semvec/pypi-packages huggingface dataset:
ImageFetchPy 1.2
A library for scraping and downloading images from the web.

# ImageFetchPy
ImageFetchPy is a Python library designed to simplify the process of scraping and downloading images from the web, specifically targeting Google Image Search based on user-provided keywords.

## Features
- Download images from Google Image Search using keywords.
- Specify the maximum number of images to download per keyword.
- Support for various image file extensions such as jpg, png, gif, etc.
- Enhanced filtering to exclude downloading images of icons, navbars, etc.
- Save image URLs in a text file for easy reference and management.
- Skips downloading duplicate images based on URL comparison.- 

## Version 1.2
Version 1.2 includes modifications:
- Includes modifications so that it does not downloads images of icons, navbar etc.
- Now saves image url in a text file inside a folder 'url' present inside the folder    which contains images based on the given query name


## Installation
You can install ImageFetchPy using pip:

```bash
pip install ImageFetchPy
```


## Usage
### Using the Library in a Python Script
Import the 'download_query' function from ImageFetchPy and use it to download images:
```python
from ImageFetchPy import download_query

# Download images based on keywords
download_query("cat, dog, bird", limit=10)
```

### Using the Command-Line Interface (CLI)
ImageFetchPy provides a command-line interface for convenient usage:
```bash
imagefetch "cat, dog, bird" --limit 10
```


## Parameters
- keywords: Keywords to search for images (comma-separated if multiple).
- limit: Maximum number of images to download per keyword.
- extensions: Set of valid image file extensions to download (default: {'jpg', 'png', 'ico', 'gif', 'jpeg'}).


## Version 1.2 Changes
In version 1.2, ImageFetchPy introduces the following improvements:

- Exclude downloading images of icons, navbars, and similar elements.
- Save image URLs in a structured format inside a 'url' folder for each keyword query.
- Skips downloading duplicate images based on URL comparison.
computer-vision-datasets 0.2.1
Easy Computer Vision Datasets
Requires Python: >=3.6

# Computer Vision Datasets

[datasetninja.com](datasetninja.com) has an impressive collection of comptuer vision datasets. This package was built to let you easily download and use these computer vision datasets. 

## Installation
`pip install computer_vision_datasets` should work. If not, go to the repository and paste the file in your code.
The repository is in [https://github.com/franchesoni/computer_vision_datasets](https://github.com/franchesoni/computer_vision_datasets).


## Available datasets (code)
To check the available datasets, simply run
```python
from computer_vision_datasets import get_released_datasets
print(sorted(get_released_datasets().keys()))
```

## Download a dataset
Find the name of the dataset you want with the code above. Here we use 'ADE20K'.
```python
from computer_vision_datasets import download
download('ADE20K', '/your/destination/directory')
```

## The `SegDataset` class
There is a `SegDataset` class for segmentation datasets. You just need to point it to the dataset path. Example:

```python
ds = SegDataset(ds_path, split='test')
```
 
You can, for instance, wrap it in a Pytorch Dataset:

```python
from torch.utils.data import Dataset

class PyTorchWrapperDataset(Dataset):
    def __init__(self, ds):
        # super().__init__() is not needed since we're not overriding anything from the parent's __init__
        self.original_dataset = ds

    def __getitem__(self, index):
        # Assuming the original dataset uses __getitem__ to access items
        # If it uses a different method (like get_item), adjust accordingly.
        return self.original_dataset[index]

    def __len__(self):
        return len(self.original_dataset)
```