SDK

The SDK class contains some core functionalities of Remo. It mainly acts as a wrapper around our API endpoints.

Use the SDK class to:

create a dataset and annotation set
list and retrieve datasets
export annotations without a need to intialize a dataset

Most of the functions documented below can be called from Python by doing

import remo

remo.function_name()

class remo.sdk.SDK¶

Creates sdk object, and checks connection to server

documentation

class remo.sdk.SDK(server: str, email: str, password: str, viewer: str = 'browser')

Parameters
- server – server host name, e.g. http://localhost:8123/
- email – user credentials
- password – user credentials
- viewer – allows to choose between browser, electron and jupyter viewer. To be able change viewer, you can use set_viewer() function. See example.

Example:

import remo

remo.set_viewer('browser')

add_annotations_to_image¶

Adds annotation to a given image

documentation

add_annotations_to_image(annotation_set_id: int, image_id: int, annotations: List[remo.domain.annotation.Annotation])

Parameters
- annotation_set_id – annotation set id
- image_id – image id
- annotations – Annotation object

add_data_to_dataset¶

Adds images and/or annotations to an existing dataset. Use local files to link (rather than copy) images. Use paths_to_upload if you want to copy image files or archive files. Use urls to download from the web images, annotations or archives. Adding images: support for jpg,``jpeg``, png, tif Adding annotations: to add annotations, you need to specify the annotation task and make sure the specific file format is one of those supported. See documentation here: https://remo.ai/docs/annotation-formats/ Adding archive files: support for zip, tar, gzip

documentation

add_data_to_dataset(dataset_id: int, local_files: List[str] = None, paths_to_upload: List[str] = None, urls: List[str] = None, annotation_task: str = None, folder_id: int = None, annotation_set_id: int = None, class_encoding=None, wait_for_complete=True)

Parameters
- dataset_id – id of the dataset to add data to
- local_files – list of files or directories containing annotations and image files Remo will create smaller copies of your images for quick previews but it will point at the original files to show original resolutions images. Folders will be recursively scanned for image files.
- paths_to_upload – list of files or directories containing images, annotations and archives. These files will be copied inside .remo folder. Folders will be recursively scanned for image files. Unpacked archive will be scanned for images, annotations and nested archives.
- urls – list of urls pointing to downloadable target, which can be image, annotation file or archive.
- annotation_task – annotation tasks tell remo how to parse annotations. See also: remo.task.
- folder_id – specifies target virtual folder in the remo dataset. If None, it adds to the root level.
- annotation_set_id – specifies target annotation set in the dataset. If None: if no annotation set exists, one will be automatically created. If exactly one annotation set already exists, it will add annotations to that annotation set, provided the task matches.
- class_encoding – specifies how to convert labels in annotation files to readable labels. If None, Remo will try to interpret the encoding automatically - which for standard words, means they will be read as they are. See also: remo.class_encodings.
- wait_for_complete – blocks function until upload data completes

Returns

Dictionary with results for linking files, upload files and upload urls:

{
    'files_link_result': ...,
    'files_upload_result': ...,
    'urls_upload_result': ...
}

create_annotation_set¶

Creates a new annotation set within the given dataset

documentation

create_annotation_set(annotation_task: str, dataset_id: int, name: str, classes: List[str] = [])

Parameters
- annotation_task – specified task for the annotation set. See also: remo.task
- dataset_id – dataset id
- name – name of the annotation set
- classes – list of classes. Default is no classes
Returns

remo.AnnotationSet

create_dataset¶

Creates a new dataset in Remo and optionally populate it with images and annotations. To add annotations, you need to specify an annotation task.

documentation

create_dataset(name: str, local_files: List[str] = None, paths_to_upload: List[str] = None, urls: List[str] = None, annotation_task: str = None, class_encoding=None, wait_for_complete=True)

Parameters
- name – name of the dataset.
- local_files – list of files or directories. These files will be linked. Folders will be recursively scanned for image files: jpg, png, tif.
- paths_to_upload – list of files or directories. These files will be copied. Supported files: images, annotations and archives.
  - image files: jpg, png, tif.
  - annotation files: json, xml, csv.
  - archive files: zip, tar, gzip.
  Unpacked archive will be scanned for images, annotations and nested archives.
- urls – list of urls pointing to downloadable target, which can be image, annotation file or archive.
- annotation_task – specifies annotation task. See also: remo.task.
- class_encoding – specifies how to convert class labels in annotation files to classes. See also: remo.class_encodings.
- wait_for_complete – blocks function until upload data completes
Returns

remo.Dataset

delete_dataset¶

Deletes dataset

documentation

delete_dataset(dataset_id: int)

Parameters

dataset_id – dataset id

export_annotations¶

Exports annotations with the chosen format.

documentation

export_annotations(annotation_set_id: int, annotation_format: str = 'json', export_coordinates: str = 'pixel', append_path: bool = True, export_tags: bool = True)

Parameters
- annotation_set_id – annotation set id
- annotation_format – can be one of [‘json’, ‘coco’, ‘csv’]. Default: ‘json’
- append_path – if True, appends the path to the filename (e.g. local path). Default: True
- export_coordinates – converts output values to percentage or pixels, can be one of [‘pixel’, ‘percent’]. Default: ‘pixel’
- export_tags – if True, exports the tags to a CSV file. Default: True
Returns

annotation file content

export_annotations_to_file¶

Exports annotations to a file with the chosen format.

documentation

export_annotations_to_file(output_file: str, annotation_set_id: int, annotation_format: str = 'json', export_coordinates: str = 'pixel', append_path: bool = True, export_tags: bool = True)

Parameters
- output_file – output file to save
- annotation_set_id – annotation set id
- annotation_format – can be one of [‘json’, ‘coco’, ‘csv’]. Default: ‘json’
- append_path – if True, appends the path to the filename (e.g. local path). Default: True
- export_coordinates – converts output values to percentage or pixels, can be one of [‘pixel’, ‘percent’]. Default: ‘pixel’
- export_tags – if True, exports also all the tags to a CSV file. Default: True

generate_annotations_from_folders¶

Creates a CSV annotation file associating images with labels, starting from folders named with labels (a common folder structure for Image Classification tasks). The CSV file is saved in the same input directory where images are stored. Example of data structure for a dog / cat dataset:

cats_and_dogs

* dog

        * img1.jpg

        * img2.jpg

        * …

* cat

        * img199.jpg

        * img200.jpg

        * …

Example::

# Download and unzip this sample dataset: s-3.s3-eu-west-1.amazonaws.com/cats_and_dogs.zip
data_path = “cats_and_dogs”
remo.generate_annotations_from_folders(path_to_data_folder=data_path)

documentation

generate_annotations_from_folders(path_to_data_folder: str, output_file_path: str = './annotations.csv', append_path: bool = True)

Parameters
- path_to_data_folder – path to the source folder where data is stored
- output_file_path – location and filename where to store the file. Default: ‘./annotations.csv’
- append_path – if True, file paths are appended to filenames in the output file, otherwise the filename alone is used. Default : True
Returns

string, path to the generated CSV annotation file. Format: ‘file_name’, ‘class_name’
Return type

output_file_path

generate_image_tags¶

Creates a CSV annotation file associating tags to images, as defined in the tags_dictionary. The CSV file is saved in the current working directory.

Example of a dictionary: {‘train’: [‘img1.jpg’, ‘img2.jpg’],’test’: [‘img3.jpg’, ‘img4.jpg’],’val’: [‘img5.jpg’, ‘img6.jpg’]}

Example::

# Download and unzip this sample dataset: [https://s-3.s3-eu-west-1.amazonaws.com/small_flowers.zip](https://s-3.s3-eu-west-1.amazonaws.com/small_flowers.zip)
import glob
import os
import random
im_list = [os.path.basename(i) for i in glob.glob(str(‘./small_flowers/images’)+’/

```
**
```

/

```
*
```

.jpg’, recursive=True)])
im_list = random.sample(im_list, len(im_list))
tags_dict = {‘train’ : im_list[0:121], ‘test’ : im_list[121:131], ‘valid’ : im_list[131:141]}
remo.generate_image_tags(tags_dict)

documentation

generate_image_tags(tags_dictionary: dict, output_file_path: str = './images_tags.csv', append_path: bool = True)

Parameters
- tags_dictionary – dictionary where each key is a tags and the value is a List of image filenames (or foder paths containing images) to which we want to assign the tags.
- output_file_path – location and filename where to store the file. Default: ‘./images_tags.csv’
- append_path – if absolute path to images is required. Default: True
Returns

string, path to the generated CSV tags file. Format: ‘file_name’, ‘tag’
Return type

output_file_path

get_annotation_info¶

Returns current annotations for the image

documentation

get_annotation_info(dataset_id: int, annotation_set_id: int, image_id: int)

Parameters
- dataset_id – dataset id
- annotation_set_id – annotation set id
- image_id – image id
Returns

annotations info - list of annotation objects or classes

get_annotation_set¶

Retrieves annotation set

documentation

get_annotation_set(annotation_set_id: int)

Parameters

annotation_set_id – annotation set id
Returns

remo.AnnotationSet

get_dataset¶

Retrieves a dataset with given dataset id.

documentation

get_dataset(dataset_id: int)

Parameters

dataset_id – dataset id
Returns

remo.Dataset

get_image¶

Retrieves image by a given image id

documentation

get_image(image_id: int)

Parameters

image_id – image id
Returns

remo.Image

get_image_content¶

Get image file content by url

documentation

get_image_content(url: str)

Parameters

url – image url
Returns

image binary data

list_annotation_set_classes¶

List classes within the annotation set

documentation

list_annotation_set_classes(annotation_set_id: int)

Parameters

annotation_set_id – annotation set id
Returns

list of classes

list_annotation_sets¶

Returns a list of AnnotationSet containing all the AnnotationSets of a given dataset

documentation

list_annotation_sets(dataset_id: int)

Parameters

dataset_id – dataset id
Returns

List[remo.AnnotationSet]

list_annotations¶

Returns all annotations for a given annotation set

documentation

list_annotations(dataset_id: int, annotation_set_id: int)

Parameters
- dataset_id – dataset id
- annotation_set_id – annotation set id
Returns

List[remo.Annotation]

list_dataset_images¶

Returns a list of images within a dataset with given dataset_id

documentation

list_dataset_images(dataset_id: int, limit: int = None, offset: int = None)

Parameters
- dataset_id – dataset id
- limit – limits result images
- offset – specifies offset
Returns

List[remo.Image]

list_datasets¶

Lists the available datasets

documentation

list_datasets()

Returns

List[remo.Dataset]

list_image_annotations¶

Returns annotations for a given image

documentation

list_image_annotations(dataset_id: int, annotation_set_id: int, image_id: int)

Parameters
- dataset_id – dataset id
- annotation_set_id – annotation set id
- image_id – image id
Returns

List[remo.Annotation]

open_ui¶

Opens the main page of Remo

documentation

open_ui()

search_images¶

Search images by class and annotation task

documentation

search_images(classes=None, task: str = None, dataset_id: int = None, limit: int = None)

Parameters
- classes – string or list of strings - search for images which match all given classes
- task – name of the annotation task to filter dataset
- dataset_id – narrows search result to given dataset
- limit – limits number of search results
Returns

image_id, dataset_id, name, annotations

set_public_url¶

documentation

set_public_url(public_url: str)

set_viewer¶

Allows to choose one of available viewers

documentation

set_viewer(viewer: str)

Parameters

viewer – choose between ‘browser’, ‘electron’ and ‘jupyter’ viewer

view_annotate_image¶

Opens browser on the annotation tool for giving image

documentation

view_annotate_image(annotation_set_id: int, image_id: int)

Parameters
- annotation_set_id – annotation set id
- image_id – image id

view_annotation_stats¶

Opens browser in annotation set insights page

documentation

view_annotation_stats(annotation_set_id: int)

Parameters

annotation_set_id – annotation set id

view_annotation_tool¶

Opens browser in annotation view for the given annotation set

documentation

view_annotation_tool(id: int)

Parameters

id – annotation set id

view_dataset¶

Opens browser for the given dataset

documentation

view_dataset(id: int)

Parameters

id – dataset id

view_image¶

Opens browser on the image view for given image

documentation

view_image(image_id: int, dataset_id: int)

Parameters
- image_id – image id
- dataset_id – dataset id

view_search¶

Opens browser in search page

documentation

view_search()