h = int(box[3])
+        bw = int(w/2)
+        bh = int(h/2)
+        tl = (xc - bw, yc - bh)
+        tr = (xc + bw, yc - bh)
+        br = (xc + bw, yc + bh)
+        bl = (xc - bw, yc + bh)
+        box = [tl, tr, br, bl]
+    # 框框左上的位置
+    if score_location == 'leftTop':
+        x0, y1 = box[0][0], box[0][1]
+    # 框框左下的位置
+    elif score_location == 'leftBottom':
+        x0, y1 = box[3][0], box[3][1]
+    else:
+        x0, y1 = box[0][0], box[0][1]
+    # x1 框框左上x位置 + 描述的宽
+    # y0 框框左上y位置 - 描述的高
+    x1, y0 = x0 + lw, y1 - lh
+    # 如果y0小于0, 说明超过上边框
+    if y0 < 0:
+        y0 = 0
+        # y1等于文字高度
+        y1 = y0 + lh
+    # 如果y1框框的高大于图片高度
+    if y1 > imh:
+        # y1等于图片高度
+        y1 = imh
+        # y0等于y1减去文字高度
+        y0 = y1 - lh
+    # 如果x0小于0
+    if x0 < 0:
+        x0 = 0
+        x1 = x0 + lw
+    if x1 > imw:
+        x1 = imw
+        x0 = x1 - lw
+    img[y0:y1, x0:x1, :] = label_array
+    pts_cls = [(x0, y0), (x1, y1)]
+
+    # 把四边形的框画上
+    # box_tl = font['boxLine_thickness'] or round(0.002 * (imh + imw) / 2) + 1
+    box_tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
+    '''
+    1. img（array） 为ndarray类型（可以为cv.imread）直接读取的数据
+    2. box（array）：为所画多边形的顶点坐标
+    3. 所画四边形是否闭合，通常为True
+    4. color（tuple）：BGR三个通道的值
+    5. thickness（int）：画线的粗细
+    6. shift：顶点坐标中小数的位数
+    '''
+    box1 = np.asarray(box, np.int32)
+    cv2.polylines(img, [box1], True, color, box_tl)
+
+    # 把英文字符score画到类别旁边
+    tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
+    label = ' %.2f' % score
+    tf = max(tl, 1)
+    fontScale = float(format(imw / 1920 * 1.1, '.2f')) or tl * 0.33
+    '''
+    1. text：要计算大小的文本内容，类型为字符串。
+    2. fontFace：字体类型，例如cv2.FONT_HERSHEY_SIMPLEX等。
+    3. fontScale：字体大小的缩放因子，例如1.2表示字体大小增加20%。
+    4. thickness：文本线条的粗细，以像素为单位。
+    5. (text_width, text_height)：给定文本在指定字体、字体大小、线条粗细下所占用的像素宽度和高度。
+    '''
+    t_size = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
+    # if   socre_location=='leftTop':
+    p1, p2 = (pts_cls[1][0], pts_cls[0][1]), (pts_cls[1][0] + t_size[0], pts_cls[1][1])
+    '''
+    1. img：要绘制矩形的图像
+    2. pt1：矩形框的左上角坐标，可以是一个包含两个整数的元组或列表，例如(x1, y1)或[x1, y1]。
+    3. pt2：矩形框的右下角坐标，可以是一个包含两个整数的元组或列表，例如(x2, y2)或[x2, y2]。
+    4. color：矩形框的颜色，可以是一个包含三个整数的元组或列表，例如(255, 0, 0)表示蓝色，或一个标量值，例如255表示白色。颜色顺序为BGR。
+    5. thickness：线条的粗细，以像素为单位。如果为负值，则表示要绘制填充矩形。默认值为1。
+    6. lineType：线条的类型，可以是cv2.LINE_AA表示抗锯齿线条，或cv2.LINE_4表示4连通线条，或cv2.LINE_8表示8连通线条。默认值为cv2.LINE_8。
+    7. shift：坐标点小数点位数。默认值为0。
+    '''
+    cv2.rectangle(img, p1, p2, color, -1, cv2.LINE_AA)
+    p3 = pts_cls[1][0], pts_cls[1][1] - (lh - t_size[1]) // 2
+    '''
+    1. img：要在其上绘制文本的图像
+    2. text：要绘制的文本内容，类型为字符串
+    3. org：文本起始位置的坐标，可以是一个包含两个整数的元组或列表，例如(x, y)或[x, y]。
+    4. fontFace：字体类型，例如cv2.FONT_HERSHEY_SIMPLEX等。
+    5. fontScale：字体大小的缩放因子，例如1.2表示字体大小增加20%。
+    6. color：文本的颜色，可以是一个包含三个整数的元组或列表，例如(255, 0, 0)表示蓝色，或一个标量值，例如255表示白色。颜色顺序为BGR。
+    7. thickness：文本线条的粗细，以像素为单位。默认值为1。
+    8. lineType：线条的类型，可以是cv2.LINE_AA表示抗锯齿线条，或cv2.LINE_4表示4连通线条，或cv2.LINE_8表示8连通线条。默认值为cv2.LINE_8。
+    9. bottomLeftOrigin：文本起始位置是否为左下角。如果为True，则文本起始位置为左下角，否则为左上角。默认值为False。
+    '''
+    cv2.putText(img, label, p3, 0, fontScale, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+    return img, box
+
+
+
+
+# def draw_painting_joint(box,img,label_array,score=0.5,color=None,font={ 'line_thickness':None,'boxLine_thickness':None, 'fontSize':None},socre_location="leftTop"):
+#     #如果box[0]不是list or 元组，则box是[ (x0,y0),(x1,y1),(x2,y2),(x3,y3)]四点格式
+#     if  isinstance(box[0],  (list, tuple,np.ndarray ) ):
+#         ###先把中文类别字体赋值到img中
+#         lh, lw, lc = label_array.shape
+#         imh, imw, imc = img.shape
+#         if socre_location=='leftTop':
+#             x0 , y1 = box[0][0],box[0][1]
+#         elif socre_location=='leftBottom':
+#             x0,y1=box[3][0],box[3][1]
+#         else:
+#             print('plot.py  line217 ,label_location:%s not implemented '%( socre_location  ))
+#             sys.exit(0)
+#
+#         x1 , y0 = x0 + lw , y1 - lh
+#         if y0<0:y0=0;y1=y0+lh
+#         if y1>imh: y1=imh;y0=y1-lh
+#         if x0<0:x0=0;x1=x0+lw
+#         if x1>imw:x1=imw;x0=x1-lw
+#         img[y0:y1,x0:x1,:] =  label_array
+#         pts_cls=[(x0,y0),(x1,y1) ]
+#
+#         #把四边形的框画上
+#         box_tl= font['boxLine_thickness'] or round(0.002 * (imh + imw) / 2) + 1
+#         cv2.polylines(img, [box], True,color , box_tl)
+#
+#         ####把英文字符score画到类别旁边
+#         tl = font['line_thickness'] or round(0.002*(imh+imw)/2)+1#line/font thickness
+#         label = ' %.2f'%(score)
+#         tf = max(tl , 1)  # font thickness
+#         fontScale = font['fontSize'] or  tl * 0.33
+#         t_size = cv2.getTextSize(label, 0, fontScale=fontScale , thickness=tf)[0]
+#
+#
+#         #if   socre_location=='leftTop':
+#         p1,p2= (pts_cls[1][0], pts_cls[0][1]),(pts_cls[1][0]+t_size[0],pts_cls[1][1])
+#         cv2.rectangle(img, p1 , p2, color, -1, cv2.LINE_AA)
+#         p3 = pts_cls[1][0],pts_cls[1][1]-(lh-t_size[1])//2
+#
+#         cv2.putText(img, label,p3, 0, fontScale, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+#         return img
+#     else:####两点格式[x0,y0,x1,y1]
+#         try:
+#             box = [int(xx.cpu()) for xx in box]
+#         except:
+#             box=[ int(x) for x in box]
+#         ###先把中文类别字体赋值到img中
+#         lh, lw, lc = label_array.shape
+#         imh, imw, imc = img.shape
+#         if socre_location=='leftTop':
+#             x0 , y1 = box[0:2]
+#         elif socre_location=='leftBottom':
+#             x0,y1=box[0],box[3]
+#         else:
+#             print('plot.py  line217 ,socre_location:%s not implemented '%( socre_location  ))
+#             sys.exit(0)
+#         x1 , y0 = x0 + lw , y1 - lh
+#         if y0<0:y0=0;y1=y0+lh
+#         if y1>imh: y1=imh;y0=y1-lh
+#         if x0<0:x0=0;x1=x0+lw
+#         if x1>imw:x1=imw;x0=x1-lw
+#         img[y0:y1,x0:x1,:] =  label_array
+#
+#
+#
+#         ###把矩形框画上，指定颜色和线宽
+#         tl = font['line_thickness'] or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
+#         box_tl= font['boxLine_thickness'] or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1
+#         c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+#         cv2.rectangle(img, c1, c2, color, thickness=box_tl, lineType=cv2.LINE_AA)
+#
+#         ###把英文字符score画到类别旁边
+#         label = ' %.2f'%(score)
+#         tf = max(tl , 1)  # font thickness
+#         fontScale = font['fontSize'] or  tl * 0.33
+#         t_size = cv2.getTextSize(label, 0, fontScale=fontScale , thickness=tf)[0]
+#
+#         if   socre_location=='leftTop':
+#             c2 = c1[0]+ lw + t_size[0], c1[1] - lh
+#             cv2.rectangle(img, (int(box[0])+lw,int(box[1])) , c2, color, -1, cv2.LINE_AA)  # filled
+#             cv2.putText(img, label, (c1[0]+lw, c1[1] - (lh-t_size[1])//2 ), 0, fontScale, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+#         elif socre_location=='leftBottom':
+#             c2 = box[0]+ lw + t_size[0], box[3] - lh
+#             cv2.rectangle(img, (int(box[0])+lw,int(box[3])) , c2, color, -1, cv2.LINE_AA)  # filled
+#             cv2.putText(img, label, ( box[0] + lw, box[3] - (lh-t_size[1])//2 ), 0, fontScale, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+#
+#             #print('#####line224 fontScale:',fontScale,'  thickness:',tf,'   line_thickness:',font['line_thickness'],' boxLine thickness:',box_tl)
+#         return img
+

util/QueRiver.py

@@ -0,0 +1,346 @@
+from kafka import KafkaProducer, KafkaConsumer
+from kafka.errors import kafka_errors
+import traceback
+import json, base64,os
+import numpy as np
+from multiprocessing import Process,Queue
+import time,cv2,string,random
+import subprocess as sp
+
+import matplotlib.pyplot as plt
+from utils.datasets import LoadStreams, LoadImages
+from models.experimental import attempt_load
+from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
+import torch,sys
+#from segutils.segmodel import SegModel,get_largest_contours
+#sys.path.extend(['../yolov5/segutils'])
+
+from segutils.segWaterBuilding import  SegModel,get_largest_contours,illBuildings
+
+#from segutils.core.models.bisenet import BiSeNet
+from segutils.core.models.bisenet import BiSeNet_MultiOutput
+
+from utils.plots import plot_one_box,plot_one_box_PIL,draw_painting_joint,get_label_arrays,get_websource
+from collections import  Counter
+#import matplotlib
+
+import matplotlib.pyplot as plt
+# get_labelnames,get_label_arrays,post_process_,save_problem_images,time_str
+#FP_DEBUG=open('debut.txt','w')
+def bsJpgCode(image_ori):            
+    jpgCode = cv2.imencode('.jpg',image_ori)[-1]###np.array,(4502009,1)
+    bsCode = str(base64.b64encode(jpgCode))[2:-1] ###str,长6002680
+    return bsCode
+def bsJpgDecode(bsCode):
+    bsDecode = base64.b64decode(bsCode)###types,长4502009
+    npString = np.frombuffer(bsDecode,np.uint8)###np.array,(长4502009,)
+    jpgDecode = cv2.imdecode(npString,cv2.IMREAD_COLOR)###np.array,(3000,4000,3)
+    return jpgDecode
+def get_ms(time0,time1):
+    str_time ='%.2f ms'%((time1-time0)*1000) 
+    return str_time  
+rainbows=[
+           (0,0,255),(0,255,0),(255,0,0),(255,0,255),(255,255,0),(255,127,0),(255,0,127),
+           (127,255,0),(0,255,127),(0,127,255),(127,0,255),(255,127,255),(255,255,127),
+           (127,255,255),(0,255,255),(255,127,255),(127,255,255),
+           (0,127,0),(0,0,127),(0,255,255)
+         ]
+
+
+def get_labelnames(labelnames):   
+    with open(labelnames,'r') as fp:
+        namesjson=json.load(fp)             
+        names_fromfile=namesjson['labelnames']                                    
+        names = names_fromfile
+    return names 
+
+def check_stream(stream):
+    cap = cv2.VideoCapture(stream)        
+    if cap.isOpened():
+        return True
+    else:
+       return False 
+#####  
+def drawWater(pred,image_array0,river={'color':(0,255,255),'line_width':3,'segRegionCnt':2}):####pred是模型的输出，只有水分割的任务
+    ##画出水体区域
+    contours, hierarchy = cv2.findContours(pred,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    water = pred.copy(); water[:,:] = 0 
+    
+    if len(contours)==0:        
+        return image_array0,water
+    max_ids = get_largest_contours(contours,river['segRegionCnt']);     
+    for max_id in max_ids:    
+        cv2.fillPoly(water, [contours[max_id][:,0,:]], 1)     
+        cv2.drawContours(image_array0,contours,max_id,river['color'],river['line_width'] )
+    return     image_array0,water
+
+
+def scale_back(boxes,padInfos):
+    top, left,r = padInfos[0:3]
+  
+    boxes[:,0] = (boxes[:,0] - left) * r
+    
+    boxes[:,2] = (boxes[:,2] - left) * r
+    boxes[:,1] = (boxes[:,1] - top) * r
+    boxes[:,3] = (boxes[:,3] - top) * r
+    return boxes
+
+def img_pad(img, size, pad_value=[114,114,114]):
+    ###填充成固定尺寸
+    H,W,_ = img.shape
+    r = max(H/size[0], W/size[1])
+    img_r = cv2.resize(img, (int(W/r), int(H/r)))
+    tb = size[0] - img_r.shape[0]
+    lr = size[1] - img_r.shape[1]
+    top = int(tb/2)
+    bottom = tb - top
+    left = int(lr/2)
+    right = lr - left
+    pad_image = cv2.copyMakeBorder(img_r, top, bottom, left, right, cv2.BORDER_CONSTANT,value=pad_value)
+    return pad_image,(top, left,r)      
+
+def post_process_(datas,conf_thres, iou_thres,names,label_arraylist,rainbows,iframe,ObjectPar={ 'object_config':[0,1,2,3,4], 'slopeIndex':[5,6,7] ,'segmodel':True,'segRegionCnt':1 },font={ 'line_thickness':None, 'fontSize':None,'boxLine_thickness':None,'waterLineColor':(0,255,255),'waterLineWidth':3},padInfos=None ):
+    object_config,slopeIndex,segmodel,segRegionCnt=ObjectPar['object_config'],ObjectPar['slopeIndex'],ObjectPar['segmodel'],ObjectPar['segRegionCnt']
+    ##输入dataset genereate 生成的数据，model预测的结果pred，nms参数
+    ##主要操作NMS ---> 坐标转换 ---> 画图
+    ##输出原图、AI处理后的图、检测结果
+    time0=time.time()
+    path, img, im0s, vid_cap ,pred,seg_pred= datas[0:6];
+    #segmodel=True
+    pred = non_max_suppression(pred, conf_thres, iou_thres, classes=None, agnostic=False)   
+    time1=time.time()
+    i=0;det=pred[0]###一次检测一张图片
+    time1_1 = time.time()
+    #p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
+    p, s, im0 = path[i], '%g: ' % i, im0s[i]
+    time1_2 = time.time()
+    gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+    time1_3 = time.time()
+    det_xywh=[];
+    #im0_brg=cv2.cvtColor(im0,cv2.COLOR_RGB2BGR);
+    if segmodel:
+        if len(seg_pred)==2:
+            im0,water = illBuildings(seg_pred,im0) 
+        else:
+            river={ 'color':font['waterLineColor'],'line_width':font['waterLineWidth'],'segRegionCnt':segRegionCnt }
+            im0,water = drawWater(seg_pred,im0,river)      
+    time2=time.time()
+    #plt.imshow(im0);plt.show()
+    if len(det)>0:
+        # Rescale boxes from img_size to im0 size
+        if not padInfos:
+            det[:, :4] = scale_coords(img.shape[2:], det[:, :4],im0.shape).round()     
+        else:
+            #print('####line131:',det[:, :])
+            det[:, :4] = scale_back( det[:, :4],padInfos).round()
+            #print('####line133:',det[:, :])
+        #用seg模型，确定有效检测匡及河道轮廓线
+        if segmodel:               
+            cls_indexs =    det[:, 5].clone().cpu().numpy().astype(np.int32)
+            ##判断哪些目标属于岸坡的
+            slope_flag = np.array([x in slopeIndex for x in cls_indexs ]  )
+            
+            det_c = det.clone(); det_c=det_c.cpu().numpy()  
+            try:
+                area_factors = np.array([np.sum(water[int(x[1]):int(x[3]), int(x[0]):int(x[2])] )*1.0/(1.0*(x[2]-x[0])*(x[3]-x[1])+0.00001) for x in det_c]  )  
+            except:
+                print('*****************************line143: error:',det_c)
+            water_flag = np.array(area_factors>0.1)            
+            det = det[water_flag|slope_flag]##如果是水上目标，则需要与水的iou超过0.1；如果是岸坡目标，则直接保留。
+        #对检测匡绘图
+        for *xyxy, conf, cls in reversed(det):
+            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+            cls_c = cls.cpu().numpy()
+            if int(cls_c) not in object_config: ###如果不是所需要的目标，则不显示
+                continue
+            conf_c = conf.cpu().numpy()
+            line = [float(cls_c), *xywh, float(conf_c)] # label format      
+            det_xywh.append(line)
+            label = f'{names[int(cls)]} {conf:.2f}'			    
+           
+            im0 = draw_painting_joint(xyxy,im0,label_arraylist[int(cls)],score=conf,color=rainbows[int(cls)%20],font=font)
+    time3=time.time()
+    strout='nms:%s drawWater:%s,copy:%s,toTensor:%s,detDraw:%s '%(get_ms(time0,time1),get_ms(time1,time2),get_ms(time1_1,time1_2),get_ms(time1_2,time1_3), get_ms(time2,time3)   ) 
+    return [im0s[0],im0,det_xywh,iframe],strout
+   
+
+          
+def preprocess(par):
+    print('#####process:',par['name'])
+    ##负责读取视频，生成原图及供检测的使用图，numpy格式   
+    #source='rtmp://liveplay.yunhengzhizao.cn/live/demo_HD5M'
+    #img_size=640;    stride=32
+    while True:
+        cap = cv2.VideoCapture(par['source'])
+        iframe = 0
+        if cap.isOpened():
+            print( '#### read %s  success!'%(par['source']))
+            try:
+                dataset = LoadStreams(par['source'], img_size=640, stride=32)           
+                for path, img, im0s, vid_cap in dataset:
+                    datas=[path, img, im0s, vid_cap,iframe]
+                    par['queOut'].put(datas)
+                    iframe +=1
+            except Exception as e:
+                print('###read error:%s '%(par['source']))
+                time.sleep(10)
+                iframe = 0
+                
+        else:
+            print('###read error:%s  '%(par['source'] ))
+            time.sleep(10)
+            iframe = 0
+
+def gpu_process(par):
+    print('#####process:',par['name'])
+    half=True
+    ##gpu运算,检测模型    
+    weights = par['weights']
+    device = par['device'] 
+    print('###line127:',par['device'])
+    model = attempt_load(par['weights'], map_location=par['device'])  # load FP32 model
+    if half:
+        model.half()
+
+    ##gpu运算，分割模型  
+    seg_nclass = par['seg_nclass']
+    seg_weights = par['seg_weights']
+
+    #segmodel = SegModel(nclass=seg_nclass,weights=seg_weights,device=device)
+    
+
+    nclass = [2,2]
+    Segmodel =  BiSeNet_MultiOutput(nclass)
+    weights='weights/segmentation/WaterBuilding.pth'
+    segmodel = SegModel(model=Segmodel,nclass=nclass,weights=weights,device='cuda:0',multiOutput=True)
+    while True:
+        if not par['queIn'].empty():
+            time0=time.time()
+            datas = par['queIn'].get() 
+            path, img, im0s, vid_cap,iframe = datas[0:5]
+            time1=time.time()
+            img = torch.from_numpy(img).to(device)
+            img = img.half() if half else img.float()  # uint8 to fp16/32
+            img /= 255.0  # 0 - 255 to 0.0 - 1.0
+            time2 = time.time()
+            pred = model(img,augment=False)[0]
+            time3 = time.time()
+            seg_pred = segmodel.eval(im0s[0],outsize=None,smooth_kernel=20)
+            time4 = time.time()
+            fpStr= 'process:%s ,iframe:%d,getdata:%s,copygpu:%s,dettime:%s,segtime:%s , time:%s, queLen:%d  '%( par['name'],iframe,get_ms(time0,time1) ,get_ms(time1,time2) ,get_ms(time2,time3) ,get_ms(time3,time4),get_ms(time0,time4)  ,par['queIn'].qsize()   )  
+            #FP_DEBUG.write( fpStr+'\n' )
+            datasOut = [path, img, im0s, vid_cap,pred,seg_pred,iframe]
+            par['queOut'].put(datasOut)
+            if par['debug']:
+                print('#####process:',par['name'],'  line107')
+        else:
+            time.sleep(1/300)  
+def get_cls(array):
+    dcs = Counter(array)
+    keys = list(dcs.keys())
+    values = list(dcs.values())
+    max_index = values.index(max(values))
+    cls = int(keys[max_index])
+    return cls
+def save_problem_images(post_results,iimage_cnt,names,streamName='live-THSAHD5M',outImaDir='problems/images_tmp',imageTxtFile=False):
+    ## [cls, x,y,w,h, conf]
+    problem_image=[[] for i in range(6)]
+
+       
+    dets_list = [x[2] for x in post_results]
+
+    mean_scores=[  np.array(x)[:,5].mean()  for x in dets_list  ] ###mean conf
+
+    best_index = mean_scores.index(max(mean_scores)) ##获取该批图片里，问题图片的index 
+    best_frame = post_results[ best_index][3] ##获取绝对帧号 
+    img_send = post_results[best_index][1]##AI处理后的图
+    img_bak = post_results[best_index][0]##原图    
+    cls_max = get_cls( x[0] for x in    dets_list[best_index] )
+
+
+    time_str = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) 
+    uid=''.join(random.sample(string.ascii_letters + string.digits, 16))
+    #ori_name = '2022-01-20-15-57-36_frame-368-720_type-漂浮物_qVh4zI08ZlwJN9on_s-live-THSAHD5M_OR.jpg'
+    #2022-01-13-15-07-57_frame-9999-9999_type-结束_9999999999999999_s-off-XJRW20220110115904_AI.jpg
+    outnameOR= '%s/%s_frame-%d-%d_type-%s_%s_s-%s_AI.jpg'%(outImaDir,time_str,best_frame,iimage_cnt,names[cls_max],uid,streamName)
+    outnameAR=  '%s/%s_frame-%d-%d_type-%s_%s_s-%s_OR.jpg'%(outImaDir,time_str,best_frame,iimage_cnt,names[cls_max],uid,streamName)
+    
+    cv2.imwrite(outnameOR,img_send)
+    cv2.imwrite(outnameAR,img_bak)
+    if imageTxtFile:
+        outnameOR_txt = outnameOR.replace('.jpg','.txt')
+        fp=open(outnameOR_txt,'w');fp.write(outnameOR+'\n');fp.close()
+        outnameAI_txt = outnameAR.replace('.jpg','.txt')
+        fp=open(outnameAI_txt,'w');fp.write(outnameAR+'\n');fp.close()
+       
+    parOut = {}; parOut['imgOR'] =  img_send; parOut['imgAR'] =  img_send; parOut['uid']=uid
+    parOut['imgORname']=os.path.basename(outnameOR);parOut['imgARname']=os.path.basename(outnameAR);
+    parOut['time_str'] = time_str;parOut['type'] = names[cls_max]
+    return parOut
+       
+
+     
+    
+def post_process(par):
+    
+    print('#####process:',par['name'])
+    ###post-process参数
+    conf_thres,iou_thres,classes=par['conf_thres'],par['iou_thres'],par['classes']
+    labelnames=par['labelnames']    
+    rainbows=par['rainbows']
+    fpsample = par['fpsample']
+    names=get_labelnames(labelnames)   
+    label_arraylist = get_label_arrays(names,rainbows,outfontsize=40)
+    iimage_cnt = 0
+    post_results=[]
+    while True:
+        if not par['queIn'].empty():
+            time0=time.time()
+            datas = par['queIn'].get() 
+            iframe = datas[6]
+            if par['debug']:
+                print('#####process:',par['name'],'  line129')
+            p_result,timeOut = post_process_(datas,conf_thres, iou_thres,names,label_arraylist,rainbows,iframe)
+            par['queOut'].put(p_result)   
+            ##输出结果
+            
+    
+
+            ##每隔 fpsample帧处理一次，如果有问题就保存图片       
+            if (iframe % fpsample == 0) and (len(post_results)>0) :
+               #print('####line204:',iframe,post_results) 
+               save_problem_images(post_results,iframe,names)
+               post_results=[]   
+
+            if len(p_result[2] )>0:  ##                
+                #post_list = p_result.append(iframe)
+                post_results.append(p_result)
+                #print('####line201:',type(p_result)) 
+                
+            time1=time.time()
+            outstr='process:%s ,iframe:%d,%s , time:%s, queLen:%d  '%( par['name'],iframe,timeOut,get_ms(time0,time1) ,par['queIn'].qsize()   ) 
+            #FP_DEBUG.write(outstr +'\n')
+            #print( 'process:%s ,iframe:%d,%s , time:%s, queLen:%d  '%( par['name'],iframe,timeOut,get_ms(time0,time1) ,par['queIn'].qsize()   )  )        
+        else:            
+            time.sleep(1/300)
+
+     
+def save_logfile(name,txt):
+    if os.path.exists(name):
+        fp=open(name,'r+')
+    else:
+        fp=open(name,'w')
+ 
+    fp.write('%s %s \n'%(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),txt))
+    fp.close()
+def time_str():     
+    return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+    
+  
+
+if __name__=='__main__':
+    jsonfile='config/queRiver.json'
+    #image_encode_decode()
+    work_stream(jsonfile)
+    #par={'name':'preprocess'}
+    #preprocess(par)

util/TorchUtils.py

@@ -0,0 +1,315 @@
+# YOLOv5 PyTorch utils
+
+import datetime
+
+import math
+import os
+import platform
+import subprocess
+import time
+from contextlib import contextmanager
+from copy import deepcopy
+from pathlib import Path
+from loguru import logger
+import torch
+import torch.backends.cudnn as cudnn
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+try:
+    import thop  # for FLOPS computation
+except ImportError:
+    thop = None
+
+
+@contextmanager
+def torch_distributed_zero_first(local_rank: int):
+    """
+    Decorator to make all processes in distributed training wait for each local_master to do something.
+    """
+    if local_rank not in [-1, 0]:
+        torch.distributed.barrier()
+    yield
+    if local_rank == 0:
+        torch.distributed.barrier()
+
+
+def init_torch_seeds(seed=0):
+    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
+    torch.manual_seed(seed)
+    if seed == 0:  # slower, more reproducible
+        cudnn.benchmark, cudnn.deterministic = False, True
+    else:  # faster, less reproducible
+        cudnn.benchmark, cudnn.deterministic = True, False
+
+
+# 文件最后修改时间
+def date_modified(path=__file__):
+    # return human-readable file modification date, i.e. '2021-3-26'
+    t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)
+    return f'{t.year}-{t.month}-{t.day}'
+
+
+def git_describe(path=Path(__file__).parent):  # path must be a directory
+    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
+    # -C 指定GIT仓库的路径
+    # describe: 命令名，表示获取最近的Git标签信息。
+    # tags: 选项，表示只考虑标签。
+    # long: 选项，表示使用完整的Git SHA-1哈希值来描述提交。
+    # always: 选项，表示如果没有Git标签，则使用Git哈希值来描述提交。
+    s = f'git -C {path} describe --tags --long --always'
+    try:
+        return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
+    except subprocess.CalledProcessError as e:
+        return ''
+
+
+def select_device(device='0'):
+    logger.info("当前torch版本: {}", torch.__version__)
+    # 设置环境变量
+    os.environ['CUDA_VISIBLE_DEVICES'] = device
+    assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'
+    return torch.device('cuda:%s' % device)
+
+
+# def select_device(device='', batch_size=None):
+#     # device = 'cpu' or '0' or '0,1,2,3'
+#     s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} '  # string
+#     cpu = device.lower() == 'cpu'
+#     if cpu:
+#         os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
+#     elif device:  # non-cpu device requested
+#         os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
+#         assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'  # check availability
+#
+#     cuda = not cpu and torch.cuda.is_available()
+#     if cuda:
+#         n = torch.cuda.device_count()
+#         if n > 1 and batch_size:  # check that batch_size is compatible with device_count
+#             assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
+#         space = ' ' * len(s)
+#         for i, d in enumerate(device.split(',') if device else range(n)):
+#             p = torch.cuda.get_device_properties(i)
+#             s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
+#     else:
+#         s += 'CPU\n'
+#     logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
+#     return torch.device('cuda:0' if cuda else 'cpu')
+
+
+def time_synchronized():
+    # pytorch-accurate time
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+    return time.time()
+
+
+def profile(x, ops, n=100, device=None):
+    # profile a pytorch module or list of modules. Example usage:
+    #     x = torch.randn(16, 3, 640, 640)  # input
+    #     m1 = lambda x: x * torch.sigmoid(x)
+    #     m2 = nn.SiLU()
+    #     profile(x, [m1, m2], n=100)  # profile speed over 100 iterations
+
+    device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    x = x.to(device)
+    x.requires_grad = True
+    print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
+    print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
+    for m in ops if isinstance(ops, list) else [ops]:
+        m = m.to(device) if hasattr(m, 'to') else m  # device
+        m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m  # type
+        dtf, dtb, t = 0., 0., [0., 0., 0.]  # dt forward, backward
+        try:
+            flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPS
+        except:
+            flops = 0
+
+        for _ in range(n):
+            t[0] = time_synchronized()
+            y = m(x)
+            t[1] = time_synchronized()
+            try:
+                _ = y.sum().backward()
+                t[2] = time_synchronized()
+            except:  # no backward method
+                t[2] = float('nan')
+            dtf += (t[1] - t[0]) * 1000 / n  # ms per op forward
+            dtb += (t[2] - t[1]) * 1000 / n  # ms per op backward
+
+        s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list'
+        s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list'
+        p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0  # parameters
+        print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}')
+
+
+def is_parallel(model):
+    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
+
+
+def intersect_dicts(da, db, exclude=()):
+    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
+    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
+
+
+def initialize_weights(model):
+    for m in model.modules():
+        t = type(m)
+        if t is nn.Conv2d:
+            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+        elif t is nn.BatchNorm2d:
+            m.eps = 1e-3
+            m.momentum = 0.03
+        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
+            m.inplace = True
+
+
+def find_modules(model, mclass=nn.Conv2d):
+    # Finds layer indices matching module class 'mclass'
+    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
+
+
+def sparsity(model):
+    # Return global model sparsity
+    a, b = 0., 0.
+    for p in model.parameters():
+        a += p.numel()
+        b += (p == 0).sum()
+    return b / a
+
+
+def prune(model, amount=0.3):
+    # Prune model to requested global sparsity
+    import torch.nn.utils.prune as prune
+    print('Pruning model... ', end='')
+    for name, m in model.named_modules():
+        if isinstance(m, nn.Conv2d):
+            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
+            prune.remove(m, 'weight')  # make permanent
+    print(' %.3g global sparsity' % sparsity(model))
+
+
+def fuse_conv_and_bn(conv, bn):
+    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+    fusedconv = nn.Conv2d(conv.in_channels,
+                          conv.out_channels,
+                          kernel_size=conv.kernel_size,
+                          stride=conv.stride,
+                          padding=conv.padding,
+                          groups=conv.groups,
+                          bias=True).requires_grad_(False).to(conv.weight.device)
+
+    # prepare filters
+    w_conv = conv.weight.clone().view(conv.out_channels, -1)
+    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
+
+    # prepare spatial bias
+    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+    return fusedconv
+
+
+def model_info(model, verbose=False, img_size=640):
+    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
+    n_p = sum(x.numel() for x in model.parameters())  # number parameters
+    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
+    if verbose:
+        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
+        for i, (name, p) in enumerate(model.named_parameters()):
+            name = name.replace('module_list.', '')
+            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
+                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
+
+    try:  # FLOPS
+        from thop import profile
+        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32
+        img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device)  # input
+        flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride GFLOPS
+        img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float
+        fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPS
+    except (ImportError, Exception):
+        fs = ''
+
+    logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
+
+
+def load_classifier(name='resnet101', n=2):
+    # Loads a pretrained model reshaped to n-class output
+    model = torchvision.models.__dict__[name](pretrained=True)
+
+    # ResNet model properties
+    # input_size = [3, 224, 224]
+    # input_space = 'RGB'
+    # input_range = [0, 1]
+    # mean = [0.485, 0.456, 0.406]
+    # std = [0.229, 0.224, 0.225]
+
+    # Reshape output to n classes
+    filters = model.fc.weight.shape[1]
+    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
+    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
+    model.fc.out_features = n
+    return model
+
+
+def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
+    # scales img(bs,3,y,x) by ratio constrained to gs-multiple
+    if ratio == 1.0:
+        return img
+    else:
+        h, w = img.shape[2:]
+        s = (int(h * ratio), int(w * ratio))  # new size
+        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
+        if not same_shape:  # pad/crop img
+            h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
+        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
+
+
+def copy_attr(a, b, include=(), exclude=()):
+    # Copy attributes from b to a, options to only include [...] and to exclude [...]
+    for k, v in b.__dict__.items():
+        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
+            continue
+        else:
+            setattr(a, k, v)
+
+
+class ModelEMA:
+    """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
+    Keep a moving average of everything in the model state_dict (parameters and buffers).
+    This is intended to allow functionality like
+    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+    A smoothed version of the weights is necessary for some training schemes to perform well.
+    This class is sensitive where it is initialized in the sequence of model init,
+    GPU assignment and distributed training wrappers.
+    """
+
+    def __init__(self, model, decay=0.9999, updates=0):
+        # Create EMA
+        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
+        # if next(model.parameters()).device.type != 'cpu':
+        #     self.ema.half()  # FP16 EMA
+        self.updates = updates  # number of EMA updates
+        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
+        for p in self.ema.parameters():
+            p.requires_grad_(False)
+
+    def update(self, model):
+        # Update EMA parameters
+        with torch.no_grad():
+            self.updates += 1
+            d = self.decay(self.updates)
+
+            msd = model.module.state_dict() if is_parallel(model) else model.state_dict()  # model state_dict
+            for k, v in self.ema.state_dict().items():
+                if v.dtype.is_floating_point:
+                    v *= d
+                    v += (1. - d) * msd[k].detach()
+
+    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
+        # Update EMA attributes
+        copy_attr(self.ema, model, include, exclude)