semvec/pypi-packages huggingface dataset:
easy-bounding-box 0.0.5
Package to simplify bounding boxes usage
Requires Python: >=3.7

# bounding_box

A Python package designed to simplify bounding box operations for object detection. It provides functions to calculate box centers, dimensions, and intersections, reducing the complexity of these tasks in your code. With Bounding_Box, you can streamline your object detection workflow, making it more efficient and straightforward.

## Installation
```
pip install easy-bounding-box
```

## Example

```python
from easy_bounding_box import BoundingBox
import numpy as np
import cv2

img = np.zeros((750,1000,3))

bounding_box = BoundingBox([547.31, 41.473, 940.4, 712.92])
bounding_box_2 = BoundingBox((500, 20, 700, 350))

bounding_box_3 = bounding_box_2.change_size(0.4, False)

line = (100, 250, 530, 250)

img = cv2.line(img, line[:2], line[2:], (255,255,0), 2)

img = cv2.rectangle(img, bounding_box[:2], bounding_box[2:], (255,0,0), 2)

img = cv2.rectangle(img, bounding_box_2[:2], bounding_box_2[2:], (0,255,0), 2)

img = cv2.rectangle(img, bounding_box_3[:2], bounding_box_3[2:], (0,0,255), 2)

print("Bounding box 2 is intercepting blue line: ", bounding_box_2.box_intercept_line(line))
print("bounding box is intercepting bounding_box_2: ", bounding_box.box_intercept_box(bounding_box_2.bounding_box))
print("How much of bounding_box_2 is in bounding_box: ", bounding_box_2.iou(bounding_box.bounding_box))
print("Bounding box 3 values: ", bounding_box_3.bounding_box)
print("Bounding box 3 listed values: ", bounding_box_3.list_bounding_box)
print("Bounding box 3 middle: ", bounding_box_3.middle)
print("Bounding box 3 listed middle: ", bounding_box_3.list_middle)
print("Bounding box 3 dimensions: ", bounding_box_3.dimensions)
print("Bounding box 3 listed dimensions: ", bounding_box_3.list_dimensions)
print("Bounding box 3 area: ", bounding_box_3.area)
print("Bounding box 3 walls coordinates: ", bounding_box_3.walls)

cv2.imshow("image", img)
  
cv2.waitKey(0)

cv2.destroyAllWindows()
```
cglearn 0.0.1
cglearn is a Python library that aims to spark probabilistic reasoning and analysis via chain graphs.
Requires Python: >=3.6

# cglearn

"cglearn" is a Python library that aims to spark probabilistic reasoning and analysis via chain graphs.

Developers: Mohammad Ali Javidian, Shantanu Deore

You can visit https://majavid.github.io/cglearnDOCs/ for details of the documentation.
fakereal 0.2.1
A Python library for seeding databases with random realistic test data.
Requires Python: >=3.6

# FakeReal

## Overview

FakeReal is a Python library designed to populate MySQL, PostgreSQL, and SQLite databases with random test data. With FakeReal, you can easily generate and insert fake but realistic data into your databases, either for testing or for other purposes. It's built on top of the Faker library for generating the data and uses Peewee for database connectivity.

## Features

- **Multiple Database Support**: FakeReal supports MySQL, PostgreSQL, and SQLite databases.
- **Intuitive API**: FakeReal provides an easy-to-use API for seeding databases and running test queries.
- **Customizable Data Generation**: Auto-generates realistic data based on your database schema, semantics of fields, and relationship between the tables.
- **Wipe Test Data**: Provides the ability to wipe test data from databases.

## Installation

You can install FakeReal via pip:

```bash
pip install fakereal
```

## Quick Start
### Pre-requisites
Before diving into FakeReal, create a sample database named OnlineStore:

```bash
mysql -u root -p < onlineStoreDB.sql
```

### Seed Your Database
Here's a Python script to quickly get you started with FakeReal:

```python
from fakereal import fakereal

# Initialize FakeReal with a connection string
connection_string = 'mysql://root:@localhost:3306/OnlineStore'
FakeReal = fakereal(connection_string)

# Seed 'all' tables with 100 rows each, in batches of 10
FakeReal.seed('all', rows=100, batch_size=10)

# Run test queries and print the result
result = FakeReal.test_queries(return_bool=True, silent=False)
print(result)
```

## Contributing
Contributions are welcome! 

## License
This project is open source.