pip package setup files

version is not hard coded in version dialog

DLTA_AI_app/labelme/__init__.py

@@ -12,7 +12,7 @@
 # 1. MAJOR version when you make incompatible API changes;
 # 2. MINOR version when you add functionality in a backwards-compatible manner;
 # 3. PATCH version when you make backwards-compatible bug fixes.
-__version__ = "4.5.10"
+__version__ = "1.0.1"
 
 QT4 = QT_VERSION[0] == "4"
 QT5 = QT_VERSION[0] == "5"

DLTA_AI_app/labelme/intelligence.py

@@ -1,7 +1,13 @@
 from ultralytics import YOLO
 import json
 import time
-from inferencing import models_inference
+try:
+    from inferencing import models_inference
+except ModuleNotFoundError:
+    import subprocess
+    print("The required package 'mmcv-full' is not currently installed. It will now be installed. This process may take some time. Note that this package will only be installed the first time you use DLTA-AI.")
+    subprocess.run(["mim", "install", "mmcv-full==1.7.0"])
+    from inferencing import models_inference
 from labelme.label_file import LabelFile
 from labelme import PY2
 from qtpy import QtCore

DLTA_AI_app/labelme/utils/help.py

@@ -412,5 +412,6 @@ def version():
     Returns:
     None
     """
+    from labelme import __version__
     # Display the version number in a message box
-    OKmsgBox("Version", "DLTA-AI Version 1.0")
+    OKmsgBox("Version", f"DLTA-AI Version {__version__}")

MANIFEST.in

@@ -0,0 +1 @@
+recursive-include . *

README.md

@@ -1,6 +1,6 @@
 <div align = "center">
 <h1>
-    <img src = "assets/icon.png" width = 200 height = 200>
+    <img src = "https://github.com/0ssamaak0/DLTA-AI/blob/master/assets/icon.png?raw=true" width = 200 height = 200>
 <br>
 
 </h1>
@@ -19,7 +19,7 @@ DLTA-AI is the next generation of annotation tools, integrating the power of Com
 [![GitHub issues](https://img.shields.io/github/issues/0ssamaak0/DLTA-AI)](https://github.com/0ssamaak0/DLTA-AI/issues)
 [![GitHub last commit](https://img.shields.io/github/last-commit/0ssamaak0/DLTA-AI)](https://github.com/0ssamaak0/DLTA-AI/commits)
 
-![gif_main](assets/gif_main.gif)
+![gif_main](https://github.com/0ssamaak0/DLTA-AI/blob/master/assets/gif_main.gif?raw=true)
 
 <!-- make p with larger font size -->
 [Installation](#installation-%EF%B8%8F)  🛠️ | [Segment Anything](#Segment-Anything-) 🪄 | [Model Selection](#model-selection-) 🤖 | [Segmentation](#segmentation-) 🎨 | [Object Tracking](#object-tracking-) 🚗 | [Export](#export-) 📤 | [Other Features](#other-features-) 🌟| [Contributing](#contributing-) 🤝| [Acknowledgements](#acknowledgements-)🙏| [Resources](#resources-) 🌐 | [License](#license-) 📜
@@ -28,23 +28,32 @@ DLTA-AI is the next generation of annotation tools, integrating the power of Com
 
 
 # Installation 🛠️
-after downloading the lastest [release](https://github.com/0ssamaak0/DLTA-AI/releases) or cloning the repoistory, do the following steps
-## 1. Install [Pytorch](https://pytorch.org/)
-preferably using anaconda
+## Install Pytorch
+preferably in a conda environment with python 3.8
 
+install pytorch according to your device from [here](https://pytorch.org/get-started/locally/)
 ```
-conda create --name DLTA-AI python=3.8 -y
+conda create -n DLTA-AI python=3.8
 conda activate DLTA-AI
 
 conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
 ```
-## 2. install requirements
+## Option 1: using pip
+```
+pip install DLTA-AI
+```
+note that first time running the tool, it will download a required module, it may take some time
+
+## Option 2: manual installation
+after downloading the lastest [release](https://github.com/0ssamaak0/DLTA-AI/releases)
+
+install requirements
 
 ```
 pip install -r requirements.txt
 mim install mmcv-full==1.7.0
 ```
-###3. Running
+then 
 Run the tool from `DLTA_AI_app` directory
 ```
 cd labelme-master
@@ -91,7 +100,7 @@ DLTA-AI takes the Annotation to the next level by integrating lastest Meta model
 
 <div align = "center">
 
-![Segment Anything](assets/SAM.gif)
+![Segment Anything](https://github.com/0ssamaak0/DLTA-AI/blob/master/assets/SAM.gif?raw=true)
 </div>
 
 # Model Selection 🤖
@@ -100,7 +109,7 @@ For model selection, DLTA-AI provides the **Model Explorer** to utilize the powe
 the to give the user the ability to compare, download and select from the library of models
 <div align = "center">
 
-![Model Explorer](assets/model_explorer.png)
+![Model Explorer](https://github.com/0ssamaak0/DLTA-AI/blob/master/assets/model_explorer.png?raw=true)
 </div>
 
 
@@ -110,7 +119,7 @@ Using the models from the **Model Explorer**, DLTA-AI provides a seamless expire
 
 <div align = "center">
 
-![Segmentation](assets/segmentation.png)
+![Segmentation](https://github.com/0ssamaak0/DLTA-AI/blob/master/assets/segmentation.png?raw=true)
 </div>
 and as mentioned before, **SAM** is fully integrated in DLTA-AI to provide zero-shot segmentation for any class, and to improve the quality of segmentation
 
@@ -123,23 +132,23 @@ Beside this, DLTA-AI provides a completely new way to modify the tracking result
 
 <div align = "center">
 
-![Object Tracking](assets/tracking.gif)
+![Object Tracking](https://github.com/0ssamaak0/DLTA-AI/blob/master/assets/tracking.gif?raw=true)
 
 </div>
 
 Beside automatic tracking models, DLTA-AI provides different methods of interpolation and filling gaps between frames to fix occlusions and unpredicted behaviors in a semi-automatic way
 
 <div align = "center">
 
-![Interpolation](assets/interpolation.png)
+![Interpolation](https://github.com/0ssamaak0/DLTA-AI/blob/master/assets/interpolation.png?raw=true)
 </div>
 
 # Export 📤
 For Instance Segmentation, DLTA-AI provides to option to export the segmentation to standard COCO format, and the results of tracking to MOT format, and a video file for the tracking results with desired visualization options e.g., show id, bbox, class name, etc.
 
 <div align = "center">
 
-![Export](assets/Export.png)
+![Export](https://github.com/0ssamaak0/DLTA-AI/blob/master/assets/Export.png?raw=true)
 
 </div>
 

setup.py

@@ -0,0 +1,36 @@
+import setuptools
+
+with open("README.md", "r", encoding="utf8") as fh:
+    long_description = fh.read()
+
+with open("requirements.txt", "r") as f:
+    requirements = f.read().splitlines()
+
+__version__ = "0.0.0"
+with open("DLTA_AI_app/labelme/__init__.py", "r") as f:
+    for line in f.readlines():
+        if line.startswith("__version__"):
+            __version__ = line.split("=")[1].strip().strip('"')
+            break
+
+setuptools.setup(
+    name="DLTA-AI",
+    version=f"{__version__}",
+    author="0ssamaak0",
+    author_email="[email protected]",
+    description="DLTA-AI is the next generation of annotation tools, integrating the power of Computer Vision SOTA models to Labelme in a seamless expirence and intuitive workflow to make creating image datasets easier than ever before",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/0ssamaak0/DLTA-AI",
+    package_dir={"DLTA_AI_app": "DLTA_AI_app"},
+    python_requires='>=3.8',
+    install_requires=requirements,
+    package_data={"": ["*"]},
+    license="GPLv3",
+    entry_points={
+        "console_scripts": [
+            "DLTA-AI=DLTA_AI_app.__main__:main"
+        ]
+    }
+)
+