Create TomAWSrekognitioncode

TomAWSrekognitioncode

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+#Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-custom-labels-developer-guide/blob/master/LICENSE-SAMPLECODE.)
+
+import boto3
+
+def start_model(project_arn, model_arn, version_name, min_inference_units):
+
+    client=boto3.client('rekognition')
+
+    try:
+        # Start the model
+        print('Starting model: ' + model_arn)
+        response=client.start_project_version(ProjectVersionArn=model_arn, MinInferenceUnits=min_inference_units)
+        # Wait for the model to be in the running state
+        project_version_running_waiter = client.get_waiter('project_version_running')
+        project_version_running_waiter.wait(ProjectArn=project_arn, VersionNames=[version_name])
+
+        #Get the running status
+        describe_response=client.describe_project_versions(ProjectArn=project_arn,
+            VersionNames=[version_name])
+        for model in describe_response['ProjectVersionDescriptions']:
+            print("Status: " + model['Status'])
+            print("Message: " + model['StatusMessage']) 
+    except Exception as e:
+        print(e)
+
+    print('Done...')
+
+def main():
+    project_arn='arn:aws:rekognition:us-east-2:833742200021:project/Foodrecognition/1624546398379'
+    model_arn='arn:aws:rekognition:us-east-2:833742200021:project/Foodrecognition/version/Foodrecognition.2021-06-24T10.53.50/1624546430747'
+    min_inference_units=1 
+    version_name='Foodrecognition.2021-06-24T10.53.50'
+    start_model(project_arn, model_arn, version_name, min_inference_units)
+
+if __name__ == "__main__":
+    main()
+
+    #Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-custom-labels-developer-guide/blob/master/LICENSE-SAMPLECODE.)
+
+import boto3
+import io
+from PIL import Image, ImageDraw, ExifTags, ImageColor, ImageFont
+
+def display_image(bucket,photo,response):
+    # Load image from S3 bucket
+    s3_connection = boto3.resource('s3')
+
+    s3_object = s3_connection.Object(bucket,photo)
+    s3_response = s3_object.get()
+
+    stream = io.BytesIO(s3_response['Body'].read())
+    image=Image.open(stream)
+
+    # Ready image to draw bounding boxes on it.
+    imgWidth, imgHeight = image.size
+    draw = ImageDraw.Draw(image)
+
+    # calculate and display bounding boxes for each detected custom label
+    print('Detected custom labels for ' + photo)
+    for customLabel in response['CustomLabels']:
+        print('Label ' + str(customLabel['Name']))
+        print('Confidence ' + str(customLabel['Confidence']))
+        if 'Geometry' in customLabel:
+            box = customLabel['Geometry']['BoundingBox']
+            left = imgWidth * box['Left']
+            top = imgHeight * box['Top']
+            width = imgWidth * box['Width']
+            height = imgHeight * box['Height']
+
+            fnt = ImageFont.truetype('/Library/Fonts/Arial.ttf', 50)
+            draw.text((left,top), customLabel['Name'], fill='#00d400', font=fnt)
+
+            print('Left: ' + '{0:.0f}'.format(left))
+            print('Top: ' + '{0:.0f}'.format(top))
+            print('Label Width: ' + "{0:.0f}".format(width))
+            print('Label Height: ' + "{0:.0f}".format(height))
+
+            points = (
+                (left,top),
+                (left + width, top),
+                (left + width, top + height),
+                (left , top + height),
+                (left, top))
+            draw.line(points, fill='#00d400', width=5)
+
+    image.show()
+
+def show_custom_labels(model,bucket,photo, min_confidence):
+    client=boto3.client('rekognition')
+
+#Call DetectCustomLabels
+    response = client.detect_custom_labels(Image={'S3Object': {'Bucket': bucket, 'Name': photo}},
+        MinConfidence=min_confidence,
+        ProjectVersionArn=model)
+
+    # For object detection use case, uncomment below code to display image.
+    # display_image(bucket,photo,response)
+
+    return len(response['CustomLabels'])
+
+def main():
+
+    bucket='MY_BUCKET'
+    photo='MY_IMAGE_KEY'
+    model='arn:aws:rekognition:us-east-2:833742200021:project/Foodrecognition/version/Foodrecognition.2021-06-24T10.53.50/1624546430747'
+    min_confidence=95
+
+    label_count=show_custom_labels(model,bucket,photo, min_confidence)
+    print("Custom labels detected: " + str(label_count))
+
+
+if __name__ == "__main__":
+    main()
+
+
+    #Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-custom-labels-developer-guide/blob/master/LICENSE-SAMPLECODE.)
+
+import boto3
+import time
+
+
+def stop_model(model_arn):
+
+    client=boto3.client('rekognition')
+
+    print('Stopping model:' + model_arn)
+
+    #Stop the model
+    try:
+        response=client.stop_project_version(ProjectVersionArn=model_arn)
+        status=response['Status']
+        print ('Status: ' + status)
+    except Exception as e:  
+        print(e)  
+
+    print('Done...')
+
+def main():
+
+    model_arn='arn:aws:rekognition:us-east-2:833742200021:project/Foodrecognition/version/Foodrecognition.2021-06-24T10.53.50/1624546430747'
+    stop_model(model_arn)