diff --git a/PyNutil/coordinate_extraction.py b/PyNutil/coordinate_extraction.py
index d16ae6fb237057823db106578e1e016ba4bfd6e1..a2a4d8d901f184ba7bc8c312faa0a15c74326c97 100644
--- a/PyNutil/coordinate_extraction.py
+++ b/PyNutil/coordinate_extraction.py
@@ -20,6 +20,7 @@ def get_centroids_and_area(segmentation, pixel_cut_off=0):
labels = measure.label(segmentation)
# This finds all the objects in the image
labels_info = measure.regionprops(labels)
+
# Remove objects that are less than pixel_cut_off
labels_info = [label for label in labels_info if label.area > pixel_cut_off]
# Get the centre points of the objects
@@ -84,7 +85,12 @@ def transform_to_atlas_space(anchoring, y, x, reg_height, reg_width):
# related to coordinate extraction
# This function returns an array of points
def folder_to_atlas_space(
- folder, quint_alignment, pixel_id=[0, 0, 0], non_linear=True, method="all", object_cutoff=0
+ folder,
+ quint_alignment,
+ pixel_id=[0, 0, 0],
+ non_linear=True,
+ method="all",
+ object_cutoff=0,
):
"""Apply Segmentation to atlas space to all segmentations in a folder."""
@@ -118,7 +124,7 @@ def folder_to_atlas_space(
centroids_list,
index,
method,
- object_cutoff
+ object_cutoff,
),
)
threads.append(x)
@@ -131,10 +137,23 @@ def folder_to_atlas_space(
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]
- centroids_list = [item for sublist in centroids_list for item in sublist]
-
- return np.array(points), np.array(centroids_list), points_len, centroids_len
+ points = np.concatenate(points_list)
+ print("points shape: ",points.shape)
+ centroids = np.concatenate(centroids_list)
+ print("centroids shape: ",centroids.shape)
+ print("Number of points: ", len(points))
+ print("Number of centroids: ", len(centroids))
+ print("points_len: ", points_len)
+ print("points: ", points)
+ print("points first: ", points[0])
+
+ return (
+ np.array(points),
+ np.array(centroids),
+ points_len,
+ centroids_len,
+ segmentations,
+ )
def segmentation_to_atlas_space(
@@ -146,7 +165,7 @@ def segmentation_to_atlas_space(
centroids_list=None,
index=None,
method="per_pixel",
- object_cutoff=0
+ object_cutoff=0,
):
"""Combines many functions to convert a segmentation to atlas space. It takes care
of deformations."""
@@ -174,9 +193,9 @@ def segmentation_to_atlas_space(
centroids, scaled_centroidsX, scaled_centroidsY = get_centroids(
segmentation, pixel_id, y_scale, x_scale, object_cutoff
)
- 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)
+ print(f"{segmentation_path} \nNumber of objects: {len(scaled_centroidsY)}\nNumber of pixels: {len(scaled_y)}")
if non_linear:
if "markers" in slice:
@@ -210,6 +229,7 @@ def segmentation_to_atlas_space(
centroids = transform_to_atlas_space(
slice["anchoring"], centroids_new_y, centroids_new_x, reg_height, reg_width
)
+ print(f"Finished and points len is: {len(points)} and centroids len is: {len(centroids)}")
points_list[index] = np.array(points)
centroids_list[index] = np.array(centroids)
@@ -217,9 +237,11 @@ def segmentation_to_atlas_space(
def get_centroids(segmentation, pixel_id, y_scale, x_scale, object_cutoff=0):
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=object_cutoff)
- centroidsY = centroids[:, 1]
- centroidsX = centroids[:, 0]
+ centroids, area, coords = get_centroids_and_area(
+ binary_seg, pixel_cut_off=object_cutoff
+ )
+ centroidsX = centroids[:, 1]
+ centroidsY = centroids[:, 0]
scaled_centroidsY, scaled_centroidsX = scale_positions(
centroidsY, centroidsX, y_scale, x_scale
)
diff --git a/PyNutil/counting_and_load.py b/PyNutil/counting_and_load.py
index 09a3f5483e115c5e58d00b0d285efbae8a844f0a..1d2dfdd4a14ecf09f4d49e9b1d4c354b96217f38 100644
--- a/PyNutil/counting_and_load.py
+++ b/PyNutil/counting_and_load.py
@@ -17,8 +17,23 @@ def label_points(points, label_volume, scale_factor=1):
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
+ print(x[0], y[0], z[0])
+
+ #make sure the points are within the volume
+ x[x < 0] = 0
+ y[y < 0] = 0
+ z[z < 0] = 0
+ mask = (x > label_volume.shape[0] - 1) | (y > label_volume.shape[1] - 1) | (z > label_volume.shape[2] - 1)
+ x[mask] = 0
+ y[mask] = 0
+ z[mask] = 0
+ print('total mask: ')
+ print(np.sum(mask))
# Get the label value for each point
+ print(x[0], y[0], z[0])
labels = label_volume[x, y, z]
+ print('label sum: ')
+ print(np.sum(labels))
return labels
diff --git a/PyNutil/main.py b/PyNutil/main.py
index 97ebdd4642e6186c0f0e9be5f1dd06e1e7d7c9fc..c65c8a8f6b6dbb6b89ae29348a47f9b0bf7281e9 100644
--- a/PyNutil/main.py
+++ b/PyNutil/main.py
@@ -123,12 +123,12 @@ class PyNutil:
current_atlas_path = self.config["annotation_volumes"][self.atlas]["volume"]
print("loading atlas volume")
start_time = datetime.now()
- atlas_volume = read_atlas_volume(atlas_root_path + current_atlas_path)
+ atlas_volume = read_atlas_volume(f"{atlas_root_path}{current_atlas_path}")
time_taken = datetime.now() - start_time
print(f"atlas volume loaded in: {time_taken} ✅")
atlas_label_path = self.config["annotation_volumes"][self.atlas]["labels"]
print("loading atlas labels")
- atlas_labels = pd.read_csv(atlas_root_path + atlas_label_path)
+ atlas_labels = pd.read_csv(f"{atlas_root_path}{atlas_label_path}")
print("atlas labels loaded ✅")
return atlas_volume, atlas_labels
@@ -160,7 +160,13 @@ class PyNutil:
f"method {method} not recognised, valid methods are: per_pixel, per_object, or all"
)
print("extracting coordinates")
- pixel_points, centroids, points_len, centroids_len = folder_to_atlas_space(
+ (
+ pixel_points,
+ centroids,
+ points_len,
+ centroids_len,
+ segmentation_filenames,
+ ) = folder_to_atlas_space(
self.segmentation_folder,
self.alignment_json,
pixel_id=self.colour,
@@ -174,6 +180,7 @@ class PyNutil:
##This will be used to split the data up later into per section files
self.points_len = points_len
self.centroids_len = centroids_len
+ self.segmentation_filenames = segmentation_filenames
def quantify_coordinates(self):
"""Quantifies the pixel coordinates by region.
@@ -203,8 +210,24 @@ class PyNutil:
self.label_df = pixel_count_per_region(
labeled_points, labeled_points_centroids, self.atlas_labels
)
+ prev_pl = 0
+ per_section_df = []
+ current_centroids = None
+ current_points = None
+ for pl in self.points_len:
+ if hasattr(self, "centroids"):
+ current_centroids = labeled_points_centroids[prev_pl : prev_pl + pl]
+ if hasattr(self, "pixel_points"):
+ current_points = labeled_points[prev_pl : prev_pl + pl]
+ current_df = pixel_count_per_region(
+ current_points, current_centroids, self.atlas_labels
+ )
+ per_section_df.append(current_df)
+ prev_pl += pl
+
self.labeled_points = labeled_points
self.labeled_points_centroids = labeled_points_centroids
+ self.per_section_df = per_section_df
print("quantification complete ✅")
@@ -226,30 +249,66 @@ class PyNutil:
if not os.path.exists(output_folder):
os.makedirs(output_folder)
- if not hasattr(self, "pixel_points"):
- raise ValueError("Please run get_coordinates before running save_analysis")
if not hasattr(self, "label_df"):
print("no quantification found so we will only save the coordinates")
print(
"if you want to save the quantification please run quantify_coordinates"
)
+ else:
+ self.label_df.to_csv(
+ f"{output_folder}/counts.csv", sep=";", na_rep="", index=False
+ )
+ if not os.path.exists(f"{output_folder}/per_section_meshview"):
+ os.makedirs(f"{output_folder}/per_section_meshview")
+ if not os.path.exists(f"{output_folder}/per_section_reports"):
+ os.makedirs(f"{output_folder}/per_section_reports")
- self.label_df.to_csv(
- output_folder + "/counts.csv", sep=";", na_rep="", index=False
- )
+ prev_pl = 0
+ prev_cl = 0
-
-
- write_points_to_meshview(
- self.pixel_points,
- self.labeled_points,
- output_folder + "/pixels_meshview.json",
- self.atlas_labels,
- )
- write_points_to_meshview(
- self.centroids,
- self.labeled_points_centroids,
- output_folder + "/objects_meshview.json",
- self.atlas_labels,
- )
+ for pl, cl, fn, df in zip(
+ self.points_len,
+ self.centroids_len,
+ self.segmentation_filenames,
+ self.per_section_df,
+ ):
+
+ split_fn = fn.split("/")[-1].split(".")[0]
+ df.to_csv(
+ f"{output_folder}/per_section_reports/{split_fn}.csv",
+ sep=";",
+ na_rep="",
+ index=False,
+ )
+ if hasattr(self, "pixel_points"):
+ write_points_to_meshview(
+ self.pixel_points[prev_pl : pl + prev_pl],
+ self.labeled_points[prev_pl : pl + prev_pl],
+ f"{output_folder}/per_section_meshview/{split_fn}_pixels.json",
+ self.atlas_labels,
+ )
+ if hasattr(self, "centroids"):
+ write_points_to_meshview(
+ self.centroids[prev_cl : cl + prev_cl],
+ self.labeled_points_centroids[prev_cl : cl + prev_cl],
+ f"{output_folder}/per_section_meshview/{split_fn}_centroids.json",
+ self.atlas_labels,
+ )
+ prev_cl += cl
+ prev_pl += pl
+
+ if hasattr(self, "pixel_points"):
+ write_points_to_meshview(
+ self.pixel_points,
+ self.labeled_points,
+ f"{output_folder}/pixels_meshview.json",
+ self.atlas_labels,
+ )
+ if hasattr(self, "centroids"):
+ write_points_to_meshview(
+ self.centroids,
+ self.labeled_points_centroids,
+ f"{output_folder}/objects_meshview.json",
+ self.atlas_labels,
+ )
print("analysis saved ✅")
diff --git a/test/PVMouse_81264_test.json b/test/PVMouse_81264_test.json
new file mode 100644
index 0000000000000000000000000000000000000000..963261b9e52e6b2de2652574d88c62e562dba571
--- /dev/null
+++ b/test/PVMouse_81264_test.json
@@ -0,0 +1,7 @@
+{ "volume_path": "allen2017",
+ "label_path": "annotation_volumes/allen2017_colours.csv",
+ "segmentation_folder": "test_data/ext-d000033_PVMouseExtraction_pub-Nutil_Quantifier_analysis-81264-Input_dir",
+ "alignment_json": "test_data/PVMouse_81264_nonlin.json",
+ "nonlinear": true,
+ "colour": [255, 0, 0]
+}
\ No newline at end of file
diff --git a/test/test1.json b/test/test1.json
index 6ce2ef414a40e9a0f0d6c1f34e30d93551379ee7..0355d8ca22ff7b021d36eac2a7dce534117df2c0 100644
--- a/test/test1.json
+++ b/test/test1.json
@@ -1,5 +1,5 @@
{ "volume_path": "allen2017",
- "label_path": "../annotation_volumes/allen2017_colours.csv",
+ "label_path": "annotation_volumes/allen2017_colours.csv",
"segmentation_folder": "test_data/ttA_2877_NOP_segmentations",
"alignment_json": "test_data/ttA_2877_NOP_horizontal_final_2017.json",
"nonlinear": true,
diff --git a/test/test3.json b/test/test3.json
index 93d869b273362cd6aa3d923b29ce0b65160f1f08..1c9343f895c732d928da65a84999da0602e8c1c6 100644
--- a/test/test3.json
+++ b/test/test3.json
@@ -1,9 +1,9 @@
-{ "volume_path": "../annotation_volumes/annotation_10_reoriented.nrrd",
- "label_path": "../annotation_volumes/allen2022_colours.csv",
- "segmentation_folder": "../test_data/oneSection15",
- "alignment_json": "../test_data/C68_nonlinear.json",
+{ "volume_path": "allen2017",
+ "label_path": "annotation_volumes/allen2017_colours.csv",
+ "segmentation_folder": "test_data/oneSection15",
+ "alignment_json": "test_data/C68_nonlinear.json",
"nonlinear": true,
"colour": [255, 0, 255],
- "points_json_path": "../outputs/test3_points.json",
- "counts_per_label_name": "../outputs/test3_counts_per_idx.csv"
+ "points_json_path": "outputs/test3_points.json",
+ "counts_per_label_name": "outputs/test3_counts_per_idx.csv"
}
\ No newline at end of file
diff --git a/testOOP.py b/testOOP.py
index fe5173ae6cc27f015d5fa59e4d8f4b8faab96f64..ce0c65d1cab9a28d37c5531d511f8b82f454ffad 100644
--- a/testOOP.py
+++ b/testOOP.py
@@ -1,6 +1,7 @@
from PyNutil import PyNutil
-pnt = PyNutil(settings_file=r"test/test4_2017.json")
+pnt = PyNutil(settings_file=r"test/PVMouse_81264_test.json")
+# pnt = PyNutil(settings_file=r"test/test3.json")
# pnt.build_quantifier()
pnt.get_coordinates(object_cutoff=0)
diff --git a/test_data/ext-d000033_PVMouseExtraction_pub-Nutil_Quantifier_analysis-81264-Input_dir/PVMouse_81264_nonlin.json b/test_data/ext-d000033_PVMouseExtraction_pub-Nutil_Quantifier_analysis-81264-Input_dir/PVMouse_81264_nonlin.json
new file mode 100644
index 0000000000000000000000000000000000000000..47498a90d9543b059f7449ea82762cc27ccbdd05
--- /dev/null
+++ b/test_data/ext-d000033_PVMouseExtraction_pub-Nutil_Quantifier_analysis-81264-Input_dir/PVMouse_81264_nonlin.json
@@ -0,0 +1,47 @@
+{"name":"file:\/\/\/\/sambaad.stud.ntnu.no\/arthurl\/TS-desktop\/QuickNIIMousev3\/sets\/PVMouse_81264\\1301_4244_6086_Ext_00006a_PVMouse_81264_Samp1__s038.png","target":"ABA_Mouse_CCFv3_2017_25um.cutlas","target-resolution":[456.0, 528.0, 320.0],"slices":[
+{"filename":"ext-d000009_PVMouse_81264_Samp1_resize15__s001_thumbnail_FinalSegmentation.png","nr":1,"width":11540,"height":8686,"anchoring":[116.41873628010956, 304.7430883068341, 304.55811790054605, 217.2083253277532, 0.0, 0.0, 0.0, -161.2429836174041, 5.630729400754519],"markers":[[4741.748013620884, 6859.779954785231, 5010.272417707152, 7147.78598342125], [5277.001051104085, 4272.504882145447, 5553.870601589102, 4431.3654860587785], [3230.7042079959483, 2380.3852639172846, 3097.8547105561856, 2402.2321024868124], [3766.703854325888, 7066.836608628541, 4073.7116912599317, 7461.974378296909], [6739.249602813287, 2815.9894967282867, 6726.208853575482, 2925.879427279578], [6595.438674172263, 4391.629801007501, 6529.72758229285, 4536.094951017331], [8431.831201662451, 2290.849389742628, 8501.089670828602, 2258.2290881688014]]},
+{"filename":"ext-d000009_PVMouse_81264_Samp1_resize15__s002_thumbnail_FinalSegmentation.png","nr":2,"width":6546,"height":10300,"anchoring":[227.15862669746707, 308.77468333189125, 298.65032693983386, 122.96766665170009, -10.75172277317006, 0.3754584284778134, -14.283069097287626, -163.15678146401638, 5.697559283303428],"markers":[[4946.384341637011, 8025.772418990204, 4992.975088967971, 8095.629238884703]]},
+{"filename":"ext-d000009_PVMouse_81264_Samp1_resize15__s003_thumbnail_FinalSegmentation.png","nr":3,"width":13398,"height":11150,"anchoring":[106.91030111371816, 334.41209206124205, 291.9880428517513, 253.10989951642432, 0.0, 0.0, 0.0, -205.87217249036576, 7.18921511145732],"markers":[[4421.171894604768, 1193.1424265259984, 4379.145545796737, 840.2411454408441], [11921.738904044989, 7994.595873730307, 12263.28858218319, 7999.095704596835], [6626.449181984648, 5721.272247239212, 6640.163111668758, 5553.99397136398]]},
+{"filename":"ext-d000009_PVMouse_81264_Samp1_resize15__s004_thumbnail_FinalSegmentation.png","nr":4,"width":15168,"height":12996,"anchoring":[86.83092971349856, 353.9287942578318, 285.5394992833533, 287.0744403215188, 0.0, 0.0, 0.0, -239.30878241661617, 8.356847329303344],"markers":[[6031.948353776629, 1165.4287867370006, 5248.578437701743, 1341.7121326299925], [3920.8139580683387, 11767.245416180474, 2751.5868302130407, 11918.712886209496], [3546.938386731142, 2101.0884832364236, 2653.66559070368, 1684.4853051996988], [1667.2508422221215, 10029.199097264513, 773.577792123951, 10175.466465712132], [8549.447330022584, 1207.0883495989465, 9047.922530664946, 1331.9186134137149], [9640.898315793856, 11695.044290490217, 9890.04519044545, 11722.842501883948], [13792.72968751934, 6334.787121082595, 14041.90574564235, 6346.200452147702]]},
+{"filename":"ext-d000009_PVMouse_81264_Samp1_resize15__s005_thumbnail_FinalSegmentation.png","nr":5,"width":16606,"height":14060,"anchoring":[79.9006912007975, 367.4765080066958, 279.299397420319, 314.8667700503469, 0.0, 0.0, 0.0, -258.20058836174684, 9.016563769560994],"markers":[[6358.391831525207, 656.910324039186, 6167.640076579451, 858.2215523737754], [3590.2276010677433, 2305.107010431674, 3115.611997447352, 2150.8515448379803], [4284.421994628601, 13029.030318554507, 4238.927887683472, 13424.280331574982], [9257.999321089883, 621.1662891948636, 10099.245692405873, 603.9336850037679], [11141.435859433179, 13118.369555329215, 11974.971282705807, 11824.385832705351], [6292.298540020072, 12134.054943562453, 6633.9221442246335, 12089.26902788244]]},
+{"filename":"ext-d000009_PVMouse_81264_Samp1_resize15__s006_thumbnail_FinalSegmentation.png","nr":6,"width":17470,"height":15877,"anchoring":[69.58149646550089, 394.8598635992396, 272.57614424961224, 331.8553583671349, 0.0, 0.0, 0.0, -290.7770530142641, 10.154159050772172],"markers":[[6741.336531871144, 1100.7415222305956, 6525.805346127486, 1196.4581763376036], [4149.009120921929, 3080.2000966815563, 3220.9938313913635, 2751.853805576488], [1520.0342176426889, 6415.486872941752, 742.3851953392733, 6209.617935192164], [8143.728150689665, 11489.644490555405, 7914.784098697738, 11426.175584024117], [9816.292615738193, 1255.3248073278048, 10070.095956134337, 1292.1748304446116], [15554.74973749931, 11836.170605930569, 15877.464016449623, 11976.54634513941]]},
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\ No newline at end of file