CN117635575A

CN117635575A - LCD flatness detection method based on mobile terminal and electronic equipment

Info

Publication number: CN117635575A
Application number: CN202311636356.XA
Authority: CN
Inventors: 李秀鹏
Original assignee: Shenzhen Shengshi Weiye Photoelectric Co ltd
Current assignee: Shenzhen Shengshi Weiye Photoelectric Co ltd
Priority date: 2023-11-30
Filing date: 2023-11-30
Publication date: 2024-03-01

Abstract

A LCD flatness detection method and electronic equipment based on mobile terminal, the method includes obtaining to-be-processed image data containing LCD screen to be tested and depth information of the LCD screen to be tested; identifying the contour identification shape of the LCD screen to be tested from the image data to be processed; dividing the contour recognition shape into a plurality of flatness detection areas by using a grid division method; constructing point cloud data of each flatness detection area based on the depth information; the method can accurately describe the geometric characteristics of the complex curved LCD without being limited by simple 2D image analysis, thus the flatness of the complex non-planar LCD can be accurately detected, and the accuracy of detecting the flatness of the complex screen shape is improved.

Description

LCD flatness detection method based on mobile terminal and electronic equipment

Technical Field

The application relates to the technical field of flatness detection, in particular to an LCD flatness detection method based on a mobile terminal and electronic equipment.

Background

With rapid development of technology and increasing demands of consumers for individualization, mobile terminal devices, such as smartphones and tablet computers, begin to use LCD screens of non-standard or special shape, such as curved screens or shaped screens. The core part of these devices is a Liquid Crystal Display (LCD), the flatness of which directly affects the display quality and user experience, and therefore, detection and analysis of the flatness of the LCD is an important link in the production process of mobile terminal devices.

Currently, the technology related to LCD flatness detection and analysis of mobile terminals is mainly based on a 2D image processing and analysis method, which generally includes capturing a 2D image of an LCD and then calculating the flatness of the LCD by extracting and analyzing features of the image.

However, when processing a complex screen shape (such as a curved screen or a deformed screen), it may be difficult to accurately extract and analyze image features due to lack of depth information of the screen, resulting in poor accuracy of flatness detection.

Disclosure of Invention

The application provides an LCD flatness detection method based on a mobile terminal and electronic equipment, which improves the accuracy of flatness detection on complex screen shapes.

In a first aspect, the present application provides a method for detecting flatness of an LCD based on a mobile terminal, including: acquiring to-be-processed image data containing an LCD screen to be tested and depth information of the LCD screen to be tested; identifying the contour identification shape of the LCD screen to be tested from the image data to be processed; dividing the contour recognition shape into a plurality of flatness detection areas by using a grid division method; constructing point cloud data of each flatness detection area based on the depth information; determining the current flatness detection area as a convex area when the Gaussian curvature of the point cloud data of the current flatness detection area is larger than a corresponding preset curvature interval, determining the current flatness detection area as a concave area when the Gaussian curvature of the point cloud data of the current flatness detection area is smaller than the corresponding preset curvature interval, and determining the current flatness detection area as a normal area when the Gaussian curvature of the point cloud data of the current flatness detection area is located in the corresponding preset curvature interval; under the condition that the sum of the screen ratio of all the raised areas in the outline identification shape and the screen ratio of all the recessed areas in the outline identification shape is larger than an overall flatness threshold value, the flatness of the LCD screen to be tested does not meet the overall production standard, and a first LCD screen flatness distribution diagram is generated, wherein the first LCD screen flatness distribution diagram comprises the outline identification shape formed by splicing all the flatness detection areas, and the normal areas are marked by a first color, the raised areas are marked by a second color and the recessed areas are marked by a third color; under the condition that the sum of the screen occupation ratios of all the bulge areas in the contour recognition shape is larger than a bulge flatness threshold value, the flatness of the LCD screen to be tested does not meet the bulge production standard, and a second LCD screen flatness distribution map is generated, wherein the second LCD screen flatness distribution map comprises the contour recognition shape formed by splicing all the flatness detection areas, and the normal areas are marked by a first color, and the bulge areas are marked by a second color; and under the condition that the sum of the screen occupation ratios of all the concave areas in the contour recognition shape is larger than a concave flatness threshold value, the flatness of the LCD screen to be tested does not meet the concave production standard, and a third LCD screen flatness distribution diagram is generated, wherein the third LCD screen flatness distribution diagram comprises the contour recognition shape formed by splicing all the flatness detection areas, and the normal areas are marked by the first color and the concave areas are marked by the third color.

In the above embodiment, by including obtaining the image data and the depth information of the LCD, constructing the point cloud data based on the depth information, and determining the flatness by gaussian curvature calculation of the point cloud data, compared with the existing technical scheme of performing flatness detection only by using the 2D image, the three-dimensional point cloud model of the LCD screen can be constructed by adding the depth information, the three-dimensional point cloud model of the LCD screen of the three-dimensional point cloud model of the LCD screen includes the spatial coordinate information of the screen surface points, and the geometric features of the complex curved LCD can be accurately described without being limited by simple 2D image analysis, so that the flatness of the complex non-planar LCD can be accurately detected, and the accuracy of detecting the flatness of the complex screen shape is improved.

With reference to some embodiments of the first aspect, in some embodiments, after constructing the point cloud data of each flatness detection area based on the depth information, the method further includes: dividing the current flatness detection area into a plurality of current sub-flatness detection areas, wherein the current flatness detection areas comprise a first current sub-flatness detection area, a second current sub-flatness detection area adjacent to the first current sub-flatness detection area and a third current sub-flatness detection area not adjacent to the first current sub-flatness detection area; calculating the Gaussian curvature of the point cloud data of the first current sub-flatness detection area; calculating the Gaussian curvature of the point cloud data of the current flatness detection area under the condition that the Gaussian curvature of the point cloud data of the first current sub-flatness detection area is not located in a preset curvature interval corresponding to the current flatness detection area; under the condition that the Gaussian curvature of the point cloud data of the first current sub-flatness detection area is located in a preset curvature interval corresponding to the current flatness detection area, calculating the Gaussian curvature of the point cloud data of the second current sub-flatness detection area and the Gaussian curvature of the point cloud data of the third current sub-flatness detection area; under the condition that the Gaussian curvature of the point cloud data of the second current sub-flatness detection area and the Gaussian curvature of the point cloud data of the third current sub-flatness detection area are simultaneously located in a preset curvature interval corresponding to the current flatness detection area, determining that the current flatness detection area is a normal area; and calculating the Gaussian curvature of the point cloud data of the current flatness detection area under the condition that the Gaussian curvature of the point cloud data of the second current sub-flatness detection area or the Gaussian curvature of the point cloud data of the third current sub-flatness detection area is not located in a preset curvature interval corresponding to the current flatness detection area.

In the above embodiment, only the gaussian curvature of the first subregion is calculated, and if the curvature of the first subregion is normal, the curvatures of the adjacent regions and the non-adjacent regions are further checked; if the curvature of any sub-region is abnormal, the curvature of the entire flatness detection area is calculated. The number of times of Gaussian curvature calculation is reduced, and the calculation amount of the detection process is reduced.

With reference to some embodiments of the first aspect, in some embodiments, identifying a contour identification shape of the LCD screen to be tested from the image data to be processed specifically includes: adjusting an image color channel of the image data to be processed to enable the outline of the LCD screen to be tested to be clearer; detecting edge characteristics of an LCD screen to be detected in image data to be processed; under the condition that the edge characteristics of the LCD screen to be tested are non-closed contours, a notch merging algorithm is adopted to process the edge characteristics of the LCD screen to be tested into contour recognition shapes; and in the case that the edge characteristic of the LCD screen to be tested is a closed contour, determining the edge characteristic of the LCD screen to be tested as a contour recognition shape.

In the above embodiment, the outline of the LCD screen to be tested is made clearer, and the edge feature of the LCD screen to be tested is processed into the outline recognition shape for the case that the edge feature of the LCD screen to be tested is a non-closed outline, so as to facilitate the subsequent segmentation.

With reference to some embodiments of the first aspect, in some embodiments, after adjusting an image color channel of the image data to be processed to make a contour of the LCD screen to be tested clearer, the method further includes: and carrying out noise filtering and smoothing processing on the image data to be processed.

In the above embodiment, noise filtering and smoothing processing are performed on the image data to be processed, so that interference of image noise on edge feature recognition of the LCD screen to be detected can be eliminated, and subsequent recognition effect is improved.

With reference to some embodiments of the first aspect, in some embodiments, the dividing the contour recognition shape into a plurality of flatness detection areas by using a meshing method specifically includes: dividing the contour recognition shape into a plurality of surrounding rectangles according to the preset grid size; and removing the part which does not contain the contour recognition shape in the surrounding rectangle to obtain a plurality of flatness detection areas.

In the above embodiment, the flatness detection area can be accurately obtained by removing the portion of the surrounding rectangle that does not include the contour recognition shape.

With reference to some embodiments of the first aspect, in some embodiments, constructing the point cloud data of each flatness detection area based on the depth information specifically includes: acquiring depth values of pixel points in the flatness detection area based on the depth information; acquiring a transverse value and a longitudinal value of a pixel point in a flatness detection area based on the contour recognition shape; and dispersing all pixel points in the flatness detection area into a three-dimensional space based on the depth value, the transverse value and the longitudinal value to obtain point cloud data.

In the above embodiment, the acquired point cloud data is made more accurate by the depth value, the lateral value, and the longitudinal value.

With reference to some embodiments of the first aspect, in some embodiments, dispersing all pixel points in the flatness detection area into the three-dimensional space based on the depth value, the lateral value and the longitudinal value to obtain the point cloud data specifically includes: dispersing all pixel points in the flatness detection area into a three-dimensional space based on the depth value, the transverse value and the longitudinal value to obtain first point cloud data; downsampling the first point cloud data to obtain second point cloud data; removing outliers in the second point cloud data to obtain third point cloud data; and carrying out smoothing treatment on the third point cloud data to obtain the point cloud data.

In the above embodiment, the structure of the point cloud data is simplified by downsampling, outlier removal, and smoothing, and the amount of computation is further reduced.

In a second aspect, an embodiment of the present application provides an LCD flatness detection electronic device based on a mobile terminal, the electronic device including:

the acquisition module is used for acquiring to-be-processed image data containing the to-be-detected LCD screen and depth information of the to-be-detected LCD screen;

the identification module is used for identifying the outline identification shape of the LCD screen to be tested from the image data to be processed;

The first dividing module is used for dividing the contour recognition shape into a plurality of flatness detection areas by using a grid dividing method;

the construction module is used for constructing point cloud data of each flatness detection area based on the depth information;

the determining module is used for determining that the current flatness detection area is a convex area when the Gaussian curvature of the point cloud data of the current flatness detection area is larger than a corresponding preset curvature interval, determining that the current flatness detection area is a concave area when the Gaussian curvature of the point cloud data of the current flatness detection area is smaller than the corresponding preset curvature interval, and determining that the current flatness detection area is a normal area when the Gaussian curvature of the point cloud data of the current flatness detection area is located in the corresponding preset curvature interval;

the first processing module is used for generating a first LCD screen flatness distribution map when the sum of the screen proportion of all the raised areas in the contour recognition shape and the screen proportion of all the recessed areas in the contour recognition shape is larger than an overall flatness threshold value, wherein the flatness of the LCD screen to be tested does not meet the overall production standard, the first LCD screen flatness distribution map comprises the contour recognition shape formed by splicing all the flatness detection areas, and the normal areas are marked by a first color, the raised areas are marked by a second color and the recessed areas are marked by a third color;

The second processing module is used for generating a second LCD screen flatness distribution map when the sum of the screen occupation ratios of all the bulge areas in the contour recognition shape is larger than a bulge flatness threshold value and the flatness of the LCD screen to be tested does not meet the bulge production standard, wherein the second LCD screen flatness distribution map comprises the contour recognition shape formed by splicing all the flatness detection areas, and the normal areas are marked by a first color and the bulge areas are marked by a second color;

and the third processing module is used for generating a third LCD screen flatness distribution map when the sum of the screen occupation ratios of all the concave areas in the contour recognition shape is larger than the concave flatness threshold value, wherein the flatness of the LCD screen to be tested does not meet the concave production standard, the third LCD screen flatness distribution map comprises the contour recognition shape formed by splicing all the flatness detection areas, and the normal areas are marked by the first color and the concave areas are marked by the third color.

With reference to some embodiments of the second aspect, in some embodiments, the electronic device further includes:

the second dividing module is used for dividing the current flatness detection area into a plurality of current sub-flatness detection areas, wherein the current flatness detection areas comprise a first current sub-flatness detection area, a second current sub-flatness detection area adjacent to the first current sub-flatness detection area and a third current sub-flatness detection area not adjacent to the first current sub-flatness detection area;

The fourth processing module is used for calculating Gaussian curvature of point cloud data of the first current sub-flatness detection area;

a fifth processing module, configured to calculate a gaussian curvature of the point cloud data of the current flatness detection area when the gaussian curvature of the point cloud data of the first current sub-flatness detection area is not located in a preset curvature interval corresponding to the current flatness detection area; a sixth processing module, configured to calculate, when the gaussian curvature of the point cloud data of the first current sub-flatness detection area is located in a preset curvature interval corresponding to the current flatness detection area, the gaussian curvature of the point cloud data of the second current sub-flatness detection area and the gaussian curvature of the point cloud data of the third current sub-flatness detection area;

a seventh processing module, configured to determine that the current flatness detection area is a normal area when the gaussian curvature of the point cloud data of the second current sub-flatness detection area and the gaussian curvature of the point cloud data of the third current sub-flatness detection area are located in a preset curvature interval corresponding to the current flatness detection area at the same time;

and the eighth processing module is used for calculating the Gaussian curvature of the point cloud data of the current flatness detection area under the condition that the Gaussian curvature of the point cloud data of the second current sub-flatness detection area or the Gaussian curvature of the point cloud data of the third current sub-flatness detection area is not located in a preset curvature interval corresponding to the current flatness detection area.

With reference to some embodiments of the second aspect, in some embodiments, the identification module specifically includes:

the adjusting sub-module is used for adjusting an image color channel of the image data to be processed, so that the outline of the LCD screen to be tested is clearer; the detection sub-module is used for detecting edge characteristics of the LCD screen to be detected in the image data to be processed;

the first processing submodule is used for processing the edge characteristics of the LCD screen to be detected into a contour recognition shape by adopting a notch merging algorithm under the condition that the edge characteristics of the LCD screen to be detected are non-closed contours;

and the second processing sub-module is used for determining the edge characteristics of the LCD screen to be detected as a contour recognition shape under the condition that the edge characteristics of the LCD screen to be detected are closed contours.

With reference to some embodiments of the second aspect, in some embodiments, the identification module further includes:

and the preprocessing sub-module is used for carrying out noise filtering and smoothing processing on the image data to be processed.

With reference to some embodiments of the second aspect, in some embodiments, the first partitioning module specifically includes:

the segmentation submodule is used for segmenting the contour recognition shape into a plurality of surrounding rectangles according to the preset grid size;

and the removing submodule is used for removing the part which does not contain the contour recognition shape in the surrounding rectangle to obtain a plurality of flatness detection areas.

With reference to some embodiments of the second aspect, in some embodiments, the building block specifically includes:

the first acquisition submodule is used for acquiring depth values of pixel points in the flatness detection area based on the depth information;

a second acquisition sub-module for acquiring a lateral value and a longitudinal value of a pixel point in the flatness detection area based on the contour recognition shape; and the dispersing sub-module is used for dispersing all pixel points in the flatness detection area into a three-dimensional space based on the depth value, the transverse value and the longitudinal value to obtain point cloud data.

With reference to some embodiments of the second aspect, in some embodiments, the dispersing submodule specifically includes:

a dispersing unit, configured to disperse all pixel points in the flatness detection area into a three-dimensional space based on the depth value, the lateral value and the longitudinal value to obtain first point cloud data;

the downsampling unit is used for downsampling the first point cloud data to obtain second point cloud data;

the removing unit is used for removing outliers in the second point cloud data to obtain third point cloud data;

and the smoothing unit is used for carrying out smoothing processing on the third point cloud data to obtain point cloud data.

In a third aspect, an embodiment of the present application provides an LCD flatness detection electronic device based on a mobile terminal, the electronic device including: one or more processors and memory;

The memory is coupled to the one or more processors for storing computer program code comprising computer instructions that are invoked by the one or more processors to cause the mobile terminal based LCD flatness detection electronic device to perform the method as described in the first aspect and any one of the possible implementations of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer program product comprising instructions which, when run on a server, cause the server to perform a method as described in the first aspect and any possible implementation of the first aspect.

In a fifth aspect, embodiments of the present application provide a computer-readable storage medium comprising instructions that, when executed on a server, cause the server to perform a method as described in the first aspect and any possible implementation of the first aspect.

It will be appreciated that the mobile terminal-based LCD flatness detection electronic device provided in the second aspect, the mobile terminal-based LCD flatness detection electronic device provided in the third aspect, the computer program product provided in the fourth aspect, and the computer storage medium provided in the fifth aspect are all configured to perform the mobile terminal-based LCD flatness detection method provided in the embodiments of the present application. Therefore, the advantages achieved by the method can be referred to as the advantages of the corresponding method, and will not be described herein.

One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

1. according to the LCD flatness detection method based on the mobile terminal, the image data and the depth information of the LCD are acquired, the point cloud data are built based on the depth information, flatness is judged through Gaussian curvature calculation of the point cloud data, compared with the existing technical scheme that flatness detection is carried out only by using a 2D image, the depth information is added, a three-dimensional point cloud model of an LCD screen can be built, the three-dimensional point cloud model of the LCD screen comprises space coordinate information of screen surface points, geometric features of the complex curved surface LCD can be accurately described without being limited by simple 2D image analysis, flatness of the complex non-planar LCD can be accurately detected, and accuracy of flatness detection of the complex screen shape is improved.

2. According to the LCD flatness detection method based on the mobile terminal, only the Gaussian curvature of the first subarea is calculated, and if the curvature of the first subarea is normal, the curvatures of the adjacent areas and the non-adjacent areas are further checked; if the curvature of any sub-region is abnormal, the curvature of the entire flatness detection area is calculated. The number of times of Gaussian curvature calculation is reduced, and the calculation amount of the detection process is reduced.

Drawings

Fig. 1 is a schematic diagram of an information interaction scenario of the LCD flatness detection system based on the mobile terminal provided in the present application.

Fig. 2 is a schematic flow chart of a mobile terminal-based LCD flatness detection method provided in the present application.

Fig. 3 is another flow chart of the mobile terminal-based LCD flatness detection method provided in the present application.

Fig. 4 is a schematic diagram of a modular virtual device of the mobile terminal-based LCD flatness detection electronic device provided in the present application.

Fig. 5 is a schematic diagram of an entity apparatus of the mobile terminal-based LCD flatness detection electronic device provided in the present application.

Detailed Description

The terminology used in the following embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It should also be understood that the term "and/or" as used in this application is intended to encompass any or all possible combinations of one or more of the listed items.

The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.

Referring to fig. 1, fig. 1 is a schematic diagram of an information interaction scenario of an LCD flatness detection system based on a mobile terminal provided in the present application. The LCD flatness detection system based on the mobile terminal comprises: monitoring acquisition equipment, a server and a display.

The monitoring acquisition equipment comprises a camera and a sensor, wherein the camera and the sensor are mainly used in the mobile terminal, the camera is used for acquiring image data containing an LCD screen to be detected, and the sensor is used for acquiring depth information of the LCD screen to be detected.

The server is used for receiving and storing the image data to be processed and the depth information sent from the mobile terminal. The server has certain graphic image processing capability, can identify the contour of the LCD screen from the image, calculate the Gaussian curvature of each detection area and judge the flatness defect type.

The display is used to show the LCD screen flatness profile.

The following describes an LCD flatness detection method based on a mobile terminal in this embodiment:

S201, obtaining to-be-processed image data containing the LCD screen to be tested and depth information of the LCD screen to be tested.

Referring to fig. 1, a camera of a mobile terminal is used to collect image data to be processed including an LCD screen to be tested. In the practical use process, in order to ensure the definition of the image data to be processed, the resolution of the camera needs to be larger than the resolution of the LCD screen to be processed, preferably, not smaller than 2 times, and meanwhile, the distance between the camera and the LCD screen to be processed is controlled within the range of 0.5-2 meters, so that the image data to be processed is prevented from being excessively distorted.

In the actual use process, the image data to be processed comprising the LCD screen to be tested is obtained to ensure sufficient and uniform ambient light, and preferably, a ring-shaped lamp tube is selected for polishing.

The depth information of the LCD screen to be detected is acquired by using the depth sensor of the mobile terminal, namely the depth of each point of the LCD screen to be detected, the depth information of the surface of the LCD screen can be acquired in a multi-scanning mode, and the distance between the depth sensor and the LCD screen to be detected is ensured to be within 0.5 meter during the preferential acquisition.

S202, recognizing the outline recognition shape of the LCD screen to be detected from the image data to be processed.

In an alternative embodiment, the method comprises the substep 1 of adjusting the image color channel of the image data to be processed to make the outline of the LCD screen to be tested clearer;

substep 1.1, decomposing the image data to be processed into different color channels, for example red, green and blue channels.

And 1.2, analyzing the edge contour display effect of the LCD screen under different channels.

Substep 1.3, selecting a channel image with the clearest and identifiable LCD outline under a certain channel, for example, selecting a blue channel image for subsequent processing when the LCD edge contrast is highest under a blue channel.

And 2, carrying out noise filtering and smoothing treatment on the image data to be processed.

And 3, detecting edge characteristics of the LCD screen to be detected in the image data to be processed.

And 3.1, detecting edge characteristics of the LCD screen to be detected in the image data to be processed by using edge detection operators such as Canny, sobel and the like.

And 3.2, obtaining a binary image of the edge of the LCD screen to be tested as an edge characteristic.

And (3) judging whether the edge features are non-closed contours or not, wherein when edge breaks or gaps exist in the screen frame, the edge features cannot directly form closed contours.

Step 4, under the condition that the edge characteristics of the LCD screen to be tested are non-closed contours, processing the edge characteristics of the LCD screen to be tested into contour recognition shapes by adopting a notch merging algorithm;

and 4.1, connecting edge break points by using algorithms such as line segment fitting, curve filling and the like, and merging the gaps.

And 4.2, processing to obtain a complete LCD closed contour serving as a contour recognition shape.

And 5, determining the edge characteristics of the LCD screen to be detected as a contour recognition shape under the condition that the edge characteristics of the LCD screen to be detected are closed contours.

Therefore, the outline of the LCD screen to be tested is clearer, the edge features of the LCD screen to be tested are processed into outline recognition shapes according to the condition that the edge features of the LCD screen to be tested are non-closed outline, subsequent segmentation is convenient, noise filtering and smoothing processing are carried out on the image data to be processed, interference of image noise on edge feature recognition of the LCD screen to be tested can be eliminated, and subsequent recognition effects are improved.

S203, dividing the contour recognition shape into a plurality of flatness detection areas by using a meshing method.

In an alternative embodiment, the method includes a substep 1 of dividing the contour recognition shape into a plurality of bounding rectangles according to a preset mesh size, for example, equally dividing the contour recognition shape into m×n small meshes, i.e., bounding rectangles, where the values of M and N are determined according to the LCD size and the detection accuracy requirement.

And 2, removing the part, which does not contain the contour recognition shape, of the surrounding rectangle to obtain a plurality of flatness detection areas.

This sub-step may create 3 cases, the first case where the bounding rectangle contains no contour recognition shape at all, where the bounding rectangle is discarded, the second case where the bounding rectangle has a contour recognition shape in part, where the part of the bounding rectangle that does not contain the contour recognition shape is removed, and the third case where the bounding rectangle is all the contour recognition shape, where the bounding rectangle is preserved.

It can be seen that the above steps remove the portion of the bounding rectangle that does not include the contour recognition shape, and the flatness detection area can be accurately obtained.

S204, constructing point cloud data of each flatness detection area based on the depth information.

In an alternative embodiment, substep 1 is included, obtaining depth values for pixel points in the flatness detection area based on the depth information.

The method comprises a substep 2 of acquiring a transverse value and a longitudinal value of a pixel point in a flatness detection area based on a contour recognition shape.

And 3, dispersing all pixel points in the flatness detection area into a three-dimensional space based on the depth value, the transverse value and the longitudinal value to obtain point cloud data.

It can be seen that the acquired point cloud data is more accurate by the depth value, the lateral value and the longitudinal value.

And 3, dispersing all pixel points in the flatness detection area into a three-dimensional space based on the depth value, the transverse value and the longitudinal value to obtain first point cloud data.

And 4, performing downsampling processing on the first point cloud data to obtain second point cloud data.

And 5, removing outliers in the second point cloud data to obtain third point cloud data.

And (6) performing smoothing processing on the third point cloud data to obtain the point cloud data.

It can be seen that the structure of the point cloud data is simplified by downsampling, outlier removal and smoothing, so that the calculation amount is further reduced.

In the actual use process, firstly, all point cloud data of the LCD screen to be tested, namely original point cloud data, are built by referring to the steps, and then corresponding point cloud data are taken out from the original point cloud data corresponding to each flatness detection area.

S205, determining the current flatness detection area as a convex area when the Gaussian curvature of the point cloud data of the current flatness detection area is larger than the corresponding preset curvature interval, determining the current flatness detection area as a concave area when the Gaussian curvature of the point cloud data of the current flatness detection area is smaller than the corresponding preset curvature interval, and determining the current flatness detection area as a normal area when the Gaussian curvature of the point cloud data of the current flatness detection area is located in the corresponding preset curvature interval.

In some embodiments, the calculation formula for the gaussian curvature is k= (LN-M ² )/(EG-F ² )

Wherein: E. f, G, L, M, N is the first and second partial derivatives of the normal vector on the curved surface, and related techniques are disclosed and will not be repeated here.

S206, when the sum of the screen ratio of all the convex areas in the outline recognition shape and the screen ratio of all the concave areas in the outline recognition shape is larger than the overall flatness threshold value, the flatness of the LCD screen to be tested does not meet the overall production standard, and a first LCD screen flatness distribution diagram is generated.

The first LCD screen flatness profile includes a contour recognition shape consisting of a concatenation of all flatness detection areas, wherein normal areas are marked with a first color, raised areas are marked with a second color, and recessed areas are marked with a third color.

In some embodiments, the first LCD screen flatness profile is constructed by the steps of:

and re-splicing all the flatness detection areas according to the original positions to form detection area distribution conforming to the contour recognition shape.

The flatness detection area is marked with different types of areas according to the judgment rules, for example, three different colors are set to represent three types of areas of the normal area, the convex area and the concave area, for example, green represents the normal area, red represents the convex area, blue represents the concave area, and other colors can be adopted in other embodiments, and the flatness detection area is not limited herein.

For the flatness detection area where the detection result is a normal area, green filling is used.

For the flatness detection area where the detection result is a convex area, red filling is used.

For the flatness detection area where the detection result is a depressed area, blue filling is used.

Of course, the first LCD screen flatness profile may also be generated in other ways, not limited herein.

S207, when the sum of the screen occupation ratios of all the convex areas in the outline recognition shape is larger than a convex flatness threshold value, the flatness of the LCD screen to be tested does not meet the convex production standard, and a second LCD screen flatness distribution diagram is generated.

The second LCD screen flatness profile includes a contour recognition shape consisting of a concatenation of all flatness detection areas, wherein normal areas are marked with a first color and raised areas are marked with a second color.

The steps adopted in this embodiment are the same as those adopted in the above embodiment, and the specific implementation process is shown in step S206, which is not repeated here.

S208, when the sum of the screen occupation ratios of all the concave areas in the outline identification shape is larger than a concave flatness threshold value, the flatness of the LCD screen to be tested does not meet the concave production standard, and a third LCD screen flatness distribution diagram is generated.

The third LCD screen flatness profile includes a contour recognition shape composed of a concatenation of all flatness detection areas, marking normal areas with a first color, marking recessed areas with a third color.

Therefore, by acquiring image data and depth information of the LCD, constructing point cloud data based on the depth information, and judging flatness through Gaussian curvature calculation of the point cloud data, compared with the existing technical scheme of detecting flatness by only using 2D images, the three-dimensional point cloud model of the LCD screen can be constructed by adding the depth information, the three-dimensional point cloud model of the LCD screen comprises space coordinate information of screen surface points, geometric features of the complex curved surface LCD can be accurately described without being limited by simple 2D image analysis, flatness of the complex non-planar LCD can be accurately detected, and accuracy of detecting flatness of the complex screen shape is improved.

The above embodiment improves accuracy of detecting flatness of a complex screen shape, but in actual use, the calculation amount involved in step S205 is too large, and the required calculation force is too large, so taking a manner of reducing the calculation amount of step S205 as an example, in conjunction with the embodiment shown in fig. 3, step S205 in the embodiment of the present application will be specifically described:

referring to fig. 3, fig. 3 is another flow chart of the mobile terminal-based LCD flatness detection method provided in the present application.

In the embodiment, the core thinking of the overall reference divide-and-conquer algorithm is to reduce the calculation of the current flatness detection area into the current sub-flatness detection area, and the whole current flatness detection area is calculated only when necessary, otherwise, the calculation of the sub-flatness detection area can make judgment, so that unnecessary calculation is avoided, and the calculation amount is greatly reduced compared with the direct calculation of the whole current flatness detection area.

S301, dividing the current flatness detection area into a plurality of current sub-flatness detection areas, wherein the current flatness detection areas comprise a first current sub-flatness detection area, a second current sub-flatness detection area adjacent to the first current sub-flatness detection area, and a third current sub-flatness detection area not adjacent to the first current sub-flatness detection area.

In the actual use process, the current sub-flatness detection areas are required to be approximately equal, so that the error is avoided being too large.

It is easily conceivable that the more the division is, the more the number of current sub-flatness detection areas is, the larger the reduction in the calculation amount is.

The first current sub-flatness detection area, the second current sub-flatness detection area, and the third current sub-flatness detection area will be described in detail later.

S302, calculating Gaussian curvature of point cloud data of the first current sub-flatness detection area.

S303, calculating the Gaussian curvature of the point cloud data of the current flatness detection area when the Gaussian curvature of the point cloud data of the first current sub-flatness detection area is not located in a preset curvature section corresponding to the current flatness detection area.

In the case where the gaussian curvature of the point cloud data of the first current sub-flatness detection area is not located within the preset curvature section corresponding to the current flatness detection area, it is considered to be necessary, and therefore it is necessary to calculate the entire current flatness detection area to verify whether the current flatness detection area is a normal area.

S304, calculating the Gaussian curvature of the point cloud data of the second current sub-flatness detection area and the Gaussian curvature of the point cloud data of the third current sub-flatness detection area under the condition that the Gaussian curvature of the point cloud data of the first current sub-flatness detection area is located in a preset curvature interval corresponding to the current flatness detection area.

In the case that the gaussian curvature of the point cloud data of the first current sub-flatness detection area is located within a preset curvature interval corresponding to the current flatness detection area, an adjacent second current sub-flatness detection area and an non-adjacent third current sub-flatness detection area need to be selected for further verification.

The second current sub-flatness detection zone may reflect inter-zone continuity, indicating that there is no abrupt change in curvature and that the entire large zone is continuous. The third current sub-flatness detection areas are similar, and the curvature of the whole large area can be judged to be globally consistent without local abnormality. The second current sub-flatness detection area reflects local information and the third current sub-flatness detection area reflects global information. Combining the two can fully judge the curvature characteristic of the whole current flatness detection area.

S305, determining that the current flatness detection area is a normal area when the Gaussian curvature of the point cloud data of the second current sub-flatness detection area and the Gaussian curvature of the point cloud data of the third current sub-flatness detection area are simultaneously located in a preset curvature interval corresponding to the current flatness detection area.

The steps adopted in this embodiment are the same as those adopted in the above embodiment, and the specific implementation process is shown in step S303 and step S304, which are not described herein again.

S306, calculating the Gaussian curvature of the point cloud data of the current flatness detection area under the condition that the Gaussian curvature of the point cloud data of the second current sub-flatness detection area or the Gaussian curvature of the point cloud data of the third current sub-flatness detection area is not located in a preset curvature interval corresponding to the current flatness detection area.

It can be seen that only the gaussian curvature of the first subregion is calculated, and if the curvature of the first subregion is normal, the curvatures of the adjacent regions and the non-adjacent regions are further checked; if the curvature of any sub-region is abnormal, the curvature of the entire flatness detection area is calculated. The number of times of Gaussian curvature calculation is reduced, and the calculation amount of the detection process is reduced.

The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

Referring to fig. 4, an embodiment of the present application provides an LCD flatness detection electronic device based on a mobile terminal, the electronic device including:

an obtaining module 401, configured to obtain to-be-processed image data including an LCD screen to be tested and depth information of the LCD screen to be tested; an identification module 402, configured to identify a contour identification shape of an LCD screen to be tested from image data to be processed;

a first dividing module 403, configured to divide the contour recognition shape into a plurality of flatness detection areas using a mesh division method; a construction module 404, configured to construct point cloud data of each flatness detection area based on the depth information;

a determining module 405, configured to determine, when the gaussian curvature of the point cloud data of the current flatness detection area is greater than the corresponding preset curvature interval, that the current flatness detection area is a convex area, determine, when the gaussian curvature of the point cloud data of the current flatness detection area is less than the corresponding preset curvature interval, that the current flatness detection area is a concave area, and determine, when the gaussian curvature of the point cloud data of the current flatness detection area is within the corresponding preset curvature interval, that the current flatness detection area is a normal area;

A first processing module 406, configured to, when a sum of a screen ratio of all the convex areas in the contour recognition shape and a screen ratio of all the concave areas in the contour recognition shape is greater than an overall flatness threshold, generate a first LCD screen flatness profile, and the first LCD screen flatness profile includes a contour recognition shape formed by stitching all the flatness detection areas, wherein the normal area is marked with a first color, the convex area is marked with a second color, and the concave area is marked with a third color;

a second processing module 407, configured to, if the sum of the screen ratios of all the bump areas in the outline identification shape is greater than the bump flatness threshold, determine that the flatness of the LCD screen to be tested does not meet the bump production standard, and generate a second LCD screen flatness distribution map, where the second LCD screen flatness distribution map includes the outline identification shape formed by stitching all the flatness detection areas, and mark the normal area with a first color and the bump area with a second color;

and a third processing module 408, configured to, when the sum of the screen ratios of all the concave regions in the contour recognition shapes is greater than the threshold of the concave flatness, determine that the flatness of the LCD screen to be tested does not meet the concave production standard, and generate a third LCD screen flatness distribution map, where the third LCD screen flatness distribution map includes the contour recognition shapes formed by stitching all the flatness detection regions, mark the normal regions with the first color, and mark the concave regions with the third color.

In some embodiments, the electronic device further comprises:

In some embodiments, the identification module specifically includes:

In some embodiments, the identification module further comprises:

In some embodiments, the first partitioning module specifically includes:

In some embodiments, the building block specifically includes:

In some embodiments, the dispersion sub-module specifically includes:

The application also discloses an LCD flatness detection electronic device based on the mobile terminal. Referring to fig. 5, a schematic diagram of an entity apparatus of a mobile terminal-based LCD flatness detection electronic device is provided. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, at least one communication bus 502.

Wherein a communication bus 502 is used to enable connected communications between these components.

The user interface 503 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 503 may further include a standard wired interface and a standard wireless interface.

The network interface 504 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.

Wherein the processor 501 may include one or more processing cores. The processor 501 utilizes various interfaces and lines to connect various portions of the overall electronic device, perform various functions of the electronic device, and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 505, and invoking data stored in the memory 505. Alternatively, the processor 501 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 501 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 501 and may be implemented by a single chip.

The Memory 505 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 505 comprises a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 505 may be used to store instructions, programs, code sets, or instruction sets. The memory 505 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described various method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. The memory 505 may also optionally be at least one storage device located remotely from the processor 501. Referring to fig. 5, an operating system, a network communication module, a user interface module, and an application program based on LCD flatness detection of a mobile terminal may be included in the memory 505 as one type of computer storage medium.

In the electronic device 500 shown in fig. 5, the user interface 503 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 501 may be configured to invoke an application program stored in the memory 505 for mobile terminal based LCD flatness detection, which when executed by the one or more processors 501, causes the electronic device 500 to perform the method as described in one or more of the embodiments above. It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In the several embodiments provided herein, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.

The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure.

This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.

Claims

1. A mobile terminal-based LCD flatness detection method, comprising:

acquiring image data to be processed containing an LCD screen to be tested and depth information of the LCD screen to be tested;

identifying the contour identification shape of the LCD screen to be tested from the image data to be processed;

dividing the contour recognition shape into a plurality of flatness detection areas by using a grid division method;

constructing point cloud data of each flatness detection area based on the depth information;

determining that the current flatness detection area is a convex area when the Gaussian curvature of the point cloud data of the current flatness detection area is larger than a corresponding preset curvature interval, determining that the current flatness detection area is a concave area when the Gaussian curvature of the point cloud data of the current flatness detection area is smaller than the corresponding preset curvature interval, and determining that the current flatness detection area is a normal area when the Gaussian curvature of the point cloud data of the current flatness detection area is within the corresponding preset curvature interval;

When the sum of the screen ratio of all the raised areas in the contour recognition shape and the screen ratio of all the recessed areas in the contour recognition shape is larger than an overall flatness threshold value, the flatness of the LCD screen to be tested does not meet the overall production standard, and a first LCD screen flatness distribution diagram is generated, wherein the first LCD screen flatness distribution diagram comprises the contour recognition shape formed by splicing all flatness detection areas, and the normal areas are marked with a first color, the raised areas are marked with a second color and the recessed areas are marked with a third color;

under the condition that the sum of screen proportion of all the raised areas in the contour recognition shape is larger than a raised flatness threshold value, the flatness of the LCD screen to be tested does not meet the raised production standard, and a second LCD screen flatness distribution diagram is generated, wherein the second LCD screen flatness distribution diagram comprises the contour recognition shape formed by splicing all the flatness detection areas, and the normal areas are marked by a first color and the raised areas are marked by a second color;

and under the condition that the sum of the screen occupation ratios of all the concave areas in the contour recognition shape is larger than a concave flatness threshold value, the flatness of the LCD screen to be tested does not meet the concave production standard, and a third LCD screen flatness distribution diagram is generated, wherein the third LCD screen flatness distribution diagram comprises the contour recognition shape formed by splicing all the flatness detection areas, and the normal areas are marked by a first color and the concave areas are marked by a third color.

2. The mobile terminal-based LCD flatness detection method of claim 1, wherein after constructing the point cloud data of each flatness detection area based on the depth information, the method further comprises:

dividing a current flatness detection area into a plurality of current sub-flatness detection areas, wherein the current flatness detection areas comprise a first current sub-flatness detection area, a second current sub-flatness detection area adjacent to the first current sub-flatness detection area and a third current sub-flatness detection area not adjacent to the first current sub-flatness detection area;

calculating the Gaussian curvature of the point cloud data of the first current sub-flatness detection area;

calculating the Gaussian curvature of the point cloud data of the current flatness detection area under the condition that the Gaussian curvature of the point cloud data of the first current sub-flatness detection area is not located in a preset curvature interval corresponding to the current flatness detection area;

calculating the Gaussian curvature of the point cloud data of the second current sub-flatness detection area and the Gaussian curvature of the point cloud data of the third current sub-flatness detection area under the condition that the Gaussian curvature of the point cloud data of the first current sub-flatness detection area is located in a preset curvature interval corresponding to the current flatness detection area;

Determining that the current flatness detection area is a normal area under the condition that the Gaussian curvature of the point cloud data of the second current sub-flatness detection area and the Gaussian curvature of the point cloud data of the third current sub-flatness detection area are simultaneously located in a preset curvature interval corresponding to the current flatness detection area;

and calculating the Gaussian curvature of the point cloud data of the current flatness detection area under the condition that the Gaussian curvature of the point cloud data of the second current sub-flatness detection area or the Gaussian curvature of the point cloud data of the third current sub-flatness detection area is not located in a preset curvature interval corresponding to the current flatness detection area.

3. The mobile terminal-based LCD flatness detection method of claim 1, wherein the identifying the contour identification shape of the LCD screen to be tested from the image data to be processed specifically comprises:

adjusting an image color channel of the image data to be processed to enable the outline of the LCD screen to be tested to be clearer;

detecting edge characteristics of an LCD screen to be detected in the image data to be processed;

under the condition that the edge characteristics of the LCD screen to be tested are non-closed contours, a notch merging algorithm is adopted to process the edge characteristics of the LCD screen to be tested into the contour recognition shape;

And under the condition that the edge characteristic of the LCD screen to be tested is a closed contour, determining the edge characteristic of the LCD screen to be tested as the contour recognition shape.

4. The method for detecting the flatness of an LCD based on a mobile terminal according to claim 3, wherein after adjusting the image color channel of the image data to be processed to make the outline of the LCD screen to be detected clearer, the method further comprises:

and carrying out noise filtering and smoothing treatment on the image data to be processed.

5. The LCD flatness detection method based on a mobile terminal according to claim 1, wherein the dividing the contour recognition shape into flatness detection areas using a mesh division method, specifically comprises:

dividing the contour recognition shape into a plurality of surrounding rectangles according to a preset grid size;

and removing the part, which does not contain the contour recognition shape, of the surrounding rectangle to obtain a plurality of flatness detection areas.

6. The LCD flatness detection method based on the mobile terminal according to claim 1, wherein the constructing the point cloud data of each flatness detection area based on the depth information specifically comprises:

Acquiring depth values of pixel points in a flatness detection area based on the depth information;

acquiring a transverse value and a longitudinal value of a pixel point in a flatness detection area based on the contour recognition shape;

and dispersing all pixel points in the flatness detection area into a three-dimensional space based on the depth value, the transverse value and the longitudinal value to obtain the point cloud data.

7. The method for detecting the flatness of an LCD based on a mobile terminal according to claim 6, wherein the dispersing all pixel points in a flatness detection area into a three-dimensional space based on the depth value, the lateral value and the longitudinal value to obtain the point cloud data specifically comprises:

dispersing all pixel points in a flatness detection area into a three-dimensional space based on the depth value, the transverse value and the longitudinal value to obtain first point cloud data;

downsampling the first point cloud data to obtain second point cloud data;

removing outliers in the second point cloud data to obtain third point cloud data;

and carrying out smoothing processing on the third point cloud data to obtain the point cloud data.

8. An LCD flatness detection electronic device based on a mobile terminal, the electronic device comprising:

The device comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring to-be-processed image data containing an LCD screen to be tested and depth information of the LCD screen to be tested;

the determining module is used for determining that the current flatness detection area is a convex area when the Gaussian curvature of the point cloud data of the current flatness detection area is larger than a corresponding preset curvature interval, determining that the current flatness detection area is a concave area when the Gaussian curvature of the point cloud data of the current flatness detection area is smaller than the corresponding preset curvature interval, and determining that the current flatness detection area is a normal area when the Gaussian curvature of the point cloud data of the current flatness detection area is within the corresponding preset curvature interval;

a first processing module, configured to, when a sum of a screen ratio of all the convex areas in the contour recognition shape and a screen ratio of all the concave areas in the contour recognition shape is greater than an overall flatness threshold, make flatness of the LCD screen to be tested not conform to an overall production standard, and generate a first LCD screen flatness distribution map, where the first LCD screen flatness distribution map includes the contour recognition shape formed by stitching all the flatness detection areas, and mark normal areas with a first color, mark convex areas with a second color, and mark concave areas with a third color;

The second processing module is used for generating a second LCD screen flatness distribution map when the sum of the screen occupation ratios of all the bulge areas in the contour recognition shape is larger than a bulge flatness threshold value, wherein the flatness of the LCD screen to be detected does not meet bulge production standards, and the second LCD screen flatness distribution map comprises the contour recognition shape formed by splicing all the flatness detection areas, and the normal areas are marked by a first color and the bulge areas are marked by a second color;

and the third processing module is used for generating a third LCD screen flatness distribution diagram when the sum of the screen occupation ratios of all the concave areas in the contour recognition shape is larger than a concave flatness threshold value, wherein the LCD screen flatness to be detected does not meet the concave production standard, the third LCD screen flatness distribution diagram comprises the contour recognition shape formed by splicing all the flatness detection areas, and the normal areas are marked by a first color and the concave areas are marked by a third color.

9. An LCD flatness detection electronic device based on a mobile terminal, comprising: one or more processors and memory;

the memory is coupled to the one or more processors, the memory for storing computer program code comprising computer instructions that are invoked by the one or more processors to cause the mobile terminal based LCD flatness detection electronic device to perform the method of any of claims 1-7.

10. A computer readable storage medium comprising instructions which, when run on a mobile terminal based LCD flatness detection electronic device, cause the mobile terminal based LCD flatness detection electronic device to perform the method according to any of claims 1-7.