def create_arrays(basepath='.'):
basepath = Path(basepath) / 'examplearrays' / 'arrays'
basepath.mkdir(parents=True, exist_ok=True)
metadata = {
"channels": [0, 9],
"comments":
"This example array has metadata, which is stored in a "
"separate JSON file. Metadata in Darr is a dictionary "
"that can contain anything that is JSON serializable.",
"datetime":
f"{datetimestring()}",
"samplingrate":
25000.0
}
# 2D
ar = np.arange(0, 16).reshape(8,
2) # sums to 120, axis 0 sums to 56, 1 to 64
car = np.array(ar, dtype='complex128') + 2.0j
for numtype in numtypesdescr.keys():
if numtype.startswith('complex'):
a = car
else:
a = ar
darr.asarray(basepath / f'array_{numtype}_2D.darr',
a,
dtype=numtype,
metadata=metadata,
overwrite=True)
# 1D
ar = [1, 3, 5, 7, 9, 11, 14] # 0:7 sums to 50
car = np.array(ar, dtype='complex128') + 2.0j
for numtype in numtypesdescr.keys():
if numtype.startswith('complex'):
a = car
elif 'int' in numtype:
imin, imax = minmaxints[numtype]
a = ar[:]
a.extend([imin, imax])
else:
a = ar
darr.asarray(basepath / f'array_{numtype}_1D.darr',
a,
dtype=numtype,
metadata=metadata,
overwrite=True)
def to_darrsnd(self,
path,
accessmode='r',
dtype='float32',
metadata=None,
overwrite=False):
ds = darr.asarray(array=self.frames,
dtype=dtype,
accessmode=accessmode,
metadata=metadata,
overwrite=overwrite)
ds.metadata['fs'] = self.fs
return DarrSnd(path=path, accessmode=accessmode)
def to_darrsnd(self,
path=None,
dtype=None,
metadata=None,
mode='r',
startframe=None,
endframe=None,
starttime=None,
endtime=None,
startdatetime=None,
enddatetime=None,
blocklen=44100,
accessmode='r',
overwrite=False,
channelindex=None):
from .darrsnd import DarrSnd
dd = create_datadir(path, overwrite=overwrite)
framespath = dd.path / DarrSnd._framespath
snds = self.iterread(startframe=startframe,
endframe=endframe,
channelindex=channelindex,
blocklen=blocklen)
snd1, snds = peek_iterable(snds)
startdatetime = str(snd1.startdatetime)
origintime = float(snd1.origintime)
frames = (snd.read_frames() for snd in snds)
da = asarray(path=framespath,
array=frames,
dtype=dtype,
accessmode=accessmode,
overwrite=overwrite)
duration = da.shape[0] / self.fs
sndinfo = {
'dtype': da.dtype.name,
'fs': self.fs,
'duration': duration,
'startdatetime': startdatetime,
'origintime': origintime,
'nchannels': da.shape[1],
'nframes': da.shape[0],
'soundversion': get_versions()['version'],
'unit': self.unit,
}
dd.write_sndinfo(sndinfo, overwrite=overwrite)
if (metadata is not None) and (not overwrite):
dd.update(metadata)
da.accessmode = accessmode
return DarrSnd(path=path, accessmode=accessmode)
def detect_movement(videofilestream,
bgs,
morphologyex=2,
color=False,
roi=None,
nroi=None,
analysispath='.',
ignore_firstnframes=10,
overwrite=False,
resultvideo=False):
"""Detects movement based on a background subtraction algorithm.
The backgound subtractor should be provided as a parameter.
Parameters
----------
videofilestream : VideoFileStream
A Birdwatcher VideoFileStream object
bgs : BaseBackgroundSubtractor
Can be any instance of child from BaseBackgroundSubtractor.
Currently included in Birdwatcher are BackgroundSubtractorMOG2,
BackgroundSubtractorKNN, BackgroundSubtractorLSBP.
morphologyex : int, default=2
Kernel size of MorphologeEx open processing.
color : bool, default=False
Should detection be done on color frames (True) or on gray frames
(False).
roi : (int, int, int, int), optional
Region of interest. Only look at this rectangular region. h1,
h2, w1, w2.
nroi : (int, int, int, int), optional
Not region of interest. Exclude this rectangular region. h1,
h2, w1, w2.
analysispath : Path or str, optional
Where to write results to. The default writes to the current working
directory.
ignore_firstnframes : int, default=10
Do not provide coordinates for the first n frames. These often have
a lot of false positives.
overwrite : bool, default=False
Overwrite results or not.
resultvideo : bool, default=False
Automatically generate a video with results, yes or no.
Returns
-------
tuple of arrays (coordinates, coordinate count, coordinate mean)
These are Darr arrays that are disk-based.
"""
if isinstance(videofilestream, VideoFileStream):
vfs = videofilestream
else:
raise TypeError(f"`videofilestream` parameter not a VideoFileStream "
f"object ({type(videofilestream)}).")
Path(analysispath).mkdir(parents=True, exist_ok=True)
movementpath = Path(analysispath) / f'{vfs.filepath.stem}_movement'
Path(movementpath).mkdir(parents=True, exist_ok=True)
metadata = {}
metadata['backgroundsegmentclass'] = str(bgs)
metadata['backgroundsegmentparams'] = bgs.get_params()
metadata['morphologyex'] = morphologyex
metadata['roi'] = roi
metadata['nroi'] = nroi
metadata['birdwatcherversion'] = get_versions()['version']
frames = (vfs.iter_frames(color=color).apply_backgroundsegmenter(
bgs, roi=roi, nroi=nroi))
if morphologyex is not None:
frames = frames.morphologyex(kernelsize=morphologyex)
cd = create_coordarray(movementpath / 'coords.darr',
framewidth=vfs.framewidth,
frameheight=vfs.frameheight,
metadata=metadata,
overwrite=overwrite)
empty = np.zeros((0, 2), dtype=np.uint16)
coords = (c if i >= ignore_firstnframes else empty
for i, c in enumerate(frames.find_nonzero()))
cd.iterappend(coords)
cc = darr.asarray(movementpath / 'coordscount.darr',
cd.get_coordcount(),
metadata=metadata,
overwrite=True)
cm = darr.asarray(movementpath / 'coordsmean.darr',
cd.get_coordmean(),
metadata=metadata,
overwrite=True)
if resultvideo:
ovfilepath = Path(movementpath) / f'{ vfs.filepath.stem}_movement.mp4'
cframes = cd.iter_frames(nchannels=3, value=(0, 0, 255))
(vfs.iter_frames().add_weighted(0.7, cframes,
0.8).draw_framenumbers().tovideo(
ovfilepath,
framerate=vfs.avgframerate,
crf=25))
return cd, cc, cm