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Last active May 31, 2023 09:53
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Amazon Rekognition - Python Code Samples

Amazon Rekognition - Python Code Samples

  1. Labels Detection
  2. Faces Detection
  3. Faces Comparison
  4. Faces Indexing
  5. Faces Search
  6. Moderation Detection
import boto3
BUCKET = "amazon-rekognition"
KEY = "test.jpg"
def detect_labels(bucket, key, max_labels=10, min_confidence=90, region="us-east-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%
"""
import boto3
BUCKET = "amazon-rekognition"
KEY = "test.jpg"
FEATURES_BLACKLIST = ("Landmarks", "Emotions", "Pose", "Quality", "BoundingBox", "Confidence")
def detect_faces(bucket, key, attributes=['ALL'], region="us-east-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%
"""
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="us-east-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%
"""
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="us-east-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
"""
import boto3
BUCKET = "amazon-rekognition"
KEY = "search.jpg"
COLLECTION = "my-collection-id"
def search_faces_by_image(bucket, key, collection_id, threshold=80, region="us-east-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
"""
import boto3
BUCKET = "amazon-rekognition"
KEY = "swimwear.jpg"
def detect_moderation_labels(bucket, key, min_confidence=75, region="us-east-1"):
rekognition = boto3.client("rekognition", region)
response = rekognition.detect_moderation_labels(
Image={
"S3Object": {
"Bucket": bucket,
"Name": key,
}
},
MinConfidence=min_confidence,
)
return response['ModerationLabels']
for label in detect_moderation_labels(BUCKET, KEY):
print "{Name} - {Confidence}%".format(**label)
"""
Expected output:
Suggestive - 98.7583084106%
Female Swimwear Or Underwear - 98.7583084106%
"""
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