Get job results

JOB RESULT

GET https://api.playment.io/v1/projects/:project_id/jobs/:job_id

This endpoint allows you to get the annotations created on a job.

Path Parameters

Name	Type	Description
job_id	string	ID of the job. You would have received this ID in the response when you created the job.
project_id	string	ID of the project where the job was created

Headers

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

200 Job results successfully retrieved.

{
    "data": {
        "project_id": "905ea3ee-6f62-4bb7-8c31-471e486d9202",
        "reference_id": "001",
        "job_id": "3f3e8675-ca69-46d7-aa34-96f90fcbb732",
        "batch_id": "468b6ae3-e98d-4d08-9427-b6e7a016054c",
        "status": "completed",
        "work_flow_id": "51bb440c-f0ed-4286-8984-ea399cbc1f9w",
        "priority_weight": 5,
        "tag": "2aae1234-acac-1234-eeff-12a22a237bbc", //tag is the workflow_id for the job  
        "result": "https://playment-data-uploads.s3.ap-south-1.amazonaws.com/sample-result.json"
    },
    "success": true
}

ATTACHMENT

GET https://api.playment.io/v1/attachments?url=:result_url

The result_url (data.result) returned by the GET Job result API is a private URL (Well, it's public till 15th July 2021, after which we'll enforce it to be private). You need to use this Attachment access API to securely fetch the results. Please refer to the Attachment Access API document for more detail.

Path Parameters

Name	Type	Description
result_url	string	Result URL returned by the GET job result API

Headers

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

200

The API will redirect to a signed URL of the attachment

Result Object

{
  "data": {
    "annotation_data": {
      "annotations": [] 
    },
    "video_data": {
      "frames": [] //This is the same list you sent while creating the job
    }
  }
}

The annotations list contains objects, where each object is a tracker. A tracker tracks an object across frames. The frames key in the tracker object maps each annotation object in the tracker to the frame_id it belongs to.

{
  "_id": "",
  "type": "rectangle", // rectangle/polygon/line/cuboid/landmark
  "label": "",
  "frames" : {
    "frame001" : {<annotation_object>}
  }
}

You can check the data structures for the various annotation objects below:

Rectangle

{
  "_id": "0e6d895e-2484-439a-b62b-d8a0afb3d190",
  "label": "Cat".
  "attributes": {
    "pose": {
      "value": "standing"
    },
    "breed": {
      "value": "Persian"
    }
  }
  "coordinates": [
    {"x": 0.00398, "y": 0.00558},
    {"x": 0.05404, "y": 0.00558},
    {"x": 0.05404, "y": 0.09096},
    {"x": 0.00398, "y": 0.09096}
  ]
}

Polygon

{
  "_id": "8f8897ad-e07b-4e55-9dbe-61f6b9df85c5",
  "label": "face_mask",
  "attributes": {
    "covers_nose": {
      "value": "Yes"
    },
  }
  "points": {
    "p1": {"x": 0.286774, "y": 0.892502},
    "p2": {"x": 0.385849, "y": 0.576888},
    "p3": {"x": 0.426721, "y": 0.469081},
    "p4": {"x": 0.288623, "y": 0.253878},
    "p5": {"x": 0.148623, "y": 0.353878}
  },
  "edges": {
    "e1": ["p1", "p2"],
    "e2": ["p2", "p3"],
    "e3": ["p3", "p4"],
    "e4": ["p4", "p4"],
    "e5": ["p5", "p1"]
  }
}

Line

{
  "_id": "8f8897ad-e07b-4e55-9dbe-61f6b9df85c5",
  "label": "lane_divider",
  "attributes": {
    "raised": {
      "value": "Yes"
    },
  }
  "points": {
    "p1": {"x": 0.286774, "y": 0.892502},
    "p2": {"x": 0.285849, "y": 0.876888},
    "p3": {"x": 0.286774, "y": 0.869081},
    "p4": {"x": 0.288623, "y": 0.853878}
  }
}

Cuboid

{
  "_id": "8f8897ad-e07b-4e55-9dbe-61f6b9df85c5",
  "label": "Car",
  "attributes" : {
    "faces_visible": {
      "value": ["right","back"]
    }
  },
  "points": {
    "p1": {"x": 0.17,"y": 0.58},
    "p2": {"x": 0.26,"y": 0.58},
    "p3": {"x": 0.26,"y": 0.49},
    "p4": {"x": 0.17,"y": 0.49},
    "p5": {"x": 0.24,"y": 0.51},
    "p6": {"x": 0.35,"y": 0.52},
    "p7": {"x": 0.35,"y": 0.36},
    "p8": {"x": 0.25,"y": 0.37}
  },
  "front": {
    "coordinates": ["p1","p2","p3","p4"]
  },
  "side": {
    "coordinates": ["p2","p3","p5","p6"]
  },
  "back": {
    "coordinates": ["p7","p8","p5","p6"]
  }
}

Landmark

{
  "_id": "e7e3353f-2ffe-432a-a14f-9cb9a6f4b735",
  "label": "nose",
  "attributes": {
    "visible": {
      "value": "Partially"
    },
  }
  "points": {
    "p1": { "x": 0.17, "y": 0.58, "label": 1 },
    "p2": { "x": 0.26, "y": 0.63, "label": 2 },
    "p3": { "x": 0.27, "y": 0.63, "label": 3 },
    "p4": { "x": 0.29, "y": 0.59, "label": 4 },
    "p5": { "x": 0.25, "y": 0.46, "label": 5 },
    "p6": { "x": 0.22, "y": 0.42, "label": 6 }
  }
}

In our annotation output, the x and y coordinates are normalised to ensure consistency across different image sizes. Normalisation is crucial for accurately representing object positions relative to the image dimensions.

X and Y Coordinates:

• X Coordinate:

  • Normalised x coordinates ($Xnorm​$) are calculated using the formula: $Xnorm = Xraw / Image Width$

  • The result ranges from 0.0 to 1.0, where 0.0(Origin) corresponds to the leftmost edge of the image, and 1.0 corresponds to the rightmost edge.

• Y Coordinate:

  • Normalised y coordinates ($Ynorm​$) are calculated using the formula: $Ynorm = Yraw / Image Height$

  • The result ranges from 0.0 to 1.0, where 0.0(Origin) corresponds to the topmost edge of the image, and 1.0 corresponds to the bottommost edge.