Run black on ipynb (#80)

notebooks/visualize_dataset.ipynb

@@ -46,6 +46,7 @@
    "outputs": [],
    "source": [
     "import goat\n",
+    "\n",
     "vis = goat.vis_utils.get_vis()"
    ]
   },
@@ -109,7 +110,7 @@
     "# goat.vis_utils.show_box_test(vis)\n",
     "num_images_to_show = 200\n",
     "for idx in tqdm(list(range(len(dataset_inputs.image_filenames)))[:num_images_to_show]):\n",
-    "    image = imageio.imread(dataset_inputs.image_filenames[idx])[:,:,:3]\n",
+    "    image = imageio.imread(dataset_inputs.image_filenames[idx])[:, :, :3]\n",
     "\n",
     "    num_intrinsics_params = len(dataset_inputs.intrinsics[idx])\n",
     "    camera_class = get_camera_model(num_intrinsics_params)\n",
@@ -120,16 +121,16 @@
     "    name = \"train/{:06d}\".format(idx)\n",
     "    # height = 0.5\n",
     "    displayed_focal_length = 0.5\n",
-    "    goat.vis_utils.draw_camera_frustum(vis, \n",
-    "        name=name, \n",
-    "        image=image, \n",
-    "        K=K.numpy(), \n",
+    "    goat.vis_utils.draw_camera_frustum(\n",
+    "        vis,\n",
+    "        name=name,\n",
+    "        image=image,\n",
+    "        K=K.numpy(),\n",
     "        pose=camera_to_world.double().numpy(),\n",
     "        height=3.0,\n",
     "        realistic=False,\n",
-    "        displayed_focal_length=0.0\n",
-    "        )\n",
-    "\n"
+    "        displayed_focal_length=0.0,\n",
+    "    )"
    ]
   },
   {
@@ -140,8 +141,8 @@
    "outputs": [],
    "source": [
     "image_dataset = ImageDataset(\n",
-    "            image_filenames=dataset_inputs.image_filenames, downscale_factor=dataset_inputs.downscale_factor\n",
-    "        )\n",
+    "    image_filenames=dataset_inputs.image_filenames, downscale_factor=dataset_inputs.downscale_factor\n",
+    ")\n",
     "iter_dataset = CollateIterDataset(\n",
     "    image_dataset,\n",
     "    collate_fn=lambda batch_list: collate_batch(\n",
@@ -188,15 +189,16 @@
     "def show_batch(batch):\n",
     "    # set the color of the sampled rays\n",
     "    c, y, x = [i.flatten() for i in torch.split(batch[\"local_indices\"], 1, dim=-1)]\n",
-    "    batch[\"image\"][c,y,x] = 1.0\n",
+    "    batch[\"image\"][c, y, x] = 1.0\n",
     "\n",
     "    # batch[\"image\"] is num_images, h, w, 3\n",
     "    images = torch.split(batch[\"image\"], 1, dim=0)\n",
     "    image_list = [image[0] for image in images]\n",
-    "    image = torch.cat(image_list, dim=1) # cat along the width dimension\n",
+    "    image = torch.cat(image_list, dim=1)  # cat along the width dimension\n",
     "\n",
     "    # the white pixels are rays\n",
-    "    media.show_image((image*255).to(torch.uint8))\n",
+    "    media.show_image((image * 255).to(torch.uint8))\n",
+    "\n",
     "\n",
     "def sample_and_show_batch():\n",
     "    batch = next(iter(dataloader))\n",
@@ -235,14 +237,16 @@
     "skip = 1\n",
     "size = 8\n",
     "data = []\n",
-    "data += [go.Scatter3d(\n",
-    "    x=ray_generator.camera_to_world[::skip,0,3],\n",
-    "    y=ray_generator.camera_to_world[::skip,1,3],\n",
-    "    z=ray_generator.camera_to_world[::skip,2,3],\n",
-    "    mode='markers',\n",
-    "    name=\"origins\",\n",
-    "    marker=dict(color='rgba(0, 0, 0, 1)', size=size)\n",
-    ")]"
+    "data += [\n",
+    "    go.Scatter3d(\n",
+    "        x=ray_generator.camera_to_world[::skip, 0, 3],\n",
+    "        y=ray_generator.camera_to_world[::skip, 1, 3],\n",
+    "        z=ray_generator.camera_to_world[::skip, 2, 3],\n",
+    "        mode=\"markers\",\n",
+    "        name=\"origins\",\n",
+    "        marker=dict(color=\"rgba(0, 0, 0, 1)\", size=size),\n",
+    "    )\n",
+    "]"
    ]
   },
   {
@@ -275,9 +279,13 @@
    "source": [
     "# near_plane = cfg.graph.network.near_plane\n",
     "# far_plane = cfg.graph.network.far_plane\n",
-    "lines = torch.stack([intersected_ray_bundle.origins + intersected_ray_bundle.directions * intersected_ray_bundle.nears,\n",
-    "                     intersected_ray_bundle.origins + intersected_ray_bundle.directions * intersected_ray_bundle.fars\n",
-    "                    ], dim=1).tolist() # (num_rays, 2, 3)\n",
+    "lines = torch.stack(\n",
+    "    [\n",
+    "        intersected_ray_bundle.origins + intersected_ray_bundle.directions * intersected_ray_bundle.nears,\n",
+    "        intersected_ray_bundle.origins + intersected_ray_bundle.directions * intersected_ray_bundle.fars,\n",
+    "    ],\n",
+    "    dim=1,\n",
+    ").tolist()  # (num_rays, 2, 3)\n",
     "lines = torch.tensor(random.sample(lines, k=100))"
    ]
   },
@@ -302,21 +310,11 @@
     "    autosize=False,\n",
     "    width=1000,\n",
     "    height=1000,\n",
-    "    margin=go.layout.Margin(\n",
-    "        l=50,\n",
-    "        r=50,\n",
-    "        b=100,\n",
-    "        t=100,\n",
-    "        pad=4\n",
-    "    ),\n",
+    "    margin=go.layout.Margin(l=50, r=50, b=100, t=100, pad=4),\n",
     "    scene=go.layout.Scene(\n",
     "        aspectmode=\"data\",\n",
-    "        camera=dict(\n",
-    "            up=dict(x=0, y=0, z=1),\n",
-    "            center=dict(x=0, y=0, z=0),\n",
-    "            eye=dict(x=1.25, y=1.25, z=1.25)\n",
-    "        )\n",
-    "    )\n",
+    "        camera=dict(up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=0), eye=dict(x=1.25, y=1.25, z=1.25)),\n",
+    "    ),\n",
     ")\n",
     "fig = go.Figure(data=data, layout=layout)\n",
     "fig.show()"
@@ -338,7 +336,7 @@
    "outputs": [],
    "source": [
     "fig.write_image(\"temp.png\")\n",
-    "plotly_image = imageio.imread(\"temp.png\")\n"
+    "plotly_image = imageio.imread(\"temp.png\")"
    ]
   },
   {