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RapidMiner Process: Searching and Analyzing a Video Stored in an Amazon S3 Bucket
joeanalytica
Member Posts: 7 Contributor II
Hi everyone.
sgenzer
This post builds and extends on the one posted by Scott Sgenzer (Using AWS Image ML inside a RapidMiner process).
Please note you need to follow the steps in the following links:
Link#1: Searching Stored Videos for Faces
Link#2: Analyzing a Video Stored in an Amazon S3 Bucket with Java or Python (SDK)
Link#3: Configuring Amazon Rekognition Video
Important Note about Video Formats and Storage :
Amazon Rekognition operations can analyze videos that are stored in Amazon S3 buckets. The video must be encoded using the H.264 codec. The supported file formats are MPEG-4 and MOV.
Note: For test purposes, upload a video that's no longer than 30 seconds in length.
Snapshot of the process:
Here is the xml:
<?xml version="1.0" encoding="UTF-8"?><process version="9.4.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.4.001" expanded="true" name="Process">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="open_file" compatibility="9.4.001" expanded="true" height="68" name="Open File" width="90" x="45" y="238">
<parameter key="resource_type" value="file"/>
<parameter key="filename" value="C:\Users\Administrator\Desktop\videos\jg.mp4"/>
</operator>
<operator activated="true" class="retrieve" compatibility="9.4.001" expanded="true" height="68" name="Retrieve" width="90" x="45" y="391">
<parameter key="repository_entry" value="/Connections/awsRekVideo"/>
</operator>
<operator activated="true" class="cloud_connectivity:write_amazons3" compatibility="9.4.000" expanded="true" height="82" name="Write Amazon S3" width="90" x="179" y="238">
<parameter key="connection_source" value="repository"/>
<parameter key="file" value="/my-rapidminer-photo-bucket01/jg.mp4"/>
</operator>
<operator activated="true" class="text:create_document" compatibility="8.2.000" expanded="true" height="68" name="Create Document" width="90" x="380" y="238">
<parameter key="text" value="#Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. #PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) import boto3 import json import sys import time class VideoDetect: jobId = '' rek = boto3.client('rekognition') sqs = boto3.client('sqs') sns = boto3.client('sns') roleArn = '' bucket = '' video = '' startJobId = '' sqsQueueUrl = '' snsTopicArn = '' processType = '' def __init__(self, role, bucket, video): self.roleArn = role self.bucket = bucket self.video = video def GetSQSMessageSuccess(self): jobFound = False succeeded = False dotLine=0 while jobFound == False: sqsResponse = self.sqs.receive_message(QueueUrl=self.sqsQueueUrl, MessageAttributeNames=['ALL'], MaxNumberOfMessages=10) if sqsResponse: if 'Messages' not in sqsResponse: if dotLine<40: print('.', end='') dotLine=dotLine+1 else: print() dotLine=0 sys.stdout.flush() time.sleep(5) continue for message in sqsResponse['Messages']: notification = json.loads(message['Body']) rekMessage = json.loads(notification['Message']) print(rekMessage['JobId']) print(rekMessage['Status']) if rekMessage['JobId'] == self.startJobId: print('Matching Job Found:' + rekMessage['JobId']) jobFound = True if (rekMessage['Status']=='SUCCEEDED'): succeeded=True self.sqs.delete_message(QueueUrl=self.sqsQueueUrl, ReceiptHandle=message['ReceiptHandle']) else: print("Job didn't match:" + str(rekMessage['JobId']) + ' : ' + self.startJobId) # Delete the unknown message. Consider sending to dead letter queue self.sqs.delete_message(QueueUrl=self.sqsQueueUrl, ReceiptHandle=message['ReceiptHandle']) return succeeded def StartLabelDetection(self): response=self.rek.start_label_detection(Video={'S3Object': {'Bucket': self.bucket, 'Name': self.video}}, NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn}) self.startJobId=response['JobId'] print('Start Job Id: ' + self.startJobId) def GetLabelDetectionResults(self): maxResults = 10 paginationToken = '' finished = False while finished == False: response = self.rek.get_label_detection(JobId=self.startJobId, MaxResults=maxResults, NextToken=paginationToken, SortBy='TIMESTAMP') print('Codec: ' + response['VideoMetadata']['Codec']) print('Duration: ' + str(response['VideoMetadata']['DurationMillis'])) print('Format: ' + response['VideoMetadata']['Format']) print('Frame rate: ' + str(response['VideoMetadata']['FrameRate'])) print() for labelDetection in response['Labels']: label=labelDetection['Label'] print("Timestamp: " + str(labelDetection['Timestamp'])) print(" Label: " + label['Name']) print(" Confidence: " + str(label['Confidence'])) print(" Instances:") for instance in label['Instances']: print (" Confidence: " + str(instance['Confidence'])) print (" Bounding box") print (" Top: " + str(instance['BoundingBox']['Top'])) print (" Left: " + str(instance['BoundingBox']['Left'])) print (" Width: " + str(instance['BoundingBox']['Width'])) print (" Height: " + str(instance['BoundingBox']['Height'])) print() print() print (" Parents:") for parent in label['Parents']: print (" " + parent['Name']) print () if 'NextToken' in response: paginationToken = response['NextToken'] else: finished = True # ============== Face Search =============== def StartFaceSearchCollection(self,collection): response = self.rek.start_face_search(Video={'S3Object':{'Bucket':self.bucket,'Name':self.video}}, CollectionId=collection, NotificationChannel={'RoleArn':self.roleArn, 'SNSTopicArn':self.snsTopicArn}) self.startJobId=response['JobId'] print('Start Job Id: ' + self.startJobId) def GetFaceSearchCollectionResults(self): maxResults = 10 paginationToken = '' finished = False while finished == False: response = self.rek.get_face_search(JobId=self.startJobId, MaxResults=maxResults, NextToken=paginationToken) print(response['VideoMetadata']['Codec']) print(str(response['VideoMetadata']['DurationMillis'])) print(response['VideoMetadata']['Format']) print(response['VideoMetadata']['FrameRate']) for personMatch in response['Persons']: print('Person Index: ' + str(personMatch['Person']['Index'])) print('Timestamp: ' + str(personMatch['Timestamp'])) if ('FaceMatches' in personMatch): for faceMatch in personMatch['FaceMatches']: print('Face ID: ' + faceMatch['Face']['FaceId']) print('Similarity: ' + str(faceMatch['Similarity'])) print() if 'NextToken' in response: paginationToken = response['NextToken'] else: finished = True print() def CreateTopicandQueue(self): millis = str(int(round(time.time() * 1000))) #Create SNS topic snsTopicName="AmazonRekognitionExample" + millis topicResponse=self.sns.create_topic(Name=snsTopicName) self.snsTopicArn = topicResponse['TopicArn'] #create SQS queue sqsQueueName="AmazonRekognitionQueue" + millis self.sqs.create_queue(QueueName=sqsQueueName) self.sqsQueueUrl = self.sqs.get_queue_url(QueueName=sqsQueueName)['QueueUrl'] attribs = self.sqs.get_queue_attributes(QueueUrl=self.sqsQueueUrl, AttributeNames=['QueueArn'])['Attributes'] sqsQueueArn = attribs['QueueArn'] # Subscribe SQS queue to SNS topic self.sns.subscribe( TopicArn=self.snsTopicArn, Protocol='sqs', Endpoint=sqsQueueArn) #Authorize SNS to write SQS queue policy = """{{ "Version":"2012-10-17", "Statement":[ {{ "Sid":"MyPolicy", "Effect":"Allow", "Principal" : {{"AWS" : "*"}}, "Action":"SQS:SendMessage", "Resource": "{}", "Condition":{{ "ArnEquals":{{ "aws:SourceArn": "{}" }} }} }} ] }}""".format(sqsQueueArn, self.snsTopicArn) response = self.sqs.set_queue_attributes( QueueUrl = self.sqsQueueUrl, Attributes = { 'Policy' : policy }) def DeleteTopicandQueue(self): self.sqs.delete_queue(QueueUrl=self.sqsQueueUrl) self.sns.delete_topic(TopicArn=self.snsTopicArn) def main(): roleArn = 'arn:aws:iam::"youriam":role/rapidminerRek' bucket = 'my-rapidminer-photo-bucket01' video = 'jg.mp4' analyzer=VideoDetect(roleArn, bucket,video) analyzer.CreateTopicandQueue() collection='rapidminer-awsrek-video' analyzer.StartFaceSearchCollection(collection) if analyzer.GetSQSMessageSuccess()==True: analyzer.GetFaceSearchCollectionResults() analyzer.DeleteTopicandQueue() if __name__ == "__main__": main()"/>
<parameter key="add label" value="false"/>
<parameter key="label_type" value="nominal"/>
</operator>
<operator activated="true" class="text:write_document" compatibility="8.2.000" expanded="true" height="82" name="Write Document" width="90" x="514" y="238">
<parameter key="file" value="C:\Python\Python37-32\awsRek-SearchingStoredVideosforFaces-Copy.py"/>
<parameter key="overwrite" value="true"/>
<parameter key="encoding" value="SYSTEM"/>
</operator>
<operator activated="true" class="productivity:execute_program" compatibility="9.4.001" expanded="true" height="103" name="Execute Program" width="90" x="715" y="238">
<parameter key="command" value="cmd /c C:\Python\Python37-32\awsRek-SearchingStoredVideosforFaces.bat"/>
<parameter key="log_stdout" value="true"/>
<parameter key="log_stderr" value="true"/>
<parameter key="working_directory" value="C:\Python\Python37-32"/>
<list key="env_variables"/>
</operator>
<operator activated="true" class="text:read_document" compatibility="8.2.000" expanded="true" height="68" name="Read Document" width="90" x="916" y="238">
<parameter key="file" value="C:\awsRek-SearchingStoredVideosforFaces.txt"/>
<parameter key="extract_text_only" value="true"/>
<parameter key="use_file_extension_as_type" value="true"/>
<parameter key="content_type" value="txt"/>
<parameter key="encoding" value="SYSTEM"/>
</operator>
<connect from_op="Open File" from_port="file" to_op="Write Amazon S3" to_port="file"/>
<connect from_op="Retrieve" from_port="output" to_op="Write Amazon S3" to_port="connection"/>
<connect from_op="Create Document" from_port="output" to_op="Write Document" to_port="document"/>
<connect from_op="Read Document" from_port="output" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<description align="center" color="yellow" colored="false" height="96" resized="true" width="490" x="176" y="67">Searching and Analyzing a Video Stored in an Amazon S3 Bucket<br></description>
<description align="center" color="transparent" colored="true" height="63" resized="true" width="119" x="35" y="309">open video from stored location</description>
<description align="center" color="transparent" colored="true" height="50" resized="true" width="126" x="163" y="320">write to S3 bucket</description>
<description align="center" color="transparent" colored="true" height="50" resized="true" width="128" x="24" y="464">AWS S3 Connection</description>
<description align="center" color="yellow" colored="false" height="68" resized="true" width="129" x="360" y="316">Python Code:<br>Searching Stored Videos for Faces</description>
<description align="center" color="yellow" colored="false" height="59" resized="true" width="112" x="505" y="324">&quot;awsVideoRek-pythonCode&quot;.py</description>
<description align="left" color="yellow" colored="false" height="107" resized="true" width="164" x="708" y="349">Batch script:<br><br>python.exe awsVideoRek-pythonCode.py&quot; &gt; &quot;c:\text.txt&quot;</description>
<description align="center" color="yellow" colored="false" height="58" resized="true" width="138" x="899" y="317">read &quot;text.txt&quot; from previous operator</description>
</process>
</operator>
</process>
Cheers
This post builds and extends on the one posted by Scott Sgenzer (Using AWS Image ML inside a RapidMiner process).
Please note you need to follow the steps in the following links:
Link#1: Searching Stored Videos for Faces
Link#2: Analyzing a Video Stored in an Amazon S3 Bucket with Java or Python (SDK)
Link#3: Configuring Amazon Rekognition Video
Important Note about Video Formats and Storage :
Amazon Rekognition operations can analyze videos that are stored in Amazon S3 buckets. The video must be encoded using the H.264 codec. The supported file formats are MPEG-4 and MOV.
Note: For test purposes, upload a video that's no longer than 30 seconds in length.
Snapshot of the process:
Here is the xml:
<?xml version="1.0" encoding="UTF-8"?><process version="9.4.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.4.001" expanded="true" name="Process">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="open_file" compatibility="9.4.001" expanded="true" height="68" name="Open File" width="90" x="45" y="238">
<parameter key="resource_type" value="file"/>
<parameter key="filename" value="C:\Users\Administrator\Desktop\videos\jg.mp4"/>
</operator>
<operator activated="true" class="retrieve" compatibility="9.4.001" expanded="true" height="68" name="Retrieve" width="90" x="45" y="391">
<parameter key="repository_entry" value="/Connections/awsRekVideo"/>
</operator>
<operator activated="true" class="cloud_connectivity:write_amazons3" compatibility="9.4.000" expanded="true" height="82" name="Write Amazon S3" width="90" x="179" y="238">
<parameter key="connection_source" value="repository"/>
<parameter key="file" value="/my-rapidminer-photo-bucket01/jg.mp4"/>
</operator>
<operator activated="true" class="text:create_document" compatibility="8.2.000" expanded="true" height="68" name="Create Document" width="90" x="380" y="238">
<parameter key="text" value="#Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. #PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) import boto3 import json import sys import time class VideoDetect: jobId = '' rek = boto3.client('rekognition') sqs = boto3.client('sqs') sns = boto3.client('sns') roleArn = '' bucket = '' video = '' startJobId = '' sqsQueueUrl = '' snsTopicArn = '' processType = '' def __init__(self, role, bucket, video): self.roleArn = role self.bucket = bucket self.video = video def GetSQSMessageSuccess(self): jobFound = False succeeded = False dotLine=0 while jobFound == False: sqsResponse = self.sqs.receive_message(QueueUrl=self.sqsQueueUrl, MessageAttributeNames=['ALL'], MaxNumberOfMessages=10) if sqsResponse: if 'Messages' not in sqsResponse: if dotLine<40: print('.', end='') dotLine=dotLine+1 else: print() dotLine=0 sys.stdout.flush() time.sleep(5) continue for message in sqsResponse['Messages']: notification = json.loads(message['Body']) rekMessage = json.loads(notification['Message']) print(rekMessage['JobId']) print(rekMessage['Status']) if rekMessage['JobId'] == self.startJobId: print('Matching Job Found:' + rekMessage['JobId']) jobFound = True if (rekMessage['Status']=='SUCCEEDED'): succeeded=True self.sqs.delete_message(QueueUrl=self.sqsQueueUrl, ReceiptHandle=message['ReceiptHandle']) else: print("Job didn't match:" + str(rekMessage['JobId']) + ' : ' + self.startJobId) # Delete the unknown message. Consider sending to dead letter queue self.sqs.delete_message(QueueUrl=self.sqsQueueUrl, ReceiptHandle=message['ReceiptHandle']) return succeeded def StartLabelDetection(self): response=self.rek.start_label_detection(Video={'S3Object': {'Bucket': self.bucket, 'Name': self.video}}, NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn}) self.startJobId=response['JobId'] print('Start Job Id: ' + self.startJobId) def GetLabelDetectionResults(self): maxResults = 10 paginationToken = '' finished = False while finished == False: response = self.rek.get_label_detection(JobId=self.startJobId, MaxResults=maxResults, NextToken=paginationToken, SortBy='TIMESTAMP') print('Codec: ' + response['VideoMetadata']['Codec']) print('Duration: ' + str(response['VideoMetadata']['DurationMillis'])) print('Format: ' + response['VideoMetadata']['Format']) print('Frame rate: ' + str(response['VideoMetadata']['FrameRate'])) print() for labelDetection in response['Labels']: label=labelDetection['Label'] print("Timestamp: " + str(labelDetection['Timestamp'])) print(" Label: " + label['Name']) print(" Confidence: " + str(label['Confidence'])) print(" Instances:") for instance in label['Instances']: print (" Confidence: " + str(instance['Confidence'])) print (" Bounding box") print (" Top: " + str(instance['BoundingBox']['Top'])) print (" Left: " + str(instance['BoundingBox']['Left'])) print (" Width: " + str(instance['BoundingBox']['Width'])) print (" Height: " + str(instance['BoundingBox']['Height'])) print() print() print (" Parents:") for parent in label['Parents']: print (" " + parent['Name']) print () if 'NextToken' in response: paginationToken = response['NextToken'] else: finished = True # ============== Face Search =============== def StartFaceSearchCollection(self,collection): response = self.rek.start_face_search(Video={'S3Object':{'Bucket':self.bucket,'Name':self.video}}, CollectionId=collection, NotificationChannel={'RoleArn':self.roleArn, 'SNSTopicArn':self.snsTopicArn}) self.startJobId=response['JobId'] print('Start Job Id: ' + self.startJobId) def GetFaceSearchCollectionResults(self): maxResults = 10 paginationToken = '' finished = False while finished == False: response = self.rek.get_face_search(JobId=self.startJobId, MaxResults=maxResults, NextToken=paginationToken) print(response['VideoMetadata']['Codec']) print(str(response['VideoMetadata']['DurationMillis'])) print(response['VideoMetadata']['Format']) print(response['VideoMetadata']['FrameRate']) for personMatch in response['Persons']: print('Person Index: ' + str(personMatch['Person']['Index'])) print('Timestamp: ' + str(personMatch['Timestamp'])) if ('FaceMatches' in personMatch): for faceMatch in personMatch['FaceMatches']: print('Face ID: ' + faceMatch['Face']['FaceId']) print('Similarity: ' + str(faceMatch['Similarity'])) print() if 'NextToken' in response: paginationToken = response['NextToken'] else: finished = True print() def CreateTopicandQueue(self): millis = str(int(round(time.time() * 1000))) #Create SNS topic snsTopicName="AmazonRekognitionExample" + millis topicResponse=self.sns.create_topic(Name=snsTopicName) self.snsTopicArn = topicResponse['TopicArn'] #create SQS queue sqsQueueName="AmazonRekognitionQueue" + millis self.sqs.create_queue(QueueName=sqsQueueName) self.sqsQueueUrl = self.sqs.get_queue_url(QueueName=sqsQueueName)['QueueUrl'] attribs = self.sqs.get_queue_attributes(QueueUrl=self.sqsQueueUrl, AttributeNames=['QueueArn'])['Attributes'] sqsQueueArn = attribs['QueueArn'] # Subscribe SQS queue to SNS topic self.sns.subscribe( TopicArn=self.snsTopicArn, Protocol='sqs', Endpoint=sqsQueueArn) #Authorize SNS to write SQS queue policy = """{{ "Version":"2012-10-17", "Statement":[ {{ "Sid":"MyPolicy", "Effect":"Allow", "Principal" : {{"AWS" : "*"}}, "Action":"SQS:SendMessage", "Resource": "{}", "Condition":{{ "ArnEquals":{{ "aws:SourceArn": "{}" }} }} }} ] }}""".format(sqsQueueArn, self.snsTopicArn) response = self.sqs.set_queue_attributes( QueueUrl = self.sqsQueueUrl, Attributes = { 'Policy' : policy }) def DeleteTopicandQueue(self): self.sqs.delete_queue(QueueUrl=self.sqsQueueUrl) self.sns.delete_topic(TopicArn=self.snsTopicArn) def main(): roleArn = 'arn:aws:iam::"youriam":role/rapidminerRek' bucket = 'my-rapidminer-photo-bucket01' video = 'jg.mp4' analyzer=VideoDetect(roleArn, bucket,video) analyzer.CreateTopicandQueue() collection='rapidminer-awsrek-video' analyzer.StartFaceSearchCollection(collection) if analyzer.GetSQSMessageSuccess()==True: analyzer.GetFaceSearchCollectionResults() analyzer.DeleteTopicandQueue() if __name__ == "__main__": main()"/>
<parameter key="add label" value="false"/>
<parameter key="label_type" value="nominal"/>
</operator>
<operator activated="true" class="text:write_document" compatibility="8.2.000" expanded="true" height="82" name="Write Document" width="90" x="514" y="238">
<parameter key="file" value="C:\Python\Python37-32\awsRek-SearchingStoredVideosforFaces-Copy.py"/>
<parameter key="overwrite" value="true"/>
<parameter key="encoding" value="SYSTEM"/>
</operator>
<operator activated="true" class="productivity:execute_program" compatibility="9.4.001" expanded="true" height="103" name="Execute Program" width="90" x="715" y="238">
<parameter key="command" value="cmd /c C:\Python\Python37-32\awsRek-SearchingStoredVideosforFaces.bat"/>
<parameter key="log_stdout" value="true"/>
<parameter key="log_stderr" value="true"/>
<parameter key="working_directory" value="C:\Python\Python37-32"/>
<list key="env_variables"/>
</operator>
<operator activated="true" class="text:read_document" compatibility="8.2.000" expanded="true" height="68" name="Read Document" width="90" x="916" y="238">
<parameter key="file" value="C:\awsRek-SearchingStoredVideosforFaces.txt"/>
<parameter key="extract_text_only" value="true"/>
<parameter key="use_file_extension_as_type" value="true"/>
<parameter key="content_type" value="txt"/>
<parameter key="encoding" value="SYSTEM"/>
</operator>
<connect from_op="Open File" from_port="file" to_op="Write Amazon S3" to_port="file"/>
<connect from_op="Retrieve" from_port="output" to_op="Write Amazon S3" to_port="connection"/>
<connect from_op="Create Document" from_port="output" to_op="Write Document" to_port="document"/>
<connect from_op="Read Document" from_port="output" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<description align="center" color="yellow" colored="false" height="96" resized="true" width="490" x="176" y="67">Searching and Analyzing a Video Stored in an Amazon S3 Bucket<br></description>
<description align="center" color="transparent" colored="true" height="63" resized="true" width="119" x="35" y="309">open video from stored location</description>
<description align="center" color="transparent" colored="true" height="50" resized="true" width="126" x="163" y="320">write to S3 bucket</description>
<description align="center" color="transparent" colored="true" height="50" resized="true" width="128" x="24" y="464">AWS S3 Connection</description>
<description align="center" color="yellow" colored="false" height="68" resized="true" width="129" x="360" y="316">Python Code:<br>Searching Stored Videos for Faces</description>
<description align="center" color="yellow" colored="false" height="59" resized="true" width="112" x="505" y="324">&quot;awsVideoRek-pythonCode&quot;.py</description>
<description align="left" color="yellow" colored="false" height="107" resized="true" width="164" x="708" y="349">Batch script:<br><br>python.exe awsVideoRek-pythonCode.py&quot; &gt; &quot;c:\text.txt&quot;</description>
<description align="center" color="yellow" colored="false" height="58" resized="true" width="138" x="899" y="317">read &quot;text.txt&quot; from previous operator</description>
</process>
</operator>
</process>
Cheers
3
Best Answer
-
sgenzer Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959 Community Managerthank you @joeanalytica !!6