A color calibration method based on color temperature consistency for a structured light camera

Abstract

This invention discloses a color calibration method for a structured light camera based on color temperature consistency, belonging to the field of camera calibration technology. The method includes: constraining color temperature consistency based on the linear relationship between the exposure time of the RGB light source and the image grayscale in the structured light camera; performing light uniformity correction on the structured light camera based on color temperature consistency; performing white balance correction on the structured light camera after completing the light uniformity correction; and completing the color calibration of the structured light camera using a color chart while ensuring the white balance constraint. Utilizing a grayscale sensor combined with an RGB light source for color calibration reduces hardware costs and system complexity while maintaining the high sensitivity and high signal-to-noise ratio advantages of the structured light camera.

Description

Technical Field

[0001] This invention relates to the field of camera calibration technology, and in particular to a color calibration method for structured light cameras based on color temperature consistency. Background Technology

[0002] Existing structured light cameras generally use grayscale sensors and cannot directly acquire color information. In applications requiring color reproduction or integration with color vision systems, traditional calibration methods often rely on a single light source or fixed lighting conditions, leading to unstable calibration results under different color temperatures and problems such as color deviation and poor consistency. Furthermore, existing methods lack adaptability to color temperature variations from multiple light sources, making it difficult to meet the high-precision color reproduction requirements of scenarios such as industrial inspection, 3D reconstruction, and precision measurement.

[0003] To address the aforementioned issues, this invention proposes a color calibration method for structured light cameras based on color temperature consistency. By utilizing combined RGB light sources and adjusting the exposure time of each channel to achieve color temperature consistency constraints, the response of the grayscale sensor remains uniform under different color temperature conditions, thus obtaining stable and reliable color calibration results. This effectively overcomes the problems of grayscale sensors not being able to directly output color images and the insufficient accuracy of traditional calibration in cross-color temperature environments, achieving accurate color reproduction and improved consistency of structured light cameras under complex lighting conditions. Summary of the Invention

[0004] This invention provides a color calibration method for structured light cameras based on color temperature consistency, comprising:

[0005] Step 1: Constrain color temperature consistency based on the linear relationship between the exposure time of the RGB light source in the structured light camera and the grayscale of the image.

[0006] Step 2: Based on the color temperature consistency, perform light uniformity correction on the structured light camera;

[0007] Step 3: After completing the light uniformity correction, perform white balance correction on the structured light camera.

[0008] Step 4: Under the condition of ensuring white balance constraints, use a color chart to complete the color calibration of the structured light camera.

[0009] The color calibration method for structured light cameras based on color temperature consistency, as described above, constrains color temperature consistency based on the linear relationship between the exposure time of the RGB light source and the image grayscale in the structured light camera. This process is specifically divided into the following sub-steps:

[0010] The structured light camera is controlled to sequentially trigger RGB light sources at different exposure times and acquire images of the white board;

[0011] Calculate the mean grayscale value of the RGB image at different exposure times;

[0012] Establish and fit a linear model of the relationship between RGB light source exposure time and image grayscale.

[0013] Based on the above linear model, a set of color temperature consistency constraint equations is constructed to solve for the exposure ratio coefficient.

[0014] The exposure ratio coefficients are stored in the configuration file of the structured light camera to complete the color temperature consistency constraint.

[0015] The structured light camera, as described above, uses a color calibration method based on color temperature consistency. In addition to color temperature consistency, the structured light camera undergoes light uniformity correction, which specifically involves the following sub-steps:

[0016] Under the exposure parameters that have been constrained for color temperature consistency, the RGB light source is triggered sequentially to acquire the white board image;

[0017] For each whiteboard image acquired, calculate its average grayscale value for the entire image;

[0018] For each color channel, substitute its average gray value across the entire image into the uniformity correction coefficient calculation formula to obtain a pixel-level gain map;

[0019] The gain maps of each color channel are stored in the configuration file of the structured light camera to complete the light uniformity correction.

[0020] The structured light camera's color calibration method based on color temperature consistency, as described above, involves performing white balance correction on the structured light camera after completing the light uniformity correction. This process is divided into the following sub-steps:

[0021] Under imaging conditions where color temperature consistency and light uniformity have been corrected, RGB three-color light sources are triggered sequentially to acquire images of the standard color chart;

[0022] The ideal gray value is calculated based on the average gray value of the white block and neutral gray block pixel regions in each image;

[0023] A white balance parameter is calculated for each color channel based on the ideal grayscale value;

[0024] Store the white balance parameters to the structured light camera's configuration file to complete white balance correction.

[0025] The color calibration method for structured light cameras based on color temperature consistency, as described above, involves using a color chart to calibrate the structured light camera while ensuring white balance constraints. This process comprises the following sub-steps:

[0026] The structured light camera is controlled to sequentially trigger RGB three-color light sources to acquire images of a standard color chart;

[0027] The color balance weight of each color block on the color chart is calculated based on the acquired image.

[0028] The color calibration matrix is ​​solved using the least squares method with color balance weights;

[0029] The solved color calibration matrix is ​​stored in the configuration file of the structured light camera to complete the color calibration.

[0030] The beneficial effects achieved by this invention are as follows: By normalizing the average gray level, color temperature consistency is constrained, effectively reducing color shift caused by differences in light source color temperature and improving the consistency and accuracy of color reproduction; color calibration is achieved by using a gray level sensor combined with an RGB light source, reducing hardware costs and system complexity while maintaining the high sensitivity and high signal-to-noise ratio advantages of structured light cameras; this method is not only applicable to structured light cameras, but can also be extended to other imaging systems based on gray level sensors, and has wide application value in fields such as industrial inspection, 3D reconstruction, and medical imaging. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0032] Figure 1 This is a flowchart of a color calibration method for a structured light camera based on color temperature consistency, provided in Embodiment 1 of this application. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1

[0035] like Figure 1 As shown, Embodiment 1 of this application provides a color calibration method for a structured light camera based on color temperature consistency, including:

[0036] Step S10: Constrain color temperature consistency based on the linear relationship between the exposure time of the RGB light source in the structured light camera and the image grayscale.

[0037] This step is the core of the invention for achieving high-precision color reproduction. Its purpose is to establish a stable image acquisition foundation that is independent of color temperature, which is achieved through the following sub-steps:

[0038] Step S11: Control the structured light camera to trigger the RGB light source sequentially at different exposure times and acquire the white board image;

[0039] The structured light camera is controlled to trigger its built-in R, G, and B color light sources in a dark room environment. For each color light source, a series of different exposure times are set. ,in These are the exposure time indices for different light sources, ranging from 1 to N, where N is the exposure sequence length. Images of a standard white board are acquired sequentially. .

[0040] Step S12: Calculate the mean grayscale value of the RGB image under different exposure times;

[0041] For each acquired image, the average grayscale value of the entire image is calculated, resulting in the average grayscale value sequences of the R, G, and B channels at different exposure times: .

[0042] Step S13: Establish and fit a linear model of the exposure time of the RGB light source and the image grayscale;

[0043] Based on the physical principles of camera imaging, within the unsaturated region, the image grayscale value and exposure time have an approximately linear relationship. Therefore, we establish the following linear model for each color channel: ,in The response slope of this channel characterizes the sensor's sensitivity to that color of light. This is an offset value used to reflect dark current noise.

[0044] Use the least squares method on the data sequence By performing fitting, the unique model parameters for each channel can be solved. and .

[0045] Step S14: Based on the above linear model, construct a set of color temperature consistency constraint equations and solve for the exposure ratio coefficient;

[0046] To achieve color temperature consistency, which requires the camera to maintain a consistent RGB response to neutral colors (such as white) under different color temperature environments, the key to this invention lies in achieving this goal by adjusting the exposure time rather than through post-processing software. First, the color temperature consistency condition is defined as follows: when illuminating a standard white board, the exposure time of the R, G, and B light sources is adjusted. This ensures that the average grayscale values ​​of the three channels are equal, i.e. Substituting the linear model of RGB light source exposure time and image grayscale into the above equation, we obtain... Then, select the exposure time of any channel as the reference unit 1, and define the exposure scaling factors for the other two channels based on this reference unit. In this embodiment, the G channel is used as the reference. and These are the scaling factors of the R and B channels relative to the G channel, respectively. Substituting the above relationship into the color temperature consistency condition, we construct a condition regarding... The system of equations: If we set the constant term and the coefficient of the first-order term on both sides of the equation to be equal, then the formula for calculating the exposure ratio coefficient is expressed as follows: This formula can be used to calculate the exposure ratio coefficients of the R and B channels relative to the G channel.

[0047] Step S15: Store the exposure ratio coefficients into the configuration file of the structured light camera to complete the color temperature consistency constraint;

[0048] The calculated exposure ratio coefficients are stored in the structured light camera's configuration file, thus completing the color temperature consistency constraint. In all subsequent image acquisition processes, the exposure times of the R and B light sources will strictly adhere to... and The relationship is linked to the exposure time of the G light source, which serves as a reference. This ensures from the outset that at different brightness settings (by adjusting...) Under these conditions, the camera's original response to white remains consistent, laying a stable and reliable foundation for subsequent color calibration.

[0049] Step S20: Based on the color temperature consistency, perform light uniformity correction on the structured light camera;

[0050] The purpose of this step is to correct illumination unevenness caused by the optical system, the light source itself, or the assembly process, ensuring uniform light intensity distribution within the field of view and providing a high-quality input image for subsequent color calibration. This step must be performed under the color temperature consistency constraint established in step S10, and is specifically implemented through the following sub-steps:

[0051] Step S21: Under the exposure parameters that have completed the color temperature consistency constraint, trigger the RGB light source to acquire the white board image in sequence;

[0052] The three images acquired are denoted as follows: ,in These are the pixel coordinates of the image.

[0053] Step S22: For each whiteboard image acquired, calculate its average grayscale value for the entire image;

[0054] For each captured whiteboard image, the average grayscale value of all pixels in the entire image is calculated and used as the ideal uniform grayscale reference for that channel, denoted as follows: .

[0055] Step S23: For each color channel, substitute its average gray value of the entire image into the uniformity correction coefficient calculation formula to obtain the pixel-level gain map;

[0056] The uniformity correction of this invention is achieved by applying a pixel-level gain map within each color channel. To achieve, gain map The uniformity correction coefficient for each pixel position is expressed as: The calculation formula is as follows: ,in Indicates channel The gray value at pixel coordinates (x, y) in the image. To smooth out the parameters and prevent the denominator from being zero.

[0057] Step S24: Store the gain map of each color channel into the configuration file of the structured light camera to complete the light uniformity correction;

[0058] In all subsequent image acquisitions, the color channel images read from the sensor are multiplied pixel by pixel in real time with the corresponding gain map to obtain the image after light uniformity correction.

[0059] Step S30: After completing the light uniformity correction, perform white balance correction on the structured light camera;

[0060] The purpose of this step is to perform precise white balance correction on the image, based on the established ideal conditions of color temperature consistency and lighting uniformity. This ensures that white and neutral gray objects in the image are accurately reproduced, eliminating overall color cast. The innovation of this step lies in its departure from the traditional global grayscale assumption. Instead, it utilizes white and neutral gray blocks from a standard color chart as objective and stable benchmarks, proposing a weighted white balance correction method based on standard color blocks. This avoids the failure problem of traditional methods when the scene has a single color or color cast. Specifically, this is achieved through the following sub-steps:

[0061] Step S31: Under the imaging conditions where color temperature consistency and light uniformity have been corrected, trigger the RGB three-color light source in sequence and acquire the image of the standard color card;

[0062] Step S32: Calculate the ideal gray value based on the average gray value of the white block and neutral gray block pixel regions in each image;

[0063] The acquired color chart images were processed using image processing techniques such as threshold segmentation and contour finding to accurately locate the white and neutral gray blocks on the color chart. The average gray value within each pixel region was calculated. The average gray value of the white block region in the three images is represented as follows: The average gray values ​​of the neutral gray block regions in the three images are respectively expressed as: Under ideal white balance conditions, the response values ​​of the R, G, and B channels of the white block and the neutral gray block should be equal. Therefore, this invention defines its ideal grayscale value as the weighted geometric average of the actual grayscale values ​​of the white block and the neutral gray block. The calculation formula is as follows: This formula combines the information from the brightest white and the most typical neutral gray in the color chart. Through geometric averaging, it obtains an ideal gray value T that is more stable and reliable than using a single white patch or global average. This value takes into account the information from highlights and midtones and is not sensitive to noise and local outliers, thus laying the foundation for high-precision white balance.

[0064] Step S33: Calculate a white balance parameter for each color channel based on the ideal grayscale value;

[0065] White balance parameters for each color channel The calculation formula is expressed as: .

[0066] Step S34: Store the white balance parameters to the configuration file of the structured light camera to complete the white balance correction;

[0067] In all subsequent image acquisitions, the image read from the sensor and after uniformity correction, the channel values ​​of each pixel will be multiplied in real time with the corresponding white balance gain coefficient, ultimately outputting a high-quality image with natural colors and accurate white balance.

[0068] Step S40: Under the condition of ensuring white balance constraints, use a color chart to complete the color calibration of the structured light camera;

[0069] This step adaptively assigns a weight to each color patch during the calibration process by analyzing its color balance after white balance correction. This significantly reduces the impact of noisy or abnormally saturated color patches on the calibration results, improving the overall accuracy and robustness of color calibration. Specifically, this is achieved through the following sub-steps:

[0070] Step S41: Control the structured light camera to sequentially trigger the RGB three-color light source and acquire the standard color chart image;

[0071] Under imaging conditions where color temperature consistency, light uniformity, and white balance correction have been completed, RGB three-color light sources are triggered sequentially to acquire images from a standard 24-color chart. The acquired images at this point possess high-quality characteristics such as uniform illumination, stable color temperature, and accurate white balance.

[0072] Step S42: Calculate the color balance weight of each color block on the color chart based on the acquired image;

[0073] First, image processing techniques such as threshold segmentation and contour finding are used to accurately locate the positions of all color blocks on the color chart image. For each color block, the average grayscale value of the pixels in its central region across the RGB channels is extracted to form the camera's RGB value vector. Where j=1,2,……,Z, are the color patch indices, and Z is the total number of color patches; then the vector Substitute into the formula: In this way, the color balance weight of each color block can be calculated. ,in It is an adjustable attenuation coefficient. , represents a template of an "ideal neutral color", used to measure how much the color vector of each color block deviates from the "ideal neutral color".

[0074] Step S43: Solve the color calibration matrix using the least squares method with color balance weights;

[0075] The goal of color calibration is to find a 3x3 color calibration matrix Q such that... Read the standard sRGB value vector corresponding to each color patch from the standard data file of the color chart. Define the target loss function as By solving The optimal solution for matrix Q can then be obtained.

[0076] Step S44: Store the solved color calibration matrix in the configuration file of the structured light camera to complete the color calibration;

[0077] The calculated 3x3 color calibration matrix Q is stored in the configuration file of the structured light camera. In subsequent practical applications, the RGB vectors of any pixel, acquired from the camera and corrected by all previous steps, are used. , will be through formula Converted to a standard color space, the final output is a high-quality color image with accurate and realistic colors.

[0078] Example 2

[0079] Embodiment 2 of this application provides an apparatus for performing a color calibration method based on color temperature consistency, comprising: an image acquisition module, a parameter calculation module, a parameter storage module, and a parameter application module;

[0080] The image acquisition module is used to acquire the images required for the color calibration method, including whiteboard images and color card images;

[0081] The structured light camera is equipped with three RGB light sources, and the exposure time can be set for each of them.

[0082] The parameter calculation module is used to calculate the parameters required for the color calibration method based on the acquired image, including the exposure ratio coefficient, uniformity correction coefficient, white balance parameters, and color calibration matrix.

[0083] The parameter storage module is used to store all parameters calculated by the parameter calculation module.

[0084] The parameter application module is used to perform color temperature consistency constraints, light uniformity correction, white balance correction, and color calibration on the structured light camera by using the parameters stored in the parameter storage module.

[0085] Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0086] The memory is used to store one or more program instructions;

[0087] A processor for running one or more program instructions to execute a color calibration method for a structured light camera based on color temperature consistency.

[0088] Corresponding to the above embodiments, this embodiment of the invention provides a computer-readable storage medium containing one or more program instructions, which are executed by a processor to provide a color calibration method for a structured light camera based on color temperature consistency.

[0089] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed on a computer, the computer performs the aforementioned color calibration method for a structured light camera based on color temperature consistency.

[0090] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0091] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0092] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0093] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0094] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0095] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0096] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0097] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A color calibration method based on color temperature consistency of a structured light camera, characterized in that, The method comprises the following steps: Step 1: According to the linear relationship between the exposure time of the RGB light source in the structured light camera and the image gray scale, the color temperature consistency constraint is performed; Step 2: On the basis of the color temperature consistency, the light uniformity correction of the structured light camera is performed again; Step 3: After completing the light uniformity correction, the white balance correction of the structured light camera is performed again; Step 4: Under the condition of ensuring the white balance constraint, the color calibration of the structured light camera is completed by using the color card. According to the linear relationship between the exposure time of the RGB light source in the structured light camera and the image gray scale, the color temperature consistency constraint is performed, which is specifically divided into the following sub-steps: Control the structured light camera to trigger the RGB light source in turn under different exposure times, and collect whiteboard images; Calculate the average gray scale of the RGB images under different exposure times; Establish a linear model of the exposure time of the RGB light source and the image gray scale and fit it; Based on the above linear model, the color temperature consistency constraint equation set is constructed to solve the exposure proportion coefficient; Store the exposure proportion coefficient in the configuration file of the structured light camera to complete the color temperature consistency constraint.

2. The structured light camera color calibration method based on color temperature consistency according to claim 1, characterized in that, On the basis of the color temperature consistency, the light uniformity correction of the structured light camera is performed again, which is specifically divided into the following sub-steps: Under the exposure parameters that have completed the color temperature consistency constraint, trigger the RGB light source in turn to collect whiteboard images; For each collected whiteboard image, calculate its full-image average gray scale; For each color channel, substitute its full-image average gray scale into the uniformity correction coefficient calculation formula to obtain a pixel-level gain map; Store the gain map of each color channel in the configuration file of the structured light camera to complete the light uniformity correction.

3. The structured light camera color calibration method based on color temperature consistency according to claim 2, characterized in that, After completing the light uniformity correction, the white balance correction of the structured light camera is performed again, which is specifically divided into the following sub-steps: Under the imaging conditions that have completed the color temperature consistency and the light uniformity correction, trigger the RGB three-color light source in turn to collect images of standard color cards; Based on the average gray scale of the white block and the neutral gray block pixel region in each image, calculate the ideal gray scale value; Based on the ideal gray scale value, calculate a white balance parameter for each color channel respectively; Store the white balance parameter in the configuration file of the structured light camera to complete the white balance correction.

4. The structured light camera color calibration method based on color temperature consistency according to claim 3, characterized in that, Under the condition of ensuring the white balance constraint, the color calibration of the structured light camera is completed by using the color card, which is specifically divided into the following sub-steps: Control the structured light camera to trigger the RGB three-color light source in turn to collect standard color card images; Based on the collected images, calculate the color balance weight of each color block on the color card; Solve the color calibration matrix by using the least square method with the color balance weight; Store the solved color calibration matrix in the configuration file of the structured light camera to complete the color calibration. 5.A color calibration device based on color temperature consistency of a structured light camera, characterized in that, The method comprises the following steps: The method comprises 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6. A computer storage medium, comprising, comprising: at least one memory and at least one processor; a memory, configured to store one or more program instructions; a processor, configured to run the one or more program instructions to perform the color calibration method based on color temperature consistency of the structured light camera according to any one of claims 1-4.

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