diff --git a/PyNutil/coordinate_extraction.py b/PyNutil/coordinate_extraction.py
index 357103592489c27cc823837dea5a5ff9f560ebbb..277c3b75ea0abce1183d44cfa6d94ee172df9a0b 100644
--- a/PyNutil/coordinate_extraction.py
+++ b/PyNutil/coordinate_extraction.py
@@ -121,6 +121,8 @@ def folder_to_atlas_space(
non_linear=True,
method="all",
object_cutoff=0,
+ atlas_volume=None,
+ use_flat = False,
):
"""Apply Segmentation to atlas space to all segmentations in a folder."""
"""Return pixel_points, centroids, points_len, centroids_len, segmentation_filenames, """
@@ -153,8 +155,12 @@ def folder_to_atlas_space(
seg_nr = int(number_sections([segmentation_path])[0])
current_slice_index = np.where([s["nr"] == seg_nr for s in slices])
current_slice = slices[current_slice_index[0][0]]
- current_flat_file_index = np.where([f == seg_nr for f in flat_file_nrs])
- current_flat = flat_files[current_flat_file_index[0][0]]
+ if use_flat == True:
+ current_flat_file_index = np.where([f == seg_nr for f in flat_file_nrs])
+ current_flat = flat_files[current_flat_file_index[0][0]]
+ else:
+ current_flat = None
+
x = threading.Thread(
target=segmentation_to_atlas_space,
args=(
@@ -170,6 +176,8 @@ def folder_to_atlas_space(
index,
method,
object_cutoff,
+ atlas_volume,
+ use_flat
),
)
threads.append(x)
@@ -199,7 +207,7 @@ def segmentation_to_atlas_space(
slice,
segmentation_path,
atlas_labels,
- flat_file_atlas,
+ flat_file_atlas=None,
pixel_id="auto",
non_linear=True,
points_list=None,
@@ -208,11 +216,13 @@ def segmentation_to_atlas_space(
index=None,
method="per_pixel",
object_cutoff=0,
+ atlas_volume=None,
+ use_flat=False
):
"""Combines many functions to convert a segmentation to atlas space. It takes care
of deformations."""
- print(f"workissssssssssng on {segmentation_path}")
+ print(f"working on {segmentation_path}")
if segmentation_path.endswith(".dzip"):
print("Reconstructing dzi")
segmentation = reconstruct_dzi(segmentation_path)
@@ -229,14 +239,17 @@ def segmentation_to_atlas_space(
# Currently only works for a single label
print("length of non background pixels: ", len(segmentation_no_background))
pixel_id = segmentation_no_background[0]
- print('detected pissssssxel_id: ', pixel_id)
+ print('detected pixel_id: ', pixel_id)
# Transform pixels to registration space (the registered image and segmentation have different dimensions)
seg_height = segmentation.shape[0]
seg_width = segmentation.shape[1]
reg_height = slice["height"]
reg_width = slice["width"]
- region_areas = flat_to_dataframe(flat_file_atlas, atlas_labels, (seg_width,seg_height))
+ if use_flat == True:
+ region_areas = flat_to_dataframe(atlas_labels, flat_file_atlas, (seg_width,seg_height))
+ else:
+ region_areas = flat_to_dataframe(atlas_labels, flat_file_atlas, (seg_width,seg_height), slice["anchoring"], atlas_volume)
# This calculates reg/seg
y_scale, x_scale = transform_to_registration(
seg_height, seg_width, reg_height, reg_width
diff --git a/PyNutil/counting_and_load.py b/PyNutil/counting_and_load.py
index 99d470a1c62badb1d7b6ddd766f30fc3ca75c446..87adbe2d683a7565e1e45ed818e791a41dedc280 100644
--- a/PyNutil/counting_and_load.py
+++ b/PyNutil/counting_and_load.py
@@ -5,7 +5,7 @@ import matplotlib.pyplot as plt
import os
import nrrd
import cv2
-# from generate_target_slice import generate_target_slice
+from .generate_target_slice import generate_target_slice
# related to counting and load
@@ -119,92 +119,82 @@ def pixel_count_per_region(
"""Read flat file and write into an np array"""
"""Read flat file, write into an np array, assign label file values, return array"""
+import struct
+import cv2
+import numpy as np
+import pandas as pd
-
-
-
-def flat_to_dataframe(file, labelfile, rescaleXY=False):
- if rescaleXY:
- rescaleX, rescaleY = rescaleXY
- if file.endswith(".flat"):
- with open(file, "rb") as f:
- # I don't know what b is, w and h are the width and height that we get from the
- # flat file header
- b, w, h = struct.unpack(">BII", f.read(9))
- # Data is a one dimensional list of values
- # It has the shape width times height
- data = struct.unpack(">" + ("xBH"[b] * (w * h)), f.read(b * w * h))
- elif file.endswith(".seg"):
- with open(file,"rb") as f:
- def byte():
- return f.read(1)[0]
- def code():
- c=byte()
- if(c<0):
- raise "!"
- return c if c<128 else (c&127)|(code()<<7)
- if "SegRLEv1" != f.read(8).decode():
- raise "Header mismatch"
- atlas=f.read(code()).decode()
- print(f"Target atlas: {atlas}")
- codes=[code() for x in range(code())]
- w=code()
- h=code()
- data=[]
- while len(data)<w*h:
- data += [codes[byte() if len(codes)<=256 else code()]]*(code()+1)
- # Convert flat file data into an array, previously data was a tuple
+def read_flat_file(file):
+ with open(file, "rb") as f:
+ b, w, h = struct.unpack(">BII", f.read(9))
+ data = struct.unpack(">" + ("xBH"[b] * (w * h)), f.read(b * w * h))
image_data = np.array(data)
-
- # Create an empty image array in the right shape, write image_data into image_array
image = np.zeros((h, w))
for x in range(w):
for y in range(h):
image[y, x] = image_data[x + y * w]
-
- image_arr = np.array(image)
- #return image_arr
- if rescaleXY:
- w,h= rescaleXY
- image_arr = cv2.resize(image_arr, (h, w), interpolation=cv2.INTER_NEAREST)
-
- print("max data value",np.max(image_arr))
-
- if file.endswith(".flat"):
- allen_id_image = np.zeros((h, w)) # create an empty image array
- coordsy, coordsx = np.meshgrid(list(range(w)), list(range(h)))
- values = image_arr[coordsy,coordsx] # assign x,y coords from image_array into values
-
- lbidx = labelfile["idx"].values
- allen_id_image = lbidx[values.astype(int)]
- elif file.endswith(".seg"):
- allen_id_image = image_arr
- """assign label file values into image array"""
-
- #return allen_id_image
-
- #def count_per_uniqueidx()
-
- """count pixels for unique idx"""
- unique_ids, counts = np.unique(allen_id_image, return_counts=True)
-
+ return image
+
+def read_seg_file(file):
+ with open(file,"rb") as f:
+ def byte():
+ return f.read(1)[0]
+ def code():
+ c=byte()
+ if(c<0):
+ raise "!"
+ return c if c<128 else (c&127)|(code()<<7)
+ if "SegRLEv1" != f.read(8).decode():
+ raise "Header mismatch"
+ atlas=f.read(code()).decode()
+ print(f"Target atlas: {atlas}")
+ codes=[code() for x in range(code())]
+ w=code()
+ h=code()
+ data=[]
+ while len(data)<w*h:
+ data += [codes[byte() if len(codes)<=256 else code()]]*(code()+1)
+ image_data = np.array(data)
+ image = np.reshape(image_data, (h, w))
+ return image
+
+def rescale_image(image, rescaleXY):
+ w, h = rescaleXY
+ return cv2.resize(image, (h, w), interpolation=cv2.INTER_NEAREST)
+
+def assign_labels_to_image(image, labelfile):
+ w,h = image.shape
+ allen_id_image = np.zeros((h, w)) # create an empty image array
+ coordsy, coordsx = np.meshgrid(list(range(w)), list(range(h)))
+
+ values = image[coordsy, coordsx]
+ lbidx = labelfile["idx"].values
+
+ allen_id_image = lbidx[values.astype(int)]
+ return allen_id_image
+
+def count_pixels_per_label(image):
+ unique_ids, counts = np.unique(image, return_counts=True)
area_per_label = list(zip(unique_ids, counts))
- # create a list of unique regions and pixel counts per region
-
df_area_per_label = pd.DataFrame(area_per_label, columns=["idx", "region_area"])
- print("df_area_per_label")
- print(df_area_per_label)
- # create a pandas df with regions and pixel counts
- return(df_area_per_label)
-
-# Import flat files, count pixels per label, np.unique... etc. nitrc.org/plugins/mwiki/index.php?title=visualign:Deformation
-
-"""
- base=slice["filename"][:-4]
-
- import struct
- with open(base+".flat","rb") as f:
- b,w,h=struct.unpack(">BII",f.read(9))
- data=struct.unpack(">"+("xBH"[b]*(w*h)),f.read(b*w*h))
-"""
+ return df_area_per_label
+
+def flat_to_dataframe(labelfile, file=None, rescaleXY=None, image_vector=None, volume=None):
+ if (image_vector is not None) and (volume is not None):
+ image = generate_target_slice(image_vector, volume)
+ image = np.float64(image)
+ elif file.endswith(".flat"):
+ image = read_flat_file(file)
+ elif file.endswith(".seg"):
+ image = read_seg_file(file)
+ print('datatype', image.dtype)
+ print('image shape open', image.shape)
+ if rescaleXY:
+ image = rescale_image(image, rescaleXY)
+ if (image_vector is None) or (volume is None):
+ allen_id_image = assign_labels_to_image(image, labelfile)
+ else:
+ allen_id_image = image
+ df_area_per_label = count_pixels_per_label(allen_id_image)
+ return df_area_per_label
\ No newline at end of file
diff --git a/PyNutil/main.py b/PyNutil/main.py
index ce537d7b767f9eeb6738debdf944a24a2fa86f3e..4d19a3b7a6fbc1e1f0dc212aee9b75b3841c97e5 100644
--- a/PyNutil/main.py
+++ b/PyNutil/main.py
@@ -134,7 +134,7 @@ class PyNutil:
return atlas_volume, atlas_labels
- def get_coordinates(self, non_linear=True, method="all", object_cutoff=0):
+ def get_coordinates(self, non_linear=True, method="all", object_cutoff=0, use_flat=False):
"""Extracts pixel coordinates from the segmentation data.
Parameters
@@ -177,6 +177,8 @@ class PyNutil:
non_linear=non_linear,
method=method,
object_cutoff=object_cutoff,
+ atlas_volume=self.atlas_volume,
+ use_flat=use_flat
)
self.pixel_points = pixel_points
self.centroids = centroids
@@ -241,43 +243,6 @@ class PyNutil:
current_df_new = all_region_df.merge(current_df, on= 'idx', how= 'left', suffixes= (None,"_y")).drop(columns=["a","VIS", "MSH", "name_y","r_y","g_y","b_y"])
current_df_new["area_fraction"] = current_df_new["pixel_count"] / current_df_new["region_area"]
current_df_new.fillna(0, inplace=True)
-
- # Several alternatives for the merge code above
- """
- new_rows = []
- for index, row in all_region_df.iterrows():
- mask = current_df["idx"] == row ["idx"]
- pnt.save_analysis("outputs/test8_PyNutil")
- object_count = current_region_row["object_count"].values
-
- row["pixel_count"] = region_area[0]
- row["object_count"] = object_count[0]
-
- new_rows.append[row]
-
- current_df_new = pd.DataFrame(new_rows)
- """
-
- """
- new_rows = []
- for index, row in current_df.iterrows():
- mask = self.atlas_labels["idx"] == row["idx"]
- current_region_row = self.atlas_labels[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)
-
- current_df_new = pd.DataFrame(new_rows)"""
-
- #per_section_df.append(current_df_new)
per_section_df.append(current_df_new)
prev_pl += pl
prev_cl += cl
diff --git a/PyNutil/read_and_write.py b/PyNutil/read_and_write.py
index 4b3d0c70ced4878dda2314ddcfdabefef5d7c5a8..5a7e26f8503caf60dd3495c3c57b83d464e8f7a9 100644
--- a/PyNutil/read_and_write.py
+++ b/PyNutil/read_and_write.py
@@ -20,29 +20,27 @@ def load_visualign_json(filename):
print(slice)
slice["nr"] = int(re.search(r"_s(\d+)", slice["filename"]).group(1))
slice["anchoring"] = slice["ouv"]
+ lz_compat_file = {
+ "name":name,
+ "target":vafile["atlas"],
+ "target-resolution":[ 456, 528, 320],
+ "slices":slices,
+ }
+ # save with .json extension need to see if i can remove this
+ with open(filename.replace(".waln", ".json").replace(".wwrp", ".json"), "w") as f:
+ json.dump(lz_compat_file, f, indent=4)
else:
slices = vafile["slices"]
# overwrite the existing file
-
name = os.path.basename(filename)
- slices = [{"filename":slice['filename'],
- "nr":slice["nr"],
- "width":slice["width"],
- "height":slice["height"],
- "anchoring":slice["anchoring"]} for slice in slices]
- lz_compat_file = {
- "name":name,
- "target":vafile["atlas"],
- "target-resolution":[ 456, 528, 320],
- "slices":slices,
-
- }
- # save with .json extension
- with open(filename.replace(".waln", ".json").replace(".wwrp", ".json"), "w") as f:
- json.dump(lz_compat_file, f, indent=4)
+
return slices
-
+def load_visualign_json(filename):
+ with open(filename) as f:
+ vafile = json.load(f)
+ slices = vafile["slices"]
+ return slices
# related to read_and_write, used in write_points_to_meshview
# this function returns a dictionary of region names
def create_region_dict(points, regions):
diff --git a/test/test8_PyNutil_fixed.json b/test/test8_PyNutil_fixed.json
index 842b226f80dec6358d62fbd09e8a5b2ca11e08aa..8969423c8ec9a40d07910b7cfe82a296ebc0a7b0 100644
--- a/test/test8_PyNutil_fixed.json
+++ b/test/test8_PyNutil_fixed.json
@@ -1,8 +1,8 @@
{ "volume_path": "allen2017",
- "label_path": "annotation_volumes/allen2017_colours.csv",
- "segmentation_folder": "test_data/PyTest_seg",
- "alignment_json": "test_data/PyNutil_testdataset_Nonlin_SY_fixed.json",
+ "label_path": "PyNutil/annotation_volumes/allen2017_colours.csv",
+ "segmentation_folder": "PyNutil/test_data/PyTest_seg",
+ "alignment_json": "PyNutil/test_data/PyNutil_testdataset_Nonlin_SY_fixed.json",
"nonlinear": true,
"colour": [0, 0, 0],
- "points_json_path": "outputs/test8_PyNutil.json"
+ "points_json_path": "PyNutil/outputs/test8_PyNutil.json"
}
\ No newline at end of file
diff --git a/testOOP.py b/testOOP.py
index c5e24774619e051678bac2ca544dc37ee2438b90..8075fb85e148db53c28a0ad30607c77930f33537 100644
--- a/testOOP.py
+++ b/testOOP.py
@@ -1,14 +1,13 @@
from PyNutil import PyNutil
-pnt = PyNutil(settings_file=r"test/test8_PyNutil_fixed.json")
-# pnt = PyNutil(settings_file=r"test/test7_PyNutil.json")
-# pnt.build_quantifier()
-
-pnt.get_coordinates(object_cutoff=0)
+pnt = PyNutil(settings_file=r"PyNutil/test/test8_PyNutil_fixed.json")
+##Use flat can be set to True if you want to use the flat file
+# instead of the visualign json (this is only useful for testing and will be removed)
+pnt.get_coordinates(object_cutoff=0, use_flat=False)
pnt.quantify_coordinates()
-pnt.save_analysis("outputs/test8_PyNutil")
+pnt.save_analysis("PyNutil/outputs/test8_PyNutil")
# remove name, r, g, b, from pixel_
# add to region_areas df
\ No newline at end of file
diff --git a/test_data/PyTest_seg/test_s001.png b/test_data/PyTest_seg/segmentations/test_s001.png
similarity index 100%
rename from test_data/PyTest_seg/test_s001.png
rename to test_data/PyTest_seg/segmentations/test_s001.png
diff --git a/test_data/PyTest_seg/test_s002.png b/test_data/PyTest_seg/segmentations/test_s002.png
similarity index 100%
rename from test_data/PyTest_seg/test_s002.png
rename to test_data/PyTest_seg/segmentations/test_s002.png
diff --git a/test_data/PyTest_seg/test_s003.png b/test_data/PyTest_seg/segmentations/test_s003.png
similarity index 100%
rename from test_data/PyTest_seg/test_s003.png
rename to test_data/PyTest_seg/segmentations/test_s003.png
diff --git a/test_data/PyTest_seg/test_s004.png b/test_data/PyTest_seg/segmentations/test_s004.png
similarity index 100%
rename from test_data/PyTest_seg/test_s004.png
rename to test_data/PyTest_seg/segmentations/test_s004.png
diff --git a/test_data/PyTest_seg/test_s005.png b/test_data/PyTest_seg/segmentations/test_s005.png
similarity index 100%
rename from test_data/PyTest_seg/test_s005.png
rename to test_data/PyTest_seg/segmentations/test_s005.png