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visual perception lib
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SSusantAchary committed Dec 28, 2020
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6 changes: 6 additions & 0 deletions .idea/inspectionProfiles/profiles_settings.xml

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21 changes: 21 additions & 0 deletions LICENSE.txt
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MIT License

Copyright (c) 2020 Susant Achary <sache.meet@yahoo.com>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
50 changes: 50 additions & 0 deletions README.md
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# Visual-Perception

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE.txt)

Visual Perception is a High Level Python Library to performs various task under visual recognition.

Currently, It Provides the Solution for Object Detection.

For Detection with pre-trained models it provides:
- YOLOv4
- TinyYOLOv4


### Dependencies:
- tensorflow >= 2.3.0
- keras
- opencv-python
- numpy
- pillow
- matplotlib
- pandas
- scikit-learn
- scikit-image
- imgaug
- labelme2coco
- progressbar2
- scipy
- h5py
- configobj


## **`Get Started:`**
```python
!pip install visual_perception
```


### **`Image Object Detection` Using `YOLOv4`**

```python
from visual_perception.Detection import Object_Detection

model = Object_Detection()
model.Det_YOLOv4()
model.Detect_From_Image(input_path='car.jpg',
output_path='./output_image.jpg')

from PIL import Image
Image.open('output_image.jpg')
```
15 changes: 15 additions & 0 deletions requirements.txt
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tensorflow >= 2.3.0
keras
opencv-python
numpy
pillow
matplotlib
pandas
scikit-learn
scikit-image
progressbar2
labelme2coco
imgaug
scipy
h5py
configobj
64 changes: 64 additions & 0 deletions setup.py
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from setuptools import setup, find_packages

with open("README.md", "r") as fh:
long_description = fh.read()

setup(
name='visual_perception',
description='A High Level Python Library for Visual Recognition ',
url="https://github.com/SSusantAchary/Visual-Perception",
author="SusantAchary",
author_email="sache.meet@yahoo.com",
maintainer="Susant Achary",
maintainer_email="sache.meet@yahoo.com",
long_description=long_description,
long_description_content_type="text/markdown",
packages=find_packages(),
python_requires='>=3.5, <4',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Intended Audience :: Research',
'License :: OSI Approved :: MIT License',
'Natural Language :: English',
'Operating System :: OS Independent',
'Programming Language :: Python :: 3 :: Only',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Topic :: Scientific/Engineering :: Image Processing',
'Topic :: Scientific/Engineering :: Image Recognition',
'Topic :: Software Development :: Libraries :: Python Modules'
],
keywords=[
"object detection",
"computer vision",
'yolo',
'yolov4',
'tinyyolo'],

install_requires=[
"tensorflow",
'keras',
'opencv-python',
'numpy',
'pillow',
'matplotlib',
'pandas',
'scikit-learn',
'progressbar2',
'scipy',
'h5py',
'imgaug',
'scikit-image',
'labelme2coco',
'configobj',
],

project_urls={

'Bug Reports': 'https://github.com/SSusantAchary/Visual-Perception/issues',
'Source': 'https://github.com/SSusantAchary/Visual-Perception/'},

)
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