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Add plotly visualization examples
Summary: Add examples of using the Plotly visualization functions to the corresponding tutorial notebooks. Reviewed By: nikhilaravi Differential Revision: D23879109 fbshipit-source-id: ea8c45aa6c828eb2f6ea2ae1c8846adc486f92e0
This commit is contained in:
Amitav Baruah
Facebook GitHub Bot
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@ -67,6 +67,7 @@
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"\n",
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"# Data structures and functions for rendering\n",
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"from pytorch3d.structures import Pointclouds\n",
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"from pytorch3d.visualization import AxisArgs, plot_pointclouds\n",
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"from pytorch3d.renderer import (\n",
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" look_at_view_transform,\n",
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" FoVOrthographicCameras, \n",
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@ -286,6 +287,86 @@
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"plt.grid(\"off\")\n",
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"plt.axis(\"off\");"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### View pointclouds in Plotly figures\n",
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"\n",
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"Here we use the PyTorch3D function `plot_pointclouds` to render the Pointcloud in a Plotly figure. `plot_pointclouds` returns a plotly figure with a trace for each pointcloud."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_pointclouds(point_cloud)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We will now render a batch of pointclouds. The first pointcloud is the same as above, and the second is all-black and offset by 2 in all dimensions so we can see them on the same plot. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"point_cloud_batch = Pointclouds(points=[verts, verts + 2], features=[rgb, torch.zeros_like(rgb)])\n",
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"# render both in the same plot\n",
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"fig = plot_pointclouds(point_cloud_batch)\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# render both in 1 row in different subplots\n",
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"fig2 = plot_pointclouds(point_cloud_batch, in_subplots=True, ncols=2)\n",
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"fig2.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# modify the plotly figure height and width\n",
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"fig2.update_layout(height=500, width=500)\n",
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"fig2.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can also modify the axis arguments and axis backgrounds, and title our plots."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig3 = plot_pointclouds(point_cloud_batch, xaxis={\"backgroundcolor\":\"rgb(200, 200, 230)\"},\n",
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" yaxis={\"backgroundcolor\":\"rgb(230, 200, 200)\"},\n",
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" zaxis={\"backgroundcolor\":\"rgb(200, 230, 200)\"}, \n",
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" subplot_titles=[\"2 pointclouds\"], # this should have a title for each subplot, titles can be \"\"\n",
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" axis_args=AxisArgs(showgrid=True))\n",
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"fig3.show()"
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]
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}
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],
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"metadata": {
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@ -298,9 +379,9 @@
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"bento/extensions/theme/main.css": true
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},
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"kernelspec": {
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"display_name": "pytorch3d (local)",
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"display_name": "pytorch3d_etc (local)",
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"language": "python",
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"name": "pytorch3d_local"
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"name": "pytorch3d_etc_local"
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},
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"language_info": {
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"codemirror_mode": {
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@ -93,6 +93,7 @@
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"\n",
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"# Data structures and functions for rendering\n",
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"from pytorch3d.structures import Meshes\n",
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"from pytorch3d.visualization import AxisArgs, plot_meshes\n",
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"from pytorch3d.renderer import (\n",
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" look_at_view_transform,\n",
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" FoVPerspectiveCameras, \n",
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@ -103,7 +104,8 @@
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" MeshRenderer, \n",
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" MeshRasterizer, \n",
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" SoftPhongShader,\n",
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" TexturesUV\n",
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" TexturesUV,\n",
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" TexturesVertex\n",
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")\n",
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"\n",
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"# add path for demo utils functions \n",
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@ -555,6 +557,111 @@
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"image_grid(images.cpu().numpy(), rows=4, cols=5, rgb=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 7. Plotly visualization \n",
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"If you only want to visualize a mesh, you don't really need to use a differentiable renderer - instead we support plotting of Meshes with plotly. For these Meshes, we use TexturesVertex to define a texture for the rendering.\n",
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"`plot_meshes` creates a Plotly figure with a trace for each Meshes object. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"verts, faces_idx, _ = load_obj(obj_filename)\n",
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"faces = faces_idx.verts_idx\n",
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"\n",
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"# Initialize each vertex to be white in color.\n",
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"verts_rgb = torch.ones_like(verts)[None] # (1, V, 3)\n",
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"textures = TexturesVertex(verts_features=verts_rgb.to(device))\n",
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"\n",
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"# Create a Meshes object\n",
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"mesh = Meshes(\n",
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" verts=[verts.to(device)], \n",
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" faces=[faces.to(device)],\n",
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" textures=textures\n",
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")\n",
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"\n",
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"# Render the plotly figure\n",
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"fig = plot_meshes(mesh)\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# use Plotly's default colors (no texture)\n",
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"mesh = Meshes(\n",
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" verts=[verts.to(device)], \n",
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" faces=[faces.to(device)]\n",
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")\n",
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"\n",
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"# Render the plotly figure\n",
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"plot_meshes(mesh)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# create a batch of meshes, and offset one to prevent overlap\n",
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"mesh_batch = Meshes(\n",
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" verts=[verts.to(device), (verts + 2).to(device)], \n",
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" faces=[faces.to(device), faces.to(device)]\n",
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")\n",
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"\n",
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"plot_meshes(mesh_batch)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# extend the batch to have 4 meshes\n",
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"mesh = mesh_batch.extend(2)\n",
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"\n",
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"# visualize the batch in different subplots, 2 per row\n",
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"# title the subplots\n",
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"fig = plot_meshes(mesh, in_subplots=True, ncols=2,\n",
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" subplot_titles = [\"cow\" + str(i) for i in range(1,5)] # this should have a title for each subplot, titles can be \"\"\n",
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" )\n",
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"# we can update the figure height and width\n",
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"fig.update_layout(height=500, width=500)\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can also modify the axis arguments and axis backgrounds."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig2 = plot_meshes(mesh_batch, xaxis={\"backgroundcolor\":\"rgb(200, 200, 230)\"},\n",
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" yaxis={\"backgroundcolor\":\"rgb(230, 200, 200)\"},\n",
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" zaxis={\"backgroundcolor\":\"rgb(200, 230, 200)\"}, \n",
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" subplot_titles=[\"2 meshes\"],\n",
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" axis_args=AxisArgs(showgrid=True))\n",
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"fig2.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@ -562,15 +669,15 @@
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"id": "t3qphI1ElUb5"
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},
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"source": [
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"## 7. Conclusion\n",
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"In this tutorial we learnt how to **load** a textured mesh from an obj file, initialize a PyTorch3D datastructure called **Meshes**, set up an **Renderer** consisting of a **Rasterizer** and a **Shader**, and modify several components of the rendering pipeline. "
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"## 8. Conclusion\n",
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"In this tutorial we learnt how to **load** a textured mesh from an obj file, initialize a PyTorch3D datastructure called **Meshes**, set up an **Renderer** consisting of a **Rasterizer** and a **Shader**, and modify several components of the rendering pipeline. We also learned how to render Meshes in Plotly figures."
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"anp_metadata": {
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"path": "fbsource/fbcode/vision/fair/pytorch3d/docs/tutorials/render_textured_meshes.ipynb"
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"path": "notebooks/render_textured_meshes.ipynb"
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},
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"bento_stylesheets": {
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"bento/extensions/flow/main.css": true,
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@ -588,9 +695,9 @@
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"backup_notebook_id": "569222367081034"
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},
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"kernelspec": {
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"display_name": "intro_to_cv",
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"display_name": "pytorch3d_etc (local)",
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"language": "python",
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"name": "bento_kernel_intro_to_cv"
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"language_info": {
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"codemirror_mode": {
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