🎉 initial rewrite and documentation for the poses module
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poses/README.md
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poses/README.md
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Contains the COLMAP wrappers and other utils to extract, read and write camera
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poses from and to different formats.
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poses/__init__.py
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poses/__init__.py
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poses/colmap_read_model.py
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poses/colmap_read_model.py
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# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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#
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# * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
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# its contributors may be used to endorse or promote products derived
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# from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
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# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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# POSSIBILITY OF SUCH DAMAGE.
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#
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# Author: Johannes L. Schoenberger (jsch at inf.ethz.ch)
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import os
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import sys
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import collections
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import numpy as np
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import struct
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CameraModel = collections.namedtuple(
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"CameraModel", ["model_id", "model_name", "num_params"])
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Camera = collections.namedtuple(
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"Camera", ["id", "model", "width", "height", "params"])
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BaseImage = collections.namedtuple(
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"Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
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Point3D = collections.namedtuple(
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"Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"])
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class Image(BaseImage):
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def qvec2rotmat(self):
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return qvec2rotmat(self.qvec)
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CAMERA_MODELS = {
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CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
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CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
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CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
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CameraModel(model_id=3, model_name="RADIAL", num_params=5),
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CameraModel(model_id=4, model_name="OPENCV", num_params=8),
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CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
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CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
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CameraModel(model_id=7, model_name="FOV", num_params=5),
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CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
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CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
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CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12)
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}
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CAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model) \
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for camera_model in CAMERA_MODELS])
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def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
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"""Read and unpack the next bytes from a binary file.
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:param fid:
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:param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
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:param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
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:param endian_character: Any of {@, =, <, >, !}
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:return: Tuple of read and unpacked values.
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"""
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data = fid.read(num_bytes)
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return struct.unpack(endian_character + format_char_sequence, data)
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def read_cameras_text(path):
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"""
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see: src/base/reconstruction.cc
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void Reconstruction::WriteCamerasText(const std::string& path)
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void Reconstruction::ReadCamerasText(const std::string& path)
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"""
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cameras = {}
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with open(path, "r") as fid:
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while True:
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line = fid.readline()
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if not line:
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break
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line = line.strip()
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if len(line) > 0 and line[0] != "#":
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elems = line.split()
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camera_id = int(elems[0])
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model = elems[1]
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width = int(elems[2])
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height = int(elems[3])
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params = np.array(tuple(map(float, elems[4:])))
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cameras[camera_id] = Camera(id=camera_id, model=model,
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width=width, height=height,
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params=params)
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return cameras
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def read_cameras_binary(path_to_model_file):
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"""
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see: src/base/reconstruction.cc
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void Reconstruction::WriteCamerasBinary(const std::string& path)
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void Reconstruction::ReadCamerasBinary(const std::string& path)
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"""
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cameras = {}
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with open(path_to_model_file, "rb") as fid:
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num_cameras = read_next_bytes(fid, 8, "Q")[0]
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for camera_line_index in range(num_cameras):
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camera_properties = read_next_bytes(
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fid, num_bytes=24, format_char_sequence="iiQQ")
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camera_id = camera_properties[0]
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model_id = camera_properties[1]
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model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
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width = camera_properties[2]
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height = camera_properties[3]
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num_params = CAMERA_MODEL_IDS[model_id].num_params
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params = read_next_bytes(fid, num_bytes=8*num_params,
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format_char_sequence="d"*num_params)
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cameras[camera_id] = Camera(id=camera_id,
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model=model_name,
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width=width,
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height=height,
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params=np.array(params))
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assert len(cameras) == num_cameras
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return cameras
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def read_images_text(path):
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"""
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see: src/base/reconstruction.cc
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void Reconstruction::ReadImagesText(const std::string& path)
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void Reconstruction::WriteImagesText(const std::string& path)
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"""
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images = {}
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with open(path, "r") as fid:
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while True:
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line = fid.readline()
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if not line:
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break
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line = line.strip()
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if len(line) > 0 and line[0] != "#":
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elems = line.split()
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image_id = int(elems[0])
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qvec = np.array(tuple(map(float, elems[1:5])))
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tvec = np.array(tuple(map(float, elems[5:8])))
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camera_id = int(elems[8])
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image_name = elems[9]
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elems = fid.readline().split()
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xys = np.column_stack([tuple(map(float, elems[0::3])),
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tuple(map(float, elems[1::3]))])
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point3D_ids = np.array(tuple(map(int, elems[2::3])))
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images[image_id] = Image(
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id=image_id, qvec=qvec, tvec=tvec,
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camera_id=camera_id, name=image_name,
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xys=xys, point3D_ids=point3D_ids)
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return images
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def read_images_binary(path_to_model_file):
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"""
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see: src/base/reconstruction.cc
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void Reconstruction::ReadImagesBinary(const std::string& path)
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void Reconstruction::WriteImagesBinary(const std::string& path)
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"""
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images = {}
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with open(path_to_model_file, "rb") as fid:
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num_reg_images = read_next_bytes(fid, 8, "Q")[0]
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for image_index in range(num_reg_images):
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binary_image_properties = read_next_bytes(
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fid, num_bytes=64, format_char_sequence="idddddddi")
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image_id = binary_image_properties[0]
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qvec = np.array(binary_image_properties[1:5])
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tvec = np.array(binary_image_properties[5:8])
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camera_id = binary_image_properties[8]
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image_name = ""
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current_char = read_next_bytes(fid, 1, "c")[0]
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while current_char != b"\x00": # look for the ASCII 0 entry
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image_name += current_char.decode("utf-8")
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current_char = read_next_bytes(fid, 1, "c")[0]
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num_points2D = read_next_bytes(fid, num_bytes=8,
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format_char_sequence="Q")[0]
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x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D,
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format_char_sequence="ddq"*num_points2D)
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xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])),
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tuple(map(float, x_y_id_s[1::3]))])
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point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))
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images[image_id] = Image(
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id=image_id, qvec=qvec, tvec=tvec,
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camera_id=camera_id, name=image_name,
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xys=xys, point3D_ids=point3D_ids)
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return images
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def read_points3D_text(path):
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"""
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see: src/base/reconstruction.cc
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void Reconstruction::ReadPoints3DText(const std::string& path)
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void Reconstruction::WritePoints3DText(const std::string& path)
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"""
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points3D = {}
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with open(path, "r") as fid:
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while True:
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line = fid.readline()
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if not line:
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break
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line = line.strip()
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if len(line) > 0 and line[0] != "#":
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elems = line.split()
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point3D_id = int(elems[0])
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xyz = np.array(tuple(map(float, elems[1:4])))
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rgb = np.array(tuple(map(int, elems[4:7])))
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error = float(elems[7])
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image_ids = np.array(tuple(map(int, elems[8::2])))
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point2D_idxs = np.array(tuple(map(int, elems[9::2])))
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points3D[point3D_id] = Point3D(id=point3D_id, xyz=xyz, rgb=rgb,
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error=error, image_ids=image_ids,
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point2D_idxs=point2D_idxs)
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return points3D
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def read_points3d_binary(path_to_model_file):
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"""
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see: src/base/reconstruction.cc
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void Reconstruction::ReadPoints3DBinary(const std::string& path)
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void Reconstruction::WritePoints3DBinary(const std::string& path)
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"""
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points3D = {}
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with open(path_to_model_file, "rb") as fid:
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num_points = read_next_bytes(fid, 8, "Q")[0]
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for point_line_index in range(num_points):
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binary_point_line_properties = read_next_bytes(
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fid, num_bytes=43, format_char_sequence="QdddBBBd")
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point3D_id = binary_point_line_properties[0]
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xyz = np.array(binary_point_line_properties[1:4])
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rgb = np.array(binary_point_line_properties[4:7])
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error = np.array(binary_point_line_properties[7])
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track_length = read_next_bytes(
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fid, num_bytes=8, format_char_sequence="Q")[0]
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track_elems = read_next_bytes(
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fid, num_bytes=8*track_length,
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format_char_sequence="ii"*track_length)
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image_ids = np.array(tuple(map(int, track_elems[0::2])))
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point2D_idxs = np.array(tuple(map(int, track_elems[1::2])))
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points3D[point3D_id] = Point3D(
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id=point3D_id, xyz=xyz, rgb=rgb,
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error=error, image_ids=image_ids,
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point2D_idxs=point2D_idxs)
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return points3D
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def read_model(path, ext):
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if ext == ".txt":
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cameras = read_cameras_text(os.path.join(path, "cameras" + ext))
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images = read_images_text(os.path.join(path, "images" + ext))
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points3D = read_points3D_text(os.path.join(path, "points3D") + ext)
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else:
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cameras = read_cameras_binary(os.path.join(path, "cameras" + ext))
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images = read_images_binary(os.path.join(path, "images" + ext))
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points3D = read_points3d_binary(os.path.join(path, "points3D") + ext)
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return cameras, images, points3D
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def qvec2rotmat(qvec):
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return np.array([
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[1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
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2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
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2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],
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[2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
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1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,
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2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],
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[2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
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2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
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1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])
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def rotmat2qvec(R):
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Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
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K = np.array([
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[Rxx - Ryy - Rzz, 0, 0, 0],
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[Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
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[Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
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[Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz]]) / 3.0
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eigvals, eigvecs = np.linalg.eigh(K)
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qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
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if qvec[0] < 0:
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qvec *= -1
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return qvec
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def main():
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if len(sys.argv) != 3:
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print("Usage: python read_model.py path/to/model/folder [.txt,.bin]")
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return
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cameras, images, points3D = read_model(path=sys.argv[1], ext=sys.argv[2])
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print("num_cameras:", len(cameras))
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print("num_images:", len(images))
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print("num_points3D:", len(points3D))
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if __name__ == "__main__":
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main()
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poses/colmap_wrapper.py
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poses/colmap_wrapper.py
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import os
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import subprocess
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import logging
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# $ DATASET_PATH=/path/to/dataset
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# $ colmap feature_extractor \
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# --database_path $DATASET_PATH/database.db \
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# --image_path $DATASET_PATH/images
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# $ colmap exhaustive_matcher \
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# --database_path $DATASET_PATH/database.db
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# $ mkdir $DATASET_PATH/sparse
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# $ colmap mapper \
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# --database_path $DATASET_PATH/database.db \
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# --image_path $DATASET_PATH/images \
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# --output_path $DATASET_PATH/sparse
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# $ mkdir $DATASET_PATH/dense
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def run_colmap(basedir, match_type):
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"""Simple wrapper for COLMAP"""
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logfile_name = os.path.join(basedir, 'colmap_output.txt')
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logfile = open(logfile_name, 'w')
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feature_extractor_args = [
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'colmap', 'feature_extractor',
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'--database_path', os.path.join(basedir, 'database.db'),
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'--image_path', os.path.join(basedir, 'images'),
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'--ImageReader.single_camera', '1',
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# '--SiftExtraction.use_gpu', '0',
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]
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logging.debug("Extracting features.")
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feat_output = subprocess.check_output(feature_extractor_args, universal_newlines=True)
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logfile.write(feat_output)
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exhaustive_matcher_args = [
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'colmap', match_type,
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'--database_path', os.path.join(basedir, 'database.db'),
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]
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logging.debug("Matching features.")
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match_output = subprocess.check_output(exhaustive_matcher_args, universal_newlines=True)
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logfile.write(match_output)
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p = os.path.join(basedir, 'sparse')
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if not os.path.exists(p):
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os.makedirs(p)
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mapper_args = [
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'colmap', 'mapper',
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'--database_path', os.path.join(basedir, 'database.db'),
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'--image_path', os.path.join(basedir, 'images'),
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'--output_path', os.path.join(basedir, 'sparse'), # --export_path changed to --output_path in colmap 3.6
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'--Mapper.num_threads', '16',
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'--Mapper.init_min_tri_angle', '4',
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'--Mapper.multiple_models', '0',
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'--Mapper.extract_colors', '0',
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]
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logging.debug("Creating sparse map")
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map_output = subprocess.check_output(mapper_args, universal_newlines=True)
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logfile.write(map_output)
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logfile.close()
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logging.info("Finished running COLMAP. Logfile written in %s",
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logfile_name)
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138
poses/pose_utils.py
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138
poses/pose_utils.py
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import os
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import logging
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import typing
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import numpy as np
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|
||||
from colmap_wrapper import run_colmap
|
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import colmap_read_model as read_model
|
||||
#from nerf_homemade.poses.colmap_wrapper import run_colmap
|
||||
#import nerf_homemade.poses.colmap_read_model as read_model
|
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|
||||
|
||||
def gen_poses(basedir: str, match_type: str='exhaustive') -> None:
|
||||
"""
|
||||
Geneate or retreive camera poses.
|
||||
|
||||
Retrieve the cameras either by generating using COLMAP or reading them if
|
||||
they already have been written.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
basedir: str
|
||||
The path of the directory either containing raw images or the
|
||||
arborescence of a COLMAP directory if camera poses have already been
|
||||
computed
|
||||
match_type: {'exhaustive', 'sequential'}
|
||||
The type of match COLMAP should perform if it needs to be run.
|
||||
Defaults to 'exhaustive'.
|
||||
"""
|
||||
|
||||
files_needed = ["cameras.bin", "images.bin", "points3D.bin"]
|
||||
|
||||
path_to_sparse = os.path.join(basedir, "sparse/0")
|
||||
if os.path.exists(path_to_sparse):
|
||||
existing_files = os.listdir(path_to_sparse)
|
||||
else:
|
||||
existing_files = []
|
||||
|
||||
if not all([f in existing_files for f in files_needed]):
|
||||
logging.info("Running COLMAP")
|
||||
run_colmap(basedir, match_type)
|
||||
else:
|
||||
logging.info("Files genreated by COLMAP found. Skipping running COLMAP.")
|
||||
|
||||
logging.debug("Loading COLMAP data")
|
||||
poses, points_3d, perm = load_colmap_data(basedir)
|
||||
|
||||
logging.debug("Saving COLMAP data to npy")
|
||||
save_poses(basedir, poses, points_3d, perm)
|
||||
|
||||
def load_colmap_data(basedir: str) -> (np.ndarray, dict, np.ndarray):
|
||||
"""
|
||||
Load data from a COLMAP arborescence.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
basedir: str
|
||||
The path of the directory which contains the COLMAP arborescence
|
||||
|
||||
Returns
|
||||
-------
|
||||
poses: numpy.ndarray
|
||||
List of poses for each image.
|
||||
pts3d:
|
||||
List of 3D points for each image.
|
||||
perm:
|
||||
Index list ordered by name of images.
|
||||
Should be [1, 2, ..., N] if well ordered.
|
||||
"""
|
||||
# append prefix
|
||||
basedir = os.path.join(basedir, "sparse/0/")
|
||||
|
||||
# read cameras data
|
||||
cameras_file = os.path.join(basedir, "cameras.bin")
|
||||
cameras_data = read_model.read_cameras_binary(cameras_file)
|
||||
logging.debug(f"Loading camera model from {cameras_file}")
|
||||
|
||||
# extract intrinsic values for the camera
|
||||
# asumption is made that it is unique
|
||||
camera_h = cameras_data[1].height
|
||||
camera_w = cameras_data[1].width
|
||||
camera_f = cameras_data[1].params[0]
|
||||
hwf = np.array([camera_h, camera_w, camera_f]).reshape([3,1])
|
||||
logging.debug(f"Number of cameras: {len(cameras_data)}")
|
||||
|
||||
# read images data
|
||||
images_file = os.path.join(basedir, "images.bin")
|
||||
images_data = read_model.read_images_binary(images_file)
|
||||
|
||||
w2c_mats = []
|
||||
bottom = np.array([0., 0., 0., 1.]).reshape([1, 4])
|
||||
|
||||
# sort by name
|
||||
names = [images_data[k].name for k in images_data]
|
||||
perm = np.argsort(names)
|
||||
# equivalent to a range since dict is index by number from 1 to N
|
||||
|
||||
# create camera matrix
|
||||
for k in images_data:
|
||||
im = images_data[k]
|
||||
R = im.qvec2rotmat()
|
||||
t = im.tvec.reshape([3,1])
|
||||
m = np.concatenate([np.concatenate([R, t], 1), bottom], 0)
|
||||
w2c_mats.append(m)
|
||||
|
||||
w2c_mats = np.stack(w2c_mats, 0)
|
||||
c2w_mats = np.linalg.inv(w2c_mats)
|
||||
|
||||
poses = c2w_mats[:, :3, :4].transpose([1,2,0])
|
||||
poses = np.concatenate([poses, np.tile(hwf[..., np.newaxis], [1,1,poses.shape[-1]])], 1)
|
||||
|
||||
# read 3d points data
|
||||
pts3d_file = os.path.join(basedir, "points3D.bin")
|
||||
pts3d = read_model.read_points3d_binary(pts3d_file)
|
||||
|
||||
# must switch to [-u, r, -t] from [r, -u, t], NOT [r, u, -t]
|
||||
poses = np.concatenate([poses[:, 1:2, :], poses[:, 0:1, :], -poses[:, 2:3, :], poses[:, 3:4, :], poses[:, 4:5, :]], 1)
|
||||
|
||||
return poses, pts3d, perm
|
||||
|
||||
def save_poses(basedir, poses, pts3d, perm) -> None:
|
||||
"""
|
||||
Save the COLMAP data in a `.npy` format.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
basedir: str
|
||||
The path of the directory in which to save the data.
|
||||
Data will be saved in `basedir/poses_bounds.npy`.
|
||||
poses:
|
||||
The list of poses
|
||||
pts3d:
|
||||
The list of 3d points
|
||||
perm:
|
||||
The sorted index of the array
|
||||
"""
|
||||
|
||||
# TODO
|
||||
Loading…
Reference in a new issue