Anna Yusef

Anna Yusef

1595417460

LabelImg is a graphical image annotation tool and label object bounding boxes in images

LabelImg

/resources/icons/app.png

LabelImg is a graphical image annotation tool.

It is written in Python and uses Qt for its graphical interface.

Annotations are saved as XML files in PASCAL VOC format, the format used by ImageNet. Besides, it also supports YOLO format

Demo Image

Demo Image

Watch a demo video

Installation

Build from source

Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8. However, Python 3 or above and PyQt5 are strongly recommended.

Ubuntu Linux

Python 2 + Qt4

sudo apt-get install pyqt4-dev-tools
sudo pip install lxml
make qt4py2
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Python 3 + Qt5 (Recommended)

sudo apt-get install pyqt5-dev-tools
sudo pip3 install -r requirements/requirements-linux-python3.txt
make qt5py3
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

macOS

Python 2 + Qt4

brew install qt qt4
brew install libxml2
make qt4py2
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Python 3 + Qt5 (Recommended)

brew install qt  # Install qt-5.x.x by Homebrew
brew install libxml2

or using pip

pip3 install pyqt5 lxml # Install qt and lxml by pip

make qt5py3
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Python 3 Virtualenv (Recommended)

Virtualenv can avoid a lot of the QT / Python version issues

brew install python3
pip3 install pipenv
pipenv run pip install pyqt5==5.13.2 lxml
pipenv run make qt5py3
python3 labelImg.py
[Optional] rm -rf build dist; python setup.py py2app -A;mv "dist/labelImg.app" /Applications

Note: The Last command gives you a nice .app file with a new SVG Icon in your /Applications folder. You can consider using the script: build-tools/build-for-macos.sh

Windows

Install Python, PyQt5 and install lxml.

Open cmd and go to the labelImg directory

pyrcc4 -o lib/resources.py resources.qrc
For pyqt5, pyrcc5 -o libs/resources.py resources.qrc

python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Windows + Anaconda

Download and install Anaconda (Python 3+)

Open the Anaconda Prompt and go to the labelImg directory

conda install pyqt=5
pyrcc5 -o libs/resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Get from PyPI but only python3.0 or above

This is the simplest (one-command) install method on modern Linux distributions such as Ubuntu and Fedora.

pip3 install labelImg
labelImg
labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Use Docker

docker run -it \
--user $(id -u) \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
-v /tmp/.X11-unix:/tmp/.X11-unix \
tzutalin/py2qt4

make qt4py2;./labelImg.py

You can pull the image which has all of the installed and required dependencies. Watch a demo video

Usage

Steps (PascalVOC)

  1. Build and launch using the instructions above.
  2. Click ‘Change default saved annotation folder’ in Menu/File
  3. Click ‘Open Dir’
  4. Click ‘Create RectBox’
  5. Click and release left mouse to select a region to annotate the rect box
  6. You can use right mouse to drag the rect box to copy or move it

The annotation will be saved to the folder you specify.

You can refer to the below hotkeys to speed up your workflow.

Steps (YOLO)

  1. In data/predefined_classes.txt define the list of classes that will be used for your training.
  2. Build and launch using the instructions above.
  3. Right below “Save” button in the toolbar, click “PascalVOC” button to switch to YOLO format.
  4. You may use Open/OpenDIR to process single or multiple images. When finished with a single image, click save.

A txt file of YOLO format will be saved in the same folder as your image with same name. A file named “classes.txt” is saved to that folder too. “classes.txt” defines the list of class names that your YOLO label refers to.

Note:

  • Your label list shall not change in the middle of processing a list of images. When you save an image, classes.txt will also get updated, while previous annotations will not be updated.
  • You shouldn’t use “default class” function when saving to YOLO format, it will not be referred.
  • When saving as YOLO format, “difficult” flag is discarded.

Create pre-defined classes

You can edit the data/predefined_classes.txt to load pre-defined classes

Hotkeys

| Ctrl + u | Load all of the images from a directory |
| Ctrl + r | Change the default annotation target dir |
| Ctrl + s | Save |
| Ctrl + d | Copy the current label and rect box |
| Space | Flag the current image as verified |
| w | Create a rect box |
| d | Next image |
| a | Previous image |
| del | Delete the selected rect box |
| Ctrl++ | Zoom in |
| Ctrl-- | Zoom out |
| ↑→↓← | Keyboard arrows to move selected rect box |

Verify Image:

When pressing space, the user can flag the image as verified, a green background will appear. This is used when creating a dataset automatically, the user can then through all the pictures and flag them instead of annotate them.

Difficult:

The difficult field is set to 1 indicates that the object has been annotated as “difficult”, for example, an object which is clearly visible but difficult to recognize without substantial use of context. According to your deep neural network implementation, you can include or exclude difficult objects during training.

How to reset the settings

In case there are issues with loading the classes, you can either:

  1. From the top menu of the labelimg click on Menu/File/Reset All

  2. Remove the .labelImgSettings.pkl from your home directory. In Linux and Mac you can do:
    rm ~/.labelImgSettings.pkl

How to contribute

Send a pull request

Download Details:

Author: tzutalin

GitHub: https://github.com/tzutalin/labelImg

#python #programming

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LabelImg is a graphical image annotation tool and label object bounding boxes in images
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How can I find the correct ulimit values for a user account or process on Linux systems?

For proper operation, we must ensure that the correct ulimit values set after installing various software. The Linux system provides means of restricting the number of resources that can be used. Limits set for each Linux user account. However, system limits are applied separately to each process that is running for that user too. For example, if certain thresholds are too low, the system might not be able to server web pages using Nginx/Apache or PHP/Python app. System resource limits viewed or set with the NA command. Let us see how to use the ulimit that provides control over the resources available to the shell and processes.

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MEAN Stack Tutorial MongoDB ExpressJS AngularJS NodeJS

We are going to build a full stack Todo App using the MEAN (MongoDB, ExpressJS, AngularJS and NodeJS). This is the last part of three-post series tutorial.

MEAN Stack tutorial series:

AngularJS tutorial for beginners (Part I)
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MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III) 👈 you are here
Before completing the app, let’s cover some background about the this stack. If you rather jump to the hands-on part click here to get started.

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Docker Applikationen mit Visual Studio Code debuggen

Mit dem integrierten Debugger von Visual Studio Code lassen sich ASP.NET Core bzw. .NET Core Applikationen einfach und problemlos debuggen. Der Debugger unterstützt auch Remote Debugging, somit lassen sich zum Beispiel .NET Core Programme, die in einem Docker-Container laufen, debuggen.

Als Beispiel Applikation reicht das Default-Template für MVC Applikationen dotnet new mvc

$ md docker-core-debugger
$ cd docker-core-debugger
$ dotnet new mvc

Mit dotnet run prüfen wir kurz, ob die Applikation läuft und unter der Adresse http://localhost:5000 erreichbar ist.

$ dotnet run
$ Hosting environment: Production
$ Content root path: D:\Temp\docker-aspnetcore
$ Now listening on: http://localhost:5000

Die .NET Core Applikation builden wir mit dotnet build und publishen alles mit Hilfe von dotnet publish

$ dotnet build
$ dotnet publish -c Debug -o out --runtime linux-x64

Dabei gilt es zu beachten, dass die Build Configuration mit -c Debug gesetzt ist und das Output Directory auf -o out. Sonst findet Docker die nötigen Binaries nicht. Für den Docker Container brauchen wir nun ein Dockerfile, dass beim Start vorgängig den .NET Core command line debugger (VSDBG) installiert. Das Installations-Script für VSDBG ist unter https://aka.ms/getvsdbgsh abfrufbar.

FROM microsoft/aspnetcore:latest
WORKDIR /app

RUN apt-get update \
    && apt-get install -y --no-install-recommends \
       unzip procps \
    && rm -rf /var/lib/apt/lists/* \
    && curl -sSL https://aka.ms/getvsdbgsh | bash /dev/stdin -v latest -l /vsdbg

COPY ./out .
ENTRYPOINT ["dotnet", "docker-core-debugger.dll"]

Den Docker Container erstellen wir mit dem docker build Kommando

$ docker build -t coreapp .

und starten die Applikation mit docker run.

$ docker run -d -p 8080:80 --name coreapp coreapp

Jetzt muss Visual Studio Code nur noch wissen, wo unsere Applikation läuft. Dazu definieren wir eine launch.json vom Typ attach und konfigurieren die nötigen Parameter für den Debugger.

{
    "version": "0.2.0",
    "configurations": [
         {
            "name": ".NET Core Remote Attach",
            "type": "coreclr",
            "request": "attach",
            "processId": "${command:pickRemoteProcess}",
            "pipeTransport": {
                "pipeProgram": "docker",
                "pipeArgs": ["exec", "-i coreapp ${debuggerCommand}"],
                "quoteArgs": false,
                "debuggerPath": "/vsdbg/vsdbg",
                "pipeCwd": "${workspaceRoot}"
            },

            "logging": {
                "engineLogging": true,
                "exceptions": true,
                "moduleLoad": true,
                "programOutput": true
            },
        }
    ]
}

Mit F5 starten wir den Debugger. Wenn alles klappt, sollte eine Auswahl der Prozesse des Docker-Containers sichtbar sein.

vscode

Nun muss der dotnet Prozess ausgewählt werden. Der Visual Studio Code Debugger verbindet sich darauf mit VSDBG und wir können wie gewohnt unseren Code debuggen. Dazu setzen wir einen Breakpoint in der Index-Action des HomeControllers und rufen mit dem Browser die URL http://localhost:8080/ auf.

vscode

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How to configure AWS SES with Postfix MTA

How do I configure Amazon SES With Postfix mail server to send email under a CentOS/RHEL/Fedora/Ubuntu/Debian Linux server?

Amazon Simple Email Service (SES) is a hosted email service for you to send and receive email using your email addresses and domains. Typically SES used for sending bulk email or routing emails without hosting MTA. We can use Perl/Python/PHP APIs to send an email via SES. Another option is to configure Linux or Unix box running Postfix to route all outgoing emails via SES.

  • » Remove sendmail
  • » Install postfix
  • » Configuring postfix for SES
  • » Test postfix

Procedure to configure AWS SES with Postfix

Before getting started with Amazon SES and Postfix, you need to sign up for AWS, including SES. You need to verify your email address and other settings. Make sure you create a user for SES access and download credentials too.

Step 1 – Uninstall Sendmail if installed

If sendmail installed remove it. Debian/Ubuntu Linux user type the following apt command/apt-get command:

$`` sudo apt --purge remove sendmail

CentOS/RHEL user type the following yum command or dnf command on Fedora/CentOS/RHEL 8.x:

$`` sudo yum remove sendmail

$`` sudo dnf remove sendmail

Sample outputs from CentOS 8 server:

Dependencies resolved.
===============================================================================
 Package           Architecture  Version               Repository         Size
===============================================================================
Removing:
 sendmail          x86_64        8.15.2-32.el8         @AppStream        2.4 M
Removing unused dependencies:
 cyrus-sasl        x86_64        2.1.27-1.el8          @BaseOS           160 k
 procmail          x86_64        3.22-47.el8           @AppStream        369 k

Transaction Summary
===============================================================================
Remove  3 Packages

Freed space: 2.9 M
Is this ok [y/N]: y

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AngularJS tutorial for beginners (Part I)
Creating RESTful APIs with NodeJS and MongoDB Tutorial (Part II) 👈 you are here
MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III)

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