CN115841480A

CN115841480A - Image detection method and device, electronic equipment and storage medium

Info

Publication number: CN115841480A
Application number: CN202211633656.8A
Authority: CN
Inventors: 荀迅; 陈红艳; 颜帅; 左唯; 张程
Original assignee: Hefei Lianbao Information Technology Co Ltd
Current assignee: Hefei Lianbao Information Technology Co Ltd
Priority date: 2022-12-19
Filing date: 2022-12-19
Publication date: 2023-03-24

Abstract

The application provides an image detection method, an image detection device, electronic equipment and a storage medium; the method comprises the following steps: acquiring a template image corresponding to a keyboard; carrying out connected domain segmentation on the template image, and determining a first connected domain of each key image included in the template image; and determining whether the template image has defects or not based on the first connection domain. Therefore, whether the image has the defects or not can be intelligently detected, and the detection precision and the detection efficiency of the defects in the image are improved.

Description

Image detection method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to image detection technologies, and in particular, to an image detection method and apparatus, an electronic device, and a storage medium.

Background

With the rapid development and wide application of image detection technology, people gradually become the mainstream of image detection technology application to detect keyboard defects by using image detection technology. However, in the process of detecting the defects of the keyboard, the existing method for detecting the defects of the keyboard is to manually detect a template image corresponding to the keyboard and then determine the defects of the keyboard based on the detected template image. People hope to automatically detect the template image corresponding to the keyboard to determine whether the template image has defects, reduce the image detection time and improve the detection precision of the defects in the image.

Therefore, how to intelligently detect whether defects exist in an image so as to improve the detection accuracy and detection efficiency of the defects in the image is a constantly pursued target.

Disclosure of Invention

The embodiment of the application provides an image detection method and device, electronic equipment and a storage medium.

According to a first aspect of the present application, there is provided an image detection method, the method comprising: acquiring a template image corresponding to a keyboard; carrying out connected domain segmentation on the template image, and determining a first connected domain of each key image included in the template image; and determining whether the template image has defects or not based on the first connection domain.

According to an embodiment of the application, before determining the first connected component of each key image included in the template image, the method further includes: performing binarization threshold segmentation on the template image to obtain a binarized template image; performing vertical projection and horizontal projection on the binarized template image to obtain an adjacent non-projection area corresponding to the binarized template image; determining the difference of the vertical coordinates of the adjacent non-projection areas; in response to the fact that the vertical coordinate difference is smaller than a preset distance threshold value, determining the adjacent non-projection area corresponding to the vertical coordinate difference as an invalid character area; deleting the invalid character area in the binarized template image to obtain an area to be detected; filling holes in the area to be detected to obtain a first keyboard image; the first keyboard image is a part of the template image corresponding to the keyboard.

According to an embodiment of the application, after determining the first connected component of each key image included in the template image, the method further includes: and determining at least one of the pixel height, the pixel width, the abscissa of the first central point and the ordinate of the upper left corner point corresponding to the first communication domain of each key image.

According to an embodiment of the present application, the determining whether the template image has a defect based on the first connection domain includes: determining an abscissa of a second center point of the first keyboard image; determining a first difference of the abscissa of the second center point and the abscissa of the first center point; and determining that the template image has a first defect in response to the pixel height being greater than a preset height threshold, the pixel width being greater than a preset width threshold and the first difference being less than a preset pixel point threshold.

According to an embodiment of the present application, the determining whether the template image has a defect based on the first connection domain includes: determining a first number of first connected domains of a first key image; the first key image is a key image of which the abscissa of the upper left corner point is within a preset abscissa range; and determining that the template image has a second defect in response to the first number being smaller than a preset first number threshold.

According to an embodiment of the present application, the determining whether the template image has a defect based on the first connection domain includes: determining the maximum value of the vertical coordinate of a pixel point of a first connected domain of the first key image; determining the vertical height of the area to be detected; and determining that the template image has a third defect in response to the fact that the difference between the vertical height and the maximum value of the vertical coordinate of the pixel point is larger than a preset difference threshold value.

According to an embodiment of the present application, the determining whether the template image has a defect based on the first connection domain includes: determining a second number of the first connected domains that the first keyboard image includes; determining that a fourth defect exists in the template image in response to the second quantity being greater than a preset second quantity threshold; carrying out connected domain segmentation on the area to be detected, and determining a second connected domain included in the area to be detected; determining a third number of the second connected domains included in the region to be detected; and determining that the template image has a fifth defect in response to the third number being less than a preset third number threshold.

According to an embodiment of the present application, the determining whether the template image has a defect based on the first connection domain includes: determining a first communication domain of the key image corresponding to a preset coordinate position based on the preset coordinate position; determining a character area with the maximum abscissa in the first communication area; determining the roundness and the area corresponding to the character area with the maximum abscissa; and determining that a sixth defect exists in the template image in response to the fact that the roundness is larger than a preset roundness threshold value and the area is larger than a preset area threshold value.

According to a second aspect of the present application, there is provided an image detection apparatus comprising: the acquisition module is used for acquiring a template image corresponding to the keyboard; the determining module is used for carrying out connected domain segmentation on the template image and determining a first connected domain of each key image included in the template image; and the detection module is used for determining whether the template image has defects or not based on the first communication domain.

According to an embodiment of the present application, the image detection apparatus further includes a projection module, and the projection module is configured to: performing binarization threshold segmentation on the template image to obtain a binarized template image; performing vertical projection and horizontal projection on the binarized template image to obtain an adjacent non-projection area corresponding to the binarized template image; determining a difference of vertical coordinates of the adjacent non-projection areas; in response to the vertical coordinate difference being smaller than a preset distance threshold, determining the adjacent non-projection area corresponding to the vertical coordinate difference as an invalid character area; deleting the invalid character area in the binarized template image to obtain an area to be detected; filling holes in the area to be detected to obtain a first keyboard image; the first keyboard image is a part of the template image corresponding to the keyboard.

According to an embodiment of the present application, the image detection apparatus further includes a calculation module, where the calculation module is configured to: and determining at least one of the pixel height, the pixel width, the abscissa of the first central point and the ordinate of the upper left corner point corresponding to the first communication domain of each key image.

According to an embodiment of the present application, the detection module is configured to: determining an abscissa of a second center point of the first keyboard image; determining a first difference of the abscissa of the second center point and the abscissa of the first center point; and determining that the template image has a first defect in response to the pixel height being greater than a preset height threshold, the pixel width being greater than a preset width threshold and the first difference being less than a preset pixel point threshold.

According to an embodiment of the present application, the detection module is configured to: determining a first number of first connected domains of a first key image; the first key image is a key image of which the abscissa of the upper left corner point is within a preset abscissa range; and determining that the template image has a second defect in response to the first number being smaller than a preset first number threshold.

According to an embodiment of the present application, the detection module is configured to: determining the maximum value of the vertical coordinate of a pixel point of a first connected domain of the first key image; determining the vertical height of the area to be detected; and determining that the template image has a third defect in response to the fact that the difference between the vertical height and the maximum value of the vertical coordinate of the pixel point is larger than a preset difference threshold value.

According to an embodiment of the present application, the detection module is configured to: determining a second number of the first connected domains that the first keyboard image includes; determining that a fourth defect exists in the template image in response to the second quantity being greater than a preset second quantity threshold; carrying out connected domain segmentation on the area to be detected, and determining a second connected domain included in the area to be detected; determining a third number of the second connected domains included in the region to be detected; and determining that the template image has a fifth defect in response to the third number being less than a preset third number threshold.

According to an embodiment of the present application, the detection module is configured to: determining a first communication domain of the key image corresponding to a preset coordinate position based on the preset coordinate position; determining a character area with the maximum abscissa in the first communication area; determining the roundness and the area corresponding to the character area with the maximum abscissa; and determining that a sixth defect exists in the template image in response to the fact that the roundness is larger than a preset roundness threshold value and the area is larger than a preset area threshold value.

According to a third aspect of the present application, there is provided an electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method described herein.

According to a fourth aspect of the present application, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method described herein.

According to the method, a template image corresponding to a keyboard is obtained; carrying out connected domain segmentation on the template image, and determining a first connected domain of each key image included in the template image; and determining whether the template image has defects or not based on the first communication domain. Therefore, whether the image has the defects or not can be intelligently detected, and the detection precision and the detection efficiency of the defects in the image are improved.

It is to be understood that the teachings of this application need not achieve all of the above-described benefits, but rather that specific embodiments may achieve specific technical results, and that other embodiments of this application may achieve benefits not mentioned above.

Drawings

The above and other objects, features and advantages of exemplary embodiments of the present application will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the present application are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:

in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

FIG. 1 is a first schematic processing flow diagram illustrating an image detection method according to an embodiment of the present disclosure;

FIG. 2 is a schematic processing flow diagram of an image detection method according to an embodiment of the present disclosure;

FIG. 3 is a schematic processing flow diagram III illustrating an image detection method provided by an embodiment of the present application;

FIG. 4 is a schematic processing flow diagram of a fourth image detection method provided by the embodiment of the present application;

FIG. 5 is a schematic processing flow diagram of an image detection method provided by an embodiment of the present application;

fig. 6 shows a processing flow diagram six of an image detection method provided by the embodiment of the present application;

FIG. 7 is a first diagram illustrating an application scenario of the image detection method according to the embodiment of the present application;

fig. 8 illustrates a second application scenario of the image detection method provided in the embodiment of the present application;

fig. 9 is a diagram illustrating an application scenario of the image detection method provided in the embodiment of the present application;

fig. 10 is a diagram illustrating an application scenario of the image detection method provided by the embodiment of the present application;

FIG. 11 is a schematic diagram illustrating an alternative image detection apparatus provided in an embodiment of the present application;

fig. 12 shows a schematic structural diagram of the electronic device provided in the embodiment of the present application.

Detailed Description

In order to make the objects, features and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

In the following description, references to the terms "first", "second", and the like, are only to distinguish similar objects and do not denote a particular order, but rather the terms "first", "second", and the like may be used interchangeably with the order specified, where permissible, to enable embodiments of the present application described herein to be practiced otherwise than as specifically illustrated or described herein.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.

In the related art, in the currently known technical scheme of image detection, a template image corresponding to a keyboard is manually detected, and then the defect of the keyboard is determined based on the detected template image. The existing image detection process is time-consuming and the detection precision of defects in images is low. In the related art, the time consumption is long in the image detection process, the detection precision of the defects in the image is low, and the problem of low detection efficiency of the defects in the image is solved.

Aiming at the problems that the time consumption is long and the image detection precision is low in the image detection process and further the image detection efficiency is low in the image detection method provided by the related technology, the method of the embodiment of the application acquires the template image corresponding to the keyboard; carrying out connected domain segmentation on the template image, and determining a first connected domain of each key image included in the template image; and determining whether the template image has defects or not based on the first connection domain. Therefore, whether the image has defects or not can be intelligently detected, the image detection time is shortened, and the detection precision and the detection efficiency of the defects in the image are improved. Therefore, compared with the prior art that the time consumption is long in the image detection process and the detection precision of the defects in the image is low, the image detection method can reduce the image detection time and improve the detection efficiency of the defects in the image.

A processing flow in the image detection method provided in the embodiment of the present application is explained. Referring to fig. 1, fig. 1 is a schematic processing flow diagram of an image detection method provided in an embodiment of the present application, which will be described with reference to steps S101 to S103 shown in fig. 1.

And step S101, acquiring a template image corresponding to the keyboard.

In some embodiments, the template image may include: and converting the design document based on the keyboard to obtain a template image.

Step S102, carrying out connected domain segmentation on the template image, and determining a first connected domain of each key image included in the template image.

In some embodiments, the key images may include: the template image includes an image of each key. The first connected domain may include: and the image of each key corresponds to a connected domain.

In some embodiments, before step S102, the image detection method may further include: performing binarization threshold segmentation on the template image to obtain a binarized template image; performing vertical projection and horizontal projection on the binarized template image to obtain an adjacent non-projection area corresponding to the binarized template image; determining the difference of vertical coordinates of adjacent non-projection areas; in response to the fact that the vertical coordinate difference is smaller than a preset distance threshold value, determining an adjacent non-projection area corresponding to the vertical coordinate difference as an invalid character area; deleting an invalid character area in the binarized template image to obtain an area to be detected; filling holes in the area to be detected to obtain a first keyboard image; the first keyboard image is a portion of the template image corresponding to the keyboard.

In specific implementation, firstly, the template image is subjected to binarization threshold segmentation to obtain a binarized template image. Then, a two-dimensional array which is the same as the length and width of the binarized template image is defined:

pro jarr [ _ rows ] [ _ cols ]. Where _colsdenotes the abscissa and _rowsdenotes the ordinate. And traversing the pixel points in the binarized template image one by one, adding 1 to the abscissa corresponding to the pixel point in the two-dimensional array in response to traversing the pixel points with nonzero gray values in the binarized template image, and keeping the abscissa consistent with the current abscissa of the pixel point. And after traversing, determining a region corresponding to the abscissa of 0 in the two-dimensional array, and taking the region as an adjacent non-projection region. And calculating the difference of the vertical coordinates of the adjacent non-projection areas. And in response to the vertical coordinate difference being smaller than a preset distance threshold, determining an adjacent non-projection area corresponding to the vertical coordinate difference as an invalid character area. And deleting the invalid character area in the binarized template image, and deducting the minimum circumscribed rectangle of the residual area in the binarized template image to obtain the area to be detected. And filling holes in the area to be detected to obtain a first keyboard image. The first keyboard image is an image of a keyboard key area in the template image. The preset distance threshold is preferably 70 pixels. The invalid character region may include: and redundant invalid character areas in the template image corresponding to the keyboard.

In a specific implementation, calculating the vertical coordinate difference _ MAXDISTENCE of the adjacent non-projection areas can be represented by the following formula (1):

_MAXDISTENCE ＝ _rowsStart-_rowsEnd (1)

wherein _rowstartrepresents the ordinate value of the start pixel of the adjacent non-projection region, and _rowendrepresents the ordinate value of the end pixel of the adjacent non-projection region.

In some embodiments, after step S102, the image detection method may further include: and determining at least one of the pixel height, the pixel width, the abscissa of the first central point and the ordinate of the upper left corner point corresponding to the first communication domain of each key image. Wherein the first center point may include: and the central point of the first connected domain of the key image.

Step S103, whether the template image has defects or not is determined based on the first connection domain.

In the first embodiment, step S103 may include: determining an abscissa of a second center point of the first keyboard image; determining a first difference between the abscissa of the second center point and the abscissa of the first center point; and determining that the template image has a first defect in response to the pixel height being greater than a preset height threshold, the pixel width being greater than a preset width threshold and the first difference being less than a preset pixel point threshold. Wherein the second center point may include: a center point of the first keyboard image. The preset height threshold is preferably 1.5 times the average height of the keys. The preset width threshold is preferably 1.2 times the average width of the keys. The preset pixel threshold is preferably 850 pixels. The presence of the first defect in the template image may include: the template image has the defect of key connection.

In the second embodiment, step S103 may include: determining a first number of first connected domains of a first key image; the first key image is a key image with the abscissa of the upper left corner point within a preset abscissa range; and determining that the template image has the second defect in response to the first number being smaller than a preset first number threshold. The preset abscissa range is preferably a range interval with an abscissa larger than 0 and an abscissa smaller than 10. The preset first number threshold is preferably 10. The presence of the second defect in the template image may include: the template image has the defect of thick contour line.

In the third embodiment, step S103 may include: determining the maximum value of the vertical coordinate of a pixel point of a first connected domain of the first key image; determining the vertical height of a region to be detected; and determining that the template image has a third defect in response to the fact that the difference between the vertical height and the maximum value of the vertical coordinate of the pixel point is larger than a preset difference threshold value. The presence of the third defect in the template image may include: the template image has the defect of redundant touch pad areas.

In specific implementation, calculating the difference _ diffRows between the vertical height and the maximum value of the ordinate of the pixel point can be represented by the following formula (2):

_diffRows ＝_imgRows - _rowMax (2)

wherein _imgRowsrepresents the vertical height of the area to be detected, and _rowMaxrepresents the maximum value of the vertical coordinate of the pixel point of the first connected domain.

In the fourth embodiment, step S103 may include: determining a second number of first connected domains comprised by the first keyboard image; in response to the second quantity being larger than a preset second quantity threshold value, determining that the template image has a fourth defect; carrying out connected domain segmentation on the region to be detected, and determining a second connected domain included in the region to be detected; determining a third number of second connected domains included in the region to be detected; and determining that the template image has a fifth defect in response to the third number being less than a preset third number threshold. The preset second quantity threshold is preferably 110. The presence of the fourth defect in the template image may include: the template image has the defect of the watermark. The template image having the fourth defect may further include: the template image has the defect that the outline area of the key is lost. The preset third number threshold is preferably 2 times the second number minus 1. The presence of the fifth defect in the template image may include: the template image has the defect that the keystroke characters are lost.

In the fifth embodiment, step S103 may include: determining a first communication domain of the key image corresponding to the preset coordinate position based on the preset coordinate position; determining a character area with the maximum abscissa in the first communication area; determining the roundness and the area corresponding to the character area with the maximum abscissa; and determining that the template image has a sixth defect in response to the roundness being greater than the preset roundness threshold and the area being greater than the preset area threshold. Wherein the preset coordinate position may include: the coordinate position of the key image of the indicator lamp exists in the template image. The preset roundness threshold is preferably 0.7. The preset area threshold is preferably 100 pixels. The presence of the sixth defect in the template image may include: the template image has the defect of blockage of the key indicator lamp.

In a specific implementation, the CIRCULARITY _ circulanity corresponding to the character region with the largest abscissa can be determined by the following equations (3), (4), (5) and (6):

wherein _avadistencerepresents an average distance from a contour point of the character region to a center point, n represents a number of contour points, _ ptX represents an abscissa of the contour point of the character region, _ ptY represents an ordinate of the contour point of the character region, _ centrx represents an abscissa of the center point of the character region, _ centry represents an ordinate of the center point of the character region, _ sumpreprval represents a deviation index, _ sigmaphase represents a deviation coefficient.

In some embodiments, one or more of the defect detection manners in the template image provided in the first to fifth embodiments of step S103 may be adopted to determine the defect in the template image based on the first connection domain.

In some embodiments, a processing flow diagram of the image detection method is shown as a second flowchart, as shown in fig. 2, and includes:

step S201, performing binarization threshold segmentation on the template image to obtain a binarized template image.

And step S202, performing vertical projection and horizontal projection on the binarized template image to obtain an adjacent non-projection area corresponding to the binarized template image.

In step S203, the difference in vertical coordinates between adjacent non-projection areas is determined.

And step S204, in response to the fact that the vertical coordinate difference is smaller than a preset distance threshold value, determining an adjacent non-projection area corresponding to the vertical coordinate difference as an invalid character area.

As an example, the preset distance threshold is 70 pixels for step S203 and step S204. The difference of vertical coordinates of adjacent non-projection areas is determined to be 50 pixels. And determining the adjacent non-projection area corresponding to the vertical coordinate difference 50 pixels as an invalid character area, wherein the vertical coordinate difference 50 pixels of the adjacent non-projection area are smaller than a preset distance threshold value 70 pixels.

And S205, deleting the invalid character area in the binarized template image to obtain the area to be detected.

Step S206, filling holes in the area to be detected to obtain a first keyboard image.

The specific description of each step of steps S201 and S206 is the same as step S103 described above, and is not repeated here.

In some embodiments, a processing flow of the image detection method is schematically illustrated in fig. 3, and includes:

in step S301a, the abscissa of the second center point of the first keyboard image is determined.

Step S301b, a first difference between the abscissa of the second center point and the abscissa of the first center point is determined.

Step S301c, in response to the pixel height being greater than the preset height threshold, the pixel width being greater than the preset width threshold and the first difference being less than the preset pixel point threshold, determining that the template image has a first defect.

As an example, for step S301c, the pixel height is greater than the preset height threshold, the pixel width is greater than the preset width threshold, and the first difference value is less than the preset pixel point threshold, which may be represented by the following formulas (7), (8), and (9):

_HEIGHT > 1.5 * _avaHeight (7)

_WIDTH > 1.2 * _avaWidth (8)

abs|_CENTERX - _curCenterX| < 850 pixel (9)

wherein _heightrepresents the pixel HEIGHT, 1.5 _ avaHeight represents the average HEIGHT of the key with the preset HEIGHT threshold value of 1.5 times, _widthrepresents the pixel WIDTH, 1.2 _ avaWidth represents the average WIDTH of the key with the preset WIDTH threshold value of 1.2 times, _centrexrepresents the abscissa of the second center point, _ currcentrx represents the abscissa of the second center point, and 850pixel represents that the preset pixel threshold value is 850 pixels.

The specific description of each step of steps S301a to S301c is the same as step S103, and is not repeated here.

In some embodiments, a processing flow diagram of the image detection method is as shown in fig. 4, and includes:

in step S302a, a first number of first connected components of a first key image is determined.

Step S302b, in response to the first number being smaller than a preset first number threshold, determining that the template image has a second defect.

Step S302c, determining the maximum value of the vertical coordinate of the pixel point of the first connected domain of the first key image.

And step S302d, determining the vertical height of the region to be detected.

Step S302e, responding to the fact that the difference between the vertical height and the maximum value of the vertical coordinate of the pixel point is larger than a preset difference threshold value, and determining that the template image has a third defect.

The specific description of each step of steps S302a to S302e is the same as step S103, and is not repeated here.

In some embodiments, a schematic processing flow diagram of the image detection method is shown in fig. 5, and includes:

in step S303a, a second number of first connected components included in the first keyboard image is determined.

Step S303b, in response to the second number being greater than the preset second number threshold, determining that the template image has a fourth defect.

And step S303c, carrying out connected domain segmentation on the area to be detected, and determining a second connected domain included in the area to be detected.

Step S303d, determining a third number of second connected domains included in the region to be detected.

Step S303e, in response to the third number being smaller than the preset third number threshold, determining that the template image has a fifth defect.

The specific description of each step of steps S303a to S303e is the same as step S103, and is not repeated here.

In some embodiments, a processing flow diagram of the image detection method is shown as six, and as shown in fig. 6, the processing flow diagram includes:

step S304a, determining a first connected domain of the key image corresponding to the preset coordinate position based on the preset coordinate position.

In step S304b, the character region with the largest abscissa in the first connected region is determined.

Step S304c, determining the roundness and the area corresponding to the character area with the maximum abscissa.

In step S304d, in response to the roundness being greater than the preset roundness threshold and the area being greater than the preset area threshold, it is determined that the template image has a sixth defect.

The specific description of each step of steps S304a-S304d is the same as step S103, and is not repeated here.

referring to fig. 7, a first application scenario of the image detection method provided in the embodiment of the present application is applied to detecting a template image based on the image detection method, and determining that the template image has a defect of an invalid character region. Detecting the template image based on an image detection method, and determining that the template image has the defect that the outline area of the key is lost.

It is understood that the application scenario of the image detection method in fig. 7 is only a partial exemplary implementation manner in the embodiment of the present application, and the application scenario of the image detection method in the embodiment of the present application includes, but is not limited to, the application scenario of the image detection method shown in fig. 7.

referring to fig. 8, a second application scenario of the image detection method provided in the embodiment of the present application is applied to detecting a template image based on the image detection method, and determining that the template image has a defect of a watermark. Detecting the template image based on an image detection method, and determining that the template image has the defect of rough contour line of the template image.

It is understood that the application scenario of the image detection method in fig. 8 is only a partial exemplary implementation manner in the embodiment of the present application, and the application scenario of the image detection method in the embodiment of the present application includes, but is not limited to, the application scenario of the image detection method shown in fig. 8.

referring to fig. 9, an application scenario of the image detection method provided in the embodiment of the present application is applied to detect a template image based on the image detection method, and determine that the template image has a defect of missing key characters. Detecting the template image based on an image detection method, and determining that the template image has the defect of key connection.

It is understood that the application scenario of the image detection method in fig. 9 is only a partial exemplary implementation manner in the embodiment of the present application, and the application scenario of the image detection method in the embodiment of the present application includes, but is not limited to, the application scenario of the image detection method shown in fig. 9.

referring to fig. 10, a fourth application scenario of the image detection method provided in the embodiment of the present application is applied to detecting a template image based on the image detection method, and determining that the template image has a defect of an unnecessary touch pad area. Detecting the template image based on an image detection method, and determining that the template image has the defect of blockage of the key indicator lamp.

It is understood that the application scenario of the image detection method in fig. 10 is only a partial exemplary implementation manner in the embodiment of the present application, and the application scenario of the image detection method in the embodiment of the present application includes, but is not limited to, the application scenario of the image detection method shown in fig. 10.

According to the method, the template image is subjected to binarization threshold segmentation to obtain a binarization template image; performing vertical projection and horizontal projection on the binarized template image to obtain an adjacent non-projection area corresponding to the binarized template image; determining the difference of vertical coordinates of adjacent non-projection areas; in response to the fact that the vertical coordinate difference is smaller than a preset distance threshold value, determining an adjacent non-projection area corresponding to the vertical coordinate difference as an invalid character area; deleting an invalid character area in the binarized template image to obtain an area to be detected; filling holes in the area to be detected to obtain a first keyboard image; the first keyboard image is a portion of the template image corresponding to the keyboard. Therefore, the template image corresponding to the keyboard can be automatically detected to determine whether the template image has defects or not, manual detection is not needed, the detection precision of the defects in the image is improved, the image detection time is shortened, and the detection efficiency of the defects in the image is improved. The method of the embodiment of the application comprises the steps of determining an abscissa of a second central point of a first keyboard image; determining a first difference between the abscissa of the second center point and the abscissa of the first center point; and determining that the template image has a first defect in response to the pixel height being greater than a preset height threshold, the pixel width being greater than a preset width threshold and the first difference being less than a preset pixel point threshold. Therefore, the template image corresponding to the keyboard can be automatically detected, the defects in the template image are determined, the detection precision of the defects in the image is improved, the image detection time is shortened, and the detection efficiency of the defects in the image is improved. The method comprises the steps of determining a first number of first connected domains of a first key image; the first key image is a key image with the abscissa of the upper left corner point within a preset abscissa range; and determining that the template image has the second defect in response to the first number being smaller than a preset first number threshold. Therefore, the template image corresponding to the keyboard can be automatically detected, the defects in the template image are determined, the detection precision of the defects in the image is improved, the image detection time is shortened, and the detection efficiency of the defects in the image is improved. The method comprises the steps of determining the maximum value of the ordinate of a pixel point of a first connected domain of a first key image; determining the vertical height of a region to be detected; and determining that the template image has a third defect in response to the fact that the difference between the vertical height and the maximum value of the vertical coordinate of the pixel point is larger than a preset difference threshold value. Therefore, the template image corresponding to the keyboard can be automatically detected, the defects in the template image are determined, the detection precision of the defects in the image is improved, the image detection time is shortened, and the detection efficiency of the defects in the image is improved. The method of the embodiment of the application determines a second number of first connected domains included in a first keyboard image; determining that the template image has a fourth defect in response to the second number being greater than a preset second number threshold; carrying out connected domain segmentation on the region to be detected, and determining a second connected domain included in the region to be detected; determining a third number of second connected domains included in the region to be detected; and determining that the template image has a fifth defect in response to the third number being less than a preset third number threshold. Therefore, the template image corresponding to the keyboard can be automatically detected, the defects in the template image are determined, the detection precision of the defects in the image is improved, the image detection time is shortened, and the detection efficiency of the defects in the image is further improved. The method comprises the steps that based on a preset coordinate position, a first communication domain of a key image corresponding to the preset coordinate position is determined; determining a character area with the maximum abscissa in the first communication area; determining the roundness and the area corresponding to the character area with the maximum abscissa; and determining that the template image has a sixth defect in response to the roundness being greater than the preset roundness threshold and the area being greater than the preset area threshold. Therefore, the template image corresponding to the keyboard can be automatically detected, the defects in the template image are determined, the detection precision of the defects in the image is improved, the image detection time is shortened, and the detection efficiency of the defects in the image is improved.

Therefore, compared with the prior art that the time consumption is long in the image detection process and the detection precision of the defects in the image is low, the image detection method can reduce the image detection time, improve the detection precision of the defects in the image and further improve the detection efficiency of the defects in the image.

Continuing with the exemplary structure of the image detection apparatus 90 provided in the embodiments of the present application as software modules, in some embodiments, as shown in fig. 11, the software modules in the image detection apparatus 90 may include: an obtaining module 901, configured to obtain a template image corresponding to a keyboard; a determining module 902, configured to perform connected domain segmentation on the template image, and determine a first connected domain of each key image included in the template image; and a detecting module 903 for determining whether the template image has defects based on the first communication field.

In some embodiments, image detection apparatus 90 may further include a projection module 904, where projection module 904 is not embodied in fig. 11, and projection module 904 may be configured to: performing binarization threshold segmentation on the template image to obtain a binarized template image; performing vertical projection and horizontal projection on the binarized template image to obtain an adjacent non-projection area corresponding to the binarized template image; determining the difference of vertical coordinates of adjacent non-projection areas; in response to the fact that the vertical coordinate difference is smaller than a preset distance threshold value, determining an adjacent non-projection area corresponding to the vertical coordinate difference as an invalid character area; deleting an invalid character area in the binarized template image to obtain an area to be detected; filling holes in the area to be detected to obtain a first keyboard image; the first keyboard image is a portion of the template image corresponding to the keyboard.

In some embodiments, the image detection apparatus 90 may further include a calculation module 905, where the calculation module 905 is not embodied in fig. 11, and the calculation module 905 may be configured to: and determining at least one of the pixel height, the pixel width, the abscissa of the first central point and the ordinate of the upper left corner point corresponding to the first communication domain of each key image.

In some embodiments, the detection module 903 may be configured to: determining an abscissa of a second center point of the first keyboard image; determining a first difference between the abscissa of the second center point and the abscissa of the first center point; and determining that the template image has a first defect in response to the pixel height being greater than a preset height threshold, the pixel width being greater than a preset width threshold and the first difference being less than a preset pixel point threshold.

In some embodiments, the detection module 903 may be configured to: determining a first number of first connected domains of a first key image; the first key image is a key image with the abscissa of the upper left corner point within a preset abscissa range; and determining that the template image has the second defects in response to the first number being smaller than a preset first number threshold.

In some embodiments, the detection module 903 may be configured to: determining the maximum value of the vertical coordinate of a pixel point of a first connected domain of the first key image; determining the vertical height of a region to be detected; and determining that the template image has a third defect in response to the fact that the difference between the vertical height and the maximum value of the vertical coordinate of the pixel point is larger than a preset difference threshold value.

In some embodiments, the detection module 903 may be configured to: determining a second number of first connected domains comprised by the first keyboard image; determining that the template image has a fourth defect in response to the second number being greater than a preset second number threshold; carrying out connected domain segmentation on the region to be detected, and determining a second connected domain included in the region to be detected; determining a third number of second connected domains included in the region to be detected; and determining that the template image has a fifth defect in response to the third number being less than a preset third number threshold.

In some embodiments, the detection module 903 may be configured to: determining a first communication domain of the key image corresponding to the preset coordinate position based on the preset coordinate position; determining a character area with the maximum abscissa in the first communication area; determining the roundness and the area corresponding to the character area with the maximum abscissa; and determining that the template image has a sixth defect in response to the roundness being greater than the preset roundness threshold and the area being greater than the preset area threshold.

It should be noted that the description of the apparatus in the embodiment of the present application is similar to the description of the method embodiment, and has similar beneficial effects to the method embodiment, and therefore, the description is not repeated. The technical details that are not used up in the image detection apparatus provided in the embodiments of the present application can be understood from the description of any one of fig. 1 to 11.

The present application also provides an electronic device and a non-transitory computer readable storage medium according to embodiments of the present application.

FIG. 12 shows a schematic block diagram of an example electronic device 800 that may be used to implement embodiments of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.

As shown in fig. 12, the electronic apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM) 802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic apparatus 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

A number of components in the electronic device 800 are connected to the I/O interface 805, including: an input unit 806 such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

Computing unit 801 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 801 executes the respective methods and processes described above, such as the image detection method. For example, in some embodiments, the image detection method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto the electronic device 800 via the ROM 802 and/or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the image detection method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the image detection method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An image detection method, characterized in that the method comprises:

acquiring a template image corresponding to a keyboard;

performing connected domain segmentation on the template image, and determining a first connected domain of each key image included in the template image;

and determining whether the template image has defects or not based on the first communication domain.

2. The method of claim 1, wherein prior to determining the first connected component of each key image included in the template image, the method further comprises:

performing binarization threshold segmentation on the template image to obtain a binarized template image;

performing vertical projection and horizontal projection on the binarized template image to obtain an adjacent non-projection area corresponding to the binarized template image;

determining the difference of the vertical coordinates of the adjacent non-projection areas;

in response to the fact that the vertical coordinate difference is smaller than a preset distance threshold value, determining the adjacent non-projection area corresponding to the vertical coordinate difference as an invalid character area;

deleting the invalid character area in the binarized template image to obtain an area to be detected;

filling holes in the area to be detected to obtain a first keyboard image; the first keyboard image is a part of the template image corresponding to the keyboard.

3. The method of claim 2, wherein after determining the first connected component of each key image included in the template image, the method further comprises:

and determining at least one of the pixel height, the pixel width, the abscissa of the first central point and the ordinate of the upper left corner point corresponding to the first communication domain of each key image.

4. The method of claim 3, wherein said determining whether the template image is defective based on the first connection field comprises:

determining an abscissa of a second center point of the first keyboard image;

determining a first difference of the abscissa of the second center point and the abscissa of the first center point;

and determining that the template image has a first defect in response to the pixel height being greater than a preset height threshold, the pixel width being greater than a preset width threshold and the first difference being less than a preset pixel point threshold.

5. The method of claim 3, wherein said determining whether the template image is defective based on the first connection field comprises:

determining a first number of first connected domains of a first key image; the first key image is a key image of which the abscissa of the upper left corner point is within a preset abscissa range;

and determining that the template image has a second defect in response to the first number being smaller than a preset first number threshold.

6. The method of claim 5, wherein determining whether the template image is defective based on the first connection field comprises:

determining the maximum value of the vertical coordinate of a pixel point of a first connected domain of the first key image;

determining the vertical height of the area to be detected;

and determining that the template image has a third defect in response to the fact that the difference between the vertical height and the maximum value of the vertical coordinate of the pixel point is larger than a preset difference threshold value.

7. The method of claim 3, wherein said determining whether the template image is defective based on the first connection field comprises:

determining a second number of the first connected domains that the first keyboard image includes;

determining that a fourth defect exists in the template image in response to the second quantity being greater than a preset second quantity threshold;

carrying out connected domain segmentation on the area to be detected, and determining a second connected domain included in the area to be detected;

determining a third number of the second connected domains included in the region to be detected;

and determining that the template image has a fifth defect in response to the third number being smaller than a preset third number threshold.

8. The method of claim 3, wherein said determining whether the template image is defective based on the first connection field comprises:

determining a first communication domain of the key image corresponding to a preset coordinate position based on the preset coordinate position;

determining a character area with the maximum abscissa in the first communication area;

determining the roundness and the area corresponding to the character area with the maximum abscissa;

and determining that a sixth defect exists in the template image in response to the fact that the roundness is larger than a preset roundness threshold value and the area is larger than a preset area threshold value.

9. An image detection apparatus, characterized in that the image detection apparatus comprises:

the acquisition module is used for acquiring a template image corresponding to the keyboard;

the determining module is used for carrying out connected domain segmentation on the template image and determining a first connected domain of each key image included in the template image;

and the detection module is used for determining whether the template image has defects or not based on the first communication domain.

10. The apparatus of claim 9, wherein the image detection apparatus further comprises a projection module configured to:

in response to the vertical coordinate difference being smaller than a preset distance threshold, determining the adjacent non-projection area corresponding to the vertical coordinate difference as an invalid character area;

11. The apparatus of claim 10, wherein the image detection apparatus further comprises a calculation module configured to:

12. The apparatus of claim 11, wherein the detection module is configured to:

determining an abscissa of a second center point of the first keyboard image;

13. The apparatus of claim 11, wherein the detection module is configured to:

14. The apparatus of claim 13, wherein the detection module is configured to:

determining the vertical height of the area to be detected;

15. The apparatus of claim 11, wherein the detection module is configured to:

in response to the second quantity being larger than a preset second quantity threshold value, determining that a fourth defect exists in the template image;

performing connected domain segmentation on the area to be detected, and determining a second connected domain included in the area to be detected;

16. The apparatus of claim 11, wherein the detection module is configured to:

17. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-8.