semvec/pypi-packages huggingface dataset:
histeqkit 0.0.6
package for histogram equalization algorithms and image accuracy measures.
Requires Python: >=3.6

# HistEQKit

HistEQKit is a Python package that provides a collection of histogram equalization algorithms and image accuracy measures for image enhancement and analysis tasks.

## Installation

You can install HistEQKit using pip:

```shell
pip install histeqkit==0.0.5
```

HistEQKit requires Python 3.6 or higher.

Usage

To use HistEQKit, import the desired histogram equalization algorithms from the package,
 along with OpenCV for image processing. Here's an example:
```shell
from histeqkit import algorithms
import cv2

# Load an image
input_image = cv2.imread("/path/to/input_image.png", cv2.IMREAD_GRAYSCALE)

# Apply histogram equalization using an algorithm
ie = algorithms.IE(input_image)
output_image = ie.INVERT()

# Save the output image
cv2.imwrite('output.jpg', output_image)
```
Make sure to replace "/path/to/input_image.png" with the path to your actual input image.

Algorithms

HistEQKit currently provides the following histogram equalization algorithms:

algorithms.IE: Inverts the intensity values of the input image.
Note: This is just an example algorithm. HistEQKit can include other algorithms as well.

Contributing

Contributions to HistEQKit are welcome! If you have any bug fixes, enhancements, or new algorithms/measures to add, please open an issue or submit a pull request on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for more details.


Feel free to customize the content further to provide additional details about the algorithms, accuracy measures, or any other relevant information about your package.
barldb 0.1.0
Easy JSON database
Requires Python: >=3.10,<4.0

# BarlDB

`BarlDB` - this is a simple local database for `Python`. It has few functions, though a very easy interface. All in `JSON`

# Example
```python
from barldb import config, db
config.debug = True #Allowing debug (Not allowed by default)

db.create("pancake") #Creates the database

data = db.connect("pancake") #Connects to database
print(data) #Print the pancake.json

answer = ["Im love pancakes!", "Im dont love pancakes!"] 
try:
    data["answer"]
except:
    data["answer"] = random.choice(f) #Adding a "answer" if a "answer" doesnt exist
#Checking if pancake.json has "answer"
db.save("pancake", data) #Saves to pancake.json var "data"

print(db.get("pancake")) #Prints the pancake.json

db.remove("pancake") #Remove the pancake.json (database)
```

# How to install
Write to console `pip install barldb`

# 5 reasons to try BarlDB

- Very easy interface
- Action logging
- There will be many updates
- Database created on `JSON` and `Python`