Merge pull request #773 from npinter/update-qupath-roi-splitter

Update QuPath ROIsplitter to v0.3.2

tools/qupath_roi_splitter/qupath_roi_splitter.py

@@ -14,29 +14,21 @@ def collect_coords(input_coords, feature_index, coord_index=0):
     return coords_with_index
 
 
-def collect_roi_coords(input_roi, feature_index):
-    all_coords = []
-    if len(input_roi["geometry"]["coordinates"]) == 1:
-        # Polygon w/o holes
-        all_coords.extend(collect_coords(input_roi["geometry"]["coordinates"][0], feature_index))
+def collect_roi_coords(input_roi):
+    coords = input_roi["geometry"]["coordinates"]
+
+    def process_coords(coord_list):
+        if isinstance(coord_list[0], (int, float)):
+            return [coord_list]
+        elif all(isinstance(c, list) for c in coord_list):
+            return coord_list
+        else:
+            return [coord_list]
+
+    if isinstance(coords[0][0], list):
+        return [process_coords(sub_coords) for sub_coords in coords]
     else:
-        coord_index = 0
-        for sub_roi in input_roi["geometry"]["coordinates"]:
-            if len(sub_roi) == 2:
-                # Special case: LMD data
-                all_coords.extend(collect_coords([sub_roi], feature_index, coord_index))
-                coord_index += 1
-            else:
-                # Polygon with holes or MultiPolygon
-                if not isinstance(sub_roi[0][0], list):
-                    all_coords.extend(collect_coords(sub_roi, feature_index, coord_index))
-                    coord_index += len(sub_roi)
-                else:
-                    # MultiPolygon with holes
-                    for sub_coord in sub_roi:
-                        all_coords.extend(collect_coords(sub_coord, feature_index, coord_index))
-                        coord_index += len(sub_coord)
-    return all_coords
+        return [process_coords(coords)]
 
 
 def split_qupath_roi(in_roi):
@@ -45,70 +37,64 @@ def split_qupath_roi(in_roi):
 
     # HE dimensions
     dim_plt = [int(qupath_roi["dim"]["width"]), int(qupath_roi["dim"]["height"])]
-
     tma_name = qupath_roi["name"]
-    cell_types = [ct.rsplit(" - ", 1)[-1] for ct in qupath_roi["featureNames"]]
+
+    if "featureNames" in qupath_roi:
+        cell_types = [ct.rsplit(" - ", 1)[-1] for ct in qupath_roi["featureNames"]]
+    else:
+        cell_types = ["all"]
 
     coords_by_cell_type = {ct: [] for ct in cell_types}
-    coords_by_cell_type['all'] = []  # For storing all coordinates if args.all is True
+    if "all" not in coords_by_cell_type:
+        coords_by_cell_type["all"] = []
 
-    for feature_index, roi in enumerate(qupath_roi["features"]):
-        feature_coords = collect_roi_coords(roi, feature_index)
+    for roi in qupath_roi["features"]:
+        feature_coords = collect_roi_coords(roi)
 
-        if args.all:
-            coords_by_cell_type['all'].extend(feature_coords)
+        if args.all or "classification" not in roi["properties"]:
+            coords_by_cell_type["all"].append(feature_coords)
         elif "classification" in roi["properties"]:
             cell_type = roi["properties"]["classification"]["name"]
             if cell_type in cell_types:
-                coords_by_cell_type[cell_type].extend(feature_coords)
-
-    for cell_type, coords in coords_by_cell_type.items():
-        if coords:
-            # Generate image (white background)
-            img = np.ones((dim_plt[1], dim_plt[0]), dtype="uint8") * 255
-
-            # Convert to numpy array and ensure integer coordinates
-            coords_arr = np.array(coords).astype(int)
-
-            # Sort by feature_index first, then by coord_index
-            coords_arr = coords_arr[np.lexsort((coords_arr[:, 3], coords_arr[:, 2]))]
-
-            # Get filled pixel coordinates
-            if args.fill:
-                filled_coords = np.column_stack(np.where(img == 0))
-                all_coords = np.unique(np.vstack((coords_arr[:, :2], filled_coords[:, ::-1])), axis=0)
-            else:
-                all_coords = coords_arr[:, :2]
-
-            # Save all coordinates to CSV
+                coords_by_cell_type[cell_type].append(feature_coords)
+
+    for cell_type, coords_list in coords_by_cell_type.items():
+        if coords_list:
+            img = np.ones((dim_plt[1], dim_plt[0], 3), dtype="uint8") * 255
+
+            all_coords = []
+            for feature in coords_list:
+                for polygon in feature:
+                    # Multiple sub_polygons in LMD data
+                    for sub_poly in polygon if isinstance(polygon[0][0], list) else [polygon]:
+                        pts = np.array(sub_poly, dtype=np.float32).reshape(-1, 2)
+                        pts = pts.astype(np.int32)
+
+                        # Get filled pixel coordinates
+                        if args.fill:
+                            temp_img = np.ones((dim_plt[1], dim_plt[0]), dtype="uint8") * 255
+                            cv2.fillPoly(temp_img, [pts], color=0)
+                            filled_coords = np.column_stack(np.where(temp_img == 0))
+                            all_coords.extend(filled_coords[:, [1, 0]])  # Swap columns to get (x, y)
+                            cv2.fillPoly(img, [pts], color=0)
+                        else:
+                            cv2.polylines(img, [pts], isClosed=True, color=(0, 0, 0), thickness=1)
+                            all_coords.extend(pts)
+
+            all_coords = np.array(all_coords)
             coords_df = pd.DataFrame(all_coords, columns=['x', 'y'], dtype=int)
             coords_df.to_csv("{}_{}.txt".format(tma_name, cell_type), sep='\t', index=False)
 
             # Generate image for visualization if --img is specified
             if args.img:
-                # Group coordinates by feature_index
-                features = {}
-                for x, y, feature_index, coord_index in coords_arr:
-                    if feature_index not in features:
-                        features[feature_index] = []
-                    features[feature_index].append((x, y))
-
-                # Draw each feature separately
-                for feature_coords in features.values():
-                    pts = np.array(feature_coords, dtype=np.int32)
-                    if args.fill:
-                        cv2.fillPoly(img, [pts], color=0)  # Black fill
-                    else:
-                        cv2.polylines(img, [pts], isClosed=True, color=0, thickness=1)  # Black outline
-
                 cv2.imwrite("{}_{}.png".format(tma_name, cell_type), img)
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Split ROI coordinates of QuPath TMA annotation by cell type (classification)")
     parser.add_argument("--qupath_roi", default=False, help="Input QuPath annotation (GeoJSON file)")
     parser.add_argument("--fill", action="store_true", required=False, help="Fill pixels in ROIs (order of coordinates will be lost)")
-    parser.add_argument('--version', action='version', version='%(prog)s 0.3.1')
+    parser.add_argument('--version', action='version', version='%(prog)s 0.3.2')
     parser.add_argument("--all", action="store_true", required=False, help="Extracts all ROIs")
     parser.add_argument("--img", action="store_true", required=False, help="Generates image of ROIs")
     args = parser.parse_args()

tools/qupath_roi_splitter/qupath_roi_splitter.xml

@@ -1,7 +1,7 @@
 <tool id="qupath_roi_splitter" name="QuPath ROI Splitter" version="@VERSION@+galaxy@VERSION_SUFFIX@">
     <description>Split ROI coordinates of QuPath TMA annotation by cell type (classification)</description>
     <macros>
-        <token name="@VERSION@">0.3.1</token>
+        <token name="@VERSION@">0.3.2</token>
         <token name="@VERSION_SUFFIX@">0</token>
     </macros>
     <requirements>
@@ -67,7 +67,7 @@
             <output_collection name="output_imgs" type="list" count="4">
                 <element name="E-5_Tumor.png">
                     <assert_contents>
-                        <has_size value="459919"/>
+                        <has_size value="1309478"/>
                     </assert_contents>
                 </element>
             </output_collection>