LSP - Leeds Sports Pose¶
The Leeds Sports Pose dataset contains 2000 pose annotated images of mostly sports people gathered from Flickr using the tags shown above. The images have been scaled such that the most prominent person is roughly 150 pixels in length. Each image has been annotated with 14 joint locations. Left and right joints are consistently labelled from a person-centric viewpoint.
Use cases¶
Human body joint detection.
Properties¶
name
: leeds_sports_posekeywords
: image_processing, detection, human_pose, keypointsdataset size
: 264,2 MBis downloadable
: yestasks
:- keypoints: (default)
primary use
: human body joint detectiondescription
: Contains image files and body parts keypoint coordinates for detecting human body joints in imagessets
: train, testmetadata file size in disk
: 473,7 kBhas annotations
: yeswhich
:- body joint keypoints
- keypoints_original:
primary use
: human body joint detectiondescription
: Contains image files and body parts keypoint coordinates for detecting human body joints in imagessets
: train, testmetadata file size in disk
: 396,0 kBhas annotations
: yeswhich
:- body joint keypoints
Note
The keypoints_original
task is essentially the same as keypoints
,
but contains full size images instead of crops of persons.
Metadata structure (HDF5)¶
Task: keypoints¶
/
├── train/
│ ├── image_filenames # dtype=np.uint8, shape=(1000,83) (note: string in ASCII format)
│ ├── keypoint_names # dtype=np.uint8, shape=(14,15) (note: string in ASCII format)
│ ├── keypoints # dtype=np.float, shape=(1000,14,3)
│ ├── object_fields # dtype=np.uint8, shape=(2,16) (note: string in ASCII format)
│ └── object_ids # dtype=np.int32, shape=(1000,2)
│
└── test/
├── image_filenames # dtype=np.uint8, shape=(1000,83) (note: string in ASCII format)
├── keypoint_names # dtype=np.uint8, shape=(14,15) (note: string in ASCII format)
├── keypoints # dtype=np.float, shape=(1000,14,3)
├── object_fields # dtype=np.uint8, shape=(2,16) (note: string in ASCII format)
└── object_ids # dtype=np.int32, shape=(1000,2)
Fields¶
image_filenames
: image file path+nameavailable in
: train, testdtype
: np.uint8is padded
: Truefill value
: 0note
: strings stored in ASCII format
keypoint_names
: body joint namesavailable in
: train, testdtype
: np.uint8is padded
: Truefill value
: 0note
: strings stored in ASCII format
keypoints
: keypoint coordinatesavailable in
: train, testdtype
: np.floatis padded
: Falsefill value
: -1note
: keypoint format [x1,y1,is_visible]
object_fields
: list of field names of the object id listavailable in
: train, testdtype
: np.uint8is padded
: Truefill value
: 0note
: strings stored in ASCII formatnote
: key field (field name aggregator)
object_ids
: list of field idsavailable in
: train, testdtype
: np.int32is padded
: Falsefill value
: -1note
: key field (field id aggregator)
Task: keypoints_original¶
/
├── train/
│ ├── image_filenames # dtype=np.uint8, shape=(1000,83) (note: string in ASCII format)
│ ├── keypoint_names # dtype=np.uint8, shape=(14,15) (note: string in ASCII format)
│ ├── keypoints # dtype=np.float, shape=(1000,14,3)
│ ├── object_fields # dtype=np.uint8, shape=(2,16) (note: string in ASCII format)
│ └── object_ids # dtype=np.int32, shape=(1000,2)
│
└── test/
├── image_filenames # dtype=np.uint8, shape=(1000,83) (note: string in ASCII format)
├── keypoint_names # dtype=np.uint8, shape=(14,15) (note: string in ASCII format)
├── keypoints # dtype=np.float, shape=(1000,14,3)
├── object_fields # dtype=np.uint8, shape=(2,16) (note: string in ASCII format)
└── object_ids # dtype=np.int32, shape=(1000,2)
Fields¶
image_filenames
: image file path+nameavailable in
: train, testdtype
: np.uint8is padded
: Truefill value
: 0note
: strings stored in ASCII format
keypoint_names
: body joint namesavailable in
: train, testdtype
: np.uint8is padded
: Truefill value
: 0note
: strings stored in ASCII format
keypoints
: keypoint coordinatesavailable in
: train, testdtype
: np.floatis padded
: Falsefill value
: -1note
: keypoint format [x1,y1,is_visible]
object_fields
: list of field names of the object id listavailable in
: train, testdtype
: np.uint8is padded
: Truefill value
: 0note
: strings stored in ASCII formatnote
: key field (field name aggregator)
object_ids
: list of field idsavailable in
: train, testdtype
: np.int32is padded
: Falsefill value
: -1note
: key field (field id aggregator)