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swagger-aimldl-auth-min-v2.yml
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openapi: 3.0.0
info:
title: >-
Mappls Skydnn AI API
description: >-
Image classification and segmentation APIs enable state-of-the-art Computer Vision AI inferences to detect and catgorise objects in the photographs and generate localisation as well as map view of geospatial objects useful for a wide range of geospatial analytics and applications. <hr><br>[<img src="https://about.mappls.com/images/mappls-logo.svg" height="100"/> </p>](https://about.mappls.com/api)
<hr>
On a Mission to <b>"Make our world better through maps, location technologies & IoT"</b>, Mappls is continuously enabling real time hyper-local information access through its map & location services. Skydnn APIs are a part of Skydnn AI platform and the document describes API's usage and specifications for their integration into any solution. <br><br>The document covers :
<ul><li>List of APIs along with use cases</ul></li>
<ul><li>Explanation of resources and query parameters</ul></li>
<ul><li>Listing of response structure and data type</ul></li>
[<img src="./images/homebutton.png" height="40"/>](./index.html)
contact:
name: Mappls API Support
url: https://about.mappls.com/api
email: apisupport@mappls.com
termsOfService: https://about.mappls.com/api/terms-&-conditions
version: V1.0.0
license:
name: Revised BSD-3 Clause
url: https://github.com/mappls-api
servers:
- url: 'https://ai.mappls.com'
security:
- bearerAuth: []
components:
securitySchemes:
bearerAuth:
type: http
scheme: bearer
bearerFormat: JWT # optional, for documentation purposes only
paths:
/predict:
post:
summary: Returns the prediction as response in form of JSON
requestBody:
content:
multipart/form-data:
schema:
type: object
properties:
image:
type: string
format: binary
responses:
'200':
description: Successful.
content:
application/json:
examples:
apires:
value:
api: null
type: vidteq-ssb-1
dnnarch: yolov3
result:
130419_093229_16718_zed_l_049.jpg:
regions:
- region_attributes:
label: shopname
score: 0.6513383388519287
shape_attributes:
name: rect
'y': 34
x: 26
height: 210
width: 326
- region_attributes:
label: shopname
score: 0.4883211851119995
shape_attributes:
name: rect
'y': 207
x: 712
height: 199
width: 81
- region_attributes:
label: shopname
score: 0.4395501911640167
shape_attributes:
name: rect
'y': 10
x: 423
height: 187
width: 103
file_attributes:
width: 1920
height: 1080
uuid: pred-b241fece-10df-4ad5-bdcf-ca14e1b18deb
size: 0
filename: 130419_093229_16718_zed_l_049.jpg
status_code: 200
timings:
image_read: 0.047499656677246094
detect_with_json: 0.3955676555633545
res_preparation: 0.00006556510925292969
tt_turnaround: 0.4431617259979248
'400':
description: Invalid Image Type
'500':
description: Internal Error. Exception in detection
/models:
get:
summary: Returns the details of all the models
responses:
'200':
description: Successful.
'400':
description: No current models in port
'500':
description: Internal Error. Exception during processing whoami.
/models/{api_model_key}:
get:
summary: Returns the details of provided model
parameters:
- name: api_model_key
in: path
required: true
description: model key
schema:
type: string
example: vidteq-ods-9
responses:
'200':
description: Successful.
content:
application/json:
examples:
apires:
value:
created_on: '2020-12-22 17:59:37 +05:30'
checkpoint_path: >-
/aimldl-dat/logs/mask_rcnn/040220_185612/train_hmd_040220_185627/mask_rcnn_hmd_{epoch:04d}.h5
classes:
- BG
- signage
- traffic_light
- traffic_sign
classinfo: null
config:
DETECTION_MIN_CONFIDENCE: 0.9
GPU_COUNT: 1
IMAGES_PER_GPU: 1
IMAGE_MAX_DIM: 1024
IMAGE_MIN_DIM: 800
STEPS_PER_EPOCH: 1000
NAME: hmd
NUM_CLASSES: 4
creator: AIE1
dataset: PXL-040220_183906
dbname: oasis
dnnarch: mask_rcnn
filename: vidteq-ods-9-mask_rcnn.yml
filepath: >-
/codehub/aihub/aimldl-dat/cfg/model/release/vidteq-ods-9-mask_rcnn.yml
framework_type: keras
id: null
load_weights:
by_name: true
exclude:
- mrcnn_class_logits
- mrcnn_bbox_fc
- mrcnn_bbox
- mrcnn_mask
log_dir: api/vidteq-ods-9
mode: inference
model_info: vidteq-hmd-040220_185627-mask_rcnn.yml
name: hmd
num_classes: 4
org_name: vidteq
problem_id: ods
rel_num: '9'
timestamp: '221220_175937'
uuid: uuid-45ff9baa-2561-4e21-9703-e22b90fe951d
weights: null
weights_path: >-
/codehub/aihub/aimldl-dat/release/vidteq/ods/9/mask_rcnn/weights/mask_rcnn_hmd_0200.h5
log_dir_path: /codehub/aihub/aimldl-dat/logs/api/vidteq-ods-9
'400':
description: No current models in port
'500':
description: Internal Error. Exception during processing whoami.
/server/whoami:
get:
summary: Returns the current models in port
responses:
'200':
description: Successful.
content:
application/json:
examples:
apires:
value:
api: null
type: vidteq-ssb-1
dnnarch: yolov3
result:
api_model_keys:
- vidteq-hmd-1
- vidteq-hmd-2
- vidteq-hmd-3
- vidteq-hmd-6
- vidteq-ods-7
- vidteq-ods-8
- vidteq-ods-9
- vidteq-rld-1
- vidteq-rbd-1
- vidteq-rod-1
- vidteq-rjd-1
- vidteq-rldc-4
- vidteq-rom-1
- vidteq-sbd-1
- vidteq-sbd-2
- vidteq-sgc-1
- vidteq-sgc-2
- vidteq-tlc-1
- vidteq-tlc-2
- vidteq-tls-1
- vidteq-tls-2
- vidteq-tsc-1
- vidteq-tsc-2
- vidteq-bhm-1
- vidteq-tsdr-1
- vidteq-ssb-1
- vidteq-rjm-1
- vidteq-rsm-1
- vidteq-hnm-1
- vidteq-vmd-1
status_code: 200
timings: null
'400':
description: No current models in port
'500':
description: Internal Error. Exception during processing whoami.