semvec/pypi-packages huggingface dataset:
mtcnn-opencv 1.0.2
MTCNN face detection using OpenCV
Requires Python: >=3.6

MTCNN-OpenCV
============
MTCNN Face Detector using OpenCV, no reqiurement for tensorflow/pytorch.

# INSTALLATION
   * `pip3 install opencv-python` or `pip3 install opencv-python-headless`
   * `pip3 install mtcnn-opencv`

# USAGE
```python
import cv2
from mtcnn_cv2 import MTCNN

detector = MTCNN()
test_pic = "t.jpg"

image = cv2.cvtColor(cv2.imread(test_pic), cv2.COLOR_BGR2RGB)
result = detector.detect_faces(image)

# Result is an array with all the bounding boxes detected. Show the first.
print(result)

if len(result) > 0:
    keypoints = result[0]['keypoints']

    cv2.rectangle(image,
                  (bounding_box[0], bounding_box[1]),
                  (bounding_box[0]+bounding_box[2], bounding_box[1] + bounding_box[3]),
                  (0,155,255),
                  2)

    cv2.circle(image,(keypoints['left_eye']), 2, (0,155,255), 2)
    cv2.circle(image,(keypoints['right_eye']), 2, (0,155,255), 2)
    cv2.circle(image,(keypoints['nose']), 2, (0,155,255), 2)
    cv2.circle(image,(keypoints['mouth_left']), 2, (0,155,255), 2)
    cv2.circle(image,(keypoints['mouth_right']), 2, (0,155,255), 2)

    cv2.imwrite("result.jpg", cv2.cvtColor(image, cv2.COLOR_RGB2BGR))

# 生成标记了的人脸的图片
with open(test_pic, "rb") as fp:
    marked_data = detector.mark_faces(fp.read())
with open("marked.jpg", "wb") as fp:
    fp.write(marked_data)
```
rwmutex 1.1
Provides a read/write mutex lock usable with context managers.
Requires Python: >=3.6

# py-rwmutex

This package provides a simple read/write mutex lock for threads, based upon
the `threading` package. It supports Python's context manager interface, so
it may be used within `with` statements.

## Purpose

This read/write lock can improve performance by allowing multiple threads to
reaad from a shared resource at once. For safety, only one thread is granted
a write lock at a time, and only when no threads have a read lock.

## Installation

This package is available from [PyPi](https://pypi.org/project/rwmutex/), which
means it can be easily acquired via [`pip`](https://pypi.org/project/pip/).

Run this command at a shell prompt:

```bash
pip3 install rwmutex
```

## Usage

To get started, import the package like so:

```py
from rwmutex import RWLock
```

Then you can declare a lock object:

```py
lock = RWLock()
```

To use the lock, you can use `with` blocks:

```py
with lock.write
    # some operation that writes
    pass

with lock.read:
    # some operation that just needs to read
    pass
```

For a working example, see [example.py](example.py).
bcikit 0.0.1.dev1
A toolbox for data manipulation and transformation for EEG and machine learning.
Requires Python: >=3.3

# Brain-Computer Interface Toolkit

The toolkit consists of datasets, model architectures, and common signal processing functions for BCI before applying on PyTorch.

## Installation

```
git clone https://github.com/ntubci/bcikit.git
pip install -r requirements.txt
```

## Contributing

If you plan to contribute new dataset, features, utility functions, or extensions to the core, please first open an [issue](https://github.com/ntubci/bcikit/issues) and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR because we might be taking the core in a different direction than you might be aware of.

To learn more about making a contribution to Pytorch, please see our [Contribution](https://github.com/ntubci/bcikit/blob/main/CONTRIBUTING.md) page.

## Communication

- GitHub Issues: If you want to report a bug, feature requests, install issues, thoughts, or suggest an enhancement, we'd love for you to open an [issue](https://github.com/ntubci/bcikit/issues) at this github repository

## The Team

## License

BCI Toolkit is licensed under the Apache License 2.0, as found in the [LICENSE](https://github.com/ntubci/bcikit/blob/main/LICENSE) file.
froxpy 1.0.1
A library to manage and test proxy lists in python

# froxpy
A python library to manage and test proxy lists

# Documentation

### froxpy.gen_proxy_dict(proxies)
Returns a dictionary object with the format the 'requests' library from python needs to use proxies in requests.

### froxpy.test_proxies(proxies, timeout, output)
Return a tuple with three lists:
  - 0: is the list with working proxies
  - 1: is the list with bad proxies (caused by an error)
  - 2: is the list with timeout proxies (cause by the timeout)
  
Defaults:
  - 'proxies': is a empty list as default
  - 'timeout': is set to 15 seconds as default
  - 'output': is set to False as default