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Point Cloud Episodes
3D Episodes labeling interface
Root folder for the project named
- Dataset folders, that represents single episode. Each named
dataset_name, which contains:
annotation.json- file with whole episode annotation
frame_pointcloud_map.json- file with pointcloud to episode frame mapping
pointcloudfolder, contains source point cloud files, for example
related_imagesoptional folder, contains photo-context data:
- Frame folder, each named according to pointcloud (
/related_images/frame1/), which contains:
- image files (
.png \ .jpg)
- image ann files (
.json) - json files, named according to image name (
1.png -> 1.json)
Point cloud Episode Project Folder
On the top level are stored Project folders, these are the elements visible on the main Supervisely dashboard. Inside them they can contain only Datasets and Poject Meta information, all other data has to be stored a level below in a Dataset (Sequence). All datasets within a project have to contain content of the same cathegory.
Project Meta contains the essential information about the project - Classes and Tags. These are defined project-wide and can be used for labeling in every dataset inside the current project.
Datasets are the second level folders inside the project, they host the individual data files (pointclouds), photo context (images) and their annotations.
Items (Point clouds)
.pcdfile in sequence has to be stored inside a
dataset/pointcloudfolder. Each file has file name and order number, that defined inside
A sequence of frames has a list of
objectsthat are shared between frames. For example, sequence might have 10 cars objects represented by theirs
figuresin 100 frames.
The list of
objectsis defined for the entire sequence, even if the object's figure occurs in only one frame.
Figures - represents individual cuboids, attached to one single frame and its object.
The sequence contains 3 objects: (car1, car2, pedestrian1) and 10 frames.
Each frame in the sequence might have a figure for every object.
Then it will be 30 figures (10 figures for each object per sequence; 1 figure for the object per frame).
Linking between point clouds , Objects and Figures
In Supervisely Annotation Format each point cloud is a frame. Frame contain
index: sequence number, that have relation to point cloud name in
In the example below, created one Car object (with key "6663ca1d20c74bea83bd48c24568989d") for the entire sequence. (Means that this object can appear in any frame of the sequence)
Then, on frames (with indices 0 and 1) figures are placed, and each figure correspond to the Car object by the
Optional fields and loading These fields are optional and are not needed when loading the project. The server can automatically fill in these fields while project is loading.
id- unique identifier of the current object
classId- unique class identifier of the current object
labelerLogin- string - the name of user who created the current figure
createdAt- string - date and time of figure creation
updatedAt- string - date and time of the last figure update
description- string - (optional) - this field is used to store the text to assign to the sequence.
key- string, unique key for a given sequence (used in key_id_map.json to get the sequence ID)
tags- list of strings that will be interpreted as episode tags
objects- list of objects that may be present on the episode
frames- list of frames of which the episode consists. List contains only frames with an object from the 'objects' field
index- integer - number of the current frame
figures- list of figures in the current frame.
framesCount- integer - total number of frames in the episode
geometryType- "cuboid_3d" - class shape
Fields definitions for
key- string - unique key for a given object (used in key_id_map.json)
classTitle- string - the title of a class. It's used to identify the class shape from the
tags- list of strings that will be interpreted as object tags (can be empty)
Fields description for
key- string - unique key for a given figure (used in key_id_map.json)
objectKey- string - unique key to link figure to object (used in key_id_map.json)
geometryType- "cuboid_3d" -class shape
geometry- geometry of the object
position3D vector X, Y, Z values matches the axes on world coordinates, defined in global frame of reference as:
- +x - forward in the direction of travel ego vehicle
- +y - left
- +z - up
dimensionsis 3D vector with:
- x - width
- y - length
- z - height
rotationis 3D Vector with:
- x - pitch
- y - roll
- z - yaw (direction)
Rotation values bound inside [-pi ; pi ] When
yaw = 0box direction will be strict
The basic idea behind key-id-map is that it maps the unique identifiers of the object to the frame on which the shape is located. The server works with an identifier, but the file system of the loaded project stores these identifiers and object keys on disk, which is necessary for navigation and use of the high-level API and applications.
When loading a
dataset(sequence), the system returns its identifier, after which it is saved to a file on disk, along with the key of the loaded sequence in key-id-map file.
objectsto the server, a sequence ID is required (to determine which sequence the object belongs to), and it can be read from the key-id-map file by key. The system then returns the IDs of the successfully loaded objects.
figuresuploading to the server, an object identifier is required (which loaded object the shape belongs to), which can again be read from the key-id-map file.
While annotating the episode inside Supervisely interface key-id-map file is created automatically, and will be downloaded with entire project. Json format of key_id_map.json:
objects- dictionary, where the key is a unique string, generated inside Supervisely environment to set correspondence of current object in annotation, and values are unique integer ID corresponding to the current object
figures- dictionary, where the key is a unique string, generated inside Supervisely environment to set correspondence of object on current frame in annotation, and values are unique integer ID corresponding to the current frame
videos- dictionary, where the key is unique string, generated inside Supervisely environment to set correspondence of sequence (dataset) in annotation, and value is a unique integer ID corresponding to the current sequence
tags- dictionary, where the keys are unique strings, generated inside Supervisely environment to set correspondence of tag on current frame in annotation, and values are a unique integer ID corresponding to the current tag
- Value - returned by server integer identifier while uploading object / figure / sequence / tag
This file create for mapping between pointcloud files and annotation frames in the correct order.
"0" : "frame1.pcd",
"1" : "frame2.pcd",
"2" : "frame3.pcd"
Keys - frame order number Values - point cloud name (with extension)
- name - string - Name of image file
- Id - (OPTIONAL) - integer >= 1 ID of the photo in the system. It is not required when upload and is filled in automatically when the project is loaded.
- entityId (OPTIONAL) - integer >= 1 ID of the Point Cloud in the system, that photo attached to. Doesn't required while uploading.
- deviceId - string- Device ID or name.
- timestamp - string - Time when the frame occurred in ISO 8601 format
- intrinsicMatrix - Array of number - 3x3 flatten matrix (dropped last zeros column) of intrinsic parameters in row-major order, also called camera matrix. It's used to denote camera calibration parameters. See Intrinsic parameters.
- extrinsicMatrix - Array of number - 4x3 flatten matrix (dropped last zeros column) of extrinsic parameters in row-major order, also called joint rotation-translation matrix. It's used to denote the coordinate system transformations from 3D world coordinates to 3D camera coordinates. See Extrinsic_parameters.
Work with Supervisely projects involves two ways:
- 1.Labeling within the interface and use of default download / upload applications.
- 2.Using the API and SDK for manual labeling and data management.
$ git clone https://github.com/supervisely-ecosystem/sdk_pointcloud_episode_example.git
$ cd sdk_pointcloud_episode_example
$ pip -r requirements.txt
This repo stores some test files, the main script
requirements.txtAfter clone the repository, install the supervise library sdk from the requirements file.
Then, you can run the code, after specifying your token, server address and workspace id.