class Azure::CognitiveServices::Face::V1_0::LargeFaceListOperations
An API for face detection, verification, and identification.
Attributes
@return [FaceClient] reference to the FaceClient
Private Class Methods
Creates and initializes a new instance of the LargeFaceListOperations
class. @param client service class for accessing basic functionality.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 17 def initialize(client) @client = client end
Private Instance Methods
Add a face to a specified large face list, up to 1,000,000 faces. <br /> To deal with an image contains multiple faces, input face can be specified as an image with a targetFace rectangle. It returns a persistedFaceId representing the added face. No image will be stored. Only the extracted face feature will be stored on server until [LargeFaceList Face
- Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a158c8ad2de3616c086f2d4) or [LargeFaceList - Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a1580d5d2de3616c086f2cd) is called. <br /> Note persistedFaceId is different from faceId generated by [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
-
Higher face image quality means better recognition precision. Please
consider high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger.
-
JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed
image file size is from 1KB to 6MB.
-
“targetFace” rectangle should contain one face. Zero or multiple faces will
be regarded as an error. If the provided “targetFace” rectangle is not returned from [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236), there’s no guarantee to detect and add the face successfully.
-
Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or
large occlusions will cause failures.
-
Adding/deleting faces to/from a same face list are processed sequentially
and to/from different face lists are in parallel.
-
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](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 [LargeFaceList - Add Face](/docs/services/563879b61984550e40cbbe8d/operations/5a158c10d2de3616c086f2d3). 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. |
Quota:
-
Free-tier subscription quota: 1,000 faces per large face list.
-
S0-tier subscription quota: 1,000,000 faces per large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param image An image stream. @param user_data [String] User-specified data about the face for any purpose. The maximum length is 1KB. @param target_face [Array<Integer>] A face rectangle to specify the target face to be added to a person in the format of “targetFace=left,top,width,height”. E.g. “targetFace=10,10,100,100”. If there is more than one face in the image, targetFace is required to specify which face to add. No targetFace means there is only one face detected in the entire image. @param detection_model [DetectionModel] 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' @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [PersistedFace] operation results.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1675 def add_face_from_stream(large_face_list_id, image, user_data:nil, target_face:nil, detection_model:nil, custom_headers:nil) response = add_face_from_stream_async(large_face_list_id, image, user_data:user_data, target_face:target_face, detection_model:detection_model, custom_headers:custom_headers).value! response.body unless response.nil? end
Add a face to a specified large face list, up to 1,000,000 faces. <br /> To deal with an image contains multiple faces, input face can be specified as an image with a targetFace rectangle. It returns a persistedFaceId representing the added face. No image will be stored. Only the extracted face feature will be stored on server until [LargeFaceList Face
- Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a158c8ad2de3616c086f2d4) or [LargeFaceList - Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a1580d5d2de3616c086f2cd) is called. <br /> Note persistedFaceId is different from faceId generated by [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
-
Higher face image quality means better recognition precision. Please
consider high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger.
-
JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed
image file size is from 1KB to 6MB.
-
“targetFace” rectangle should contain one face. Zero or multiple faces will
be regarded as an error. If the provided “targetFace” rectangle is not returned from [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236), there’s no guarantee to detect and add the face successfully.
-
Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or
large occlusions will cause failures.
-
Adding/deleting faces to/from a same face list are processed sequentially
and to/from different face lists are in parallel.
-
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](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 [LargeFaceList - Add Face](/docs/services/563879b61984550e40cbbe8d/operations/5a158c10d2de3616c086f2d3). 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. |
Quota:
-
Free-tier subscription quota: 1,000 faces per large face list.
-
S0-tier subscription quota: 1,000,000 faces per large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param image An image stream. @param user_data [String] User-specified data about the face for any purpose. The maximum length is 1KB. @param target_face [Array<Integer>] A face rectangle to specify the target face to be added to a person in the format of “targetFace=left,top,width,height”. E.g. “targetFace=10,10,100,100”. If there is more than one face in the image, targetFace is required to specify which face to add. No targetFace means there is only one face detected in the entire image. @param detection_model [DetectionModel] 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' @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1822 def add_face_from_stream_async(large_face_list_id, image, user_data:nil, target_face:nil, detection_model:nil, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? fail ArgumentError, "'user_data' should satisfy the constraint - 'MaxLength': '1024'" if !user_data.nil? && user_data.length > 1024 fail ArgumentError, 'image is nil' if image.nil? request_headers = {} request_headers['Content-Type'] = 'application/octet-stream' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? # Serialize Request request_mapper = { client_side_validation: true, required: true, serialized_name: 'Image', type: { name: 'Stream' } } request_content = @client.serialize(request_mapper, image) path_template = 'largefacelists/{largeFaceListId}/persistedfaces' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id}, query_params: {'userData' => user_data,'targetFace' => target_face.nil? ? nil : target_face.join(','),'detectionModel' => detection_model}, body: request_content, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:post, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRest::HttpOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? # Deserialize Response if status_code == 200 begin parsed_response = response_content.to_s.empty? ? nil : JSON.load(response_content) result_mapper = Azure::CognitiveServices::Face::V1_0::Models::PersistedFace.mapper() result.body = @client.deserialize(result_mapper, parsed_response) rescue Exception => e fail MsRest::DeserializationError.new('Error occurred in deserializing the response', e.message, e.backtrace, result) end end result end promise.execute end
Add a face to a specified large face list, up to 1,000,000 faces. <br /> To deal with an image contains multiple faces, input face can be specified as an image with a targetFace rectangle. It returns a persistedFaceId representing the added face. No image will be stored. Only the extracted face feature will be stored on server until [LargeFaceList Face
- Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a158c8ad2de3616c086f2d4) or [LargeFaceList - Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a1580d5d2de3616c086f2cd) is called. <br /> Note persistedFaceId is different from faceId generated by [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
-
Higher face image quality means better recognition precision. Please
consider high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger.
-
JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed
image file size is from 1KB to 6MB.
-
“targetFace” rectangle should contain one face. Zero or multiple faces will
be regarded as an error. If the provided “targetFace” rectangle is not returned from [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236), there’s no guarantee to detect and add the face successfully.
-
Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or
large occlusions will cause failures.
-
Adding/deleting faces to/from a same face list are processed sequentially
and to/from different face lists are in parallel.
-
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](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 [LargeFaceList - Add Face](/docs/services/563879b61984550e40cbbe8d/operations/5a158c10d2de3616c086f2d3). 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. |
Quota:
-
Free-tier subscription quota: 1,000 faces per large face list.
-
S0-tier subscription quota: 1,000,000 faces per large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param image An image stream. @param user_data [String] User-specified data about the face for any purpose. The maximum length is 1KB. @param target_face [Array<Integer>] A face rectangle to specify the target face to be added to a person in the format of “targetFace=left,top,width,height”. E.g. “targetFace=10,10,100,100”. If there is more than one face in the image, targetFace is required to specify which face to add. No targetFace means there is only one face detected in the entire image. @param detection_model [DetectionModel] 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' @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1749 def add_face_from_stream_with_http_info(large_face_list_id, image, user_data:nil, target_face:nil, detection_model:nil, custom_headers:nil) add_face_from_stream_async(large_face_list_id, image, user_data:user_data, target_face:target_face, detection_model:detection_model, custom_headers:custom_headers).value! end
Add a face to a specified large face list, up to 1,000,000 faces. <br /> To deal with an image contains multiple faces, input face can be specified as an image with a targetFace rectangle. It returns a persistedFaceId representing the added face. No image will be stored. Only the extracted face feature will be stored on server until [LargeFaceList Face
- Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a158c8ad2de3616c086f2d4) or [LargeFaceList - Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a1580d5d2de3616c086f2cd) is called. <br /> Note persistedFaceId is different from faceId generated by [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
-
Higher face image quality means better recognition precision. Please
consider high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger.
-
JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed
image file size is from 1KB to 6MB.
-
“targetFace” rectangle should contain one face. Zero or multiple faces will
be regarded as an error. If the provided “targetFace” rectangle is not returned from [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236), there’s no guarantee to detect and add the face successfully.
-
Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or
large occlusions will cause failures.
-
Adding/deleting faces to/from a same face list are processed sequentially
and to/from different face lists are in parallel.
-
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](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 [LargeFaceList - Add Face](/docs/services/563879b61984550e40cbbe8d/operations/5a158c10d2de3616c086f2d3). 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. |
Quota:
-
Free-tier subscription quota: 1,000 faces per large face list.
-
S0-tier subscription quota: 1,000,000 faces per large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param url [String] Publicly reachable URL of an image @param user_data [String] User-specified data about the face for any purpose. The maximum length is 1KB. @param target_face [Array<Integer>] A face rectangle to specify the target face to be added to a person in the format of “targetFace=left,top,width,height”. E.g. “targetFace=10,10,100,100”. If there is more than one face in the image, targetFace is required to specify which face to add. No targetFace means there is only one face detected in the entire image. @param detection_model [DetectionModel] 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' @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [PersistedFace] operation results.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1262 def add_face_from_url(large_face_list_id, url, user_data:nil, target_face:nil, detection_model:nil, custom_headers:nil) response = add_face_from_url_async(large_face_list_id, url, user_data:user_data, target_face:target_face, detection_model:detection_model, custom_headers:custom_headers).value! response.body unless response.nil? end
Add a face to a specified large face list, up to 1,000,000 faces. <br /> To deal with an image contains multiple faces, input face can be specified as an image with a targetFace rectangle. It returns a persistedFaceId representing the added face. No image will be stored. Only the extracted face feature will be stored on server until [LargeFaceList Face
- Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a158c8ad2de3616c086f2d4) or [LargeFaceList - Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a1580d5d2de3616c086f2cd) is called. <br /> Note persistedFaceId is different from faceId generated by [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
-
Higher face image quality means better recognition precision. Please
consider high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger.
-
JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed
image file size is from 1KB to 6MB.
-
“targetFace” rectangle should contain one face. Zero or multiple faces will
be regarded as an error. If the provided “targetFace” rectangle is not returned from [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236), there’s no guarantee to detect and add the face successfully.
-
Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or
large occlusions will cause failures.
-
Adding/deleting faces to/from a same face list are processed sequentially
and to/from different face lists are in parallel.
-
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](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 [LargeFaceList - Add Face](/docs/services/563879b61984550e40cbbe8d/operations/5a158c10d2de3616c086f2d3). 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. |
Quota:
-
Free-tier subscription quota: 1,000 faces per large face list.
-
S0-tier subscription quota: 1,000,000 faces per large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param url [String] Publicly reachable URL of an image @param user_data [String] User-specified data about the face for any purpose. The maximum length is 1KB. @param target_face [Array<Integer>] A face rectangle to specify the target face to be added to a person in the format of “targetFace=left,top,width,height”. E.g. “targetFace=10,10,100,100”. If there is more than one face in the image, targetFace is required to specify which face to add. No targetFace means there is only one face detected in the entire image. @param detection_model [DetectionModel] 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' @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1409 def add_face_from_url_async(large_face_list_id, url, user_data:nil, target_face:nil, detection_model:nil, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? fail ArgumentError, "'user_data' should satisfy the constraint - 'MaxLength': '1024'" if !user_data.nil? && user_data.length > 1024 fail ArgumentError, 'url is nil' if url.nil? image_url = ImageUrl.new unless url.nil? image_url.url = url end request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? # Serialize Request request_mapper = Azure::CognitiveServices::Face::V1_0::Models::ImageUrl.mapper() request_content = @client.serialize(request_mapper, image_url) request_content = request_content != nil ? JSON.generate(request_content, quirks_mode: true) : nil path_template = 'largefacelists/{largeFaceListId}/persistedfaces' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id}, query_params: {'userData' => user_data,'targetFace' => target_face.nil? ? nil : target_face.join(','),'detectionModel' => detection_model}, body: request_content, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:post, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRest::HttpOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? # Deserialize Response if status_code == 200 begin parsed_response = response_content.to_s.empty? ? nil : JSON.load(response_content) result_mapper = Azure::CognitiveServices::Face::V1_0::Models::PersistedFace.mapper() result.body = @client.deserialize(result_mapper, parsed_response) rescue Exception => e fail MsRest::DeserializationError.new('Error occurred in deserializing the response', e.message, e.backtrace, result) end end result end promise.execute end
Add a face to a specified large face list, up to 1,000,000 faces. <br /> To deal with an image contains multiple faces, input face can be specified as an image with a targetFace rectangle. It returns a persistedFaceId representing the added face. No image will be stored. Only the extracted face feature will be stored on server until [LargeFaceList Face
- Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a158c8ad2de3616c086f2d4) or [LargeFaceList - Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a1580d5d2de3616c086f2cd) is called. <br /> Note persistedFaceId is different from faceId generated by [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).
-
Higher face image quality means better recognition precision. Please
consider high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger.
-
JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed
image file size is from 1KB to 6MB.
-
“targetFace” rectangle should contain one face. Zero or multiple faces will
be regarded as an error. If the provided “targetFace” rectangle is not returned from [Face - Detect](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236), there’s no guarantee to detect and add the face successfully.
-
Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or
large occlusions will cause failures.
-
Adding/deleting faces to/from a same face list are processed sequentially
and to/from different face lists are in parallel.
-
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](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 [LargeFaceList - Add Face](/docs/services/563879b61984550e40cbbe8d/operations/5a158c10d2de3616c086f2d3). 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. |
Quota:
-
Free-tier subscription quota: 1,000 faces per large face list.
-
S0-tier subscription quota: 1,000,000 faces per large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param url [String] Publicly reachable URL of an image @param user_data [String] User-specified data about the face for any purpose. The maximum length is 1KB. @param target_face [Array<Integer>] A face rectangle to specify the target face to be added to a person in the format of “targetFace=left,top,width,height”. E.g. “targetFace=10,10,100,100”. If there is more than one face in the image, targetFace is required to specify which face to add. No targetFace means there is only one face detected in the entire image. @param detection_model [DetectionModel] 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' @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1336 def add_face_from_url_with_http_info(large_face_list_id, url, user_data:nil, target_face:nil, detection_model:nil, custom_headers:nil) add_face_from_url_async(large_face_list_id, url, user_data:user_data, target_face:target_face, detection_model:detection_model, custom_headers:custom_headers).value! end
Create an empty large face list with user-specified largeFaceListId, name, an optional userData and recognitionModel. <br /> Large face list is a list of faces, up to 1,000,000 faces, and used by [Face - Find Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237). <br /> After creation, user should use [LargeFaceList Face
- Add](/docs/services/563879b61984550e40cbbe8d/operations/5a158c10d2de3616c086f2d3) to import the faces and [LargeFaceList - Train](/docs/services/563879b61984550e40cbbe8d/operations/5a158422d2de3616c086f2d1) to make it ready for [Face - Find Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237). No image will be stored. Only the extracted face features are stored on server until [LargeFaceList - Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a1580d5d2de3616c086f2cd) is called. <br /> Find Similar is used for scenario like finding celebrity-like faces, similar face filtering, or as a light way face identification. But if the actual use is to identify person, please use [PersonGroup](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395244) / [LargePersonGroup](/docs/services/563879b61984550e40cbbe8d/operations/599acdee6ac60f11b48b5a9d) and [Face - Identify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395239). <br/>'recognitionModel' should be specified to associate with this large face list. The default value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly specify the model you need in this parameter. New faces that are added to an existing large face list will use the recognition model that's already associated with the collection. Existing face features in a large face list can't be updated to features extracted by another version of recognition model.
-
'recognition_01': The default recognition model for [LargeFaceList-
Create](/docs/services/563879b61984550e40cbbe8d/operations/5a157b68d2de3616c086f2cc). All those large face lists 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'.
Large face list quota:
-
Free-tier subscription quota: 64 large face lists.
-
S0-tier subscription quota: 1,000,000 large face lists.
@param large_face_list_id [String] Id referencing a particular large face list. @param name [String] User defined name, maximum length is 128. @param user_data [String] User specified data. Length should not exceed 16KB. @param recognition_model [RecognitionModel] Possible values include: 'recognition_01', 'recognition_02' @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 77 def create(large_face_list_id, name:nil, user_data:nil, recognition_model:nil, custom_headers:nil) response = create_async(large_face_list_id, name:name, user_data:user_data, recognition_model:recognition_model, custom_headers:custom_headers).value! nil end
Create an empty large face list with user-specified largeFaceListId, name, an optional userData and recognitionModel. <br /> Large face list is a list of faces, up to 1,000,000 faces, and used by [Face - Find Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237). <br /> After creation, user should use [LargeFaceList Face
- Add](/docs/services/563879b61984550e40cbbe8d/operations/5a158c10d2de3616c086f2d3) to import the faces and [LargeFaceList - Train](/docs/services/563879b61984550e40cbbe8d/operations/5a158422d2de3616c086f2d1) to make it ready for [Face - Find Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237). No image will be stored. Only the extracted face features are stored on server until [LargeFaceList - Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a1580d5d2de3616c086f2cd) is called. <br /> Find Similar is used for scenario like finding celebrity-like faces, similar face filtering, or as a light way face identification. But if the actual use is to identify person, please use [PersonGroup](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395244) / [LargePersonGroup](/docs/services/563879b61984550e40cbbe8d/operations/599acdee6ac60f11b48b5a9d) and [Face - Identify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395239). <br/>'recognitionModel' should be specified to associate with this large face list. The default value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly specify the model you need in this parameter. New faces that are added to an existing large face list will use the recognition model that's already associated with the collection. Existing face features in a large face list can't be updated to features extracted by another version of recognition model.
-
'recognition_01': The default recognition model for [LargeFaceList-
Create](/docs/services/563879b61984550e40cbbe8d/operations/5a157b68d2de3616c086f2cc). All those large face lists 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'.
Large face list quota:
-
Free-tier subscription quota: 64 large face lists.
-
S0-tier subscription quota: 1,000,000 large face lists.
@param large_face_list_id [String] Id referencing a particular large face list. @param name [String] User defined name, maximum length is 128. @param user_data [String] User specified data. Length should not exceed 16KB. @param recognition_model [RecognitionModel] Possible values include: 'recognition_01', 'recognition_02' @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 194 def create_async(large_face_list_id, name:nil, user_data:nil, recognition_model:nil, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? fail ArgumentError, "'name' should satisfy the constraint - 'MaxLength': '128'" if !name.nil? && name.length > 128 fail ArgumentError, "'user_data' should satisfy the constraint - 'MaxLength': '16384'" if !user_data.nil? && user_data.length > 16384 body = MetaDataContract.new unless name.nil? && user_data.nil? && recognition_model.nil? body.name = name body.user_data = user_data body.recognition_model = recognition_model end request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? # Serialize Request request_mapper = Azure::CognitiveServices::Face::V1_0::Models::MetaDataContract.mapper() request_content = @client.serialize(request_mapper, body) request_content = request_content != nil ? JSON.generate(request_content, quirks_mode: true) : nil path_template = 'largefacelists/{largeFaceListId}' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id}, body: request_content, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:put, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRest::HttpOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? result end promise.execute end
Create an empty large face list with user-specified largeFaceListId, name, an optional userData and recognitionModel. <br /> Large face list is a list of faces, up to 1,000,000 faces, and used by [Face - Find Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237). <br /> After creation, user should use [LargeFaceList Face
- Add](/docs/services/563879b61984550e40cbbe8d/operations/5a158c10d2de3616c086f2d3) to import the faces and [LargeFaceList - Train](/docs/services/563879b61984550e40cbbe8d/operations/5a158422d2de3616c086f2d1) to make it ready for [Face - Find Similar](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395237). No image will be stored. Only the extracted face features are stored on server until [LargeFaceList - Delete](/docs/services/563879b61984550e40cbbe8d/operations/5a1580d5d2de3616c086f2cd) is called. <br /> Find Similar is used for scenario like finding celebrity-like faces, similar face filtering, or as a light way face identification. But if the actual use is to identify person, please use [PersonGroup](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395244) / [LargePersonGroup](/docs/services/563879b61984550e40cbbe8d/operations/599acdee6ac60f11b48b5a9d) and [Face - Identify](/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395239). <br/>'recognitionModel' should be specified to associate with this large face list. The default value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly specify the model you need in this parameter. New faces that are added to an existing large face list will use the recognition model that's already associated with the collection. Existing face features in a large face list can't be updated to features extracted by another version of recognition model.
-
'recognition_01': The default recognition model for [LargeFaceList-
Create](/docs/services/563879b61984550e40cbbe8d/operations/5a157b68d2de3616c086f2cc). All those large face lists 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'.
Large face list quota:
-
Free-tier subscription quota: 64 large face lists.
-
S0-tier subscription quota: 1,000,000 large face lists.
@param large_face_list_id [String] Id referencing a particular large face list. @param name [String] User defined name, maximum length is 128. @param user_data [String] User specified data. Length should not exceed 16KB. @param recognition_model [RecognitionModel] Possible values include: 'recognition_01', 'recognition_02' @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 136 def create_with_http_info(large_face_list_id, name:nil, user_data:nil, recognition_model:nil, custom_headers:nil) create_async(large_face_list_id, name:name, user_data:user_data, recognition_model:recognition_model, custom_headers:custom_headers).value! end
Delete a specified large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 470 def delete(large_face_list_id, custom_headers:nil) response = delete_async(large_face_list_id, custom_headers:custom_headers).value! nil end
Delete a specified large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 499 def delete_async(large_face_list_id, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? path_template = 'largefacelists/{largeFaceListId}' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id}, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:delete, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRestAzure::AzureOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? result end promise.execute end
Delete a face from a large face list by specified largeFaceListId and persistedFaceId. <br /> Adding/deleting faces to/from a same large face list are processed sequentially and to/from different large face lists are in parallel.
@param large_face_list_id [String] Id referencing a particular large face list. @param persisted_face_id Id referencing a particular persistedFaceId of an existing face. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 896 def delete_face(large_face_list_id, persisted_face_id, custom_headers:nil) response = delete_face_async(large_face_list_id, persisted_face_id, custom_headers:custom_headers).value! nil end
Delete a face from a large face list by specified largeFaceListId and persistedFaceId. <br /> Adding/deleting faces to/from a same large face list are processed sequentially and to/from different large face lists are in parallel.
@param large_face_list_id [String] Id referencing a particular large face list. @param persisted_face_id Id referencing a particular persistedFaceId of an existing face. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 935 def delete_face_async(large_face_list_id, persisted_face_id, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? fail ArgumentError, 'persisted_face_id is nil' if persisted_face_id.nil? request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? path_template = 'largefacelists/{largeFaceListId}/persistedfaces/{persistedFaceId}' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id,'persistedFaceId' => persisted_face_id}, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:delete, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRestAzure::AzureOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? result end promise.execute end
Delete a face from a large face list by specified largeFaceListId and persistedFaceId. <br /> Adding/deleting faces to/from a same large face list are processed sequentially and to/from different large face lists are in parallel.
@param large_face_list_id [String] Id referencing a particular large face list. @param persisted_face_id Id referencing a particular persistedFaceId of an existing face. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 916 def delete_face_with_http_info(large_face_list_id, persisted_face_id, custom_headers:nil) delete_face_async(large_face_list_id, persisted_face_id, custom_headers:custom_headers).value! end
Delete a specified large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 485 def delete_with_http_info(large_face_list_id, custom_headers:nil) delete_async(large_face_list_id, custom_headers:custom_headers).value! end
Retrieve a large face list’s largeFaceListId, name, userData and recognitionModel.
@param large_face_list_id [String] Id referencing a particular large face list. @param return_recognition_model [Boolean] A value indicating whether the operation should return 'recognitionModel' in response. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [LargeFaceList] operation results.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 267 def get(large_face_list_id, return_recognition_model:false, custom_headers:nil) response = get_async(large_face_list_id, return_recognition_model:return_recognition_model, custom_headers:custom_headers).value! response.body unless response.nil? end
Retrieve a large face list’s largeFaceListId, name, userData and recognitionModel.
@param large_face_list_id [String] Id referencing a particular large face list. @param return_recognition_model [Boolean] A value indicating whether the operation should return 'recognitionModel' in response. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 302 def get_async(large_face_list_id, return_recognition_model:false, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? path_template = 'largefacelists/{largeFaceListId}' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id}, query_params: {'returnRecognitionModel' => return_recognition_model}, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:get, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRest::HttpOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? # Deserialize Response if status_code == 200 begin parsed_response = response_content.to_s.empty? ? nil : JSON.load(response_content) result_mapper = Azure::CognitiveServices::Face::V1_0::Models::LargeFaceList.mapper() result.body = @client.deserialize(result_mapper, parsed_response) rescue Exception => e fail MsRest::DeserializationError.new('Error occurred in deserializing the response', e.message, e.backtrace, result) end end result end promise.execute end
Retrieve information about a persisted face (specified by persistedFaceId and its belonging largeFaceListId).
@param large_face_list_id [String] Id referencing a particular large face list. @param persisted_face_id Id referencing a particular persistedFaceId of an existing face. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [PersistedFace] operation results.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 994 def get_face(large_face_list_id, persisted_face_id, custom_headers:nil) response = get_face_async(large_face_list_id, persisted_face_id, custom_headers:custom_headers).value! response.body unless response.nil? end
Retrieve information about a persisted face (specified by persistedFaceId and its belonging largeFaceListId).
@param large_face_list_id [String] Id referencing a particular large face list. @param persisted_face_id Id referencing a particular persistedFaceId of an existing face. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1029 def get_face_async(large_face_list_id, persisted_face_id, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? fail ArgumentError, 'persisted_face_id is nil' if persisted_face_id.nil? request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? path_template = 'largefacelists/{largeFaceListId}/persistedfaces/{persistedFaceId}' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id,'persistedFaceId' => persisted_face_id}, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:get, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRest::HttpOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? # Deserialize Response if status_code == 200 begin parsed_response = response_content.to_s.empty? ? nil : JSON.load(response_content) result_mapper = Azure::CognitiveServices::Face::V1_0::Models::PersistedFace.mapper() result.body = @client.deserialize(result_mapper, parsed_response) rescue Exception => e fail MsRest::DeserializationError.new('Error occurred in deserializing the response', e.message, e.backtrace, result) end end result end promise.execute end
Retrieve information about a persisted face (specified by persistedFaceId and its belonging largeFaceListId).
@param large_face_list_id [String] Id referencing a particular large face list. @param persisted_face_id Id referencing a particular persistedFaceId of an existing face. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1012 def get_face_with_http_info(large_face_list_id, persisted_face_id, custom_headers:nil) get_face_async(large_face_list_id, persisted_face_id, custom_headers:custom_headers).value! end
Retrieve the training status of a large face list (completed or ongoing).
@param large_face_list_id [String] Id referencing a particular large face list. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [TrainingStatus] operation results.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 554 def get_training_status(large_face_list_id, custom_headers:nil) response = get_training_status_async(large_face_list_id, custom_headers:custom_headers).value! response.body unless response.nil? end
Retrieve the training status of a large face list (completed or ongoing).
@param large_face_list_id [String] Id referencing a particular large face list. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 583 def get_training_status_async(large_face_list_id, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? path_template = 'largefacelists/{largeFaceListId}/training' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id}, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:get, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRest::HttpOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? # Deserialize Response if status_code == 200 begin parsed_response = response_content.to_s.empty? ? nil : JSON.load(response_content) result_mapper = Azure::CognitiveServices::Face::V1_0::Models::TrainingStatus.mapper() result.body = @client.deserialize(result_mapper, parsed_response) rescue Exception => e fail MsRest::DeserializationError.new('Error occurred in deserializing the response', e.message, e.backtrace, result) end end result end promise.execute end
Retrieve the training status of a large face list (completed or ongoing).
@param large_face_list_id [String] Id referencing a particular large face list. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 569 def get_training_status_with_http_info(large_face_list_id, custom_headers:nil) get_training_status_async(large_face_list_id, custom_headers:custom_headers).value! end
Retrieve a large face list’s largeFaceListId, name, userData and recognitionModel.
@param large_face_list_id [String] Id referencing a particular large face list. @param return_recognition_model [Boolean] A value indicating whether the operation should return 'recognitionModel' in response. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 285 def get_with_http_info(large_face_list_id, return_recognition_model:false, custom_headers:nil) get_async(large_face_list_id, return_recognition_model:return_recognition_model, custom_headers:custom_headers).value! end
List large face lists’ information of largeFaceListId, name, userData and recognitionModel. <br /> To get face information inside largeFaceList use [LargeFaceList Face
- Get](/docs/services/563879b61984550e40cbbe8d/operations/5a158cf2d2de3616c086f2d5)<br />
-
Large face lists are stored in alphabetical order of largeFaceListId.
-
“start” parameter (string, optional) is a user-provided largeFaceListId
value that returned entries have larger ids by string comparison. “start” set to empty to indicate return from the first item.
-
“top” parameter (int, optional) specifies the number of entries to return.
A maximal of 1000 entries can be returned in one call. To fetch more, you can specify “start” with the last returned entry’s Id of the current call. <br /> For example, total 5 large person lists: “list1”, …, “list5”. <br /> “start=&top=” will return all 5 lists. <br /> “start=&top=2” will return “list1”, “list2”. <br /> “start=list2&top=3” will return “list3”, “list4”, “list5”.
@param return_recognition_model [Boolean] A value indicating whether the operation should return 'recognitionModel' in response. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Array] operation results.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 665 def list(return_recognition_model:false, custom_headers:nil) response = list_async(return_recognition_model:return_recognition_model, custom_headers:custom_headers).value! response.body unless response.nil? end
List large face lists’ information of largeFaceListId, name, userData and recognitionModel. <br /> To get face information inside largeFaceList use [LargeFaceList Face
- Get](/docs/services/563879b61984550e40cbbe8d/operations/5a158cf2d2de3616c086f2d5)<br />
-
Large face lists are stored in alphabetical order of largeFaceListId.
-
“start” parameter (string, optional) is a user-provided largeFaceListId
value that returned entries have larger ids by string comparison. “start” set to empty to indicate return from the first item.
-
“top” parameter (int, optional) specifies the number of entries to return.
A maximal of 1000 entries can be returned in one call. To fetch more, you can specify “start” with the last returned entry’s Id of the current call. <br /> For example, total 5 large person lists: “list1”, …, “list5”. <br /> “start=&top=” will return all 5 lists. <br /> “start=&top=2” will return “list1”, “list2”. <br /> “start=list2&top=3” will return “list3”, “list4”, “list5”.
@param return_recognition_model [Boolean] A value indicating whether the operation should return 'recognitionModel' in response. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 728 def list_async(return_recognition_model:false, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? path_template = 'largefacelists' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], query_params: {'returnRecognitionModel' => return_recognition_model}, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:get, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRest::HttpOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? # Deserialize Response if status_code == 200 begin parsed_response = response_content.to_s.empty? ? nil : JSON.load(response_content) result_mapper = { client_side_validation: true, required: false, serialized_name: 'parsed_response', type: { name: 'Sequence', element: { client_side_validation: true, required: false, serialized_name: 'LargeFaceListElementType', type: { name: 'Composite', class_name: 'LargeFaceList' } } } } result.body = @client.deserialize(result_mapper, parsed_response) rescue Exception => e fail MsRest::DeserializationError.new('Error occurred in deserializing the response', e.message, e.backtrace, result) end end result end promise.execute end
List all faces in a large face list, and retrieve face information (including userData and persistedFaceIds of registered faces of the face).
@param large_face_list_id [String] Id referencing a particular large face list. @param start [String] Starting face id to return (used to list a range of faces). @param top [Integer] Number of faces to return starting with the face id indicated by the 'start' parameter. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Array] operation results.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1493 def list_faces(large_face_list_id, start:nil, top:nil, custom_headers:nil) response = list_faces_async(large_face_list_id, start:start, top:top, custom_headers:custom_headers).value! response.body unless response.nil? end
List all faces in a large face list, and retrieve face information (including userData and persistedFaceIds of registered faces of the face).
@param large_face_list_id [String] Id referencing a particular large face list. @param start [String] Starting face id to return (used to list a range of faces). @param top [Integer] Number of faces to return starting with the face id indicated by the 'start' parameter. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1532 def list_faces_async(large_face_list_id, start:nil, top:nil, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? fail ArgumentError, "'top' should satisfy the constraint - 'InclusiveMaximum': '1000'" if !top.nil? && top > 1000 fail ArgumentError, "'top' should satisfy the constraint - 'InclusiveMinimum': '1'" if !top.nil? && top < 1 request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? path_template = 'largefacelists/{largeFaceListId}/persistedfaces' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id}, query_params: {'start' => start,'top' => top}, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:get, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRest::HttpOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? # Deserialize Response if status_code == 200 begin parsed_response = response_content.to_s.empty? ? nil : JSON.load(response_content) result_mapper = { client_side_validation: true, required: false, serialized_name: 'parsed_response', type: { name: 'Sequence', element: { client_side_validation: true, required: false, serialized_name: 'PersistedFaceElementType', type: { name: 'Composite', class_name: 'PersistedFace' } } } } result.body = @client.deserialize(result_mapper, parsed_response) rescue Exception => e fail MsRest::DeserializationError.new('Error occurred in deserializing the response', e.message, e.backtrace, result) end end result end promise.execute end
List all faces in a large face list, and retrieve face information (including userData and persistedFaceIds of registered faces of the face).
@param large_face_list_id [String] Id referencing a particular large face list. @param start [String] Starting face id to return (used to list a range of faces). @param top [Integer] Number of faces to return starting with the face id indicated by the 'start' parameter. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1513 def list_faces_with_http_info(large_face_list_id, start:nil, top:nil, custom_headers:nil) list_faces_async(large_face_list_id, start:start, top:top, custom_headers:custom_headers).value! end
List large face lists’ information of largeFaceListId, name, userData and recognitionModel. <br /> To get face information inside largeFaceList use [LargeFaceList Face
- Get](/docs/services/563879b61984550e40cbbe8d/operations/5a158cf2d2de3616c086f2d5)<br />
-
Large face lists are stored in alphabetical order of largeFaceListId.
-
“start” parameter (string, optional) is a user-provided largeFaceListId
value that returned entries have larger ids by string comparison. “start” set to empty to indicate return from the first item.
-
“top” parameter (int, optional) specifies the number of entries to return.
A maximal of 1000 entries can be returned in one call. To fetch more, you can specify “start” with the last returned entry’s Id of the current call. <br /> For example, total 5 large person lists: “list1”, …, “list5”. <br /> “start=&top=” will return all 5 lists. <br /> “start=&top=2” will return “list1”, “list2”. <br /> “start=list2&top=3” will return “list3”, “list4”, “list5”.
@param return_recognition_model [Boolean] A value indicating whether the operation should return 'recognitionModel' in response. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 697 def list_with_http_info(return_recognition_model:false, custom_headers:nil) list_async(return_recognition_model:return_recognition_model, custom_headers:custom_headers).value! end
Queue a large face list training task, the training task may not be started immediately.
@param large_face_list_id [String] Id referencing a particular large face list. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 806 def train(large_face_list_id, custom_headers:nil) response = train_async(large_face_list_id, custom_headers:custom_headers).value! nil end
Queue a large face list training task, the training task may not be started immediately.
@param large_face_list_id [String] Id referencing a particular large face list. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 837 def train_async(large_face_list_id, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? path_template = 'largefacelists/{largeFaceListId}/train' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id}, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:post, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 202 error_model = JSON.load(response_content) fail MsRest::HttpOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? result end promise.execute end
Queue a large face list training task, the training task may not be started immediately.
@param large_face_list_id [String] Id referencing a particular large face list. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 822 def train_with_http_info(large_face_list_id, custom_headers:nil) train_async(large_face_list_id, custom_headers:custom_headers).value! end
Update information of a large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param name [String] User defined name, maximum length is 128. @param user_data [String] User specified data. Length should not exceed 16KB. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 369 def update(large_face_list_id, name:nil, user_data:nil, custom_headers:nil) response = update_async(large_face_list_id, name:name, user_data:user_data, custom_headers:custom_headers).value! nil end
Update information of a large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param name [String] User defined name, maximum length is 128. @param user_data [String] User specified data. Length should not exceed 16KB. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 402 def update_async(large_face_list_id, name:nil, user_data:nil, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? fail ArgumentError, "'name' should satisfy the constraint - 'MaxLength': '128'" if !name.nil? && name.length > 128 fail ArgumentError, "'user_data' should satisfy the constraint - 'MaxLength': '16384'" if !user_data.nil? && user_data.length > 16384 body = NameAndUserDataContract.new unless name.nil? && user_data.nil? body.name = name body.user_data = user_data end request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? # Serialize Request request_mapper = Azure::CognitiveServices::Face::V1_0::Models::NameAndUserDataContract.mapper() request_content = @client.serialize(request_mapper, body) request_content = request_content != nil ? JSON.generate(request_content, quirks_mode: true) : nil path_template = 'largefacelists/{largeFaceListId}' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id}, body: request_content, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:patch, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRestAzure::AzureOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? result end promise.execute end
Update a persisted face's userData field.
@param large_face_list_id [String] Id referencing a particular large face list. @param persisted_face_id Id referencing a particular persistedFaceId of an existing face. @param user_data [String] User-provided data attached to the face. The size limit is 1KB. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1098 def update_face(large_face_list_id, persisted_face_id, user_data:nil, custom_headers:nil) response = update_face_async(large_face_list_id, persisted_face_id, user_data:user_data, custom_headers:custom_headers).value! nil end
Update a persisted face's userData field.
@param large_face_list_id [String] Id referencing a particular large face list. @param persisted_face_id Id referencing a particular persistedFaceId of an existing face. @param user_data [String] User-provided data attached to the face. The size limit is 1KB. @param [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [Concurrent::Promise] Promise object which holds the HTTP response.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1135 def update_face_async(large_face_list_id, persisted_face_id, user_data:nil, custom_headers:nil) fail ArgumentError, '@client.endpoint is nil' if @client.endpoint.nil? fail ArgumentError, 'large_face_list_id is nil' if large_face_list_id.nil? fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'MaxLength': '64'" if !large_face_list_id.nil? && large_face_list_id.length > 64 fail ArgumentError, "'large_face_list_id' should satisfy the constraint - 'Pattern': '^[a-z0-9-_]+$'" if !large_face_list_id.nil? && large_face_list_id.match(Regexp.new('^^[a-z0-9-_]+$$')).nil? fail ArgumentError, 'persisted_face_id is nil' if persisted_face_id.nil? fail ArgumentError, "'user_data' should satisfy the constraint - 'MaxLength': '1024'" if !user_data.nil? && user_data.length > 1024 body = UpdateFaceRequest.new unless user_data.nil? body.user_data = user_data end request_headers = {} request_headers['Content-Type'] = 'application/json; charset=utf-8' # Set Headers request_headers['x-ms-client-request-id'] = SecureRandom.uuid request_headers['accept-language'] = @client.accept_language unless @client.accept_language.nil? # Serialize Request request_mapper = Azure::CognitiveServices::Face::V1_0::Models::UpdateFaceRequest.mapper() request_content = @client.serialize(request_mapper, body) request_content = request_content != nil ? JSON.generate(request_content, quirks_mode: true) : nil path_template = 'largefacelists/{largeFaceListId}/persistedfaces/{persistedFaceId}' request_url = @base_url || @client.base_url request_url = request_url.gsub('{Endpoint}', @client.endpoint) options = { middlewares: [[MsRest::RetryPolicyMiddleware, times: 3, retry: 0.02], [:cookie_jar]], path_params: {'largeFaceListId' => large_face_list_id,'persistedFaceId' => persisted_face_id}, body: request_content, headers: request_headers.merge(custom_headers || {}), base_url: request_url } promise = @client.make_request_async(:patch, path_template, options) promise = promise.then do |result| http_response = result.response status_code = http_response.status response_content = http_response.body unless status_code == 200 error_model = JSON.load(response_content) fail MsRestAzure::AzureOperationError.new(result.request, http_response, error_model) end result.request_id = http_response['x-ms-request-id'] unless http_response['x-ms-request-id'].nil? result.correlation_request_id = http_response['x-ms-correlation-request-id'] unless http_response['x-ms-correlation-request-id'].nil? result.client_request_id = http_response['x-ms-client-request-id'] unless http_response['x-ms-client-request-id'].nil? result end promise.execute end
Update a persisted face's userData field.
@param large_face_list_id [String] Id referencing a particular large face list. @param persisted_face_id Id referencing a particular persistedFaceId of an existing face. @param user_data [String] User-provided data attached to the face. The size limit is 1KB. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 1117 def update_face_with_http_info(large_face_list_id, persisted_face_id, user_data:nil, custom_headers:nil) update_face_async(large_face_list_id, persisted_face_id, user_data:user_data, custom_headers:custom_headers).value! end
Update information of a large face list.
@param large_face_list_id [String] Id referencing a particular large face list. @param name [String] User defined name, maximum length is 128. @param user_data [String] User specified data. Length should not exceed 16KB. @param custom_headers [Hash{String => String}] A hash of custom headers that will be added to the HTTP request.
@return [MsRestAzure::AzureOperationResponse] HTTP response information.
# File lib/1.0/generated/azure_cognitiveservices_face/large_face_list_operations.rb, line 386 def update_with_http_info(large_face_list_id, name:nil, user_data:nil, custom_headers:nil) update_async(large_face_list_id, name:name, user_data:user_data, custom_headers:custom_headers).value! end