Initial commit

label/backend/rekognition.py

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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
+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('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.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)
+
+    output_filename = 'output_' + photo.split('/')[-1]
+    image.save(output_filename)
+    print(f'\nImage saved as: {output_filename}')
+
+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='custom-labels-valvulas-bloco-funcao'
+    photo='clean_image/DE-5400.00-4710-944-TYS-009=C_row2_col4.png'
+    model='arn:aws:rekognition:us-east-1:173378533286:project/labels-valvula/version/labels-valvula.2025-11-24T15.44.16/1764009856090'
+    min_confidence=80
+
+
+    label_count=show_custom_labels(model,bucket,photo, min_confidence)
+    print("Custom labels detected: " + str(label_count))
+
+
+if __name__ == "__main__":
+    main()