def test_match_coordinates_shuffled_40x40(self):
"""
Test MatchCoordinates for shuffled optical coordinates, comparing the order of the shuffled optical list and the estimated coordinates
generated by MatchCoordinates
"""
electron_coordinates = self.electron_coordinates_10x10
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
shuffled_coordinates = coordinates._TransformCoordinates(
electron_coordinates, (translation_x, translation_y), rotation,
(scale_x, scale_y))
shuffle(shuffled_coordinates)
known_estimated_coordinates, known_optical_coordinates = coordinates.MatchCoordinates(
shuffled_coordinates, electron_coordinates, 0.25, 0.25)
if known_estimated_coordinates != []:
(calc_translation_x, calc_translation_y), (
calc_scaling_x,
calc_scaling_y), calc_rotation = transform.CalculateTransform(
known_optical_coordinates, known_estimated_coordinates)
numpy.testing.assert_almost_equal(
(calc_translation_x, calc_translation_y, calc_scaling_x,
calc_scaling_y, calc_rotation),
(translation_x, translation_y, scale_x, scale_y, rotation), 1)
def test_match_coordinates_shuffled__distorted_10x10(self):
"""
Test MatchCoordinates for shuffled and distorted optical coordinates, comparing the order of the shuffled optical list and the estimated coordinates
generated by MatchCoordinates
"""
electron_coordinates = self.electron_coordinates_10x10
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
shuffled_coordinates = coordinates._TransformCoordinates(
electron_coordinates, (translation_x, translation_y), rotation,
(scale_x, scale_y))
shuffle(shuffled_coordinates)
distorted_coordinates = []
# Add noise to the coordinates
for i in xrange(shuffled_coordinates.__len__()):
distortion = tuple((uniform(-0.1, 0.1), uniform(-0.1, 0.1)))
distorted_coordinates.append(
tuple(map(operator.add, shuffled_coordinates[i], distortion)))
known_estimated_coordinates, known_optical_coordinates = coordinates.MatchCoordinates(
distorted_coordinates, electron_coordinates, 0.25, 0.25)
if known_estimated_coordinates != []:
(calc_translation_x, calc_translation_y), (
calc_scaling_x,
calc_scaling_y), calc_rotation = transform.CalculateTransform(
shuffled_coordinates, known_estimated_coordinates)
numpy.testing.assert_almost_equal(
(calc_translation_x, calc_translation_y, calc_scaling_x,
calc_scaling_y, calc_rotation),
(translation_x, translation_y, scale_x, scale_y, rotation), 1)
def test_precomputed_transformation_40x40(self):
"""
Test MatchCoordinates for applied transformation
"""
electron_coordinates = self.electron_coordinates_40x40
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
transformed_coordinates = coordinates._TransformCoordinates(electron_coordinates, (translation_x, translation_y), rotation, (scale_x, scale_y))
known_estimated_coordinates, known_optical_coordinates, max_dist = coordinates.MatchCoordinates(transformed_coordinates, electron_coordinates, 0.25, 0.25)
(calc_translation_x, calc_translation_y), (calc_scaling_x, calc_scaling_y), calc_rotation = transform.CalculateTransform(known_optical_coordinates, known_estimated_coordinates)
numpy.testing.assert_almost_equal((calc_translation_x, calc_translation_y, calc_scaling_x, calc_scaling_y, calc_rotation), (translation_x, translation_y, scale_x, scale_y, rotation), 0)
def test_precomputed_output_missing_point_40x40(self):
"""
Test MatchCoordinates if NaN is returned in the corresponding position in case of missing point
"""
electron_coordinates = self.electron_coordinates_40x40
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
transformed_coordinates = coordinates._TransformCoordinates(electron_coordinates, (translation_x, translation_y), rotation, (scale_x, scale_y))
rand = random.randint(0, len(transformed_coordinates) - 1)
del transformed_coordinates[rand]
known_estimated_coordinates, known_optical_coordinates, max_dist = coordinates.MatchCoordinates(transformed_coordinates, electron_coordinates, 0.25, 0.25)
self.assertEqual(len(known_estimated_coordinates), len(electron_coordinates) - 1)
def test_shuffled_40x40(self):
"""
Test MatchCoordinates for shuffled optical coordinates, comparing the order of the shuffled optical list and the estimated coordinates
generated by MatchCoordinates
"""
electron_coordinates = self.electron_coordinates_40x40
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
shuffled_coordinates = coordinates._TransformCoordinates(electron_coordinates, (translation_x, translation_y), rotation, (scale_x, scale_y))
shuffle(shuffled_coordinates)
known_estimated_coordinates, known_optical_coordinates, max_dist = coordinates.MatchCoordinates(shuffled_coordinates, electron_coordinates, 0.25, 0.25)
(calc_translation_x, calc_translation_y), (calc_scaling_x, calc_scaling_y), calc_rotation = transform.CalculateTransform(known_optical_coordinates, known_estimated_coordinates)
numpy.testing.assert_almost_equal((calc_translation_x, calc_translation_y, calc_scaling_x, calc_scaling_y, calc_rotation), (translation_x, translation_y, scale_x, scale_y, rotation), 1)
def test_match_coordinates_precomputed_output_missing_point_3x3(self):
"""
Test MatchCoordinates if NaN is returned in the corresponding position in case of missing point
"""
electron_coordinates = self.electron_coordinates_3x3
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
transformed_coordinates = coordinates._TransformCoordinates(electron_coordinates, (translation_x, translation_y), rotation, (scale_x, scale_y))
rand = random.randint(0, len(transformed_coordinates) - 1)
del transformed_coordinates[rand]
known_estimated_coordinates, known_optical_coordinates = coordinates.MatchCoordinates(transformed_coordinates, electron_coordinates, 0.25, 0.25)
if known_estimated_coordinates != []:
numpy.testing.assert_equal(known_estimated_coordinates.__len__(), electron_coordinates.__len__() - 1)
def test_precomputed_output_missing_point_40x40(self):
"""
Test MatchCoordinates if NaN is returned in the corresponding position in case of missing point
"""
electron_coordinates = self.electron_coordinates_40x40
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
transformed_coordinates = coordinates._TransformCoordinates(
electron_coordinates, (translation_x, translation_y), rotation,
(scale_x, scale_y))
rand = random.randint(0, len(transformed_coordinates) - 1)
del transformed_coordinates[rand]
known_estimated_coordinates, known_optical_coordinates, max_dist = coordinates.MatchCoordinates(
transformed_coordinates, electron_coordinates, 0.25, 0.25)
self.assertEqual(len(known_estimated_coordinates),
len(electron_coordinates) - 1)
def test_match_coordinates_precomputed_output_missing_point_3x3(self):
"""
Test MatchCoordinates if NaN is returned in the corresponding position in case of missing point
"""
electron_coordinates = self.electron_coordinates_3x3
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
transformed_coordinates = coordinates._TransformCoordinates(
electron_coordinates, (translation_x, translation_y), rotation,
(scale_x, scale_y))
rand = random.randint(0, transformed_coordinates.__len__() - 1)
del transformed_coordinates[rand]
known_estimated_coordinates, known_optical_coordinates = coordinates.MatchCoordinates(
transformed_coordinates, electron_coordinates, 0.25, 0.25)
if known_estimated_coordinates != []:
numpy.testing.assert_equal(known_estimated_coordinates.__len__(),
electron_coordinates.__len__() - 1)
def test_shuffled__distorted_10x10(self):
"""
Test MatchCoordinates for shuffled and distorted optical coordinates, comparing the order of the shuffled optical list and the estimated coordinates
generated by MatchCoordinates
"""
electron_coordinates = self.electron_coordinates_10x10
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
shuffled_coordinates = coordinates._TransformCoordinates(electron_coordinates, (translation_x, translation_y), rotation, (scale_x, scale_y))
shuffle(shuffled_coordinates)
distorted_coordinates = []
# Add noise to the coordinates
for i in xrange(len(shuffled_coordinates)):
distortion = tuple((uniform(-0.1, 0.1), uniform(-0.1, 0.1)))
distorted_coordinates.append(tuple(map(operator.add, shuffled_coordinates[i], distortion)))
known_estimated_coordinates, known_optical_coordinates, max_dist = coordinates.MatchCoordinates(distorted_coordinates, electron_coordinates, 0.25, 0.25)
(calc_translation_x, calc_translation_y), (calc_scaling_x, calc_scaling_y), calc_rotation = transform.CalculateTransform(shuffled_coordinates, known_estimated_coordinates)
numpy.testing.assert_almost_equal((calc_translation_x, calc_translation_y, calc_scaling_x, calc_scaling_y, calc_rotation), (translation_x, translation_y, scale_x, scale_y, rotation), 1)
def test_precomputed_transformation_40x40(self):
"""
Test MatchCoordinates for applied transformation
"""
electron_coordinates = self.electron_coordinates_40x40
translation_x, translation_y = self.translation_x, self.translation_y
scale_x, scale_y = self.scale_x, self.scale_y
rotation = self.rotation
transformed_coordinates = coordinates._TransformCoordinates(
electron_coordinates, (translation_x, translation_y), rotation,
(scale_x, scale_y))
known_estimated_coordinates, known_optical_coordinates, max_dist = coordinates.MatchCoordinates(
transformed_coordinates, electron_coordinates, 0.25, 0.25)
(calc_translation_x, calc_translation_y), (
calc_scaling_x,
calc_scaling_y), calc_rotation = transform.CalculateTransform(
known_optical_coordinates, known_estimated_coordinates)
numpy.testing.assert_almost_equal(
(calc_translation_x, calc_translation_y, calc_scaling_x,
calc_scaling_y, calc_rotation),
(translation_x, translation_y, scale_x, scale_y, rotation), 0)
def main(args):
"""
Handles the command line arguments
args is the list of arguments passed
return (int): value to return to the OS as program exit code
"""
# arguments handling
parser = argparse.ArgumentParser(description=
"Automated AR acquisition at multiple spot locations")
parser.add_argument("--repetitions_x", "-x", dest="repetitions_x", required=True,
help="repetitions defines the number of CL spots in the grid (x dimension)")
parser.add_argument("--repetitions_y", "-y", dest="repetitions_y", required=True,
help="repetitions defines the number of CL spots in the grid (y dimension)")
parser.add_argument("--dwell_time", "-t", dest="dwell_time", required=True,
help="dwell_time indicates the time to scan each spot (unit: s)")
parser.add_argument("--max_allowed_diff", "-d", dest="max_allowed_diff", required=True,
help="max_allowed_diff indicates the maximum allowed difference in electron coordinates (unit: m)")
options = parser.parse_args(args[1:])
repetitions = (int(options.repetitions_x), int(options.repetitions_y))
dwell_time = float(options.dwell_time)
max_allowed_diff = float(options.max_allowed_diff)
try:
escan = None
detector = None
ccd = None
# find components by their role
for c in model.getComponents():
if c.role == "e-beam":
escan = c
elif c.role == "se-detector":
detector = c
elif c.role == "ccd":
ccd = c
if not all([escan, detector, ccd]):
logging.error("Failed to find all the components")
raise KeyError("Not all components found")
# ccd.data.get()
future_scan = images.ScanGrid(repetitions, dwell_time, escan, ccd, detector)
# Wait for ScanGrid to finish
optical_image, electron_coordinates, electron_scale = future_scan.result()
hdf5.export("scanned_image.h5", optical_image)
logging.debug("electron coord = %s", electron_coordinates)
############## TO BE REMOVED ON TESTING##############
# grid_data = hdf5.read_data("scanned_image.h5")
# C, T, Z, Y, X = grid_data[0].shape
# grid_data[0].shape = Y, X
# optical_image = grid_data[0]
#####################################################
logging.debug("Isolating spots...")
subimages, subimage_coordinates = coordinates.DivideInNeighborhoods(optical_image, repetitions, optical_scale)
logging.debug("Number of spots found: %d", len(subimages))
hdf5.export("spot_found.h5", subimages,thumbnail=None)
logging.debug("Finding spot centers...")
spot_coordinates = coordinates.FindCenterCoordinates(subimages)
logging.debug("center coord = %s", spot_coordinates)
optical_coordinates = coordinates.ReconstructCoordinates(subimage_coordinates, spot_coordinates)
logging.debug(optical_coordinates)
rgb_optical = img.DataArray2RGB(optical_image)
for ta in optical_coordinates:
rgb_optical[ta[1] - 1:ta[1] + 1, ta[0] - 1:ta[0] + 1, 0] = 255
rgb_optical[ta[1] - 1:ta[1] + 1, ta[0] - 1:ta[0] + 1, 1] *= 0.5
rgb_optical[ta[1] - 1:ta[1] + 1, ta[0] - 1:ta[0] + 1, 2] *= 0.5
misc.imsave('spots_image.png', rgb_optical)
# TODO: Make function for scale calculation
sorted_coordinates = sorted(optical_coordinates, key=lambda tup: tup[1])
tab = tuple(map(operator.sub, sorted_coordinates[0], sorted_coordinates[1]))
optical_scale = math.hypot(tab[0], tab[1])
scale = electron_scale[0] / optical_scale
print scale
# max_allowed_diff in pixels
max_allowed_diff_px = max_allowed_diff / escan.pixelSize.value[0]
logging.debug("Matching coordinates...")
known_electron_coordinates, known_optical_coordinates = coordinates.MatchCoordinates(optical_coordinates, electron_coordinates, scale, max_allowed_diff_px)
logging.debug("Calculating transformation...")
(calc_translation_x, calc_translation_y), (calc_scaling_x, calc_scaling_y), calc_rotation = transform.CalculateTransform(known_electron_coordinates, known_optical_coordinates)
logging.debug("Electron->Optical: ")
print calc_translation_x, calc_translation_y, calc_scaling_x, calc_scaling_y, calc_rotation
final_electron = coordinates._TransformCoordinates(known_optical_coordinates, (calc_translation_x, calc_translation_y), calc_rotation, (calc_scaling_x, calc_scaling_y))
logging.debug("Overlay done.")
# Calculate distance between the expected and found electron coordinates
coord_diff = []
for ta, tb in zip(final_electron, known_electron_coordinates):
tab = tuple(map(operator.sub, ta, tb))
coord_diff.append(math.hypot(tab[0], tab[1]))
mean_difference = numpy.mean(coord_diff) * escan.pixelSize.value[0]
variance_sum = 0
for i in range(0, len(coord_diff)):
variance_sum += (mean_difference - coord_diff[i]) ** 2
variance = (variance_sum / len(coord_diff)) * escan.pixelSize.value[0]
not_found_spots = len(electron_coordinates) - len(final_electron)
# Generate overlay image
logging.debug("Generating images...")
(calc_translation_x, calc_translation_y), (calc_scaling_x, calc_scaling_y), calc_rotation = transform.CalculateTransform(known_optical_coordinates, known_electron_coordinates)
logging.debug("Optical->Electron: ")
print calc_translation_x, calc_translation_y, calc_scaling_x, calc_scaling_y, calc_rotation
overlay_coordinates = coordinates._TransformCoordinates(known_electron_coordinates, (calc_translation_y, calc_translation_x), -calc_rotation, (calc_scaling_x, calc_scaling_y))
for ta in overlay_coordinates:
rgb_optical[ta[0] - 1:ta[0] + 1, ta[1] - 1:ta[1] + 1, 1] = 255
misc.imsave('overlay_image.png', rgb_optical)
misc.imsave('optical_image.png', optical_image)
logging.debug("Done. Check electron_image.png, optical_image.png and overlay_image.png.")
except:
logging.exception("Unexpected error while performing action.")
return 127
logging.info("\n**Overlay precision stats (Resulted to expected electron coordinates comparison)**\n Mean distance: %f (unit: m)\n Variance: %f (unit: m)\n Not found spots: %d", mean_difference, variance, not_found_spots)
return 0
def main(args):
"""
Handles the command line arguments
args is the list of arguments passed
return (int): value to return to the OS as program exit code
"""
# arguments handling
parser = argparse.ArgumentParser(
description="Automated AR acquisition at multiple spot locations")
parser.add_argument(
"--repetitions_x",
"-x",
dest="repetitions_x",
required=True,
help=
"repetitions defines the number of CL spots in the grid (x dimension)")
parser.add_argument(
"--repetitions_y",
"-y",
dest="repetitions_y",
required=True,
help=
"repetitions defines the number of CL spots in the grid (y dimension)")
parser.add_argument(
"--dwell_time",
"-t",
dest="dwell_time",
required=True,
help="dwell_time indicates the time to scan each spot (unit: s)")
parser.add_argument(
"--max_allowed_diff",
"-d",
dest="max_allowed_diff",
required=True,
help=
"max_allowed_diff indicates the maximum allowed difference in electron coordinates (unit: m)"
)
options = parser.parse_args(args[1:])
repetitions = (int(options.repetitions_x), int(options.repetitions_y))
dwell_time = float(options.dwell_time)
max_allowed_diff = float(options.max_allowed_diff)
try:
escan = None
detector = None
ccd = None
# find components by their role
for c in model.getComponents():
if c.role == "e-beam":
escan = c
elif c.role == "se-detector":
detector = c
elif c.role == "ccd":
ccd = c
if not all([escan, detector, ccd]):
logging.error("Failed to find all the components")
raise KeyError("Not all components found")
# ccd.data.get()
gscanner = GridScanner(repetitions, dwell_time, escan, ccd, detector)
# Wait for ScanGrid to finish
optical_image, electron_coordinates, electron_scale = gscanner.DoAcquisition(
)
hdf5.export("scanned_image.h5", optical_image)
logging.debug("electron coord = %s", electron_coordinates)
############## TO BE REMOVED ON TESTING##############
# grid_data = hdf5.read_data("scanned_image.h5")
# C, T, Z, Y, X = grid_data[0].shape
# grid_data[0].shape = Y, X
# optical_image = grid_data[0]
#####################################################
logging.debug("Isolating spots...")
opxs = optical_image.metadata[model.MD_PIXEL_SIZE]
optical_dist = escan.pixelSize.value[0] * electron_scale[0] / opxs[0]
subimages, subimage_coordinates = coordinates.DivideInNeighborhoods(
optical_image, repetitions, optical_dist)
logging.debug("Number of spots found: %d", len(subimages))
hdf5.export("spot_found.h5", subimages, thumbnail=None)
logging.debug("Finding spot centers...")
spot_coordinates = spot.FindCenterCoordinates(subimages)
logging.debug("center coord = %s", spot_coordinates)
optical_coordinates = coordinates.ReconstructCoordinates(
subimage_coordinates, spot_coordinates)
logging.debug(optical_coordinates)
rgb_optical = img.DataArray2RGB(optical_image)
for ta in optical_coordinates:
rgb_optical[ta[1] - 1:ta[1] + 1, ta[0] - 1:ta[0] + 1, 0] = 255
rgb_optical[ta[1] - 1:ta[1] + 1, ta[0] - 1:ta[0] + 1, 1] *= 0.5
rgb_optical[ta[1] - 1:ta[1] + 1, ta[0] - 1:ta[0] + 1, 2] *= 0.5
misc.imsave('spots_image.png', rgb_optical)
# TODO: Make function for scale calculation
sorted_coordinates = sorted(optical_coordinates,
key=lambda tup: tup[1])
tab = tuple(
map(operator.sub, sorted_coordinates[0], sorted_coordinates[1]))
optical_scale = math.hypot(tab[0], tab[1])
scale = electron_scale[0] / optical_scale
print(scale)
# max_allowed_diff in pixels
max_allowed_diff_px = max_allowed_diff / escan.pixelSize.value[0]
logging.debug("Matching coordinates...")
known_electron_coordinates, known_optical_coordinates, max_diff = coordinates.MatchCoordinates(
optical_coordinates, electron_coordinates, scale,
max_allowed_diff_px)
logging.debug("Calculating transformation...")
(calc_translation_x, calc_translation_y), (
calc_scaling_x,
calc_scaling_y), calc_rotation = transform.CalculateTransform(
known_electron_coordinates, known_optical_coordinates)
logging.debug("Electron->Optical: ")
print(calc_translation_x, calc_translation_y, calc_scaling_x,
calc_scaling_y, calc_rotation)
final_electron = coordinates._TransformCoordinates(
known_optical_coordinates,
(calc_translation_x, calc_translation_y), calc_rotation,
(calc_scaling_x, calc_scaling_y))
logging.debug("Overlay done.")
# Calculate distance between the expected and found electron coordinates
coord_diff = []
for ta, tb in zip(final_electron, known_electron_coordinates):
tab = tuple(map(operator.sub, ta, tb))
coord_diff.append(math.hypot(tab[0], tab[1]))
mean_difference = numpy.mean(coord_diff) * escan.pixelSize.value[0]
variance_sum = 0
for i in range(0, len(coord_diff)):
variance_sum += (mean_difference - coord_diff[i])**2
variance = (variance_sum / len(coord_diff)) * escan.pixelSize.value[0]
not_found_spots = len(electron_coordinates) - len(final_electron)
# Generate overlay image
logging.debug("Generating images...")
(calc_translation_x, calc_translation_y), (
calc_scaling_x,
calc_scaling_y), calc_rotation = transform.CalculateTransform(
known_optical_coordinates, known_electron_coordinates)
logging.debug("Optical->Electron: ")
print(calc_translation_x, calc_translation_y, calc_scaling_x,
calc_scaling_y, calc_rotation)
overlay_coordinates = coordinates._TransformCoordinates(
known_electron_coordinates,
(calc_translation_y, calc_translation_x), -calc_rotation,
(calc_scaling_x, calc_scaling_y))
for ta in overlay_coordinates:
rgb_optical[ta[0] - 1:ta[0] + 1, ta[1] - 1:ta[1] + 1, 1] = 255
misc.imsave('overlay_image.png', rgb_optical)
misc.imsave('optical_image.png', optical_image)
logging.debug(
"Done. Check electron_image.png, optical_image.png and overlay_image.png."
)
except:
logging.exception("Unexpected error while performing action.")
return 127
logging.info(
"\n**Overlay precision stats (Resulted to expected electron coordinates comparison)**\n Mean distance: %f (unit: m)\n Variance: %f (unit: m)\n Not found spots: %d",
mean_difference, variance, not_found_spots)
return 0