def compute_threshold(self, image, mask):
'''
Compute the threshold.
:param image: The image to process
:param mask: The pixel mask on the image
:returns: A boolean mask showing foreground/background pixels
'''
from dials.algorithms.spot_finding.helen import BlobThresholdAlgorithm
params = self.params.spotfinder.threshold.helen
self._algorithm = BlobThresholdAlgorithm(
pixels_per_row=image.all()[1],
row_count=image.all()[0],
exp_spot_dimension=params.exp_spot_dimension,
global_threshold=params.global_threshold,
min_blob_score=params.min_blob_score,
num_passes=params.num_passes)
result = self._algorithm.threshold(image, mask)
if self.params.spotfinder.threshold.helen.debug:
from dials.array_family import flex
corr = self._algorithm.correlation(image, mask)
import six.moves.cPickle as pickle
with open("correlation.pickle", "wb") as fh:
pickle.dump(corr, fh, pickle.HIGHEST_PROTOCOL)
return result
def compute_threshold(self, image, mask):
'''
Compute the threshold.
:param image: The image to process
:param mask: The pixel mask on the image
:returns: A boolean mask showing foreground/background pixels
'''
from dials.algorithms.spot_finding.helen import BlobThresholdAlgorithm
params = self.params.spotfinder.threshold.helen
self._algorithm = BlobThresholdAlgorithm(
pixels_per_row = image.all()[1],
row_count = image.all()[0],
exp_spot_dimension = params.exp_spot_dimension,
global_threshold = params.global_threshold,
min_blob_score = params.min_blob_score,
num_passes = params.num_passes)
result = self._algorithm.threshold(image, mask)
if self.params.spotfinder.threshold.helen.debug:
from dials.array_family import flex
corr = self._algorithm.correlation(image, mask)
import cPickle as pickle
pickle.dump(corr, open("correlation.pickle", "wb"))
return result
class HelenSpotFinderThresholdExt(object):
''' Extensions to do spot finding threshold. '''
name = 'helen'
@classmethod
def phil(cls):
from libtbx.phil import parse
phil = parse('''
exp_spot_dimension = 3
.type = int
.expert_level = 2
.help = "The expected spot dimensions in pixels"
global_threshold = 100
.type = float
.expert_level = 2
.help = "The global threshold value."
min_blob_score = 0.7
.type = float
.expert_level = 2
.help = "The correlation threshold between putative spot and model spot"
num_passes = 0
.type = int
.expert_level = 2
.help = "Number of passes after updating ideal spot"
debug = False
.type = bool
.expert_level = 2
.help = "Write out correlation"
''')
return phil
def __init__(self, params):
'''
Initialise the algorithm.
:param params: The input parameters
'''
self.params = params
def compute_threshold(self, image, mask):
'''
Compute the threshold.
:param image: The image to process
:param mask: The pixel mask on the image
:returns: A boolean mask showing foreground/background pixels
'''
from dials.algorithms.spot_finding.helen import BlobThresholdAlgorithm
params = self.params.spotfinder.threshold.helen
self._algorithm = BlobThresholdAlgorithm(
pixels_per_row=image.all()[1],
row_count=image.all()[0],
exp_spot_dimension=params.exp_spot_dimension,
global_threshold=params.global_threshold,
min_blob_score=params.min_blob_score,
num_passes=params.num_passes)
result = self._algorithm.threshold(image, mask)
if self.params.spotfinder.threshold.helen.debug:
from dials.array_family import flex
corr = self._algorithm.correlation(image, mask)
import six.moves.cPickle as pickle
with open("correlation.pickle", "wb") as fh:
pickle.dump(corr, fh, pickle.HIGHEST_PROTOCOL)
return result
class HelenSpotFinderThresholdExt(SpotFinderThresholdIface):
''' Extensions to do spot finding threshold. '''
name = 'helen'
@classmethod
def phil(cls):
from libtbx.phil import parse
phil = parse('''
exp_spot_dimension = 3
.type = int
.help = "The expected spot dimensions"
global_threshold = 100
.type = float
.help = "The global threshold value."
min_blob_score = 0.7
.type = float
.help = "The minimum score for a blob"
num_passes = 0
.type = int
.help = "Number of passes after updating ideal spot"
debug = False
.type = bool
.help = "Write out correlation"
''')
return phil
def __init__(self, params):
'''
Initialise the algorithm.
:param params: The input parameters
'''
self.params = params
def compute_threshold(self, image, mask):
'''
Compute the threshold.
:param image: The image to process
:param mask: The pixel mask on the image
:returns: A boolean mask showing foreground/background pixels
'''
from dials.algorithms.spot_finding.helen import BlobThresholdAlgorithm
params = self.params.spotfinder.threshold.helen
self._algorithm = BlobThresholdAlgorithm(
pixels_per_row = image.all()[1],
row_count = image.all()[0],
exp_spot_dimension = params.exp_spot_dimension,
global_threshold = params.global_threshold,
min_blob_score = params.min_blob_score,
num_passes = params.num_passes)
result = self._algorithm.threshold(image, mask)
if self.params.spotfinder.threshold.helen.debug:
from dials.array_family import flex
corr = self._algorithm.correlation(image, mask)
import cPickle as pickle
pickle.dump(corr, open("correlation.pickle", "wb"))
return result
