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MLDatasetBuilder-1.0.0
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مشاهده بیشترتوضیحات
MLDatasetBuilder is a python package which is helping to prepare the image for your ML dataset.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | MLDatasetBuilder-1.0.0 |
| نام | MLDatasetBuilder |
| نسخه کتابخانه | 1.0.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Karthick Nagarajan |
| ایمیل نویسنده | karthick965938@gmail.com |
| آدرس صفحه اصلی | https://github.com/karthick965938/ML-Dataset-Builder |
| آدرس اینترنتی | https://pypi.org/project/MLDatasetBuilder/ |
| مجوز | MIT |
# 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.
[](https://pypi.org/project/MLDatasetBuilder/)
[](https://pypi.org/project/MLDatasetBuilder/)
[](https://pepy.tech/project/mldatasetbuilder)
[](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()
```
### 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!
## 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.
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')
```
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)
نیازمندی
| مقدار | نام |
|---|---|
| - | opencv-python |
| - | Pillow |
| - | art |
| - | termcolor |
| - | progress |
| - | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl MLDatasetBuilder-1.0.0:
pip install MLDatasetBuilder-1.0.0.whl
نصب پکیج tar.gz MLDatasetBuilder-1.0.0:
pip install MLDatasetBuilder-1.0.0.tar.gz