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Commit 181976df authored by Kimberly Isobel Meechan's avatar Kimberly Isobel Meechan
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split main function into two

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scripts/extension/attributes/morphology_impl.py
+ 147
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......@@ -99,7 +99,7 @@ def load_data(ds, row, scale):
# load the data from the bounding box
return ds[bb]
# @profile
def morphology_row_features(mask, scale):
# Calculate stats from skimage
ski_morph = regionprops(mask.astype('uint8'))
......@@ -131,7 +131,7 @@ def morphology_row_features(mask, scale):
return (volume_in_microns, extent, equiv_diameter, major_axis,
minor_axis, surface_area, sphericity, max_radius)
# @profile
def intensity_row_features(raw, mask):
intensity_vals_in_mask = raw[mask]
# mean and stdev - use float64 to avoid silent overflow errors
......@@ -146,7 +146,7 @@ def intensity_row_features(raw, mask):
return mean_intensity, st_dev, median_intensity, interquartile_range_intensity, total
# @profile
def radial_intensity_row_features(raw, mask, scale, stops=(0.0, 0.25, 0.5, 0.75, 1.0)):
result = ()
......@@ -163,7 +163,7 @@ def radial_intensity_row_features(raw, mask, scale, stops=(0.0, 0.25, 0.5, 0.75,
return result
# @profile
def texture_row_features(raw, mask):
# errors if there are small, isolated spots (because I'm using ignore zeros as true)
# so here remove components that are < 10 pixels
......@@ -187,7 +187,7 @@ def texture_row_features(raw, mask):
return tuple(hara)
# @profile
def radial_distribution(edt, mask, stops=(0.0, 0.25, 0.5, 0.75, 1.0)):
result = ()
......@@ -201,7 +201,7 @@ def radial_distribution(edt, mask, stops=(0.0, 0.25, 0.5, 0.75, 1.0)):
return result
# @profile
def chromatin_row_features(chromatin, edt, raw, scale_chromatin):
result = ()
......@@ -224,7 +224,7 @@ def chromatin_row_features(chromatin, edt, raw, scale_chromatin):
return result
# @profile
# compute morphology (and intensity features) for label range
def morphology_features_for_label_range(table, ds, ds_raw,
ds_chromatin,
......@@ -404,6 +404,145 @@ def compute_morphology_features(table, segmentation_path, raw_path,
return stats
def morphology_impl_nucleus (nucleus_segmentation_path, raw_path, chromatin_path,
table,
min_size, max_size,
max_bb,
nucleus_resolution, chromatin_resolution,
nucleus_seg_scale, raw_scale, chromatin_scale,
label_starts, label_stops):
# keys for the different scales
nucleus_seg_key_full = 't00000/s00/0/cells'
nucleus_seg_key = 't00000/s00/%i/cells' % nucleus_seg_scale
raw_key_full = 't00000/s00/0/cells'
raw_key = 't00000/s00/%i/cells' % raw_scale
chromatin_key_full = 't00000/s00/0/cells'
chromatin_key = 't00000/s00/%i/cells' % chromatin_scale
# get scale factors
scale_factor_nucleus_seg = get_scale_factor(nucleus_segmentation_path, nucleus_seg_key_full, nucleus_seg_key,
nucleus_resolution)
if raw_path is not None:
log("Have raw path; compute intensity features")
# NOTE for now we can hard-code the resolution for the raw data here,
# but we might need to change this if we get additional dataset(s)
raw_resolution = [0.025, 0.01, 0.01]
scale_factor_raw = get_scale_factor(raw_path, raw_key_full, raw_key, raw_resolution)
else:
log("Don't have raw path; do not compute intensity features")
scale_factor_raw = None
if chromatin_path is not None:
log("Have chromatin path; compute chromatin features")
scale_factor_chromatin = get_scale_factor(chromatin_path, chromatin_key_full, chromatin_key,
chromatin_resolution)
else:
log("Don't have chromatin path; do not compute chromatin features")
scale_factor_chromatin = None
# remove zero label if it exists
table = table.loc[table['label_id'] != 0, :]
# filter by size
if min_size is not None or max_size is not None:
table = filter_table(table, min_size, max_size)
log("Number of labels after size filter: %i" % table.shape[0])
# filter by bounding box size
if max_bb is not None:
table = filter_table_bb(table, max_bb)
log("Number of labels after bounding box size filter %i" % table.shape[0])
log("Computing morphology features")
stats = compute_morphology_features(table, segmentation_path, raw_path,
chromatin_path, exclude_nuc_path,
seg_key, raw_key, chromatin_key,
exclude_key,
scale_factor_seg, scale_factor_raw,
scale_factor_chromatin,
scale_factor_exclude,
label_starts, label_stops)
return stats
def morphology_impl_cell (cell_segmentation_path, raw_path,
nucleus_segmentation_path,
table, mapping_path,
region_mapping_path,
min_size, max_size,
max_bb,
cell_resolution, nucleus_resolution,
cell_seg_scale, raw_scale, nucleus_seg_scale,
label_starts, label_stops):
# keys for the different scales
cell_seg_key_full = 't00000/s00/0/cells'
cell_seg_key = 't00000/s00/%i/cells' % cell_seg_scale
raw_key_full = 't00000/s00/0/cells'
raw_key = 't00000/s00/%i/cells' % raw_scale
nucleus_seg_key_full = 't00000/s00/0/cells'
nucleus_seg_key = 't00000/s00/%i/cells' % nucleus_seg_scale
# get scale factors
cell_seg_scale_factor = get_scale_factor(cell_segmentation_path, cell_seg_key_full, cell_seg_key, cell_resolution)
if raw_path is not None:
log("Have raw path; compute intensity features")
# NOTE for now we can hard-code the resolution for the raw data here,
# but we might need to change this if we get additional dataset(s)
raw_resolution = [0.025, 0.01, 0.01]
scale_factor_raw = get_scale_factor(raw_path, raw_key_full, raw_key, raw_resolution)
else:
log("Don't have raw path; do not compute intensity features")
scale_factor_raw = None
if nucleus_segmentation_path is not None:
log("Have nucleus path; exclude nucleus for intensity measures")
scale_factor_nucleus = get_scale_factor(nucleus_segmentation_path, nucleus_seg_key_full, nucleus_seg_key,
nucleus_resolution)
else:
log("Don't have exclude path; don't exclude nucleus area for intensity measures")
scale_factor_nucleus = None
# remove zero label if it exists
table = table.loc[table['label_id'] != 0, :]
# filter to only keep cells with assigned nuclei)
if mapping_path is not None:
log("Have mapping path %s" % mapping_path)
table = filter_table_from_mapping(table, mapping_path)
log("Number of labels after filter with mapping: %i" % table.shape[0])
# filter to exclude certain regions
if region_mapping_path is not None:
log("Have region mapping path %s" % region_mapping_path)
table = filter_table_region(table, region_mapping_path)
log("Number of labels after region filter: %i" % table.shape[0])
# filter by size of object (no. pixels)
if min_size is not None or max_size is not None:
table = filter_table(table, min_size, max_size)
log("Number of labels after size filter: %i" % table.shape[0])
# filter by bounding box size
if max_bb is not None:
table = filter_table_bb(table, max_bb)
log("Number of labels after bounding box size filter %i" % table.shape[0])
log("Computing morphology features")
stats = compute_morphology_features(table, segmentation_path, raw_path,
chromatin_path, exclude_nuc_path,
seg_key, raw_key, chromatin_key,
exclude_key,
scale_factor_seg, scale_factor_raw,
scale_factor_chromatin,
scale_factor_exclude,
label_starts, label_stops)
return stats
def morphology_impl(segmentation_path, raw_path, chromatin_path,
exclude_nuc_path,
table, mapping_path,
......@@ -530,5 +669,6 @@ def morphology_impl(segmentation_path, raw_path, chromatin_path,
return stats
if __name__ == '__main__':
pass
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