diff --git a/PyNutil/coordinate_extraction.py b/PyNutil/coordinate_extraction.py
index 5a7b98978f8c23787d004445e5ecdfc9de59c3d7..c220e821ed347172ed1de8c92f7bf8702f720ea0 100644
--- a/PyNutil/coordinate_extraction.py
+++ b/PyNutil/coordinate_extraction.py
@@ -72,7 +72,6 @@ def transform_to_atlas_space(anchoring, y, x, reg_height, reg_width):
# Scale X and Y to between 0 and 1 using the registration width and height
y_scale = y / reg_height
x_scale = x / reg_width
- # print("width: ", reg_width, " height: ", reg_height, " Xmax: ", np.max(x), " Ymax: ", np.max(y), " Xscale: ", np.max(x_scale), " Yscale: ", np.max(y_scale))
xyz_v = np.array([y_scale * v[0], y_scale * v[1], y_scale * v[2]])
xyz_u = np.array([x_scale * u[0], x_scale * u[1], x_scale * u[2]])
o = np.reshape(o, (3, 1))
@@ -102,6 +101,7 @@ def folder_to_atlas_space(
# segmentations = [x for _,x in sorted(zip(section_numbers,segmentations))]
# section_numbers.sort()
points_list = [None] * len(segmentations)
+ centroids_list = [None] * len(segmentations)
threads = []
for segmentation_path, index in zip(segmentations, range(len(segmentations))):
seg_nr = int(number_sections([segmentation_path])[0])
@@ -115,6 +115,7 @@ def folder_to_atlas_space(
pixel_id,
non_linear,
points_list,
+ centroids_list,
index,
method,
),
@@ -126,18 +127,22 @@ def folder_to_atlas_space(
# Wait for threads to finish
[t.join() for t in threads]
# Flatten points_list
+
+ points_len = [len(points) for points in points_list]
+ centroids_len = [len(centroids) for centroids in centroids_list]
points = [item for sublist in points_list for item in sublist]
- return np.array(points)
+ centroids_list = [item for sublist in centroids_list for item in sublist]
+
+ return np.array(points), np.array(centroids_list), points_len, centroids_len
-# related to coordinate extraction
-# This function returns an array of points
def segmentation_to_atlas_space(
slice,
segmentation_path,
pixel_id="auto",
non_linear=True,
points_list=None,
+ centroids_list=None,
index=None,
method="per_pixel",
):
@@ -162,38 +167,65 @@ def segmentation_to_atlas_space(
y_scale, x_scale = transform_to_registration(
seg_height, seg_width, reg_height, reg_width
)
-
+ centroids, points = None, None
if method in ["per_object", "all"]:
- # This function returns the centroids, area and coordinates of all the objects in the segmentation
- # Right now we only use centroids
- binary_seg = segmentation == pixel_id
- binary_seg = np.all(binary_seg, axis=2)
- centroids, area, coords = get_centroids_and_area(binary_seg, pixel_cut_off=0)
- print("Number of objects: ", len(centroids))
- # print(centroids)
-
- if method in ["per_pixel", "all"]:
- id_pixels = find_matching_pixels(segmentation, pixel_id)
- # Scale the seg coordinates to reg/seg
- scaled_y, scaled_x = scale_positions(
- id_pixels[0], id_pixels[1], y_scale, x_scale
+ centroids, scaled_centroidsX, scaled_centroidsY = get_centroids(
+ segmentation, pixel_id, y_scale, x_scale
)
+ print("Number of objects: ", len(scaled_centroidsY))
+ if method in ["per_pixel", "all"]:
+ scaled_y, scaled_x = get_scaled_pixels(segmentation, pixel_id, y_scale, x_scale)
if non_linear:
if "markers" in slice:
# This creates a triangulation using the reg width
triangulation = triangulate(reg_width, reg_height, slice["markers"])
- new_x, new_y = transform_vec(triangulation, scaled_x, scaled_y)
+ if method in ["per_pixel", "all"]:
+ new_x, new_y = transform_vec(triangulation, scaled_x, scaled_y)
+ if method in ["per_object", "all"]:
+ centroids_new_x, centroids_new_y = transform_vec(
+ triangulation, scaled_centroidsX, scaled_centroidsY
+ )
else:
print(
f"No markers found for {slice['filename']}, result for section will be linear."
)
- new_x, new_y = scaled_x, scaled_y
+ if method in ["per_pixel", "all"]:
+ new_x, new_y = scaled_x, scaled_y
+ if method in ["per_object", "all"]:
+ centroids_new_x, centroids_new_y = scaled_centroidsX, scaled_centroidsY
else:
- new_x, new_y = scaled_x, scaled_y
+ if method in ["per_pixel", "all"]:
+ new_x, new_y = scaled_x, scaled_y
+ if method in ["per_object", "all"]:
+ centroids_new_x, centroids_new_y = scaled_centroidsX, scaled_centroidsY
# Scale U by Uxyz/RegWidth and V by Vxyz/RegHeight
- points = transform_to_atlas_space(
- slice["anchoring"], new_y, new_x, reg_height, reg_width
- )
- # points = points.reshape(-1)
+ if method in ["per_pixel", "all"]:
+ points = transform_to_atlas_space(
+ slice["anchoring"], new_y, new_x, reg_height, reg_width
+ )
+ if method in ["per_object", "all"]:
+ centroids = transform_to_atlas_space(
+ slice["anchoring"], centroids_new_y, centroids_new_x, reg_height, reg_width
+ )
points_list[index] = np.array(points)
+ centroids_list[index] = np.array(centroids)
+
+
+def get_centroids(segmentation, pixel_id, y_scale, x_scale):
+ binary_seg = segmentation == pixel_id
+ binary_seg = np.all(binary_seg, axis=2)
+ centroids, area, coords = get_centroids_and_area(binary_seg, pixel_cut_off=0)
+ centroidsY = centroids[:, 1]
+ centroidsX = centroids[:, 0]
+ scaled_centroidsY, scaled_centroidsX = scale_positions(
+ centroidsY, centroidsX, y_scale, x_scale
+ )
+ return centroids, scaled_centroidsX, scaled_centroidsY
+
+
+def get_scaled_pixels(segmentation, pixel_id, y_scale, x_scale):
+ id_pixels = find_matching_pixels(segmentation, pixel_id)
+ # Scale the seg coordinates to reg/seg
+ scaled_y, scaled_x = scale_positions(id_pixels[0], id_pixels[1], y_scale, x_scale)
+ return scaled_y, scaled_x
diff --git a/PyNutil/counting_and_load.py b/PyNutil/counting_and_load.py
index efb80174d0f0d7f0b83ca32529c2806e2570898f..09a3f5483e115c5e58d00b0d285efbae8a844f0a 100644
--- a/PyNutil/counting_and_load.py
+++ b/PyNutil/counting_and_load.py
@@ -23,15 +23,46 @@ def label_points(points, label_volume, scale_factor=1):
# related to counting_and_load
-def pixel_count_per_region(labels_dict, df_label_colours):
+def pixel_count_per_region(
+ labels_dict_points, labeled_dict_centroids, df_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(labels_dict, 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=["idx", "pixel_count"])
+ if labels_dict_points is not None and labeled_dict_centroids is not None:
+ counted_labels_points, label_counts_points = np.unique(
+ labels_dict_points, return_counts=True
+ )
+ counted_labels_centroids, label_counts_centroids = np.unique(
+ labeled_dict_centroids, return_counts=True
+ )
+ # Which regions have pixels, and how many pixels are there per region
+ counts_per_label = list(
+ zip(counted_labels_points, label_counts_points, label_counts_centroids)
+ )
+ # Create a list of unique regions and pixel counts per region
+ df_counts_per_label = pd.DataFrame(
+ counts_per_label, columns=["idx", "pixel_count", "object_count"]
+ )
+ elif labels_dict_points is None and labeled_dict_centroids is not None:
+ counted_labels_centroids, label_counts_centroids = np.unique(
+ labeled_dict_centroids, return_counts=True
+ )
+ # Which regions have pixels, and how many pixels are there per region
+ counts_per_label = list(zip(counted_labels_centroids, label_counts_centroids))
+ # Create a list of unique regions and pixel counts per region
+ df_counts_per_label = pd.DataFrame(
+ counts_per_label, columns=["idx", "object_count"]
+ )
+ elif labels_dict_points is not None and labeled_dict_centroids is None:
+ counted_labels_points, label_counts_points = np.unique(
+ labels_dict_points, return_counts=True
+ )
+ # Which regions have pixels, and how many pixels are there per region
+ counts_per_label = list(zip(counted_labels_points, label_counts_points))
+ # Create a list of unique regions and pixel counts per region
+ df_counts_per_label = pd.DataFrame(
+ counts_per_label, columns=["idx", "pixel_count"]
+ )
# Create a pandas df with regions and pixel counts
# df_label_colours = pd.read_csv(label_colours, sep=",")
diff --git a/PyNutil/main.py b/PyNutil/main.py
index af5a7e84e48c106824f154df0926883ebf8d65f5..73eae5818911035deff9220fec8dc6604844e053 100644
--- a/PyNutil/main.py
+++ b/PyNutil/main.py
@@ -157,7 +157,7 @@ class PyNutil:
f"method {method} not recognised, valid methods are: per_pixel, per_object, or all"
)
print("extracting coordinates")
- pixel_points = folder_to_atlas_space(
+ pixel_points, centroids, points_len, centroids_len = folder_to_atlas_space(
self.segmentation_folder,
self.alignment_json,
pixel_id=self.colour,
@@ -165,8 +165,11 @@ class PyNutil:
method=method,
)
self.pixel_points = pixel_points
-
-
+ self.centroids = centroids
+ ##points len and centroids len tell us how many points were extracted from each section
+ ##This will be used to split the data up later into per section files
+ self.points_len = points_len
+ self.centroids_len = centroids_len
def quantify_coordinates(self):
"""Quantifies the pixel coordinates by region.
@@ -177,20 +180,29 @@ class PyNutil:
If the pixel coordinates have not been extracted.
"""
- if not hasattr(self, "pixel_points"):
+ if not hasattr(self, "pixel_points") and not hasattr(self, "centroids"):
raise ValueError(
"Please run get_coordinates before running quantify_coordinates"
)
print("quantifying coordinates")
- labeled_points = label_points(self.pixel_points, self.atlas_volume, scale_factor=1)
+ labeled_points_centroids = None
+ labeled_points = None
+ if hasattr(self, "centroids"):
+ labeled_points_centroids = label_points(
+ self.centroids, self.atlas_volume, scale_factor=1
+ )
+ if hasattr(self, "pixel_points"):
+ labeled_points = label_points(
+ self.pixel_points, self.atlas_volume, scale_factor=1
+ )
- self.label_df = pixel_count_per_region(labeled_points, self.atlas_labels)
+ self.label_df = pixel_count_per_region(
+ labeled_points, labeled_points_centroids, self.atlas_labels
+ )
self.labeled_points = labeled_points
print("quantification complete ✅")
-
-
def save_analysis(self, output_folder):
"""Saves the pixel coordinates and pixel counts to different files in the specified
output folder.
@@ -226,4 +238,4 @@ class PyNutil:
output_folder + "/pixels_meshview.json",
self.atlas_labels,
)
- print("analysis saved ✅")
\ No newline at end of file
+ print("analysis saved ✅")