rekognition-testing.ipnyb

notebook.ipynb

notebook.py

import io
import boto3
from PIL import Image, ImageDraw, ExifTags, ImageColor, ImageFont
r = boto3.client("rekognition")

# ignore anything below this confidence level
confidence = 80

#filepath
testimage = "testing/glass81.jpg"

image = Image.open(testimage)

def display_bbox(image, labels):
    # Ready image to draw bounding boxes on it.
    imgWidth, imgHeight = image.size
    draw = ImageDraw.Draw(image)

    for label in labels:
        print('Label ' + str(label['Name']))
        print('Confidence ' + str(label['Confidence']))
        if 'Instances' in label:
            for instance in label['Instances']:
                box = instance['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), f"{label['Name']} - {label['Confidence']}", 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()
    return image
    
    
    
%time
fd = open(testimage, 'rb')
img = fd.read()
fd.close()

response = r.detect_labels(Image={'Bytes': img})

labels = [{ x['Name'],x['Confidence']} for i,x in enumerate(response['Labels']) if x['Confidence'] > confidence]

display_bbox(image,response['Labels'])


# start model

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...')
    
    
project_arn='arn:aws:rekognition:xxxxx'
model_arn='arn:aws:rekognition:xxxx'
min_inference_units=1 
version_name='xxxx'

start_model(project_arn, model_arn, version_name, min_inference_units)

custom_labels = r.detect_custom_labels(Image={'Bytes': img},ProjectVersionArn=model_arn)

[{ x['Name'],x['Confidence']} for i,x in enumerate(custom_labels['CustomLabels']) if x['Confidence']]

display_bbox(image,custom_labels['CustomLabels'])