Zakaria
117 lines
4.8 KiB
Markdown
117 lines
4.8 KiB
Markdown
# Rekognition
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**Protocol:** JSON 1.1 (`X-Amz-Target: RekognitionService.*`)
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**Endpoint:** `POST http://localhost:4566/`
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## Supported Actions
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<!-- floci:actions:start -->
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| Action | Description |
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| --- | --- |
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| `DetectLabels` | Detect objects, scenes, and concepts in an image |
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| `DetectFaces` | Detect faces in an image and their attributes |
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| `DetectText` | Detect text in an image |
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| `CompareFaces` | Compare a face in a source image with faces in a target image |
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| `DetectModerationLabels` | Detect unsafe or inappropriate content in an image |
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<!-- floci:actions:end -->
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## Emulation Behavior
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- **Stub responses:** every action returns a fixed, AWS-shaped response — no real
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image analysis is performed. `Image`/`SourceImage`/`TargetImage` content is
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accepted but never decoded, the same way Textract accepts `Document` without
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reading it.
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- **Content-listing operations return one canned entry:** `DetectLabels` and
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`DetectText` always report a single stub label/text detection, matching the
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Textract stub's block-hierarchy convention.
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- **Face/moderation operations return empty results:** `DetectFaces`,
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`CompareFaces`, and `DetectModerationLabels` always report nothing found
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(empty `FaceDetails`/`FaceMatches`/`ModerationLabels`) rather than fabricating
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biometric attributes or a moderation flag for content that was never analyzed
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— an honest "nothing detected" response, matching the Comprehend PII-detection
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stub's precedent.
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- **Real input validation:** `Image` (or `SourceImage`/`TargetImage` for
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`CompareFaces`) is required, must be a JSON structure, and must specify
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`Bytes` (string-typed) or `S3Object` (structure-typed) — matching AWS's own
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modeling and rejecting wrong JSON types as `SerializationException`. This is
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protocol compatibility, not content analysis.
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- **Intentional deviations:** `Bytes`'s content is not validated as
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well-formed base64 (the `S3Object` shape has no required members in AWS's
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own model, so its fields aren't validated either), and supplying both
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`Bytes` and `S3Object` is accepted rather than rejected — nothing in the
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Rekognition API model declares that combination invalid, and the stub
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never reads either field's content regardless.
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- **Out of scope:** face-collection persistence (`CreateCollection`,
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`IndexFaces`, `SearchFaces`), custom-model training (`Projects`, `Datasets`,
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`ProjectVersions`), live video (`StreamProcessor`), async video jobs
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(`Start*`/`Get*` Celebrity/Content/Face/Label/Person/Segment/Text detection),
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and Face Liveness sessions are not implemented.
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## Configuration
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| Variable | Default | Description |
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|---|---|---|
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| `FLOCI_SERVICES_REKOGNITION_ENABLED` | `true` | Enable or disable the service |
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| `AI_MOCK_CONFIG` | unset | Path to a shared mock-response config file — see "Mock Responses" below |
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## Mock Responses
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The default stub responses above are the same for every call. To exercise application
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logic that branches on detection results, point `AI_MOCK_CONFIG` at a JSON file shared
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across Rekognition, Textract, and Comprehend:
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```json
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{
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"rekognition": {
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"my-bucket/cat.jpg": {
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"DetectLabels": {
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"Labels": [{ "Name": "Cat", "Confidence": 97.2 }, { "Name": "Animal", "Confidence": 98.1 }],
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"LabelModelVersion": "1.0"
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}
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}
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}
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}
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```
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The lookup key is **`"<Bucket>/<Name>"`** from `Image.S3Object` (or `SourceImage.S3Object`
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for `CompareFaces` — `TargetImage` has no independent key in this scheme). A `Bytes`-backed
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image has no such key, so mocking only applies to S3Object-based calls. The file is re-read
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when its modification time changes, so it can be edited without restarting the emulator. A
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missing file, an unset `AI_MOCK_CONFIG`, or no matching entry all fall back to the default
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stub.
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## Examples
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```bash
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export AWS_ENDPOINT_URL=http://localhost:4566
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aws rekognition detect-labels \
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--image '{"S3Object":{"Bucket":"my-bucket","Name":"photo.jpg"}}' \
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--endpoint-url $AWS_ENDPOINT_URL
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aws rekognition detect-text \
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--image '{"S3Object":{"Bucket":"my-bucket","Name":"photo.jpg"}}' \
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--endpoint-url $AWS_ENDPOINT_URL
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aws rekognition compare-faces \
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--source-image '{"S3Object":{"Bucket":"my-bucket","Name":"source.jpg"}}' \
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--target-image '{"S3Object":{"Bucket":"my-bucket","Name":"target.jpg"}}' \
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--endpoint-url $AWS_ENDPOINT_URL
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```
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## SDK Example (Java)
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```java
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RekognitionClient rekognition = RekognitionClient.builder()
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.endpointOverride(URI.create("http://localhost:4566"))
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.region(Region.US_EAST_1)
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.credentialsProvider(StaticCredentialsProvider.create(
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AwsBasicCredentials.create("test", "test")))
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.build();
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DetectLabelsResponse response = rekognition.detectLabels(req -> req
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.image(img -> img.s3Object(s3 -> s3.bucket("my-bucket").name("photo.jpg"))));
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System.out.println("Labels: " + response.labels());
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```
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