Ada lebih banyak contoh AWS SDK yang tersedia di repo Contoh SDK AWS Doc
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Contoh Rekognition Amazon menggunakan SDK for Python (Boto3)
Contoh kode berikut menunjukkan cara melakukan tindakan dan mengimplementasikan skenario umum dengan menggunakan AWS SDK for Python (Boto3) With Amazon Rekognition.
Tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Sementara tindakan menunjukkan cara memanggil fungsi layanan individual, Anda dapat melihat tindakan dalam konteks dalam skenario terkait.
Skenario adalah contoh kode yang menunjukkan kepada Anda bagaimana menyelesaikan tugas tertentu dengan memanggil beberapa fungsi dalam layanan atau dikombinasikan dengan yang lain Layanan AWS.
Setiap contoh menyertakan tautan ke kode sumber lengkap, di mana Anda dapat menemukan instruksi tentang cara mengatur dan menjalankan kode dalam konteks.
Tindakan
Contoh kode berikut menunjukkan cara menggunakanCompareFaces.
Untuk informasi selengkapnya, lihat Membandingkan wajah dalam gambar.
- SDK untuk Python (Boto3)
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Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 def compare_faces(self, target_image, similarity): """ Compares faces in the image with the largest face in the target image. :param target_image: The target image to compare against. :param similarity: Faces in the image must have a similarity value greater than this value to be included in the results. :return: A tuple. The first element is the list of faces that match the reference image. The second element is the list of faces that have a similarity value below the specified threshold. """ try: response = self.rekognition_client.compare_faces( SourceImage=self.image, TargetImage=target_image.image, SimilarityThreshold=similarity, ) matches = [ RekognitionFace(match["Face"]) for match in response["FaceMatches"] ] unmatches = [RekognitionFace(face) for face in response["UnmatchedFaces"]] logger.info( "Found %s matched faces and %s unmatched faces.", len(matches), len(unmatches), ) except ClientError: logger.exception( "Couldn't match faces from %s to %s.", self.image_name, target_image.image_name, ) raise else: return matches, unmatches-
Untuk detail API, lihat CompareFacesdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanCreateCollection.
Untuk informasi selengkapnya, lihat Membuat koleksi.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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-
Untuk detail API, lihat CreateCollectiondi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanDeleteCollection.
Untuk informasi selengkapnya, lihat Menghapus koleksi.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 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-
Untuk detail API, lihat DeleteCollectiondi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanDeleteFaces.
Untuk informasi selengkapnya, lihat Menghapus wajah dari koleksi.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 delete_faces(self, face_ids): """ Deletes faces from the collection. :param face_ids: The list of IDs of faces to delete. :return: The list of IDs of faces that were deleted. """ try: response = self.rekognition_client.delete_faces( CollectionId=self.collection_id, FaceIds=face_ids ) deleted_ids = response["DeletedFaces"] logger.info( "Deleted %s faces from %s.", len(deleted_ids), self.collection_id ) except ClientError: logger.exception("Couldn't delete faces from %s.", self.collection_id) raise else: return deleted_ids-
Untuk detail API, lihat DeleteFacesdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanDescribeCollection.
Untuk informasi selengkapnya, lihat Menjelaskan koleksi.
- SDK untuk Python (Boto3)
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Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 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()-
Untuk detail API, lihat DescribeCollectiondi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanDetectFaces.
Untuk informasi selengkapnya, lihat Mendeteksi wajah dalam gambar.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 def detect_faces(self): """ Detects faces in the image. :return: The list of faces found in the image. """ try: response = self.rekognition_client.detect_faces( Image=self.image, Attributes=["ALL"] ) faces = [RekognitionFace(face) for face in response["FaceDetails"]] logger.info("Detected %s faces.", len(faces)) except ClientError: logger.exception("Couldn't detect faces in %s.", self.image_name) raise else: return faces-
Untuk detail API, lihat DetectFacesdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanDetectLabels.
Untuk informasi selengkapnya, lihat Mendeteksi label dalam gambar.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 def detect_labels(self, max_labels): """ Detects labels in the image. Labels are objects and people. :param max_labels: The maximum number of labels to return. :return: The list of labels detected in the image. """ try: response = self.rekognition_client.detect_labels( Image=self.image, MaxLabels=max_labels ) labels = [RekognitionLabel(label) for label in response["Labels"]] logger.info("Found %s labels in %s.", len(labels), self.image_name) except ClientError: logger.info("Couldn't detect labels in %s.", self.image_name) raise else: return labels-
Untuk detail API, lihat DetectLabelsdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanDetectModerationLabels.
Untuk informasi selengkapnya, lihat Mendeteksi gambar yang tidak pantas.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 def detect_moderation_labels(self): """ Detects moderation labels in the image. Moderation labels identify content that may be inappropriate for some audiences. :return: The list of moderation labels found in the image. """ try: response = self.rekognition_client.detect_moderation_labels( Image=self.image ) labels = [ RekognitionModerationLabel(label) for label in response["ModerationLabels"] ] logger.info( "Found %s moderation labels in %s.", len(labels), self.image_name ) except ClientError: logger.exception( "Couldn't detect moderation labels in %s.", self.image_name ) raise else: return labels-
Untuk detail API, lihat DetectModerationLabelsdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanDetectText.
Untuk informasi selengkapnya, lihat Mendeteksi teks dalam gambar.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 def detect_text(self): """ Detects text in the image. :return The list of text elements found in the image. """ try: response = self.rekognition_client.detect_text(Image=self.image) texts = [RekognitionText(text) for text in response["TextDetections"]] logger.info("Found %s texts in %s.", len(texts), self.image_name) except ClientError: logger.exception("Couldn't detect text in %s.", self.image_name) raise else: return texts-
Untuk detail API, lihat DetectTextdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanDisassociateFaces.
- SDK untuk Python (Boto3)
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from botocore.exceptions import ClientError import boto3 import logging logger = logging.getLogger(__name__) session = boto3.Session(profile_name='profile-name') client = session.client('rekognition') def disassociate_faces(collection_id, user_id, face_ids): """ Disassociate stored faces within collection to the given user :param collection_id: The ID of the collection where user and faces are stored. :param user_id: The ID of the user that we want to disassociate faces from :param face_ids: The list of face IDs to be disassociated from the given user :return: response of AssociateFaces API """ logger.info(f'Disssociating faces from user: {user_id}, {face_ids}') try: response = client.disassociate_faces( CollectionId=collection_id, UserId=user_id, FaceIds=face_ids ) print(f'- disassociated {len(response["DisassociatedFaces"])} faces') except ClientError: logger.exception("Failed to disassociate faces from the given user") raise else: print(response) return response def main(): face_ids = ["faceId1", "faceId2"] collection_id = "collection-id" user_id = "user-id" disassociate_faces(collection_id, user_id, face_ids) if __name__ == "__main__": main()-
Untuk detail API, lihat DisassociateFacesdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanIndexFaces.
Untuk informasi selengkapnya, lihat Menambahkan wajah ke koleksi.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 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-
Untuk detail API, lihat IndexFacesdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanListCollections.
Untuk informasi selengkapnya, lihat Daftar koleksi.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 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-
Untuk detail API, lihat ListCollectionsdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanListFaces.
Untuk informasi selengkapnya, lihat Daftar wajah dalam koleksi.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 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-
Untuk detail API, lihat ListFacesdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanRecognizeCelebrities.
Untuk informasi selengkapnya, lihat Mengenali selebriti dalam sebuah gambar.
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 def recognize_celebrities(self): """ Detects celebrities in the image. :return: A tuple. The first element is the list of celebrities found in the image. The second element is the list of faces that were detected but did not match any known celebrities. """ try: response = self.rekognition_client.recognize_celebrities(Image=self.image) celebrities = [ RekognitionCelebrity(celeb) for celeb in response["CelebrityFaces"] ] other_faces = [ RekognitionFace(face) for face in response["UnrecognizedFaces"] ] logger.info( "Found %s celebrities and %s other faces in %s.", len(celebrities), len(other_faces), self.image_name, ) except ClientError: logger.exception("Couldn't detect celebrities in %s.", self.image_name) raise else: return celebrities, other_faces-
Untuk detail API, lihat RecognizeCelebritiesdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanSearchFaces.
Untuk informasi selengkapnya, lihat Mencari wajah (ID wajah).
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 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-
Untuk detail API, lihat SearchFacesdi AWS SDK for Python (Boto3) Referensi API.
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Contoh kode berikut menunjukkan cara menggunakanSearchFacesByImage.
Untuk informasi selengkapnya, lihat Mencari wajah (gambar).
- SDK untuk Python (Boto3)
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catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. 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 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-
Untuk detail API, lihat SearchFacesByImagedi AWS SDK for Python (Boto3) Referensi API.
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Skenario
Contoh kode berikut ini menunjukkan cara:
Buat koleksi Amazon Rekognition.
Tambahkan gambar ke koleksi dan deteksi wajah di dalamnya.
Cari koleksi untuk wajah yang cocok dengan gambar referensi.
Hapus koleksi.
Untuk informasi selengkapnya, lihat Mencari wajah dalam koleksi.
- SDK untuk Python (Boto3)
-
catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. Buat kelas yang membungkus fungsi 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 renderingGunakan kelas pembungkus untuk membangun koleksi wajah dari satu set gambar dan kemudian mencari wajah dalam koleksi.
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)
Contoh kode berikut ini menunjukkan cara:
Mendeteksi elemen dalam gambar dengan menggunakan Amazon Rekognition.
Tampilkan gambar dan gambar kotak pembatas di sekitar elemen yang terdeteksi.
Untuk informasi selengkapnya, lihat Menampilkan kotak pembatas.
- SDK untuk Python (Boto3)
-
catatan
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara pengaturan dan menjalankannya di Repositori Contoh Kode AWS
. Buat kelas untuk membungkus fungsi Amazon Rekognition.
import logging from pprint import pprint import boto3 from botocore.exceptions import ClientError import requests from rekognition_objects import ( RekognitionFace, RekognitionCelebrity, RekognitionLabel, RekognitionModerationLabel, RekognitionText, show_bounding_boxes, show_polygons, ) 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) @classmethod def from_bucket(cls, s3_object, rekognition_client): """ Creates a RekognitionImage object from an Amazon S3 object. :param s3_object: An Amazon S3 object that identifies the image. The image is not retrieved until needed for a later call. :param rekognition_client: A Boto3 Rekognition client. :return: The RekognitionImage object, initialized with Amazon S3 object data. """ image = {"S3Object": {"Bucket": s3_object.bucket_name, "Name": s3_object.key}} return cls(image, s3_object.key, rekognition_client) def detect_faces(self): """ Detects faces in the image. :return: The list of faces found in the image. """ try: response = self.rekognition_client.detect_faces( Image=self.image, Attributes=["ALL"] ) faces = [RekognitionFace(face) for face in response["FaceDetails"]] logger.info("Detected %s faces.", len(faces)) except ClientError: logger.exception("Couldn't detect faces in %s.", self.image_name) raise else: return faces def detect_labels(self, max_labels): """ Detects labels in the image. Labels are objects and people. :param max_labels: The maximum number of labels to return. :return: The list of labels detected in the image. """ try: response = self.rekognition_client.detect_labels( Image=self.image, MaxLabels=max_labels ) labels = [RekognitionLabel(label) for label in response["Labels"]] logger.info("Found %s labels in %s.", len(labels), self.image_name) except ClientError: logger.info("Couldn't detect labels in %s.", self.image_name) raise else: return labels def recognize_celebrities(self): """ Detects celebrities in the image. :return: A tuple. The first element is the list of celebrities found in the image. The second element is the list of faces that were detected but did not match any known celebrities. """ try: response = self.rekognition_client.recognize_celebrities(Image=self.image) celebrities = [ RekognitionCelebrity(celeb) for celeb in response["CelebrityFaces"] ] other_faces = [ RekognitionFace(face) for face in response["UnrecognizedFaces"] ] logger.info( "Found %s celebrities and %s other faces in %s.", len(celebrities), len(other_faces), self.image_name, ) except ClientError: logger.exception("Couldn't detect celebrities in %s.", self.image_name) raise else: return celebrities, other_faces def compare_faces(self, target_image, similarity): """ Compares faces in the image with the largest face in the target image. :param target_image: The target image to compare against. :param similarity: Faces in the image must have a similarity value greater than this value to be included in the results. :return: A tuple. The first element is the list of faces that match the reference image. The second element is the list of faces that have a similarity value below the specified threshold. """ try: response = self.rekognition_client.compare_faces( SourceImage=self.image, TargetImage=target_image.image, SimilarityThreshold=similarity, ) matches = [ RekognitionFace(match["Face"]) for match in response["FaceMatches"] ] unmatches = [RekognitionFace(face) for face in response["UnmatchedFaces"]] logger.info( "Found %s matched faces and %s unmatched faces.", len(matches), len(unmatches), ) except ClientError: logger.exception( "Couldn't match faces from %s to %s.", self.image_name, target_image.image_name, ) raise else: return matches, unmatches def detect_moderation_labels(self): """ Detects moderation labels in the image. Moderation labels identify content that may be inappropriate for some audiences. :return: The list of moderation labels found in the image. """ try: response = self.rekognition_client.detect_moderation_labels( Image=self.image ) labels = [ RekognitionModerationLabel(label) for label in response["ModerationLabels"] ] logger.info( "Found %s moderation labels in %s.", len(labels), self.image_name ) except ClientError: logger.exception( "Couldn't detect moderation labels in %s.", self.image_name ) raise else: return labels def detect_text(self): """ Detects text in the image. :return The list of text elements found in the image. """ try: response = self.rekognition_client.detect_text(Image=self.image) texts = [RekognitionText(text) for text in response["TextDetections"]] logger.info("Found %s texts in %s.", len(texts), self.image_name) except ClientError: logger.exception("Couldn't detect text in %s.", self.image_name) raise else: return textsBuat fungsi pembantu untuk menggambar kotak pembatas dan poligon.
import io import logging from PIL import Image, ImageDraw logger = logging.getLogger(__name__) def show_bounding_boxes(image_bytes, box_sets, colors): """ Draws bounding boxes on an image and shows it with the default image viewer. :param image_bytes: The image to draw, as bytes. :param box_sets: A list of lists of bounding boxes to draw on the image. :param colors: A list of colors to use to draw the bounding boxes. """ image = Image.open(io.BytesIO(image_bytes)) draw = ImageDraw.Draw(image) for boxes, color in zip(box_sets, colors): for box in boxes: left = image.width * box["Left"] top = image.height * box["Top"] right = (image.width * box["Width"]) + left bottom = (image.height * box["Height"]) + top draw.rectangle([left, top, right, bottom], outline=color, width=3) image.show() def show_polygons(image_bytes, polygons, color): """ Draws polygons on an image and shows it with the default image viewer. :param image_bytes: The image to draw, as bytes. :param polygons: The list of polygons to draw on the image. :param color: The color to use to draw the polygons. """ image = Image.open(io.BytesIO(image_bytes)) draw = ImageDraw.Draw(image) for polygon in polygons: draw.polygon( [ (image.width * point["X"], image.height * point["Y"]) for point in polygon ], outline=color, ) image.show()Buat kelas untuk mengurai objek yang dikembalikan oleh Amazon Rekognition.
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 class RekognitionCelebrity: """Encapsulates an Amazon Rekognition celebrity.""" def __init__(self, celebrity, timestamp=None): """ Initializes the celebrity object. :param celebrity: Celebrity data, in the format returned by Amazon Rekognition functions. :param timestamp: The time when the celebrity was detected, if the celebrity was detected in a video. """ self.info_urls = celebrity.get("Urls") self.name = celebrity.get("Name") self.id = celebrity.get("Id") self.face = RekognitionFace(celebrity.get("Face")) self.confidence = celebrity.get("MatchConfidence") self.bounding_box = celebrity.get("BoundingBox") self.timestamp = timestamp def to_dict(self): """ Renders some of the celebrity data to a dict. :return: A dict that contains the celebrity data. """ rendering = self.face.to_dict() if self.name is not None: rendering["name"] = self.name if self.info_urls: rendering["info URLs"] = self.info_urls if self.timestamp is not None: rendering["timestamp"] = self.timestamp return rendering class RekognitionPerson: """Encapsulates an Amazon Rekognition person.""" def __init__(self, person, timestamp=None): """ Initializes the person object. :param person: Person data, in the format returned by Amazon Rekognition functions. :param timestamp: The time when the person was detected, if the person was detected in a video. """ self.index = person.get("Index") self.bounding_box = person.get("BoundingBox") face = person.get("Face") self.face = RekognitionFace(face) if face is not None else None self.timestamp = timestamp def to_dict(self): """ Renders some of the person data to a dict. :return: A dict that contains the person data. """ rendering = self.face.to_dict() if self.face is not None else {} if self.index is not None: rendering["index"] = self.index if self.bounding_box is not None: rendering["bounding_box"] = self.bounding_box if self.timestamp is not None: rendering["timestamp"] = self.timestamp return rendering class RekognitionLabel: """Encapsulates an Amazon Rekognition label.""" def __init__(self, label, timestamp=None): """ Initializes the label object. :param label: Label data, in the format returned by Amazon Rekognition functions. :param timestamp: The time when the label was detected, if the label was detected in a video. """ self.name = label.get("Name") self.confidence = label.get("Confidence") self.instances = label.get("Instances") self.parents = label.get("Parents") self.timestamp = timestamp def to_dict(self): """ Renders some of the label data to a dict. :return: A dict that contains the label data. """ rendering = {} if self.name is not None: rendering["name"] = self.name if self.timestamp is not None: rendering["timestamp"] = self.timestamp return rendering class RekognitionModerationLabel: """Encapsulates an Amazon Rekognition moderation label.""" def __init__(self, label, timestamp=None): """ Initializes the moderation label object. :param label: Label data, in the format returned by Amazon Rekognition functions. :param timestamp: The time when the moderation label was detected, if the label was detected in a video. """ self.name = label.get("Name") self.confidence = label.get("Confidence") self.parent_name = label.get("ParentName") self.timestamp = timestamp def to_dict(self): """ Renders some of the moderation label data to a dict. :return: A dict that contains the moderation label data. """ rendering = {} if self.name is not None: rendering["name"] = self.name if self.parent_name is not None: rendering["parent_name"] = self.parent_name if self.timestamp is not None: rendering["timestamp"] = self.timestamp return rendering class RekognitionText: """Encapsulates an Amazon Rekognition text element.""" def __init__(self, text_data): """ Initializes the text object. :param text_data: Text data, in the format returned by Amazon Rekognition functions. """ self.text = text_data.get("DetectedText") self.kind = text_data.get("Type") self.id = text_data.get("Id") self.parent_id = text_data.get("ParentId") self.confidence = text_data.get("Confidence") self.geometry = text_data.get("Geometry") def to_dict(self): """ Renders some of the text data to a dict. :return: A dict that contains the text data. """ rendering = {} if self.text is not None: rendering["text"] = self.text if self.kind is not None: rendering["kind"] = self.kind if self.geometry is not None: rendering["polygon"] = self.geometry.get("Polygon") return renderingGunakan kelas pembungkus untuk mendeteksi elemen dalam gambar dan menampilkan kotak pembatasnya. Gambar yang digunakan dalam contoh ini dapat ditemukan GitHub bersama dengan instruksi dan lebih banyak kode.
def usage_demo(): print("-" * 88) print("Welcome to the Amazon Rekognition image detection demo!") print("-" * 88) logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") rekognition_client = boto3.client("rekognition") street_scene_file_name = ".media/pexels-kaique-rocha-109919.jpg" celebrity_file_name = ".media/pexels-pixabay-53370.jpg" one_girl_url = "https://dhei5unw3vrsx.cloudfront.net/images/source3_resized.jpg" three_girls_url = "https://dhei5unw3vrsx.cloudfront.net/images/target3_resized.jpg" swimwear_object = boto3.resource("s3").Object( "console-sample-images-pdx", "yoga_swimwear.jpg" ) book_file_name = ".media/pexels-christina-morillo-1181671.jpg" street_scene_image = RekognitionImage.from_file( street_scene_file_name, rekognition_client ) print(f"Detecting faces in {street_scene_image.image_name}...") faces = street_scene_image.detect_faces() print(f"Found {len(faces)} faces, here are the first three.") for face in faces[:3]: pprint(face.to_dict()) show_bounding_boxes( street_scene_image.image["Bytes"], [[face.bounding_box for face in faces]], ["aqua"], ) input("Press Enter to continue.") print(f"Detecting labels in {street_scene_image.image_name}...") labels = street_scene_image.detect_labels(100) print(f"Found {len(labels)} labels.") for label in labels: pprint(label.to_dict()) names = [] box_sets = [] colors = ["aqua", "red", "white", "blue", "yellow", "green"] for label in labels: if label.instances: names.append(label.name) box_sets.append([inst["BoundingBox"] for inst in label.instances]) print(f"Showing bounding boxes for {names} in {colors[:len(names)]}.") show_bounding_boxes( street_scene_image.image["Bytes"], box_sets, colors[: len(names)] ) input("Press Enter to continue.") celebrity_image = RekognitionImage.from_file( celebrity_file_name, rekognition_client ) print(f"Detecting celebrities in {celebrity_image.image_name}...") celebs, others = celebrity_image.recognize_celebrities() print(f"Found {len(celebs)} celebrities.") for celeb in celebs: pprint(celeb.to_dict()) show_bounding_boxes( celebrity_image.image["Bytes"], [[celeb.face.bounding_box for celeb in celebs]], ["aqua"], ) input("Press Enter to continue.") girl_image_response = requests.get(one_girl_url) girl_image = RekognitionImage( {"Bytes": girl_image_response.content}, "one-girl", rekognition_client ) group_image_response = requests.get(three_girls_url) group_image = RekognitionImage( {"Bytes": group_image_response.content}, "three-girls", rekognition_client ) print("Comparing reference face to group of faces...") matches, unmatches = girl_image.compare_faces(group_image, 80) print(f"Found {len(matches)} face matching the reference face.") show_bounding_boxes( group_image.image["Bytes"], [[match.bounding_box for match in matches]], ["aqua"], ) input("Press Enter to continue.") swimwear_image = RekognitionImage.from_bucket(swimwear_object, rekognition_client) print(f"Detecting suggestive content in {swimwear_object.key}...") labels = swimwear_image.detect_moderation_labels() print(f"Found {len(labels)} moderation labels.") for label in labels: pprint(label.to_dict()) input("Press Enter to continue.") book_image = RekognitionImage.from_file(book_file_name, rekognition_client) print(f"Detecting text in {book_image.image_name}...") texts = book_image.detect_text() print(f"Found {len(texts)} text instances. Here are the first seven:") for text in texts[:7]: pprint(text.to_dict()) show_polygons( book_image.image["Bytes"], [text.geometry["Polygon"] for text in texts], "aqua" ) print("Thanks for watching!") print("-" * 88)
Contoh kode berikut menunjukkan cara membuat aplikasi yang menggunakan Amazon Rekognition untuk mendeteksi objek berdasarkan kategori dalam gambar.
- SDK untuk Python (Boto3)
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Menunjukkan cara menggunakan AWS SDK for Python (Boto3) untuk membuat aplikasi web yang memungkinkan Anda melakukan hal berikut:
Mengunggah foto ke bucket Amazon Simple Storage Service (Amazon S3).
Menggunakan Amazon Rekognition untuk menganalisis dan memberi label pada foto.
Menggunakan Amazon Simple Email Service (Amazon SES) untuk mengirim laporan email analisis gambar.
Contoh ini berisi dua komponen utama: halaman web yang ditulis di dalamnya JavaScript yang dibangun dengan React, dan layanan REST yang ditulis dengan Python yang dibangun dengan Flask-. RESTful
Anda dapat menggunakan halaman web React untuk:
Menampilkan daftar gambar yang disimpan di bucket S3 Anda.
Mengunggah gambar dari komputer ke bucket S3.
Menampilkan gambar dan label yang mengidentifikasi item yang terdeteksi dalam gambar.
Mendapatkan laporan semua gambar di bucket S3 Anda dan mengirimkan email laporan tersebut.
Halaman web memanggil layanan REST. Layanan mengirimkan permintaan ke AWS untuk melakukan tindakan berikut:
Mendapatkan dan memfilter daftar gambar dalam bucket S3 Anda.
Merngunggah foto ke bucket S3 Anda.
Menggunakan Amazon Rekognition untuk menganalisis foto individual dan mendapatkan daftar label yang mengidentifikasi item yang terdeteksi dalam foto.
Menganlisis semua foto di bucket S3 Anda dan menggunakan Amazon SES untuk mengirim laporan melalui email.
Untuk kode sumber lengkap dan instruksi tentang cara mengatur dan menjalankan, lihat contoh lengkapnya di GitHub
. Layanan yang digunakan dalam contoh ini
Amazon Rekognition
Amazon S3
Amazon SES
Contoh kode berikut menunjukkan cara mendeteksi orang dan objek dalam video dengan Amazon Rekognition.
- SDK untuk Python (Boto3)
-
Gunakan Amazon Rekognition untuk mendeteksi wajah, objek, dan orang dalam video dengan memulai tugas deteksi asinkron. Contoh ini juga mengonfigurasi Amazon Rekognition untuk memberi tahu topik Amazon Simple Notification Service (Amazon SNS) saat pekerjaan selesai dan berlangganan antrian Amazon Simple Queue Service (Amazon SQS) ke topik tersebut. Ketika antrian menerima pesan tentang pekerjaan, pekerjaan diambil dan hasilnya adalah output.
Contoh ini paling baik dilihat di GitHub. Untuk kode sumber lengkap dan instruksi tentang cara mengatur dan menjalankan, lihat contoh lengkapnya di GitHub
. Layanan yang digunakan dalam contoh ini
Amazon Rekognition
Amazon SNS
Amazon SQS
