Update detection visualization tutorial

detection/luna16_visualization/README.md

@@ -5,7 +5,7 @@ Visualizing box prediction/annotation in 3D medical image detection is not strai
 ## Prerequisite
 
 - The Version of 3D Slicer should be **4.11.20210226** or later.
-- Box information should be stored in a ".json" file. The "data\_sample.json" file is an example. The information of *N* boxes is stored under the key "box" as *N* lists. The six values are the *X*/*Y*/*Z* coordinates of the box center and the box length in the *X*/*Y*/*Z* axes. All the coordinate values are in the world coordinate system.
+- Box information should be stored in a ".json" file. The information of *N* boxes is stored under the key "box" as *N* lists. The six values could be in different 3D [modes](https://github.com/Project-MONAI/MONAI/blob/edf3b742a4ae85d1f30462ed0c7511c520fae888/monai/data/box_utils.py#L447-L456) (e.g., corner or center coordinates). All the coordinate values are in either world coordinate system or image coordinate system. The "data\_sample.json" file is an example with X/Y/Z-axial center coordinates and box lengths (box mode **cccwhd**).
 
 ```
 "box": [
@@ -32,13 +32,30 @@ Visualizing box prediction/annotation in 3D medical image detection is not strai
 
 ### 1. Create ".obj" file for predictions/annotation using the "save\_obj.sh" script.
 
-```
-#!/bin/bash
+User needs to specify the box mode and whether to use image coordinates. If image coordinates are specified, the image root directory needs to be provided.
+
+#### Example of boxes in world coordinate
 
+```
 INPUT_DATASET_JSON="./data_sample.json"
 OUTPUT_DIR="./out"
 
-python save_obj.py  --input_dataset_json ${INPUT_DATASET_JSON} \
+python save_obj.py  --input_box_mode "cccwhd" \
+                    --input_dataset_json ${INPUT_DATASET_JSON} \
+                    --output_dir ${OUTPUT_DIR}
+```
+
+#### Example of boxes in image coordinate
+
+```
+IMAGE_DATA_ROOT="/data_root"
+INPUT_DATASET_JSON="./data_sample_xyzxyz_image-coordinate.json"
+OUTPUT_DIR="./out_image_coord"
+
+python save_obj.py  --image_coordinate \
+                    --image_data_root ${IMAGE_DATA_ROOT} \
+                    --input_box_mode "xyzxyz" \
+                    --input_dataset_json ${INPUT_DATASET_JSON} \
                     --output_dir ${OUTPUT_DIR}
 ```
 

detection/luna16_visualization/data_sample_xyzxyz_image-coordinate.json

@@ -0,0 +1,45 @@
+{
+    "validation": [
+        {
+            "image": "1.3.6.1.4.1.14519.5.2.1.6279.6001.108197895896446896160048741492.mhd",
+            "box": [
+                [
+                    106,
+                    342.25,
+                    31.453125,
+                    114.75,
+                    351,
+                    34.0625
+                ]
+            ],
+            "label": [
+                0
+            ]
+        },
+        {
+            "image": "1.3.6.1.4.1.14519.5.2.1.6279.6001.109002525524522225658609808059.mhd",
+            "box": [
+                [
+                    413.75,
+                    273.25,
+                    62.90625,
+                    438.5,
+                    298,
+                    73.8125
+                ],
+                [
+                    404.5,
+                    333.25,
+                    54.8125,
+                    412.25,
+                    341.25,
+                    58.3125
+                ]
+            ],
+            "label": [
+                0,
+                0
+            ]
+        }
+    ]
+}

detection/luna16_visualization/save_obj.py

@@ -14,10 +14,13 @@
 import argparse
 import csv
 import json
+import monai
 import nibabel as nib
 import numpy as np
 import os
 
+from monai.data.box_utils import convert_box_mode
+
 
 def save_obj(vertices, faces, filename):
 
@@ -29,20 +32,44 @@ def save_obj(vertices, faces, filename):
         for t in faces:
             f.write("f {} {} {} {}\n".format(*(np.array(t) + 1)))
 
+    return
+
 
 def main():
-    parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser(
+        description="Save .obj files of boxes for visualization using 3D Slicer.",
+    )
+    parser.add_argument(
+        "--image_coordinate",
+        action="store_true",
+        help="if box coordinates in image coordinate",
+    )
+    parser.add_argument(
+        "--image_data_root",
+        type=str,
+        default="",
+        help="image data root",
+    )
+    parser.add_argument(
+        "--input_box_mode",
+        action="store",
+        type=str,
+        required=True,
+        help="input box coordinate mode",
+    ),
     parser.add_argument(
         "--input_dataset_json",
         action="store",
         type=str,
         required=True,
+        help="the dataset .json with box information",
     )
     parser.add_argument(
         "--output_dir",
         action="store",
         type=str,
         required=True,
+        help="output directory",
     )
     args = parser.parse_args()
 
@@ -52,6 +79,9 @@ def main():
     with open(os.path.join(args.input_dataset_json)) as f:
         input_dataset = json.load(f)
 
+    if args.image_coordinate:
+        image_loader = monai.transforms.LoadImage(reader=None, image_only=False)
+
     for key in input_dataset.keys():
         section = input_dataset[key]
 
@@ -61,17 +91,36 @@ def main():
             box_filename = box_filename.split(os.sep)[-1]
             print("-- {0:d}th case name:".format(_k + 1), box_filename)
 
+            if args.image_coordinate:
+                image_name = os.path.join(args.image_data_root, section[_k]["image"])
+                image_data = image_loader(image_name)
+                affine = image_data[1]["original_affine"]
+
+                # convert to RAS coordinate system (required by 3D Slicer)
+                for _i in range(3):
+                    if affine[_i, _i] < 0:
+                        affine[_i, _i] *= -1.0
+                        affine[_i, 3] *= -1.0
+
             vertices = []
             faces = []
             _i = 0
-            for vec in box_data:
-                xmin = vec[0] - 0.5 * vec[3]
-                ymin = vec[1] - 0.5 * vec[4]
-                zmin = vec[2] - 0.5 * vec[5]
-
-                xmax = vec[0] + 0.5 * vec[3]
-                ymax = vec[1] + 0.5 * vec[4]
-                zmax = vec[2] + 0.5 * vec[5]
+            for _vec in box_data:
+                vec = convert_box_mode(
+                    np.expand_dims(np.array(_vec), axis=0),
+                    src_mode=args.input_box_mode,
+                    dst_mode="xyzxyz",
+                )
+                vec = vec.squeeze()
+                xmin, ymin, zmin = vec[0], vec[1], vec[2]
+                xmax, ymax, zmax = vec[3], vec[4], vec[5]
+
+                if args.image_coordinate:
+                    _out = affine @ np.transpose(np.array([xmin, ymin, zmin, 1]))
+                    xmin, ymin, zmin = _out[0], _out[1], _out[2]
+
+                    _out = affine @ np.transpose(np.array([xmax, ymax, zmax, 1]))
+                    xmax, ymax, zmax = _out[0], _out[1], _out[2]
 
                 vertices += [
                     (xmax, ymax, zmin),
@@ -95,7 +144,9 @@ def main():
 
                 _i += 1
 
-            save_obj(vertices, faces, os.path.join(args.output_dir, box_filename + ".obj"))
+            save_obj(
+                vertices, faces, os.path.join(args.output_dir, box_filename + ".obj")
+            )
 
     return
 

detection/luna16_visualization/save_obj.sh

@@ -1,7 +1,17 @@
 #!/bin/bash
 
 INPUT_DATASET_JSON="./data_sample.json"
-OUTPUT_DIR="./out"
+OUTPUT_DIR="./out_world_coord"
 
-python save_obj.py  --input_dataset_json ${INPUT_DATASET_JSON} \
+python save_obj.py  --input_box_mode "cccwhd" \
+                    --input_dataset_json ${INPUT_DATASET_JSON} \
                     --output_dir ${OUTPUT_DIR}
+
+IMAGE_DATA_ROOT="/data_root"
+INPUT_DATASET_JSON="./data_sample_xyzxyz_image-coordinate.json"
+OUTPUT_DIR="./out_image_coord"
+
+python save_obj.py  --image_coordinate \
+                    --image_data_root ${IMAGE_DATA_ROOT} \
+                    --input_box_mode "xyzxyz" \
+                    --input_dataset_json ${INPUT_DATASET_JSON} \