# Copyright CNRS/Inria/UNS
# Contributor(s): Eric Debreuve (since 2019), Morgane Nadal (2020)
#
# eric.debreuve@cnrs.fr
#
# This software is governed by the CeCILL license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
# Time profiling:
# python -m cProfile -o runtime/profiling.log -s name main.py
# Memory profiling:
# python -m memory_profiler main.py
# or
# mprof run main.py
# mprof plot
import brick.component.connection as cn_
import brick.component.extension as xt_
import brick.component.soma as sm_
import brick.general.feedback as fb_
import brick.processing.input as in_
import brick.processing.plot as po_
import brick.processing.map_labeling as ml_
# import brick.processing.image_verification as iv_
from sklgraph.skl_fgraph import skl_graph_t
from sklgraph.skl_map import skl_map_t
import brick.processing.graph_feat_extraction as ge_
import os as os_
import sys as sy_
import time as tm_
import pathlib
import psutil as mu_
import matplotlib.pyplot as pl_
import numpy as np_
import imageio as io_
from skimage.segmentation import relabel_sequential
import skimage.morphology as mp_
import skimage.measure as ms_
import skimage.exposure as ex_
import pandas as pd_
import networkx as nx_
print(sy_.argv, sy_.argv.__len__())
if sy_.argv.__len__() < 2:
print("Missing parameter file argument")
sy_.exit(0)
if not (os_.path.isfile(sy_.argv[1]) and os_.access(sy_.argv[1], os_.R_OK)):
print("Wrong parameter file path or parameter file unreadable")
sy_.exit(0)
data_path = None
channel = None
size_voxel_in_micron = None
soma_low_c = None
soma_high_c = None
soma_selem_micron_c = None
soma_min_area_c = None
soma_max_area_c = None
adapt_hist_equalization = None
ext_low_c = None
ext_high_c = None
ext_selem_micron_c = None
ext_min_area_c = None
ext_max_length_c = None
max_straight_sq_dist_c = None
max_weighted_length_c = None
scale_range = None
scale_step = None
alpha = None
beta = None
frangi_c = None
diff_mode = None
bright_on_dark = None
method = None
hist_min_length = None
hist_step_length = None
number_of_bins_length = None
hist_bins_borders_length = None
hist_min_curvature = None
hist_step_curvature = None
number_of_bins_curvature = None
hist_bins_borders_curvature = None
with_plot = None
in_parallel = None
exec(open(sy_.argv[1]).read()) # Only with search_parameters.py (stable version)
soma_t = sm_.soma_t
extension_t = xt_.extension_t
print(f"STARTED: {tm_.strftime('%a, %b %d %Y @ %H:%M:%S')}")
start_time = tm_.time()
# TODO add proper warning with warn module
# --- Images
# Find the name of the file, eq. to the biological tested condition number x
name_file = os_.path.basename(data_path)
print("FILE: ", name_file)
name_file = name_file.replace(".tif", "")
name_dir = os_.path.dirname(data_path)
print("DIR: ", name_dir)
# Find the dimension of the image voxels in micron
# TODO do something more general, here too specific to one microscope metadata
size_voxel_in_micron = in_.FindVoxelDimensionInMicron(data_path, size_voxel_in_micron=size_voxel_in_micron)
# Read the image
image = io_.volread(data_path)
# Image size verification - simple version without user interface
image = in_.ImageVerification(image, channel)
# iv_.image_verification(image, channel) # -> PySide2 user interface # TODO: must return the modified image!
# /!\ conflicts between some versions of PySide2 and Python3
image = image
img_shape = image.shape
#
print(f"IMAGE: s.{img_shape} t.{image.dtype} m.{image.min()} M.{image.max()}")
# Intensity relative normalization (between 0 and 1).
# Image for soma normalized for hysteresis
image_for_soma = in_.IntensityNormalizedImage(image)
# Image for extensions not normalized for frangi enhancement
image_for_ext = image.copy()
print(f"NRM-IMG: t.{image_for_soma.dtype} m.{image_for_soma.min():.2f} M.{image_for_soma.max():.2f}")
# --- Initialization
som_nfo = {}
ext_nfo = {}
axes = {}
#
# --- Somas
print("\n--- Soma Detection")
# Change the soma parameters from micron to pixel dimensions, using the previously determined voxel size
soma_min_area_c = in_.ToPixel(soma_min_area_c, size_voxel_in_micron, dimension=(0, 1))
soma_max_area_c = in_.ToPixel(soma_max_area_c, size_voxel_in_micron, dimension=(0, 1))
soma_selem_c = mp_.disk(in_.ToPixel(soma_selem_micron_c, size_voxel_in_micron))
# - Create the maps for enhancing and detecting the soma
# Hysteresis thresholding and closing/opening
som_nfo["map_small"] = soma_t.Map(image_for_soma, soma_low_c, soma_high_c, soma_selem_c)
# Deletion of too small elements
som_nfo["map_small"], som_lmp_small = soma_t.DeleteSmallSomas(som_nfo["map_small"], soma_min_area_c)
# Deletion of too big elements (probably aggregation of cells)
# Separated from the deletion of small elements to avoid extensions to be detected where too big somas were just deleted
som_nfo["map"], som_lmp = soma_t.DeleteBigSomas(som_nfo["map_small"], som_lmp_small, soma_max_area_c)
# Labelling of somas, find the number of somas
# som_nfo["lmp"], n_somas = ms_.label(som_nfo["map"], return_num=True)
# Use relabel instead of label to optimize the algorithm. /!\ But BUG.
som_nfo["lmp"] = relabel_sequential(som_lmp)[0]
n_somas = som_nfo["lmp"].max()
id_soma = 1
for id in range(n_somas + 1):
if id != id_soma:
print(id)
som_nfo["lmp"][som_nfo["lmp"] == id] = 0
som_nfo["lmp"], n_somas = ms_.label(som_nfo["lmp"], return_num=True)
# po_.MaximumIntensityProjectionZ(som_nfo["lmp"])
# Create the distance and influence maps used next for connexions between somas-extensions and extensions-extensions
som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(som_nfo["lmp"])
# po_.MaximumIntensityProjectionZ(som_nfo["influence_map"])
# Create the tuple somas, containing all the objects 'soma'
somas = tuple(soma_t().FromMap(som_nfo["lmp"], uid) for uid in range(1, n_somas + 1))
print(f" n = {n_somas}")
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
fb_.PlotSomas(somas, som_nfo, axes)
#
# -- Extentions
print("\n--- Extension Detection")
# Set to zeros the pixels belonging to the somas.
# Take into account the aggregation of cells detected as one big soma to avoid extension creation there
del_soma = (som_nfo["map_small"] > 0).nonzero()
image_for_ext[del_soma] = 0
# Change the extensions parameters from micron to pixel dimensions
ext_min_area_c = in_.ToPixel(ext_min_area_c, size_voxel_in_micron, dimension=(0, 1))
ext_max_length_c = in_.ToPixel(ext_max_length_c, size_voxel_in_micron, dimension=(0,))
if ext_selem_micron_c == 0:
ext_selem_pixel_c = None
else:
ext_selem_pixel_c = mp_.disk(in_.ToPixel(ext_selem_micron_c, size_voxel_in_micron))
# scale_range_pixel = []
# #
# for value in scale_range:
# value_in_pixel = in_.ToPixel(value, size_voxel_in_micron, decimals=1)
# scale_range_pixel.append(value_in_pixel)
# scale_range = tuple(scale_range_pixel)
#
# scale_step = in_.ToPixel(scale_step, size_voxel_in_micron, decimals=1)
# - Perform frangi feature enhancement (via python or c - faster - implementation)
enhanced_ext, ext_scales = extension_t.EnhancedForDetection(
image_for_ext,
scale_range,
scale_step,
alpha,
beta,
frangi_c,
bright_on_dark,
method,
diff_mode=diff_mode,
in_parallel=in_parallel)
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"Elapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}\n")
# po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
# - Creation of the enhanced maps
# enhanced_ext = ex_.adjust_log(enhanced_ext, 1) # Not necessary, log contrast adjustment
# Enhanced the contrast in frangi output
if adapt_hist_equalization:
# necessary but not sufficient, histogram equalisation (adaptative)
enhanced_ext = ex_.equalize_adapthist(enhanced_ext)
else:
# necessary but not sufficient, histogram equalisation (global)
enhanced_ext = ex_.equalize_hist(enhanced_ext)
# po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
# Rescale the image by stretching the intensities between 0 and 255, necessary but not sufficient
enhanced_ext = ex_.rescale_intensity(enhanced_ext, out_range=(0, 255))
# Hysteresis thersholding
ext_nfo["coarse_map"] = extension_t.CoarseMap(enhanced_ext, ext_low_c, ext_high_c, ext_selem_pixel_c)
# po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
# Deletion of too small elements
ext_nfo["coarse_map"], ext_lmp = extension_t.FilteredCoarseMap(ext_nfo["coarse_map"], ext_min_area_c)
# po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
# Creation of the extensions skeleton
ext_nfo["map"] = extension_t.FineMapFromCoarseMap(ext_nfo["coarse_map"])
# ext_nfo["map"] = extension_t.FilteredSkeletonMap(ext_nfo["coarse_map"], ext_max_length_c)
# po_.MaximumIntensityProjectionZ(ext_nfo["map"])
# Deletion of extensions found within the somas
ext_nfo["map"][som_nfo["map"] > 0] = 0
# Labelling of extensions and number of extensions determined
ext_nfo["lmp"], n_extensions = ms_.label(ext_nfo["map"], return_num=True)
# Use relabel instead of label to optimize the algorithm. BUT PROBLEM WITH THE NUMBER OF EXTENSIONS DETECTED !
# ext_nfo["lmp"] = relabel_sequential(ext_lmp)[0]
# n_extensions = ext_nfo["lmp"].max()
# Create the tuple extensions, containing all the objects 'extension'
extensions = tuple(
extension_t().FromMap(ext_nfo["lmp"], ext_scales, uid)
for uid in range(1, n_extensions + 1))
print(f" n = {n_extensions}")
#
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
fb_.PlotExtensions(extensions, ext_nfo, img_shape)
# -- Preparation for Connections
# Calculate the costs of each pixel on the image
dijkstra_costs = in_.DijkstraCosts(image, som_nfo["map"], ext_nfo["map"])
# -- Soma-Extention
print("\n--- Soma <-> Extension")
# Change the extensions parameters from micron to pixel dimensions
max_straight_sq_dist_c = in_.ToPixel(max_straight_sq_dist_c, size_voxel_in_micron)
max_weighted_length_c = in_.ToPixel(max_weighted_length_c, size_voxel_in_micron)
# Find the candidate extensions, with their endpoints, for connexion to the somas
candidate_conn_nfo = cn_.CandidateConnections(
somas,
som_nfo["influence_map"],
som_nfo["dist_to_closest"],
extensions,
max_straight_sq_dist_c,
)
# For each candidate, verify that it is valid based on the shortest path and the maximum allowed straight distance
for ep_idx, soma, extension, end_point in candidate_conn_nfo:
# Only try to connect if the extension is not already connected
if extension.is_unconnected:
print(f" Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end="")
# Use of Dijkstra shortest weighted path
path, length = cn_.ShortestPathFromToN(
start_time,
end_point,
dijkstra_costs,
soma.ContourPointsCloseTo,
max_straight_sq_dist=max_straight_sq_dist_c,
)
# Keep the connexion only if inferior to the allowed max weighted distance
if length <= max_weighted_length_c:
cn_.ValidateConnection(soma, extension, end_point, path, dijkstra_costs)
print(": Made")
else:
print("")
# for soma in somas:
# soma.Extend(extensions, som_nfo["dist_to_closest"], dijkstra_costs)
fb_.PrintConnectedExtensions(extensions)
#
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
fb_.PlotSomasWithExtensions(somas, som_nfo, "all")
# -- Extention-Extention
print("\n--- Extension <-> Extension")
should_look_for_connections = True
# Update the soma maps with next Ext-Ext connexions
while should_look_for_connections:
# Update the final somas + ext map by adding the new extensions and their connexion paths
som_nfo["soma_w_ext_lmp"] = soma_t.SomasLMap(somas)
# Update the influence map
som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(som_nfo["soma_w_ext_lmp"])
# Find the candidate extensions for connexion to the primary extensions
candidate_conn_nfo = cn_.CandidateConnections(
somas,
som_nfo["influence_map"],
som_nfo["dist_to_closest"],
extensions,
max_straight_sq_dist_c,
)
should_look_for_connections = False
# For each candidate, verify that it is valid based on the shortest path and the maximum allowed straight distance
for ep_idx, soma, extension, end_point in candidate_conn_nfo:
# Only try to connect if the extension is not already connected
if extension.is_unconnected:
print(f" Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end="")
# Use of Dijkstra shortest weighted path
path, length = cn_.ShortestPathFromToN(
start_time,
end_point,
dijkstra_costs,
soma.ExtensionPointsCloseTo,
max_straight_sq_dist=max_straight_sq_dist_c,
)
# Keep the connexion only if inferior to the allowed max weighted distance
if length <= max_weighted_length_c:
# Verify that the last element of the connexion path is contained in the extension to be connected
# If so, returns the extensions containing the path last site.
tgt_extenstion = extension_t.ExtensionContainingSite(extensions, path[-1])
# Validate connexion: update the glial component objects and store more info in their fields
cn_.ValidateConnection(tgt_extenstion, extension, end_point, path, dijkstra_costs)
should_look_for_connections = True
print(": Made")
else:
print("")
# Create an extension map containing all the ext + connexions, without the somas.
# Used for the graphs extraction
ext_nfo["lmp_soma"] = som_nfo['soma_w_ext_lmp'] - som_nfo['lmp']
#
fb_.PrintConnectedExtensions(extensions)
#
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
fb_.PlotSomasWithExtensions(somas, som_nfo, "with_ext_of_ext")
# -- Summary
print("\n")
for soma in somas:
print(soma)
# snapshot = tr_.take_snapshot()
# top_file_stats = snapshot.statistics('lineno')
# print("Memory Profiling: Top 10 FILES")
# for stat in top_file_stats[:10]:
# print(stat)
# top_block_stats = snapshot.statistics('traceback')
# top_block_stats = top_block_stats[0]
# print(f"Memory Profiling: {top_block_stats.count} memory blocks: {top_block_stats.size / 1024:.1f} KiB")
# for line in top_block_stats.traceback.format():
# print(line)
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
pl_.show()
po_.MaximumIntensityProjectionZ(som_nfo['soma_w_ext_lmp'])
# , output_image_file_name=f"D:\\MorganeNadal\\Results\\Images\\{name_file}.png")
# --- Extract all the extensions of all somas as a graph
print('\n--- Graph extraction')
# Create the graphs with SKLGraph module (available on Eric Debreuve Gitlab)
for soma in somas:
print(f" Soma {soma.uid}", end="")
# Create SKLGraph skeletonized map
ext_map = skl_map_t.FromShapeMap(ext_nfo['lmp_soma'] == soma.uid,
store_widths=True,
skeletonize=False,
do_post_thinning=False)
# do_post_thinning = True, in order to remove pixels that are not breaking connectivity
# Create the graph from the SKLGaph skeletonized map
soma.skl_graph = skl_graph_t.FromSkeleton(ext_map, size_voxel=size_voxel_in_micron)
# --- Find the root of the {ext+conn} graphs.
# Roots are the nodes of degree 1 that are to be linked to the soma
soma.graph_roots = ge_.FindGraphsRootWithNodes(soma)
# Add a node "soma" and link it to the root nodes
soma_node = f"S-{int(soma.centroid[0])}-{int(soma.centroid[1])}-{int(soma.centroid[2])}"
soma.skl_graph.add_node(soma_node, soma=True, soma_nfo=soma)
for node in soma.graph_roots.values():
soma.skl_graph.add_edge(node, soma_node, root=True)
# nx_.draw_networkx(soma.skl_graph)
# pl_.show(block=True)
# pl_.close()
print(": Done")
elapsed_time = tm_.gmtime(tm_.time() - start_time)
# --- Extract features
print('\n--- Features Extraction\n')
# Parameters
if hist_bins_borders_length is None:
number_of_bins_length = int(number_of_bins_length)
bins_length = np_.linspace(hist_min_length, hist_min_length + hist_step_length * number_of_bins_length,
num=number_of_bins_length)
bins_length[-1] = np_.inf
else:
bins_length = np_.array(hist_bins_borders_length)
bins_length[-1] = np_.inf
if hist_bins_borders_curvature is None:
number_of_bins_curvature = int(number_of_bins_curvature)
bins_curvature = np_.linspace(hist_min_curvature,
hist_min_curvature + hist_step_curvature * number_of_bins_curvature,
num=number_of_bins_curvature)
bins_curvature[-1] = np_.inf
else:
bins_curvature = np_.array(hist_bins_borders_curvature)
bins_curvature[-1] = np_.inf
# Pandas dataframe creation with all the measured features
features_df = ge_.ExtractFeaturesInDF(name_file, somas, size_voxel_in_micron, bins_length, bins_curvature,
ext_scales)
# Save the pandas df into .csv
features_df.to_csv(f"{name_dir}\\{name_file}.csv")
#
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
print(f"DONE: {tm_.strftime('%a, %b %d %Y @ %H:%M:%S')}")
#########----------- Executable version with files AND repositories ------------###########
# def NutriMorph(data_path: str,
# channel=channel,
# size_voxel_in_micron=size_voxel_in_micron,
# soma_low_c=soma_low_c,
# soma_high_c=soma_high_c,
# soma_selem_micron_c=soma_selem_micron_c,
# soma_min_area_c=soma_min_area_c,
# soma_max_area_c=soma_max_area_c,
# adapt_hist_equalization=adapt_hist_equalization,
# ext_low_c=ext_low_c,
# ext_high_c=ext_high_c,
# ext_selem_micron_c=ext_selem_micron_c,
# ext_min_area_c=ext_min_area_c,
# ext_max_length_c=ext_max_length_c,
# max_straight_sq_dist_c=max_straight_sq_dist_c,
# max_weighted_length_c=max_weighted_length_c,
# scale_range=scale_range,
# scale_step=scale_step,
# alpha=alpha,
# beta=beta,
# frangi_c=frangi_c,
# diff_mode=diff_mode,
# bright_on_dark=bright_on_dark,
# method=method,
# hist_min_length=hist_min_length,
# hist_step_length=hist_step_length,
# number_of_bins_length=number_of_bins_length,
# hist_bins_borders_length=hist_bins_borders_length,
# hist_min_curvature=hist_min_curvature,
# hist_step_curvature=hist_step_curvature,
# number_of_bins_curvature=number_of_bins_curvature,
# hist_bins_borders_curvature=hist_bins_borders_curvature,
# with_plot=with_plot,
# in_parallel=in_parallel) -> pd_.DataFrame():
#
# soma_t = sm_.soma_t
# extension_t = xt_.extension_t
#
# print(f"STARTED: {tm_.strftime('%a, %b %d %Y @ %H:%M:%S')}")
# start_time = tm_.time()
#
# # TODO add proper warning with warn module
# # --- Images
#
# # Find the name of the file, eq. to the biological tested condition number x
# name_file = os_.path.basename(data_path)
# print("FILE: ", name_file)
# name_file = name_file.replace(".tif", "")
#
# name_dir = os_.path.dirname(data_path)
# print("DIR: ", name_dir)
#
# # Find the dimension of the image voxels in micron
# # TODO do something more general, here too specific to one microscope metadata
# size_voxel_in_micron = in_.FindVoxelDimensionInMicron(data_path, size_voxel_in_micron=size_voxel_in_micron)
#
# # Read the image
# image = io_.volread(data_path)
#
# # Image size verification - simple version without user interface
# image = in_.ImageVerification(image, channel)
#
# # iv_.image_verification(image, channel) # -> PySide2 user interface # TODO: must return the modified image!
# # /!\ conflicts between some versions of PySide2 and Python3
#
# image = image[:, 500:, 500:]
# img_shape = image.shape
#
# #
# print(f"IMAGE: s.{img_shape} t.{image.dtype} m.{image.min()} M.{image.max()}")
#
# # Intensity relative normalization (between 0 and 1).
# # Image for soma normalized for hysteresis
# image_for_soma = in_.IntensityNormalizedImage(image)
# # Image for extensions not normalized for frangi enhancement
# image_for_ext = image.copy()
#
# print(f"NRM-IMG: t.{image_for_soma.dtype} m.{image_for_soma.min():.2f} M.{image_for_soma.max():.2f}")
#
# # --- Initialization
# som_nfo = {}
# ext_nfo = {}
# axes = {}
#
# #
# # --- Somas
# print("\n--- Soma Detection")
#
# # Change the soma parameters from micron to pixel dimensions, using the previously determined voxel size
# soma_min_area_c = in_.ToPixel(soma_min_area_c, size_voxel_in_micron, dimension=(0, 1))
# soma_max_area_c = in_.ToPixel(soma_max_area_c, size_voxel_in_micron, dimension=(0, 1))
# soma_selem_c = mp_.disk(in_.ToPixel(soma_selem_micron_c, size_voxel_in_micron))
#
# # - Create the maps for enhancing and detecting the soma
# # Hysteresis thresholding and closing/opening
# som_nfo["map_small"] = soma_t.Map(image_for_soma, soma_low_c, soma_high_c, soma_selem_c)
# # Deletion of too small elements
# som_nfo["map_small"], som_lmp_small = soma_t.DeleteSmallSomas(som_nfo["map_small"], soma_min_area_c)
# # Deletion of too big elements (probably aggregation of cells)
# # Separated from the deletion of small elements to avoid extensions to be detected where too big somas were just deleted
# som_nfo["map"], som_lmp = soma_t.DeleteBigSomas(som_nfo["map_small"], som_lmp_small, soma_max_area_c)
#
# # Labelling of somas, find the number of somas
# # som_nfo["lmp"], n_somas = ms_.label(som_nfo["map"], return_num=True)
#
# # Use relabel instead of label to optimize the algorithm. /!\ But BUG.
# som_nfo["lmp"] = relabel_sequential(som_lmp)[0]
# n_somas = som_nfo["lmp"].max()
#
# # po_.MaximumIntensityProjectionZ(som_nfo["lmp"])
#
# # Create the distance and influence maps used next for connexions between somas-extensions and extensions-extensions
# som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(som_nfo["lmp"])
#
# # po_.MaximumIntensityProjectionZ(som_nfo["influence_map"])
#
# # Create the tuple somas, containing all the objects 'soma'
# somas = tuple(soma_t().FromMap(som_nfo["lmp"], uid) for uid in range(1, n_somas + 1))
#
# print(f" n = {n_somas}")
#
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
#
# if with_plot:
# fb_.PlotSomas(somas, som_nfo, axes)
#
# #
# # -- Extentions
# print("\n--- Extension Detection")
#
# # Set to zeros the pixels belonging to the somas.
# # Take into account the aggregation of cells detected as one big soma to avoid extension creation there
# del_soma = (som_nfo["map_small"] > 0).nonzero()
# image_for_ext[del_soma] = 0
#
# # Change the extensions parameters from micron to pixel dimensions
# ext_min_area_c = in_.ToPixel(ext_min_area_c, size_voxel_in_micron, dimension=(0, 1))
# ext_max_length_c = in_.ToPixel(ext_max_length_c, size_voxel_in_micron, dimension=(0,))
#
# if ext_selem_micron_c == 0:
# ext_selem_pixel_c = None
# else:
# ext_selem_pixel_c = mp_.disk(in_.ToPixel(ext_selem_micron_c, size_voxel_in_micron))
#
# # scale_range_pixel = []
# # #
# # for value in scale_range:
# # value_in_pixel = in_.ToPixel(value, size_voxel_in_micron, decimals=1)
# # scale_range_pixel.append(value_in_pixel)
# # scale_range = tuple(scale_range_pixel)
# #
# # scale_step = in_.ToPixel(scale_step, size_voxel_in_micron, decimals=1)
#
# # - Perform frangi feature enhancement (via python or c - faster - implementation)
# enhanced_ext, ext_scales = extension_t.EnhancedForDetection(
# image_for_ext,
# scale_range,
# scale_step,
# alpha,
# beta,
# frangi_c,
# bright_on_dark,
# method,
# diff_mode=diff_mode,
# in_parallel=in_parallel)
#
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"Elapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}\n")
#
# # po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
#
# # - Creation of the enhanced maps
# # enhanced_ext = ex_.adjust_log(enhanced_ext, 1) # Not necessary, log contrast adjustment
#
# # Enhanced the contrast in frangi output
# if adapt_hist_equalization:
# # necessary but not sufficient, histogram equalisation (adaptative)
# enhanced_ext = ex_.equalize_adapthist(enhanced_ext)
# else:
# # necessary but not sufficient, histogram equalisation (global)
# enhanced_ext = ex_.equalize_hist(enhanced_ext)
#
# # po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
#
# # Rescale the image by stretching the intensities between 0 and 255, necessary but not sufficient
# enhanced_ext = ex_.rescale_intensity(enhanced_ext, out_range=(0, 255))
# # Hysteresis thersholding
# ext_nfo["coarse_map"] = extension_t.CoarseMap(enhanced_ext, ext_low_c, ext_high_c, ext_selem_pixel_c)
# # po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
# # Deletion of too small elements
# ext_nfo["coarse_map"], ext_lmp = extension_t.FilteredCoarseMap(ext_nfo["coarse_map"], ext_min_area_c)
# # po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
# # Creation of the extensions skeleton
# ext_nfo["map"] = extension_t.FineMapFromCoarseMap(ext_nfo["coarse_map"])
# # TODO delete too long extensions
# # ext_nfo["map"] = extension_t.FilteredSkeletonMap(ext_nfo["coarse_map"], ext_max_length_c)
# # po_.MaximumIntensityProjectionZ(ext_nfo["map"])
# # Deletion of extensions found within the somas
# ext_nfo["map"][som_nfo["map"] > 0] = 0
# # Labelling of extensions and number of extensions determined
# ext_nfo["lmp"], n_extensions = ms_.label(ext_nfo["map"], return_num=True)
#
# # Use relabel instead of label to optimize the algorithm. BUT PROBLEM WITH THE NUMBER OF EXTENSIONS DETECTED !
# # ext_nfo["lmp"] = relabel_sequential(ext_lmp)[0]
# # n_extensions = ext_nfo["lmp"].max()
#
# # Create the tuple extensions, containing all the objects 'extension'
# extensions = tuple(
# extension_t().FromMap(ext_nfo["lmp"], ext_scales, uid)
# for uid in range(1, n_extensions + 1))
#
# print(f" n = {n_extensions}")
#
# #
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
#
# if with_plot:
# fb_.PlotExtensions(extensions, ext_nfo, img_shape)
#
# # -- Preparation for Connections
# # Calculate the costs of each pixel on the image
# dijkstra_costs = in_.DijkstraCosts(image, som_nfo["map"], ext_nfo["map"])
#
# # -- Soma-Extention
# print("\n--- Soma <-> Extension")
#
# # Change the extensions parameters from micron to pixel dimensions
# max_straight_sq_dist_c = in_.ToPixel(max_straight_sq_dist_c, size_voxel_in_micron)
# max_weighted_length_c = in_.ToPixel(max_weighted_length_c, size_voxel_in_micron)
#
# # Find the candidate extensions, with their endpoints, for connexion to the somas
# candidate_conn_nfo = cn_.CandidateConnections(
# somas,
# som_nfo["influence_map"],
# som_nfo["dist_to_closest"],
# extensions,
# max_straight_sq_dist_c,
# )
#
# # For each candidate, verify that it is valid based on the shortest path and the maximum allowed straight distance
# for ep_idx, soma, extension, end_point in candidate_conn_nfo:
# # Only try to connect if the extension is not already connected
# if extension.is_unconnected:
# print(f" Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end="")
# # Use of Dijkstra shortest weighted path
# path, length = cn_.ShortestPathFromToN(
# start_time,
# end_point,
# dijkstra_costs,
# soma.ContourPointsCloseTo,
# max_straight_sq_dist=max_straight_sq_dist_c,
# )
#
# # Keep the connexion only if inferior to the allowed max weighted distance
# if length <= max_weighted_length_c:
# cn_.ValidateConnection(soma, extension, end_point, path, dijkstra_costs)
# print(": Made")
# else:
# print("")
#
# # for soma in somas:
# # soma.Extend(extensions, som_nfo["dist_to_closest"], dijkstra_costs)
#
# fb_.PrintConnectedExtensions(extensions)
#
# #
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
#
# if with_plot:
# fb_.PlotSomasWithExtensions(somas, som_nfo, "all")
#
# # -- Extention-Extention
# print("\n--- Extension <-> Extension")
#
# should_look_for_connections = True
#
# # Update the soma maps with next Ext-Ext connexions
# while should_look_for_connections:
# # Update the final somas + ext map by adding the new extensions and their connexion paths
# som_nfo["soma_w_ext_lmp"] = soma_t.SomasLMap(somas)
# # Update the influence map
# som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(som_nfo["soma_w_ext_lmp"])
#
# # Find the candidate extensions for connexion to the primary extensions
# candidate_conn_nfo = cn_.CandidateConnections(
# somas,
# som_nfo["influence_map"],
# som_nfo["dist_to_closest"],
# extensions,
# max_straight_sq_dist_c,
# )
#
# should_look_for_connections = False
#
# # For each candidate, verify that it is valid based on the shortest path and the maximum allowed straight distance
# for ep_idx, soma, extension, end_point in candidate_conn_nfo:
# # Only try to connect if the extension is not already connected
# if extension.is_unconnected:
# print(f" Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end="")
# # Use of Dijkstra shortest weighted path
# path, length = cn_.ShortestPathFromToN(
# start_time,
# end_point,
# dijkstra_costs,
# soma.ExtensionPointsCloseTo,
# max_straight_sq_dist=max_straight_sq_dist_c,
# )
#
# # Keep the connexion only if inferior to the allowed max weighted distance
# if length <= max_weighted_length_c:
# # Verify that the last element of the connexion path is contained in the extension to be connected
# # If so, returns the extensions containing the path last site.
# tgt_extenstion = extension_t.ExtensionContainingSite(extensions, path[-1])
# # Validate connexion: update the glial component objects and store more info in their fields
# cn_.ValidateConnection(tgt_extenstion, extension, end_point, path, dijkstra_costs)
# # TODO delete too long extensions
#
# should_look_for_connections = True
#
# print(": Made")
# else:
# print("")
#
# # Create an extension map containing all the ext + connexions, without the somas.
# # Used for the graphs extraction
# ext_nfo["lmp_soma"] = som_nfo['soma_w_ext_lmp'] - som_nfo['lmp']
#
# fb_.PrintConnectedExtensions(extensions)
#
# #
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
#
# if with_plot:
# fb_.PlotSomasWithExtensions(somas, som_nfo, "with_ext_of_ext")
#
# # -- Summary
# print("\n")
# for soma in somas:
# print(soma)
#
# # snapshot = tr_.take_snapshot()
# # top_file_stats = snapshot.statistics('lineno')
# # print("Memory Profiling: Top 10 FILES")
# # for stat in top_file_stats[:10]:
# # print(stat)
# # top_block_stats = snapshot.statistics('traceback')
# # top_block_stats = top_block_stats[0]
# # print(f"Memory Profiling: {top_block_stats.count} memory blocks: {top_block_stats.size / 1024:.1f} KiB")
# # for line in top_block_stats.traceback.format():
# # print(line)
#
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
#
# if with_plot:
# pl_.show()
#
# po_.MaximumIntensityProjectionZ(som_nfo['soma_w_ext_lmp'])
# # , output_image_file_name=f"D:\\MorganeNadal\\Results\\Images\\{name_file}.png")
#
# # --- Extract all the extensions of all somas as a graph
# print('\n--- Graph extraction')
#
# # Create the graphs with SKLGraph module (available on Eric Debreuve Gitlab)
# for soma in somas:
# print(f" Soma {soma.uid}", end="")
# # Create SKLGraph skeletonized map
# ext_map = skl_map_t.FromShapeMap(ext_nfo['lmp_soma'] == soma.uid, store_widths=True, skeletonize=False,
# do_post_thinning=True)
# # do_post_thinning = True, in order to remove pixels that are not breaking connectivity
#
# # Create the graph from the SKLGaph skeletonized map
# soma.skl_graph = skl_graph_t.FromSkeleton(ext_map, size_voxel=size_voxel_in_micron)
#
# # --- Find the root of the {ext+conn} graphs.
# # Roots are the nodes of degree 1 that are to be linked to the soma
# soma.graph_roots = ge_.FindGraphsRootWithNodes(soma)
#
# # Add a node "soma" and link it to the root nodes
# soma_node = f"S-{int(soma.centroid[0])}-{int(soma.centroid[1])}-{int(soma.centroid[2])}"
# soma.skl_graph.add_node(soma_node, soma=True, soma_nfo=soma)
#
# for node in soma.graph_roots.values():
# soma.skl_graph.add_edge(node, soma_node, root=True)
# #
# # nx_.draw_networkx(soma.skl_graph)
# # if with_plot:
# # pl_.show(block=True)
# #
# print(": Done")
#
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
#
# # --- Extract features
# print('\n--- Features Extraction\n')
#
# # Parameters
# if hist_bins_borders_length is None:
# number_of_bins_length = int(number_of_bins_length)
# bins_length = np_.linspace(hist_min_length, hist_min_length + hist_step_length * number_of_bins_length,
# num=number_of_bins_length)
# bins_length[-1] = np_.inf
# else:
# bins_length = np_.array(hist_bins_borders_length)
# bins_length[-1] = np_.inf
#
# if hist_bins_borders_curvature is None:
# number_of_bins_curvature = int(number_of_bins_curvature)
# bins_curvature = np_.linspace(hist_min_curvature,
# hist_min_curvature + hist_step_curvature * number_of_bins_curvature,
# num=number_of_bins_curvature)
# bins_curvature[-1] = np_.inf
# else:
# bins_curvature = np_.array(hist_bins_borders_curvature)
# bins_curvature[-1] = np_.inf
#
# # Pandas dataframe creation with all the measured features
# features_df = ge_.ExtractFeaturesInDF(name_file, somas, size_voxel_in_micron, bins_length, bins_curvature,
# ext_scales)
#
# # Save the pandas df into .csv
# features_df.to_csv(f"{name_dir}\\{name_file}.csv")
#
# #
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
# print(f"DONE: {tm_.strftime('%a, %b %d %Y @ %H:%M:%S')}")
#
# return features_df
#
#
# if __name__ == '__main__':
#
# # --- Extract cell graphs and features from microscope images using NutriMorph function.
# #
# # Differentiate between path to a tiff file or to a repository
# if pathlib.Path(data_path).is_file():
# # Perform NutriMorph algorithm on the file entered in parameters
# features_df = NutriMorph(data_path)
#
# elif pathlib.Path(data_path).is_dir():
# # Keep the directory to the repository
# name_dir = os_.path.dirname(data_path)
# # Initialize the future concatenated features
# concatenated_features_df = pd_.DataFrame()
# # Find all the tiff files in the parent and child repositories
# for path in pathlib.Path(data_path).glob("**/*.tif"):
# if path.is_file():
# # Perform NutriMorph algorithm
# features_df = NutriMorph(path)
# # Concatenate all the dataframes
# concatenated_features_df = concatenated_features_df.append(features_df)
# # Save to .csv in the parent repository
# concatenated_features_df.to_csv(f"{data_path}_features.csv")
#
# # --- TODO Clustering with this df and module cell_clustering.py