CustomS3AutoDeleteObjectsCustomResourceProviderHandler9D90184F:
Type: AWS::Lambda::Function
Properties:
Code:
S3Bucket:
Fn::Sub: cdk-hnb659fds-assets-${AWS::AccountId}-${AWS::Region}
S3Key: b7f33614a69548d6bafe224d751a7ef238cde19097415e553fe8b63a4c8fd8a6.zip

CustomS3AutoDeleteObjectsCustomResourceProviderHandler9D90184F:
Type: AWS::Lambda::Function
Properties:
Code:
ZipFile: |
"use strict";var C=Object.create,i=Object.defineProperty,I=Object.getOwnPropertyDescriptor,w=Object.getOwnPropertyNames,P=Object.getPrototypeOf,A=Object.prototype.hasOwnProperty,L=(e,t)=>{for(var o in t)i(e,o,{get:t[o],enumerable:!0})},d=(e,t,o,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let s of w(t))!A.call(e,s)&&s!==o&&i(e,s,{get:()=>t[s],enumerable:!(r=I(t,s))||r.enumerable});return e},l=(e,t,o)=>(o=e!=null?C(P(e)):{},d(t||!e||!e.__esModule?i(o,"default",{value:e,enumerable:!0}):o,e)),k=e=>d(i({},"__esModule",{value:!0}),e),U={};L(U,{autoDeleteHandler:()=>S,handler:()=>_}),module.exports=k(U);var h=require("@aws-sdk/client-s3"),y=l(require("https")),m=l(require("url")),a={sendHttpRequest:T,log:b,includeStackTraces:!0,userHandlerIndex:"./index"},p="AWSCDK::CustomResourceProviderFramework::CREATE_FAILED",B="AWSCDK::CustomResourceProviderFramework::MISSING_PHYSICAL_ID";function R(e){return async(t,o)=>{let r={...t,ResponseURL:"..."};if(a.log(JSON.stringify(r,void 0,2)),t.RequestType==="Delete"&&t.PhysicalResourceId===p){a.log("ignoring DELETE event caused by a failed CREATE event"),await u("SUCCESS",t);return}try{let s=await e(r,o),n=D(t,s);await u("SUCCESS",n)}catch(s){let n={...t,Reason:a.includeStackTraces?s.stack:s.message};n.PhysicalResourceId||(t.RequestType==="Create"?(a.log("CREATE failed, responding with a marker physical resource id so that the subsequent DELETE will be ignored"),n.PhysicalResourceId=p):a.log(`ERROR: Malformed event. "PhysicalResourceId" is required: ${JSON.stringify(t)}`)),await u("FAILED",n)}}}function D(e,t={}){let o=t.PhysicalResourceId??e.PhysicalResourceId??e.RequestId;if(e.RequestType==="Delete"&&o!==e.PhysicalResourceId)throw new Error(`DELETE: cannot change the physical resource ID from "${e.PhysicalResourceId}" to "${t.PhysicalResourceId}" during deletion`);return{...e,...t,PhysicalResourceId:o}}async function u(e,t){let o={Status:e,Reason:t.Reason??e,StackId:t.StackId,RequestId:t.RequestId,PhysicalResourceId:t.PhysicalResourceId||B,LogicalResourceId:t.LogicalResourceId,NoEcho:t.NoEcho,Data:t.Data};a.log("submit response to cloudformation",o);let r=JSON.stringify(o),s=m.parse(t.ResponseURL),n={hostname:s.hostname,path:s.path,method:"PUT",headers:{"content-type":"","content-length":Buffer.byteLength(r,"utf8")}};await O({attempts:5,sleep:1e3},a.sendHttpRequest)(n,r)}async function T(e,t){return new Promise((o,r)=>{try{let s=y.request(e,n=>o());s.on("error",r),s.write(t),s.end()}catch(s){r(s)}})}function b(e,...t){console.log(e,...t)}function O(e,t){return async(...o)=>{let r=e.attempts,s=e.sleep;for(;;)try{return await t(...o)}catch(n){if(r--<=0)throw n;await x(Math.floor(Math.random()*s)),s*=2}}}async function x(e){return new Promise(t=>setTimeout(t,e))}var g="aws-cdk:auto-delete-objects",H=JSON.stringify({Version:"2012-10-17",Statement:[]}),c=new h.S3({}),_=R(S);async function S(e){switch(e.RequestType){case"Create":return;case"Update":return F(e);case"Delete":return f(e.ResourceProperties?.BucketName)}}async function F(e){let t=e,o=t.OldResourceProperties?.BucketName,r=t.ResourceProperties?.BucketName;if(r!=null&&o!=null&&r!==o)return f(o)}async function N(e){try{let t=(await c.getBucketPolicy({Bucket:e}))?.Policy??H,o=JSON.parse(t);o.Statement.push({Principal:"*",Effect:"Deny",Action:["s3:PutObject"],Resource:[`arn:aws:s3:::${e}/*`]}),await c.putBucketPolicy({Bucket:e,Policy:JSON.stringify(o)})}catch(t){if(t.name==="NoSuchBucket")throw t;console.log(`Could not set new object deny policy on bucket '${e}' prior to deletion.`)}}async function E(e){let t=await c.listObjectVersions({Bucket:e}),o=[...t.Versions??[],...t.DeleteMarkers??[]];if(o.length===0)return;let r=o.map(s=>({Key:s.Key,VersionId:s.VersionId}));await c.deleteObjects({Bucket:e,Delete:{Objects:r}}),t?.IsTruncated&&await E(e)}async function f(e){if(!e)throw new Error("No BucketName was provided.");try{if(!await W(e)){console.log(`Bucket does not have '${g}' tag, skipping cleaning.`);return}await N(e),await E(e)}catch(t){if(t.name==="NoSuchBucket"){console.log(`Bucket '${e}' does not exist.`);return}throw t}}async function W(e){return(await c.getBucketTagging({Bucket:e})).TagSet?.some(o=>o.Key===g&&o.Value==="true")}

***************************************************************************************

Welcome to the AWS HealthImaging working with image sets and frames workflow.

***************************************************************************************

This workflow will import DICOM files into a HealthImaging data store.
DICOM® — Digital Imaging and Communications in Medicine — is the international
standard for medical images and related information.

The workflow will then download all the image frames created during the DICOM import and decode
the image frames from their HTJ2K format to a bitmap format.
The bitmaps will then be validated with a checksum to ensure they are correct.

This workflow requires a number of AWS resources to run.

It requires a HealthImaging data store, an Amazon Simple Storage Service (Amazon S3)
bucket for uploaded DICOM files, an Amazon S3 bucket for the output of a DICOM import, and
an AWS Identity and Access Management (IAM) role for importing the DICOM files into
the data store.

These resources can be created for you using an AWS CloudFormation stack.

Would you like to let this workflow create the resources for you? (y/n) y
Enter a name for the AWS CloudFormation stack to create. workflow-stack
Enter a name for the HealthImaging datastore to create. workflow-datastore
Stack creation initiated.
Waiting for the stack to be created.
Stack creation completed.
The following resources have been created.
A HealthImaging datastore with ID: 12345678901234567890123456789012.
An Amazon S3 input bucket named: workflow-stack-docexampledicominbucket1234567.
An Amazon S3 output bucket named: workflow-stack-docexampledicomoutbucket1234567.
An IAM role with the ARN: arn:aws:iam::12345678901:role/workflow-stack-docexampleimportrole1234567.
Enter return to continue.

***************************************************************************************

This workflow uses DICOM files from the National Cancer Institute Imaging Data
Commons (IDC) Collections.
Here is the link to their website.
https://registry.opendata.aws/nci-imaging-data-commons/
We will use DICOM files stored in an S3 bucket managed by the IDC.
First one of the DICOM folders in the IDC collection must be copied to your
input S3 bucket.
You have the choice of one of the following 4 folders to copy.
1 - CT of chest (2 images)
2 - CT of pelvis (57 images)
3 - MRI of head (192 images)
4 - MRI of breast (92 images)
Choose DICOM files to import: 3
The files in the directory '0002d261-8a5d-4e63-8e2e-0cbfac87b904' in the bucket 'idc-open-data' will be copied 
to the folder 'input/12345678901234567890123456789012' in the bucket 'workflow-stack-docexampledicominbucket1234567'.
Enter return to start the copy.
192 DICOM files were copied.

***************************************************************************************

Now the DICOM images will be imported into the datastore with ID '12345678901234567890123456789012'
Enter return to start the DICOM import job.
DICOM import job started with job ID 12345678901234567890123456789012.
DICOM import job status: IN_PROGRESS
DICOM import job status: IN_PROGRESS
DICOM import job status: IN_PROGRESS
DICOM import job status: IN_PROGRESS
DICOM import job status: IN_PROGRESS
DICOM import job status: COMPLETED
DICOM import job completed.
The DICOM files were successfully imported. The import job ID is '12345678901234567890123456789012'.
Information about the import job, including the IDs of the created image sets,
is located in a file named 'job-output-manifest.json'. This file is located in a
folder specified by the import job's 'outputS3Uri'.
The 'outputS3Uri' is retrieved by calling the 'GetDICOMImportJob' action.

***************************************************************************************

The image set IDs will be retrieved by downloading 'job-output-manifest.json' file from the output S3 bucket.
Enter return to continue.
The image sets created by this import job are: 
Image set: 12345678901234567890123456789012
If you would like information about how HealthImaging organizes image sets,
go to the following link.
https://docs.aws.amazon.com/healthimaging/latest/devguide/understanding-image-sets.html
Enter return to continue.
Enter return to continue.

***************************************************************************************

Next this workflow will download all the image frames created in this import job.
The IDs of all the image frames in an image set are stored in the image set metadata.
The image set metadata will be downloaded and parsed for the image frame IDs.
Enter return to continue.
192 image frames were created by this import job.

***************************************************************************************

The image frames are encoded in the HTJ2K format. This example will convert
the image frames to bitmaps. The decoded images will be verified using
a CRC32 checksum retrieved from the image set metadata,
The OpenJPEG open-source library will be used for the conversion.
The following link contains information about HTJ2K decoding libraries.
https://docs.aws.amazon.com/healthimaging/latest/devguide/reference-htj2k.html
Enter return to download and convert the images.
192 image files were downloaded.
The image files were successfully decoded and validated.
The HTJ2K image files are located in the directory
'output/import_job_12345678901234567890123456789012' in the working directory
of this application.

***************************************************************************************

This concludes this workflow.

***************************************************************************************

Would you like to delete the stack workflow-stack? (y/n)y
Deleting the image sets in the stack.
Successfully deleted image set 12345678901234567890123456789012 from data store 12345678901234567890123456789012

***************************************************************************************

Deleting the stack.
Stack deletion initiated.
Stack deletion completed.

4 HealthImaging data stores were retrieved.
  Datastore: cpp-test
  Datastore ID: 12345678901234567890123456789012
  Datastore: java_auto_test
  Datastore ID: 12345678901234567890123456789012
  Datastore: java_test_1414
  Datastore ID: 12345678901234567890123456789012
  Datastore: java_test_1896
  Datastore ID: 12345678901234567890123456789012