nvidia driver on ubuntu

$ lspci -k | grep -A 2 -i "VGA"

$ ubuntu-drivers devices
$ sudo ubuntu-drivers autoinstall

不建議下載 nvidia 網站的 driver

CUDNN 安裝

顯卡 驅動 CUDA CUDNN 版本關係

https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html

安裝

https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

ubuntu 換接顯示器 intel nvidia

圖形模式選擇

$ nvidia-settings

開啟 NVIDIA X Server Settings

選擇左邊 Tab 之 PRIME Profiles

右邊 Tab 出現

NVIDIA (Performance Mode)

NVIDIA On-Demand

Intel (Prower Saving Mode)

三種選項

命令模式選擇

$ sudo prime-select -?

$ sudo prime-select query

$ sudo prime-select on-deamand

開機直接進入圖形介面
$ sudo systemctl set-default graphical.target
開機直接進入文字介面
$ sudo systemctl set-default multi-user.target

Console 切換 Ctrl + Alt + Fn

Intel 上會顯示文字模式的 login:

Nvidia 上只有 Ctrl + Alt + F1 的圖形模式

LPD LPR on DeepStream

參考

https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app

https://github.com/NVIDIA-AI-IOT/deepstream_lpr_app

https://blogs.nvidia.com.tw/2021/05/12/creating-a-real-time-license-plate-detection-and-recognition-app/

依據 CUDA cuDNN TensorRT 的版本，下載 tlt-converter

https://developer.nvidia.com/tlt-get-started

完整程序

~/Data/DeepStream$ mkdir deepstream_lpr_app

~/Data/DeepStream$ cd deepstream_lpr_app/

~/Data/DeepStream/deepstream_lpr_app$ git clone https://github.com/NVIDIA-AI-IOT/deepstream_lpr_app.git

~/Data/DeepStream/deepstream_lpr_app$ cd deepstream_lpr_app/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app$ ./download_us.sh

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app$ ../../tlt-converter/cuda11.0_cudnn8.0_trt7.2/tlt-converter -k nvidia_tlt -p image_input,1x3x48x96,4x3x48x96,16x3x48x96 models/LP/LPR/us_lprnet_baseline18_deployable.etlt -t fp16 -e models/LP/LPR/lpr_us_onnx_b16.engine

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app$ make

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app$ cd deepstream-lpr-app/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ cp dict_us.txt dict.txt

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ ./deepstream-lpr-app 1 2 0 /opt/nvidia/deepstream/deepstream-5.1/samples/streams/sample_720p.mp4 output.264

以上可以執行，並在第一次執行時產生 engine

也可以利用下列命令 tlt-converter 產生 engine

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ cd ../models/tlt_pretrained_models/trafficcamnet/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/tlt_pretrained_models/trafficcamnet$ mv trafficnet_int8.txt trafficnet_int8.bin

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/tlt_pretrained_models/trafficcamnet$ ../../../../../tlt-converter/cuda11.0_cudnn8.0_trt7.2/tlt-converter -k tlt_encode -o output_bbox/BiasAdd,output_cov/Sigmoid -d 3,544,960 -t int8 -c trafficnet_int8.bin -e resnet18_trafficcamnet_pruned.etlt_b1_gpu0_int8.engine resnet18_trafficcamnet_pruned.etlt

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/tlt_pretrained_models/trafficcamnet$ cd ../../LP/LPD/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/LP/LPD$ ../../../../../tlt-converter/cuda11.0_cudnn8.0_trt7.2/tlt-converter -k nvidia_tlt -o output_bbox/BiasAdd,output_cov/Sigmoid -d 3,480,640 -t int8 -c usa_lpd_cal.bin -e usa_pruned.etlt_b16_gpu0_int8.engine usa_pruned.etlt

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/LP/LPD$ cd ../../../deepstream-lpr-app/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ vi lpd_us_config.txt

不顯示

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ ./deepstream-lpr-app 1 2 0 /opt/nvidia/deepstream/deepstream-5.1/samples/streams/sample_720p.mp4 output.264

有顯示

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ ./deepstream-lpr-app 1 3 0 /opt/nvidia/deepstream/deepstream-5.1/samples/streams/sample_720p.mp4 output.264

重新訓練模型

使用 tlt-launcher

python3 -m venv --system-site-packages tlt

source ~/envs/tlt/bin/activate

(tlt) $ pip3 install nvidia-pyindex

(tlt) $ pip3 install nvidia-tlt

(tlt) $ cat ~/.tlt_mounts.json

{

    "Mounts": [

        {

            "source": "/home/username/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments",

            "destination": "/workspace/tlt-experiments"

        },

        {

            "source": "/home/username/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr",

            "destination": "/workspace/openalpr"

        }

    ]

}

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr$ git clone https://github.com/openalpr/benchmarks benchmarks

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr$ mkdir tlt-experiments

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr$ mkdir openalpr

重新訓練 LPD 模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr$ cd openalpr/

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpd/lpd_prepare_data.py

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ python3 lpd_prepare_data.py --input_dir ../benchmarks/endtoend/us --output_dir lpd --target_width 640 --target_height 480

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpd/SPECS_tfrecord.txt

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 dataset_convert -d /workspace/openalpr/SPECS_tfrecord.txt -o /workspace/openalpr/lpd_tfrecord/lpd

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpd/SPECS_train.txt

下載預先訓練模型 (tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ ngc registry model download-version "nvidia/tlt_lpdnet:unpruned_v1.0"

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ mv tlt_lpdnet_vunpruned_v1.0/usa_unpruned.tlt .

訓練

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 train -e /workspace/openalpr/SPECS_train.txt -r /workspace/openalpr/exp_unpruned -k nvidia_tlt

修剪

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 prune -m /workspace/openalpr/exp_unpruned/weights/model.tlt -o /workspace/openalpr/exp_pruned/model_pruned_06.tlt -pth 0.6 -k nvidia_tlt

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ cp SPECS_train.txt SPECS_retrain.txt

修改 pretrained_model_file: "/workspace/openalpr/exp_pruned/model_pruned_06.tlt"

再次訓練

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 train -e /workspace/openalpr/SPECS_retrain.txt -r /workspace/openalpr/exp_pruned -k nvidia_tlt

匯出

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 export -m /workspace/openalpr/exp_unpruned/weights/model.tlt -o /workspace/openalpr/export/unpruned_model.etlt --cal_cache_file /workspace/openalpr/export/unpruned_calibration.bin -e /workspace/openalpr/SPECS_train.txt -k nvidia_tlt --cal_image_dir /workspace/openalpr/lpd/data/image --data_type int8 --batch_size 4 --batches 10 --engine_file /workspace/openalpr/export/unpruned_int8.trt

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 export -m /workspace/openalpr/exp_pruned/weights/model.tlt -o /workspace/openalpr/export/pruned_model.etlt --cal_cache_file /workspace/openalpr/export/pruned_calibration.bin -e /workspace/openalpr/SPECS_train.txt -k nvidia_tlt --cal_image_dir /workspace/openalpr/lpd/data/image --data_type int8 --batch_size 4 --batches 10 --engine_file /workspace/openalpr/export/pruned_int8.trt

評估

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 evaluate -m /workspace/openalpr/exp_unpruned/weights/model.tlt -k nvidia_tlt -e /workspace/openalpr/SPECS_train.txt

(tlt) mark@z390:~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 evaluate -m /workspace/openalpr/exp_pruned/weights/model.tlt -k nvidia_tlt -e /workspace/openalpr/SPECS_train.txt

重新訓練 LPR 模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments$ mkdir lprnet

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments$ cd lprnet/

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpr/preprocess_openalpr_benchmark.py

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ python3 preprocess_openalpr_benchmark.py --input_dir=../../benchmarks/endtoend/us --output_dir=../data/openalpr

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpr/tutorial_spec.txt

修改 tutorial_spec.txt

augmentation_config {

    output_width: 96

    output_height: 48

    output_channel: 3

    keep_original_prob: 0.3

    transform_prob: 0.5

    rotate_degree: 5

}

成為

augmentation_config {

    output_width: 96

    output_height: 48

    output_channel: 3

    keep_original_prob: 0.3

    max_rotate_degree: 5

    rotate_prob: 0.5

    blur_prob: 0.5

}

下載預先訓練模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ ngc registry model download-version "nvidia/tlt_lprnet:trainable_v1.0"

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ cp tlt_lprnet_vtrainable_v1.0/us_lprnet_baseline18_trainable.tlt .

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ mkdir specs

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ cp tlt_lprnet_vtrainable_v1.0/us_lp_characters.txt specs/

訓練模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ tlt lprnet train -e /workspace/tlt-experiments/lprnet/tutorial_spec.txt -r /workspace/tlt-experiments/lprnet/ -k nvidia_tlt -m /workspace/tlt-experiments/lprnet/us_lprnet_baseline18_trainable.tlt --use_amp

匯出模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ tlt lprnet export -m /workspace/tlt-experiments/lprnet/weights/lprnet_epoch-24.tlt -k nvidia_tlt -e /workspace/tlt-experiments/lprnet/tutorial_spec.txt

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ ls weights/lprnet_epoch-24.etlt

評估

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ tlt lprnet evaluate -m /workspace/tlt-experiments/lprnet/weights/lprnet_epoch-24.tlt -k nvidia_tlt -e /workspace/tlt-experiments/lprnet/tutorial_spec.txt

Ubuntu 18.04 的 launcher 或 sidebar 消失

發生時機，當保護螢幕啟動，由遠端登入後發現
工作列不見了

壓 Alt+F2, 會出現一個對話框, 輸入 r

$ gnome-shell -r

$ gnome-shell --replace

$ busctl --user call org.gnome.Shell /org/gnome/Shell org.gnome.Shell Eval s 'Meta.restart("Restarting…")'

Restart GNOME Shell

$ killall -HUP gnome-shell

Ubuntu 18.04 重灌

下載 iso 檔，利用 rufus 寫入 usb
搜尋工具/Disks 確認硬碟位置

備份資料含檔案屬性

$ sudo cp -a /path/source/. /path/dest

gitea 備份和還原

/var/lib/gitea

$ sudo cp /etc/gitea/app.ini .

$ sudo -u git gitea dump -c /etc/gitea/app.ini

$ unzip gitea-dump-xxxx.zip

$ cd gitea-dump-xxxx

$ mv data/conf/app.ini /etc/gitea/conf/app.ini

$ mv data/* /var/lib/gitea/data/

$ mv log/* /var/lib/gitea/log/

$ mv repos/* /var/lib/gitea/repositories/

$ chown -R gitea:gitea /etc/gitea/conf/app.ini /var/lib/gitea

$ mysql --default-character-set=utf8mb4 -u$USER -p$PASS $DATABASE <gitea-db.sql

$ service gitea restart

mariadb backup and restore

/var/lib/mysql

$ sudo mysql

> SHOW DATABASES;

$ sudo mysqldump --all-databases > all.sql

$ sudo mysql --one-database db_name < all.sql

$ sudo cp -a /opt/tomcat /backup/opt/tomcat

$ sudo cp -a /opt/nvidia /backup/opt/nvidia

$ sudo cp -a /etc/nginx /backup/etc/nginx

$ sudo cp -a /etc/systemd /backup/etc/systemd

$ sudo cp -a /etc/udev/rules.d /backup/udev

$ sudo vi /etc/default/grub

$ sudo update-grub

nvidia driver 更新

$ sudo apt-get install gcc

$ sudo apt-get install make

下載 https://www.nvidia.com/download/driverResults.aspx/168347/en-us 驅動

$ chmod 755 NVIDIA-Linux-x86_64-460.32.03.run

$ sudo ./ NVIDIA-Linux-x86_64-460.32.03.run

但會說 nvidia-drm 已經啟動

所以要卸載 舊驅動

$ sudo apt-get --purge remove "*cublas*" "*cufft*" "*curand*" \

 "*cusolver*" "*cusparse*" "*npp*" "*nvjpeg*" "cuda*" "nsight*"

$ sudo apt-get purge 'nvidia*'

$ sudo apt-get autoremove

$ sudo reboot

$ sudo ./ NVIDIA-Linux-x86_64-460.32.03.run

使用 --no-opengl-files 參數，以免使用DeepStream時，只開啟一下黑幕，出現下列錯誤

cuGraphicsGLRegisterBuffer failed with error(304) gst_eglglessink_cuda_init texture = 1

$ sudo ./ NVIDIA-Linux-x86_64-460.32.03.run --no-opengl-files --dkms --no-drm

因為 ubuntu 還是用了一個驅動，所以不能直接更新驅動

但可以設定停用此驅動，小心回答問題，在執行一遍

Would you like ton register the kernel module sources with DKMS? 回答 Yes

Install NVIDIA’s 32-bit compatibility libraries? 回答 No

$ nvidia-smi

安裝 CUDA, 不要使用 deb(network), 好像在版本安裝上會出錯

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin

$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600

$ wget https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda-repo-ubuntu1804-11-1-local_11.1.1-455.32.00-1_amd64.deb

$ sudo dpkg -i cuda-repo-ubuntu1804-11-1-local_11.1.1-455.32.00-1_amd64.deb

$ sudo apt-key add /var/cuda-repo-ubuntu1804-11-1-local/7fa2af80.pub

$ sudo apt-get update

$ sudo apt-get -y install cuda

$ sudo apt-get -y install cuda-11-1

指定版本很重要，不然會裝最新的

$ nvidia-smi

Failed to initialize NVML: Driver/library version mismatch

重新開機即可

但顯示出來的 Driver 和 CUDA Version 都會改變

TensorRT 安裝

https://developer.nvidia.com/nvidia-tensorrt-download

由此進入選擇所需版本，並選擇 deb 版

由此選擇開啟文件

進入 TensorRT Installation Guide

https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-723/install-guide/index.html

跳至 4.1. Debian Installation

跟著步驟安裝

設定環境變數

PATH

LD_LIBRARY_PATH

pycuda 只能搭配 python 3.7

$ sudo apt-get install python3.7

$ sudo apt-get install python3.7-dev

$ python3.7 -m pip install 'pycuda>=2019.1.1'

Install Nvidia Docker

$ curl https://get.docker.com | sh && sudo systemctl --now enable docker

$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \

   && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \

   && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

$ curl -s -L https://nvidia.github.io/nvidia-container-runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list

$ sudo apt-get update

$ sudo apt-get install -y nvidia-docker2

$ sudo systemctl restart docker

$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

$ sudo groupadd docker

$ sudo usermod -a -G docker $USER

$ sudo reboot

Install TensorRT 7.2 OSS

參考 https://github.com/NVIDIA/TensorRT/tree/master

Install TensorRT 7.2 OSS

$ git clone -b master https://github.com/nvidia/TensorRT TensorRT_OSS-7.2.3.4

$ cd TensorRT_OSS-7.2.3.4/

$ git submodule update --init --recursive

$ cd /pathto/TensorRT-7.2.2.3/

$ export TRT_LIBPATH=`pwd`

$ cd /pathto/TensorRT_OSS-7.2.3.4/

$ ./docker/build.sh --file docker/ubuntu-18.04.Dockerfile --tag tensorrt-ubuntu-1804 --cuda 11.1

$ ./docker/launch.sh --tag tensorrt-ubuntu-1804 --gpus all

trtuser@c2936e108d43:/workspace$ cd $TRT_OSSPATH

trtuser@c2936e108d43:/workspace/TensorRT$ mkdir -p build && cd build

trtuser@c2936e108d43:/workspace/TensorRT/build$ cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out

trtuser@c2936e108d43:/workspace/TensorRT/build$ make -j$(nproc)

trtuser@c2936e108d43:/workspace/TensorRT/build$ exit

$ cd ..

$ mkdir backup

$ mv TensorRT-7.2.2.3/targets/x86_64-linux-gnu/lib/libnvinfer_plugin.so.7.2.2 backup

$ cp TensorRT_OSS-7.2.3.4/build/out/libnvinfer_plugin.so.7.2.3 TensorRT-7.2.2.3/targets/x86_64-linux-gnu/lib/

install xpra

https://github.com/Xpra-org/xpra/blob/master/docs/Build/Debian.md

$ git clone https://github.com/Xpra-org/xpra.git

$ sudo ./setup.py install

Exception: ERROR: cannot find a valid pkg-config entry for nvjpeg-11.4 using PKG_CONFIG_PATH=(empty)

$ vi setup.py

/if nvjpeg_ENABLED:

在底下不遠處有兩處 for v in ("11.4", "11.3"...):

皆改成 for v in ("11.1"): 即可

另外 ld: connot find -lcuda

$ sudo ln -s /usr/local/cuda-11.1/lib64/stubs/libcuda.so /usr/lib

pip 更新出錯

$ pip install --upgrade --no-cache-dir pip

$ python3 -m pip install --upgrade --no-cache-dir pip -i https://pypi.python.org/simple

python 之 pip install --upgrade pip 出錯

Exception:

Traceback (most recent call last):

  File "/usr/lib/python3/dist-packages/pip/basecommand.py", line 215, in main

    status = self.run(options, args)

  File "/usr/lib/python3/dist-packages/pip/commands/install.py", line 290, in run

    with self._build_session(options) as session:

  File "/usr/lib/python3/dist-packages/pip/basecommand.py", line 69, in _build_session

    if options.cache_dir else None

  File "/usr/lib/python3.6/posixpath.py", line 80, in join

    a = os.fspath(a)

TypeError: expected str, bytes or os.PathLike object, not int

pip 更新出錯
$ pip install --upgrade --no-cache-dir pip
$ python3 -m pip install --upgrade --no-cache-dir pip -i https://pypi.python.org/simple

在 windows 10 利用 frp 執行 vnc

編輯 frps.ini

[common]

bind_port = 7000

token = password

編輯 frps.vbx

set ws=Wscript.CreateObject("Wscript.Shell")

ws.Run "C:\frp_0.31.2_windows_amd64\frps.exe -c C:\frp_0.31.2_windows_amd64\frps.ini", 0

gpedit.msc

本機電腦 原則/電腦設定/指令碼 - (啟動/關機)

啟動/內容

新增 ftps.vbs

編輯 frpc.ini

[common]

server_addr = remote.server.ip

server_port = 7000

token = password

[vnc]

type = tcp

local_ip = 127.0.0.1

local_port = 5800

remote_port = 5900

use_encryption = true

use_compression = true

編輯 frpc.vbx

set ws=Wscript.CreateObject("Wscript.Shell")

ws.Run "C:\frp_0.31.2_windows_amd64\frpc.exe -c C:\frp_0.31.2_windows_amd64\frpc.ini", 0

gpedit.msc

本機電腦 原則/電腦設定/指令碼 - (啟動/關機)

啟動/內容

新增 ftpc.vbs

Tesseract 在 Docker 上訓練 (二)

$ docker start t4cmp

$ docker exec -it t4cmp bash

# cd /home/workspace/tesseract

# TESSDATA_PREFIX=/root/tesstutorial/tesseract/tessdata

# vi src/training/tesstrain_util.sh

尋找 phase_E_extract_features()

在 run_command tesseract ${img_file} ${img_file%.*} 後面加入 --psm 7

在 ${box_config} ${config} & 去除 &

移除  jobs="$jobs $!" 和 wait $jobs

# vi src/training/language-specific.sh

加入 你要增加的語言

src/training/tesstrain.sh --fonts_dir /usr/share/fonts --lang plate \

--linedata_only --my_boxtiff_dir /home/tmp --noextract_font_properties \

--langdata_dir ../langdata --tessdata_dir ~/tesstutorial/tesseract/tessdata  \

--output_dir ~/tesstutorial/platetrain

lstmtraining --model_output ~/tesstutorial/impact_from_full/impact \

--continue_from ~/tesstutorial/impact_from_full/eng.lstm \

--traineddata ~/tesstutorial/tesseract/tessdata/eng.traineddata \

--train_listfile ~/tesstutorial/platetrain/plate.training_files.txt \

--max_iterations 400 

lstmtraining --stop_training \

--continue_from ~/tesstutorial/impact_from_full/impact_0.031000_2_400.checkpoint \

--traineddata ~/tesstutorial/tesseract/tessdata/best/eng.traineddata \

--model_output ~/tesstutorial/impact_from_full/plate.traineddata

刪除無法訓練的圖

for i in `find /tmp/plate-2021-04-13.u4C -name "*.tif"`

do

LSTMF=${i//.tif/.lstmf}

if [ ! -f "$LSTMF" ]; then

  LSTMF=`basename "$LSTMF"`

  TIF=${LSTMF//.lstmf/.tif}

  BOX=${LSTMF//.lstmf/.box}

  rm /home/tmp/$TIF /home/tmp/$BOX

fi

done