diff --git a/PyNutil/coordinate_extraction.py b/PyNutil/coordinate_extraction.py
index b6740dd44f562c9a81945a0017b752424c8a52ae..db162acca958b3784cab93610952d73a38252a84 100644
--- a/PyNutil/coordinate_extraction.py
+++ b/PyNutil/coordinate_extraction.py
@@ -3,7 +3,7 @@ import pandas as pd
from DeepSlice.coord_post_processing.spacing_and_indexing import number_sections
import json
from .read_and_write import load_visualign_json
-from .counting_and_load import label_points
+from .counting_and_load import label_points, flat_to_dataframe
from .visualign_deformations import triangulate, transform_vec
from glob import glob
from tqdm import tqdm
@@ -87,6 +87,7 @@ def transform_to_atlas_space(anchoring, y, x, reg_height, reg_width):
def folder_to_atlas_space(
folder,
quint_alignment,
+ atlas_labels,
pixel_id=[0, 0, 0],
non_linear=True,
method="all",
@@ -103,25 +104,37 @@ def folder_to_atlas_space(
for file in glob(folder + "/*")
if any([file.endswith(type) for type in segmentation_file_types])
]
+ flat_files = [
+ file
+ for file in glob(folder + "/flat_files/*")
+ if any([file.endswith('.flat')])
+ ]
# Order segmentations and section_numbers
# segmentations = [x for _,x in sorted(zip(section_numbers,segmentations))]
# section_numbers.sort()
points_list = [None] * len(segmentations)
centroids_list = [None] * len(segmentations)
+ region_areas_list = [None] * len(segmentations)
threads = []
+ flat_file_nrs = [int(number_sections([ff])[0]) for ff in flat_files]
for segmentation_path, index in zip(segmentations, range(len(segmentations))):
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]]
x = threading.Thread(
target=segmentation_to_atlas_space,
args=(
current_slice,
segmentation_path,
+ atlas_labels,
+ current_flat,
pixel_id,
non_linear,
points_list,
centroids_list,
+ region_areas_list,
index,
method,
object_cutoff,
@@ -143,6 +156,7 @@ def folder_to_atlas_space(
return (
np.array(points),
np.array(centroids),
+ region_areas_list,
points_len,
centroids_len,
segmentations,
@@ -152,13 +166,17 @@ def folder_to_atlas_space(
def segmentation_to_atlas_space(
slice,
segmentation_path,
+ atlas_labels,
+ flat_file_atlas,
pixel_id="auto",
non_linear=True,
points_list=None,
centroids_list=None,
+ region_areas_list=None,
index=None,
method="per_pixel",
object_cutoff=0,
+
):
"""Combines many functions to convert a segmentation to atlas space. It takes care
of deformations."""
@@ -177,6 +195,7 @@ def segmentation_to_atlas_space(
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))
# This calculates reg/seg
y_scale, x_scale = transform_to_registration(
seg_height, seg_width, reg_height, reg_width
@@ -226,6 +245,7 @@ def segmentation_to_atlas_space(
)
points_list[index] = np.array(points)
centroids_list[index] = np.array(centroids)
+ region_areas_list[index] = region_areas
def get_centroids(segmentation, pixel_id, y_scale, x_scale, object_cutoff=0):
diff --git a/PyNutil/counting_and_load.py b/PyNutil/counting_and_load.py
index b5faacade63770b7f818d6c181d1ef03cf1bffbc..b48af03d8f920d1a43a2ea031b93ff52603a6613 100644
--- a/PyNutil/counting_and_load.py
+++ b/PyNutil/counting_and_load.py
@@ -4,6 +4,7 @@ import struct
import matplotlib.pyplot as plt
import os
import nrrd
+import cv2
# related to counting and load
@@ -87,6 +88,7 @@ def pixel_count_per_region(
# 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 "idx"
+ #Sharon, remove this here
new_rows = []
for index, row in df_counts_per_label.iterrows():
mask = df_label_colours["idx"] == row["idx"]
@@ -117,11 +119,14 @@ 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"""
-def flat_to_dataframe(flat_file, labelfile):
+def flat_to_dataframe(flat_file, labelfile, rescaleXY=False):
+ if rescaleXY:
+ rescaleX, rescaleY = rescaleXY
with open(flat_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))
+ print(w, h)
# 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))
@@ -137,12 +142,15 @@ def flat_to_dataframe(flat_file, labelfile):
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)
+
"""assign label file values into image array"""
- labelfile = pd.read_csv(labelfile)
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[coordsx, coordsy] # assign x,y coords from image_array into values
+ 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)]
#return allen_id_image
diff --git a/PyNutil/load_workflow.py b/PyNutil/load_workflow.py
index c99fd13a3be3e58d6f4356a072d55191400d4e1d..ac3dd3d0c49eab6c8947cc467b01749424fe34af 100644
--- a/PyNutil/load_workflow.py
+++ b/PyNutil/load_workflow.py
@@ -3,6 +3,7 @@
import struct
import matplotlib.pyplot as plt
import pandas as pd
+import cv2
import numpy as np
#from read_and_write import flat_to_array, label_to_array
@@ -10,12 +11,16 @@ from counting_and_load import flat_to_dataframe
base = r"../test_data/tTA_2877_NOP_s037_atlasmap/2877_NOP_tTA_lacZ_Xgal_s037_nl.flat"
label = r"../annotation_volumes\allen2017_colours.csv"
-
+##optional
+seg = r"../test_data/tTA_2877_NOP_s037_seg/2877_NOP_tTA_lacZ_Xgal_resize_Simple_Seg_s037.png"
+segim = cv2.imread(seg)
+#the indexing [:2] means the first two values and [::-1] means reverse the list
+segXY = segim.shape[:2][::-1]
#image_arr = flat_to_array(base, label)
#plt.imshow(flat_to_array(base, label))
-df_area_per_label = flat_to_dataframe(base, label)
+df_area_per_label = flat_to_dataframe(base, label, segXY)
"""count pixels in np array for unique idx, return pd df"""
#unique_ids, counts = np.unique(allen_id_image, return_counts=True)
@@ -54,7 +59,7 @@ df_area_per_label_name = pd.DataFrame(new_rows)
print(df_area_per_label_name)
df_area_per_label_name.to_csv(
- "../outputs/test5_s037_area_per_idx.csv", sep=";", na_rep="", index=False
+ "../outputs/NOP_s037_regionareas.csv", sep=";", na_rep="", index=False
)
diff --git a/PyNutil/main.py b/PyNutil/main.py
index 8e045e6bb055a76d14e7aa9f17dd4cf7ea28b45d..b2da63c3e8cb02c35c9b40df365c6655b1fab2a6 100644
--- a/PyNutil/main.py
+++ b/PyNutil/main.py
@@ -163,12 +163,14 @@ class PyNutil:
(
pixel_points,
centroids,
+ region_areas_list,
points_len,
centroids_len,
segmentation_filenames,
) = folder_to_atlas_space(
self.segmentation_folder,
self.alignment_json,
+ self.atlas_labels,
pixel_id=self.colour,
non_linear=non_linear,
method=method,
@@ -181,6 +183,7 @@ class PyNutil:
self.points_len = points_len
self.centroids_len = centroids_len
self.segmentation_filenames = segmentation_filenames
+ self.region_areas_list = region_areas_list
def quantify_coordinates(self):
"""Quantifies the pixel coordinates by region.
@@ -207,15 +210,12 @@ class PyNutil:
self.pixel_points, self.atlas_volume, scale_factor=1
)
- self.label_df = pixel_count_per_region(
- labeled_points, labeled_points_centroids, self.atlas_labels
- )
prev_pl = 0
prev_cl = 0
per_section_df = []
current_centroids = None
current_points = None
- for pl,cl in zip(self.points_len, self.centroids_len):
+ for pl,cl,ra in zip(self.points_len, self.centroids_len, self.region_areas_list):
if hasattr(self, "centroids"):
current_centroids = labeled_points_centroids[prev_cl : prev_cl + cl]
if hasattr(self, "pixel_points"):
@@ -223,9 +223,14 @@ class PyNutil:
current_df = pixel_count_per_region(
current_points, current_centroids, self.atlas_labels
)
+
+ current_df = ra.merge(current_df, on='idx', how='left')
+ ##Sharon. I would guess you should add the rgb and name adding code here
per_section_df.append(current_df)
prev_pl += pl
prev_cl += cl
+ ##Sharon. and then here you should group on r,g,b,idx, and name since you dont want any of these summed
+ self.label_df = pd.concat(per_section_df).groupby(['idx']).sum().reset_index()
self.labeled_points = labeled_points
self.labeled_points_centroids = labeled_points_centroids
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