kwcoco 0.2.0

The kwcoco Module

The main webpage for this project is: https://gitlab.kitware.com/computer-vision/kwcoco

The Kitware COCO module defines a variant of the Microsoft COCO format, originally developed for the “collected images in context” object detection challenge. We are backwards compatible with the original module, but we also have improved implementations in several places, including segmentations and keypoints.

The main data structure in this model is largely based on the implementation in https://github.com/cocodataset/cocoapi It uses the same efficient core indexing data structures, but in our implementation the indexing can be optionally turned off, functions are silent by default (with the exception of long running processes, which optionally show progress by default). We support helper functions that add and remove images, categories, and annotations.

We have reimplemented the object detection scoring code in the kwcoco.metrics submodule.

The original pycocoutils API is exposed via the kwcoco.compat_dataset.COCO class for drop-in replacement with existing tools that use pycocoutils.

There is some support for kw18 files in the kwcoco.kw18 module.

The kwcoco CLI

After installing kwcoco, you will also have the kwcoco command line tool. This uses a scriptconfig / argparse CLI interface. Running kwcoco --help should provide a good starting point.

usage: kwcoco [-h] [--version] {stats,union,split,show,reroot,toydata,conform,eval,modify_categories,validate} ...

The Kitware COCO CLI

positional arguments:
  {stats,union,split,show,reroot,toydata,conform,eval,modify_categories,validate}
                        specify a command to run
    stats               Compute summary statistics about a COCO dataset
    union               Combine multiple COCO datasets into a single merged dataset.
    split               Split a single COCO dataset into two sub-datasets.
    show                Visualize a COCO image using matplotlib, optionally writing it to disk
    reroot              Reroot image paths onto a new image root.
    toydata             Create COCO toydata
    conform             Reroot image paths onto a new image root.
    eval                argparse CLI generated by scriptconfig
    modify_categories   Rename or remove categories
    validate            Validate that a coco file conforms to the json schema, that assets exist, and potentially fix corrupted assets by removing them.

optional arguments:
  -h, --help            show this help message and exit
  --version             show version number and exit (default: False)

This should help you inspect (via stats and show), combine (via union), and make training splits (via split) using the command line. Also ships with toydata, which generates a COCO file you can use for testing.

Toy Data

Don’t have a dataset with you, but you still want to test out your algorithms? Try the kwcoco shapes demo dataset, and generate an arbitrarilly large dataset.

The toydata submodule renders simple objects on a noisy background — optionally with auxillary channels — and provides bounding boxes, segmentations, and keypoint annotations. The following example illustrates a generated toy image with and without overlaid annotations.

The CocoDataset object

The kwcoco.CocoDataset class is capable of dynamic addition and removal of categories, images, and annotations. Has better support for keypoints and segmentation formats than the original COCO format. Despite being written in Python, this data structure is reasonably efficient.

>>> import kwcoco
>>> import json
>>> # Create demo data
>>> demo = CocoDataset.demo()
>>> # could also use demo.dump / demo.dumps, but this is more explicit
>>> text = json.dumps(demo.dataset)
>>> with open('demo.json', 'w') as file:
>>>    file.write(text)

>>> # Read from disk
>>> self = CocoDataset('demo.json')

>>> # Add data
>>> cid = self.add_category('Cat')
>>> gid = self.add_image('new-img.jpg')
>>> aid = self.add_annotation(image_id=gid, category_id=cid, bbox=[0, 0, 100, 100])

>>> # Remove data
>>> self.remove_annotations([aid])
>>> self.remove_images([gid])
>>> self.remove_categories([cid])

>>> # Look at data
>>> print(ub.repr2(self.basic_stats(), nl=1))
>>> print(ub.repr2(self.extended_stats(), nl=2))
>>> print(ub.repr2(self.boxsize_stats(), nl=3))
>>> print(ub.repr2(self.category_annotation_frequency()))


>>> # Inspect data
>>> import kwplot
>>> kwplot.autompl()
>>> self.show_image(gid=1)

>>> # Access single-item data via imgs, cats, anns
>>> cid = 1
>>> self.cats[cid]
{'id': 1, 'name': 'astronaut', 'supercategory': 'human'}

>>> gid = 1
>>> self.imgs[gid]
{'id': 1, 'file_name': 'astro.png', 'url': 'https://i.imgur.com/KXhKM72.png'}

>>> aid = 3
>>> self.anns[aid]
{'id': 3, 'image_id': 1, 'category_id': 3, 'line': [326, 369, 500, 500]}

# Access multi-item data via the annots and images helper objects
>>> aids = self.index.gid_to_aids[2]
>>> annots = self.annots(aids)

>>> print('annots = {}'.format(ub.repr2(annots, nl=1, sv=1)))
annots = <Annots(num=2)>

>>> annots.lookup('category_id')
[6, 4]

>>> annots.lookup('bbox')
[[37, 6, 230, 240], [124, 96, 45, 18]]

>>> # built in conversions to efficient kwimage array DataStructures
>>> print(ub.repr2(annots.detections.data))
{
    'boxes': <Boxes(xywh,
                 array([[ 37.,   6., 230., 240.],
                        [124.,  96.,  45.,  18.]], dtype=float32))>,
    'class_idxs': np.array([5, 3], dtype=np.int64),
    'keypoints': <PointsList(n=2) at 0x7f07eda33220>,
    'segmentations': <PolygonList(n=2) at 0x7f086365aa60>,
}

>>> gids = list(self.imgs.keys())
>>> images = self.images(gids)
>>> print('images = {}'.format(ub.repr2(images, nl=1, sv=1)))
images = <Images(num=3)>

>>> images.lookup('file_name')
['astro.png', 'carl.png', 'stars.png']

>>> print('images.annots = {}'.format(images.annots))
images.annots = <AnnotGroups(n=3, m=3.7, s=3.9)>

>>> print('images.annots.cids = {!r}'.format(images.annots.cids))
images.annots.cids = [[1, 2, 3, 4, 5, 5, 5, 5, 5], [6, 4], []]

The JSON Spec

A COCO file is a json file that follows a particular spec. It is used for storing computer vision datasets: namely images, categories, and annotations. Images have an id and a file name, which holds a relative or absolute path to the image data. Images can also have auxillary files (e.g. for depth masks, infared, or motion). A category has an id, a name, and an optional supercategory. Annotations always have an id, an image-id, and a bounding box. Usually they also contain a category-id. Sometimes they contain keypoints, segmentations. The dataset can also store videos, in which case images should have video_id field, and annotations should have a track_id field.

An implementation and extension of the original MS-COCO API [1].

Dataset Spec:

An informal description of the spec is written here:

# All object categories are defined here.
category = {
    'id': int,
    'name': str,  # unique name of the category
    'supercategory': str,   # parent category name
}

# Videos are used to manage collections of sequences of images.
video = {
    'id': int,
    'name': str,  # a unique name for this video.
}

# Specifies how to find sensor data of a particular scene at a particular
# time. This is usually paths to rgb images, but auxiliary information
# can be used to specify multiple bands / etc...
image = {
    'id': int,

    'name': str,  # an encouraged but optional unique name
    'file_name': str,  # relative path to the "base" image data

    'width': int,   # pixel width of "base" image
    'height': int,  # pixel height of "base" image

    'channels': <ChannelSpec>,   # a string encoding of the channels in the main image

    'auxiliary': [  # information about any auxiliary channels / bands
        {
            'file_name': str,     # relative path to associated file
            'channels': <ChannelSpec>,   # a string encoding
            'width':     <int>    # pixel width of auxiliary image
            'height':    <int>    # pixel height of auxiliary image
            'base_to_aux': <TransformSpec>,  # tranform from "base" image space to auxiliary image space. (identity if unspecified)
        }, ...
    ]

    'video_id': str  # if this image is a frame in a video sequence, this id is shared by all frames in that sequence.
    'timestamp': str | int  # a iso-string timestamp or an integer in flicks.
    'frame_index': int  # ordinal frame index which can be used if timestamp is unknown.
}

TransformSpec:
    Currently there is only one spec that works with anything:
        {'type': 'affine': 'matrix': <a-3x3 matrix>},

    In the future we may do something like this:
        {'type': 'scale', 'factor': <float|Tuple[float, float]>},
        {'type': 'translate', 'offset': <float|Tuple[float, float]>},
        {'type': 'rotate', 'radians_ccw': <float>},

ChannelSpec:
    This is a string that describes the channel composition of an image.
    For the purposes of kwcoco, separate different channel names with a
    pipe ('|'). If the spec is not specified, methods may fall back on
    grayscale or rgb processing. There are special string. For instance
    'rgb' will expand into 'r|g|b'. In other applications you can "late
    fuse" inputs by separating them with a "," and "early fuse" by
    separating with a "|". Early fusion returns a solid array/tensor, late
    fusion returns separated arrays/tensors.

# Ground truth is specified as annotations, each belongs to a spatial
# region in an image. This must reference a subregion of the image in pixel
# coordinates. Additional non-schma properties can be specified to track
# location in other coordinate systems. Annotations can be linked over time
# by specifying track-ids.
annotation = {
    'id': int,
    'image_id': int,
    'category_id': int,

    'track_id': <int | str | uuid>  # indicates association between annotations across frames

    'bbox': [tl_x, tl_y, w, h],  # xywh format)
    'score' : float,
    'prob' : List[float],
    'weight' : float,

    'caption': str,  # a text caption for this annotation
    'keypoints' : <Keypoints | List[int] > # an accepted keypoint format
    'segmentation': <RunLengthEncoding | Polygon | MaskPath | WKT >,  # an accepted segmentation format
}

# A dataset bundles a manifest of all aformentioned data into one structure.
dataset = {
    'categories': [category, ...],
    'videos': [video, ...]
    'images': [image, ...]
    'annotations': [annotation, ...]
    'licenses': [],
    'info': [],
}

Polygon:
    A flattned list of xy coordinates.
    [x1, y1, x2, y2, ..., xn, yn]

    or a list of flattned list of xy coordinates if the CCs are disjoint
    [[x1, y1, x2, y2, ..., xn, yn], [x1, y1, ..., xm, ym],]

    Note: the original coco spec does not allow for holes in polygons.

    We also allow a non-standard dictionary encoding of polygons
        {'exterior': [(x1, y1)...],
         'interiors': [[(x1, y1), ...], ...]}

    TODO: Support WTK

RunLengthEncoding:
    The RLE can be in a special bytes encoding or in a binary array
    encoding. We reuse the original C functions are in [2]_ in
    ``kwimage.structs.Mask`` to provide a convinient way to abstract this
    rather esoteric bytes encoding.

    For pure python implementations see kwimage:
        Converting from an image to RLE can be done via kwimage.run_length_encoding
        Converting from RLE back to an image can be done via:
            kwimage.decode_run_length

        For compatibility with the COCO specs ensure the binary flags
        for these functions are set to true.

Keypoints:
    Annotation keypoints may also be specified in this non-standard (but
    ultimately more general) way:

    'annotations': [
        {
            'keypoints': [
                {
                    'xy': <x1, y1>,
                    'visible': <0 or 1 or 2>,
                    'keypoint_category_id': <kp_cid>,
                    'keypoint_category': <kp_name, optional>,  # this can be specified instead of an id
                }, ...
            ]
        }, ...
    ],
    'keypoint_categories': [{
        'name': <str>,
        'id': <int>,  # an id for this keypoint category
        'supercategory': <kp_name>  # name of coarser parent keypoint class (for hierarchical keypoints)
        'reflection_id': <kp_cid>  # specify only if the keypoint id would be swapped with another keypoint type
    },...
    ]

    In this scheme the "keypoints" property of each annotation (which used
    to be a list of floats) is now specified as a list of dictionaries that
    specify each keypoints location, id, and visibility explicitly. This
    allows for things like non-unique keypoints, partial keypoint
    annotations. This also removes the ordering requirement, which makes it
    simpler to keep track of each keypoints class type.

    We also have a new top-level dictionary to specify all the possible
    keypoint categories.

    TODO: Support WTK

Auxiliary Channels:
    For multimodal or multispectral images it is possible to specify
    auxiliary channels in an image dictionary as follows:

    {
        'id': int,
        'file_name': str,    # path to the "base" image (may be None)
        'name': str,         # a unique name for the image (must be given if file_name is None)
        'channels': <spec>,  # a spec code that indicates the layout of the "base" image channels.
        'auxiliary': [  # information about auxiliary channels
            {
                'file_name': str,
                'channels': <spec>
            }, ... # can have many auxiliary channels with unique specs
        ]
    }

Video Sequences:
    For video sequences, we add the following video level index:

    'videos': [
        { 'id': <int>, 'name': <video_name:str> },
    ]

    Note that the videos might be given as encoded mp4/avi/etc.. files (in
    which case the name should correspond to a path) or as a series of
    frames in which case the images should be used to index the extracted
    frames and information in them.

    Then image dictionaries are augmented as follows:

    {
        'video_id': str  # optional, if this image is a frame in a video sequence, this id is shared by all frames in that sequence.
        'timestamp': int  # optional, timestamp (ideally in flicks), used to identify the timestamp of the frame. Only applicable video inputs.
        'frame_index': int  # optional, ordinal frame index which can be used if timestamp is unknown.
    }

    And annotations are augmented as follows:

    {
        'track_id': <int | str | uuid>  # optional, indicates association between annotations across frames
    }

For a formal description of the spec see the kwcoco/coco_schema.json.

Converting your data to COCO

Assuming you have programmatic access to your dataset you can easily convert to a coco file using process similar to the following code:

# ASSUME INPUTS
# my_classes: a list of category names
# my_annots: a list of annotation objects with bounding boxes, images, and categories
# my_images: a list of image files.

my_images = [
    'image1.png',
    'image2.png',
    'image3.png',
]

my_classes = [
    'spam', 'eggs', 'ham', 'jam'
]

my_annots = [
    {'image': 'image1.png', 'box': {'tl_x':  2, 'tl_y':  3, 'br_x':  5, 'br_y':  7}, 'category': 'spam'},
    {'image': 'image1.png', 'box': {'tl_x': 11, 'tl_y': 13, 'br_x': 17, 'br_y': 19}, 'category': 'spam'},
    {'image': 'image3.png', 'box': {'tl_x': 23, 'tl_y': 29, 'br_x': 31, 'br_y': 37}, 'category': 'eggs'},
    {'image': 'image3.png', 'box': {'tl_x': 41, 'tl_y': 43, 'br_x': 47, 'br_y': 53}, 'category': 'spam'},
    {'image': 'image3.png', 'box': {'tl_x': 59, 'tl_y': 61, 'br_x': 67, 'br_y': 71}, 'category': 'jam'},
    {'image': 'image3.png', 'box': {'tl_x': 73, 'tl_y': 79, 'br_x': 83, 'br_y': 89}, 'category': 'spam'},
]

# The above is just an example input, it is left as an exercise for the
# reader to translate that to your own dataset.

import kwcoco
import kwimage

# A kwcoco.CocoDataset is simply an object that manages an underlying
# `dataset` json object. It contains methods to dynamically, add, remove,
# and modify these data structures, efficient lookup tables, and many more
# conveniences when working and playing with vision datasets.
my_dset = kwcoco.CocoDataset()

for catname in my_classes:
    my_dset.add_category(name=catname)

for image_path in my_images:
    my_dset.add_image(file_name=image_path)

for annot in my_annots:
    # The index property provides fast lookups into the json data structure
    cat = my_dset.index.name_to_cat[annot['category']]
    img = my_dset.index.file_name_to_img[annot['image']]
    # One quirk of the coco format is you need to be aware that
    # boxes are in <top-left-x, top-left-y, width-w, height-h> format.
    box = annot['box']
    # Use kwimage.Boxes to preform quick, reliable, and readable
    # conversions between common bounding box formats.
    tlbr = [box['tl_x'], box['tl_y'], box['br_x'], box['br_y']]
    xywh = kwimage.Boxes([tlbr], 'tlbr').toformat('xywh').data[0].tolist()
    my_dset.add_annotation(bbox=xywh, image_id=img['id'], category_id=cat['id'])

# Dump the underlying json `dataset` object to a file
my_dset.fpath = 'my-converted-dataset.mscoco.json'
my_dset.dump(my_dset.fpath, newlines=True)

# Dump the underlying json `dataset` object to a string
print(my_dset.dumps(newlines=True))

[1]

http://cocodataset.org/#format-data

[2]

https://github.com/nightrome/cocostuffapi/blob/master/PythonAPI/pycocotools/mask.py