vidstab 1.5.2

Video Stabilization using OpenCV

Python Video Stabilization

Python video stabilization using OpenCV.

This module contains a single class (VidStab) used for video stabilization. This class is based on the work presented by Nghia Ho in SIMPLE VIDEO STABILIZATION USING OPENCV. The foundation code was found in a comment on Nghia Ho's post by the commenter with username koala.

Input	Output

Video used with permission from HappyLiving

Installation

+ Please report issues if you install/try to install and run into problems!

Install vidstab without installing OpenCV

If you've already built OpenCV with python bindings on your machine it is recommended to install vidstab without installing the pypi versions of OpenCV. The opencv-python python module can cause issues if you've already built OpenCV from source in your environment.

The below commands will install vidstab without OpenCV included.

From PyPi

pip install vidstab

From Github

pip install git+https://github.com/AdamSpannbauer/python_video_stab.git

Install vidstab & OpenCV

If you don't have OpenCV installed already there are a couple options.

1. You can build OpenCV using one of the great online tutorials from PyImageSearch, LearnOpenCV, or OpenCV themselves. When building from source you have more options (e.g. platform optimization), but more responsibility. Once installed you can use the pip install command shown above.
2. You can install a pre-built distribution of OpenCV from pypi as a dependency for vidstab (see command below)

The below commands will install vidstab with opencv-contrib-python as dependencies.

From PyPi

pip install vidstab[cv2]

From Github

 pip install -e git+https://github.com/AdamSpannbauer/python_video_stab.git#egg=vidstab[cv2]

Usage

The VidStab class can be used as a command line script or in your own custom python code.

Using from command line

# Using defaults
python3 -m vidstab --input input_video.mov --output stable_video.avi

# Using a specific keypoint detector
python3 -m vidstab -i input_video.mov -o stable_video.avi -k GFTT

Using VidStab class

from vidstab import VidStab

# Using defaults
stabilizer = VidStab()
stabilizer.stabilize(input_path='input_video.mov', output_path='stable_video.avi')

# Using a specific keypoint detector
stabilizer = VidStab(kp_method='ORB')
stabilizer.stabilize(input_path='input_video.mp4', output_path='stable_video.avi')

# Using a specific keypoint detector and customizing keypoint parameters
stabilizer = VidStab(kp_method='FAST', threshold=42, nonmaxSuppression=False)
stabilizer.stabilize(input_path='input_video.mov', output_path='stable_video.avi')

Plotting frame to frame transformations

from vidstab import VidStab
import matplotlib.pyplot as plt

stabilizer = VidStab()
stabilizer.stabilize(input_path='input_video.mov', output_path='stable_video.avi')

stabilizer.plot_trajectory()
plt.show()

stabilizer.plot_transforms()
plt.show()

Trajectories	Transforms

Using borders

from vidstab import VidStab

stabilizer = VidStab()

# black borders
stabilizer.stabilize(input_path='input_video.mov', 
                     output_path='stable_video.avi', 
                     border_type='black')
stabilizer.stabilize(input_path='input_video.mov', 
                     output_path='wide_stable_video.avi', 
                     border_type='black', 
                     border_size=100)
stabilizer.stabilize(input_path='input_video.mov', 
                     output_path='wide_stable_video.avi', 
                     border_type='black', 
                     border_size='auto')

# filled in borders
stabilizer.stabilize(input_path='input_video.mov', 
                     output_path='ref_stable_video.avi', 
                     border_type='reflect')
stabilizer.stabilize(input_path='input_video.mov', 
                     output_path='rep_stable_video.avi', 
                     border_type='replicate')

border_size=0	border_size=100
border_size='auto'

border_type='reflect'	border_type='replicate'

Video used with permission from HappyLiving

Using Frame Layering

from vidstab import VidStab, layer_overlay, layer_blend

# init vid stabilizer
stabilizer = VidStab()

# use vidstab.layer_overlay for generating a trail effect
stabilizer.stabilize(input_path=input_vid,
                     output_path='trail_stable_video.avi',
                     border_type='black',
                     border_size=100,
                     layer_func=layer_overlay)


# create custom overlay function
# here we use vidstab.layer_blend with custom alpha
#   layer_blend will generate a fading trail effect with some motion blur
def layer_custom(foreground, background):
    return layer_blend(foreground, background, foreground_alpha=.8)

# use custom overlay function
stabilizer.stabilize(input_path=input_vid,
                     output_path='blend_stable_video.avi',
                     border_type='black',
                     border_size=100,
                     layer_func=layer_custom)

layer_func=vidstab.layer_overlay	layer_func=vidstab.layer_blend

Video used with permission from HappyLiving

Working with live video

The VidStab class can also process live video streams. The underlying video reader is cv2.VideoCapture(documentation). The relevant snippet from the documentation for stabilizing live video is:

Its argument can be either the device index or the name of a video file. Device index is just the number to specify which camera. Normally one camera will be connected (as in my case). So I simply pass 0 (or -1). You can select the second camera by passing 1 and so on.

The input_path argument of the VidStab.stabilize method can accept integers that will be passed directly to cv2.VideoCapture as a device index. You can also pass a device index to the --input argument for command line usage.

One notable difference between live feeds and video files is that webcam footage does not have a definite end point. The options for ending a live video stabilization are to set the max length using the max_frames argument or to manually stop the process by pressing the Esc key or the Q key. If max_frames is not provided then no progress bar can be displayed for live video stabilization processes.

Example

from vidstab import VidStab

stabilizer = VidStab()
stabilizer.stabilize(input_path=0,
                     output_path='stable_webcam.avi',
                     max_frames=1000,
                     playback=True)