Applications - Create

Applications can be created by posting to application endpoint with following arguments, with only name and type are required. Input and output subjects are optional but the application is not useful until they are specified. And they can only be specified if the subjects already exists.

An application json object with full attributes are returned.

Name	Example	Description
name string	"Person detector"	(required) Name should be brief and descriptive
type string	"classification"	(required) Cogniac appliction type, see type string in valid application type below
description string	"Find people walking into the front door"	(optional) A full description of the purpose of the application. Use this field to capture detailed subject and any exceptional feedback instructions.
input_subjects array	["flicker_cats", "animals_pics"]	(optional) Array of input subjects id's (note that input subjects' must be already created and have a valid id before adding to an Application)
output_subjects array	["cat","dog","face"]	(optional) List of subject tags corresponding to objects, patterns, or features that are of interest in this application (note that output subjects' must be already created and have a valid id before adding to an Application)
release_metrics string	"best_F1"	(optional) The performance measure used when to assess model performance. Must be one of 'best_recall', 'best_precision', 'best_F1'. Defaults to 'best_F1'
detection_ thresholds dictionary	{"cat": 0.5, "dog": 0.7, "face": 0.5}	(optional) Map between subject_uid's and associated probability thresholds. Detections below the specified probability threshold will not be forwarded to subsequent applications (if any). Detections below the threshold will not be posted to the detection_post_urls (if any).
detection_ post_urls array	["http://127.0.0.1:99/ my_model_output.net", "https://detections-now.com/detections"]	(optional) A list of URL's where model detections will be surfaced in addition to web and iOS interfaces. Posts are retried for thirty seconds, but URL's that fail retried posts after thirty seconds are blacklisted for five minutes.
gateway_ post_urls array	["http://127.0.0.1:99/ my_model_output.net", "https://detections-now.com/detections"]	(optional) A list of URL's where model detections will be surfaced from the gateway. Posts are retried for thirty seconds, but URL's that fail retried posts after thirty seconds are blacklisted for five minutes. Specifying a gateway post URL implies that the gateway will implement the application along with any linked applications.
active bool	true	(optional) Flag to control if the the application is active or not. Inactive applications do not process images submitted to their input subjects or requests feed back.
requested_ feedback_per_hour integer	50	(optional) Override the target rate of feedback to surface per hour. A Null or value indicates the system feedback rate should be used. The default value is Null: system selects feedback rate. Select a higher value to schedule more feedback feedback requests, or a lower value to schedule fewer feedback requests.
refresh_feedback bool	false	(optional) Flag to control whether the images waiting for user feedback should be re-evaluated by the new model when a new model is released.
app_managers array	["[email protected]", "[email protected]"]	_(optional)_List of user email address, the users are given the app_manager role that is authorized to manage application settings and maintain feedback control.

Create Application

POST /1/applications
Host: https://api.cogniac.io

Example: Create a New Application

curl -X POST https://api.cogniac.io/1/applications \
-H "Authorization: Bearer abcdefg.hijklmnop.qrstuvwxyz" \
-H "Content-Type: application/json" \
-d '{ 
"name": "Test App",
"type": "classification",
"description": "App For Testing",
"output_subjects": ["cat", "dog"],
"detection_post_urls": ["http://example.com/cogniac-post"]
}'

import cogniac

# connect to the tenant
cc = cogniac.CogniacConnection(username="[email protected]", 
                               password="myPassword", 
                               tenant_id="63QhzFLc9tg4")

# create the application with keyword arguments
cc.create_application(name='Test App',
                      application_type='classification,
                      description='App For Testing',
                      output_subjects=['cat', 'dog'],
                      detection_post_urls=['http://example.com/cogniac-post'])

🚧
Application Subjects
The input and output subjects must already exist in the system. Otherwise the above call will result in and error. One can always create applications without input and output subjects and add them later with an application update.

{
    "application_id": "di71rG94",
    "tenant_id": "abcdefghijk",
    "name": "Test App",
    "description": "App For Testing",
    "type": "classification",
    "release_metrics": "best_F1",
    "output_subjects": [
        "cat",
        "dog"
    ],
    "detection_thresholds": {
        "dog": 0.0,
        "cat": 0.0
    },
    "active": true,
    "refresh_feedback": false,
    "app_managers": [
        "[email protected]"
    ],
    "detection_post_urls": [
        "http://example.com/cogniac-post"
    ],
    "gateway_post_urls": [],
    "custom_fields": {},
    "hpo_credit": 0,
    "created_by": "[email protected]",
    "created_at": 1508537094.552487,
    "modified_at": 1508537094.552487,
    "system_feedback_per_hour": 0,
    "requested_feedback_per_hour": null,   
    "release_model_count": null,
    "best_model_ccp_filename": null,
    "candidate_model_count": null,
    "current_performance": null,
    "last_candidate_at": null,
    "last_released_at": null,
    "training_data_count": null,
    "validation_data_count": null,
    "replay": false,
}

Valid Application Types

Name	Type string	Description
Classification	classification	Classify the full input image as exactly one subject to the mutual exclusion of the other subjects
Detection Full-frame	detection_fullframe	Detect the presence of one or more objects of interest in a full-frame image
Detection	box_detection	Detection with bounding boxes
Optical Character Recognition	ocr	reads a limited set of characters against a noisy real-world (non-document) image background
Static Count	static_count	count number of objects of interest in an image
Left Offset	offset_x	Measure the pixel distances from the LEFT frame edge to the subjects of interest
Top Offset	offset_y	Measure the pixel distances from the TOP frame edge to the subjects of interest
Point Detection	point	Locate a point coordinate for each object of interest
Motion Detection	motion	Perform motion detection based on background subtraction
Static Crop	static_crop	Perform a static crop at fixed coordinates. Negative numbers indicate the right-most or bottom pixel dimension of the image
Network Camera	network_camera	Pull images from network cameras and capture to a subject.
Twitter	twitter	Capture images from twitter to the indicated subjects
Video Segmentation	video_segmentation	Segment video files into individual frames or smaller segments

Other application types available by special arrangement include:

Name	Type string	Description
Line Crossing	line_crossing	Counts number of objects crossing lines set by users! A dynamic tracking app must be an immediate upstream app!
Dynamic Tracking	dynamic_tracking	Track boxed objects in videos or real-time video streams! A detection app must be an immediate upstream app
DroneDeploy Retiler	retiler	Recombine DroneDeploy plan tiles into larger sub-plans of their original plan. Sub-plan size is given by configuration items 'max_height' and 'max_width'
Static Focus	static_focus	Add the fixed focus on the media to indicate the area of interests to downstream apps. If a fixed coordinate is beyond the corresponding limit of the media, the focus at the coordinate will be set at the limit. If constant_focus_box_size is set to 1, the size of the focus box will be maintained at the time when a coordinate is beyond a media limit. If all the fixed coordinates are beyond the limits of media, no focus will be added.
NSFW Detection	nsfw	Detect the presence of explicit (NSFW) content.

Create Application

Example: Create a New Application

🚧Application Subjects

Valid Application Types

🚧
Application Subjects