Source code for segments.dataset

from __future__ import annotations

import json
import logging
import os
from multiprocessing.pool import ThreadPool
from typing import Any, Dict, List, Optional, Tuple, Union, cast
from urllib.parse import urlparse

import numpy as np
import numpy.typing as npt
import requests
from PIL import Image
from pydantic import parse_obj_as
from segments.typing import LabelStatus, Release, SegmentsDatasetCategory
from segments.utils import (
from tqdm import tqdm

# Variables #
logger = logging.getLogger(__name__)

[docs]class SegmentsDataset: """A class that represents a Segments dataset. .. code-block:: python # pip install --upgrade segments-ai from segments import SegmentsClient, SegmentsDataset from segments.utils import export_dataset # Initialize a SegmentsDataset from the release file client = SegmentsClient('YOUR_API_KEY') release = client.get_release('jane/flowers', 'v1.0') # Alternatively: release = 'flowers-v1.0.json' dataset = SegmentsDataset(release, labelset='ground-truth', filter_by=['LABELED', 'REVIEWED']) # Export to COCO panoptic format export_format = 'coco-panoptic' export_dataset(dataset, export_format) Alternatively, you can use the initialized :class:`SegmentsDataset` to loop through the samples and labels, and visualize or process them in any way you please: .. code-block:: python import matplotlib.pyplot as plt from segments.utils import get_semantic_bitmap for sample in dataset: # Print the sample name and list of labeled objects print(sample['name']) print(sample['annotations']) # Show the image plt.imshow(sample['image']) # Show the instance segmentation label plt.imshow(sample['segmentation_bitmap']) # Show the semantic segmentation label semantic_bitmap = get_semantic_bitmap(sample['segmentation_bitmap'], sample['annotations']) plt.imshow(semantic_bitmap) Args: release_file: Path to a release file, or a release class resulting from :meth:`.get_release`. labelset: The labelset that should be loaded. Defaults to ``ground-truth``. filter_by: A list of label statuses to filter by. Defaults to :obj:`None`. filter_by_metadata: A dict of metadata key:value pairs to filter by. Filters are ANDed together. Defaults to :obj:`None`. segments_dir: The directory where the data will be downloaded to for caching. Set to :obj:`None` to disable caching. Defaults to ``segments``. preload: Whether the data should be pre-downloaded when the dataset is initialized. Ignored if ``segments_dir`` is :obj:`None`. Defaults to :obj:`True`. s3_client: A boto3 S3 client, e.g. ``s3_client = boto3.client("s3")``. Needs to be provided if your images are in a private S3 bucket. Defaults to :obj:`None`. Raises: :exc:`ValueError`: If the release task type is not one of: ``segmentation-bitmap``, ``segmentation-bitmap-highres``, ``image-vector-sequence``, ``bboxes``, ``vector``, ``pointcloud-cuboid``, ``pointcloud-cuboid-sequence``, ``pointcloud-segmentation``, ``pointcloud-segmentation-sequence``, ``text-named-entities``, or ``text-span-categorization``. :exc:`ValueError`: If there is no labelset with this name. """ # def __init__( self, release_file: Union[str, Release], labelset: str = "ground-truth", filter_by: Optional[Union[LabelStatus, List[LabelStatus]]] = None, filter_by_metadata: Optional[Dict[str, str]] = None, segments_dir: str = "segments", preload: bool = True, s3_client: Optional[Any] = None, ): self.labelset = labelset if isinstance(filter_by, list): filter_by = [f.upper() for f in filter_by] elif filter_by: filter_by = [filter_by.upper()] self.filter_by = filter_by # if self.filter_by: # self.filter_by = [s.lower() for s in self.filter_by] self.filter_by_metadata = filter_by_metadata self.segments_dir = segments_dir self.caching_enabled = segments_dir is not None self.preload = preload self.s3_client = s3_client # if urlparse(release_file).scheme in ('http', 'https'): # If it's a url if isinstance(release_file, str): # If it's a file path with open(release_file) as f: self.release = json.load(f) else: # If it's a release object release_file_url = release_file.attributes.url content = requests.get(cast(str, release_file_url)) # TODO Fix in backend. self.release = json.loads(content.content) self.release_file = release_file self.dataset_identifier = "{}_{}".format( self.release["dataset"]["owner"], self.release["dataset"]["name"] ) self.image_dir = ( None if segments_dir is None else os.path.join( segments_dir, self.dataset_identifier, self.release["name"] ) ) # First some checks if self.labelset not in [ labelset["name"] for labelset in self.release["dataset"]["labelsets"] ]: raise ValueError(f"There is no labelset with name '{self.labelset}'.") self.task_type = self.release["dataset"]["task_type"] if self.task_type not in [ "segmentation-bitmap", "segmentation-bitmap-highres", "vector", "bboxes", "keypoints", "image-vector-sequence", "pointcloud-cuboid", "pointcloud-segmentation", ]: raise ValueError( "You can only create a dataset for tasks of type 'segmentation-bitmap', 'segmentation-bitmap-highres', 'vector', 'bboxes', 'keypoints', 'image-vector-sequence', 'pointcloud-cuboid', 'pointcloud-segmentation' for now." ) self.load_dataset() def load_dataset(self) -> None: print("Initializing dataset...") # Setup cache if ( self.caching_enabled and self.image_dir and not os.path.exists(self.image_dir) ): os.makedirs(self.image_dir) # Load and filter the samples samples = self.release["dataset"]["samples"] if self.filter_by: filtered_samples = [] for sample in samples: if sample["labels"][self.labelset]: label_status = sample["labels"][self.labelset]["label_status"] else: label_status = "UNLABELED" if self.filter_by and label_status in self.filter_by: filtered_samples.append(sample) samples = filtered_samples if self.filter_by_metadata: filtered_samples = [] for sample in samples: # if self.filter_by_metadata.items() <= sample["metadata"].items(): filtered_samples.append(sample) samples = filtered_samples self.samples = samples # # Preload all samples (sequentially) # for i in tqdm(range(self.__len__())): # item = self.__getitem__(i) # To avoid memory overflow or "Too many open files" error when using tqdm in combination with multiprocessing. def _load_image(i: int) -> int: self.__getitem__(i) return i # Preload all samples (in parallel) # # # num_samples = self.__len__() if ( self.caching_enabled and self.preload and self.task_type not in ["pointcloud-segmentation", "pointcloud-cuboid"] ): print("Preloading all samples. This may take a while...") with ThreadPool(16) as pool: # list(tqdm(pool.imap_unordered(self.__getitem__, range(num_samples)), total=num_samples)) list( tqdm( pool.imap_unordered(_load_image, range(num_samples)), total=num_samples, ) ) print(f"Initialized dataset with {num_samples} images.") def _load_image_from_cache( self, sample: Dict[str, Any] ) -> Tuple[Optional[Image.Image], str]: sample_name = os.path.splitext(sample["name"])[0] image_url = sample["attributes"]["image"]["url"] image_url_parsed = urlparse(image_url) url_extension = os.path.splitext(image_url_parsed.path)[1] # image_filename_rel = '{}{}'.format(sample['uuid'], url_extension) image_filename_rel = f"{sample_name}{url_extension}" if image_url_parsed.scheme == "s3": image = None else: if self.caching_enabled: image_filename = os.path.join(self.image_dir, image_filename_rel) if not os.path.exists(image_filename): image = load_image_from_url( image_url, image_filename, self.s3_client ) else: image = else: image = load_image_from_url(image_url, self.s3_client) image = handle_exif_rotation(image) return image, image_filename_rel def _load_segmentation_bitmap_from_cache( self, sample: Dict[str, Any], labelset: str ) -> Union[npt.NDArray[np.uint32], Image.Image]: sample_name = os.path.splitext(sample["name"])[0] label = sample["labels"][labelset] segmentation_bitmap_url = label["attributes"]["segmentation_bitmap"]["url"] url_extension = os.path.splitext(urlparse(segmentation_bitmap_url).path)[1] if self.caching_enabled: # segmentation_bitmap_filename = os.path.join(self.image_dir, '{}{}'.format(label['uuid'], url_extension)) segmentation_bitmap_filename = os.path.join( self.image_dir, f"{sample_name}_label_{labelset}{url_extension}", ) if not os.path.exists(segmentation_bitmap_filename): return load_label_bitmap_from_url( segmentation_bitmap_url, segmentation_bitmap_filename ) else: return else: return load_label_bitmap_from_url(segmentation_bitmap_url) @property def categories(self) -> List[SegmentsDatasetCategory]: return parse_obj_as( List[SegmentsDatasetCategory], self.release["dataset"]["task_attributes"]["categories"], ) # categories = {} # for category in self.release['dataset']['labelsets'][self.labelset]['attributes']['categories']: # categories[category['id']] = category['name'] # return categories def __len__(self) -> int: return len(self.samples) def __getitem__(self, index: int) -> Dict[str, Any]: sample: Dict[str, Any] = self.samples[index] if self.task_type in [ "pointcloud-segmentation", "pointcloud-cuboid", "image-vector-sequence", ]: return sample # Load the image try: image, image_filename = self._load_image_from_cache(sample) except TypeError: logger.error( f"Something went wrong loading sample {sample['name']}: {sample}" ) item = { "uuid": sample["uuid"], "name": sample["name"], "file_name": image_filename, "image": image, "metadata": sample["metadata"], } # Segmentation bitmap if ( self.task_type == "segmentation-bitmap" or self.task_type == "segmentation-bitmap-highres" ): # Load the label try: label = sample["labels"][self.labelset] segmentation_bitmap = self._load_segmentation_bitmap_from_cache( sample, self.labelset ) attributes = label["attributes"] annotations = attributes["annotations"] item.update( { "segmentation_bitmap": segmentation_bitmap, "annotations": annotations, "attributes": attributes, } ) except TypeError: item.update( { "segmentation_bitmap": None, "annotations": None, "attributes": None, } ) # Vector labels elif ( self.task_type == "vector" or self.task_type == "bboxes" or self.task_type == "keypoints" ): try: label = sample["labels"][self.labelset] attributes = label["attributes"] annotations = attributes["annotations"] item.update({"annotations": annotations, "attributes": attributes}) except (KeyError, TypeError): item.update({"annotations": None, "attributes": None}) else: raise ValueError("This task type is not yet supported.") # # transform # if self.transform: # item = self.transform(item) return item