def get_filtered_segmentation(dataitem, params):
nuc_label_image = segmentation_from_nuclear_channel_and_markers(
dataitem.fishimage,
skimage.measure.label(dataitem.scaled_markers),
params
)
segmentation = segmentation_from_cellmask_and_label_image(
dataitem.cell_mask(params),
nuc_label_image
)
scaled_good_mask = scale_segmentation(dataitem.good_mask, dataitem.maxproj)
labelled_points = skimage.measure.label(scaled_good_mask)
rprops = skimage.measure.regionprops(labelled_points)
region_centroids = [r.centroid for r in rprops]
icentroids = [(int(r), int(c)) for r, c in region_centroids]
good_regions = [segmentation[r, c] for r, c in icentroids]
filtered_segmentation = filter_segmentation_by_region_list(
segmentation,
good_regions
)
return filtered_segmentation
def get_filtered_segmentation(dataitem, params):
# nuc_label_image = segmentation_from_nuclear_channel_and_markers(
# dataitem.fishimage,
# skimage.measure.label(dataitem.scaled_markers),
# params
# )
# nuc_label_image.pretty_color_image.view(dbiImage).save("nuc_label_img.png")
# dataitem.cell_mask(params).view(dbiImage).save("cell_mask.png")
segmentation = segmentation_from_cellmask_and_label_image(
dataitem.cell_mask(params),
skimage.measure.label(dataitem.scaled_markers)
# nuc_label_image
)
scaled_good_mask = scale_segmentation(dataitem.good_mask, dataitem.maxproj)
labelled_points = skimage.measure.label(scaled_good_mask)
rprops = skimage.measure.regionprops(labelled_points)
region_centroids = [r.centroid for r in rprops]
icentroids = [(int(r), int(c)) for r, c in region_centroids]
good_regions = [segmentation[r, c] for r, c in icentroids]
filtered_segmentation = filter_segmentation_by_region_list(
segmentation,
good_regions
)
return filtered_segmentation
def visualise_marker_positions(dataitem):
r = dataitem.bad_mask
g = dataitem.good_mask
b = dataitem.nuc_mask
import numpy as np
from dtoolbioimage import scale_to_uint8
merged = np.dstack([r, g, b])
scaled = scale_to_uint8(scale_segmentation(merged, dataitem.maxproj))
maxproj_rgb = scale_to_uint8(np.dstack(3 * [dataitem.maxproj]))
scaled.view(dbiImage).save("scaled.png")
v = 0.5 * scaled + 0.5 * maxproj_rgb
v.view(dbiImage).save("v.png")
visd = np.array(vis).view(dbiImage)
(0.5 * visd + 0.5 * scaled).view(dbiImage).save("svis.png")
def scaled_markers(self):
scaled_markers = scale_segmentation(self.all_mask, self.maxproj)
return scaled_markers
def scaled_markers(self):
# print(f"Scaling to {self.maxproj.shape}")
# print(f"Diag {self.deconv_stack.shape}, {self.fishimage.nuclei.shape}")
scaled_markers = scale_segmentation(self.all_mask, self.maxproj)
return scaled_markers