vidgear: A Python repository from eideo

VidGear is a Python library for Turbo Multi-Threaded Video Processing

VidGear is a high-level All-in-One Video Processing framework build on top of OpenCV, PiCamera and FFmpeg. It's simple to use and easy to extend.

It contains following Powerful Gears (Multi-Threaded Video Processing classes) to handle/control different device-specific video streams and writer:

Gear	Function
CamGear	Targets any IP-Camera/USB-Camera/Network-Stream/YouTube-Video
PiGear	Targets any Raspberry Pi Camera Modules
VideoGear	Common Gear to access any of the video capture gear
WriteGear	enables Lossless Video Writer with flexible Video Compression capabilities

It comes with various in-built features like Flexible Control over Video Source properties like resolution, framerate, colorspace, etc. manipulation and supports easy direct Network stream pipelining (support for GStreamer, YouTube and other network streams like http(s), rtp, rstp, mms, etc.).
This library is also very well compatible with the Raspberry-Pi Camera modules (RPiCam) and provides us the ability to exploit its various features like brightness, saturation, sensor_mode, etc. easily.
Furthermore, VidGear utilizes FFmpeg's powerful encoders to encode and reduce the output to a smaller size, without sacrificing the video quality. It provide us full-control over FFmpeg output parameters.

Key Features

What makes VidGear stand out from all other Video Processing python tools?

Documentation and Usage

You can checkout VidGear WIKI-SECTION for in-depth documentation with examples for each VidGear Class

Basic example:

The bare minimum basic example of WriteGear(Compression Mode) with VideoGear class for a common Live WebCamera stream is as follows :

from vidgear.gears import VideoGear
from vidgear.gears import WriteGear
import cv2

stream = VideoGear(source=0).start() #Open live webcam video stream on first index(i.e. 0) device

writer = WriteGear(output_filename = 'Output.mp4') #Define writer with output filename 'Output.mp4'

# infinite loop
while True:
	
	frame = stream.read()
	# read frames

	# check if frame is None
	if frame is None:
		#if True break the infinite loop
		break
	

	# do something with frame here

	# write frame to writer
        writer.write(frame) 
       
        # Show output window
	cv2.imshow("Output Frame", frame)

	key = cv2.waitKey(1) & 0xFF
	# check for 'q' key-press
	if key == ord("q"):
		#if 'q' key-pressed break out
		break

cv2.destroyAllWindows()
# close output window

stream.stop()
# safely close video stream
writer.close()
# safely close writer

Prerequisites

Note: Vidgear automatically handles all required prerequisites(except FFmpeg) installation according to your system requirements

Critical:

OpenCV(with contrib): VidGear must require OpenCV with contrib (3.0+) python library to be installed on your machine which is critical for its core algorithm functioning. You can build it from from scratch (Raspberry Pi) or Vidgear automatically installs OpenCV with contrib python library for you based on your system from PyPi.
FFmpeg: VidGear requires FFmpeg installation for Compression capabilities. Follow this WIKI Page for latest FFmpeg installation. ⚠️

Additional:

PiCamera: If you are using Raspberry Pi Camera Modules such as OmniVision OV5647 Camera Module and Sony IMX219 Camera Module. It requires additional Picamera library installation on your Raspberry Pi machine prior to its installation (Latest versions recommended).

Also, make sure to enable Raspberry Pi hardware specific settings prior using this library.

pafy: For direct YouTube Video Pipelining into OpenCV, Vidgear requires Pafy python library.

Installation

From PyPI(Stable Only): VidGear can be easily installed as follows(available on Python Package Index (PyPI)):
```
$ pip install vidgear
```

Contribution and Development

You are welcome to contribute with suggestions, feature requests and pull requests.

Clone this repository(development): You can clone this repository for latest releases (maybe experimental) for development purposes and thereby can install as follows:

$ git clone https://github.com/abhiTronix/vidgear.git
$ cd vidgear
$ pip install .

Supported Python versions

Python 2.7 is the only supported version in 2.x series. Python 2.7 support will be dropped in the end of 2019.
Python 3.x releases follow OpenCV releases.

Author

Abhishek Thakur @abhiTronix

License

This project is under the MIT License. See the LICENSE file for the full license text.

eideo/vidgear