ToF camera fppn calibration method and device, and electronic device

Abstract

The application discloses a kind of FPPN calibration method and device of ToF camera, electronic equipment.The method comprises the following steps: obtaining the gray scale chart of checkerboard as calibration board in the field of view of ToF camera;The gray scale chart is carried out luminance binary, and the black part of checkerboard in the gray scale chart is rejected;The FPPN value of the white part of checkerboard in the gray scale chart is obtained;The gray scale chart is divided into multiple blocks according to predetermined block size, and the characteristic value of each block is obtained;And according to the coordinates of each pixel point in the gray scale chart, the block where the corresponding pixel point is located is determined, and then the correction value of the corresponding pixel point is obtained according to the characteristic value of the block where the corresponding pixel point is located, to complete the FPPN calibration of ToF camera.The technical scheme provided by the application can save calibration time and calibration cost, and avoid introducing light intensity error into the solution of FPPN, avoiding the influence of single pixel point noise, improving calibration accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ToF ranging technology, and particularly to a FPPN calibration method and device for a ToF camera and electronic equipment. BACKGROUND

[0002] Binocular ranging, structured light and time-of-flight (ToF) are three major 3D imaging technologies today. Among them, ToF has gradually been applied in gesture recognition, 3D modeling, unmanned driving and machine vision due to its simple principle, simple and stable structure, long measurement distance and other advantages. The working principle of ToF technology is as follows: an external light source (VCSEL or LED, etc.) emits continuous modulated emission light, the emission light is reflected after irradiating the surface of the object to be measured, and the reflected light is captured by the image sensor of the ToF camera. The depth / distance of the object from the camera is obtained by calculating the time difference or phase difference between the emission light and the reflected light. Among them, the method of calculating the distance by the time difference is called pulsed ToF, and the method of calculating the distance by the phase difference is called continuous-wave ToF.

[0003] Due to its own imaging reasons and external environmental interference, the data directly obtained by the ToF camera usually has certain errors, so a series of calibrations need to be performed on the ToF camera before application to improve the measurement accuracy. The system error of the ToF camera mainly comes from: the external light source (VCSEL or LED, etc.) is usually driven by the square wave generated by the image sensor, but as the modulation frequency increases, the light waveform gradually approaches the sine wave, and the high-order harmonic wave in the square wave will bring periodic error to the measurement, and the existence of the related waveform will cause wiggling error in the measurement process; and due to the manufacturing process of the image sensor, each pixel point may be different, resulting in independent distance deviation of each pixel point, resulting in the existence of fixed phase pattern noise (FPPN).

[0004] In order to ensure the measurement accuracy of the ToF camera, a series of calibrations need to be performed on the ToF camera, such as lens calibration, wiggling calibration, FPPN calibration and the like. Different calibrations require different test environments and equipment. For example, a chessboard calibration plate is usually required for lens calibration, but a white board is required for FPPN calibration to calibrate each pixel point. The difference between black and white of the chessboard will cause an error in the light intensity of the distance measurement. If the FPPN calibration is directly performed on the chessboard calibration plate, the light intensity error will be directly introduced into the FPPN, which will cause a large deviation of the FPPN. In addition, different calibrations require different test patterns on the production line. If the existing production line is replaced with new test equipment to switch different test patterns, it will cause a large increase in production cost.

[0005] The depth image calibration method and system of a ToF camera system disclosed in Chinese patent publication CN109903241A uses a white target plate for FPPN calibration. The influence of noise is considered and the curve fitting is performed after the average of 5*5 windows. However, for the production line, the white board required for switching to FPPN calibration requires a certain cost, and the error caused by the curve fitting method may affect the accuracy requirement.

[0006] Therefore, how to reduce the FPPN calibration cost of the ToF camera and improve the calibration accuracy of the ToF camera is a technical problem to be solved at present. SUMMARY

[0007] The purpose of the present application is to provide a ToF camera FPPN calibration method and device, which solves the problems of high FPPN calibration cost of the existing ToF camera and the need to switch different test patterns on the production line. The FPPN calibration is performed by using the chessboard used for lens calibration as a calibration plate, which saves the calibration cost while ensuring the calibration accuracy.

[0008] To achieve the above purpose, the present application provides a ToF camera FPPN calibration method, comprising the following steps: obtaining a gray scale image of a chessboard as a calibration plate in the field of view of a ToF camera, wherein the chessboard is a chessboard used for lens calibration; performing brightness binarization on the gray scale image and removing the black part of the chessboard in the gray scale image; obtaining the FPPN value of the white part of the chessboard in the gray scale image; dividing the gray scale image into a plurality of blocks according to a predetermined block size and obtaining the characteristic value of each block; and determining the block where the corresponding pixel point is located according to the coordinates of each pixel point in the gray scale image, and then obtaining the correction value of the corresponding pixel point according to the characteristic value of the block where the pixel point is located, to complete the FPPN calibration of the ToF camera.

[0009] Optionally, before the step of determining the block where each pixel point in the gray scale image is located according to the coordinates of each pixel point, and then obtaining the correction value of the corresponding pixel point according to the characteristic value of the block, the method further comprises: performing linear interpolation compensation on the information missing block according to the characteristic value of the surrounding block corresponding to the information missing block, wherein the information missing block is a block in the gray scale image that is all black.

[0010] To achieve the above object, the application further provides a FPPN calibration device of a ToF camera, comprising: a first obtaining module, configured to obtain a gray scale image of a checkerboard as a calibration board in a field of view of the ToF camera, wherein the checkerboard is a checkerboard used for lens calibration; a processing module, configured to perform brightness binarization on the gray scale image, and remove the black part of the checkerboard in the gray scale image; a second obtaining module, configured to obtain the FPPN value of the white part of the checkerboard in the gray scale image; a third obtaining module, configured to divide the gray scale image into a plurality of blocks according to a predetermined block size, and obtain the characteristic value of each block; and a fourth obtaining module, configured to determine the block where each pixel point in the gray scale image is located according to the coordinates of each pixel point, and then obtain the correction value of the corresponding pixel point according to the characteristic value of the block, so as to complete the FPPN calibration of the ToF camera.

[0011] Optionally, the device further comprises a compensation module, configured to perform linear interpolation compensation on the information missing block according to the characteristic value of the surrounding block corresponding to the information missing block, wherein the information missing block is a block in the gray scale image that is all black.

[0012] To achieve the above object, the application further provides an electronic device, comprising a memory, a processor, and a computer executable program stored in the memory and executable on the processor, wherein the processor implements the steps of the FPPN calibration method of the ToF camera according to the computer executable program.

[0013] The FPPN calibration method and device of the ToF camera provided by the application saves calibration time and cost by using the checkerboard used for lens calibration as the calibration board for FPPN calibration; the calibration accuracy is improved by avoiding introducing light intensity error into the solution of FPPN by performing median filtering to binarize the brightness of the whole image; the calibration accuracy is improved by avoiding the influence of single pixel noise by dividing the whole image into blocks according to a certain block size, and obtaining the characteristic value of a single block by performing Gaussian function weighting calculation on all pixel points in the block. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to make the technical solutions in the embodiments of the present application clearer, the accompanying drawings needed in the embodiment description will be briefly introduced. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0015] Figure 1 A schematic diagram for measuring by the continuous wave method;

[0016] Figure 2 A flow chart of the FPPN calibration method of the ToF camera provided by an embodiment of the present application;

[0017] Figure 3 A schematic diagram of FPPN calibration provided by an embodiment of the present application;

[0018] Figure 4 A schematic diagram of block division of a gray scale image provided by an embodiment of the present application;

[0019] Figure 5 A schematic diagram of linear interpolation compensation principle provided by an embodiment of the present application;

[0020] Figure 6 A structural block diagram of the FPPN calibration device of the ToF camera provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort belong to the scope of protection of the present application.

[0022] Please refer to Figure 1 , which is a schematic diagram for measuring by the continuous wave method. Since the light speed c is a known quantity and the modulation frequency f of the emitted light is a known quantity, the phase difference between the emitted light and the reflected light at different distances The corresponding distance d can be calculated, i.e.:

[0023]

[0024] The FPPN deviation is due to the fact that each pixel point of the gray scale image sensor may be different in the manufacturing process, resulting in independent distance deviation of each pixel point.

[0025] The usual correction process is to irradiate a whiteboard parallel to the gray scale image sensor, and to correct the FPPN deviation by the following formula: wherein, is the measured phase value, The wiggling is a wiggling error corresponding to the pixel point.

[0026] Generally, lens calibration needs to pass through a checkerboard calibration plate, and the difference between black and white of the checkerboard will bring an error of light intensity in distance measurement. If FPPN calibration is directly performed through the checkerboard calibration plate, the light intensity error will be directly introduced into FPPN, and FPPN deviation will be large. For the calibration mode that the lens calibration adopts a checkerboard and the FPPN calibration adopts a white plate, different test modes need to be switched for different calibrations on the production line, which will increase the calibration time and calibration cost.

[0027] In view of the problems existing in the ToF camera calibration, the present application saves the calibration time and the calibration cost by taking the checkerboard used for lens calibration as a calibration plate for FPPN calibration. The light intensity error is avoided from being introduced into the solution of FPPN by binarizing the brightness of the whole picture through median filtering, so as to improve the calibration accuracy. The single-pixel-point noise is avoided by dividing the whole picture according to a certain block size, and the characteristic value of a single block is obtained by Gaussian function weighting calculation of all pixel points in the block, so as to improve the calibration accuracy. The following gives a detailed explanation.

[0028] An embodiment of the present application provides a FPPN calibration method of a ToF camera. Figures 2 to 5 Wherein, Figure 2 A FPPN calibration method of a ToF camera provided by an embodiment of the present application is shown in the flow chart, Figure 3 A FPPN calibration schematic diagram provided by an embodiment of the present application is shown in the flow chart, Figure 4 A block division schematic diagram of a gray scale diagram provided by an embodiment of the present application is shown in the flow chart, Figure 5 A linear interpolation compensation principle schematic diagram provided by an embodiment of the present application is shown in the flow chart.

[0029] As shown in the flow chart, Figure 2 The method provided by the embodiment includes the following steps: S21, a gray scale diagram of a checkerboard as a calibration plate in a field of view of a ToF camera is acquired, wherein the checkerboard is a checkerboard used for lens calibration; S22, the gray scale diagram is binarized in brightness, and the black part of the checkerboard in the gray scale diagram is removed; S23, the FPPN value of the white part of the checkerboard in the gray scale diagram is acquired; S24, the gray scale diagram is divided into multiple blocks according to a predetermined block size, and the characteristic value of each block is acquired; and S25, the block where each pixel point in the gray scale diagram is located is determined according to the coordinates of each pixel point, and then the correction value of the corresponding pixel point is acquired according to the characteristic value of the block where the pixel point is located, so as to complete the FPPN calibration of the ToF camera.

[0030] Regarding step S21, acquiring a grayscale image of the checkerboard used as a calibration board in the ToF camera's field of view, wherein the checkerboard is the checkerboard used for lens calibration. Specifically, the ToF camera captures an image of the checkerboard used for lens calibration, acquiring a grayscale image of the checkerboard in the ToF camera's field of view (fov). The ToF camera outputs a grayscale image (i.e., a brightness image) and a depth image; this invention primarily focuses on grayscale image processing.

[0031] like Figure 3 As shown, assuming the area corresponding to the field of view of the ToF camera 31 in the checkerboard 32 is the field of view region Fov1, the ToF camera 31 captures an image of the checkerboard 32 to obtain a grayscale image of the field of view region Fov1 of the checkerboard 32. The calibration plane of the calibration plate is the surface of the checkerboard facing the lens of the ToF camera, that is, the plane in the checkerboard used to reflect the light emitted by the ToF camera. In this embodiment, the checkerboard used as the calibration plate is installed with a tilt angle θ (that is, the angle θ between the checkerboard and the vertical plane corresponding to the field of view of the ToF camera is less than or equal to 90 degrees). By using the black and white checkerboard used in lens calibration as the calibration plate for FPPN calibration, there is no need to provide an additional white board covering the entire field of view for FPPN calibration, so that different calibration plate patterns do not need to be switched during calibration on the production line, saving calibration time and cost.

[0032] Regarding step S22, the grayscale image is binarized for brightness, and the black portions of the checkerboard pattern in the grayscale image are removed. Specifically, by binarizing the grayscale image for brightness, intensity error is avoided from being introduced into the FPPN solution, thus improving calibration accuracy.

[0033] In some embodiments, the grayscale image can be binarized for brightness using median filtering, thereby avoiding the introduction of light intensity errors into the FPPN solution and improving calibration accuracy. Median filtering of a grayscale image is a processing method that sorts the sampled data within the neighborhood of a pixel in the grayscale image, obtains the median, and uses the median to replace the brightness value of that pixel. This makes the surrounding pixel values ​​closer to the true values, thereby eliminating isolated noise points and effectively eliminating a small amount of discrete noise in the grayscale image. Assuming a row of brightness values ​​in the grayscale image is as follows: A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12; median filtering of this row using a 1×5 window can be understood as: taking the grayscale values ​​of the five adjacent pixels centered on the current pixel, sorting them (if there are no adjacent pixels, their brightness value is considered 0), and taking the median as the brightness value of the current pixel; for example, the result of median filtering for pixel A3 is the median of A1, A2, A3, A4, A5 after sorting.

[0034] In some embodiments, step S22 further includes: performing brightness binarization on the grayscale image using the following formula to obtain the binarized brightness of each pixel; and, treating pixels with a binarized brightness of 0 as the black portion and removing them from the grayscale image. The formula for performing brightness binarization on the grayscale image to obtain the binarized brightness of each pixel is:

[0035]

[0036] Where binary_amp is the binary brightness of the pixel, amplitude is the grayscale value (i.e., brightness value) of the pixel, and medium is the median brightness of the row containing the pixel. The checkerboard pattern can be installed at a certain tilt angle. Each row along the tilt angle of the checkerboard pattern has a corresponding median brightness value. The binary grayscale value of the pixel is obtained by comparing the grayscale value of the pixel in that row with the median brightness value of that row.

[0037] Regarding step S23, obtaining the FPPN value of the white portion of the checkerboard pattern in the grayscale image. Specifically, after removing the black portion of the checkerboard pattern in the grayscale image through brightness binarization, the FPPN value can be calculated for the remaining white portion.

[0038] In some embodiments, the FPPN value of the white portion of the checkerboard pattern in the grayscale image is calculated using the following formula:

[0039]

[0040] in, To measure the phase value, The true phase value is represented by `wriggling`, which is the wriggling error corresponding to the pixel, and `binary_amp` is the binary brightness of the corresponding pixel.

[0041] Regarding step S24, the grayscale image is divided into multiple blocks according to a predetermined block size, and the feature value of each block is obtained. Specifically, since the black parts are removed after brightness binarization, the black part information in the image is missing, resulting in incomplete FPPN information. Therefore, by dividing the entire image according to a predetermined block size (block_size), the grayscale image is divided into multiple blocks, and the feature value of each block is calculated to restore the image information of the grayscale image.

[0042] In some embodiments, the feature values ​​of each block are obtained by performing a Gaussian weighted calculation on each pixel of that block.

[0043] like Figure 4 As shown in section (a), the grayscale image 41 can be divided into n*m blocks according to a predetermined block size; asFigure 4 As shown in section (b), the predetermined block size contains k*l pixels in each block 411 (e.g., k*l = 12*12). Then, the feature values ​​of each block are obtained by performing a Gaussian weighted average on all pixels in each block using the following formula:

[0044]

[0045] Where block represents the corresponding block, block_size is the predefined block size, and 0 <i≤k,0<j≤l。

[0046] Individual pixels can have noise due to various factors. This invention divides the entire image into blocks of a certain size, and the feature value of each block is obtained by performing a Gaussian weighted calculation on all pixels in that block. This avoids the influence of noise from individual pixels and improves calibration accuracy.

[0047] Regarding step S25, the block in which each pixel belongs is determined based on the coordinates of each pixel in the grayscale image, and then the correction value of the corresponding pixel is obtained based on the feature value of the block, thereby completing the FPPN calibration of the ToF camera. Specifically, when the FPPN information for the entire block has been calculated, the block in which each pixel belongs can be determined based on its coordinates during the calibration process, and then the correction value of the corresponding pixel can be obtained based on the feature value of the block.

[0048] In some embodiments, before performing step S25, the method further includes: performing linear interpolation compensation on the information-missing block based on the feature values ​​of the surrounding blocks corresponding to the information-missing block, wherein the information-missing block is a block in the grayscale image that is entirely black. Due to the uncertainty of black and white positions, some blocks may be entirely black, which would cause information loss in those blocks. Therefore, in this embodiment, based on the principle of linear interpolation, the information-missing block is interpolated and compensated based on the surrounding blocks, so that the FPPN value of all blocks in the grayscale image can be calculated.

[0049] like Figure 5 As shown, label 51 indicates the missing information block for which the feature value is to be determined. The coordinates of its surrounding blocks are (x1, y1), (x1, y2), (x2, y1), and (x2, y2), respectively. When the feature values ​​of the surrounding blocks are known, the missing information block is interpolated and compensated based on the surrounding blocks according to the principle of linear interpolation, thereby obtaining the feature value of the missing information block.

[0050] Based on the same inventive concept, this invention also provides an FPPN calibration device for a ToF camera. The provided FPPN calibration device for a ToF camera can be used as follows: Figure 2The FPPN calibration method of the ToF camera shown calibrates the FPPN of the ToF camera.

[0051] Please refer to Figure 6 which is a structural block diagram of the FPPN calibration device of the ToF camera according to an embodiment of the present application. As shown in the figure, Figure 6 The FPPN calibration device of the ToF camera includes a first acquisition module 61, a processing module 62, a second acquisition module 63, a third acquisition module 64, and a fourth acquisition module 65.

[0052] Specifically, the first acquisition module 61 is configured to acquire a grayscale image of a checkerboard in the field of view of the ToF camera as a calibration board, wherein the checkerboard is a checkerboard used for lens calibration. The processing module 62 is configured to perform brightness binarization on the grayscale image and remove the black part of the checkerboard in the grayscale image. The second acquisition module 63 is configured to acquire the FPPN value of the white part of the checkerboard in the grayscale image. The third acquisition module 64 is configured to divide the grayscale image into a plurality of blocks according to a predetermined block size and acquire the feature value of each block. The fourth acquisition module 65 is configured to determine the block in which each pixel point in the grayscale image is located according to the coordinates of each pixel point, and then acquire the correction value of the corresponding pixel point according to the feature value of the block in which the pixel point is located, so as to complete the FPPN calibration of the ToF camera. The specific working mode of each module can be referred to the description of the corresponding steps in the FPPN calibration method of the ToF camera shown in Figure 2 The FPPN calibration method of the ToF camera shown in the figure calibrates the FPPN of the ToF camera.

[0053] In some embodiments, the FPPN calibration device of the ToF camera further includes a compensation module 66 (which is a preferred component shown by a dashed line), configured to perform linear interpolation compensation on an information missing block according to the feature values of the surrounding blocks of the information missing block, wherein the information missing block is a block in the grayscale image that is all black. In the uncertainty of black and white positions, there may be a part of the block that is all black, which will cause the information missing of the block. Therefore, in this embodiment, the information missing block is compensated by linear interpolation according to the surrounding blocks, so that the FPPN values of all blocks in the grayscale image can be calculated.

[0054] The FPPN calibration method and device of the ToF camera provided by the present application saves calibration time and cost by using the checkerboard used for lens calibration as a calibration board for FPPN calibration. The brightness of the entire image is binarized by median filtering, which avoids introducing light intensity errors into the solution of FPPN and improves the calibration accuracy. By dividing the entire image into blocks of a certain size, the feature value of a single block is obtained by Gaussian function weighting calculation on all pixel points in the block, which avoids the influence of single pixel point noise and improves the calibration accuracy.

[0055] Based on the same inventive concept, the present application also provides an electronic device comprising a memory, a processor, and a computer executable program stored on the memory and executable on the processor; the processor implements the method for FPPN calibration of a ToF camera as shown in Figure 2 the steps of the FPPN calibration method of the ToF camera as shown in

[0056] Embodiments in the inventive concept can be described and explained in terms of modules that perform described one or more functions. These modules (which can also be referred to herein as units, etc.) can be physically implemented by analog and / or digital circuits such as logic gates, integrated circuits, microprocessors, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, etc., and can optionally be driven by firmware and / or software. The circuits can be implemented, for example, in one or more semiconductor chips. The circuits making up a module can be implemented by specialized hardware, or by a processor such as one or more programmed microprocessors and related circuitry, or by a combination of specialized hardware to perform some functions of the module, and a processor to perform other functions of the module. Each module of an embodiment can be physically separated into two or more interacting and discrete modules without departing from the scope of the inventive concept. Likewise, modules of an embodiment can be physically combined into more complex modules without departing from the scope of the inventive concept.

[0057] In general, terminology can be understood at least in part from usage in context. For example, the term "one or more" as used herein, depending at least in part upon context, can be used to describe either a singular number or plural numbers (i.e., one or more), or it can be used to describe a singular characteristic or a combination of characteristics, either a singular instance or a combination of instances (i.e., one or more). In addition, the term "based on" can be understood as not necessarily of exclusive

[0058] It should be noted that the terms "comprise" and "have" and variations thereof in the present document are intended to cover non-exclusive inclusions. The terms "first", "second", and the like in the description do not necessarily imply a specific order or sequence, unless otherwise explicitly stated. It should be understood that data used in the specification can be interchangeable with appropriate context, where appropriate. In addition, embodiments in the present application and features in the embodiments can be combined with each other, if not in conflict. Furthermore, in the above description, the description of well-known components and techniques has been omitted to avoid unnecessarily obscuring the inventive concept. In each of the above embodiments, each embodiment focuses on the differences from other embodiments, and the same / similar parts between the embodiments can be referred to each other.

[0059] The above merely describes the preferred embodiments of the present application, and it should be pointed out that, for those skilled in the art, several improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be considered as falling within the protection scope of the present application.

Claims

1. A method for FPPN calibration of a ToF camera, characterized in that, The method comprises the following steps: acquiring a gray image of a checkerboard in a field of view of a ToF camera, the checkerboard being used for lens calibration; performing brightness binarization on the gray image and removing black parts of the checkerboard in the gray image; acquiring FPPN values of white parts of the checkerboard in the gray image; dividing the gray image into a plurality of blocks according to a predetermined block size and acquiring feature values of each block; and determining a block where each pixel point in the gray image is located according to coordinates of the pixel point, and then acquiring a correction value of the pixel point according to the feature value of the block, so as to complete FPPN calibration of the ToF camera; before the step of determining a block where each pixel point in the gray image is located according to coordinates of the pixel point, and then acquiring a correction value of the pixel point according to the feature value of the block, the method further comprises: performing linear interpolation compensation on an information missing block according to feature values of surrounding blocks of the information missing block, wherein the information missing block is a block in the gray image that is all black.

2. The method of claim 1, wherein, The step of performing brightness binarization on the gray image further comprises: performing brightness binarization on the gray image in a median filter manner.

3. The method of claim 1, wherein, The step of performing luminance binarization on the grayscale image to remove the black part of the checkerboard in the grayscale image further includes: performing luminance binarization on the grayscale image by the following formula to obtain the binarized luminance of each pixel point: binary_amp = amplitude - medium, where binary_amp is the binarized luminance of the pixel point, amplitude is the grayscale value of the pixel point, and medium is the luminance median of the row in which the pixel point is located; the pixel point with the binarized luminance of 0 is regarded as the black part, and is removed from the grayscale image.

4. The method of claim 3, wherein, The step of acquiring the FPPN value of the white part of the chessboard in the grayscale image further includes: calculating the FPPN value of the white part of the chessboard in the grayscale image by the following formula: wherein, is a measured phase value, is a true phase value, wiggling is a wiggling error corresponding to a corresponding pixel point, and binary_amp is a binary brightness of the corresponding pixel point.

5. The method of claim 1, wherein, The feature value of each block is acquired by performing Gaussian function weighting calculation on each pixel point in the block.

6. The method of claim 1, wherein, The step of dividing the gray-scale image into a plurality of blocks according to a predetermined block size and obtaining a feature value of each block further comprises: dividing the gray-scale image into n*m blocks according to a predetermined block size, wherein the predetermined block size is that each block contains k*l pixel points; and obtaining the feature value of each block by performing Gaussian function weighting calculation on all pixel points in each block according to the following formula: wherein block is a corresponding block, block_size is the predetermined block size, 0<i≤k, and 0<j≤l.

7. An FPPN calibration device of a ToF camera, characterized in that, The method comprises: A first acquisition module is configured to acquire a gray image of a checkerboard in a field of view of a ToF camera, the checkerboard being used for lens calibration; a processing module is configured to perform brightness binarization on the gray image and remove black parts of the checkerboard in the gray image; a second acquisition module is configured to acquire FPPN values of white parts of the checkerboard in the gray image; a third acquisition module is configured to divide the gray image into a plurality of blocks according to a predetermined block size and acquire feature values of each block; and a fourth acquisition module is configured to determine a block where each pixel point in the gray image is located according to coordinates of the pixel point, and then acquire a correction value of the pixel point according to the feature value of the block, so as to complete FPPN calibration of the ToF camera; the device further comprises: a compensation module configured to perform linear interpolation compensation on an information missing block according to feature values of surrounding blocks of the information missing block, wherein the information missing block is a block in the gray image that is all black.

8. An electronic device comprising a memory, a processor, and a computer- executable program stored on the memory and executable on the processor, wherein, The processor performs the computer executable program to realize the steps of the FPPN calibration method of the ToF camera according to any one of claims 1-6.

Images