Efficient and standard algorithm interface docking method

Abstract

The invention relates to an efficient and standard algorithm interface docking method, which comprises the following steps of: S1, introducing a unified open framework in an algorithm docking module; s2, based on the unified open framework introduced in S1, standardization processing of the interface is carried out; and S3, based on the interface after standardization processing in the S2, completing butt joint for different face recognition algorithms. By introducing a unified open framework and a standardized interface design, the problems of interface incompatibility, difficulty in upgrading, long development period, poor system flexibility and the like are solved, and finally the flexibility and the application efficiency of algorithm integration are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of interface systems and processing methods, and relates to an algorithm interface docking method, in particular to an efficient and standard algorithm interface docking method. Background Art

[0002] As facial recognition algorithms from different manufacturers and versions continue to emerge, the interface incompatibility and difficulty in replacement between algorithms have become increasingly prominent, posing huge challenges to developers and the implementation of application scenarios.

[0003] At present, there are many different face recognition algorithms on the market, each with its own unique interface design and data format requirements. This diversity means that in practical applications, if different algorithms are to be integrated into the same system or application, a lot of custom development and adaptation work must be done, which leads to longer development cycles, increased costs, and reduced system flexibility and maintainability.

[0004] In addition, due to the inconsistency of the interfaces between algorithms, once a specific face recognition algorithm is selected, it becomes very difficult to replace or upgrade it to another algorithm. This situation not only limits the rapid iteration of technology and the flexible response to application scenarios, but also limits the widespread adoption and promotion of new algorithms and technologies in the market.

[0005] The existing "interface system and processing method" mainly has the following defects and shortcomings:

[0006] Incompatible interfaces: The interface designs and data formats provided by different algorithms are not unified, resulting in a lot of custom development and adaptation work when integrating multiple algorithms. Difficulty in upgrading: Once a specific algorithm is selected, it becomes complicated and time-consuming to replace or upgrade to another algorithm, which limits the rapid iteration and flexible response of technology. Long development cycle: Due to inconsistent interfaces, developers need to spend extra time and resources to solve compatibility issues, which prolongs the development cycle and increases costs. Poor system flexibility: The flexibility and maintainability of the system are reduced, making it difficult to adapt to changing technical requirements and market environments.

[0007] To solve the above problems, we proposed an efficient and standard algorithm interface docking method.

[0008] After searching, no public documents of the prior art identical or similar to the present invention were found. Summary of the invention

[0009] The purpose of the present invention is to overcome the shortcomings of the prior art and propose an efficient and standard algorithm interface docking method. By introducing a unified open framework and standardized interface design, the problems of interface incompatibility, upgrade difficulties, long development cycle and poor system flexibility are solved, thereby ultimately improving the flexibility and application efficiency of algorithm integration.

[0010] The present invention solves the practical problem by adopting the following technical solutions:

[0011] An efficient and standard algorithm interface docking method includes the following steps:

[0012] S1: Introduce a unified open framework in the algorithm docking module;

[0013] S2: Based on the unified open framework introduced in S1, standardize the interface;

[0014] S3: Based on the interface standardized by S2, complete the docking of different face recognition algorithms.

[0015] Moreover, the open framework of step S1 enables all face recognition algorithms to be designed and packaged according to the API standard, and allows face recognition algorithms from different manufacturers or versions to be seamlessly replaced and integrated.

[0016] Moreover, the specific steps of step S2 include:

[0017] Step 2.1: Algorithm registration

[0018] Function description: Register the face recognition algorithm and initialize the required resources.

[0019] Interface definition: register_algorithm(api_key)

[0020] Implementation method: Implement the register_algorithm(api_key) function, verify the API key, and initialize the algorithm resources.

[0021] Step 2.2: Algorithm initialization and deinitialization

[0022] Function description: Initialize and release algorithm resources.

[0023] Interface definition: initialize_algorithm(model_path), uninitialize_algorithm()

[0024] Implementation method: initialize_algorithm(model_path) loads the model, and uninitialize_algorithm() releases resources.

[0025] Step 2.3: Algorithm key verification

[0026] Function description: Authorization key used by the verification algorithm.

[0027] Interface definition: verify_key(api_key)

[0028] Implementation method: Write the verify_key(api_key) function to verify the validity of the API key.

[0029] Step 2.4: Algorithm parameter setting

[0030] Function description: Set the parameters related to face detection and recognition.

[0031] Interface definition: set_resolution(width,height), set_detection_region(region), set_min_face_size(min_size)

[0032] Implementation method: Set the image resolution, detection area, and minimum face size to ensure that the parameters are legal.

[0033] Step 2.5: Face detection and feature extraction

[0034] Function description: Detect faces in input images and extract features.

[0035] Interface definition: detect_faces(image), extract_face_features(face)

[0036] Implementation method: detect_faces(image) performs face detection and extract_face_features(face) extracts features.

[0037] Step 2.6: Face attribute detection

[0038] Function description: Detect facial attributes, such as age, gender, etc.

[0039] Interface definition: detect_face_attributes(face)

[0040] Implementation method: detect_face_attributes(face) analyzes and returns the attribute information of the face.

[0041] Step 2.7: Face matching and image selection

[0042] Function description: Compare the extracted facial features and filter the matching results.

[0043] Interface definition: match_faces(features_list)

[0044] Implementation method: match_faces(features_list) performs feature comparison and returns the matching results.

[0045] Step 2.8: Algorithm result output

[0046] Function description: Output face detection, feature extraction, attribute detection, and comparison results.

[0047] Interface definition: get_algorithm_results()

[0048] Implementation method: get_algorithm_results() obtains the results after algorithm processing.

[0049] Step 2.9: Output face information

[0050] Function description: Output detailed information of the recognized face.

[0051] Interface definition: get_face_info(face)

[0052] Implementation method: get_face_info(face) returns detailed information about a face, including its location, features, and attributes.

[0053] Moreover, the specific steps of step S3 include:

[0054] (1) Encapsulate different algorithms into different algorithm libraries, which provide standardized APIs developed by S2.

[0055] (2) Then, the algorithm docking layer is implemented. The algorithm docking layer loads the algorithm library and the standardized API developed by S2 to realize the functions of algorithm registration, algorithm initialization and deinitialization, algorithm key verification, algorithm parameter setting, face detection and feature extraction, face attribute detection, face comparison and image selection, algorithm result output, and face information output.

[0056] (3) Finally, when it is necessary to replace the algorithm of a different manufacturer, the algorithm docking layer only needs to replace the algorithm library to support the algorithm of that manufacturer.

[0057] Advantages and beneficial effects of the present invention:

[0058] The present invention provides an efficient and standardized algorithm interface docking method, which introduces a unified open framework and standardized interface design of an algorithm, so that algorithms of different manufacturers and versions can be seamlessly docked into the same platform, thereby greatly improving the flexibility and application efficiency of the algorithm;

[0059] Specifically manifested as:

[0060] Standardized interface definition: provides a unified interface standard, solves the problems of inconsistent formats and poor compatibility among different algorithms, and improves the simplicity and consistency of algorithm integration.

[0061] Optimize input and output formats: By optimizing the input and output formats, the data exchange process is simplified, the complexity of data conversion and processing is reduced, and the efficiency of algorithm docking is improved.

[0062] Improved docking process: The algorithm docking process has been improved, reducing the difficulty and time cost of technology integration, and quickly realizing the application and promotion of technology.

[0063] Enhanced flexibility: The flexibility of algorithm docking has been improved, allowing the algorithm to adapt to the needs of different application scenarios more quickly, supporting technological innovation and ecological expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the algorithm docking interface of the present invention. DETAILED DESCRIPTION

[0065] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings:

[0066] The implementation of the present invention can be implemented in the form of a software program, including but not limited to: writing the code of the algorithm docking framework, defining the calling method of the interface, formulating the input and output data format specifications, etc. During the implementation process, the interface can also be customized, expanded and optimized according to specific application requirements. The method for implementing the present invention mainly includes the following key steps and technical solutions:

[0067] An efficient and standard algorithm interface docking method, such as Figure 1 As shown, the following steps are included:

[0068] S1: Introduce a unified open framework in the algorithm docking module;

[0069] The open framework of step S1 enables all face recognition algorithms to be designed and packaged according to the API standard, and allows face recognition algorithms from different manufacturers or versions to be seamlessly replaced and integrated.

[0070] In this embodiment, the open framework of step S1 is intended to provide a complete and unified algorithm docking interface standard, so that all face recognition algorithms can be designed and packaged according to this standard, thereby achieving external standardization. This framework allows face recognition algorithms from different manufacturers or versions to be seamlessly replaced and integrated. By establishing a common interface and standard, we reduce compatibility issues between different algorithm systems and enhance the flexibility and scalability of the system.

[0071] S2: Based on the unified open framework introduced in S1, standardize the interface;

[0072] The specific steps of step S2 include:

[0073] Step 2.1: Algorithm registration

[0074] Function description: Register the face recognition algorithm and initialize the required resources.

[0075] Interface definition: register_algorithm(api_key)

[0076] Implementation method: Implement the register_algorithm(api_key) function, verify the API key, and initialize the algorithm resources.

[0077] Step 2.2: Algorithm initialization and deinitialization

[0078] Function description: Initialize and release algorithm resources.

[0079] Interface definition: initialize_algorithm(model_path), uninitialize_algorithm()

[0080] Implementation method: initialize_algorithm(model_path) loads the model, and uninitialize_algorithm() releases resources.

[0081] Step 2.3: Algorithm key verification

[0082] Function description: Authorization key used by the verification algorithm.

[0083] Interface definition: verify_key(api_key)

[0084] Implementation method: Write the verify_key(api_key) function to verify the validity of the API key.

[0085] Step 2.4: Algorithm parameter setting

[0086] Function description: Set the parameters related to face detection and recognition.

[0087] Interface definition: set_resolution(width,height), set_detection_region(region), set_min_face_size(min_size)

[0088] Implementation method: Set the image resolution, detection area, and minimum face size to ensure that the parameters are legal.

[0089] Step 2.5: Face detection and feature extraction

[0090] Function description: Detect faces in input images and extract features.

[0091] Interface definition: detect_faces(image), extract_face_features(face)

[0092] Implementation method: detect_faces(image) performs face detection and extract_face_features(face) extracts features.

[0093] Step 2.6: Face attribute detection

[0094] Function description: Detect facial attributes, such as age, gender, etc.

[0095] Interface definition: detect_face_attributes(face)

[0096] Implementation method: detect_face_attributes(face) analyzes and returns the attribute information of the face.

[0097] Step 2.7: Face matching and image selection

[0098] Function description: Compare the extracted facial features and filter the matching results.

[0099] Interface definition: match_faces(features_list)

[0100] Implementation method: match_faces(features_list) performs feature comparison and returns the matching results.

[0101] Step 2.8: Algorithm result output

[0102] Function description: Output face detection, feature extraction, attribute detection, and comparison results.

[0103] Interface definition: get_algorithm_results()

[0104] Implementation method: get_algorithm_results() obtains the results after algorithm processing.

[0105] Step 2.9: Output face information

[0106] Function description: Output detailed information of the recognized face.

[0107] Interface definition: get_face_info(face)

[0108] Implementation method: get_face_info(face) returns detailed information about a face, including its location, features, and attributes.

[0109] The above steps reflect the logical connection between each sub-step, from registration algorithm to final output result, ensuring the complete algorithm docking and implementation process.

[0110] S3: Based on the interface standardized by S2, it completes the connection for different face recognition algorithms;

[0111] According to the standardized interface provided by S2, the face recognition algorithm is connected.

[0112] The specific steps of step S3 include:

[0113] (3) Encapsulate different algorithms into different algorithm libraries, which provide standardized APIs developed by S2.

[0114] (4) Then, the algorithm docking layer is implemented. The algorithm docking layer loads the algorithm library and the standardized API developed by S2 to implement functions such as algorithm registration, algorithm initialization and deinitialization, algorithm key verification, algorithm parameter setting, face detection and feature extraction, face attribute detection, face comparison and image selection, algorithm result output, and face information output.

[0115] (5) Finally, when it is necessary to replace algorithms from different manufacturers, since the API provided by the algorithm library has been standardized, the algorithm docking layer only needs to replace the algorithm library to support the algorithm of the manufacturer, which greatly simplifies the development process and reduces development complexity.

[0116] The working principle of the present invention is:

[0117] The present invention relates to an efficient and standard algorithm interface docking method, the core purpose of which is to provide a unified docking standard and process for face recognition algorithms and other types of algorithms to achieve seamless integration and compatibility of the algorithms.

[0118] 1. Introduction of AI open framework:

[0119] The present invention introduces the concept of AI open framework and provides a unified docking interface standard. This framework allows face recognition algorithms of different manufacturers or versions to be seamlessly replaced and integrated. By establishing a universal interface and standard, the compatibility issues between different algorithm systems are reduced, and the flexibility and scalability of the system are enhanced.

[0120] 2. Interface specifications:

[0121] Detailed docking processes and interface specifications have been determined, including algorithm registration, initialization, deinitialization, key verification, parameter setting, etc. These specifications ensure the compatibility and stability of the algorithm when it is connected to the AI ​​open framework, thereby achieving seamless access to the algorithm and efficient operation of the system.

[0122] 3. Data interaction structure:

[0123] The input and output data structures of face recognition algorithms are standardized. The interface standard specifies the data transmission and processing flow, including key steps such as image processing, face feature detection, and attribute recognition. The standardized data structure simplifies the data interaction process and improves the efficiency of algorithm development and integration.

[0124] 4. General Application:

[0125] This process is not only applicable to face recognition algorithms, but can also be extended to other types of algorithm docking applications. Through universal interfaces and standardized processes, the present invention promotes the development of algorithm application ecology and provides a flexible solution for the integration and replacement of different algorithms.

[0126] 5. Detailed interface design:

[0127] The present invention provides a detailed interface design, including algorithm registration, initialization, deinitialization, key verification, parameter setting, face feature detection, image selection, attribute detection, result output, feature background comparison, etc. These designs ensure the full compatibility and stability of the algorithm, so that various functional modules can be smoothly connected and interacted.

[0128] 6. Document output:

[0129] Provide detailed algorithm documentation, including interface description, docking process, input and output structure, usage restrictions, etc. These documents ensure the ease of use and standardization of the interface, allowing developers to quickly understand and apply the docking interface, improving the efficiency of system integration.

[0130] In summary, the present invention establishes an efficient and standard algorithm docking interface through a unified AI open framework, standardized interface standards, standardized data structures, general application processes, detailed interface design, and complete document output, ensuring the compatibility, stability, and scalability of the algorithm system. This method not only improves the integration efficiency of face recognition algorithms, but also provides a flexible solution for docking other types of algorithms.

[0131] The innovation of the present invention lies in:

[0132] Standardization and normalization: This invention proposes a set of unified algorithm docking interface standards, which solves the problems of inconsistent formats and poor compatibility in existing algorithm docking, and provides a standard platform for sharing and exchanging algorithms for the industry.

[0133] Efficiency improvement: By optimizing the docking process and input and output formats, the efficiency and flexibility of algorithm docking are greatly improved, the development and deployment costs are reduced, and the application of technology is accelerated.

[0134] Ecological promotion: The implementation of this invention can greatly promote the development of the algorithm application ecology and promote the widespread application and innovation of artificial intelligence technology in various industries.

[0135] Based on the above technical description, the present invention proposes an algorithm docking interface based on efficient standards. By defining unified interface standards and process specifications, it realizes seamless docking and integration between different algorithms, and provides technical guarantee and support for the rapid application and promotion of AI technology in various application scenarios. The present invention has important application and economic significance in improving algorithm application efficiency, reducing development costs and promoting the development of industry ecology.

[0136] The present invention achieves seamless docking between algorithms by defining a unified interface standard and optimizing the docking process, greatly improving the efficiency of algorithm application, reducing compatibility issues, lowering development and deployment costs, and promoting the widespread application of AI technology in various industries.

[0137] It should be emphasized that the embodiments of the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation modes. Any other implementation modes derived by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. An efficient and standard algorithm interface docking method, characterized by: The following steps are involved: S1: Introduce a unified open framework in the algorithm docking module; S2: Based on the unified open framework introduced in S1, standardize the interface; S3: Based on the interface standardized by S2, complete the docking of different face recognition algorithms.

2. According to claim 1, an efficient and standard algorithm interface docking method is characterized by: The open framework of step S1 enables all face recognition algorithms to be designed and packaged according to the API standard, and allows face recognition algorithms from different manufacturers or versions to be seamlessly replaced and integrated.

3. The efficient and standard algorithm interface docking method according to claim 1 is characterized in that: The specific steps of step S2 include: Step 2.1: Algorithm registration Function description: Register the face recognition algorithm and initialize the required resources; Interface definition: register_algorithm(api_key) Implementation method: Implement the register_algorithm(api_key) function, verify the API key, and initialize the algorithm resources; Step 2.2: Algorithm initialization and deinitialization Function description: Initialize and release algorithm resources; Interface definition: initialize_algorithm(model_path), uninitialize_algorithm() Implementation method: initialize_algorithm(model_path) loads the model, uninitialize_algorithm() releases resources; Step 2.3: Algorithm key verification Function description: Verify the authorization key used by the algorithm; Interface definition: verify_key(api_key) Implementation method: Write the verify_key(api_key) function to verify the validity of the API key; Step 2.4: Algorithm parameter setting Function description: Set the relevant parameters of face detection and recognition; Interface definition: set_resolution(width,height), set_detection_region(region), set_min_face_size(min_size) Implementation method: Set the image resolution, detection area and minimum face size to ensure the parameters are legal; Step 2.5: Face detection and feature extraction Function description: Detect faces in input images and extract features; Interface definition: detect_faces(image), extract_face_features(face) Implementation method: detect_faces(image) performs face detection, extract_face_features(face) extracts features; Step 2.6: Face attribute detection Functional description: Detect facial attributes, such as age, gender, etc. Interface definition: detect_face_attributes(face) Implementation method: detect_face_attributes(face) analyzes and returns the attribute information of the face; Step 2.7: Face matching and image selection Function description: Compare the extracted facial features and filter the matching results; Interface definition: match_faces(features_list) Implementation method: match_faces(features_list) performs feature comparison and returns the matching results; Step 2.8: Algorithm result output Function description: Output face detection, feature extraction, attribute detection, and comparison results; Interface definition: get_algorithm_results() Implementation method: get_algorithm_results() obtains the results after algorithm processing; Step 2.9: Output face information Function description: Output detailed information of the recognized face; Interface definition: get_face_info(face) Implementation method: get_face_info(face) returns detailed information about a face, including its location, features, and attributes.

4. The efficient and standard algorithm interface docking method according to claim 1, characterized in that: The specific steps of step S3 include: (1) Encapsulate different algorithms into different algorithm libraries, which provide standardized APIs developed by S2. (2) Then, the algorithm docking layer is implemented. The algorithm docking layer loads the algorithm library and the standardized API developed by S2 to realize the functions of algorithm registration, algorithm initialization and deinitialization, algorithm key verification, algorithm parameter setting, face detection and feature extraction, face attribute detection, face comparison and image selection, algorithm result output, and face information output. (3) Finally, when it is necessary to replace the algorithm of a different manufacturer, the algorithm docking layer only needs to replace the algorithm library to support the algorithm of that manufacturer.

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