mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-11-04 18:02:14 +08:00
new tests demonstrating pixel matching
Summary: Demonstrate current behavior of pixels with new tests of all renderers. Reviewed By: gkioxari Differential Revision: D32651141 fbshipit-source-id: 3ca30b4274ed2699bc5e1a9c6437eb3f0b738cbf
This commit is contained in:
Jeremy Reizenstein
Facebook GitHub Bot
parent
bf3bc6f8e3
commit
70acb3e415
256
tests/test_camera_pixels.py
Normal file
256
tests/test_camera_pixels.py
Normal file
@ -0,0 +1,256 @@
|
||||
# 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 unittest
|
||||
|
||||
import torch
|
||||
from common_testing import TestCaseMixin
|
||||
from pytorch3d.renderer import (
|
||||
MeshRasterizer,
|
||||
NDCGridRaysampler,
|
||||
PerspectiveCameras,
|
||||
PointsRasterizationSettings,
|
||||
PointsRasterizer,
|
||||
PulsarPointsRenderer,
|
||||
RasterizationSettings,
|
||||
)
|
||||
from pytorch3d.structures import Meshes, Pointclouds
|
||||
|
||||
|
||||
"""
|
||||
PyTorch3D renderers operate in an align_corners=False manner.
|
||||
This file demonstrates the pixel-perfect calculation by very simple
|
||||
examples.
|
||||
"""
|
||||
|
||||
|
||||
class _CommonData:
|
||||
"""
|
||||
Contains data for all these tests.
|
||||
|
||||
- Firstly, a non-square at the origin specified in ndc space and
|
||||
screen space. Principal point is in the center of the image.
|
||||
Focal length is 1.0 in world space.
|
||||
This camera has the identity as its world to view transformation, so
|
||||
it is facing down the positive z axis with y being up and x being left.
|
||||
A point on the z=1.0 focal plane has its x,y world coordinate equal to
|
||||
its NDC.
|
||||
|
||||
- Secondly, batched together with that, is a camera with the same
|
||||
focal length facing in the same direction but located so that it faces
|
||||
the corner of the corner pixel of the first image, with its principal
|
||||
point located at its corner, so that it maps the z=1 plane to NDC just
|
||||
like the first.
|
||||
|
||||
- a single point self.point in world space which is located on a plane 1.0
|
||||
in front from the camera which is located exactly in the center
|
||||
of a known pixel (self.x, self.y), specifically with negative x and slightly
|
||||
positive y, so it is in the top right quadrant of the image.
|
||||
|
||||
- A second batch of cameras defined in screen space which exactly match the
|
||||
first ones.
|
||||
|
||||
So that this data can be copied for making demos, it is easiest to leave
|
||||
it as a freestanding class.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.H, self.W = 249, 125
|
||||
self.image_size = (self.H, self.W)
|
||||
self.camera_ndc = PerspectiveCameras(
|
||||
focal_length=1.0,
|
||||
image_size=(self.image_size,),
|
||||
in_ndc=True,
|
||||
T=torch.tensor([[0.0, 0.0, 0.0], [-1.0, self.H / self.W, 0.0]]),
|
||||
principal_point=((-0.0, -0.0), (1.0, -self.H / self.W)),
|
||||
)
|
||||
# Note how principal point is specifiied
|
||||
self.camera_screen = PerspectiveCameras(
|
||||
focal_length=self.W / 2.0,
|
||||
principal_point=((self.W / 2.0, self.H / 2.0), (0.0, self.H)),
|
||||
image_size=(self.image_size,),
|
||||
T=torch.tensor([[0.0, 0.0, 0.0], [-1.0, self.H / self.W, 0.0]]),
|
||||
in_ndc=False,
|
||||
)
|
||||
|
||||
# 81 is more than half of 125, 113 is a bit less than half of 249
|
||||
self.x, self.y = 81, 113
|
||||
self.point = [-0.304, 0.176, 1]
|
||||
# The point is in the center of pixel (81, 113)
|
||||
# where pixel (0,0) is the top left.
|
||||
# 81 is 38/2 pixels over the midpoint (125-1)/2=62
|
||||
# and 38/125=0.304
|
||||
# 113 is 22/2 pixels under the midpoint (249-1)/2=124
|
||||
# and 22/125=0.176
|
||||
|
||||
|
||||
class TestPixels(TestCaseMixin, unittest.TestCase):
|
||||
def test_mesh(self):
|
||||
data = _CommonData()
|
||||
# Three points on the plane at unit 1 from the camera in
|
||||
# world space, whose mean is the known point.
|
||||
verts = torch.tensor(
|
||||
[[-0.288, 0.192, 1], [-0.32, 0.192, 1], [-0.304, 0.144, 1]]
|
||||
)
|
||||
self.assertClose(verts.mean(0), torch.tensor(data.point))
|
||||
faces = torch.LongTensor([[0, 1, 2]])
|
||||
# A mesh of one triangular face whose centroid is the known point
|
||||
# duplicated so it can be rendered from two cameras.
|
||||
meshes = Meshes(verts=[verts], faces=[faces]).extend(2)
|
||||
faces_per_pixel = 2
|
||||
for camera in (data.camera_ndc, data.camera_screen):
|
||||
rasterizer = MeshRasterizer(
|
||||
cameras=camera,
|
||||
raster_settings=RasterizationSettings(
|
||||
image_size=data.image_size, faces_per_pixel=faces_per_pixel
|
||||
),
|
||||
)
|
||||
barycentric_coords_found = rasterizer(meshes).bary_coords
|
||||
self.assertTupleEqual(
|
||||
barycentric_coords_found.shape,
|
||||
(2,) + data.image_size + (faces_per_pixel, 3),
|
||||
)
|
||||
# We see that the barycentric coordinates at the expected
|
||||
# pixel are (1/3, 1/3, 1/3), indicating that this pixel
|
||||
# hits the centroid of the triangle.
|
||||
self.assertClose(
|
||||
barycentric_coords_found[:, data.y, data.x, 0],
|
||||
torch.full((2, 3), 1 / 3.0),
|
||||
atol=1e-5,
|
||||
)
|
||||
|
||||
def test_pointcloud(self):
|
||||
data = _CommonData()
|
||||
clouds = Pointclouds(points=torch.tensor([[data.point]])).extend(2)
|
||||
colorful_cloud = Pointclouds(
|
||||
points=torch.tensor([[data.point]]), features=torch.ones(1, 1, 3)
|
||||
).extend(2)
|
||||
points_per_pixel = 2
|
||||
# for camera in [data.camera_screen]:
|
||||
for camera in (data.camera_ndc, data.camera_screen):
|
||||
rasterizer = PointsRasterizer(
|
||||
cameras=camera,
|
||||
raster_settings=PointsRasterizationSettings(
|
||||
image_size=data.image_size,
|
||||
radius=0.0001,
|
||||
points_per_pixel=points_per_pixel,
|
||||
),
|
||||
)
|
||||
# when rasterizing we expect only one pixel to be occupied
|
||||
rasterizer_output = rasterizer(clouds).idx
|
||||
self.assertTupleEqual(
|
||||
rasterizer_output.shape, (2,) + data.image_size + (points_per_pixel,)
|
||||
)
|
||||
found = torch.nonzero(rasterizer_output != -1)
|
||||
self.assertTupleEqual(found.shape, (2, 4))
|
||||
self.assertListEqual(found[0].tolist(), [0, data.y, data.x, 0])
|
||||
self.assertListEqual(found[1].tolist(), [1, data.y, data.x, 0])
|
||||
|
||||
if camera.in_ndc():
|
||||
# Pulsar not currently working in screen space.
|
||||
pulsar_renderer = PulsarPointsRenderer(rasterizer=rasterizer)
|
||||
pulsar_output = pulsar_renderer(
|
||||
colorful_cloud, gamma=(0.1, 0.1), znear=(0.1, 0.1), zfar=(70, 70)
|
||||
)
|
||||
self.assertTupleEqual(
|
||||
pulsar_output.shape, (2,) + data.image_size + (3,)
|
||||
)
|
||||
# Look for points rendered in the red channel only, expecting our one.
|
||||
# Check the first batch element only.
|
||||
# TODO: Something is odd with the second.
|
||||
found = torch.nonzero(pulsar_output[0, :, :, 0])
|
||||
self.assertTupleEqual(found.shape, (1, 2))
|
||||
self.assertListEqual(found[0].tolist(), [data.y, data.x])
|
||||
# Should be:
|
||||
# found = torch.nonzero(pulsar_output[:, :, :, 0])
|
||||
# self.assertTupleEqual(found.shape, (2, 3))
|
||||
# self.assertListEqual(found[0].tolist(), [0, data.y, data.x])
|
||||
# self.assertListEqual(found[1].tolist(), [1, data.y, data.x])
|
||||
|
||||
def test_raysampler(self):
|
||||
data = _CommonData()
|
||||
gridsampler = NDCGridRaysampler(
|
||||
image_width=data.W,
|
||||
image_height=data.H,
|
||||
n_pts_per_ray=2,
|
||||
min_depth=1.0,
|
||||
max_depth=2.0,
|
||||
)
|
||||
for camera in (data.camera_ndc, data.camera_screen):
|
||||
bundle = gridsampler(camera)
|
||||
self.assertTupleEqual(bundle.xys.shape, (2,) + data.image_size + (2,))
|
||||
self.assertTupleEqual(
|
||||
bundle.directions.shape, (2,) + data.image_size + (3,)
|
||||
)
|
||||
self.assertClose(
|
||||
bundle.xys[:, data.y, data.x],
|
||||
torch.tensor(data.point[:2]).expand(2, -1),
|
||||
)
|
||||
# We check only the first batch element.
|
||||
# Second element varies because of camera location.
|
||||
self.assertClose(
|
||||
bundle.directions[0, data.y, data.x],
|
||||
torch.tensor(data.point),
|
||||
)
|
||||
|
||||
def test_camera(self):
|
||||
data = _CommonData()
|
||||
# Our point, plus the image center, and a corner of the image.
|
||||
# Located at the focal-length distance away
|
||||
points = torch.tensor([data.point, [0, 0, 1], [1, data.H / data.W, 1]])
|
||||
for cameras in (data.camera_ndc, data.camera_screen):
|
||||
ndc_points = cameras.transform_points_ndc(points)
|
||||
screen_points = cameras.transform_points_screen(points)
|
||||
camera_points = cameras.transform_points(points)
|
||||
for batch_idx in range(2):
|
||||
# NDC space agrees with the original
|
||||
self.assertClose(ndc_points[batch_idx], points, atol=1e-5)
|
||||
# First point in screen space is the center of our expected pixel
|
||||
self.assertClose(
|
||||
screen_points[batch_idx][0],
|
||||
torch.tensor([data.x + 0.5, data.y + 0.5, 1.0]),
|
||||
atol=1e-5,
|
||||
)
|
||||
# Second point in screen space is the center of the screen
|
||||
self.assertClose(
|
||||
screen_points[batch_idx][1],
|
||||
torch.tensor([data.W / 2.0, data.H / 2.0, 1.0]),
|
||||
atol=1e-5,
|
||||
)
|
||||
# Third point in screen space is the corner of the screen
|
||||
# (corner of corner pixels)
|
||||
self.assertClose(
|
||||
screen_points[batch_idx][2],
|
||||
torch.tensor([0.0, 0.0, 1.0]),
|
||||
atol=1e-5,
|
||||
)
|
||||
|
||||
if cameras.in_ndc():
|
||||
self.assertClose(camera_points[batch_idx], ndc_points[batch_idx])
|
||||
else:
|
||||
# transform_points does something strange for screen cameras
|
||||
if batch_idx == 0:
|
||||
wanted = torch.stack(
|
||||
[
|
||||
data.W - screen_points[batch_idx, :, 0],
|
||||
data.H - screen_points[batch_idx, :, 1],
|
||||
torch.ones(3),
|
||||
],
|
||||
dim=1,
|
||||
)
|
||||
else:
|
||||
wanted = torch.stack(
|
||||
[
|
||||
-screen_points[batch_idx, :, 0],
|
||||
2 * data.H - screen_points[batch_idx, :, 1],
|
||||
torch.ones(3),
|
||||
],
|
||||
dim=1,
|
||||
)
|
||||
|
||||
print(wanted)
|
||||
print(camera_points[batch_idx])
|
||||
self.assertClose(camera_points[batch_idx], wanted)
|
||||
Loading…
x
Reference in New Issue
Block a user