Package: kwcoco
Read the docshttps://kwcoco.readthedocs.ioGitlab (main)https://gitlab.kitware.com/computer-vision/kwcocoGithub (mirror)https://github.com/Kitware/kwcocoPypihttps://pypi.org/project/kwcocoSlideshttps://docs.google.com/presentation/d/1OqljzzaZdjzmMg9876J4wFOmdFlJsH2Lc8_7tcqAPUE/editThe main webpage for this project is:https://gitlab.kitware.com/computer-vision/kwcocoThe kwcoco package is a Python module and command line utility for reading,
writing, modifying, and interacting with computer vision datasets — i.e.
images or videos with raster or vector annotations. A dataset is defined using
a json file or SQL database that points to assets that exist on disk or on the
cloud. The Python API provides a way to efficiently load image data and
associated metadata (e.g. for writing torch datasets). The command line
application provides a way to quickly summarize, visualize, and perform common
modifications (e.g. subset, union) on the data.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,
keypoints, annotation tracks, multi-spectral images, and videos (which
represents a generic sequence of images).A KWCOCO file is a “manifest” that serves as a single reference that points to
all images, categories, and annotations in a computer vision dataset. Thus,
when applying an algorithm to a dataset, it is sufficient to have the algorithm
take one dataset parameter: the path to the KWCOCO file.  Generally a KWCOCO
file will live in a “bundle” directory along with the data that it references,
and paths in the KWCOCO file will be relative to the location of the KWCOCO
file itself.The main data structure in this model is largely based on the implementation inhttps://github.com/cocodataset/cocoapiIt 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 thekwcoco.metricssubmodule.The originalpycocoutilsAPI is exposed via thekwcoco.compat_dataset.COCOclass for drop-in replacement with existing tools that usepycocoutils.There is some support for kw18 files in thekwcoco.kw18module.InstallationThekwcoco.  package can be installed via pip:# From pypipipinstallkwcocoFeature OverviewAt its core kwcoco’s goal is to make it easy to organize, query, manipulate,
and distribute image and video datasets. To accomplish this goal it offers
several features.An fast in-memory dictionary-based backend data structure with random access and indexed lookups.Ansqlalchemybackend that supports sqlite3 or postgresql (currently read-only).Efficient random sampling of image subregions usingdelayed_image.A Command Line Interface (CLI) for manipulating / inspecting coco files usingscriptconfig.Transparent coco file compression (e.g. i.e. read from / write to zipfiles)Support for videos as lists of image framesSupport for multispectral imagery via image assetsMetrics for classification and bounding box object detection (segmentation and polygon object detection coming soon).Toydata generation for easy CI testing and demos.Backwards compatability with the originalcocoapi.The KWCOCO CLIAfter installing KWCOCO, you will also have thekwcococommand line tool.
This uses ascriptconfig/argparseCLI interface. Runningkwcoco--helpshould provide a good starting point.usage: kwcoco [-h] [--version] {stats,union,split,show,toydata,eval,conform,modify_categories,reroot,validate,subset,grab} ...

The Kitware COCO CLI

positional arguments:
  {stats,union,split,show,toydata,eval,conform,modify_categories,reroot,validate,subset,grab}
                        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 or opencv, optionally writing
    toydata             Create COCO toydata for demo and testing purposes.
    eval                Evaluate and score predicted versus truth detections / classifications in a COCO dataset
    conform             Infer properties to make the COCO file conform to different specs.
    modify_categories   Rename or remove categories
    reroot              Reroot image paths onto a new image root.
    validate            Validates that a coco file satisfies expected properties.
    subset              Take a subset of this dataset and write it to a new file
    grab                Grab standard datasets.

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. The kwcoco CLI
has tab-complete features, but requiresenabling argcomplete.Toy DataDon’t have a dataset with you, but you still want to test out your algorithms?
Try the KWCOCO shapes demo dataset, and generate an arbitrarily large dataset.The toydata submodule renders simple objects on a noisy background —
optionally with auxiliary channels — and provides bounding boxes,
segmentations, and keypoint annotations. The following example illustrates a
generated toy image with and without overlaid annotations.Video data, multispectral, and multi-sensor demo data is supported.The CocoDataset objectThekwcoco.CocoDatasetclass 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.>>>importkwcoco>>>importjson>>># Create demo data>>>demo=kwcoco.CocoDataset.demo()>>># Modify it such that all paths are absolute wrt to your machine>>>demo.reroot(absolute=True)>>># could also use demo.dump / demo.dumps, but this is more explicit>>>text=json.dumps(demo.dataset)>>>withopen('demo.json','w')asfile:>>>file.write(text)>>># Read from disk>>>self=kwcoco.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.urepr(self.basic_stats(),nl=1))>>>print(ub.urepr(self.extended_stats(),nl=2))>>>print(ub.urepr(self.boxsize_stats(),nl=3))>>>print(ub.urepr(self.category_annotation_frequency()))>>># Inspect data>>>importkwplot>>>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.urepr(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.urepr(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)at0x7f07eda33220>,'segmentations':<PolygonList(n=2)at0x7f086365aa60>,}>>>gids=list(self.imgs.keys())>>>images=self.images(gids)>>>print('images ={}'.format(ub.urepr(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 SpecA 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 auxiliary files (e.g. for depth masks,
infrared, 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 given in:kwcoco/coco_schema_informal.rst.For a formal description of the spec see thekwcoco/coco_schema.json.For more information on the “warp” transforms seewarping_and_spaces.A high level overview of the structure - entities and their relationships - is shown in the following overview slide:The CocoDatset API Grouped by FunctinoalityThe following are grouped attribute/method names of akwcoco.CocoDataset.
See the in-code documentation for further details.{'classmethod':['coerce','demo','from_coco_paths','from_data','from_image_paths','random',],'slots':['index','hashid','hashid_parts','tag','dataset','bundle_dpath','assets_dpath','cache_dpath',],'property':['anns','cats','cid_to_aids','data_fpath','data_root','fpath','gid_to_aids','img_root','imgs','n_annots','n_cats','n_images','n_videos','name_to_cat',],'method(via MixinCocoAddRemove)':['add_annotation','add_annotations','add_category','add_image','add_images','add_video','clear_annotations','clear_images','ensure_category','ensure_image','remove_annotation','remove_annotation_keypoints','remove_annotations','remove_categories','remove_images','remove_keypoint_categories','remove_videos','set_annotation_category',],'method(via MixinCocoObjects)':['annots','categories','images','videos',],'method(via MixinCocoStats)':['basic_stats','boxsize_stats','category_annotation_frequency','category_annotation_type_frequency','conform','extended_stats','find_representative_images','keypoint_annotation_frequency','stats','validate',],'method(via MixinCocoAccessors)':['category_graph','delayed_load','get_auxiliary_fpath','get_image_fpath','keypoint_categories','load_annot_sample','load_image','object_categories',],'method(via CocoDataset)':['copy','dump','dumps','subset','union','view_sql',],'method(via MixinCocoExtras)':['corrupted_images','missing_images','rename_categories','reroot',],'method(via MixinCocoDraw)':['draw_image','imread','show_image',],}Converting your RGB data to KWCOCOAssuming 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.importkwcocoimportkwimage# 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()forcatnameinmy_classes:my_dset.add_category(name=catname)forimage_pathinmy_images:my_dset.add_image(file_name=image_path)forannotinmy_annots:# The index property provides fast lookups into the json data structurecat=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.Box to preform quick, reliable, and readable# conversions between common bounding box formats.ltrb=[box['tl_x'],box['tl_y'],box['br_x'],box['br_y']]xywh=kwimage.Box.coerce(ltrb,format='ltrb').to_coco()my_dset.add_annotation(bbox=xywh,image_id=img['id'],category_id=cat['id'])# Dump the underlying json `dataset` object to a filemy_dset.fpath='my-converted-dataset.mscoco.json'my_dset.dump(my_dset.fpath,newlines=True)# Dump the underlying json `dataset` object to a stringprint(my_dset.dumps(newlines=True))KWCOCO SpacesThere are 3 spaces that a user of kwcoco may need to be concerned with
depending on their dataset: (1) video space, (2) image space, and (3)
asset/auxiliary space.Videos can contain multiple images, images can contain multiple asset/auxiliary
items, and kwcoco needs to know about any transformation that relates between
different levels in this heirarchy.Video space - In a sequence of images, each individual image might be at a
different resolution, or misaligned with other images in the sequence.
This space is only important when working with images in “video” sequences.Image space - If an image contains multiple auxiliary / asset items, this is
the space that they are all re sampled to at the “image level”. Note all
annotations on images should always be given in image space by convention.Auxiliary / Asset Space - This is the native space/resolution of the raster
image data that lives on disk that KWCOCO points to. When an image consists of
only a single asset. This space is only important when an image contains
multiple files at different resolutions.When an item is registered in a space. (i.e. you register a video, image, or
auxiliary/asset item), kwcoco will benefit from knowing (1) the width/height of
the object in it’s own space, and any transformation from that object to it’s
parent space — i.e. an auxiliary/asset item needs to know how to be
transformed into image space, and an image needs to know how to be transformed
into video space (if applicable). This warping can be as simple as a scale
factor or as complex as a full homography matrix (and we may generalize beyond
this), and is specified via theTransformSpec. When this transform is
unspecified it is assumed to be the identity transform, so for pre-aligned
datasets, the user does not need to worry about the differentiation between
spaces and simply work in “image space”.Converting your Multispectral Multiresolution Data to KWCOCOKWCOCO has the ability to work with multispectral images. More generally, a
KWCOCO image can contain any number of “raster assets”. The motivating use case
is multispectral imagery, but this also incorporates more general use cases
where rasters can represent metadata from a depth sensor, or stereo images,
etc.Put plainly, a KWCOCO image can consist of multiple image files, and each of
those image file can have any number of channels. Furthermore, these image
files do not need to have the same resolution. However, the channels
within a single image currently must be unique.Because images can be in different resolutions, we need to bring up the topic
of “KWCOCO spaces”. For full info on this, see the discussion on “KWCOCO
spaces”, but briefly, there are 3 spaces that a user of kwcoco needs to be
concerned with: (1) video space, (2) image space, and (3) asset/auxiliary
space, and KWCOCO will want to know how.As a simple example, lets assume you have a dataset containing sequences of RGB
images, corresponding infrared images, depth estimations, and optical flow
estimations. The infrared images are stored in half-resolution of the RGB
images, but the depth and flow data is at the same resolution as the RGB data.
The RGB images have 3 channels the flow images have 2 channels, and depth and
ir have 1 channel.If our images on disk look like:- video1/vid1_frame1_rgb.tif
- video1/vid1_frame1_ir.tif
- video1/vid1_frame1_depth.tif
- video1/vid1_frame1_flow.tif
- video1/vid1_frame2_rgb.tif
- video1/vid1_frame2_ir.tif
- video1/vid1_frame2_depth.tif
- video1/vid1_frame2_flow.tif
- video1/vid1_frame3_rgb.tif
- video1/vid1_frame3_ir.tif
- video1/vid1_frame3_depth.tif
- video1/vid1_frame3_flow.tifWe can add them to a custom kwcoco file using the following code.First, lets’s actually make dummy data for those images on disk.importnumpyasnpimportkwimageimportubeltasubnum_frames=3num_videos=1width,height=64,64bundle_dpath=ub.Path('demo_bundle').ensuredir()forvididinrange(1,num_videos+1):vid_dpath=(bundle_dpath/f'video{vidid}').ensuredir()forframe_numinrange(1,num_frames+1):kwimage.imwrite(vid_dpath/f'vid{vidid}_frame{frame_num}_rgb.tif',np.random.rand(height,width,3))kwimage.imwrite(vid_dpath/f'vid{vidid}_frame{frame_num}_ir.tif',np.random.rand(height//2,width//2))kwimage.imwrite(vid_dpath/f'vid{vidid}_frame{frame_num}_depth.tif',np.random.rand(height,width,1))kwimage.imwrite(vid_dpath/f'vid{vidid}_frame{frame_num}_flow.tif',np.random.rand(height,width,2))Now lets create a kwcoco dataset to register them. We use the channel spec to denote what the channels are.importubeltasubimportosbundle_dpath=ub.Path('demo_bundle')importkwcocoimportkwimagedset=kwcoco.CocoDataset()dset.fpath=bundle_dpath/'data.kwcoco.json'# We will define a map from our suffix codes in the filename to# kwcoco channel specs that indicate the number of channelschannel_spec_mapping={'rgb':'red|green|blue',# rgb is 3 channels'flow':'fx|fy',# flow is 2 channels'ir':'ir','depth':'depth',}forvideo_dpathinbundle_dpath.glob('video*'):# Add a video and give it a name.vidid=dset.add_video(name=video_dpath.name)# Parse out information that we need from the filenames.# Lots of different ways to do this depending on the use case.assets=[]forfpathinvideo_dpath.glob('*.tif'):_,frame_part,chan_part=fpath.stem.split('_')frame_index=int(frame_part[5:])assets.append({'frame_num':frame_index,'channels':channel_spec_mapping[chan_part],'fpath':fpath,})# Group all data from the same frame together.frame_to_group=ub.group_items(assets,lambdax:x['frame_num'])forframe_index,groupinframe_to_group.items():# Let us lookup data by channelschan_to_item={item['channels']:itemforitemingroup}# Grab the RGB data as it will be our "primary" assetrgbdata=chan_to_item['red|green|blue']# Use the prefix for the image namename=rgbdata['fpath'].stem.split('_rgb')[0]height,width=kwimage.load_image_shape(rgbdata['fpath'])[0:2]# First add the base image. We will add this image as# without a file_name because all of its data will be stored# in its auxiliary list. We will assume all images in the# video are aligned, so we set `warp_img_to_vid` to be the# identity matrix.gid=dset.add_image(name=name,width=width,height=height,warp_img_to_vid=kwimage.Affine.eye().concise())# We could have constructed the auxiliary item dictionaries# explicitly and added them in the previous step, but we# will use the CocoImage api to do this instead.coco_img=dset.coco_image(gid)foritemingroup:fpath=item['fpath']height,width=kwimage.load_image_shape(fpath)[0:2]file_name=os.fspath(fpath.relative_to(bundle_dpath))coco_img.add_auxiliary_item(file_name=file_name,channels=item['channels'],width=width,height=height)# We can always double check we did not make errors using kwcoco validatedset.validate()Now we have a multispectral multi-resolution dataset. You can load specific
subsets of channels (in specific subregions is your data is stored in the COG
or a RAW format) using the delayed load interface.# Get a coco image.gid=1coco_img=dset.coco_image(gid)# Tell delayed load what channels we want. We can# also specify which "space" we want to load it in.# Note: that when specifying channels from multiple asset items# it is not possible to sample in the the auxiliary / asset space# so only image and video are allowed there.delayed_img=coco_img.imdelay('fx|depth|red',space='image')# We finalize the data to load itimdata=delayed_img.finalize()# We can show it if we want, but it's just random data.importkwplotkwplot.autompl()kwplot.imshow(imdata)Somewhat more interesting is to use the KWCOCO demodata. We can see here that
videos can contain multiple images at different resolutions and each image can
contain different number of channels.importkwcocoimportkwarrayimportkwimagedset=kwcoco.CocoDataset.demo('vidshapes8-msi-multisensor')gid=1coco_img=dset.coco_image(gid)# Randomly select 3 channels to useavail_channels=coco_img.channels.fuse().as_list()channels='|'.join(kwarray.shuffle(avail_channels)[0:3])print('channels ={!r}'.format(channels))delayed_img=coco_img.imdelay(channels,space='video')imdata=delayed_img.finalize()# Depending on the sensor intensity might be out of standard ranges,# we can use kwimage to robustly normalize for this. This lets# us visualize data with false color.canvas=kwimage.normalize_intensity(imdata,axis=2)canvas=np.ascontiguousarray(canvas)# We can draw the annotations on the image, but be cognizant of the spaces.# Annotations are always in "image" space, so if we loaded in "video" space# then we need to warp to that.imgspace_dets=dset.annots(gid=gid).detectionsvidspace_dets=imgspace_dets.warp(coco_img.warp_vid_from_img)canvas=vidspace_dets.draw_on(canvas)importkwplotkwplot.autompl()kwplot.imshow(canvas)The result of the above code is (note the data is random, so it may differ on your machine):Key notes to takeaway:KWCOCO can register many assets at different resolutions, register groups depicting the same scene at a particular time into an “image”, and then groups of images can be grouped into “videos”.Annotations are always specified in image spaceChannel code within a single image should never be duplicated.The KWCOCO Channel SpecificationTo summarizekwcoco.ChannelSpecandkwcoco.FusedChannelSpecrepresent a
set of channels or bands in an image.  A FusedChannelSpec could be as simple asred|green|blue, or more complex like:red|green|blue|nir|swir16|swir22and a ChannelSpec can be a collection of one or more FusedChannelSpecs
separated by a comma.The home of the channel specification has moved todelayed_image.
See thedelayed image channel spec docsfor more details.Related WorkThere are several other COCO libraries available:https://pypi.org/project/coco-lib/https://github.com/cocodataset/cocoapihttps://pypi.org/project/fast-coco-eval/https://pypi.org/project/coco-assistant/https://pypi.org/project/cocorepr/https://github.com/jaspersiebring/GeoCOCOhttps://www.researchgate.net/figure/COCO-CameraTraps-data-format_fig5_326412262https://github.com/microsoft/CameraTrapsTools that work with COCO files:https://voxel51.com/docs/fiftyone/https://labelstud.io/guide/export.html#COCOProjects that use the COCO format:https://github.com/arpanmukherjee/Into-The-Wild-Animal-Detection-and-Classificationhttps://github.com/amurto/jeev-rakshak[1]http://cocodataset.org/#format-data[2]https://github.com/nightrome/cocostuffapi/blob/master/PythonAPI/pycocotools/mask.py