Add Rekognition support. (#4957)

moto/__init__.py

@@ -131,6 +131,9 @@ mock_redshift = lazy_load(".redshift", "mock_redshift")
 mock_redshiftdata = lazy_load(
     ".redshiftdata", "mock_redshiftdata", boto3_name="redshift-data"
 )
+mock_rekognition = lazy_load(
+    ".rekognition", "mock_rekognition", boto3_name="rekognition"
+)
 mock_resourcegroups = lazy_load(
     ".resourcegroups", "mock_resourcegroups", boto3_name="resource-groups"
 )

moto/backend_index.py

@@ -102,6 +102,7 @@ backend_url_patterns = [
     ("rds", re.compile("https?://rds\\.amazonaws\\.com")),
     ("redshift", re.compile("https?://redshift\\.(.+)\\.amazonaws\\.com")),
     ("redshift-data", re.compile("https?://redshift-data\\.(.+)\\.amazonaws\\.com")),
+    ("rekognition", re.compile("https?://rekognition\\.(.+)\\.amazonaws\\.com")),
     (
         "resource-groups",
         re.compile("https?://resource-groups(-fips)?\\.(.+)\\.amazonaws.com"),

moto/rekognition/__init__.py

@@ -0,0 +1,5 @@
+"""rekognition module initialization; sets value for base decorator."""
+from .models import rekognition_backends
+from ..core.models import base_decorator
+
+mock_rekognition = base_decorator(rekognition_backends)

moto/rekognition/exceptions.py

@@ -0,0 +1 @@
+"""Exceptions raised by the rekognition service."""

moto/rekognition/models.py

@@ -0,0 +1,215 @@
+"""RekognitionBackend class with methods for supported APIs."""
+
+import random
+import string
+
+from moto.core import BaseBackend
+from moto.core.utils import BackendDict
+
+
+class RekognitionBackend(BaseBackend):
+    """Implementation of Rekognition APIs."""
+
+    def __init__(self, region_name=None):
+        self.region_name = region_name
+
+    def reset(self):
+        """Re-initialize all attributes for this instance."""
+        region_name = self.region_name
+        self.__dict__ = {}
+        self.__init__(region_name)
+
+    def start_text_detection(self):
+        return self._job_id()
+
+    def get_text_detection(self):
+        """
+        This returns hardcoded values and none of the parameters are taken into account.
+        """
+        return (
+            self._job_status(),
+            self._status_message(),
+            self._video_metadata(),
+            self._text_detections(),
+            self._next_token(),
+            self._text_model_version(),
+        )
+
+    # private
+
+    def _job_id(self):
+        return "".join(
+            random.choice(string.ascii_uppercase + string.digits) for _ in range(64)
+        )
+
+    def _job_status(self):
+        return "SUCCEEDED"
+
+    def _next_token(self):
+        return ""
+
+    def _status_message(self):
+        return ""
+
+    def _text_model_version(self):
+        return "3.1"
+
+    def _video_metadata(self):
+        return {
+            "Codec": "h264",
+            "DurationMillis": 15020,
+            "Format": "QuickTime / MOV",
+            "FrameRate": 24.0,
+            "FrameHeight": 720,
+            "FrameWidth": 1280,
+            "ColorRange": "LIMITED",
+        }
+
+    def _text_detections(self):
+        return [
+            {
+                "Timestamp": 0,
+                "TextDetection": {
+                    "DetectedText": "Hello world",
+                    "Type": "LINE",
+                    "Id": 0,
+                    "Confidence": 97.89398956298828,
+                    "Geometry": {
+                        "BoundingBox": {
+                            "Width": 0.1364741027355194,
+                            "Height": 0.0318513885140419,
+                            "Left": 0.4310702085494995,
+                            "Top": 0.876121461391449,
+                        },
+                        "Polygon": [
+                            {"X": 0.4310702085494995, "Y": 0.8769540190696716},
+                            {"X": 0.5673548579216003, "Y": 0.876121461391449},
+                            {"X": 0.5675443410873413, "Y": 0.90714031457901},
+                            {"X": 0.4312596917152405, "Y": 0.9079728722572327},
+                        ],
+                    },
+                },
+            },
+            {
+                "Timestamp": 0,
+                "TextDetection": {
+                    "DetectedText": "Hello",
+                    "Type": "WORD",
+                    "Id": 1,
+                    "ParentId": 0,
+                    "Confidence": 99.1568832397461,
+                    "Geometry": {
+                        "BoundingBox": {
+                            "Width": 0.0648193359375,
+                            "Height": 0.0234375,
+                            "Left": 0.43121337890625,
+                            "Top": 0.876953125,
+                        },
+                        "Polygon": [
+                            {"X": 0.43121337890625, "Y": 0.876953125},
+                            {"X": 0.49603271484375, "Y": 0.876953125},
+                            {"X": 0.49603271484375, "Y": 0.900390625},
+                            {"X": 0.43121337890625, "Y": 0.900390625},
+                        ],
+                    },
+                },
+            },
+            {
+                "Timestamp": 0,
+                "TextDetection": {
+                    "DetectedText": "world",
+                    "Type": "WORD",
+                    "Id": 2,
+                    "ParentId": 0,
+                    "Confidence": 96.63108825683594,
+                    "Geometry": {
+                        "BoundingBox": {
+                            "Width": 0.07103776931762695,
+                            "Height": 0.02804870530962944,
+                            "Left": 0.4965003430843353,
+                            "Top": 0.8795245885848999,
+                        },
+                        "Polygon": [
+                            {"X": 0.4965003430843353, "Y": 0.8809727430343628},
+                            {"X": 0.5673661231994629, "Y": 0.8795245885848999},
+                            {"X": 0.5675381422042847, "Y": 0.9061251282691956},
+                            {"X": 0.4966723322868347, "Y": 0.9075732827186584},
+                        ],
+                    },
+                },
+            },
+            {
+                "Timestamp": 1000,
+                "TextDetection": {
+                    "DetectedText": "Goodbye world",
+                    "Type": "LINE",
+                    "Id": 0,
+                    "Confidence": 98.9729995727539,
+                    "Geometry": {
+                        "BoundingBox": {
+                            "Width": 0.13677978515625,
+                            "Height": 0.0302734375,
+                            "Left": 0.43121337890625,
+                            "Top": 0.876953125,
+                        },
+                        "Polygon": [
+                            {"X": 0.43121337890625, "Y": 0.876953125},
+                            {"X": 0.5679931640625, "Y": 0.876953125},
+                            {"X": 0.5679931640625, "Y": 0.9072265625},
+                            {"X": 0.43121337890625, "Y": 0.9072265625},
+                        ],
+                    },
+                },
+            },
+            {
+                "Timestamp": 1000,
+                "TextDetection": {
+                    "DetectedText": "Goodbye",
+                    "Type": "WORD",
+                    "Id": 1,
+                    "ParentId": 0,
+                    "Confidence": 99.7258529663086,
+                    "Geometry": {
+                        "BoundingBox": {
+                            "Width": 0.0648193359375,
+                            "Height": 0.0234375,
+                            "Left": 0.43121337890625,
+                            "Top": 0.876953125,
+                        },
+                        "Polygon": [
+                            {"X": 0.43121337890625, "Y": 0.876953125},
+                            {"X": 0.49603271484375, "Y": 0.876953125},
+                            {"X": 0.49603271484375, "Y": 0.900390625},
+                            {"X": 0.43121337890625, "Y": 0.900390625},
+                        ],
+                    },
+                },
+            },
+            {
+                "Timestamp": 1000,
+                "TextDetection": {
+                    "DetectedText": "world",
+                    "Type": "WORD",
+                    "Id": 2,
+                    "ParentId": 0,
+                    "Confidence": 98.22015380859375,
+                    "Geometry": {
+                        "BoundingBox": {
+                            "Width": 0.0703125,
+                            "Height": 0.0263671875,
+                            "Left": 0.4976806640625,
+                            "Top": 0.880859375,
+                        },
+                        "Polygon": [
+                            {"X": 0.4976806640625, "Y": 0.880859375},
+                            {"X": 0.5679931640625, "Y": 0.880859375},
+                            {"X": 0.5679931640625, "Y": 0.9072265625},
+                            {"X": 0.4976806640625, "Y": 0.9072265625},
+                        ],
+                    },
+                },
+            },
+        ]
+
+
+rekognition_backends = BackendDict(RekognitionBackend, "rekognition")

moto/rekognition/responses.py

@@ -0,0 +1,45 @@
+"""Handles incoming rekognition requests, invokes methods, returns responses."""
+import json
+
+from moto.core.responses import BaseResponse
+from .models import rekognition_backends
+
+
+class RekognitionResponse(BaseResponse):
+    """Handler for Rekognition requests and responses."""
+
+    @property
+    def rekognition_backend(self):
+        """Return backend instance specific for this region."""
+        return rekognition_backends[self.region]
+
+    def get_text_detection(self):
+        (
+            job_status,
+            status_message,
+            video_metadata,
+            text_detections,
+            next_token,
+            text_model_version,
+        ) = self.rekognition_backend.get_text_detection()
+
+        return json.dumps(
+            dict(
+                JobStatus=job_status,
+                StatusMessage=status_message,
+                VideoMetadata=video_metadata,
+                TextDetections=text_detections,
+                NextToken=next_token,
+                TextModelVersion=text_model_version,
+            )
+        )
+
+    def start_text_detection(self):
+        headers = {"Content-Type": "application/x-amz-json-1.1"}
+        job_id = self.rekognition_backend.start_text_detection()
+        response = ('{"JobId":"' + job_id + '"}').encode()
+
+        return 200, headers, response
+
+
+# add templates from here

moto/rekognition/urls.py

@@ -0,0 +1,10 @@
+"""rekognition base URL and path."""
+from .responses import RekognitionResponse
+
+url_bases = [
+    r"https?://rekognition\.(.+)\.amazonaws\.com",
+]
+
+url_paths = {
+    "{0}/$": RekognitionResponse.dispatch,
+}

tests/test_rekognition/test_rekognition.py

@@ -0,0 +1,45 @@
+"""Unit tests for rekognition-supported APIs."""
+import random
+import string
+
+import boto3
+
+import sure  # noqa # pylint: disable=unused-import
+from moto import mock_rekognition
+
+# See our Development Tips on writing tests for hints on how to write good tests:
+# http://docs.getmoto.org/en/latest/docs/contributing/development_tips/tests.html
+
+
+@mock_rekognition
+def test_start_text_detection():
+    client = boto3.client("rekognition", region_name="ap-southeast-1")
+    video = {
+        "S3Object": {
+            "Bucket": "bucket",
+            "Name": "key",
+        }
+    }
+
+    resp = client.start_text_detection(Video=video)
+
+    resp["ResponseMetadata"]["HTTPStatusCode"].should.equal(200)
+
+    resp.should.have.key("JobId")
+
+
+@mock_rekognition
+def test_get_text_detection():
+    client = boto3.client("rekognition", region_name="us-east-2")
+    job_id = "".join(
+        random.choice(string.ascii_uppercase + string.digits) for _ in range(64)
+    )
+
+    resp = client.get_text_detection(JobId=job_id)
+
+    resp["ResponseMetadata"]["HTTPStatusCode"].should.equal(200)
+
+    resp["JobStatus"].should.equal("SUCCEEDED")
+    resp["TextDetections"][0]["TextDetection"]["DetectedText"].should.equal(
+        "Hello world"
+    )