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
6. Moderation Detection

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="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%

"""

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="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%

"""

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="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%

"""

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="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

"""

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="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

"""

test-6-detect-moderation.py

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%
	
"""