Delete folder_of_segmentations_to_meshview_sharon_comments.py

PyNutil/folder_of_segmentations_to_meshview_sharon_comments.py

-
-#pandas is used for working with csv files
-import pandas as pd
-#nrrd just lets us open nrrd files
-import nrrd
-import numpy as np
-import csv
-
-from datetime import datetime
-
-#import our function for converting a folder of segmentations to points
-from PyNutil import FolderToAtlasSpace, labelPoints, WritePointsToMeshview
-
-volume_path = "../annotation_volumes//annotation_10_reoriented.nrrd"
-data, header = nrrd.read(volume_path)
-
-startTime = datetime.now()
-
-segmentation_folder = "../test_data/tTA_2877_NOP/"
-alignment_json = "../test_data/tTA_2877_NOP_horizontal_final_2017.json"
-#now we can use our function to convert the folder of segmentations to points
-# colour is BGR (not RGB)
-points = FolderToAtlasSpace(segmentation_folder,alignment_json, pixelID=[0, 0, 255], nonLinear=True)
-#first we need to find the label file for the volume
-label_path = "../annotation_volumes//allen2022_colours.csv"
-#then the path to the volume
-
-#read the label files
-label_df = pd.read_csv(label_path)
-#read the annotation volume, it also has a header but we don't need it
-#now we can get the labels for each point
-labels = labelPoints(points, data, scale_factor=2.5)
-#save points to a meshview json
-WritePointsToMeshview(points, labels,"../outputs/points.json", label_df)
-
-#Task:
-# Make a pandas dataframe
-# Column 1: counted_labels
-# Column 2: label_counts
-# Column 3: region_name (look up by reading Allen2022_colours.csv, look up name and RGB)
-# Save dataframe in output as CSV
-# next task is to create functions from this. 
-counted_labels, label_counts = np.unique(labels, return_counts=True)
-counts_per_label = list(zip(counted_labels,label_counts))
-
-df_counts_per_label = pd.DataFrame(counts_per_label, columns=["allenID","pixel count"])
-
-allen_colours = "../annotation_volumes//allen2022_colours.csv"
-
-df_allen_colours =pd.read_csv(allen_colours, sep=",")
-df_allen_colours
-
-#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_allen_colours["allenID"] == row["allenID"] 
-    current_region_row = df_allen_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)
-#df_counts_per_label_name
-
-# write to csv file
-df_counts_per_label_name.to_csv("../outputs/counts_per_allenID.csv", sep=";", na_rep='', index= False)
-
-#r = df_allen_colours["r"]
-#g = df_allen_colours["g"]
-#b = df_allen_colours["b"]
-#region_name = df_allen_colours["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"time taken was: {time_taken}")
-
-
-#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.
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