API reference/Mappls AI / ML
POSThybridexact public snapshot

Returns different output depending upon the inputs given.

/facedetection

Using these APIs developers can fully leverage and embed the power of AI / ML Analytics, intelligence. These APIs allow our client to integrate into their own application the power and full set of functionalities of the mGIS product and platform. It’s a fact: people have a right of getting digital privacy. Here’s one more: online publishers and businesses worldwide must respect those rights and provide visual anonymity when it’s needed. That’s why we decided to give you the fastest and easiest way to keep people’s privacy in photos. Meet Face Detection & Blurring by MapmyIndia. This tool completely automates Face Detection, Face Blurring in photos and helps you get the job done in just a few clicks without graphic editors and manual work. Keep reading to find out how it works, when it comes in handy, and how you can actually use it in MapmyIndia.

Exact public source snapshot

This operation is retained because its version-labelled public specification differs structurally from another generation. It is comparison evidence, not a promise of current support, compatibility, entitlement, or availability.

Call the operation

POSThttps://mgis-api.mapmyindia.com/facedetection
Base URLhttps://mgis-api.mapmyindia.com/
Authenticationbearer
Journey rolecommand
Content typesmultipart/form-data

Parameters

NameLocationTypeDescriptionExample
No explicit parameters are declared by the source operation.

Body schema

FieldTypeDescriptionExample
filerequired
string

Image file in .jpg, .jpeg and .png formats

example
Request body example
{
  "file": "example"
}

Build your request in eight languages

Change source-backed examples once, validate required inputs, and copy the synchronized cURL, JavaScript, Python, Java, C#, Go, PHP, or Ruby request. Credentials remain environment references, and this page never sends a provider request.

Local request composerCode stays local · fixtures are explicit
POSTRequest inputs
0 editable · 0 protected
This operation has no editable path or query parameters.
Generated in eight languages
Environment-backed credentials
POSThttps://mgis-api.mapmyindia.com/facedetection
Credential-free sandboxExecute this contract without a provider call
How fixtures work
Generated from your inputs
curl --request POST \
  --url 'https://mgis-api.mapmyindia.com/facedetection' \
  --header "Accept: application/json" \
  --header "Authorization: Bearer ${MAPPLS_ACCESS_TOKEN}" \
  --header "Content-Type: multipart/form-data" \
  --data-raw '{"file":"example"}'

Credential-safe by constructionSet MAPPLS_ACCESS_TOKEN only in your trusted runtime. Code generation stays local. Sandbox execution sends only bounded fixture inputs to this developer platform and never reads a credential or calls Mappls.

Send a production-shaped request

Examples use environment-backed access tokens, explicit timeouts where supported, status checks, and JSON bodies derived from the source schema. Replace sample identifiers and coordinates with values from your application.

POST /facedetection
curl --request POST \
  --url 'https://mgis-api.mapmyindia.com/facedetection' \
  --header 'Authorization: Bearer YOUR_ACCESS_TOKEN' \
  --header 'Content-Type: application/json' \
  --data '{
  "file": "example"
}'

Responses

200

OK

400

Bad Request, User made an error while creating a valid request.

500

Internal server error.

Response example
[
  0,
  0,
  1
]

Operate it safely

command

This operation creates or triggers durable state. Supply an idempotency strategy and persist the returned identifier before continuing.

Keep credentials and raw tokens out of source, client bundles, prompts, and logs.Record the operation ID, status, latency, retry count, and source contract version in traces.Classify 4xx responses before retrying; back off and add jitter for retryable 5xx or transport failures.Cache only when request inputs, authorization scope, and freshness requirements permit it.

Contract provenance

Source file
mappls-developer-platform/data/corpus/public-api-contracts/mappls-ai-apis/docs/custom/AI_ML_API.yaml
SHA-256
c89f36fb053ab44fc9caeebf96326da5b69c7b5ee1a7f5a85fd14d095565687e
Normalization
Fields are parsed from OpenAPI. Operational guidance and language samples are generated and labeled separately.