# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. """ Sanity checks for output images from the pointcloud renderer. """ import unittest import warnings from os import path import numpy as np import torch from PIL import Image from pytorch3d.renderer.cameras import ( FoVOrthographicCameras, FoVPerspectiveCameras, look_at_view_transform, OrthographicCameras, PerspectiveCameras, ) from pytorch3d.renderer.compositing import alpha_composite, norm_weighted_sum from pytorch3d.renderer.fisheyecameras import FishEyeCameras from pytorch3d.renderer.points import ( AlphaCompositor, NormWeightedCompositor, PointsRasterizationSettings, PointsRasterizer, PointsRenderer, PulsarPointsRenderer, ) from pytorch3d.structures.pointclouds import Pointclouds from pytorch3d.utils.ico_sphere import ico_sphere from .common_testing import ( get_pytorch3d_dir, get_tests_dir, load_rgb_image, TestCaseMixin, ) # If DEBUG=True, save out images generated in the tests for debugging. # All saved images have prefix DEBUG_ DEBUG = False DATA_DIR = get_tests_dir() / "data" class TestRenderPoints(TestCaseMixin, unittest.TestCase): def test_simple_sphere(self): device = torch.device("cuda:0") sphere_mesh = ico_sphere(1, device) verts_padded = sphere_mesh.verts_padded() # Shift vertices to check coordinate frames are correct. verts_padded[..., 1] += 0.2 verts_padded[..., 0] += 0.2 pointclouds = Pointclouds( points=verts_padded, features=torch.ones_like(verts_padded) ) R, T = look_at_view_transform(2.7, 0.0, 0.0) cameras = FoVPerspectiveCameras(device=device, R=R, T=T) raster_settings = PointsRasterizationSettings( image_size=256, radius=5e-2, points_per_pixel=1 ) rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings) compositor = NormWeightedCompositor() renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor) # Load reference image filename = "simple_pointcloud_sphere.png" image_ref = load_rgb_image("test_%s" % filename, DATA_DIR) for bin_size in [0, None]: # Check both naive and coarse to fine produce the same output. renderer.rasterizer.raster_settings.bin_size = bin_size images = renderer(pointclouds) rgb = images[0, ..., :3].squeeze().cpu() if DEBUG: filename = "DEBUG_%s" % filename Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( DATA_DIR / filename ) self.assertClose(rgb, image_ref) def test_simple_sphere_fisheye(self): device = torch.device("cuda:0") sphere_mesh = ico_sphere(1, device) verts_padded = sphere_mesh.verts_padded() # Shift vertices to check coordinate frames are correct. verts_padded[..., 1] += 0.2 verts_padded[..., 0] += 0.2 pointclouds = Pointclouds( points=verts_padded, features=torch.ones_like(verts_padded) ) R, T = look_at_view_transform(2.7, 0.0, 0.0) cameras = FishEyeCameras( device=device, R=R, T=T, use_radial=False, use_tangential=False, use_thin_prism=False, world_coordinates=True, ) raster_settings = PointsRasterizationSettings( image_size=256, radius=5e-2, points_per_pixel=1 ) rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings) compositor = NormWeightedCompositor() renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor) # Load reference image filename = "render_fisheye_sphere_points.png" image_ref = load_rgb_image("test_%s" % filename, DATA_DIR) for bin_size in [0, None]: # Check both naive and coarse to fine produce the same output. renderer.rasterizer.raster_settings.bin_size = bin_size images = renderer(pointclouds) rgb = images[0, ..., :3].squeeze().cpu() if DEBUG: filename = "DEBUG_%s" % filename Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( DATA_DIR / filename ) self.assertClose(rgb, image_ref) def test_simple_sphere_pulsar(self): for device in [torch.device("cpu"), torch.device("cuda")]: sphere_mesh = ico_sphere(1, device) verts_padded = sphere_mesh.verts_padded() # Shift vertices to check coordinate frames are correct. verts_padded[..., 1] += 0.2 verts_padded[..., 0] += 0.2 pointclouds = Pointclouds( points=verts_padded, features=torch.ones_like(verts_padded) ) for azimuth in [0.0, 90.0]: R, T = look_at_view_transform(2.7, 0.0, azimuth) for camera_name, cameras in [ ("fovperspective", FoVPerspectiveCameras(device=device, R=R, T=T)), ( "fovorthographic", FoVOrthographicCameras(device=device, R=R, T=T), ), ("perspective", PerspectiveCameras(device=device, R=R, T=T)), ("orthographic", OrthographicCameras(device=device, R=R, T=T)), ]: raster_settings = PointsRasterizationSettings( image_size=256, radius=5e-2, points_per_pixel=1 ) rasterizer = PointsRasterizer( cameras=cameras, raster_settings=raster_settings ) renderer = PulsarPointsRenderer(rasterizer=rasterizer).to(device) # Load reference image filename = ( "pulsar_simple_pointcloud_sphere_" f"azimuth{azimuth}_{camera_name}.png" ) image_ref = load_rgb_image("test_%s" % filename, DATA_DIR) images = renderer( pointclouds, gamma=(1e-3,), znear=(1.0,), zfar=(100.0,) ) rgb = images[0, ..., :3].squeeze().cpu() if DEBUG: filename = "DEBUG_%s" % filename Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( DATA_DIR / filename ) self.assertClose(rgb, image_ref, rtol=7e-3, atol=5e-3) def test_unified_inputs_pulsar(self): # Test data on different devices. for device in [torch.device("cpu"), torch.device("cuda")]: sphere_mesh = ico_sphere(1, device) verts_padded = sphere_mesh.verts_padded() pointclouds = Pointclouds( points=verts_padded, features=torch.ones_like(verts_padded) ) R, T = look_at_view_transform(2.7, 0.0, 0.0) # Test the different camera types. for _, cameras in [ ("fovperspective", FoVPerspectiveCameras(device=device, R=R, T=T)), ( "fovorthographic", FoVOrthographicCameras(device=device, R=R, T=T), ), ("perspective", PerspectiveCameras(device=device, R=R, T=T)), ("orthographic", OrthographicCameras(device=device, R=R, T=T)), ]: # Test different ways for image size specification. for image_size in (256, (256, 256)): raster_settings = PointsRasterizationSettings( image_size=image_size, radius=5e-2, points_per_pixel=1 ) rasterizer = PointsRasterizer( cameras=cameras, raster_settings=raster_settings ) # Test that the compositor can be provided. It's value is ignored # so use a dummy. _ = PulsarPointsRenderer(rasterizer=rasterizer, compositor=1).to( device ) # Constructor without compositor. _ = PulsarPointsRenderer(rasterizer=rasterizer).to(device) # Constructor with n_channels. _ = PulsarPointsRenderer(rasterizer=rasterizer, n_channels=3).to( device ) # Constructor with max_num_spheres. renderer = PulsarPointsRenderer( rasterizer=rasterizer, max_num_spheres=1000 ).to(device) # Test the forward function. if isinstance(cameras, (PerspectiveCameras, OrthographicCameras)): # znear and zfar is required in this case. self.assertRaises( ValueError, lambda renderer=renderer, pointclouds=pointclouds: renderer.forward( point_clouds=pointclouds, gamma=(1e-4,) ), ) renderer.forward( point_clouds=pointclouds, gamma=(1e-4,), znear=(1.0,), zfar=(2.0,), ) # znear and zfar must be batched. self.assertRaises( TypeError, lambda renderer=renderer, pointclouds=pointclouds: renderer.forward( point_clouds=pointclouds, gamma=(1e-4,), znear=1.0, zfar=(2.0,), ), ) self.assertRaises( TypeError, lambda renderer=renderer, pointclouds=pointclouds: renderer.forward( point_clouds=pointclouds, gamma=(1e-4,), znear=(1.0,), zfar=2.0, ), ) else: # gamma must be batched. self.assertRaises( TypeError, lambda renderer=renderer, pointclouds=pointclouds: renderer.forward( point_clouds=pointclouds, gamma=1e-4 ), ) renderer.forward(point_clouds=pointclouds, gamma=(1e-4,)) # rasterizer width and height change. renderer.rasterizer.raster_settings.image_size = 0 self.assertRaises( ValueError, lambda renderer=renderer, pointclouds=pointclouds: renderer.forward( point_clouds=pointclouds, gamma=(1e-4,) ), ) def test_pointcloud_with_features(self): device = torch.device("cuda:0") file_dir = get_pytorch3d_dir() / "docs/tutorials/data" pointcloud_filename = file_dir / "PittsburghBridge/pointcloud.npz" # Note, this file is too large to check in to the repo. # Download the file to run the test locally. if not path.exists(pointcloud_filename): url = ( "https://dl.fbaipublicfiles.com/pytorch3d/data/" "PittsburghBridge/pointcloud.npz" ) msg = ( "pointcloud.npz not found, download from %s, save it at the path %s, and rerun" % (url, pointcloud_filename) ) warnings.warn(msg) return True # Load point cloud pointcloud = np.load(pointcloud_filename) verts = torch.Tensor(pointcloud["verts"]).to(device) rgb_feats = torch.Tensor(pointcloud["rgb"]).to(device) verts.requires_grad = True rgb_feats.requires_grad = True point_cloud = Pointclouds(points=[verts], features=[rgb_feats]) R, T = look_at_view_transform(20, 10, 0) cameras = FoVOrthographicCameras(device=device, R=R, T=T, znear=0.01) raster_settings = PointsRasterizationSettings( # Set image_size so it is not a multiple of 16 (min bin_size) # in order to confirm that there are no errors in coarse rasterization. image_size=500, radius=0.003, points_per_pixel=10, ) renderer = PointsRenderer( rasterizer=PointsRasterizer( cameras=cameras, raster_settings=raster_settings ), compositor=AlphaCompositor(), ) images = renderer(point_cloud) # Load reference image filename = "bridge_pointcloud.png" image_ref = load_rgb_image("test_%s" % filename, DATA_DIR) for bin_size in [0, None]: # Check both naive and coarse to fine produce the same output. renderer.rasterizer.raster_settings.bin_size = bin_size images = renderer(point_cloud) rgb = images[0, ..., :3].squeeze().cpu() if DEBUG: filename = "DEBUG_%s" % filename Image.fromarray((rgb.detach().numpy() * 255).astype(np.uint8)).save( DATA_DIR / filename ) self.assertClose(rgb, image_ref, atol=0.015) # Check grad exists. grad_images = torch.randn_like(images) images.backward(grad_images) self.assertIsNotNone(verts.grad) self.assertIsNotNone(rgb_feats.grad) def test_simple_sphere_batched(self): device = torch.device("cuda:0") sphere_mesh = ico_sphere(1, device) verts_padded = sphere_mesh.verts_padded() verts_padded[..., 1] += 0.2 verts_padded[..., 0] += 0.2 pointclouds = Pointclouds( points=verts_padded, features=torch.ones_like(verts_padded) ) batch_size = 20 pointclouds = pointclouds.extend(batch_size) R, T = look_at_view_transform(2.7, 0.0, 0.0) cameras = FoVPerspectiveCameras(device=device, R=R, T=T) raster_settings = PointsRasterizationSettings( image_size=256, radius=5e-2, points_per_pixel=1 ) rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings) compositor = NormWeightedCompositor() renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor) # Load reference image filename = "simple_pointcloud_sphere.png" image_ref = load_rgb_image("test_%s" % filename, DATA_DIR) images = renderer(pointclouds) for i in range(batch_size): rgb = images[i, ..., :3].squeeze().cpu() if i == 0 and DEBUG: filename = "DEBUG_%s" % filename Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( DATA_DIR / filename ) self.assertClose(rgb, image_ref) def test_compositor_background_color_rgba(self): N, H, W, K, C, P = 1, 15, 15, 20, 4, 225 ptclds = torch.randn((C, P)) alphas = torch.rand((N, K, H, W)) pix_idxs = torch.randint(-1, 20, (N, K, H, W)) # 20 < P, large amount of -1 background_color = [0.5, 0, 1] compositor_funcs = [ (NormWeightedCompositor, norm_weighted_sum), (AlphaCompositor, alpha_composite), ] for compositor_class, composite_func in compositor_funcs: compositor = compositor_class(background_color) # run the forward method to generate masked images masked_images = compositor.forward(pix_idxs, alphas, ptclds) # generate unmasked images for testing purposes images = composite_func(pix_idxs, alphas, ptclds) is_foreground = pix_idxs[:, 0] >= 0 # make sure foreground values are unchanged self.assertClose( torch.masked_select(masked_images, is_foreground[:, None]), torch.masked_select(images, is_foreground[:, None]), ) is_background = ~is_foreground[..., None].expand(-1, -1, -1, C) # permute masked_images to correctly get rgb values masked_images = masked_images.permute(0, 2, 3, 1) for i in range(3): channel_color = background_color[i] # check if background colors are properly changed self.assertTrue( masked_images[is_background] .view(-1, C)[..., i] .eq(channel_color) .all() ) # check background color alpha values self.assertTrue( masked_images[is_background].view(-1, C)[..., 3].eq(1).all() ) def test_compositor_background_color_rgb(self): N, H, W, K, C, P = 1, 15, 15, 20, 3, 225 ptclds = torch.randn((C, P)) alphas = torch.rand((N, K, H, W)) pix_idxs = torch.randint(-1, 20, (N, K, H, W)) # 20 < P, large amount of -1 background_color = [0.5, 0, 1] compositor_funcs = [ (NormWeightedCompositor, norm_weighted_sum), (AlphaCompositor, alpha_composite), ] for compositor_class, composite_func in compositor_funcs: compositor = compositor_class(background_color) # run the forward method to generate masked images masked_images = compositor.forward(pix_idxs, alphas, ptclds) # generate unmasked images for testing purposes images = composite_func(pix_idxs, alphas, ptclds) is_foreground = pix_idxs[:, 0] >= 0 # make sure foreground values are unchanged self.assertClose( torch.masked_select(masked_images, is_foreground[:, None]), torch.masked_select(images, is_foreground[:, None]), ) is_background = ~is_foreground[..., None].expand(-1, -1, -1, C) # permute masked_images to correctly get rgb values masked_images = masked_images.permute(0, 2, 3, 1) for i in range(3): channel_color = background_color[i] # check if background colors are properly changed self.assertTrue( masked_images[is_background] .view(-1, C)[..., i] .eq(channel_color) .all() )