Crie uma coleção do Amazon Rekognition e encontre rostos nela usando um AWS SDK - AWS SDKExemplos de código

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Crie uma coleção do Amazon Rekognition e encontre rostos nela usando um AWS SDK

O exemplo de código a seguir mostra como:

  • Criar uma coleção do Amazon Rekognition.

  • Adicionar imagens à coleção e detectar faces nela.

  • Pesquisar na coleção faces que correspondam a uma imagem de referência.

  • Excluir uma coleção.

Para obter mais informações, consulte Pesquisar faces em uma coleção.

Python
SDKpara Python (Boto3)
nota

Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no Repositório de exemplos de código da AWS.

Criar classes que envolvam as funções do Amazon Rekognition.

import logging from pprint import pprint import boto3 from botocore.exceptions import ClientError from rekognition_objects import RekognitionFace from rekognition_image_detection import RekognitionImage logger = logging.getLogger(__name__) class RekognitionImage: """ Encapsulates an Amazon Rekognition image. This class is a thin wrapper around parts of the Boto3 Amazon Rekognition API. """ def __init__(self, image, image_name, rekognition_client): """ Initializes the image object. :param image: Data that defines the image, either the image bytes or an Amazon S3 bucket and object key. :param image_name: The name of the image. :param rekognition_client: A Boto3 Rekognition client. """ self.image = image self.image_name = image_name self.rekognition_client = rekognition_client @classmethod def from_file(cls, image_file_name, rekognition_client, image_name=None): """ Creates a RekognitionImage object from a local file. :param image_file_name: The file name of the image. The file is opened and its bytes are read. :param rekognition_client: A Boto3 Rekognition client. :param image_name: The name of the image. If this is not specified, the file name is used as the image name. :return: The RekognitionImage object, initialized with image bytes from the file. """ with open(image_file_name, "rb") as img_file: image = {"Bytes": img_file.read()} name = image_file_name if image_name is None else image_name return cls(image, name, rekognition_client) class RekognitionCollectionManager: """ Encapsulates Amazon Rekognition collection management functions. This class is a thin wrapper around parts of the Boto3 Amazon Rekognition API. """ def __init__(self, rekognition_client): """ Initializes the collection manager object. :param rekognition_client: A Boto3 Rekognition client. """ self.rekognition_client = rekognition_client def create_collection(self, collection_id): """ Creates an empty collection. :param collection_id: Text that identifies the collection. :return: The newly created collection. """ try: response = self.rekognition_client.create_collection( CollectionId=collection_id ) response["CollectionId"] = collection_id collection = RekognitionCollection(response, self.rekognition_client) logger.info("Created collection %s.", collection_id) except ClientError: logger.exception("Couldn't create collection %s.", collection_id) raise else: return collection def list_collections(self, max_results): """ Lists collections for the current account. :param max_results: The maximum number of collections to return. :return: The list of collections for the current account. """ try: response = self.rekognition_client.list_collections(MaxResults=max_results) collections = [ RekognitionCollection({"CollectionId": col_id}, self.rekognition_client) for col_id in response["CollectionIds"] ] except ClientError: logger.exception("Couldn't list collections.") raise else: return collections class RekognitionCollection: """ Encapsulates an Amazon Rekognition collection. This class is a thin wrapper around parts of the Boto3 Amazon Rekognition API. """ def __init__(self, collection, rekognition_client): """ Initializes a collection object. :param collection: Collection data in the format returned by a call to create_collection. :param rekognition_client: A Boto3 Rekognition client. """ self.collection_id = collection["CollectionId"] self.collection_arn, self.face_count, self.created = self._unpack_collection( collection ) self.rekognition_client = rekognition_client @staticmethod def _unpack_collection(collection): """ Unpacks optional parts of a collection that can be returned by describe_collection. :param collection: The collection data. :return: A tuple of the data in the collection. """ return ( collection.get("CollectionArn"), collection.get("FaceCount", 0), collection.get("CreationTimestamp"), ) def to_dict(self): """ Renders parts of the collection data to a dict. :return: The collection data as a dict. """ rendering = { "collection_id": self.collection_id, "collection_arn": self.collection_arn, "face_count": self.face_count, "created": self.created, } return rendering def describe_collection(self): """ Gets data about the collection from the Amazon Rekognition service. :return: The collection rendered as a dict. """ try: response = self.rekognition_client.describe_collection( CollectionId=self.collection_id ) # Work around capitalization of Arn vs. ARN response["CollectionArn"] = response.get("CollectionARN") ( self.collection_arn, self.face_count, self.created, ) = self._unpack_collection(response) logger.info("Got data for collection %s.", self.collection_id) except ClientError: logger.exception("Couldn't get data for collection %s.", self.collection_id) raise else: return self.to_dict() def delete_collection(self): """ Deletes the collection. """ try: self.rekognition_client.delete_collection(CollectionId=self.collection_id) logger.info("Deleted collection %s.", self.collection_id) self.collection_id = None except ClientError: logger.exception("Couldn't delete collection %s.", self.collection_id) raise def index_faces(self, image, max_faces): """ Finds faces in the specified image, indexes them, and stores them in the collection. :param image: The image to index. :param max_faces: The maximum number of faces to index. :return: A tuple. The first element is a list of indexed faces. The second element is a list of faces that couldn't be indexed. """ try: response = self.rekognition_client.index_faces( CollectionId=self.collection_id, Image=image.image, ExternalImageId=image.image_name, MaxFaces=max_faces, DetectionAttributes=["ALL"], ) indexed_faces = [ RekognitionFace({**face["Face"], **face["FaceDetail"]}) for face in response["FaceRecords"] ] unindexed_faces = [ RekognitionFace(face["FaceDetail"]) for face in response["UnindexedFaces"] ] logger.info( "Indexed %s faces in %s. Could not index %s faces.", len(indexed_faces), image.image_name, len(unindexed_faces), ) except ClientError: logger.exception("Couldn't index faces in image %s.", image.image_name) raise else: return indexed_faces, unindexed_faces def list_faces(self, max_results): """ Lists the faces currently indexed in the collection. :param max_results: The maximum number of faces to return. :return: The list of faces in the collection. """ try: response = self.rekognition_client.list_faces( CollectionId=self.collection_id, MaxResults=max_results ) faces = [RekognitionFace(face) for face in response["Faces"]] logger.info( "Found %s faces in collection %s.", len(faces), self.collection_id ) except ClientError: logger.exception( "Couldn't list faces in collection %s.", self.collection_id ) raise else: return faces def search_faces(self, face_id, threshold, max_faces): """ Searches for faces in the collection that match another face from the collection. :param face_id: The ID of the face in the collection to search for. :param threshold: The match confidence must be greater than this value for a face to be included in the results. :param max_faces: The maximum number of faces to return. :return: The list of matching faces found in the collection. This list does not contain the face specified by `face_id`. """ try: response = self.rekognition_client.search_faces( CollectionId=self.collection_id, FaceId=face_id, FaceMatchThreshold=threshold, MaxFaces=max_faces, ) faces = [RekognitionFace(face["Face"]) for face in response["FaceMatches"]] logger.info( "Found %s faces in %s that match %s.", len(faces), self.collection_id, face_id, ) except ClientError: logger.exception( "Couldn't search for faces in %s that match %s.", self.collection_id, face_id, ) raise else: return faces def search_faces_by_image(self, image, threshold, max_faces): """ Searches for faces in the collection that match the largest face in the reference image. :param image: The image that contains the reference face to search for. :param threshold: The match confidence must be greater than this value for a face to be included in the results. :param max_faces: The maximum number of faces to return. :return: A tuple. The first element is the face found in the reference image. The second element is the list of matching faces found in the collection. """ try: response = self.rekognition_client.search_faces_by_image( CollectionId=self.collection_id, Image=image.image, FaceMatchThreshold=threshold, MaxFaces=max_faces, ) image_face = RekognitionFace( { "BoundingBox": response["SearchedFaceBoundingBox"], "Confidence": response["SearchedFaceConfidence"], } ) collection_faces = [ RekognitionFace(face["Face"]) for face in response["FaceMatches"] ] logger.info( "Found %s faces in the collection that match the largest " "face in %s.", len(collection_faces), image.image_name, ) except ClientError: logger.exception( "Couldn't search for faces in %s that match %s.", self.collection_id, image.image_name, ) raise else: return image_face, collection_faces class RekognitionFace: """Encapsulates an Amazon Rekognition face.""" def __init__(self, face, timestamp=None): """ Initializes the face object. :param face: Face data, in the format returned by Amazon Rekognition functions. :param timestamp: The time when the face was detected, if the face was detected in a video. """ self.bounding_box = face.get("BoundingBox") self.confidence = face.get("Confidence") self.landmarks = face.get("Landmarks") self.pose = face.get("Pose") self.quality = face.get("Quality") age_range = face.get("AgeRange") if age_range is not None: self.age_range = (age_range.get("Low"), age_range.get("High")) else: self.age_range = None self.smile = face.get("Smile", {}).get("Value") self.eyeglasses = face.get("Eyeglasses", {}).get("Value") self.sunglasses = face.get("Sunglasses", {}).get("Value") self.gender = face.get("Gender", {}).get("Value", None) self.beard = face.get("Beard", {}).get("Value") self.mustache = face.get("Mustache", {}).get("Value") self.eyes_open = face.get("EyesOpen", {}).get("Value") self.mouth_open = face.get("MouthOpen", {}).get("Value") self.emotions = [ emo.get("Type") for emo in face.get("Emotions", []) if emo.get("Confidence", 0) > 50 ] self.face_id = face.get("FaceId") self.image_id = face.get("ImageId") self.timestamp = timestamp def to_dict(self): """ Renders some of the face data to a dict. :return: A dict that contains the face data. """ rendering = {} if self.bounding_box is not None: rendering["bounding_box"] = self.bounding_box if self.age_range is not None: rendering["age"] = f"{self.age_range[0]} - {self.age_range[1]}" if self.gender is not None: rendering["gender"] = self.gender if self.emotions: rendering["emotions"] = self.emotions if self.face_id is not None: rendering["face_id"] = self.face_id if self.image_id is not None: rendering["image_id"] = self.image_id if self.timestamp is not None: rendering["timestamp"] = self.timestamp has = [] if self.smile: has.append("smile") if self.eyeglasses: has.append("eyeglasses") if self.sunglasses: has.append("sunglasses") if self.beard: has.append("beard") if self.mustache: has.append("mustache") if self.eyes_open: has.append("open eyes") if self.mouth_open: has.append("open mouth") if has: rendering["has"] = has return rendering

Use as classes wrapper para criar uma coleção de faces a partir de um conjunto de imagens e, em seguida, pesquisar faces na coleção.

def usage_demo(): print("-" * 88) print("Welcome to the Amazon Rekognition face collection demo!") print("-" * 88) logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") rekognition_client = boto3.client("rekognition") images = [ RekognitionImage.from_file( ".media/pexels-agung-pandit-wiguna-1128316.jpg", rekognition_client, image_name="sitting", ), RekognitionImage.from_file( ".media/pexels-agung-pandit-wiguna-1128317.jpg", rekognition_client, image_name="hopping", ), RekognitionImage.from_file( ".media/pexels-agung-pandit-wiguna-1128318.jpg", rekognition_client, image_name="biking", ), ] collection_mgr = RekognitionCollectionManager(rekognition_client) collection = collection_mgr.create_collection("doc-example-collection-demo") print(f"Created collection {collection.collection_id}:") pprint(collection.describe_collection()) print("Indexing faces from three images:") for image in images: collection.index_faces(image, 10) print("Listing faces in collection:") faces = collection.list_faces(10) for face in faces: pprint(face.to_dict()) input("Press Enter to continue.") print( f"Searching for faces in the collection that match the first face in the " f"list (Face ID: {faces[0].face_id}." ) found_faces = collection.search_faces(faces[0].face_id, 80, 10) print(f"Found {len(found_faces)} matching faces.") for face in found_faces: pprint(face.to_dict()) input("Press Enter to continue.") print( f"Searching for faces in the collection that match the largest face in " f"{images[0].image_name}." ) image_face, match_faces = collection.search_faces_by_image(images[0], 80, 10) print(f"The largest face in {images[0].image_name} is:") pprint(image_face.to_dict()) print(f"Found {len(match_faces)} matching faces.") for face in match_faces: pprint(face.to_dict()) input("Press Enter to continue.") collection.delete_collection() print("Thanks for watching!") print("-" * 88)