Amazon Rekognition - Python Code Samples

amazon-rekognition.md

Amazon Rekognition - Python Code Samples

1. Labels Detection
2. Faces Detection
3. Faces Comparison
4. Faces Indexing
5. Faces Search

test-1-detect-labels.py

import boto3

BUCKET = "amazon-rekognition"
KEY = "test.jpg"

def detect_labels(bucket, key, max_labels=10, min_confidence=90, region="eu-west-1"):
	rekognition = boto3.client("rekognition", region)
	response = rekognition.detect_labels(
		Image={
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
		MaxLabels=max_labels,
		MinConfidence=min_confidence,
	)
	return response['Labels']


for label in detect_labels(BUCKET, KEY):
	print "{Name} - {Confidence}%".format(**label)

"""
	Expected output:

	People - 99.2436447144%
	Person - 99.2436447144%
	Human - 99.2351226807%
	Clothing - 96.7797698975%
	Suit - 96.7797698975%

"""

test-2-detect-faces.py

import boto3

BUCKET = "amazon-rekognition"
KEY = "test.jpg"
FEATURES_BLACKLIST = ("Landmarks", "Emotions", "Pose", "Quality", "BoundingBox", "Confidence")

def detect_faces(bucket, key, attributes=['ALL'], region="eu-west-1"):
	rekognition = boto3.client("rekognition", region)
	response = rekognition.detect_faces(
	    Image={
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
	    Attributes=attributes,
	)
	return response['FaceDetails']

for face in detect_faces(BUCKET, KEY):
	print "Face ({Confidence}%)".format(**face)
	# emotions
	for emotion in face['Emotions']:
		print "  {Type} : {Confidence}%".format(**emotion)
	# quality
	for quality, value in face['Quality'].iteritems():
		print "  {quality} : {value}".format(quality=quality, value=value)
	# facial features
	for feature, data in face.iteritems():
		if feature not in FEATURES_BLACKLIST:
			print "  {feature}({data[Value]}) : {data[Confidence]}%".format(feature=feature, data=data)

"""
	Expected output:

	Face (99.945602417%)
	  SAD : 14.6038293839%
	  HAPPY : 12.3668470383%
	  DISGUSTED : 3.81404161453%
	  Sharpness : 10.0
	  Brightness : 31.4071826935
	  Eyeglasses(False) : 99.990234375%
	  Sunglasses(False) : 99.9500656128%
	  Gender(Male) : 99.9291687012%
	  EyesOpen(True) : 99.9609146118%
	  Smile(False) : 99.8329467773%
	  MouthOpen(False) : 98.3746566772%
	  Mustache(False) : 98.7549591064%
	  Beard(False) : 92.758682251%

"""

test-3-compare-faces.py

import boto3

BUCKET = "amazon-rekognition"
KEY_SOURCE = "test.jpg"
KEY_TARGET = "target.jpg"

def compare_faces(bucket, key, bucket_target, key_target, threshold=80, region="eu-west-1"):
	rekognition = boto3.client("rekognition", region)
	response = rekognition.compare_faces(
	    SourceImage={
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
		TargetImage={
			"S3Object": {
				"Bucket": bucket_target,
				"Name": key_target,
			}
		},
	    SimilarityThreshold=threshold,
	)
	return response['SourceImageFace'], response['FaceMatches']


source_face, matches = compare_faces(BUCKET, KEY_SOURCE, BUCKET, KEY_TARGET)

# the main source face
print "Source Face ({Confidence}%)".format(**source_face)

# one match for each target face
for match in matches:
	print "Target Face ({Confidence}%)".format(**match['Face'])
	print "  Similarity : {}%".format(match['Similarity'])

"""
    Expected output:
    
    Source Face (99.945602417%)
    Target Face (99.9963378906%)
      Similarity : 89.0%

"""

test-4-index-faces.py

import boto3

BUCKET = "amazon-rekognition"
KEY = "test.jpg"
IMAGE_ID = KEY  # S3 key as ImageId
COLLECTION = "my-collection-id"

# Note: you have to create the collection first!
# rekognition.create_collection(CollectionId=COLLECTION)

def index_faces(bucket, key, collection_id, image_id=None, attributes=(), region="eu-west-1"):
	rekognition = boto3.client("rekognition", region)
	response = rekognition.index_faces(
		Image={
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
		CollectionId=collection_id,
		ExternalImageId=image_id,
	    DetectionAttributes=attributes,
	)
	return response['FaceRecords']


for record in index_faces(BUCKET, KEY, COLLECTION, IMAGE_ID):
	face = record['Face']
	# details = record['FaceDetail']
	print "Face ({}%)".format(face['Confidence'])
	print "  FaceId: {}".format(face['FaceId'])
	print "  ImageId: {}".format(face['ImageId'])


"""
	Expected output:

	Face (99.945602417%)
	  FaceId: dc090f86-48a4-5f09-905f-44e97fb1d455
	  ImageId: f974c8d3-7519-5796-a08d-b96e0f2fc242

"""

test-5-search-faces.py

import boto3

BUCKET = "amazon-rekognition"
KEY = "search.jpg"
COLLECTION = "my-collection-id"

def search_faces_by_image(bucket, key, collection_id, threshold=80, region="eu-west-1"):
	rekognition = boto3.client("rekognition", region)
	response = rekognition.search_faces_by_image(
		Image={
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
		CollectionId=collection_id,
		FaceMatchThreshold=threshold,
	)
	return response['FaceMatches']

for record in search_faces_by_image(BUCKET, KEY, COLLECTION):
	face = record['Face']
	print "Matched Face ({}%)".format(record['Similarity'])
	print "  FaceId : {}".format(face['FaceId'])
	print "  ImageId : {}".format(face['ExternalImageId'])


"""
	Expected output:

	Matched Face (96.6647949219%)
	  FaceId : dc090f86-48a4-5f09-905f-44e97fb1d455
	  ImageId : test.jpg

"""

how to pass image name when S3 trigger Lambda Function?

@Dingjian412 When you trigger Lambda, the event has all the details of the object. You can extract the name from it.

Thanks @alexcasalboni for this!

Can you please specify what is wrong in the syntax of print?
I am a beginner in the AWS as well as python so I am not able to figure this out.

Kindly provide the guidance ASAP :)

Hi @maithily1905,

The problem is that % indicates a placeholder (e.g. %s for strings, %d for numbers, etc.). If you want to print the % symbol you need to escape it as %% :)

StackOverflow answer here: https://stackoverflow.com/a/28343785

-------------------------------------------------------HELP--------------------------------------------------------------------------------------
Hi, I am trying to execute the code with Python as mentioned above. My bucket name is "celebimages123" . and i create collectionID "guest_collection" and my dynamodb table name is "guest_collection" . While executing, I am experiencing errors every time as shown below. Can you help me?

Hi @qu2toy, maybe you have another region like us-east-1. and you must create de collection in that region :

collectionId='MyCollection'
region="eu-west-1"
client=boto3.client("rekognition", region)

#Create a collection
print('Creating collection:' + collectionId)
response=client.create_collection(CollectionId=collectionId)

Hope to help you!

[ERROR] ParamValidationError: Parameter validation failed:
Invalid type for parameter Image.S3Object.Bucket, value: {'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}, type: <class 'dict'>, valid types: <class 'str'>
Invalid type for parameter Image.S3Object.Name, value: <bootstrap.LambdaContext object at 0x7f6ec3a2cc50>, type: <class 'bootstrap.LambdaContext'>, valid types: <class 'str'>
Traceback (most recent call last):
  File "/var/task/lambda_function.py", line 16, in detect_faces
    Attributes=attributes
  File "/var/runtime/botocore/client.py", line 320, in _api_call
    return self._make_api_call(operation_name, kwargs)
  File "/var/runtime/botocore/client.py", line 596, in _make_api_call
    api_params, operation_model, context=request_context)
  File "/var/runtime/botocore/client.py", line 632, in _convert_to_request_dict
    api_params, operation_model)
  File "/var/runtime/botocore/validate.py", line 291, in serialize_to_request
    raise ParamValidationError(report=report.generate_report())
END RequestId: 51e16fc4-6229-4e76-82ed-a1de3f2aa8f8

Thanks for this.

Here's my python 3 implementation which also supports passing in a file pointer, as well as an S3 bucket. I've only implemented the method for detect_labels.

Code

class Rekognition():
    def __init__(self, session, max_labels = 10, min_confidence = 80):
        self.rek_cli = session.client('rekognition')
        self.max_labels = max_labels
        self.min_confidence = min_confidence

    def labels_from_s3(self, bucket_str, key):
        response = self.rek_cli.detect_labels(
            Image=
            {
                "S3Object": {
                    "Bucket": bucket_str,
                    "Name": key,
                }
            },
            MaxLabels=self.max_labels,
            MinConfidence=self.min_confidence,
        )
        return response['Labels']

    def labels_from_file(self, fp):

        fp = fp.read()

        response = self.rek_cli.detect_labels(
            Image=
            {
                'Bytes': fp,
            },
            MaxLabels=self.max_labels,
            MinConfidence=self.min_confidence,
        )
        return response['Labels']

Usage

Create a Rekognition object passing in a boto3.Session

r = Rekognition(session)

Rekognise an image from an S3 bucket:

result = r.labels_from_s3('your-bucket', 'your-key)

Rekognise an image from a file pointer.

im = open('any-old.jpg', 'rb')
result = r.labels_from_file(im)

Print the results:

for label in result:
    print ("{Name} - {Confidence}%".format(**label))

Output

Field - 99.86431121826172%
Outdoors - 99.8351058959961%
Grassland - 99.8351058959961%
Nature - 99.07447052001953%
Plant - 95.42001342773438%
Grass - 95.42001342773438%

import boto3

BUCKET = "*****"
KEY_SOURCE = "photo.jpg"
KEY_TARGET = "grp1.jpg"

def compare_faces(bucket, key, bucket_target, key_target, threshold=80, region="eu-east-1"):
rekognition = boto3.client("rekognition", aws_access_key_id='',
aws_secret_access_key='',
region_name='us-east-1')
response = rekognition.compare_faces(
SourceImage={
"S3Object": {
"Bucket": bucket,
"Name": key,
}
},
TargetImage={
"S3Object": {
"Bucket": bucket_target,
"Name": key_target,
}
},
SimilarityThreshold=threshold,
)
return response['SourceImageFace'], response['FaceMatches']

source_face, matches = compare_faces(BUCKET, KEY_SOURCE, BUCKET, KEY_TARGET)

the main source face

print("Source Face ({Confidence}%)".format(**source_face))

one match for each target face

for match in matches:
print("Target Face ({Confidence}%)".format(**match['Face']))
print("Similarity : {}%".format(match['Similarity']))

iam typing the above code and i am getting the following error even after giving public access to my s3 objects and bucket and full access permissions to amazon rekognition also can anyone please let me know the permissions

But still i am facing this error kindly give me the solution:

ERROR:

" botocore.errorfactory.InvalidS3ObjectException: An error occurred (InvalidS3ObjectException) when calling the CompareFaces operation: Unable to get object metadata from S3. Check object key, region and/or access permissions. "

import boto3

BUCKET = "xxxx"
KEY_SOURCE = "xxx.jpg"
KEY_TARGET = "xxxx1.jpg"

def compare_faces(bucket, key, bucket_target, key_target, threshold=80, region="xxxxx"):
rekognition = boto3.client("rekognition", region)
response = rekognition.compare_faces(
SourceImage={
"S3Object": {
"Bucket": bucket,
"Name": key,
}
},
TargetImage={
"S3Object": {
"Bucket": bucket_target,
"Name": key_target,
}
},
SimilarityThreshold=threshold,
)
return response['SourceImageFace'], response['FaceMatches']

source_face, matches = compare_faces(BUCKET, KEY_SOURCE, BUCKET, KEY_TARGET)

the main source face

print("Source Face ({Confidence}%)".format(**source_face))

one match for each target face

for match in matches:
print("Target Face ({Confidence}%)".format(**match['Face']))
print("Similarity : {}%".format(match['Similarity']))

This one should work...replace xxx with all your credentials

Hello,

I am very new to all of this so this may be a silly questions but when I try and run the facial comparison code I get the following error:

ClientError: An error occurred (UnrecognizedClientException) when calling the CompareFaces operation: The security token included in the request is invalid.

I have set up and IAM user "admin" and given the user permissions for AmazonRekognizeFullAccess, AmazonS3ReadOnlyAccess and AdministratorAccess. I am running the code using Anadona/Spyder/Python3.7 and I have boto installed. How do you specify credentials so it can use Rekognize? or should I be running it some other way?

Hi @jessekotsch how did you configure your credentials?

If you have configured an AWS CLI profile and set it as the default profile, it should work.

Something like this:

export AWS_PROFILE=your-profile-name

Hey, Thanks this is so helpful.
Any idea how i can parse(or filter) the response of detectLabel, say I would like to get images with no human instance.

@toto88 you can just remove them from the labels list in your favorite programming language (if you are using one of the available AWS SDK). The detectLabel response is just a JSON object so you should be able to parse it with any JSON parser out there, exclude the "Person"/"People"/"Human" labels and use/store all the others.

@alexcasalboni thanks for your reply, how i can get the full list of labels, I have checked the Q&A page, it's just have some examples. https://aws.amazon.com/rekognition/faqs/

@toto88 unfortunately there is no public list of labels available yet.. Rekognition uses thousands of labels and the model is updated periodically under the hood.. for now, the best way to know if a given label is recognized is to submit a few images with that object/entity and verify it directly :)

@alexcasalboni thanks for all your effort! I tried your script but having an issue with print. Do you have any idea what I can do? Also, MaxLables and MinConfidence does not seem to work either.

@sunhoro I bet you're using Python3 :) You just need to convert the script to Python 3 (hint: use the print function, with parenthesis).

The face emotion part displays all the types of available emotions. How can I display the emotion with highest confidence?

Just parse through the json like output that it gives using the dictionary key for what you want to display. I don’t remember the output exactly right now but a simple for loop and key pair logic should get you what you want.

…

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