-
Create the video job as mentioned in Creating video annotation-task(Job) check the sidebar for a sample input
POSThttps://api.playment.io/v0/projects/<project_id>/jobs
Parameters
project_id: To be passed in the URL
x-api-key: Secret key to be passed as a header
{
"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"
}
]
}
},
"tag": "track_bounding_boxes",
"batch_id": "72c888f6-b365-4f27-ad57-d7841da2de0c"
}
{
"data": {
"job_id": "3f3e8675-ca69-46d7-aa34-96f90fcbb732",
"reference_id": "001",
"tag": "Sample-task"
},
"success": true
}
import requests
import json
"""
Details for creating JOBS,
project_id ->> ID of project in which job needed to be created
x_api_key ->> secret api key to create JOBS
tag ->> You can ask this from playment side
batch_id ->> The batch in which JOB needed to be created
"""
project_id = ''
x_api_key = ''
tag = ''
batch_id = ''
#method that can be used to call the job creation api
def Upload_jobs( DATA):
base_url = f"https://api.playment.io/v0/projects/{project_id}/jobs"
response = requests.post(base_url, headers={'x-api-key': x_api_key}, json=DATA)
print(response.json())
if response.status_code >= 500:
raise Exception("Something wrong at Playment's end")
if 400 <= response.status_code < 500:
raise Exception("Something wrong!!")
return response.json()
#method that can be used for batch creation
def create_batch(batch_name,batch_description):
headers = {'x-api-key':x_api_key}
url = 'https://api.playment.io/v1/project/{}/batch'.format(project_id)
data = {"project_id":project_id,"label":batch_name,"name":batch_name,"description":batch_description}
response = requests.post(url=url,headers=headers,json=data)
print(response.json())
if response.status_code >= 500:
raise Exception("Something wrong at Playment's end")
if 400 <= response.status_code < 500:
raise Exception("Something wrong!!")
return response.json()['data']['batch_id']
#list of frames in a single job
frames = [
"https://example.com/image_url_1",
"https://example.com/image_url_2"
]
video_data = {'frames' : []}
for frame_url in frames:
frame_id = os.path.basename(os.path.normpath(frame_url))
frame_obj = {'src':frame_url,'frame_id':frame_id}
video_data['frames'].append(frame_obj)
#reference_id should be unique for each job
reference_id="job1"
job_data = {
'reference_id':reference_id,
'tag':api_tag,
'data':{'video_data':video_data},
'batch_id' : new_batch_id
}
#helper method to print json data structure
def to_dict(obj):
return json.loads(
json.dumps(obj, default=lambda o: getattr(o, '__dict__', str(o)))
)
print(json.dumps(to_dict(job_data)))
response = Upload_jobs(DATA=job_data)
print(response.json())
-
Fetching result as mentioned in Fetching Job Result
GEThttps://api.playment.in/v0/projects/<project_id>/jobs/<job_id>
Parameters
project_id: To be passed in the URL
x-api-key: Secret key to be passed as a header
{
"data": {
"project_id": "",
"reference_id": "001",
"job_id": "fde54589-ebty-48lp-677a-03a0428ca836",
"batch_id": "b99d241a-bb80-ghyi-po90-c37d4fead593",
"status": "completed",
"tag": "sample_project",
"priority_weight": 5,
"result": "https://playment-data-uploads.s3.ap-south-1.amazonaws.com/sample-result.json"
},
"success": true
}
{
"data": {
"annotation_data": {
"annotations": [
{
"_id": "1a620df1-64a8-46ea-bc63-7446534e4870",
"attributes": null,
"color": "rgb(128,216,218)",
"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%"}
}
}
},
"label": "bear",
"origin": "manual",
"source": "images",
"state": "editable",
"type": "line"
}
],
"groups": null
},
"video_data": {
"frames": [
{
"_id": "00000000-0000-0000-0000-000000000000",
"frame_id": "frame001",
"meta_data": null,
"ref_id": "",
"src": "http://dfnq1fss3rnqc.cloudfront.net/play/original/3e4379ec-2a5a-4d30-9772-c02d408b2fe9",
"src_original": "https://playment-asmobi.s3.amazonaws.com/pilot_data/2D_animals/bear/bear_0000.png"
},
{
"_id": "00000000-0000-0000-0000-000000000000",
"frame_id": "frame002",
"meta_data": null,
"ref_id": "",
"src": "http://dfnq1fss3rnqc.cloudfront.net/play/original/9b282fcc-f328-4f2b-88e9-f0efad08fd05",
"src_original": "https://playment-asmobi.s3.amazonaws.com/pilot_data/2D_animals/bear/bear_0001.png"
}
]
}
}
}
Result JSON Structure
| Response Key | Description |
|---|---|
| data | object having annotation_data object and video_data |
| data.annotation_data.annotations | List of all the annotation tracks each having _id, label, frames and other attributes |
| data.annotation_data.annotations.[i]._id | String The unique tracking ID for an instance of an object across the sequential data. |
| data.annotation_data.annotations.[i].frames | A dictionary with the frame_id key and annotation details as the value |
| data.annotation_data.annotations.[i].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. |
| data.annotation_data.annotations.[i].frames.<frame_id>._id | String The unique ID for a frame instance of a tracker ie. an annotation |
| data.annotation_data.annotations.[i].frames.<frame_id>.attributes | Frame level attribute dictionary for the given annotation |
| data.video_data | Object It is the input data, having list of frames |
| data.video_data.frames.[i].src_original | String source of input frame provided by client |