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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Modified from https://github.com/facebookresearch/vggt
import numpy as np
import pycolmap
from mapanything.third_party.projection import project_3D_points_np
def batch_np_matrix_to_pycolmap(
points3d,
extrinsics,
intrinsics,
tracks,
image_size,
masks=None,
max_reproj_error=None,
max_points3D_val=3000,
shared_camera=False,
camera_type="SIMPLE_PINHOLE",
extra_params=None,
min_inlier_per_frame=64,
points_rgb=None,
):
"""
Convert Batched NumPy Arrays to PyCOLMAP
Check https://github.com/colmap/pycolmap for more details about its format
NOTE that colmap expects images/cameras/points3D to be 1-indexed
so there is a +1 offset between colmap index and batch index
NOTE: different from VGGSfM, this function:
1. Use np instead of torch
2. Frame index and camera id starts from 1 rather than 0 (to fit the format of PyCOLMAP)
"""
# points3d: Px3
# extrinsics: Nx3x4
# intrinsics: Nx3x3
# tracks: NxPx2
# masks: NxP
# image_size: 2, assume all the frames have been padded to the same size
# where N is the number of frames and P is the number of tracks
N, P, _ = tracks.shape
assert len(extrinsics) == N
assert len(intrinsics) == N
assert len(points3d) == P
assert image_size.shape[0] == 2
reproj_mask = None
if max_reproj_error is not None:
projected_points_2d, projected_points_cam = project_3D_points_np(
points3d, extrinsics, intrinsics
)
projected_diff = np.linalg.norm(projected_points_2d - tracks, axis=-1)
projected_points_2d[projected_points_cam[:, -1] <= 0] = 1e6
reproj_mask = projected_diff < max_reproj_error
if masks is not None and reproj_mask is not None:
masks = np.logical_and(masks, reproj_mask)
elif masks is not None:
masks = masks
else:
masks = reproj_mask
assert masks is not None
if masks.sum(1).min() < min_inlier_per_frame:
print("Not enough inliers per frame, skip BA.")
return None, None
# Reconstruction object, following the format of PyCOLMAP/COLMAP
reconstruction = pycolmap.Reconstruction()
inlier_num = masks.sum(0)
valid_mask = inlier_num >= 2 # a track is invalid if without two inliers
valid_idx = np.nonzero(valid_mask)[0]
# Only add 3D points that have sufficient 2D points
for vidx in valid_idx:
# Use RGB colors if provided, otherwise use zeros
rgb = points_rgb[vidx] if points_rgb is not None else np.zeros(3)
reconstruction.add_point3D(points3d[vidx], pycolmap.Track(), rgb)
num_points3D = len(valid_idx)
camera = None
# frame idx
for fidx in range(N):
# set camera
if camera is None or (not shared_camera):
pycolmap_intri = _build_pycolmap_intri(
fidx, intrinsics, camera_type, extra_params
)
camera = pycolmap.Camera(
model=camera_type,
width=image_size[0],
height=image_size[1],
params=pycolmap_intri,
camera_id=fidx + 1,
)
# add camera
reconstruction.add_camera(camera)
# set image
cam_from_world = pycolmap.Rigid3d(
pycolmap.Rotation3d(extrinsics[fidx][:3, :3]), extrinsics[fidx][:3, 3]
) # Rot and Trans
image = pycolmap.Image(
id=fidx + 1,
name=f"image_{fidx + 1}",
camera_id=camera.camera_id,
cam_from_world=cam_from_world,
)
points2D_list = []
point2D_idx = 0
# NOTE point3D_id start by 1
for point3D_id in range(1, num_points3D + 1):
original_track_idx = valid_idx[point3D_id - 1]
if (reconstruction.points3D[point3D_id].xyz < max_points3D_val).all():
if masks[fidx][original_track_idx]:
# It seems we don't need +0.5 for BA
point2D_xy = tracks[fidx][original_track_idx]
# Please note when adding the Point2D object
# It not only requires the 2D xy location, but also the id to 3D point
points2D_list.append(pycolmap.Point2D(point2D_xy, point3D_id))
# add element
track = reconstruction.points3D[point3D_id].track
track.add_element(fidx + 1, point2D_idx)
point2D_idx += 1
assert point2D_idx == len(points2D_list)
try:
image.points2D = pycolmap.ListPoint2D(points2D_list)
image.registered = True
except: # noqa
print(f"frame {fidx + 1} is out of BA")
image.registered = False
# add image
reconstruction.add_image(image)
return reconstruction, valid_mask
def pycolmap_to_batch_np_matrix(
reconstruction, device="cpu", camera_type="SIMPLE_PINHOLE"
):
"""
Convert a PyCOLMAP Reconstruction Object to batched NumPy arrays.
Args:
reconstruction (pycolmap.Reconstruction): The reconstruction object from PyCOLMAP.
device (str): Ignored in NumPy version (kept for API compatibility).
camera_type (str): The type of camera model used (default: "SIMPLE_PINHOLE").
Returns:
tuple: A tuple containing points3D, extrinsics, intrinsics, and optionally extra_params.
"""
num_images = len(reconstruction.images)
max_points3D_id = max(reconstruction.point3D_ids())
points3D = np.zeros((max_points3D_id, 3))
for point3D_id in reconstruction.points3D:
points3D[point3D_id - 1] = reconstruction.points3D[point3D_id].xyz
extrinsics = []
intrinsics = []
extra_params = [] if camera_type == "SIMPLE_RADIAL" else None
for i in range(num_images):
# Extract and append extrinsics
pyimg = reconstruction.images[i + 1]
pycam = reconstruction.cameras[pyimg.camera_id]
matrix = pyimg.cam_from_world.matrix()
extrinsics.append(matrix)
# Extract and append intrinsics
calibration_matrix = pycam.calibration_matrix()
intrinsics.append(calibration_matrix)
if camera_type == "SIMPLE_RADIAL":
extra_params.append(pycam.params[-1])
# Convert lists to NumPy arrays instead of torch tensors
extrinsics = np.stack(extrinsics)
intrinsics = np.stack(intrinsics)
if camera_type == "SIMPLE_RADIAL":
extra_params = np.stack(extra_params)
extra_params = extra_params[:, None]
return points3D, extrinsics, intrinsics, extra_params
########################################################
def batch_np_matrix_to_pycolmap_wo_track(
points3d,
points_xyf,
points_rgb,
extrinsics,
intrinsics,
image_size,
shared_camera=False,
camera_type="SIMPLE_PINHOLE",
):
"""
Convert Batched NumPy Arrays to PyCOLMAP
Different from batch_np_matrix_to_pycolmap, this function does not use tracks.
It saves points3d to colmap reconstruction format only to serve as init for Gaussians or other nvs methods.
Do NOT use this for BA.
"""
# points3d: Px3
# points_xyf: Px3, with x, y coordinates and frame indices
# points_rgb: Px3, rgb colors
# extrinsics: Nx3x4
# intrinsics: Nx3x3
# image_size: 2, assume all the frames have been padded to the same size
# where N is the number of frames and P is the number of tracks
N = len(extrinsics)
P = len(points3d)
# Reconstruction object, following the format of PyCOLMAP/COLMAP
reconstruction = pycolmap.Reconstruction()
for vidx in range(P):
reconstruction.add_point3D(points3d[vidx], pycolmap.Track(), points_rgb[vidx])
camera = None
# frame idx
for fidx in range(N):
# set camera
if camera is None or (not shared_camera):
pycolmap_intri = _build_pycolmap_intri(fidx, intrinsics, camera_type)
camera = pycolmap.Camera(
model=camera_type,
width=image_size[0],
height=image_size[1],
params=pycolmap_intri,
camera_id=fidx + 1,
)
# add camera
reconstruction.add_camera(camera)
# set image
cam_from_world = pycolmap.Rigid3d(
pycolmap.Rotation3d(extrinsics[fidx][:3, :3]), extrinsics[fidx][:3, 3]
) # Rot and Trans
image = pycolmap.Image(
id=fidx + 1,
name=f"image_{fidx + 1}",
camera_id=camera.camera_id,
cam_from_world=cam_from_world,
)
points2D_list = []
point2D_idx = 0
points_belong_to_fidx = points_xyf[:, 2].astype(np.int32) == fidx
points_belong_to_fidx = np.nonzero(points_belong_to_fidx)[0]
for point3D_batch_idx in points_belong_to_fidx:
point3D_id = point3D_batch_idx + 1
point2D_xyf = points_xyf[point3D_batch_idx]
point2D_xy = point2D_xyf[:2]
points2D_list.append(pycolmap.Point2D(point2D_xy, point3D_id))
# add element
track = reconstruction.points3D[point3D_id].track
track.add_element(fidx + 1, point2D_idx)
point2D_idx += 1
assert point2D_idx == len(points2D_list)
try:
image.points2D = pycolmap.ListPoint2D(points2D_list)
image.registered = True
except: # noqa
print(f"frame {fidx + 1} does not have any points")
image.registered = False
# add image
reconstruction.add_image(image)
return reconstruction
def _build_pycolmap_intri(fidx, intrinsics, camera_type, extra_params=None):
"""
Helper function to get camera parameters based on camera type.
Args:
fidx: Frame index
intrinsics: Camera intrinsic parameters
camera_type: Type of camera model
extra_params: Additional parameters for certain camera types
Returns:
pycolmap_intri: NumPy array of camera parameters
"""
if camera_type == "PINHOLE":
pycolmap_intri = np.array(
[
intrinsics[fidx][0, 0],
intrinsics[fidx][1, 1],
intrinsics[fidx][0, 2],
intrinsics[fidx][1, 2],
]
)
elif camera_type == "SIMPLE_PINHOLE":
focal = (intrinsics[fidx][0, 0] + intrinsics[fidx][1, 1]) / 2
pycolmap_intri = np.array(
[focal, intrinsics[fidx][0, 2], intrinsics[fidx][1, 2]]
)
elif camera_type == "SIMPLE_RADIAL":
raise NotImplementedError("SIMPLE_RADIAL is not supported yet")
focal = (intrinsics[fidx][0, 0] + intrinsics[fidx][1, 1]) / 2
pycolmap_intri = np.array(
[
focal,
intrinsics[fidx][0, 2],
intrinsics[fidx][1, 2],
extra_params[fidx][0],
]
)
else:
raise ValueError(f"Camera type {camera_type} is not supported yet")
return pycolmap_intri
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