modified: F1_CellSegment.py

	modified:   F3_FeatureExtract.py
	modified:   WSIGraph.py
	deleted:    __pycache__/F1_CellSegment.cpython-38.pyc
	deleted:    __pycache__/F4_Visualization.cpython-38.pyc
	deleted:    __pycache__/utils_xml.cpython-38.pyc
	modified:   main.py
	modified:   utils_xml.py

F1_CellSegment.py

@@ -4,8 +4,8 @@
 def fun1(input_dir, output_dir):
     print(input_dir)
     print(output_dir)
-    model_path = './hovernet_fast_pannuke_type_tf2pytorch.tar'
-    args = {'gpu':'0', 'nr_types':6, 'type_info_path':'type_info.json', 'model_mode':'fast',
+    model_path = 'Hover/hovernet_fast_pannuke_type_tf2pytorch.tar'
+    args = {'gpu':'0', 'nr_types':6, 'type_info_path':'Hover/type_info.json', 'model_mode':'fast',
             'model_path':model_path, 'nr_inference_workers':8, 'nr_post_proc_workers':0,
             'batch_size':16}
     sub_args = {'input_dir': input_dir,

F3_FeatureExtract.py

@@ -33,7 +33,7 @@ def fun3(json_path, wsi_path, output_path, xml_path=None):
         vertex_dataframe = vertex_dataframe.iloc[index_x]
 
     col_dist = defaultdict(list)
-    cellType = ['T', 'I', 'S', 'N']
+    cellType = ['T', 'I', 'S']
     for featname in vertex_dataframe.columns.values:
         if 'Graph' not in featname:
             # public feature, including cell information, Morph feature and GLCM feature

WSIGraph.py

@@ -474,13 +474,14 @@ def constructGraphFromDict(
                     ## 字典的前3个value，都是[[1,2,3,......]]这样列表套列表的list；后面则是列表里面2个列表，类似于[[1,2,3,....],[2,3,4,...]]
                     'I-I': [inflamIDs, inflamIDs],
                     'S-S': [connecIDs, connecIDs],
-                    'N-N': [normalIDs, normalIDs],
+                    # 'N-N': [normalIDs, normalIDs],
                     'T-I': [neoplaIDs, inflamIDs],
                     'T-S': [neoplaIDs, connecIDs],
-                    'T-N': [neoplaIDs, normalIDs],
+                    # 'T-N': [neoplaIDs, normalIDs],
                     'I-S': [inflamIDs, connecIDs],
-                    'I-N': [inflamIDs, normalIDs],
-                    'S-N': [connecIDs, normalIDs]}
+                    # 'I-N': [inflamIDs, normalIDs],
+                    # 'S-N': [connecIDs, normalIDs]
+    }
 
     for featype, featype_index_list in zip(featype_dict.keys(),
                                            featype_dict.values()):  ##这里对featype_dict的value进行迭代，featype_index_list要么是列表套列表，要么是列表里面套2个列表

main.py

@@ -29,22 +29,20 @@
 Arguments for feature extract.
 
 Usage:
-    feature (--json_path=<filepath>) (--wsi_path=<filepath>)\
-            (--output_path=<folderpath>) [--xml_path=<path>]
+    feature --json_path=<filepath> --wsi_path=<filepath> --output_path=<folderpath> [--xml_path=<path>]
 
 Option:
-    --json_path     Path to HoVer-Net output, it show be a json file
-    --wsi_path      Path to wsi file
-    --output_path   Path to output
-    --xml_path      Path to xml. The xml is an annotation file of ImageScope.\
-                    Only extract the feature in the annotation.[default: None]
+    --json_path=<filepath>     Path to HoVer-Net output, it show be a json file.
+    --wsi_path=<filepath>      Path to wsi file.
+    --output_path=<folderpath>   Path to output.
+    --xml_path=<path>      Path to xml. The xml is an annotation file of ImageScope. Only extract the feature in the annotation.[default: None]
 """
 
 visual_cli = """
 Arguments for visual.
 
 Usage:
-    feature --feature_path=<filepath> --wsi_path=<filepath> --xml_path=<filepath>
+    visual --feature_path=<filepath> --wsi_path=<filepath> --xml_path=<filepath>
 
 Option:
     --feature_path=<filepath>  Path to feature folder, it show be a folder including feature and edge .csv file.
@@ -78,9 +76,13 @@
     print(sub_args)
     if sub_cmd=='segment':
         from F1_CellSegment import fun1
+        import sys
+        sys.path.append('Hover')
         fun1(**sub_args)
     elif sub_cmd=='feature':
         from F3_FeatureExtract import fun3
+        if sub_args['xml_path'] == 'None':
+            sub_args['xml_path'] = None
         fun3(**sub_args)
     elif sub_cmd=='visual':
         from F4_Visualization import fun4

utils_xml.py

@@ -53,20 +53,20 @@
         clusters_RGB.append([int(colormap(i, bytes=True)[c]) for c in range(3)])
 assert len(clusters_id) < len(clusters_RGB), f"the num of classes ({len(clusters_id)}) can't more than the num of RGB list ({len(clusters_RGB)})"
 
-cellType_RGB = [[0  ,   0,   0],#nolabe no color 
-                [255,   0,   0],#neopla red
-                [0  , 255,   0],#inflam green
-                [0  ,   0, 255],#connec blue
-                [255, 255,   0],#necros
-                [255, 165,   0]]#normal
+cellType_RGB = [(0  ,   0,   0),#nolabe no color 
+                (255,   0,   0),#neopla red
+                (0  , 255,   0),#inflam green
+                (0  ,   0, 255),#connec blue
+                (255, 255,   0),#necros
+                (255, 165,   0)]#normal
 
 colormap = mpl.cm.get_cmap('tab20')
 edge_RGB= []
 for i in np.linspace(0, 1, 20)[::2]:
         edge_RGB.append([int(colormap(i, bytes=True)[c]) for c in range(3)])
 for i in np.linspace(0, 1, 20)[1::2]:
         edge_RGB.append([int(colormap(i, bytes=True)[c]) for c in range(3)])
-HEX2RGB = lambda x:[int(x[1:3], 16), int(x[3:5], 16), int(x[5:7], 16)]
+HEX2RGB = lambda x:(int(x[1:3], 16), int(x[3:5], 16), int(x[5:7], 16))
 edge_RGB[0] = HEX2RGB('#FF0000')
 edge_RGB[1] = HEX2RGB('#00FF00')
 edge_RGB[2] = HEX2RGB('#0000FF')
@@ -158,9 +158,9 @@ def read_csv(self, feature_path,
         n_data = 0
         for c in csv_file:
             if csv_data is None:
-                csv_data = pd.read_csv(os.path.join(feature_path, sample_name+'_Feats_'+c+'.csv'))
+                csv_data = pd.read_csv(os.path.join(feature_path, sample_name+'_vertex_'+c+'.csv'))
             else:
-                temp = pd.read_csv(os.path.join(feature_path, sample_name+'_Feats_'+c+'.csv'))
+                temp = pd.read_csv(os.path.join(feature_path, sample_name+'_vertex_'+c+'.csv'))
                 csv_data = pd.concat([csv_data, temp])
         inbox_name = np.empty((0), dtype=np.int64)
         inbox_centroid = np.empty((0, 2), dtype=np.float32)
@@ -251,7 +251,7 @@ def read_csv(self, feature_path,
         # %%
         # Read edge csv
         ## Prepare index from table data
-        csv_data = pd.read_csv(os.path.join(rootpath, sample_name, sample_name+'_edge'+'.csv'))
+        csv_data = pd.read_csv(os.path.join(feature_path, sample_name+'_edge'+'.csv'))
         source = csv_data.source
         target = csv_data.target
 
@@ -312,21 +312,21 @@ def make_img_circle(self):
         def draw_circle(img, bbox, myclass, radius=7):
             center = bbox.mean(axis=0).astype('int')
             center[center<0] = 0
-            cv2.circle(img, center, radius=radius, color=cellType_RGB[int(myclass)], thickness=-1)
+            cv2.circle(img, tuple(center), radius=radius, color=cellType_RGB[int(myclass)], thickness=-1)
         self.make_img_element(type=2, draw_fun=draw_circle)
 
     def make_img_line(self, thickness=5):
         def draw_line(img, bbox, myclass):
             pt1 = bbox[0].astype('int')
             pt2 = bbox[1].astype('int')
-            cv2.line(img, pt1, pt2, color=edge_RGB[edgeDictReverse[myclass]], thickness=thickness)
+            cv2.line(img, tuple(pt1), tuple(pt2), color=edge_RGB[edgeDictReverse[myclass]], thickness=thickness)
         self.make_img_element(type=0, draw_fun=draw_line)
 
     def make_img_rect(self, thickness=5):
         def draw_line(img, bbox, myclass):
             pt1 = bbox[0].astype('int')
             pt2 = bbox[1].astype('int')
-            cv2.rectangle(img, pt1, pt2, color=clusters_RGB[myclass], thickness=thickness)
+            cv2.rectangle(img, tuple(pt1), tuple(pt2), color=clusters_RGB[myclass], thickness=thickness)
         self.make_img_element(type=1, draw_fun=draw_line)
 
     def make_img(self, line_size=5, rect_size=5):