update distance evaluation

Author: taigw

pymic/test/test_assd.py

@@ -0,0 +1,37 @@
+from scipy import ndimage
+from PIL import Image
+import numpy as np 
+import SimpleITK as sitk
+import matplotlib.pyplot as plt
+from pymic.util.evaluation_seg import get_edge_points
+
+def test_assd_2d():
+    img_name = "/home/x/projects/PyMIC_project/PyMIC_examples/PyMIC_data/JSRT/label/JPCLN001.png"
+    img = Image.open(img_name)
+    img_array = np.asarray(img)
+    img_edge = get_edge_points(img_array > 0)
+    s_dis = ndimage.distance_transform_edt(1-img_edge)
+    plt.subplot(1,2,1)
+    plt.imshow(img_edge)
+    plt.subplot(1,2,2)
+    plt.imshow(s_dis)
+    plt.show()
+
+def test_assd_3d():
+    img_name = "/home/x/projects/PyMIC_project/PyMIC_examples/seg_ssl/ACDC/result/unet2d_baseline/patient001_frame01.nii.gz"
+    img_obj = sitk.ReadImage(img_name) 
+    spacing = img_obj.GetSpacing()
+    spacing = spacing[::-1]
+    img_data = sitk.GetArrayFromImage(img_obj)
+    print(img_data.shape)
+    print(spacing)
+    img_edge = get_edge_points(img_data > 0)
+    s_dis = ndimage.distance_transform_edt(1-img_edge, sampling=spacing)
+    dis_obj = sitk.GetImageFromArray(s_dis)
+    dis_obj.CopyInformation(img_obj)
+    sitk.WriteImage(dis_obj, "test_dis.nii.gz")
+
+
+
+if __name__ == "__main__":
+    test_assd_3d()

pymic/util/evaluation_seg.py

@@ -6,11 +6,7 @@
 import csv
 import os
 import sys
-import math
 import pandas as pd
-import random
-import GeodisTK
-import configparser
 import numpy as np
 from scipy import ndimage
 from pymic.io.image_read_write import *
@@ -90,7 +86,7 @@ def get_edge_points(img):
         strt = ndimage.generate_binary_structure(2,1)
     else:
         strt = ndimage.generate_binary_structure(3,1)
-    ero  = ndimage.morphology.binary_erosion(img, strt)
+    ero  = ndimage.binary_erosion(img, strt)
     edge = np.asarray(img, np.uint8) - np.asarray(ero, np.uint8) 
     return edge 
 
@@ -114,14 +110,9 @@ def binary_hd95(s, g, spacing = None):
         spacing = [1.0] * image_dim
     else:
         assert(image_dim == len(spacing))
-    img = np.zeros_like(s)
-    if(image_dim == 2):
-        s_dis = GeodisTK.geodesic2d_raster_scan(img, s_edge, 0.0, 2)
-        g_dis = GeodisTK.geodesic2d_raster_scan(img, g_edge, 0.0, 2)
-    elif(image_dim ==3):
-        s_dis = GeodisTK.geodesic3d_raster_scan(img, s_edge, spacing, 0.0, 2)
-        g_dis = GeodisTK.geodesic3d_raster_scan(img, g_edge, spacing, 0.0, 2)
-
+    s_dis = ndimage.distance_transform_edt(1-s_edge, sampling = spacing)
+    g_dis = ndimage.distance_transform_edt(1-g_edge, sampling = spacing)
+  
     dist_list1 = s_dis[g_edge > 0]
     dist_list1 = sorted(dist_list1)
     dist1 = dist_list1[int(len(dist_list1)*0.95)]
@@ -150,13 +141,8 @@ def binary_assd(s, g, spacing = None):
         spacing = [1.0] * image_dim
     else:
         assert(image_dim == len(spacing))
-    img = np.zeros_like(s)
-    if(image_dim == 2):
-        s_dis = GeodisTK.geodesic2d_raster_scan(img, s_edge, 0.0, 2)
-        g_dis = GeodisTK.geodesic2d_raster_scan(img, g_edge, 0.0, 2)
-    elif(image_dim ==3):
-        s_dis = GeodisTK.geodesic3d_raster_scan(img, s_edge, spacing, 0.0, 2)
-        g_dis = GeodisTK.geodesic3d_raster_scan(img, g_edge, spacing, 0.0, 2)
+    s_dis = ndimage.distance_transform_edt(1-s_edge, sampling = spacing)
+    g_dis = ndimage.distance_transform_edt(1-g_edge, sampling = spacing)
 
     ns = s_edge.sum()
     ng = g_edge.sum()

requirements.txt

@@ -5,7 +5,7 @@ scikit-image>=0.16.2
 scikit-learn>=0.22
 scipy>=1.3.3
 SimpleITK>=2.0.0
-tensorboard>=2.1.0
-tensorboardX>=1.9
+tensorboard
+tensorboardX
 torch>=1.1.12
 torchvision>=0.13.0

setup.py

@@ -11,7 +11,7 @@
 
 setuptools.setup(
     name    = 'PYMIC',
-    version = "0.3.1",
+    version = "0.3.2.2",
     author  ='PyMIC Consortium',
     author_email = 'wguotai@gmail.com',
     description  = description,
@@ -28,8 +28,8 @@
         "scikit-learn>=0.22",
         "scipy>=1.3.3",
         "SimpleITK>=2.0.0",
-        "tensorboard>=2.1.0",
-        "tensorboardX>=1.9",
+        "tensorboard",
+        "tensorboardX",
     ],
     classifiers=[
         'License :: OSI Approved :: Apache Software License',