fixed extent in notebooks and rendering

add cropped volumes
appliedAI-Initiative · Aug 13, 2024 · 329fdcf · 329fdcf
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diff --git a/docs/02_notebooks/L0_MRI_and_Labelmaps.ipynb b/docs/02_notebooks/L0_MRI_and_Labelmaps.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    ")# Visualization of MRI Data and Labelmaps"
+    "# Visualization of MRI Data and Labelmaps"
    ]
   },
   {

diff --git a/docs/02_notebooks/L4_normalize_volumes.ipynb b/docs/02_notebooks/L4_normalize_volumes.ipynb
@@ -153,6 +153,8 @@
     "sliced_img = sitk.GetArrayFromImage(sliced_volume)\n",
     "cluster = TissueClusters.from_labelmap_slice(sliced_img.T)\n",
     "show_clusters(cluster, sliced_img.T, extent=transversal_extent)\n",
+    "print(f\"Slice value range: {np.min(sliced_img)} - {np.max(sliced_img)}\")\n",
+    "plt.axis(\"off\")\n",
     "plt.show()"
    ]
   },
@@ -174,6 +176,14 @@
     "reward = anatomy_based_rwd(cluster)\n",
     "print(reward)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b0966298f2bc74c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/docs/02_notebooks/L5_random_volume_transformations.ipynb b/docs/02_notebooks/L5_random_volume_transformations.ipynb
@@ -121,7 +121,6 @@
     "sliced_img = sitk.GetArrayFromImage(sliced_volume)\n",
     "print(f\"Slice value range: {np.min(sliced_img)} - {np.max(sliced_img)}\")\n",
     "\n",
-    "extent_xz = (0, size[0], size[2], 0)\n",
     "cluster = TissueClusters.from_labelmap_slice(sliced_img.T)\n",
     "show_clusters(cluster, sliced_img.T)\n",
     "reward = anatomy_based_rwd(cluster)\n",

diff --git a/docs/02_notebooks/L6_environment.ipynb b/docs/02_notebooks/L6_environment.ipynb
@@ -30,12 +30,11 @@
     "from armscan_env.clustering import TissueClusters\n",
     "from armscan_env.config import get_config\n",
     "from armscan_env.envs.labelmaps_navigation import (\n",
-    "    LabelmapClusteringBasedReward,\n",
     "    ArmscanEnv,\n",
+    "    LabelmapClusteringBasedReward,\n",
     "    LabelmapEnvTerminationCriterion,\n",
     ")\n",
     "from armscan_env.envs.observations import (\n",
-    "    ActionRewardObservation,\n",
     "    LabelmapSliceAsChannelsObservation,\n",
     ")\n",
     "from armscan_env.envs.rewards import anatomy_based_rwd\n",
@@ -87,6 +86,11 @@
     "z = [0, -5, 0, 0, 5, 15, 19.3, -10, 0, 0, 0, 5, -8, 8, 0, -10, -10, 10, 19.3]\n",
     "o = volumes[0].GetOrigin()\n",
     "slice_shape = (volumes[0].GetSize()[0], volumes[0].GetSize()[2])\n",
+    "size = np.array(volumes[0].GetSize()) * np.array(volumes[0].GetSpacing())\n",
+    "\n",
+    "transversal_extent = (0, size[0], 0, size[2])\n",
+    "longitudinal_extent = (0, size[1], 0, size[2])\n",
+    "frontal_extent = (0, size[0], size[1], 0)\n",
     "\n",
     "\n",
     "# Sample functions for demonstration\n",
@@ -106,9 +110,9 @@
     "\n",
     "for i in range(len(t)):\n",
     "    # Subplot 1: Image with dashed line\n",
-    "    ax1.imshow(img_array_1[40, :, :])\n",
-    "    x_dash = np.arange(img_array_1.shape[2])\n",
-    "    b = volumes[0].TransformPhysicalPointToIndex([o[0], o[1] + t[i], o[2]])[1]\n",
+    "    ax1.imshow(img_array_1[40, :, :], extent=frontal_extent)\n",
+    "    x_dash = np.arange(size[0])\n",
+    "    b = t[i]\n",
     "    y_dash = linear_function(x_dash, np.tan(np.deg2rad(z[i])), b)\n",
     "    ax1.set_title(f\"Section {0}\")\n",
     "    line = ax1.plot(x_dash, y_dash, linestyle=\"--\", color=\"red\")[0]\n",
@@ -120,12 +124,16 @@
     "        action=ManipulatorAction(rotation=(z[i], 0.0), translation=(0.0, t[i])),\n",
     "    )\n",
     "    sliced_img = sitk.GetArrayFromImage(sliced_volume).T\n",
-    "    ax2.imshow(sliced_img.T, aspect=6, origin=\"lower\")\n",
+    "    ax2.imshow(\n",
+    "        sliced_img.T,\n",
+    "        origin=\"lower\",\n",
+    "        extent=transversal_extent,\n",
+    "    )\n",
     "    ax2.set_title(f\"Slice {i}\")\n",
     "\n",
     "    # OBSERVATION\n",
     "    clusters = TissueClusters.from_labelmap_slice(sliced_img)\n",
-    "    ax3 = show_clusters(clusters, sliced_img, ax3)\n",
+    "    ax3 = show_clusters(clusters, sliced_img, ax3, extent=transversal_extent)\n",
     "    ax3.set_title(f\"Clusters {i}\")\n",
     "\n",
     "    # REWARD\n",
@@ -194,7 +202,7 @@
     "    termination_criterion=LabelmapEnvTerminationCriterion(),\n",
     "    max_episode_len=10,\n",
     "    rotation_bounds=(30.0, 10.0),\n",
-    "    translation_bounds=(None, None),\n",
+    "    translation_bounds=(0.0, None),\n",
     "    render_mode=\"animation\",\n",
     "    apply_volume_transformation=True,\n",
     ")\n",
@@ -216,44 +224,6 @@
     "HTML(animation.to_jshtml())"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "volume_size = volumes[0].GetSize()\n",
-    "\n",
-    "env = ArmscanEnv(\n",
-    "    name2volume={\"1\": volumes[6]},\n",
-    "    observation=ActionRewardObservation(action_shape=(2,)).to_array_observation(),\n",
-    "    slice_shape=(volume_size[0], volume_size[2]),\n",
-    "    reward_metric=LabelmapClusteringBasedReward(),\n",
-    "    termination_criterion=LabelmapEnvTerminationCriterion(min_reward_threshold=-0.05),\n",
-    "    max_episode_len=10,\n",
-    "    rotation_bounds=(30.0, 10.0),\n",
-    "    translation_bounds=(None, None),\n",
-    "    render_mode=\"animation\",\n",
-    "    project_actions_to=\"zy\",\n",
-    "    apply_volume_transformation=True,\n",
-    ")\n",
-    "\n",
-    "observation, info = env.reset()\n",
-    "for _ in range(50):\n",
-    "    action = env.action_space.sample()\n",
-    "    epsilon = 0.1\n",
-    "    if np.random.rand() > epsilon:\n",
-    "        observation, reward, terminated, truncated, info = env.step(action)\n",
-    "    else:\n",
-    "        observation, reward, terminated, truncated, info = env.step_to_optimal_state()\n",
-    "    env.render()\n",
-    "\n",
-    "    if terminated or truncated:\n",
-    "        observation, info = env.reset(reset_render=False)\n",
-    "animation = env.get_cur_animation()\n",
-    "env.get_cur_animation_as_html()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

diff --git a/docs/02_notebooks/L7_linear_sweep.ipynb b/docs/02_notebooks/L7_linear_sweep.ipynb
@@ -24,8 +24,8 @@
     "from armscan_env.config import get_config\n",
     "from armscan_env.envs.base import EnvRollout\n",
     "from armscan_env.envs.labelmaps_navigation import (\n",
-    "    LabelmapClusteringBasedReward,\n",
     "    ArmscanEnv,\n",
+    "    LabelmapClusteringBasedReward,\n",
     "    LabelmapEnvTerminationCriterion,\n",
     ")\n",
     "from armscan_env.envs.observations import (\n",