def prepare(self):
directory = os.path.join(path, 'video', 'image_sequence')
self.v = tp.invert_image(ImageSequence(os.path.join(directory, '*.png')))
# mass depends on pixel dtype, which differs per reader
minmass = self.v[0].max() * 2
self.PARAMS = {'diameter': 11, 'minmass': minmass}
self.expected = tp.batch(self.v[[0, 1]], engine='python', meta=False,
**self.PARAMS)
def prepare(self, batch_params=None):
directory = os.path.join(path, 'video', 'image_sequence')
v = TrackpyImageSequence(os.path.join(directory, '*.png'))
self.v = [tp.invert_image(v[i]) for i in range(2)]
# mass depends on pixel dtype, which differs per reader
minmass = self.v[0].max() * 2
self.PARAMS = {'diameter': 11, 'minmass': minmass}
if batch_params is not None:
self.PARAMS.update(batch_params)
self.expected = tp.batch(self.v,
engine='python',
meta=False,
**self.PARAMS)
import numpy as np
import pims
version = 'VERSION' # adjust this
pos_columns = ['y', 'x']
char_columns = ['mass', 'size', 'ecc', 'signal', 'raw_mass', 'ep']
testpath = os.path.join(os.path.dirname(tp.__file__), 'tests')
impath = os.path.join(testpath, 'video', 'image_sequence', '*.png')
npzpath = os.path.join(testpath, 'data',
'reproducibility_v{}.npz'.format(version))
v = pims.ImageSequence(impath)
# take reader that provides uint8!
assert np.issubdtype(v.dtype, np.uint8)
v0 = tp.invert_image(v[0])
v0_bp = tp.bandpass(v0, lshort=1, llong=9)
expected_find = tp.grey_dilation(v0, separation=9)
expected_find_bandpass = tp.grey_dilation(v0_bp, separation=9)
expected_refine = tp.refine_com(v0,
v0_bp,
radius=4,
coords=expected_find_bandpass)
expected_refine = expected_refine[expected_refine['mass'] >= 140]
expected_refine_coords = expected_refine[pos_columns].values
expected_locate = tp.locate(v0, diameter=9, minmass=140)
expected_locate_coords = expected_locate[pos_columns].values
df = tp.locate(v0, diameter=9)
df = df[(df['x'] < 64) & (df['y'] < 64)]
expected_characterize = df[pos_columns + char_columns].values
import numpy as np
import pims
version = 'VERSION' # adjust this
pos_columns = ['y', 'x']
char_columns = ['mass', 'size', 'ecc', 'signal', 'raw_mass', 'ep']
testpath = os.path.join(os.path.dirname(tp.__file__), 'tests')
impath = os.path.join(testpath, 'video', 'image_sequence', '*.png')
npzpath = os.path.join(testpath, 'data',
'reproducibility_v{}.npz'.format(version))
v = pims.ImageSequence(impath)
# take reader that provides uint8!
assert np.issubdtype(v.dtype, np.uint8)
v0 = tp.invert_image(v[0])
v0_bp = tp.bandpass(v0, lshort=1, llong=9)
expected_find = tp.grey_dilation(v0, separation=9)
expected_find_bandpass = tp.grey_dilation(v0_bp, separation=9)
expected_refine = tp.refine_com(v0, v0_bp, radius=4,
coords=expected_find_bandpass)
expected_refine = expected_refine[expected_refine['mass'] >= 140]
expected_refine_coords = expected_refine[pos_columns].values
expected_locate = tp.locate(v0, diameter=9, minmass=140)
expected_locate_coords = expected_locate[pos_columns].values
df = tp.locate(v0, diameter=9)
df = df[(df['x'] < 64) & (df['y'] < 64)]
expected_characterize = df[pos_columns + char_columns].values
f = tp.batch(tp.invert_image(v), 9, minmass=140)
f_crop = f[(f['x'] < 320) & (f['x'] > 280) & (f['y'] < 280) & (f['x'] > 240)]