allow cameras to be None in rasterizer initialization

Summary: Fix to enable a mesh/point rasterizer to be initialized without having to specify the camera.

Reviewed By: jcjohnson, gkioxari

Differential Revision: D21362359

fbshipit-source-id: 4f84ea18ad9f179c7b7c2289ebf9422a2f5e26de

tests/test_rasterizer.py

@@ -9,6 +9,11 @@ import torch
 from PIL import Image
 from pytorch3d.renderer.cameras import OpenGLPerspectiveCameras, look_at_view_transform
 from pytorch3d.renderer.mesh.rasterizer import MeshRasterizer, RasterizationSettings
+from pytorch3d.renderer.points.rasterizer import (
+    PointsRasterizationSettings,
+    PointsRasterizer,
+)
+from pytorch3d.structures import Pointclouds
 from pytorch3d.utils.ico_sphere import ico_sphere
 
 
@@ -99,3 +104,101 @@ class TestMeshRasterizer(unittest.TestCase):
                 DATA_DIR / "DEBUG_test_rasterized_sphere_zoom.png"
             )
         self.assertTrue(torch.allclose(image, image_ref))
+
+        #################################
+        #  4. Test init without cameras.
+        ##################################
+
+        # Create a new empty rasterizer:
+        rasterizer = MeshRasterizer()
+
+        # Check that omitting the cameras in both initialization
+        # and the forward pass throws an error:
+        with self.assertRaisesRegex(ValueError, "Cameras must be specified"):
+            rasterizer(sphere_mesh)
+
+        # Now pass in the cameras as a kwarg
+        fragments = rasterizer(
+            sphere_mesh, cameras=cameras, raster_settings=raster_settings
+        )
+        image = fragments.pix_to_face[0, ..., 0].squeeze().cpu()
+        # Convert pix_to_face to a binary mask
+        image[image >= 0] = 1.0
+        image[image < 0] = 0.0
+
+        if DEBUG:
+            Image.fromarray((image.numpy() * 255).astype(np.uint8)).save(
+                DATA_DIR / "DEBUG_test_rasterized_sphere.png"
+            )
+
+        self.assertTrue(torch.allclose(image, image_ref))
+
+
+class TestPointRasterizer(unittest.TestCase):
+    def test_simple_sphere(self):
+        device = torch.device("cuda:0")
+
+        # Load reference image
+        ref_filename = "test_simple_pointcloud_sphere.png"
+        image_ref_filename = DATA_DIR / ref_filename
+
+        # Rescale image_ref to the 0 - 1 range and convert to a binary mask.
+        image_ref = convert_image_to_binary_mask(image_ref_filename).to(torch.int32)
+
+        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)
+        R, T = look_at_view_transform(2.7, 0.0, 0.0)
+        cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
+        raster_settings = PointsRasterizationSettings(
+            image_size=256, radius=5e-2, points_per_pixel=1
+        )
+
+        #################################
+        #  1. Test init without cameras.
+        ##################################
+
+        # Initialize without passing in the cameras
+        rasterizer = PointsRasterizer()
+
+        # Check that omitting the cameras in both initialization
+        # and the forward pass throws an error:
+        with self.assertRaisesRegex(ValueError, "Cameras must be specified"):
+            rasterizer(pointclouds)
+
+        ##########################################
+        # 2. Test rasterizing a single pointcloud
+        ##########################################
+
+        fragments = rasterizer(
+            pointclouds, cameras=cameras, raster_settings=raster_settings
+        )
+
+        # Convert idx to a binary mask
+        image = fragments.idx[0, ..., 0].squeeze().cpu()
+        image[image >= 0] = 1.0
+        image[image < 0] = 0.0
+
+        if DEBUG:
+            Image.fromarray((image.numpy() * 255).astype(np.uint8)).save(
+                DATA_DIR / "DEBUG_test_rasterized_sphere_points.png"
+            )
+
+        self.assertTrue(torch.allclose(image, image_ref[..., 0]))
+
+        ########################################
+        #  3. Test with a batch of pointclouds
+        ########################################
+
+        batch_size = 10
+        pointclouds = pointclouds.extend(batch_size)
+        fragments = rasterizer(
+            pointclouds, cameras=cameras, raster_settings=raster_settings
+        )
+        for i in range(batch_size):
+            image = fragments.idx[i, ..., 0].squeeze().cpu()
+            image[image >= 0] = 1.0
+            image[image < 0] = 0.0
+            self.assertTrue(torch.allclose(image, image_ref[..., 0]))