def ReadInputFile(self, filename, nSmp=None):
# Read configuration file.
self.cvConfig = CVConfig(ConfigObj(filename))
# For performance statistics.
# Correct the 'nSmp' in configs with what passed into program.
if nSmp is not None:
self.cvConfig.nSmp = nSmp
self.cvConfig.meshes['wall'].nSmp = nSmp
self.cvConfig.solver.nSmp = nSmp
# Loading meshes.
config = self.cvConfig
meshes = self.cvConfig.meshes
equations = self.cvConfig.equations
self.meshes = {
'lumen':
FluidMesh(self.comm, config, meshes['lumen'], equations['fluid']),
'wall':
SolidMesh(self.comm, config, meshes['wall'], equations['solid'])
}
self.name = config.name
# Debug info printout.
if self.rank == 0:
print('Model name: {}'.format(config.name))
print('Fluid mesh size: {} elements, {} nodes'.format(
self.meshes['lumen'].nElements, self.meshes['lumen'].nNodes))
print('Structure mesh size: {} elements, {} nodes'.format(
self.meshes['wall'].nElements, self.meshes['wall'].nNodes))
print('Solver method: {}'.format(config.solver.method))
print('Number of samples: {}'.format(config.solver.nSmp))
def main():
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
cf = CVConfig()
print('Initializing CVSegmenter at', cf.DIRECTORY_PATH)
if cf.IS_CODEX_OUTPUT:
print('Picking channel', cf.NUCLEAR_CHANNEL_NAME, 'from',
len(cf.CHANNEL_NAMES), 'total to segment on')
print('Channel names:')
print(cf.CHANNEL_NAMES)
print("Working with images of shape:", cf.SHAPE)
stitcher = CVMaskStitcher(overlap=cf.OVERLAP)
segmenter = CVSegmenter(
cf.SHAPE,
cf.MODEL_PATH,
cf.OVERLAP,
cf.INCREASE_FACTOR,
cf.THRESHOLD
)
growth = cf.GROWTH_PIXELS
rows, cols = None, None
dataframe_regs = defaultdict(list)
if cf.OUTPUT_METHOD not in ['imagej_text_file', 'statistics', 'visual_image_output', 'visual_overlay_output', 'all']:
raise NameError(
'Output method is not supported. Check the OUTPUT_METHOD variable in cvconfig.py.')
count = 0
for filename in cf.FILENAMES:
count += 1
path = os.path.join(cf.DIRECTORY_PATH, filename)
image = np.array(cf.READ_METHOD(path))
if cf.IS_CODEX_OUTPUT:
image = np.transpose(image, (2, 3, 0, 1))
image = image.reshape(cf.SHAPE)
nuclear_index = None
if cf.N_DIMS == 4:
nuclear_index = cvutils.get_channel_index(cf.NUCLEAR_CHANNEL_NAME, cf.CHANNEL_NAMES)
cf.N_DIMS = 3
nuclear_image = cvutils.get_nuclear_image(cf.N_DIMS, image, nuclear_index=nuclear_index)
if cf.BOOST == 'auto':
print('Using auto boosting - may be inaccurate for empty or noisy images.')
image_max = np.percentile(nuclear_image, cf.AUTOBOOST_PERCENTILE)
cf.BOOST = cvutils.EIGHT_BIT_MAX / image_max
print('Boosting with value of', cf.BOOST, ', check that this makes sense.')
nuclear_image = cvutils.boost_image(nuclear_image, cf.BOOST)
print('\nSegmenting with CellVision:', filename)
masks, rows, cols = segmenter.segment_image(nuclear_image)
print('Stitching:', filename)
stitched_mask = CVMask(stitcher.stitch_masks(masks, rows, cols))
instances = stitched_mask.n_instances()
print(instances, 'cell masks found by segmenter')
if instances == 0:
print('No cells found in', filename, ', skipping to next')
continue
print('Growing cells by', growth, 'pixels:', filename)
stitched_mask.grow_by(growth)
print('Removing overlaps by nearest neighbor:', filename)
stitched_mask.remove_overlaps_nearest_neighbors()
if cf.OUTPUT_METHOD == 'imagej_text_file':
print('Sort into strips and outputting:', filename)
new_path = os.path.join(
cf.IMAGEJ_OUTPUT_PATH, (filename[:-4] + '-coords.txt'))
stitched_mask.sort_into_strips()
stitched_mask.output_to_file(new_path)
if cf.OUTPUT_METHOD == 'statistics' or cf.OUTPUT_METHOD == 'all':
print('Calculating statistics:', filename)
reg, tile_row, tile_col, tile_z = 0, 1, 1, 0
if cf.IS_CODEX_OUTPUT:
reg, tile_row, tile_col, tile_z = cvutils.extract_tile_information(
filename)
channel_means, size = None, None
if cf.SHOULD_COMPENSATE == True:
channel_means, size = stitched_mask.compute_channel_means_sums_compensated(image)
else:
channel_means, size = stitched_mask.compute_channel_means_sums(image)
centroids = stitched_mask.compute_centroids()
absolutes = stitched_mask.absolute_centroids(tile_row, tile_col)
if centroids.size != 0:
metadata_list = np.array([reg, tile_row, tile_col, tile_z])
metadata = np.broadcast_to(
metadata_list, (stitched_mask.n_instances(), len(metadata_list)))
semi_dataframe = np.concatenate(
[metadata, centroids, absolutes, size[:, None], channel_means], axis=1)
dataframe_regs[reg].append(semi_dataframe)
if cf.OUTPUT_METHOD == 'visual_image_output' or cf.OUTPUT_METHOD == 'all':
print('Creating visual output saved to', cf.VISUAL_OUTPUT_PATH)
new_path = os.path.join(cf.VISUAL_OUTPUT_PATH, filename[:-4]) + 'visual_growth' + str(growth)
figsize = (cf.SHAPE[1] // 25, cf.SHAPE[0] // 25)
cvvisualize.generate_instances_and_save(
new_path + '.png', nuclear_image, stitched_mask.masks, figsize=figsize)
if cf.OUTPUT_METHOD == 'visual_overlay_output' or cf.OUTPUT_METHOD == 'all':
print('Creating visual overlay output saved to', cf.VISUAL_OUTPUT_PATH)
new_path = os.path.join(cf.VISUAL_OUTPUT_PATH, filename[:-4]) + 'growth' + str(growth) + 'mask.tif'
cvvisualize.generate_masks_and_save(
new_path, nuclear_image, stitched_mask.masks)
if cf.OUTPUT_METHOD == 'statistics' or cf.OUTPUT_METHOD == 'all':
for reg in dataframe_regs:
dataframe_list = dataframe_regs[reg]
full_df_array = np.concatenate(dataframe_list, axis=0)
descriptive_labels = [
'Reg',
'Tile Row',
'Tile Col',
'Tile Z',
'In-Tile Y',
'In-Tile X',
'Absolute Y',
'Absolute X',
'Cell Size'
]
# Output to CSV
if not cf.IS_CODEX_OUTPUT:
cf.CHANNEL_NAMES = ['single-channel']
n_channels = cf.SHAPE[2]
if n_channels == 3:
cf.CHANNEL_NAMES = ['Red', 'Green', 'Blue']
columns = descriptive_labels + list(cf.CHANNEL_NAMES)
dataframe = pd.DataFrame(full_df_array, columns=columns)
path = os.path.join(cf.QUANTIFICATION_OUTPUT_PATH, filename[:-4]+ '_reg' + str(reg) + '_growth' + str(growth))
dataframe.to_csv(path + '.csv')
# Output to .fcs file
fcswrite.write_fcs(path + '.fcs', columns, full_df_array)
def main():
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
cf = CVConfig()
print('Initializing CSSegmenter at', cf.DIRECTORY_PATH)
if cf.IS_CODEX_OUTPUT:
print('Picking channel', cf.NUCLEAR_CHANNEL_NAME, 'from',
len(cf.CHANNEL_NAMES), 'total to segment on')
print('Channel names:')
print(cf.CHANNEL_NAMES)
print("Working with images of shape:", cf.SHAPE)
stitcher = CVMaskStitcher(overlap=cf.OVERLAP)
#imshape = cf.SHAPE
#if cf.HALF_RESOLUTION:
# imshape = (round(imshape[0] / 2), round(imshape[1] / 2), imshape[2])
segmenter = CVSegmenter(
cf.SHAPE,
cf.MODEL_PATH,
cf.OVERLAP,
cf.INCREASE_FACTOR,
cf.THRESHOLD
)
growth = cf.GROWTH_PIXELS
rows, cols = None, None
dataframe_regs = defaultdict(list)
columns = []
path=''
if cf.OUTPUT_METHOD not in ['imagej_text_file', 'statistics', 'visual_image_output', 'visual_overlay_output', 'all']:
raise NameError(
'Output method is not supported. Check the OUTPUT_METHOD variable in cvconfig.py.')
print("Checking previous segmentation progress...")
progress_table = cf.PROGRESS_TABLE
print("These tiles already segmented: ")
print(progress_table)
cf.FILENAMES = [item for item in cf.FILENAMES if item not in progress_table]
cf.FILENAMES.sort()
if cf.BOOST == 'auto':
path = os.path.join(cf.DIRECTORY_PATH, cf.AUTOBOOST_REFERENCE_IMAGE)
image = np.array(cf.READ_METHOD(path))
if cf.IS_CODEX_OUTPUT:
image = np.transpose(image, (2, 3, 0, 1))
image = image.reshape(cf.SHAPE)
nuclear_index = None
if cf.N_DIMS == 4:
nuclear_index = cvutils.get_channel_index(cf.NUCLEAR_CHANNEL_NAME, cf.CHANNEL_NAMES)
nuclear_image = cvutils.get_nuclear_image(cf.N_DIMS-1, image, nuclear_index=nuclear_index)
print('Using auto boosting - may be inaccurate for empty or noisy images.')
image_max = np.percentile(nuclear_image, cf.AUTOBOOST_PERCENTILE)
cf.BOOST = cvutils.EIGHT_BIT_MAX / image_max
print('Boosting with value of', cf.BOOST, ', check that this makes sense.')
count = 0
for filename in cf.FILENAMES:
count += 1
path = os.path.join(cf.DIRECTORY_PATH, filename)
image = np.array(cf.READ_METHOD(path))
if cf.IS_CODEX_OUTPUT:
image = np.transpose(image, (2, 3, 0, 1))
image = image.reshape(cf.SHAPE)
nuclear_index = None
if cf.N_DIMS == 4:
nuclear_index = cvutils.get_channel_index(cf.NUCLEAR_CHANNEL_NAME, cf.CHANNEL_NAMES)
nuclear_image = cvutils.get_nuclear_image(cf.N_DIMS-1, image, nuclear_index=nuclear_index)
nuclear_image = cvutils.boost_image(nuclear_image, cf.BOOST)
print('\nSegmenting with CellSeg:', filename)
masks, rows, cols = segmenter.segment_image(nuclear_image)
print('Stitching:', filename)
stitched_mask = CVMask(stitcher.stitch_masks(masks, rows, cols))
#inside the stitcher, split the masks back into crops
del masks
instances = stitched_mask.n_instances()
print(instances, 'cell masks found by segmenter')
if instances == 0:
print('No cells found in', filename, ', skipping to next')
continue
print('Growing cells by', growth, 'pixels:', filename)
if cf.USE_GROWTH:
print("Computing centroids and bounding boxes for the masks.")
stitched_mask.compute_centroids()
stitched_mask.compute_boundbox()
print(f"Growing masks by {cf.GROWTH_PIXELS} pixels")
stitched_mask.grow_masks(cf.GROWTH_PIXELS, cf.GROWTH_METHOD)
#restitch and squash after growth
if not os.path.exists(cf.IMAGEJ_OUTPUT_PATH):
os.makedirs(cf.IMAGEJ_OUTPUT_PATH)
if not os.path.exists(cf.VISUAL_OUTPUT_PATH):
os.makedirs(cf.VISUAL_OUTPUT_PATH)
if not os.path.exists(cf.QUANTIFICATION_OUTPUT_PATH):
os.makedirs(cf.QUANTIFICATION_OUTPUT_PATH)
if cf.OUTPUT_METHOD == 'imagej_text_file':
print('Sort into strips and outputting:', filename)
new_path = os.path.join(
cf.IMAGEJ_OUTPUT_PATH, (filename[:-4] + '-coords.txt'))
stitched_mask.sort_into_strips()
stitched_mask.output_to_file(new_path)
if cf.OUTPUT_METHOD == 'visual_image_output' or cf.OUTPUT_METHOD == 'all':
print('Creating visual output saved to', cf.VISUAL_OUTPUT_PATH)
new_path = os.path.join(cf.VISUAL_OUTPUT_PATH, filename[:-4]) + 'visual_growth' + str(growth) + ".png"
figsize = (cf.SHAPE[1] // 25, cf.SHAPE[0] // 25)
outlines = cvvisualize.generate_mask_outlines(stitched_mask.flatmasks)
cvvisualize.overlay_outlines_and_save(nuclear_image, outlines, new_path, figsize=figsize)
if cf.OUTPUT_METHOD == 'visual_overlay_output' or cf.OUTPUT_METHOD == 'all':
print('Creating visual overlay output saved to', cf.VISUAL_OUTPUT_PATH)
new_path = os.path.join(cf.VISUAL_OUTPUT_PATH, filename[:-4]) + 'growth' + str(growth) + 'mask.tif'
outlines = cvvisualize.generate_mask_outlines(stitched_mask.flatmasks)
imsave(new_path, outlines)
if cf.OUTPUT_METHOD == 'statistics' or cf.OUTPUT_METHOD == 'all': #must rewrite this
print('Calculating statistics:', filename)
reg, tile_row, tile_col, tile_z = 0, 1, 1, 0
regname = ''
if cf.IS_CODEX_OUTPUT:
reg, tile_row, tile_col, tile_z = cvutils.extract_tile_information(
filename)
regname = filename.split("_")[0]
channel_means, size = None, None
channel_means_comp, channel_means_uncomp, size = stitched_mask.compute_channel_means_sums_compensated(image)
centroids = stitched_mask.centroids
absolutes = stitched_mask.absolute_centroids(tile_row, tile_col)
semi_dataframe_comp = 1
if centroids:
metadata_list = np.array([reg, tile_row, tile_col, tile_z])
metadata = np.broadcast_to(
metadata_list, (stitched_mask.n_instances(), len(metadata_list)))
semi_dataframe = np.concatenate(
[metadata, np.array(centroids), absolutes, size[:, None], channel_means_uncomp], axis=1)
semi_dataframe_comp = np.concatenate(
[metadata, np.array(centroids), absolutes, size[:, None], channel_means_comp], axis=1)
descriptive_labels = [
'Reg',
'Tile Row',
'Tile Col',
'Tile Z',
'In-Tile Y',
'In-Tile X',
'Absolute Y',
'Absolute X',
'Cell Size'
]
# Output to CSV
if not cf.IS_CODEX_OUTPUT:
cf.CHANNEL_NAMES = ['single-channel']
n_channels = cf.SHAPE[2]
if n_channels == 3:
cf.CHANNEL_NAMES = ['Red', 'Green', 'Blue']
regname = filename.replace("." + filename.split(".")[-1], "")
columns = descriptive_labels + [s for s in cf.CHANNEL_NAMES]
dataframe = None
path = ''
if cf.SHOULD_COMPENSATE:
dataframe = pd.DataFrame(semi_dataframe_comp, columns=columns)
path = os.path.join(cf.QUANTIFICATION_OUTPUT_PATH, regname + '_statistics_growth' + str(growth)+'_comp')
else:
dataframe = pd.DataFrame(semi_dataframe, columns=columns)
path = os.path.join(cf.QUANTIFICATION_OUTPUT_PATH, regname + '_statistics_growth' + str(growth)+'_uncomp')
if os.path.exists(path+'.csv'):
dataframe.to_csv(path + '.csv',mode='a',header=False)
else:
dataframe.to_csv(path + '.csv')
#save intermediate progress in case of mid-run crash
with open(cf.PROGRESS_TABLE_PATH, "a") as myfile:
myfile.write(filename + "\n")
#duplicate existing csv files in fcs format at the end of run
if cf.OUTPUT_METHOD == 'statistics' or cf.OUTPUT_METHOD == 'all':
print("Duplicating existing csv files in fcs format")
descriptive_labels = [
'Reg',
'Tile Row',
'Tile Col',
'Tile Z',
'In-Tile Y',
'In-Tile X',
'Absolute Y',
'Absolute X',
'Cell Size'
]
columns = descriptive_labels + [s for s in cf.CHANNEL_NAMES]
filenames = os.listdir(cf.QUANTIFICATION_OUTPUT_PATH)
filenames = [f for f in filenames if f.endswith("csv")]
for filename in filenames:
path = os.path.join(cf.QUANTIFICATION_OUTPUT_PATH,filename)
dataframe = pd.read_csv(path,index_col=0)
path = path.replace('.csv','')
fcswrite.write_fcs(path + '.fcs', columns, dataframe)
print("Segmentation Completed")