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nvidia_nims/vista_3d_remote_nim.ipynb

@@ -109,7 +109,7 @@
     "import zipfile\n",
     "\n",
     "import itk\n",
-    "import pyvista as pv\n"
+    "import pyvista as pv"
    ]
   },
   {
@@ -133,28 +133,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def post_image_to_fileio(input_image:itk.Image) -> str:\n",
-    "    '''Post an image to file.io and return the link.'''\n",
+    "def post_image_to_fileio(input_image: itk.Image) -> str:\n",
+    "    \"\"\"Post an image to file.io and return the link.\"\"\"\n",
     "\n",
     "    # Save the image to a temporary file\n",
     "    # Note: The Vista-3D NIM on NVAIE is limited to reading .nii.gz files\n",
     "    tmp_dir = tempfile.mkdtemp()\n",
     "    tmp_filename = os.path.join(tmp_dir, \"tmp.nii.gz\")\n",
     "    itk.imwrite(input_image, tmp_filename)\n",
-    "    \n",
+    "\n",
     "    # Upload the file\n",
     "    with open(os.path.join(tmp_filename), \"rb\") as f:\n",
-    "        res = requests.post(\n",
-    "            \"https://file.io/\",\n",
-    "            files={\"file\": f}\n",
-    "        )\n",
+    "        res = requests.post(\"https://file.io/\", files={\"file\": f})\n",
     "    if res.status_code != 200:\n",
     "        raise RuntimeError(f\"Cannot upload file. The response {res}.\")\n",
-    "    \n",
+    "\n",
     "    # Get the link\n",
     "    res = res.json()\n",
     "    link = res[\"link\"]\n",
-    "    \n",
+    "\n",
     "    return link"
    ]
   },
@@ -177,49 +174,53 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def nvaie_vista3d_nim(api_key:str, input_image:itk.Image) -> list:\n",
-    "    '''Run the MONAI VISTA 3D model on the input image and return the result.'''\n",
+    "def nvaie_vista3d_nim(api_key: str, input_image: itk.Image) -> list:\n",
+    "    \"\"\"Run the MONAI VISTA 3D model on the input image and return the result.\"\"\"\n",
     "\n",
     "    # The API endpoint for the MONAI VISTA 3D model on NVIDIA AI Enterprise\n",
     "    invoke_url = \"https://health.api.nvidia.com/v1/medicalimaging/nvidia/vista-3d\"\n",
-    "    \n",
+    "\n",
     "    # Check the input image\n",
     "    assert len(input_image.GetSpacing()) == 3, \"The input image must be 3D.\"\n",
-    "    isotropy = ((input_image.GetSpacing()[1] / input_image.GetSpacing()[0]) +\n",
-    "                (input_image.GetSpacing()[2] / input_image.GetSpacing()[0])) / 2\n",
+    "    isotropy = (\n",
+    "        (input_image.GetSpacing()[1] / input_image.GetSpacing()[0])\n",
+    "        + (input_image.GetSpacing()[2] / input_image.GetSpacing()[0])\n",
+    "    ) / 2\n",
     "    if isotropy < 0.9 or isotropy > 1.1 or input_image.GetSpacing()[0] != 1.5:\n",
     "        print(\"WARNING: The input image should have 1.5 mm isotropic spacing.  Performance will be degraded.\")\n",
     "        print(\"    The input image has spacing:\", input_image.GetSpacing())\n",
     "    input_image_arr = itk.array_view_from_image(input_image)\n",
     "    minv = input_image_arr.min()\n",
     "    maxv = input_image_arr.max()\n",
     "    if minv < -1024 or maxv > 3071:\n",
-    "        print(\"WARNING: The input image should have Hounsfield Units in the range [-1024, 3071].  Performance will be degraded.\")\n",
+    "        print(\n",
+    "            \"WARNING: The input image should have Hounsfield Units in the range [-1024, 3071].  Performance will be degraded.\"\n",
+    "        )\n",
     "        print(\"    The input image has Hounsfield Units in the range:\", [minv, maxv])\n",
     "\n",
     "    # Post the image to file.io and get the link\n",
     "    input_image_url = post_image_to_fileio(input_image)\n",
     "\n",
     "    # Define the header and payload for the API call\n",
     "    header = {\n",
-    "    \"Authorization\": \"Bearer \" + api_key, \n",
+    "        \"Authorization\": \"Bearer \" + api_key,\n",
     "    }\n",
-    "    \n",
+    "\n",
     "    payload = {\n",
     "        \"image\": input_image_url,\n",
     "        # Optionally limited processing to specific classes\n",
-    "        #\"prompts\": {\n",
+    "        # \"prompts\": {\n",
     "        #    \"classes\": [\"liver\", \"spleen\"]\n",
-    "        #}\n",
+    "        # }\n",
     "    }\n",
     "\n",
     "    # Call the API\n",
     "    session = requests.Session()\n",
     "    response = session.post(invoke_url, headers=header, json=payload)\n",
-    "    \n",
+    "\n",
     "    # Check the response\n",
     "    response.raise_for_status()\n",
-    "    \n",
+    "\n",
     "    # Get the result\n",
     "    with tempfile.TemporaryDirectory() as temp_dir:\n",
     "        z = zipfile.ZipFile(io.BytesIO(response.content))\n",
@@ -230,7 +231,7 @@
     "            if os.path.isfile(filepath) and filename.endswith(\".nrrd\"):\n",
     "                # SUCCESS: Return the results\n",
     "                return itk.imread(filepath, pixel_type=itk.SS)\n",
-    "                    \n",
+    "\n",
     "    # FAILURE: Return None\n",
     "    return None"
    ]
@@ -252,7 +253,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ngc_api_key = os.environ['NGC_API_KEY']"
+    "ngc_api_key = os.environ[\"NGC_API_KEY\"]"
    ]
   },
   {
@@ -280,13 +281,10 @@
     "        os.makedirs(monai_data_directory, exist_ok=True)\n",
     "    else:\n",
     "        monai_data_directory = tempfile.mkdtemp()\n",
-    "    input_image_filename = os.path.join(\n",
-    "        monai_data_directory,\n",
-    "        \"vista3d-example-1.nii.gz\"\n",
-    "    )\n",
+    "    input_image_filename = os.path.join(monai_data_directory, \"vista3d-example-1.nii.gz\")\n",
     "    if not os.path.exists(input_image_filename):\n",
     "        resp = requests.get(\"https://assets.ngc.nvidia.com/products/api-catalog/vista3d/example-1.nii.gz\")\n",
-    "        with open(input_image_filename, \"wb\") as f: \n",
+    "        with open(input_image_filename, \"wb\") as f:\n",
     "            f.write(resp.content)"
    ]
   },
@@ -386,14 +384,14 @@
    ],
    "source": [
     "# For visualization purposes, we will clip the contours half-way along the x-axis\n",
-    "clipped = contours.clip('x')\n",
+    "clipped = contours.clip(\"x\")\n",
     "\n",
     "# Render the clipped contours and the original image\n",
     "pl = pv.Plotter()\n",
     "pl.add_mesh(clipped, cmap=\"pink\", show_scalar_bar=False, opacity=1.0)\n",
-    "pl.add_volume(image, clim=[50,800], cmap=\"pink\", show_scalar_bar=False)\n",
+    "pl.add_volume(image, clim=[50, 800], cmap=\"pink\", show_scalar_bar=False)\n",
     "pl.set_background(\"black\")\n",
-    "pl.camera_position = 'xz'\n",
+    "pl.camera_position = \"xz\"\n",
     "pl.show()"
    ]
   },
@@ -423,15 +421,15 @@
     "pl2 = pv.Plotter(shape=(3, 1))\n",
     "\n",
     "# The normal directions (dirs) of the slices and the positions (pos) of the camera for each view\n",
-    "dirs = ['x', 'y', 'z']\n",
-    "pos = ['yz', 'xz', 'xy']\n",
+    "dirs = [\"x\", \"y\", \"z\"]\n",
+    "pos = [\"yz\", \"xz\", \"xy\"]\n",
     "\n",
     "# Extract the slices and generate the views for each subplot\n",
     "for i, dir in enumerate(dirs):\n",
     "    image_slice = image.slice(normal=dir)\n",
     "    contours_slice = labels.slice(normal=dir, contour=True)\n",
-    "    pl2.subplot(i,0)\n",
-    "    pl2.add_mesh(image_slice, cmap=\"bone\", clim=[-200,500], show_scalar_bar=False, opacity=1.0)\n",
+    "    pl2.subplot(i, 0)\n",
+    "    pl2.add_mesh(image_slice, cmap=\"bone\", clim=[-200, 500], show_scalar_bar=False, opacity=1.0)\n",
     "    pl2.add_mesh(contours_slice, cmap=\"viridis\", show_scalar_bar=False, opacity=1.0, line_width=5)\n",
     "    pl2.camera_position = pos[i]\n",
     "\n",