Uploading Annotations or Predictions

In this tutorial, we explore different options to upload annotations in Remo from code. In particular, we can:

  • add annotations from a file in a format supported by remo
  • add annotations from code, which enables uploading annotations or model predictions from any input format

We start off by creating a dataset and populating it with some images

import remo
import os
import pandas as pd
remo.set_viewer('jupyter')

urls = ['https://remo-scripts.s3-eu-west-1.amazonaws.com/open_images_sample_dataset.zip']
my_dataset = remo.create_dataset(name = 'D1', urls = urls)

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Add annotations from file supported by remo

To add annotations from a supported file format, we can pass the file via dataset.add_data

Remo automatically parses annotation files in a variety of formats (such as Pascal XML, COCO JSON, Open Images CSV, etc). You can read more about file formats supported by remo in our documentation.

Example: let's add some annotations for an Object Detection task from a CSV file with encoded classes

In this case, annotations are stored in a CSV file in a format already supported by Remo. Class labels were encoded using GoogleKnowledgeGraph. Remo automatically detects the class encoding and translates it into the corresponding labels

annotation_files=[os.getcwd() + '/assets/open_sample.csv']

df = pd.read_csv(annotation_files[0])
df.columns

Index(['ImageID', 'Source', 'LabelName', 'Confidence', 'XMin', 'XMax', 'YMin', 'YMax', 'IsOccluded', 'IsTruncated', 'IsGroupOf', 'IsDepiction', 'IsInside'], dtype='object')

my_dataset.add_data(local_files=annotation_files, annotation_task = 'Object detection')

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{'session_id': '810e23ad-ff63-42a9-89ed-400fbb7d4fc0', 'created_at': '2020-05-29T13:56:17.832956Z', 'dataset': {'id': 14, 'name': 'D1'}, 'status': 'done', 'substatus': '', 'images': {'pending': 0, 'total': 0, 'successful': 0, 'failed': 0, 'errors': []}, 'annotations': {'pending': 0, 'total': 1, 'successful': 1, 'failed': 0, 'errors': []}, 'errors': [], 'uploaded': {'total': {'items': 0, 'size': 0, 'human_size': '0 b'}, 'images': {'items': 0, 'size': 0, 'human_size': '0 b'}, 'annotations': {'items': 0, 'size': 0, 'human_size': '0 b'}, 'archives': {'items': 0, 'size': 0, 'human_size': '0 b'}}}

We can now see annotation statistics, explore the dataset and further leverage Remo

my_dataset.get_annotation_statistics()

[{'AnnotationSet ID': 44, 'AnnotationSet name': 'Object detection', 'n_images': 9, 'n_classes': 15, 'n_objects': 84, 'top_3_classes': [{'name': 'Fruit', 'count': 27}, {'name': 'Sports equipment', 'count': 12}, {'name': 'Human arm', 'count': 7}], 'creation_date': None, 'last_modified_date': '2020-05-29T13:56:05.883892Z'}]

my_dataset.view()

dataset_added_annotation.jpeg

Add annotations o model predictions from code

We can also easily to add annotations from code via the Annotation object

This can be useful to:

  • visualize model predictions
  • upload annotations from any custom file format
  • create an active learning workflow

Example: let's add annotations to a specific image using add_annotations() method of the dataset class

image_name = '000a1249af2bc5f0.jpg'

annotations = []

annotation = remo.Annotation()
annotation.img_filename = image_name
annotation.classes='Human hand'
annotation.bbox=[227, 284, 678, 674]
annotations.append(annotation)

annotation = remo.Annotation()
annotation.img_filename = image_name
annotation.classes='Fashion accessory'
annotation.bbox=[496, 322, 544,370]
annotations.append(annotation)

my_dataset.add_annotations(annotations)

Progress 100% - 1/1 - elapsed 0:00:00.001000 - speed: 1000.00 img / s, ETA: 0:00:00 Acquiring data - completed
Processing data - completed
Data upload completed

We can now retrieve the picture and visualise it:

my_image = my_dataset.image(image_name)
my_image.view()

dataset_added_annotation.jpeg

Annotation sets

Behind the scenes, Remo organises annotations in Annotation sets. An annotation set is simply a collection of all the annotations of Dataset.

The advantage of grouping annotations in an Annotation Set is that it allows for high-level group operations on all the annotations, such as:

  • grouping classes together
  • deleting objects of specific classes
  • comparing of different annotations (such as ground truth vs prediction, or annotations coming from different annotators)

In the examples we have seen before, Remo automatically creates an annotation set and sets it as default. For more control, it's however possible to explicit manipulate Annotation sets objects.

To read more about annotation sets, you can check the remo documentation: https://remo.ai/docs/.