1086 update url (#1087)

Signed-off-by: Wenqi Li <[email protected]>

Fixes #1086


### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Notebook runs automatically `./runner [-p <regex_pattern>]`

Signed-off-by: Wenqi Li <[email protected]>

Author: wyli

modules/resample_benchmark.ipynb

@@ -132,7 +132,7 @@
     "\n",
     "# dict of file name and corresponding urls\n",
     "url_dict = {\n",
-    "    \"SimpleITK.jpg\": \"https://s3.amazonaws.com/simpleitk/public/notebooks/SHA512/f1b5ce1bf9d7ebc0bd66f1c3bc0f90d9e9798efc7d0c5ea7bebe0435f173355b27df632971d1771dc1fc3743c76753e6a28f6ed43c5531866bfa2b38f1b8fd46\",  # noqa: E501\n",
+    "    \"monai.png\": \"https://github.com/Project-MONAI/project-monai.github.io/raw/master/assets/logo/MONAI-logo_color.png\",  # noqa: E501\n",
     "    \"mri.nii\": \"https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/Prostate_T2W_AX_1.nii\"\n",
     "}\n",
     "\n",
@@ -243,7 +243,7 @@
     "# from ipywidgets import interact, fixed\n",
     "# interact(\n",
     "#     transformation_display,\n",
-    "#     img_path=fixed(f\"{root_dir}/SimpleITK.jpg\"),\n",
+    "#     img_path=fixed(f\"{root_dir}/monai.png\"),\n",
     "#     tx=(-128.0, 128.0, 2.0),\n",
     "#     ty=(-64.0, 64.0),\n",
     "#     theta=(-np.pi/4.0, np.pi/4.0)\n",
@@ -263,7 +263,7 @@
     "The following example randomly sample 10 physical points (locations) on an image and assign ascending displacements in y(H) direction accordingly.\n",
     "Resampled intensities obtained by both MONAI and SimpleITK implementations are reported at all 10 points.\n",
     "\n",
-    "First, 10 physical points are randomly sampled from the downloaded SimpleITK logo."
+    "First, 10 physical points are randomly sampled from the downloaded logo."
    ]
   },
   {
@@ -275,7 +275,7 @@
    "outputs": [],
    "source": [
     "# Read image\n",
-    "img_path = f\"{root_dir}/SimpleITK.jpg\"\n",
+    "img_path = f\"{root_dir}/monai.png\"\n",
     "monai_img = np.asarray(PIL.Image.open(img_path))\n",
     "monai_img = monai_img.transpose((2, 0, 1))\n",
     "monai_img = torch.tensor(monai_img, dtype=torch.float)\n",