Camera module tuning method

Abstract

The invention relates to the technical field of camera debugging, in particular to a camera module tuning method, and aims to solve the technical problem of how to realize tuning of a camera module more efficiently. In order to achieve the purpose, the collaborative interaction system is created, and the collaborative interaction system comprises a local code bin, an automatic file verification module and a temporary storage area. In response to the adjustment and optimization service provider submitting adjustment and optimization data to the local code warehouse, triggering an automatic file verification module to execute automatic verification; if the automatic verification is passed, submitting the tuning data to a temporary storage area and generating a notification, otherwise, returning the tuning data to a local code bin and prompting a failure reason; in response to the notification, pulling the tuning data to a local bin for the personnel of the tuning demand side to perform local verification; if the local verification is passed, the tuning data is combined into the code general warehouse of the tuning demand side, otherwise, the problem feedback information is transmitted back to the local code warehouse for further tuning of the tuning demand side, the tuning efficiency is improved, and the communication cost is reduced.

Description

Technical Field

[0001] This application relates to the field of camera tuning technology, specifically to a camera module tuning method. Background Technology

[0002] A camera imaging system mainly includes key components such as a lens, an image sensor, and an image signal processor (ISP). These components work together to complete the imaging process, including light sensing, signal transmission, and image processing.

[0003] 1. Lens: Responsible for focusing light onto the image sensor, affecting image sharpness and distortion.

[0004] 2. Image sensor: Converts light signals into electrical signals. It is the "eye" of the camera, determining the image resolution, light sensitivity, and signal response.

[0005] 3. ISP: Responsible for processing the raw image data acquired from the sensor, performing image processing such as white balance calibration, color correction, noise reduction, and sharpening to optimize the output image quality. MIPI cameras do not include an ISP, so the ISP is integrated into the motherboard. A better and more sophisticated ISP algorithm can excel in optimizing image quality.

[0006] Camera modules developed for Android mobile devices such as smartphones, VR (Virtual Reality), and PADs require camera tuning after the OEM (Original Equipment Manufacturer) has completed camera driver debugging. There are generally two approaches to camera tuning: The first involves providing the camera module and driver motherboard to a chip manufacturer like Rockchip or a tuning company for optimization. This method requires advance booking and is time-consuming. The second approach involves the chip manufacturer (such as Qualcomm) providing the OEM with a basic algorithm engine, but the OEM needs to perform tuning to determine which algorithm to use in which scenarios and its strength. Furthermore, there is no unified standard for image quality. Different product applications expect different levels of image quality because the same camera can be used in different scenarios. For example, a 4M color camera can be used for VR perspective or face tracking algorithms. In this example, when the camera is used for perspective, its color and sharpness characteristics will be the focus of tuning. When using algorithms for face tracking, noise becomes a key area for optimization because it interferes with the algorithm's ability to extract feature points of objects in an image.

[0007] Camera tuning is a systematic, modular process that requires extensive expertise and experience. Different environments, modules, and platforms all exhibit variations. Even with the same module, different models on the same platform will produce inconsistent image quality. The core objective of tuning is to adjust software parameters on a given camera to output images that best meet human needs. For research projects, the requirements for camera image quality are focused on specific criteria such as noise and motion blur, requiring engineers to continuously adjust and verify settings to meet development requirements.

[0008] The basic Tuning workflow includes the following: Module calibration, which is the process of measuring the inherent physical characteristics of the camera hardware to provide basic data for subsequent debugging. This stage is mostly automated. Module debugging is the core of the optimization work, performed manually by Tuning engineers, and is the most time-consuming, heavily reliant on the engineer's experience and aesthetic sense. Verification and regression testing: After all parameters are modified, rigorous testing must be conducted to ensure stability and consistency. Delivery and solidification: This involves confirming the final parameter tuning file and integrating it into the system version, performing final flashing tests for verification, and recording key decisions and final parameters during the debugging process to provide a basis for subsequent project or problem troubleshooting.

[0009] When OEMs are involved in tuning, they need to repeatedly verify and provide feedback on externally generated tuning configuration files. This process incurs excessive communication costs and is inefficient. Each acceptance test requires integration engineers and algorithm engineers to verify the effectiveness of the algorithm (such as a face tracking algorithm). If, after verification, the customer finds that some parts of the images captured in the tuning configuration file do not meet the algorithm requirements, the tuning team needs to optimize and calibrate the images again based on the customer's feedback. This process is repeated until the requirements are met.

[0010] During this process, the OEM (customer) and the external tuning company exchanged code and images via online chat tools or email. This method clearly suffers from high communication costs and low efficiency.

[0011] Accordingly, there is a need in the field for a new camera module optimization scheme to solve the above problems. Summary of the Invention

[0012] In order to overcome the above-mentioned shortcomings, this application is proposed to solve, or at least partially solve, the technical problem of how to achieve camera module optimization more efficiently.

[0013] In a first aspect, a camera module optimization method is provided, characterized in that the method includes:

[0014] In response to receiving a request from the tuning service provider to establish a collaborative interaction system, a collaborative interaction system for file interaction with the tuning service provider is created. The collaborative interaction system includes a local code repository, an automated file verification module connected to the local code repository, and a temporary storage area for storing files that have passed verification.

[0015] In response to the optimization service provider submitting optimization data to the local code repository, the automated file verification module is triggered to perform automated verification.

[0016] If the automated verification passes, the optimization data is submitted to the temporary storage area and a notification is generated; otherwise, the optimization data is returned to the local code repository and the reason for the failure is displayed.

[0017] In response to the notification, the optimization data is pulled to the local warehouse for local verification by the personnel of the optimization request party;

[0018] In response to receiving a notification that local verification has passed, the optimization data is merged into the main code repository of the optimization requester; otherwise, the issue feedback information is sent back to the local code repository for further optimization by the optimization service provider.

[0019] In one technical solution of the above camera module tuning method, in the case of the first tuning, the submitted tuning data includes a tuning file, a rendering based on the tuning file, and a tuning index file;

[0020] In cases where it is not the first time optimizing, the submitted optimization data includes the effect diagram and the optimization index file;

[0021] The tuning index file includes the parameters of the tuning file and the identifier of the corresponding tuning effect diagram.

[0022] In one technical solution of the above camera module tuning method, the tuning file parameters include the tuning file name, path and / or version identifier.

[0023] In one technical solution of the above camera module optimization method, the automated file verification module performs automated verification, including:

[0024] Verify the version consistency, syntax compliance, and validity of file changes in the submitted optimization data.

[0025] In one technical solution of the above camera module tuning method, before triggering the automated file verification module to perform automated verification, the method further includes: responding to the tuning service provider submitting tuning data to the local code repository, determining whether the submitted tuning data is invalid information, and if so, returning the tuning data to the local code repository.

[0026] In one technical solution of the above-mentioned camera module optimization method, in the case of the first optimization, the optimization data is obtained by the optimization service provider burning the optimization file to the camera module and optimizing the camera module according to the optimization file;

[0027] In cases other than the first optimization, the optimization data is obtained by the optimization service provider after adjusting the optimization file based on the annotated local effect diagram and the updated optimization index file in the local code repository, and then re-optimizing the camera module.

[0028] In one technical solution of the above camera module optimization method, the step of sending the problem feedback information back to the local code repository includes:

[0029] In response to the optimization requester's personnel annotating the local effect diagram obtained based on the optimization data, a local effect diagram with annotations is generated.

[0030] The tuning index file is updated based on the annotated local renderings, so that the updated tuning index file contains the identifiers of the annotated local renderings.

[0031] The labeled local rendering and the updated tuning index file are sent back to the local code repository.

[0032] In one technical solution of the above camera module tuning method, the step of merging the tuning data into the code repository of the tuning requester includes:

[0033] Record the account information of the optimization service provider.

[0034] One technical solution of the above camera module optimization method also includes:

[0035] At least two local effect diagrams obtained from the input tuning data;

[0036] Obtain the image differences between the at least two local effect images;

[0037] Obtain the variable parameters and their corresponding attributes for the at least two local effect diagrams from the optimization index file;

[0038] Based on the image differences, changing parameters, and associated attributes, tagged text is generated to guide the personnel of the optimization requesting the optimization to perform independent optimization.

[0039] In one technical solution of the above camera module tuning method, the two local effect images obtained from the input tuning data include:

[0040] Based on the desired optimization direction, select the order in which to input the effect diagrams.

[0041] In one technical solution of the above-mentioned camera module tuning method, the marked text includes the exposure parameter value and / or gain parameter value that need to be adjusted to achieve the image change between the effect images corresponding to the at least two local effect image identifiers.

[0042] The above-described technical solutions of this application have at least one or more of the following beneficial effects:

[0043] In implementing the camera module optimization method provided in this application, a collaborative interaction system is created to interact with the optimization service provider. This system includes a local code repository, an automated file verification module connected to the local code repository, and a temporary storage area for storing verified files. In response to the optimization service provider submitting optimization data to the local code repository, the automated file verification module is triggered to perform automated verification. If the automated verification passes, the optimization data is submitted to the temporary storage area and a notification is generated; otherwise, the optimization data is returned to the local code repository with the reason for failure. In response to the notification, the optimization data is pulled to the local repository for local verification by the optimization requester. If the local verification passes, the optimization data is merged into the optimization requester's main code repository; otherwise, the problem feedback information is sent back to the local code repository for further optimization by the optimization requester. Through this configuration, this application enables interaction between the optimization requester and the optimization service provider based on the collaborative interaction system, improving the optimization efficiency and effectively reducing the communication costs of the optimization process.

[0044] Furthermore, in the case of initial optimization, the submitted optimization data includes optimization files, effect images taken based on the optimization files, and optimization index files; in the case of subsequent optimizations, the submitted optimization data includes effect images and optimization index files; the optimization index files include the parameters of the optimization files and the corresponding identifiers of the optimization effect images. This effectively improves the readability of the optimization data and further reduces the manual and communication costs of the optimization process. Attached Figure Description

[0045] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Wherein:

[0046] Figure 1 This is a schematic flowchart of the main steps of a camera module optimization method according to an embodiment of this application;

[0047] Figure 2 This is an example schematic diagram of an optimized index file according to one embodiment of the present application;

[0048] Figure 3 This is a flowchart illustrating the main steps of an optimization service provider accessing the code repository of an optimization requester according to one embodiment of this application. Detailed Implementation

[0049] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0050] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a camera module optimization method according to an embodiment of this application. Figure 1 As shown, the camera module optimization method in this application embodiment is applied to the optimization requester. The camera module optimization method mainly includes the following steps S101 to S105.

[0051] Step S101: In response to receiving a collaborative interaction system establishment request from the tuning service provider, create a collaborative interaction system for file interaction with the tuning service provider. The collaborative interaction system includes a local code repository, an automated file verification module connected to the local code repository, and a temporary storage area for storing files that have passed verification.

[0052] In this embodiment, a collaborative interaction system can be created for file exchange between the tuning requester and the tuning provider. The collaborative interaction system may include a local code repository, an automated file verification module, and a temporary storage area. The automated file verification module can be connected to the local code repository. The temporary storage area can be used to store files that have passed verification.

[0053] In one implementation, the local code repository can be built based on the main code repository of the optimization requester. The optimization requester can provide the download address, account, and key of the local code repository to the optimization service provider, thereby enabling the optimization service provider to access the local code repository. The local code repository can contain one or more files from the main code repository, or it can contain the entire source code library of the corresponding project in the main code repository, depending on the project background and actual needs.

[0054] In one implementation, the optimization service provider can be an external optimization vendor or perform internal optimization for the optimization requester.

[0055] In one implementation, the automated file verification module establishes a first-level security channel between the optimization service provider and the local code repository by verifying the optimization data of the optimization service provider, thereby improving the efficiency of submitting valid code for the optimization service.

[0056] In one implementation, the temporary storage area can be used for interaction between the tuning service provider and the tuning requester. It allows the tuning requester's engineers to locally verify the tuning data by checking compilation issues. If the code corresponding to the tuning data compiles successfully, the tuning data can be merged into the main code repository. If the code fails to compile, the tuning requester's engineers can analyze the compilation errors, mark the code, and feed the marked tuning data back to the local code repository, enabling the tuning service provider to adjust the tuning data.

[0057] In one implementation, an automated file verification module and a staging area can be established based on the Git repository creation method.

[0058] Step S102: In response to the tuning service provider submitting tuning data to the local code repository, the automated file verification module is triggered to perform automated verification.

[0059] In this embodiment, when there is tuning data submitted by the tuning service provider in the local code repository, the tuning data can be automatically verified based on the automated file verification module.

[0060] In one implementation, the engineers of the tuning service provider can, based on the materials of the camera module provided by the tuning requester (e.g., motherboard, camera, battery, connecting cables, etc.), turn on the camera module to take pictures, tune the camera module according to the image quality, output the corresponding tuning data, integrate it into a tuning file according to a preset format, and then embed the tuning file into the motherboard to take pictures again. If the pictures have overexposure problems, the corresponding parameters can be modified in the tuning file and embedded into the motherboard again for debugging. This process is repeated to tune the image effect. The tuning of the camera module provided by the tuning service provider can be based on commonly used tuning methods in the prior art, and this application does not limit this approach.

[0061] In one implementation, the preset format can be a JSON file format, with each camera module corresponding to a JSON file. The JSON format of different camera modules can be consistent, with only differences in parameters.

[0062] In one implementation, for the initial optimization, the submitted optimization data may include optimization files, effect images taken based on the optimization files, and an optimization index file. For subsequent optimizations, the submitted optimization data may include effect images and an optimization index file. The optimization index file may include the parameters of the optimization files and the identifiers of the corresponding optimization effect images. Including optimization effect images in the optimization data effectively enhances the readability of the optimization data, further improves the efficiency of the optimization process, and reduces optimization communication costs.

[0063] In one implementation, the tuning file parameters may include parameter information such as the tuning file name, path, and version identifier.

[0064] In a specific example, tuning the index file can be done as follows: Figure 2 As shown, the tuning index file can include parameters of the tuning file, such as recording the tuning file name, parameters that the tuning file format is consistent with the platform configuration file; recording the diff parameters and corresponding attributes compared to 1; recording the diff parameters and corresponding attributes compared to 2; recording the diff parameters and corresponding attributes compared to 3. The tuning index file can also include identifiers of corresponding tuning effect diagrams, such as effect diagram filename1.jpeg, effect diagram filename2.jpeg, effect diagram filename3.jpeg, effect diagram filename4.jpeg, customer feedback marker1.jpeg, customer feedback marker2.jpeg, and customer feedback marker3.jpeg.

[0065] In one implementation, the automated verification performed by the automated file verification module may include verifying the version consistency, syntax compliance, and validity of file changes in the submitted tuning data. The specific verification methods and rules can be set according to the needs of the actual application, and this application does not limit them.

[0066] In one implementation, in response to the tuning service provider submitting tuning data to the local code repository, it can be determined whether the submitted tuning data is invalid. If so, the tuning data can be returned to the local code repository.

[0067] Step S103: If the automated verification passes, submit the optimization data to the temporary storage area and generate a notification; otherwise, return the optimization data to the local code repository and indicate the reason for the failure.

[0068] In this embodiment, if the automated file verification module passes the automated verification of the tuning data, the tuning data can be submitted to the temporary storage area and a notification can be generated to notify the main code repository to merge the tuning data. If the automated decomposition verification module fails the automated verification of the tuning data, the tuning data can be returned to the local code repository with the reason for the failure displayed, so that the tuning service provider can adjust the tuning data.

[0069] Step S104: In response to the notification, pull the tuning data to the local warehouse for local verification by the personnel of the tuning request party.

[0070] In this implementation, if a notification is received that optimization data has been submitted to the temporary storage area, the optimization data can be pulled to the local repository, thereby enabling local verification by the personnel of the optimization request party.

[0071] In one implementation, the tuning data pulled from the local repository can be imported into the camera module, and photos can be taken to verify the effect. If it is believed that the images captured by this version of the tuning data have too much noise, the noise locations can be marked on the images, and feedback marker image names (i.e., identifiers of the tuning effect images) can be added to the tuning index file. The marked tuning effect images and the tuning index file are then uploaded to the local code repository for confirmation by the tuning service provider.

[0072] Step S105: In response to receiving a notification that the local verification has passed, merge the optimization data into the code repository of the optimization requester; otherwise, send the problem feedback information back to the local code repository for further optimization by the optimization service provider.

[0073] In this implementation, if the local verification passes, the optimization data can be merged into the central code repository of the party requesting the optimization. If the verification fails, the issue can be reported to the local code repository, allowing the optimization service provider to perform further optimization.

[0074] In one implementation, in response to the user's annotation of issues on the local rendering obtained based on the tuning data, a local rendering with annotations is generated; the tuning index file is updated based on the local rendering with annotations, so that the updated tuning index file contains the identifiers of the local rendering with annotations; the local rendering with annotations and the updated tuning index file are sent back to the local code repository, thereby enabling the tuning service provider to perform further tuning.

[0075] In one implementation, in the case of initial optimization, the optimization data is obtained by the optimization service provider burning the optimization file to the camera module and optimizing the camera module according to the optimization file.

[0076] In cases other than the first optimization, the optimization data is obtained by the optimization service provider after adjusting the optimization files based on the annotated local effect diagrams and updated optimization index files in the local code repository, and then re-optimizing the camera module.

[0077] In one implementation, after the tuning data from the tuning service provider passes local verification, the permissions of the automated file verification module corresponding to the tuning service provider can be disabled.

[0078] In one implementation, the process of inputting optimization data can obtain at least two local effect images; and obtain the image differences between the at least two local effect images; obtain the variable parameters and attributes corresponding to the at least two local effect images from the optimization index file; and generate labeled text based on the image differences, variable parameters, and attributes to guide the optimization requesters to perform self-optimization.

[0079] In one implementation, the process of inputting optimization data to obtain two local effect diagrams can be based on the desired optimization direction, selecting the order of input effect diagrams so as to achieve optimization according to the order of effect diagrams.

[0080] In one implementation, the tag text for the tuning data may include numerical values ​​such as exposure parameter values ​​and gain parameter values ​​that need to be adjusted to achieve image changes between renderings corresponding to at least two local rendering identifiers.

[0081] In a specific example, consider the scenario where the client requests optimization and performs their own optimization:

[0082] You can enter the image names of two local mockups, such as mockup filename2 and mockup filename4. The order in which the image names are entered determines the desired optimization direction. For example, mockup filename4 has lower exposure and higher gain than mockup filename2. If the client needs a valid marker file to adjust from mockup filename2 to mockup filename4 for reference optimization, then they need to first enter mockup filename2, then enter the name of mockup filename4, and then perform a query, comparison, and output based on script commands.

[0083] The corresponding script instructions can be traced back to the cam_tuning_test_list file (i.e., the tuning index file) based on the name of the effect image, and find the 2 and 4 marked for these two tuning effect images respectively. <tuning>(Optimization) Attribute Parameters. If the script detects that there is an optimization attribute parameter for optimization image file name 3 between optimization image file name 2 and optimization image file name 4, it will also add the optimization attribute parameter of optimization image file name 3 to the subsequent comparison sequence. That is, the parameters within the optimization attributes corresponding to 2, 3, and 4 will be compared in an orderly manner. For example, if 3 only reduces the exposure compared to 2, then the corresponding exposure parameter will be adjusted, and the script needs to record and store the optimization parameters in this process. Continuing the comparison, if 4 only increases the gain compared to 3, then the corresponding gain parameter will be adjusted, and the script needs to record and store the optimization parameters in this process as well. At this point, the script will output a valid marker text containing exposure and gain adjustments. The personnel requesting optimization can learn from this valid marker file to achieve independent optimization. They can manually add the changed attributes and parameters in the valid marker text to the optimization file, and then import the optimization file into the camera module to verify the image shooting effect of the camera module.

[0084] In one implementation, when merging tuning data into the code repository of the tuning requester, the account information of the tuning service provider can be recorded to clarify which tuning service provider provided the tuning data.

[0085] In one implementation, such as Figure 3 As shown, a local code repository can be established and updated based on the client's master code repository for the optimization requester. Automated verification of optimization data can be performed via an external channel (i.e., the automated file verification module). If verification passes, the optimization data can be submitted to a temporary channel (i.e., the temporary storage area) for local verification by the optimization requester's personnel. If local verification passes, the optimization data is merged into the client's master code repository for the optimization requester. If local verification fails, code issues can be marked, and issue feedback information can be sent back to the local code repository for further optimization by the optimization service provider, who will then update the `cam_tuning_test_list` file (i.e., the optimization index file).

[0086] Based on the methods described in steps S101 to S105 above, this embodiment of the application creates a collaborative interaction system for interacting with the optimization service provider. The collaborative interaction system includes a local code repository, an automated file verification module connected to the local code repository, and a temporary storage area for storing files that have passed verification. In response to the optimization service provider submitting optimization data to the local code repository, the automated file verification module is triggered to perform automated verification. If the automated verification passes, the optimization data is submitted to the temporary storage area and a notification is generated; otherwise, the optimization data is returned to the local code repository with a failure reason displayed. In response to the notification, the optimization data is pulled to the local repository for local verification by the optimization requester. If the local verification passes, the optimization data is merged into the optimization requester's main code repository; otherwise, the problem feedback information is sent back to the local code repository for further optimization by the optimization requester. Through the above configuration, this embodiment of the application can realize interaction between the optimization requester and the optimization service provider based on the collaborative interaction system, improving the optimization efficiency of the optimization process and effectively reducing the communication cost of the optimization process.

[0087] Meanwhile, in the case of initial optimization, the submitted optimization data in this application includes optimization files, effect images taken based on the optimization files, and optimization index files; in cases of subsequent optimizations, the submitted optimization data includes effect images and optimization index files; the optimization index files include the parameters of the optimization files and the corresponding identifiers of the optimization effect images. This effectively improves the readability of the optimization data and further reduces the manual and communication costs of the optimization process.

[0088] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application and therefore will also fall within the protection scope of this application.

[0089] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0090] The technical solution of this application has been described above with reference to one embodiment shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.< / tuning>

Claims

1. A camera module optimization method, characterized in that, The method includes: In response to receiving a request from the tuning service provider to establish a collaborative interaction system, a collaborative interaction system for file interaction with the tuning service provider is created. The collaborative interaction system includes a local code repository, an automated file verification module connected to the local code repository, and a temporary storage area for storing files that have passed verification. In response to the optimization service provider submitting optimization data to the local code repository, the automated file verification module is triggered to perform automated verification. If the automated verification passes, the optimization data is submitted to the temporary storage area and a notification is generated; otherwise, the optimization data is returned to the local code repository and the reason for the failure is displayed. In response to the notification, the optimization data is pulled to the local warehouse for local verification by the personnel of the optimization request party; In response to receiving a notification that local verification has passed, the optimization data is merged into the main code repository of the optimization requester; otherwise, the issue feedback information is sent back to the local code repository for further optimization by the optimization service provider.

2. The method according to claim 1, characterized in that, In the case of initial optimization, the submitted optimization data includes optimization files, effect images taken based on the optimization files, and optimization index files; In cases where it is not the first time optimizing, the submitted optimization data includes the aforementioned effect diagram and optimization index file. The tuning index file includes the parameters of the tuning file and the identifier of the corresponding tuning effect diagram.

3. The method according to claim 2, characterized in that, The tuning file parameters include the tuning file name, path, and / or version identifier.

4. The method according to claim 1, characterized in that, The automated file verification module performs automated verification, including: Verify the version consistency, syntax compliance, and validity of file changes in the submitted optimization data.

5. The method according to claim 1, characterized in that, Before triggering the automated file verification module to perform automated verification, the method further includes: in response to the optimization service provider submitting optimization data to the local code repository, determining whether the submitted optimization data is invalid information; if so, returning the optimization data to the local code repository.

6. The method according to claim 2, characterized in that, The step of sending the problem feedback information back to the local code repository includes: In response to the optimization requester's personnel annotating the local effect diagram obtained based on the optimization data, a local effect diagram with annotations is generated. The tuning index file is updated based on the annotated local renderings, so that the updated tuning index file contains the identifiers of the annotated local renderings. The labeled local rendering and the updated tuning index file are sent back to the local code repository.

7. The method according to claim 6, characterized in that, In the case of initial optimization, the optimization data is obtained by the optimization service provider burning the optimization file to the camera module and optimizing the camera module according to the optimization file; In cases other than the first optimization, the optimization data is obtained by the optimization service provider after adjusting the optimization file based on the annotated local effect diagram and the updated optimization index file in the local code repository, and then re-optimizing the camera module.

8. The method according to claim 1, characterized in that, The process of merging the optimization data into the code repository of the optimization requester includes: Record the account information of the optimization service provider.

9. The method according to claim 6, characterized in that, Also includes: At least two local effect diagrams obtained from the input optimization data; Obtain the image differences between the at least two local effect images; Obtain the variable parameters and their corresponding attributes for the at least two local effect diagrams from the optimization index file; Based on the image differences, changing parameters, and associated attributes, tagged text is generated to guide the personnel of the optimization requesting the optimization to perform independent optimization.

10. The method according to claim 8, characterized in that, The two local effect graphs obtained from the input optimization data include: Based on the desired optimization direction, select the order in which to input the effect diagrams.

11. The method according to claim 8 or 9, characterized in that, The labeled text contains the exposure parameter values ​​and / or gain parameter values ​​that need to be adjusted to achieve image changes between the effect images corresponding to the at least two local effect image identifiers.

Images