D'autres exemples de AWS SDK sont disponibles dans le référentiel [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub .
Les traductions sont fournies par des outils de traduction automatique. En cas de conflit entre le contenu d'une traduction et celui de la version originale en anglais, la version anglaise prévaudra.
# Exemples Amazon Rekognition avec le kit SDK pour Python (Boto3)
Les exemples de code suivants vous montrent comment effectuer des actions et implémenter des scénarios courants à l' AWS SDK pour Python (Boto3) aide d'Amazon Rekognition.
Les *actions* sont des extraits de code de programmes plus larges et doivent être exécutées dans leur contexte. Alors que les actions vous indiquent comment appeler des fonctions de service individuelles, vous pouvez les voir en contexte dans leurs scénarios associés.
Les *scénarios* sont des exemples de code qui vous montrent comment accomplir des tâches spécifiques en appelant plusieurs fonctions au sein d’un même service ou combinés à d’autres Services AWS.
Chaque exemple inclut un lien vers le code source complet, où vous trouverez des instructions sur la configuration et l’exécution du code en contexte.
**Topics**
+ [Actions](#actions)
+ [Scénarios](#scenarios)
## Actions
### `CompareFaces`
L'exemple de code suivant montre comment utiliser`CompareFaces`.
Pour plus d’informations, consultez [Comparaison de visages dans des images](https://docs.aws.amazon.com/rekognition/latest/dg/faces-comparefaces.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [CompareFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/CompareFaces)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `CreateCollection`
L'exemple de code suivant montre comment utiliser`CreateCollection`.
Pour plus d’informations, consultez [Création d’une collection](https://docs.aws.amazon.com/rekognition/latest/dg/create-collection-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [CreateCollection](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/CreateCollection)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeleteCollection`
L'exemple de code suivant montre comment utiliser`DeleteCollection`.
Pour plus d’informations, consultez [Suppression d’une collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [DeleteCollection](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DeleteCollection)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeleteFaces`
L'exemple de code suivant montre comment utiliser`DeleteFaces`.
Pour plus d’informations, veuillez consulter [Supprimer des visages d’une collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-faces-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [DeleteFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DeleteFaces)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DescribeCollection`
L'exemple de code suivant montre comment utiliser`DescribeCollection`.
Pour plus d’informations, veuillez consulter [Description d’une collection](https://docs.aws.amazon.com/rekognition/latest/dg/describe-collection-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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()
```
+ Pour plus de détails sur l'API, consultez [DescribeCollection](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DescribeCollection)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectFaces`
L'exemple de code suivant montre comment utiliser`DetectFaces`.
Pour plus d’informations, veuillez consulter [Détecter des visages dans une image](https://docs.aws.amazon.com/rekognition/latest/dg/faces-detect-images.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [DetectFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DetectFaces)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectLabels`
L'exemple de code suivant montre comment utiliser`DetectLabels`.
Pour plus d’informations, veuillez consulter [Détection des étiquettes dans une image](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [DetectLabels](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DetectLabels)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectModerationLabels`
L'exemple de code suivant montre comment utiliser`DetectModerationLabels`.
Pour plus d’informations, veuillez consulter [Détecter des images inappropriées](https://docs.aws.amazon.com/rekognition/latest/dg/procedure-moderate-images.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [DetectModerationLabels](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DetectModerationLabels)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DetectText`
L'exemple de code suivant montre comment utiliser`DetectText`.
Pour plus d’informations, consultez [Détection de texte dans une image](https://docs.aws.amazon.com/rekognition/latest/dg/text-detecting-text-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [DetectText](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/DetectText)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `IndexFaces`
L'exemple de code suivant montre comment utiliser`IndexFaces`.
Pour plus d’informations, veuillez consulter [Ajouter des visages à une collection](https://docs.aws.amazon.com/rekognition/latest/dg/add-faces-to-collection-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [IndexFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/IndexFaces)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListCollections`
L'exemple de code suivant montre comment utiliser`ListCollections`.
Pour plus d’informations, consultez [Créer une liste de collections](https://docs.aws.amazon.com/rekognition/latest/dg/list-collection-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [ListCollections](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/ListCollections)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListFaces`
L'exemple de code suivant montre comment utiliser`ListFaces`.
Pour plus d’informations, consultez [Répertoriage de visages d’une collection](https://docs.aws.amazon.com/rekognition/latest/dg/list-faces-in-collection-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [ListFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/ListFaces)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `RecognizeCelebrities`
L'exemple de code suivant montre comment utiliser`RecognizeCelebrities`.
Pour plus d’informations, consultez [Reconnaissance de célébrités dans une image](https://docs.aws.amazon.com/rekognition/latest/dg/celebrities-procedure-image.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [RecognizeCelebrities](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/RecognizeCelebrities)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `SearchFaces`
L'exemple de code suivant montre comment utiliser`SearchFaces`.
Pour plus d’informations, veuillez consulter [Recherche d’un visage (identification faciale)](https://docs.aws.amazon.com/rekognition/latest/dg/search-face-with-id-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [SearchFaces](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/SearchFaces)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `SearchFacesByImage`
L'exemple de code suivant montre comment utiliser`SearchFacesByImage`.
Pour plus d’informations, voir [Recherche d’un visage (image)](https://docs.aws.amazon.com/rekognition/latest/dg/search-face-with-image-procedure.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
```
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
```
+ Pour plus de détails sur l'API, consultez [SearchFacesByImage](https://docs.aws.amazon.com/goto/boto3/rekognition-2016-06-27/SearchFacesByImage)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
## Scénarios
### Créez une collection et trouvez-y des visages
L’exemple de code suivant illustre comment :
+ Créer une collection Amazon Rekognition.
+ Ajouter des images à la collection et détecter les visages qu’elle contient.
+ Rechercher dans la collection les visages qui correspondent à une image de référence.
+ Supprimer une collection.
Pour plus d’informations, veuillez consulter [Recherche de visages dans une collection](https://docs.aws.amazon.com/rekognition/latest/dg/collections.html).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
Créez des classes qui encapsulent les fonctions 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
```
Utilisez les classes wrapper pour créer une collection de visages à partir d’un ensemble d’images, puis recherchez des visages dans la collection.
```
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)
```
### Détecter et afficher des éléments dans des images
L’exemple de code suivant illustre comment :
+ Détecter des éléments dans des images à l’aide d’Amazon Rekognition.
+ Afficher des images et tracer des cadres autour des éléments détectés.
Pour plus d’informations, veuillez consulter [Affichage des cadres de délimitation.](https://docs.aws.amazon.com/rekognition/latest/dg/images-displaying-bounding-boxes.html)
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition#code-examples).
Créez des classes pour encapsuler les fonctions 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 texts
```
Créez des fonctions d’assistance pour dessiner des cadres de délimitation et des polygones.
```
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()
```
Créer des classes pour analyser les objets renvoyés par 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 rendering
```
Utilisez les classes wrapper pour détecter des éléments dans les images et afficher leurs cadres de délimitation. Les images utilisées dans cet exemple se trouvent ici, GitHub ainsi que des instructions et du code supplémentaire.
```
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)
```
### Détecter des objets dans des images
L’exemple de code suivant montre comment créer une application qui utilise Amazon Rekognition afin de détecter des objets par catégorie dans des images.
**Kit SDK for Python (Boto3)**
Vous montre comment utiliser le AWS SDK pour Python (Boto3) pour créer une application Web qui vous permet d'effectuer les opérations suivantes :
+ Chargez les photos dans un compartiment Amazon Simple Storage Service (Amazon S3).
+ Utilisez Amazon Rekognition pour analyser et étiqueter les photos.
+ Utilisez Amazon Simple Email Service (Amazon SES) pour envoyer des rapports d’analyse d’images par e-mail.
Cet exemple contient deux composants principaux : une page Web écrite en JavaScript React et un service REST écrit en Python construit avec Flask-RESTful.
Vous pouvez utiliser la page web React pour :
+ Affichez une liste d’images stockées dans votre compartiment S3.
+ Chargez des images depuis votre ordinateur dans votre compartiment S3.
+ Affichez des images et des étiquettes qui identifient les éléments détectés dans l’image.
+ Obtenez un rapport de toutes les images de votre compartiment S3 et envoyez un e-mail du rapport.
La page web appelle le service REST. Le service envoie des demandes à AWS pour effectuer les opérations suivantes :
+ Obtenez et filtrez la liste des images de votre compartiment S3.
+ Chargez des photos dans votre compartiment S3.
+ Utilisez Amazon Rekognition pour analyser des photos individuelles et obtenir une liste d’étiquettes qui identifient les éléments détectés sur la photo.
+ Analysez toutes les photos de votre compartiment S3 et utilisez Amazon SES pour envoyer un rapport par e-mail.
Pour obtenir le code source complet et les instructions de configuration et d'exécution, consultez l'exemple complet sur [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/photo_analyzer).
**Les services utilisés dans cet exemple**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES
### Détecter des personnes et des objets dans une vidéo
L’exemple de code suivant montre comment détecter des personnes et des objets dans une vidéo avec Amazon Rekognition.
**SDK pour Python (Boto3)**
Utilisez Amazon Rekognition pour détecter des visages, des objets et des personnes dans des vidéos en démarrant des tâches de détection asynchrone. Cet exemple montre également comment configurer Amazon Rekognition pour notifier une rubrique Amazon Simple Notification Service (Amazon SNS) lorsque les tâches sont terminées et abonner une file d’attente Amazon Simple Queue Service (Amazon SQS) à la rubrique. Lorsque la file d’attente reçoit un message concernant une tâche, elle est récupérée et les résultats sont affichés.
Il est préférable de visionner cet exemple sur GitHub. Pour obtenir le code source complet et les instructions de configuration et d'exécution, consultez l'exemple complet sur [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/rekognition).
**Les services utilisés dans cet exemple**
+ Amazon Rekognition
+ Amazon S3
+ Amazon SES
+ Amazon SNS
+ Amazon SQS