Fix circular import

Summary: This fixes a recently introduced circular import: the problem went unnoticed by having `pytorch3d.renderer` imported first...

Reviewed By: bottler

Differential Revision: D29686235

fbshipit-source-id: 4b9f2faecec2cc8347ee259cfc359dc9e4f67784

pytorch3d/renderer/camera_conversions.py

@@ -0,0 +1,176 @@
+# Copyright (c) Facebook, Inc. and its 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.
+
+import logging
+from typing import Tuple
+
+import torch
+
+from ..transforms import matrix_to_rotation_6d
+from .cameras import PerspectiveCameras
+
+
+LOGGER = logging.getLogger(__name__)
+
+
+def _cameras_from_opencv_projection(
+    R: torch.Tensor,
+    tvec: torch.Tensor,
+    camera_matrix: torch.Tensor,
+    image_size: torch.Tensor,
+) -> PerspectiveCameras:
+    focal_length = torch.stack([camera_matrix[:, 0, 0], camera_matrix[:, 1, 1]], dim=-1)
+    principal_point = camera_matrix[:, :2, 2]
+
+    # Retype the image_size correctly and flip to width, height.
+    image_size_wh = image_size.to(R).flip(dims=(1,))
+
+    # Get the PyTorch3D focal length and principal point.
+    focal_pytorch3d = focal_length / (0.5 * image_size_wh)
+    p0_pytorch3d = -(principal_point / (0.5 * image_size_wh) - 1)
+
+    # For R, T we flip x, y axes (opencv screen space has an opposite
+    # orientation of screen axes).
+    # We also transpose R (opencv multiplies points from the opposite=left side).
+    R_pytorch3d = R.clone().permute(0, 2, 1)
+    T_pytorch3d = tvec.clone()
+    R_pytorch3d[:, :, :2] *= -1
+    T_pytorch3d[:, :2] *= -1
+
+    return PerspectiveCameras(
+        R=R_pytorch3d,
+        T=T_pytorch3d,
+        focal_length=focal_pytorch3d,
+        principal_point=p0_pytorch3d,
+    )
+
+
+def _opencv_from_cameras_projection(
+    cameras: PerspectiveCameras,
+    image_size: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    R_pytorch3d = cameras.R.clone()  # pyre-ignore
+    T_pytorch3d = cameras.T.clone()  # pyre-ignore
+    focal_pytorch3d = cameras.focal_length
+    p0_pytorch3d = cameras.principal_point
+    T_pytorch3d[:, :2] *= -1
+    R_pytorch3d[:, :, :2] *= -1
+    tvec = T_pytorch3d
+    R = R_pytorch3d.permute(0, 2, 1)
+
+    # Retype the image_size correctly and flip to width, height.
+    image_size_wh = image_size.to(R).flip(dims=(1,))
+
+    principal_point = (-p0_pytorch3d + 1.0) * (0.5 * image_size_wh)  # pyre-ignore
+    focal_length = focal_pytorch3d * (0.5 * image_size_wh)
+
+    camera_matrix = torch.zeros_like(R)
+    camera_matrix[:, :2, 2] = principal_point
+    camera_matrix[:, 2, 2] = 1.0
+    camera_matrix[:, 0, 0] = focal_length[:, 0]
+    camera_matrix[:, 1, 1] = focal_length[:, 1]
+    return R, tvec, camera_matrix
+
+
+def _pulsar_from_opencv_projection(
+    R: torch.Tensor,
+    tvec: torch.Tensor,
+    camera_matrix: torch.Tensor,
+    image_size: torch.Tensor,
+    znear: float = 0.1,
+) -> torch.Tensor:
+    assert len(camera_matrix.size()) == 3, "This function requires batched inputs!"
+    assert len(R.size()) == 3, "This function requires batched inputs!"
+    assert len(tvec.size()) in (2, 3), "This function reuqires batched inputs!"
+
+    # Validate parameters.
+    image_size_wh = image_size.to(R).flip(dims=(1,))
+    assert torch.all(
+        image_size_wh > 0
+    ), "height and width must be positive but min is: %s" % (
+        str(image_size_wh.min().item())
+    )
+    assert (
+        camera_matrix.size(1) == 3 and camera_matrix.size(2) == 3
+    ), "Incorrect camera matrix shape: expected 3x3 but got %dx%d" % (
+        camera_matrix.size(1),
+        camera_matrix.size(2),
+    )
+    assert (
+        R.size(1) == 3 and R.size(2) == 3
+    ), "Incorrect R shape: expected 3x3 but got %dx%d" % (
+        R.size(1),
+        R.size(2),
+    )
+    if len(tvec.size()) == 2:
+        tvec = tvec.unsqueeze(2)
+    assert (
+        tvec.size(1) == 3 and tvec.size(2) == 1
+    ), "Incorrect tvec shape: expected 3x1 but got %dx%d" % (
+        tvec.size(1),
+        tvec.size(2),
+    )
+    # Check batch size.
+    batch_size = camera_matrix.size(0)
+    assert R.size(0) == batch_size, "Expected R to have batch size %d. Has size %d." % (
+        batch_size,
+        R.size(0),
+    )
+    assert (
+        tvec.size(0) == batch_size
+    ), "Expected tvec to have batch size %d. Has size %d." % (
+        batch_size,
+        tvec.size(0),
+    )
+    # Check image sizes.
+    image_w = image_size_wh[0, 0]
+    image_h = image_size_wh[0, 1]
+    assert torch.all(
+        image_size_wh[:, 0] == image_w
+    ), "All images in a batch must have the same width!"
+    assert torch.all(
+        image_size_wh[:, 1] == image_h
+    ), "All images in a batch must have the same height!"
+    # Focal length.
+    fx = camera_matrix[:, 0, 0].unsqueeze(1)
+    fy = camera_matrix[:, 1, 1].unsqueeze(1)
+    # Check that we introduce less than 1% error by averaging the focal lengths.
+    fx_y = fx / fy
+    if torch.any(fx_y > 1.01) or torch.any(fx_y < 0.99):
+        LOGGER.warning(
+            "Pulsar only supports a single focal lengths. For converting OpenCV "
+            "focal lengths, we average them for x and y directions. "
+            "The focal lengths for x and y you provided differ by more than 1%, "
+            "which means this could introduce a noticeable error."
+        )
+    f = (fx + fy) / 2
+    # Normalize f into normalized device coordinates.
+    focal_length_px = f / image_w
+    # Transfer into focal_length and sensor_width.
+    focal_length = torch.tensor([znear - 1e-5], dtype=torch.float32, device=R.device)
+    focal_length = focal_length[None, :].repeat(batch_size, 1)
+    sensor_width = focal_length / focal_length_px
+    # Principal point.
+    cx = camera_matrix[:, 0, 2].unsqueeze(1)
+    cy = camera_matrix[:, 1, 2].unsqueeze(1)
+    # Transfer principal point offset into centered offset.
+    cx = -(cx - image_w / 2)
+    cy = cy - image_h / 2
+    # Concatenate to final vector.
+    param = torch.cat([focal_length, sensor_width, cx, cy], dim=1)
+    R_trans = R.permute(0, 2, 1)
+    cam_pos = -torch.bmm(R_trans, tvec).squeeze(2)
+    cam_rot = matrix_to_rotation_6d(R_trans)
+    cam_params = torch.cat([cam_pos, cam_rot, param], dim=1)
+    return cam_params
+
+
+def _pulsar_from_cameras_projection(
+    cameras: PerspectiveCameras,
+    image_size: torch.Tensor,
+) -> torch.Tensor:
+    opencv_R, opencv_T, opencv_K = _opencv_from_cameras_projection(cameras, image_size)
+    return _pulsar_from_opencv_projection(opencv_R, opencv_T, opencv_K, image_size)

pytorch3d/renderer/points/pulsar/unified.py

@@ -11,7 +11,7 @@ from typing import Any, Dict, Optional, Tuple, Union
 import torch
 import torch.nn as nn
 
-from ....utils import pulsar_from_cameras_projection
+from ...camera_conversions import _pulsar_from_cameras_projection
 from ...cameras import (
     FoVOrthographicCameras,
     FoVPerspectiveCameras,
@@ -378,7 +378,7 @@ class PulsarPointsRenderer(nn.Module):
         size_tensor = torch.tensor(
             [[self.renderer._renderer.height, self.renderer._renderer.width]]
         )
-        pulsar_cam = pulsar_from_cameras_projection(tmp_cams, size_tensor)
+        pulsar_cam = _pulsar_from_cameras_projection(tmp_cams, size_tensor)
         cam_pos = pulsar_cam[0, :3]
         cam_rot = pulsar_cam[0, 3:9]
         return cam_pos, cam_rot

pytorch3d/utils/__init__.py

@@ -7,8 +7,8 @@
 from .camera_conversions import (
     cameras_from_opencv_projection,
     opencv_from_cameras_projection,
-    pulsar_from_opencv_projection,
     pulsar_from_cameras_projection,
+    pulsar_from_opencv_projection,
 )
 from .ico_sphere import ico_sphere
 from .torus import torus

pytorch3d/utils/camera_conversions.py

@@ -4,16 +4,17 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-import logging
 from typing import Tuple
 
 import torch
 
 from ..renderer import PerspectiveCameras
-from ..transforms import matrix_to_rotation_6d
+from ..renderer.camera_conversions import (
-
+    _cameras_from_opencv_projection,
-
+    _opencv_from_cameras_projection,
-LOGGER = logging.getLogger(__name__)
+    _pulsar_from_cameras_projection,
+    _pulsar_from_opencv_projection,
+)
 
 
 def cameras_from_opencv_projection(
@@ -58,30 +59,7 @@ def cameras_from_opencv_projection(
     Returns:
         cameras_pytorch3d: A batch of `N` cameras in the PyTorch3D convention.
     """
-    focal_length = torch.stack([camera_matrix[:, 0, 0], camera_matrix[:, 1, 1]], dim=-1)
+    return _cameras_from_opencv_projection(R, tvec, camera_matrix, image_size)
-    principal_point = camera_matrix[:, :2, 2]
-
-    # Retype the image_size correctly and flip to width, height.
-    image_size_wh = image_size.to(R).flip(dims=(1,))
-
-    # Get the PyTorch3D focal length and principal point.
-    focal_pytorch3d = focal_length / (0.5 * image_size_wh)
-    p0_pytorch3d = -(principal_point / (0.5 * image_size_wh) - 1)
-
-    # For R, T we flip x, y axes (opencv screen space has an opposite
-    # orientation of screen axes).
-    # We also transpose R (opencv multiplies points from the opposite=left side).
-    R_pytorch3d = R.clone().permute(0, 2, 1)
-    T_pytorch3d = tvec.clone()
-    R_pytorch3d[:, :, :2] *= -1
-    T_pytorch3d[:, :2] *= -1
-
-    return PerspectiveCameras(
-        R=R_pytorch3d,
-        T=T_pytorch3d,
-        focal_length=focal_pytorch3d,
-        principal_point=p0_pytorch3d,
-    )
 
 
 def opencv_from_cameras_projection(
@@ -114,27 +92,7 @@ def opencv_from_cameras_projection(
         tvec: A batch of translation vectors of shape `(N, 3)`.
         camera_matrix: A batch of camera calibration matrices of shape `(N, 3, 3)`.
     """
-    R_pytorch3d = cameras.R.clone()  # pyre-ignore
+    return _opencv_from_cameras_projection(cameras, image_size)
-    T_pytorch3d = cameras.T.clone()  # pyre-ignore
-    focal_pytorch3d = cameras.focal_length
-    p0_pytorch3d = cameras.principal_point
-    T_pytorch3d[:, :2] *= -1
-    R_pytorch3d[:, :, :2] *= -1
-    tvec = T_pytorch3d
-    R = R_pytorch3d.permute(0, 2, 1)
-
-    # Retype the image_size correctly and flip to width, height.
-    image_size_wh = image_size.to(R).flip(dims=(1,))
-
-    principal_point = (-p0_pytorch3d + 1.0) * (0.5 * image_size_wh)  # pyre-ignore
-    focal_length = focal_pytorch3d * (0.5 * image_size_wh)
-
-    camera_matrix = torch.zeros_like(R)
-    camera_matrix[:, :2, 2] = principal_point
-    camera_matrix[:, 2, 2] = 1.0
-    camera_matrix[:, 0, 0] = focal_length[:, 0]
-    camera_matrix[:, 1, 1] = focal_length[:, 1]
-    return R, tvec, camera_matrix
 
 
 def pulsar_from_opencv_projection(
@@ -170,90 +128,7 @@ def pulsar_from_opencv_projection(
             convention `(N, 13)` (3 translation, 6 rotation, focal_length, sensor_width,
             c_x, c_y).
     """
-    assert len(camera_matrix.size()) == 3, "This function requires batched inputs!"
+    return _pulsar_from_opencv_projection(R, tvec, camera_matrix, image_size, znear)
-    assert len(R.size()) == 3, "This function requires batched inputs!"
-    assert len(tvec.size()) in (2, 3), "This function reuqires batched inputs!"
-
-    # Validate parameters.
-    image_size_wh = image_size.to(R).flip(dims=(1,))
-    assert torch.all(
-        image_size_wh > 0
-    ), "height and width must be positive but min is: %s" % (
-        str(image_size_wh.min().item())
-    )
-    assert (
-        camera_matrix.size(1) == 3 and camera_matrix.size(2) == 3
-    ), "Incorrect camera matrix shape: expected 3x3 but got %dx%d" % (
-        camera_matrix.size(1),
-        camera_matrix.size(2),
-    )
-    assert (
-        R.size(1) == 3 and R.size(2) == 3
-    ), "Incorrect R shape: expected 3x3 but got %dx%d" % (
-        R.size(1),
-        R.size(2),
-    )
-    if len(tvec.size()) == 2:
-        tvec = tvec.unsqueeze(2)
-    assert (
-        tvec.size(1) == 3 and tvec.size(2) == 1
-    ), "Incorrect tvec shape: expected 3x1 but got %dx%d" % (
-        tvec.size(1),
-        tvec.size(2),
-    )
-    # Check batch size.
-    batch_size = camera_matrix.size(0)
-    assert R.size(0) == batch_size, "Expected R to have batch size %d. Has size %d." % (
-        batch_size,
-        R.size(0),
-    )
-    assert (
-        tvec.size(0) == batch_size
-    ), "Expected tvec to have batch size %d. Has size %d." % (
-        batch_size,
-        tvec.size(0),
-    )
-    # Check image sizes.
-    image_w = image_size_wh[0, 0]
-    image_h = image_size_wh[0, 1]
-    assert torch.all(
-        image_size_wh[:, 0] == image_w
-    ), "All images in a batch must have the same width!"
-    assert torch.all(
-        image_size_wh[:, 1] == image_h
-    ), "All images in a batch must have the same height!"
-    # Focal length.
-    fx = camera_matrix[:, 0, 0].unsqueeze(1)
-    fy = camera_matrix[:, 1, 1].unsqueeze(1)
-    # Check that we introduce less than 1% error by averaging the focal lengths.
-    fx_y = fx / fy
-    if torch.any(fx_y > 1.01) or torch.any(fx_y < 0.99):
-        LOGGER.warning(
-            "Pulsar only supports a single focal lengths. For converting OpenCV "
-            "focal lengths, we average them for x and y directions. "
-            "The focal lengths for x and y you provided differ by more than 1%, "
-            "which means this could introduce a noticeable error."
-        )
-    f = (fx + fy) / 2
-    # Normalize f into normalized device coordinates.
-    focal_length_px = f / image_w
-    # Transfer into focal_length and sensor_width.
-    focal_length = torch.tensor([znear - 1e-5], dtype=torch.float32, device=R.device)
-    focal_length = focal_length[None, :].repeat(batch_size, 1)
-    sensor_width = focal_length / focal_length_px
-    # Principal point.
-    cx = camera_matrix[:, 0, 2].unsqueeze(1)
-    cy = camera_matrix[:, 1, 2].unsqueeze(1)
-    # Transfer principal point offset into centered offset.
-    cx = -(cx - image_w / 2)
-    cy = cy - image_h / 2
-    # Concatenate to final vector.
-    param = torch.cat([focal_length, sensor_width, cx, cy], dim=1)
-    R_trans = R.permute(0, 2, 1)
-    cam_pos = -torch.bmm(R_trans, tvec).squeeze(2)
-    cam_rot = matrix_to_rotation_6d(R_trans)
-    cam_params = torch.cat([cam_pos, cam_rot, param], dim=1)
-    return cam_params
 
 
 def pulsar_from_cameras_projection(
@@ -281,5 +156,4 @@ def pulsar_from_cameras_projection(
             convention `(N, 13)` (3 translation, 6 rotation, focal_length, sensor_width,
             c_x, c_y).
     """
-    opencv_R, opencv_T, opencv_K = opencv_from_cameras_projection(cameras, image_size)
+    return _pulsar_from_cameras_projection(cameras, image_size)
-    return pulsar_from_opencv_projection(opencv_R, opencv_T, opencv_K, image_size)