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$encrypted_6e98b3e8e789_iv -in .env.enc -out .env -d || true' +- '[ "${TRAVIS_PULL_REQUEST}" = "false" ] && openssl aes-256-cbc -K $encrypted_cebf25e6c525_key + -iv $encrypted_cebf25e6c525_iv -in .env.enc -out .env -d || true' install: pip install tox-travis script: - pip install -U python-dotenv diff --git a/examples/visual_recognition_v3.py b/examples/visual_recognition_v3.py index 5f024b49a..0f8afd962 100644 --- a/examples/visual_recognition_v3.py +++ b/examples/visual_recognition_v3.py @@ -7,7 +7,6 @@ '/ginni_bio_780x981_v4_03162016.jpg' visual_recognition = VisualRecognitionV3('2016-05-20', api_key='YOUR API KEY') -classifier_id = 'CarsvsTrucksxDO_NOT_DELETE_771019274' # with open(join(dirname(__file__), '../resources/cars.zip'), 'rb') as cars, \ # open(join(dirname(__file__), '../resources/trucks.zip'), 'rb') as @@ -19,7 +18,7 @@ car_path = join(dirname(__file__), '../resources/cars.zip') with open(car_path, 'rb') as images_file: - parameters = json.dumps({'threshold': 0.1, 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Must contain a minimum of 10 images. The swagger limits you to training only one class. To train more classes, use the API functionality. :param file negative_examples: A compressed (.zip) file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images. + :param dict headers: A `dict` containing the request headers :return: A `dict` containing the `Classifier` response. :rtype: dict """ if name is None: raise ValueError('name must be provided') + headers = {} + if 'headers' in kwargs: + headers.update(kwargs.get('headers')) params = {'version': self.version} data = {'name': name} url = '/v3/classifiers' response = self.request( method='POST', url=url, + headers=headers, params=params, data=data, files=kwargs, accept_json=True) return response - def delete_classifier(self, classifier_id): + def delete_classifier(self, classifier_id, **kwargs): """ Delete a classifier. :param str classifier_id: The ID of the classifier. + :param dict headers: A `dict` containing the request headers :rtype: None """ if classifier_id is None: raise ValueError('classifier_id must be provided') + headers = {} + if 'headers' in kwargs: + headers.update(kwargs.get('headers')) params = {'version': self.version} url = '/v3/classifiers/{0}'.format( *self._encode_path_vars(classifier_id)) - self.request(method='DELETE', url=url, params=params, accept_json=True) + self.request( + method='DELETE', + url=url, + headers=headers, + params=params, + accept_json=True) return None - def get_classifier(self, classifier_id): + def get_classifier(self, classifier_id, **kwargs): """ Retrieve information about a custom classifier. :param str classifier_id: The ID of the classifier. + :param dict headers: A `dict` containing the request headers :return: A `dict` containing the `Classifier` response. :rtype: dict """ if classifier_id is None: raise ValueError('classifier_id must be provided') + headers = {} + if 'headers' in kwargs: + headers.update(kwargs.get('headers')) params = {'version': self.version} url = '/v3/classifiers/{0}'.format( *self._encode_path_vars(classifier_id)) response = self.request( - method='GET', url=url, params=params, accept_json=True) + method='GET', + url=url, + headers=headers, + params=params, + accept_json=True) return response - def list_classifiers(self, verbose=None): + def list_classifiers(self, verbose=None, **kwargs): """ Retrieve a list of classifiers. :param bool verbose: Specify `true` to return details about the classifiers. Omit this parameter to return a brief list of classifiers. + :param dict headers: A `dict` containing the request headers :return: A `dict` containing the `Classifiers` response. :rtype: dict """ + headers = {} + if 'headers' in kwargs: + headers.update(kwargs.get('headers')) params = {'version': self.version, 'verbose': verbose} url = '/v3/classifiers' response = self.request( - method='GET', url=url, params=params, accept_json=True) + method='GET', + url=url, + headers=headers, + params=params, + accept_json=True) return response def update_classifier(self, @@ -276,17 +316,22 @@ def update_classifier(self, :param str classifier_id: The ID of the classifier. :param file _positive_examples: A compressed (.zip) file of images that depict the visual subject for a class within the classifier. Must contain a minimum of 10 images. :param file negative_examples: A compressed (.zip) file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images. + :param dict headers: A `dict` containing the request headers :return: A `dict` containing the `Classifier` response. :rtype: dict """ if classifier_id is None: raise ValueError('classifier_id must be provided') + headers = {} + if 'headers' in kwargs: + headers.update(kwargs.get('headers')) params = {'version': self.version} url = '/v3/classifiers/{0}'.format( *self._encode_path_vars(classifier_id)) response = self.request( method='POST', url=url, + headers=headers, params=params, files=kwargs, accept_json=True) @@ -296,7 +341,7 @@ def update_classifier(self, # Core ML ######################### - def get_core_ml_model(self, classifier_id): + def get_core_ml_model(self, classifier_id, **kwargs): """ Retrieve a Core ML model of a classifier. @@ -304,16 +349,24 @@ def get_core_ml_model(self, classifier_id): core_ml_enabled: true in the classifier details. :param str classifier_id: The ID of the classifier. + :param dict headers: A `dict` containing the request headers :return: A `Response ` object representing the response. :rtype: requests.models.Response """ if classifier_id is None: raise ValueError('classifier_id must be provided') + headers = {} + if 'headers' in kwargs: + headers.update(kwargs.get('headers')) params = {'version': self.version} url = '/v3/classifiers/{0}/core_ml_model'.format( *self._encode_path_vars(classifier_id)) response = self.request( - method='GET', url=url, params=params, accept_json=False) + method='GET', + url=url, + headers=headers, + params=params, + accept_json=False) return response ############################################################################## @@ -375,7 +428,7 @@ class ClassResult(object): :attr str class_name: The name of the class. :attr float score: (optional) Confidence score for the property in the range of 0 to 1. A higher score indicates greater likelihood that the class is depicted in the image. The default threshold for returning scores from a classifier is 0.5. - :attr str type_hierarchy: (optional) Knowledge graph of the property. For example, `People/Leaders/Presidents/USA/Barack Obama`. Included only if identified. + :attr str type_hierarchy: (optional) Knowledge graph of the property. For example, `/fruit/pome/apple/eating apple/Granny Smith`. Included only if identified. """ def __init__(self, class_name, score=None, type_hierarchy=None): @@ -384,7 +437,7 @@ def __init__(self, class_name, score=None, type_hierarchy=None): :param str class_name: The name of the class. :param float score: (optional) Confidence score for the property in the range of 0 to 1. A higher score indicates greater likelihood that the class is depicted in the image. The default threshold for returning scores from a classifier is 0.5. - :param str type_hierarchy: (optional) Knowledge graph of the property. For example, `People/Leaders/Presidents/USA/Barack Obama`. Included only if identified. + :param str type_hierarchy: (optional) Knowledge graph of the property. For example, `/fruit/pome/apple/eating apple/Granny Smith`. Included only if identified. """ self.class_name = class_name self.score = score @@ -996,26 +1049,19 @@ class Face(object): :attr FaceAge age: (optional) :attr FaceGender gender: (optional) :attr FaceLocation face_location: (optional) - :attr FaceIdentity identity: (optional) """ - def __init__(self, - age=None, - gender=None, - face_location=None, - identity=None): + def __init__(self, age=None, gender=None, face_location=None): """ Initialize a Face object. :param FaceAge age: (optional) :param FaceGender gender: (optional) :param FaceLocation face_location: (optional) - :param FaceIdentity identity: (optional) """ self.age = age self.gender = gender self.face_location = face_location - self.identity = identity @classmethod def _from_dict(cls, _dict): @@ -1028,8 +1074,6 @@ def _from_dict(cls, _dict): if 'face_location' in _dict: args['face_location'] = FaceLocation._from_dict( _dict.get('face_location')) - if 'identity' in _dict: - args['identity'] = FaceIdentity._from_dict(_dict.get('identity')) return cls(**args) def _to_dict(self): @@ -1041,8 +1085,6 @@ def _to_dict(self): _dict['gender'] = self.gender._to_dict() if hasattr(self, 'face_location') and self.face_location is not None: _dict['face_location'] = self.face_location._to_dict() - if hasattr(self, 'identity') and self.identity is not None: - _dict['identity'] = self.identity._to_dict() return _dict def __str__(self): @@ -1062,12 +1104,11 @@ def __ne__(self, other): class FaceAge(object): """ - Provides age information about a face. If there are more than 10 faces in an image, - the response might return the confidence score `0`. + Provides age information about a face. :attr int min: (optional) Estimated minimum age. :attr int max: (optional) Estimated maximum age. - :attr float score: (optional) Confidence score for the property in the range of 0 to 1. A higher score indicates greater likelihood that the class is depicted in the image. The default threshold for returning scores from a classifier is 0.5. + :attr float score: (optional) Confidence score in the range of 0 to 1. A higher score indicates greater confidence in the estimated value for the property. """ def __init__(self, min=None, max=None, score=None): @@ -1076,7 +1117,7 @@ def __init__(self, min=None, max=None, score=None): :param int min: (optional) Estimated minimum age. :param int max: (optional) Estimated maximum age. - :param float score: (optional) Confidence score for the property in the range of 0 to 1. A higher score indicates greater likelihood that the class is depicted in the image. The default threshold for returning scores from a classifier is 0.5. + :param float score: (optional) Confidence score in the range of 0 to 1. A higher score indicates greater confidence in the estimated value for the property. """ self.min = min self.max = max @@ -1122,11 +1163,10 @@ def __ne__(self, other): class FaceGender(object): """ - Provides information about the gender of the face. If there are more than 10 faces in - an image, the response might return the confidence score 0. + Provides information about the gender of the face. :attr str gender: Gender identified by the face. For example, `MALE` or `FEMALE`. - :attr float score: (optional) Confidence score for the property in the range of 0 to 1. A higher score indicates greater likelihood that the class is depicted in the image. The default threshold for returning scores from a classifier is 0.5. + :attr float score: (optional) Confidence score in the range of 0 to 1. A higher score indicates greater confidence in the estimated value for the property. """ def __init__(self, gender, score=None): @@ -1134,7 +1174,7 @@ def __init__(self, gender, score=None): Initialize a FaceGender object. :param str gender: Gender identified by the face. For example, `MALE` or `FEMALE`. - :param float score: (optional) Confidence score for the property in the range of 0 to 1. A higher score indicates greater likelihood that the class is depicted in the image. The default threshold for returning scores from a classifier is 0.5. + :param float score: (optional) Confidence score in the range of 0 to 1. A higher score indicates greater confidence in the estimated value for the property. """ self.gender = gender self.score = score @@ -1176,69 +1216,6 @@ def __ne__(self, other): return not self == other -class FaceIdentity(object): - """ - Provides information about a celebrity who is detected in the image. Not returned when - a celebrity is not detected. - - :attr str name: Name of the person. - :attr float score: (optional) Confidence score for the property in the range of 0 to 1. A higher score indicates greater likelihood that the class is depicted in the image. The default threshold for returning scores from a classifier is 0.5. - :attr str type_hierarchy: (optional) Knowledge graph of the property. For example, `People/Leaders/Presidents/USA/Barack Obama`. Included only if identified. - """ - - def __init__(self, name, score=None, type_hierarchy=None): - """ - Initialize a FaceIdentity object. - - :param str name: Name of the person. - :param float score: (optional) Confidence score for the property in the range of 0 to 1. A higher score indicates greater likelihood that the class is depicted in the image. The default threshold for returning scores from a classifier is 0.5. - :param str type_hierarchy: (optional) Knowledge graph of the property. For example, `People/Leaders/Presidents/USA/Barack Obama`. Included only if identified. - """ - self.name = name - self.score = score - self.type_hierarchy = type_hierarchy - - @classmethod - def _from_dict(cls, _dict): - """Initialize a FaceIdentity object from a json dictionary.""" - args = {} - if 'name' in _dict: - args['name'] = _dict.get('name') - else: - raise ValueError( - 'Required property \'name\' not present in FaceIdentity JSON') - if 'score' in _dict: - args['score'] = _dict.get('score') - if 'type_hierarchy' in _dict: - args['type_hierarchy'] = _dict.get('type_hierarchy') - return cls(**args) - - def _to_dict(self): - """Return a json dictionary representing this model.""" - _dict = {} - if hasattr(self, 'name') and self.name is not None: - _dict['name'] = self.name - if hasattr(self, 'score') and self.score is not None: - _dict['score'] = self.score - if hasattr(self, 'type_hierarchy') and self.type_hierarchy is not None: - _dict['type_hierarchy'] = self.type_hierarchy - return _dict - - def __str__(self): - """Return a `str` version of this FaceIdentity object.""" - return json.dumps(self._to_dict(), indent=2) - - def __eq__(self, other): - """Return `true` when self and other are equal, false otherwise.""" - if not isinstance(other, self.__class__): - return False - return self.__dict__ == other.__dict__ - - def __ne__(self, other): - """Return `true` when self and other are not equal, false otherwise.""" - return not self == other - - class FaceLocation(object): """ Defines the location of the bounding box around the face.