diff --git a/PyNutil/coordinate_extraction.py b/PyNutil/coordinate_extraction.py
index 1edb063f6406a5154362ce6fbec85da9edcb1974..270fc37106027a6bd6ac4b7e11bdee29e21578c1 100644
--- a/PyNutil/coordinate_extraction.py
+++ b/PyNutil/coordinate_extraction.py
@@ -4,7 +4,7 @@ import pandas as pd
from DeepSlice.coord_post_processing.spacing_and_indexing import number_sections
import json
from read_and_write import loadVisuAlignJson
-from object_counting import labelPoints
+from counting_and_load import labelPoints
from visualign_deformations import triangulate, transform_vec
from glob import glob
from tqdm import tqdm
@@ -216,7 +216,7 @@ def SegmentationToAtlasSpaceMultiThreaded(slice, SegmentationPath, pixelID='auto
pointsList[index] = np.array(points)
-#related to coordinate extraction or object_counting
+#related to counting_and_load
def createRegionDict(points, regions):
"""points is a list of points and regions is an id for each point"""
regionDict = {region:points[regions==region].flatten().tolist() for region in np.unique(regions)}
@@ -224,7 +224,6 @@ def createRegionDict(points, regions):
#related to read and write: WritePoints
-
def WritePoints(pointsDict, filename, infoFile):
meshview = [
@@ -246,68 +245,14 @@ def WritePoints(pointsDict, filename, infoFile):
# related to read and write: WritePointsToMeshview
-
def WritePointsToMeshview(points, pointNames, filename, infoFile):
regionDict = createRegionDict(points, pointNames)
WritePoints(regionDict, filename, infoFile)
-# related to object_counting: labelPoints
-def labelPoints(points, label_volume, scale_factor=1):
- """this function takes a list of points and assigns them to a region based on the regionVolume.
- These regions will just be the values in the regionVolume at the points.
- it returns a dictionary with the region as the key and the points as the value"""
- #first convert the points to 3 columns
- points = np.reshape(points, (-1,3))
- #scale the points
- points = points * scale_factor
- #round the points to the nearest whole number
- points = np.round(points).astype(int)
- x = points[:,0]
- y = points[:,1]
- z = points[:,2]
- #get the label value for each point
- labels = label_volume[x,y,z]
- return labels
-
-# related to object_counting
-# consider separating out writing to CSV in future
-def PixelCountPerRegion(labelsDict, label_colours):
- """Function for counting no. of pixels per region and writing to CSV based on
- a dictionary with the region as the key and the points as the value, """
- counted_labels, label_counts = np.unique(labelsDict, return_counts=True)
- # which regions have pixels, and how many pixels are there per region
- counts_per_label = list(zip(counted_labels,label_counts))
- # create a list of unique regions and pixel counts per region
-
- df_counts_per_label = pd.DataFrame(counts_per_label, columns=["allenID","pixel count"])
- # create a pandas df with regions and pixel counts
-
- df_label_colours =pd.read_csv(label_colours, sep=",")
- # find colours corresponding to each region ID and add to the pandas dataframe
-
- #look up name, r, g, b in df_allen_colours in df_counts_per_label based on "allenID"
- new_rows = []
- for index, row in df_counts_per_label.iterrows():
- mask = df_label_colours["allenID"] == row["allenID"]
- current_region_row = df_label_colours[mask]
- current_region_name = current_region_row["name"].values
- current_region_red = current_region_row["r"].values
- current_region_green = current_region_row["g"].values
- current_region_blue = current_region_row["b"].values
-
- row["name"] = current_region_name[0]
- row["r"] = current_region_red[0]
- row["g"] = current_region_green[0]
- row["b"] = current_region_blue[0]
-
- new_rows.append(row)
-
- df_counts_per_label_name = pd.DataFrame(new_rows)
- return df_counts_per_label_name
-
-
-#def SaveDataframeasCSV(df_to_save):
- #df_to_save.to_csv(output_csv, sep=";", na_rep='', index= False)
+# related to counting_and_load: labelPoints.
+# this has been moved successfully.
+# related to counting_and_load: PixelCountPerRegion
+# This has been moved successfully.
diff --git a/PyNutil/counting_and_load.py b/PyNutil/counting_and_load.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e4887b988d9ff7db63c3bdb2d9527c3e9f85883
--- /dev/null
+++ b/PyNutil/counting_and_load.py
@@ -0,0 +1,57 @@
+import numpy as np
+import pandas as pd
+
+# related to counting and load
+def labelPoints(points, label_volume, scale_factor=1):
+ """this function takes a list of points and assigns them to a region based on the regionVolume.
+ These regions will just be the values in the regionVolume at the points.
+ it returns a dictionary with the region as the key and the points as the value"""
+ #first convert the points to 3 columns
+ points = np.reshape(points, (-1,3))
+ #scale the points
+ points = points * scale_factor
+ #round the points to the nearest whole number
+ points = np.round(points).astype(int)
+ x = points[:,0]
+ y = points[:,1]
+ z = points[:,2]
+ #get the label value for each point
+ labels = label_volume[x,y,z]
+ return labels
+
+
+# related to counting_and_load
+def PixelCountPerRegion(labelsDict, label_colours):
+ """Function for counting no. of pixels per region and writing to CSV based on
+ a dictionary with the region as the key and the points as the value, """
+ counted_labels, label_counts = np.unique(labelsDict, return_counts=True)
+ # which regions have pixels, and how many pixels are there per region
+ counts_per_label = list(zip(counted_labels,label_counts))
+ # create a list of unique regions and pixel counts per region
+
+ df_counts_per_label = pd.DataFrame(counts_per_label, columns=["allenID","pixel count"])
+ # create a pandas df with regions and pixel counts
+
+ df_label_colours =pd.read_csv(label_colours, sep=",")
+ # find colours corresponding to each region ID and add to the pandas dataframe
+
+ #look up name, r, g, b in df_allen_colours in df_counts_per_label based on "allenID"
+ new_rows = []
+ for index, row in df_counts_per_label.iterrows():
+ mask = df_label_colours["allenID"] == row["allenID"]
+ current_region_row = df_label_colours[mask]
+ current_region_name = current_region_row["name"].values
+ current_region_red = current_region_row["r"].values
+ current_region_green = current_region_row["g"].values
+ current_region_blue = current_region_row["b"].values
+
+ row["name"] = current_region_name[0]
+ row["r"] = current_region_red[0]
+ row["g"] = current_region_green[0]
+ row["b"] = current_region_blue[0]
+
+ new_rows.append(row)
+
+ df_counts_per_label_name = pd.DataFrame(new_rows)
+ return df_counts_per_label_name
+
diff --git a/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py b/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py
index 7c1d6955dc25ec06ea38a49c5b02d5497ea1ff28..8705412833664e321b5adfa0417e673aedb288ab 100644
--- a/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py
+++ b/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py
@@ -15,8 +15,9 @@ with open('../test/test2.json', 'r') as f:
#print(input)
#import our function for converting a folder of segmentations to points
-from coordinate_extraction import FolderToAtlasSpace, labelPoints, WritePointsToMeshview, FolderToAtlasSpaceMultiThreaded, PixelCountPerRegion
+from coordinate_extraction import FolderToAtlasSpace, WritePointsToMeshview, FolderToAtlasSpaceMultiThreaded
from read_and_write import SaveDataframeasCSV
+from counting_and_load import PixelCountPerRegion, labelPoints
startTime = datetime.now()
@@ -41,16 +42,13 @@ df_counts_per_label_name = PixelCountPerRegion(labels, input["allen_colours"])
SaveDataframeasCSV(df_counts_per_label_name, input["counts_per_label_name"])
-#while we havent added it here it would be good to next quantify the number of cells for each label.
time_taken = datetime.now() - startTime
print(f"overall time taken was: {time_taken}")
+#while we havent added it here it would be good to next quantify the number of cells for each label.
#get centroids and areas returns a list of objects and the center coordinate.
-
#we need to deform the center coordinate according to visualign deformations¨
-
#we need to then transform the coordinate into atlas space
-
#and then save labels like before.
\ No newline at end of file
diff --git a/PyNutil/object_counting.py b/PyNutil/object_counting.py
deleted file mode 100644
index e237457fc11240e2f6f77e4b57f33de0c2927236..0000000000000000000000000000000000000000
--- a/PyNutil/object_counting.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# related to object_counting: labelPoints
-def labelPoints(points, label_volume, scale_factor=1):
- """this function takes a list of points and assigns them to a region based on the regionVolume.
- These regions will just be the values in the regionVolume at the points.
- it returns a dictionary with the region as the key and the points as the value"""
- #first convert the points to 3 columns
- points = np.reshape(points, (-1,3))
- #scale the points
- points = points * scale_factor
- #round the points to the nearest whole number
- points = np.round(points).astype(int)
- x = points[:,0]
- y = points[:,1]
- z = points[:,2]
- #get the label value for each point
- labels = label_volume[x,y,z]
- return labels
\ No newline at end of file