Metadata-Version: 2.1
Name: MLDatasetBuilder
Version: 1.0.0
Summary: MLDatasetBuilder is a python package which is helping to prepare the image for your ML dataset.
Home-page: https://github.com/karthick965938/ML-Dataset-Builder
Author: Karthick Nagarajan
Author-email: karthick965938@gmail.com
License: MIT
Description: # MLDatasetBuilder
        
        **[MLDatasetBuilder-Version 1.0.0](https://pypi.org/project/MLDatasetBuilder/)** - A Python package to build Dataset for Machine Learning
        Whenever we begin a machine learning project, the first thing that we need is a dataset. Datasets will be the pillar of the training model. You can build the dataset either automatically or manually. MLDatasetBuilder is a python package which is helping to prepare the image for your ML dataset.
        
        
        [![python version](https://img.shields.io/badge/Python-3.6-yellow)](https://pypi.org/project/MLDatasetBuilder/)
        [![PyPI](https://img.shields.io/badge/pypi-v1.0.0-blue)](https://pypi.org/project/MLDatasetBuilder/)
        [![Downloads](https://pepy.tech/badge/mldatasetbuilder)](https://pepy.tech/project/mldatasetbuilder)
        [![Downloads](https://pepy.tech/badge/mldatasetbuilder/month)](https://pepy.tech/project/mldatasetbuilder/month)
        
        **Author**: Karthick Nagarajan
        
        **Email**: karthick965938@gmail.com
        
        ## Installation
        We can install ***MLDatasetBuilder*** package using this command
        
        ```sh
        pip install MLDatasetBuilder
        ```
        
        ### How to test?
        When you run python3 in the terminal, it will produce output like this:
        
        ```sh
        Python 3.6.9 (default, Apr 18 2020, 01:56:04) 
        [GCC 8.4.0] on linux
        Type "help", "copyright", "credits" or "license" for more information.
        >>> 
        ```
        
        Run the following code to you can get the Initialize process output for the MLDatasetBuilder package.
        
        ```sh
        >>> from MLDatasetBuilder import *
        >>> MLDatasetBuilder()
        ```
        ![package_sample_output](https://i1.wp.com/cdn-images-1.medium.com/max/800/1*h4KOBQoEjCaoUWjj0PzVjg.gif?ssl=1)
        
        ### Available Operations
        
        1) ***PrepareImage***  —  Remove unwanted format images and Rename your images
        
        ```sh
        #PrepareImage(folder_name, image_name)
        PrepareImage('images', 'dog')
        ```
        2) ***ExtractImages***  —  Extract images from video file
        ```sh
        #ExtractImages(video_path, file_name, frame_size)
        ExtractImages('video.mp4', 'frame', 10)
        #OR
        #ExtractImages(video_path, filename)
        ExtractImages('video.mp4', 'frame')
        #Default FPS will be 5
        ```
        
        ## Step1 — Get images from google
        
        Yes, we can get images from Google. Using the [Download All Images](https://chrome.google.com/webstore/detail/download-all-images/ifipmflagepipjokmbdecpmjbibjnakm?hl=en) browser extension we can easily get images in a few minutes. You can check out [here](https://www.youtube.com/watch?v=ik1VxrtN7m8&feature=youtu.be) for more details about this extension!
        
        
        ![step_1](https://raw.githubusercontent.com/karthick965938/ML-Dataset-Builder/master/assets/step_01.gif)
        
        ## Step2 — Create a Python file
        
        Once you have downloaded the images using this extension, you can create a python file called ***test.py*** the same directory as below.
        
        ```sh
        download_image_folder/
           _14e839ba-9691-11ea-a968-2ed746e9a968.jpg
           5e5f7af12600004018b602c0.jpeg
           A471529_Alice_b-1.jpg
           image1.png
           image2.png
           ...
        test.py
        ```
        Inside the images folder, you can see lots of png images and random filenames.
        
        ## Step3 — PrepareImage
        
        MLDatasetBuilder provides a method called PrepareImage. Using this method to we can remove the unwanted images and rename your image files which are already you have downloaded from the browser’s extensions.
        
        ```sh
        PrepareImage(folder_name, image_name)
        #PrepareImage('images', 'dog')
        ```
        As per the above code, we need to mention the image folder path and class name.
        
        ![step_1](https://raw.githubusercontent.com/karthick965938/ML-Dataset-Builder/master/assets/step_2.gif)
        
        After completing the process your image folder structure will look like below 
        
        ```sh
        download_image_folder/
           dog_0.jpg
           dog_1.jpg
           dog_2.jpg
           dog_3.png
           dog_4.png
           ...
        test.py
        ```
        
        This process very helps to annotate your images while labeling. And of course, it will be like one of the standardized things.
        
        ### Step4 — ExtractImage
        
        MLDatasetBuilder also provides a method called ExtractImages. Using this method we can extract the images from the video files.
        
        ```sh
        download_image_folder/
        video.mp4
        test.py
        ```
        As per the below code, we need to mention the video path, folder name, and framesize. Folder name will the class name and framesize’s default value 5 and it’s not mandatory.
        
        ```sh
        ExtractImages(video_path, folder_name, framesize)
        #ExtractImages('video.mp4', 'frame', 10)
        ExtractImages(video_path, folder_name)
        #ExtractImages('video.mp4', 'frame')
        ```
        ![step_1](https://raw.githubusercontent.com/karthick965938/ML-Dataset-Builder/master/assets/step_3.gif)
        
        After completing the process your image folder structure will look like below
        
        ```sh
        download_image_folder/
        dog/
           dog_0.jpg
           dog_1.jpg
           dog_2.jpg
           dog_3.png
           dog_4.png
           ...
        dog.mp4
        test.py
        ```
        
        # Contributing
        All issues and pull requests are welcome! To run the code locally, first, fork the repository and then run the following commands on your computer:
        
        ```sh
        git clone https://github.com/<your-username>/ML-Dataset-Builder.git
        cd ML-Dataset-Builder
        # Recommended creating a virtual environment before the next step
        pip3 install -r requirements.txt
        ```
        When adding code, be sure to write unit tests where necessary.
        
        # Contact
        MLDatasetBuilder was created by [Karthick Nagarajan](https://stackoverflow.com/users/6295641/karthick-nagarajan?tab=profile). Feel free to reach out on [Twitter](https://twitter.com/Karthick965938) or through [Email!](karthick965938@gmail.com)
        
Keywords: image data datascience imagedataset preparedataset prepareimage dataset mldataset datasetbuilder mldatasetbuilder ML ml machinelearning AI ai
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
