Luna Remote SDK API (v.0.2.12)

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'VisionLabs Luna Remote SDK'

version

get version

get service version

header Parameters

Luna-Request-Id

string <timestamp,uuid> ^[0-9]{10},[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4...

Example: 1536751345,8b8b5937-2e9c-4e8b-a7a7-5caf86621b5a

external request id. Helps to uniquely identify messages that correspond to particular requests, in system logs. If it was not set, system will set it in default format ("timestamp,UUID"). It will be returned with response.

Responses

Response samples

Content type

application/json

{"Version": {"api": 1,
"major": 0,
"minor": 0,
"patch": 0
}
}

sdk

sdk resource

Detect faces, human bodies and estimate attributes on input images. Resource estimates only available attributes for corresponding images. For example, if user sends warped face image and wants to calculate a body descriptor, landmarks5 and basic attributes - only basic attributes will be estimated.

Order of input images is corresponding to order elements in output json. If an image is broken resource will returns error for it image, other images will be correct processing.

WARNING: this resource is unstable. A resource API can be changes in any release.

Notes for incoming data:

face or body bounding boxes passed with face or body wapred image will be ignored.

query Parameters

image_type

integer

Default: 0

Enum: 0 1 2

Image type of input image. 0 - raw image, 1 - face warped image, 2 - body warped image.

estimate_people_count

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate people count on the image.

⚠ People counter licensing feature is required to be enabled to perform estimation.

multiface_policy

integer (multiface_policy)

Default: 1

Enum: 0 1 2

Multiple face detection policy:

0 - do not process images with several detected faces.

If detect_body is enabled, the system will attempt to detect a human body related to the detected face. If a human body is detected, its detection will be associated with the same event as the face.

If there is no face detected in the image, no human body detections will be returned. Thus there will be no human body detection returned if detect_face is disabled.
1 - process images with several detected faces and/or human bodies.

If detect_body is enabled, all the detections of human bodies will be returned. Face detection and human body detection of the same person will be associated with the same event.

The detections of human bodies are returned even if there were no faces found.
2 - get the best face detection from the image. A single face of the best quality is detected in the image.

If detect_body is enabled, the system will attempt to detect a human body related to the detected face. If a human body is detected, its detection will be associated with the same event as the face.

If there is no face detected in the image, no human body detections will be returned. Thus there will be no human body detection returned if detect_face is disabled.

detect_face

integer (detect_face)

Default: 0

Enum: 0 1

Whether to detect faces on the image.

detect_body

integer (detect_body)

Default: 0

Enum: 0 1

Whether to detect human bodies on the image.

estimate_head_pose

integer (estimate_head_pose)

Default: 0

Enum: 0 1

Whether to estimate head pose on the image.

Not supported with warped images (see warped_image or image_type parameter).

estimate_emotions

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate emotions on the image.

estimate_landmarks68

integer (detect_landmarks68)

Default: 0

Enum: 0 1

Whether to detect basic 68-point facial landmarks on the image.

Not supported with warped images (see warped_image or image_type parameter).

estimate_landmarks5

integer (detect_landmarks5)

Default: 0

Enum: 0 1

Whether to detect basic 5-point facial landmarks on the image.

Not supported with warped images (see warped_image or image_type parameter).

estimate_quality

integer (estimate_quality)

Default: 0

Enum: 0 1

Whether to estimate input face image quality.

estimate_gaze

integer (estimate_gaze)

Default: 0

Enum: 0 1

Whether to estimate eye gaze on the image.

Not supported with warped images (see warped_image or image_type parameter).

estimate_eyes_attributes

integer (estimate_eyes_attributes)

Default: 0

Enum: 0 1

Whether to estimate eye attributes on the image.

Not supported with warped images (see warped_image or image_type parameter).

estimate_mouth_attributes

integer (estimate_mouth_attributes)

Default: 0

Enum: 0 1

Whether to estimate mouth attributes on the image.

estimate_mask

integer (estimate_mask)

Default: 0

Enum: 0 1

Whether to estimate mask on the image.

estimate_glasses

integer (estimate_glasses)

Default: 0

Enum: 0 1

Whether to estimate glasses on the image.

estimate_liveness

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate liveness on the image.

⚠ Good quality only on webcam or selfie photos: otherwise the result may be incorrect.

⚠ Liveness v.2 licensing feature is required to be enabled to perform liveness estimation.

estimate_deepfake

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate deepfake feature on the image.

⚠ Deepfake licensing feature is required to be enabled to perform deepfake estimation.

estimate_face_descriptor

integer (extract_descriptor)

Default: 0

Enum: 0 1

Whether to estimate face descriptor(s).

estimate_basic_attributes

integer (extract_basic_attributes)

Default: 0

Enum: 0 1

Whether to estimate face basic attributes (gender, age, ethnicity).

estimate_body_descriptor

integer (extract_descriptor)

Default: 0

Enum: 0 1

Whether to estimate human body descriptor(s).

estimate_upper_body

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate human upper body attributes on the image (headwear type, clothing color, sleeve length).

estimate_lower_body

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate human lower body attributes on the image (lower garment type, clothing color, shoes color).

estimate_accessories

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate human body accessories on the image (backpack presence).

estimate_body_basic_attributes

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate human body basic attributes on the image (age, gender).

estimate_body_warp

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate human body warp on the image.

estimate_face_warp

integer (int01)

Default: 0

Enum: 0 1

Whether to estimate human face warp on the image.

aggregate_attributes

integer (aggregate_attributes)

Default: 0

Enum: 0 1

Whether to aggregate face descriptor(s).

If true, all extracted attributes will be aggregated and stored as a single attribute. Otherwise, all attributes will be stored for each sample.

pitch_threshold

integer (pitch_threshold) [ 0 .. 180 ]

Maximum deviation pitch angle from 0.

roll_threshold

integer (roll_threshold) [ 0 .. 180 ]

Maximum deviation roll angle from 0.

yaw_threshold

integer (yaw_threshold) [ 0 .. 180 ]

Maximum deviation yaw angle from 0.

score_threshold

number (score_threshold) [ 0 .. 1 ]

Default: 0

Example: score_threshold=0.99

Face descriptor quality score threshold. All the attributes with quality score below the threshold will be ignored (and not stored in the DB). The function will proceed as usual with all the remaining descriptors (if left).

mask_states

Array of integers (mask_states) non-empty

Items Enum: 1 2 3

Filter by mask states.

masks	value
missing	1
medical_mask	2
occluded	3

liveness_states

Array of integers (liveness_states) non-empty

Items Enum: 0 1 2 null

Filter by liveness states.

liveness state	value
spoof	0
real	1
unknown	2

deepfake_states

Array of integers (deepfake_states) non-empty

Items Enum: 0 1 null

Filter by deepfake states.

liveness state	value
fake	0
real	1

use_exif_info

integer

Default: 1

Enum: 0 1

Example: use_exif_info=1

Whether to try to auto orient image based on exif data

Has no effect with tiff images (they're always auto oriented).

Ignored with warped images (see warped_image or image_type parameter).

extract_exif

integer (extract_exif)

Default: 0

Enum: 0 1

Whether to extract EXIF meta information from input JPEG images.

Exact output will vary since there are no mandatory requirements for both authoring software and digital cameras how to write the data.

This function will only parse the tags and output their names and values as they are. Please refer to JEITA CP-3451 EXIF specification for details.

header Parameters

Accept	string Default: application/json Enum: "application/json" "application/msgpack" The response content type header.
Luna-Request-Id	string <timestamp,uuid> ^[0-9]{10},[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4... Example: 1536751345,8b8b5937-2e9c-4e8b-a7a7-5caf86621b5a external request id. Helps to uniquely identify messages that correspond to particular requests, in system logs. If it was not set, system will set it in default format ("timestamp,UUID"). It will be returned with response.
Content-Type	string Enum: "application/json" "multipart/form-data" "image/jpeg" "image/png" "image/bmp" "image/x-portable-pixmap" "image/tiff" "image/x-jpeg-base64" "image/x-png-base64" "image/x-bmp-base64" "image/x-tiff-base64" "image/x-portable-pixmap-base64"

Request Body schema:
image/jpeg

string <binary>

Responses

Request samples

Payload

Content type

image/jpeg

No sample

Response samples

Content type

application/json

Example

all

{"images_estimations": [{"filename": "raw image",
"exif": {"make": "Apple",
"model": "iPhone 11",
"gps": {"latitude": "55.0, 45.0, 18.29",
"longitude": "37.0, 39.0, 16.32"
},
"software": "13.6.1",
"orientation": 6
},
"estimations": [{"face": {"detection": {"rect": {"x": 71,
"y": 45,
"width": 134,
"height": 178
},
"landmarks5": [[25,
25
],
[83,
83
],
[47,
47
],
[33,
33
],
[82,
82
]
],
"landmarks68": [[1,
1
],
[1,
1
],
[4,
4
],
[8,
8
],
[14,
14
],
[23,
23
],
[33,
33
],
[43,
43
],
[60,
60
],
[78,
78
],
[96,
96
],
[112,
112
],
[124,
124
],
[132,
132
],
[135,
135
],
[137,
137
],
[138,
138
],
[1,
1
],
[8,
8
],
[17,
17
],
[26,
26
],
[35,
35
],
[59,
59
],
[73,
73
],
[86,
86
],
[100,
100
],
[113,
113
],
[47,
47
],
[45,
45
],
[44,
44
],
[43,
43
],
[37,
37
],
[42,
42
],
[47,
47
],
[54,
54
],
[61,
61
],
[14,
14
],
[20,
20
],
[29,
29
],
[36,
36
],
[28,
28
],
[19,
19
],
[69,
69
],
[77,
77
],
[86,
86
],
[95,
95
],
[88,
88
],
[78,
78
],
[33,
33
],
[38,
38
],
[45,
45
],
[50,
50
],
[57,
57
],
[69,
69
],
[82,
82
],
[72,
72
],
[60,
60
],
[52,
52
],
[45,
45
],
[38,
38
],
[36,
36
],
[46,
46
],
[51,
51
],
[58,
58
],
[77,
77
],
[58,
58
],
[51,
51
],
[45,
45
]
],
"attributes": {"eyes_attributes": {"left_eye": {"iris_landmarks": [[92,
92
],
[92,
92
],
[93,
93
],
[93,
93
],
[94,
94
],
[95,
95
],
[96,
96
],
[98,
98
],
[99,
99
],
[100,
100
],
[101,
101
],
[102,
102
],
[103,
103
],
[104,
104
],
[105,
105
],
[105,
105
],
[105,
105
],
[105,
105
],
[105,
105
],
[104,
104
],
[103,
103
],
[102,
102
],
[101,
101
],
[100,
100
],
[99,
99
],
[98,
98
],
[97,
97
],
[96,
96
],
[95,
95
],
[94,
94
],
[93,
93
],
[93,
93
]
],
"state": "open"
},
"right_eye": {"iris_landmarks": [[152,
152
],
[153,
153
],
[153,
153
],
[154,
154
],
[154,
154
],
[155,
155
],
[157,
157
],
[158,
158
],
[159,
159
],
[161,
161
],
[162,
162
],
[163,
163
],
[164,
164
],
[165,
165
],
[166,
166
],
[166,
166
],
[166,
166
],
[166,
166
],
[166,
166
],
[165,
165
],
[164,
164
],
[163,
163
],
[162,
162
],
[161,
161
],
[159,
159
],
[158,
158
],
[158,
158
],
[157,
157
],
[156,
156
],
[154,
154
],
[154,
154
],
[153,
153
]
],
"state": "open"
}
},
"head_pose": {"pitch": 12.7464532852,
"roll": -2.9892115593,
"yaw": 17.7173442841
},
"gaze": {"yaw": -5.9912848473,
"pitch": -10.2646684647
},
"emotions": {"predominant_emotion": "neutral",
"estimations": {"anger": 8.682e-7,
"disgust": 1.0670000000000001e-7,
"fear": 4.386e-7,
"happiness": 0.45658952,
"sadness": 4.951e-7,
"surprise": 0.0000029291,
"neutral": 0.543405652
}
},
"mask": {"estimations": {"medical_mask": 0.7108324766,
"missing": 0.0700698048,
"occluded": 0.2190976739
},
"predominant_mask": "medical_mask"
},
"liveness": {"estimations": {"quality": 0.9984464645385742,
"score": 0.9872102737426758
},
"prediction": "real"
},
"deepfake": {"score": 0.525161345345,
"prediction": "real"
},
"glasses": {"glasses": "no_glasses"
},
"mouth_attributes": {"score": 1,
"opened": 0.3884400725,
"occluded": 1.4000000000000001e-9,
"smile": 0.6115599275
},
"basic_attributes": {"ethnicities": {"predominant_ethnicity": "caucasian",
"estimations": {"asian": 0.0000025556,
"indian": 6.417e-7,
"caucasian": 0.9999949932000001,
"african_american": 0.0000017718000000000001
}
},
"age": 24,
"gender": 0
},
"descriptor": {"sdk_descriptor": "ZHAAADgAAAB0k4SAf...IB6gHyAgH19fQ==",
"score": 0.7854047418000001
}
},
"quality": {"blurriness": 0.9769582748000001,
"dark": 0.9542820454000001,
"illumination": 0.8248310089,
"specularity": 0.8472354412,
"light": 0.8179533482
},
"warp": "/9j/4AAQSkZJRgABAQAAAQABA..C4gb+KneZD/erEHWn0/asOTzP/9k="
}
}
},
{"body": {"detection": {"rect": {"x": 32,
"y": 4,
"width": 224,
"height": 255
},
"warp": "/9j/4AAQSkZJRgABAQAAAQABAA...CX6kdapTi1qS00f/Z",
"attributes": {"descriptor": {"sdk_descriptor": "ZHAAAGUAAAC....AfX9",
"score": 1
},
"basic_attributes": {"apparent_age": 0,
"apparent_gender": 0
},
"upper_body": {"headwear": {"state": 0
},
"sleeve": {"length": "short"
},
"upper_clothing": {"colors": ["white",
"black"
]
}
},
"accessories": {"backpack": {"state": 0
}
}
}
}
}
}
],
"image_estimations": {"people": {"count": 1
}
}
}
]
}

videosdk

Video Analytics

Request for video analysis based on the specified analytics parameters eg facial estimations.

header Parameters

Luna-Request-Id	string <timestamp,uuid> ^[0-9]{10},[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4... Example: 1536751345,8b8b5937-2e9c-4e8b-a7a7-5caf86621b5a external request id. Helps to uniquely identify messages that correspond to particular requests, in system logs. If it was not set, system will set it in default format ("timestamp,UUID"). It will be returned with response.
Content-Type	string Value: "application/json"

Request Body schema: application/json

required	object Video URL.
required	Array of objects (human_tracking_analytics) A list of parameters for comprehensive video analytics.

Responses

Request samples

Payload

Content type

application/json

{"video": {"url": "https://example.com/"
},
"analytics": [{"name": "human_tracking",
"parameters": {"track": {"min_length": 4
},
"roi": {"x": 3327,
"y": 3327,
"width": 3327,
"height": 3327,
"mode": "abs"
},
"droi": {"x": 3327,
"y": 3327,
"width": 3327,
"height": 3327,
"mode": "abs"
},
"face": {"aggregation_count": 13,
"estimations": {"basic_attributes": {"estimate": 1
},
"deepfake": {"estimate": 1,
"score_threshold": 0.84
},
"descriptor": {"estimate": 1
},
"detection": {"estimate": 1
},
"detection_background": {"estimate": 1
},
"dynamic_range": {"estimate": 1
},
"emotions": {"estimate": 1
},
"eyebrow": {"estimate": 1
},
"eyes": {"estimate": 1
},
"fisheye": {"estimate": 1
},
"gaze_direction": {"estimate": 1
},
"glasses": {"estimate": 1
},
"head_pose": {"estimate": 1
},
"headwear": {"estimate": 1
},
"landmarks5": {"estimate": 1
},
"landmarks68": {"estimate": 1
},
"liveness": {"estimate": 1
},
"mask": {"estimate": 1
},
"mouth": {"estimate": 1
},
"natural_light": {"estimate": 1
},
"portrait_style": {"estimate": 1
},
"red_eyes": {"estimate": 1
}
},
"bestshot_filters": {"ags": 0.96,
"mask_states": [1,
3
],
"pitch_threshold": 77,
"roll_threshold": 23,
"yaw_threshold": 138
}
},
"body": {"aggregation_count": 13,
"estimations": {"basic_attributes": {"estimate": 1
},
"accessories": {"estimate": 1
},
"descriptor": {"estimate": 1
},
"detection": {"estimate": 1
}
}
}
}
}
]
}

Response samples

Content type

application/json

{"error_code": 12017,
"desc": "Bad/incomplete input data",
"detail": "Bad content type",
"link": "https://docs.visionlabs.ai/info/luna/troubleshooting/errors-description/code-12017"
}

iso

iso resource

The resource performs a visual examination of frontal type images based on ISO/IEC 19794-5 standard. This means that the images that have passed the validation are standardized and attributes of the person's face meet the established requirements. For more information on the requirements for face attributes in the image, see ISO/IEC 19794-5 Clauses 7-10.

The response contains the result of the check for each estimator. The image check is considered successful if the set of these results complies with the ISO standard.

The order of input images is corresponding to elements order in response. If an image is broken, the resource will return an error for this image and other images will be correctly processed.

Notes for incoming data:

face or body bounding boxes passed with face or body wapred image will be ignored.

query Parameters

image_type	integer Default: 0 Enum: 0 1 Image type of input image. 0 - raw image, 1 - face warped image.
multiface_policy	integer (multiface_policy) Default: 0 Enum: 0 1 2 Multiple face detection policy: `0` - do not process images with several detected faces. If `detect_body` is enabled, the system will attempt to detect a human body related to the detected face. If a human body is detected, its detection will be associated with the same event as the face. If there is no face detected in the image, no human body detections will be returned. Thus there will be no human body detection returned if `detect_face` is disabled. `1` - process images with several detected faces and/or human bodies. If `detect_body` is enabled, all the detections of human bodies will be returned. Face detection and human body detection of the same person will be associated with the same event. The detections of human bodies are returned even if there were no faces found. `2` - get the best face detection from the image. A single face of the best quality is detected in the image. If `detect_body` is enabled, the system will attempt to detect a human body related to the detected face. If a human body is detected, its detection will be associated with the same event as the face. If there is no face detected in the image, no human body detections will be returned. Thus there will be no human body detection returned if `detect_face` is disabled.
extract_exif	integer (extract_exif) Default: 0 Enum: 0 1 Whether to extract EXIF meta information from input JPEG images. Exact output will vary since there are no mandatory requirements for both authoring software and digital cameras how to write the data. This function will only parse the tags and output their names and values as they are. Please refer to JEITA CP-3451 EXIF specification for details.

header Parameters

Accept	string Default: application/json Enum: "application/json" "application/msgpack" The response content type header.
Luna-Request-Id	string <timestamp,uuid> ^[0-9]{10},[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4... Example: 1536751345,8b8b5937-2e9c-4e8b-a7a7-5caf86621b5a external request id. Helps to uniquely identify messages that correspond to particular requests, in system logs. If it was not set, system will set it in default format ("timestamp,UUID"). It will be returned with response.
Content-Type	string Enum: "application/json" "multipart/form-data" "image/jpeg" "image/png" "image/bmp" "image/x-portable-pixmap" "image/tiff" "image/x-jpeg-base64" "image/x-png-base64" "image/x-bmp-base64" "image/x-tiff-base64" "image/x-portable-pixmap-base64"

Request Body schema:
image/jpeg

string <binary>

Responses

Request samples

Payload

Content type

image/jpeg

No sample

Response samples

Content type

application/json

{"images": [{"filename": "raw image",
"status": 1,
"error": {"error_code": 0,
"desc": "Success",
"detail": "Success",
"link": "https://docs.visionlabs.ai/info/luna/troubleshooting/errors-description/code-0"
},
"estimations": [{"face": {"detection": {"iso": {"status": 1,
"checks": [{"name": "image_format",
"object_value": "JPEG",
"threshold_value": ["JPEG",
"JPEG2000",
"PNG"
],
"result": 1
},
{"name": "illumination_quality",
"object_value": 0.6005162000656128,
"threshold_value": {"min": 0.3,
"max": 1
},
"result": 1
},
{"name": "specularity_quality",
"object_value": 0.7662366628646851,
"threshold_value": {"min": 0.3,
"max": 1
},
"result": 1
},
{"name": "blurriness_quality",
"object_value": 0.9429352283477783,
"threshold_value": {"min": 0.61,
"max": 1
},
"result": 1
},
{"name": "dark_quality",
"object_value": 0.9020983576774597,
"threshold_value": {"min": 0.5,
"max": 1
},
"result": 1
},
{"name": "light_quality",
"object_value": 0.7881984114646912,
"threshold_value": {"min": 0.57,
"max": 1
},
"result": 1
},
{"name": "head_yaw",
"object_value": 2.818983316421509,
"threshold_value": {"min": -5,
"max": 5
},
"result": 1
},
{"name": "head_pitch",
"object_value": 3.816443920135498,
"threshold_value": {"min": -5,
"max": 5
},
"result": 1
},
{"name": "head_roll",
"object_value": 5.434040069580078,
"threshold_value": {"min": -8,
"max": 8
},
"result": 1
},
{"name": "gaze_yaw",
"object_value": -3.773012399673462,
"threshold_value": {"min": -5,
"max": 5
},
"result": 1
},
{"name": "gaze_pitch",
"object_value": 0.7140519022941589,
"threshold_value": {"min": -5,
"max": 5
},
"result": 1
},
{"name": "mouth_smiling",
"object_value": 0.000290759839117527,
"threshold_value": {"min": 0,
"max": 0.5
},
"result": 1
},
{"name": "mouth_occluded",
"object_value": 0.00009619363845558837,
"threshold_value": {"min": 0,
"max": 0.5
},
"result": 1
},
{"name": "mouth_open",
"object_value": 0.6226108074188232,
"threshold_value": {"min": 0,
"max": 0.5
},
"result": 1
},
{"name": "glasses",
"object_value": "no_glasses",
"threshold_value": ["no_glasses",
"eyeglasses"
],
"result": 1
},
{"name": "left_eye",
"object_value": "open",
"threshold_value": ["open"
],
"result": 1
},
{"name": "right_eye",
"object_value": "open",
"threshold_value": ["open"
],
"result": 1
},
{"name": "head_horizontal_center",
"object_value": 0.51,
"threshold_value": {"min": 0.45,
"max": 0.55
},
"result": 1
},
{"name": "head_vertical_center",
"object_value": 0.51,
"threshold_value": {"min": 0.3,
"max": 0.5
},
"result": 1
},
{"name": "head_width",
"object_value": 0.61,
"threshold_value": {"min": 0.5,
"max": 0.75
},
"result": 1
},
{"name": "head_height",
"object_value": 0.71,
"threshold_value": {"min": 0.6,
"max": 0.9
},
"result": 1
},
{"name": "eye_distance",
"object_value": 110,
"threshold_value": {"min": 90,
"max": null
},
"result": 1
},
{"name": "background_lightness",
"object_value": 0.5780888795852661,
"threshold_value": {"min": 0.2,
"max": 1
},
"result": 1
},
{"name": "background_uniformity",
"object_value": 0.8874394297599792,
"threshold_value": {"min": 0.5,
"max": 1
},
"result": 1
},
{"name": "shoulders_position",
"object_value": "parallel",
"threshold_value": ["parallel"
],
"result": 1
}
]
},
"rect": {"x": 108,
"y": 58,
"width": 113,
"height": 165
}
}
}
}
]
}
]
}

estimator

estimator resource

estimator

header Parameters

Luna-Request-Id	string <timestamp,uuid> ^[0-9]{10},[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4... Example: 1536751345,8b8b5937-2e9c-4e8b-a7a7-5caf86621b5a external request id. Helps to uniquely identify messages that correspond to particular requests, in system logs. If it was not set, system will set it in default format ("timestamp,UUID"). It will be returned with response.
Content-Type	string Value: "application/msgpack"

Request Body schema: application/msgpack

required	object (estimator_parameters)
required	Array of objects (estimator_image)

Responses

Response samples

Content type

application/json

{"error_code": 12017,
"desc": "Bad/incomplete input data",
"detail": "Bad content type",
"link": "https://docs.visionlabs.ai/info/luna/troubleshooting/errors-description/code-12017"
}

documents

get openapi documentation

Get service OpenApi documentation. If Accept request header is of type application/x-yaml, returns documentation in yaml format or returns html documentation, if Accept-Type is text/html

header Parameters

Luna-Request-Id	string <timestamp,uuid> ^[0-9]{10},[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4... Example: 1536751345,8b8b5937-2e9c-4e8b-a7a7-5caf86621b5a external request id. Helps to uniquely identify messages that correspond to particular requests, in system logs. If it was not set, system will set it in default format ("timestamp,UUID"). It will be returned with response.
Accept required	string (accept_docs_handler) Enum: "application/x-yaml" "text/html" acceptable type of receiving data

Responses

Response samples

Content type

application/json

{"error_code": 12024,
"detail": "Bad/incomplete input data",
"desc": "Unsupported media type",
"link": "https://docs.visionlabs.ai/info/luna/troubleshooting/errors-description/code-12024"
}

get development manual

Get sphinx documentation - Development Manual. After the request you will be redirected to the page /docs/dev/index.html

header Parameters

Accept	string Value: "application/json" Preferred response content type
Luna-Request-Id	string <timestamp,uuid> ^[0-9]{10},[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4... Example: 1536751345,8b8b5937-2e9c-4e8b-a7a7-5caf86621b5a external request id. Helps to uniquely identify messages that correspond to particular requests, in system logs. If it was not set, system will set it in default format ("timestamp,UUID"). It will be returned with response.

Responses

Response samples

Content type

application/json

{"error_code": 1,
"detail": "internal server error",
"desc": "internal server error",
"link": "https://docs.visionlabs.ai/info/luna/troubleshooting/errors-description/code-1"
}

config

get service configuration

Get service configuration. Passwords and tokens will be hidden in the response.

header Parameters

Luna-Request-Id	string <timestamp,uuid> ^[0-9]{10},[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4... Example: 1536751345,8b8b5937-2e9c-4e8b-a7a7-5caf86621b5a external request id. Helps to uniquely identify messages that correspond to particular requests, in system logs. If it was not set, system will set it in default format ("timestamp,UUID"). It will be returned with response.
Accept	string Enum: "application/json" "text/plain" acceptable type of receiving data

Responses

Response samples

Content type

application/json

{"INFLUX_MONITORING": {"SEND_DATA_FOR_MONITORING": 0,
"ORGANIZATION": "ORGANIZATION_NAME",
"TOKEN": "********",
"BUCKET": "luna_monitoring",
"HOST": "127.0.0.1",
"PORT": 8086,
"USE_SSL": 0,
"FLUSHING_PERIOD": 1
}
}

health

get health

get health of service

query Parameters

include_luna_services

integer

Default: 0

Enum: 0 1

Whether to perform healthchecks for dependent luna services.

header Parameters

Luna-Request-Id

string <timestamp,uuid> ^[0-9]{10},[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4...

Example: 1536751345,8b8b5937-2e9c-4e8b-a7a7-5caf86621b5a

Responses

Response samples

Content type

application/json

{"execution_time": 0.123
}

Luna Remote SDK API (v.0.2.12)

version

get version

header Parameters

Responses

Response samples

sdk

sdk resource

query Parameters

header Parameters

Request Body schema: image/jpegimage/pngimage/bmpimage/x-portable-pixmapimage/tiffimage/x-jpeg-base64image/x-png-base64image/x-bmp-base64image/x-tiff-base64image/x-portable-pixmap-base64multipart/form-dataapplication/jsonimage/jpeg

Responses

Request samples

Response samples

videosdk

Video Analytics

header Parameters

Request Body schema: application/json

Responses

Request samples

Response samples

iso

iso resource

query Parameters

header Parameters

Request Body schema: image/jpegimage/pngimage/bmpimage/x-portable-pixmapimage/tiffimage/x-jpeg-base64image/x-png-base64image/x-bmp-base64image/x-tiff-base64image/x-portable-pixmap-base64multipart/form-dataapplication/jsonimage/jpeg

Responses

Request samples

Response samples

estimator

estimator resource

header Parameters

Request Body schema: application/msgpack

Responses

Response samples

documents

get openapi documentation

header Parameters

Responses

Response samples

get development manual

header Parameters

Responses

Response samples

config

get service configuration

header Parameters

Responses

Response samples

health

get health

query Parameters

header Parameters

Responses

Response samples

Request Body schema:
image/jpeg

Request Body schema:
image/jpeg