This repository compare a selfie with images from identity documents and response if the selfie match.
This code was made in a Python Notebook under SageMaker.
- Create a Notebook Instance in SageMaker
- Notebook instance type : ml.t2.medium
- Volume Size : 5GB EBS
- Create a role for SageMaker with the following policies:
- AmazonS3FullAccess
- AmazonRekognitionFullAccess
- AmazonSageMakerFullAccess
- Create a S3 Bucket
- Inside bucket create folder to insert the dataset images
boto3 is needed to use the aws client of S3 and Rekognition. Just like what we do with variables, data can be kept as bytes in an in-memory buffer when we use the io module’s Byte IO operations, so we can load images froms S3. At least Pillow is needed for image plotting.
import boto3
import io
from PIL import Image, ImageDraw, ExifTags, ImageColor
rekognition_client=boto3.client('rekognition')
s3_resource = boto3.resource('s3')In this notebook I use two functions of AWS Rekognition
- detect_faces : Detect faces in the image. It also evaluate different metrics and create different landmarks for all elements of the face like eyes positions.
- compare_faces : Evaluate the similarity of two faces.
Here I explain how to compare two images
The compare function
IMG_SOURCE ="dataset-CI/imgsource.jpg"
IMG_TARGET ="dataset-CI/img20.jpg"
response = rekognition_client.compare_faces(
SourceImage={
'S3Object': {
'Bucket': BUCKET,
'Name': IMG_SOURCE
}
},
TargetImage={
'S3Object': {
'Bucket': BUCKET,
'Name': IMG_TARGET
}
}
)response
{'SourceImageFace': {'BoundingBox': {'Width': 0.3676206171512604,
'Height': 0.5122320055961609,
'Left': 0.33957839012145996,
'Top': 0.18869829177856445},
'Confidence': 99.99957275390625},
'FaceMatches': [{'Similarity': 99.99634552001953,
'Face': {'BoundingBox': {'Width': 0.14619407057762146,
'Height': 0.26241832971572876,
'Left': 0.13103649020195007,
'Top': 0.40437373518943787},
'Confidence': 99.99955749511719,
'Landmarks': [{'Type': 'eyeLeft',
'X': 0.17260463535785675,
'Y': 0.5030772089958191},
{'Type': 'eyeRight', 'X': 0.23902645707130432, 'Y': 0.5023221969604492},
{'Type': 'mouthLeft', 'X': 0.17937719821929932, 'Y': 0.5977044105529785},
{'Type': 'mouthRight', 'X': 0.23477530479431152, 'Y': 0.5970458984375},
{'Type': 'nose', 'X': 0.20820103585720062, 'Y': 0.5500822067260742}],
'Pose': {'Roll': 0.4675966203212738,
'Yaw': 1.592366099357605,
'Pitch': 8.6331205368042},
'Quality': {'Brightness': 85.35185241699219,
'Sharpness': 89.85481262207031}}}],
'UnmatchedFaces': [],
'ResponseMetadata': {'RequestId': '3ae9032d-de8a-41ef-b22f-f95c70eed783',
'HTTPStatusCode': 200,
'HTTPHeaders': {'x-amzn-requestid': '3ae9032d-de8a-41ef-b22f-f95c70eed783',
'content-type': 'application/x-amz-json-1.1',
'content-length': '911',
'date': 'Wed, 26 Jan 2022 17:21:53 GMT'},
'RetryAttempts': 0}}
If the source image match with the target image, the json return a key "FaceMatches" with a non-empty, otherwise it returns a key "UnmatchedFaces" with a non-empty array.
# Analisis imagen source
s3_object = s3_resource.Object(BUCKET,IMG_SOURCE)
s3_response = s3_object.get()
stream = io.BytesIO(s3_response['Body'].read())
image=Image.open(stream)
imgWidth, imgHeight = image.size
draw = ImageDraw.Draw(image)
box = response['SourceImageFace']['BoundingBox']
left = imgWidth * box['Left']
top = imgHeight * box['Top']
width = imgWidth * box['Width']
height = imgHeight * box['Height']
print('Left: ' + '{0:.0f}'.format(left))
print('Top: ' + '{0:.0f}'.format(top))
print('Face Width: ' + "{0:.0f}".format(width))
print('Face 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=2)
image.show()Left: 217
Top: 121
Face Width: 235
Face Height: 328
# Analisis imagen target
s3_object = s3_resource.Object(BUCKET,IMG_TARGET)
s3_response = s3_object.get()
stream = io.BytesIO(s3_response['Body'].read())
image=Image.open(stream)
imgWidth, imgHeight = image.size
draw = ImageDraw.Draw(image)
if len(response['UnmatchedFaces']) > 0:
for face in response['UnmatchedFaces']:
box = face['BoundingBox']
left = imgWidth * box['Left']
top = imgHeight * box['Top']
width = imgWidth * box['Width']
height = imgHeight * box['Height']
print('UnmatchedFaces')
print('Left: ' + '{0:.0f}'.format(left))
print('Top: ' + '{0:.0f}'.format(top))
print('Face Width: ' + "{0:.0f}".format(width))
print('Face Height: ' + "{0:.0f}".format(height))
points = (
(left,top),
(left + width, top),
(left + width, top + height),
(left , top + height),
(left, top)
)
draw.line(points, fill='#ff0000', width=2)
if len(response['FaceMatches']) > 0:
for face in response['FaceMatches']:
face_match = face['Face']
box = face_match['BoundingBox']
left = imgWidth * box['Left']
top = imgHeight * box['Top']
width = imgWidth * box['Width']
height = imgHeight * box['Height']
print('FaceMatches')
print('Left: ' + '{0:.0f}'.format(left))
print('Top: ' + '{0:.0f}'.format(top))
print('Face Width: ' + "{0:.0f}".format(width))
print('Face 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=2)
image.show()FaceMatches
Left: 671
Top: 1553
Face Width: 749
Face Height: 1008
