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add test for recipe localization modules coordinate conventions #1663

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50 changes: 50 additions & 0 deletions tests/PYME/recipes/test_measurement.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,56 @@ def test_ChunkedTravelingSalesman():
ordered = recipe.execute()
assert ordered.mdh['TravelingSalesperson.Distance'] < ordered.mdh['TravelingSalesperson.OriginalDistance']

def test_DensityMapping_DetectPoints2D_and_FitPoints():
from PYME.recipes.measurement import DetectPoints2D, FitPoints
from PYME.recipes.localisations import DensityMapping
from PYME.IO.tabular import DictSource
from PYME.IO.MetaDataHandler import DictMDHandler
from PYME.recipes.tablefilters import Mapping
# generate gt points
n = 15
voxelsize_nm = 100
im_size = 256
# generate points, avoiding edges
x_px_gt = np.linspace(20, im_size - 20, n).astype('f')
y_px_gt = np.linspace(20, im_size - 20, n).astype('f')

res = DictSource({'x': x_px_gt *voxelsize_nm, 'y': y_px_gt * voxelsize_nm,
'z': np.zeros_like(x_px_gt), 'tIndex': np.zeros_like(x_px_gt, dtype=int)})
res.mdh = DictMDHandler()
res.mdh['Camera.ROIWidth'] = im_size
res.mdh['Camera.ROIHeight'] = im_size
res.mdh['voxelsize.x'] = voxelsize_nm / 1e3 # nm -> um
res.mdh['voxelsize.y'] = voxelsize_nm / 1e3 # nm -> um
res.mdh['Camera.ReadNoise'] = 1.0 # e-
res.mdh['Camera.ElectronsPerCount'] = 1.0
res.mdh['Camera.NoiseFactor'] = 1.0
res.mdh['Camera.TrueEMGain'] = 1.0
res.mdh['Camera.ADOffset'] = 0

rendered = DensityMapping(renderingModule='Gaussian', xyBoundsMode='metadata',
pixelSize=voxelsize_nm).apply_simple(res)
detected = DetectPoints2D(threshold=0.001).apply_simple(rendered)
assert len(detected) == n

# Note that DetectPoints2D returns [pixels] rather than [nm]
detected_nm = Mapping(mappings={
'voxelsize_nmx': '%f' % voxelsize_nm,
'voxelsize_nmy': '%f' % voxelsize_nm,
'x': 'x * voxelsize_nmx', 'y': 'y * voxelsize_nmy'
}).apply_simple(detected)

# now fit the points and check they are close to gt
fitted = FitPoints(fitModule='LatGaussFitFR',
roiHalfSize=5).apply_simple(inputImage=rendered, inputPositions=detected_nm)

assert len(fitted) == n
# --> systematic 0.5 pixel shift
x_expected = (x_px_gt - 0.5) * voxelsize_nm
y_expected = (y_px_gt - 0.5) * voxelsize_nm
assert np.allclose(fitted['x'], x_expected, atol=0.1 * voxelsize_nm)
assert np.allclose(fitted['y'], y_expected, atol=0.1 * voxelsize_nm)


if __name__ == '__main__':
import time
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