WO2018120097A1 - Computer vision-based method and system for recognizing black piece and white piece - Google Patents

Abstract

Disclosed in embodiments of the present invention are a computer vision-based method and system for recognizing a black piece and a white piece. The method comprises: obtaining an image to be recognized on a chessboard in an actual light condition; arranging a first detection region and a second detection region on the image to be recognized; computing a first region average gray scale value in the first detection region and a second region average gray scale value in the second detection region; determining a difference and a sum between the second region average gray scale value and a preset determining threshold; comparing the first region average gray scale value with the difference and the sum, so as to determine a chess piece movement state in the first detection region. In this way, in the method, the determining threshold is set according an actual environment, so that the method is applicable to different light conditions, thereby reducing faulty detections caused by a light factor, and improving the accuracy rate for recognizing a black piece and a white piece.

Description

Black and white sub-identification method and system based on computer vision [Technical Field]

Embodiments of the present invention relate to the field of image recognition technologies, and in particular, to a black and white sub-identification method and system based on computer vision.

【Background technique】

At present, the game system of various types of robots is increasingly rich. However, the traditional means of detecting chess pieces in the traditional game system is to place various sensors on the board, such as photosensitive sensors or Hall sensors. This detection method requires the chess pieces and the chessboard. The transformation will result in a corresponding increase in costs and a complicated construction.

The visual-based chessboard recognition system can not only reduce the cost of transforming the chess pieces and the chessboard, but also make the game system more concise and easy to maintain, and more and more popularization. However, the existing visual-based black and white chess recognition system has the intensity of the light source. Uniformity and stability have higher requirements, and the requirements for lighting conditions are also more demanding, otherwise it is prone to false detection.

[Summary of the Invention]

The embodiment of the invention provides a black-and-white sub-identification method and system based on computer vision, so as to solve the technical problem of the false detection phenomenon caused by the light factor in the prior art visual-based black and white chess recognition system.

In order to solve the above technical problem, a technical solution adopted by the embodiment of the present invention is to provide a black and white sub-identification method based on computer vision, the method comprising:

Obtaining an image to be recognized of the board under actual lighting conditions;

Setting a first detection area and a second detection area on the image to be identified, wherein an area of the second detection area is larger than the first detection area and surrounds the first detection area;

Calculating a first region average gray value of the to-be-identified image in the first detection area and a second region average gray value of the to-be-identified image in the second detection area;

Calculating a difference between the average grayscale value of the second region and a preset determination threshold, and calculating a summation value of the average grayscale value of the second region and the preset determination threshold;

Comparing the first region average gray value with the difference value and the summation value to determine a falling state of the first detection region.

The step of comparing the first region average gray value with the difference value and the summation value, and further determining the falling state of the first detection region includes:

If the first region average gray value is smaller than the difference, the first detection region is provided with a black child, and if the first region average gray value is greater than the summation value, the first A white sub is disposed in the detection area, and if the first area average gray value is between the difference and the summation value, the first detection area is in a sub-state.

Wherein, the method further comprises:

Obtaining an adjustment reference image when the checkerboard is in a sub-state without the actual illumination condition;

Calculating an actual chessboard average gray value of the chessboard according to the adjusted reference image;

Determining the determination threshold according to the actual checkerboard average gray value and the reference chessboard average gray value under the reference illumination condition, wherein the difference between the actual checkerboard average grayscale value and the reference checkerboard average grayscale value The larger the decision threshold, the smaller.

The determination threshold is a product of a reference determination threshold and a one-hund score, wherein the difference between the actual average chessboard grayscale value and the reference chessboard average grayscale value is smaller, and the percentage is smaller.

Wherein, the determination threshold is calculated by the following formula:

T = abs (H _{b -} abs (HH _b )) × T _b / H _b ;

Where T is the decision threshold, H _b is the reference checkerboard average gray value, T _b is the reference decision threshold, H is the actual checkerboard average gray value, and abs is an absolute value function.

The method for determining the reference determination threshold includes:

Obtaining, in a reference illumination condition, a first reference image when the checkerboard is in a sub-state;

Calculating the reference chessboard average gray value of the chessboard according to the first reference image;

Obtaining, in the reference illumination condition, a second reference image when the chessboard is provided with sunspots and whites;

Calculating a reference gray value of the sunspot and a reference gray value of the white light according to the second reference image;

The reference determination threshold is set based on the reference checkerboard average grayscale value, the reference grayscale value of the sunspot, and the reference grayscale value of the white matter.

Wherein the reference illumination condition is such that the reference checkerboard average gray value is between 100 and 150, and the reference gray value of the sunspot is less than the reference checkerboard average gray value exceeds 50, the white matter The reference grayscale value is greater than the illumination condition in which the reference checkerboard average grayscale value exceeds 50.

Wherein, the method further comprises: acquiring actual ambient light brightness under the actual lighting condition;

Adjusting according to the actual ambient light brightness and the reference ambient light brightness under the reference illumination condition The determination threshold, the difference between the actual ambient light brightness and the reference ambient light brightness, the smaller the determination threshold.

The step of setting the first detection area and the second detection area on the image to be identified includes: setting the first detection area and the second detection area respectively at each grid point of the board And determining a fall state of each of the grid points, wherein the first detection area and the second detection area are set to be square, and the width of the first detection area is equal to the sunspot and/or the The diameter of the white, the width of the second detection area is equal to the three grid widths of the board.

The method further includes: generating corresponding characters according to the determined falling state of each of the grid points, thereby forming a character string indicating a falling state of all the grid points, wherein different falling sub-states are Different characters are represented.

In order to solve the above technical problem, a technical solution adopted by the embodiment of the present invention is to provide a black and white sub-identification system based on computer vision, the system comprising an image acquisition device and an image processor, wherein the image acquisition device is used in actual Acquiring an image to be recognized of the board in an illumination condition; the image processor is configured to set a first detection area and a second detection area on the image to be identified, and calculate the image to be recognized in the first detection area a first region average gray value and a second region average gray value of the image to be identified in the second detection region, wherein an area of the second detection region is larger than the first detection region and surrounds the a first detection area, the image processor further calculating a difference between the average grayscale value of the second region and the determination threshold, and a summation value of the average grayscale value of the second region and the determination threshold, and The first region average gray value is compared with the difference value and the summation value, thereby determining a falling state of the first detection region.

If the comparison result of the image processor is that the average grayscale value of the first region is smaller than the difference, it is determined that a black spot is set in the first detection region, and if the comparison result of the image processor is If the first region average gray value is greater than the summation value, it is determined that the first detection region is provided with white, if the comparison result of the image processor is that the first region average gray value is between And between the difference value and the summation value, determining that the first detection area is in a sub-state.

The image capture device is further configured to acquire an adjusted reference image when the checkerboard is in a sub-state without the sub-state, and the image processor calculates an actual checkerboard average of the checkboard according to the adjusted reference image. a gray value, and determining the decision threshold based on the actual checkerboard average grayscale value and a reference checkerboard average grayscale value under the reference illumination condition, wherein the actual checkerboard average grayscale value and the reference checkerboard average The larger the difference in the gray value, the smaller the decision threshold.

Wherein the determination threshold is a product of a reference determination threshold and a one hundred fraction, wherein the actual chess The greater the difference between the disc average gray value and the reference board average gray value, the smaller the percentage.

Wherein, the determination threshold is calculated by the following formula:

T = abs (H _{b -} abs (HH _b )) × T _b / H _b ;

Where T is the decision threshold, H _b is the reference checkerboard average gray value, T _b is the reference decision threshold, H is the actual checkerboard average gray value, and abs is an absolute value function.

The image capture device is further configured to acquire, according to a reference illumination condition, a first reference image when the checkerboard is in a sub-state, and obtain, when the checkerboard is set, a black and white when the checkerboard is set. a second reference image, the image processor further calculating the reference chessboard average gray value of the chessboard according to the first reference image, calculating a reference grayscale value of the sunspot according to the second reference image, and the The reference gradation value of the white matter, and further setting the reference determination threshold value based on the reference checkerboard average gradation value, the reference gradation value of the sunspot, and the reference gradation value of the white matter.

Wherein the reference illumination condition is such that the reference checkerboard average gray value is between 100 and 150, and the reference gray value of the sunspot is less than the reference checkerboard average gray value exceeds 50, the white matter The reference grayscale value is greater than the illumination condition in which the reference checkerboard average grayscale value exceeds 50.

Wherein the system further comprises a light sensor for acquiring actual ambient light brightness under the actual lighting condition, the image processor according to the actual ambient light brightness and the reference ambient light brightness under the reference lighting condition The determination threshold is adjusted, and the difference between the actual ambient light brightness and the reference ambient light brightness is larger, and the determination threshold is smaller.

The image processor sets the first detection area and the second detection area respectively at each grid point to be detected of the board, and further determines a falling state of each grid point to be detected. Wherein the first detection area and the second detection area are arranged in a square shape, and the width of the first detection area is equal to the diameter of the sunspot and/or the white matter, and the width of the second detection area is equal to The three grid widths of the board.

The image processor respectively generates corresponding characters according to the determined falling state of each of the to-be-detected grid points, thereby forming a character string representing all the falling state of the grid point to be detected, wherein different The drop state is represented by different characters.

The beneficial effects of the embodiment of the present invention are: in the computer vision-based black-and-white sub-identification method and system provided by the embodiment of the present invention, the difference between the average gray value of the second region and the determination threshold is calculated according to the determination threshold value. And the summation value of the two, and then comparing the average gray value of the first region with the difference and the sum value, thereby determining the state of the falling of the first detection region, because the determination threshold is set according to the actual environment, so that the The method is adapted to different lighting conditions and reduces false detection due to light factors. Improve the recognition accuracy of black and white.

[Description of the Drawings]

1 is a schematic flow chart of a first embodiment of a computer vision-based black and white sub-identification method provided by the present invention;

2 is a schematic structural view of a first detection area and a second detection area in the first embodiment;

3 is a schematic flow chart of a second embodiment of a computer vision-based black and white sub-identification method provided by the present invention;

4 is a partial flow chart of a third embodiment of a computer vision-based black and white sub-identification method provided by the present invention;

5 is a partial flow chart of a fourth embodiment of a computer vision-based black and white sub-identification method provided by the present invention;

6 is a schematic flow chart of a fifth embodiment of a computer vision-based black and white sub-identification method provided by the present invention;

7 is a schematic structural diagram of a computer vision-based black and white sub-identification system according to a first embodiment of the present invention;

FIG. 8 is a schematic structural diagram of a second embodiment of a computer vision based black and white sub-identification system provided by the present invention.

【detailed description】

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

1 and FIG. 2, FIG. 1 is a schematic flowchart of a computer vision-based black and white sub-identification method according to a first embodiment of the present invention, and FIG. 2 is a first detection area and a second detection area in the first embodiment. Schematic diagram of the structure. In this embodiment, the difference between the average gray value of the second region and the preset determination threshold and the sum of the two are calculated, and then the average gray value of the first region is compared with the difference and the sum. Further, the state of the falling of the first detection area is determined. Specifically, the calibration method of this embodiment includes the following steps:

Step S11: Acquire an image to be recognized of the board under actual lighting conditions.

Obtain the image to be recognized of the board under actual lighting conditions. The actual lighting conditions include excessive light, too dark light, uneven light or unstable light. The image to be recognized is the image of the entire board, including the 19*19 grid. The line area (see the grid line area of Figure 2), not just the image of the area to be detected.

Step S12: setting a first detection area and a second detection area on the image to be identified, wherein the area of the second detection area is larger than the first detection area and surrounds the first detection area.

As shown in FIG. 2, setting the first detection area and the second detection area on the image to be identified means that the first detection area and the second detection area are respectively set at each grid point of the board, and then each grid point is further The falling state is determined, wherein the first detecting area and the second detecting area are arranged in a square shape, and the width of the first detecting area is equal to the diameter of the black and/or white, and the width of the second detecting area is equal to three grids of the board The width, where the three grid widths are specifically two full grid widths plus two half grid widths.

Specifically, when the first detection area is located in the middle of the board, the first detection area and the second detection area are concentrically arranged, that is, the centers of the first detection area and the second detection area are consistent; when the first detection area is located at the edge of the board The first detection area and the second detection area may be concentrically arranged, or may be set differently. For example, when the second detection area does not exceed the checkerboard, the first detection area and the second detection area may be concentrically set. When the second detection area exceeds the board, the first detection area and the second detection area may be disposed differently, and the center of the first detection area is farther from the center of the second detection area than the edge of the board.

Step S13: calculating a first region average gray value of the image to be recognized in the first detection area and a second region average gray value of the image to be identified in the second detection area.

The gray value refers to the color depth or the difference between light and dark in the black and white image. The gray value ranges from 0 to 255, where white is 255 and black is 0. The average gray value is obtained by summing the gray values of all the pixels and dividing by the sum of the number of pixels, thereby obtaining the first region average gray value H _n and the image to be recognized in the first detection region. The second region average gray value H _m in the second detection region.

Step S14: Calculating a difference between the second region average gray value and the preset determination threshold and a summation value of the second region average gray value and the preset determination threshold.

The decision threshold can be fixed. For example, the determination threshold is a fixed value set according to experience, or a reference determination threshold under reference illumination conditions, wherein the specific determination manner of the reference determination threshold is as follows. The determination threshold may also be changed, for example, calculated according to a preset algorithm, and the specific calculation manner of calculating the determination threshold according to a preset algorithm is as follows. The unit of the determination threshold is a gray value, and the determination threshold is set to T, and the difference between the average gray value of the second region and the determination threshold is calculated as (H _m -T), the average gray value of the second region and the determination threshold. The summed value is set to (H _m +T).

Step S15: comparing the first region average gray value with the difference value and the summation value, thereby determining the falling state of the first detection region.

In the computer vision-based black-and-white sub-identification method and system provided by the embodiment of the present invention, since the determination threshold is provided, the difference between the average gray value of the second region and the determination threshold and the sum of the two values are calculated, and then The first region average gray value is compared with the difference value and the summation value, thereby determining the falling state of the first detection region, because the determination threshold is set according to the actual environment, so that the method is adapted to different lighting conditions, reducing The false detection caused by the light factor improves the recognition accuracy of the black and white sub-score.

The step S15 is further specifically: if the first region average gray value is less than the difference, the first detection region is provided with a sunspot, and if the first region average gray value is greater than the summation value, the first detection region is set. There is a white sub-, if the first region average gray value is between the difference and the summation value, the first detection region is in a sub-state.

Specifically, if the first region average gray value is less than the difference, that is, (H _n <(H _m -T)), the first detection region is provided with a sunspot; if the first region average gray value is greater than the summation value , that is, (H _n >(H _m +T)), white matter is set in the first detection region; if the average gray value of the first region is between the difference and the summation value, ie ((H _m -T) ) ≤ H _n ≤ (H _m + T)), the first detection area is in a sub-state.

As shown in FIG. 3, FIG. 3 is a schematic flowchart diagram of a second embodiment of a computer vision-based black and white sub-identification method provided by the present invention. In the present embodiment, the determination threshold is further determined based on the actual board average gray value and the reference board average gray value under the reference lighting condition. Specifically, the calibration method of this embodiment includes the following steps:

Steps S21-S25 are substantially the same as steps S11-S15 of the first embodiment, and are not described herein again. The difference between this embodiment and the first embodiment is that the embodiment further includes the following steps before step S21:

Step S201: Acquire an adjustment reference image when the board is in a sub-state without being in actual lighting conditions.

Under the actual lighting conditions, the adjustment reference image is obtained when the board is in the sub-state. The actual lighting conditions include excessive light, too dark light, uneven light or unstable light. When there is no sub-state, it means that there is no chessboard. Falling into the sunspot or white, adjusting the reference image is just a 19*19 gridline area.

Step S202: Calculate the actual average gray value of the chessboard according to the adjusted reference image.

According to the adjustment reference image, the actual average gray value H of the board of the 19*19 grid line is calculated.

Step S203: determining a determination threshold according to the actual checkerboard average gray value and the reference checkerboard average gray value under the reference illumination condition, wherein the difference between the actual checkerboard average grayscale value and the reference checkerboard average grayscale value is larger, and the determination threshold is smaller. .

The image to be recognized of the board is obtained under a preset set of illumination conditions, and the black sub-gray value and the white sub-gray value on the to-be-identified image satisfy a preset condition, and the set of illumination conditions is a reference illumination condition. The average gray value of the image to be recognized that acquires the board without the sub-state under the reference illumination condition is the average gray value of the reference board. When the preset lighting conditions are determined, the reference board average gray value is a fixed value. The preset condition may be that the average value of the reference board is within a specified range.

The determination threshold is a product of the reference determination threshold and the one hundred fraction, wherein the difference between the actual average chessboard gradation value and the reference chessboard average gradation value is larger, and the percentage is smaller. For the specific calculation method of the reference determination threshold, please refer to the following.

Specifically, the decision threshold is calculated by the following formula:

T = abs (H _{b -} abs (HH _b )) × T _b / H _b ;

Where T is the decision threshold, H _b is the reference checkerboard average gray value, T _b is the reference decision threshold, H is the actual checkerboard average gray value, and abs is the absolute value function. The determination threshold T is an integer.

In a practical embodiment, provided reference board mean gray value H _b 128, the reference determination threshold value T _b is 30, then the determination threshold calculated: T = abs (128-abs (H-128)) × 30/128. Wherein, the greater the difference between the actual checkerboard average gray value and the reference checkerboard average gray value, the smaller the decision threshold.

As shown in FIG. 4, FIG. 4 is a schematic flowchart of determining a reference determination threshold in a computer vision-based black and white sub-identification method provided by the present invention. In the present embodiment, the reference determination threshold is set based on the reference checkerboard average gradation value, the reference gradation value of the sunspot, and the reference gradation value of the white matter. Specifically, the calibration method of this embodiment includes the following steps:

Step S301: Acquire a first reference image when the board is in a sub-state without under the reference illumination condition.

The reference illumination condition is such that the reference board average gray value is between 100 and 150, and the reference gray value of the sunspot is less than the reference board average gray value exceeds 50, and the white matter reference gray value is greater than the reference board average gray value exceeds 50 lighting conditions. In practical applications, the reference illumination condition refers to the first reference image when the board is in the sub-state without natural light and the light is uniform, that is, a gray image or a color image.

Step S302: Calculate a reference chessboard average gray value of the board according to the first reference image.

Under the reference lighting condition, when the light is uniform under normal lighting conditions, the average value of the reference board is a fixed value, and the gray level is actually obtained or calculated. Gray level refers to black and white display The difference between the brightness and the darkness of the displayed pixel points is different in color display in the color display. The more the gray level, the clearer the image hierarchy is. The gray level depends on the number of bits of the refresh memory cell corresponding to each pixel and the performance of the display itself. When the display is black and white, the gray level can be directly obtained; when the display is colored, calculation is required to obtain the gray level.

Among them, any color is composed of three primary colors of red, green and blue. When converting the color of a certain point to RGB (R, G, B) to grayscale gray, the following five methods can be used:

First, the floating point algorithm: Gray = R * 0.3 + G * 0.59 + B * 0.11;

Second, the integer method: Gray = (R * 30 + G * 59 + B * 11) / 100;

Third, the shift method: Gray = (R * 77 + G * 151 + B * 28) >> 8;

Fourth, the average method: Gray = (R + G + B) / 3;

5. Only take green: Gray=G;

After obtaining Gray by any of the above methods, R, G, and B in the original RGB (R, G, B) are uniformly replaced by Gray, and generally obtained by the average method. In the actual application example, the average gray value of the reference board in the reference lighting condition is generally between 100 and 150, and may be 120, 130 or 140. When the actual measured reference board average gray value exceeds 100 to 150 under the reference illumination condition, it is set such that the reference board average gray value is between 100 and 150.

Step S303: Acquire a second reference image when the board is provided with sunspots and whites under the reference illumination condition.

Step S304: Calculate the reference gray value of the sunspot and the reference gray value of the white matter according to the second reference image.

The sunspots and whites were taken under reference lighting conditions to obtain the reference gray values of the sunspots and whites, respectively. See the above for specific calculation methods. Wherein, the reference gray value of the sunspot is less than 50 of the reference board average gray value, and the white matter reference gray value is greater than the reference board average gray value by more than 50. In other words, the reference gray value of the sunspot ranges from 0 to 50, optionally 20, 30 or 40; the reference gray value of the white sub-range ranges from 200 to 255, optionally 210, 225 or 235.

Step S305: The reference determination threshold is set based on the reference checkerboard average gradation value, the reference gradation value of the sunspot, and the reference gradation value of the white matter.

The reference determination threshold is set according to the reference checkerboard average gray value, the reference gray value of the sunspot, and the reference gray value of the white matter, wherein the reference gray value of the sunspot and the reference gray value of the white matter do not change much, and the reference is mainly made to the reference chessboard. Average gray value. In this embodiment, the reference determination threshold is one-half of a difference between a reference chessboard average gray value and a sunspot reference gray value or a difference between a white sub-reference gray value and a reference chessboard average gray value. One of the points, at this time, according to the benchmark decision threshold can better separate the state of the board black, white or no child; wherein the two differences are subject to the smaller, and one-half of the difference is rounded to Integer. For example, when the reference gray value of the sunspot is 25, the reference gray value of the white matter is 235, and the average value of the reference chessboard is 125, that is, the difference between the reference checkerboard average gray value 125 and the sunspot reference gray value 25 One-half of the value 100 or one-half of the difference 110 between the white sub-reference gray value 235 and the reference chessboard average gray value 125, so that the reference determination threshold can be determined to be one-half of the difference 100, and the reference is obtained. The decision threshold is 50. In the case of better illumination conditions, according to the above calculation method for determining the state of the drop, that is, the gray value is 75 or less for the sunspot, the gray value is 175 or more for the white, and the gray value is between 75 and 175. Substate. When the lighting conditions are not good, the average checkerboard gray value is 125, and the actual checkerboard average gray value is 100, according to the formula T=abs(H _b -abs(HH _b ))×T _b /H _b ; It can be calculated that T=40. That is, the gray value is 60 or less for the sunspot, the gray value is 140 or more for the white, and the gray value is between 60 and 140 for the no-sub state.

In summary, in the computer vision-based black-and-white sub-identification method provided by the embodiment of the present invention, the steps of calculating the reference chessboard average gray value and the reference determination threshold are added, so that the determination threshold is based on the reference chessboard average gray value and the reference. The decision threshold is set such that the method is further adapted to the light conditions of natural light, further reducing false detections due to the light factor of the checkerboard.

As shown in FIG. 5, FIG. 5 is a partial flow chart of a fourth embodiment of a computer vision-based black and white sub-identification method provided by the present invention. In the present embodiment, the determination threshold is adjusted by the actual ambient light luminance and the reference ambient light luminance under the reference illumination conditions. Specifically, the calibration method of this embodiment includes the following steps:

The process steps in this embodiment are basically the same as the process steps in the second embodiment, and are not described herein again. This embodiment is different from the second embodiment in that step S201 - step S203 is replaced with step S401 - step S402:

Step S401: Acquire actual ambient light brightness under actual lighting conditions.

Calculate the actual ambient light level under actual lighting conditions. Luminance refers to the physical quantity of the illuminant surface, and the unit of brightness is candela/square meter (cd/m2), which is the human perception of the intensity of light. Adjusting the decision threshold based on the actual ambient light brightness and the reference ambient light level under the reference illumination conditions is another embodiment.

Step S402: The determination threshold is adjusted according to the actual ambient light brightness and the reference ambient light brightness under the reference illumination condition, and the difference between the actual ambient light brightness and the reference ambient light brightness is larger, and the determination threshold is smaller.

The reference ambient light brightness is a fixed value under preset baseline ambient lighting conditions. Assume that the reference ambient light brightness is 400 cd/m2, and the reference determination threshold is 50. If the actual ambient light brightness is 100 cd/m2. When the brightness is substituted into the above formula for calculating the determination threshold T=abs(400-abs(100-400))×50/400, wherein the actual board average gray value is replaced with the actual ambient light level, and the reference board average gray level. The value is replaced with the reference ambient light brightness, which can be calculated to obtain an adjustment decision threshold of 12.5, rounded to an integer of 13. The difference between the actual ambient light brightness and the reference ambient light brightness is larger, and the determination threshold is smaller.

As shown in FIG. 6, FIG. 6 is a schematic flowchart diagram of a fifth embodiment of a computer vision-based black-and-white sub-identification method provided by the present invention. In the present embodiment, the determination threshold is adjusted in accordance with the actual ambient light luminance and the reference ambient light luminance under the reference illumination conditions. Specifically, the calibration method of this embodiment includes the following steps:

Steps S51-S55 are substantially the same as steps S11-S15 of the first embodiment, and are not described herein again. The difference between this embodiment and the first embodiment is that the embodiment further includes after step S55:

Step S56: respectively generate corresponding characters according to the determined falling state of each grid point, thereby forming a character string indicating the falling state of all the grid points, wherein different falling sub-states are represented by different characters.

Specifically, setting the first detection area and the second detection area on the image to be identified means that the first detection area and the second detection area are respectively set on each grid point of the board, and then the falling state of each grid point is further Determining, wherein the first detection area and the second detection area are arranged in a square shape, and the width of the first detection area is equal to the diameter of the black and/or white, and the width of the second detection area is greater than or equal to the two grid widths of the board . In this embodiment, the width of the second detection area is greater than the width of the two grids of the board, including the area of 3*3. When the piece is a black box, the corresponding character is generated as b, and when the piece is white, the corresponding character is generated. For w, when there is no child, the corresponding character is generated as n. Includes three. The result is packed before the output, which can be started from the upper left corner and packed in rows, for example, the result of the 3x3 grid is packaged as the string nnnbnwnnn, and the string can be sent out.

Please refer to FIG. 7. FIG. 7 is a schematic structural diagram of a computer vision-based black and white sub-identification system according to a first embodiment of the present invention.

As shown in FIG. 7, the computer vision-based black and white sub-identification system 10 includes an image acquisition device 11 and an image processor 13, and the image acquisition device 11 is electrically connected to the image processor 13, wherein the image acquisition device 11 is composed of a camera and a lens. Specifically, it is a CCD camera and a CCTV lens ("Closed Circuit Television", CCTV for short). The CCTV lens is a lens used by a closed circuit television, and can also be called a surveillance lens. The CCD camera is mounted at an appropriate distance directly above the board, with a CCTV lens of appropriate focal length to enable the CCD camera to capture the entire board exactly; the image processor 13 is composed of a visual industrial control machine and visual processing software.

In this embodiment, the image capturing device 11 is configured to acquire an image to be recognized of the board under actual lighting conditions; the image processor 13 is configured to set a first detecting area and a second detecting area on the image to be recognized, and calculate an image to be recognized. a first region average gray value in the first detection area and a second region average gray value in the second detection area, wherein the area of the second detection area is larger than the first detection area and surrounds the first detection The image processor 13 further calculates a difference between the average gray value of the second region and the determination threshold, and a sum of the average gray value of the second region and the determination threshold, and compares the average gray value of the first region with the difference The summation value is compared to determine the drop state of the first detection area, wherein the difference between the actual illumination condition and the reference illumination condition is larger, and the determination threshold is smaller.

The image acquisition device 11 is further configured to: when the actual illumination condition is obtained, obtain an adjustment reference image when the board is in a sub-state, obtain a first reference image when the board is in a sub-state under the reference illumination condition, and under the reference illumination condition Obtain a second reference image when the board is set with sunspots and whites. The other corresponding steps in the foregoing method embodiment are all performed by the image processor 13 of the identification system 10. Therefore, the image processor 13 will not be described here. For details, refer to the description of the corresponding steps above.

Please refer to FIG. 8. FIG. 8 is a schematic structural diagram of a second embodiment of a computer vision based black and white sub-identification system provided by the present invention.

As shown in FIG. 8, the computer vision based black and white sub-identification system 20 further includes a light sensor 22, and the image capture device 21 and the light sensor 22 are electrically coupled to the image processor 23, respectively. The photosensor 22 of the present embodiment is used to obtain the actual ambient light brightness under actual illumination conditions, and the image processor 23 determines the determination threshold according to the actual ambient light brightness and the reference ambient light brightness under the reference illumination condition, the actual ambient light brightness and the reference environment. The greater the difference in brightness, the smaller the decision threshold. The light sensor 22 is a natural light sensor, that is, a solar light sensor. The solar sensor can identify 180 degrees in the horizontal and vertical directions, or specify the angle; it can also identify the location of the sun and identify cloudy, cloudy, semi-cloudy, sunny and evening days; tracking position recognition can also be performed. The lightness measurement range of the photosensor 22 is 0-100 cd/m2. In other embodiments, the image capturing device 21 and the light sensor 22 may be used in combination, and a specific light source may be further added to improve the lighting conditions, or only natural light may be used instead of the light source.

In summary, those skilled in the art can easily understand that in the computer vision-based black-and-white sub-identification method and system provided by the embodiments of the present invention, since the determination threshold is provided, the average gray value and the preset value of the second region are calculated. The difference between the decision thresholds and the sum of the two, and then compare the first region average gray value with the difference and the sum value, thereby determining the fall state of the first detection region, because the decision threshold is based on the actual environment Set to adapt the method to different lighting conditions, reducing the cause of light The false detection caused by the element improves the recognition accuracy of the black and white sub.

The above is only the embodiment of the present invention, and is not intended to limit the scope of the invention, and the equivalent structure or equivalent process transformations made by the description of the invention and the drawings are directly or indirectly applied to other related technologies. The fields are all included in the scope of patent protection of the present invention.

Claims (20)

1. A black and white sub-identification method based on computer vision, characterized in that the method comprises:

   Obtaining an image to be recognized of the board under actual lighting conditions;

   Setting a first detection area and a second detection area on the image to be identified, wherein an area of the second detection area is larger than the first detection area and surrounds the first detection area;

   Calculating a first region average gray value of the to-be-identified image in the first detection area and a second region average gray value of the to-be-identified image in the second detection area;

   Calculating a difference between the average grayscale value of the second region and a preset determination threshold, and calculating a summation value of the average grayscale value of the second region and the preset determination threshold;

   Comparing the first region average gray value with the difference value and the summation value to determine a falling state of the first detection region.
2. The computer vision-based black and white sub-recognition method according to claim 1, wherein said comparing said first region average gray value with said difference value and said summation value, thereby determining said said The step of the falling state of the first detecting area includes:

   If the first region average gray value is smaller than the difference, the first detection region is provided with a black child, and if the first region average gray value is greater than the summation value, the first A white sub is disposed in the detection area, and if the first area average gray value is between the difference and the summation value, the first detection area is in a sub-state.
3. The method according to claim 1, wherein the method further comprises:

   Obtaining an adjustment reference image when the checkerboard is in a sub-state without the actual illumination condition;

   Calculating an actual chessboard average gray value of the chessboard according to the adjusted reference image;

   Determining the determination threshold according to the actual board average gray value and the reference board average gray value under the reference illumination condition, wherein the difference between the actual board average gray value and the reference board average gray value is greater The decision threshold is smaller.
4. The computer vision-based black-and-white sub-recognition method according to claim 3, wherein the determination threshold is a product of a reference determination threshold and a one-hund score, wherein the actual checkerboard average grayscale value and the reference checkerboard average grayscale The greater the difference in degrees, the smaller the percentage.
5. The computer vision-based black and white sub-recognition method according to claim 4, wherein the determination threshold is calculated by the following formula:

   T = abs (H _{b -} abs (HH _b )) × T _b / H _b ;

   Where T is the decision threshold, H _b is the reference checkerboard average gray value, T _b is the reference decision threshold, H is the actual checkerboard average gray value, and abs is an absolute value function.
6. The computer vision-based black and white sub-identification method according to claim 4, wherein the method for determining the reference determination threshold comprises:

   Obtaining, in a reference illumination condition, a first reference image when the checkerboard is in a sub-state;

   Calculating the reference chessboard average gray value of the chessboard according to the first reference image;

   Obtaining, in the reference illumination condition, a second reference image when the chessboard is provided with sunspots and whites;

   Calculating a reference gray value of the sunspot and a reference gray value of the white light according to the second reference image;

   The reference determination threshold is set based on the reference checkerboard average grayscale value, the reference grayscale value of the sunspot, and the reference grayscale value of the white matter.
7. The computer vision-based black and white sub-recognition method according to claim 6, wherein the reference illumination condition is such that the reference checkerboard average gray value is between 100 and 150, and the reference gray of the sunspot The degree value is less than 50 of the reference checkerboard average gray value, and the white matter's reference gray value is greater than the reference board average gray value exceeding 50.
8. The method according to claim 1, wherein the method further comprises:

   Obtaining actual ambient light brightness under the actual lighting conditions;

   The determination threshold is adjusted according to the actual ambient light brightness and the reference ambient light brightness under the reference illumination condition, and the difference between the actual ambient light brightness and the reference ambient light brightness is smaller, and the determination threshold is smaller.
9. The computer vision-based black and white sub-recognition method according to claim 1, wherein the step of setting the first detection area and the second detection area on the image to be recognized comprises:

   Setting the first detection area and the second detection area respectively at each grid point of the checkerboard, and further determining a drop state of each grid point, wherein the first detection area and the first The second detection area is set to a square, and the width of the first detection area is equal to the diameter of the sunspot and/or the white, and the width of the second detection area is equal to three grid widths of the board.
10. The method according to claim 9, wherein the method further comprises:

    Generating corresponding characters according to the determined falling state of each of the grid points, thereby forming a character string representing the falling state of all the grid points, wherein different falling sub-states are different characters Make a representation.
11. A black and white sub-identification system based on computer vision, characterized in that the system comprises an image acquisition device and an image processor, wherein the image acquisition device is configured to acquire an image to be recognized of a chessboard under actual illumination conditions; And configured to set a first detection area and a second detection area on the image to be identified, and calculate an average gray value of the first area of the image to be recognized in the first detection area and the image to be recognized a second region average gray value in the second detection area, wherein an area of the second detection area is larger than the first detection area and surrounding the first detection area, the image processor further calculates the a difference between the second region average gray value and a preset determination threshold, and a summation value of the second region average gray value and the preset determination threshold, and the first region average gray value Comparing the difference with the summed value to determine a falling state of the first detection area.
12. The computer vision-based black and white sub-identification system according to claim 11, wherein if the comparison result of the image processor is that the first region average gray value is smaller than the difference, determining the a black spot is disposed in a detection area, and if a comparison result of the image processor is that the first area average gray value is greater than the summation value, determining that the first detection area is provided with a white sub- The comparison result of the image processor is that the first region average gray value is between the difference value and the summation value, and then determining that the first detection region is in a sub-state.
13. The computer vision-based black and white sub-identification system according to claim 11, wherein the image acquisition device is further configured to acquire an adjustment reference image when the chessboard is in a sub-state without the sub-state under the actual illumination condition. The image processor calculates an actual board average gray value of the board according to the adjusted reference image, and determines the average board value according to the actual board average gray value and the reference board average gray value under the reference lighting condition. A threshold is determined, wherein the greater the difference between the actual checkerboard average grayscale value and the reference checkerboard average grayscale value, the smaller the decision threshold.
14. The computer vision-based black-and-white sub-identification system according to claim 13, wherein the determination threshold is a product of a reference determination threshold and a one-hund score, wherein the actual chessboard average gray value and the reference chessboard average gray The greater the difference in degrees, the smaller the percentage.
15. The computer vision based black and white sub-identification system according to claim 14, wherein the determination threshold is calculated by the following formula:

    T = abs (H _{b -} abs (HH _b )) × T _b / H _b ;

    Where T is the decision threshold, H _b is the reference checkerboard average gray value, T _b is the reference decision threshold, H is the actual checkerboard average gray value, and abs is an absolute value function.
16. The computer vision-based black and white sub-identification system according to claim 14, wherein the image acquisition device is further configured to acquire a first reference image when the checkerboard is in a sub-state without under a reference illumination condition, and Acquiring, in the reference illumination condition, a second reference image when the chessboard is set with black and white, the image processor further calculating, according to the first reference image, the average average gray value of the reference chessboard according to the first reference image, according to The second reference image calculates a reference gradation value of the sunspot and a reference gradation value of the white matter, and further according to the reference checkerboard average gradation value, the reference gradation value of the sunspot, and the white matter The reference gradation value sets the reference determination threshold.
17. The computer vision-based black and white sub-identification system according to claim 16, wherein the reference illumination condition is such that the reference checkerboard average gray value is between 100 and 150, and the reference gray of the sunspot The degree value is less than 50 of the reference checkerboard average gray value, and the white matter's reference gray value is greater than the reference board average gray value exceeding 50.
18. The computer vision-based black and white sub-identification system according to claim 11, wherein the system further comprises a light sensor for acquiring actual ambient light brightness under the actual lighting condition, the image processor according to the The determination threshold is adjusted by the actual ambient light brightness and the reference ambient light brightness under the reference illumination condition, and the difference between the actual ambient light brightness and the reference ambient light brightness is smaller, and the determination threshold is smaller.
19. The computer vision-based black and white sub-identification system according to claim 11, wherein the image processor sets the first detection area and the second respectively at each grid point to be detected of the board Detecting an area, and further determining a falling state of each grid point to be detected, wherein the first detecting area and the second detecting area are arranged in a square, and a width of the first detecting area is equal to the sunspot and / or the diameter of the white, the width of the second detection area is equal to the three grid widths of the board.
20. The computer vision-based black and white sub-identification system according to claim 19, wherein the image processor generates corresponding characters according to the determined falling state of each of the detected grid points, thereby forming a representation A string of all the state of the drop of the grid point to be detected, wherein different down states are represented by different characters.

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