text: # coding=utf-8
  # --------------------------------------------------------------------------
  # Copyright (c) Microsoft Corporation. All rights reserved.
  # Licensed under the MIT License. See License.txt in the project root for
  # license information.
  #
  # Code generated by Microsoft (R) AutoRest Code Generator.
  # Changes may cause incorrect behavior and will be lost if the code is
  # regenerated.
  # --------------------------------------------------------------------------
  
  from msrest.pipeline import ClientRawResponse
  
  from .. import models
  
  
  class FaceOperations(object):
      """FaceOperations operations.
  
      You should not instantiate directly this class, but create a Client instance that will create it for you and attach it as attribute.
  
      :param client: Client for service requests.
      :param config: Configuration of service client.
      :param serializer: An object model serializer.
      :param deserializer: An object model deserializer.
      """
  
      models = models
  
      def __init__(self, client, config, serializer, deserializer):
  
          self._client = client
          self._serialize = serializer
          self._deserialize = deserializer
  
          self.config = config
  
      def find_similar(
              self, face_id, face_list_id=None, large_face_list_id=None, face_ids=None, max_num_of_candidates_returned=20, mode="matchPerson", custom_headers=None, raw=False, **operation_config):
          """Given query face's faceId, to search the similar-looking faces from a
          faceId array, a face list or a large face list. faceId array contains
          the faces created by [Face -
          Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236),
          which will expire 24 hours after creation. A "faceListId" is created by
          [FaceList -
          Create](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039524b)
          containing persistedFaceIds that will not expire. And a
          "largeFaceListId" is created by [LargeFaceList -
          Create](/docs/services/563879b61984550e40cbbe8d/operations/5a157b68d2de3616c086f2cc)
          containing persistedFaceIds that will also not expire. Depending on the
          input the returned similar faces list contains faceIds or
          persistedFaceIds ranked by similarity.
          <br/>Find similar has two working modes, "matchPerson" and "matchFace".
          "matchPerson" is the default mode that it tries to find faces of the
          same person as possible by using internal same-person thresholds. It is
          useful to find a known person's other photos. Note that an empty list
          will be returned if no faces pass the internal thresholds. "matchFace"
          mode ignores same-person thresholds and returns ranked similar faces
          anyway, even the similarity is low. It can be used in the cases like
          searching celebrity-looking faces.
          <br/>The 'recognitionModel' associated with the query face's faceId
          should be the same as the 'recognitionModel' used by the target faceId
          array, face list or large face list.
          .
  
          :param face_id: FaceId of the query face. User needs to call Face -
           Detect first to get a valid faceId. Note that this faceId is not
           persisted and will expire 24 hours after the detection call
          :type face_id: str
          :param face_list_id: An existing user-specified unique candidate face
           list, created in Face List - Create a Face List. Face list contains a
           set of persistedFaceIds which are persisted and will never expire.
           Parameter faceListId, largeFaceListId and faceIds should not be
           provided at the same time.
          :type face_list_id: str
          :param large_face_list_id: An existing user-specified unique candidate
           large face list, created in LargeFaceList - Create. Large face list
           contains a set of persistedFaceIds which are persisted and will never
           expire. Parameter faceListId, largeFaceListId and faceIds should not
           be provided at the same time.
          :type large_face_list_id: str
          :param face_ids: An array of candidate faceIds. All of them are
           created by Face - Detect and the faceIds will expire 24 hours after
           the detection call. The number of faceIds is limited to 1000.
           Parameter faceListId, largeFaceListId and faceIds should not be
           provided at the same time.
          :type face_ids: list[str]
          :param max_num_of_candidates_returned: The number of top similar faces
           returned. The valid range is [1, 1000].
          :type max_num_of_candidates_returned: int
          :param mode: Similar face searching mode. It can be "matchPerson" or
           "matchFace". Possible values include: 'matchPerson', 'matchFace'
          :type mode: str or
           ~azure.cognitiveservices.vision.face.models.FindSimilarMatchMode
          :param dict custom_headers: headers that will be added to the request
          :param bool raw: returns the direct response alongside the
           deserialized response
          :param operation_config: :ref:`Operation configuration
           overrides<msrest:optionsforoperations>`.
          :return: list or ClientRawResponse if raw=true
          :rtype: list[~azure.cognitiveservices.vision.face.models.SimilarFace]
           or ~msrest.pipeline.ClientRawResponse
          :raises:
           :class:`APIErrorException<azure.cognitiveservices.vision.face.models.APIErrorException>`
          """
          body = models.FindSimilarRequest(face_id=face_id, face_list_id=face_list_id, large_face_list_id=large_face_list_id, face_ids=face_ids, max_num_of_candidates_returned=max_num_of_candidates_returned, mode=mode)
  
          # Construct URL
          url = self.find_similar.metadata['url']
          path_format_arguments = {
              'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
          }
          url = self._client.format_url(url, **path_format_arguments)
  
          # Construct parameters
          query_parameters = {}
  
          # Construct headers
          header_parameters = {}
          header_parameters['Accept'] = 'application/json'
          header_parameters['Content-Type'] = 'application/json; charset=utf-8'
          if custom_headers:
              header_parameters.update(custom_headers)
  
          # Construct body
          body_content = self._serialize.body(body, 'FindSimilarRequest')
  
          # Construct and send request
          request = self._client.post(url, query_parameters, header_parameters, body_content)
          response = self._client.send(request, stream=False, **operation_config)
  
          if response.status_code not in [200]:
              raise models.APIErrorException(self._deserialize, response)
  
          deserialized = None
          if response.status_code == 200:
              deserialized = self._deserialize('[SimilarFace]', response)
  
          if raw:
              client_raw_response = ClientRawResponse(deserialized, response)
              return client_raw_response
  
          return deserialized
      find_similar.metadata = {'url': '/findsimilars'}
  
      def group(
              self, face_ids, custom_headers=None, raw=False, **operation_config):
          """Divide candidate faces into groups based on face similarity.<br />
          * The output is one or more disjointed face groups and a messyGroup. A
          face group contains faces that have similar looking, often of the same
          person. Face groups are ranked by group size, i.e. number of faces.
          Notice that faces belonging to a same person might be split into
          several groups in the result.
          * MessyGroup is a special face group containing faces that cannot find
          any similar counterpart face from original faces. The messyGroup will
          not appear in the result if all faces found their counterparts.
          * Group API needs at least 2 candidate faces and 1000 at most. We
          suggest to try [Face -
          Verify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039523a)
          when you only have 2 candidate faces.
          * The 'recognitionModel' associated with the query faces' faceIds
          should be the same.
          .
  
          :param face_ids: Array of candidate faceId created by Face - Detect.
           The maximum is 1000 faces
          :type face_ids: list[str]
          :param dict custom_headers: headers that will be added to the request
          :param bool raw: returns the direct response alongside the
           deserialized response
          :param operation_config: :ref:`Operation configuration
           overrides<msrest:optionsforoperations>`.
          :return: GroupResult or ClientRawResponse if raw=true
          :rtype: ~azure.cognitiveservices.vision.face.models.GroupResult or
           ~msrest.pipeline.ClientRawResponse
          :raises:
           :class:`APIErrorException<azure.cognitiveservices.vision.face.models.APIErrorException>`
          """
          body = models.GroupRequest(face_ids=face_ids)
  
          # Construct URL
          url = self.group.metadata['url']
          path_format_arguments = {
              'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
          }
          url = self._client.format_url(url, **path_format_arguments)
  
          # Construct parameters
          query_parameters = {}
  
          # Construct headers
          header_parameters = {}
          header_parameters['Accept'] = 'application/json'
          header_parameters['Content-Type'] = 'application/json; charset=utf-8'
          if custom_headers:
              header_parameters.update(custom_headers)
  
          # Construct body
          body_content = self._serialize.body(body, 'GroupRequest')
  
          # Construct and send request
          request = self._client.post(url, query_parameters, header_parameters, body_content)
          response = self._client.send(request, stream=False, **operation_config)
  
          if response.status_code not in [200]:
              raise models.APIErrorException(self._deserialize, response)
  
          deserialized = None
          if response.status_code == 200:
              deserialized = self._deserialize('GroupResult', response)
  
          if raw:
              client_raw_response = ClientRawResponse(deserialized, response)
              return client_raw_response
  
          return deserialized
      group.metadata = {'url': '/group'}
  
      def identify(
              self, face_ids, person_group_id=None, large_person_group_id=None, max_num_of_candidates_returned=1, confidence_threshold=None, custom_headers=None, raw=False, **operation_config):
          """1-to-many identification to find the closest matches of the specific
          query person face from a person group or large person group.
          <br/> For each face in the faceIds array, Face Identify will compute
          similarities between the query face and all the faces in the person
          group (given by personGroupId) or large person group (given by
          largePersonGroupId), and return candidate person(s) for that face
          ranked by similarity confidence. The person group/large person group
          should be trained to make it ready for identification. See more in
          [PersonGroup -
          Train](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395249)
          and [LargePersonGroup -
          Train](/docs/services/563879b61984550e40cbbe8d/operations/599ae2d16ac60f11b48b5aa4).
          <br/>
          Remarks:<br />
          * The algorithm allows more than one face to be identified
          independently at the same request, but no more than 10 faces.
          * Each person in the person group/large person group could have more
          than one face, but no more than 248 faces.
          * Higher face image quality means better identification precision.
          Please consider high-quality faces: frontal, clear, and face size is
          200x200 pixels (100 pixels between eyes) or bigger.
          * Number of candidates returned is restricted by
          maxNumOfCandidatesReturned and confidenceThreshold. If no person is
          identified, the returned candidates will be an empty array.
          * Try [Face - Find
          Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237)
          when you need to find similar faces from a face list/large face list
          instead of a person group/large person group.
          * The 'recognitionModel' associated with the query faces' faceIds
          should be the same as the 'recognitionModel' used by the target person
          group or large person group.
          .
  
          :param face_ids: Array of query faces faceIds, created by the Face -
           Detect. Each of the faces are identified independently. The valid
           number of faceIds is between [1, 10].
          :type face_ids: list[str]
          :param person_group_id: PersonGroupId of the target person group,
           created by PersonGroup - Create. Parameter personGroupId and
           largePersonGroupId should not be provided at the same time.
          :type person_group_id: str
          :param large_person_group_id: LargePersonGroupId of the target large
           person group, created by LargePersonGroup - Create. Parameter
           personGroupId and largePersonGroupId should not be provided at the
           same time.
          :type large_person_group_id: str
          :param max_num_of_candidates_returned: The range of
           maxNumOfCandidatesReturned is between 1 and 5 (default is 1).
          :type max_num_of_candidates_returned: int
          :param confidence_threshold: Confidence threshold of identification,
           used to judge whether one face belong to one person. The range of
           confidenceThreshold is [0, 1] (default specified by algorithm).
          :type confidence_threshold: float
          :param dict custom_headers: headers that will be added to the request
          :param bool raw: returns the direct response alongside the
           deserialized response
          :param operation_config: :ref:`Operation configuration
           overrides<msrest:optionsforoperations>`.
          :return: list or ClientRawResponse if raw=true
          :rtype:
           list[~azure.cognitiveservices.vision.face.models.IdentifyResult] or
           ~msrest.pipeline.ClientRawResponse
          :raises:
           :class:`APIErrorException<azure.cognitiveservices.vision.face.models.APIErrorException>`
          """
          body = models.IdentifyRequest(face_ids=face_ids, person_group_id=person_group_id, large_person_group_id=large_person_group_id, max_num_of_candidates_returned=max_num_of_candidates_returned, confidence_threshold=confidence_threshold)
  
          # Construct URL
          url = self.identify.metadata['url']
          path_format_arguments = {
              'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
          }
          url = self._client.format_url(url, **path_format_arguments)
  
          # Construct parameters
          query_parameters = {}
  
          # Construct headers
          header_parameters = {}
          header_parameters['Accept'] = 'application/json'
          header_parameters['Content-Type'] = 'application/json; charset=utf-8'
          if custom_headers:
              header_parameters.update(custom_headers)
  
          # Construct body
          body_content = self._serialize.body(body, 'IdentifyRequest')
  
          # Construct and send request
          request = self._client.post(url, query_parameters, header_parameters, body_content)
          response = self._client.send(request, stream=False, **operation_config)
  
          if response.status_code not in [200]:
              raise models.APIErrorException(self._deserialize, response)
  
          deserialized = None
          if response.status_code == 200:
              deserialized = self._deserialize('[IdentifyResult]', response)
  
          if raw:
              client_raw_response = ClientRawResponse(deserialized, response)
              return client_raw_response
  
          return deserialized
      identify.metadata = {'url': '/identify'}
  
      def verify_face_to_face(
              self, face_id1, face_id2, custom_headers=None, raw=False, **operation_config):
          """Verify whether two faces belong to a same person or whether one face
          belongs to a person.
          <br/>
          Remarks:<br />
          * Higher face image quality means better identification precision.
          Please consider high-quality faces: frontal, clear, and face size is
          200x200 pixels (100 pixels between eyes) or bigger.
          * For the scenarios that are sensitive to accuracy please make your own
          judgment.
          * The 'recognitionModel' associated with the query faces' faceIds
          should be the same as the 'recognitionModel' used by the target face,
          person group or large person group.
          .
  
          :param face_id1: FaceId of the first face, comes from Face - Detect
          :type face_id1: str
          :param face_id2: FaceId of the second face, comes from Face - Detect
          :type face_id2: str
          :param dict custom_headers: headers that will be added to the request
          :param bool raw: returns the direct response alongside the
           deserialized response
          :param operation_config: :ref:`Operation configuration
           overrides<msrest:optionsforoperations>`.
          :return: VerifyResult or ClientRawResponse if raw=true
          :rtype: ~azure.cognitiveservices.vision.face.models.VerifyResult or
           ~msrest.pipeline.ClientRawResponse
          :raises:
           :class:`APIErrorException<azure.cognitiveservices.vision.face.models.APIErrorException>`
          """
          body = models.VerifyFaceToFaceRequest(face_id1=face_id1, face_id2=face_id2)
  
          # Construct URL
          url = self.verify_face_to_face.metadata['url']
          path_format_arguments = {
              'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
          }
          url = self._client.format_url(url, **path_format_arguments)
  
          # Construct parameters
          query_parameters = {}
  
          # Construct headers
          header_parameters = {}
          header_parameters['Accept'] = 'application/json'
          header_parameters['Content-Type'] = 'application/json; charset=utf-8'
          if custom_headers:
              header_parameters.update(custom_headers)
  
          # Construct body
          body_content = self._serialize.body(body, 'VerifyFaceToFaceRequest')
  
          # Construct and send request
          request = self._client.post(url, query_parameters, header_parameters, body_content)
          response = self._client.send(request, stream=False, **operation_config)
  
          if response.status_code not in [200]:
              raise models.APIErrorException(self._deserialize, response)
  
          deserialized = None
          if response.status_code == 200:
              deserialized = self._deserialize('VerifyResult', response)
  
          if raw:
              client_raw_response = ClientRawResponse(deserialized, response)
              return client_raw_response
  
          return deserialized
      verify_face_to_face.metadata = {'url': '/verify'}
  
      def detect_with_url(
              self, url, return_face_id=True, return_face_landmarks=False, return_face_attributes=None, recognition_model="recognition_01", return_recognition_model=False, detection_model="detection_01", custom_headers=None, raw=False, **operation_config):
          """Detect human faces in an image, return face rectangles, and optionally
          with faceIds, landmarks, and attributes.<br />
          * No image will be stored. Only the extracted face feature will be
          stored on server. The faceId is an identifier of the face feature and
          will be used in [Face -
          Identify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395239),
          [Face -
          Verify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039523a),
          and [Face - Find
          Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237).
          The stored face feature(s) will expire and be deleted 24 hours after
          the original detection call.
          * Optional parameters include faceId, landmarks, and attributes.
          Attributes include age, gender, headPose, smile, facialHair, glasses,
          emotion, hair, makeup, occlusion, accessories, blur, exposure and
          noise. Some of the results returned for specific attributes may not be
          highly accurate.
          * JPEG, PNG, GIF (the first frame), and BMP format are supported. The
          allowed image file size is from 1KB to 6MB.
          * Up to 100 faces can be returned for an image. Faces are ranked by
          face rectangle size from large to small.
          * For optimal results when querying [Face -
          Identify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395239),
          [Face -
          Verify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039523a),
          and [Face - Find
          Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237)
          ('returnFaceId' is true), please use faces that are: frontal, clear,
          and with a minimum size of 200x200 pixels (100 pixels between eyes).
          * The minimum detectable face size is 36x36 pixels in an image no
          larger than 1920x1080 pixels. Images with dimensions higher than
          1920x1080 pixels will need a proportionally larger minimum face size.
          * Different 'detectionModel' values can be provided. To use and compare
          different detection models, please refer to [How to specify a detection
          model](https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/specify-detection-model)
          | Model | Recommended use-case(s) |
          | ---------- | -------- |
          | 'detection_01': | The default detection model for [Face -
          Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
          Recommend for near frontal face detection. For scenarios with
          exceptionally large angle (head-pose) faces, occluded faces or wrong
          image orientation, the faces in such cases may not be detected. |
          | 'detection_02': | Detection model released in 2019 May with improved
          accuracy especially on small, side and blurry faces. |
          * Different 'recognitionModel' values are provided. If follow-up
          operations like Verify, Identify, Find Similar are needed, please
          specify the recognition model with 'recognitionModel' parameter. The
          default value for 'recognitionModel' is 'recognition_01', if latest
          model needed, please explicitly specify the model you need in this
          parameter. Once specified, the detected faceIds will be associated with
          the specified recognition model. More details, please refer to [How to
          specify a recognition
          model](https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/specify-recognition-model)
          | Model | Recommended use-case(s) |
          | ---------- | -------- |
          | 'recognition_01': | The default recognition model for [Face -
          Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
          All those faceIds created before 2019 March are bonded with this
          recognition model. |
          | 'recognition_02': | Recognition model released in 2019 March.
          'recognition_02' is recommended since its overall accuracy is improved
          compared with 'recognition_01'. |.
  
          :param url: Publicly reachable URL of an image
          :type url: str
          :param return_face_id: A value indicating whether the operation should
           return faceIds of detected faces.
          :type return_face_id: bool
          :param return_face_landmarks: A value indicating whether the operation
           should return landmarks of the detected faces.
          :type return_face_landmarks: bool
          :param return_face_attributes: Analyze and return the one or more
           specified face attributes in the comma-separated string like
           "returnFaceAttributes=age,gender". Supported face attributes include
           age, gender, headPose, smile, facialHair, glasses and emotion. Note
           that each face attribute analysis has additional computational and
           time cost.
          :type return_face_attributes: list[str or
           ~azure.cognitiveservices.vision.face.models.FaceAttributeType]
          :param recognition_model: Name of recognition model. Recognition model
           is used when the face features are extracted and associated with
           detected faceIds, (Large)FaceList or (Large)PersonGroup. A recognition
           model name can be provided when performing Face - Detect or
           (Large)FaceList - Create or (Large)PersonGroup - Create. The default
           value is 'recognition_01', if latest model needed, please explicitly
           specify the model you need. Possible values include: 'recognition_01',
           'recognition_02'
          :type recognition_model: str or
           ~azure.cognitiveservices.vision.face.models.RecognitionModel
          :param return_recognition_model: A value indicating whether the
           operation should return 'recognitionModel' in response.
          :type return_recognition_model: bool
          :param detection_model: Name of detection model. Detection model is
           used to detect faces in the submitted image. A detection model name
           can be provided when performing Face - Detect or (Large)FaceList - Add
           Face or (Large)PersonGroup - Add Face. The default value is
           'detection_01', if another model is needed, please explicitly specify
           it. Possible values include: 'detection_01', 'detection_02'
          :type detection_model: str or
           ~azure.cognitiveservices.vision.face.models.DetectionModel
          :param dict custom_headers: headers that will be added to the request
          :param bool raw: returns the direct response alongside the
           deserialized response
          :param operation_config: :ref:`Operation configuration
           overrides<msrest:optionsforoperations>`.
          :return: list or ClientRawResponse if raw=true
          :rtype: list[~azure.cognitiveservices.vision.face.models.DetectedFace]
           or ~msrest.pipeline.ClientRawResponse
          :raises:
           :class:`APIErrorException<azure.cognitiveservices.vision.face.models.APIErrorException>`
          """
          image_url = models.ImageUrl(url=url)
  
          # Construct URL
          url = self.detect_with_url.metadata['url']
          path_format_arguments = {
              'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
          }
          url = self._client.format_url(url, **path_format_arguments)
  
          # Construct parameters
          query_parameters = {}
          if return_face_id is not None:
              query_parameters['returnFaceId'] = self._serialize.query("return_face_id", return_face_id, 'bool')
          if return_face_landmarks is not None:
              query_parameters['returnFaceLandmarks'] = self._serialize.query("return_face_landmarks", return_face_landmarks, 'bool')
          if return_face_attributes is not None:
              query_parameters['returnFaceAttributes'] = self._serialize.query("return_face_attributes", return_face_attributes, '[FaceAttributeType]', div=',')
          if recognition_model is not None:
              query_parameters['recognitionModel'] = self._serialize.query("recognition_model", recognition_model, 'str')
          if return_recognition_model is not None:
              query_parameters['returnRecognitionModel'] = self._serialize.query("return_recognition_model", return_recognition_model, 'bool')
          if detection_model is not None:
              query_parameters['detectionModel'] = self._serialize.query("detection_model", detection_model, 'str')
  
          # Construct headers
          header_parameters = {}
          header_parameters['Accept'] = 'application/json'
          header_parameters['Content-Type'] = 'application/json; charset=utf-8'
          if custom_headers:
              header_parameters.update(custom_headers)
  
          # Construct body
          body_content = self._serialize.body(image_url, 'ImageUrl')
  
          # Construct and send request
          request = self._client.post(url, query_parameters, header_parameters, body_content)
          response = self._client.send(request, stream=False, **operation_config)
  
          if response.status_code not in [200]:
              raise models.APIErrorException(self._deserialize, response)
  
          deserialized = None
          if response.status_code == 200:
              deserialized = self._deserialize('[DetectedFace]', response)
  
          if raw:
              client_raw_response = ClientRawResponse(deserialized, response)
              return client_raw_response
  
          return deserialized
      detect_with_url.metadata = {'url': '/detect'}
  
      def verify_face_to_person(
              self, face_id, person_id, person_group_id=None, large_person_group_id=None, custom_headers=None, raw=False, **operation_config):
          """Verify whether two faces belong to a same person. Compares a face Id
          with a Person Id.
  
          :param face_id: FaceId of the face, comes from Face - Detect
          :type face_id: str
          :param person_id: Specify a certain person in a person group or a
           large person group. personId is created in PersonGroup Person - Create
           or LargePersonGroup Person - Create.
          :type person_id: str
          :param person_group_id: Using existing personGroupId and personId for
           fast loading a specified person. personGroupId is created in
           PersonGroup - Create. Parameter personGroupId and largePersonGroupId
           should not be provided at the same time.
          :type person_group_id: str
          :param large_person_group_id: Using existing largePersonGroupId and
           personId for fast loading a specified person. largePersonGroupId is
           created in LargePersonGroup - Create. Parameter personGroupId and
           largePersonGroupId should not be provided at the same time.
          :type large_person_group_id: str
          :param dict custom_headers: headers that will be added to the request
          :param bool raw: returns the direct response alongside the
           deserialized response
          :param operation_config: :ref:`Operation configuration
           overrides<msrest:optionsforoperations>`.
          :return: VerifyResult or ClientRawResponse if raw=true
          :rtype: ~azure.cognitiveservices.vision.face.models.VerifyResult or
           ~msrest.pipeline.ClientRawResponse
          :raises:
           :class:`APIErrorException<azure.cognitiveservices.vision.face.models.APIErrorException>`
          """
          body = models.VerifyFaceToPersonRequest(face_id=face_id, person_group_id=person_group_id, large_person_group_id=large_person_group_id, person_id=person_id)
  
          # Construct URL
          url = self.verify_face_to_person.metadata['url']
          path_format_arguments = {
              'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
          }
          url = self._client.format_url(url, **path_format_arguments)
  
          # Construct parameters
          query_parameters = {}
  
          # Construct headers
          header_parameters = {}
          header_parameters['Accept'] = 'application/json'
          header_parameters['Content-Type'] = 'application/json; charset=utf-8'
          if custom_headers:
              header_parameters.update(custom_headers)
  
          # Construct body
          body_content = self._serialize.body(body, 'VerifyFaceToPersonRequest')
  
          # Construct and send request
          request = self._client.post(url, query_parameters, header_parameters, body_content)
          response = self._client.send(request, stream=False, **operation_config)
  
          if response.status_code not in [200]:
              raise models.APIErrorException(self._deserialize, response)
  
          deserialized = None
          if response.status_code == 200:
              deserialized = self._deserialize('VerifyResult', response)
  
          if raw:
              client_raw_response = ClientRawResponse(deserialized, response)
              return client_raw_response
  
          return deserialized
      verify_face_to_person.metadata = {'url': '/verify'}
  
      def detect_with_stream(
              self, image, return_face_id=True, return_face_landmarks=False, return_face_attributes=None, recognition_model="recognition_01", return_recognition_model=False, detection_model="detection_01", custom_headers=None, raw=False, callback=None, **operation_config):
          """Detect human faces in an image, return face rectangles, and optionally
          with faceIds, landmarks, and attributes.<br />
          * No image will be stored. Only the extracted face feature will be
          stored on server. The faceId is an identifier of the face feature and
          will be used in [Face -
          Identify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395239),
          [Face -
          Verify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039523a),
          and [Face - Find
          Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237).
          The stored face feature(s) will expire and be deleted 24 hours after
          the original detection call.
          * Optional parameters include faceId, landmarks, and attributes.
          Attributes include age, gender, headPose, smile, facialHair, glasses,
          emotion, hair, makeup, occlusion, accessories, blur, exposure and
          noise. Some of the results returned for specific attributes may not be
          highly accurate.
          * JPEG, PNG, GIF (the first frame), and BMP format are supported. The
          allowed image file size is from 1KB to 6MB.
          * Up to 100 faces can be returned for an image. Faces are ranked by
          face rectangle size from large to small.
          * For optimal results when querying [Face -
          Identify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395239),
          [Face -
          Verify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039523a),
          and [Face - Find
          Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237)
          ('returnFaceId' is true), please use faces that are: frontal, clear,
          and with a minimum size of 200x200 pixels (100 pixels between eyes).
          * The minimum detectable face size is 36x36 pixels in an image no
          larger than 1920x1080 pixels. Images with dimensions higher than
          1920x1080 pixels will need a proportionally larger minimum face size.
          * Different 'detectionModel' values can be provided. To use and compare
          different detection models, please refer to [How to specify a detection
          model](https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/specify-detection-model)
          | Model | Recommended use-case(s) |
          | ---------- | -------- |
          | 'detection_01': | The default detection model for [Face -
          Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
          Recommend for near frontal face detection. For scenarios with
          exceptionally large angle (head-pose) faces, occluded faces or wrong
          image orientation, the faces in such cases may not be detected. |
          | 'detection_02': | Detection model released in 2019 May with improved
          accuracy especially on small, side and blurry faces. |
          * Different 'recognitionModel' values are provided. If follow-up
          operations like Verify, Identify, Find Similar are needed, please
          specify the recognition model with 'recognitionModel' parameter. The
          default value for 'recognitionModel' is 'recognition_01', if latest
          model needed, please explicitly specify the model you need in this
          parameter. Once specified, the detected faceIds will be associated with
          the specified recognition model. More details, please refer to [How to
          specify a recognition
          model](https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/specify-recognition-model)
          | Model | Recommended use-case(s) |
          | ---------- | -------- |
          | 'recognition_01': | The default recognition model for [Face -
          Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
          All those faceIds created before 2019 March are bonded with this
          recognition model. |
          | 'recognition_02': | Recognition model released in 2019 March.
          'recognition_02' is recommended since its overall accuracy is improved
          compared with 'recognition_01'. |.
  
          :param image: An image stream.
          :type image: Generator
          :param return_face_id: A value indicating whether the operation should
           return faceIds of detected faces.
          :type return_face_id: bool
          :param return_face_landmarks: A value indicating whether the operation
           should return landmarks of the detected faces.
          :type return_face_landmarks: bool
          :param return_face_attributes: Analyze and return the one or more
           specified face attributes in the comma-separated string like
           "returnFaceAttributes=age,gender". Supported face attributes include
           age, gender, headPose, smile, facialHair, glasses and emotion. Note
           that each face attribute analysis has additional computational and
           time cost.
          :type return_face_attributes: list[str or
           ~azure.cognitiveservices.vision.face.models.FaceAttributeType]
          :param recognition_model: Name of recognition model. Recognition model
           is used when the face features are extracted and associated with
           detected faceIds, (Large)FaceList or (Large)PersonGroup. A recognition
           model name can be provided when performing Face - Detect or
           (Large)FaceList - Create or (Large)PersonGroup - Create. The default
           value is 'recognition_01', if latest model needed, please explicitly
           specify the model you need. Possible values include: 'recognition_01',
           'recognition_02'
          :type recognition_model: str or
           ~azure.cognitiveservices.vision.face.models.RecognitionModel
          :param return_recognition_model: A value indicating whether the
           operation should return 'recognitionModel' in response.
          :type return_recognition_model: bool
          :param detection_model: Name of detection model. Detection model is
           used to detect faces in the submitted image. A detection model name
           can be provided when performing Face - Detect or (Large)FaceList - Add
           Face or (Large)PersonGroup - Add Face. The default value is
           'detection_01', if another model is needed, please explicitly specify
           it. Possible values include: 'detection_01', 'detection_02'
          :type detection_model: str or
           ~azure.cognitiveservices.vision.face.models.DetectionModel
          :param dict custom_headers: headers that will be added to the request
          :param bool raw: returns the direct response alongside the
           deserialized response
          :param callback: When specified, will be called with each chunk of
           data that is streamed. The callback should take two arguments, the
           bytes of the current chunk of data and the response object. If the
           data is uploading, response will be None.
          :type callback: Callable[Bytes, response=None]
          :param operation_config: :ref:`Operation configuration
           overrides<msrest:optionsforoperations>`.
          :return: list or ClientRawResponse if raw=true
          :rtype: list[~azure.cognitiveservices.vision.face.models.DetectedFace]
           or ~msrest.pipeline.ClientRawResponse
          :raises:
           :class:`APIErrorException<azure.cognitiveservices.vision.face.models.APIErrorException>`
          """
          # Construct URL
          url = self.detect_with_stream.metadata['url']
          path_format_arguments = {
              'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
          }
          url = self._client.format_url(url, **path_format_arguments)
  
          # Construct parameters
          query_parameters = {}
          if return_face_id is not None:
              query_parameters['returnFaceId'] = self._serialize.query("return_face_id", return_face_id, 'bool')
          if return_face_landmarks is not None:
              query_parameters['returnFaceLandmarks'] = self._serialize.query("return_face_landmarks", return_face_landmarks, 'bool')
          if return_face_attributes is not None:
              query_parameters['returnFaceAttributes'] = self._serialize.query("return_face_attributes", return_face_attributes, '[FaceAttributeType]', div=',')
          if recognition_model is not None:
              query_parameters['recognitionModel'] = self._serialize.query("recognition_model", recognition_model, 'str')
          if return_recognition_model is not None:
              query_parameters['returnRecognitionModel'] = self._serialize.query("return_recognition_model", return_recognition_model, 'bool')
          if detection_model is not None:
              query_parameters['detectionModel'] = self._serialize.query("detection_model", detection_model, 'str')
  
          # Construct headers
          header_parameters = {}
          header_parameters['Accept'] = 'application/json'
          header_parameters['Content-Type'] = 'application/octet-stream'
          if custom_headers:
              header_parameters.update(custom_headers)
  
          # Construct body
          body_content = self._client.stream_upload(image, callback)
  
          # Construct and send request
          request = self._client.post(url, query_parameters, header_parameters, body_content)
          response = self._client.send(request, stream=False, **operation_config)
  
          if response.status_code not in [200]:
              raise models.APIErrorException(self._deserialize, response)
  
          deserialized = None
          if response.status_code == 200:
              deserialized = self._deserialize('[DetectedFace]', response)
  
          if raw:
              client_raw_response = ClientRawResponse(deserialized, response)
              return client_raw_response
  
          return deserialized
      detect_with_stream.metadata = {'url': '/detect'}
  