API Reference
Playment's API helps you setup various data annotation tasks that are solved with Playment's state of the art annotation tools by our highly trained annotators.
Playment offers fully managed enterprise-grade solutions to build high quality training data for computer vision and vehicle perception models at scale. You just need to send us the raw data the defined format and we'll take care of everything else.
Contact us at [email protected] to get started.
Security & Authentication
Your data is safe with us.
The following measures are in place to securing the two way communication with Playment servers
Authentication:
While creating a task authenticate your account by including your secret key in API requests.
The secret x-client-key ensures that only you are able to access your projects.
We expect the API key to be included in all API requests sent to our server.
Callback Authorization:
Playment can also provide to you the final annotation data via API by subscribing to our callback APIs.
If you’d like to authenticate our callbacks, Playment can send a x-playment-key header with each callback. You will need to share the key with us via a secure channel.
Network Security:
To ensure network security, you can whitelist our IP addresses so that you accept callback requests originating only from our servers
Creating a request
Each request represents one unit of data that you would want annotated.
In case of a non-sequential data it would be one image or point cloud data.
In case of sequential data it would be an array of data links corresponding to the annotation task of interest
When you send a POST request to our Feedline API to create a request, we create what is called as a FLU or Feed Line Unit
Create Feed Line Unit
POST https://api.playment.in/v1/project/ project_id/feedline
Path Parameters
project_id : This is a unique ID for a given annotation requirement and sensor setup.
Header Parameters
x-client-key : Secret key used for authentication to be passed as a header
* Playment will share the project_id and x-client-key separately with you via a secure channel.
Contact us at [email protected] to get started.
Payload
{
"reference_id":"001",
"data":{
"depends on the type of task"
},
"tag":"categorise_image",
}Check out the examples below to see different types of request structures.
Request Key
Request Value
reference_id
reference_id is a unique identifier for a request. We'll fail a request if you've sent another request with the same reference_id previously. This helps us ensure that we don't charge you for work which we've already done for you.
tag
Each request should have a tag which tells us what operation needs to be performed on that request. We'll share this tag with you during the integration process.
data
This has the complete data that is to be annotated as per the examples below
QPS
Playment's feedline POST API currently supports up to 20 requests per second.
Error codes
The following describe the possible issues with the request data when sending a POST request to Playment's feedline API
Error Code
Reason
FS_0001
Duplicate reference_id. Note: For each unique FLU/sequence you must use a unique reference_id
FS_0002
reference_id missing
FS_0003
- Data URL invalid
- Duplicate
frame_iddetected in case of sequential data. - Invalid string for
frame_iddetected in case of sequential data.
FS_0004
Data missing
FS_0005
tag missing
FS_0006
Feed Line Unit not found
FS_0007
Image URLs not valid
GE_0001
Malformed JSON/Invalid UUID passed
GE_0002
Parameter missing
Bounding Boxes
Bounding boxes for object detection and localization in images
Detect Facial attributes
Note:
Note for existing customers: This will be Playment's new data structure. The old data structure will be deprecated and non-functional from September 1st, 2018.
{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"draw_bounding_boxes"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"draw_bounding_boxes"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"draw_bounding_boxes\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"draw_bounding_boxes\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache"
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body:
{ reference_id: '001',
data: { image_url: 'https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1' },
tag: 'draw_bounding_boxes' },
json: true };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [{
"reference_id": "001",
"flu_id": "33abc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "draw_bounding_boxes",
"result": [
{
"label": "television",
"coordinates": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}]
},
{
"label": "plant",
"coordinates": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}]
}
]
]
}
LiDAR Annotation
An endpoint to annotate cuboids in LiDAR data and track them across frames
Two cycles on top on the car
The coordinate system for the point cloud are described below:
- positive x-axis : Red(in direction of the ego-car motion)
- positive y-axis : Green(towards the left side of the ego-car)
- positive z-axis : Blue(vertically upwards, follows the right-hand rule)
Notes:
- R|T matrices (in the calibration key) should be with respect to the origin of the point cloud data(i..e LiDAR)
- Calibration and IMU are optional fields.
- Reference: Calibration
Requirement:
Camera matrix
Rotation Matrix
Camera Matrix = [[fx, 0, cx],
[0, fy, cy],
[0, 0, 1]]
Rotation Matrix = [[r11,r12,r13,0],
[r21,r22,r23,0],
[r31,r32,r33,0],
[0, 0, 0, 1]]
Translation Matrix = [[1,0,0,tx],
[0,0,0,ty],
[0,0,0,tz],
[0,0,0,1]]
Distortion Coefficients - [k1,k2,p1,p2, k3]; if not mentioned, assumed to be zeros.
- Definitions:
- (fx, fy) - focal lengths expressed in pixel units.
- (cx, cy) - principal point that is usually at the image center.
- 'rij's - rotation components between LiDAR and camera in LiDAR frame of reference.
- (tx, ty, tz)-translation between LiDAR and camera in LiDAR frame of reference.
- 'k' - radial distortion coefficients.
- 'p' - tangential distortion coefficients.
{
"reference_id":"001",
"data":{
"sensor_data": {
"frames": [
{
"cameras": [
{
"camera_id": "0",
"image_url": "https://s3.amazonaws.com/bucket/____.JPG"
}
],
"frame_id": "000000001",
"point_cloud": "https://s3.amazonaws.com/bucket/____.PCD"
},
{
"cameras": [
{
"camera_id": "0",
"image_url": "https://s3.amazonaws.com/bucket/____.JPG"
}
],
"frame_id": "000000002",
"point_cloud": "https://s3.amazonaws.com/bucket/____.PCD"
},
{}
],
"calibration": {
"cameras": [
{
"id": 0,
"rotation_matrix": [
[r11, r12, r12, 0],
[r21, r22, r23, 0],
[r31, r32, r33, 0],
[0, 0, 0, 1]
],
"camera_origin_3D": [x0,y0,z0], //camera-origin in 3D world
"projection_matrix": [
[fx, 0, cx, 0],
[0, fy, cy, 0],
[0, 0, 1, 0]
],
"translation_matrix": [
[1, 0, 0, -tx],
[0, 1, 0, -ty],
[0, 0, 1, -tz],
[0, 0, 0, 1 ]
]
}
],
"point_cloud": [
{
"type": "pcd",
"format": [
{
"key": "x",
"datatype": "float32"
},
{
"key": "y",
"datatype": "float32"
},
{
"key": "z",
"datatype": "float32"
},
{
"key": "intensity",
"datatype": "float32"
}
],
"hardware": "Velodyne32"
}
]
}
}
},
"tag":"lidar_track_cuboids"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id":"001",
"data":{
"frames": [
{
"imu": {
"delX": 0,
"delY": 0,
"delZ": 0,
"del_rotX": 0, //value in radians
"del_rotY": 0,
"del_rotZ": 0
},
"cameras": [
{
"camera_id": "0",
"image_url": "https://s3.amazonaws.com/bucket/____.JPG"
}
],
"frame_id": 0,
"point_cloud": "https://s3.amazonaws.com/bucket/____.PCD"
},
{},
{}
],
"calibration": {
"cameras": [
{
"id": 0,
"rotation_matrix": [
[r11, r12, r12, 0],
[r21, r22, r23, 0],
[r31, r32, r33, 0],
[0, 0, 0, 1]
],
"camera_origin_3D": [x0,y0,z0], //camera-origin in 3D world
"projection_matrix": [
[fx, 0, cx, 0],
[0, fy, cy, 0],
[0, 0, 1, 0]
],
"translation_matrix": [
[1, 0, 0, -tx],
[0, 1, 0, -ty],
[0, 0, 1, -tz],
[0, 0, 0, 1 ]
]
}
],
"point_cloud": [
{
"type": "pcd",
"format": [
{
"key": "x",
"datatype": "float32"
},
{
"key": "y",
"datatype": "float32"
},
{
"key": "z",
"datatype": "float32"
},
{
"key": "intensity",
"datatype": "float32"
}
],
"hardware": "lidar"
}
]
}
},
"tag":"lidar_track_cuboids"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{ \n \"reference_id\":\"001\",\n \"data\":{\n \"frames\": [\n {\n \"imu\": {\n \"delX\": 0,\n \"delY\": 0,\n \"delZ\": 0,\n \"del_rotX\": 0, //value in radians \n \"del_rotY\": 0,\n \"del_rotZ\": 0\n },\n \"cameras\": [\n {\n \"camera_id\": \"0\",\n \"image_url\": \"https://s3.amazonaws.com/bucket/____.JPG\"\n }\n ],\n \"frame_id\": 0,\n \"point_cloud\": \"https://s3.amazonaws.com/bucket/____.PCD\"\n },\n {}, \n {}\n ], \n \"calibration\": {\n \"cameras\": [\n {\n \"id\": 0,\n \"rotation_matrix\": [\n [r11, r12, r12, 0],\n [r21, r22, r23, 0],\n [r31, r32, r33, 0],\n [0, 0, 0, 1]\n ],\n \"camera_origin_3D\": [x0,y0,z0], //camera-origin in 3D world\n \"projection_matrix\": [\n [fx, 0, cx, 0],\n [0, fy, cy, 0],\n [0, 0, 1, 0]\n ],\n \"translation_matrix\": [\n [1, 0, 0, -tx],\n [0, 1, 0, -ty],\n [0, 0, 1, -tz],\n [0, 0, 0, 1 ]\n ]\n }\n ],\n \"point_cloud\": [\n {\n \"type\": \"pcd\",\n \"format\": [\n {\n \"key\": \"x\",\n \"datatype\": \"float32\"\n },\n {\n \"key\": \"y\",\n \"datatype\": \"float32\"\n },\n {\n \"key\": \"z\",\n \"datatype\": \"float32\"\n },\n {\n \"key\": \"intensity\",\n \"datatype\": \"float32\"\n }\n ],\n \"hardware\": \"lidar\"\n }\n ]\n }\n },\n \"tag\":\"lidar_track_cuboids\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{ \n \"reference_id\":\"001\",\n \"data\":{\n \"frames\": [\n {\n \"imu\": {\n \"delX\": 0,\n \"delY\": 0,\n \"delZ\": 0,\n \"del_rotX\": 0, //value in radians \n \"del_rotY\": 0,\n \"del_rotZ\": 0\n },\n \"cameras\": [\n {\n \"camera_id\": \"0\",\n \"image_url\": \"https://s3.amazonaws.com/bucket/____.JPG\"\n }\n ],\n \"frame_id\": 0,\n \"point_cloud\": \"https://s3.amazonaws.com/bucket/____.PCD\"\n },\n {}, \n {}\n ], \n \"calibration\": {\n \"cameras\": [\n {\n \"id\": 0,\n \"rotation_matrix\": [\n [r11, r12, r12, 0],\n [r21, r22, r23, 0],\n [r31, r32, r33, 0],\n [0, 0, 0, 1]\n ],\n \"camera_origin_3D\": [x0,y0,z0], //camera-origin in 3D world\n \"projection_matrix\": [\n [fx, 0, cx, 0],\n [0, fy, cy, 0],\n [0, 0, 1, 0]\n ],\n \"translation_matrix\": [\n [1, 0, 0, -tx],\n [0, 1, 0, -ty],\n [0, 0, 1, -tz],\n [0, 0, 0, 1 ]\n ]\n }\n ],\n \"point_cloud\": [\n {\n \"type\": \"pcd\",\n \"format\": [\n {\n \"key\": \"x\",\n \"datatype\": \"float32\"\n },\n {\n \"key\": \"y\",\n \"datatype\": \"float32\"\n },\n {\n \"key\": \"z\",\n \"datatype\": \"float32\"\n },\n {\n \"key\": \"intensity\",\n \"datatype\": \"float32\"\n }\n ],\n \"hardware\": \"lidar\"\n }\n ]\n }\n },\n \"tag\":\"lidar_track_cuboids\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache",
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body: '{ \n "reference_id":"001",\n "data":{\n "frames": [\n {\n "imu": {\n "delX": 0,\n "delY": 0,\n "delZ": 0,\n "del_rotX": 0, //value in radians \n "del_rotY": 0,\n "del_rotZ": 0\n },\n "cameras": [\n {\n "camera_id": "0",\n "image_url": "https://s3.amazonaws.com/bucket/____.JPG"\n }\n ],\n "frame_id": 0,\n "point_cloud": "https://s3.amazonaws.com/bucket/____.PCD"\n },\n {}, \n {}\n ], \n "calibration": {\n "cameras": [\n {\n "id": 0,\n "rotation_matrix": [\n [r11, r12, r12, 0],\n [r21, r22, r23, 0],\n [r31, r32, r33, 0],\n [0, 0, 0, 1]\n ],\n "camera_origin_3D": [x0,y0,z0], //camera-origin in 3D world\n "projection_matrix": [\n [fx, 0, cx, 0],\n [0, fy, cy, 0],\n [0, 0, 1, 0]\n ],\n "translation_matrix": [\n [1, 0, 0, -tx],\n [0, 1, 0, -ty],\n [0, 0, 1, -tz],\n [0, 0, 0, 1 ]\n ]\n }\n ],\n "point_cloud": [\n {\n "type": "pcd",\n "format": [\n {\n "key": "x",\n "datatype": "float32"\n },\n {\n "key": "y",\n "datatype": "float32"\n },\n {\n "key": "z",\n "datatype": "float32"\n },\n {\n "key": "intensity",\n "datatype": "float32"\n }\n ],\n "hardware": "lidar"\n }\n ]\n }\n },\n "tag":"lidar_track_cuboids"\n}' };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [
{
"reference_id": "001",
"flu_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "lidar_track_cuboids",
"result": {
"annotations": [
{
"tracking_id": "85164320-42cb-47b7-9f3d-de004d6a5aba",
"type": "cuboid3d",
"label": "Obstruction",
"frames": {
"000000001": {
"center": {
"x": 34.915330335708234,
"y": -2.1073208887785713,
"z": -1.2032084902657283
},
"rotation": {
"x": 0,
"y": 0,
"z": 1
},
"dimensions": {
"width": 0.2260065451,
"height": 0.7935830195,
"length": 0.2046092338
},
"attributes": {
"Object State": {
"value": "Moving"
},
"Occlusion State": {
"value": "Not Occluded"
}
}
},
"000000002": {
"center": {
"x": 34.915330335708234,
"y": -2.1073208887785713,
"z": -1.2032084902657283
},
"rotation": {
"x": 0,
"y": 0,
"z": 1.1
},
"dimensions": {
"width": 0.2260065451,
"height": 0.7935830195,
"length": 0.2046092338
},
"attributes": {
"Object State": {
"value": "Moving"
},
"Occlusion State": {
"value": "Not Occluded"
}
}
},
"000000003": {
"center": {
"x": 34.915330335708234,
"y": -2.1073208887785713,
"z": -1.2032084902657283
},
"rotation": {
"x": 0,
"y": 0,
"z": 1.2
},
"dimensions": {
"width": 0.2260065451,
"height": 0.7935830195,
"length": 0.2046092338
},
"attributes": {
"Object State": {
"value": "Moving"
},
"Occlusion State": {
"value": "Not Occluded"
}
}
}
}
}
]
}
}
]
}Video Annotation
API Request Body
Key
Description
reference_id
The unique identifier for each FLU.
The final response will also be provided WRT the corresponding reference_id.
tag
This defines the process to be followed to complete the annotation process.
This is a value that will be provided by Playment.
data.video_data.frames
This contains a list of frames each for a particular timestamp in the order of annotation.
data.video_data.frames.frame_id
This is a unique string identifier for each frame in the sequence.
NOTE: You cannot send multiple frames with the same ID.
Annotation Response
Key
Description
reference_id
The unique identifier with which the FLU was created. If you have integrated with Playment's Feedline API you will receive the response corresponding to the same reference_id
flu_id
A Playment level UUID identifier for each FLU
status
PENDING: The data is currently being processed
COMPLETED: All the data has been processed and is ready for consumption for training your models.
tag
The string that denotes the process in which the annotation took place. This will be shared by Playment during the integration process.
result
The list of trackers/annotations in the corresponding FLU.
TrackerData
This is an object that defines the complete data across frames for a given object grouped by tracking id each object will have a key called frames which is a dictionary with key as the frame_id and the value as the annotation details.
Object Tracking with Bounding Boxes
Playment can help you annotate 2D bounding boxes on the image while maintaining the tracking id for objects across frames.
Bounding Box
Key
Description
_id
The unique tracking ID for an instance of an object across the sequential data.
label
The label for the unique annotation instance.
type
This defines the type of annotation rectangles
frames
A dictionary with the frame id key and annotation details as the value
frames.<frame_id>.coordinates
The coordinates of the 2D bounding box in the order from left top in the clockwise direction.
frames.<frame_id>._id
The unique ID for a frame instance of a tracker ie. an annotation
frames.<frame_id>.attributes
Frame level attribute dictionary for the given annotation
Cuboids
_id
The unique tracking ID for an instance of an object across the sequential data.
label
The label for the unique annotation instance.
type
This defines the type of annotation rectangles
frames
A dictionary with the frame id key and annotation details as the value
frames.<frame_id>._id
The unique ID for a frame instance of a tracker ie. an annotation
frames.<frame_id>.points
The normalized coordinates dictionary of the points that correspond to the annotated cuboid.
frames.<frame_id>.front
The list of points that correspond to the front face of the cuboid
frames.<frame_id>.side
The list of points that correspond to the side face of the cuboid.
frames.<frame_id>.back
The list of points that correspond to the ba face of the cuboid.
frames.<frame_id>.attributes
Frame level attribute dictionary for the given annotation.
Key
Description
_id
The unique tracking ID for an instance of an object across the sequential data.
label
The label for the unique annotation instance.
type
This defines the type of annotation rectangles
frames
A dictionary with the frame id key and annotation details as the value
frames.<frame_id>.points
The normalized coordinates dictionary of the points that correspond to the annotated line. The order of points is p1→p2→p3... so on.
frames.<frame_id>._id
The unique ID for a frame instance of a tracker ie. an annotation
frames.<frame_id>.attributes
Frame level attribute dictionary for the given annotation
{
"feed_line_units": [
{
"reference_id": "001",
"_id": "cf8ba34a-78ab-402b-b386-daa607cda443"
"flu_id": "sample_reference_id",
"status": "COMPLETED",
"tag": "line_tracking",
"result": [
{
"type": "line",
"label": "Lane",
"frames": {
"frame001": {
"_id": "555397a8-d916-443d-a973-e04300bb3d59",
"points": {
"p1": {"x": 0.5976247,"y": 0.776666},
"p2": {"x": 0.5040257,"y": 0.5233333}
},
"attributes": {
"Occlusion": {"value": "10%"}
}
},
"frame002": {
"_id": "057cf1f5-6c15-4f29-9775-940cf27643f4",
"points": {
"p1": {"x": 0.5976247,"y": 0.77333333},
"p2": {"x": 0.5040257,"y": 0.5233333}
},
"attributes": {
"Occlusion": {"value": "50%"}
}
},
"frame003": {
"_id": "057cf1f5-6c15-4f29-9775-940cf27643f4",
"points": {
"p1": {"x": 0.5976247,"y": 0.7766666},
"p2": {"x": 0.5040257,"y": 0.5233333}
},
"attributes": {
"Occlusion": {"value": "10%"}
}
}
}
}
]
}
]
}Key
Description
_id
The unique tracking ID for an instance of an object across the sequential data.
label
The label for the unique annotation instance.
type
This defines the type of annotation rectangles
frames
A dictionary with the frame id key and annotation details as the value
frames.<frame_id>.points
The normalized coordinates dictionary of the points that correspond to the annotated line. Each point also has a label associated with it which defines the order of the points.
frames.<frame_id>._id
The unique ID for a frame instance of a tracker ie. an annotation
frames.<frame_id>.attributes
Frame level attribute dictionary for the given annotation
{
"feed_line_units": [{
"reference_id": "002",
"flu_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "draw_landmarks",
"result": [
{
"type": "landmarks",
"label": "Left Eye",
"frames": {
"frame001": {
"points": {
"p1": { "x": 0.17, "y": 0.58, "label": 7 },
"p2": { "x": 0.26, "y": 0.63, "label": 8 },
"p3": { "x": 0.27, "y": 0.63, "label": 9 },
"p4": { "x": 0.29, "y": 0.59, "label": 10 },
"p5": { "x": 0.25, "y": 0.46, "label": 11 }
"attributes": {
"Occlusion": {"value": "10%"}
}
},
"frame002": {
"points": {
"p1": { "x": 0.18, "y": 0.59, "label": 7 },
"p2": { "x": 0.26, "y": 0.63, "label": 8 },
"p3": { "x": 0.28, "y": 0.62, "label": 9 },
"p4": { "x": 0.29, "y": 0.59, "label": 10 },
"p5": { "x": 0.25, "y": 0.46, "label": 11 }
},
"attributes": {
"Occlusion": {"value": "50%"}
}
},
"frame003": {
"points": {
"p1": { "x": 0.19, "y": 0.62, "label": 7 },
"p2": { "x": 0.28, "y": 0.63, "label": 8 },
"p3": { "x": 0.30, "y": 0.61, "label": 9 },
"p4": { "x": 0.24, "y": 0.55, "label": 10 },
"p5": { "x": 0.22, "y": 0.43, "label": 11 }
},
"attributes": {
"Occlusion": {"value": "10%"}
}
}
}
}
]
}]
}Key
Description
_id
The unique tracking ID for an instance of an object across the sequential data.
label
The label for the unique annotation instance.
frames.<frame_id>.points
The normalized coordinates dictionary of the points that correspond to the annotated line.
frames.<frame_id>.edges
This gives a list of pairs of points which correspond to the order in which the points are joined to make the final polygon.
type
This defines the type of annotation rectangles
frames
A dictionary with the frame id key and annotation details as the value
frames.<frame_id>._id
The unique ID for a frame instance of a tracker ie. an annotation
frames.<frame_id>.attributes
Frame level attribute dictionary for the given annotation
{
"feed_line_units": [{
"reference_id": "001",
"flu_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "draw_polygons",
"result": [
{
"_id": "563bc3fe-9fbc-4d7c-b951-a13bcbc3fe",
"type":"polygons",
"label": "Cyclist",
"frames":{
"frame_1": {
"_id": "563bc3fe-9fbc-4d7c-b951-a13bcbc3fe",
"points": {
"p1": {"x": 0.17,"y": 0.58},
"p2": {"x": 0.26,"y": 0.63},
"p3": {"x": 0.27,"y": 0.63},
"p4": {"x": 0.29,"y": 0.59},
"p5": {"x": 0.25,"y": 0.46},
"p6": {"x": 0.22,"y": 0.42}
},
"edges": {
"e1": ["p1","p2"],
"e2": ["p2","p3"],
"e3": ["p3","p4"],
"e4": ["p4","p5"],
"e5": ["p5","p6"],
"e6": ["p6","p7"],
"e7": ["p8","p9"]
},
"attributes":{
"Occlusion":{
"value": "50%"
}
}
},
"frame_2": {
"_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"points": {
"p1": {"x": 0.18,"y": 0.45},
"p2": {"x": 0.24,"y": 0.58},
"p3": {"x": 0.23,"y": 0.60},
"p4": {"x": 0.26,"y": 0.55},
"p5": {"x": 0.21,"y": 0.43},
"p6": {"x": 0.22,"y": 0.42}
},
"edges": {
"e1": ["p1","p2"],
"e2": ["p2","p3"],
"e3": ["p3","p4"],
"e4": ["p4","p5"],
"e5": ["p5","p6"],
"e6": ["p6","p7"],
"e7": ["p8","p9"]
},
"attributes":{
"Occlusion":{
"value": "60%"
}
}
}
}
}
]
}]
}{
"reference_id": "sample_reference_id",
"data": {
"video_data": {
"frames": [
{
"frame_id": "frame001",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
{
"frame_id": "frame002",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2"
},
{
"frame_id": "frame003",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3"
},
{
"frame_id": "frame004",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4"
},
{
"frame_id": "frame005",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5"
}
],
}
},
"tag": "object_tracking"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id": "sample_reference_id",
"data": {
"frames": [
{
"frame_id": "frame001",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
{
"frame_id": "frame002",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2"
},
{
"frame_id": "frame003",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3"
},
{
"frame_id": "frame004",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4"
},
{
"frame_id": "frame005",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5"
}
],
},
"tag": "object_tracking"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{\n \"reference_id\": \"sample_reference_id\",\n \"data\": {\n \"frames\": [\n {\n \"frame_id\": \"frame001\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n {\n \"frame_id\": \"frame002\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2\"\n },\n {\n \"frame_id\": \"frame003\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3\"\n },\n {\n \"frame_id\": \"frame004\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4\"\n },\n {\n \"frame_id\": \"frame005\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5\"\n }\n ],\n \n },\n \"tag\": \"object_tracking\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{\n \"reference_id\": \"sample_reference_id\",\n \"data\": {\n \"frames\": [\n {\n \"frame_id\": \"frame001\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n {\n \"frame_id\": \"frame002\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2\"\n },\n {\n \"frame_id\": \"frame003\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3\"\n },\n {\n \"frame_id\": \"frame004\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4\"\n },\n {\n \"frame_id\": \"frame005\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5\"\n }\n ],\n \n },\n \"tag\": \"object_tracking\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache",
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body: '{\n "reference_id": "sample_reference_id",\n "data": {\n "frames": [\n {\n "frame_id": "frame001",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"\n },\n {\n "frame_id": "frame002",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2"\n },\n {\n "frame_id": "frame003",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3"\n },\n {\n "frame_id": "frame004",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4"\n },\n {\n "frame_id": "frame005",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5"\n }\n ],\n \n },\n "tag": "object_tracking"\n}' };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [
{
"reference_id": "001",
"flu_id": "sample_reference_id",
"status": "COMPLETED",
"tag": "object_tracking",
"result": [
{
"_id": "555397a8-d916-443d-a973-e04300bb3d59",
"type": "rectangle",
"label": "traffic_light",
"frames": {
"frame001": {
"_id": "555397a8-d916-443d-a973-e04300bb3d59",
"type": "rectangle",
"label": "traffic_light",
"attributes": {
"color": {
"value": "red"
}
},
"coordinates": [
{
"x": 0.5435780444531554,
"y": 0.3925569183910122
},
{
"x": 0.5477217039572666,
"y": 0.3925569183910122
},
{
"x": 0.5477217039572666,
"y": 0.41283176820041856
},
{
"x": 0.5435780444531554,
"y": 0.41283176820041856
}
]
},
"frame002": {
"_id": "555397a8-d916-443d-a973-e04300bb3d59",
"type": "rectangle",
"label": "traffic_light",
"attributes": {
"color": {
"value": "green"
}
},
"coordinates": [
{
"x": 0.5464092560993109,
"y": 0.4103705019588349
},
{
"x": 0.5508899646089167,
"y": 0.4103705019588349
},
{
"x": 0.5508899646089167,
"y": 0.4327607148169202
},
{
"x": 0.5464092560993109,
"y": 0.4327607148169202
}
]
}
}
},
{
"_id": "057cf1f5-6c15-4f29-9775-940cf27643f4",
"type": "rectangle",
"label": "traffic_light",
"frames": {
"frame002": {
"_id": "057cf1f5-6c15-4f29-9775-940cf27643f4",
"type": "rectangle",
"label": "traffic_light",
"attributes": {
"color": {
"value": "off"
}
},
"coordinates": [
{
"x": 0.3480726842916043,
"y": 0.5059539147455372
},
{
"x": 0.3526237778854049,
"y": 0.5059539147455372
},
{
"x": 0.3526237778854049,
"y": 0.5252503794751654
},
{
"x": 0.3480726842916043,
"y": 0.5252503794751654
}
]
},
"frame003": {
"_id": "057cf1f5-6c15-4f29-9775-940cf27643f4",
"type": "rectangle",
"label": "traffic_light",
"attributes": {
"color": {
"value": "off"
}
},
"coordinates": [
{
"x": 0.3418031924762347,
"y": 0.5049184373876437
},
{
"x": 0.3465110233654195,
"y": 0.5049184373876437
},
{
"x": 0.3465110233654195,
"y": 0.5252503794751653
},
{
"x": 0.3418031924762347,
"y": 0.5252503794751653
}
]
}
}
}
]
}
]
}{
"feed_line_units": [{
"reference_id": "002",
"flu_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "draw_cuboids",
"result": [
{
"_id": "555397a8-d916-443d-a973-e04300bb3d59",
"type": "rectangles",
"label": "Car",
"frames":{
"frame_001":{
"_id": "057cf1f5-6c15-4f29-9775-940cf27643f4",
"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"]
},
"attributes":{
"model_type" : {"value": "Sedan"},
"faces_visible": {"value": ["right","back"]}
}
},
"frame_002":{
"_id": "33abc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"points": {
"p1": {"x": 0.19,"y": 0.58},
"p2": {"x": 0.24,"y": 0.56},
"p3": {"x": 0.23,"y": 0.49},
"p4": {"x": 0.12,"y": 0.44},
"p5": {"x": 0.27,"y": 0.52},
"p6": {"x": 0.33,"y": 0.52},
"p7": {"x": 0.38,"y": 0.38},
"p8": {"x": 0.21,"y": 0.32}
},
"front": {
"coordinates": ["p1","p2","p3","p4"]
},
"side": {
"coordinates": ["p2","p3","p5","p6"]
},
"back": {
"coordinates": ["p7","p8","p5","p6"]
},
"attributes":{
"model_type" : {"value": "Sedan"},
"faces_visible": {"value": ["right","back"]}
}
}
}
}
]
}]
}Line Tracking
Lines for object tracking in video across frames.
{
"reference_id": "sample_reference_id",
"data": {
"video_data": {
"frames": [
{
"frame_id": "frame001",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
{
"frame_id": "frame002",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2"
},
{
"frame_id": "frame003",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3"
},
{
"frame_id": "frame004",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4"
},
{
"frame_id": "frame005",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5"
}
],
}
},
"tag": "polygon_tracking"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id": "sample_reference_id",
"data": {
"frames": [
{
"frame_id": "frame001",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
{
"frame_id": "frame002",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2"
},
{
"frame_id": "frame003",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3"
},
{
"frame_id": "frame004",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4"
},
{
"frame_id": "frame005",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5"
}
],
},
"tag": "polygon_tracking"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{\n \"reference_id\": \"sample_reference_id\",\n \"data\": {\n \"frames\": [\n {\n \"frame_id\": \"frame001\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n {\n \"frame_id\": \"frame002\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2\"\n },\n {\n \"frame_id\": \"frame003\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3\"\n },\n {\n \"frame_id\": \"frame004\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4\"\n },\n {\n \"frame_id\": \"frame005\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5\"\n }\n ],\n \n },\n \"tag\": \"polygon_tracking\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{\n \"reference_id\": \"sample_reference_id\",\n \"data\": {\n \"frames\": [\n {\n \"frame_id\": \"frame001\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n {\n \"frame_id\": \"frame002\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2\"\n },\n {\n \"frame_id\": \"frame003\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3\"\n },\n {\n \"frame_id\": \"frame004\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4\"\n },\n {\n \"frame_id\": \"frame005\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5\"\n }\n ],\n \n },\n \"tag\": \"polygon_tracking\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache",
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body: '{\n "reference_id": "sample_reference_id",\n "data": {\n "frames": [\n {\n "frame_id": "frame001",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"\n },\n {\n "frame_id": "frame002",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2"\n },\n {\n "frame_id": "frame003",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3"\n },\n {\n "frame_id": "frame004",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4"\n },\n {\n "frame_id": "frame005",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5"\n }\n ],\n \n },\n "tag": "polygon_tracking"\n}' };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [
{
"reference_id": "001",
"flu_id": "sample_reference_id",
"status": "COMPLETED",
"tag": "polygon_tracking",
"result": [
{
"type": "line",
"label": "Lane",
"frames": {
"frame001": {
"points": {
"p1": {
"x": 0.5976247987117552,
"y": 0.7766666666666666
},
"p2": {
"x": 0.5040257648953301,
"y": 0.5233333333333333
}
},
"attributes": {
"Occlusion": {
"value": "10%"
}
}
},
"frame002": {
"points": {
"p1": {
"x": 0.5976247987117552,
"y": 0.7733333333333332
},
"p2": {
"x": 0.5040257648953301,
"y": 0.52
}
},
"attributes": {
"Occlusion": {
"value": "50%"
}
}
},
"frame003": {
"points": {
"p1": {
"x": 0.5976247987117552,
"y": 0.7766666666666666
},
"p2": {
"x": 0.5040257648953301,
"y": 0.5233333333333333
}
},
"attributes": {
"Occlusion": {
"value": "10%"
}
}
},
"frame004": {
"points": {
"p1": {
"x": 0.5976247987117552,
"y": 0.7766666666666666
},
"p2": {
"x": 0.5040257648953301,
"y": 0.5233333333333333
}
},
"attributes": {
"Occlusion": {
"value": "10%"
}
}
},
"frame005": {
"points": {
"p1": {
"x": 0.5976247987117552,
"y": 0.7766666666666666
},
"p2": {
"x": 0.5040257648953301,
"y": 0.5233333333333333
}
},
"attributes": {
"Occlusion": {
"value": "40%"
}
}
}
},
"tracker_id": "cf8ba34a-78ab-402b-b386-daa607cda443"
}
]
}
]
}Cuboid Tracking
2D Cuboids for object tracking in image videos across frames.
{
"reference_id": "sample_reference_id",
"data": {
"video_data": {
"frames": [
{
"frame_id": "frame001",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
{
"frame_id": "frame002",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2"
},
{
"frame_id": "frame003",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3"
},
{
"frame_id": "frame004",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4"
},
{
"frame_id": "frame005",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5"
}
],
}
},
"tag": "cuboid_tracking"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id": "sample_reference_id",
"data": {
"frames": [
{
"frame_id": "frame001",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
{
"frame_id": "frame002",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2"
},
{
"frame_id": "frame003",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3"
},
{
"frame_id": "frame004",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4"
},
{
"frame_id": "frame005",
"src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5"
}
],
},
"tag": "cuboid_tracking"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{\n \"reference_id\": \"sample_reference_id\",\n \"data\": {\n \"frames\": [\n {\n \"frame_id\": \"frame001\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n {\n \"frame_id\": \"frame002\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2\"\n },\n {\n \"frame_id\": \"frame003\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3\"\n },\n {\n \"frame_id\": \"frame004\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4\"\n },\n {\n \"frame_id\": \"frame005\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5\"\n }\n ],\n \n },\n \"tag\": \"cuboid_tracking\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{\n \"reference_id\": \"sample_reference_id\",\n \"data\": {\n \"frames\": [\n {\n \"frame_id\": \"frame001\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n {\n \"frame_id\": \"frame002\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2\"\n },\n {\n \"frame_id\": \"frame003\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3\"\n },\n {\n \"frame_id\": \"frame004\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4\"\n },\n {\n \"frame_id\": \"frame005\",\n \"src\": \"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5\"\n }\n ],\n \n },\n \"tag\": \"cuboid_tracking\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache",
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body: '{\n "reference_id": "sample_reference_id",\n "data": {\n "frames": [\n {\n "frame_id": "frame001",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"\n },\n {\n "frame_id": "frame002",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+2"\n },\n {\n "frame_id": "frame003",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+3"\n },\n {\n "frame_id": "frame004",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+4"\n },\n {\n "frame_id": "frame005",\n "src": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+5"\n }\n ],\n \n },\n "tag": "cuboid_tracking"\n}' };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [
{
"reference_id": "001",
"flu_id": "sample_reference_id",
"status": "COMPLETED",
"tag": "polygon_tracking",
"result": [
{
"type": "cuboid",
"label": "Car",
"frames": {
"frame001": {
"points": {
"p1": {
"x": 0.15982286634460555,
"y": 0.54
},
"p2": {
"x": 0.267512077294686,
"y": 0.54
},
"p3": {
"x": 0.267512077294686,
"y": 0.8
},
"p4": {
"x": 0.15982286634460555,
"y": 0.8
},
"p5": {
"x": 0.3852657004830917,
"y": 0.48833333333333334
},
"p6": {
"x": 0.3852657004830917,
"y": 0.7283333333333334
},
"p7": {
"x": 0.28586027499070976,
"y": 0.48833333333333334
},
"p8": {
"x": 0.28586027499070976,
"y": 0.7283333333333334
}
},
"attributes": {
"Lane": {
"value": "Same as ego car"
}
},
"front": {
"coordinates": [
"p1",
"p2",
"p3",
"p4"
]
},
"side": {
"coordinates": [
"p2",
"p5",
"p6",
"p3"
]
},
"back": {
"coordinates": [
"p5",
"p7",
"p8",
"p6"
]
}
},
"frame002": {
"points": {
"p1": {
"x": 0.11352657004830924,
"y": 0.56
},
"p2": {
"x": 0.22121578099838982,
"y": 0.56
},
"p3": {
"x": 0.22121578099838982,
"y": 0.82
},
"p4": {
"x": 0.11352657004830924,
"y": 0.82
},
"p5": {
"x": 0.33896940418679555,
"y": 0.5083333333333332
},
"p6": {
"x": 0.33896940418679555,
"y": 0.7483333333333332
},
"p7": {
"x": 0.23956397869441343,
"y": 0.5083333333333332
},
"p8": {
"x": 0.23956397869441343,
"y": 0.7483333333333332
}
},
"attributes": {
"Lane": {
"value": "Same as ego car"
}
},
"front": {
"coordinates": [
"p1",
"p2",
"p3",
"p4"
]
},
"side": {
"coordinates": [
"p2",
"p5",
"p6",
"p3"
]
},
"back": {
"coordinates": [
"p5",
"p7",
"p8",
"p6"
]
}
},
"frame003": {
"points": {
"p1": {
"x": 0.036030595813204405,
"y": 0.5966666666666667
},
"p2": {
"x": 0.14371980676328497,
"y": 0.5966666666666667
},
"p3": {
"x": 0.14371980676328497,
"y": 0.8566666666666667
},
"p4": {
"x": 0.036030595813204405,
"y": 0.8566666666666667
},
"p5": {
"x": 0.26147342995169087,
"y": 0.5449999999999999
},
"p6": {
"x": 0.26147342995169087,
"y": 0.7849999999999998
},
"p7": {
"x": 0.16206800445930886,
"y": 0.5449999999999999
},
"p8": {
"x": 0.16206800445930886,
"y": 0.7849999999999998
}
},
"attributes": {
"Lane": {
"value": "Side"
}
},
"front": {
"coordinates": [
"p1",
"p2",
"p3",
"p4"
]
},
"side": {
"coordinates": [
"p2",
"p5",
"p6",
"p3"
]
},
"back": {
"coordinates": [
"p5",
"p7",
"p8",
"p6"
]
}
},
"frame004": {
"points": {
"p1": {
"x": -0.06763285024154582,
"y": 0.6466666666666666
},
"p2": {
"x": 0.04005636070853473,
"y": 0.6466666666666666
},
"p3": {
"x": 0.04005636070853473,
"y": 0.9066666666666666
},
"p4": {
"x": -0.06763285024154582,
"y": 0.9066666666666666
},
"p5": {
"x": 0.15780998389694048,
"y": 0.5949999999999999
},
"p6": {
"x": 0.15780998389694048,
"y": 0.8349999999999999
},
"p7": {
"x": 0.05840455840455844,
"y": 0.5949999999999999
},
"p8": {
"x": 0.05840455840455844,
"y": 0.8349999999999999
}
},
"attributes": {
"Lane": {
"value": "Side"
}
},
"front": {
"coordinates": [
"p1",
"p2",
"p3",
"p4"
]
},
"side": {
"coordinates": [
"p2",
"p5",
"p6",
"p3"
]
},
"back": {
"coordinates": [
"p5",
"p7",
"p8",
"p6"
]
}
},
"frame005": {
"points": {
"p1": {
"x": -0.13103864734299503,
"y": 0.5833333333333334
},
"p2": {
"x": -0.015096618357487952,
"y": 0.5833333333333334
},
"p3": {
"x": -0.015096618357487952,
"y": 0.9
},
"p4": {
"x": -0.13103864734299503,
"y": 0.9
},
"p5": {
"x": 0.1869967793880838,
"y": 0.49833333333333335
},
"p6": {
"x": 0.1869967793880838,
"y": 0.8283333333333334
},
"p7": {
"x": 0.06617298076108166,
"y": 0.49833333333333335
},
"p8": {
"x": 0.06617298076108166,
"y": 0.8283333333333334
}
},
"attributes": {
"Lane": {
"value": "Side"
}
},
"front": {
"coordinates": [
"p1",
"p2",
"p3",
"p4"
]
},
"side": {
"coordinates": [
"p2",
"p5",
"p6",
"p3"
]
},
"back": {
"coordinates": [
"p5",
"p7",
"p8",
"p6"
]
}
}
},
"tracker_id": "bab6690f-c6de-446b-b372-0ef3743abe35"
}
]
}
]
}Semantic Segmentation
Assign a label to each pixel in an image
Full Pixel Semantic Segmentation for Autonomous driving
{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"semantic_segmentation"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"semantic_segmentation"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"semantic_segmentation\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"semantic_segmentation\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache",
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body:
{ reference_id: '001',
data: { image_url: 'https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1' },
tag: 'semantic_segmentation' },
json: true };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [
{
"reference_id": "001",
"flu_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "semantic_segmentation",
"image_url": "https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1",
"result": {
"grayscale_mask": "https://example.playment.io/001_grayscale.png",
"grayscale_label_map": {
"car" : "1",
"pedestrian" : "2"
},
"rgb_mask": "https://example.playment.io/001_rgb.png",
"rgb_label_map": {
"car" : "#001ADE",
"pedestrian" : "#BDF032"
}
}
}
]
}Instance Segmentation
Assign an instanceID to each object present in the image.
Semantic Instance Segmented street scene
{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"instance_segmentation"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"semantic_segmentation"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"instance_segmentation\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"instance_segmentation\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache",
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body:
{ reference_id: '001',
data: { image_url: 'https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1' },
tag: 'instance_segmentation' },
json: true };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [
{
"reference_id": "001",
"flu_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "instance_segmentation",
"result": {
"mask_url": "https://example.playment.io/001_rgb.png",
"legend": {
"Car # 1": "#fdffd34",
"Car # 2": "#f1fd34",
"Sky": "#f23334",
"Tree": "#fd2d34"
}
}
}
]
}Cuboids
Label perspective cuboids with coordinates of 6 vertices for any object in a 2D image for camera based depth perception models
{
"reference_id":"002",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"draw_cuboids"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"draw_cuboids"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"draw_cuboids\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"draw_cuboids\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache"
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body:
{ reference_id: '001',
data: { image_url: 'https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1' },
tag: 'draw_cuboids' },
json: true };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [{
"reference_id": "002",
"flu_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "draw_cuboids",
"result": {
"cuboids": [{
"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"] //clockwise
},
"side": {
"coordinates": ["p2","p3","p5","p6"]
},
"back": {
"coordinates": ["p7","p8","p5","p6"]
},
"label": "Car - Sedan",
"faces_visible": ["right","back"]
}
]
}
}]
}Landmarks
Facial landmark annotations for facial feature understanding
Landmarks on Human Face
{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"draw_landmarks"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"draw_landmarks"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"draw_landmarks\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"draw_landmarks\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache"
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body:
{ reference_id: '001',
data: { image_url: 'https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1' },
tag: 'draw_landmarks' },
json: true };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [{
"reference_id": "002",
"flu_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "draw_landmarks",
"result": {
"landmarks": [{
"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 }
},
"label": "nose"
}, {
"points": {
"p1": { "x": 0.17, "y": 0.58, "label": 7 },
"p2": { "x": 0.26, "y": 0.63, "label": 8 },
"p3": { "x": 0.27, "y": 0.63, "label": 9 },
"p4": { "x": 0.29, "y": 0.59, "label": 10 },
"p5": { "x": 0.25, "y": 0.46, "label": 11 },
"p6": { "x": 0.22, "y": 0.42, "label": 12 }
},
"label": "left-ear"
}]
}
}]
}Polygons
Annotate object instances with polygons instead of pixel segmentation
Detect vehicles on road
{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"draw_polygons"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1"
},
"tag":"draw_polygons"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"draw_polygons\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\"\n },\n \"tag\":\"draw_polygons\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache"
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body:
{ reference_id: '001',
data: { image_url: 'https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1' },
tag: 'draw_polygons' },
json: true };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [{
"reference_id": "001",
"flu_id": "3bcbc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "draw_polygons",
"result": {
"polygons": [{
"points": {
"p1": {"x": 0.17,"y": 0.58},
"p2": {"x": 0.26,"y": 0.63},
"p3": {"x": 0.27,"y": 0.63},
"p4": {"x": 0.29,"y": 0.59},
"p5": {"x": 0.25,"y": 0.46},
"p6": {"x": 0.22,"y": 0.42}
},
"edges": {
"e1": ["p1","p2"],
"e2": ["p2","p3"],
"e3": ["p3","p4"],
"e4": ["p4","p5"],
"e5": ["p5","p6"],
"e6": ["p6","p7"],
"e7": ["p8","p9"]
},
"label": "Sofa"
}
]
}
}]
}Bounding Box Validation
Validate the performance of your object detection models
{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1",
"boxes": [{
"id" : "box_0001",
"label": "car",
"coordinates": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}]
},
{
"id" : "box_0002",
"label": "van",
"coordinates": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}]
}]
},
"tag":"validate_boxes"
}curl -X POST \
https://api.playment.in/v1/project/:projectId/feedline \
-H 'Content-Type: application/json' \
-H 'cache-control: no-cache' \
-H 'x-client-key: <your x-client-key here>' \
-d '{
"reference_id":"001",
"data":{
"image_url":"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1",
"boxes": [{
"id" : "box_0001",
"label": "car",
"coordinates": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}]
},
{
"id" : "box_0002",
"label": "van",
"coordinates": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}]
}]
},
"tag":"validate_boxes"
}'package main
import (
"fmt"
"strings"
"net/http"
"io/ioutil"
)
func main() {
url := "https://api.playment.in/v1/project/:projectId/feedline"
payload := strings.NewReader("{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\",\n \"boxes\": [{\n \t \"id\" : \"box_0001\",\n \"label\": \"car\",\n \"coordinates\": [\n {\"x\": 0.38, \"y\": 0.19}, \n {\"x\": 0.57, \"y\": 0.19}, \n {\"x\": 0.57, \"y\": 0.44}, \n {\"x\": 0.38, \"y\": 0.44}]\n },\n {\n \"id\" : \"box_0002\",\n \"label\": \"van\",\n \"coordinates\": [\n {\"x\": 0.38, \"y\": 0.19}, \n {\"x\": 0.57, \"y\": 0.19}, \n {\"x\": 0.57, \"y\": 0.44}, \n {\"x\": 0.38, \"y\": 0.44}]\n }]\n },\n \"tag\":\"validate_boxes\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-client-key", "<your x-client-key here>")
req.Header.Add("Content-Type", "application/json")
req.Header.Add("cache-control", "no-cache")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := ioutil.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}import http.client
conn = http.client.HTTPConnection("api,playment,in")
payload = "{ \n \"reference_id\":\"001\",\n \"data\":{\n \"image_url\":\"https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1\",\n \"boxes\": [{\n \t \"id\" : \"box_0001\",\n \"label\": \"car\",\n \"coordinates\": [\n {\"x\": 0.38, \"y\": 0.19}, \n {\"x\": 0.57, \"y\": 0.19}, \n {\"x\": 0.57, \"y\": 0.44}, \n {\"x\": 0.38, \"y\": 0.44}]\n },\n {\n \"id\" : \"box_0002\",\n \"label\": \"van\",\n \"coordinates\": [\n {\"x\": 0.38, \"y\": 0.19}, \n {\"x\": 0.57, \"y\": 0.19}, \n {\"x\": 0.57, \"y\": 0.44}, \n {\"x\": 0.38, \"y\": 0.44}]\n }]\n },\n \"tag\":\"validate_boxes\"\n}"
headers = {
'x-client-key': "<your x-client-key here>",
'Content-Type': "application/json",
'cache-control': "no-cache",
}
conn.request("POST", "v1,project,:projectId,feedline", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))var request = require("request");
var options = { method: 'POST',
url: 'https://api.playment.in/v1/project/:projectId/feedline',
headers:
{ 'cache-control': 'no-cache',
'Content-Type': 'application/json',
'x-client-key': '<your x-client-key here>' },
body:
{ reference_id: '001',
data:
{ image_url: 'https://dummyimage.com/600x400/000/fff.jpg&text=Dummy+Image+1',
boxes:
[ { id: 'box_0001',
label: 'car',
coordinates:
[ { x: 0.38, y: 0.19 },
{ x: 0.57, y: 0.19 },
{ x: 0.57, y: 0.44 },
{ x: 0.38, y: 0.44 } ] },
{ id: 'box_0002',
label: 'van',
coordinates:
[ { x: 0.38, y: 0.19 },
{ x: 0.57, y: 0.19 },
{ x: 0.57, y: 0.44 },
{ x: 0.38, y: 0.44 } ] } ] },
tag: 'validate_boxes' },
json: true };
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
{
"feed_line_units": [
{
"reference_id": "001",
"flu_id": "33abc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "validate_boxes",
"result": [
{
"id": "box_0001",
"label": {
"judgement": "valid",
"value": "car"
},
"coordinates": {
"judgement": "invalid",
"value": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}]
}
},
{
"id": "box_0002",
"label": {
"judgement": "invalid",
"value": "truck"
},
"coordinates": {
"judgement": "valid",
"value": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}]
}
}
]
]
}
}Annotation Attributes
In addition to the basic shape annotations, we provide labelling of any attributes associated with the parent annotations
Check the feed_line_units[0].result.rectangles[0].attributes key in the callback body shown on the right
{
"feed_line_units": [{
"reference_id": "001",
"flu_id": "33abc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "draw_bounding_boxes",
"result": {
"rectangles": [
{
"label": "television",
"coordinates": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}],
"attributes": {
"occlusion": 0,
"leading_vehicle": True
}
},
{
"label": "plant",
"coordinates": [
{"x": 0.38, "y": 0.19},
{"x": 0.57, "y": 0.19},
{"x": 0.57, "y": 0.44},
{"x": 0.38, "y": 0.44}],
"attributes": {
"occlusion": 1,
"leading_vehicle": False
}
}
]
}
}]
}
What we send
You will be required to configure a callback_url, where we will POST the results of your request once it is completed.
POST https://<some_custom_callback_url>
We send the response as a JSON body, which looks like this:
The result object is a JSON which can contain one or more key-value pairs, depending on your requirements. Check the examples to understand how it can be used in various scenarios.
In case the images shared in the Feedline API are invalid or unavailable, we will retry multiple times before responding asynchronously to the Callback API with "status": "FAILED"
reference_id
reference_id is a unique identifier for a request. We'll fail a request if you've sent another request with the same reference_id previously. This helps us ensure that we don't charge you for work which we've already done for you.
flu_id
This is the unique ID that we generate for every request
status
PENDING : The data is currently being processedCOMPLETED : All the data has been processed and is ready for consumption by clientFAILED : Data validation or file download failed
As of today, you will not receive a response which has a `FAILED' status
tag
Each request should have a tag which tells us what operation needs to be performed on that request. Don't worry, We'll share this tag with you during the integration process.
{
"feed_line_units": [{
"reference_id": "001",
"flu_id": "33abc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "COMPLETED",
"tag": "cat_or_dog",
"result": {
"category" : "cat"
}]
}{
"feed_line_units": [
{
"reference_id": "001",
"flu_id": "33abc3fe-9fbc-4d7c-b951-a1d1bfb6f840",
"status": "FAILED",
"tag": "cat_or_dog",
"error": {
"code": "FS_0003",
"message": "Invalid data passed",
"meta_data": {
"failures": [
{
"field": "product_image_url",
"type": "INVALID_IMAGE_LINK"
},
{
"field": "review_image_url",
"type": "INVALID_IMAGE_LINK"
}
]
}
}
}
]
}Error codes
On a callback request, you should respond with 200 HttpStatusCode to be considered as a successful request. Any non-200 HttpStatusCode will be considered as unsuccessful. In case of following non-200 HttpStatusCodes, we retry upto 3 times in an interval of 30 seconds:
Error Code
Description
408
Request Timeout
500
Internal Server Error
501
Not Implemented
502
Bad Gateway
503
Service Unavailable
504
Gateway Timeout
505
HTTP Version Not Supported
511
Network Authentication Required