neidiom · August 17, 2022 13:43 · Dec 7, 2019 · Oct 6, 2019 · Oct 6, 2019 · Oct 6, 2019
diff --git a/cognitive-actions-vision-image-aws.md b/cognitive-actions-vision-image-aws.md
@@ -321,7 +321,7 @@ request.json
 ```
 * Perform (input: JSON file "request.json"; output: JSON file "result$RANDOM.json)
 ```
-./run-request.sh detect-labels request.json 
+$ ./run-request.sh detect-labels request.json 
 result24519.json
 ```
 * Review (text from output JSON) - for expanded view use `jq . <output JSON filename>`

diff --git a/cognitive-actions-vision-image-aws.md b/cognitive-actions-vision-image-aws.md
@@ -9,6 +9,7 @@
 1. Detect multiple objects in images
 1. ~~Detect web references to an image~~ N/A
 1. Detect landmarks in images
+1. Recognition of Face in images
 
 ***
 
@@ -394,7 +395,7 @@ Monument 55.30182647705078
 * Target image
 <br><img src="https://www.nih.gov/sites/default/files/news-events/research-matters/2014/20140428-attention.jpg" width="50%" /><br>
 * Source image (to find in the target image (show as downsampled to 2kb from original 4kb)
-<br><img src="https://github.com/realBjornRoden/cognition/blob/master/data/face2match2kb.jpg" /><br>
+<br><img src="https://raw.githubusercontent.com/realBjornRoden/cognition/master/data/face2match2kb.jpg" /><br>
 
 * Verify that the files are in the S3 Bucket
 ```

diff --git a/cognitive-actions-vision-image-aws.md b/cognitive-actions-vision-image-aws.md
@@ -347,8 +347,8 @@ Housing 55.82354736328125
 
 ***
 
-### Detect web references to an image
-NOOP
+### ~~Detect web references to an image~~
+N/A
 
 ***
 

diff --git a/cognitive-actions-vision-image-aws.md b/cognitive-actions-vision-image-aws.md
@@ -0,0 +1,423 @@
+# Cognitive Artificial Intelligence
+* Cloud Vendor Based NoOps
+
+### Use Cases
+1. Detect text in images
+1. Detect handwriting in images
+1. Detect text in files
+1. Detect faces in images
+1. Detect multiple objects in images
+1. ~~Detect web references to an image~~ N/A
+1. Detect landmarks in images
+
+***
+
+# AWS (Amazon Web Services) NoOps
+
+* [Amazon (AWS) Artificial Intelligence](https://aws.amazon.com/machine-learning/)
+
+* [Amazon Rekognition](https://docs.aws.amazon.com/en_pv/rekognition/latest/dg/what-is.html)
+
+* Prerequisites are to have a valid and activated AWS account and permissions to use "Rekognition" cognitive services
+
+1. Prepare to configure AWS CLI
+   <br><i>NB. Do not use the AWS account root user access key. The access key for the AWS account root user gives full access to all resources for all AWS services, including billing information. The permissions cannot be reduce for the AWS account root user access key.</i>
+   1. Create a GROUP in the Console, such as `cognitive`, and assign `AmazonRekognitionFullAccess` and `AmazonS3FullAccess` as Policy [create-admin-group](https://docs.aws.amazon.com/IAM/latest/UserGuide/getting-started_create-admin-group.html)
+   <br>Select one or more policies to attach. Each group can have up to 10 policies attached.
+   1. Create a USER in the Console, such as `aiuser`, assign it to the GROUP, and save the `credentials.csv` file (store and keep it secret) [create-admin-user](https://docs.aws.amazon.com/IAM/latest/UserGuide/getting-started_create-admin-group.html)
+   1. Set a PASSWORD for the user [aws-password](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_passwords_admin-change-user.html)
+1. Run the `aws configure` command to configure the AWS CLI using the keys for the USER (`aiuser`)
+   <br><i>NB. The command prompts for: access key, secret access key, AWS Region, and output format; stores this in a profile ("default"), this is used when running  an AWS CLI command without explicitly specify another profile.</i>
+   ```
+   $ aws configure list
+         Name                    Value             Type    Location
+         ----                    -----             ----    --------
+      profile                <not set>             None    None
+   access_key     ****************MYVZ shared-credentials-file    
+   secret_key     ****************nEac shared-credentials-file    
+       region                <not set>             None    None
+   ```
+1. [Creae S3 Bucket](https://docs.aws.amazon.com/cli/latest/reference/s3api/create-bucket.html)
+   * In this case the bucket is named `blobbucket` and set to `private`, with LocationConstraint set to the specified region
+   ```
+   $ aws s3api create-bucket --bucket blobbucket --acl private --region us-east-2 --create-bucket-configuration LocationConstraint=us-east-2
+   http://blobbucket.s3.amazonaws.com/
+   ```
+   * Upload files to the S3 Bucket (s3 and s3api commands)
+   ```
+   $ aws s3 cp --recursive ../data/ s3://blobbucket/
+   upload: ../data/letter1.pdf to s3://blobbucket/letter1.pdf        
+   upload: ../data/faces1.jpeg to s3://blobbucket/faces1.jpeg        
+   upload: ../data/texthandwriting1.png to s3://blobbucket/texthandwriting1.png
+   upload: ../data/landmark1.jpeg to s3://blobbucket/landmark1.jpeg    
+   upload: ../data/multiple1.jpeg to s3://blobbucket/multiple1.jpeg    
+   upload: ../data/texttyped1.png to s3://blobbucket/texttyped1.png    
+   upload: ../data/faces2.png to s3://blobbucket/faces2.png
+   
+   $ aws s3api put-object --bucket blobbucket --key texttyped1.png --body ../data/texttyped1.png --acl private
+   {
+       "ETag": "\"c9ad5d3165a38a49281c693f0a4bc694\""
+   }
+   ```
+   * List objects (files) in the S3 Bucket  (s3 and s3api commands)
+   ```
+   $ aws s3 ls s3://blobbucket
+   2019-10-02 02:43:13      26107 faces1.jpeg
+   2019-10-02 02:43:21     458795 faces2.png
+   2019-10-02 02:43:35     181092 landmark1.jpeg
+   2019-10-02 02:43:44      21578 letter1.pdf
+   2019-10-02 02:43:50      96078 multiple1.jpeg
+   2019-10-02 02:43:59      61840 texthandwriting1.png
+   2019-10-02 10:49:46      26107 texttyped1.png
+
+   $ aws s3api list-objects --bucket blobbucket --query 'Contents[].{Key: Key}' | jq -r '.[].Key'
+   faces1.jpeg
+   faces2.png
+   landmark1.jpeg
+   letter1.pdf
+   multiple1.jpeg
+   texthandwriting1.png
+   texttyped1.png
+   ```
+   * Trying to access this bucket over HTTP without authenticating is denied
+   ```
+   <Error>
+         <Code>AccessDenied</Code>
+         <Message>Access Denied</Message>
+         <RequestId>090832BE4B92F4DC</RequestId>
+      <HostId>
+         27Ec+Sx6rPwGJFpWIQ4ktZrdlG5m710m+yUKjXJ9IfWE3GWXde6e2OdaY0OdKnV6Y3NEUSOI4iw=
+      </HostId>
+   </Error>
+   ```
+
+
+### Detect text in images (Recognition DetectText)
+
+* [detect-text](https://docs.aws.amazon.com/cli/latest/reference/rekognition/detect-text.html)
+* [DetectText can detect <i>up to 50 words</i> in an image](https://docs.aws.amazon.com/en_pv/rekognition/latest/dg/limits.html)
+
+* Verify that the file is in the S3 Bucket; create JSON request content file
+```
+$ ./pre-request.sh detect-text texttyped1.png
+2019-10-02 11:06:19     186536 texttyped1.png
+request.json
+```
+* Perform (input: JSON file "request.json"; output: JSON file "result$RANDOM.json)
+```
+$ ./run-request.sh detect-text request.json 
+result12541.json
+```
+* Review (text from output JSON) <i>NB. It is capped at 100 words</i>
+```
+$ jq -r '.TextDetections[].DetectedText' result12541.json | tr '\n' ' ' ; echo 
+
+Google is using deepfakes to fight deepfakes. With the 2020 US presidential election approaching, the race is on to figure how to prevent widespread out deepfake disinformation. On Tuesday, Google offered the latest contribution: an open-source database containing 3,000 original manipulated videos. The goal is to help train and test Google is using deepfakes to fight deepfakes. With the 2020 US presidential election approaching, the race is on to figure out how to prevent widespread deepfake disinformation. On Tuesday, Google offered the latest contribution: an open-source database containing 3,000 original manipulated videos. The goal is to help train and test 
+```
+
+### Detect text in images (Textract DetectDocumentText)
+
+* [Detecting and Analyzing Text in Single-Page Documents](https://docs.aws.amazon.com/en_pv/textract/latest/dg/sync.html)
+* NB. This require Security Policy `AmazonTextractFullAccess`, not `AmazonRekognitionFullAccess`
+
+* Verify that the file is in the S3 Bucket; create JSON request content file
+```
+$ ./pre-request.sh detect-document-text texttyped1.png
+2019-10-02 11:06:19     186536 texttyped1.png
+request.json
+```
+* Perform (input: JSON file "request.json"; output: JSON file "result$RANDOM.json)
+```
+$ ./run-request.sh detect-document-text request.json 
+result22224.json
+```
+* Review (text from output JSON) <i>NB. Missed "l" in "presidential"</i>
+```
+$ jq -r '.Blocks[]|select(.BlockType=="LINE")|.Text'  result22224.json| tr '\n' ' '; echo
+
+Google is using deepfakes to fight deepfakes. With the 2020 US presidentia election approaching, the race is on to figure out how to prevent widespread deepfake disinformation. On Tuesday, Google offered the latest contribution: an open-source database containing 3,000 original manipulated videos. The goal is to help train and test automated detection tools. The company compiled the data by working with 28 actors to record videos of them speaking, making common expressions, and doing mundane tasks. It then used publicly available deepfake algorithms to alter their faces. Google isn't the first to take this approach. As we covered in The Algorithm earlier this month, Facebook announced that it would be releasing a similar database near the end of the year. In January, an academic team led by a researcher from the Technical University of Munich also created another called FaceForensics++. The trouble is technical solutions like these can only go so far because synthetic media could soon become indistinguishable from reality. Read more here. 
+```
+
+***
+
+### Detect handwriting in images
+
+* [detect-text](https://docs.aws.amazon.com/cli/latest/reference/rekognition/detect-text.html)
+* [DetectText can detect <i>up to 50 words</i> in an image](https://docs.aws.amazon.com/en_pv/rekognition/latest/dg/limits.html)
+
+* Verify that the file is in the S3 Bucket; create JSON request content file
+```
+$ ./pre-request.sh detect-text texthandwriting1.png 
+2019-10-02 11:06:18      61840 texthandwriting1.png
+request.json
+```
+* Perform (input: JSON file "request.json"; output: JSON file "result$RANDOM.json)
+```
+$ ./run-request.sh detect-text request.json 
+result3587.json
+```
+* Review (text from output JSON)
+```
+$ jq -r '.TextDetections[].DetectedText' result3587.json | tr '\n' ' ' ; echo
+oople CLoud 0 gle P fatform oople gle CLoud 0 P fatform 
+```
+
+***
+
+### Detect text in files (Textract DocumentTextDetection)
+
+* [Detecting and Analyzing Text in Multipage Documents](https://docs.aws.amazon.com/en_pv/textract/latest/dg/async.html)
+* [Calling Amazon Textract Asynchronous Operations](https://docs.aws.amazon.com/en_pv/textract/latest/dg/api-async.html)
+<br><img src="https://docs.aws.amazon.com/en_pv/textract/latest/dg/images/asynchronous.png" alt="DocumentTextDetection" width="50%"/><br>
+
+* NB. Here using  `AmazonTextractFullAccess` Policy, not `AmazonRekognitionFullAccess`
+* NB. Here using `AmazonSNSFullAccess` for SNS  and `AmazonSQSFullAccess` for SQS and `IAMFullAccess` for IAM
+
+* Multi-step process:
+   1. Setup SQS to receive SNS status notification [aws-sqs](https://docs.aws.amazon.com/cli/latest/reference/sqs/index.html)
+      * Add Operaiton Permissions for Principal to the Queue, such as for "Everybody (*)"
+   1. Setup SNS Topic and Subscription to recieve notification from StartDocumentTextDetection [aws-sns](https://docs.aws.amazon.com/cli/latest/reference/sns/index.html)
+   1. Create IAM Role to allow Textract to publish to SNS, with the `AmazonTextractServiceRole` Policy
+   1. StartDocumentTextDetection operation to submit the OCR operation, returns a job identifier (JobId) for the next step [aws-textract](https://docs.aws.amazon.com/cli/latest/reference/textract/start-document-text-detection.html)
+   1. Check completion status queued in SQS from SNS
+   1. GetDocumentTextDetection with job identifier (JobId) to access the OCR results in JSON output format [aws-textract](https://docs.aws.amazon.com/cli/latest/reference/textract/get-document-text-detection.html)
+
+* Example
+
+|SERVICE                                                  |ARN|
+|---|---|
+|SQS ARN                   |`arn:aws:sqs:us-east-2:deadbeef7898:SNStopic123`|
+|SQS URL                   |`https://sqs.us-east-2.amazonaws.com/deadbeef7898/SNStopic123`|
+|SQS Principal Operation Permissions |`SQS:AddPermission`, `SQS:DeleteMessage` and `SQS:ReceiveMessage`|
+|SNS Topic ARN             |`arn:aws:sns:us-east-2:deadbeef7898:topic123`|
+|SNS Subscription ARN      |`arn:aws:sns:us-east-2:deadbeef7898:topic123:deadbeef-9863-41e8-b283-78290f63d316`|
+|SNS Subscription Endpoint |`arn:aws:sqs:us-east-2:deadbeef7898:SNStopic123`|
+|Textract ARN Role         |`arn:aws:sqs:us-east-2:deadbeef7898:SNStopic123`|
+
+```
+$ aws sqs list-queues --region us-east-2
+{
+    "QueueUrls": [
+        "https://us-east-2.queue.amazonaws.com/deadbeef7898/SNStopic123"
+    ]
+}
+
+$ aws sns list-topics --region us-east-2
+{
+    "Topics": [
+        {
+            "TopicArn": "arn:aws:sns:us-east-2:deadbeef7898:topic123"
+        }
+    ]
+}
+
+$ aws sns list-subscriptions --region us-east-2
+{
+    "Subscriptions": [
+        {
+            "SubscriptionArn": "arn:aws:sns:us-east-2:deadbeef7898:topic123:b39a91fc-9863-41e8-b283-78290f63d316",
+            "Owner": "deadbeef7898",
+            "Protocol": "sqs",
+            "Endpoint": "arn:aws:sqs:us-east-2:deadbeef7898:SNStopic123",
+            "TopicArn": "arn:aws:sns:us-east-2:deadbeef7898:topic123"
+        }
+    ]
+}
+
+$ aws iam list-roles | jq '.Roles[]|select(.RoleName=="Textract2SNS")'
+{
+  "Path": "/",
+  "RoleName": "Textract2SNS",
+  "RoleId": "AROAYWZGLN25L6XSDYTI4",
+  "Arn": "arn:aws:iam::deadbeef7898:role/Textract2SNS",
+  "CreateDate": "2019-10-03T06:37:23Z",
+  "AssumeRolePolicyDocument": {
+    "Version": "2012-10-17",
+    "Statement": [
+      {
+        "Sid": "",
+        "Effect": "Allow",
+        "Principal": {
+          "Service": "textract.amazonaws.com"
+        },
+        "Action": "sts:AssumeRole"
+      }
+    ]
+  },
+  "Description": "Allows AWS Textract to call other AWS services on your behalf.",
+  "MaxSessionDuration": 3600
+}
+
+$ aws textract start-document-text-detection --document '{"S3Object":{"Bucket":"blobbucket","Name":"sample.pdf"}}' --notification-channel '{"SNSTopicArn":"arn:aws:sns:us-east-2:deadbeef7898:topic123","RoleArn":"arn:aws:iam::deadbeef7898:role/Textract2SNS"}' --region us-east-2
+{
+    "JobId": "6014e9101702c9da1c505a4acdbd2e5675d630155dfa8da9205f77c34981b9b8"
+}
+
+$ aws sqs receive-message --queue-url https://us-east-2.queue.amazonaws.com/deadbeef7898/SNStopic123 --region us-east-2
+
+$ aws textract get-document-text-detection --job-id "6014e9101702c9da1c505a4acdbd2e5675d630155dfa8da9205f77c34981b9b8" --region us-east-2 > textract-output-sample.json
+
+$ jq -r '.Blocks[]|select(.BlockType=="LINE")|.Text' textract-output-sample.json | tr '\n' ' '; echo
+A Simple PDF File This is a small demonstration .pdf file -- just for use in the Virtual Mechanics tutorials. More text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. Boring, ZZZZZ. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. And more text. Even more. Continued on page 2 Simple PDF File 2 .continued from page 1. Yet more text And more text. And more text. And more text. And more text. And more text. And more text. And more text. Oh, how boring typing this stuff. But not as boring as watching paint dry. And more text And more text. And more text. And more text. Boring. More, a little more text. The end, and just as well. 
+```
+
+***
+
+### Detect Faces in images
+
+* [Detecting Faces in an Image](https://docs.aws.amazon.com/en_pv/rekognition/latest/dg/faces-detect-images.html)
+* [detect-faces](https://docs.aws.amazon.com/cli/latest/reference/rekognition/detect-faces.html)
+<br><img src="https://docs.aws.amazon.com/rekognition/latest/dg/images/landmarkface.png" alt="Face Landmarks" width="50%" /><br>
+
+
+* Verify that the file is in the S3 Bucket; create JSON request content file
+```
+$ ./pre-request.sh detect-faces faces1.jpeg
+2019-10-02 11:06:19      26107 faces1.jpeg
+request.json
+```
+* Perform (input: JSON file "request.json"; output: JSON file "result$RANDOM.json)
+```
+$ ./run-request.sh detect-faces request.json 
+result10579.json
+```
+* Review (text from output JSON) - for expanded view use `jq . <output JSON filename>`
+```
+$ jq -r '.FaceDetails[].Confidence' result10579.json
+99.9998550415039
+99.85765075683594
+99.99970245361328
+99.99711608886719
+99.99925231933594
+99.98178100585938
+99.9980697631836
+99.70393371582031
+99.99464416503906
+99.99987030029297
+99.99915313720703
+99.99749755859375
+99.99079895019531
+99.99903106689453
+91.42262268066406
+99.89049530029297
+91.33202362060547
+65.57625579833984
+99.625244140625
+81.71480560302734
+```
+
+***
+
+### Detect multiple objects in images
+
+* [Detecting Labels in an Image](https://docs.aws.amazon.com/en_pv/rekognition/latest/dg/labels-detect-labels-image.html)
+
+* Verify that the file is in the S3 Bucket; create JSON request content file
+```
+$ ./pre-request.sh detect-labels multiple1.jpeg
+2019-10-02 11:06:18      96078 multiple1.jpeg
+request.json
+```
+* Perform (input: JSON file "request.json"; output: JSON file "result$RANDOM.json)
+```
+./run-request.sh detect-labels request.json 
+result24519.json
+```
+* Review (text from output JSON) - for expanded view use `jq . <output JSON filename>`
+```
+$ jq -r '.Labels[]| "\(.Name) \(.Confidence)"' result24519.json
+Bicycle 99.98738098144531
+Transportation 99.98738098144531
+Vehicle 99.98738098144531
+Bike 99.98738098144531
+Machine 99.97575378417969
+Wheel 99.97575378417969
+Handrail 85.58196258544922
+Banister 85.58196258544922
+Door 71.61861419677734
+Staircase 66.0941390991211
+Indoors 63.238643646240234
+Interior Design 63.238643646240234
+Walkway 62.390384674072266
+Path 62.390384674072266
+Wall 61.80942916870117
+Building 55.82354736328125
+Housing 55.82354736328125
+```
+
+***
+
+### Detect web references to an image
+NOOP
+
+***
+
+### Detect landmarks in images
+
+* Verify that the file is in the S3 Bucket; create JSON request content file
+```
+$ ./pre-request.sh detect-labels landmark1.jpeg
+2019-10-02 11:06:19     181092 landmark1.jpeg
+request.json
+```
+* Perform (input: JSON file "request.json"; output: JSON file "result$RANDOM.json)
+```
+./run-request.sh detect-labels request.json 
+result27651.json
+```
+* Review (text from output JSON) - for expanded view use `jq . <output JSON filename>`
+```
+$ jq -r '.Labels[]| "\(.Name) \(.Confidence)"' result27651.json
+Architecture 99.82380676269531
+Dome 99.82380676269531
+Building 99.82380676269531
+Spire 99.72821044921875
+Tower 99.72821044921875
+Steeple 99.72821044921875
+City 88.84866333007812
+Town 88.84866333007812
+Urban 88.84866333007812
+Downtown 88.47482299804688
+Metropolis 82.51697540283203
+Person 73.4709701538086
+Human 73.4709701538086
+Church 59.03559494018555
+Cathedral 59.03559494018555
+Monument 55.30182647705078
+```
+
+***
+
+### Recognition of Face in images
+
+* [compare-faces](https://docs.aws.amazon.com/cli/latest/reference/rekognition/compare-faces.html)
+* Target image
+<br><img src="https://www.nih.gov/sites/default/files/news-events/research-matters/2014/20140428-attention.jpg" width="50%" /><br>
+* Source image (to find in the target image (show as downsampled to 2kb from original 4kb)
+<br><img src="https://github.com/realBjornRoden/cognition/blob/master/data/face2match2kb.jpg" /><br>
+
+* Verify that the files are in the S3 Bucket
+```
+$ aws s3 ls s3://blobbucket/faces1.jpeg
+2019-10-02 11:06:19      26107 faces1.jpeg
+
+$ aws s3 ls s3://blobbucket/face2match4kb.jpeg
+2019-10-04 11:08:08       3859 face2match4kb.jpeg
+
+$ aws s3 ls s3://blobbucket/face2match2kb.jpg
+2019-10-04 11:52:34       1829 face2match2kb.jpg
+```
+* Perform output: JSON file "facematch.json
+```
+$ aws rekognition compare-faces --target-image '{"S3Object":{"Bucket":"blobbucket","Name":"faces1.jpeg"}}' --source-image '{"S3Object":{"Bucket":"blobbucket","Name":"face2match4kb.jpeg"}}' > facematch4kb.out
+
+$ aws rekognition compare-faces --target-image '{"S3Object":{"Bucket":"blobbucket","Name":"faces1.jpeg"}}' --source-image '{"S3Object":{"Bucket":"blobbucket","Name":"face2match2kb.jpg"}}' > facematch2kb.out
+```
+* Review (text from output JSON) - for expanded view use `jq . <file>` or `cat <file>`
+```
+$ jq -r '.FaceMatches[]| "Similarity: \(.Similarity) Face.Confidence: \(.Face.Confidence)"' facematch4kb.out
+Similarity: 99.99290466308594 Face.Confidence: 99.98178100585938
+
+$ jq -r '.FaceMatches[]| "Similarity: \(.Similarity) Face.Confidence: \(.Face.Confidence)"' facematch2kb.out
+Similarity: 99.61591339111328 Face.Confidence: 99.98178100585938
+```
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