update

README.md

@@ -70,16 +70,18 @@ print(next(iter(male_train)))
 ```
 
 ### Visualize data
+Download `vis_hit_sample.py` from the repo or `git clone https://huggingface.co/datasets/varora/HIT`
 ```angular2html
-pip install -r requirements.txt
+pip install datasets, open3d, pyvista
 ```
 ```angular2html
-git clone https://huggingface.co/datasets/varora/HIT
+python vis_hit_sample.py --gender male --split test --idx 5 --show_skin
 ```
+![alt text](extras/vis_script_output.png)
 ```angular2html
-python vis_hit_sample.py --gender male --split test --idx 5
+python vis_hit_sample.py --gender male --split test --idx 5 --show_tissue
 ```
-![alt text](extras/vis_script_output.png)
+![alt text](extras/tissue_slice_frontal.png)
 
 ## Dataset Structure
 The dataset is structured as follows:

requirements.txt

@@ -1,2 +1,3 @@
 datasets
-open3d
+open3d
+pyvista

vis_hit_sample.py

@@ -3,10 +3,77 @@ import argparse
 from datasets import load_dataset
 
 import open3d as o3d
+import pyvista as pv
+from PIL import Image
+import matplotlib
+import matplotlib.pyplot as plt
+
 import numpy as np
 import random
 
 
+def plot_3D_image(values, resolution, p=None, interactive_slice=False, orthogonal_slices=True):
+    ''' Interactive plot of the 3D volume'''
+
+    # Create the spatial reference
+    grid = pv.ImageData()
+    values = np.transpose(values, (1,2,0))
+    # Set the grid dimensions: shape + 1 because we want to inject our values on
+    # the CELL data
+    grid.dimensions = np.array(values.shape) + 1
+
+    # Edit the spatial reference
+    # The bottom left corner of the data set
+    origin = np.array(resolution[0]) * np.array(values.shape) * 0.5
+    grid.origin = origin
+    #print(f'Scan size in meter: {origin * 2}')
+    grid.spacing = resolution[0]  # These are the cell sizes along each axis
+
+    # Add the data values to the cell data
+    grid.cell_data["values"] = values.flatten(order="F")  # Flatten the array!
+
+    if p is None:
+        p = pv.Plotter()
+
+    if orthogonal_slices:
+        slices = grid.slice_orthogonal()
+        cmap = matplotlib.colors.ListedColormap(['black', 'indianred', 'goldenrod', 'steelblue', 'ghostwhite'])
+        p.add_mesh(slices, cmap=cmap)
+
+    if interactive_slice:
+        p.add_mesh_clip_plane(grid)
+
+    return p
+
+
+def get_sliced_mri_png(sample):
+    # get data
+    mri = np.asarray(sample['mri_seg'])
+    resolution = np.asarray(sample['resolution'])
+    # set plotter
+    p = pv.Plotter(shape=(1, 1), off_screen=True)
+    p.subplot(0, 0)
+    plotter = plot_3D_image(mri, resolution, p, interactive_slice=False, orthogonal_slices=True)
+    plotter.view_yz()
+    plotter.remove_scalar_bar()
+    # store screenshot
+    img = p.screenshot("./extras/img.png", return_img=True)
+    # read screenshot
+    img = Image.fromarray(img)
+    # set plotter lateral
+    p = pv.Plotter(shape=(1, 1), off_screen=True)
+    p.subplot(0, 0)
+    plotter = plot_3D_image(mri, resolution, p, interactive_slice=False, orthogonal_slices=True)
+    plotter.remove_scalar_bar()
+    plotter.view_xz()
+    img_lateral = p.screenshot("./extras/img_lateral.png", return_img=True)
+    img_lateral = Image.fromarray(img_lateral)
+    # resize
+    img = img.resize((512+128, 372+128))
+    img_lateral = img_lateral.resize((512+128, 372+128))
+    return img, img_lateral
+
+
 def vis_hit_sample(sample):
     """
     :param sample: HIT dataset sample
@@ -65,6 +132,8 @@ if __name__ == '__main__':
     parser.add_argument('--gender', type=str, default='male')
     parser.add_argument('--split', type=str, default='train')
     parser.add_argument('--idx', type=int, default=None)
+    parser.add_argument('--show_skin', action='store_true')
+    parser.add_argument('--show_tissue', action='store_true')
 
     # get args
     args = parser.parse_args()
@@ -98,4 +167,16 @@ if __name__ == '__main__':
     hit_sample = hit_dataset[idx]
     # visualize the sample
     print(f"Visualizing sample no. {idx} in {args.gender}:{args.split}.")
-    vis_hit_sample(hit_sample)
+    if args.show_tissue:
+        img, img_lateral = get_sliced_mri_png(hit_sample)
+        img.show()
+        img_lateral.show()
+    elif args.show_skin:
+        vis_hit_sample(hit_sample)
+    else:
+        img, img_lateral = get_sliced_mri_png(hit_sample)
+        img.show()
+        img_lateral.show()
+        vis_hit_sample(hit_sample)
+
+