mirror of
https://github.com/graphdeco-inria/gaussian-splatting
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283 lines
11 KiB
Python
283 lines
11 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import numpy as np
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import collections
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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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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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CAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model)
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for camera_model in CAMERA_MODELS])
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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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class Image(BaseImage):
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def qvec2rotmat(self):
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return qvec2rotmat(self.qvec)
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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_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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xyzs = None
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rgbs = None
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errors = None
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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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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 = np.array(float(elems[7]))
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if xyzs is None:
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xyzs = xyz[None, ...]
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rgbs = rgb[None, ...]
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errors = error[None, ...]
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else:
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xyzs = np.append(xyzs, xyz[None, ...], axis=0)
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rgbs = np.append(rgbs, rgb[None, ...], axis=0)
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errors = np.append(errors, error[None, ...], axis=0)
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return xyzs, rgbs, errors
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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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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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xyzs = np.empty((num_points, 3))
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rgbs = np.empty((num_points, 3))
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errors = np.empty((num_points, 1))
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for p_id 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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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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xyzs[p_id] = xyz
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rgbs[p_id] = rgb
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errors[p_id] = error
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return xyzs, rgbs, errors
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def read_intrinsics_text(path):
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"""
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Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py
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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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assert model == "PINHOLE", "While the loader support other types, the rest of the code assumes PINHOLE"
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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_extrinsics_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 _ 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_intrinsics_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 _ 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_extrinsics_text(path):
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"""
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Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py
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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_colmap_bin_array(path):
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"""
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Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_dense.py
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:param path: path to the colmap binary file.
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:return: nd array with the floating point values in the value
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"""
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with open(path, "rb") as fid:
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width, height, channels = np.genfromtxt(fid, delimiter="&", max_rows=1,
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usecols=(0, 1, 2), dtype=int)
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fid.seek(0)
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num_delimiter = 0
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byte = fid.read(1)
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while True:
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if byte == b"&":
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num_delimiter += 1
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if num_delimiter >= 3:
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break
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byte = fid.read(1)
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array = np.fromfile(fid, np.float32)
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array = array.reshape((width, height, channels), order="F")
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return np.transpose(array, (1, 0, 2)).squeeze()
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