Camera hardware testing method and apparatus, computer-readable medium, and electronic device

Abstract

The present disclosure provides a camera hardware testing method and device, a computer readable medium and an electronic device, and relates to the technical field of camera testing. The method comprises the following steps: acquiring an original image collected by camera hardware; performing image conversion on the original image to obtain a target image; and performing hardware testing on the camera hardware based on the target image. The present disclosure can integrate an image format conversion algorithm in a camera engineering testing application program, does not need to rely on a hardware abstraction layer, decouples the camera hardware testing process from the imaging effect debugging process, and improves the testing efficiency of the camera hardware.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of camera testing, in particular to a camera hardware testing method, a camera hardware testing device, a computer readable medium and an electronic device. BACKGROUND

[0002] With the continuous improvement of people's living standards, the image system on the smart phone becomes more and more indispensable function. Before the smart phone is shipped, the mobile phone manufacturer generally tests the camera hardware through the production line.

[0003] At present, in the related camera hardware testing process, the hardware testing process is strongly coupled with the camera imaging effect debugging process, which leads to the fact that the camera hardware testing process cannot be performed before the camera imaging effect debugging is completed, and further leads to the fact that the camera hardware testing efficiency is low. SUMMARY

[0004] The purpose of the present disclosure is to provide a camera hardware testing method, a camera hardware testing device, a computer readable medium and an electronic device, so as to at least decouple the camera hardware testing process and the camera imaging effect debugging process to some extent, and improve the testing efficiency of the camera hardware.

[0005] According to a first aspect of the present disclosure, a camera hardware testing method is provided, comprising:

[0006] obtaining an original image collected by the camera hardware;

[0007] performing image conversion on the original image to obtain a target image;

[0008] performing hardware testing on the camera hardware based on the target image to obtain a hardware testing result.

[0009] According to a second aspect of the present disclosure, a camera hardware testing device is provided, comprising:

[0010] an original image obtaining module configured to obtain an original image collected by the camera hardware;

[0011] an image conversion module configured to perform image conversion on the original image to obtain a target image;

[0012] a hardware testing module configured to perform hardware testing on the camera hardware based on the target image to obtain a hardware testing result.

[0013] According to a third aspect of the present disclosure, a computer readable medium having a computer program stored thereon is provided, and the computer program is executed by a processor to implement the above method.

[0014] According to a fourth aspect of the present disclosure, an electronic device is provided, characterized in that it comprises:

[0015] a processor; and

[0016] a memory storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the above method.

[0017] The camera hardware testing method provided by the embodiment of the present disclosure can obtain an original image collected by camera hardware, perform image conversion on the original image to obtain a target image, perform hardware testing on the camera hardware based on the target image, and obtain a hardware testing result. The original image can be directly converted to the target image, without the need of image conversion on the original image by means of a hardware abstraction layer, and thus the need for waiting for completion of algorithm debugging related to camera imaging effect in the hardware abstraction layer is eliminated. The camera hardware testing process is decoupled from the camera imaging effect debugging process, and the testing efficiency of the camera hardware is improved. Meanwhile, an image format conversion scheme that can be integrated in a camera engineering testing application program is provided, and the application range of the camera hardware testing is improved.

[0018] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure. It is clear that the accompanying drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings. In the drawings:

[0020] Figure 1 a schematic diagram showing an exemplary system architecture to which the embodiments of the present disclosure can be applied;

[0021] Figure 2 a schematic flow diagram of a camera hardware testing method in an exemplary embodiment of the present disclosure;

[0022] Figure 3 a schematic flow diagram of generating a target image based on an original image in an exemplary embodiment of the present disclosure;

[0023] Figure 4 a schematic flow diagram of image data conversion on an original image in an exemplary embodiment of the present disclosure;

[0024] Figure 5This illustration schematically depicts a process diagram for implementing camera hardware testing in an exemplary embodiment of this disclosure;

[0025] Figure 6 This schematic diagram illustrates the composition of a camera hardware testing apparatus in an exemplary embodiment of the present disclosure.

[0026] Figure 7 A schematic diagram of an electronic device to which embodiments of the present disclosure may be applied is shown. Detailed Implementation

[0027] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0028] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0029] Figure 1 A schematic diagram of a system architecture for an exemplary application environment in which a camera hardware testing method and apparatus according to embodiments of the present disclosure can be applied is shown.

[0030] like Figure 1 As shown, system architecture 100 may include one or more of terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables. Terminal devices 101, 102, and 103 may be various electronic devices integrated with camera engineering applications, including but not limited to desktop computers, laptops, smartphones, and tablets. It should be understood that... Figure 1The number of terminal devices, networks and servers in the figure is only illustrative. According to the implementation needs, there can be any number of terminal devices, networks and servers. For example, the server 105 can be a server cluster composed of multiple servers, etc.

[0031] The camera hardware testing method provided by the embodiment of the present disclosure is generally executed in the terminal device 101, 102, 103, and correspondingly, the camera hardware testing apparatus is generally arranged in the terminal device 101, 102, 103. However, it is easily understood by those skilled in the art that the camera hardware testing method provided by the embodiment of the present disclosure can also be executed by the server 105, and correspondingly, the camera hardware testing apparatus can also be arranged in the server 105, which is not specially limited in the present exemplary embodiment. For example, in an exemplary embodiment, the user can obtain the original image collected by the camera hardware through the terminal device 101, 102, 103, and then upload the original image to the server 105. The server generates the hardware testing result through the camera hardware testing method provided by the embodiment of the present disclosure, and then transmits the hardware testing result to the terminal device 101, 102, 103, etc.

[0032] In the related art, the test process of the mobile phone manufacturer on the camera hardware in the production line is generally: the factory test machine sends an instruction, the camera engineering test application program accepts the instruction and then issues a test requirement, the test requirement is transmitted to the hardware abstraction layer (HAL) through the original system framework layer (Framework) of Google, the hardware abstraction layer (HAL) utilizes the platform 3A algorithm (which can include Auto White Balance (AWB), Auto Exposure (AE), and Anto-Focus (AF)) to issue 3A parameters to the driver of the camera hardware through the kernel layer (Kernel). After the 3A algorithm converges, the camera engineering test application program is notified, and the camera engineering test application program issues a shooting instruction to capture a picture stream and transmit it to the spatial frequency response (SFR), whiteboard (cleanliness), optical anti-shake, and multi-camera calibration algorithm for testing to determine whether the camera hardware meets the test specifications in the smart phone device. The camera hardware testing process state is strongly coupled with the imaging effect debugging of the camera and the platform 3A algorithm. In the case where the imaging effect debugging is not done in the early stage of the project, the camera hardware cannot be tested.

[0033] Therefore, in the current related test scheme, an ITS (Image Test System) test scheme is generally used. The test scheme uses a self-developed simple 3A algorithm integrated in a camera engineering test application program to replace the platform 3A algorithm, does not depend on the software debugging progress, and uses a unified simple 3A algorithm for different projects and different platforms, thereby truly decoupling the hardware test process and the camera imaging effect debugging. The ITS test process is that the camera engineering test application program uses the simple 3A algorithm to perform AE and AF convergence on the uploaded preview image frame, takes the preview image frame meeting the requirements after the convergence for corresponding processing, and in different scenes, can include: definition test, that is, taking the preview Raw image into the algorithm for definition calculation; whiteboard test, that is, taking the preview Raw image into the algorithm for imaging cleanliness test; optical anti-shake test, that is, taking the preview Raw image into the anti-shake algorithm for calculation after being converted into a YUV image; multi-camera calibration, that is, taking the preview Raw image into the calibration algorithm for calculation.

[0034] In the original production line test, although the pictures required by the definition test and the multi-camera calibration are Raw images, which are less affected by the effect debugging, the pictures used for the whiteboard test and the optical anti-shake test are deeply coupled with the imaging effect debugging and are greatly affected. In the new ITS test scheme, in order to avoid the influence of the imaging effect debugging process, the original Raw image is used to perform simple 3A calculation in the camera engineering test application program. However, the Raw image that has not been processed by the hardware abstraction layer (HAL) cannot be directly used for preview or simple 3A algorithm calculation, and needs to be converted into a YUV image to be input into the algorithm or displayed on the display screen for the convenience of engineers to check the test environment. Therefore, whether it is a test requirement or a display requirement, the camera engineering test application program needs to add a simple Raw-to-YUV algorithm to generate the images required by the ITS test scheme in the test process.

[0035] Based on one or more schemes in the related test scheme, the camera hardware test method in the embodiment of the disclosure is first provided. The camera hardware test method and the camera hardware test device of the exemplary embodiment of the disclosure are specifically described below by taking the terminal device executing the method as an example.

[0036] Figure 3 A flowchart of a camera hardware test method in the exemplary embodiment is shown, which can include the following steps S310 to S330:

[0037] In step S310, an original image captured by the camera hardware is obtained.

[0038] In an example embodiment, the camera hardware can include optical lenses, driving circuits, image sensors based on a Bayer array filter pattern, and the like, and of course, the camera hardware can also include other various hardware elements constituting an imaging system, which are conventional knowledge in the technical field and will not be described here.

[0039] The image sensor can be a complementary metal oxide semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor, and of course, the image sensor based on the Bayer array filter pattern can also be other types of image sensors, which are not specially limited in the example embodiment.

[0040] The raw image refers to original data converted by the image sensor from light source signals into digital signals, and has not undergone digital image signal processing. For example, the raw image can be image information in a Raw format, and of course, the raw image can also be original data output by the image sensor in other formats, and the example embodiment does not specially limit the image data format of the raw image.

[0041] In step S320, the raw image is image-converted to obtain a target image.

[0042] In an example embodiment, the target image refers to image information in a format required by each test item in the camera hardware test process. For example, the target image can be image information in a YUV color space, and of course, the target image can also be image information in an RGB color space, and the example embodiment does not specially limit the form of the target image.

[0043] Image conversion refers to a conversion process of converting the raw image into the target image away from the hardware abstraction layer (HAL). For example, the image conversion can be a process of performing down-sampling, image compression processing, and then format conversion on the raw image.

[0044] In step S330, the camera hardware is tested based on the target image to obtain a hardware test result.

[0045] In an example embodiment, the hardware test refers to a process of verifying a test of a problem of camera hardware, for example, the hardware test can include but is not limited to a sharpness SFR test, an imaging cleanliness test, an optical anti-shake function test, and a multi-camera calibration test of camera hardware, of course, the hardware test can also be other types of camera hardware tests, such as a camera aperture function test, a camera light amount test, etc., which are not specially limited in the present example embodiment. Wherein, the sharpness SFR is mainly used to measure the influence on a single image caused by the increase of lines with spatial frequency.

[0046] The original image can be directly converted to obtain the target image, without the need for image conversion of the original image by the hardware abstraction layer, and thus without the need to wait for the completion of the debugging of the camera imaging effect related algorithm in the hardware abstraction layer, and directly using the target image to complete the hardware test of the camera hardware, decoupling the camera hardware test process and the camera imaging effect debugging process, improving the test efficiency of the camera hardware; At the same time, an image format conversion scheme that can be integrated in the camera engineering test application program is provided, which improves the application range of the camera hardware test.

[0047] The steps in steps S210 to S230 are described in detail below.

[0048] In an example embodiment, the image conversion of the original image can be implemented by the steps in Figure 3 Referring to FIG. 3, the image conversion of the original image can specifically include: Figure 3

[0049] Step S310: Channel screening is performed on the original image to obtain an original image with reduced channels;

[0050] Step S320: The original image with reduced channels is compressed to obtain a compressed original image;

[0051] Step S330: Image data conversion is performed on the compressed original image to obtain a target image.

[0052] Wherein, the channel screening refers to a down-sampling process of the color channels in the original image, for example, assuming that the original image collected can be image information collected based on a RGGB channel Bayer array, the G channel in the original image can be screened, and only the channel value of one G channel is retained, of course, this is only an illustrative example, and the original image can also be image information collected based on a RYYB channel Bayer array, which is not specially limited in the present embodiment.

[0053] ​The image compression refers to a downsampling process of the resolution of the original image after the channel reduction. For example, the original image after the channel reduction can be reduced in size to obtain an original image with a lower resolution. Of course, in some application scenarios, the image compression can also refer to an image enlargement process of the original image after the channel reduction, and the present example is not limited thereto.

[0054] The image data conversion refers to a data conversion process of the original image after the image compression. For example, the original image can be a Raw format image, and the target image can be a YUV format image. Then, the channel value of the Y channel can be generated according to the image brightness information stored in the original image, and the channel values of the U channel and the V channel can be generated according to the color information stored in the original image, so as to finally obtain the target image in the YUV format, and the process can be implemented without the participation of the hardware abstraction layer (HAL). Of course, the image data conversion can also be a process of converting images between different image formats or image color controls without the participation of the hardware abstraction layer (HAL), and the present example does not make any special limitation thereto.

[0055] Optionally, the original image can be subjected to the channel screening to obtain the original image after the channel reduction by the following steps: the target color channel in the original image can be determined, the target color channel can include at least two color channels with the same color component, and then the target color channel can be subjected to the downsampling process to obtain the original image after the channel reduction.

[0056] The target color channel can be a color channel with redundant channel values in the original image, and the target color channel can include a plurality of color channels with the same color component in the original image. For example, the original image can be image information collected based on a Bayer array with RGGB channels, and the original image includes two color channels with the same G color component. Therefore, the G channel in the original image can be subjected to the channel screening to retain only the value of one G channel, so as to obtain the original image after the channel reduction.

[0057] The channel screening of the original image can reduce the amount of redundant data in the original image, effectively reduce the channel values stored in the original image, and improve the conversion efficiency from the original image to the target image.

[0058] Optionally, the original image after the channel reduction can be subjected to the image compression to obtain the original image after the image compression by the following steps: a preset image size can be obtained, and the original image after the channel reduction can be subjected to the image scaling according to the image size to obtain the original image after the image compression.

[0059] The image size refers to the size information of an image that is pre-set for camera hardware testing and can achieve better testing results. For example, the image size can be an image resolution of 1280*720 (720p) or an image resolution of 1920*1080 (1080p). The specific data of the image size is not specially limited in the embodiment. Of course, the image size can also be the side length information of the image. The form of the image size is not specially limited in the example embodiment.

[0060] The image size of the original image after channel reduction can be compared with the preset image size. If the image size of the original image after channel reduction is greater than or equal to the preset image size, the original image after channel reduction can be reduced in size based on the preset image size. For example, the original image after channel reduction can be reduced in size by a proportion based on the aspect ratio of the preset image size. If the image size of the original image after channel reduction is less than the preset image size, the original image after channel reduction can be enlarged in size based on the preset image size. Of course, other processing methods that can make the image size of the original image after channel reduction match the preset image size can also be used. The example embodiment is not limited in this regard.

[0061] The original image after channel reduction is compressed in size by the preset image size, which can effectively reduce the size of the original image to be processed, reduce the calculation amount of the subsequent generation of a preview image or hardware testing algorithm, improve the system calculation efficiency, and further improve the hardware testing efficiency.

[0062] Optionally, the image data conversion of the original image after image compression can be implemented by the steps in Figure 4 Referring to Figure 4 , the steps can specifically include

[0063] In step S410, image brightness information in the original image after image compression is obtained.

[0064] In step S420, the image brightness information is stored in a first image channel, and a second image channel and a third image channel are filled according to a preset color value.

[0065] In step S430, a target image is obtained based on the first image channel, the second image channel, and the third image channel.

[0066] The first image channel, the second image channel, and the third image channel can respectively represent different image channels in the target image. For example, the target image can be image information in YUV format, and the first image channel can be a Y image channel, the second image channel can be a U image channel, and the third image channel can be a V image channel. Of course, the target image can also be image information in RGB format, and the first image channel, the second image channel, and the third image channel can correspond to an R image channel, a G image channel, and a B image channel, respectively, without special limitation in this example embodiment.

[0067] It should be noted that the "first", "second", and "third" in the "first image channel", "second image channel", and "third image channel" of this embodiment are only used to distinguish different color channels in the target image and do not have any special meaning, and should not cause any special limitation to this example embodiment.

[0068] The preset color value is a pre-set filling value for filling the color channel storing non-image brightness information. For example, the preset color value can be 0x7F, which is equivalent to converting the color channel storing non-image brightness information to grayscale, discarding color information that is useless for AE algorithm and AF algorithm in the hardware test process, effectively reducing the number of AE and AF, and improving the calculation efficiency. Of course, the preset color value can also be other numerical values, and this example embodiment does not have special limitation.

[0069] In an example embodiment, after obtaining the target image, the camera hardware can be tested based on the target image, and the target image can be input to different processes for processing:

[0070] The first process can perform image rendering on the target image to generate a preview image and display it to real-time show the test environment of the camera hardware through the preview image.

[0071] The second process can input the target image into a camera test algorithm to test the camera hardware through the camera test algorithm. The camera test algorithm can include one or more combinations of automatic exposure convergence, automatic focusing convergence, optical image stabilization test, and imaging cleanliness test.

[0072] Optionally, in the second process, the target image can be first input to the automatic exposure convergence (AE) algorithm and the automatic focusing convergence (AF) algorithm for convergence. When performing the optical image stabilization test, the converged results can be input to the optical image stabilization algorithm for testing after the automatic exposure convergence (AE) algorithm and the automatic focusing convergence (AF) algorithm converge on the target image.

[0073] Optionally, the target image can also be input to a third process for processing, and a test image is generated based on the target image through the third process.

[0074] Figure 5 A flowchart schematically showing a process for implementing camera hardware testing in an exemplary embodiment of the present disclosure is shown.

[0075] Reference Figure 5 As shown, in step S510, the original image 501 is acquired, and a target color channel in the original image is determined, and channel screening (such as sampling processing) is performed on the target color channel, for example, for an original image with RGGB channels, only the value of one G channel is retained, which is equivalent to down-sampling to obtain half the precision of the original image, reduces redundant information, and effectively improves processing efficiency.

[0076] In step S520, for different image sizes of the original image 501, a preset image size can be acquired, and the image size of the original image 501 is resized according to the preset image size to obtain an original image 501 with a required image size, for example, taking an original image size of 4096*3072 as an example, the image size of the G channel after down-sampling is 2048*1536, and the original image 501 can be resized to 720p or other sizes according to the preset image size. The purposes of channel screening and image resizing are to reduce the image size, reduce redundant image information, and improve the calculation efficiency.

[0077] In step S530, the original image after channel screening and image resizing is subjected to image data conversion, for example, the target image can be YUV format image information, then the image brightness information in the original image can be all stored in the Y channel, and the U and V channels are all filled with a preset color value 0x7F to obtain the target image 502. Filling 0x7F in the U and V channels is equivalent to directly converting the target image to grayscale and discarding color information useless for AE and AF, further reducing the amount of calculation.

[0078] In step S540, the target image 502 is subjected to image rendering to generate a preview image and display, so as to display the test environment of the camera hardware in real time through the preview image.

[0079] In step S550, the target image 502 is input into a camera test algorithm to perform hardware testing on the camera hardware through the camera test algorithm; wherein the camera test algorithm can include one or a combination of multiple of automatic exposure convergence, automatic focusing convergence, optical image stabilization test, and imaging cleanliness test.

[0080] In step S560, a test image is generated based on the target image.

[0081] The Raw-to-YUV algorithm integrated in the camera engineering test application is a necessary function for realizing real-time preview of pictures, 3A convergence and test image generation in the ITS test process. The Raw image processing flow is transplanted from the platform hardware abstraction layer (HAL) to the camera engineering test application, thereby decoupling the hardware test process and the imaging effect debugging process and effectively improving the hardware test efficiency.

[0082] In summary, in the example embodiment, the original image collected by the camera hardware can be acquired, the original image is converted to obtain a target image, and the camera hardware is tested based on the target image. The original image can be directly converted to obtain the target image, without the need for image conversion of the original image by the hardware abstraction layer, and thus without the need to wait for the completion of the algorithm debugging related to the camera imaging effect in the hardware abstraction layer. The camera hardware testing process is decoupled from the camera imaging effect debugging process, the testing efficiency of the camera hardware is improved, and an image format conversion scheme that can be integrated in the camera engineering test application is provided to improve the application range of the camera hardware testing.

[0083] It should be noted that the above-described figures are only schematic illustrations of the processes included in the method according to the example embodiments of the present disclosure, and are not intended for limitation purposes. It is easy to understand that the processes shown in the above-described figures do not indicate or limit the time sequence of the processes. In addition, it is also easy to understand that the processes can be executed synchronously or asynchronously in multiple modules, for example.

[0084] Further, referring to Figure 6 In the example embodiment, a camera hardware testing apparatus 600 is also provided, which can include an original image acquisition module 610, an image conversion module 620 and a hardware testing module 630. Wherein:

[0085] The original image acquisition module 610 can be used to acquire the original image collected by the camera hardware;

[0086] The image conversion module 620 can be used to convert the original image to obtain a target image;

[0087] The hardware testing module 630 can be used to test the camera hardware based on the target image to obtain a hardware testing result.

[0088] In an example embodiment, the image conversion module 620 can include:

[0089] A channel screening unit configured to screen the channels of the original image to obtain a channel-reduced original image;

[0090] An image compression unit is used to compress the original image after the channel reduction to obtain the compressed original image.

[0091] The data conversion unit is used to convert the compressed original image into image data to obtain the target image.

[0092] In one exemplary embodiment, the channel filtering unit can be used to:

[0093] Determine the target color channel in the original image, wherein the target color channel includes at least two color channels with the same color components;

[0094] The target color channel is downsampled to obtain the original image with fewer channels.

[0095] In one exemplary embodiment, the image compression unit may be used for:

[0096] Get the preset image size;

[0097] The original image after reducing the number of channels is scaled according to the image size to obtain the original image after image compression.

[0098] In one exemplary embodiment, the data conversion unit may be used to:

[0099] Obtain the image brightness information from the original image after image compression;

[0100] The image brightness information is stored in the first image channel, and the second and third image channels are filled according to preset color values;

[0101] The target image is obtained based on the first image channel, the second image channel, and the third image channel.

[0102] In one exemplary embodiment, the hardware testing module 630 can be used to:

[0103] The target image is rendered to generate a preview image, which is then displayed to showcase the testing environment of the camera hardware in real time.

[0104] In one exemplary embodiment, the hardware testing module 630 can be used to:

[0105] The target image is input into the camera testing algorithm to perform hardware testing on the camera hardware.

[0106] The camera testing algorithm includes one or more combinations of automatic exposure convergence, automatic focus convergence, optical image stabilization testing, and image cleanliness testing.

[0107] The specific details of the modules in the above apparatus have been described in detail in the method embodiments, and the details not disclosed can refer to the content of the method embodiments, and thus will not be described again.

[0108] Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a system, a method or a program product. Therefore, various aspects of the present disclosure can be embodied as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system" here.

[0109] The exemplary embodiments of the present disclosure also provide an electronic device. The electronic device can be the terminal devices 101, 102, 103 and the server 105 described above. Generally, the electronic device can include a processor and a memory for storing executable instructions of the processor, and the processor is configured to perform the camera hardware test method described above by executing the executable instructions.

[0110] The following will take the mobile terminal 700 in Figure 7 as an example to exemplarily describe the structure of the electronic device. Those skilled in the art should understand that, in addition to the components specially used for mobile purposes, Figure 7 , the structure in can also be applied to devices of fixed type.

[0111] As shown in Figure 7 , the electronic device 700 is in the form of a general computing device. The components of the electronic device 700 can include but are not limited to: at least one processing unit 710, at least one storage unit 720, a bus 730 connecting different system components (including the storage unit 720 and the processing unit 710), a display unit 740.

[0112] Among them, the storage unit 720 stores program codes, which can be executed by the processing unit 710, so that the processing unit 710 executes the camera hardware test method in the present specification.

[0113] The storage unit 720 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 721 and / or a cache memory unit 722, and can further include a read-only memory (ROM) 723.

[0114] The storage unit 720 can also include program / utilities 724 with a set of (at least one) program modules 725, such as but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which can include the implementation of a network environment.

[0115] Bus 730 can be one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, a graphics bus, a processor or local bus using any of a variety of bus architectures.

[0116] Electronic device 700 can also communicate with one or more external devices 770 such as a sensor device, a Bluetooth device, a head set, or other devices, and can communicate with one or more devices that enable additional functionality, such as a microphone for hands free operation, or a modem for placing and receiving phone calls. The electronic device 700 can communicate with such external devices 770 through input / output (I / O) interface 750. Further, electronic device 700 can communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN) or an Internet network, through network adapter 760. As depicted, network adapter 760 communicates with the other components of electronic device 700 via bus 730. It should be appreciated that the network adapter 760 can also be connected to one or more components of electronic device 700 in some other fashion such as over a wireless communication protocol, over a wired connection, etc. Although not shown, other hardware and / or software modules could be used in conjunction with the electronic device 700. Examples include microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data archival storage systems, and many others.

[0117] From the above description of the embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, or the like) or a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to perform the methods according to the embodiments of the disclosure.

[0118] The example embodiments of the disclosure also provide a computer-readable storage medium having stored thereon a program product capable of implementing the methods described above. In some possible embodiments, various aspects of the disclosure can also be implemented in the form of a program product including a program code for causing an end device to perform the steps described in the above “Example Method” section according to various example embodiments of the disclosure when the program product is run on the end device.

[0119] It should be noted that the computer-readable medium shown in the disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0120] In the disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, device or apparatus. In the disclosure, the computer-readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium that can send, propagate or transmit the program for use by or in connection with an instruction execution system, device or apparatus. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0121] In addition, the program code for performing the operations of the disclosure can be written in any combination of one or more programming languages, including an object-oriented programming language, such as Java, C++, etc., and a conventional procedural programming language, such as the "C" programming language or similar programming languages. The program code can be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0122] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

[0123] It should be understood that the present disclosure is not limited to the precise structures herein described and illustrated in the drawings, and that various modifications and changes can be made without departing from its scope. The scope of the present disclosure is limited only by the claims that follow.

Claims

1. A camera hardware testing method, characterized by, The method comprises the following steps: acquiring an original image collected by the camera hardware; performing image conversion on the original image to obtain a target image; performing hardware testing on the camera hardware based on the target image to obtain a hardware testing result; wherein the step of performing image conversion on the original image to obtain a target image comprises: performing down-sampling and image compression on the original image to obtain an original image after image compression; acquiring image brightness information in the original image after image compression; storing the image brightness information to a first image channel, and filling a second image channel and a third image channel according to a preset color value; obtaining a target image based on the first image channel, the second image channel and the third image channel; wherein the step of performing hardware testing on the camera hardware based on the target image comprises: performing image rendering on the target image to generate a preview image and display, so as to display the testing environment of the camera hardware in real time through the preview image.

2. The method of claim 1, wherein, The step of performing image conversion on the original image to obtain a target image comprises: performing channel screening on the original image to obtain an original image after channel reduction; performing image compression on the original image after channel reduction to obtain an original image after image compression; performing image data conversion on the original image after image compression to obtain a target image.

3. The method of claim 2, wherein, The step of performing channel screening on the original image to obtain an original image after channel reduction comprises: determining a target color channel in the original image, the target color channel comprising at least two color channels of the same color component; performing down-sampling processing on the target color channel to obtain an original image after channel reduction.

4. The method of claim 2, wherein, The step of performing image compression on the original image after channel reduction to obtain an original image after image compression comprises: acquiring a preset image size; performing image scaling on the original image after channel reduction according to the image size to obtain an original image after image compression.

5. The method of claim 1, wherein, The step of performing hardware testing on the camera hardware based on the target image comprises: inputting the target image into a camera testing algorithm to perform hardware testing on the camera hardware through the camera testing algorithm; wherein the camera testing algorithm comprises one or more combinations of automatic exposure convergence, automatic focusing convergence, optical image stabilization testing and imaging cleanliness testing.

6. A camera hardware testing apparatus, characterized by, The method comprises the following steps: an original image acquisition module configured to acquire an original image collected by the camera hardware; an image conversion module configured to perform image conversion on the original image to obtain a target image; a hardware testing module configured to perform hardware testing on the camera hardware based on the target image to obtain a hardware testing result; The image conversion is performed on the original image to obtain a target image, including: performing down-sampling on the original image to obtain an original image after image compression; obtaining image brightness information in the original image after image compression; storing the image brightness information into a first image channel, and filling a second image channel and a third image channel according to a preset color value; and obtaining the target image based on the first image channel, the second image channel and the third image channel. The hardware test of the camera hardware based on the target image includes: performing image rendering on the target image to generate a preview image and display, so as to display the test environment of the camera hardware in real time through the preview image.

7. A computer readable medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the method in any one of claims 1 to 5.

8. An electronic device, comprising: It comprises: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the method in any one of claims 1 to 5 by executing the executable instructions.

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