Create a job

A JOB

POST https://api.playment.io/v1/projects/:project_id/jobs

This endpoint allows you to create a job

Path Parameters

Name	Type	Description
project_id	string	ID of the project in which you want to create the job

Headers

Name	Type	Description
x-api-key	string	API key for authentication

Request Body

Name	Type	Description
batch_id	string	A batch is a way to organize multiple jobs under one batch_id. You can create new batches from the dashboard or by using the batch creation API. If batch_id is left empty or the key is not present, the job is created in the Default batch in your project.
work_flow_id	string	The ID of the workflow inside which you want to create the job
data	object	The data object contains all the information and attachments required to label a job. The data object is defined below
reference_id	string	The unique identifier of the job

200

{
  "data": {
    "job_id": "3f3e8675-ca69-46d7-aa34-96f90fcbb732",
    "reference_id": "001",
    "work_flow_id": "2aae1234-acac-1234-eeff-12a22a237bbc"
  },
  "success": true
}

Payload

{
  "reference_id": "001",
  "data": {
    "sensor_data": {
      "sensors": [
        {
          "sensor_id": "lidar",
          "data_url": "https://xyz.s3.com/123.pcd",
          "sensor_pose": {
            "position": {
              "x": 0,
              "y": 0,
              "z": 0
            },
            "heading": {
              "w": 1,
              "x": 0,
              "y": 0,
              "z": 0
            }
          }
        },
        {
          "sensor_id": "18158562",
          "data_url": "https://xyz.s3.com/123.jpg",
          "sensor_pose": {
            "position": {
              "x": -0.81,
              "y": 1.64,
              "z": -1.52
            },
            "heading": {
              "w": 0.68,
              "x": 0.66,
              "y": -0.21,
              "z": 0.19
            }
          }
        }
      ],
      "ego_pose": {
        "position": {
          "x": 0,
          "y": 0,
          "z": 0
        },
        "heading": {
          "w": 1,
          "x": 0,
          "y": 0,
          "z": 0
        }
      },
      "sensor_meta": [
        {
          "id": "lidar",
          "name": "lidar",
          "state": "editable",
          "modality": "lidar",
          "primary_view": true
        },
        {
          "id": "18158562",
          "name": "18158562",
          "state": "editable",
          "modality": "camera",
          "camera_model": "brown_conrady",
          "primary_view": false,
          "intrinsics": {
            "cx": 600,
            "cy": 400,
            "fx": 1200,
            "fy": 800,
            "k1": 0,
            "k2": 0,
            "k3": 0,
            "k4": 0,
            "p1": 0,
            "p2": 0,
            "skew": 0,
            "scale_factor": 1
          }
        }
      ]
    }
  },
  "work_flow_id": "2aae1234-acac-1234-eeff-12a22a237bbc"
}

Key	Description	Type
data.sensor_data	Contains a sensors list, ego_pose object and sensor metadata list	Object
data.sensor_data.sensor_meta	Contains a list of all the sensors with each having metadata information like id : id of sensor name : name of sensor modality : lidar / camera If the sensor is a camera, you can add the camera intrinsic values as well as the camera_model. These values are used along with the sensor_pose to create projections between sensors. camera_model: one of brown_conrady or fisheye If this key doesn't exist or is null, the tool will assume brown_conrady. The intrinsics object contains the following keys: cx: principal point x value cy: principal point y value fx: focal length in x-axis fy: focal length in y-axis k1, k2, k3, k4, k5, k6: Radial distortion coefficients p1, p2: Tangential distortion coefficients skew: camera skew coefficient scale_factor: The factor by which the image has been downscaled (For example, scale_factor will be 2 if the original image is twice as large as the downscaled image) If the camera_model is brown_conrady then the distortion coefficients should be one of the following combinations: k1, k2, p1, p2 k1, k2, p1, p2, k3 k1, k2, p1, p2, k3, k4, k5, k6 If the camera_model is fisheye then the distortion coefficients should be the following combination: k1, k2, k3, k4 The remaining coefficients can be ignored or be assigned a value of 0 OpenCV reference: Camera calibration and 3D reconstruction	List
data.sensor_data.ego_pose	Contains the pose of a fixed point on the ego vehicle in the world frame of reference in the form of position (in (x, y, z)) and orientation (as quaternion (w, x, y, z)) In case the pose of the ego vehicle is available in the world frame of reference, The tool can allow annotators to mark objects as stationary and toggle APC (Aggregated point cloud) mode. Usually, if a vehicle is equipped with an IMU or Odometry sensor, then it is possible to get the pose of the ego-vehicle in the world frame of reference.	Object
data.sensor_data.sensors	List of all the sensors associated with this particular frame with each having: sensor_id : id of the sensor. This is a foreign key to the sensor id mentioned in the sensor_meta of the sequence data data_url : A URL to the file containing the data captured from the sensor for this frame. In order to annotate lidar data, please share point clouds in ascii encoded PCD format. sensor_pose : This key specifies the pose of respective sensors in a common frame of reference. If the ego_pose is available in the world frame of reference, then you should specify the sensor_pose of individual sensors in the same world frame of reference. In such cases, the pose might change in every frame, as the vehicle moves. If the ego_pose is not available, then all sensor_pose can be specified with respect to a fixed point on the vehicle. In such cases, the pose will not change between frames.	List

Please share point clouds in ascii encoded PCD format

PCD format Specification: https://pcl.readthedocs.io/projects/tutorials/en/latest/pcd_file_format.html

# .PCD v0.7 - Point Cloud Data file format 
VERSION 0.7
FIELDS x y z
SIZE 4 4 4
TYPE F F F
COUNT 1 1 1
WIDTH 47286
HEIGHT 1
VIEWPOINT 0 0 0 1 0 0 0
POINTS 47286
DATA ascii
5075.773 3756.887 107.923
5076.011 3756.876 107.865
5076.116 3756.826 107.844
5076.860 3756.975 107.648
5077.045 3756.954 107.605
5077.237 3756.937 107.559
5077.441 3756.924 107.511
5077.599 3756.902 107.474
5077.780 3756.885 107.432
5077.955 3756.862 107.391
...

Visualizing intensity/reflectivity information

You can send additional data like Intensity or reflectivity values for each point in the PCD file. This can help annotators segment reflective surfaces like lane markings.

You can refer to a sample PCD structure below.

# .PCD v0.7 - Point Cloud Data file format
VERSION 0.7
FIELDS x y z intensity
SIZE 4 4 4 4
TYPE F F F F
COUNT 1 1 1 1
WIDTH 33345
HEIGHT 1
VIEWPOINT 0 0 0 1 0 0 0
POINTS 33345
DATA ascii
6.381713394829211 20.46125301261318 -2.413081058852035 0.02745098
5.28544895349773 16.913142630625543 -2.283128795562235 0.023529412
4.50899949078274 14.40011307442226 -2.1906859549297164 0.023529412
3.90763970412707 12.453374568477361 -2.108260110209615 0.023529412
2.9475040373348147 11.063306463789342 -2.0292308745898255 0.03137255
2.648762017271138 9.92654497521169 -1.991709336214386 0.03529412
3.4192604688595 9.368384486158 -1.99749275125155 0.03137255
...

Once you share the data with the above format, annotators can utilize it to view point-cloud colors based on the intesity. It'll look like this:

Helper Python script to create jobs

import json
import requests
from copy import deepcopy

## Functions
def create_batch(BATCH_NAME):
    base_url = f"https://api.playment.io/v1/projects/{PROJECT_ID}/batch"
    DATA = {"name":BATCH_NAME}
    response = requests.post(base_url, headers={'x-api-key': CLIENT_KEY}, json=DATA)
    response_data = response.json()
    if response.status_code >= 500:
        raise Exception(f"Something went wrong at Playment's end {response.status_code}")
    if 400 <= response.status_code < 500:
        raise Exception(f"{response_data['error']['message']} {response.status_code}")
    print(response_data)
    return response_data

def Upload_jobs(DATA):
    base_url = f"https://api.playment.io/v1/projects/{PROJECT_ID}/jobs"
    response = requests.post(base_url, headers={'x-api-key': CLIENT_KEY}, json=DATA)
    response_data = response.json()
    if response.status_code >= 500:
        raise Exception(f"Something went wrong at Playment's end {response.status_code}")
    if 400 <= response.status_code < 500:
        raise Exception(f"{response_data['error']['message']} {response.status_code}")
    print(response_data)
    return response_data

## Set project details

# Details for creating JOBS,
# PROJECT_ID ->> ID of project in which job needed to be created
# CLIENT_KEY ->> secret client key to create JOBS
# WORK_FLOW_ID ->> You can ask this from playment side
# BATCH_ID ->> The batch in which JOB needed to be created

PROJECT_ID = ''
CLIENT_KEY = ''
WORK_FLOW_ID = ''
BATCH_ID = ''


## Job creation payload structure

payload_data_structure ={
    "reference_id": "",                 # reference_id is the unique reference id for the job
    "data": {
        "sensor_data": {
            "sensors": [],
            "ego_pose": {               # ego_pose is the ego vehicle's pose
                "position": {           # ego vehicle's position
                    "x": 0,
                    "y": 0,
                    "z": 0
                },
                "heading": {            # ego vehicle's heading in quaternion format
                    "w": 1,
                    "x": 0,
                    "y": 0,
                    "z": 0
                }
            },
            "sensor_meta": []           # sensor_meta contains meta data for lidar and camera sensors
        }
    },
    "work_flow_id": "",                 # this is the id of the workflow in which the jobs are to be created
    "batch_id": ""                     # this is the batch_id of the batch in which jobs are to be created

}

sensor_data_structure = {
  "sensor_id": "",                      # This is the sensor's id
  "data_url": "",                       # sensor's data's URL
  "sensor_pose": {                      # This are the sensor's extrinsic values
    "position": {                       # sensor's position
      "x": 0,
      "y": 0,
      "z": 0
    },
    "heading": {                        # sensor's heading in quaternion format
      "w": 1,
      "x": 0,
      "y": 0,
      "z": 0
    }
  }
}

lidar_sensor_meta_structure = {         # lidar sensor's metadata. There can only be one lidar sesnor
  "id": "",                             # lidar sensor's ID
  "name": "",                           # should be kept same as lidar sensor's ID
  "state": "editable",                  # should be kept as editable only
  "modality": "lidar",                  # moality specifies the type of sensor [lidar/camera]
  "primary_view": True                  # should be kept True for lidar sensor
}

camera_sensor_meta_structure = {        # camer sensor's metadata. There can be multiple camera sensors
  "id": "",                             # camera sensor's ID
  "name": "",                           # should be kept same as camera sensor's ID
  "state": "editable",                  # should be kept as editable only           
  "modality": "camera",                 # moality specifies the type of sensor [lidar/camera]
  "camera_model": "brown_conrady",      # specifies the model of camera
  "primary_view": False,                # should be kept as False for all camera sensors
  "intrinsics": {                       # camera sensor's intrinsic values
    "cx": 0,
    "cy": 0,
    "fx": 0,
    "fy": 0,
    "k1": 0,
    "k2": 0,
    "k3": 0,
    "k4": 0,
    "p1": 0,
    "p2": 0,
    "skew": 0,
    "scale_factor": 1
  }
}

if __name__ == "__main__":
    
    ## Set sensor id and data url (Only 1 lidar can be present per job)

    # Here lidar's sensor_id is the key and pcd file for each job is in the list
    lidar_data = [
            "https://example.com/pcd_url_1",
            "https://example.com/pcd_url_2"
        ]
    

    ## Set camera sensor ids and data urls (1 or more cameras can be present per job)

    # Here camera's sensor_id is the key and image url for each job is in the list corresponding to that camera
    camera_data = {
        'camera_1': [
            "https://example.com/image_url_1",
            "https://example.com/image_url_2"        ],
        "camera_2": [
            "https://example.com/image_url_3",
            "https://example.com/image_url_4"
        ]
    }



    lidar_sensor_id = 'lidar'  # set your lidar sensor id


    # validate the number of urls are equal for each sensor
    for camera_sensor_id, camer_sensor_urls in camera_data.items():
        assert len(camer_sensor_urls) == len(lidar_data), f"Number of URLs are not equal for {camera_sensor_id}"

    # get number of jobs
    numer_of_jobs = len(lidar_data)


    # create the jobs
    for i in range(numer_of_jobs):
       
        # define the payload for job creation
        payload_data = deepcopy(payload_data_structure)
        payload_data['reference_id'] = lidar_data[i].replace('.pcd', '') # you can set unique reference id as per your wish
        payload_data['work_flow_id'] = WORK_FLOW_ID
        payload_data['batch_id'] = BATCH_ID

        # define lidar sensor meta and add it to payload data
        lidar_sensor_meta = deepcopy(lidar_sensor_meta_structure)
        lidar_sensor_meta['id'] = lidar_sensor_id
        lidar_sensor_meta['name'] = lidar_sensor_id
        payload_data['data']['sensor_data']['sensor_meta'].append(lidar_sensor_meta)

        # define lidar sensor data and add it to payload data
        lidar_sensor_data = deepcopy(sensor_data_structure)
        lidar_sensor_data['sensor_id'] = lidar_sensor_id
        lidar_sensor_data['data_url'] = lidar_data[i]
        # set appropriate lidar position and heading, corrently defaulted it to (0,0,0),(1,0,0,0) 
        lidar_sensor_data['sensor_pose']['position']['x'] = 0
        lidar_sensor_data['sensor_pose']['position']['y'] = 0
        lidar_sensor_data['sensor_pose']['position']['z'] = 0
        
        lidar_sensor_data['sensor_pose']['heading']['w'] = 1
        lidar_sensor_data['sensor_pose']['heading']['x'] = 0
        lidar_sensor_data['sensor_pose']['heading']['y'] = 0
        lidar_sensor_data['sensor_pose']['heading']['z'] = 0
        payload_data['data']['sensor_data']['sensors'].append(lidar_sensor_data)

        for camera_sensor_id, camera_sensor_urls in camera_data.items():
            # define camera sensor meta and add it to payload
            camera_sensor_meta = deepcopy(camera_sensor_meta_structure)
            camera_sensor_meta['id'] = camera_sensor_id
            camera_sensor_meta['name'] = camera_sensor_id
            payload_data['data']['sensor_data']['sensor_meta'].append(camera_sensor_meta)

            # define camera sensor data and add it to payload data
            camera_sensor_data = deepcopy(sensor_data_structure)
            camera_sensor_data['sensor_id'] = camera_sensor_id
            camera_sensor_data['data_url'] = camera_sensor_urls[i]
            # set appropriate camera position and heading, corrently defaulted it to (0,0,0),(1,0,0,0) 
            camera_sensor_data['sensor_pose']['position']['x'] = 0
            camera_sensor_data['sensor_pose']['position']['y'] = 0
            camera_sensor_data['sensor_pose']['position']['z'] = 0

            camera_sensor_data['sensor_pose']['heading']['w'] = 1
            camera_sensor_data['sensor_pose']['heading']['x'] = 0
            camera_sensor_data['sensor_pose']['heading']['y'] = 0
            camera_sensor_data['sensor_pose']['heading']['z'] = 0
            
            payload_data['data']['sensor_data']['sensors'].append(camera_sensor_data)

        #print(json.dumps(payload_data))
        Upload_jobs(payload_data)