WO2025247111A1 - Method and device for measuring spatial distribution of ambient luminance - Google Patents

Abstract

A method and device for measuring spatial distribution of ambient luminance, relating to the technical field of luminance measurement. The method comprises: receiving an ambient luminance measurement instruction, and triggering an image sensor of a camera to acquire an initial image of an environment to be measured (S110); on the basis of the initial image, determining target exposure parameters (S120); receiving a photographing instruction, triggering, on the basis of the target exposure parameters, the camera to perform photographing, and acquiring a target image file in said environment that is obtained by photographing (S130); on the basis of the target image file and the target exposure parameters, calculating an object-space luminance value of each pixel in the target image file (S140); and on the basis of the object-space luminance value, generating and displaying a pseudo-color map of luminance spatial distribution in said environment (S150). The method and device achieve visual display of ambient luminance, thereby providing intuitive design references for lighting designers.

Description

Method and apparatus for measuring spatial distribution of ambient brightness

Cross-references to related applications

This application claims priority to Chinese Patent Application No. 2024106784177, filed on May 28, 2024, entitled “Method and Apparatus for Measuring Spatial Distribution of Ambient Brightness”, which is incorporated herein by reference in its entirety.

Technical Field

This application relates to the field of brightness measurement technology, and in particular to a method and apparatus for measuring the spatial distribution of ambient brightness.

Background Technology

In traditional related technologies, research on identifying ambient brightness through photographs is scarce. Photometric stereochemistry in computational photography is one of the few similar studies. In photometric stereochemistry, it is assumed that illumination is uniform and directional, and the direction of illumination is known. By taking images of an object under different lighting conditions, a series of images can be obtained. The pixel value in each image is affected by the object's surface normal and the direction of illumination. According to the photometric equation, the object-side brightness value of a pixel (the actual brightness value of each pixel in the photograph under the shooting environment, in candela per square meter) can be expressed as a function of the illumination direction and the surface normal. By analyzing these images, the normal information of the pixels can be estimated by solving a system of equations, thus obtaining the object-side brightness value of the pixels.

The above method can only estimate the object-side brightness value of pixels, and cannot obtain a visual display of the spatial distribution of ambient brightness, thus failing to provide intuitive design references for lighting designers.

Summary of the Invention

This application provides a method and apparatus for measuring the spatial distribution of ambient brightness, which solves the problem that traditional ambient brightness measurement methods cannot obtain a visual display of the spatial distribution of ambient brightness, and cannot intuitively provide design references for lighting designers.

This application provides a method for measuring the spatial distribution of ambient brightness, including the following steps:

Upon receiving an ambient brightness measurement command, the camera's image sensor is triggered to acquire an initial image of the environment to be measured.

Based on the initial image, determine the target exposure parameters;

Receive the shooting instruction, trigger the camera to take a picture according to the target exposure parameters, and acquire the target image file under the measured environment;

Based on the target image file and the target exposure parameters, calculate the object-side brightness value of each pixel in the target image file;

Based on the object luminance value, a pseudo-color map of the luminance spatial distribution under the measured environment is generated and displayed.

According to the ambient brightness spatial distribution measurement method provided in this application, after calculating the object-space brightness value of each pixel in the target image file based on the target image file and the target exposure parameters, the method further includes:

Based on the object luminance values, a luminance distribution file is generated, which is configured to store the object luminance values of each pixel in the target image file in tabular form.

In this table, the row and column positions of the object-side brightness value of each pixel correspond one-to-one with the row and column coordinates of the corresponding pixel in the target image file.

According to the method for measuring the spatial distribution of ambient brightness provided in this application, after generating and displaying a pseudo-color map of the spatial distribution of brightness in the environment to be measured based on the object brightness value, the method further includes:

Obtain the row and column coordinates of the user-specified target pixel on the luminance spatial distribution pseudo-color map;

Based on the row and column coordinates of the target pixel, query the target object brightness value recorded at the corresponding row and column position in the brightness distribution file;

Display the brightness value of the target object.

According to the method for measuring the spatial distribution of ambient brightness provided in this application, after generating and displaying a pseudo-color map of the spatial distribution of brightness in the environment to be measured based on the object brightness value, the method further includes:

Obtain the user-specified target region on the luminance spatial distribution pseudocolor map;

Using all pixels in the target region as target pixels, obtain the row and column coordinates of the target pixels;

Based on the row and column coordinates of the target pixel, query the target object brightness value recorded at the corresponding row and column position in the brightness distribution file;

This displays the statistical values of the brightness of the target object.

According to the ambient brightness spatial distribution measurement method provided in this application, after calculating the object-space brightness value of each pixel in the target image file based on the target image file and the target exposure parameters, the method further includes:

Calculate and display the statistical values of the object-side brightness of all pixels in the target image file.

According to the method for measuring the spatial distribution of ambient brightness provided in this application, after generating and displaying a pseudo-color map of the spatial distribution of brightness in the environment to be measured based on the object brightness value, the method further includes:

Receive the scene category of the environment to be measured, as input by the user;

Based on the scene category, a recommended statistical value for the brightness corresponding to the environment to be measured is determined.

According to the method for measuring the spatial distribution of ambient brightness provided in this application, a target image file captured under the environment to be measured is obtained, including:

Obtain RAW format files captured by the camera;

The raw format file is converted to an RGB three-channel format file according to a preset conversion method, and the RGB three-channel format file is determined to be the target image file.

According to the method for measuring the spatial distribution of ambient luminance provided in this application, based on the object luminance value, a pseudo-color map of the spatial distribution of luminance in the environment to be measured is generated and displayed, including:

The object-side brightness value of each pixel is normalized from 0 to ²ⁿ -1 to generate an n-bit grayscale image representing the brightness distribution, where n is a multiple of 8.

The grayscale image is converted into a brightness distribution pseudo-color image.

According to the ambient brightness spatial distribution measurement method provided in this application, based on the target image file and the target exposure parameters, the object-space brightness value of each pixel in the target image file is calculated, including:

Extract the pixel values of each pixel in the target image file in the r, g, and b channels respectively;

The pixel value and the target exposure parameter are input into the object-space luminance regression model to obtain the object-space luminance value of each pixel output by the object-space luminance regression model.

The object-side brightness regression model is obtained by fitting and regressing the exposure parameters of the sample target image file, the pixel values of the pixels in the sample target image file, and the standard object-side brightness values corresponding to the pixels in the sample target image file.

An ambient brightness spatial distribution measurement device according to this application is applied to a terminal, and the device includes the following modules:

The measurement command receiving module is configured to receive ambient brightness measurement commands and trigger the camera's image sensor to acquire an initial image of the environment to be measured.

An exposure parameter determination module is configured to determine target exposure parameters based on the initial image;

The target image file acquisition module is configured to receive a shooting command, trigger the camera to take a picture according to the target exposure parameters, and acquire the target image file under the measured environment.

The brightness value calculation module is configured to calculate the object-side brightness value of each pixel in the target image file based on the target image file and the target exposure parameters;

The pseudo-color map generation module is configured to generate and display a pseudo-color map of the luminance spatial distribution under the measured environment based on the object luminance value.

This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the ambient brightness spatial distribution measurement method as described above.

This application also provides a computer program product, including a computer program that, when executed by a processor, implements the ambient brightness spatial distribution measurement method as described above.

The method and apparatus for measuring the spatial distribution of ambient brightness provided in this application involve receiving an ambient brightness measurement command, triggering the camera's image sensor to acquire an initial image of the environment to be measured; determining target exposure parameters based on the initial image; receiving a shooting command, triggering the camera to take a picture according to the target exposure parameters, and acquiring a target image file of the environment to be measured; calculating the object-side brightness value of each pixel in the target image file based on the target image file and the target exposure parameters; and generating and displaying a pseudo-color map of the spatial distribution of brightness in the environment to be measured based on the object-side brightness values. This application achieves a visual display of ambient brightness by capturing a target image file of the current environment to be measured in real time, calculating the object-side brightness value of each pixel in the target image file based on the exposure parameters at the time of shooting, and generating and displaying a pseudo-color map of the spatial distribution of brightness in the environment to be measured based on the object-side brightness value of each pixel. This provides an intuitive design reference for lighting designers.

Attached Figure Description

To more clearly illustrate the technical solutions in this application or conventional technology, the drawings used in the description of the embodiments or conventional technology will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

Figure 1 is one of the flowcharts of the method for measuring the spatial distribution of ambient brightness provided in the embodiments of this application.

Figure 2 is an interface display diagram of the photograph of the environment to be measured taken by the terminal in the method for measuring the spatial distribution of ambient brightness provided in the embodiment of this application.

Figure 3 is a schematic diagram of the display interface of the pseudo-color map of the spatial distribution of brightness in the ambient brightness measurement method provided in the embodiment of this application.

Figure 4 is a second schematic flowchart of the method for measuring the spatial distribution of ambient brightness provided in the embodiments of this application.

Figure 5 is a schematic diagram showing the object-side brightness value of a pixel at a user-specified point in the ambient brightness spatial distribution measurement method provided in this application embodiment.

Figure 6 is a flowchart of the third embodiment of the method for measuring the spatial distribution of ambient brightness provided in this application.

Figure 7 is a schematic diagram showing the object-space brightness value of a target pixel in a user-specified area in the ambient brightness spatial distribution measurement method provided in the embodiments of this application.

Figure 8 is a flowchart of the method for measuring the spatial distribution of ambient brightness provided in the embodiments of this application.

Figure 9 is a flowchart of the fifth embodiment of the method for measuring the spatial distribution of ambient brightness provided in this application.

Figure 10 is a schematic flowchart of the method for measuring the spatial distribution of ambient brightness provided in the embodiments of this application.

Figure 11 is the seventh flowchart of the method for measuring the spatial distribution of ambient brightness provided in the embodiments of this application.

Figure 12 is a schematic diagram of the structure of the ambient light spatial distribution measurement device provided in the embodiment of this application.

Figure 13 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application.

Detailed Implementation

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

The method for measuring the spatial distribution of ambient brightness according to this application is applied to a terminal, which may be a device such as a smartphone or tablet computer. The method includes the following steps S110 to S140.

Step S110: Receive an ambient brightness measurement command and trigger the camera's image sensor to acquire an initial image of the environment to be measured. Specifically, the terminal receives an ambient brightness measurement command triggered by the user, triggering the image sensor of the camera in the terminal to acquire an initial image of the environment to be measured. That is, upon receiving the ambient brightness measurement command, the camera module is activated, and the image sensor displays the acquired initial image on the terminal's display screen. The user can observe the initial image and adjust the shooting angle.

Step S120: Based on the initial image, determine the target exposure parameters. Specifically, the camera's default exposure parameters (the camera automatically generates a set of default exposure parameters for different scenes) can be determined as the target exposure parameters. Preferably, the user's adjustment of the default exposure parameters is received, the adjusted exposure parameters are determined as the target exposure parameters, and the target image file is obtained by exposing according to the adjusted exposure parameters. The overall brightness of the image will be close to the actual scene in the user's perception, making the subsequently calculated object-side brightness values of pixels more accurate.

Step S130: Receive a shooting command, trigger the camera to take a picture according to the target exposure parameters, and acquire the target image file of the environment to be measured. After determining the target exposure parameters, the user issues a shooting command, the terminal receives the shooting command, and triggers the camera to take a picture according to the target exposure parameters, thereby acquiring the target image file of the environment to be measured. As shown in Figure 2, this is a schematic diagram of a target image file taken by a smartphone in a bedroom environment. The area below the image can display the corresponding exposure parameters.

Step S140: Based on the target image file and the target exposure parameters, calculate the object-side brightness value of each pixel in the target image file.

Step S150: Based on the object luminance value, generate and display a pseudo-color map of the luminance spatial distribution in the environment to be measured. As shown in Figure 3, this is the pseudo-color map of the luminance spatial distribution of the target image file corresponding to Figure 2. The pseudo-color map of the luminance spatial distribution can more intuitively show the distribution of light and dark in the entire environment to be measured. Users can understand the luminance spatial distribution of the entire environment to be measured by observing this pseudo-color map, providing a more intuitive reference for lighting design in this environment.

The ambient brightness spatial distribution measurement method of this embodiment acquires target image files by real-time shooting of the current environment to be measured, calculates the object-side brightness value of each pixel in the target image file by combining the exposure parameters at the time of shooting, and generates and displays a pseudo-color map of the brightness spatial distribution in the environment to be measured based on the object-side brightness value of each pixel, thereby realizing the visualization of ambient brightness and providing intuitive design reference for lighting designers.

In some embodiments, after step S140, the method further includes: calculating and displaying statistical values of the object-side luminance values of all pixels in the target image file, wherein the statistical values include: average object-side luminance value, maximum object-side luminance value, and minimum object-side luminance value. As shown in Figure 3, the terminal interface displays not only a pseudo-color map of luminance spatial distribution, but also the average object-side luminance value, maximum object-side luminance value, and minimum object-side luminance value on one side of the pseudo-color map. Preferably, the pseudo-color map of luminance spatial distribution also displays markers for the maximum and minimum object-side luminance values to mark the locations of the brightest and darkest areas in the environment to be measured. In practical applications, multiple lamps of the same power may be installed in the environment to be measured. Therefore, a marker for the maximum object-side luminance value is marked at each lamp, meaning there can be multiple markers for the maximum object-side luminance value. Similarly, there can also be multiple markers for the minimum object-side luminance value.

In some embodiments, after step S140, the method further includes: generating a brightness distribution file based on the object-side brightness values. The brightness distribution file is configured to store the object-side brightness values of each pixel in the target image file in tabular form, wherein the row and column positions of the table containing the object-side brightness value of each pixel correspond one-to-one with the row and column coordinates of the corresponding pixel in the target image file. Specifically, the brightness distribution file is a CSV file. A CSV file is a file that stores tabular data in plain text format. It can be understood that the total number of rows and columns in the table in the CSV file is the same as the total number of rows and columns of pixels in the target image file. The object-side brightness values of each pixel in the target image file can be stored in the CSV file for subsequent querying of the object-side brightness value of any pixel.

In some embodiments, as shown in FIG4, steps S410 to S430 are further included after step S150.

Step S410: Obtain the row and column coordinates of the target pixel point specified by the user on the luminance spatial distribution pseudo-color map. Specifically, the target pixel point specified by the user is the touch point clicked by the user on the luminance spatial distribution pseudo-color map. The row and column coordinates of the target pixel point can be determined by sensing the user's touch point position through the terminal screen.

Step S420: Based on the row and column coordinates of the target pixel, query the target object brightness value recorded at the corresponding row and column position in the brightness distribution file. That is, according to the one-to-one correspondence between the row and column position of the table in the brightness distribution file where the object brightness value of each pixel is located and the row and column coordinates of the corresponding pixel in the target image file, query the target object brightness value corresponding to the target pixel in the brightness distribution file.

Step S430: Display the target object brightness value. As shown in Figure 5, the target object brightness value of two specified target pixels on the brightness spatial distribution pseudo-color map is displayed.

This embodiment enables real-time display of the target object brightness value of a user-specified target pixel. In practical applications, it can display the target object brightness values corresponding to multiple target pixels in a user-specified spatial area, allowing the user to view the differences between the brightness values of multiple target objects. Large differences indicate uneven brightness distribution in that spatial area. For example, in Figure 5, multiple target pixels in the space where the bed is located are specified, thus displaying the real-time brightness values of multiple target objects in that space. If the differences between the brightness values of multiple target objects in the space where the bed is located are large (e.g., the difference between the brightness values of one or two points and other points is too large), it indicates uneven brightness distribution in the space where the bed is located, which will affect sleep and necessitates a proper lighting arrangement around the bed.

Preferably, after step S430, the method further includes: calculating the target average value of the brightness values of multiple target objects in real time, comparing any target object brightness value with the target average value, and if the difference between any target object brightness value and the target average value is greater than a preset threshold (the preset threshold can be set according to actual conditions, for example, 20% of the target average value), displaying a message on the terminal's display interface indicating uneven brightness distribution. Specifically, for each target pixel specified by the user, an average value is calculated and compared, providing a real-time message indicating whether the brightness distribution is uniform.

In some embodiments, as shown in FIG6, steps S610 to S640 are further included after step S150.

Step S610: Obtain the target area specified by the user on the luminance spatial distribution pseudo-color map. Specifically, the target area is obtained by sensing the closed area circled by the user on the terminal interface, as shown in Figure 7, where the user has drawn the area of the space where the bed is located.

Step S620: Using all pixels in the target region as target pixels, obtain the row and column coordinates of the target pixels. Specifically, by determining all target pixels within the target region, the row and column coordinates of the target pixels can be obtained.

Step S630: Based on the row and column coordinates of the target pixel, query the target object-side brightness value recorded at the corresponding row and column position in the brightness distribution file. Specifically, for each target pixel, according to the one-to-one correspondence between the row and column position of the table in the brightness distribution file where the object-side brightness value of each pixel is located and the row and column coordinates of the corresponding pixel in the target image file, query the brightness distribution file to obtain the target object-side brightness value corresponding to that target pixel.

Step S640: Display the statistical value of the target object brightness value. As shown in Figure 7, the statistical value of the target area brightness in the target area on the brightness spatial distribution pseudo-color map is displayed, that is, the statistical value of the target object brightness value in the area. For example, the average value of the target object brightness value of all target pixels in the target area is displayed, and the average value is used to represent the overall brightness of the target area.

In this embodiment, by displaying the regional object brightness statistics of the target area defined by the user, a regional brightness reference is provided to the user.

It should be noted that: based on the execution of steps S610 to S640, combined with the execution of the above steps S410 to S430, that is, further specifying target pixels in the target area, so as to obtain the overall brightness of the target area and determine whether the brightness distribution uniformity of the target area meets the requirements.

In some embodiments, as shown in FIG8, steps S810 and S820 are further included after step S150.

Step S810: Receive the scene category of the environment to be measured, input by the user. Scene categories include, for example, bedroom, living room, study, tea room, KTV room, and gymnasium. Specifically, as shown in Figures 3, 5, and 7, the scene category is entered in the edit box on one side of the pseudo-color map of the brightness spatial distribution on the terminal interface.

Step S820: Based on the scene category, determine the recommended statistical value of the brightness corresponding to the environment to be measured. Specifically, a mapping table of scene categories and their corresponding recommended statistical values of brightness can be preset. After receiving the scene category input by the user, the corresponding recommended statistical value is found according to the mapping table, and the recommended statistical value is displayed in the area for displaying recommended statistical values. The recommended statistical value corresponding to the scene category can be the average brightness, reflecting the average brightness of different scenes. In this embodiment, outputting the recommended statistical value of brightness through scene category can quickly provide users with a reference for brightness distribution design.

In some embodiments, as shown in FIG9, step S130 specifically includes steps S910 and S920.

Step S910: Obtain the raw format file captured by the camera. The raw format file is unprocessed and uncompressed image encoding data, which records the original information of the camera sensor.

Step S920: Convert the raw format file to an RGB three-channel format file using a preset conversion method, and determine that the RGB three-channel format file is the target image file. For example, convert the raw format file to a JPG or PNG format file. The preset conversion method is a unified ISP (Image Signal Processor) processing flow to obtain the RGB three-channel format file. The unified ISP flow can be implemented using the libraw C++ library or the postprocess function in the Python-encapsulated rawpy library. Specifically, the raw format file is input as a parameter to the postprocess function, which uses the unified ISP flow to convert the raw format file to an RGB three-channel format file and outputs it.

In this embodiment, regardless of the shooting device used, the raw format file of the shooting device is converted into an RGB three-channel format file according to a unified ISP process. This shields the adverse effects of different target image files obtained by different mobile phone or camera manufacturers using different ISP processes on brightness calculation, making the final measured object-side pixel value more accurate and stable.

In some embodiments, as shown in FIG10, step S150 specifically includes steps S1010 and S1020.

Step S1010: Normalize the object-side luminance value of each pixel from 0 to ²ⁿ -1 to generate an n-bit grayscale image representing the luminance distribution, where n is a multiple of 8. For example, if n is 16, normalize the object-side luminance value of each pixel to the range of 0-65535 (2 ^{^16} - 1) to generate a 16-bit PNG format grayscale image representing the luminance distribution.

Step S1020: Convert the grayscale image into a brightness distribution pseudo-color map. In this embodiment, the grayscale image can be converted into a brightness distribution pseudo-color map using the opencv:cv2.applyColorMap function.

In some embodiments, as shown in FIG11, step S140 specifically includes step S1110 and step S1120.

Step S1110: Extract the pixel values of each pixel in the target image file in the r, g, and b channels. Each pixel in the target image file corresponds to three channels: r, g, and b, and each channel has a pixel value in the range of 0 to 255. In this step, by extracting the pixel values of each pixel in the r, g, and b channels of the target image file, the pixel values of each pixel in the r, g, and b channels are obtained: IR value, IG value, and IB value, and the IR value, IG value, and IB value are all integers between 0 and 255.

Step S1120: Input the pixel value and the target exposure parameters into the object-space brightness regression model to obtain the object-space brightness value of each pixel output by the object-space brightness regression model. The object-space brightness value of each pixel is the ambient brightness value of the corresponding point in the shooting environment, thus realizing the measurement of ambient brightness through the captured image file.

The object-side brightness regression model is obtained by fitting and regressing the exposure parameters of the sample target image file, the pixel values of the pixels in the sample target image file, and the standard object-side brightness values corresponding to the pixels in the sample target image file.

Specifically, when fitting the object-side brightness regression model, the exposure parameters of the sample target image file and the pixel values of the pixels in the sample target image file are known quantities. For the standard object-side brightness value corresponding to the pixel in the sample target image file, it can be measured by devices such as an imaging luminance meter under the same environment as the shooting environment. Since the imaging luminance meter can measure the brightness of objects in the shooting environment, it can obtain an accurate object-side brightness value (i.e., the standard object-side brightness value). Based on the standard object-side brightness value, the fitting coefficient of the object-side brightness regression model that is adapted to the standard object-side brightness value is obtained. Thus, the object-side brightness regression model can be used to accurately regress the object-side brightness value of each pixel in the real-time captured target image file, i.e., the ambient brightness value, without the need for other brightness measurement devices, eliminating the interference of device factors, and the output object-side brightness value has high stability.

It should be noted that when fitting the object-side brightness regression model, the sample image files are also converted from raw format files to RGB three-channel format files using a unified ISP process, which makes the object-side brightness value of each pixel output by the final object-side brightness regression model more accurate.

In some embodiments, the object-side brightness regression model includes: a first sub-model and a second sub-model, wherein the first sub-model is configured to determine the tristimulus value corresponding to the pixel value based on the pixel value, and to determine the floating-point grayscale value of each pixel based on the tristimulus value of each pixel.

The second sub-model is configured to determine the object-side brightness value of each pixel based on the floating-point grayscale value of each pixel and the exposure parameters.

In some embodiments, the first sub-model can be obtained by regression fitting using the following formula.
GF＝α×R+β×G+γ×B (1)

Wherein, GF is the floating-point gray value of the pixel, IR, IG and IB are the pixel values of the three channels respectively, R, G and B are the tristimulus values of IR, IG and IB respectively, _A1 , _A2 , _A3 , _B1 , _B2 and _B3 are fitting coefficients, _A′1 , _A′2 and _A′3 are intermediate variables, and α, β and γ are constants. According to the relationship between the floating-point gray value and the tristimulus value, in formula (1), α = 0.299, β = 0.587, and γ = 0.114. Substituting the above formulas (2), (3) and (4) into formula (1) yields formula (5), where _A1 = α × _A′1 , _A2 = β × _A′2 , and _A3 = γ × _A′3 .

In the first sub-model, the relationship between pixel values and corresponding tristimulus values can be represented by a power function with the pixel values IR, IG, and IB as bases. During the fitting regression of the first sub-model, the accurate tristimulus values of pixels in the sample target image file are obtained through camera optical characteristic calibration experiments. Multiple (at least 6) accurate tristimulus values and the corresponding pixel values in the sample target image file are substituted into formulas (2), (3), and (4) to obtain fitting coefficients _A′1 , _A′2 , _A′3 , _B1 , _B2 , and _B3 , that is _{, A1} , _A2 , _A3 , _B1 , _B2 , and _B3 are obtained, thus completing the fitting regression process of the first sub-model.

In some embodiments, the second sub-model can be obtained by regression fitting using the following formula.

Where _A4 and _B4 are fitting coefficients, L is the object-side brightness value of the pixel, which can be measured by a luminance meter during the fitting process, and F, T, and ISO are exposure parameters, representing aperture, exposure time, and ISO, respectively. log can be a logarithmic function with any base, such as ln (based on the natural constant e) or lg (based on 10). Different bases of the logarithmic functions will result in different _A4 and B4 values. The first sub-model obtains the GF of the pixels in the sample target image files. The fitting coefficients A4 and _B4 are then obtained by fitting the exposure parameters corresponding to multiple ₍ at least two) sample target image files and the standard object-side brightness values corresponding to the pixels in multiple sets of sample target image files, thus completing the fitting regression of the second sub _- model.

In some embodiments, the object-side brightness regression model is obtained by regression fitting using the following formula.

Wherein, IR, IG, and IB are the pixel values of the three channels, _A1 , _A2 , _A3 , _A4 , B1, _B2 , _B3 , and _B4 are fitting coefficients, L is the object-side brightness value of the pixel, _and F, T, and ISO are exposure parameters, representing the aperture coefficient, exposure time, and ISO sensitivity, respectively.

In this embodiment, there are a total of eight fitting coefficients. The three-channel pixel values of pixels in at least eight different sample target image files and the corresponding different exposure parameters can be substituted into formula (7) to obtain _A1 , _A2 , _A3 , _A4 , _B1 , _B2 , _B3 and _B4 in one regression. In this embodiment, it is not necessary to conduct multi-step experiments, and it is not necessary to first conduct camera optical characteristic calibration experiments to find the relationship between tristimulus values and image RGB values. That is, it is not necessary to fit the intermediate variables _A′1 , _A′2 and A′3 in the above formulas (2), (3) and ( ₄ ), and then fit the relationship between the floating-point gray value calculated by the tristimulus values and the object brightness value. Instead, it directly uses sample target image files under multiple exposure parameters to obtain _A1 , _A2 , _A3 , _A4 , _B1 , _B2 , _B3 and _B4 in one step through regression. This results in higher regression fitting efficiency, eliminates the need for multiple fittings, reduces fitting errors, and improves the accuracy and stability of the final calculated object-side brightness values of the pixels.

Images often exhibit vignetting, where pixels at the image edges become darker, and the RGB values of these edge pixels are inaccurate, affecting the measurement of object-side brightness. Therefore, in some embodiments, before step S120, the method further includes vignetting correction on the target image file. Specifically, this is achieved by using a `resize` function (e.g., the `resize` function in Python or C++) to reduce the vignetting effect. Reducing the vignetting effect results in more accurate object-side brightness values. The simplest way to correct vignetting is to reduce the size of the target image file using the `resize` function, thereby minimizing the impact of darkened edge pixels on the overall brightness distribution of the measured environment.

Vignetting can also be corrected by adjusting the vignetting coefficient of the camera device, thus reducing the darkening of edge pixels in the target image file.

It should be noted that when fitting the object-side brightness regression model, the vignetting effect can also be corrected for the sample image files, so that the object-side brightness value of each pixel output by the final object-side brightness regression model is more accurate.

The ambient light spatial distribution measurement device provided in this application is described below. The ambient light spatial distribution measurement device described below can be referred to in correspondence with the ambient light spatial distribution measurement method described above.

The ambient brightness spatial distribution measurement device of this application embodiment is applied to a terminal, as shown in FIG12. The device includes: a measurement command receiving module 1210, an exposure parameter determining module 1220, a target image file acquisition module 1230, a brightness value calculation module 1240, and a pseudo color map generation module 1250.

The measurement command receiving module 1210 is configured to receive an ambient brightness measurement command and trigger the camera's image sensor to acquire an initial image of the environment to be measured.

The exposure parameter determination module 1220 is configured to determine the target exposure parameters based on the initial image.

The target image file acquisition module 1230 is configured to receive a shooting command, trigger the camera to take a picture according to the target exposure parameters, and acquire the target image file under the measured environment.

The brightness value calculation module 1240 is configured to calculate the object-side brightness value of each pixel in the target image file based on the target image file and the target exposure parameters.

The pseudo-color map generation module 1250 is configured to generate and display a pseudo-color map of the luminance spatial distribution under the measured environment based on the object luminance value.

The ambient brightness spatial distribution measurement device of this application acquires target image files by capturing the current environment to be measured in real time, calculates the object-side brightness value of each pixel in the target image file by combining the exposure parameters at the time of shooting, and generates and displays a pseudo-color map of the brightness spatial distribution under the environment to be measured based on the object-side brightness value of each pixel, thereby realizing the visualization of ambient brightness and providing intuitive design reference for lighting designers.

Optionally, the ambient brightness spatial distribution measurement device further includes: a brightness distribution file generation module, configured to, after calculating the object-space brightness value of each pixel in the target image file based on the target image file and the target exposure parameters, generate a brightness distribution file based on the object-space brightness value, wherein the brightness distribution file is configured to store the object-space brightness value of each pixel in the target image file in the form of a table; wherein the row and column positions of the table containing the object-space brightness value of each pixel correspond one-to-one with the row and column coordinates of the corresponding pixel in the target image file.

Optionally, the ambient brightness spatial distribution measurement device further includes: a first row and column coordinate acquisition module, configured to acquire the row and column coordinates of a target pixel point specified by the user on the brightness spatial distribution pseudo-color map after generating and displaying the brightness spatial distribution pseudo-color map of the environment to be measured based on the object brightness value.

The first brightness value query module is configured to query the target object brightness value recorded at the corresponding row and column position in the brightness distribution file based on the row and column coordinates of the target pixel.

The pixel brightness value display module is configured to display the brightness value of the target object.

Optionally, the ambient brightness spatial distribution measurement device further includes a target area acquisition module, configured to acquire a user-specified target area on the brightness spatial distribution pseudo-color map after generating and displaying the brightness spatial distribution pseudo-color map of the environment to be measured based on the object brightness value.

The second row and column coordinate acquisition module is configured to acquire the row and column coordinates of the target pixels, taking all pixels in the target region as target pixels.

The second brightness value query module is configured to query the target object brightness value recorded at the corresponding row and column position in the brightness distribution file based on the row and column coordinates of the target pixel.

The area brightness value display module is configured to display statistical values of the brightness value of the target object.

Optionally, the ambient brightness spatial distribution measurement device further includes: a brightness statistics display module, configured to calculate and display statistical values of the object-space brightness values of all pixels in the target image file after calculating the object-space brightness value of each pixel in the target image file based on the target image file and the target exposure parameters.

Optionally, the ambient brightness spatial distribution measurement device further includes: a scene category receiving module, configured to receive the scene category of the environment to be measured, input by the user, after generating and displaying a pseudo-color map of the brightness spatial distribution of the environment to be measured based on the object brightness value.

The recommended statistical value determination module is configured to determine the recommended statistical value of the brightness corresponding to the environment to be measured based on the scene category.

Optionally, the target image file acquisition module 1230 specifically includes a raw format file acquisition module, configured to acquire raw format files captured by the camera.

The file format conversion module is configured to convert the raw format file into an RGB three-channel format file according to a preset conversion method, and determine the RGB three-channel format file as the target image file.

Optionally, the pseudo-color image generation module 1250 specifically includes: a grayscale image generation module, configured to normalize the object-side brightness value of each pixel from 0 to ²ⁿ -1 to generate an n-bit storage grayscale image representing the brightness distribution, where n is a multiple of 8.

The pseudo-color map conversion module is configured to convert the grayscale image into a brightness distribution pseudo-color map.

Optionally, the brightness value calculation module 1240 specifically includes a pixel value extraction module, configured to extract the pixel values of each pixel in the target image file in the r, g and b channels respectively.

The regression model execution module is configured to input the pixel value and the target exposure parameter into the object-space luminance regression model to obtain the object-space luminance value of each pixel output by the object-space luminance regression model.

The object-side brightness regression model is obtained by fitting and regressing the exposure parameters of the sample target image file, the pixel values of the pixels in the sample target image file, and the standard object-side brightness values corresponding to the pixels in the sample target image file.

Figure 13 illustrates a schematic diagram of the physical structure of an electronic device. As shown in Figure 13, the electronic device may include: a processor 1310, a communication interface 1320, a memory 1330, and a communication bus 1340. The processor 1310, communication interface 1320, and memory 1330 communicate with each other via the communication bus 1340. The processor 1310 can call logical instructions in the memory 1330 to execute an ambient brightness spatial distribution measurement method, which includes the following steps.

Upon receiving an ambient brightness measurement command, the camera's image sensor is triggered to acquire an initial image of the environment to be measured.

Based on the initial image, the target exposure parameters are determined.

Upon receiving the shooting instruction, the camera is triggered to take a picture according to the target exposure parameters, and the captured target image file under the measured environment is obtained.

Based on the target image file and the target exposure parameters, calculate the object-side brightness value of each pixel in the target image file.

Based on the object luminance value, a pseudo-color map of the luminance spatial distribution under the measured environment is generated and displayed.

Furthermore, the logical instructions in the aforementioned memory 1330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to conventional technology, 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 described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

On the other hand, this application also provides a computer program product, which may be on a non-transitory computer-readable medium or a downloaded app, etc. The computer program product includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the ambient brightness spatial distribution measurement method provided by the above methods, which includes the following steps.

Upon receiving an ambient brightness measurement command, the camera's image sensor is triggered to acquire an initial image of the environment to be measured.

Based on the initial image, the target exposure parameters are determined.

Upon receiving the shooting instruction, the camera is triggered to take a picture according to the target exposure parameters, and the captured target image file under the measured environment is obtained.

Based on the target image file and the target exposure parameters, calculate the object-side brightness value of each pixel in the target image file.

Based on the object luminance value, a pseudo-color map of the luminance spatial distribution under the measured environment is generated and displayed.

In another aspect, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the ambient brightness spatial distribution measurement method provided by the above methods, the method comprising the following steps.

Upon receiving an ambient brightness measurement command, the camera's image sensor is triggered to acquire an initial image of the environment to be measured.

Based on the initial image, the target exposure parameters are determined.

Upon receiving the shooting instruction, the camera is triggered to take a picture according to the target exposure parameters, and the captured target image file under the measured environment is obtained.

Based on the target image file and the target exposure parameters, calculate the object-side brightness value of each pixel in the target image file.

Based on the object luminance value, a pseudo-color map of the luminance spatial distribution under the measured environment is generated and displayed.

The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to conventional technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims (12)

A method for measuring the spatial distribution of ambient brightness, applied to a terminal, includes: Upon receiving an ambient brightness measurement command, the camera's image sensor is triggered to acquire an initial image of the environment to be measured. Based on the initial image, determine the target exposure parameters; Receive the shooting instruction, trigger the camera to take a picture according to the target exposure parameters, and acquire the target image file under the measured environment; Based on the target image file and the target exposure parameters, calculate the object-side brightness value of each pixel in the target image file; Based on the object luminance value, a pseudo-color map of the luminance spatial distribution under the measured environment is generated and displayed. According to the method for measuring the spatial distribution of ambient brightness as described in claim 1, after calculating the object-side brightness value of each pixel in the target image file based on the target image file and the target exposure parameters, the method further includes: Based on the object luminance values, a luminance distribution file is generated, which is configured to store the object luminance values of each pixel in the target image file in tabular form. In this table, the row and column positions of the object-side brightness value of each pixel correspond one-to-one with the row and column coordinates of the corresponding pixel in the target image file. According to claim 2, the method for measuring the spatial distribution of ambient luminance, after generating and displaying a pseudo-color map of the spatial distribution of luminance under the measured environment based on the object luminance value, further includes: Obtain the row and column coordinates of the user-specified target pixel on the luminance spatial distribution pseudo-color map; Based on the row and column coordinates of the target pixel, query the target object brightness value recorded at the corresponding row and column position in the brightness distribution file; Display the brightness value of the target object. According to claim 2, the method for measuring the spatial distribution of ambient luminance, after generating and displaying a pseudo-color map of the spatial distribution of luminance under the measured environment based on the object luminance value, further includes: Obtain the user-specified target region on the luminance spatial distribution pseudocolor map; Using all pixels in the target region as target pixels, obtain the row and column coordinates of the target pixels; Based on the row and column coordinates of the target pixel, query the target object brightness value recorded at the corresponding row and column position in the brightness distribution file; This displays the statistical values of the brightness of the target object. According to the method for measuring the spatial distribution of ambient brightness as described in claim 1, after calculating the object-side brightness value of each pixel in the target image file based on the target image file and the target exposure parameters, the method further includes: Calculate and display the statistical values of the object-side brightness of all pixels in the target image file. According to the method for measuring the spatial distribution of ambient luminance as described in claim 1, after generating and displaying a pseudo-color map of the spatial distribution of luminance under the measured environment based on the object luminance value, the method further includes: Receive the scene category of the environment to be measured, as input by the user; Based on the scene category, a recommended statistical value for the brightness corresponding to the environment to be measured is determined. The method for measuring the spatial distribution of ambient brightness according to any one of claims 1 to 6, wherein acquiring a target image file captured under the environment to be measured includes: Obtain RAW format files captured by the camera; The raw format file is converted to an RGB three-channel format file according to a preset conversion method, and the RGB three-channel format file is determined to be the target image file. The method for measuring the spatial distribution of ambient luminance according to any one of claims 1 to 6, wherein generating and displaying a pseudo-color map of the spatial distribution of luminance under the measured environment based on the object luminance value includes: The object-side brightness value of each pixel is normalized from 0 to ²ⁿ -1 to generate an n-bit grayscale image representing the brightness distribution, where n is a multiple of 8. The grayscale image is converted into a brightness distribution pseudo-color image. The method for measuring the spatial distribution of ambient brightness according to any one of claims 1 to 6, wherein calculating the object-side brightness value of each pixel in the target image file based on the target image file and the target exposure parameters includes: Extract the pixel values of each pixel in the target image file in the r, g, and b channels respectively; The pixel value and the target exposure parameter are input into the object-space luminance regression model to obtain the object-space luminance value of each pixel output by the object-space luminance regression model. The object-side brightness regression model is obtained by fitting and regressing the exposure parameters of the sample target image file, the pixel values of the pixels in the sample target image file, and the standard object-side brightness values corresponding to the pixels in the sample target image file. An ambient light spatial distribution measurement device, applied to a terminal, includes: The measurement command receiving module is configured to receive ambient brightness measurement commands and trigger the camera's image sensor to acquire an initial image of the environment to be measured. An exposure parameter determination module is configured to determine target exposure parameters based on the initial image; The target image file acquisition module is configured to receive a shooting command, trigger the camera to take a picture according to the target exposure parameters, and acquire the target image file under the measured environment. The brightness value calculation module is configured to calculate the object-side brightness value of each pixel in the target image file based on the target image file and the target exposure parameters; The pseudo-color map generation module is configured to generate and display a pseudo-color map of the luminance spatial distribution under the measured environment based on the object luminance value. An electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method for measuring the spatial distribution of ambient brightness as described in any one of claims 1 to 9. A computer program product includes a computer program that, when executed by a processor, implements the method for measuring the spatial distribution of ambient brightness as described in any one of claims 1 to 9.