From 416c3345bcbb9e0c9599319ec7d86ba2a0dcf4b4 Mon Sep 17 00:00:00 2001 From: Adilkhan Sarsen <54854336+adolkhan@users.noreply.github.com> Date: Tue, 23 Aug 2022 20:45:05 +0600 Subject: [PATCH] [AL-1775] Point Cloud htype (#1685) * Rebased on main * Removed unecessary changes after rebase * Added point cloud decompression test * Added bbox.3d, segmet_mask.3d and binary_mask.3d * Fixed python linting * Added instance segmentation dtype * Removed uneccessary htype * Changed tests.txt * Fixed error * Changed the code to support downgraded version of numpy * Fixed python linting * Small test fixes * Removed pickle parser * Fixed python linting * Increased code coverage rate * Fixed python linting * Added corrupted sample * Fixed a type in test_point_cloud.py * Few more tests * Changed according to comments * Removed get_full_point_cloud_numpy_array method from tensor.py * Removed uneccessary code block * Fixed typo * Changed according to comments --- hub/api/read.py | 7 +- hub/api/tests/test_api.py | 24 ++- hub/api/tests/test_point_cloud.py | 140 ++++++++++++++++++ hub/compression.py | 10 +- hub/core/chunk_engine.py | 3 +- hub/core/compression.py | 120 ++++++++++++++- hub/core/dataset/dataset.py | 9 +- hub/core/meta/tensor_meta.py | 9 +- hub/core/sample.py | 17 ++- hub/core/serialize.py | 1 + hub/core/tensor.py | 55 +++++++ hub/core/tests/test_compression.py | 10 ++ hub/htype.py | 10 ++ hub/integrations/tf/hubtensorflowdataset.py | 2 +- hub/requirements/common.txt | 1 + .../point_cloud/corrupted_point_cloud.las | Bin 0 -> 1924288 bytes .../dummy_data/point_cloud/point_cloud.bin | Bin 0 -> 1924288 bytes .../dummy_data/point_cloud/point_cloud.las | Bin 0 -> 524205 bytes .../dummy_data/point_cloud/point_cloud.laz | Bin 0 -> 156534 bytes hub/tests/path_fixtures.py | 12 ++ hub/util/json.py | 2 - hub/util/point_cloud.py | 72 +++++++++ setup.py | 1 + 23 files changed, 482 insertions(+), 23 deletions(-) create mode 100644 hub/api/tests/test_point_cloud.py create mode 100644 hub/tests/dummy_data/point_cloud/corrupted_point_cloud.las create mode 100644 hub/tests/dummy_data/point_cloud/point_cloud.bin create mode 100644 hub/tests/dummy_data/point_cloud/point_cloud.las create mode 100644 hub/tests/dummy_data/point_cloud/point_cloud.laz create mode 100644 hub/util/point_cloud.py diff --git a/hub/api/read.py b/hub/api/read.py index 1a0b235834..b26b7ed0ee 100644 --- a/hub/api/read.py +++ b/hub/api/read.py @@ -1,11 +1,13 @@ +import pathlib from hub.core.sample import Sample # type: ignore -from typing import Optional, Dict +from typing import Optional, Dict, Union from hub.core.storage.provider import StorageProvider +from hub.util.path import convert_pathlib_to_string_if_needed def read( - path: str, + path: Union[str, pathlib.Path], verify: bool = False, creds: Optional[Dict] = None, compression: Optional[str] = None, @@ -53,6 +55,7 @@ def read( Returns: Sample: Sample object. Call `sample.array` to get the `np.ndarray`. """ + path = convert_pathlib_to_string_if_needed(path) return Sample( path, verify=verify, compression=compression, creds=creds, storage=storage ) diff --git a/hub/api/tests/test_api.py b/hub/api/tests/test_api.py index 070931cbc8..adc140c97e 100755 --- a/hub/api/tests/test_api.py +++ b/hub/api/tests/test_api.py @@ -7,7 +7,7 @@ from hub.core.dataset import Dataset from hub.core.tensor import Tensor -from hub.tests.common import assert_array_lists_equal, is_opt_true +from hub.tests.common import assert_array_lists_equal, is_opt_true, get_dummy_data_path from hub.tests.storage_fixtures import enabled_remote_storages from hub.core.storage import GCSProvider from hub.util.exceptions import ( @@ -560,6 +560,12 @@ def test_htype(memory_ds: Dataset): segment_mask = memory_ds.create_tensor("segment_mask", htype="segment_mask") keypoints_coco = memory_ds.create_tensor("keypoints_coco", htype="keypoints_coco") point = memory_ds.create_tensor("point", htype="point") + point_cloud = memory_ds.create_tensor( + "point_cloud", htype="point_cloud", sample_compression="las" + ) + memory_ds.create_tensor( + "point_cloud_calibration_matrix", htype="point_cloud.calibration_matrix" + ) image.append(np.ones((28, 28, 3), dtype=np.uint8)) bbox.append(np.array([1.0, 1.0, 0.0, 0.5], dtype=np.float32)) @@ -572,6 +578,17 @@ def test_htype(memory_ds: Dataset): keypoints_coco.append(np.ones((51, 2), dtype=np.int32)) point.append(np.ones((11, 2), dtype=np.int32)) + point_cloud.append( + hub.read(os.path.join(get_dummy_data_path("point_cloud"), "point_cloud.las")) + ) + point_cloud_dummy_data_path = pathlib.Path(get_dummy_data_path("point_cloud")) + point_cloud.append(hub.read(point_cloud_dummy_data_path / "point_cloud.las")) + # Along the forst direcection three matrices are concatenated, the first matrix is P, + # the second one is Tr and the third one is R + memory_ds.point_cloud_calibration_matrix.append( + np.zeros((3, 4, 4), dtype=np.float32) + ) + def test_dtype(memory_ds: Dataset): tensor = memory_ds.create_tensor("tensor") @@ -969,6 +986,7 @@ def test_compressions_list(): "ico", "jpeg", "jpeg2000", + "las", "lz4", "mkv", "mp3", @@ -991,6 +1009,7 @@ def test_htypes_list(): assert hub.htypes == [ "audio", "bbox", + "bbox.3d", "binary_mask", "class_label", "dicom", @@ -998,10 +1017,13 @@ def test_htypes_list(): "image", "image.gray", "image.rgb", + "instance_label", "json", "keypoints_coco", "list", "point", + "point_cloud", + "point_cloud.calibration_matrix", "segment_mask", "text", "video", diff --git a/hub/api/tests/test_point_cloud.py b/hub/api/tests/test_point_cloud.py new file mode 100644 index 0000000000..3474857bdb --- /dev/null +++ b/hub/api/tests/test_point_cloud.py @@ -0,0 +1,140 @@ +import pytest + +import hub +from hub.core.dataset import Dataset +from hub.core.compression import compress_multiple +from hub.tests.common import get_dummy_data_path +from hub.util.exceptions import CorruptedSampleError + +import numpy as np + + +def test_point_cloud(local_ds, point_cloud_paths): + for i, (compression, path) in enumerate(point_cloud_paths.items()): + if compression == "las": + tensor = local_ds.create_tensor( + f"point_cloud_{i}", htype="point_cloud", sample_compression=compression + ) + sample = hub.read(path) + + if "point_cloud" in path: # check shape only for internal test point_clouds + assert sample.shape[0] == 20153 + + assert len(sample.meta) == 6 + assert len(sample.meta["dimension_names"]) == 18 + assert len(sample.meta["las_header"]) == 19 + + assert sample.meta["las_header"]["DEFAULT_VERSION"] == { + "major": 1, + "minor": 2, + } + assert sample.meta["las_header"]["creation_date"] == { + "year": 2022, + "month": 5, + "day": 24, + } + assert sample.meta["las_header"]["version"] == {"major": 1, "minor": 2} + assert ( + sample.meta["las_header"]["uuid"] + == "00000000-0000-0000-0000-000000000000" + ) + + tensor.append(sample) + tensor.append(sample) + tensor.append(sample) + assert tensor.shape == (3, 20153, 18) + + shape_tester(local_ds, path, sample, tensor, feature_size=18) + + with pytest.raises(NotImplementedError): + arrays = np.zeros((5, 1000, 3)) + compress_multiple(arrays, compression) + + local_ds.create_tensor( + "point_cloud_without_sample_compression", + htype="point_cloud", + sample_compression=None, + ) + local_ds.point_cloud_without_sample_compression.append( + np.zeros((1000, 3), dtype=np.float32) + ) + np.testing.assert_array_equal( + local_ds.point_cloud_without_sample_compression[0].numpy(), + np.zeros((1000, 3), dtype=np.float32), + ) + local_ds.point_cloud_without_sample_compression.data() + assert len(local_ds.point_cloud_without_sample_compression.data()) == 0 + + local_ds.point_cloud_without_sample_compression.append(hub.read(path)) + assert local_ds.point_cloud_without_sample_compression[1].numpy().shape == ( + 20153, + 3, + ) + assert len(local_ds.point_cloud_without_sample_compression.numpy(aslist=True)) == 2 + assert len(local_ds.point_cloud_without_sample_compression.data(aslist=True)) == 2 + local_ds.create_tensor( + "point_cloud_with_sample_compression", + htype="point_cloud", + sample_compression="las", + ) + with pytest.raises(NotImplementedError): + local_ds.point_cloud_with_sample_compression.append( + np.zeros((1000, 3), dtype=np.float32) + ) + + with pytest.raises(CorruptedSampleError): + local_ds.point_cloud_with_sample_compression.append( + hub.read(get_dummy_data_path("point_cloud/corrupted_point_cloud.las")) + ) + + local_ds.point_cloud_with_sample_compression.append(hub.read(path, verify=True)) + assert local_ds.point_cloud_with_sample_compression.shape == (1, 20153, 18) + + local_ds.create_tensor( + "point_cloud_data_method_type_tester", + htype="point_cloud", + sample_compression="las", + ) + local_ds.point_cloud_data_method_type_tester.append(sample) + assert isinstance(local_ds.point_cloud_data_method_type_tester.data(), dict) + + local_ds.point_cloud_data_method_type_tester.append(sample) + assert isinstance( + local_ds.point_cloud_data_method_type_tester.data(aslist=True), list + ) + + +def shape_tester(local_ds, path, sample, tensor, feature_size): + with local_ds: + for _ in range(5): + tensor.append(hub.read(path)) # type: ignore + tensor.extend([hub.read(path) for _ in range(5)]) # type: ignore + + for i in range(10): + assert tensor[i].numpy().shape[0] == sample.shape[0] # type: ignore + assert len(tensor[i].data()) == feature_size + + +def test_point_cloud_slicing(local_ds: Dataset, point_cloud_paths): + for compression, path in point_cloud_paths.items(): + if compression == "las": + dummy = np.zeros((20153, 3)) + local_ds.create_tensor( + "point_cloud", htype="point_cloud", sample_compression=compression + ) + local_ds.point_cloud.append(hub.read(path)) + assert local_ds.point_cloud[0][0:5].numpy().shape == dummy[0:5].shape + assert local_ds.point_cloud[0][100:120].numpy().shape == dummy[100:120].shape + assert local_ds.point_cloud[0][120].numpy().shape == dummy[120].shape + assert local_ds.point_cloud[0][-1].numpy().shape == dummy[-1].shape + assert local_ds.point_cloud[0][10:5:-2].numpy().shape == dummy[10:5:-2].shape + assert ( + local_ds.point_cloud[0][-3:-10:-1].numpy().shape == dummy[-3:-10:-1].shape + ) + assert ( + local_ds.point_cloud[0][-25:100:-2].numpy().shape == dummy[-25:100:-2].shape + ) + assert local_ds.point_cloud[0][::-1].numpy().shape == dummy[::-1].shape + assert local_ds.point_cloud[0][:5:-1].numpy().shape == dummy[:5:-1].shape + assert local_ds.point_cloud[0][-1].numpy().shape == dummy[-1].shape + return diff --git a/hub/compression.py b/hub/compression.py index 0689a47d96..b7c2f7e724 100644 --- a/hub/compression.py +++ b/hub/compression.py @@ -80,15 +80,15 @@ AUDIO_COMPRESSIONS = ["mp3", "flac", "wav"] +POINT_CLOUD_COMPRESSIONS = ["las"] + # Just constants BYTE_COMPRESSION = "byte" IMAGE_COMPRESSION = "image" VIDEO_COMPRESSION = "video" AUDIO_COMPRESSION = "audio" - - -COMPRESSION_TYPES = [BYTE_COMPRESSION, IMAGE_COMPRESSION, AUDIO_COMPRESSION] +POINT_CLOUD_COMPRESSION = "point_cloud" COMPRESSION_TYPES = [ @@ -96,6 +96,7 @@ IMAGE_COMPRESSION, AUDIO_COMPRESSION, VIDEO_COMPRESSION, + POINT_CLOUD_COMPRESSION, ] # Pillow plugins for some formats might not be installed: @@ -113,6 +114,7 @@ *IMAGE_COMPRESSIONS, *AUDIO_COMPRESSIONS, *VIDEO_COMPRESSIONS, + *POINT_CLOUD_COMPRESSIONS, ] SUPPORTED_COMPRESSIONS = list(sorted(set(SUPPORTED_COMPRESSIONS))) # type: ignore SUPPORTED_COMPRESSIONS.append(None) # type: ignore @@ -133,6 +135,8 @@ _compression_types[c] = VIDEO_COMPRESSION for c in AUDIO_COMPRESSIONS: _compression_types[c] = AUDIO_COMPRESSION +for c in POINT_CLOUD_COMPRESSIONS: + _compression_types[c] = POINT_CLOUD_COMPRESSION def get_compression_type(c): diff --git a/hub/core/chunk_engine.py b/hub/core/chunk_engine.py index 54296361c3..9702b839bb 100644 --- a/hub/core/chunk_engine.py +++ b/hub/core/chunk_engine.py @@ -1479,7 +1479,8 @@ def get_basic_sample(self, global_sample_index, index, fetch_chunks=False): chunk_id, partial_chunk_bytes=worst_case_header_size ) return chunk.read_sample( - local_sample_index, cast=self.tensor_meta.htype != "dicom" + local_sample_index, + cast=self.tensor_meta.htype != "dicom", )[tuple(entry.value for entry in index.values[1:])] def get_non_tiled_sample(self, global_sample_index, index, fetch_chunks=False): diff --git a/hub/core/compression.py b/hub/core/compression.py index 24477ad9d6..9bdbb95da3 100644 --- a/hub/core/compression.py +++ b/hub/core/compression.py @@ -1,3 +1,4 @@ +import io from logging import warning import hub from hub.util.exceptions import ( @@ -6,18 +7,20 @@ UnsupportedCompressionError, CorruptedSampleError, ) +from hub.util.point_cloud import LAS_HEADER_FILED_NAME_TO_PARSER from hub.compression import ( get_compression_type, BYTE_COMPRESSION, - IMAGE_COMPRESSION, VIDEO_COMPRESSION, AUDIO_COMPRESSION, + POINT_CLOUD_COMPRESSION, ) from typing import Union, Tuple, Sequence, List, Optional, BinaryIO import numpy as np from pathlib import Path from PIL import Image, UnidentifiedImageError # type: ignore from io import BytesIO + import mmap import struct import sys @@ -40,7 +43,6 @@ except ImportError: _LZ4_INSTALLED = False - if sys.byteorder == "little": _NATIVE_INT32 = " bytes: raise NotImplementedError( "In order to store video data, you should use `hub.read(path_to_file)`. Compressing raw data is not yet supported." ) - + elif compr_type == POINT_CLOUD_COMPRESSION: + raise NotImplementedError( + "In order to store point cloud data, you should use `hub.read(path_to_file)`. Compressing raw data is not yet supported." + ) if compression == "apng": return _compress_apng(array) try: @@ -281,6 +286,8 @@ def decompress_array( return _decompress_audio(buffer) elif compr_type == VIDEO_COMPRESSION: return _decompress_video(buffer, start_idx, end_idx, step, reverse) # type: ignore + elif compr_type == POINT_CLOUD_COMPRESSION: + return _decompress_full_point_cloud(buffer) if compression == "apng": return _decompress_apng(buffer) # type: ignore @@ -352,6 +359,10 @@ def compress_multiple( raise NotImplementedError("compress_multiple does not support audio samples.") elif compr_type == VIDEO_COMPRESSION: raise NotImplementedError("compress_multiple does not support video samples.") + elif compr_type == POINT_CLOUD_COMPRESSION: + raise NotImplementedError( + "compress_multiple does not support point cloud samples." + ) elif compression == "apng": raise NotImplementedError("compress_multiple does not support apng samples.") canvas = np.zeros(_get_bounding_shape([arr.shape for arr in arrays]), dtype=dtype) @@ -424,6 +435,8 @@ def verify_compressed_file( return _read_video_shape(file), "|u1" # type: ignore elif compression == "dcm": return _read_dicom_shape_and_dtype(file) + elif compression == "las": + return _read_point_cloud_shape_and_dtype(file) else: return _fast_decompress(file) except Exception as e: @@ -438,7 +451,16 @@ def verify_compressed_file( def get_compression(header=None, path=None): if path: # These formats are recognized by file extension for now - file_formats = [".mp3", ".flac", ".wav", ".mp4", ".mkv", ".avi", ".dcm"] + file_formats = [ + ".mp3", + ".flac", + ".wav", + ".mp4", + ".mkv", + ".avi", + ".dcm", + ".las", + ] path = str(path).lower() for fmt in file_formats: if path.endswith(fmt): @@ -623,6 +645,11 @@ def read_meta_from_compressed_file( shape, typestr = _read_video_shape(file), "|u1" # type: ignore except Exception as e: raise CorruptedSampleError(compression) + elif compression == "las": + try: + shape, typestr = _read_point_cloud_shape_and_dtype(file) + except Exception as e: + raise CorruptedSampleError(compression) from e else: img = Image.open(f) if isfile else Image.open(BytesIO(f)) # type: ignore shape, typestr = Image._conv_type_shape(img) @@ -1058,3 +1085,88 @@ def _decompress_audio( ) sample_count += frame.samples return audio + + +def _open_lidar_file(file): + try: + import laspy as lp # type: ignore + except: + raise ModuleNotFoundError("laspy not found. Install using `pip install laspy`") + return lp.read(file) + + +def _load_lidar_point_cloud_data(file): + point_cloud = _open_lidar_file(file) + dimension_names = list(point_cloud.point_format.dimension_names) + return point_cloud, dimension_names + + +def _open_point_cloud_data(file: Union[bytes, memoryview, str]): + if isinstance(file, str): + point_cloud, dimension_names = _load_lidar_point_cloud_data(file) + return point_cloud, dimension_names + + point_cloud, dimension_names = _load_lidar_point_cloud_data(BytesIO(file)) + return point_cloud, dimension_names + + +def _read_point_cloud_meta(file): + point_cloud, dimension_names = _open_point_cloud_data(file) + meta_data = { + "dimension_names": dimension_names, + } + if type(point_cloud) != np.ndarray: + meta_data.update( + { + "las_header": { + "DEFAULT_VERSION": LAS_HEADER_FILED_NAME_TO_PARSER[ + "DEFAULT_VERSION" + ](point_cloud), + "file_source_id": point_cloud.header.file_source_id, + "system_identifier": point_cloud.header.system_identifier, + "generating_software": point_cloud.header.generating_software, + "creation_date": LAS_HEADER_FILED_NAME_TO_PARSER["creation_date"]( + point_cloud + ), + "point_count": point_cloud.header.point_count, + "scales": point_cloud.header.scales.tolist(), + "offsets": point_cloud.header.offsets.tolist(), + "number_of_points_by_return": point_cloud.header.number_of_points_by_return.tolist(), + "start_of_waveform_data_packet_record": point_cloud.header.start_of_waveform_data_packet_record, + "start_of_first_evlr": point_cloud.header.start_of_first_evlr, + "number_of_evlrs": point_cloud.header.number_of_evlrs, + "version": LAS_HEADER_FILED_NAME_TO_PARSER["version"](point_cloud), + "maxs": point_cloud.header.maxs.tolist(), + "mins": point_cloud.header.mins.tolist(), + "major_version": point_cloud.header.major_version, + "minor_version": point_cloud.header.minor_version, + "global_encoding": LAS_HEADER_FILED_NAME_TO_PARSER[ + "global_encoding" + ](point_cloud), + "uuid": str(point_cloud.header.uuid), + }, + "vlrs": point_cloud.vlrs, + } + ) + return meta_data + + +def _read_point_cloud_shape_and_dtype(file): + point_cloud = _decompress_full_point_cloud(file) + shape = point_cloud.shape + return shape, point_cloud.dtype + + +def _decompress_full_point_cloud(file: Union[bytes, memoryview, str]): + decompressed_point_cloud, _ = _open_point_cloud_data(file) + meta = _read_point_cloud_meta(file) + + decompressed_point_cloud = np.concatenate( + [ + np.expand_dims(decompressed_point_cloud[dim_name], -1) + for dim_name in meta["dimension_names"] + ], + axis=1, + ) + decompressed_point_cloud = decompressed_point_cloud.astype(np.float32) + return decompressed_point_cloud diff --git a/hub/core/dataset/dataset.py b/hub/core/dataset/dataset.py index ef5192dadb..3e6e63fadb 100755 --- a/hub/core/dataset/dataset.py +++ b/hub/core/dataset/dataset.py @@ -403,6 +403,7 @@ def create_tensor( ds.create_tensor("images", htype="image", sample_compression="jpg") ds.create_tensor("videos", htype="video", sample_compression="mp4") ds.create_tensor("data") + ds.create_tensor("point_clouds", htype="point_cloud") # append data ds.images.append(np.ones((400, 400, 3), dtype='uint8')) @@ -552,7 +553,13 @@ def create_tensor( if info_kwargs: tensor.info.update(info_kwargs) self.storage.maybe_flush() - if create_sample_info_tensor and htype in ("image", "audio", "video", "dicom"): + if create_sample_info_tensor and htype in ( + "image", + "audio", + "video", + "dicom", + "point_cloud", + ): self._create_sample_info_tensor(name) if create_shape_tensor and htype not in ("text", "json"): self._create_sample_shape_tensor(name, htype=htype) diff --git a/hub/core/meta/tensor_meta.py b/hub/core/meta/tensor_meta.py index 1916849ce6..2eed9ce663 100644 --- a/hub/core/meta/tensor_meta.py +++ b/hub/core/meta/tensor_meta.py @@ -262,7 +262,7 @@ def _validate_htype_overwrites(htype: str, htype_overwrite: dict): raise TensorMetaMissingRequiredValue( htype, ["chunk_compression", "sample_compression"] # type: ignore ) - if htype in ("audio", "video"): + if htype in ("audio", "video", "point_cloud"): if cc not in (UNSPECIFIED, None): raise UnsupportedCompressionError("Chunk compression", htype=htype) elif sc == UNSPECIFIED: @@ -289,7 +289,10 @@ def _replace_unspecified_values(htype: str, htype_overwrite: dict): if v == UNSPECIFIED: htype_overwrite[k] = defaults[k] - if htype in ("json", "list", "text") and not htype_overwrite["dtype"]: + if ( + htype in ("json", "list", "text", "point_cloud_calibration_matrix") + and not htype_overwrite["dtype"] + ): htype_overwrite["dtype"] = HTYPE_CONFIGURATIONS[htype]["dtype"] @@ -335,7 +338,7 @@ def _format_values(htype: str, htype_overwrite: dict): dtype = htype_overwrite["dtype"] if dtype is not None: - if htype in ("json", "list"): + if htype in ("json", "list", "point_cloud_calibration_matrix"): if getattr(dtype, "__module__", None) == "typing": htype_overwrite["dtype"] = str(dtype) else: diff --git a/hub/core/sample.py b/hub/core/sample.py index 627e8bb0d4..cc6916d1ed 100644 --- a/hub/core/sample.py +++ b/hub/core/sample.py @@ -8,17 +8,14 @@ _open_video, _read_metadata_from_vstream, _read_audio_meta, + _read_point_cloud_meta, ) from hub.compression import ( get_compression_type, AUDIO_COMPRESSION, IMAGE_COMPRESSION, VIDEO_COMPRESSION, -) -from hub.compression import ( - get_compression_type, - AUDIO_COMPRESSION, - IMAGE_COMPRESSION, + POINT_CLOUD_COMPRESSION, ) from hub.util.exceptions import UnableToReadFromUrlError from hub.util.exif import getexif @@ -88,6 +85,7 @@ def __init__( self._typestr = None self._shape = shape or None self._dtype = dtype or None + self.path = None self.storage = storage self._buffer = None @@ -235,6 +233,13 @@ def _get_audio_meta(self) -> dict: info = _read_audio_meta(self.buffer) return info + def _get_point_cloud_meta(self) -> dict: + if self.path and get_path_type(self.path) == "local": + info = _read_point_cloud_meta(self.path) + else: + info = _read_point_cloud_meta(self.buffer) + return info + @property def is_lazy(self) -> bool: return self._array is None @@ -453,6 +458,8 @@ def meta(self) -> dict: meta.update(self._get_video_meta()) elif compression_type == AUDIO_COMPRESSION: meta.update(self._get_audio_meta()) + elif compression_type == POINT_CLOUD_COMPRESSION: + meta.update(self._get_point_cloud_meta()) meta["shape"] = self.shape meta["format"] = self.compression if self.path: diff --git a/hub/core/serialize.py b/hub/core/serialize.py index a9cd3f450e..ab994e2747 100644 --- a/hub/core/serialize.py +++ b/hub/core/serialize.py @@ -2,6 +2,7 @@ BYTE_COMPRESSION, VIDEO_COMPRESSION, AUDIO_COMPRESSION, + POINT_CLOUD_COMPRESSION, get_compression_type, ) from hub.core.tiling.sample_tiles import SampleTiles diff --git a/hub/core/tensor.py b/hub/core/tensor.py index 80445cda1d..ca196efd7b 100644 --- a/hub/core/tensor.py +++ b/hub/core/tensor.py @@ -57,6 +57,10 @@ from hub.compression import get_compression_type, VIDEO_COMPRESSION from hub.util.notebook import is_jupyter, video_html, is_colab +from hub.util.point_cloud import ( + POINT_CLOUD_FIELD_NAME_TO_TYPESTR, + cast_point_cloud_array_to_proper_dtype, +) import warnings import webbrowser @@ -669,6 +673,18 @@ def numpy( Returns: A numpy array containing the data represented by this tensor. """ + if self.htype == "point_cloud": + full_numpy_arr = self.chunk_engine.numpy( + self.index, + aslist=aslist, + fetch_chunks=fetch_chunks, + pad_tensor=self.pad_tensor, + ) + + if isinstance(full_numpy_arr, list): + return [arr[..., :3] for arr in full_numpy_arr] + return full_numpy_arr[..., :3] + return self.chunk_engine.numpy( self.index, aslist=aslist, @@ -793,6 +809,45 @@ def data(self, aslist: bool = False) -> Any: "value": self.numpy(aslist=aslist), "sample_info": self.sample_info or {}, } + elif htype == "point_cloud": + full_arr = self.chunk_engine.numpy( + self.index, + aslist=aslist, + pad_tensor=self.pad_tensor, + ) + + if self.ndim == 2: + meta = {} # type: ignore + + if len(self.sample_info) == 0: + return meta + + for i, dimension_name in enumerate(self.sample_info["dimension_names"]): + typestr = POINT_CLOUD_FIELD_NAME_TO_TYPESTR[dimension_name] + meta[dimension_name] = full_arr[..., i].astype(np.dtype(typestr)) # type: ignore + return meta + + meta = [] # type: ignore + for sample_index in range(len(full_arr)): + meta_dict = {} # type: ignore + + if len(self.sample_info[sample_index]) == 0: + meta.append(meta_dict) # type: ignore + continue + + for dimension_index, dimension_name in enumerate( + self.sample_info[sample_index]["dimension_names"] + ): + dtype = POINT_CLOUD_FIELD_NAME_TO_TYPESTR[dimension_name] + meta_dict[dimension_name] = cast_point_cloud_array_to_proper_dtype( + full_arr, sample_index, dimension_index, dtype + ) + meta.append(meta_dict) # type: ignore + + if len(full_arr) == 1: + meta = meta[0] # type: ignore + return meta + else: return { "value": self.numpy(aslist=aslist), diff --git a/hub/core/tests/test_compression.py b/hub/core/tests/test_compression.py index 6baf34ea4d..c23188990b 100644 --- a/hub/core/tests/test_compression.py +++ b/hub/core/tests/test_compression.py @@ -22,6 +22,7 @@ AUDIO_COMPRESSIONS, VIDEO_COMPRESSIONS, SUPPORTED_COMPRESSIONS, + POINT_CLOUD_COMPRESSIONS, ) from hub.util.exceptions import CorruptedSampleError from PIL import Image # type: ignore @@ -161,6 +162,15 @@ def test_video(compression, video_paths): assert sample.compressed_bytes(compression) == f.read() +@pytest.mark.parametrize("compression", POINT_CLOUD_COMPRESSIONS) +def test_point_cloud(point_cloud_paths, compression): + path = point_cloud_paths[compression] + sample = hub.read(path) + arr = np.array(sample) + if compression == "las": + assert len(arr[0]) == 18 + + def test_apng(memory_ds): ds = memory_ds diff --git a/hub/htype.py b/hub/htype.py index 60154cf61e..9e45d8cb38 100644 --- a/hub/htype.py +++ b/hub/htype.py @@ -125,6 +125,7 @@ AUDIO_COMPRESSIONS, BYTE_COMPRESSIONS, COMPRESSION_ALIASES, + POINT_CLOUD_COMPRESSIONS, ) @@ -135,8 +136,10 @@ class htype: IMAGE_GRAY = "image.gray" CLASS_LABEL = "class_label" BBOX = "bbox" + BBOX_3D = "bbox.3d" VIDEO = "video" BINARY_MASK = "binary_mask" + INSTANCE_LABEL = "instance_label" SEGMENT_MASK = "segment_mask" KEYPOINTS_COCO = "keypoints_coco" POINT = "point" @@ -145,6 +148,8 @@ class htype: JSON = "json" LIST = "list" DICOM = "dicom" + POINT_CLOUD = "point_cloud" + POINT_CLOUD_CALIBRATION_MATRIX = "point_cloud.calibration_matrix" # used for requiring the user to specify a value for htype properties. notates that the htype property has no default. @@ -172,11 +177,13 @@ class htype: "_disable_temp_transform": False, }, htype.BBOX: {"dtype": "float32", "coords": {}, "_info": ["coords"]}, + htype.BBOX_3D: {"dtype": "float32", "coords": {}, "_info": ["coords"]}, htype.AUDIO: {"dtype": "float64"}, htype.VIDEO: {"dtype": "uint8"}, htype.BINARY_MASK: { "dtype": "bool" }, # TODO: pack numpy arrays to store bools as 1 bit instead of 1 byte + htype.INSTANCE_LABEL: {"dtype": "uint32"}, htype.SEGMENT_MASK: { "dtype": "uint32", "class_names": [], @@ -190,6 +197,8 @@ class htype: htype.LIST: {"dtype": "List"}, htype.TEXT: {"dtype": "str"}, htype.DICOM: {"sample_compression": "dcm"}, + htype.POINT_CLOUD: {"sample_compression": "las"}, + htype.POINT_CLOUD_CALIBRATION_MATRIX: {"dtype": "float32"}, } HTYPE_VERIFICATIONS: Dict[str, Dict] = { @@ -210,6 +219,7 @@ class htype: htype.TEXT: BYTE_COMPRESSIONS[:], htype.LIST: BYTE_COMPRESSIONS[:], htype.JSON: BYTE_COMPRESSIONS[:], + htype.POINT_CLOUD: POINT_CLOUD_COMPRESSIONS[:], htype.DICOM: ["dcm"], } diff --git a/hub/integrations/tf/hubtensorflowdataset.py b/hub/integrations/tf/hubtensorflowdataset.py index c43e6056dc..c30d4c8d83 100644 --- a/hub/integrations/tf/hubtensorflowdataset.py +++ b/hub/integrations/tf/hubtensorflowdataset.py @@ -3,7 +3,7 @@ """ import abc -import tensorflow as tf +import tensorflow as tf # type: ignore def hub_tf_adapter(_fn): diff --git a/hub/requirements/common.txt b/hub/requirements/common.txt index 8d79483cff..2bc50e7a08 100644 --- a/hub/requirements/common.txt +++ b/hub/requirements/common.txt @@ -16,3 +16,4 @@ pydicom IPython flask pyjwt +laspy diff --git a/hub/tests/dummy_data/point_cloud/corrupted_point_cloud.las 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