adding acdc dataset example for readme

README.md

@@ -17,6 +17,19 @@
 | ![Python](https://img.shields.io/badge/python-3.6/3.7/3.8-orange) | ![System](https://img.shields.io/badge/Windows-blue) | ![Unittests Windows](https://github.com/PhoenixDL/rising/workflows/Unittests%20Windows/badge.svg) |
 | ![Python](https://img.shields.io/badge/python-3.6/3.7/3.8-orange) | ![System](https://img.shields.io/badge/MacOS-blue)   | ![Unittests macOS](https://github.com/PhoenixDL/rising/workflows/Unittests%20MacOS/badge.svg)     |
 
+## What I have modified from the original repo?
+
+I noticed that rising under its current form has small bugs and inconsistency in terms of design and performance, especially for medical image segmentation task with 3D volumes. Based on project requirement, several improvements have been made so that it can now enjoy a better compatibility with 3D datas and intenstive augmentation operations using GPUs. Issues and PRs are welcome.
+
+Thanks a lot @Yuxiang1990 for the kind PR.
+
+## Working example on ACDC dataset
+
+[ACDC example](notebooks/medical_seg/acdc_seg.py) should provide a resonable example of how to use rising besides the below
+explanations.
+
+______________________________________________________________________
+
 ## What is `rising`?
 
 Rising is a high-performance data loading and augmentation library for 2D *and* 3D data completely written in PyTorch.
@@ -27,13 +40,6 @@ Multiple examples for different use cases can be found in our [tutorial docs](ht
 [Example Transformation Output](https://rising.readthedocs.io/en/latest/transformations.html),
 [Integration of External Frameworks](https://rising.readthedocs.io/en/latest/external_augmentation.html)
 
-## What I have modified from the original repo?
-
-I noticed that rising under its current form has small bugs and inconsistency in terms of design and performance, especially for medical image segmentation task with 3D volumes. Based on project requirement, several improvements have been made so that it can now enjoy a better compatibility with 3D datas and intenstive augmentation operations using GPUs. Issues and PRs are welcome.
-
-Thanks a lot @Yuxiang1990 for the kind PR.
-
-
 ## Why another framework?
 
 |            | rising  |   TorchIO    | Batchgenerators | Kornia  | DALI | Vanilla PyTorch | Albumentations |

notebooks/medical_seg/acdc_seg.py

@@ -25,7 +25,7 @@
     transforms.GammaCorrection(gamma=UniformParameter(0.8, 2), keys=("image",)),
     transforms.RicianNoiseTransform(keys=("image",), std=0.05, keep_range=False),
     transforms.BaseAffine(
-        scale=(1, UniformParameter(0.5, 2), UniformParameter(0.9, 1.2)),
+        scale=(1, UniformParameter(0.5, 4), UniformParameter(0.9, 1.2)),
         rotation=(0, UniformParameter(-10, 10), UniformParameter(-10, 10)),
         degree=True,
         p=1,
@@ -56,6 +56,9 @@
 
 for data in tqdm(tra_loader):
     image, label = data["image"], data["label"]
+    from tests.realtime_viewer import multi_slice_viewer_debug
+
+    multi_slice_viewer_debug([*image.squeeze()], *label.squeeze(), block=True, no_contour=True)
     # from tests.realtime_viewer import multi_slice_viewer_debug
     #
     # for img, lab in zip(image, label):

tests/realtime_viewer.py

@@ -92,7 +92,7 @@ def previous_slice(ax):
             else:
                 ax.con.remove()
                 ax.con = ax.imshow(ax.gt_volume[ax.index], alpha=alpha, cmap="rainbow")
-        ax.set_title(f"plane = {ax.index}")
+        # ax.set_title(f"plane = {ax.index}")
 
     def next_slice(ax):
         img_volume = ax.img_volume
@@ -107,7 +107,7 @@ def next_slice(ax):
             else:
                 ax.con.remove()
                 ax.con = ax.imshow(ax.gt_volume[ax.index], alpha=alpha, cmap="rainbow")
-        ax.set_title(f"plane = {ax.index}")
+        # ax.set_title(f"plane = {ax.index}")
 
     try:
         import matplotlib
@@ -147,7 +147,7 @@ def next_slice(ax):
             except Exception as e:
                 pass
             ax.axis("off")
-            ax.set_title(f"plane = {ax.index}")
+            # ax.set_title(f"plane = {ax.index}")
 
     fig.canvas.mpl_connect("key_press_event", process_key)
     fig.canvas.mpl_connect("scroll_event", process_mouse_wheel)