Working with flat files

PyNutil/coordinate_extraction.py

@@ -136,6 +136,8 @@ def FolderToAtlasSpace(folder, QUINT_alignment, pixelID=[0, 0, 0], nonLinear=Tru
         points.extend(SegmentationToAtlasSpace(current_slice, SegmentationPath, pixelID, nonLinear))
     return np.array(points)
 
+# points.append would make list of lists, keeping sections separate. 
+
 
 #related to coordinate extraction
 #this function returns an array of points

PyNutil/pyflat.py

+   import sys
+   for arg in sys.argv:
+       if arg.startswith("flat="):
+           flatfile=arg[len("flat="):]
+       if arg.startswith("label="):
+           labelfile=arg[len("label="):]
+       if arg.startswith("json="):
+           jsonfile=arg[len("json="):]
+       if arg.startswith("output="):
+           outfile=arg[len("output="):]
+   if "flatfile" not in vars():
+       print("flat=<some .flat file> parameter missing")
+       sys.exit()
+   if "outfile" not in vars():
+       print("output=<new .png file> parameter missing")
+       sys.exit()
+   
+   palette=False
+   if "labelfile" in vars():
+       import re
+       palette=[]
+       with open(labelfile) as f:
+           for line in f:
+               lbl=re.match(r'\s*\d+\s+(\d+)\s+(\d+)\s+(\d+)',line)
+               if lbl:
+                   palette.append((int(lbl[1]),int(lbl[2]),int(lbl[3])))
+       print(f"{len(palette)} labels parsed")
+   elif "jsonfile" in vars():
+       import json
+       with open(jsonfile) as f:
+           palette=[(i["red"],i["green"],i["blue"]) for i in json.load(f)]
+       print(f"{len(palette)} labels loaded")
+   
+   import struct
+   with open(flatfile,"rb") as f:
+       b,w,h=struct.unpack(">BII",f.read(9))
+       data=struct.unpack(">"+("xBH"[b]*(w*h)),f.read(b*w*h))
+   print(f"{b} bytes per pixel, {w} x {h} resolution")
+   
+   import PIL.Image
+   image=PIL.Image.new("RGB" if palette else "L",(w,h))
+   for y in range(h):
+       for x in range(w):
+           image.putpixel((x,y),palette[data[x+y*w]] if palette else data[x+y*w] & 255)
+   image.save(outfile,"PNG")
\ No newline at end of file

PyNutil/testing_openflatfile.py

+base= r"../test_data\ttA_2877_NOP_atlasmaps\2877_NOP_tTA_lacZ_Xgal_s037_nl.flat"
+import numpy as np
+import struct
+with open(base,"rb") as f:
+    b,w,h=struct.unpack(">BII",f.read(9))
+    data=struct.unpack(">"+("xBH"[b]*(w*h)),f.read(b*w*h))
+    
+from PIL import Image
+import random
+image= Image.new("RGB",(w,h))
+for y in range(h):
+    for x in range(w):
+    image.putpixel((x,y),palette[d])
+
+image_arr = np.array(image)
+
+image_arr.min()
\ No newline at end of file

scripts/reformat_label_files.py

@@ -20,7 +20,8 @@ reformat_allen_label("../annotation_volumes/itksnap_label_description_2022.txt",
 """reformat AllenMouseBrain_atlas_CCF_2017.label"""
 def reformat_label(inputpath, outputpath):
     df = pd.read_csv(inputpath, sep = "\t", header=None, skiprows=15 ,names=["idx", "r", "g", "b", "a", "VIS", "MSH", "name"] )
-    df = df.append({"idx": 0, "name": "Clear Label", "r": 0, "g": 0, "b": 0, "a": 1.0, "VIS":1.0, "MSH":1.0}, ignore_index=True)
+    df_clear = pd.DataFrame({"idx": 0, "name": "Clear Label", "r": 0, "g": 0, "b": 0, "a": 1.0, "VIS":1.0, "MSH":1.0}, ignore_index=True)
+    df = pd.concat([df_clear, df])
     df.to_csv(outputpath, index=False)
 
 reformat_label("../annotation_volumes/AllenMouseBrain_Atlas_CCF_2017.label","../annotation_volumes/allen2017_colours.csv")