Add OpenCV camera conversion; fix bug for camera unified PyTorch3D interface.

Summary: This commit adds a new camera conversion function for OpenCV style parameters to Pulsar parameters to the library. Using this function it addresses a bug reported here: https://fb.workplace.com/groups/629644647557365/posts/1079637302558095, by using the PyTorch3D->OpenCV->Pulsar chain instead of the original direct conversion function. Both conversions are well-tested and an additional test for the full chain has been added, resulting in a more reliable solution requiring less code.

Reviewed By: patricklabatut

Differential Revision: D29322106

fbshipit-source-id: 13df13c2e48f628f75d9f44f19ff7f1646fb7ebd

tests/test_camera_conversions.py

@@ -12,10 +12,12 @@ import numpy as np
 import torch
 from common_testing import TestCaseMixin, get_tests_dir
 from pytorch3d.ops import eyes
+from pytorch3d.renderer.points.pulsar import Renderer as PulsarRenderer
 from pytorch3d.transforms import so3_exp_map, so3_log_map
 from pytorch3d.utils import (
     cameras_from_opencv_projection,
     opencv_from_cameras_projection,
+    pulsar_from_opencv_projection,
 )
 
 
@@ -111,6 +113,9 @@ class TestCameraConversions(TestCaseMixin, unittest.TestCase):
             [105.0, 105.0],
             [120.0, 120.0],
         ]
+        # These values are in y, x format, but they should be in x, y format.
+        # The tests work like this because they only test for consistency,
+        # but this format is misleading.
         principal_point = [
             [240, 320],
             [240.5, 320.3],
@@ -160,3 +165,80 @@ class TestCameraConversions(TestCaseMixin, unittest.TestCase):
         self.assertClose(R, R_i)
         self.assertClose(tvec, tvec_i)
         self.assertClose(camera_matrix, camera_matrix_i)
+
+    def test_pulsar_conversion(self):
+        """
+        Tests that the cameras converted from opencv to pulsar convention
+        return correct projections of random 3D points. The check is done
+        against a set of results precomputed using `cv2.projectPoints` function.
+        """
+        image_size = [[480, 640]]
+        R = [
+            [
+                [1.0, 0.0, 0.0],
+                [0.0, 1.0, 0.0],
+                [0.0, 0.0, 1.0],
+            ],
+            [
+                [0.1968, -0.6663, -0.7192],
+                [0.7138, -0.4055, 0.5710],
+                [-0.6721, -0.6258, 0.3959],
+            ],
+        ]
+        tvec = [
+            [10.0, 10.0, 3.0],
+            [-0.0, -0.0, 20.0],
+        ]
+        focal_length = [
+            [100.0, 100.0],
+            [10.0, 10.0],
+        ]
+        principal_point = [
+            [320, 240],
+            [320, 240],
+        ]
+
+        principal_point, focal_length, R, tvec, image_size = [
+            torch.FloatTensor(x)
+            for x in (principal_point, focal_length, R, tvec, image_size)
+        ]
+        camera_matrix = eyes(dim=3, N=2)
+        camera_matrix[:, 0, 0] = focal_length[:, 0]
+        camera_matrix[:, 1, 1] = focal_length[:, 1]
+        camera_matrix[:, :2, 2] = principal_point
+        rvec = so3_log_map(R)
+        pts = torch.tensor(
+            [[[0.0, 0.0, 120.0]], [[0.0, 0.0, 120.0]]], dtype=torch.float32
+        )
+        radii = torch.tensor([[1e-5], [1e-5]], dtype=torch.float32)
+        col = torch.zeros((2, 1, 1), dtype=torch.float32)
+
+        # project the 3D points with the opencv projection function
+        pts_proj_opencv = cv2_project_points(pts, rvec, tvec, camera_matrix)
+        pulsar_cam = pulsar_from_opencv_projection(
+            R, tvec, camera_matrix, image_size, znear=100.0
+        )
+        pulsar_rend = PulsarRenderer(
+            640, 480, 1, right_handed_system=False, n_channels=1
+        )
+        rendered = torch.flip(
+            pulsar_rend(
+                pts,
+                col,
+                radii,
+                pulsar_cam,
+                1e-5,
+                max_depth=150.0,
+                min_depth=100.0,
+            ),
+            dims=(1,),
+        )
+        for batch_id in range(2):
+            point_pos = torch.where(rendered[batch_id] == rendered[batch_id].min())
+            point_pos = point_pos[1][0], point_pos[0][0]
+            self.assertLess(
+                torch.abs(point_pos[0] - pts_proj_opencv[batch_id, 0, 0]), 2
+            )
+            self.assertLess(
+                torch.abs(point_pos[1] - pts_proj_opencv[batch_id, 0, 1]), 2
+            )