modeyolo 0.1.3

ModeYOLO is a versatile Python package designed for efficient color space transformations and simplified dataset modification for deep learning applications. Seamlessly integrating into your workflow, this package empowers users to effortlessly perform diverse color operations and streamline the creation of modified datasets, enhancing the flexibility and convenience of machine learning model training processes.

ModeYOLO Python Package

Introduction

ModeYOLO is a Python package designed to perform color space transformations on images and facilitate the creation of modified datasets for training deep learning models. The package consists of two main modules: ColorOperation.py and Operation.py.

Folder Structure

Before using the package, ensure that your source dataset follows the following folder structure:

dataset/
|-- train/
|   |-- images/
|   |-- labels/
|-- test/
|   |-- images/
|   |-- labels/
|-- val/
|   |-- images/
|   |-- labels/
|-- data.yaml

ColorOperation Module (ColorOperation.py)

Class: colorcng

Constructor

def __init__(self, path: str, mode: str = 'all') -> None:
    """
    Initializes the colorcng object.

    Parameters:
    - path: str, path to the target directory.
    - mode: str, mode of operation ('all', 'rgb', 'bgr', 'gray', 'hsv', 'crcb', 'lab').
    """

Methods

1. cng_rgb

   def cng_rgb(self, opt: str, img: np.ndarray, idx: int | str = 0) -> None:
       """
       Converts the image to RGB color space.
   
       Parameters:
       - opt: str, operation type ('train', 'test', 'val').
       - img: np.ndarray, input image.
       - idx: int | str, index for the output file name.
       """
   

2. cng_bgr

   def cng_bgr(self, opt: str, img: np.ndarray, idx: int | str = 0) -> None:
       """
       Saves the image in BGR color space.
   
       Parameters:
       - opt: str, operation type ('train', 'test', 'val').
       - img: np.ndarray, input image.
       - idx: int | str, index for the output file name.
       """
   

3. cng_gray

   def cng_gray(self, opt: str, img: np.ndarray, idx: int | str = 0) -> None:
       """
       Converts the image to grayscale.
   
       Parameters:
       - opt: str, operation type ('train', 'test', 'val').
       - img: np.ndarray, input image.
       - idx: int | str, index for the output file name.
       """
   

4. cng_hsv

   def cng_hsv(self, opt: str, img: np.ndarray, idx: int | str = 0) -> None:
       """
       Converts the image to HSV color space.
   
       Parameters:
       - opt: str, operation type ('train', 'test', 'val').
       - img: np.ndarray, input image.
       - idx: int | str, index for the output file name.
       """
   

5. cng_crcb

   def cng_crcb(self, opt: str, img: np.ndarray, idx: int | str = 0) -> None:
       """
       Converts the image to YCrCb color space.
   
       Parameters:
       - opt: str, operation type ('train', 'test', 'val').
       - img: np.ndarray, input image.
       - idx: int | str, index for the output file name.
       """
   

6. cng_lab

   def cng_lab(self, opt: str, img: np.ndarray, idx: int | str = 0) -> None:
       """
       Converts the image to LAB color space.
   
       Parameters:
       - opt: str, operation type ('train', 'test', 'val').
       - img: np.ndarray, input image.
       - idx: int | str, index for the output file name.
       """
   

7. execute

   def execute(self, opt: str, file: str, idx: int | str = 0) -> None:
       """
       Executes the specified color space transformation.
   
       Parameters:
       - opt: str, operation type ('train', 'test', 'val').
       - file: str, path to the input image.
       - idx: int | str, index for the output file name.
       """
   

Operation Module (Operation.py)

Class: InitOperation

Constructor

def __init__(self, target_directory: str = 'modified_dataset', src_directory: str = 'dataset', mode: str = 'all') -> None:
    """
    Initializes the InitOperation object.

    Parameters:
    - target_directory: str, path to the target directory.
    - src_directory: str, path to the source dataset directory.
    - mode: str, mode of operation ('all', 'rgb', 'bgr', 'gray', 'hsv', 'crcb', 'lab').
    """

Methods

1. start_train

   def start_train(self) -> None:
       """
       Creates the modified training dataset.
       """
   

2. start_test

   def start_test(self) -> None:
       """
       Creates the modified testing dataset.
       """
   

3. start_val

   def start_val(self) -> None:
       """
       Creates the modified validation dataset.
       """
   

4. reform_dataset

   def reform_dataset(self) -> None:
       """
       Reformats the entire dataset.
       """
   

Example Usage

# Import the InitOperation class
from ModeYOLO.Operation import InitOperation

# Create an InitOperation object
init_op = InitOperation(target_directory='modified_dataset', src_directory='dataset', mode='all')

# Create the modified dataset
init_op.reform_dataset()

This example assumes that the source dataset is structured according to the specified folder structure. Adjust the paths and parameters accordingly based on your dataset structure.