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| | import collections |
| | import struct |
| |
|
| | import numpy as np |
| |
|
| | CameraModel = collections.namedtuple( |
| | "CameraModel", ["model_id", "model_name", "num_params"] |
| | ) |
| | Camera = collections.namedtuple("Camera", ["id", "model", "width", "height", "params"]) |
| | BaseImage = collections.namedtuple( |
| | "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"] |
| | ) |
| | Point3D = collections.namedtuple( |
| | "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"] |
| | ) |
| | CAMERA_MODELS = { |
| | CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), |
| | CameraModel(model_id=1, model_name="PINHOLE", num_params=4), |
| | CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), |
| | CameraModel(model_id=3, model_name="RADIAL", num_params=5), |
| | CameraModel(model_id=4, model_name="OPENCV", num_params=8), |
| | CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), |
| | CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), |
| | CameraModel(model_id=7, model_name="FOV", num_params=5), |
| | CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), |
| | CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), |
| | CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), |
| | } |
| | CAMERA_MODEL_IDS = dict( |
| | [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] |
| | ) |
| | CAMERA_MODEL_NAMES = dict( |
| | [(camera_model.model_name, camera_model) for camera_model in CAMERA_MODELS] |
| | ) |
| |
|
| |
|
| | def qvec2rotmat(qvec): |
| | return np.array( |
| | [ |
| | [ |
| | 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, |
| | 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], |
| | 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], |
| | ], |
| | [ |
| | 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], |
| | 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, |
| | 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], |
| | ], |
| | [ |
| | 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], |
| | 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], |
| | 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, |
| | ], |
| | ] |
| | ) |
| |
|
| |
|
| | def rotmat2qvec(R): |
| | Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat |
| | K = ( |
| | np.array( |
| | [ |
| | [Rxx - Ryy - Rzz, 0, 0, 0], |
| | [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0], |
| | [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0], |
| | [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz], |
| | ] |
| | ) |
| | / 3.0 |
| | ) |
| | eigvals, eigvecs = np.linalg.eigh(K) |
| | qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)] |
| | if qvec[0] < 0: |
| | qvec *= -1 |
| | return qvec |
| |
|
| |
|
| | class Image(BaseImage): |
| | def qvec2rotmat(self): |
| | return qvec2rotmat(self.qvec) |
| |
|
| |
|
| | def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): |
| | """Read and unpack the next bytes from a binary file. |
| | :param fid: |
| | :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. |
| | :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. |
| | :param endian_character: Any of {@, =, <, >, !} |
| | :return: Tuple of read and unpacked values. |
| | """ |
| | data = fid.read(num_bytes) |
| | return struct.unpack(endian_character + format_char_sequence, data) |
| |
|
| |
|
| | def read_points3D_text(path): |
| | """ |
| | see: src/base/reconstruction.cc |
| | void Reconstruction::ReadPoints3DText(const std::string& path) |
| | void Reconstruction::WritePoints3DText(const std::string& path) |
| | """ |
| | xyzs = None |
| | rgbs = None |
| | errors = None |
| | num_points = 0 |
| | with open(path, "r") as fid: |
| | while True: |
| | line = fid.readline() |
| | if not line: |
| | break |
| | line = line.strip() |
| | if len(line) > 0 and line[0] != "#": |
| | num_points += 1 |
| |
|
| | xyzs = np.empty((num_points, 3)) |
| | rgbs = np.empty((num_points, 3)) |
| | errors = np.empty((num_points, 1)) |
| | count = 0 |
| | with open(path, "r") as fid: |
| | while True: |
| | line = fid.readline() |
| | if not line: |
| | break |
| | line = line.strip() |
| | if len(line) > 0 and line[0] != "#": |
| | elems = line.split() |
| | xyz = np.array(tuple(map(float, elems[1:4]))) |
| | rgb = np.array(tuple(map(int, elems[4:7]))) |
| | error = np.array(float(elems[7])) |
| | xyzs[count] = xyz |
| | rgbs[count] = rgb |
| | errors[count] = error |
| | count += 1 |
| |
|
| | return xyzs, rgbs, errors |
| |
|
| |
|
| | def read_points3D_binary(path_to_model_file): |
| | """ |
| | see: src/base/reconstruction.cc |
| | void Reconstruction::ReadPoints3DBinary(const std::string& path) |
| | void Reconstruction::WritePoints3DBinary(const std::string& path) |
| | """ |
| |
|
| | with open(path_to_model_file, "rb") as fid: |
| | num_points = read_next_bytes(fid, 8, "Q")[0] |
| |
|
| | xyzs = np.empty((num_points, 3)) |
| | rgbs = np.empty((num_points, 3)) |
| | errors = np.empty((num_points, 1)) |
| |
|
| | for p_id in range(num_points): |
| | binary_point_line_properties = read_next_bytes( |
| | fid, num_bytes=43, format_char_sequence="QdddBBBd" |
| | ) |
| | xyz = np.array(binary_point_line_properties[1:4]) |
| | rgb = np.array(binary_point_line_properties[4:7]) |
| | error = np.array(binary_point_line_properties[7]) |
| | track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ |
| | 0 |
| | ] |
| | track_elems = read_next_bytes( |
| | fid, |
| | num_bytes=8 * track_length, |
| | format_char_sequence="ii" * track_length, |
| | ) |
| | xyzs[p_id] = xyz |
| | rgbs[p_id] = rgb |
| | errors[p_id] = error |
| | return xyzs, rgbs, errors |
| |
|
| |
|
| | def read_intrinsics_text(path): |
| | """ |
| | Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py |
| | """ |
| | cameras = {} |
| | with open(path, "r") as fid: |
| | while True: |
| | line = fid.readline() |
| | if not line: |
| | break |
| | line = line.strip() |
| | if len(line) > 0 and line[0] != "#": |
| | elems = line.split() |
| | camera_id = int(elems[0]) |
| | model = elems[1] |
| | assert ( |
| | model == "PINHOLE" |
| | ), "While the loader support other types, the rest of the code assumes PINHOLE" |
| | width = int(elems[2]) |
| | height = int(elems[3]) |
| | params = np.array(tuple(map(float, elems[4:]))) |
| | cameras[camera_id] = Camera( |
| | id=camera_id, model=model, width=width, height=height, params=params |
| | ) |
| | return cameras |
| |
|
| |
|
| | def read_extrinsics_binary(path_to_model_file): |
| | """ |
| | see: src/base/reconstruction.cc |
| | void Reconstruction::ReadImagesBinary(const std::string& path) |
| | void Reconstruction::WriteImagesBinary(const std::string& path) |
| | """ |
| | images = {} |
| | with open(path_to_model_file, "rb") as fid: |
| | num_reg_images = read_next_bytes(fid, 8, "Q")[0] |
| | for _ in range(num_reg_images): |
| | binary_image_properties = read_next_bytes( |
| | fid, num_bytes=64, format_char_sequence="idddddddi" |
| | ) |
| | image_id = binary_image_properties[0] |
| | qvec = np.array(binary_image_properties[1:5]) |
| | tvec = np.array(binary_image_properties[5:8]) |
| | camera_id = binary_image_properties[8] |
| | image_name = "" |
| | current_char = read_next_bytes(fid, 1, "c")[0] |
| | while current_char != b"\x00": |
| | image_name += current_char.decode("utf-8") |
| | current_char = read_next_bytes(fid, 1, "c")[0] |
| | num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ |
| | 0 |
| | ] |
| | x_y_id_s = read_next_bytes( |
| | fid, |
| | num_bytes=24 * num_points2D, |
| | format_char_sequence="ddq" * num_points2D, |
| | ) |
| | xys = np.column_stack( |
| | [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] |
| | ) |
| | point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) |
| | images[image_id] = Image( |
| | id=image_id, |
| | qvec=qvec, |
| | tvec=tvec, |
| | camera_id=camera_id, |
| | name=image_name, |
| | xys=xys, |
| | point3D_ids=point3D_ids, |
| | ) |
| | return images |
| |
|
| |
|
| | def read_intrinsics_binary(path_to_model_file): |
| | """ |
| | see: src/base/reconstruction.cc |
| | void Reconstruction::WriteCamerasBinary(const std::string& path) |
| | void Reconstruction::ReadCamerasBinary(const std::string& path) |
| | """ |
| | cameras = {} |
| | with open(path_to_model_file, "rb") as fid: |
| | num_cameras = read_next_bytes(fid, 8, "Q")[0] |
| | for _ in range(num_cameras): |
| | camera_properties = read_next_bytes( |
| | fid, num_bytes=24, format_char_sequence="iiQQ" |
| | ) |
| | camera_id = camera_properties[0] |
| | model_id = camera_properties[1] |
| | model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name |
| | width = camera_properties[2] |
| | height = camera_properties[3] |
| | num_params = CAMERA_MODEL_IDS[model_id].num_params |
| | params = read_next_bytes( |
| | fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params |
| | ) |
| | cameras[camera_id] = Camera( |
| | id=camera_id, |
| | model=model_name, |
| | width=width, |
| | height=height, |
| | params=np.array(params), |
| | ) |
| | assert len(cameras) == num_cameras |
| | return cameras |
| |
|
| |
|
| | def read_extrinsics_text(path): |
| | """ |
| | Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py |
| | """ |
| | images = {} |
| | with open(path, "r") as fid: |
| | while True: |
| | line = fid.readline() |
| | if not line: |
| | break |
| | line = line.strip() |
| | if len(line) > 0 and line[0] != "#": |
| | elems = line.split() |
| | image_id = int(elems[0]) |
| | qvec = np.array(tuple(map(float, elems[1:5]))) |
| | tvec = np.array(tuple(map(float, elems[5:8]))) |
| | camera_id = int(elems[8]) |
| | image_name = elems[9] |
| | elems = fid.readline().split() |
| | xys = np.column_stack( |
| | [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] |
| | ) |
| | point3D_ids = np.array(tuple(map(int, elems[2::3]))) |
| | images[image_id] = Image( |
| | id=image_id, |
| | qvec=qvec, |
| | tvec=tvec, |
| | camera_id=camera_id, |
| | name=image_name, |
| | xys=xys, |
| | point3D_ids=point3D_ids, |
| | ) |
| | return images |
| |
|
| |
|
| | def read_colmap_bin_array(path): |
| | """ |
| | Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_dense.py |
| | |
| | :param path: path to the colmap binary file. |
| | :return: nd array with the floating point values in the value |
| | """ |
| | with open(path, "rb") as fid: |
| | width, height, channels = np.genfromtxt( |
| | fid, delimiter="&", max_rows=1, usecols=(0, 1, 2), dtype=int |
| | ) |
| | fid.seek(0) |
| | num_delimiter = 0 |
| | byte = fid.read(1) |
| | while True: |
| | if byte == b"&": |
| | num_delimiter += 1 |
| | if num_delimiter >= 3: |
| | break |
| | byte = fid.read(1) |
| | array = np.fromfile(fid, np.float32) |
| | array = array.reshape((width, height, channels), order="F") |
| | return np.transpose(array, (1, 0, 2)).squeeze() |
| |
|