Loading results movement detection

Movement detection fuctions return three results: the coords, coordscount and coordsmean, which are numpy-like arrays that can immediately be used for further analyses. However, they directly map to disk-based data folders saved next to the video and can also be loaded in a later session without having to run the detection algoritm again.

myvideo.mp4 (file)
myvideo (folder)
├── coords.darr (folder)
├── coordscount.darr (folder)
├── coordmean.darr (folder)
└── movementvideo.mp4 (file)

To read the coords data in a later session, use the CoordinateArrays class:

>>> import birdwatcher as bw
>>> ca = bw.CoordinateArrays('myvideo/coords.darr/')
>>> ca
RaggedArray (497 subarrays with atom shape (2,), r)
>>> ca.show(framerate=25) # shows the results as a video on screen

The other two disk-based arrays are derived from the coords data and are plain Darr arrays:

>>> import darr as da
>>> cm = da.open('movement_zf20s_low/coordsmean.darr/')
>>> cm[150:160] # get means of frames 150 to 160
array([[517.34666667, 390.72666667],
       [523.04255319, 392.50483559],
       [538.40122511, 386.70444104],
       [559.11492418, 375.74700718],
       [600.23291367, 366.65827338],
       [650.84895833, 372.53559028],
       [683.68726236, 372.55323194],
       [714.62362972, 384.35809988],
       [742.3156708 , 403.63472379],
       [747.93397231, 403.20979766]])

Darr is a separate library that has been installed when you installed Birdwatcher. Darr arrays are disk-based but can be indexed with numpy indexing methods, which return numpy arrays.