semvec/pypi-packages huggingface dataset:
fiona-settings 0.1.0
Wrappers for fiona settings.
License: MIT
Requires Python: >=3.6.0


## fiona-settings

Lightweight wrapper for `fiona` settings & schemas to make it a little easier to manipulate.
Fiona includes a lot of "magic strings" representings GDAL drivers, CRS values, data types, and more. 
While it's all quite intuitive and relatively simple, I have always wished there
was a neater way to manage them all, and so I wrote this.


## install

Requires Python3.6+.

with pip:

```bash
pip install fiona-settings
```

from source:

```bash
git clone git@github.com:tomplex/fiona_settings.git
pip install fiona_settings/
```

## examples

```python
import fiona
from fiona_settings import Settings, Type, Driver, CRS

src = fiona.open('my_file.shp')
# from_collection copies the following settings from an already opened collection:
# - driver
# - schema
# - crs
# - encoding
settings = Settings.from_collection(src)
# add a string column with width 25
settings += ('my_column', Type.str(width=25))
settings += ('my_other_column', 'float')  # don't need to use Enum types
# Remove a column by name
settings -= 'column_i_dont_want'

# unpack the settings directly into the fiona.open() function
sink = fiona.open('my_output_file.shp', 'w', **settings)

# Override any option which would be copied with a kwarg. Either
# string or enum values will work.
new_settings = Settings.from_collection(src, driver=Driver.GeoJSON)
sink = fiona.open('my_file.geojson', 'w', **new_settings)
```
pyvision3 0.1.8
Facilitates computer vision research and prototyping using python and openCV
License: MIT
Requires Python: >=3.6
Keywords: images video vision


A python 3 computer vision library that complements OpenCV 3.x to add many useful features for developers
and researchers alike. Pyvision provides utilities that help in these core areas: training data preparation,
displaying results, and applying vision algorithms to video streams.

Training data preparation:

* Apply affine transformations
* Label points and polygons in images
* Generate defined or random crops in images
* Select good vs. bad tiles in a montage display

Displaying results:

* Easy and powerful image annotations of shapes, lines, text, and overlay masks
* Compatible with shapely -- annotate shapely polygons to images
* Annotations are kept on a separate layer from the underlying image
* Display the image and save images with or without annotations
* Show results in an "image montage" -- multiple images or tiles shown in a grid
* Use image montage to select results (or to provide manual QA on the results)
* Play back sequences of images like a video, with pause-and-play interface

Video processing:

* Pyvision Video class makes it easy to view and process common video files (avi, mpg, etc.), directories of images,
  or live streams from USB or IP cameras.
* Video objects are iterators. ```for frame in vid: ...```
* Create videos of your results, save a sequence of results as an avi file, for example. Simple and pythonic!
* Apply background subtraction and motion detection to your video streams. Several common algorithms included
  or invent your own.
* Video interface provides ability to buffer the video, to pause playback, to step through frame-by-frame,
  to resume playing with a certain per-frame delay, etc.
* Video montage allows you to display multiple videos side-by-side in lockstep! And save the result
  as another video!
* Register a call-back with a video object to allow you to process each frame with your own encapsulated code.
* Create an image buffer from images or video
* Treat image buffer as a 3D array of grayscale images, or display as a montage

Pyvision3 is intended to be a successor to Pyvision that takes advantage of OpenCV 3.x and Python 3.x features.
Huge thanks to David Bolme for being the originator of Pyvision. Many of the ease-of-use and interface ideas from
the original Pyvision are carried forward, albeit with new implementations for Pyvision3.