Target image adjusting method and system, display equipment and storage medium

Abstract

The invention discloses a target image adjusting method and system, display equipment and a storage medium, and belongs to the technical field of computers. The method comprises the steps of obtaining ambient light intensity of an environment where a vehicle is located; according to the mapping relation between the target gray level transformation strategy and the light intensity range and the environment light intensity, gray level transformation parameters are determined; and in combination with the ambient light of the current environment, controlling a gray scale transformation parameter corresponding to the target gray scale transformation strategy in real time, and further performing gray scale adjustment on the initial target image matched with the vehicle according to the gray scale transformation parameter and the target gray scale transformation strategy to obtain the target target image. The adjusted gray scale can adapt to the ambient light of the current environment, and the reliability and applicability of the target image are improved. And the target image is displayed to calibrate the image sensor of the vehicle, so that the image sensor can accurately and quickly capture the image features of the target image, and the image recognition efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, system, display device and storage medium for adjusting a target image. Background Art

[0002] Advanced driving assistant system (ADAS) uses vehicle cameras, radars, range finders and other sensors to sense the surrounding environment in real time during vehicle driving, collect environmental data, and then analyze the environmental data such as static and dynamic object recognition, observation and tracking, and combine it with navigation map data for comprehensive calculations, so as to make the driver aware of possible dangers in advance to improve safety. ADAS is an active safety technology that effectively increases the reliability and safety of vehicle driving. Therefore, the image sensor (i.e., camera) in ADAS must be accurately calibrated and adjusted to reduce the system output of erroneous results or failure.

[0003] In the current ADAS calibration service, in an environment with moderate lighting, the calibration process of the image sensor in the ADAS can be completed by using the target image displayed on the client of the display device. The target image is usually a fixed rectangular QR code pattern.

[0004] However, under different ambient lighting conditions, the target image may be difficult for the camera to capture image features due to insufficient contrast or excessive brightness, resulting in low image recognition efficiency. Summary of the invention

[0005] The present application provides a method, system, display device and storage medium for adjusting a target image, which can increase the applicable environment of the target image and improve the efficiency of image recognition. The technical solution is as follows:

[0006] In a first aspect, a method for adjusting a target image is provided, which is applicable to a calibration scenario of an advanced driver assistance system, and the method comprises: obtaining the ambient light intensity of an environment in which a vehicle is located; determining grayscale transformation parameters according to a mapping relationship between a target grayscale transformation strategy and a light intensity range, and the ambient light intensity; performing grayscale adjustment on an initial target image that matches the vehicle according to the grayscale transformation parameters and the target grayscale transformation strategy to obtain a target target image; and displaying the target target image to calibrate an image sensor of the vehicle.

[0007] In a second aspect, a target image adjustment system is provided, which is suitable for a calibration scenario of an advanced driver assistance system, and includes a light sensor, a control module, an image generation module and a display module; the light sensor is used to detect the ambient light intensity of the vehicle's environment; the control module is used to determine the grayscale transformation parameters according to the mapping relationship between the grayscale transformation strategy and the light intensity range, and the ambient light intensity; the image generation module is used to adjust the grayscale of an initial target image matching the vehicle according to the grayscale transformation parameters and the grayscale transformation strategy to obtain a target image; the display module is used to display the target image to calibrate the image sensor of the vehicle.

[0008] In a third aspect, a display device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method described in the first aspect when executed by the processor.

[0009] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0010] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute the method described in the first aspect.

[0011] The embodiment of the present application provides a method, system, display device and storage medium for adjusting a target image. According to the scheme provided by the present application, the method is applicable to the calibration scenario of an advanced driver assistance system, and the method includes: obtaining the ambient light intensity of the environment in which the vehicle is located; determining the grayscale transformation parameters according to the mapping relationship between the target grayscale transformation strategy and the light intensity range, as well as the ambient light intensity; combining the ambient light of the current environment, controlling the grayscale transformation parameters corresponding to the target grayscale transformation strategy in real time, and then adjusting the grayscale of the initial target image matched with the vehicle according to the grayscale transformation parameters and the target grayscale transformation strategy to obtain the target target image, so that the adjusted grayscale can adapt to the ambient light of the current environment, increase the applicable environment of the target image, and improve the reliability and applicability of the target image. Display the target target image to calibrate the image sensor of the vehicle, so that the image sensor can accurately and quickly capture the image features of the target target image, and improve the image recognition efficiency and calibration accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 This is a calibration scenario of an advanced driver assistance system provided by an embodiment of the present application;

[0014] Figure 2 is a flow chart of a target image adjustment method provided in an embodiment of the present application;

[0015] Figure 3 is a flow chart of another target image adjustment method provided in an embodiment of the present application;

[0016] Figure 4 is a flow chart of another target image adjustment method provided in an embodiment of the present application;

[0017] Figure 5 It is a structural schematic diagram of a target image adjustment system provided in an embodiment of the present application;

[0018] Figure 6 A schematic diagram of the structure of a display device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0020] It should be understood that the "multiple" mentioned in this application refers to two or more. In the description of this application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in order to facilitate the clear description of the technical solution of this application, the words "first" and "second" are used to distinguish between the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the words "first" and "second" do not limit the quantity and execution order, and the words "first" and "second" do not limit them to be different.

[0021] Before explaining the embodiments of the present application in detail, the application scenarios and related technologies of the embodiments of the present application are first explained.

[0022] In the related technology, taking the four-wheel alignment scenario as an example, the four-wheel alignment technology can be used in vehicle maintenance and inspection to ensure the vehicle's driving stability and safety. In the four-wheel alignment technology, a target image with a fixed contrast composed of black and white blocks is used as a reference mark to calculate the spatial position of the vehicle's wheels. However, in different ambient light conditions such as strong light and weak light, for example, in outdoor environments or scenes with uneven light intensity, the target image may be difficult to accurately capture the image features by the vehicle's camera equipment (i.e., image sensor) due to insufficient or excessive contrast, affecting image recognition efficiency and calibration accuracy.

[0023] The target image adjustment method provided in the embodiment of the present application is applicable to the calibration scenario of ADAS, such as Figure 1 As shown, Figure 1 This is a calibration scenario of an advanced driver assistance system provided in an embodiment of the present application. The ADAS calibration device is used to determine the position of the calibration vehicle and the calibration device, and complete the calibration in combination with the calibration software. Among them, the ADAS calibration device includes a target display screen (i.e., a display device), a calibration main frame ( Figure 1 The ADAS calibration equipment is composed of a main frame with a crossbeam, a target bracket (for supporting the display device), a rangefinder, a rangefinder panel, etc., wherein the rangefinder and the rangefinder panel are arranged on the crossbeam. The ADAS calibration equipment may also include a wheel clamp, which is used to fix on the wheels of the calibration vehicle to achieve four-wheel alignment calibration. The target display screen in the ADAS calibration equipment can emit a calibration alignment line so that the calibration vehicle is placed in an appropriate position according to the calibration alignment line. The target display screen is used in conjunction with a tablet computer (i.e., an intelligent detection device) to perform diagnostic detection on the camera device (i.e., image sensor) and sensor of the calibration vehicle.

[0024] Based on the above Figure 1 The ADAS calibration scenario provided, the embodiment of the present application provides a method for adjusting a target image, which can be applied to the ADAS calibration scenario. It is intended to solve the technical problem that the target image in the current ADAS calibration equipment has insufficient contrast or is too bright in a complex lighting environment, resulting in low image recognition efficiency. Exemplarily, during calibration in an outdoor detection environment, the light sensor detects a high-brightness environment caused by direct sunlight, and automatically adjusts the grayscale range of the target image. By compressing the grayscale range, the effect of changing the image contrast is achieved, so that the camera device can capture the image clearly. During calibration in a dim workshop environment, the grayscale range of the target image is automatically adjusted, and the image brightness is enhanced by stretching the grayscale range, making the edge of the target image clearer and improving image recognition efficiency.

[0025] like Figure 2 As shown, Figure 2 : is a flow chart of a target image adjustment method provided in an embodiment of the present application, and the target image adjustment method includes:

[0026] S101, obtaining the ambient light intensity of the environment where the vehicle is located.

[0027] The target image adjustment method can be performed by a display device, and a light sensor is integrated in the display setting. The light sensor can also be called an ambient light sensor or a light sensor, which can automatically measure the light intensity of the surrounding environment. Among them, the working principle of the light sensor is the photoelectric effect, that is, a certain material produces an electrical effect after absorbing the energy of photons. The light sensor can be a photoresistor, a photodiode, a phototransistor, an IC-type ambient light sensor, etc., which is not limited to the embodiments of the present application.

[0028] In the embodiment of the present application, the light sensor can detect the ambient light intensity in real time in lux. A high-precision sensor with a wide detection range can be selected to improve the accuracy of the ambient light intensity. The light sensor can also support bidirectional two-wire synchronous serial bus (inter integrated circuit, I2C) communication to communicate with other modules (e.g., control module).

[0029] S102: Determine grayscale transformation parameters according to the mapping relationship between the target grayscale transformation strategy and the light intensity range, as well as the ambient light intensity.

[0030] In an embodiment of the present application, the light intensity range can be divided in advance. For example, an environment with an ambient light intensity greater than or equal to 10000 Lux is classified as a strong light environment; an environment with an ambient light intensity greater than or equal to 1000 Lux and less than 10000 Lux is classified as a normal environment; and an environment with an ambient light intensity less than 1000 Lux is classified as a weak light environment.

[0031] The target grayscale transformation strategy includes a transformation strategy for stretching the grayscale range and a transformation strategy for compressing the grayscale range, so that the grayscale range becomes larger or smaller. Correspondingly, the grayscale transformation parameters include a first grayscale transformation parameter for stretching the grayscale range and a second grayscale transformation parameter for compressing the grayscale range. Different transformation strategies need to be implemented for different environments. For example, for a strong light environment, the grayscale range of pixels in the image needs to be compressed to enhance the contrast of black and white blocks in the target image; for a weak light environment, the grayscale range of the pixel values ​​of the pixels in the image needs to be stretched to enhance the brightness of the target image and improve the visual effect.

[0032] According to the range of the current ambient light intensity, it is determined whether the grayscale range needs to be stretched or compressed, and then the grayscale transformation parameters are determined.

[0033] The target grayscale transformation strategy may be any one of a plurality of grayscale transformation strategies, including linear transformation, logarithmic transformation, exponential transformation and histogram equalization, which are introduced below respectively.

[0034] Linear transformation is to stretch (also called expansion) each pixel of an image through a linear function, thereby improving the visual effect of the image. For example, assuming that the original grayscale range of a grayscale image is 0 to 255 (i.e., 8-bit image), if you need to stretch (also called expand) the grayscale range of this image, you can change the original grayscale value 0 to 0, and the original grayscale value 255 to 340, so that the grayscale range of the image is enlarged. If you need to compress the grayscale range of this image, you can change the original grayscale value 0 to 0, and the original grayscale value 255 to 160, so that the grayscale range of the image is compressed. The linear transformation formula is g(x, y) = αf(x, y) + β, where f(x, y) is the original grayscale value of the original image, g(x, y) is the grayscale value of the transformed image, and α and β are linear transformation parameters.

[0035] Exemplarily, taking the stretching of the grayscale range as an example, assuming that the original grayscale range [0, 255] needs to be stretched to [0, 340], that is, 0 is mapped to 0, 255 is mapped to 340, and α is set to (340-0) / (255-0)=340 / 255, β=0-α×0=0, and the linear transformation formula becomes g(x, y)=340 / 255×f(x, y). Grayscale transformation is performed on each pixel in the original image according to the transformation formula. For example, if the grayscale value of a pixel is 128, the transformed grayscale value will be: g(x, y)=340 / 255×128=161.5686. Since the grayscale value is an integer, the result can be rounded or truncated. Exemplarily, 161.5686 is rounded to 162, or 161.5686 is truncated to 161. Next, the process of stretching the original grayscale range is explained. A grayscale image is read and the linear transformation parameters are defined: alpha = 340 / 255, beta = 0, where alpha represents α and beta represents β. Then a linear transformation is applied using a scaling and absolute value function (e.g., convertScaleAbs function), where convertScaleAbs function can automatically handle data type conversion to ensure that the output grayscale value is in the range of 0 to 340, and the transformed image is obtained.

[0036] Taking the compression of grayscale range as an example, suppose the original grayscale range [0, 255] needs to be compressed to [0, 160], that is, 0 is mapped to 0, 255 is mapped to 160, set α = (160-0) / (255-0) = 160 / 255, β = 0-α×0 = 0, and the linear transformation formula becomes g(x, y) = 160 / 255×f(x, y). Perform grayscale transformation on each pixel in the original image according to the transformation formula. For example, if the grayscale value of a pixel is 100, the grayscale value after transformation will be: g(x, y) = 160 / 255×100 = 62.745, 62.745 is rounded to 63, or 62.745 is truncated to 62. Next, the process of compressing the original grayscale range is explained. Read a grayscale image and define the linear transformation parameters: alpha = 160 / 255, beta = 0. Then, a linear transformation is applied using a scaling and absolute value function (eg, convertScaleAbs function), and the output grayscale value is in the range of 0 to 160 to obtain the transformed image.

[0037] Logarithmic transformation is a nonlinear transformation, which is usually used to expand the low grayscale range of an image while compressing the high grayscale range. The logarithmic transformation formula is s=clog(1+r), where r represents the grayscale of the original image, s represents the grayscale of the transformed image, and c is a constant. Logarithmic transformation has a better effect on image enhancement with low overall contrast and low grayscale values.

[0038] Exponential transformation (also called gamma transformation) is a nonlinear transformation. The exponential transformation formula is Where r represents the grayscale of the original image, s represents the grayscale of the transformed image, and a, b, c, and d are constants. The exponential transformation can stretch the areas with higher grayscale levels in the image and compress the areas with lower grayscale levels, or vice versa, depending on the size of the gamma value.

[0039] Histogram equalization is an image enhancement technology. Its basic principle is to make the grayscale distribution of the image more uniform through grayscale mapping, thereby enhancing the contrast of the image. For the case of continuous grayscale values, the cumulative distribution function (CDF) of the grayscale is used as the conversion function to make the grayscale of the output image conform to a uniform distribution. Histogram equalization can be regarded as an adaptive exponential transformation or a piecewise transformation. No parameters need to be specified, and all operations are based on the image itself.

[0040] It should be noted that, for any grayscale transformation strategy, the first grayscale transformation parameter for stretching the grayscale range and the second grayscale transformation parameter for compressing the grayscale range can be pre-set by a person skilled in the art according to actual conditions. Exemplarily, for linear transformation, the first grayscale transformation parameter is set to α=340 / 255, β=0; the second grayscale transformation parameter is set to α=160 / 255, β=0. Of course, the first grayscale transformation parameter and the second grayscale transformation parameter can also be set to other values, which are not limited to the embodiments of the present application, as long as the grayscale range can be stretched and compressed. Similarly, for logarithmic transformation, different c values ​​(i.e., grayscale transformation parameters) can be pre-set by a person skilled in the art according to actual conditions to achieve stretching and compression of the grayscale range. For exponential transformation, different a, b, c, d values ​​(i.e., grayscale transformation parameters) can be pre-set by a person skilled in the art according to actual conditions to achieve stretching and compression of the grayscale range. For histogram equalization, different CDF functions (i.e., grayscale transformation parameters) can be pre-set by a person skilled in the art according to actual conditions to achieve stretching and compression of the grayscale range.

[0041] Of course, for any grayscale transformation strategy, the first grayscale transformation parameter for stretching the grayscale range and the second grayscale transformation parameter for compressing the grayscale range can also be multi-gradient, for example, the ambient light intensity less than the first preset light intensity is divided into multiple gradients, and the first grayscale transformation parameter is configured for each gradient; the ambient light intensity greater than or equal to the second preset light intensity is divided into multiple gradients, and the second grayscale transformation parameter is configured for each gradient, and the embodiment of the present application does not limit this. In this way, after determining the multi-gradient first grayscale transformation parameter and the multi-gradient second grayscale transformation parameter, the grayscale is adjusted using the grayscale transformation parameter that matches the gradient where the ambient light intensity is located, which can improve the image enhancement effect.

[0042] S103 , adjusting the grayscale of the initial target image that matches the vehicle according to the grayscale transformation parameters and the target grayscale transformation strategy to obtain the target image.

[0043] The grayscale transformation parameter corresponds to the target grayscale transformation strategy, and the target grayscale transformation strategy is used to adjust the grayscale of the initial target image that matches the vehicle according to the grayscale transformation parameter. If the grayscale transformation parameter is a first grayscale transformation parameter for stretching the grayscale range, the target grayscale transformation strategy is used to stretch (also called expand) the grayscale value of each pixel in the initial target image to achieve stretching adjustment of the entire grayscale range, and obtain the transformed image (i.e., the target target image). If the grayscale transformation parameter is a second grayscale transformation parameter for compressing the grayscale range, the target grayscale transformation strategy is used to compress the grayscale value of each pixel in the initial target image to achieve compression adjustment of the entire grayscale range, and obtain the transformed image (i.e., the target target image).

[0044] S104: Display the target image to calibrate the image sensor of the vehicle.

[0045] In an embodiment of the present application, the target image adjustment method can be executed by a display device, and after grayscale adjustment is performed on the initial target image to obtain the target target image, the target target image is displayed. After the target target image is displayed on the display device, the intelligent detection device can perform a calibration process for the image sensor of the vehicle using the target target image, and generate calibration feedback information after completing the calibration process. The intelligent detection device can also send the calibration feedback information to the display device.

[0046] The embodiments of the present application relate to the field of computer vision and image display technology, and provide a method for dynamically adjusting the display of a target image during ADAS calibration, which can solve the technical problem of insufficient or excessive image contrast of the target image in a complex lighting environment in the ADAS calibration scenario. A method for dynamically adjusting the grayscale range of the target image based on the ambient light intensity to achieve a change in image contrast or brightness improves the recognition capability of the vehicle's image sensor, thereby improving image recognition efficiency. This allows the ADAS calibration device to perform vehicle calibration testing in complex lighting environments or under dim lighting, increases the applicable scenarios of the ADAS calibration device, enhances the practicality of the ADAS calibration device, and optimizes the user experience.

[0047] According to the scheme provided by the present application, the method is applicable to the calibration scenario of the advanced driver assistance system, and the method includes: obtaining the ambient light intensity of the environment where the vehicle is located; determining the grayscale transformation parameters according to the mapping relationship between the target grayscale transformation strategy and the light intensity range, and the ambient light intensity; combining the ambient light of the current environment, controlling the grayscale transformation parameters corresponding to the target grayscale transformation strategy in real time, and then adjusting the grayscale of the initial target image matching the vehicle according to the grayscale transformation parameters and the target grayscale transformation strategy to obtain the target target image, so that the adjusted grayscale can adapt to the ambient light of the current environment, increase the applicable environment of the target image, and improve the reliability and applicability of the target image. Display the target target image to calibrate the image sensor of the vehicle, so that the image sensor can accurately and quickly capture the image features of the target target image, and improve the image recognition efficiency and calibration accuracy.

[0048] In some embodiments, after S104, the display device may continue to detect the ambient light intensity in real time or every preset period (e.g., 30 seconds, 1 minute, etc.). If it is detected that the light intensity range of the ambient light intensity has changed, for example, it is inconsistent with the light intensity range of the previous period, the above S101-S104 will continue to be re-executed to cyclically update the target image. Through the polling mechanism, the real-time detection of the ambient light intensity and the dynamic update of the target image are realized, thereby improving the image recognition efficiency.

[0049] Based on the above Figure 2 ,like Figure 3 As shown, Figure 3 is a flow chart of another target image adjustment method provided in an embodiment of the present application. Figure 2 The grayscale transformation parameters in include the first grayscale transformation parameter and the second grayscale transformation parameter. Figure 3 Provide explanation.

[0050] S201 : Obtain the ambient light intensity of the environment where the vehicle is located.

[0051] S201 is consistent with the above S101. Its implementation method and technical effects can be found in the description of the above S101, which will not be repeated here.

[0052] S202: When the ambient light intensity is less than a first preset light intensity, determine a first grayscale transformation parameter for stretching the grayscale range according to a mapping relationship between a target grayscale transformation strategy and a light intensity range.

[0053] In an embodiment of the present application, the light intensity range is divided in advance, and the situation where the ambient light intensity is less than the first preset light intensity is divided into a low-light environment. For example, in a dim workshop environment, the grayscale range needs to be stretched, and the brightness of the target image is enhanced by stretching the grayscale range, so that the edge of the target image is clearer, and the image recognition efficiency is improved. Based on this, in this environment, according to the mapping relationship between the target grayscale transformation strategy and the light intensity range, the first grayscale transformation parameter for stretching the grayscale range is determined. For example, the target grayscale transformation strategy is a linear transformation, and the first grayscale transformation parameter is set to α=340 / 255, β=0.

[0054] S203. According to the first grayscale transformation parameter and the target grayscale transformation strategy, the grayscale values ​​of the pixels in the initial target image are adjusted to obtain the target target image, so that the upper limit value of the grayscale distribution range of the pixels in the target target image is greater than the upper limit value of the grayscale distribution range of the pixels in the initial target image.

[0055] In the embodiment of the present application, the target grayscale transformation strategy and the first grayscale transformation parameter are used to stretch the grayscale value of each pixel in the initial target image to achieve stretching adjustment of the entire grayscale range, and obtain the transformed image (i.e., the target target image). The grayscale distribution range of the pixel in the target target image includes the grayscale distribution range of the pixel in the initial target image, that is, the upper limit value of the grayscale distribution range of the pixel in the target target image is greater than the upper limit value of the grayscale distribution range of the pixel in the initial target image, for example, the grayscale distribution range of 0 to 340 includes the grayscale distribution range of 0 to 255.

[0056] According to the current ambient light intensity, the parameters of the target image are adjusted dynamically in real time. In a low-light environment, the grayscale distribution range of the initial target image is stretched to enhance the visual effect, make the edge of the target image clearer, and improve image recognition efficiency.

[0057] S204: When the ambient light intensity is greater than or equal to the second preset light intensity, determine a second grayscale transformation parameter for compressing the grayscale range according to a mapping relationship between the target grayscale transformation strategy and the light intensity range.

[0058] In an embodiment of the present application, the light intensity range is divided in advance, and the situation where the ambient light intensity is greater than or equal to the second preset light intensity is divided into a strong light environment. For example, in an outdoor environment, the grayscale range needs to be compressed, and the effect of changing the image contrast is achieved by compressing the grayscale range, so that the camera device can recognize the image. Based on this, in this environment, the second grayscale transformation parameters for compressing the grayscale range are determined according to the mapping relationship between the target grayscale transformation strategy and the light intensity range. For example, the target grayscale transformation strategy is a linear transformation, and the second grayscale transformation parameters are set to α=160 / 255, β=0.

[0059] S205. According to the second grayscale transformation parameter and the target grayscale transformation strategy, the grayscale values ​​of the pixels in the initial target image are adjusted to obtain the target target image, so that the upper limit value of the grayscale distribution range of the pixels in the target target image is smaller than the upper limit value of the grayscale distribution range of the pixels in the initial target image.

[0060] In the embodiment of the present application, the target grayscale transformation strategy and the second grayscale transformation parameter are used to compress the grayscale value of each pixel in the initial target image, so as to achieve compression adjustment of the entire grayscale range and obtain the transformed image (i.e., the target target image). The grayscale distribution range of the pixels in the initial target image includes the grayscale distribution range of the pixels in the target target image, that is, the upper limit value of the grayscale distribution range of the pixels in the target target image is less than the upper limit value of the grayscale distribution range of the pixels in the initial target image, for example, the grayscale distribution range of 0 to 255 includes the grayscale distribution range of 0 to 160.

[0061] According to the current ambient light intensity, the parameters of the target image are adjusted dynamically in real time. In a strong light environment, the grayscale distribution range of the initial target image is compressed to enhance the contrast effect of black and white blocks and improve image recognition efficiency.

[0062] S206: When the ambient light intensity is greater than or equal to the first preset light intensity and less than the second preset light intensity, use the initial target image as the target target image.

[0063] It should be noted that after S201, if the ambient light intensity is less than the first preset light intensity, S202-S203 are executed, and if the ambient light intensity is greater than or equal to the second preset light intensity, S204-S205 are executed. If the ambient light intensity is greater than or equal to the first preset light intensity and less than the second preset light intensity, S206 is executed.

[0064] Among them, S207 is executed after S203; S207 is executed after S205; S207 is executed after S206.

[0065] In the embodiment of the present application, the light intensity range is divided in advance, and the situation where the ambient light intensity is greater than or equal to the first preset light intensity and less than the second preset light intensity is classified as a normal environment. In this environment, there is no need to adjust the grayscale value of the pixel points in the initial target image, and the initial target image is directly used as the target target image for subsequent display and calibration. By adding judgment conditions and adopting a grayscale adjustment strategy that matches the ambient light intensity (including stretching, compression, and no change), the reliability and applicability of the target image are improved.

[0066] S207: Display the target image to calibrate the image sensor of the vehicle.

[0067] S207 is consistent with the above S104. Its implementation method and technical effects can be found in the description of the above S104, which will not be repeated here.

[0068] The calibration method in the related art cannot adapt to the changing light environment. The target image adjustment method provided in the embodiment of the present application can automatically adjust the grayscale of the target image according to the ambient light intensity to achieve the effect of changing the contrast and brightness, thereby improving the reliability and applicability of the ADAS calibration equipment.

[0069] The embodiment of the present application proposes a dynamic target image adjustment method that combines optical sensors, image generation and real-time display technology to solve the technical problem that the current ADAS calibration equipment has low image recognition efficiency in complex lighting environments. By dynamically adjusting the target image parameters, it is suitable for complex environments such as strong outdoor light and weak workshop light, and is also suitable for normal environments, which increases the applicable environment of the target image and improves the reliability and applicability of the target image. By enhancing the contrast or brightness of the target image, the recognizability is improved, the recognition error of the camera equipment is reduced, and the image recognition efficiency and calibration accuracy are improved. The dynamic adjustment of the target image is achieved through a fast feedback mechanism to meet the real-time requirements of the ADAS calibration equipment. The adjustment method can be implemented by adding software control logic using optical sensors and display devices, with few hardware changes, simplified implementation process, and reduced costs.

[0070] In some embodiments, the target grayscale transformation strategy is any one of a plurality of grayscale transformation strategies; the plurality of grayscale transformation strategies include linear transformation, logarithmic transformation, exponential transformation and histogram equalization; Figure 2 S104 or Figure 3 After S207, the target image adjustment method further includes the following steps: receiving calibration feedback information; the calibration feedback information is generated after calibrating the image sensor of the vehicle; when the calibration feedback information indicates that the calibration is unsuccessful, continue to determine new grayscale transformation parameters according to the mapping relationship between other grayscale transformation strategies and the light intensity range, as well as the ambient light intensity; the other grayscale transformation strategy is any grayscale transformation strategy among the multiple grayscale transformation strategies except the target grayscale transformation strategy; according to the new grayscale transformation parameters and the other grayscale transformation strategies, grayscale adjustment is performed on the initial target image to obtain other target images; until the new calibration feedback information indicates that the calibration is successful.

[0071] In the embodiment of the present application, any one of linear transformation, logarithmic transformation, exponential transformation and histogram equalization is selected as the target grayscale transformation strategy. When executing the target image adjustment method provided in the embodiment of the present application, the display device first uses the target grayscale transformation strategy to adjust the initial target image to obtain the adjusted target image (i.e., the target target image). Then the intelligent detection device uses the adjusted target image to calibrate the image sensor of the vehicle, generates calibration feedback information, and sends the calibration feedback information to the display device. The calibration feedback information includes calibration failure or calibration success. When the calibration feedback information indicates that the calibration is unsuccessful, it means that the target grayscale transformation strategy cannot meet the calibration requirements, and the initial target image is adjusted according to other grayscale transformation strategies (different from the target grayscale transformation strategy). The adjustment process of other grayscale transformation strategies is the same as the adjustment process of the target grayscale transformation strategy, except that the mapping relationship between the other grayscale transformation strategies and the light intensity range is used. After obtaining other target images, the intelligent detection device then uses other target images to calibrate the image sensor of the vehicle, generates calibration feedback information, and sends the calibration feedback information to the display device. If the calibration feedback information indicates that the calibration is unsuccessful, the initial target image is adjusted by using a grayscale transformation strategy that is different from other grayscale transformation strategies and the target grayscale transformation strategy, and so on, until the new calibration feedback information indicates that the calibration is successful, indicating that the calibration requirements are met.

[0072] Different grayscale transformation strategies have different basic principles, and their corresponding image enhancement effects will also be different. Please refer to the above descriptions of linear transformation, logarithmic transformation, exponential transformation and histogram equalization. By changing multiple grayscale transformation strategies in turn, the initial target image is adjusted until a grayscale transformation strategy that meets the calibration requirements is selected. Compared with a single grayscale transformation strategy, the flexibility of the target image adjustment process is improved, and the image recognition efficiency and calibration accuracy are improved.

[0073] In some embodiments, based on the above Figure 2 and Figure 3 ,like Figure 4 As shown, Figure 4 : is a flow chart of another target image adjustment method provided in an embodiment of the present application, the target image adjustment method comprising:

[0074] S301: Obtain the ambient light intensity of the environment where the vehicle is located.

[0075] S301 is consistent with the above-mentioned S101. Its implementation method and technical effects achieved can be found in the description of the above-mentioned S101, which will not be repeated here.

[0076] S302: Determine linear transformation parameters according to the mapping relationship between the linear transformation strategy and the light intensity range, and the ambient light intensity.

[0077] S303 , adjusting the grayscale of the initial target image matched with the vehicle according to the linear transformation parameters and the linear transformation strategy to obtain the target image.

[0078] In an embodiment of the present application, compared with other transformation strategies (for example, logarithmic transformation, exponential transformation and histogram equalization), the linear transformation strategy has a simpler transformation process and a better image enhancement effect. Based on this, linear transformation is used as the target grayscale transformation strategy, and the initial target image is first adjusted according to the linear transformation strategy. This can improve the calibration success rate and reduce the number of times the grayscale transformation strategy is changed.

[0079] Exemplarily, the linear transformation process can be implemented by the following code.

[0080] import cv2

[0081] import numpy as np

[0082] #Read the image

[0083] image=cv2.imread('path_to_image',cv2.IMREAD_GRAYSCALE)

[0084] #Define alpha and beta

[0085] alpha=340 / 255

[0086] beta = 0

[0087] # Apply linear transformation

[0088] new_image=cv2.convertScaleAbs(image,alpha=alpha,beta=beta)

[0089] #Save or display the transformed image

[0090] cv2.imwrite('path_to_new_image',new_image)

[0091] cv2.imshow('Transformed',new_image)

[0092] cv2.waitkey(0)

[0093] cv2.destoryAllWindows()

[0094] The initial target image (image) is read, and the linear transformation parameters are defined as: alpha = 340 / 255, beta = 0, where alpha represents α and beta represents β. Then the convertScaleAbs function (for scaling and taking absolute values) is used to apply the linear transformation, ensuring that the output grayscale value is within the range of 0 to 340, and the target image (new_image) is obtained.

[0095] S304: Display the target image to calibrate the image sensor of the vehicle.

[0096] S304 is consistent with the above S104. Its implementation method and technical effects can be found in the description of the above S104, which will not be repeated here.

[0097] S305, receiving calibration feedback information; the calibration feedback information is generated after calibrating the image sensor of the vehicle.

[0098] S306 . When the calibration feedback information indicates that the calibration is unsuccessful, continue to adjust the grayscale of the initial target image in sequence according to the preset arrangement order of the multiple grayscale transformation strategies until new calibration feedback information indicates that the calibration is successful.

[0099] Among them, the preset arrangement order of multiple grayscale transformation strategies is logarithmic transformation, exponential transformation and histogram equalization.

[0100] In an embodiment of the present application, an arrangement order of multiple grayscale transformation strategies is pre-set, and the initial target image is adjusted in sequence according to the arrangement order. If the target target image obtained by adjusting the linear transformation can meet the calibration requirements, that is, the calibration feedback information indicates that the calibration is successful, there is no need to execute subsequent transformation strategies. If the target target image obtained by adjusting the linear transformation cannot meet the calibration requirements, that is, the calibration feedback information indicates that the calibration is unsuccessful, the logarithmic transformation is continued to be used to adjust the initial target image, and so on. Next, the exponential transformation and histogram equalization can be used to perform the adjustment process of the initial target image until the adjusted target target image can meet the calibration requirements.

[0101] When the display device executes the target image adjustment method, it can first use the default linear transformation for image enhancement. If the enhancement effect is not good, for example, the target image cannot be recognized or the calibration cannot be completed, that is, the calibration is unsuccessful, then the logarithmic transformation is used instead, and so on, and different transformation strategies (linear transformation, logarithmic transformation, gamma transformation and histogram equalization) are used in turn. Just like changing the shooting parameters of the camera device, until the displayed target target image can be recognized or calibrated. In this way, compared with a single grayscale transformation strategy, the flexibility of the target image adjustment process is improved, and the image recognition efficiency and calibration accuracy are improved.

[0102] Based on the target image adjustment method provided in the above embodiment, Figure 5 is a schematic diagram of the structure of a target image adjustment system provided in an embodiment of the present application. The target image adjustment system 50 can be used as a display device itself or integrated in a display device. The system can be implemented as part or all of the display device by software, hardware or a combination of both. Figure 5 The target image adjustment system 50 is suitable for the calibration scenario of the advanced driver assistance system. The target image adjustment system 50 includes a light sensor 501, a control module 502, an image generation module 503 and a display module 504; the light sensor 501 is used to detect the ambient light intensity of the environment where the vehicle is located; the control module 502 is used to determine the grayscale transformation parameters according to the mapping relationship between the grayscale transformation strategy and the light intensity range, and the ambient light intensity; the image generation module 503 is used to adjust the grayscale of the initial target image matching the vehicle according to the grayscale transformation parameters and the grayscale transformation strategy to obtain the target image; the display module 504 is used to display the target image to calibrate the image sensor of the vehicle.

[0103] Optionally, the control module 502 is also used to determine a first grayscale transformation parameter for stretching the grayscale range according to a mapping relationship between a target grayscale transformation strategy and the light intensity range when the ambient light intensity is less than a first preset light intensity; and to determine a second grayscale transformation parameter for compressing the grayscale range according to a mapping relationship between a target grayscale transformation strategy and the light intensity range when the ambient light intensity is greater than or equal to a second preset light intensity; wherein the grayscale transformation parameters include a first grayscale transformation parameter and a second grayscale transformation parameter.

[0104] Optionally, the image generation module 503 is also used to adjust the grayscale values ​​of the pixels in the initial target image according to the first grayscale transformation parameter and the target grayscale transformation strategy to obtain a target target image, so that the upper limit value of the grayscale distribution range of the pixels in the target target image is greater than the upper limit value of the grayscale distribution range of the pixels in the initial target image.

[0105] Optionally, the image generation module 503 is also used to adjust the grayscale values ​​of the pixels in the initial target image according to the second grayscale transformation parameters and the target grayscale transformation strategy to obtain the target target image, so that the upper limit value of the grayscale distribution range of the pixels in the target target image is smaller than the upper limit value of the grayscale distribution range of the pixels in the initial target image.

[0106] Optionally, the target grayscale transformation strategy is any one of a plurality of grayscale transformation strategies; the plurality of grayscale transformation strategies include linear transformation, logarithmic transformation, exponential transformation and histogram equalization; the target image adjustment system 50 also includes a receiving module 505; the receiving module 505 is used to receive calibration feedback information; the calibration feedback information is generated after the vehicle's image sensor is calibrated; the control module 502 is also used to, when the calibration feedback information indicates that the calibration is unsuccessful, continue to determine new grayscale transformation parameters based on the mapping relationship between other grayscale transformation strategies and the light intensity range, as well as the ambient light intensity; the other grayscale transformation strategies are any grayscale transformation strategies among the plurality of grayscale transformation strategies except the target grayscale transformation strategy; the image generation module 503 is also used to adjust the grayscale of the initial target image according to the new grayscale transformation parameters and the other grayscale transformation strategies to obtain other target images; until the new calibration feedback information indicates that the calibration is successful.

[0107] Optionally, the target grayscale transformation strategy is a linear transformation;

[0108] The receiving module 505 is also used to receive calibration feedback information; the calibration feedback information is generated after the vehicle's image sensor is calibrated; the image generation module 503 is also used to continue to adjust the grayscale of the initial target image in sequence according to the preset arrangement order of multiple grayscale transformation strategies when the calibration feedback information indicates that the calibration is unsuccessful, until new calibration feedback information indicates that the calibration is successful; wherein the preset arrangement order of the multiple grayscale transformation strategies is logarithmic transformation, exponential transformation and histogram equalization.

[0109] Optionally, the image generating module 503 is further configured to use the initial target image as the target target image when the ambient light intensity is greater than or equal to the first preset light intensity and less than the second preset light intensity.

[0110] The embodiment of the present application provides a display device for adjusting a target image based on the ambient light intensity. The display device may be the above-mentioned Figure 5The target image adjustment system 50 in the embodiment of the present invention, or the target image adjustment system 50. The light sensor 501 is used to detect the ambient light intensity or ambient brightness in real time, and converts the brightness value of the current environment (i.e., the ambient light intensity) collected in real time into a digital signal and transmits it to the control module 502. The control module 502 is used to connect the light sensor 501 and the image generation module 503, and dynamically control the grayscale transformation parameters used to adjust the target image in real time according to the brightness value (i.e., the ambient light intensity) fed back by the light sensor 501. The image generation module 503 is used to adjust the pixel value of each pixel in the initial target image based on the preset algorithm according to the grayscale transformation parameter to generate a target target image. By adjusting the grayscale value of the pixel of the image, a technical effect of changing the contrast or brightness of the image can be produced. The display module 504 is used to display the dynamically adjusted target image (i.e., the target target image), and the display module can be a high-brightness liquid crystal display (LCD) or an electronic ink screen. If the display module adopts a high-brightness LCD screen, the resolution can be set to 1024×1024. The target image is updated in real time through the display module, ensuring that the camera equipment can quickly identify in different lighting environments, improving image recognition efficiency and thus improving calibration accuracy.

[0111] It should be noted that the target image adjustment system provided in the above embodiment only uses the division of the above-mentioned functional modules as an example when adjusting the target image. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the system is divided into different functional modules to complete all or part of the functions described above.

[0112] The functional units and modules in the above embodiments may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the above integrated units may be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present application.

[0113] The target image adjustment system and the target image adjustment method provided in the above embodiments belong to the same concept. The specific working process of the units and modules in the above embodiments and the technical effects brought about can be found in the method embodiment part and will not be repeated here.

[0114] Based on the target image adjustment method provided in the above embodiment, Figure 6 A schematic diagram of a display device provided in an embodiment of the present application is shown in FIG. Figure 6As shown, the display device 60 includes: a processor 601, a memory 602, and a computer program 603 stored in the memory 602 and executable on the processor 601. When the processor 601 executes the computer program 603, the steps in the target image adjustment method in the above embodiment are implemented.

[0115] The display device 60 may be a general-purpose computer device or a dedicated computer device. In a specific implementation, the display device 60 may be a desktop computer, a portable computer, a network server, a PDA, a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of the display device 60. Those skilled in the art will understand that Figure 6 This is merely an example of the display device 60 and does not constitute a limitation on the display device 60 . The display device 60 may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, etc.

[0116] The processor 601 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0117] In some embodiments, the memory 602 may be an internal storage unit of the display device 60, such as a hard disk or memory of the display device 60. In other embodiments, the memory 602 may also be an external storage device of the display device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the display device 60. Further, the memory 602 may also include both an internal storage unit of the display device 60 and an external storage device. The memory 602 is used to store an operating system, an application program, a boot loader, data, and other programs. The memory 602 may also be used to temporarily store data that has been output or is to be output.

[0118] An embodiment of the present application also provides a display device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the computer program.

[0119] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0120] An embodiment of the present application provides a computer program product, which, when executed on a computer, enables the computer to execute the steps in the above-mentioned method embodiments.

[0121] If the integrated unit is implemented in the form of 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 present application implements all or part of the processes in the above method embodiments, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk and optical data storage device. The computer-readable storage medium mentioned in the present application can be a non-volatile storage medium, in other words, it can be a non-transient storage medium.

[0122] It should be understood that all or part of the steps to implement the above embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions can be stored in the above-mentioned computer readable storage medium.

[0123] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0124] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for adjusting a target image, characterized in that: The method is applicable to a calibration scenario of an advanced driver assistance system, and the method comprises: Get the ambient light intensity of the vehicle's environment; Determining grayscale transformation parameters according to a mapping relationship between a target grayscale transformation strategy and a light intensity range, and the ambient light intensity; According to the grayscale transformation parameters and the target grayscale transformation strategy, grayscale adjustment is performed on the initial target image matched with the vehicle to obtain a target target image; The target image is displayed to calibrate an image sensor of the vehicle.

2. The method according to claim 1, characterized in that The step of determining the grayscale transformation parameters according to the mapping relationship between the target grayscale transformation strategy and the light intensity range, and the ambient light intensity, includes: When the ambient light intensity is less than the first preset light intensity, determining a first grayscale transformation parameter for stretching the grayscale range according to a mapping relationship between the target grayscale transformation strategy and the light intensity range; When the ambient light intensity is greater than or equal to a second preset light intensity, determining a second grayscale transformation parameter for compressing the grayscale range according to a mapping relationship between the target grayscale transformation strategy and the light intensity range; The grayscale transformation parameters include the first grayscale transformation parameters and the second grayscale transformation parameters.

3. The method according to claim 2, characterized in that The grayscale adjustment of the initial target image matched with the vehicle according to the grayscale transformation parameter and the target grayscale transformation strategy to obtain the target image includes: According to the first grayscale transformation parameter and the target grayscale transformation strategy, the grayscale values ​​of the pixels in the initial target image are adjusted to obtain the target target image, so that the upper limit value of the grayscale distribution range of the pixels in the target target image is greater than the upper limit value of the grayscale distribution range of the pixels in the initial target image.

4. The method according to claim 2, characterized in that The grayscale adjustment of the initial target image matched with the vehicle according to the grayscale transformation parameter and the target grayscale transformation strategy to obtain the target image includes: According to the second grayscale transformation parameter and the target grayscale transformation strategy, the grayscale values ​​of the pixels in the initial target image are adjusted to obtain the target target image, so that the upper limit value of the grayscale distribution range of the pixels in the target target image is less than the upper limit value of the grayscale distribution range of the pixels in the initial target image.

5. The method according to any one of claims 1 to 4, characterized in that: The target grayscale transformation strategy is any one of a plurality of grayscale transformation strategies; the plurality of grayscale transformation strategies include linear transformation, logarithmic transformation, exponential transformation and histogram equalization; After displaying the target image to calibrate the image sensor of the vehicle, the method further includes: Receiving calibration feedback information; the calibration feedback information is generated after calibrating the image sensor of the vehicle; When the calibration feedback information indicates that the calibration is unsuccessful, continue to determine new grayscale transformation parameters according to the mapping relationship between other grayscale transformation strategies and the light intensity range, and the ambient light intensity; the other grayscale transformation strategy is any grayscale transformation strategy among the multiple grayscale transformation strategies except the target grayscale transformation strategy; According to the new grayscale transformation parameters and the other grayscale transformation strategies, the grayscale of the initial target image is adjusted to obtain other target images; until new calibration feedback information indicates that the calibration is successful.

6. The method according to any one of claims 1 to 4, characterized in that: The target grayscale transformation strategy is a linear transformation; After displaying the target image to calibrate the image sensor of the vehicle, the method further includes: Receiving calibration feedback information; the calibration feedback information is generated after calibrating the image sensor of the vehicle; When the calibration feedback information indicates that the calibration is unsuccessful, continue to adjust the grayscale of the initial target image in sequence according to the preset arrangement order of the multiple grayscale transformation strategies until new calibration feedback information indicates that the calibration is successful; Among them, the preset arrangement order of the multiple grayscale transformation strategies is logarithmic transformation, exponential transformation and histogram equalization.

7. The method according to any one of claims 1 to 4, characterized in that: After obtaining the ambient light intensity of the environment where the vehicle is located, the method further includes: When the ambient light intensity is greater than or equal to the first preset light intensity and less than the second preset light intensity, the initial target image is used as the target target image.

8. A target image adjustment system, characterized in that: The system is suitable for calibration scenarios of advanced driver assistance systems, and the system includes a light sensor, a control module, an image generation module, and a display module; The light sensor is used to detect the ambient light intensity of the environment where the vehicle is located; The control module is used to determine the grayscale transformation parameters according to the mapping relationship between the grayscale transformation strategy and the light intensity range, and the ambient light intensity; The image generation module is used to adjust the grayscale of the initial target image matching the vehicle according to the grayscale transformation parameter and the grayscale transformation strategy to obtain a target image; The display module is used to display a target image to calibrate the image sensor of the vehicle.

9. A display device, characterized in that: The display device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images