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.
{% hint style="info" %} The section import-data shows how you can obtain URLs for your assets. {% endhint %}
Supported image formats: jpeg, png, bmp.
{
"image": {
"url": "https://example.com/image.jpg"
}
}{% hint style="warning" %}
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.
{% endhint %}
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"
}
]
} {% hint style="info" %} 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. {% endhint %}
{
"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
"bounds": { // optional
"min_z": -1,
"max_z": 3
}
}| Name | Type | Description |
|---|---|---|
pcd | Point cloud data | Required. Point cloud data. |
images | array of camera images | Reference camera images. |
name | string | Name of the sample. |
timestamp | int, float, or string | Timestamp of the sample. Should be in nanoseconds for accurate velocity/acceleration calculations. Will also be used for interpolation unless disabled in dataset settings. |
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). |
bounds | dict of <string, float> | Point cloud bounds: a Supported values: |
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" | "ply" | Required. Type of the point cloud data. See #3d-point-cloud file formats for the list of supported file formats. |
{% hint style="warning" %}
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.
{% endhint %}
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.
{
"name": "Camera example 1", // optional
"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
"rotation": 1.5708 // optional
}| Name | Type | Description |
|---|---|---|
name | string | Name of the camera image. |
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 | Camera intrinsics | Intrinsic parameters of the camera. |
extrinsics | Camera extrinsics | Extrinsic parameters of the camera relative to the ego pose. |
distortion | 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. |
rotation | float | The rotation that needs to be applied when displaying the image. Valid options are 0, \frac{\pi}{4} , \frac{\pi}{2}, and \frac{3 \pi}{4}. Useful for when a camera is mounted upside-down. |
{% hint style="warning" %}
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.
{% endhint %}
{
"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 Kin row-major order | Required. Intrinsic matrix K used in the pinhole camera model. f_x and f_y are the focal lengths in pixels. We assume square pixels, so f_x = f_y. o_x and o_y are the offsets (in pixels) of the principal point from the top-left corner of the image frame. |
{
"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 |
"qw": | 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. |
.png)
Diagram 1: camera convention for calibrated camera images on Segments.ai.
// 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: "k1": "k2": "k3": "k4": }
"k1": "k2": "k3": "p1": "p2": } | 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 k_4 and k_5 are not used). |
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 |
"qw": | Required. Orientation of the sensor. Defined as a rotation quaternion. |
{% hint style="warning" %} 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. {% endhint %}
{
"frames": [
{ ... },
{ ... },
{ ... }
]
} | Name | Type | Description |
|---|---|---|
frames | array of 3D point clouds | Required. List of 3D point cloud frames in the 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. |
| 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. |