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Sample formats

A sample is a data point you want to label. Samples come in different types, like an image, a 3D point cloud, or a video sequence. When uploading (client.add_sample()) or downloading (client.get_sample()) a sample using the Python SDK, the format of the attributes field depends on the type of sample. The different formats are described here.
The section Import data shows how you can obtain URLs for your assets.

Image

Supported image formats: jpeg, png, bmp.
{
"image": {
"url": "https://example.com/image.jpg"
}
}
If the image file is on your local computer, you should first upload it to our asset storage service (using upload_asset()) or to another cloud storage service.

Image sequence

Supported image formats: jpeg, png, bmp.
{
"frames": [
{
"image": {
"url": "https://example.com/frame_00001.jpg"
},
"name": "frame_00001" // optional
},
{
"image": {
"url": "https://example.com/frame_00002.jpg"
},
"name": "frame_00002"
},
{
"image": {
"url": "https://example.com/frame_00003.jpg"
},
"name": "frame_00003"
}
]
}

3D point cloud

On Segments.ai, the up direction is defined along the z-axis, i.e. the vector (0, 0, 1) points up. If you upload point clouds with a different up direction, you might have trouble navigating the point cloud.
{
"pcd": {
"url": "https://example.com/pointcloud.pcd",
"type": "pcd"
},
"images": [
{ ... },
{ ... },
{ ... }
], // optional
"name": "frame_00001", // optional
"timestamp": "00001", // optional
"ego_pose": {
"position": {
"x": -2.7161461413869947,
"y": 116.25822288149078,
"z": 1.8348751887989483
},
"heading": {
"qx": -0.02111296123795955,
"qy": -0.006495469416730261,
"qz": -0.008024565904865688,
"qw": 0.9997181192298087
}
},
"default_z": -1 // optional, 0 by default
}
Name
Type
Description
pcd
Required. Point cloud data.
images
array of camera images
Reference camera images.
name
string
Name of the sample.
timestamp
int or string
Timestamp of the sample. Object kinematics will only be calculated if the timestamp is an integer representing the time in nanoseconds.
ego_pose
Ego pose
Pose of the sensor that captured the point cloud data.
default_z
float
Default z-value of the ground plane. 0 by default. Only valid in the point cloud cuboid editor. New cuboids will be drawn on top of the ground plane, i.e. the default z-position of a new cuboid is 0.5 (since the default height of a new cuboid is 1).

Point cloud data

See 3D point cloud formats for the supported file formats.
{
"url": "https://example.com/pointcloud.bin",
"type": "kitti"
}
Name
Type
Description
url
string
Required. URL of the point cloud data.
type
string: "pcd" | "binary-xyzi" | "kitti" | "binary-xyzir" | "nuscenes"
Required. Type of the point cloud data. See 3D point cloud file formats for the list of supported file formats.
If the point cloud file is on your local computer, you should first upload it to our asset storage service (using upload_asset()) or to another cloud storage service.

Camera image

A calibrated or uncalibrated reference image corresponding to a point cloud. The reference images can be opened in a new tab from within the labeling interface. You can determine the layout of the images by setting the row and col attributes on each image. If you also supply the calibration parameters (and distortion parameters if necessary), the main point cloud view can be set to the image to obtain a fused view.
{
"url": "https://example.com/image.jpg",
"row": 0,
"col": 0,
"intrinsics": { // optional
"intrinsic_matrix": [
[1266.417203046554, 0, 816.2670197447984],
[0, 1266.417203046554, 491.50706579294757],
[0, 0, 1]
]
},
"extrinsics": { // optional
"translation": {
"x": -0.012463384576629082,
"y": 0.76486688894964,
"z": -0.3109103442096661
},
"rotation": {
"qx": 0.713640516187247,
"qy": -0.001134052598226082,
"qz": 0.0036449450274057696,
"qw": 0.7005017073187271
}
},
"distortion": { // optional
"model": "fisheye",
"coefficients": {
"k1": -0.0539124,
"k2": -0.0101993,
"k3": -0.00202017,
"k4": 0.00120938
}
},
"camera_convention": "OpenCV" // optional
}
Name
Type
Description
url
string
Required. URL of the camera image.
row
int
Required. Row of this image in the images viewer.
col
int
Required. Column of this image in the images viewer.
intrinsics
Intrinsic parameters of the camera.
extrinsics
Extrinsic parameters of the camera relative to the ego pose.
distortion
Distortion parameters of the camera.
camera_convention
string: "OpenGL" | "OpenCV"
Convention of the camera coordinates. We use the OpenGL/Blender coordinate convention for cameras. +X is right, +Y is up, and +Z is pointing back and away from the camera. -Z is the look-at direction. Other codebases may use the OpenCV convention, where the Y and Z axes are flipped but the +X axis remains the same. See diagram 1.
If the image file is on your local computer, you should first upload it to our asset storage service (using upload_asset()) or to another cloud storage service.

Camera intrinsics

{
"intrinsic_matrix": [
[1266.417203046554, 0, 816.2670197447984],
[0, 1266.417203046554, 491.50706579294757],
[0, 0, 1]
]
}
Name
Type
Description
intrinsic_matrix
2D array of floats representing 3x3 matrix
KK
in row-major order​
Required. Intrinsic matrix
KK
used in the pinhole camera model.
K=[fx0ox0fyoy001]K = \begin{bmatrix} f_x & 0 & o_x\\ 0 & f_y & o_y \\ 0 & 0 & 1 \end{bmatrix}
fxf_x
and
fyf_y
​ are the focal lengths in pixels. We assume square pixels, so
fx=fyf_x = f_y
​.
oxo_x
and
oyo_y
are the offsets (in pixels) of the principal point from the top-left corner of the image frame.

Camera extrinsics

{
"translation": {
"x": -0.012463384576629082,
"y": 0.76486688894964,
"z": -0.3109103442096661
},
"rotation": {
"qx": 0.713640516187247,
"qy": -0.001134052598226082,
"qz": 0.0036449450274057696,
"qw": 0.7005017073187271
}
}
Name
Type
Description
translation
object: { "x": float, "y": float, "z": float }
Required. Translation of the camera in lidar coordinates, i.e., relative to the ego pose.
rotation
object: { "qx": float, "qy": float, "qz": float,
"qw": float }
Required. Rotation of the camera in lidar coordinates, i.e., relative to the ego pose (or equivalently: a transformation from camera frame to ego frame). Defined as a rotation quaternion. By default, we use the OpenGL/Blender coordinate convention for cameras. +X is right, +Y is up, and +Z is pointing back and away from the camera. -Z is the look-at direction. Other codebases may use the OpenCV convention, where the Y and Z axes are flipped but the +X axis remains the same. See diagram 1. You can specify the camera convention in Camera image.
Diagram 1: camera convention for calibrated camera images on Segments.ai.

Distortion

// Fisheye
{
"model": "fisheye",
"coefficients": {
"k1": -0.0539124,
"k2": -0.0101993,
"k3": -0.00202017,
"k4": 0.00120938
}
// Brown-Conrady
{
"model": "brown-conrady",
"coefficients": {
"k1": -0.2916058942,
"k2": 0.0763231072,
"k3": 0.0,
"p1": 0.0014829263,
"p2": -0.0019540316
}
}
Name
Type
Description
model
string: "fisheye" | "brown-conrady"
Required. Type of the distortion model: fisheye or Brown-Conrady.
coefficients
Fisheye: object: {
"k1": float,
"k2": float,
"k3": float,
"k4": float,
}
Brown-Conrady: object: {
"k1": float,
"k2": float,
"k3": float,
"p1": float,
"p2": float
}
Required. Coefficients of the distortion model: k1, k2, k3, k4 for fisheye (see the OpenCV fisheye model) and k1, k2, k3, p1, p2 for Brown-Conrady (see the OpenCV distortion model, note that
k4k_4
and
k5k_5
are not used).

Ego pose

The pose of the sensor used to capture the 3D point cloud data. This can be helpful if you want to obtain cuboids in world coordinates, or when your sensor is moving. In the latter situation, supplying an ego pose with each frame will ensure that static objects do not move when switching between frames.
{
"position": {
"x": -2.7161461413869947,
"y": 116.25822288149078,
"z": 1.8348751887989483
},
"heading": {
"qx": -0.02111296123795955,
"qy": -0.006495469416730261,
"qz": -0.008024565904865688,
"qw": 0.9997181192298087
}
},
Name
Type
Description
position
object: { "x": float, "y": float, "z": float }
Required. XYZ position of the sensor in world coordinates.
heading
object: { "qx": float, "qy": float, "qz": float,
"qw": float }
Required. Orientation of the sensor. Defined as a rotation quaternion.
Segments.ai uses 32-bit floats for the point positions. Keep in mind that 32-bit floats have limited precision. In fact, only 24 bits can be used to represent the number itself (the significand, excluding the sign bit), or about 7.22 decimal digits. If you want to keep two decimal places, this only leaves 5.22 decimal digits, so the numbers shouldn't be larger than 10^5.22 = 165958.
To avoid rounding problems, it is best practice to subtract the ego position of the first frame from all other ego positions. This way, the first ego position is set to (0, 0, 0) and the subsequent ego positions are relative to (0, 0, 0) . In your export script, you can add the ego position of the first frame back to the object positions.

3D point cloud sequence

{
"frames": [
{ ... },
{ ... },
{ ... }
]
}
Name
Type
Description
frames
array of 3D point clouds
Required. List of 3D point cloud frames in the sequence.

Multi-sensor sequence

{
"sensors": [
{
"name": "Lidar",
"task_type": "pointcloud-cuboid-sequence",
"attributes": { ... }
},
{
"name": "Camera 1",
"task_type": "image-vector-sequence",
"attributes": { ... }
},
...
]
}
Name
Type
Description
sensors
array of sensors
Required. List of the sensors that can be labeled.

Sensor

Name
Type
Description
name
string
Required. The name of the sensor.
task_type
string
Required. The task type of the sensor. Currently, pointcloud-cuboid-sequence and image-vector-sequence are supported.
attributes
object
Required. The sample attributes for the sensor. Currently, 3D point cloud sequence and image sequence are supported.

Text

{
"text": "Example text sample."
}
Name
Type
Description
text
string
Required. Text data.
To upload text samples in bulk, see file formats.