Add Rekognition support. (#4957)
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CoderDonohoe
GitHub
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@ -131,6 +131,9 @@ mock_redshift = lazy_load(".redshift", "mock_redshift")
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mock_redshiftdata = lazy_load(
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".redshiftdata", "mock_redshiftdata", boto3_name="redshift-data"
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)
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mock_rekognition = lazy_load(
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".rekognition", "mock_rekognition", boto3_name="rekognition"
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)
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mock_resourcegroups = lazy_load(
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".resourcegroups", "mock_resourcegroups", boto3_name="resource-groups"
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)
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@ -102,6 +102,7 @@ backend_url_patterns = [
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("rds", re.compile("https?://rds\\.amazonaws\\.com")),
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("redshift", re.compile("https?://redshift\\.(.+)\\.amazonaws\\.com")),
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("redshift-data", re.compile("https?://redshift-data\\.(.+)\\.amazonaws\\.com")),
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("rekognition", re.compile("https?://rekognition\\.(.+)\\.amazonaws\\.com")),
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(
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"resource-groups",
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re.compile("https?://resource-groups(-fips)?\\.(.+)\\.amazonaws.com"),
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5
moto/rekognition/__init__.py
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5
moto/rekognition/__init__.py
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@ -0,0 +1,5 @@
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"""rekognition module initialization; sets value for base decorator."""
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from .models import rekognition_backends
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from ..core.models import base_decorator
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mock_rekognition = base_decorator(rekognition_backends)
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1
moto/rekognition/exceptions.py
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1
moto/rekognition/exceptions.py
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@ -0,0 +1 @@
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"""Exceptions raised by the rekognition service."""
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215
moto/rekognition/models.py
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215
moto/rekognition/models.py
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@ -0,0 +1,215 @@
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"""RekognitionBackend class with methods for supported APIs."""
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import random
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import string
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from moto.core import BaseBackend
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from moto.core.utils import BackendDict
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class RekognitionBackend(BaseBackend):
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"""Implementation of Rekognition APIs."""
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def __init__(self, region_name=None):
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self.region_name = region_name
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def reset(self):
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"""Re-initialize all attributes for this instance."""
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region_name = self.region_name
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self.__dict__ = {}
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self.__init__(region_name)
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def start_text_detection(self):
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return self._job_id()
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def get_text_detection(self):
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"""
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This returns hardcoded values and none of the parameters are taken into account.
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"""
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return (
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self._job_status(),
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self._status_message(),
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self._video_metadata(),
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self._text_detections(),
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self._next_token(),
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self._text_model_version(),
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)
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# private
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def _job_id(self):
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return "".join(
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random.choice(string.ascii_uppercase + string.digits) for _ in range(64)
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)
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def _job_status(self):
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return "SUCCEEDED"
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def _next_token(self):
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return ""
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def _status_message(self):
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return ""
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def _text_model_version(self):
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return "3.1"
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def _video_metadata(self):
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return {
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"Codec": "h264",
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"DurationMillis": 15020,
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"Format": "QuickTime / MOV",
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"FrameRate": 24.0,
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"FrameHeight": 720,
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"FrameWidth": 1280,
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"ColorRange": "LIMITED",
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}
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def _text_detections(self):
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return [
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{
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"Timestamp": 0,
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"TextDetection": {
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"DetectedText": "Hello world",
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"Type": "LINE",
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"Id": 0,
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"Confidence": 97.89398956298828,
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"Geometry": {
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"BoundingBox": {
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"Width": 0.1364741027355194,
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"Height": 0.0318513885140419,
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"Left": 0.4310702085494995,
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"Top": 0.876121461391449,
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},
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"Polygon": [
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{"X": 0.4310702085494995, "Y": 0.8769540190696716},
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{"X": 0.5673548579216003, "Y": 0.876121461391449},
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{"X": 0.5675443410873413, "Y": 0.90714031457901},
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{"X": 0.4312596917152405, "Y": 0.9079728722572327},
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],
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},
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},
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},
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{
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"Timestamp": 0,
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"TextDetection": {
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"DetectedText": "Hello",
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"Type": "WORD",
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"Id": 1,
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"ParentId": 0,
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"Confidence": 99.1568832397461,
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"Geometry": {
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"BoundingBox": {
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"Width": 0.0648193359375,
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"Height": 0.0234375,
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"Left": 0.43121337890625,
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"Top": 0.876953125,
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},
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"Polygon": [
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{"X": 0.43121337890625, "Y": 0.876953125},
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{"X": 0.49603271484375, "Y": 0.876953125},
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{"X": 0.49603271484375, "Y": 0.900390625},
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{"X": 0.43121337890625, "Y": 0.900390625},
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],
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},
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},
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},
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{
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"Timestamp": 0,
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"TextDetection": {
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"DetectedText": "world",
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"Type": "WORD",
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"Id": 2,
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"ParentId": 0,
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"Confidence": 96.63108825683594,
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"Geometry": {
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"BoundingBox": {
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"Width": 0.07103776931762695,
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"Height": 0.02804870530962944,
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"Left": 0.4965003430843353,
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"Top": 0.8795245885848999,
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},
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"Polygon": [
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{"X": 0.4965003430843353, "Y": 0.8809727430343628},
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{"X": 0.5673661231994629, "Y": 0.8795245885848999},
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{"X": 0.5675381422042847, "Y": 0.9061251282691956},
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{"X": 0.4966723322868347, "Y": 0.9075732827186584},
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],
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},
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},
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},
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{
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"Timestamp": 1000,
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"TextDetection": {
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"DetectedText": "Goodbye world",
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"Type": "LINE",
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"Id": 0,
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"Confidence": 98.9729995727539,
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"Geometry": {
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"BoundingBox": {
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"Width": 0.13677978515625,
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"Height": 0.0302734375,
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"Left": 0.43121337890625,
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"Top": 0.876953125,
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},
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"Polygon": [
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{"X": 0.43121337890625, "Y": 0.876953125},
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{"X": 0.5679931640625, "Y": 0.876953125},
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{"X": 0.5679931640625, "Y": 0.9072265625},
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{"X": 0.43121337890625, "Y": 0.9072265625},
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],
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},
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},
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},
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{
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"Timestamp": 1000,
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"TextDetection": {
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"DetectedText": "Goodbye",
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"Type": "WORD",
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"Id": 1,
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"ParentId": 0,
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"Confidence": 99.7258529663086,
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"Geometry": {
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"BoundingBox": {
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"Width": 0.0648193359375,
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"Height": 0.0234375,
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"Left": 0.43121337890625,
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"Top": 0.876953125,
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},
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"Polygon": [
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{"X": 0.43121337890625, "Y": 0.876953125},
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{"X": 0.49603271484375, "Y": 0.876953125},
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{"X": 0.49603271484375, "Y": 0.900390625},
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{"X": 0.43121337890625, "Y": 0.900390625},
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],
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},
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},
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},
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{
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"Timestamp": 1000,
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"TextDetection": {
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"DetectedText": "world",
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"Type": "WORD",
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"Id": 2,
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"ParentId": 0,
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"Confidence": 98.22015380859375,
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"Geometry": {
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"BoundingBox": {
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"Width": 0.0703125,
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"Height": 0.0263671875,
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"Left": 0.4976806640625,
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"Top": 0.880859375,
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},
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"Polygon": [
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{"X": 0.4976806640625, "Y": 0.880859375},
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{"X": 0.5679931640625, "Y": 0.880859375},
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{"X": 0.5679931640625, "Y": 0.9072265625},
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{"X": 0.4976806640625, "Y": 0.9072265625},
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],
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},
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},
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},
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]
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rekognition_backends = BackendDict(RekognitionBackend, "rekognition")
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45
moto/rekognition/responses.py
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45
moto/rekognition/responses.py
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"""Handles incoming rekognition requests, invokes methods, returns responses."""
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import json
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from moto.core.responses import BaseResponse
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from .models import rekognition_backends
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class RekognitionResponse(BaseResponse):
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"""Handler for Rekognition requests and responses."""
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@property
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def rekognition_backend(self):
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"""Return backend instance specific for this region."""
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return rekognition_backends[self.region]
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def get_text_detection(self):
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(
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job_status,
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status_message,
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video_metadata,
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text_detections,
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next_token,
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text_model_version,
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) = self.rekognition_backend.get_text_detection()
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return json.dumps(
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dict(
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JobStatus=job_status,
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StatusMessage=status_message,
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VideoMetadata=video_metadata,
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TextDetections=text_detections,
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NextToken=next_token,
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TextModelVersion=text_model_version,
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)
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)
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def start_text_detection(self):
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headers = {"Content-Type": "application/x-amz-json-1.1"}
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job_id = self.rekognition_backend.start_text_detection()
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response = ('{"JobId":"' + job_id + '"}').encode()
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return 200, headers, response
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# add templates from here
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10
moto/rekognition/urls.py
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10
moto/rekognition/urls.py
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"""rekognition base URL and path."""
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from .responses import RekognitionResponse
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url_bases = [
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r"https?://rekognition\.(.+)\.amazonaws\.com",
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]
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url_paths = {
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"{0}/$": RekognitionResponse.dispatch,
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}
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0
tests/test_rekognition/__init__.py
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0
tests/test_rekognition/__init__.py
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45
tests/test_rekognition/test_rekognition.py
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tests/test_rekognition/test_rekognition.py
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"""Unit tests for rekognition-supported APIs."""
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import random
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import string
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import boto3
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import sure # noqa # pylint: disable=unused-import
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from moto import mock_rekognition
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# See our Development Tips on writing tests for hints on how to write good tests:
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# http://docs.getmoto.org/en/latest/docs/contributing/development_tips/tests.html
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@mock_rekognition
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def test_start_text_detection():
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client = boto3.client("rekognition", region_name="ap-southeast-1")
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video = {
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"S3Object": {
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"Bucket": "bucket",
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"Name": "key",
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}
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}
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resp = client.start_text_detection(Video=video)
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resp["ResponseMetadata"]["HTTPStatusCode"].should.equal(200)
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resp.should.have.key("JobId")
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@mock_rekognition
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def test_get_text_detection():
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client = boto3.client("rekognition", region_name="us-east-2")
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job_id = "".join(
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random.choice(string.ascii_uppercase + string.digits) for _ in range(64)
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)
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resp = client.get_text_detection(JobId=job_id)
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resp["ResponseMetadata"]["HTTPStatusCode"].should.equal(200)
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resp["JobStatus"].should.equal("SUCCEEDED")
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resp["TextDetections"][0]["TextDetection"]["DetectedText"].should.equal(
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"Hello world"
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)
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