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As a Ms.Sc. in for Chemistry & Physics I started out working in the field of environmental analysis as a laboratory technician.

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Execution"})}),"\n",(0,t.jsx)(r.li,{children:(0,t.jsx)(r.a,{href:"#working-with-shell-scripts",children:"Working with Shell Scripts"})}),"\n"]}),"\n"]}),"\n"]}),"\n",(0,t.jsx)(r.h2,{id:"install-smartmontools-and-lm-sensors",children:"Install Smartmontools and LM Sensors"}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"apt install lm-sensors smartmontools\n"})}),"\n",(0,t.jsx)(r.h3,{id:"harddrive-monitoring",children:"Harddrive Monitoring"}),"\n",(0,t.jsx)(r.p,{children:"S.M.A.R.T. (Self-Monitoring, Analysis and Reporting Technology; often written as SMART) is a monitoring system included in computer hard disk drives (HDDs), solid-state drives (SSDs), and eMMC drives"}),"\n",(0,t.jsx)(r.p,{children:"The smartmontools package comes with two utilities, smartctl which you can use to check your hard drives on the command line, and smartd, a daemon that checks your hard disks at a specified interval and logs warnings/errors to the syslog and can also send warnings and errors to a specified email address (usually the admin of the system)."}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"smartctl -v\r\n\r\nsmartctl 7.2 2020-12-30 r5155 [x86_64-linux-5.10.0-11-amd64] (local build)\r\nCopyright (C) 2002-20, Bruce Allen, Christian Franke, www.smartmontools.org\n"})}),"\n",(0,t.jsx)(r.h3,{id:"using-smartctl",children:"Using Smartctl"}),"\n",(0,t.jsx)(r.p,{children:"Find partition:"}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"df -h\r\n\r\nFilesystem Size Used Avail Use% Mounted on\r\ntmpfs 32G 2.9M 32G 1% /run\r\n/dev/mapper/ubuntu--vg--1-ubuntu--lv 438G 81G 338G 20% /\r\ntmpfs 63G 914M 62G 2% /dev/shm\r\ntmpfs 5.0M 0 5.0M 0% /run/lock\r\n/dev/nvme0n1p2 2.0G 304M 1.5G 17% /boot\r\n/dev/nvme0n1p1 1.1G 6.1M 1.1G 1% /boot/efi\r\ntmpfs 13G 4.0K 13G 1% /run/user/1000\n"})}),"\n",(0,t.jsxs)(r.p,{children:["In the case of my test server below we have a virtual machine - that, obviously, does not have access to the underlying HDD hardware ",(0,t.jsx)(r.code,{children:"/dev/sda1"}),":"]}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"smartctl -a /dev/sda\r\nsmartctl 7.2 2020-12-30 r5155 [x86_64-linux-5.15.0-97-generic] (local build)\r\nCopyright (C) 2002-20, Bruce Allen, Christian Franke, www.smartmontools.org\r\n\r\n=== START OF INFORMATION SECTION ===\r\nModel Family: Western Digital Red\r\nDevice Model: WDC WD10EFRX-68FYTN0\r\nSerial Number: WD-WCC4J4NHYJJ2\r\nLU WWN Device Id: 5 0014ee 269c5648a\r\nFirmware Version: 82.00A82\r\nUser Capacity: 1,000,204,886,016 bytes [1.00 TB]\r\nSector Sizes: 512 bytes logical, 4096 bytes physical\r\nRotation Rate: 5400 rpm\r\nDevice is: In smartctl database [for details use: -P show]\r\nATA Version is: ACS-2 (minor revision not indicated)\r\nSATA Version is: SATA 3.0, 6.0 Gb/s (current: 6.0 Gb/s)\r\nLocal Time is: Sat Mar 9 09:21:06 2024 UTC\r\nSMART support is: Available - device has SMART capability.\r\nSMART support is: Enabled\r\n\r\n=== START OF READ SMART DATA SECTION ===\r\nSMART overall-health self-assessment test result: PASSED\r\n\r\nGeneral SMART Values:\r\nOffline data collection status: (0x00) Offline data collection activity\r\n was never started.\r\n Auto Offline Data Collection: Disabled.\r\nSelf-test execution status: ( 0) The previous self-test routine completed\r\n without error or no self-test has ever \r\n been run.\r\nTotal time to complete Offline \r\ndata collection: (14100) seconds.\r\nOffline data collection\r\ncapabilities: (0x7b) SMART execute Offline immediate.\r\n Auto Offline data collection on/off support.\r\n Suspend Offline collection upon new\r\n command.\r\n Offline surface scan supported.\r\n Self-test supported.\r\n Conveyance Self-test supported.\r\n Selective Self-test supported.\r\nSMART capabilities: (0x0003) Saves SMART data before entering\r\n power-saving mode.\r\n Supports SMART auto save timer.\r\nError logging capability: (0x01) Error logging supported.\r\n General Purpose Logging supported.\r\nShort self-test routine \r\nrecommended polling time: ( 2) minutes.\r\nExtended self-test routine\r\nrecommended polling time: ( 160) minutes.\r\nConveyance self-test routine\r\nrecommended polling time: ( 5) minutes.\r\nSCT capabilities: (0x303d) SCT Status supported.\r\n SCT Error Recovery Control supported.\r\n SCT Feature Control supported.\r\n SCT Data Table supported.\r\n\r\nSMART Attributes Data Structure revision number: 16\r\nVendor Specific SMART Attributes with Thresholds:\r\nID# ATTRIBUTE_NAME FLAG VALUE WORST THRESH TYPE UPDATED WHEN_FAILED RAW_VALUE\r\n 1 Raw_Read_Error_Rate 0x002f 200 200 051 Pre-fail Always - 0\r\n 3 Spin_Up_Time 0x0027 169 133 021 Pre-fail Always - 2516\r\n 4 Start_Stop_Count 0x0032 100 100 000 Old_age Always - 63\r\n 5 Reallocated_Sector_Ct 0x0033 200 200 140 Pre-fail Always - 0\r\n 7 Seek_Error_Rate 0x002e 200 200 000 Old_age Always - 0\r\n 9 Power_On_Hours 0x0032 075 075 000 Old_age Always - 18640\r\n 10 Spin_Retry_Count 0x0032 100 253 000 Old_age Always - 0\r\n 11 Calibration_Retry_Count 0x0032 100 253 000 Old_age Always - 0\r\n 12 Power_Cycle_Count 0x0032 100 100 000 Old_age Always - 62\r\n192 Power-Off_Retract_Count 0x0032 200 200 000 Old_age Always - 59\r\n193 Load_Cycle_Count 0x0032 199 199 000 Old_age Always - 5458\r\n194 Temperature_Celsius 0x0022 121 094 000 Old_age Always - 22\r\n196 Reallocated_Event_Count 0x0032 200 200 000 Old_age Always - 0\r\n197 Current_Pending_Sector 0x0032 200 200 000 Old_age Always - 0\r\n198 Offline_Uncorrectable 0x0030 100 253 000 Old_age Offline - 0\r\n199 UDMA_CRC_Error_Count 0x0032 200 200 000 Old_age Always - 0\r\n200 Multi_Zone_Error_Rate 0x0008 100 253 000 Old_age Offline - 0\r\n\r\nSMART Error Log Version: 1\r\nNo Errors Logged\r\n\r\nSMART Self-test log structure revision number 1\r\nNum Test_Description Status Remaining LifeTime(hours) LBA_of_first_error\r\n# 1 Short offline Completed without error 00% 18594 -\r\n\r\nSMART Selective self-test log data structure revision number 1\r\n SPAN MIN_LBA MAX_LBA CURRENT_TEST_STATUS\r\n 1 0 0 Not_testing\r\n 2 0 0 Not_testing\r\n 3 0 0 Not_testing\r\n 4 0 0 Not_testing\r\n 5 0 0 Not_testing\r\nSelective self-test flags (0x0):\r\n After scanning selected spans, do NOT read-scan remainder of disk.\r\nIf Selective self-test is pending on power-up, resume after 0 minute delay.\n"})}),"\n",(0,t.jsx)(r.p,{children:"Additional NVME drive:"}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"smartctl -a /dev/nvme0\r\n\r\nsmartctl 7.2 2020-12-30 r5155 [x86_64-linux-5.15.0-91-generic] (local build)\r\nCopyright (C) 2002-20, Bruce Allen, Christian Franke, www.smartmontools.org\r\n\r\n=== START OF INFORMATION SECTION ===\r\nModel Number: SanDisk Extreme Pro 500GB\r\nSerial Number: 212181449612\r\nFirmware Version: 111130WD\r\nPCI Vendor/Subsystem ID: 0x15b7\r\nIEEE OUI Identifier: 0x001b44\r\nTotal NVM Capacity: 500,107,862,016 [500 GB]\r\nUnallocated NVM Capacity: 0\r\nController ID: 8215\r\nNVMe Version: 1.3\r\nNumber of Namespaces: 1\r\nNamespace 1 Size/Capacity: 500,107,862,016 [500 GB]\r\nNamespace 1 Formatted LBA Size: 512\r\nNamespace 1 IEEE EUI-64: 001b44 4a4944db11\r\nLocal Time is: Sat Mar 9 09:45:02 2024 CET\r\nFirmware Updates (0x14): 2 Slots, no Reset required\r\nOptional Admin Commands (0x0017): Security Format Frmw_DL Self_Test\r\nOptional NVM Commands (0x005f): Comp Wr_Unc DS_Mngmt Wr_Zero Sav/Sel_Feat Timestmp\r\nLog Page Attributes (0x0e): Cmd_Eff_Lg Ext_Get_Lg Telmtry_Lg\r\nMaximum Data Transfer Size: 128 Pages\r\nWarning Comp. Temp. Threshold: 84 Celsius\r\nCritical Comp. Temp. Threshold: 88 Celsius\r\nNamespace 1 Features (0x02): NA_Fields\r\n\r\nSupported Power States\r\nSt Op Max Active Idle RL RT WL WT Ent_Lat Ex_Lat\r\n 0 + 5.50W - - 0 0 0 0 0 0\r\n 1 + 3.50W - - 1 1 1 1 0 0\r\n 2 + 3.00W - - 2 2 2 2 0 0\r\n 3 - 0.0700W - - 3 3 3 3 4000 10000\r\n 4 - 0.0035W - - 4 4 4 4 4000 40000\r\n\r\nSupported LBA Sizes (NSID 0x1)\r\nId Fmt Data Metadt Rel_Perf\r\n 0 + 512 0 2\r\n 1 - 4096 0 1\r\n\r\n=== START OF SMART DATA SECTION ===\r\nSMART overall-health self-assessment test result: PASSED\r\n\r\nSMART/Health Information (NVMe Log 0x02)\r\nCritical Warning: 0x00\r\nTemperature: 51 Celsius\r\nAvailable Spare: 100%\r\nAvailable Spare Threshold: 10%\r\nPercentage Used: 3%\r\nData Units Read: 7,580,500 [3.88 TB]\r\nData Units Written: 37,759,770 [19.3 TB]\r\nHost Read Commands: 38,034,128\r\nHost Write Commands: 1,870,013,477\r\nController Busy Time: 279\r\nPower Cycles: 25\r\nPower On Hours: 13,542\r\nUnsafe Shutdowns: 17\r\nMedia and Data Integrity Errors: 0\r\nError Information Log Entries: 1\r\nWarning Comp. Temperature Time: 0\r\nCritical Comp. Temperature Time: 0\r\n\r\nError Information (NVMe Log 0x01, 16 of 256 entries)\r\nNo Errors Logged\n"})}),"\n",(0,t.jsxs)(r.p,{children:["When you see that the ",(0,t.jsx)(r.code,{children:"SMART support is:"})," ",(0,t.jsx)(r.strong,{children:"disabled"})," run the following command to enable it:"]}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"smartctl -s on -a /dev/sda1\n"})}),"\n",(0,t.jsx)(r.h3,{id:"cpu-temperature",children:"CPU Temperature"}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"sensors\r\n\r\ncoretemp-isa-0000\r\nAdapter: ISA adapter\r\nPackage id 0: +50.0\xb0C (high = +100.0\xb0C, crit = +100.0\xb0C)\r\nCore 0: +50.0\xb0C (high = +100.0\xb0C, crit = +100.0\xb0C)\r\nCore 1: +42.0\xb0C (high = +100.0\xb0C, crit = +100.0\xb0C)\r\nCore 2: +46.0\xb0C (high = +100.0\xb0C, crit = +100.0\xb0C)\r\nCore 3: +48.0\xb0C (high = +100.0\xb0C, crit = +100.0\xb0C)\r\nCore 4: +44.0\xb0C (high = +100.0\xb0C, crit = +100.0\xb0C)\r\nCore 5: +49.0\xb0C (high = +100.0\xb0C, crit = +100.0\xb0C)\r\nCore 6: +44.0\xb0C (high = +100.0\xb0C, crit = +100.0\xb0C)\r\nCore 7: +47.0\xb0C (high = +100.0\xb0C, crit = +100.0\xb0C)\r\n\r\nnvme-pci-0900\r\nAdapter: PCI adapter\r\nComposite: +48.9\xb0C (low = -5.2\xb0C, high = +83.8\xb0C)\r\n (crit = +87.8\xb0C)\r\n\r\nacpitz-acpi-0\r\nAdapter: ACPI interface\r\ntemp1: +16.8\xb0C (crit = +20.8\xb0C)\r\ntemp2: +27.8\xb0C (crit = +105.0\xb0C)\r\n\r\niwlwifi_1-virtual-0\r\nAdapter: Virtual device\r\ntemp1: N/A \r\n\r\nenp7s0-pci-0700\r\nAdapter: PCI adapter\r\nPHY Temperature: +47.9\xb0C \r\nMAC Temperature: +48.5\xb0C \r\n\r\nnvme-pci-0300\r\nAdapter: PCI adapter\r\nComposite: +50.9\xb0C (low = -5.2\xb0C, high = +83.8\xb0C)\r\n (crit = +87.8\xb0C)\n"})}),"\n",(0,t.jsx)(r.h2,{id:"zabbix",children:"Zabbix"}),"\n",(0,t.jsx)(r.h3,{id:"preparing-zabbix-agent2",children:"Preparing Zabbix-Agent2"}),"\n",(0,t.jsxs)(r.p,{children:["Add the ",(0,t.jsx)(r.code,{children:"zabbix"})," agent to ",(0,t.jsx)(r.strong,{children:"Sudoers"}),":"]}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"nano /etc/sudoers\n"})}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"# Zabbix user SMART control\r\nzabbix ALL=(ALL) NOPASSWD:/usr/sbin/smartctl\r\n\r\n# Zabbix user LM Sensors\r\nzabbix ALL=NOPASSWD:/bin/sensors\n"})}),"\n",(0,t.jsx)(r.p,{children:"Let's start by allowing the Zabbix server to execute ANY script (parental supervision advised):"}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"nano /etc/zabbix/zabbix_agent2.conf\n"})}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:'### Option: AllowKey\r\n# Allow execution of item keys matching pattern.\r\n# Multiple keys matching rules may be defined in combination with DenyKey.\r\n# Key pattern is wildcard expression, which support "*" character to match any number of any characters in ce>\r\n# Parameters are processed one by one according their appearance order.\r\n# If no AllowKey or DenyKey rules defined, all keys are allowed.\r\n#\r\n# Mandatory: no\r\nAllowKey=system.run[*]\n'})}),"\n",(0,t.jsx)(r.h3,{id:"direct-cli-command-execution",children:"Direct CLI Command Execution"}),"\n",(0,t.jsx)(r.p,{children:"Now prepare a few sensor/smarttools commands to extract single values of interest:"}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"smartctl -a /dev/sda | grep Temperature_Celsius | awk {'print $10'}\r\n23\r\n\r\nsmartctl -a /dev/nvme0 | grep Temperature | awk {'print $2'} | grep -o '[0-9]\\+'\r\n51\r\n\r\nsudo sensors | grep 'Core 0' | awk -F'[+|.]' {'print $2'}\r\n30\n"})}),"\n",(0,t.jsxs)(r.p,{children:["We can add these scripts to our Zabbix Server ",(0,t.jsx)(r.strong,{children:"Scripts"})," config:"]}),"\n",(0,t.jsx)(r.p,{children:(0,t.jsx)(r.img,{alt:"Zabbix v6 SMART HDD and CPU Temperature Check",src:n(281681).Z+"",width:"1047",height:"728"})}),"\n",(0,t.jsx)(r.p,{children:(0,t.jsx)(r.img,{alt:"Zabbix v6 SMART HDD and CPU Temperature Check",src:n(950072).Z+"",width:"1047",height:"506"})}),"\n",(0,t.jsx)(r.p,{children:"As a manual script item we can now execute those scripts directly from our global dashboard:"}),"\n",(0,t.jsx)(r.p,{children:(0,t.jsx)(r.img,{alt:"Zabbix v6 SMART HDD and CPU Temperature Check",src:n(831036).Z+"",width:"1045",height:"494"})}),"\n",(0,t.jsxs)(r.p,{children:["If you run into the following error message ",(0,t.jsx)(r.strong,{children:"Cannot execute script. Unknown metric system.run"})," you skipped the step above of adding the zabbix agent to your host ",(0,t.jsx)(r.code,{children:"sudoers"})," - or forgot to restart the Zabbix Agent service:"]}),"\n",(0,t.jsx)(r.p,{children:(0,t.jsx)(r.img,{alt:"Zabbix v6 SMART HDD and CPU Temperature Check",src:n(325070).Z+"",width:"1042",height:"231"})}),"\n",(0,t.jsx)(r.p,{children:"If everything is set up right your server should now be able to retrieve the Temperature value from your host system:"}),"\n",(0,t.jsx)(r.p,{children:(0,t.jsx)(r.img,{alt:"Zabbix v6 SMART HDD and CPU Temperature Check",src:n(934431).Z+"",width:"1042",height:"330"})}),"\n",(0,t.jsx)(r.h3,{id:"working-with-shell-scripts",children:"Working with Shell Scripts"}),"\n",(0,t.jsx)(r.p,{children:"To replace the nasty wildcard execution permission we can now replace the direct commands with a shell script. 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wildcard with the explicit script file calls to exclude any script not specifically defined by you:"}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:"nano nano /etc/zabbix/zabbix_agent2.conf\n"})}),"\n",(0,t.jsx)(r.pre,{children:(0,t.jsx)(r.code,{className:"language-bash",children:'### Option: AllowKey\r\n# Allow execution of item keys matching pattern.\r\n# Multiple keys matching rules may be defined in combination with DenyKey.\r\n# Key pattern is wildcard expression, which support "*" character to match any number of any characters in ce>\r\n# Parameters are processed one by one according their appearance order.\r\n# If no AllowKey or DenyKey rules defined, all keys are allowed.\r\n#\r\n# Mandatory: no\r\nAllowKey=system.run[sh /opt/zabbix/temp_nvme0.sh]\r\nAllowKey=system.run[sh /opt/zabbix/temp_nvme1.sh]\r\nAllowKey=system.run[sh /opt/zabbix/temp_sda.sh]\r\nAllowKey=system.run[sh /opt/zabbix/temp_sdb.sh]\r\nAllowKey=system.run[sh 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to `./datasets`\ndataset = \'datasets/data.yaml\'\n\n# load a model\n# backbone = YOLO("yolov8n.yaml") # build a new model from scratch\nbackbone = YOLO("yolov8n.pt") # load a pre-trained model (recommended for training)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Use the model\nresults = backbone.train(data=dataset, epochs=20) # train the model\n"})}),"\n",(0,i.jsx)(n.p,{children:"20 epochs completed in 2.530 hours.\nOptimizer stripped from runs/detect/train11/weights/last.pt, 6.2MB\nOptimizer stripped from runs/detect/train11/weights/best.pt, 6.2MB"}),"\n",(0,i.jsx)(n.p,{children:"Validating runs/detect/train11/weights/best.pt...\nUltralytics YOLOv8.0.173 \ud83d\ude80 Python-3.10.11 torch-2.0.1 CUDA:0 (NVIDIA GeForce GTX 1060 6GB, 6070MiB)\nModel summary (fused): 168 layers, 3005843 parameters, 0 gradients\nClass Images Instances Box(P R mAP50 mAP50-95): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 64/64 [00:17, 3.61it/s]\nall 2046 2132 0.986 0.954 0.984 0.701\nSpeed: 0.3ms preprocess, 5.1ms inference, 0.0ms loss, 0.5ms postprocess per image\nResults saved to \x1b[1mruns/detect/train11\x1b[0m"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(587708).Z+"",width:"2400",height:"1200"})}),"\n",(0,i.jsxs)(n.table,{children:[(0,i.jsx)(n.thead,{children:(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.th,{children:"Class"}),(0,i.jsx)(n.th,{children:"Images"}),(0,i.jsx)(n.th,{children:"Instances"}),(0,i.jsx)(n.th,{children:"Box( P"}),(0,i.jsx)(n.th,{children:"R"}),(0,i.jsx)(n.th,{children:"mAP50"}),(0,i.jsx)(n.th,{children:"mAP50-95)"})]})}),(0,i.jsxs)(n.tbody,{children:[(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"all"}),(0,i.jsx)(n.td,{children:"2046"}),(0,i.jsx)(n.td,{children:"2132"}),(0,i.jsx)(n.td,{children:"0.986"}),(0,i.jsx)(n.td,{children:"0.954"}),(0,i.jsx)(n.td,{children:"0.984"}),(0,i.jsx)(n.td,{children:"0.701"})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:(0,i.jsx)(n.em,{children:"Speed: 0.3ms preprocess, 5.1ms inference, 0.0ms loss, 0.5ms postprocess per image"})}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:(0,i.jsx)(n.em,{children:"Model summary (fused): 168 layers, 3005843 parameters, 0 gradients"})}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{})]})]})]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Evaluate the model's performance on the validation set\nresults = backbone.val()\n"})}),"\n",(0,i.jsx)(n.p,{children:"Ultralytics YOLOv8.0.173 \ud83d\ude80 Python-3.10.11 torch-2.0.1 CUDA:0 (NVIDIA GeForce GTX 1060 6GB, 6070MiB)\nModel summary (fused): 168 layers, 3005843 parameters, 0 gradients\n\x1b[34m\x1b[1mval: \x1b[0mScanning /opt/app/03_object_detection_with_text_extraction_easyocr/datasets/valid/labels.cache... 2046 images, 3 backgrounds, 0 corrupt: 100%|\u2588\u2588\u2588\u2588\x1b[0m\nClass Images Instances Box(P R mAP50 mAP50-95): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 128/128 [00:18, 6.74it/s]\nall 2046 2132 0.986 0.954 0.984 0.701\nSpeed: 0.3ms preprocess, 5.7ms inference, 0.0ms loss, 0.6ms postprocess per image\nResults saved to \x1b[1mruns/detect/val\x1b[0m"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(766487).Z+"",width:"1721",height:"925"})}),"\n",(0,i.jsxs)(n.table,{children:[(0,i.jsx)(n.thead,{children:(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.th,{children:"Class"}),(0,i.jsx)(n.th,{children:"Images"}),(0,i.jsx)(n.th,{children:"Instances"}),(0,i.jsx)(n.th,{children:"Box( P"}),(0,i.jsx)(n.th,{children:"R"}),(0,i.jsx)(n.th,{children:"mAP50"}),(0,i.jsx)(n.th,{children:"mAP50-95)"})]})}),(0,i.jsxs)(n.tbody,{children:[(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"all"}),(0,i.jsx)(n.td,{children:"2046"}),(0,i.jsx)(n.td,{children:"2132"}),(0,i.jsx)(n.td,{children:"0.986"}),(0,i.jsx)(n.td,{children:"0.954"}),(0,i.jsx)(n.td,{children:"0.984"}),(0,i.jsx)(n.td,{children:"0.701"})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:(0,i.jsx)(n.em,{children:"Speed: 0.3ms preprocess, 5.7ms inference, 0.0ms loss, 0.6ms postprocess per image"})}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{})]})]})]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Perform object detection on an image using the model\nresults = backbone('inputs/cars.png')\n"})}),"\n",(0,i.jsx)(n.p,{children:"image 1/1 /opt/app/03_object_detection_with_text_extraction_easyocr/inputs/cars.png: 384x640 2 License_Plates, 35.9ms\nSpeed: 1.7ms preprocess, 35.9ms inference, 2.0ms postprocess per image at shape (1, 3, 384, 640)"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Export the model to ONNX format\n# success = model.export(imgsz=(640, 480), format='onnx', opset=12, optimize=False, half=False)\n# Export to PyTorch format\nsuccess = backbone.export(imgsz=640, format='torchscript', optimize=False, half=False, int8=False)\n# TorchScript: export success \u2705 1.5s, saved as 'runs/detect/train11/weights/best.torchscript' (11.9 MB)\n"})}),"\n",(0,i.jsx)(n.p,{children:"Ultralytics YOLOv8.0.173 \ud83d\ude80 Python-3.10.11 torch-2.0.1 CPU (Intel Core(TM) i7-7700 3.60GHz)"}),"\n",(0,i.jsx)(n.p,{children:"\x1b[34m\x1b[1mPyTorch:\x1b[0m starting from 'runs/detect/train11/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 5, 8400) (6.0 MB)"}),"\n",(0,i.jsx)(n.p,{children:"\x1b[34m\x1b[1mTorchScript:\x1b[0m starting export with torch 2.0.1...\n\x1b[34m\x1b[1mTorchScript:\x1b[0m export success \u2705 1.3s, saved as 'runs/detect/train11/weights/best.torchscript' (11.9 MB)"}),"\n",(0,i.jsxs)(n.p,{children:["Export complete (2.6s)\nResults saved to \x1b[1m/opt/app/03_object_detection_with_text_extraction_easyocr/runs/detect/train11/weights\x1b[0m\nPredict: yolo predict task=detect model=runs/detect/train11/weights/best.torchscript imgsz=640",(0,i.jsx)(n.br,{}),"\n","Validate: yolo val task=detect model=runs/detect/train11/weights/best.torchscript imgsz=640 data=datasets/data.yaml",(0,i.jsx)(n.br,{}),"\n","Visualize: ",(0,i.jsx)(n.a,{href:"https://netron.app",children:"https://netron.app"})]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# pick pre-trained model\nnp_model = YOLO('runs/detect/train11/weights/best.torchscript')\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read video by index\nvideo = cv.VideoCapture(videos[1])\nret, frame = video.read()\n\n# get video dims\nframe_width = int(video.get(3))\nframe_height = int(video.get(4))\nsize = (frame_width, frame_height)\n\n# Define the codec and create VideoWriter object\nfourcc = cv.VideoWriter_fourcc(*'DIVX')\nout = cv.VideoWriter('./outputs/uk_dash_np_2.avi', fourcc, 20.0, size)\n\n# read frames\nret = True\n\nwhile ret:\n ret, frame = video.read()\n\n if ret:\n # detect & track objects\n results = np_model.track(frame, persist=True)\n\n # plot results\n composed = results[0].plot()\n\n # save video\n out.write(composed)\n\nout.release()\nvideo.release()\n"})}),"\n",(0,i.jsx)(n.p,{children:"And now we have a model that is only interested in number plates:"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(526491).Z+"",width:"1933",height:"391"})}),"\n",(0,i.jsx)(n.p,{children:"Though, the confusion matrix shows us that it also sees a lot of plates that do not exist - but at least it does not miss that many:"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(553629).Z+"",width:"3000",height:"2250"})}),"\n",(0,i.jsx)(n.h2,{id:"improving-training-results",children:"Improving Training Results"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:'# unzip downloaded dataset to `./datasets`\ndataset = \'datasets/data.yaml\'\n\n# load a model\n# backbone = YOLO("yolov8n.yaml") # build a new model from scratch\nbackbone_small = YOLO("yolov8s.pt") # load a pre-trained model (recommended for training)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Use the model\nresults_medium = backbone_small.train(data=dataset, epochs=100) # train the model\n"})}),"\n",(0,i.jsx)(n.p,{children:"Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n57/100 4.57G 0.9052 0.3966 1.064 7 640: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1324/1324 [13:43, 1.61it/s]\nClass Images Instances Box(P R mAP50 mAP50-95): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 64/64 [00:26, 2.38it/s]\nall 2046 2132 0.981 0.968 0.984 0.709"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# pick pre-trained model\nnp2_model = YOLO('runs/detect/train4/weights/best.pt')\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Evaluate the model's performance on the validation set\nresults = np2_model.val()\n"})}),"\n",(0,i.jsxs)(n.p,{children:["Ultralytics YOLOv8.0.173 \ud83d\ude80 Python-3.10.11 torch-2.0.1 CUDA:0 (NVIDIA GeForce GTX 1060 6GB, 6070MiB)\nModel summary (fused): 168 layers, 11125971 parameters, 0 gradients\nDownloading ",(0,i.jsx)(n.a,{href:"https://ultralytics.com/assets/Arial.ttf",children:"https://ultralytics.com/assets/Arial.ttf"})," to '/root/.config/Ultralytics/Arial.ttf'...\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 755k/755k [00:00, 2.35MB/s]\nClass Images Instances Box(P R mAP50 mAP50-95): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 128/128 [00:35, 3.59it/s]\nall 2046 2132 0.981 0.968 0.984 0.71\nSpeed: 0.3ms preprocess, 13.4ms inference, 0.0ms loss, 0.6ms postprocess per image\nResults saved to \x1b[1mruns/detect/val2\x1b[0m"]}),"\n",(0,i.jsx)(n.h2,{id:"license-plate-detection",children:"License Plate Detection"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"import ast\nimport cv2 as cv\nimport easyocr\nfrom glob import glob\nimport numpy as np\nimport pandas as pd\nimport string\nfrom ultralytics import YOLO\n"})}),"\n",(0,i.jsx)(n.p,{children:"As seen during the training - the model, just using the COCO training weights, is very capable of detecting cars, trucks and buses. But number plates seem to be a bit harder - the model often confuses street signs or just basic backgound noise as a car registration plate. The positive is that it rarely misses a plate."}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# regular pre-trained yolov8 model for car recognition\n# coco_model = YOLO('yolov8n.pt')\ncoco_model = YOLO('yolov8s.pt')\n# yolov8 model trained to detect number plates\nnp_model = YOLO('runs/detect/train4/weights/best.pt')\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read in test video paths\nvideos = glob('inputs/*.mp4')\nprint(videos)\n"})}),"\n",(0,i.jsx)(n.p,{children:"['inputs/uk_dash_1.mp4', 'inputs/uk_dash_2.mp4']"}),"\n",(0,i.jsx)(n.h3,{id:"step-1-implementing-the-car-detection",children:"STEP 1 Implementing the Car Detection"}),"\n",(0,i.jsx)(n.p,{children:"Get the bounding boxes of all vehicles in your video recording with prediction confidence score and object tracking ID"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read video by index\nvideo = cv.VideoCapture(videos[1])\n\nret = True\nframe_number = -1\n# all vehicle class IDs from the COCO dataset (car, motorbike, truck) https://docs.ultralytics.com/datasets/detect/coco/#dataset-yaml\nvehicles = [2,3,5]\nvehicle_bounding_boxes = []\n\n# read the 10 first frames\nwhile ret:\n frame_number += 1\n ret, frame = video.read()\n\n if ret and frame_number < 10:\n # use track() to identify instances and track them frame by frame\n detections = coco_model.track(frame, persist=True)[0]\n # save cropped detections\n # detections.save_crop('outputs')\n # print nodel predictions for debugging\n # print(results)\n\n for detection in detections.boxes.data.tolist():\n # print detection bounding boxes for debugging\n # print(detection)\n x1, y1, x2, y2, track_id, score, class_id = detection\n # I am only interested in class IDs that belong to vehicles\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n\n# print found bounding boxes for debugging\nprint(vehicle_bounding_boxes)\nvideo.release()\n"})}),"\n",(0,i.jsxs)(n.p,{children:["This code now collects all vehicle bounding boxes from the video and writes them into the ",(0,i.jsx)(n.code,{children:"vehicle_bounding_boxes"})," list. Besides the bbox coordinates this list also contains the tracking ID of the detected vehicle - they should stay the same frame-to-frame for every detected vehicle and serve as a unique identifier. And the score - how confident is the model that this bbox acutally contains a vehicle with calues from ",(0,i.jsx)(n.code,{children:"0"}),"-",(0,i.jsx)(n.code,{children:"1"}),":"]}),"\n",(0,i.jsxs)(n.blockquote,{children:["\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.code,{children:"[[762.6422729492188, 614.1869506835938, 1121.368408203125, 911.6900024414062, 1.0, 0.9254793524742126], [1196.008056640625, 635.3404541015625, 1526.3975830078125, 828.6062622070312, 2.0, 0.8488578200340271], [1672.98193359375, 613.9304809570312, 1912.382080078125, 819.3222045898438, 3.0, 0.5385741591453552], [758.7203369140625, 612.6467895507812, 1119.0892333984375, 917.7677612304688, 1.0, 0.925308883190155], [1195.9505615234375, 635.4146118164062, 1527.97412109375, 830.3245239257812, 2.0, 0.865635871887207], [1692.5439453125, 613.0050659179688, 1917.7542724609375, 819.2852783203125, 3.0, 0.5493771433830261], [754.7435302734375, 612.3658447265625, 1115.0045166015625, 919.653076171875, 1.0, 0.9127519130706787], [1194.00341796875, 634.9168090820312, 1527.029541015625, 832.383544921875, 2.0, 0.8814489841461182], [1688.8155517578125, 615.6485595703125, 1920.0, 812.7891235351562, 3.0, 0.6132197976112366], [752.8799438476562, 611.2362060546875, 1111.976318359375, 920.200927734375, 1.0, 0.9137689471244812], [1192.805908203125, 634.3713989257812, 1526.1273193359375, 832.46337890625, 2.0, 0.8671290278434753], [1680.4443359375, 616.384033203125, 1920.0, 813.3687744140625, 3.0, 0.6371273994445801], [750.9274291992188, 611.5806884765625, 1110.2657470703125, 915.3110961914062, 1.0, 0.9381350874900818], [1189.63916015625, 634.7803955078125, 1525.4072265625, 833.2440185546875, 2.0, 0.888615071773529], [1669.8206787109375, 616.796142578125, 1920.0, 808.6288452148438, 3.0, 0.5068169236183167], [748.747802734375, 609.5638427734375, 1109.0101318359375, 912.808837890625, 1.0, 0.9158740639686584], [1187.832275390625, 634.11328125, 1524.633056640625, 832.628173828125, 2.0, 0.8583219647407532], [1659.7103271484375, 615.9025268554688, 1920.0, 823.25048828125, 3.0, 0.7755634784698486], [745.1077270507812, 609.5160522460938, 1107.8214111328125, 912.8062133789062, 1.0, 0.9354495406150818], [1186.91455078125, 634.5582885742188, 1524.004150390625, 832.4244995117188, 2.0, 0.8758277297019958], [1650.2227783203125, 613.749267578125, 1920.0, 828.9586791992188, 3.0, 0.7407982349395752], [742.2940673828125, 610.4445190429688, 1106.597900390625, 912.6227416992188, 1.0, 0.9281387329101562], [1186.1158447265625, 634.223876953125, 1523.3406982421875, 832.6515502929688, 2.0, 0.8710047006607056], [1638.47705078125, 614.6183471679688, 1919.968017578125, 833.5314331054688, 3.0, 0.8480165600776672], [741.3974609375, 610.8768920898438, 1105.543701171875, 912.5601806640625, 1.0, 0.9410984516143799], [1185.1246337890625, 633.4691162109375, 1523.3682861328125, 832.612060546875, 2.0, 0.8842733502388], [1627.5872802734375, 616.9085693359375, 1919.9117431640625, 829.2400512695312, 3.0, 0.85666424036026], [741.3576049804688, 610.9183959960938, 1103.5601806640625, 914.4734497070312, 1.0, 0.9404377937316895], [1183.273681640625, 633.708984375, 1522.5953369140625, 833.3422241210938, 2.0, 0.8721591234207153], [1618.3934326171875, 619.4539794921875, 1919.864013671875, 827.6344604492188, 3.0, 0.8759608864784241]]"})}),"\n"]}),"\n",(0,i.jsxs)(n.p,{children:["Using the ",(0,i.jsx)(n.code,{children:"save_crop()"})," function shows me that the first 10 frames of my video contain three different cars:"]}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(841616).Z+"",width:"651",height:"533"})}),"\n",(0,i.jsx)(n.h3,{id:"step-2-implementing-the-license-plate-detection",children:"STEP 2 Implementing the License Plate Detection"}),"\n",(0,i.jsx)(n.p,{children:"Use the bounding box for each vehicle and use the number plate detector model to try to find the corresponding plate within in the confinement of those boxes."}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read video by index\nvideo = cv.VideoCapture(videos[0])\n\nret = True\nframe_number = -1\nvehicles = [2,3,5]\n\n# read the 10 first frames\nwhile ret:\n frame_number += 1\n ret, frame = video.read()\n\n if ret and frame_number < 10:\n \n # vehicle detector\n detections = coco_model.track(frame, persist=True)[0]\n for detection in detections.boxes.data.tolist():\n x1, y1, x2, y2, track_id, score, class_id = detection\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes = []\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n for bbox in vehicle_bounding_boxes:\n print(bbox)\n roi = frame[int(y1):int(y2), int(x1):int(x2)]\n # debugging check if bbox lines up with detected vehicles (should be identical to save_crops() above\n # cv.imwrite(str(track_id) + '.jpg', roi)\n \n # license plate detector for region of interest\n license_plates = np_model(roi)[0]\n # check every bounding box for a license plate\n for license_plate in license_plates.boxes.data.tolist():\n plate_x1, plate_y1, plate_x2, plate_y2, plate_score, _ = license_plate\n # verify detections\n print(license_plate, 'track_id: ' + str(bbox[4]))\n plate = roi[int(plate_y1):int(plate_y2), int(plate_x1):int(plate_x2)]\n cv.imwrite(str(track_id) + '.jpg', plate)\n \nvideo.release()\n"})}),"\n",(0,i.jsx)(n.p,{children:"By using the tracking ID I can make sure that every license plate - as seen above the video contained several instances of the same 3 cars - is only returned ones:"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(173943).Z+"",width:"704",height:"271"})}),"\n",(0,i.jsx)(n.h3,{id:"step-3-preprocess-license-plates",children:"STEP 3 Preprocess License Plates"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read video by index\nvideo = cv.VideoCapture(videos[0])\n\nret = True\nframe_number = -1\nvehicles = [2,3,5]\n\n# read the 10 first frames\nwhile ret:\n frame_number += 1\n ret, frame = video.read()\n\n if ret and frame_number < 100:\n \n # vehicle detector\n detections = coco_model.track(frame, persist=True)[0]\n for detection in detections.boxes.data.tolist():\n x1, y1, x2, y2, track_id, score, class_id = detection\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes = []\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n for bbox in vehicle_bounding_boxes:\n print(bbox)\n roi = frame[int(y1):int(y2), int(x1):int(x2)]\n \n # license plate detector for region of interest\n license_plates = np_model(roi)[0]\n # process license plate\n for license_plate in license_plates.boxes.data.tolist():\n plate_x1, plate_y1, plate_x2, plate_y2, plate_score, _ = license_plate\n # crop plate from region of interest\n plate = roi[int(plate_y1):int(plate_y2), int(plate_x1):int(plate_x2)]\n # de-colorize\n plate_gray = cv.cvtColor(plate, cv.COLOR_BGR2GRAY)\n # posterize\n _, plate_treshold = cv.threshold(plate_gray, 64, 255, cv.THRESH_BINARY_INV)\n cv.imwrite(str(track_id) + '_gray.jpg', plate_gray)\n cv.imwrite(str(track_id) + '_thresh.jpg', plate_treshold)\n \nvideo.release()\n"})}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(560549).Z+"",width:"658",height:"289"})}),"\n",(0,i.jsx)(n.h3,{id:"step-4-read-license-plates",children:"STEP 4 Read License Plates"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Initialize the OCR reader\nreader = easyocr.Reader(['en'], gpu=True)\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def read_license_plate(license_plate_crop):\n detections = reader.readtext(license_plate_crop)\n\n for detection in detections:\n bbox, text, score = detection\n\n text = text.upper().replace(' ', '')\n \n return text, score\n\n return None, None\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def write_csv(results, output_path):\n \n with open(output_path, 'w') as f:\n f.write('{},{},{},{},{},{},{},{}\\n'.format(\n 'frame_number', 'track_id', 'car_bbox', 'car_bbox_score',\n 'license_plate_bbox', 'license_plate_bbox_score', 'license_plate_number',\n 'license_text_score'))\n\n for frame_number in results.keys():\n for track_id in results[frame_number].keys():\n print(results[frame_number][track_id])\n if 'car' in results[frame_number][track_id].keys() and \\\n 'license_plate' in results[frame_number][track_id].keys() and \\\n 'number' in results[frame_number][track_id]['license_plate'].keys():\n f.write('{},{},{},{},{},{},{},{}\\n'.format(\n frame_number,\n track_id,\n '[{} {} {} {}]'.format(\n results[frame_number][track_id]['car']['bbox'][0],\n results[frame_number][track_id]['car']['bbox'][1],\n results[frame_number][track_id]['car']['bbox'][2],\n results[frame_number][track_id]['car']['bbox'][3]\n ),\n results[frame_number][track_id]['car']['bbox_score'],\n '[{} {} {} {}]'.format(\n results[frame_number][track_id]['license_plate']['bbox'][0],\n results[frame_number][track_id]['license_plate']['bbox'][1],\n results[frame_number][track_id]['license_plate']['bbox'][2],\n results[frame_number][track_id]['license_plate']['bbox'][3]\n ),\n results[frame_number][track_id]['license_plate']['bbox_score'],\n results[frame_number][track_id]['license_plate']['number'],\n results[frame_number][track_id]['license_plate']['text_score'])\n )\n f.close()\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"results = {}\n\n# read video by index\nvideo = cv.VideoCapture(videos[0])\n\nret = True\nframe_number = -1\nvehicles = [2,3,5]\n\n# read the 10 first frames\nwhile ret:\n frame_number += 1\n ret, frame = video.read()\n\n if ret and frame_number < 100:\n results[frame_number] = {}\n \n # vehicle detector\n detections = coco_model.track(frame, persist=True)[0]\n for detection in detections.boxes.data.tolist():\n x1, y1, x2, y2, track_id, score, class_id = detection\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes = []\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n for bbox in vehicle_bounding_boxes:\n print(bbox)\n roi = frame[int(y1):int(y2), int(x1):int(x2)]\n \n # license plate detector for region of interest\n license_plates = np_model(roi)[0]\n # process license plate\n for license_plate in license_plates.boxes.data.tolist():\n plate_x1, plate_y1, plate_x2, plate_y2, plate_score, _ = license_plate\n # crop plate from region of interest\n plate = roi[int(plate_y1):int(plate_y2), int(plate_x1):int(plate_x2)]\n # de-colorize\n plate_gray = cv.cvtColor(plate, cv.COLOR_BGR2GRAY)\n # posterize\n _, plate_treshold = cv.threshold(plate_gray, 64, 255, cv.THRESH_BINARY_INV)\n \n # OCR\n np_text, np_score = read_license_plate(plate_treshold)\n # if plate could be read write results\n if np_text is not None:\n results[frame_number][track_id] = {\n 'car': {\n 'bbox': [x1, y1, x2, y2],\n 'bbox_score': score\n },\n 'license_plate': {\n 'bbox': [plate_x1, plate_y1, plate_x2, plate_y2],\n 'bbox_score': plate_score,\n 'number': np_text,\n 'text_score': np_score\n }\n }\n\nwrite_csv(results, './results.csv')\nvideo.release()\n"})}),"\n",(0,i.jsx)(n.p,{children:"This returns a list with bounding box metrics for every frame with a successful detection:"}),"\n",(0,i.jsx)("br",{}),"\n",(0,i.jsxs)(n.table,{children:[(0,i.jsx)(n.thead,{children:(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.th,{children:"frame_number"}),(0,i.jsx)(n.th,{children:"track_id"}),(0,i.jsx)(n.th,{children:"car_bbox"}),(0,i.jsx)(n.th,{children:"license_plate_bbox"}),(0,i.jsx)(n.th,{children:"license_plate_bbox_score"}),(0,i.jsx)(n.th,{children:"license_number"}),(0,i.jsx)(n.th,{children:"license_number_score"})]})}),(0,i.jsxs)(n.tbody,{children:[(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"0"}),(0,i.jsx)(n.td,{children:"1.0"}),(0,i.jsx)(n.td,{children:"[760.1986694335938 614.2100830078125 1123.09130859375 914.9498901367188]"}),(0,i.jsx)(n.td,{children:"[110.20427703857422 133.25326538085938 238.5574493408203 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180.43345642089844]"}),(0,i.jsx)(n.td,{children:"0.7666334509849548"}),(0,i.jsx)(n.td,{children:"BP66UXN"}),(0,i.jsx)(n.td,{children:"0.7696779876170268"})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"3"}),(0,i.jsx)(n.td,{children:"1.0"}),(0,i.jsx)(n.td,{children:"[753.9749755859375 611.0296630859375 1115.607421875 920.6179809570312]"}),(0,i.jsx)(n.td,{children:"[109.80683898925781 134.79702758789062 239.79380798339844 180.0568389892578]"}),(0,i.jsx)(n.td,{children:"0.7609436511993408"}),(0,i.jsx)(n.td,{children:"BPG6UXN"}),(0,i.jsx)(n.td,{children:"0.5947437696221942"})]})]})]}),"\n",(0,i.jsx)(n.h3,{id:"step-5-clean-up-license-plate-format",children:"STEP 5 Clean-Up License Plate Format"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Mapping dictionaries for character conversion\n# characters that can easily be confused can be \n# verified by their location - an `O` in a place\n# where a number is expected is probably a `0`\ndict_char_to_int = {'O': '0',\n 'I': '1',\n 'J': '3',\n 'A': '4',\n 'G': '6',\n 'S': '5'}\n\ndict_int_to_char = {'0': 'O',\n '1': 'I',\n '3': 'J',\n '4': 'A',\n '6': 'G',\n '5': 'S'}\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def license_complies_format(text):\n # True if the license plate complies with the format, False otherwise.\n if len(text) != 7:\n return False\n\n if (text[0] in string.ascii_uppercase or text[0] in dict_int_to_char.keys()) and \\\n (text[1] in string.ascii_uppercase or text[1] in dict_int_to_char.keys()) and \\\n (text[2] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] or text[2] in dict_char_to_int.keys()) and \\\n (text[3] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] or text[3] in dict_char_to_int.keys()) and \\\n (text[4] in string.ascii_uppercase or text[4] in dict_int_to_char.keys()) and \\\n (text[5] in string.ascii_uppercase or text[5] in dict_int_to_char.keys()) and \\\n (text[6] in string.ascii_uppercase or text[6] in dict_int_to_char.keys()):\n return True\n else:\n return False\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def format_license(text):\n license_plate_ = ''\n mapping = {0: dict_int_to_char, 1: dict_int_to_char, 4: dict_int_to_char, 5: dict_int_to_char, 6: dict_int_to_char,\n 2: dict_char_to_int, 3: dict_char_to_int}\n for j in [0, 1, 2, 3, 4, 5, 6]:\n if text[j] in mapping[j].keys():\n license_plate_ += mapping[j][text[j]]\n else:\n license_plate_ += text[j]\n\n return license_plate_\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def read_license_plate(license_plate_crop):\n detections = reader.readtext(license_plate_crop)\n\n for detection in detections:\n bbox, text, score = detection\n\n text = text.upper().replace(' ', '')\n\n # verify that text is conform to a standard license plate\n if license_complies_format(text):\n # bring text into the default license plate format\n return format_license(text), score\n\n return None, None\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"results = {}\n\n# read video by index\nvideo = cv.VideoCapture(videos[1])\n\nret = True\nframe_number = -1\nvehicles = [2,3,5]\n\n# read the entire video\nwhile ret:\n ret, frame = video.read()\n frame_number += 1\n if ret:\n results[frame_number] = {}\n \n # vehicle detector\n detections = coco_model.track(frame, persist=True)[0]\n for detection in detections.boxes.data.tolist():\n x1, y1, x2, y2, track_id, score, class_id = detection\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes = []\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n for bbox in vehicle_bounding_boxes:\n print(bbox)\n roi = frame[int(y1):int(y2), int(x1):int(x2)]\n \n # license plate detector for region of interest\n license_plates = np_model(roi)[0]\n # process license plate\n for license_plate in license_plates.boxes.data.tolist():\n plate_x1, plate_y1, plate_x2, plate_y2, plate_score, _ = license_plate\n # crop plate from region of interest\n plate = roi[int(plate_y1):int(plate_y2), int(plate_x1):int(plate_x2)]\n # de-colorize\n plate_gray = cv.cvtColor(plate, cv.COLOR_BGR2GRAY)\n # posterize\n _, plate_treshold = cv.threshold(plate_gray, 64, 255, cv.THRESH_BINARY_INV)\n \n # OCR\n np_text, np_score = read_license_plate(plate_treshold)\n # if plate could be read write results\n if np_text is not None:\n results[frame_number][track_id] = {\n 'car': {\n 'bbox': [x1, y1, x2, y2],\n 'bbox_score': score\n },\n 'license_plate': {\n 'bbox': [plate_x1, plate_y1, plate_x2, plate_y2],\n 'bbox_score': plate_score,\n 'number': np_text,\n 'text_score': np_score\n }\n }\n\nwrite_csv(results, './results.csv')\nvideo.release()\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"results = pd.read_csv('./results.csv')\n\n# show results for tracking ID 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Plate Format",id:"step-5-clean-up-license-plate-format",level:3},{value:"STEP 6 Visualize the Results",id:"step-6-visualize-the-results",level:3}];function o(e){const n={a:"a",blockquote:"blockquote",br:"br",code:"code",em:"em",h1:"h1",h2:"h2",h3:"h3",img:"img",li:"li",p:"p",pre:"pre",table:"table",tbody:"tbody",td:"td",th:"th",thead:"thead",tr:"tr",ul:"ul",...(0,s.ah)(),...e.components};return(0,i.jsxs)(i.Fragment,{children:[(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"Guangzhou, China",src:t(653493).Z+"",width:"1500",height:"871"})}),"\n",(0,i.jsx)(n.h1,{id:"yolov8-license-plate-detection",children:"YOLOv8 License Plate Detection"}),"\n",(0,i.jsxs)(n.p,{children:["Using the ",(0,i.jsx)(n.a,{href:"https://docs.ultralytics.com/tasks/detect/",children:"YOLOv8 Object Tracker"})," and ",(0,i.jsx)(n.a,{href:"https://www.jaided.ai/easyocr/",children:"EasyOCR"})," to record License Plates."]}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsxs)(n.li,{children:["This guide is based on the ",(0,i.jsx)(n.a,{href:"https://github.com/computervisioneng/automatic-number-plate-recognition-python-yolov8",children:"DeepSORT & EasyOCR Repository"})," by ",(0,i.jsx)(n.a,{href:"https://github.com/computervisioneng",children:"@computervisioneng"}),". But I replaced the ",(0,i.jsx)(n.a,{href:"https://github.com/abewley/sort",children:"DeepSORT Dependency"})," with the YOLOv8 included Track function."]}),"\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"https://github.com/mpolinowski/yolo-i-know-flowers",children:"Github Repository"})}),"\n"]}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.a,{href:"#yolov8-license-plate-detection",children:"YOLOv8 License Plate Detection"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"#pre-trained-yolov8",children:"Pre-Trained YOLOv8"})}),"\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"#retraining-yolov8",children:"Retraining YOLOv8"})}),"\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"#improving-training-results",children:"Improving Training Results"})}),"\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.a,{href:"#license-plate-detection",children:"License Plate Detection"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"#step-1-implementing-the-car-detection",children:"STEP 1 Implementing the Car Detection"})}),"\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"#step-2-implementing-the-license-plate-detection",children:"STEP 2 Implementing the License Plate Detection"})}),"\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"#step-3-preprocess-license-plates",children:"STEP 3 Preprocess License Plates"})}),"\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"#step-4-read-license-plates",children:"STEP 4 Read License Plates"})}),"\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"#step-5-clean-up-license-plate-format",children:"STEP 5 Clean-Up License Plate Format"})}),"\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"#step-6-visualize-the-results",children:"STEP 6 Visualize the Results"})}),"\n"]}),"\n"]}),"\n"]}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"import cv2 as cv\nfrom glob import glob\nimport os\nimport random\nfrom ultralytics import YOLO\n"})}),"\n",(0,i.jsx)(n.h2,{id:"pre-trained-yolov8",children:"Pre-Trained YOLOv8"}),"\n",(0,i.jsxs)(n.p,{children:["The regular YOLOv8 training weights do not contain a ",(0,i.jsx)(n.code,{children:"number_plate"})," class and cannot be used directly for a number plate detection:"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read in video paths\nvideos = glob('inputs/*.mp4')\nprint(videos)\n"})}),"\n",(0,i.jsx)(n.p,{children:"['inputs/uk_dash_1.mp4', 'inputs/uk_dash_2.mp4']"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# pick pre-trained model\nmodel_pretrained = YOLO('yolov8n.pt')\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read video by index\nvideo = cv.VideoCapture(videos[1])\n\n# get video dims\nframe_width = int(video.get(3))\nframe_height = int(video.get(4))\nsize = (frame_width, frame_height)\n\n# Define the codec and create VideoWriter object\nfourcc = cv.VideoWriter_fourcc(*'DIVX')\nout = cv.VideoWriter('./outputs/uk_dash_2.avi', fourcc, 20.0, size)\n\n# read frames\nret = True\n\nwhile ret:\n ret, frame = video.read()\n\n if ret:\n # detect & track objects\n results = model_pretrained.track(frame, persist=True)\n\n # plot results\n composed = results[0].plot()\n\n # save video\n out.write(composed)\n\nout.release()\nvideo.release()\n"})}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(579768).Z+"",width:"1946",height:"438"})}),"\n",(0,i.jsx)(n.h2,{id:"retraining-yolov8",children:"Retraining YOLOv8"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsx)(n.li,{children:(0,i.jsx)(n.a,{href:"https://universe.roboflow.com/roboflow-universe-projects/license-plate-recognition-rxg4e",children:"Dataset: Roboflow - License Plate Recognition Computer Vision Project"})}),"\n"]}),"\n",(0,i.jsxs)(n.p,{children:["Download the Dataset with YOLOv8 annotation and point YOLO to the ",(0,i.jsx)(n.code,{children:"data.yaml"})," file that comes with the dataset:"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-yml",children:"train: ../train/images\nval: ../valid/images\ntest: ../test/images\n\nnc: 1\nnames: ['License_Plate']\n\nroboflow:\n workspace: roboflow-universe-projects\n project: license-plate-recognition-rxg4e\n version: 4\n license: CC BY 4.0\n url: https://universe.roboflow.com/roboflow-universe-projects/license-plate-recognition-rxg4e/dataset/4\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:'# unzip downloaded dataset to `./datasets`\ndataset = \'datasets/data.yaml\'\n\n# load a model\n# backbone = YOLO("yolov8n.yaml") # build a new model from scratch\nbackbone = YOLO("yolov8n.pt") # load a pre-trained model (recommended for training)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Use the model\nresults = backbone.train(data=dataset, epochs=20) # train the model\n"})}),"\n",(0,i.jsx)(n.p,{children:"20 epochs completed in 2.530 hours.\nOptimizer stripped from runs/detect/train11/weights/last.pt, 6.2MB\nOptimizer stripped from runs/detect/train11/weights/best.pt, 6.2MB"}),"\n",(0,i.jsx)(n.p,{children:"Validating runs/detect/train11/weights/best.pt...\nUltralytics YOLOv8.0.173 \ud83d\ude80 Python-3.10.11 torch-2.0.1 CUDA:0 (NVIDIA GeForce GTX 1060 6GB, 6070MiB)\nModel summary (fused): 168 layers, 3005843 parameters, 0 gradients\nClass Images Instances Box(P R mAP50 mAP50-95): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 64/64 [00:17, 3.61it/s]\nall 2046 2132 0.986 0.954 0.984 0.701\nSpeed: 0.3ms preprocess, 5.1ms inference, 0.0ms loss, 0.5ms postprocess per image\nResults saved to \x1b[1mruns/detect/train11\x1b[0m"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(587708).Z+"",width:"2400",height:"1200"})}),"\n",(0,i.jsxs)(n.table,{children:[(0,i.jsx)(n.thead,{children:(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.th,{children:"Class"}),(0,i.jsx)(n.th,{children:"Images"}),(0,i.jsx)(n.th,{children:"Instances"}),(0,i.jsx)(n.th,{children:"Box( P"}),(0,i.jsx)(n.th,{children:"R"}),(0,i.jsx)(n.th,{children:"mAP50"}),(0,i.jsx)(n.th,{children:"mAP50-95)"})]})}),(0,i.jsxs)(n.tbody,{children:[(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"all"}),(0,i.jsx)(n.td,{children:"2046"}),(0,i.jsx)(n.td,{children:"2132"}),(0,i.jsx)(n.td,{children:"0.986"}),(0,i.jsx)(n.td,{children:"0.954"}),(0,i.jsx)(n.td,{children:"0.984"}),(0,i.jsx)(n.td,{children:"0.701"})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:(0,i.jsx)(n.em,{children:"Speed: 0.3ms preprocess, 5.1ms inference, 0.0ms loss, 0.5ms postprocess per image"})}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:(0,i.jsx)(n.em,{children:"Model summary (fused): 168 layers, 3005843 parameters, 0 gradients"})}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{})]})]})]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Evaluate the model's performance on the validation set\nresults = backbone.val()\n"})}),"\n",(0,i.jsx)(n.p,{children:"Ultralytics YOLOv8.0.173 \ud83d\ude80 Python-3.10.11 torch-2.0.1 CUDA:0 (NVIDIA GeForce GTX 1060 6GB, 6070MiB)\nModel summary (fused): 168 layers, 3005843 parameters, 0 gradients\n\x1b[34m\x1b[1mval: \x1b[0mScanning /opt/app/03_object_detection_with_text_extraction_easyocr/datasets/valid/labels.cache... 2046 images, 3 backgrounds, 0 corrupt: 100%|\u2588\u2588\u2588\u2588\x1b[0m\nClass Images Instances Box(P R mAP50 mAP50-95): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 128/128 [00:18, 6.74it/s]\nall 2046 2132 0.986 0.954 0.984 0.701\nSpeed: 0.3ms preprocess, 5.7ms inference, 0.0ms loss, 0.6ms postprocess per image\nResults saved to \x1b[1mruns/detect/val\x1b[0m"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(766487).Z+"",width:"1721",height:"925"})}),"\n",(0,i.jsxs)(n.table,{children:[(0,i.jsx)(n.thead,{children:(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.th,{children:"Class"}),(0,i.jsx)(n.th,{children:"Images"}),(0,i.jsx)(n.th,{children:"Instances"}),(0,i.jsx)(n.th,{children:"Box( P"}),(0,i.jsx)(n.th,{children:"R"}),(0,i.jsx)(n.th,{children:"mAP50"}),(0,i.jsx)(n.th,{children:"mAP50-95)"})]})}),(0,i.jsxs)(n.tbody,{children:[(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"all"}),(0,i.jsx)(n.td,{children:"2046"}),(0,i.jsx)(n.td,{children:"2132"}),(0,i.jsx)(n.td,{children:"0.986"}),(0,i.jsx)(n.td,{children:"0.954"}),(0,i.jsx)(n.td,{children:"0.984"}),(0,i.jsx)(n.td,{children:"0.701"})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:(0,i.jsx)(n.em,{children:"Speed: 0.3ms preprocess, 5.7ms inference, 0.0ms loss, 0.6ms postprocess per image"})}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{}),(0,i.jsx)(n.td,{})]})]})]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Perform object detection on an image using the model\nresults = backbone('inputs/cars.png')\n"})}),"\n",(0,i.jsx)(n.p,{children:"image 1/1 /opt/app/03_object_detection_with_text_extraction_easyocr/inputs/cars.png: 384x640 2 License_Plates, 35.9ms\nSpeed: 1.7ms preprocess, 35.9ms inference, 2.0ms postprocess per image at shape (1, 3, 384, 640)"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Export the model to ONNX format\n# success = model.export(imgsz=(640, 480), format='onnx', opset=12, optimize=False, half=False)\n# Export to PyTorch format\nsuccess = backbone.export(imgsz=640, format='torchscript', optimize=False, half=False, int8=False)\n# TorchScript: export success \u2705 1.5s, saved as 'runs/detect/train11/weights/best.torchscript' (11.9 MB)\n"})}),"\n",(0,i.jsx)(n.p,{children:"Ultralytics YOLOv8.0.173 \ud83d\ude80 Python-3.10.11 torch-2.0.1 CPU (Intel Core(TM) i7-7700 3.60GHz)"}),"\n",(0,i.jsx)(n.p,{children:"\x1b[34m\x1b[1mPyTorch:\x1b[0m starting from 'runs/detect/train11/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 5, 8400) (6.0 MB)"}),"\n",(0,i.jsx)(n.p,{children:"\x1b[34m\x1b[1mTorchScript:\x1b[0m starting export with torch 2.0.1...\n\x1b[34m\x1b[1mTorchScript:\x1b[0m export success \u2705 1.3s, saved as 'runs/detect/train11/weights/best.torchscript' (11.9 MB)"}),"\n",(0,i.jsxs)(n.p,{children:["Export complete (2.6s)\nResults saved to \x1b[1m/opt/app/03_object_detection_with_text_extraction_easyocr/runs/detect/train11/weights\x1b[0m\nPredict: yolo predict task=detect model=runs/detect/train11/weights/best.torchscript imgsz=640",(0,i.jsx)(n.br,{}),"\n","Validate: yolo val task=detect model=runs/detect/train11/weights/best.torchscript imgsz=640 data=datasets/data.yaml",(0,i.jsx)(n.br,{}),"\n","Visualize: ",(0,i.jsx)(n.a,{href:"https://netron.app",children:"https://netron.app"})]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# pick pre-trained model\nnp_model = YOLO('runs/detect/train11/weights/best.torchscript')\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read video by index\nvideo = cv.VideoCapture(videos[1])\nret, frame = video.read()\n\n# get video dims\nframe_width = int(video.get(3))\nframe_height = int(video.get(4))\nsize = (frame_width, frame_height)\n\n# Define the codec and create VideoWriter object\nfourcc = cv.VideoWriter_fourcc(*'DIVX')\nout = cv.VideoWriter('./outputs/uk_dash_np_2.avi', fourcc, 20.0, size)\n\n# read frames\nret = True\n\nwhile ret:\n ret, frame = video.read()\n\n if ret:\n # detect & track objects\n results = np_model.track(frame, persist=True)\n\n # plot results\n composed = results[0].plot()\n\n # save video\n out.write(composed)\n\nout.release()\nvideo.release()\n"})}),"\n",(0,i.jsx)(n.p,{children:"And now we have a model that is only interested in number plates:"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(526491).Z+"",width:"1933",height:"391"})}),"\n",(0,i.jsx)(n.p,{children:"Though, the confusion matrix shows us that it also sees a lot of plates that do not exist - but at least it does not miss that many:"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(553629).Z+"",width:"3000",height:"2250"})}),"\n",(0,i.jsx)(n.h2,{id:"improving-training-results",children:"Improving Training Results"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:'# unzip downloaded dataset to `./datasets`\ndataset = \'datasets/data.yaml\'\n\n# load a model\n# backbone = YOLO("yolov8n.yaml") # build a new model from scratch\nbackbone_small = YOLO("yolov8s.pt") # load a pre-trained model (recommended for training)\n'})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Use the model\nresults_medium = backbone_small.train(data=dataset, epochs=100) # train the model\n"})}),"\n",(0,i.jsx)(n.p,{children:"Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n57/100 4.57G 0.9052 0.3966 1.064 7 640: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1324/1324 [13:43, 1.61it/s]\nClass Images Instances Box(P R mAP50 mAP50-95): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 64/64 [00:26, 2.38it/s]\nall 2046 2132 0.981 0.968 0.984 0.709"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# pick pre-trained model\nnp2_model = YOLO('runs/detect/train4/weights/best.pt')\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Evaluate the model's performance on the validation set\nresults = np2_model.val()\n"})}),"\n",(0,i.jsxs)(n.p,{children:["Ultralytics YOLOv8.0.173 \ud83d\ude80 Python-3.10.11 torch-2.0.1 CUDA:0 (NVIDIA GeForce GTX 1060 6GB, 6070MiB)\nModel summary (fused): 168 layers, 11125971 parameters, 0 gradients\nDownloading ",(0,i.jsx)(n.a,{href:"https://ultralytics.com/assets/Arial.ttf",children:"https://ultralytics.com/assets/Arial.ttf"})," to '/root/.config/Ultralytics/Arial.ttf'...\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 755k/755k [00:00, 2.35MB/s]\nClass Images Instances Box(P R mAP50 mAP50-95): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 128/128 [00:35, 3.59it/s]\nall 2046 2132 0.981 0.968 0.984 0.71\nSpeed: 0.3ms preprocess, 13.4ms inference, 0.0ms loss, 0.6ms postprocess per image\nResults saved to \x1b[1mruns/detect/val2\x1b[0m"]}),"\n",(0,i.jsx)(n.h2,{id:"license-plate-detection",children:"License Plate Detection"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"import ast\nimport cv2 as cv\nimport easyocr\nfrom glob import glob\nimport numpy as np\nimport pandas as pd\nimport string\nfrom ultralytics import YOLO\n"})}),"\n",(0,i.jsxs)(n.p,{children:["As seen during the training - the model, just using the COCO training weights, is very capable of detecting cars, trucks and buses. But number plates seem to be a bit harder - the model often confuses street signs or just basic backgound noise as a car registration plate. The positive is that it rarely misses a plate.\n",(0,i.jsx)(n.a,{href:"https://www.youtube.com/watch?v=uVOHMFqILEAox",children:"https://www.youtube.com/watch?v=uVOHMFqILEAox"}),"."]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# regular pre-trained yolov8 model for car recognition\n# coco_model = YOLO('yolov8n.pt')\ncoco_model = YOLO('yolov8s.pt')\n# yolov8 model trained to detect number plates\nnp_model = YOLO('runs/detect/train4/weights/best.pt')\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read in test video paths\nvideos = glob('inputs/*.mp4')\nprint(videos)\n"})}),"\n",(0,i.jsx)(n.p,{children:"['inputs/uk_dash_1.mp4', 'inputs/uk_dash_2.mp4']"}),"\n",(0,i.jsx)(n.h3,{id:"step-1-implementing-the-car-detection",children:"STEP 1 Implementing the Car Detection"}),"\n",(0,i.jsx)(n.p,{children:"Get the bounding boxes of all vehicles in your video recording with prediction confidence score and object tracking ID"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read video by index\nvideo = cv.VideoCapture(videos[1])\n\nret = True\nframe_number = -1\n# all vehicle class IDs from the COCO dataset (car, motorbike, truck) https://docs.ultralytics.com/datasets/detect/coco/#dataset-yaml\nvehicles = [2,3,5]\nvehicle_bounding_boxes = []\n\n# read the 10 first frames\nwhile ret:\n frame_number += 1\n ret, frame = video.read()\n\n if ret and frame_number < 10:\n # use track() to identify instances and track them frame by frame\n detections = coco_model.track(frame, persist=True)[0]\n # save cropped detections\n # detections.save_crop('outputs')\n # print nodel predictions for debugging\n # print(results)\n\n for detection in detections.boxes.data.tolist():\n # print detection bounding boxes for debugging\n # print(detection)\n x1, y1, x2, y2, track_id, score, class_id = detection\n # I am only interested in class IDs that belong to vehicles\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n\n# print found bounding boxes for debugging\nprint(vehicle_bounding_boxes)\nvideo.release()\n"})}),"\n",(0,i.jsxs)(n.p,{children:["This code now collects all vehicle bounding boxes from the video and writes them into the ",(0,i.jsx)(n.code,{children:"vehicle_bounding_boxes"})," list. Besides the bbox coordinates this list also contains the tracking ID of the detected vehicle - they should stay the same frame-to-frame for every detected vehicle and serve as a unique identifier. And the score - how confident is the model that this bbox acutally contains a vehicle with calues from ",(0,i.jsx)(n.code,{children:"0"}),"-",(0,i.jsx)(n.code,{children:"1"}),":"]}),"\n",(0,i.jsxs)(n.blockquote,{children:["\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.code,{children:"[[762.6422729492188, 614.1869506835938, 1121.368408203125, 911.6900024414062, 1.0, 0.9254793524742126], [1196.008056640625, 635.3404541015625, 1526.3975830078125, 828.6062622070312, 2.0, 0.8488578200340271], [1672.98193359375, 613.9304809570312, 1912.382080078125, 819.3222045898438, 3.0, 0.5385741591453552], [758.7203369140625, 612.6467895507812, 1119.0892333984375, 917.7677612304688, 1.0, 0.925308883190155], [1195.9505615234375, 635.4146118164062, 1527.97412109375, 830.3245239257812, 2.0, 0.865635871887207], [1692.5439453125, 613.0050659179688, 1917.7542724609375, 819.2852783203125, 3.0, 0.5493771433830261], [754.7435302734375, 612.3658447265625, 1115.0045166015625, 919.653076171875, 1.0, 0.9127519130706787], [1194.00341796875, 634.9168090820312, 1527.029541015625, 832.383544921875, 2.0, 0.8814489841461182], [1688.8155517578125, 615.6485595703125, 1920.0, 812.7891235351562, 3.0, 0.6132197976112366], [752.8799438476562, 611.2362060546875, 1111.976318359375, 920.200927734375, 1.0, 0.9137689471244812], [1192.805908203125, 634.3713989257812, 1526.1273193359375, 832.46337890625, 2.0, 0.8671290278434753], [1680.4443359375, 616.384033203125, 1920.0, 813.3687744140625, 3.0, 0.6371273994445801], [750.9274291992188, 611.5806884765625, 1110.2657470703125, 915.3110961914062, 1.0, 0.9381350874900818], [1189.63916015625, 634.7803955078125, 1525.4072265625, 833.2440185546875, 2.0, 0.888615071773529], [1669.8206787109375, 616.796142578125, 1920.0, 808.6288452148438, 3.0, 0.5068169236183167], [748.747802734375, 609.5638427734375, 1109.0101318359375, 912.808837890625, 1.0, 0.9158740639686584], [1187.832275390625, 634.11328125, 1524.633056640625, 832.628173828125, 2.0, 0.8583219647407532], [1659.7103271484375, 615.9025268554688, 1920.0, 823.25048828125, 3.0, 0.7755634784698486], [745.1077270507812, 609.5160522460938, 1107.8214111328125, 912.8062133789062, 1.0, 0.9354495406150818], [1186.91455078125, 634.5582885742188, 1524.004150390625, 832.4244995117188, 2.0, 0.8758277297019958], [1650.2227783203125, 613.749267578125, 1920.0, 828.9586791992188, 3.0, 0.7407982349395752], [742.2940673828125, 610.4445190429688, 1106.597900390625, 912.6227416992188, 1.0, 0.9281387329101562], [1186.1158447265625, 634.223876953125, 1523.3406982421875, 832.6515502929688, 2.0, 0.8710047006607056], [1638.47705078125, 614.6183471679688, 1919.968017578125, 833.5314331054688, 3.0, 0.8480165600776672], [741.3974609375, 610.8768920898438, 1105.543701171875, 912.5601806640625, 1.0, 0.9410984516143799], [1185.1246337890625, 633.4691162109375, 1523.3682861328125, 832.612060546875, 2.0, 0.8842733502388], [1627.5872802734375, 616.9085693359375, 1919.9117431640625, 829.2400512695312, 3.0, 0.85666424036026], [741.3576049804688, 610.9183959960938, 1103.5601806640625, 914.4734497070312, 1.0, 0.9404377937316895], [1183.273681640625, 633.708984375, 1522.5953369140625, 833.3422241210938, 2.0, 0.8721591234207153], [1618.3934326171875, 619.4539794921875, 1919.864013671875, 827.6344604492188, 3.0, 0.8759608864784241]]"})}),"\n"]}),"\n",(0,i.jsxs)(n.p,{children:["Using the ",(0,i.jsx)(n.code,{children:"save_crop()"})," function shows me that the first 10 frames of my video contain three different cars:"]}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(841616).Z+"",width:"651",height:"533"})}),"\n",(0,i.jsx)(n.h3,{id:"step-2-implementing-the-license-plate-detection",children:"STEP 2 Implementing the License Plate Detection"}),"\n",(0,i.jsx)(n.p,{children:"Use the bounding box for each vehicle and use the number plate detector model to try to find the corresponding plate within in the confinement of those boxes."}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read video by index\nvideo = cv.VideoCapture(videos[0])\n\nret = True\nframe_number = -1\nvehicles = [2,3,5]\n\n# read the 10 first frames\nwhile ret:\n frame_number += 1\n ret, frame = video.read()\n\n if ret and frame_number < 10:\n \n # vehicle detector\n detections = coco_model.track(frame, persist=True)[0]\n for detection in detections.boxes.data.tolist():\n x1, y1, x2, y2, track_id, score, class_id = detection\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes = []\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n for bbox in vehicle_bounding_boxes:\n print(bbox)\n roi = frame[int(y1):int(y2), int(x1):int(x2)]\n # debugging check if bbox lines up with detected vehicles (should be identical to save_crops() above\n # cv.imwrite(str(track_id) + '.jpg', roi)\n \n # license plate detector for region of interest\n license_plates = np_model(roi)[0]\n # check every bounding box for a license plate\n for license_plate in license_plates.boxes.data.tolist():\n plate_x1, plate_y1, plate_x2, plate_y2, plate_score, _ = license_plate\n # verify detections\n print(license_plate, 'track_id: ' + str(bbox[4]))\n plate = roi[int(plate_y1):int(plate_y2), int(plate_x1):int(plate_x2)]\n cv.imwrite(str(track_id) + '.jpg', plate)\n \nvideo.release()\n"})}),"\n",(0,i.jsx)(n.p,{children:"By using the tracking ID I can make sure that every license plate - as seen above the video contained several instances of the same 3 cars - is only returned ones:"}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(173943).Z+"",width:"704",height:"271"})}),"\n",(0,i.jsx)(n.h3,{id:"step-3-preprocess-license-plates",children:"STEP 3 Preprocess License Plates"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# read video by index\nvideo = cv.VideoCapture(videos[0])\n\nret = True\nframe_number = -1\nvehicles = [2,3,5]\n\n# read the 10 first frames\nwhile ret:\n frame_number += 1\n ret, frame = video.read()\n\n if ret and frame_number < 100:\n \n # vehicle detector\n detections = coco_model.track(frame, persist=True)[0]\n for detection in detections.boxes.data.tolist():\n x1, y1, x2, y2, track_id, score, class_id = detection\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes = []\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n for bbox in vehicle_bounding_boxes:\n print(bbox)\n roi = frame[int(y1):int(y2), int(x1):int(x2)]\n \n # license plate detector for region of interest\n license_plates = np_model(roi)[0]\n # process license plate\n for license_plate in license_plates.boxes.data.tolist():\n plate_x1, plate_y1, plate_x2, plate_y2, plate_score, _ = license_plate\n # crop plate from region of interest\n plate = roi[int(plate_y1):int(plate_y2), int(plate_x1):int(plate_x2)]\n # de-colorize\n plate_gray = cv.cvtColor(plate, cv.COLOR_BGR2GRAY)\n # posterize\n _, plate_treshold = cv.threshold(plate_gray, 64, 255, cv.THRESH_BINARY_INV)\n cv.imwrite(str(track_id) + '_gray.jpg', plate_gray)\n cv.imwrite(str(track_id) + '_thresh.jpg', plate_treshold)\n \nvideo.release()\n"})}),"\n",(0,i.jsx)(n.p,{children:(0,i.jsx)(n.img,{alt:"YOLOv8 License Plate Detection",src:t(560549).Z+"",width:"658",height:"289"})}),"\n",(0,i.jsx)(n.h3,{id:"step-4-read-license-plates",children:"STEP 4 Read License Plates"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Initialize the OCR reader\nreader = easyocr.Reader(['en'], gpu=True)\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def read_license_plate(license_plate_crop):\n detections = reader.readtext(license_plate_crop)\n\n for detection in detections:\n bbox, text, score = detection\n\n text = text.upper().replace(' ', '')\n \n return text, score\n\n return None, None\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def write_csv(results, output_path):\n \n with open(output_path, 'w') as f:\n f.write('{},{},{},{},{},{},{},{}\\n'.format(\n 'frame_number', 'track_id', 'car_bbox', 'car_bbox_score',\n 'license_plate_bbox', 'license_plate_bbox_score', 'license_plate_number',\n 'license_text_score'))\n\n for frame_number in results.keys():\n for track_id in results[frame_number].keys():\n print(results[frame_number][track_id])\n if 'car' in results[frame_number][track_id].keys() and \\\n 'license_plate' in results[frame_number][track_id].keys() and \\\n 'number' in results[frame_number][track_id]['license_plate'].keys():\n f.write('{},{},{},{},{},{},{},{}\\n'.format(\n frame_number,\n track_id,\n '[{} {} {} {}]'.format(\n results[frame_number][track_id]['car']['bbox'][0],\n results[frame_number][track_id]['car']['bbox'][1],\n results[frame_number][track_id]['car']['bbox'][2],\n results[frame_number][track_id]['car']['bbox'][3]\n ),\n results[frame_number][track_id]['car']['bbox_score'],\n '[{} {} {} {}]'.format(\n results[frame_number][track_id]['license_plate']['bbox'][0],\n results[frame_number][track_id]['license_plate']['bbox'][1],\n results[frame_number][track_id]['license_plate']['bbox'][2],\n results[frame_number][track_id]['license_plate']['bbox'][3]\n ),\n results[frame_number][track_id]['license_plate']['bbox_score'],\n results[frame_number][track_id]['license_plate']['number'],\n results[frame_number][track_id]['license_plate']['text_score'])\n )\n f.close()\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"results = {}\n\n# read video by index\nvideo = cv.VideoCapture(videos[0])\n\nret = True\nframe_number = -1\nvehicles = [2,3,5]\n\n# read the 10 first frames\nwhile ret:\n frame_number += 1\n ret, frame = video.read()\n\n if ret and frame_number < 100:\n results[frame_number] = {}\n \n # vehicle detector\n detections = coco_model.track(frame, persist=True)[0]\n for detection in detections.boxes.data.tolist():\n x1, y1, x2, y2, track_id, score, class_id = detection\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes = []\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n for bbox in vehicle_bounding_boxes:\n print(bbox)\n roi = frame[int(y1):int(y2), int(x1):int(x2)]\n \n # license plate detector for region of interest\n license_plates = np_model(roi)[0]\n # process license plate\n for license_plate in license_plates.boxes.data.tolist():\n plate_x1, plate_y1, plate_x2, plate_y2, plate_score, _ = license_plate\n # crop plate from region of interest\n plate = roi[int(plate_y1):int(plate_y2), int(plate_x1):int(plate_x2)]\n # de-colorize\n plate_gray = cv.cvtColor(plate, cv.COLOR_BGR2GRAY)\n # posterize\n _, plate_treshold = cv.threshold(plate_gray, 64, 255, cv.THRESH_BINARY_INV)\n \n # OCR\n np_text, np_score = read_license_plate(plate_treshold)\n # if plate could be read write results\n if np_text is not None:\n results[frame_number][track_id] = {\n 'car': {\n 'bbox': [x1, y1, x2, y2],\n 'bbox_score': score\n },\n 'license_plate': {\n 'bbox': [plate_x1, plate_y1, plate_x2, plate_y2],\n 'bbox_score': plate_score,\n 'number': np_text,\n 'text_score': np_score\n }\n }\n\nwrite_csv(results, './results.csv')\nvideo.release()\n"})}),"\n",(0,i.jsx)(n.p,{children:"This returns a list with bounding box metrics for every frame with a successful detection:"}),"\n",(0,i.jsx)("br",{}),"\n",(0,i.jsxs)(n.table,{children:[(0,i.jsx)(n.thead,{children:(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.th,{children:"frame_number"}),(0,i.jsx)(n.th,{children:"track_id"}),(0,i.jsx)(n.th,{children:"car_bbox"}),(0,i.jsx)(n.th,{children:"license_plate_bbox"}),(0,i.jsx)(n.th,{children:"license_plate_bbox_score"}),(0,i.jsx)(n.th,{children:"license_number"}),(0,i.jsx)(n.th,{children:"license_number_score"})]})}),(0,i.jsxs)(n.tbody,{children:[(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"0"}),(0,i.jsx)(n.td,{children:"1.0"}),(0,i.jsx)(n.td,{children:"[760.1986694335938 614.2100830078125 1123.09130859375 914.9498901367188]"}),(0,i.jsx)(n.td,{children:"[110.20427703857422 133.25326538085938 238.5574493408203 175.96791076660156]"}),(0,i.jsx)(n.td,{children:"0.7692280411720276"}),(0,i.jsx)(n.td,{children:"BPG6UXN"}),(0,i.jsx)(n.td,{children:"0.7290849695998655"})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"1"}),(0,i.jsx)(n.td,{children:"1.0"}),(0,i.jsx)(n.td,{children:"[758.7349243164062 612.4984741210938 1122.470458984375 919.1956787109375]"}),(0,i.jsx)(n.td,{children:"[109.57369995117188 134.78448486328125 238.8947296142578 179.6195831298828]"}),(0,i.jsx)(n.td,{children:"0.767607569694519"}),(0,i.jsx)(n.td,{children:"BP6EUXN"}),(0,i.jsx)(n.td,{children:"0.27891552972114064"})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"2"}),(0,i.jsx)(n.td,{children:"1.0"}),(0,i.jsx)(n.td,{children:"[755.6078491210938 612.161865234375 1118.7542724609375 920.3657836914062]"}),(0,i.jsx)(n.td,{children:"[109.76798248291016 134.661376953125 239.85276794433594 180.43345642089844]"}),(0,i.jsx)(n.td,{children:"0.7666334509849548"}),(0,i.jsx)(n.td,{children:"BP66UXN"}),(0,i.jsx)(n.td,{children:"0.7696779876170268"})]}),(0,i.jsxs)(n.tr,{children:[(0,i.jsx)(n.td,{children:"3"}),(0,i.jsx)(n.td,{children:"1.0"}),(0,i.jsx)(n.td,{children:"[753.9749755859375 611.0296630859375 1115.607421875 920.6179809570312]"}),(0,i.jsx)(n.td,{children:"[109.80683898925781 134.79702758789062 239.79380798339844 180.0568389892578]"}),(0,i.jsx)(n.td,{children:"0.7609436511993408"}),(0,i.jsx)(n.td,{children:"BPG6UXN"}),(0,i.jsx)(n.td,{children:"0.5947437696221942"})]})]})]}),"\n",(0,i.jsx)(n.h3,{id:"step-5-clean-up-license-plate-format",children:"STEP 5 Clean-Up License Plate Format"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"# Mapping dictionaries for character conversion\n# characters that can easily be confused can be \n# verified by their location - an `O` in a place\n# where a number is expected is probably a `0`\ndict_char_to_int = {'O': '0',\n 'I': '1',\n 'J': '3',\n 'A': '4',\n 'G': '6',\n 'S': '5'}\n\ndict_int_to_char = {'0': 'O',\n '1': 'I',\n '3': 'J',\n '4': 'A',\n '6': 'G',\n '5': 'S'}\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def license_complies_format(text):\n # True if the license plate complies with the format, False otherwise.\n if len(text) != 7:\n return False\n\n if (text[0] in string.ascii_uppercase or text[0] in dict_int_to_char.keys()) and \\\n (text[1] in string.ascii_uppercase or text[1] in dict_int_to_char.keys()) and \\\n (text[2] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] or text[2] in dict_char_to_int.keys()) and \\\n (text[3] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] or text[3] in dict_char_to_int.keys()) and \\\n (text[4] in string.ascii_uppercase or text[4] in dict_int_to_char.keys()) and \\\n (text[5] in string.ascii_uppercase or text[5] in dict_int_to_char.keys()) and \\\n (text[6] in string.ascii_uppercase or text[6] in dict_int_to_char.keys()):\n return True\n else:\n return False\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def format_license(text):\n license_plate_ = ''\n mapping = {0: dict_int_to_char, 1: dict_int_to_char, 4: dict_int_to_char, 5: dict_int_to_char, 6: dict_int_to_char,\n 2: dict_char_to_int, 3: dict_char_to_int}\n for j in [0, 1, 2, 3, 4, 5, 6]:\n if text[j] in mapping[j].keys():\n license_plate_ += mapping[j][text[j]]\n else:\n license_plate_ += text[j]\n\n return license_plate_\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def read_license_plate(license_plate_crop):\n detections = reader.readtext(license_plate_crop)\n\n for detection in detections:\n bbox, text, score = detection\n\n text = text.upper().replace(' ', '')\n\n # verify that text is conform to a standard license plate\n if license_complies_format(text):\n # bring text into the default license plate format\n return format_license(text), score\n\n return None, None\n"})}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"results = {}\n\n# read video by index\nvideo = cv.VideoCapture(videos[1])\n\nret = True\nframe_number = -1\nvehicles = [2,3,5]\n\n# read the entire video\nwhile ret:\n ret, frame = video.read()\n frame_number += 1\n if ret:\n results[frame_number] = {}\n \n # vehicle detector\n detections = coco_model.track(frame, persist=True)[0]\n for detection in detections.boxes.data.tolist():\n x1, y1, x2, y2, track_id, score, class_id = detection\n if int(class_id) in vehicles and score > 0.5:\n vehicle_bounding_boxes = []\n vehicle_bounding_boxes.append([x1, y1, x2, y2, track_id, score])\n for bbox in vehicle_bounding_boxes:\n print(bbox)\n roi = frame[int(y1):int(y2), int(x1):int(x2)]\n \n # license plate detector for region of interest\n license_plates = np_model(roi)[0]\n # process license plate\n for license_plate in 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