Adjustment method and device of shooting equipment, equipment and storage medium

Abstract

The embodiment of the invention provides an adjusting method and device for shooting equipment, equipment and a storage medium, and the method comprises the steps: determining a key image region in a pre-obtained target image, calculating and obtaining an overall brightness evaluation value corresponding to the key image region based on the pixel brightness information in the key image region, and carrying out the adjustment of the overall brightness evaluation value. And comparing the overall brightness evaluation value with a preset target brightness range to obtain a comparison result, and generating an adjustment instruction based on the comparison result. Through the method, more accurate parameter adjustment of the shooting equipment is realized, the brightness stability is improved, and algorithm false detection caused by brightness fluctuation is eliminated.

Description

Technical Field

[0001] This application relates to the field of computer vision technology, and in particular to a method, apparatus, device, and storage medium for adjusting a shooting device. Background Technology

[0002] Using machine vision systems to capture and analyze images of electronic device screens has become a crucial technology. However, the screen's own light-emitting characteristics and ambient light interference often lead to uneven brightness, overexposure, or underexposure in the captured images. Unstable image brightness severely impacts the accuracy and reliability of subsequent advanced vision tasks such as defect detection and content recognition based on deep learning and traditional algorithms. Therefore, achieving precise control and stable maintenance of the captured image brightness is a prerequisite for improving the robustness and detection accuracy of the entire vision system.

[0003] Currently, existing technologies typically determine overexposure by calculating the percentage of pixels in the entire image whose brightness exceeds a certain high threshold, or by simply calculating the average brightness of all pixels in the image. For parameter adjustment, simple heuristic rules are generally used; for example, once overexposure is detected, the camera's exposure time is reduced by a fixed percentage (e.g., halved). While this approach is simple to implement, it heavily relies on fixed thresholds set by human experience.

[0004] In conclusion, how to achieve more precise adjustments to shooting equipment is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for adjusting a shooting device, in order to solve the problem of how to achieve more precise adjustment of the shooting device.

[0006] In a first aspect, embodiments of this application provide a method for adjusting a shooting device, including:

[0007] Key image regions are identified in a pre-acquired target image, which is obtained by the shooting device from the current subject.

[0008] Based on the pixel brightness information within the key image region, the overall brightness evaluation value corresponding to the key image region is calculated.

[0009] The overall brightness assessment value is compared with the preset target brightness range to obtain the comparison result;

[0010] An adjustment instruction is generated based on the comparison results, and the adjustment instruction is used to adjust the shooting parameters of the shooting device.

[0011] In one possible implementation, determining the key image region in the pre-acquired target image includes:

[0012] The effective display area of ​​the target image is divided into multiple sub-regions;

[0013] Calculate the brightness characterization value for each sub-region;

[0014] Based on the brightness characterization value of each sub-region, the key image region is determined in the plurality of sub-regions.

[0015] In one possible implementation, determining the key image region based on the brightness characterization value of each sub-region includes:

[0016] The multiple sub-regions are sorted in descending order according to their brightness characterization values ​​to obtain a sorted sequence.

[0017] The Nth sub-region that ranks first in the sorted sequence is determined as the key image region, where N is a preset positive integer.

[0018] In one possible implementation, calculating the overall brightness assessment value corresponding to the key image region based on the pixel brightness information within the key image region includes:

[0019] The brightness values ​​of pixels within the key image region are statistically sorted.

[0020] Based on the statistical sorting results, pixels within a preset range are selected as target pixels;

[0021] Calculate the average brightness of the target pixel and use the average brightness as the overall brightness evaluation value.

[0022] In one possible implementation, generating adjustment instructions based on the comparison results includes:

[0023] When the comparison result indicates that the overall brightness assessment value is not within the target brightness range, the estimated brightness level corresponding to the target image is determined based on the overall brightness assessment value, the current shooting parameters of the shooting device, and the preset parameter mapping database.

[0024] Based on the estimated brightness level and the parameter mapping database, the target shooting parameters are obtained;

[0025] The adjustment command is generated based on the target shooting parameters.

[0026] In one possible implementation, determining the estimated brightness level of the target image based on the overall brightness assessment value, the current shooting parameters of the shooting device, and a preset parameter mapping database includes:

[0027] In the parameter mapping database, obtain a reference shooting parameter group that matches the current shooting parameters of the shooting device;

[0028] Within the data points of the reference shooting parameter group, determine the position range of the overall brightness evaluation value;

[0029] Based on the reference target brightness levels corresponding to the data points at both ends of the location interval, the estimated brightness level of the target image is calculated by linear interpolation.

[0030] In one possible implementation, the method further includes:

[0031] Based on multiple subjects with known brightness levels, the shooting parameters of the shooting device are iteratively adjusted until the overall brightness assessment value corresponding to the image of each subject is within the target brightness range. The parameter mapping database is then constructed based on the brightness level-shooting parameter pairs of the subjects.

[0032] Secondly, embodiments of this application provide an adjustment device for a shooting device, comprising:

[0033] The determination module is used to determine key image regions in a pre-acquired target image, wherein the target image is obtained by the shooting device from the current shooting object;

[0034] The calculation module is used to calculate the overall brightness evaluation value corresponding to the key image region based on the pixel brightness information in the key image region.

[0035] The comparison module is used to compare the overall brightness assessment value with the preset target brightness range to obtain the comparison result;

[0036] The generation module is used to generate adjustment instructions based on the comparison results, and the adjustment instructions are used to adjust the shooting parameters of the shooting device.

[0037] In one possible implementation, the determining module includes:

[0038] The effective display area of ​​the target image is divided into multiple sub-regions;

[0039] Calculate the brightness characterization value for each sub-region;

[0040] Based on the brightness characterization value of each sub-region, the key image region is determined in the plurality of sub-regions.

[0041] In one possible implementation, the determining module identifies the key image region from the plurality of sub-regions based on the brightness characterization value of each sub-region, specifically including:

[0042] The multiple sub-regions are sorted in descending order according to their brightness characterization values ​​to obtain a sorted sequence.

[0043] The Nth sub-region that ranks first in the sorted sequence is determined as the key image region, where N is a preset positive integer.

[0044] In one possible implementation, the computing module includes:

[0045] The brightness values ​​of pixels within the key image region are statistically sorted.

[0046] Based on the statistical sorting results, pixels within a preset range are selected as target pixels;

[0047] Calculate the average brightness of the target pixel and use the average brightness as the overall brightness evaluation value.

[0048] In one possible implementation, the generation module includes:

[0049] When the comparison result indicates that the overall brightness assessment value is not within the target brightness range, the estimated brightness level corresponding to the target image is determined based on the overall brightness assessment value, the current shooting parameters of the shooting device, and the preset parameter mapping database.

[0050] Based on the estimated brightness level and the parameter mapping database, the target shooting parameters are obtained;

[0051] The adjustment command is generated based on the target shooting parameters.

[0052] In one possible implementation, the generation module determines the estimated brightness level corresponding to the target image based on the overall brightness assessment value, the current shooting parameters of the shooting device, and a preset parameter mapping database, specifically including:

[0053] In the parameter mapping database, obtain a reference shooting parameter group that matches the current shooting parameters of the shooting device;

[0054] Within the data points of the reference shooting parameter group, determine the position range of the overall brightness evaluation value;

[0055] Based on the reference target brightness levels corresponding to the data points at both ends of the location interval, the estimated brightness level of the target image is calculated by linear interpolation.

[0056] In one possible implementation, the device further includes:

[0057] The component module is used to iteratively adjust the shooting parameters of the shooting device based on multiple shooting objects with known brightness levels, until the overall brightness assessment value corresponding to the image of each shooting object is within the target brightness range, and to form the parameter mapping database based on the brightness level of the shooting object and the shooting parameter pair.

[0058] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0059] The memory stores computer-executed instructions;

[0060] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0061] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0062] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0063] The method, apparatus, device, and storage medium for adjusting the shooting device provided in this application determine key image regions in a pre-acquired target image. Based on the pixel brightness information within the key image regions, an overall brightness evaluation value corresponding to the key image regions is calculated. The overall brightness evaluation value is compared with a preset target brightness range to obtain a comparison result, and an adjustment command is generated based on the comparison result. This method achieves more precise parameter adjustment of the shooting device, improves brightness stability, and eliminates algorithmic false detections caused by brightness fluctuations. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0065] Figure 1 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 1 ;

[0066] Figure 2 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 2;

[0067] Figure 3 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 3 ;

[0068] Figure 4 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 4 ;

[0069] Figure 5 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 5 ;

[0070] Figure 6 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 6 ;

[0071] Figure 7 A graph showing screen brightness, average grayscale value, and the 10th largest grayscale value.

[0072] Figure 8 A schematic diagram of the adjustment device for the shooting equipment provided in this application;

[0073] Figure 9 A schematic diagram of the structure of the electronic device provided in this application.

[0074] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0075] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0076] Using machine vision systems to capture and analyze images of electronic device screens has become a crucial technology. However, the screen's own light-emitting characteristics and ambient light interference often lead to uneven brightness, overexposure, or underexposure in the captured images. Unstable image brightness severely affects the accuracy and reliability of subsequent advanced vision tasks such as defect detection and content recognition based on deep learning and traditional algorithms. Therefore, achieving precise control and stable maintenance of the brightness of captured images is a prerequisite for improving the robustness and detection accuracy of the entire vision system. Currently, existing technologies typically determine overexposure by calculating the percentage of pixels in the entire image whose brightness exceeds a certain high threshold, or by simply calculating the average brightness value of all pixels in the image. Parameter adjustment generally relies on simple heuristic rules; for example, once overexposure is detected, the camera's exposure time is reduced by a fixed percentage (e.g., halved). While this approach is simple to implement, it heavily relies on fixed thresholds set by human experience.

[0077] To address the aforementioned problems, this application provides a method, apparatus, device, and storage medium for adjusting a shooting device, eliminating algorithm detection failures caused by overexposure or underexposure, and improving adaptability and accuracy. Specifically, existing technologies typically determine overexposure by calculating the percentage of pixels in the entire image whose brightness exceeds a certain high threshold, or by simply calculating the average brightness of all pixels in the image. Parameter adjustment generally relies on simple heuristic rules; for example, once overexposure is detected, the camera's exposure time is reduced by a fixed proportion (e.g., halved). While this approach is simple to implement, it heavily relies on fixed thresholds set by human experience. Considering these issues, the inventors investigated whether the screen image could be divided into several rectangular sub-regions, excluding the highest and lowest brightness extreme regions, and selecting the second brightest region as the analysis sample; then, the average value of the highest brightness pixels within this region would be calculated; next, using this average value and the current camera exposure time as input, reverse positioning would be performed in a pre-calibrated database, and finally, a linear interpolation algorithm would be used to accurately calculate the camera parameters that would capture the current screen in the optimal brightness range. Based on this, the solution proposed in this application is proposed.

[0078] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0079] Figure 1 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0080] S101: Identify key image regions in the pre-acquired target image.

[0081] In existing technologies, global pixel statistics are used, so even a small but strong reflective spot on the screen can cause the entire image to be judged as overexposed. This step, however, effectively immunizes against interference from local overexposed or underexposed areas by partitioning and selecting representative regions.

[0082] Specifically, the effective display area of ​​the target image is divided into multiple sub-regions, the brightness characterization value of each sub-region is calculated, and the key image region is determined from the multiple sub-regions based on the brightness characterization value of each sub-region.

[0083] S102: Calculate the overall brightness assessment value corresponding to the key image region based on the pixel brightness information within the key image region.

[0084] In this step, if we simply calculate the average value of the entire key area, any black icons or dark content within that area could lower the calculation result, leading to a misjudgment as too dark. Therefore, this step does not calculate the average value of all pixels within the selected key image area, but instead performs the calculation based on the pixel brightness values.

[0085] Specifically, the brightness values ​​of pixels within the key image region are statistically sorted. Based on the results of the statistical sorting, pixels within a preset range are selected as target pixels, the average brightness of the target pixels is calculated, and the average brightness is used as the overall brightness evaluation value.

[0086] S103: Compare the overall brightness assessment value with the preset target brightness range to obtain the comparison result.

[0087] In this step, the previous judgment criteria relied on human experience, which was not accurate enough. Therefore, the overall brightness assessment value calculated in the above steps is compared with the preset target brightness range to obtain the comparison result.

[0088] For example, if the overall brightness assessment value is 195 and the target brightness range is 180-210, then the comparison result obtained by comparison is normal. The comparison result also includes overexposure and underexposure. Within the target brightness range, it is normal; outside the target brightness range, it is overexposure; and below the range, it is underexposure.

[0089] S104: Generate adjustment instructions based on the comparison results.

[0090] In this step, instead of the previous approach of halving the exposure parameters for overexposure, which was considered a rather drastic measure, adjustment instructions are generated based on the comparison results obtained in the previous steps. These instructions are then used to adjust the shooting parameters of the camera.

[0091] Optionally, the comparison result can be normal, overexposed, or underexposed.

[0092] The method for adjusting the shooting device provided in this application involves identifying key image regions in a pre-acquired target image, calculating the overall brightness assessment value corresponding to the key image regions based on pixel brightness information within these regions, comparing the overall brightness assessment value with a preset target brightness range to obtain a comparison result, and generating an adjustment command based on the comparison result. This method achieves more precise parameter adjustment of the shooting device, improves brightness stability, eliminates algorithmic false detections caused by brightness fluctuations, and enhances the robustness of screens with different brightness levels.

[0093] Figure 2 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 2 ,like Figure 2 As shown, based on the above embodiments, step S101 specifically includes:

[0094] S201: Divide the effective display area of ​​the target image into multiple sub-regions.

[0095] Traditional methods for performing whole-image statistics are contaminated by local overexposed / underexposed areas, such as dark corners at the edges of the screen or local reflections. In addition, there is natural brightness decay between the center and the edges of the screen, and the average value of the whole image cannot reflect the brightness of the subject. Therefore, a grid partitioning algorithm, such as a 3×3 or 4×4 uniform grid, is used to divide the effective display area of ​​the target image into multiple sub-regions, for example, dividing the effective display area into 9-16 sub-regions.

[0096] S202: Calculate the brightness characterization value for each sub-region.

[0097] After dividing the sub-regions, the brightness characterization value of each sub-region is calculated. Specifically, the mean value of each sub-region can be calculated, or the weighted median brightness value can be used to reduce the influence of a small number of extreme pixels on the mean value.

[0098] S203: Based on the brightness characterization value of each sub-region, the key image region is identified in multiple sub-regions.

[0099] In this step, after calculating the brightness characterization value of each sub-region through the above steps, the multiple sub-regions are sorted in descending order according to the size of the brightness characterization value to obtain a sorted sequence. The Nth sub-region in the sorted sequence is determined as the key image region.

[0100] The adjustment method for the shooting device provided in this application divides the effective display area of ​​the target image into multiple sub-regions, calculates the brightness characterization value of each sub-region, and identifies the key image region among the multiple sub-regions based on the brightness characterization value of each sub-region. This method improves the evaluation accuracy, solves the problem of non-uniform brightness characterization, and reduces the overexposure false alarm rate.

[0101] Figure 3 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 3 ,like Figure 3 As shown, based on the above embodiment, step S203 specifically includes:

[0102] S301: Sort multiple sub-regions in descending order according to their brightness characterization values ​​to obtain a sorted sequence.

[0103] S302: The Nth sub-region that ranks first in the sorted sequence is identified as the key image region.

[0104] To accurately determine the key image region, after dividing the image into multiple sub-regions, the brightness characterization values ​​of each sub-region calculated in the aforementioned embodiment are sorted in descending order to obtain a sorted sequence. For example, a dual-axis fast sort can be used. Then, the Nth sub-region ranking first in the sorted sequence is determined as the key image region, where N is a positive integer.

[0105] It should be noted that, in order to avoid interference from a single extreme brightness, the brightest sub-region is not selected when selecting the key image region.

[0106] The adjustment method for the imaging device provided in this application sorts multiple sub-regions in descending order according to their brightness characterization values ​​to obtain a sorted sequence. The Nth sub-region at the top of the sorted sequence is then identified as the key image region. This method avoids the selection of interfering regions and improves the accuracy of subsequent operations.

[0107] Figure 4 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 4 ,like Figure 4 As shown, based on the above embodiment, step S102 specifically includes:

[0108] S401: Statistically sort the brightness values ​​of pixels within the key image region.

[0109] S402: Based on the results of statistical sorting, select pixels within a preset range as target pixels.

[0110] S403: Calculate the average brightness of the target pixel and use the average brightness as the overall brightness evaluation value.

[0111] In order to intelligently adjust the shooting parameters, after the key image area is determined, the brightness values ​​of the pixels in the area are sorted to obtain the sorting result. Due to the difference in the light emission mechanism of different screens, the brightness distribution will be different. Therefore, a dynamic preset range can be preset to select target pixels, thereby eliminating evaluation bias. Pixels with local brightness can be excluded from the target pixels. After the target pixels are determined, the average brightness of the target pixels is calculated and the average brightness is determined as the overall brightness evaluation value.

[0112] Optionally, the maximum pixel brightness in the key image area can be used as the overall brightness evaluation value, or the Kth largest value can be used as the overall brightness evaluation value.

[0113] The adjustment method for the shooting device provided in this application embodiment statistically sorts the brightness values ​​of pixels within a key image area. Based on the statistical sorting results, pixels within a preset range are selected as target pixels, and the average brightness of the target pixels is calculated. This average brightness is then used as the overall brightness evaluation value. This method avoids misjudgment of overexposure and improves the stability of brightness evaluation.

[0114] Figure 5 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 5 ,like Figure 5 As shown, based on the above embodiment, step S104 specifically includes:

[0115] S501: When the comparison result indicates that the overall brightness assessment value is not within the target brightness range, the estimated brightness level corresponding to the target image is determined based on the overall brightness assessment value, the current shooting parameters of the shooting device, and the preset parameter mapping database.

[0116] S502: Obtain target shooting parameters based on estimated brightness levels and parameter mapping database.

[0117] S503: Generates adjustment instructions based on target shooting parameters.

[0118] When the overall brightness assessment value is detected to exceed the target range, the absolute physical brightness of the screen is calculated in reverse based on the parameters of the current shooting device (exposure / aperture) and a pre-established parameter mapping database. This is the estimated brightness level corresponding to the target image. This transforms unstable image brightness values ​​into inherent physical properties of the screen, eliminating the influence of differences in screen hardware.

[0119] Based on the estimated brightness level obtained from the inversion, the optimal shooting parameters are interpolated from the database to make the brightness of the newly captured image approach the optimal point in the target range. This directly obtains the target shooting parameters, avoiding the oscillating adjustments of traditional "halving the exposure." After obtaining the target shooting parameters, adjustment commands are generated to allow for parameter adjustments, thereby enabling adjustments to the shooting equipment parameters.

[0120] Optionally, the comparison result indicates that the overall brightness assessment value is not within the target brightness range. It can be that it exceeds the maximum limit of the target brightness range, which is overexposed; it is less than the minimum limit, which is underexposed; and it is normal if it is within the target brightness range.

[0121] The method for adjusting the shooting device provided in this application, when the comparison result indicates that the overall brightness assessment value is not within the target brightness range, determines the estimated brightness level corresponding to the target image based on the overall brightness assessment value, the current shooting parameters of the shooting device, and a preset parameter mapping database. Then, it obtains the target shooting parameters based on the estimated brightness level and the parameter mapping database, and generates an adjustment command based on the target shooting parameters. This method improves brightness stability and increases parameter adjustment efficiency.

[0122] Figure 6 Flowchart of the adjustment method for the shooting equipment provided in this application Figure 6 ,like Figure 6 As shown, based on the above embodiment, step S501 specifically includes:

[0123] S601: In the parameter mapping database, obtain a reference shooting parameter group that matches the current shooting parameters of the shooting device.

[0124] S602: Determine the position range of the overall brightness assessment value from the data points of the reference shooting parameter group.

[0125] S603: Based on the reference target brightness levels corresponding to the data points at both ends of the position interval, the estimated brightness level of the target image is calculated by linear interpolation.

[0126] The parameter mapping database is stored hierarchically according to the shooting device parameter groups, with each group containing the same exposure time / aperture. Each group contains multiple rows of data: screen physical brightness → image brightness value. Using the current shooting parameters as an index, all calibration data under the same parameter group are retrieved. Then, the reference group data is sorted from highest to lowest image brightness value, and a binary search is used to locate the position of the overall brightness assessment value in the ordered sequence. Finally, linear interpolation is used to calculate the estimated brightness level corresponding to the target image.

[0127] The method for adjusting the shooting device provided in this application involves obtaining a reference shooting parameter group that matches the current shooting parameters of the shooting device from a parameter mapping database. Within the data points of the reference shooting parameter group, the position interval of the overall brightness assessment value is determined. Based on the reference target brightness levels corresponding to the data points at both ends of the position interval, the estimated brightness level of the target image is calculated through linear interpolation. This method establishes physical brightness anchor points for screen shooting, improving the stability of image brightness.

[0128] Optionally, the method further includes iteratively adjusting the shooting parameters of the shooting device based on multiple shooting objects with known brightness levels until the overall brightness assessment value corresponding to the image of each shooting object is within the target brightness range, and forming a parameter mapping database based on the brightness level-shooting parameter pairs of the shooting objects.

[0129] For example, regarding the parameter adjustment method, firstly, a batch of devices to be photographed is selected using a brightness testing instrument according to a predefined brightness level. The device with the highest brightness level is selected, and the brightness value of the captured image is calculated using two comparison schemes. Then, the shooting parameter P is adjusted so that the image brightness value is at the required value T. h With T l Between, closer to T l Even better, for example, if the required range is 180-210, then the shooting parameters need to be adjusted so that the image brightness value calculated according to the second comparison scheme is less than or equal to 210, the closer the better. Based on the shooting parameter P, capture screen images with different brightness levels and calculate their brightness values. Based on the image brightness value T... h Adjust the shooting parameters P and capture screen images with different brightness levels so that the image brightness falls within T. h With T l For a new device to be photographed, first, capture an image according to parameter P and calculate the brightness value to deduce the screen brightness value. Then, select the shooting parameters that are closest to the value that will yield an image with a brightness value of 210, or calculate the shooting parameters using linear interpolation. For example, according to Table 1 below, assuming that the brightness value of the image captured by the new screen with an exposure time of 100,000 μs and an aperture of f / 1.4 is calculated to be 185, then the screen brightness can be determined to be between 2400 and 2800 nits. To obtain an image with a brightness value near 210, the shooting parameters should be between an exposure time of 170,000 and 220,000 μs, or 195,000 μs using linear interpolation. Therefore, the image brightness value only needs to be calibrated once for different hardware and does not depend on other factors. Furthermore, if the calculated current image brightness value is already within the required range, the parameters do not need to be adjusted, and the next image capture can continue.

[0130] Table 1

[0131]

[0132] For example, the following is an analysis of the impact of screen brightness on imaging:

[0133] Adjust the device screen brightness to different levels, then capture images and calculate the average grayscale value of the images.

[0134] Camera exposure: 105004

[0135] Aperture: Between 8 and 16 on the scale, closer to 16 (at 1 / 4).

[0136] The relationship between device screen display brightness (percentage) and changes in average image grayscale value is shown in Table 2 below:

[0137] Table 2

[0138]

[0139] The device screen displays grayscale values ​​in different brightness areas. Taking device 5 as an example, the environment is as follows: the first screen display detection device (dark environment, isolated from external light sources), camera exposure: 105004, aperture: between 8 and 16, closer to 16 (1 / 4). Data acquisition method: different images are captured at different brightness levels. The images are divided into 32 areas, and the second largest average grayscale value of each area is taken. The tenth largest grayscale value of each pixel in the image area is also taken.

[0140] Note: Camera exposure value adjustable range (5-1000000.00) unit: us. Nit is a unit of brightness, 1 nit = 1 cd / m2. Brightness refers to the physical quantity of the intensity of light (reflection) emitted by the surface of a light-emitting (reflective) object.

[0141] Table 3

[0142]

[0143] Based on Table 3 above, the screen brightness, average grayscale value of the region, and the curve of the 10th largest grayscale value of the region were plotted. Figure 7 The graph shows the screen brightness, average grayscale value, and the 10th largest grayscale value. Figure 7 As shown in the chart (horizontal axis represents screen brightness, vertical axis represents the corresponding image grayscale value), screen brightness and image grayscale value have a roughly linear relationship. (Device screen brightness values ​​below 50 nits are too dim to be tested).

[0144] For different brightness values, adjust the exposure value and record the corresponding image grayscale values, as shown in Table 4.

[0145] Table 4

[0146]

[0147] Exposure parameter upper limit: 10,000,000us. The screen brightness must be greater than 60 nits for the image grayscale value to reach the required range (180-210).

[0148] Exposure values ​​are calculated based on image grayscale values, as shown in Table 5.

[0149] Table 5

[0150]

[0151] Based on the aforementioned data, adjusting the exposure value in 50-degree intervals of brightness can yield an exposure parameter value suitable for that brightness.

[0152] To ensure compatibility with higher brightness device screens, the first exposure value was changed from 105004 to 90000, while other conditions remained unchanged, as shown in Table 6.

[0153] Table 6

[0154]

[0155] Verification was conducted using different mobile phone models, including Model A and Model B. The testing environment was within a screen display monitoring device, with camera exposure at 90000 and aperture at f / 8. Between 16, close to 16 (at 1 / 4).

[0156] Model A, with automatic brightness adjustment enabled, constantly adjusts the screen brightness internally, making it impossible to match suitable exposure parameters and resulting in uncontrollable grayscale values ​​in the final screen image. Manually disabling automatic brightness adjustment allows for suitable exposure parameters, ensuring the image grayscale values ​​are within the required range (180-210). Model B, based on the above data, can adapt to suitable exposure parameters, and the image grayscale values ​​are within the required range (180-210).

[0157] Figure 8 A schematic diagram of the structure of the adjustment device for the shooting equipment provided in this application is shown below. Figure 8 As shown, the adjustment device 800 of the shooting equipment provided in this embodiment includes:

[0158] The determining module 801 is used to determine the key image region in the pre-acquired target image, which is obtained by the shooting device from the current shooting object;

[0159] The calculation module 802 is used to calculate the overall brightness evaluation value of the key image region based on the pixel brightness information of the key image region.

[0160] The comparison module 803 is used to compare the overall brightness assessment value with the preset target brightness range to obtain the comparison result;

[0161] The generation module 804 is used to generate adjustment instructions based on the comparison results. The adjustment instructions are used to adjust the shooting parameters of the shooting device.

[0162] Optionally, the determination module 801 specifically includes:

[0163] The effective display area of ​​the target image is divided into multiple sub-regions;

[0164] Calculate the brightness characterization value for each sub-region;

[0165] Based on the brightness characterization value of each sub-region, key image regions are identified among multiple sub-regions.

[0166] In one possible implementation, the determining module 801 identifies key image regions among multiple sub-regions based on the brightness characterization value of each sub-region, specifically including:

[0167] The multiple sub-regions are sorted in descending order according to their brightness characterization values ​​to obtain a sorted sequence;

[0168] The Nth sub-region in the sorted sequence is identified as the key image region, where N is a preset positive integer.

[0169] In one possible implementation, the computing module 802 includes:

[0170] Statistical sorting of the brightness values ​​of pixels within key image regions;

[0171] Based on the statistical sorting results, pixels within a preset range are selected as target pixels;

[0172] Calculate the average brightness of the target pixels and use the average brightness as the overall brightness evaluation value.

[0173] Optionally, the generation module 804 includes:

[0174] When the comparison result indicates that the overall brightness assessment value is not within the target brightness range, the estimated brightness level corresponding to the target image is determined based on the overall brightness assessment value, the current shooting parameters of the shooting device, and the preset parameter mapping database.

[0175] Target shooting parameters are obtained based on the estimated brightness level and parameter mapping database;

[0176] Adjustment instructions are generated based on the target shooting parameters.

[0177] In one possible implementation, the generation module 804 determines the estimated brightness level corresponding to the target image based on the overall brightness assessment value, the current shooting parameters of the shooting device, and a preset parameter mapping database, specifically including:

[0178] In the parameter mapping database, obtain the reference shooting parameter set that matches the current shooting parameters of the shooting device;

[0179] Determine the location range of the overall brightness assessment value from the data points of the reference shooting parameter group;

[0180] Based on the reference target brightness levels corresponding to the data points at both ends of the location interval, the estimated brightness level of the target image is calculated by linear interpolation.

[0181] In one possible implementation, the adjustment device 800 of the shooting equipment further includes:

[0182] The component module 805 is used to iteratively adjust the shooting parameters of the shooting device based on multiple shooting objects with known brightness levels until the overall brightness evaluation value corresponding to the image of each shooting object is within the target brightness range, and to form a parameter mapping database based on the brightness level of the shooting object and the shooting parameter pair.

[0183] The adjustment device for the shooting equipment provided in this embodiment can perform the adjustment method for the shooting equipment provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0184] Figure 9 A schematic diagram of the structure of the electronic device provided in this application. Figure 9 As shown, the electronic device provided in this embodiment includes at least one processor 901 and a memory 902.

[0185] Optionally, the electronic device further includes a communication component 903. The processor 901, memory 902, and communication component 903 are connected via a bus 904.

[0186] In a specific implementation, at least one processor 901 executes computer execution instructions stored in memory 902, causing at least one processor 901 to perform the methods of the above embodiments.

[0187] The specific implementation process of processor 901 can be found in the above-mentioned method embodiments, and its implementation principle and technical effect are similar. Therefore, it will not be repeated here.

[0188] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0189] The memory may include high-speed memory (Random Access Memory, RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0190] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0191] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods of the various embodiments described above.

[0192] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the methods of the above embodiments.

[0193] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0194] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in application-specific integrated circuits (ASICs). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0195] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units 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 indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0196] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0197] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0198] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part 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, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0199] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0200] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for adjusting a shooting device, characterized in that, include: Key image regions are identified in a pre-acquired target image, which is obtained by the shooting device from the current subject. Based on the pixel brightness information within the key image region, the overall brightness evaluation value corresponding to the key image region is calculated. The overall brightness assessment value is compared with the preset target brightness range to obtain the comparison result; An adjustment instruction is generated based on the comparison results, and the adjustment instruction is used to adjust the shooting parameters of the shooting device.

2. The method according to claim 1, characterized in that, The step of determining the key image region in the pre-acquired target image includes: The effective display area of ​​the target image is divided into multiple sub-regions; Calculate the brightness characterization value for each sub-region; Based on the brightness characterization value of each sub-region, the key image region is determined in the plurality of sub-regions.

3. The method according to claim 2, characterized in that, The determination of the key image region based on the brightness characterization value of each sub-region among the plurality of sub-regions includes: The multiple sub-regions are sorted in descending order according to their brightness characterization values ​​to obtain a sorted sequence. The Nth sub-region that ranks first in the sorted sequence is determined as the key image region, where N is a preset positive integer.

4. The method according to any one of claims 1-3, characterized in that, The step of calculating the overall brightness evaluation value corresponding to the key image region based on the pixel brightness information within the key image region includes: The brightness values ​​of pixels within the key image region are statistically sorted. Based on the statistical sorting results, pixels within a preset range are selected as target pixels; Calculate the average brightness of the target pixel and use the average brightness as the overall brightness evaluation value.

5. The method according to any one of claims 1-3, characterized in that, The step of generating adjustment instructions based on the comparison results includes: When the comparison result indicates that the overall brightness assessment value is not within the target brightness range, the estimated brightness level corresponding to the target image is determined based on the overall brightness assessment value, the current shooting parameters of the shooting device, and the preset parameter mapping database. Based on the estimated brightness level and the parameter mapping database, the target shooting parameters are obtained; The adjustment command is generated based on the target shooting parameters.

6. The method according to claim 5, characterized in that, The step of determining the estimated brightness level of the target image based on the overall brightness assessment value, the current shooting parameters of the shooting device, and a preset parameter mapping database includes: In the parameter mapping database, obtain a reference shooting parameter group that matches the current shooting parameters of the shooting device; Within the data points of the reference shooting parameter group, determine the position range of the overall brightness evaluation value; Based on the reference target brightness levels corresponding to the data points at both ends of the location interval, the estimated brightness level of the target image is calculated by linear interpolation.

7. The method according to claim 5, characterized in that, The method further includes: Based on multiple subjects with known brightness levels, the shooting parameters of the shooting device are iteratively adjusted until the overall brightness assessment value corresponding to the image of each subject is within the target brightness range. The parameter mapping database is then constructed based on the brightness level-shooting parameter pairs of the subjects.

8. An adjustment device for a shooting equipment, characterized in that, include: The determination module is used to determine key image regions in a pre-acquired target image, wherein the target image is obtained by the shooting device from the current shooting object; The calculation module is used to calculate the overall brightness evaluation value corresponding to the key image region based on the pixel brightness information in the key image region. The comparison module is used to compare the overall brightness assessment value with the preset target brightness range to obtain the comparison result; The generation module is used to generate adjustment instructions based on the comparison results, and the adjustment instructions are used to adjust the shooting parameters of the shooting device.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the adjustment method of the shooting device as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the adjustment method of the shooting device as described in any one of claims 1 to 7.

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