Source code for supervisely.project.project

# coding: utf-8

from __future__ import annotations

import os
import random
import shutil
from collections import namedtuple
from enum import Enum
from typing import Callable, Dict, Generator, List, NamedTuple, Optional, Tuple, Union

import numpy as np
from tqdm import tqdm

import supervisely as sly
from supervisely._utils import abs_url, batched, is_development
from supervisely.annotation.annotation import ANN_EXT, Annotation, TagCollection
from supervisely.annotation.obj_class import ObjClass
from supervisely.annotation.obj_class_collection import ObjClassCollection
from supervisely.api.api import Api
from supervisely.api.image_api import ImageInfo
from supervisely.collection.key_indexed_collection import (
    KeyIndexedCollection,
    KeyObject,
)
from supervisely.geometry.bitmap import Bitmap
from supervisely.geometry.rectangle import Rectangle
from supervisely.imaging import image as sly_image
from supervisely.io.fs import (
    copy_file,
    dir_empty,
    dir_exists,
    ensure_base_path,
    file_exists,
    get_file_name_with_ext,
    get_subdirs,
    list_dir_recursively,
    list_files,
    list_files_recursively,
    mkdir,
    silent_remove,
)
from supervisely.io.fs_cache import FileCache
from supervisely.io.json import dump_json_file, load_json_file
from supervisely.project.project_meta import ProjectMeta
from supervisely.project.project_type import ProjectType
from supervisely.sly_logger import logger
from supervisely.task.progress import Progress, tqdm_sly


# @TODO: rename img_path to item_path (maybe convert namedtuple to class and create fields and props)
[docs]class ItemPaths(NamedTuple): #: :class:`str`: Full image file path of item img_path: str #: :class:`str`: Full annotation file path of item ann_path: str
[docs]class ItemInfo(NamedTuple): #: :class:`str`: Item's dataset name dataset_name: str #: :class:`str`: Item name name: str #: :class:`str`: Full image file path of item img_path: str #: :class:`str`: Full annotation file path of item ann_path: str
[docs]class OpenMode(Enum): """ Defines the mode of using the :class:`Project<Project>` and :class:`Dataset<Dataset>`. """ #: :class:`int`: READ open mode. #: Loads project from given project directory. Checks that item and annotation directories #: exist and dataset is not empty. Consistency checks. Checks that every image has #: an annotation and the correspondence is one to one. READ = 1 #: :class:`int`: CREATE open mode. #: Creates a leaf directory and empty meta.json file. Generates error if #: project directory already exists and is not empty. CREATE = 2
def _get_effective_ann_name(img_name, ann_names): new_format_name = img_name + ANN_EXT if new_format_name in ann_names: return new_format_name else: old_format_name = os.path.splitext(img_name)[0] + ANN_EXT return old_format_name if (old_format_name in ann_names) else None
[docs]class Dataset(KeyObject): """ Dataset is where your labeled and unlabeled images and other data files live. :class:`Dataset<Dataset>` object is immutable. :param directory: Path to dataset directory. :type directory: str :param mode: Determines working mode for the given dataset. :type mode: :class:`OpenMode<OpenMode>` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) """ #: :class:`str`: Items data directory name item_dir_name = "img" #: :class:`str`: Annotations directory name ann_dir_name = "ann" #: :class:`str`: Items info directory name item_info_dir_name = "img_info" #: :class:`str`: Segmentation masks directory name seg_dir_name = "seg" annotation_class = Annotation item_info_class = ImageInfo def __init__(self, directory: str, mode: OpenMode): if type(mode) is not OpenMode: raise TypeError( "Argument 'mode' has type {!r}. Correct type is OpenMode".format(type(mode)) ) self._directory = directory self._item_to_ann = {} # item file name -> annotation file name project_dir, ds_name = os.path.split(directory.rstrip("/")) self._project_dir = project_dir self._name = ds_name if mode is OpenMode.READ: self._read() else: self._create() @property def project_dir(self) -> str: """ Path to the project containing the dataset. :return: Path to the project. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds0" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.project_dir) # Output: "/home/admin/work/supervisely/projects/lemons_annotated" """ return self._project_dir @property def name(self) -> str: """ Dataset name. :return: Dataset Name. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.name) # Output: "ds1" """ return self._name def key(self): # TODO: add docstring return self.name @property def directory(self) -> str: """ Path to the dataset directory. :return: Path to the dataset directory. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.directory) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1' """ return self._directory @property def item_dir(self) -> str: """ Path to the dataset items directory. :return: Path to the dataset directory with items. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.item_dir) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/img' """ return os.path.join(self.directory, self.item_dir_name) @property def img_dir(self) -> str: """ Path to the dataset images directory. Property is alias of item_dir. :return: Path to the dataset directory with images. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.img_dir) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/img' """ return self.item_dir @property def ann_dir(self) -> str: """ Path to the dataset annotations directory. :return: Path to the dataset directory with annotations. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.ann_dir) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/ann' """ return os.path.join(self.directory, self.ann_dir_name) @property def img_info_dir(self): """ Path to the dataset image info directory. Property is alias of item_info_dir. :return: Path to the dataset directory with images info. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.img_info_dir) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/img_info' """ return self.item_info_dir @property def item_info_dir(self): """ Path to the dataset item info directory. :return: Path to the dataset directory with items info. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.item_info_dir) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/img_info' """ return os.path.join(self.directory, self.item_info_dir_name) @property def seg_dir(self): """ Path to the dataset segmentation masks directory. :return: Path to the dataset directory with masks. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.seg_dir) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/seg' """ return os.path.join(self.directory, self.seg_dir_name) @classmethod def _has_valid_ext(cls, path: str) -> bool: """ The function _has_valid_ext checks if a given file has a supported extension('.jpg', '.jpeg', '.mpo', '.bmp', '.png', '.webp') :param path: the path to the file :return: bool (True if a given file has a supported extension, False - in otherwise) """ return sly_image.has_valid_ext(path) def _read(self): """ Fills out the dictionary items: item file name -> annotation file name. Checks item and annotation directoris existing and dataset not empty. Consistency checks. Every item must have an annotation, and the correspondence must be one to one. If not - it generate exception error. """ if not dir_exists(self.item_dir): raise FileNotFoundError("Item directory not found: {!r}".format(self.item_dir)) if not dir_exists(self.ann_dir): raise FileNotFoundError("Annotation directory not found: {!r}".format(self.ann_dir)) raw_ann_paths = list_files(self.ann_dir, [ANN_EXT]) img_paths = list_files(self.item_dir, filter_fn=self._has_valid_ext) raw_ann_names = set(os.path.basename(path) for path in raw_ann_paths) img_names = [os.path.basename(path) for path in img_paths] if len(img_names) == 0 and len(raw_ann_names) == 0: raise RuntimeError("Dataset {!r} is empty".format(self.name)) if len(img_names) == 0: # items_names polyfield img_names = [os.path.splitext(ann_name)[0] for ann_name in raw_ann_names] # Consistency checks. Every image must have an annotation, and the correspondence must be one to one. effective_ann_names = set() for img_name in img_names: ann_name = _get_effective_ann_name(img_name, raw_ann_names) if ann_name is None: raise RuntimeError( "Item {!r} in dataset {!r} does not have a corresponding annotation file.".format( img_name, self.name ) ) if ann_name in effective_ann_names: raise RuntimeError( "Annotation file {!r} in dataset {!r} matches two different image files.".format( ann_name, self.name ) ) effective_ann_names.add(ann_name) self._item_to_ann[img_name] = ann_name def _create(self): """ Creates a leaf directory and all intermediate ones for items and annotations. """ mkdir(self.ann_dir) mkdir(self.item_dir)
[docs] def get_items_names(self) -> list: """ List of dataset item names. :return: List of item names. :rtype: :class:`list` [ :class:`str` ] :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.get_item_names()) # Output: ['IMG_0002.jpg', 'IMG_0005.jpg', 'IMG_0008.jpg', ...] """ return list(self._item_to_ann.keys())
[docs] def item_exists(self, item_name: str) -> bool: """ Checks if given item name belongs to the dataset. :param item_name: Item name. :type item_name: :class:`str` :return: True if item exist, otherwise False. :rtype: :class:`bool` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) ds.item_exists("IMG_0748") # False ds.item_exists("IMG_0748.jpeg") # True """ return item_name in self._item_to_ann
[docs] def get_item_path(self, item_name: str) -> str: """ Path to the given item. :param item_name: Item name. :type item_name: :class:`str` :return: Path to the given item. :rtype: :class:`str` :raises: :class:`RuntimeError` if item not found in the project :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.get_item_path("IMG_0748")) # Output: RuntimeError: Item IMG_0748 not found in the project. print(ds.get_item_path("IMG_0748.jpeg")) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/img/IMG_0748.jpeg' """ if not self.item_exists(item_name): raise RuntimeError("Item {} not found in the project.".format(item_name)) return os.path.join(self.item_dir, item_name)
[docs] def get_img_path(self, item_name: str) -> str: """ Path to the given image. Method is alias of get_item_path(item_name). :param item_name: Image name. :type item_name: :class:`str` :return: Path to the given image. :rtype: :class:`str` :raises: :class:`RuntimeError` if item not found in the project. :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.get_img_path("IMG_0748")) # Output: RuntimeError: Item IMG_0748 not found in the project. print(ds.get_img_path("IMG_0748.jpeg")) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/ann/IMG_0748.jpeg.json' """ return self.get_item_path(item_name)
[docs] def get_ann(self, item_name, project_meta: ProjectMeta) -> Annotation: """ Read annotation of item from json. :param item_name: Item name. :type item_name: :class:`str` :param project_meta: ProjectMeta object. :type project_meta: :class:`ProjectMeta<supervisely.project.project_meta.ProjectMeta>` :return: Annotation object. :rtype: :class:`Annotation<supervisely.annotation.annotation.Annotation>` :raises: :class:`RuntimeError` if item not found in the project :Usage example: .. code-block:: python import supervisely as sly project_path = "/home/admin/work/supervisely/projects/lemons_annotated" project = sly.Project(project_path, sly.OpenMode.READ) ds = project.datasets.get('ds1') annotation = ds.get_ann("IMG_0748", project.meta) # Output: RuntimeError: Item IMG_0748 not found in the project. annotation = ds.get_ann("IMG_0748.jpeg", project.meta) print(annotation.to_json()) # Output: { # "description": "", # "size": { # "height": 500, # "width": 700 # }, # "tags": [], # "objects": [], # "customBigData": {} # } """ ann_path = self.get_ann_path(item_name) return self.annotation_class.load_json_file(ann_path, project_meta)
[docs] def get_ann_path(self, item_name: str) -> str: """ Path to the given annotation json file. :param item_name: Item name. :type item_name: :class:`str` :return: Path to the given annotation json file. :rtype: :class:`str` :raises: :class:`RuntimeError` if item not found in the project :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.get_ann_path("IMG_0748")) # Output: RuntimeError: Item IMG_0748 not found in the project. print(ds.get_ann_path("IMG_0748.jpeg")) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/ann/IMG_0748.jpeg.json' """ ann_path = self._item_to_ann.get(item_name, None) if ann_path is None: raise RuntimeError("Item {} not found in the project.".format(item_name)) ann_path = ann_path.strip("/") return os.path.join(self.ann_dir, ann_path)
[docs] def get_img_info_path(self, img_name: str) -> str: """ Get path to the image info json file without checking if the file exists. Method is alias of get_item_info_path(item_name). :param item_name: Image name. :type item_name: :class:`str` :return: Path to the given image info json file. :rtype: :class:`str` :raises: :class:`RuntimeError` if image not found in the project. :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.get_img_info_path("IMG_0748")) # Output: RuntimeError: Item IMG_0748 not found in the project. print(ds.get_img_info_path("IMG_0748.jpeg")) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/img_info/IMG_0748.jpeg.json' """ return self.get_item_info_path(img_name)
[docs] def get_item_info_path(self, item_name: str) -> str: """ Get path to the item info json file without checking if the file exists. :param item_name: Item name. :type item_name: :class:`str` :return: Path to the given item info json file. :rtype: :class:`str` :raises: :class:`RuntimeError` if item not found in the project. :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.get_item_info_path("IMG_0748")) # Output: RuntimeError: Item IMG_0748 not found in the project. print(ds.get_item_info_path("IMG_0748.jpeg")) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/img_info/IMG_0748.jpeg.json' """ ann_path = self._item_to_ann.get(item_name, None) if ann_path is None: raise RuntimeError("Item {} not found in the project.".format(item_name)) return os.path.join(self.item_info_dir, ann_path)
[docs] def get_image_info(self, item_name: str) -> ImageInfo: """ Information for Item with given name. :param item_name: Item name. :type item_name: :class:`str` :return: ImageInfo object. :rtype: :class:`ImageInfo<supervisely.api.image_api.ImageInfo>` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds0" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.get_image_info("IMG_0748.jpeg")) # Output: # ImageInfo( # id=770915, # name='IMG_0748.jpeg', # link=None, # hash='ZdpMD+ZMJx0R8BgsCzJcqM7qP4M8f1AEtoYc87xZmyQ=', # mime='image/jpeg', # ext='jpeg', # size=148388, # width=1067, # height=800, # labels_count=4, # dataset_id=2532, # created_at='2021-03-02T10:04:33.973Z', # updated_at='2021-03-02T10:04:33.973Z', # meta={}, # path_original='/h5un6l2bnaz1vj8a9qgms4-public/images/original/7/h/Vo/...jpeg', # full_storage_url='http://app.supervise.ly/h5un6l2bnaz1vj8a9qgms4-public/images/original/7/h/Vo/...jpeg'), # tags=[] # ) """ return self.get_item_info(item_name)
[docs] def get_item_info(self, item_name: str) -> ImageInfo: """ Information for Item with given name. :param item_name: Item name. :type item_name: :class:`str` :return: ImageInfo object. :rtype: :class:`ImageInfo<supervisely.api.image_api.ImageInfo>` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds0" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.get_item_info("IMG_0748.jpeg")) # Output: # ImageInfo( # id=770915, # name='IMG_0748.jpeg', # link=None, # hash='ZdpMD+ZMJx0R8BgsCzJcqM7qP4M8f1AEtoYc87xZmyQ=', # mime='image/jpeg', # ext='jpeg', # size=148388, # width=1067, # height=800, # labels_count=4, # dataset_id=2532, # created_at='2021-03-02T10:04:33.973Z', # updated_at='2021-03-02T10:04:33.973Z', # meta={}, # path_original='/h5un6l2bnaz1vj8a9qgms4-public/images/original/7/h/Vo/...jpeg', # full_storage_url='http://app.supervise.ly/h5un6l2bnaz1vj8a9qgms4-public/images/original/7/h/Vo/...jpeg'), # tags=[] # ) """ item_info_path = self.get_item_info_path(item_name) item_info_dict = load_json_file(item_info_path) item_info_named_tuple = namedtuple(self.item_info_class.__name__, item_info_dict) return item_info_named_tuple(**item_info_dict)
[docs] def get_seg_path(self, item_name: str) -> str: """ Get path to the png segmentation mask file without checking if the file exists. Use :class:`Project.to_segmentation_task()<supervisely.project.project.Project.to_segmentation_task>` to create segmentation masks from annotations in your project. :param item_name: Item name. :type item_name: :class:`str` :return: Path to the given png mask file. :rtype: :class:`str` :raises: :class:`RuntimeError` if item not found in the project. :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.get_seg_path("IMG_0748")) # Output: RuntimeError: Item IMG_0748 not found in the project. print(ds.get_seg_path("IMG_0748.jpeg")) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/seg/IMG_0748.jpeg.png' """ ann_path = self._item_to_ann.get(item_name, None) if ann_path is None: raise RuntimeError("Item {} not found in the project.".format(item_name)) return os.path.join(self.seg_dir, f"{item_name}.png")
[docs] def add_item_file( self, item_name: str, item_path: str, ann: Optional[Union[Annotation, str]] = None, _validate_item: Optional[bool] = True, _use_hardlink: Optional[bool] = False, item_info: Optional[Union[ImageInfo, Dict, str]] = None, img_info: Optional[Union[ImageInfo, Dict, str]] = None, ) -> None: """ Adds given item file to dataset items directory, and adds given annotation to dataset annotations directory. if ann is None, creates empty annotation file. :param item_name: Item name. :type item_name: :class:`str` :param item_path: Path to the item. :type item_path: :class:`str` :param ann: Annotation object or path to annotation json file. :type ann: :class:`Annotation<supervisely.annotation.annotation.Annotation>` or :class:`str`, optional :param _validate_item: Checks input files format. :type _validate_item: :class:`bool`, optional :param _use_hardlink: If True creates a hardlink pointing to src named dst, otherwise don't. :type _use_hardlink: :class:`bool`, optional :param item_info: ImageInfo object or ImageInfo object converted to dict or path to item info json file for copying to dataset item info directory. :type item_info: :class:`ImageInfo<supervisely.api.image_api.ImageInfo>` or :class:`dict` or :class:`str`, optional :param img_info: Deprecated version of item_info parameter. Can be removed in future versions. :type img_info: :class:`ImageInfo<supervisely.api.image_api.ImageInfo>` or :class:`dict` or :class:`str`, optional :return: None :rtype: NoneType :raises: :class:`RuntimeError` if item_name already exists in dataset or item name has unsupported extension. :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) ann = "/home/admin/work/supervisely/projects/lemons_annotated/ds1/ann/IMG_8888.jpeg.json" ds.add_item_file("IMG_8888.jpeg", "/home/admin/work/supervisely/projects/lemons_annotated/ds1/img/IMG_8888.jpeg", ann=ann) print(ds.item_exists("IMG_8888.jpeg")) # Output: True """ # item_path is None when image is cached if item_path is None and ann is None and img_info is None: raise RuntimeError("No item_path or ann or img_info provided.") if item_info is not None and img_info is not None: raise RuntimeError( "At least one parameter of two (item_info and img_info) must be None." ) if img_info is not None: logger.warn( "img_info parameter of add_item_file() method is deprecated and can be removed in future versions. Use item_info parameter instead." ) item_info = img_info self._add_item_file( item_name, item_path, _validate_item=_validate_item, _use_hardlink=_use_hardlink, ) self._add_ann_by_type(item_name, ann) self._add_item_info(item_name, item_info)
[docs] def add_item_np( self, item_name: str, img: np.ndarray, ann: Optional[Union[Annotation, str]] = None, img_info: Optional[Union[ImageInfo, Dict, str]] = None, ) -> None: """ Adds given numpy matrix as an image to dataset items directory, and adds given annotation to dataset ann directory. if ann is None, creates empty annotation file. :param item_name: Item name. :type item_name: :class:`str` :param img: numpy Image matrix in RGB format. :type img: np.ndarray :param ann: Annotation object or path to annotation json file. :type ann: :class:`Annotation<supervisely.annotation.annotation.Annotation>` or :class:`str`, optional :param img_info: ImageInfo object or ImageInfo object converted to dict or path to item info json file for copying to dataset item info directory. :type img_info: :class:`ImageInfo<supervisely.api.image_api.ImageInfo>` or :class:`dict` or :class:`str`, optional :return: None :rtype: NoneType :raises: :class:`RuntimeError` if item_name already exists in dataset or item name has unsupported extension :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) img_path = "/home/admin/Pictures/Clouds.jpeg" img_np = sly.image.read(img_path) ds.add_item_np("IMG_050.jpeg", img_np) print(ds.item_exists("IMG_050.jpeg")) # Output: True """ if img is None and ann is None and img_info is None: raise RuntimeError("No img or ann or img_info provided.") self._add_img_np(item_name, img) self._add_ann_by_type(item_name, ann) self._add_item_info(item_name, img_info)
[docs] def add_item_raw_bytes( self, item_name: str, item_raw_bytes: bytes, ann: Optional[Union[Annotation, str]] = None, img_info: Optional[Union[ImageInfo, Dict, str]] = None, ) -> None: """ Adds given binary object as an image to dataset items directory, and adds given annotation to dataset ann directory. if ann is None, creates empty annotation file. :param item_name: Item name. :type item_name: :class:`str` :param item_raw_bytes: Binary object. :type item_raw_bytes: :class:`bytes` :param ann: Annotation object or path to annotation json file. :type ann: :class:`Annotation<supervisely.annotation.annotation.Annotation>` or :class:`str`, optional :param img_info: ImageInfo object or ImageInfo object converted to dict or path to item info json file for copying to dataset item info directory. :type img_info: :class:`ImageInfo<supervisely.api.image_api.ImageInfo>` or :class:`dict` or :class:`str`, optional :return: None :rtype: NoneType :raises: :class:`RuntimeError` if item_name already exists in dataset or item name has unsupported extension :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) img_path = "/home/admin/Pictures/Clouds.jpeg" img_np = sly.image.read(img_path) img_bytes = sly.image.write_bytes(img_np, "jpeg") ds.add_item_raw_bytes("IMG_050.jpeg", img_bytes) print(ds.item_exists("IMG_050.jpeg")) # Output: True """ if item_raw_bytes is None and ann is None and img_info is None: raise RuntimeError("No item_raw_bytes or ann or img_info provided.") self._add_item_raw_bytes(item_name, item_raw_bytes) self._add_ann_by_type(item_name, ann) self._add_item_info(item_name, img_info)
def get_classes_stats( self, project_meta: Optional[ProjectMeta] = None, return_objects_count: Optional[bool] = True, return_figures_count: Optional[bool] = True, return_items_count: Optional[bool] = True, ): if project_meta is None: project = Project(self.project_dir, OpenMode.READ) project_meta = project.meta class_items = {} class_objects = {} class_figures = {} for obj_class in project_meta.obj_classes: class_items[obj_class.name] = 0 class_objects[obj_class.name] = 0 class_figures[obj_class.name] = 0 for item_name in self: item_ann = self.get_ann(item_name, project_meta) item_class = {} for label in item_ann.labels: class_objects[label.obj_class.name] += 1 item_class[label.obj_class.name] = True for obj_class in project_meta.obj_classes: if obj_class.name in item_class.keys(): class_items[obj_class.name] += 1 result = {} if return_items_count: result["items_count"] = class_items if return_objects_count: result["objects_count"] = class_objects if return_figures_count: class_figures = class_objects.copy() # for Images project result["figures_count"] = class_figures return result def _get_empty_annotaion(self, item_name): """ Create empty annotation from given item. Generate exception error if item not found in project :param item_name: str :return: Annotation class object """ img_size = sly_image.read(self.get_img_path(item_name)).shape[:2] return self.annotation_class(img_size) def _add_ann_by_type(self, item_name, ann): """ Add given annotation to dataset annotations dir and to dictionary items: item file name -> annotation file name :param item_name: str :param ann: Annotation class object, str, dict, None (generate exception error if param type is another) """ # This is a new-style annotation name, so if there was no image with this name yet, there should not have been # an annotation either. self._item_to_ann[item_name] = item_name + ANN_EXT if ann is None: self.set_ann(item_name, self._get_empty_annotaion(item_name)) elif type(ann) is self.annotation_class: self.set_ann(item_name, ann) elif type(ann) is str: self.set_ann_file(item_name, ann) elif type(ann) is dict: self.set_ann_dict(item_name, ann) else: raise TypeError("Unsupported type {!r} for ann argument".format(type(ann))) def _add_item_info(self, item_name, item_info=None): if item_info is None: return dst_info_path = self.get_item_info_path(item_name) ensure_base_path(dst_info_path) if type(item_info) is dict: dump_json_file(item_info, dst_info_path, indent=4) elif type(item_info) is str and os.path.isfile(item_info): shutil.copy(item_info, dst_info_path) else: # item info named tuple (ImageInfo, VideoInfo, PointcloudInfo, ..) dump_json_file(item_info._asdict(), dst_info_path, indent=4) def _check_add_item_name(self, item_name): """ Generate exception error if item name already exists in dataset or has unsupported extension :param item_name: str """ if item_name in self._item_to_ann: raise RuntimeError( "Item {!r} already exists in dataset {!r}.".format(item_name, self.name) ) if not self._has_valid_ext(item_name): raise RuntimeError("Item name {!r} has unsupported extension.".format(item_name)) def _add_item_raw_bytes(self, item_name, item_raw_bytes): """ Write given binary object to dataset items directory, Generate exception error if item_name already exists in dataset or item name has unsupported extension. Make sure we actually received a valid image file, clean it up and fail if not so. :param item_name: str :param item_raw_bytes: binary object """ if item_raw_bytes is None: return self._check_add_item_name(item_name) item_name = item_name.strip("/") dst_img_path = os.path.join(self.item_dir, item_name) os.makedirs(os.path.dirname(dst_img_path), exist_ok=True) with open(dst_img_path, "wb") as fout: fout.write(item_raw_bytes) self._validate_added_item_or_die(dst_img_path)
[docs] def generate_item_path(self, item_name: str) -> str: """ Generates full path to the given item. :param item_name: Item name. :type item_name: :class:`str` :return: Full path to the given item :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(ds.generate_item_path("IMG_0748.jpeg")) # Output: '/home/admin/work/supervisely/projects/lemons_annotated/ds1/img/IMG_0748.jpeg' """ # TODO: what the difference between this and ds.get_item_path() ? return os.path.join(self.item_dir, item_name)
def _add_img_np(self, item_name, img): """ Write given image(RGB format(numpy matrix)) to dataset items directory. Generate exception error if item_name already exists in dataset or item name has unsupported extension :param item_name: str :param img: image in RGB format(numpy matrix) """ if img is None: return self._check_add_item_name(item_name) dst_img_path = os.path.join(self.item_dir, item_name) sly_image.write(dst_img_path, img) def _add_item_file(self, item_name, item_path, _validate_item=True, _use_hardlink=False): """ Add given item file to dataset items directory. Generate exception error if item_name already exists in dataset or item name has unsupported extension :param item_name: str :param item_path: str :param _validate_item: bool :param _use_hardlink: bool """ if item_path is None: return self._check_add_item_name(item_name) dst_item_path = os.path.join(self.item_dir, item_name) if ( item_path != dst_item_path and item_path is not None ): # used only for agent + api during download project + None to optimize internal usage hardlink_done = False if _use_hardlink: try: os.link(item_path, dst_item_path) hardlink_done = True except OSError: pass if not hardlink_done: copy_file(item_path, dst_item_path) if _validate_item: self._validate_added_item_or_die(item_path) def _validate_added_item_or_die(self, item_path): """ Make sure we actually received a valid image file, clean it up and fail if not so :param item_path: str """ # Make sure we actually received a valid image file, clean it up and fail if not so. try: sly_image.validate_format(item_path) except (sly_image.UnsupportedImageFormat, sly_image.ImageReadException): os.remove(item_path) raise
[docs] def set_ann(self, item_name: str, ann: Annotation) -> None: """ Replaces given annotation for given item name to dataset annotations directory in json format. :param item_name: Item name. :type item_name: :class:`str` :param ann: Annotation object. :type ann: :class:`Annotation<supervisely.annotation.annotation.Annotation>` :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) height, width = 500, 700 new_ann = sly.Annotation((height, width)) ds.set_ann("IMG_0748.jpeg", new_ann) """ if type(ann) is not self.annotation_class: raise TypeError( f"Type of 'ann' should be {self.annotation_class.__name__}, not a {type(ann).__name__}" ) dst_ann_path = self.get_ann_path(item_name) dump_json_file(ann.to_json(), dst_ann_path, indent=4)
[docs] def set_ann_file(self, item_name: str, ann_path: str) -> None: """ Replaces given annotation json file for given item name to dataset annotations directory in json format. :param item_name: Item Name. :type item_name: :class:`str` :param ann_path: Path to the :class:`Annotation<supervisely.annotation.annotation.Annotation>` json file. :type ann_path: :class:`str` :return: None :rtype: NoneType :raises: :class:`RuntimeError` if ann_path is not str :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) new_ann = "/home/admin/work/supervisely/projects/kiwi_annotated/ds1/ann/IMG_1812.jpeg.json" ds.set_ann_file("IMG_1812.jpeg", new_ann) """ if type(ann_path) is not str: raise TypeError("Annotation path should be a string, not a {}".format(type(ann_path))) dst_ann_path = self.get_ann_path(item_name) copy_file(ann_path, dst_ann_path)
[docs] def set_ann_dict(self, item_name: str, ann: Dict) -> None: """ Replaces given annotation json for given item name to dataset annotations directory in json format. :param item_name: Item name. :type item_name: :class:`str` :param ann: :class:`Annotation<supervisely.annotation.annotation.Annotation>` as a dict in json format. :type ann: :class:`dict` :return: None :rtype: NoneType :raises: :class:`RuntimeError` if ann_path is not str :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) new_ann_json = { "description":"", "size":{ "height":500, "width":700 }, "tags":[], "objects":[], "customBigData":{} } ds.set_ann_dict("IMG_8888.jpeg", new_ann_json) """ if type(ann) is not dict: raise TypeError("Ann should be a dict, not a {}".format(type(ann))) dst_ann_path = self.get_ann_path(item_name) os.makedirs(os.path.dirname(dst_ann_path), exist_ok=True) dump_json_file(ann, dst_ann_path, indent=4)
[docs] def get_item_paths(self, item_name: str) -> ItemPaths: """ Generates :class:`ItemPaths<ItemPaths>` object with paths to item and annotation directories for item with given name. :param item_name: Item name. :type item_name: :class:`str` :return: ItemPaths object :rtype: :class:`ItemPaths<ItemPaths>` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) img_path, ann_path = dataset.get_item_paths("IMG_0748.jpeg") print("img_path:", img_path) print("ann_path:", ann_path) # Output: # img_path: /home/admin/work/supervisely/projects/lemons_annotated/ds1/img/IMG_0748.jpeg # ann_path: /home/admin/work/supervisely/projects/lemons_annotated/ds1/ann/IMG_0748.jpeg.json """ return ItemPaths( img_path=self.get_item_path(item_name), ann_path=self.get_ann_path(item_name), )
def __len__(self): return len(self._item_to_ann) def __next__(self): for item_name in self._item_to_ann.keys(): yield item_name def __iter__(self): return next(self)
[docs] def items(self) -> Generator[Tuple[str]]: """ This method is used to iterate over dataset items, receiving item name, path to image and path to annotation json file. It is useful when you need to iterate over dataset items and get paths to images and annotations. :return: Generator object, that yields tuple of item name, path to image and path to annotation json file. :rtype: Generator[Tuple[str]] :Usage example: .. code-block:: python import supervisely as sly input = "path/to/local/directory" # Creating Supervisely project from local directory. project = sly.Project(input, sly.OpenMode.READ) for dataset in project.datasets: for item_name, image_path, ann_path in dataset.items(): print(f"Item '{item_name}': image='{image_path}', ann='{ann_path}'") """ for item_name in self._item_to_ann.keys(): img_path, ann_path = self.get_item_paths(item_name) yield item_name, img_path, ann_path
[docs] def delete_item(self, item_name: str) -> bool: """ Delete image, image info and annotation from :class:`Dataset<Dataset>`. :param item_name: Item name. :type item_name: :class:`str` :return: True if item was successfully deleted, False if item wasn't found in dataset. :rtype: :class:`bool` :Usage example: .. code-block:: python import supervisely as sly dataset_path = "/home/admin/work/supervisely/projects/lemons_annotated/ds1" ds = sly.Dataset(dataset_path, sly.OpenMode.READ) print(dataset.delete_item("IMG_0748")) # Output: False print(dataset.delete_item("IMG_0748.jpeg")) # Output: True """ if self.item_exists(item_name): data_path, ann_path = self.get_item_paths(item_name) img_info_path = self.get_img_info_path(item_name) silent_remove(data_path) silent_remove(ann_path) silent_remove(img_info_path) self._item_to_ann.pop(item_name) return True return False
[docs] @staticmethod def get_url(project_id: int, dataset_id: int) -> str: """ Get URL to dataset items list in Supervisely. :param project_id: :class:`Project<Project>` ID in Supervisely. :type project_id: :class:`int` :param dataset_id: :class:`Dataset<Dataset>` ID in Supervisely. :type dataset_id: :class:`int` :return: URL to dataset items list. :rtype: :class:`str` :Usage example: .. code-block:: python from supervisely import Dataset project_id = 10093 dataset_id = 45330 ds_items_link = Dataset.get_url(project_id, dataset_id) print(ds_items_link) # Output: "/projects/10093/datasets/45330/entities" """ res = f"/projects/{project_id}/datasets/{dataset_id}/entities" if is_development(): res = abs_url(res) return res
[docs]class Project: """ Project is a parent directory for dataset. Project object is immutable. :param directory: Path to project directory. :type directory: :class:`str` :param mode: Determines working mode for the given project. :type mode: :class:`OpenMode<OpenMode>` :Usage example: .. code-block:: python import supervisely as sly project_path = "/home/admin/work/supervisely/projects/lemons_annotated" project = sly.Project(project_path, sly.OpenMode.READ) """ dataset_class = Dataset
[docs] class DatasetDict(KeyIndexedCollection): """ :class:`Datasets<Dataset>` collection of :class:`Project<Project>`. """ item_type = Dataset
def __init__(self, directory: str, mode: OpenMode): if type(mode) is not OpenMode: raise TypeError( "Argument 'mode' has type {!r}. Correct type is OpenMode".format(type(mode)) ) parent_dir, name = Project._parse_path(directory) self._parent_dir = parent_dir self._name = name self._datasets = Project.DatasetDict() # ds_name -> dataset object self._meta = None if mode is OpenMode.READ: self._read() else: self._create()
[docs] @staticmethod def get_url(id: int) -> str: """ Get URL to datasets list in Supervisely. :param id: :class:`Project<Project>` ID in Supervisely. :type id: :class:`int` :return: URL to datasets list. :rtype: :class:`str` :Usage example: .. code-block:: python from supervisely import Project project_id = 10093 datasets_link = Project.get_url(project_id) print(datasets_link) # Output: "/projects/10093/datasets" """ res = f"/projects/{id}/datasets" if is_development(): res = abs_url(res) return res
@property def parent_dir(self) -> str: """ Project parent directory. :return: Path to project parent directory :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) print(project.parent_dir) # Output: '/home/admin/work/supervisely/projects' """ return self._parent_dir @property def name(self) -> str: """ Project name. :return: Project name. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) print(project.name) # Output: 'lemons_annotated' """ return self._name @property def type(self) -> str: """ Project type. :return: Project type. :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) print(project.type) # Output: 'images' """ return ProjectType.IMAGES.value @property def datasets(self) -> Project.DatasetDict: """ Project datasets. :return: Datasets :rtype: :class:`DatasetDict<supervisely.project.project.Project.DatasetDict>` :Usage example: .. code-block:: python import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) for dataset in project.datasets: print(dataset.name) # Output: ds1 # ds2 """ return self._datasets @property def meta(self) -> ProjectMeta: """ Project meta. :return: ProjectMeta object :rtype: :class:`ProjectMeta<supervisely.project.project_meta.ProjectMeta>` :Usage example: .. code-block:: python import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) print(project.meta) # Output: # +-------+--------+----------------+--------+ # | Name | Shape | Color | Hotkey | # +-------+--------+----------------+--------+ # | kiwi | Bitmap | [255, 0, 0] | | # | lemon | Bitmap | [81, 198, 170] | | # +-------+--------+----------------+--------+ # Tags # +------+------------+-----------------+--------+---------------+--------------------+ # | Name | Value type | Possible values | Hotkey | Applicable to | Applicable classes | # +------+------------+-----------------+--------+---------------+--------------------+ """ return self._meta @property def directory(self) -> str: """ Path to the project directory. :return: Path to the project directory :rtype: :class:`str` :Usage example: .. code-block:: python import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) print(project.directory) # Output: '/home/admin/work/supervisely/projects/lemons_annotated' """ return os.path.join(self.parent_dir, self.name) @property def total_items(self) -> int: """ Total number of items in project. :return: Total number of items in project :rtype: :class:`int` :Usage example: .. code-block:: python import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) print(project.total_items) # Output: 12 """ return sum(len(ds) for ds in self._datasets) def get_classes_stats( self, dataset_names: Optional[List[str]] = None, return_objects_count: Optional[bool] = True, return_figures_count: Optional[bool] = True, return_items_count: Optional[bool] = True, ): result = {} for ds in self.datasets: ds: Dataset if dataset_names is not None and ds.name not in dataset_names: continue ds_stats = ds.get_classes_stats( self.meta, return_objects_count, return_figures_count, return_items_count, ) for stat_name, classes_stats in ds_stats.items(): if stat_name not in result.keys(): result[stat_name] = {} for class_name, class_count in classes_stats.items(): if class_name not in result[stat_name].keys(): result[stat_name][class_name] = 0 result[stat_name][class_name] += class_count return result def _get_project_meta_path(self): """ :return: str (path to project meta file(meta.json)) """ return os.path.join(self.directory, "meta.json") def _read(self): meta_json = load_json_file(self._get_project_meta_path()) self._meta = ProjectMeta.from_json(meta_json) possible_datasets = get_subdirs(self.directory) for ds_name in possible_datasets: try: current_dataset = self.dataset_class( os.path.join(self.directory, ds_name), OpenMode.READ ) self._datasets = self._datasets.add(current_dataset) except Exception as ex: logger.warning(ex, exc_info=True) if self.total_items == 0: raise RuntimeError("Project is empty") def _create(self): if dir_exists(self.directory): if len(list_files_recursively(self.directory)) > 0: raise RuntimeError( "Cannot create new project {!r}. Directory {!r} already exists and is not empty".format( self.name, self.directory ) ) else: mkdir(self.directory) self.set_meta(ProjectMeta()) def validate(self): # @TODO: remove? pass
[docs] def set_meta(self, new_meta: ProjectMeta) -> None: """ Saves given :class:`meta<supervisely.project.project_meta.ProjectMeta>` to project directory in json format. :param new_meta: ProjectMeta object. :type new_meta: :class:`ProjectMeta<supervisely.project.project_meta.ProjectMeta>` :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly proj_lemons = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) proj_kiwi = sly.Project("/home/admin/work/supervisely/projects/kiwi_annotated", sly.OpenMode.READ) proj_lemons.set_meta(proj_kiwi.meta) print(project.proj_lemons) # Output: # +-------+--------+----------------+--------+ # | Name | Shape | Color | Hotkey | # +-------+--------+----------------+--------+ # | kiwi | Bitmap | [255, 0, 0] | | # +-------+--------+----------------+--------+ """ self._meta = new_meta dump_json_file(self.meta.to_json(), self._get_project_meta_path(), indent=4)
def __iter__(self): return next(self) def __next__(self): for dataset in self._datasets: yield dataset
[docs] def create_dataset(self, ds_name: str) -> Dataset: """ Creates a subdirectory with given name and all intermediate subdirectories for items and annotations in project directory, and also adds created dataset to the collection of all datasets in the project. :param ds_name: Dataset name. :type ds_name: :class:`str` :return: Dataset object :rtype: :class:`Dataset<Dataset>` :Usage example: .. code-block:: python import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) for dataset in project.datasets: print(dataset.name) # Output: ds1 # ds2 project.create_dataset("ds3") for dataset in project.datasets: print(dataset.name) # Output: ds1 # ds2 # ds3 """ ds = self.dataset_class(os.path.join(self.directory, ds_name), OpenMode.CREATE) self._datasets = self._datasets.add(ds) return ds
[docs] def copy_data( self, dst_directory: str, dst_name: Optional[str] = None, _validate_item: Optional[bool] = True, _use_hardlink: Optional[bool] = False, ) -> Project: """ Makes a copy of the :class:`Project<Project>`. :param dst_directory: Path to project parent directory. :type dst_directory: :class:`str` :param dst_name: Project name. :type dst_name: :class:`str`, optional :param _validate_item: Checks input files format. :type _validate_item: :class:`bool`, optional :param _use_hardlink: If True creates a hardlink pointing to src named dst, otherwise don't. :type _use_hardlink: :class:`bool`, optional :return: Project object. :rtype: :class:`Project<Project>` :Usage example: .. code-block:: python import supervisely as sly project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) print(project.total_items) # Output: 6 new_project = project.copy_data("/home/admin/work/supervisely/projects/", "lemons_copy") print(new_project.total_items) # Output: 6 """ dst_name = dst_name if dst_name is not None else self.name new_project = Project(os.path.join(dst_directory, dst_name), OpenMode.CREATE) new_project.set_meta(self.meta) for ds in self: new_ds = new_project.create_dataset(ds.name) for item_name in ds: item_path, ann_path = ds.get_item_paths(item_name) item_info_path = ds.get_item_info_path(item_name) item_path = item_path if os.path.isfile(item_path) else None ann_path = ann_path if os.path.isfile(ann_path) else None item_info_path = item_info_path if os.path.isfile(item_info_path) else None new_ds.add_item_file( item_name, item_path, ann_path, _validate_item=_validate_item, _use_hardlink=_use_hardlink, item_info=item_info_path, ) return new_project
@staticmethod def _parse_path(project_dir): """ Split given path to project on parent directory and directory where project is located :param project_dir: str :return: str, str """ # alternative implementation # temp_parent_dir = os.path.dirname(parent_dir) # temp_name = os.path.basename(parent_dir) parent_dir, pr_name = os.path.split(project_dir.rstrip("/")) if not pr_name: raise RuntimeError("Unable to determine project name.") return parent_dir, pr_name
[docs] @staticmethod def to_segmentation_task( src_project_dir: str, dst_project_dir: Optional[str] = None, inplace: Optional[bool] = False, target_classes: Optional[List[str]] = None, progress_cb: Optional[Union[tqdm, Callable]] = None, segmentation_type: Optional[str] = "semantic", ) -> None: """ Makes a copy of the :class:`Project<Project>`, converts annotations to :class:`Bitmaps<supervisely.geometry.bitmap.Bitmap>` and updates :class:`project meta<supervisely.project.project_meta.ProjectMeta>`. You will able to get item's segmentation masks location by :class:`dataset.get_seg_path(item_name)<supervisely.project.project.Dataset.get_seg_path>` method. :param src_project_dir: Path to source project directory. :type src_project_dir: :class:`str` :param dst_project_dir: Path to destination project directory. Must be None If inplace=True. :type dst_project_dir: :class:`str`, optional :param inplace: Modifies source project If True. Must be False If dst_project_dir is specified. :type inplace: :class:`bool`, optional :param target_classes: Classes list to include to destination project. If segmentation_type="semantic", background class "__bg__" will be added automatically. :type target_classes: :class:`list` [ :class:`str` ], optional :param progress_cb: Function for tracking download progress. :type progress_cb: tqdm or callable, optional :param segmentation_type: One of: {"semantic", "instance"}. If segmentation_type="semantic", background class "__bg__" will be added automatically and instances will be converted to non overlapping semantic segmentation mask. :type segmentation_type: :class:`str` :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly source_project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) seg_project_path = "/home/admin/work/supervisely/projects/lemons_segmentation" sly.Project.to_segmentation_task( src_project_dir=source_project.directory, dst_project_dir=seg_project_path ) seg_project = sly.Project(seg_project_path, sly.OpenMode.READ) """ _bg_class_name = "__bg__" _bg_obj_class = ObjClass(_bg_class_name, Bitmap, color=[0, 0, 0]) if dst_project_dir is None and inplace is False: raise ValueError( f"Original project in folder {src_project_dir} will be modified. Please, set 'inplace' " f"argument (inplace=True) directly" ) if inplace is True and dst_project_dir is not None: raise ValueError("dst_project_dir has to be None if inplace is True") if dst_project_dir is not None: if not dir_exists(dst_project_dir): mkdir(dst_project_dir) elif not dir_empty(dst_project_dir): raise ValueError(f"Destination directory {dst_project_dir} is not empty") src_project = Project(src_project_dir, OpenMode.READ) dst_meta = src_project.meta.clone() dst_meta, dst_mapping = dst_meta.to_segmentation_task(target_classes=target_classes) if segmentation_type == "semantic" and dst_meta.obj_classes.get(_bg_class_name) is None: dst_meta = dst_meta.add_obj_class(_bg_obj_class) if target_classes is not None: if segmentation_type == "semantic": if _bg_class_name not in target_classes: target_classes.append(_bg_class_name) # check that all target classes are in destination project meta for class_name in target_classes: if dst_meta.obj_classes.get(class_name) is None: raise KeyError(f"Class {class_name} not found in destination project meta") dst_meta = dst_meta.clone( obj_classes=ObjClassCollection( [dst_meta.obj_classes.get(class_name) for class_name in target_classes] ) ) if inplace is False: dst_project = Project(dst_project_dir, OpenMode.CREATE) dst_project.set_meta(dst_meta) for src_dataset in src_project.datasets: if inplace is False: dst_dataset = dst_project.create_dataset(src_dataset.name) for item_name in src_dataset: img_path, ann_path = src_dataset.get_item_paths(item_name) ann = Annotation.load_json_file(ann_path, src_project.meta) if segmentation_type == "semantic": seg_ann = ann.add_bg_object(_bg_obj_class) dst_mapping[_bg_obj_class] = _bg_obj_class seg_ann = seg_ann.to_nonoverlapping_masks(dst_mapping) # get_labels with bg seg_ann = seg_ann.to_segmentation_task() elif segmentation_type == "instance": seg_ann = ann.to_nonoverlapping_masks( dst_mapping ) # rendered instances and filter classes elif segmentation_type == "panoptic": raise NotImplementedError seg_path = None if inplace is False: dst_dataset.add_item_file(item_name, img_path, seg_ann) seg_path = dst_dataset.get_seg_path(item_name) else: # replace existing annotation src_dataset.set_ann(item_name, seg_ann) seg_path = src_dataset.get_seg_path(item_name) # save rendered segmentation # seg_ann.to_indexed_color_mask(seg_path, palette=palette["colors"], colors=len(palette["names"])) seg_ann.to_indexed_color_mask(seg_path) if progress_cb is not None: progress_cb(1) if inplace is True: src_project.set_meta(dst_meta)
[docs] @staticmethod def to_detection_task( src_project_dir: str, dst_project_dir: Optional[str] = None, inplace: Optional[bool] = False, progress_cb: Optional[Union[tqdm, Callable]] = None, ) -> None: """ Makes a copy of the :class:`Project<Project>`, converts annotations to :class:`Rectangles<supervisely.geometry.rectangle.Rectangle>` and updates :class:`project meta<supervisely.project.project_meta.ProjectMeta>`. :param src_project_dir: Path to source project directory. :type src_project_dir: :class:`str` :param dst_project_dir: Path to destination project directory. Must be None If inplace=True. :type dst_project_dir: :class:`str`, optional :param inplace: Modifies source project If True. Must be False If dst_project_dir is specified. :type inplace: :class:`bool`, optional :param progress_cb: Function for tracking download progress. :type progress_cb: tqdm or callable, optional :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly source_project = sly.Project("/home/admin/work/supervisely/projects/lemons_annotated", sly.OpenMode.READ) det_project_path = "/home/admin/work/supervisely/projects/lemons_detection" sly.Project.to_detection_task( src_project_dir=source_project.directory, dst_project_dir=det_project_path ) det_project = sly.Project(det_project_path, sly.OpenMode.READ) """ if dst_project_dir is None and inplace is False: raise ValueError( f"Original project in folder {src_project_dir} will be modified. Please, set 'inplace' " f"argument (inplace=True) directly" ) if inplace is True and dst_project_dir is not None: raise ValueError("dst_project_dir has to be None if inplace is True") if dst_project_dir is not None: if not dir_exists(dst_project_dir): mkdir(dst_project_dir) elif not dir_empty(dst_project_dir): raise ValueError(f"Destination directory {dst_project_dir} is not empty") src_project = Project(src_project_dir, OpenMode.READ) det_meta, det_mapping = src_project.meta.to_detection_task(convert_classes=True) if inplace is False: dst_project = Project(dst_project_dir, OpenMode.CREATE) dst_project.set_meta(det_meta) for src_dataset in src_project.datasets: if inplace is False: dst_dataset = dst_project.create_dataset(src_dataset.name) for item_name in src_dataset: img_path, ann_path = src_dataset.get_item_paths(item_name) ann = Annotation.load_json_file(ann_path, src_project.meta) det_ann = ann.to_detection_task(det_mapping) if inplace is False: dst_dataset.add_item_file(item_name, img_path, det_ann) else: # replace existing annotation src_dataset.set_ann(item_name, det_ann) if progress_cb is not None: progress_cb(1) if inplace is True: src_project.set_meta(det_meta)
[docs] @staticmethod def remove_classes_except( project_dir: str, classes_to_keep: Optional[List[str]] = None, inplace: Optional[bool] = False, ) -> None: """ Removes classes from Project with the exception of some classes. :param project_dir: Path to project directory. :type project_dir: :class:`str` :param classes_to_keep: Classes to keep in project. :type classes_to_keep: :class:`list` [ :class:`str` ], optional :param inplace: Checkbox that determines whether to change the source data in project or not. :type inplace: :class:`bool`, optional :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly project = sly.Project(project_path, sly.OpenMode.READ) project.remove_classes_except(project_path, inplace=True) """ if classes_to_keep is None: classes_to_keep = [] classes_to_remove = [] project = Project(project_dir, OpenMode.READ) for obj_class in project.meta.obj_classes: if obj_class.name not in classes_to_keep: classes_to_remove.append(obj_class.name) Project.remove_classes(project_dir, classes_to_remove, inplace)
[docs] @staticmethod def remove_classes( project_dir: str, classes_to_remove: Optional[List[str]] = None, inplace: Optional[bool] = False, ) -> None: """ Removes given classes from Project. :param project_dir: Path to project directory. :type project_dir: :class:`str` :param classes_to_remove: Classes to remove. :type classes_to_remove: :class:`list` [ :class:`str` ], optional :param inplace: Checkbox that determines whether to change the source data in project or not. :type inplace: :class:`bool`, optional :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly project = sly.Project(project_path, sly.OpenMode.READ) classes_to_remove = ['lemon'] project.remove_classes(project_path, classes_to_remove, inplace=True) """ if classes_to_remove is None: classes_to_remove = [] if inplace is False: raise ValueError( f"Original data will be modified. Please, set 'inplace' argument (inplace=True) directly" ) project = Project(project_dir, OpenMode.READ) for dataset in project.datasets: for item_name in dataset: img_path, ann_path = dataset.get_item_paths(item_name) ann = Annotation.load_json_file(ann_path, project.meta) new_labels = [] for label in ann.labels: if label.obj_class.name not in classes_to_remove: new_labels.append(label) new_ann = ann.clone(labels=new_labels) dataset.set_ann(item_name, new_ann) new_classes = [] for obj_class in project.meta.obj_classes: if obj_class.name not in classes_to_remove: new_classes.append(obj_class) new_meta = project.meta.clone(obj_classes=ObjClassCollection(new_classes)) project.set_meta(new_meta)
@staticmethod def _remove_items( project_dir, without_objects=False, without_tags=False, without_objects_and_tags=False, inplace=False, ): if inplace is False: raise ValueError( f"Original data will be modified. Please, set 'inplace' argument (inplace=True) directly" ) if without_objects is False and without_tags is False and without_objects_and_tags is False: raise ValueError( "One of the flags (without_objects / without_tags or without_objects_and_tags) have to be defined" ) project = Project(project_dir, OpenMode.READ) for dataset in project.datasets: items_to_delete = [] for item_name in dataset: img_path, ann_path = dataset.get_item_paths(item_name) ann = Annotation.load_json_file(ann_path, project.meta) if ( (without_objects and len(ann.labels) == 0) or (without_tags and len(ann.img_tags) == 0) or (without_objects_and_tags and ann.is_empty()) ): items_to_delete.append(item_name) for item_name in items_to_delete: dataset.delete_item(item_name)
[docs] @staticmethod def remove_items_without_objects(project_dir: str, inplace: Optional[bool] = False) -> None: """ Remove items(images and annotations) without objects from Project. :param project_dir: Path to project directory. :type project_dir: :class:`str` :param inplace: Checkbox that determines whether to change the source data in project or not. :type inplace: :class:`bool`, optional :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly sly.Project.remove_items_without_objects(project_path, inplace=True) """ Project._remove_items(project_dir=project_dir, without_objects=True, inplace=inplace)
[docs] @staticmethod def remove_items_without_tags(project_dir: str, inplace: Optional[bool] = False) -> None: """ Remove items(images and annotations) without tags from Project. :param project_dir: Path to project directory. :type project_dir: :class:`str` :param inplace: Checkbox that determines whether to change the source data in project or not. :type inplace: :class:`bool`, optional :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly sly.Project.remove_items_without_tags(project_path, inplace=True) """ Project._remove_items(project_dir=project_dir, without_tags=True, inplace=inplace)
[docs] @staticmethod def remove_items_without_both_objects_and_tags( project_dir: str, inplace: Optional[bool] = False ) -> None: """ Remove items(images and annotations) without objects and tags from Project. :param project_dir: Path to project directory. :type project_dir: :class:`str` :param inplace: Checkbox that determines whether to change the source data in project or not. :type inplace: :class:`bool`, optional :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly sly.Project.remove_items_without_both_objects_and_tags(project_path, inplace=True) """ Project._remove_items( project_dir=project_dir, without_objects_and_tags=True, inplace=inplace )
def get_item_paths(self, item_name) -> ItemPaths: # TODO: remove? raise NotImplementedError("Method available only for dataset")
[docs] @staticmethod def get_train_val_splits_by_count( project_dir: str, train_count: int, val_count: int ) -> Tuple[List[ItemInfo], List[ItemInfo]]: """ Get train and val items information from project by given train and val counts. :param project_dir: Path to project directory. :type project_dir: :class:`str` :param train_count: Number of train items. :type train_count: :class:`int` :param val_count: Number of val items. :type val_count: :class:`int` :raises: :class:`ValueError` if total_count != train_count + val_count :return: Tuple with lists of train items information and val items information :rtype: :class:`list` [ :class:`ItemInfo<ItemInfo>` ], :class:`list` [ :class:`ItemInfo<ItemInfo>` ] :Usage example: .. code-block:: python import supervisely as sly train_count = 4 val_count = 2 train_items, val_items = sly.Project.get_train_val_splits_by_count( project_path, train_count, val_count ) """ def _list_items_for_splits(project) -> List[ItemInfo]: items = [] for dataset in project.datasets: for item_name in dataset: items.append( ItemInfo( dataset_name=dataset.name, name=item_name, img_path=dataset.get_img_path(item_name), ann_path=dataset.get_ann_path(item_name), ) ) return items project = Project(project_dir, OpenMode.READ) if project.total_items != train_count + val_count: raise ValueError("total_count != train_count + val_count") all_items = _list_items_for_splits(project) random.shuffle(all_items) train_items = all_items[:train_count] val_items = all_items[train_count:] return train_items, val_items
[docs] @staticmethod def get_train_val_splits_by_tag( project_dir: str, train_tag_name: str, val_tag_name: str, untagged: Optional[str] = "ignore", ) -> Tuple[List[ItemInfo], List[ItemInfo]]: """ Get train and val items information from project by given train and val tags names. :param project_dir: Path to project directory. :type project_dir: :class:`str` :param train_tag_name: Train tag name. :type train_tag_name: :class:`str` :param val_tag_name: Val tag name. :type val_tag_name: :class:`str` :param untagged: Actions in case of absence of train_tag_name and val_tag_name in project. :type untagged: :class:`str`, optional :raises: :class:`ValueError` if untagged not in ["ignore", "train", "val"] :return: Tuple with lists of train items information and val items information :rtype: :class:`list` [ :class:`ItemInfo<ItemInfo>` ], :class:`list` [ :class:`ItemInfo<ItemInfo>` ] :Usage example: .. code-block:: python import supervisely as sly train_tag_name = 'train' val_tag_name = 'val' train_items, val_items = sly.Project.get_train_val_splits_by_tag( project_path, train_tag_name, val_tag_name ) """ untagged_actions = ["ignore", "train", "val"] if untagged not in untagged_actions: raise ValueError( f"Unknown untagged action {untagged}. Should be one of {untagged_actions}" ) project = Project(project_dir, OpenMode.READ) train_items = [] val_items = [] for dataset in project.datasets: for item_name in dataset: img_path, ann_path = dataset.get_item_paths(item_name) info = ItemInfo(dataset.name, item_name, img_path, ann_path) ann = Annotation.load_json_file(ann_path, project.meta) if ann.img_tags.get(train_tag_name) is not None: train_items.append(info) if ann.img_tags.get(val_tag_name) is not None: val_items.append(info) if ( ann.img_tags.get(train_tag_name) is None and ann.img_tags.get(val_tag_name) is None ): # untagged item if untagged == "ignore": continue elif untagged == "train": train_items.append(info) elif untagged == "val": val_items.append(info) return train_items, val_items
[docs] @staticmethod def get_train_val_splits_by_dataset( project_dir: str, train_datasets: List[str], val_datasets: List[str] ) -> Tuple[List[ItemInfo], List[ItemInfo]]: """ Get train and val items information from project by given train and val datasets names. :param project_dir: Path to project directory. :type project_dir: :class:`str` :param train_datasets: List of train datasets names. :type train_datasets: :class:`list` [ :class:`str` ] :param val_datasets: List of val datasets names. :type val_datasets: :class:`list` [ :class:`str` ] :raises: :class:`KeyError` if dataset name not found in project :return: Tuple with lists of train items information and val items information :rtype: :class:`list` [ :class:`ItemInfo<ItemInfo>` ], :class:`list` [ :class:`ItemInfo<ItemInfo>` ] :Usage example: .. code-block:: python import supervisely as sly train_datasets = ['ds1', 'ds2'] val_datasets = ['ds3', 'ds4'] train_items, val_items = sly.Project.get_train_val_splits_by_dataset( project_path, train_datasets, val_datasets ) """ def _add_items_to_list(project, datasets_names, items_list): for dataset_name in datasets_names: dataset = project.datasets.get(dataset_name) if dataset is None: raise KeyError(f"Dataset '{dataset_name}' not found") for item_name in dataset: img_path, ann_path = dataset.get_item_paths(item_name) info = ItemInfo(dataset.name, item_name, img_path, ann_path) items_list.append(info) project = Project(project_dir, OpenMode.READ) train_items = [] _add_items_to_list(project, train_datasets, train_items) val_items = [] _add_items_to_list(project, val_datasets, val_items) return train_items, val_items
[docs] @staticmethod def download( api: Api, project_id: int, dest_dir: str, dataset_ids: Optional[List[int]] = None, log_progress: Optional[bool] = False, batch_size: Optional[int] = 50, cache: Optional[FileCache] = None, progress_cb: Optional[Union[tqdm, Callable]] = None, only_image_tags: Optional[bool] = False, save_image_info: Optional[bool] = False, save_images: bool = True, save_image_meta: bool = False, ) -> None: """ Download project from Supervisely to the given directory. :param api: Supervisely API address and token. :type api: :class:`Api<supervisely.api.api.Api>` :param project_id: Supervisely downloadable project ID. :type project_id: :class:`int` :param dest_dir: Destination directory. :type dest_dir: :class:`str` :param dataset_ids: Dataset IDs. :type dataset_ids: :class:`list` [ :class:`int` ], optional :param log_progress: Show uploading progress bar. :type log_progress: :class:`bool`, optional :param batch_size: The number of images in the batch when they are loaded to a host. :type batch_size: :class:`int`, optional :param cache: FileCache object. :type cache: :class:`FileCache<supervisely.io.fs_cache.FileCache>`, optional :param progress_cb: Function for tracking download progress. :type progress_cb: tqdm or callable, optional :param only_image_tags: Download project with only images tags (without objects tags). :type only_image_tags: :class:`bool`, optional :param save_image_info: Download images infos or not. :type save_image_info: :class:`bool`, optional :param save_images: Download images or not. :type save_images: :class:`bool`, optional :param save_image_meta: Download images metadata in JSON format or not. :type save_image_meta: :class:`bool`, optional :return: None :rtype: NoneType :Usage example: .. code-block:: python import supervisely as sly # Local destination Project folder save_directory = "/home/admin/work/supervisely/source/project" # Obtain server address and your api_token from environment variables # Edit those values if you run this notebook on your own PC address = os.environ['SERVER_ADDRESS'] token = os.environ['API_TOKEN'] # Initialize API object api = sly.Api(address, token) project_id = 8888 # Download Project sly.Project.download(api, project_id, save_directory) project_fs = sly.Project(save_directory, sly.OpenMode.READ) """ download_project( api=api, project_id=project_id, dest_dir=dest_dir, dataset_ids=dataset_ids, log_progress=log_progress, batch_size=batch_size, cache=cache, progress_cb=progress_cb, only_image_tags=only_image_tags, save_image_info=save_image_info, save_images=save_images, save_image_meta=save_image_meta, )
[docs] @staticmethod def upload( dir: str, api: Api, workspace_id: int, project_name: Optional[str] = None, log_progress: Optional[bool] = True, progress_cb: Optional[Union[tqdm, Callable]] = None, ) -> Tuple[int, str]: """ Uploads project to Supervisely from the given directory. :param dir: Path to project directory. :type dir: :class:`str` :param api: Supervisely API address and token. :type api: :class:`Api<supervisely.api.api.Api>` :param workspace_id: Workspace ID, where project will be uploaded. :type workspace_id: :class:`int` :param project_name: Name of the project in Supervisely. Can be changed if project with the same name is already exists. :type project_name: :class:`str`, optional :param log_progress: Show uploading progress bar. :type log_progress: :class:`bool`, optional :param progress_cb: Function for tracking download progress. :type progress_cb: tqdm or callable, optional :return: Project ID and name. It is recommended to check that returned project name coincides with provided project name. :rtype: :class:`int`, :class:`str` :Usage example: .. code-block:: python import supervisely as sly # Local folder with Project project_directory = "/home/admin/work/supervisely/source/project" # Obtain server address and your api_token from environment variables # Edit those values if you run this notebook on your own PC address = os.environ['SERVER_ADDRESS'] token = os.environ['API_TOKEN'] # Initialize API object api = sly.Api(address, token) # Upload Project project_id, project_name = sly.Project.upload( project_directory, api, workspace_id=45, project_name="My Project" ) """ return upload_project( dir=dir, api=api, workspace_id=workspace_id, project_name=project_name, log_progress=log_progress, progress_cb=progress_cb, )
def read_single_project( dir: str, project_class: Optional[ Union[ Project, sly.VideoProject, sly.VolumeProject, sly.PointcloudProject, sly.PointcloudEpisodeProject, ] ] = Project, ) -> Union[ Project, sly.VideoProject, sly.VolumeProject, sly.PointcloudProject, sly.PointcloudEpisodeProject, ]: """ Read project from given directory or tries to find project directory in subdirectories. :param dir: Path to directory, which contains project folder or have project folder in any subdirectory. :type dir: :class:`str` :param project_class: Project object of arbitrary modality :type project_class: :class: `Project` or `VideoProject` or `VolumeProject` or `PointcloudProject` or `PointcloudEpisodeProject`, optional :return: Project class object of arbitrary modality :rtype: :class: `Project` or `VideoProject` or `VolumeProject` or `PointcloudProject` or `PointcloudEpisodeProject` :raises: RuntimeError if the given directory and it's subdirectories contains more than one valid project folder. :raises: FileNotFoundError if the given directory or any of it's subdirectories doesn't contain valid project folder. :Usage example: .. code-block:: python import supervisely as sly proj_dir = "/home/admin/work/supervisely/source/project" # Project directory or directory with project subdirectory. project = sly.read_single_project(proj_dir) """ project_dirs = [project_dir for project_dir in find_project_dirs(dir, project_class)] if len(project_dirs) > 1: raise RuntimeError( f"The given directory {dir} and it's subdirectories contains more than one valid project folder. " f"The following project folders were found: {project_dirs}. " "Ensure that you have only one project in the given directory and it's subdirectories." ) elif len(project_dirs) == 0: raise FileNotFoundError( f"The given directory {dir} or any of it's subdirectories doesn't contain valid project folder." ) return project_class(project_dirs[0], OpenMode.READ) def find_project_dirs(dir: str, project_class: Optional[Project] = Project) -> str: """Yields directories, that contain valid project folder in the given directory or in any of it's subdirectories. :param dir: Path to directory, which contains project folder or have project folder in any subdirectory. :type dir: str :param project_class: Project object :type project_class: :class:`Project<Project>` :return: Path to directory, that contain meta.json file. :rtype: str :Usage example: .. code-block:: python import supervisely as sly # Local folder (or any of it's subdirectories) which contains sly.Project files. input_directory = "/home/admin/work/supervisely/source" for project_dir in sly.find_project_dirs(input_directory): project_fs = sly.Project(meta_json_dir, sly.OpenMode.READ) # Do something with project_fs """ paths = list_dir_recursively(dir) for path in paths: if get_file_name_with_ext(path) == "meta.json": parent_dir = os.path.dirname(path) project_dir = os.path.join(dir, parent_dir) try: project_class(project_dir, OpenMode.READ) yield project_dir except Exception: pass def _download_project( api, project_id, dest_dir, dataset_ids=None, log_progress=False, batch_size=50, only_image_tags=False, save_image_info=False, save_images=True, progress_cb=None, save_image_meta=False, ): dataset_ids = set(dataset_ids) if (dataset_ids is not None) else None project_fs = Project(dest_dir, OpenMode.CREATE) meta = ProjectMeta.from_json(api.project.get_meta(project_id, with_settings=True)) project_fs.set_meta(meta) if only_image_tags is True: id_to_tagmeta = meta.tag_metas.get_id_mapping() for dataset_info in api.dataset.get_list(project_id): dataset_id = dataset_info.id if dataset_ids is not None and dataset_id not in dataset_ids: continue dataset_fs = project_fs.create_dataset(dataset_info.name) images = api.image.get_list(dataset_id) if save_image_meta: meta_dir = os.path.join(dest_dir, dataset_info.name, "meta") sly.fs.mkdir(meta_dir) for image_info in images: meta_paths = os.path.join(meta_dir, image_info.name + ".json") sly.json.dump_json_file(image_info.meta, meta_paths) ds_progress = None if log_progress: ds_progress = Progress( "Downloading dataset: {!r}".format(dataset_info.name), total_cnt=len(images), ) for batch in batched(images, batch_size): image_ids = [image_info.id for image_info in batch] image_names = [image_info.name for image_info in batch] # download images in numpy format if save_images: batch_imgs_bytes = api.image.download_bytes(dataset_id, image_ids) else: batch_imgs_bytes = [None] * len(image_ids) # download annotations in json format if only_image_tags is False: ann_infos = api.annotation.download_batch(dataset_id, image_ids) ann_jsons = [ann_info.annotation for ann_info in ann_infos] else: ann_jsons = [] for image_info in batch: tags = TagCollection.from_api_response( image_info.tags, meta.tag_metas, id_to_tagmeta ) tmp_ann = Annotation( img_size=(image_info.height, image_info.width), img_tags=tags ) ann_jsons.append(tmp_ann.to_json()) for img_info, name, img_bytes, ann in zip( batch, image_names, batch_imgs_bytes, ann_jsons ): dataset_fs.add_item_raw_bytes( item_name=name, item_raw_bytes=img_bytes if save_images is True else None, ann=ann, img_info=img_info if save_image_info is True else None, ) if log_progress: ds_progress.iters_done_report(len(batch)) if progress_cb is not None: progress_cb(len(batch)) def upload_project( dir: str, api: Api, workspace_id: int, project_name: Optional[str] = None, log_progress: Optional[bool] = True, progress_cb: Optional[Union[tqdm, Callable]] = None, ) -> Tuple[int, str]: project_fs = read_single_project(dir) if project_name is None: project_name = project_fs.name if api.project.exists(workspace_id, project_name): project_name = api.project.get_free_name(workspace_id, project_name) project = api.project.create(workspace_id, project_name, change_name_if_conflict=True) api.project.update_meta(project.id, project_fs.meta.to_json()) if progress_cb is not None: log_progress = False image_id_dct, anns_paths_dct = {}, {} for ds_fs in project_fs.datasets: dataset = api.dataset.create(project.id, ds_fs.name) ds_fs: Dataset names, img_paths, img_infos, ann_paths = [], [], [], [] for item_name in ds_fs: img_path, ann_path = ds_fs.get_item_paths(item_name) img_info_path = ds_fs.get_img_info_path(item_name) names.append(item_name) img_paths.append(img_path) ann_paths.append(ann_path) if os.path.isfile(img_info_path): img_infos.append(ds_fs.get_image_info(item_name=item_name)) img_paths = list(filter(lambda x: os.path.isfile(x), img_paths)) ann_paths = list(filter(lambda x: os.path.isfile(x), ann_paths)) metas = [{} for _ in names] meta_dir = os.path.join(dir, ds_fs.name, "meta") if os.path.isdir(meta_dir): metas = [] for name in names: meta_path = os.path.join(meta_dir, name + ".json") if os.path.isfile(meta_path): metas.append(sly.json.load_json_file(meta_path)) else: metas.append({}) ds_progress = progress_cb if log_progress: ds_progress = tqdm_sly( desc="Uploading images to {!r}".format(dataset.name), total=len(names), ) if len(img_paths) != 0: uploaded_img_infos = api.image.upload_paths( dataset.id, names, img_paths, ds_progress, metas=metas ) elif len(img_paths) == 0 and len(img_infos) != 0: # uploading links and hashes (the code from api.image.upload_ids) img_metas = [{}] * len(names) links, links_names, links_order, links_metas = [], [], [], [] hashes, hashes_names, hashes_order, hashes_metas = [], [], [], [] dataset_id = dataset.id for idx, (name, info, meta) in enumerate(zip(names, img_infos, img_metas)): if info.link is not None: links.append(info.link) links_names.append(name) links_order.append(idx) links_metas.append(meta) else: hashes.append(info.hash) hashes_names.append(name) hashes_order.append(idx) hashes_metas.append(meta) result = [None] * len(names) if len(links) > 0: res_infos_links = api.image.upload_links( dataset_id, links_names, links, ds_progress, metas=links_metas, ) for info, pos in zip(res_infos_links, links_order): result[pos] = info if len(hashes) > 0: res_infos_hashes = api.image.upload_hashes( dataset_id, hashes_names, hashes, ds_progress, metas=hashes_metas, ) for info, pos in zip(res_infos_hashes, hashes_order): result[pos] = info uploaded_img_infos = result else: raise ValueError( "Cannot upload Project: img_paths is empty and img_infos_paths is empty" ) # image_id_dct[ds_fs.name] = image_ids = [img_info.id for img_info in uploaded_img_infos] # anns_paths_dct[ds_fs.name] = ann_paths anns_progress = None if log_progress or progress_cb is not None: anns_progress = tqdm_sly( desc="Uploading annotations to {!r}".format(dataset.name), total=len(image_ids), leave=False, ) api.annotation.upload_paths(image_ids, ann_paths, anns_progress) return project.id, project.name def download_project( api: Api, project_id: int, dest_dir: str, dataset_ids: Optional[List[int]] = None, log_progress: Optional[bool] = False, batch_size: Optional[int] = 50, cache: Optional[FileCache] = None, progress_cb: Optional[Union[tqdm, Callable]] = None, only_image_tags: Optional[bool] = False, save_image_info: Optional[bool] = False, save_images: bool = True, save_image_meta: bool = False, ) -> None: """ Download image project to the local directory. :param api: Supervisely API address and token. :type api: Api :param project_id: Project ID to download :type project_id: int :param dest_dir: Destination path to local directory. :type dest_dir: str :param dataset_ids: Specified list of Dataset IDs which will be downloaded. Datasets could be downloaded from different projects but with the same data type. :type dataset_ids: list(int), optional :param log_progress: Show downloading logs in the output. :type log_progress: bool, optional :param batch_size: Size of a downloading batch. :type batch_size: int, optional :param cache: Cache of downloading files. :type cache: FileCache, optional :param progress_cb: Function for tracking download progress. :type progress_cb: tqdm or callable, optional :param only_image_tags: Specify if downloading images only with image tags. Alternatively, full annotations will be downloaded. :type only_image_tags: bool, optional :param save_image_info: Include image info in the download. :type save_image_info, bool, optional :param save_images: Include images in the download. :type save_images, bool, optional :param save_image_meta: Include images metadata in JSON format in the download. :type save_imgge_meta: bool, optional :return: None. :rtype: NoneType :Usage example: .. code-block:: python import os from dotenv import load_dotenv from tqdm import tqdm import supervisely as sly # Load secrets and create API object from .env file (recommended) # Learn more here: https://developer.supervisely.com/getting-started/basics-of-authentication if sly.is_development(): load_dotenv(os.path.expanduser("~/supervisely.env")) api = sly.Api.from_env() # Pass values into the API constructor (optional, not recommended) # api = sly.Api(server_address="https://app.supervise.ly", token="4r47N...xaTatb") dest_dir = 'your/local/dest/dir' # Download image project project_id = 17732 project_info = api.project.get_info_by_id(project_id) num_images = project_info.items_count p = tqdm(desc="Downloading image project", total=num_images) sly.download( api, project_id, dest_dir, progress_cb=p, ) """ if cache is None: _download_project( api, project_id, dest_dir, dataset_ids, log_progress, batch_size, only_image_tags=only_image_tags, save_image_info=save_image_info, save_images=save_images, progress_cb=progress_cb, save_image_meta=save_image_meta, ) else: _download_project_optimized( api, project_id, dest_dir, dataset_ids, cache, progress_cb, only_image_tags=only_image_tags, save_image_info=save_image_info, save_images=save_images, ) def _download_project_optimized( api: Api, project_id, project_dir, datasets_whitelist=None, cache=None, progress_cb=None, only_image_tags=False, save_image_info=False, save_images=True, ): project_info = api.project.get_info_by_id(project_id) project_id = project_info.id logger.info(f"Annotations are not cached (always download latest version from server)") project_fs = Project(project_dir, OpenMode.CREATE) meta = ProjectMeta.from_json(api.project.get_meta(project_id, with_settings=True)) project_fs.set_meta(meta) for dataset_info in api.dataset.get_list(project_id): dataset_name = dataset_info.name dataset_id = dataset_info.id need_download = True if datasets_whitelist is not None and dataset_id not in datasets_whitelist: need_download = False if need_download is True: dataset = project_fs.create_dataset(dataset_name) _download_dataset( api, dataset, dataset_id, cache=cache, progress_cb=progress_cb, project_meta=meta, only_image_tags=only_image_tags, save_image_info=save_image_info, save_images=save_images, ) def _split_images_by_cache(images, cache): images_to_download = [] images_in_cache = [] images_cache_paths = [] for image in images: _, effective_ext = os.path.splitext(image.name) if len(effective_ext) == 0: # Fallback for the old format where we were cutting off extensions from image names. effective_ext = image.ext cache_path = cache.check_storage_object(image.hash, effective_ext) if cache_path is None: images_to_download.append(image) else: images_in_cache.append(image) images_cache_paths.append(cache_path) return images_to_download, images_in_cache, images_cache_paths def _maybe_append_image_extension(name, ext): name_split = os.path.splitext(name) if name_split[1] == "": normalized_ext = ("." + ext).replace("..", ".") result = name + normalized_ext sly_image.validate_ext(result) else: result = name return result def _download_dataset( api: Api, dataset, dataset_id, cache=None, progress_cb=None, project_meta: ProjectMeta = None, only_image_tags=False, save_image_info=False, save_images=True, ): images = api.image.get_list(dataset_id) images_to_download = images if only_image_tags is True: if project_meta is None: raise ValueError("Project Meta is not defined") id_to_tagmeta = project_meta.tag_metas.get_id_mapping() # copy images from cache to task folder and download corresponding annotations if cache: ( images_to_download, images_in_cache, images_cache_paths, ) = _split_images_by_cache(images, cache) if len(images_to_download) + len(images_in_cache) != len(images): raise RuntimeError("Error with images cache during download. Please contact support.") logger.info( f"Download dataset: {dataset.name}", extra={ "total": len(images), "in cache": len(images_in_cache), "to download": len(images_to_download), }, ) if len(images_in_cache) > 0: img_cache_ids = [img_info.id for img_info in images_in_cache] if only_image_tags is False: ann_info_list = api.annotation.download_batch( dataset_id, img_cache_ids, progress_cb ) img_name_to_ann = {ann.image_id: ann.annotation for ann in ann_info_list} else: img_name_to_ann = {} for image_info in images_in_cache: tags = TagCollection.from_api_response( image_info.tags, project_meta.tag_metas, id_to_tagmeta ) tmp_ann = Annotation( img_size=(image_info.height, image_info.width), img_tags=tags ) img_name_to_ann[image_info.id] = tmp_ann.to_json() if progress_cb is not None: progress_cb(len(images_in_cache)) for batch in batched(list(zip(images_in_cache, images_cache_paths)), batch_size=50): for img_info, img_cache_path in batch: item_name = _maybe_append_image_extension(img_info.name, img_info.ext) img_info_to_add = None if save_image_info is True: img_info_to_add = img_info dataset.add_item_file( item_name, item_path=img_cache_path if save_images is True else None, ann=img_name_to_ann[img_info.id], _validate_item=False, _use_hardlink=True, item_info=img_info_to_add, ) if progress_cb is not None: progress_cb(len(batch)) # download images from server if len(images_to_download) > 0: # prepare lists for api methods img_ids = [] img_paths = [] for img_info in images_to_download: img_ids.append(img_info.id) img_paths.append( os.path.join( dataset.item_dir, _maybe_append_image_extension(img_info.name, img_info.ext), ) ) # download annotations if only_image_tags is False: ann_info_list = api.annotation.download_batch(dataset_id, img_ids, progress_cb) img_name_to_ann = {ann.image_id: ann.annotation for ann in ann_info_list} else: img_name_to_ann = {} for image_info in images_to_download: tags = TagCollection.from_api_response( image_info.tags, project_meta.tag_metas, id_to_tagmeta ) tmp_ann = Annotation(img_size=(image_info.height, image_info.width), img_tags=tags) img_name_to_ann[image_info.id] = tmp_ann.to_json() if progress_cb is not None: progress_cb(len(images_to_download)) # download images and write to dataset for img_info_batch in batched(images_to_download): if save_images: images_ids_batch = [image_info.id for image_info in img_info_batch] images_nps = api.image.download_nps( dataset_id, images_ids_batch, progress_cb=progress_cb ) else: images_nps = [None] * len(img_info_batch) for index, image_np in enumerate(images_nps): img_info = img_info_batch[index] image_name = _maybe_append_image_extension(img_info.name, img_info.ext) dataset.add_item_np( item_name=image_name, img=image_np if save_images is True else None, ann=img_name_to_ann[img_info.id], img_info=img_info if save_image_info is True else None, ) if cache is not None and save_images is True: img_hashes = [img_info.hash for img_info in images_to_download] cache.write_objects(img_paths, img_hashes) DatasetDict = Project.DatasetDict