Detection method, device, storage medium, equipment and program product

Abstract

The application discloses a detection method and device, a storage medium, equipment and a program product, which are applied to game, game operation, testing and the like. The method comprises the following steps: acquiring a to-be-detected resource; acquiring a first object and a second object corresponding to the to-be-detected resource based on a preset resource reference relationship, the first object and the second object being resource files of the same resource in different versions; rendering the first object and the second object, and shooting the rendered first object and the second object at the same time interval to generate a first rendering image and a second rendering image respectively; and detecting image differences between the first rendering image and the second rendering image to generate detection information. The differences in display effects of target objects caused by resource modification do not need to be compared manually, but detection information is directly generated, and users can directly check the detection information to quickly realize detection, thereby improving detection efficiency and improving the development progress of software or games.

Description

Technical Field

[0001] This application relates to the field of testing technology, specifically to a testing method, apparatus, storage medium, equipment, and program product. Background Technology

[0002] Currently, when software or games are updated, resource files (such as model files, material files, texture files, etc.) are usually modified. Modification of resource files will lead to differences in the final display effect. These differences may not meet the expectations of the modification (such as the updated image display having significant defects (such as missing parts, discoloration, deformation, etc.)). Therefore, it is necessary to actively detect the differences in the final display effect of different versions in order to repair the resource files. However, software or games display a large number of objects, and relying on manual inspection of each one is extremely inefficient and affects the development progress of software or games. Summary of the Invention

[0003] This application provides a detection method, apparatus, storage medium, device, and program product, which can improve detection efficiency and accelerate the development progress of software or games.

[0004] On the one hand, a detection method is provided, the method comprising: acquiring a resource to be detected; acquiring a first object and a second object corresponding to the resource to be detected based on a preset resource reference relationship, wherein the first object and the second object are resource files of the same resource in different versions; rendering the first object and the second object, and taking pictures of the rendered first object and the second object at the same time interval to generate a first rendered image and a second rendered image respectively; detecting the image differences between the first rendered image and the second rendered image to generate detection information.

[0005] On the other hand, a detection device is provided, comprising a first acquisition module, a rendering module, and a first detection module. The first acquisition module is used to acquire a resource to be detected; the first acquisition module is used to acquire a first object and a second object corresponding to the resource to be detected based on a preset resource reference relationship, wherein the first object and the second object are resource files of the same resource in different versions; the rendering module is used to render the first object and the second object, and to capture images of the rendered first object and the second object at equal time intervals to generate a first rendered image and a second rendered image respectively; the first detection module is used to detect image differences between the first rendered image and the second rendered image to generate detection information.

[0006] In another aspect, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program adapted for loading by a processor to perform the steps in the detection method as described in any of the above embodiments.

[0007] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing a computer program, the processor executing the steps of the detection method as described in any of the above embodiments by calling the computer program stored in the memory.

[0008] On the other hand, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps in the detection method as described in any of the above embodiments.

[0009] The detection method, detection device, computer-readable storage medium, and computer equipment of this application embodiment acquire the target object (such as a first object and a second object) corresponding to different versions of each resource to be detected, then render the two different versions of the target object to obtain two rendered images, and detect the image differences between the two rendered images to obtain detection information, which includes image difference information between the two rendered images. Thus, there is no need for manual comparison of differences in the display effect of the target object caused by resource modifications; instead, detection information is directly generated, and users can quickly achieve detection by directly checking the detection information, thereby improving detection efficiency and accelerating the development progress of software or games. Furthermore, when acquiring the first or second rendered image, images are taken at the same time interval, ensuring that the motion states of corresponding frames in the first and second rendered images are identical, which improves the accuracy of the subsequently obtained detection information. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the detection system provided in an embodiment of this application.

[0012] Figure 2 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0013] Figure 3 This is a schematic diagram of a scenario for the detection method provided in the embodiments of this application.

[0014] Figure 4This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0015] Figure 5 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0016] Figure 6 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0017] Figure 7 This is a schematic diagram of a scenario for the detection method provided in the embodiments of this application.

[0018] Figure 8 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0019] Figure 9 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0020] Figure 10 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0021] Figure 11 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0022] Figure 12 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0023] Figure 13 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0024] Figure 14 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0025] Figure 15 This is a schematic flowchart of the detection method provided in the embodiments of this application.

[0026] Figure 16 This is a schematic diagram of the detection device provided in an embodiment of this application.

[0027] Figure 17 This application provides a schematic diagram of the structure of a computer device. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] This application provides a detection method, apparatus, computer device, and storage medium. Specifically, the detection method of this application can be executed by a computer device, which can be a terminal or a cloud server, etc. The terminal can be a smartphone, tablet, laptop, desktop computer, smart TV, smart speaker, wearable smart device, smart vehicle terminal, etc. The terminal can also include a client, which can be a cloud gaming client, a client applet, a video client, a browser client, or an instant messaging client, etc. The cloud server can be an independent physical cloud server, a cloud server cluster or a distributed system composed of multiple physical cloud servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0030] The embodiments of this application can be applied to scenarios such as games, game operation, and testing.

[0031] First, some of the nouns or terms that appear in the description of the embodiments of this application are explained as follows:

[0032] Unity Engine: A cross-platform game engine developed by Unity Technologies. It is one of the most widely used game engines in the industry, and many excellent games are developed and implemented based on the Unity engine.

[0033] Art assets: Resource files created by artists in other software or Unity, such as modelers, animators, and special effects artists. These include model files (with extensions like .fbx, .max, .blend, etc.), texture files (with extensions like .png, .tga, etc.), material files (.mat), and so on.

[0034] GameObject: All objects (visible and invisible) that exist in the game scene are called GameObjects, which can include characters, weapons, skill effects, maps, UI, lighting, etc.

[0035] Components: Components can be attached to GameObjects to give them different functions. All GameObjects must have a Transform component (used to record the object's position, rotation, and scaling information). For example, adding a Particle System component to an empty GameObject can give it the ability to emit particles, and adding an Animation component and an AnimationClip (animation clip) to an empty GameObject can give it animation effects, etc.

[0036] Prefab: A file type (with the .prefab extension) in the Unity game engine that organizes fragmented art assets into an independent whole. For example, it can organize character models, skin textures, materials, shaders, etc. into an independent and complete character and save it. When needed, you can directly load the file and instantiate it to get the pre-made character.

[0037] Game camera: The content displayed on the final screen of the game is obtained through the game camera. According to the projection type, it is divided into orthographic camera and perspective camera. The image under the orthographic camera is "the same size for near and far", while the image under the perspective camera is "larger for near and smaller for far".

[0038] RenderTexture: A special type of texture, which is essentially a FrameBufferObject attached to a server-side Texture object. "Client-side texture" refers to the texture that exists in the CPU, while "server-side texture" refers to the texture that exists in the GPU.

[0039] Precise testing: For a specific iteration of agile development of a game product, using data from the previous version as baseline data, the program explicitly exposes the data differences between the two versions, providing them to testers to check for anomalies, missing data, and other issues.

[0040] A cloud server is a server that runs games in the cloud and has functions such as image processing.

[0041] A terminal refers to a type of device that has rich human-computer interaction methods, internet access capabilities, typically runs various operating systems, and possesses strong processing power. Terminals include smartphones, living room TVs, tablets, in-vehicle terminals, handheld game consoles, etc.

[0042] Please refer to Figure 1 , Figure 1This is a schematic diagram of the detection system provided in an embodiment of this application. The detection system includes a terminal 10 and a cloud server 20, etc.; the terminal 10 and the cloud server 20 are connected via a network, such as a wired or wireless network.

[0043] Terminal 10 can be used to display a graphical user interface (GUI). This GUI allows interaction with the user, such as downloading and installing a client application, running a mini-program, or accessing a website. In this embodiment, terminal 10 can receive and display rendered images and detection information generated by cloud server 20, allowing users to compare the display effects of software or games after version updates and quickly identify problematic resources.

[0044] The following sections provide detailed descriptions of each example. It should be noted that the order in which the embodiments are described is not intended to limit the priority of the embodiments.

[0045] The embodiments of this application provide a detection method, which can be executed by a detection system, such as by terminal 10 or cloud server 20, or by both terminal 10 and cloud server 20. The embodiments of this application use the execution of the detection method by cloud server 20 as an example for illustration.

[0046] Please see Figures 2 to 14 , Figure 2 , Figure 4 , Figure 5 , Figure 6 , Figures 8 to 15 These are all schematic flowcharts of the data processing methods provided in the embodiments of this application. Figure 3 and Figure 7 These are all schematic diagrams illustrating scenarios of the detection methods provided in the embodiments of this application. The detection methods include:

[0047] Step 011: Obtain the resource to be tested.

[0048] Optionally, the resource to be detected can be any resource among all the resources of the software or game. For example, the resource to be detected can be an art resource, such as model files (with suffixes like .fbx, .max, .blend, etc.), texture files (with suffixes like .png, .tga, etc.), material files (.mat), etc., so that all resources can be detected to ensure the accuracy of the detection.

[0049] Optionally, the resource to be tested can also be a changed resource.

[0050] It's understandable that when software or games are updated, resources are iteratively modified. For resources that haven't been modified, the display remains largely unchanged after the update. Therefore, we only need to focus on the resources that have changed during the update—the modified resources—to reduce processing load and improve detection efficiency.

[0051] Optionally, when acquiring changed resources, the first and second resource files corresponding to the same resource in different versions can be acquired first. It can be understood that when a version is updated, there is a one-to-one correspondence between the resources in the previous and updated versions. Therefore, the first and second resource files corresponding to the same resource in both the previous and updated versions can be acquired.

[0052] Then, by comparing the first resource file and the second resource file, it can be determined whether the first resource file and the second resource file have been modified. For example, by comparing the code sets in the first resource file and the code sets in the second resource file, it can be determined whether the code has been modified, thereby determining whether the first resource file and the second resource file have been modified.

[0053] Based on the resource comparison results, a resource file comparison table is generated. See Table 1 below:

[0054] Resource file A Resource file B Version A Version B Are there differences? Table of Contents 1 Table of Contents 2 A1 B1 yes Table of Contents 3 Table of Contents 4 A1 B1 no Table of Contents 5 Table of Contents 6 A1 B1 yes

[0055] In this table, resource file A represents the resource file directory of the previous version, and resource file B represents the resource file directory of the updated version. By comparing the resource files, the differences can be quickly identified, thus allowing for rapid determination of the changed resources.

[0056] It's understandable that if no resources are modified during a version update, the first and second resource files corresponding to those resources should be identical. Therefore, a difference between the code sets in the first and second resource files indicates that modifications have occurred.

[0057] If the first resource file and the second resource file corresponding to the same resource are at least partially different, the resource can be determined to be a changed resource, and thus the changed resource can be used as the resource to be tested.

[0058] Optionally, upon detecting a software or game version update, resources to be tested can be acquired to detect defects after the update; or, upon receiving a trigger operation (such as a user manually entering a detection command), resources to be tested can be acquired to detect defects after the update; or, regardless of whether a version update occurs, resources to be tested can be acquired at predetermined time intervals to periodically detect defects after the update; or, based on the update schedule of the software or game, such as some games undergoing maintenance updates every Tuesday, resources to be tested can be acquired to detect defects after the update when the current time reaches a preset time (such as 8:00 AM every Tuesday).

[0059] Step 012: Based on the preset resource reference relationship, obtain the first object and the second object corresponding to the resource to be detected. The first object and the second object are resource files of the same resource in different versions.

[0060] Once the resource to be tested is determined, the first and second objects corresponding to the resource to be tested can be obtained based on the preset resource reference relationships.

[0061] Optionally, the preset resource reference relationships are established based on the reference relationships between art resources, prefabs, etc. For example, when a prefab is created, it will reference one or more art resources, and there are also mutual reference relationships between art resources, such as character model resources referencing skin texture resources, material resources, etc. In this way, the preset resource reference relationships can be accurately established based on the reference relationships between art resources, prefabs, etc.

[0062] Furthermore, resource reference relationships generally remain unchanged when versions change. Therefore, the preset resource reference relationships can adapt to both the pre-update and post-update versions, thus quickly determining the first object of the resource to be detected in the pre-update version and the second object in the post-update version based on the preset resource reference relationships.

[0063] Optionally, resource reference relationships adapted to the previous version can be established based on the reference relationships between art resources, prefabs, etc. in the previous version, and resource reference relationships adapted to the updated version can be established based on the reference relationships between art resources, prefabs, etc. in the updated version, thereby more accurately determining the first object and the second object.

[0064] As is understandable, when software or games are finally displayed, they are generally displayed as objects (such as GameObjects, software objects, prefabs, etc.). Specifically, these include characters, weapons, skill effects, maps, UI, lighting, etc. in a game scene, and UI, click animations, etc. in software. Each object can reference one or more prefabs.

[0065] Therefore, by identifying the first and second objects of the resource to be tested before and after the version update, and comparing the display differences between the first and second objects, the impact of resource modifications on the display effect can be quickly detected. For example, resource modifications may cause the character to appear entirely black or skill effects to malfunction.

[0066] Optionally, the changed resources include resources before the change and resources after the change. When the resource to be detected is a changed resource, the first object corresponding to the resource before the change (i.e., the resource before the version update) and the second object corresponding to the resource after the change (i.e., the resource after the version update) can be obtained.

[0067] Step 013: Render the first object and the second object, and take pictures of the rendered first object and the second object at the same time interval to generate the first rendered image and the second rendered image respectively.

[0068] After determining the first object and the second object corresponding to the resource to be detected, the first object and the second object can be rendered respectively, thereby generating the first rendered image and the second rendered image respectively.

[0069] Before rendering, some preparatory work needs to be done, such as passing parameters between different versions and setting the control parameters required for rendering.

[0070] Alternatively, the Unity engine offers two modes:

[0071] 1. Edit Mode: In this mode, game developers can make adjustments and previews to the scene and objects. Changes made in edit mode are permanent.

[0072] 2. Run Mode: This mode is for the entire game process. Changes made to the scene and objects during this mode will only take effect during the current game run.

[0073] When the Unity engine starts, it is in edit mode by default. This mode cannot obtain the actual rendered images of multiple GameObjects at the same time (it can only do preview). Therefore, it is necessary to switch to run mode to render and capture multiple objects.

[0074] Taking the first and second objects as game objects as an example, in the running mode, multiple game objects can be obtained at the same time, and multiple game objects can be rendered at the same time (such as creating multiple threads to render multiple game objects at the same time). After rendering, the rendered game objects can be screenshotted, thereby obtaining the first rendered image corresponding to the first object and the second rendered image corresponding to the second object.

[0075] It is understandable that, in the case where the first and second objects are moving objects, in order to avoid the impact of system resource control on the acquisition of rendered images, which would cause the motion states of the first and second rendered images to be different and affect the detection accuracy, it is necessary to perform frame alignment on the acquired first and second rendered images so that the motion states of the aligned first and second rendered images are the same.

[0076] Furthermore, for moving objects (such as the animation of a character running), the motion state is the same at the same time interval. In order to achieve frame alignment, when acquiring the first or second rendered image, the same time interval is used to capture the images. This ensures that when capturing the first and second objects after rendering, they are both in the same motion state. This makes the motion state of the Nth frame of the first rendered image (N is a positive integer) and the Nth frame of the second rendered image the same, which can improve the accuracy of the detection information obtained subsequently.

[0077] Step 014: Detect the image differences between the first rendered image and the second rendered image to generate detection information.

[0078] After obtaining the first and second rendered images, the differences between the first and second rendered images can be detected.

[0079] Optionally, the detection information includes difference regions, which are determined by calculating the difference between pixels at the same location in the first and second rendered images. If the difference between pixels at the same location in the first and second rendered images is greater than a preset difference, then the pixel is identified as a difference pixel.

[0080] Then, the difference region is determined based on multiple difference pixels, such as by connecting adjacent difference pixels to determine the difference region, thereby generating detection information, such as the location information of the difference region.

[0081] Optionally, the detection information may also include defect information. Defect information can be determined by comparing color differences in the difference areas, shape differences of the displayed objects in the difference areas, etc. In this way, not only can the location of the difference areas be determined, but also the specific defects corresponding to the difference areas can be identified, such as partial missing parts of the updated second object, darkening of the color, deformation of the second object, etc., thereby further reducing the workload of testers in interpreting images and improving detection efficiency.

[0082] Please see Figure 3 Images P1 and P2 are the first and second rendered images, respectively. The first object and the second object are "trees". The difference region of the first object is M1, and the difference region of the second object is M2. By comparing the difference region M1 and the difference region M2, the defect can be determined to be a missing branch.

[0083] Please see Figure 4 Optionally, the detection method further includes:

[0084] Step 015: Obtain the preset resource reference relationships.

[0085] Optionally, the preset resource reference relationships are established based on the reference relationships between art resources, prefabs, etc.

[0086] Please see Figure 5 Optionally, step 015 includes:

[0087] Step 0151: Parse the preset resource files to obtain the identification information of each resource and the reference information between different resources.

[0088] The Unity game engine creates a ".meta" file with the same name for every file (folder) in the project directory. The ".meta" file records the GUID (Globally Unique Identifier) ​​assigned to the file, which is used to identify the resource file.

[0089] Non-binary format resource files generated by Unity itself (such as .mat material files, .anim animation files, .prefab prefab files, etc.) are essentially text files in YAML format. The files record information about some objects (properties, reference relationships). Each object in a document is identified by a pair of "ClassID + FileID".

[0090] The reference chain between different resource files is indexed using the format "GUID + FileID". If it is a reference to an object within a resource, that is, a reference to an object in the same file, only the "FileID" needs to be provided. Of course, objects in all resource files (between the same resource file or between different resource files) can be referenced using the "GUID + FileID" method.

[0091] Step 0152: Establish resource reference relationships based on the identification information and reference information.

[0092] Therefore, by parsing resource files to obtain the identification information of each resource (such as GUID, FileID, ClassID, etc.) and the reference information between resources (such as the identification information of the resources referenced by each resource), resource reference relationships between different resources can be established. For example, by recording the identification information of each resource and the identification information of the resources referenced by each resource in an XML file, the resource reference relationship text is established. The resource reference relationship text is as follows:

[0093] Identification information: Identification information 1 for resource A;

[0094] Reference information for resource A: Identification information 2 for resource B, and identification information 3 for resource C;

[0095] Identification information: Identification information 4 for resource D;

[0096] Reference information for resource D: Identification information for resource E (5), Identification information for resource F (6).

[0097] Alternatively, you can use an EXEL spreadsheet to record the identifier information of each resource and the identifier information of the resources referenced by each resource, thus creating a resource reference relationship table.

[0098] As shown in Table 2 below:

[0099]

[0100] In this way, resource reference relationships can be quickly established based on identification and reference information.

[0101] Please see Figure 6 In one embodiment, step 013 includes:

[0102] Step 0131: Render the first object and capture first rendered images of the first object from different perspectives at a first preset time interval;

[0103] Step 0132: Render the second object and take second rendered images of the second object from different perspectives at a second preset time interval. The first preset time interval and the second preset time interval are the same.

[0104] Please see Figure 7 In order to cover game objects (such as Figure 7 As shown in the diagram (S), different perspectives are presented. This solution renders the same game object in engine mode using six cameras positioned in different directions, with perspectives including top, bottom, left, right, front, and back (e.g., ...). Figure 7 As shown in the image, each view corresponds to one camera, and six cameras can be called a camera group. One camera group corresponds to one game object. Multiple camera groups can exist in the same scene, meaning that multiple resources can be rendered and captured simultaneously.

[0105] Thus, after rendering the first object, the camera group corresponding to the first object can acquire first rendered images of the first object from different perspectives at a first preset time interval. After rendering the second object, the camera group corresponding to the second object can acquire second rendered images of the second object from different perspectives at a second preset time interval. Since the first preset time interval and the second preset time interval are the same, first rendered images and second rendered images with the same motion state and the same perspective can be obtained.

[0106] Specifically, when acquiring the rendered image, you can first acquire the camera group bound to the resource. For each camera in the camera group, create a new RenderTexture and name it rt1. Set the camera's TargetTexture (target texture, which is a camera property used to bind the image captured by the camera to a specific texture object) to rt1, and set RenderTexture.active to rt1 (meaning that the RenderTexture bound to the current camera is activated, so that the rendered image data can be obtained from rt1 after the camera renders).

[0107] Please see Figure 8 The overall rendering process is as follows:

[0108] The rendering process begins and the run mode is initiated (step S1). In run mode, the resource list is obtained (step S2) and the resource paths are read (step S3). Then, the validity of the resource paths is determined (step S4), for example, whether the resource paths are correct and accessible. If the resource paths are valid, the objects in the resource list are stored sequentially in the object queue (e.g., ...). Figure 8 The system first extracts objects 1 through 3 from the queue, and then renders them sequentially to obtain the rendered image for each object (step S5). If the resource path is invalid, the resource path of the next object is read.

[0109] After one object is rendered, check if the object queue is empty (step S6), that is, whether all objects have been rendered. If the object queue is empty, end the rendering process (step S7).

[0110] Optionally, in order to further improve the detection accuracy of the image difference between the first rendered image and the second rendered image, multiple frames of rendered images can be acquired at each viewpoint; that is, multiple frames of the first rendered image and the second rendered image can be acquired at each viewpoint.

[0111] It is understandable that both the first and second objects can be dynamic. Therefore, determining the image differences between them by only acquiring rendered images of the first and second objects under one motion state is inaccurate. Therefore, acquiring multiple frames of the first and second rendered images from each viewpoint allows for comparison of the image differences between the rendered images of the first and second objects under different motion states, thus improving the accuracy of the generated detection information.

[0112] Optionally, under each viewpoint, the rendered first object is continuously photographed at a first preset time interval to obtain multiple frames of the first rendered image under each viewpoint; under each viewpoint, the rendered second object is continuously photographed at a second preset time interval to obtain multiple frames of the second rendered image under each viewpoint, wherein the first preset time interval and the second preset time interval are the same.

[0113] It is understandable that, in the case that the first object and the second object are moving objects, in order to avoid the influence of system resource control on the camera group to acquire rendered images, which would cause the motion states of the first rendered image and the second rendered image to be different and affect the detection accuracy, it is necessary to perform frame alignment on the acquired multiple frames of first rendered images and multiple frames of second rendered images so that the aligned first rendered images and second rendered images have the same viewpoint and the same motion state.

[0114] Furthermore, for moving objects (such as the animation of a character running), the motion state is the same at the same time interval. In order to achieve frame alignment, when acquiring the first or second rendered image from each viewpoint, the images can be taken at fixed time intervals (such as the first preset time interval and the second preset time interval), so as to ensure that when the camera captures the first and second objects after rendering, both are in the same motion state.

[0115] Please see Figure 9 In one embodiment, step 014 includes:

[0116] Step 0141: Detect the image differences between the first and second rendered images from the same viewpoint to generate multiple detection information.

[0117] After capturing multiple first-rendered images and multiple second-rendered images from different perspectives, the image differences between the first-rendered images and the second-rendered images from the same perspective can be detected, thereby generating detection information from different perspectives.

[0118] In this way, the differences in the display effect of each resource to be tested from different perspectives of the game object before and after the version update can be obtained, thus achieving comprehensive testing of each resource to be tested.

[0119] Please see Figure 10 In one embodiment, step 014 includes:

[0120] Step 0142: Obtain multiple frames of first rendered images and multiple frames of second rendered images from the same viewpoint;

[0121] Step 0143: Align multiple frames of the first rendered image and multiple frames of the second rendered image according to the frame number of the first rendered image and the frame number of the second rendered image.

[0122] Step 0144: Detect the image differences between the aligned first and second rendered images to generate detection information.

[0123] When performing frame alignment, multiple first-frame rendered images and multiple second-frame rendered images can be obtained from the same viewpoint. Both the first-frame rendered images and the second-frame rendered images are obtained by continuous shooting at the same time interval.

[0124] Based on the time when the first and second rendered images were captured, multiple frames of the first and second rendered images can be numbered respectively, so that each frame has a frame number. For example, the earlier the capture time, the smaller the frame number.

[0125] Then, based on the frame numbers of the first and second rendered images, the first and second rendered images are aligned; for example, the first and second rendered images with the same frame number are aligned.

[0126] Finally, the differences between the first and second rendered images, which are from the same and aligned viewpoints, are detected to generate detection information.

[0127] In this way, by aligning the frames of the first and second rendered images, it is ensured that the differences between the first and second rendered images used for image comparison are only due to modifications to the resource to be detected, thus improving detection accuracy.

[0128] Optionally, in order to ensure that the image difference between the first rendered image and the second rendered image is caused only by the modification of the resource to be detected, conventional image processing may not be performed on the first rendered image and the second rendered image to prevent changes in the image difference between the first rendered image and the second rendered image after image processing, which would affect the detection accuracy.

[0129] Please see Figure 11 In one embodiment, step 014 includes:

[0130] Step 0145: Identify the foreground and background images of the first rendered image and the foreground and background images of the second rendered image.

[0131] Optionally, when the camera captures the rendered first and second objects, it simultaneously acquires the foreground image (i.e., the images of the first and second objects) and the background image, thereby generating a first and second rendered image of a fixed size (e.g., both 400*400) to facilitate subsequent detection of image differences.

[0132] Therefore, after obtaining the first rendered image and the second rendered image, the foreground and background images of the first rendered image and the foreground and background images of the second rendered image can be identified.

[0133] Specifically, when capturing the first and second rendered images, to facilitate the identification of background and foreground in subsequent image comparison tasks, the camera background needs to be set to transparent beforehand. The captured images are 4-channel images, i.e., RGBA, where the RGB channels are color channels used to distinguish the color of each pixel, and the A channels are used to distinguish between foreground and background. For example, an A value of 1 for pixels in the foreground area indicates complete opacity, while an A value of 0 for pixels in the background area indicates complete transparency.

[0134] Step 0146: Detect the image difference between the foreground image of the first rendered image and the foreground image of the second rendered image to generate detection information.

[0135] It is understandable that the background image is not caused by the modification of the resource to be detected. Therefore, it is only necessary to detect the image difference between the foreground image of the first rendered image and the foreground image of the second rendered image to generate detection information, thereby improving the accuracy of the detection information and preventing the background image from affecting the detection.

[0136] Please see Figure 12 The detection methods also include:

[0137] Step 016: Display the first rendered image and the second rendered image based on the detection information.

[0138] After obtaining the detection information, the first rendered image and the second rendered image can be displayed based on the detection information, such as displaying the first rendered image, the second rendered image, and the detection information on the display screen.

[0139] Optionally, the detection information includes difference regions. Based on the difference regions, the regions where there are image differences can be marked in the first and second rendered images, thereby facilitating testers to quickly verify the differences between the first and second rendered images, reducing the workload of manual image review, and improving detection efficiency.

[0140] Please see Figure 13 In one embodiment, the detection method further includes:

[0141] Step 017: Detect the image differences between the difference regions of the first rendered image and the difference regions of the second rendered image to determine the similarity.

[0142] Optionally, similarity can be determined by calculating the sum of the differences between pixels at the same location in the first and second rendered images. The larger the sum of these differences, the smaller the similarity.

[0143] Step 018: Determine the detection results of the first rendered image and the second rendered image based on their similarity.

[0144] After determining the similarity, the detection result can be determined based on the similarity. The detection result can be categorized as normal or abnormal. If the similarity is greater than the preset similarity (e.g., 98%, 99%), it indicates that the difference between the first and second rendered images is small, possibly due to system shooting errors, and the detection result can be determined as normal. Conversely, if the similarity is less than or equal to the preset similarity, it indicates that the difference between the first and second rendered images is large, most likely due to modifications to the resource being detected, and the detection result can be determined as abnormal. In this way, users only need to focus on the first and second rendered images with abnormal detection results, further reducing the workload of testers in interpreting images and improving detection efficiency.

[0145] Please see Figure 14 In one embodiment, the detection method further includes:

[0146] Step 019: Receive input operation to determine the detection results of the first rendered image and the second rendered image. The detection results include normal or abnormal.

[0147] It is understandable that after the difference between the first and second rendered images is displayed based on the detection results, the user can quickly compare the difference areas to determine whether the detection results of the first and second rendered images are normal or abnormal. Then, the user can perform input operations to confirm the detection results.

[0148] All of the above technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.

[0149] This application embodiment obtains the target object corresponding to each resource to be detected in different versions, then renders two different versions of the target object to obtain two rendered images, and detects the image differences between the two rendered images to obtain detection information, which includes the image difference information between the two rendered images. In this way, there is no need for manual comparison of the differences in the display effect of the target object caused by resource modifications; instead, detection information is directly generated. Users can directly verify the detection information to quickly achieve detection, thereby improving detection efficiency and accelerating the development progress of software or games.

[0150] For a better illustration of the image processing method provided in the embodiments of this application, please refer to... Figure 15 The detection method provided in this application can be summarized into the following steps:

[0151] Step 021: Trigger the test process;

[0152] The detection process can be triggered when a version update is detected, when a user manually inputs a detection command, or when the scheduled detection time is reached, so as to carry out subsequent detection.

[0153] Step 022: Obtain the preset resource reference relationships;

[0154] Specifically, please refer to the description of step 015 for step 022.

[0155] Step 023: Obtain the changed resources and start the rendering preprocessing process;

[0156] Acquire the changed resources (see step 011), and at the same time as acquiring the changed resources, execute the preprocessing process required for the rendering process, so that the subsequent rendering process can be carried out quickly after the changed resources are acquired.

[0157] Step 024: Render the two objects corresponding to the resources before and after the change to generate two rendered images;

[0158] Specifically, please refer to the description of step 013 for step 024.

[0159] Step 025: Detect image differences between the two rendered images to generate detection information;

[0160] Specifically, please refer to the description of step 014 for step 024.

[0161] Step 026: Display the rendered image based on the detection information, receive input operations to determine the detection result, and store the rendered image and the detection result.

[0162] Testers manually review the images to determine the detection results for each set of rendered images. If the result is normal or abnormal, the rendered images and detection results are stored for subsequent targeted processing.

[0163] In one example, either incremental precise testing or full precise testing can be performed. During incremental precise testing, the resource files before and after the version update are compared to identify the changed resources. Then, based on the parsed resource reference table, the first and second objects corresponding to the changed resources are obtained, forming a list of changed resources. During full precise testing, all resource files can be directly obtained to form a full resource list.

[0164] Then, preparatory work is carried out before rendering, such as passing parameters between different versions and setting the control parameters required for rendering.

[0165] Next, the objects in the resource list (such as the modified resource list or the full resource list) are rendered to obtain rendered images. Then, the rendered images are compared to determine the differences between the images before and after the version update, which can be used as detection information.

[0166] Finally, the testing platform presents the rendered image and detection information, which are then checked by the testers to complete the entire testing process.

[0167] To facilitate better implementation of the detection method in the embodiments of this application, the embodiments of this application also provide a detection device. Please refer to... Figure 16 , Figure 16 This is a schematic diagram of the structure of the detection device 1000 provided in an embodiment of this application. The detection device 1000 may include:

[0168] The first acquisition module 1010 is used to acquire the resource to be detected.

[0169] The first acquisition module 1010 is specifically used for:

[0170] Obtain any one of the all resources as the resource to be detected; or,

[0171] Obtain changed resources from all resources as resources to be detected. The changed resources are those resources that have changed in the case of version updates.

[0172] The first acquisition module 1010 is also specifically used for:

[0173] Retrieve the first and second resource files corresponding to the same resource in different versions;

[0174] Resources that are at least partially different from the first resource file and the second resource file are identified as changed resources, and these changed resources are used as resources to be detected. The changed resources include resources before the change and resources after the change. The resources before the change are the first resource file, and the resources after the change are the second resource file.

[0175] The second acquisition module 1020 is used to acquire the first object and the second object corresponding to the resource to be detected based on the preset resource reference relationship. The first object and the second object are resource files of the same resource in different versions.

[0176] The second acquisition module 1020 is specifically used for:

[0177] Get the first object corresponding to the resource before the change and the second object corresponding to the resource after the change.

[0178] The rendering module 1030 is used to render the first object and the second object, and to take pictures of the rendered first object and the second object at the same time interval to generate the first rendered image and the second rendered image respectively.

[0179] Rendering module 1030 is specifically used for:

[0180] Render a first object, and capture first rendered images of the first object from different perspectives at a first preset time interval; and

[0181] Render the second object, and take second rendered images of the second object from different perspectives at a second preset time interval.

[0182] Rendering module 1030 is also specifically used for:

[0183] Under each viewpoint, the rendered first object is continuously photographed at a first preset time interval to obtain multiple frames of the first rendered image under each viewpoint;

[0184] Under each viewpoint, the rendered second object is continuously photographed at a second preset time interval to obtain multiple frames of the second rendered image under each viewpoint. The first preset time interval and the second preset time interval are the same.

[0185] The first detection module 1040 is used to detect the image differences between the first rendered image and the second rendered image in order to generate detection information.

[0186] The first detection module 1040 is specifically used for:

[0187] Detect the image differences between a first rendered image and a second rendered image from the same viewpoint to generate multiple detection information.

[0188] The first detection module 1040 is also specifically used for:

[0189] Obtain multiple frames of first-rendered images and multiple frames of second-rendered images from the same viewpoint;

[0190] Align multiple frames of the first rendered image and multiple frames of the second rendered image according to the frame number of the first rendered image and the frame number of the second rendered image.

[0191] The image differences between the aligned first and second rendered images are detected to generate detection information.

[0192] The first detection module 1040 is also specifically used for:

[0193] Identify the foreground and background images of the first rendered image, and the foreground and background images of the second rendered image;

[0194] The image differences between the foreground image of the first rendered image and the foreground image of the second rendered image are detected to generate detection information.

[0195] Trigger module 1050 is used to acquire the resource to be detected upon receiving a trigger operation; or

[0196] If the current time reaches the preset time, acquire the resource to be detected.

[0197] The third acquisition module 1060 is used to acquire preset resource reference relationships before acquiring the resource to be detected.

[0198] The third acquisition module 1060 is specifically used for:

[0199] Parse the preset resource files to obtain the identification information of each resource and the reference information between different resources; establish resource reference relationships based on the identification information and reference information.

[0200] Display module 1070 is used to display the first rendered image and the second rendered image based on the detection information.

[0201] The display module 1070 is specifically used to mark the difference areas in the first rendered image and the second rendered image.

[0202] The second detection module 1080 is used for:

[0203] The image differences between the first rendered image and the second rendered image are detected to determine similarity.

[0204] The detection results of the first and second rendered images are determined based on their similarity; or,

[0205] Receive input operations to determine the detection results of the first and second rendered images, whereby the detection results include normal or abnormal.

[0206] Each module in the aforementioned detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0207] The detection device can be integrated into a terminal 10 and / or a cloud server 20 that has storage and a processor and thus computing power, or the detection device can be the terminal 10 and / or the cloud server 20.

[0208] Optionally, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0209] Figure 17 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. The computer device may be... Figure 1 The terminal 10 or cloud server 20 shown. Figure 17As shown, the computer device 4000 may include: a communication interface 4010, a memory 4020, a processor 4030, and a communication bus 4040. The communication interface 4010, memory 4020, and processor 4030 communicate with each other via the communication bus 4040. The communication interface 4010 is used for data communication between the detection device 1000 and external devices. The memory 4020 can be used to store software programs and modules, and the processor 4030 runs the software programs and modules stored in the memory 4020, such as the software programs for corresponding operations in the aforementioned method embodiments.

[0210] Optionally, the processor 4030 can call software programs and modules stored in the memory 4020 to perform the following operations: acquire a test sample, the test sample including multiple data points, the data points including dispersion values ​​and signal-to-noise ratio; determine the target data point as the peak among the multiple data points; based on a preset Lorentz distribution function, fit multiple data points in the test sample within a preset width range of the target data point to determine a first fitting function, the first fitting function including a fitting width; determine the outer envelope data points based on the multiple data points within the fitting width range of the target data points, where the signal-to-noise ratio of the outer envelope data points is the largest when the dispersion values ​​are the same; fit the outer envelope data points based on the preset Lorentz distribution function to determine a second fitting function, the second fitting function including fitting parameters; and determine the test sample as a suspected pulsar sample if the similarity between the first fitting function and the second fitting function is greater than a preset similarity and the fitting parameters meet preset conditions.

[0211] This application also provides a computer-readable storage medium for storing a computer program. This computer-readable storage medium can be applied to a computer device, and the computer program causes the computer device to execute the corresponding processes in the detection method described in the embodiments of this application; for brevity, these will not be elaborated further here.

[0212] This application also provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the corresponding process in the detection method described in the embodiments of this application. For simplicity, further details are omitted here.

[0213] This application also provides a computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the corresponding process in the detection method described in the embodiments of this application. For brevity, further details are omitted here.

[0214] It should be understood that the processor in the embodiments of this application may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0215] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0216] It should be understood that the above-described memory is exemplary and not a limiting description. For example, the memory in the embodiments of this application may also be static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM), etc. That is to say, the memory in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0217] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0218] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0219] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0220] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0221] In addition, the functional modules in the embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0222] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer or cloud server 20) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0223] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of detection, characterized in that, The method comprises the following steps: acquiring a to-be-detected resource, the to-be-detected resource comprising a changed resource in all resources, the changed resource being a resource that has changed in the case of version update; parsing a preset resource file to acquire identification information of each resource and reference information between different resources; establishing a resource reference relationship according to the identification information and the reference information, the resource reference relationship being automatically updated based on updated identification information and reference information; acquiring a first object and a second object corresponding to the to-be-detected resource based on a preset resource reference relationship, the first object and the second object being resource files of the same resource in different versions respectively; rendering the first object and the second object and respectively shooting the rendered first object and the second object at the same time interval through a multi-view camera group to respectively generate a plurality of frames of first rendered images and a plurality of frames of second rendered images under each view angle; detecting image differences between the first rendered images and the second rendered images to generate detection information, comprising: acquiring a plurality of frames of the first rendered images and a plurality of frames of the second rendered images under the same view angle; aligning the plurality of frames of the first rendered images and the plurality of frames of the second rendered images according to frame numbers of the first rendered images and frame numbers of the second rendered images, so that the aligned first rendered images and second rendered images have the same view angle and the same motion state; identifying foreground images and background images of the aligned first rendered images and foreground images and background images of the aligned second rendered images, the foreground images of the aligned second rendered images being determined by the changed resource; detecting image differences between the foreground images of the aligned first rendered images and the foreground images of the aligned second rendered images, and determining defect information according to color differences of difference regions and shape differences of display objects of the difference regions to generate detection information containing difference information and the defect information; displaying the difference information and the defect information corresponding to the first rendered images and the second rendered images according to the detection information, and determining a detection result, the detection result comprising normal or abnormal.

2. The detection method according to claim 1, characterized in that, The acquiring of the changed resource in all resources as the to-be-detected resource comprises: acquiring a first resource file and a second resource file corresponding to the same resource in different versions; determining a resource that is at least partially different from the first resource file and the second resource file as the changed resource, so as to take the changed resource as the to-be-detected resource, wherein the changed resource comprises a pre-change resource and a post-change resource, the pre-change resource being a resource corresponding to the first resource file, and the post-change resource being a resource corresponding to the second resource file; The acquiring of the first object and the second object corresponding to the to-be-detected resource comprises: acquiring the first object corresponding to the pre-change resource and the second object corresponding to the post-change resource.

3. The detection method according to claim 1, characterized in that, The first object and the second object are rendered, and the rendered first object and the second object are respectively photographed by a multi-view camera group at the same time interval to generate a plurality of frames of first rendering images and a plurality of frames of second rendering images under each view angle, comprising: The first object is rendered, and a plurality of frames of the first rendering images of different views of the rendered first object are photographed by the multi-view camera group at a first preset time interval; and The second object is rendered, and a plurality of frames of the second rendering images of different views of the rendered second object are photographed by the multi-view camera group at a second preset time interval, and the first preset time interval is equal to the second preset time interval.

4. The detection method according to claim 3, characterized in that, The plurality of frames of the first rendering images of different views of the rendered first object are photographed at the first preset time interval, comprising: At each view angle, the rendered first object is continuously photographed at the first preset time interval to obtain a plurality of frames of the first rendering images under each view angle; The plurality of frames of the second rendering images of different views of the rendered second object are photographed at the second preset time interval, comprising: At each view angle, the rendered second object is continuously photographed at the second preset time interval to obtain a plurality of frames of the second rendering images under each view angle, and the first preset time interval and the second preset time interval are the same.

5. The method of claim 1, wherein Further comprising: According to the detection information, the first rendering image and the second rendering image are displayed.

6. The detection method according to claim 5, characterized in that, The detection information includes a difference region, and the first rendering image and the second rendering image are displayed according to the detection information, comprising: The difference region is marked in the first rendering image and the second rendering image.

7. The detection method according to claim 6, characterized in that, The detection method further comprises: Detecting image differences of the difference region of the first rendering image and the difference region of the second rendering image to determine a similarity; According to the similarity, a detection result of the first rendering image and the second rendering image is determined; or An input operation is received to determine a detection result of the first rendering image and the second rendering image, and the detection result includes normal or abnormal.

8. A detection device, characterized in that The device comprises: A first acquisition module is configured to acquire a to-be-detected resource, wherein the to-be-detected resource includes a changed resource in all resources, and the changed resource is a resource that is changed in a version update; A third acquisition module is configured to parse a preset resource file to acquire identification information of each resource and reference information between different resources, and establish a resource reference relationship based on the identification information and the reference information, wherein the resource reference relationship is automatically updated based on updated identification information and reference information; A second acquisition module is configured to acquire a first object and a second object corresponding to the to-be-detected resource based on a preset resource reference relationship, wherein the first object and the second object are resource files of the same resource in different versions, respectively. a rendering module, configured to render the first object and the second object, and capture the rendered first object and the rendered second object by the multi-view camera group at the same time interval respectively to generate a first rendering image and a second rendering image respectively; a first detection module, configured to detect image difference of the first rendering image and the second rendering image to generate detection information, including: acquiring multiple frames of the first rendering image and multiple frames of the second rendering image under the same view angle; aligning the multiple frames of the first rendering image and the multiple frames of the second rendering image according to frame numbers of the first rendering image and frame numbers of the second rendering image, so that the aligned first rendering image and the aligned second rendering image have the same view angle and the same motion state; identifying foreground images and background images of the aligned first rendering image and foreground images and background images of the aligned second rendering image, the foreground images of the aligned second rendering image being determined by the changed resource; detecting image difference of the foreground images of the aligned first rendering image and the foreground images of the aligned second rendering image, and determining defect information according to color difference of a difference region and shape difference of a display object in the difference region to generate detection information containing the difference information and the defect information; a display module, configured to display the difference information and the defect information corresponding to the first rendering image and the second rendering image according to the detection information, and determine a detection result, the detection result including normal or abnormal.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is adapted to be loaded by the processor to execute the steps in the detection method according to any one of claims 1-7.

10. A computer device, comprising: The computer device includes a processor and a memory, and the memory stores a computer program, and the processor is configured to execute the steps in the detection method according to any one of claims 1-7 by calling the computer program stored in the memory.

11. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to implement the steps in the detection method according to any one of claims 1-7.

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