CN111627009A - Screen detection method and device and head-mounted display equipment - Google Patents
Screen detection method and device and head-mounted display equipment Download PDFInfo
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Abstract
The application discloses a screen detection method and device and a head-mounted display device. The screen detection method comprises the following steps: acquiring an original image of a screen to be detected, and removing interference information in the original image to obtain a detection image; extracting an edge area of the detected image according to the screen contour; detecting lines in the edge area and the positions of line center points by using a line extraction algorithm, taking the screen contour as a boundary line, and selecting an area block where the line center points are located as a suspected defect area when the positions of the line center points are within the screen contour; selecting key pixel points in the suspected defect area, carrying out binarization on the key pixel points based on a binarization threshold value, and obtaining a defect index detection value according to a binarization result of the key pixel points; and comparing the defect index detection value with the corresponding defect index to obtain a detection result. The embodiment of the application solves the technical problem that the edge defect detection accuracy is low due to the adhesion of the edge defect and the boundary background, and meets the requirement of practical application.
Description
Technical Field
The application relates to the technical field of head-mounted display equipment, in particular to a screen detection method and device and the head-mounted display equipment.
Background
In recent years, head-mounted display devices such as VR (Virtual Reality) products have been exploded, and according to market prediction, the next generation of VR head-mounted devices will adopt an OLED (Organic Light-Emitting semiconductor) screen. In order to ensure that the VR product can be widely applied and the user experience is good, the VR screen must achieve good resolution, and no dead spots or dirt exists, so that the product detection is an essential process.
In the detection of the OLED display screen of the VR head-mounted display device at the present stage, edge defects such as edge black lines and edge scratches are difficult to distinguish due to the fact that the edge defects are adhered to the boundary background, and the detection accuracy is low.
Disclosure of Invention
In view of the above, the present application is proposed to provide a screen detection method, apparatus and head mounted display device that overcome or at least partially solve the above problems.
According to an aspect of the present application, there is provided a screen detecting method including:
acquiring an original image of a screen to be detected, removing interference information in the original image, and obtaining a detection image containing a screen outline;
extracting one or more edge regions of the detected image according to the screen outline;
detecting and determining lines in the edge region and the positions of line central points by using a line extraction algorithm, taking the screen contour as a boundary, and selecting an area block containing a plurality of pixel points where the line central points are located as a suspected defect area when the positions of the line central points are within the screen contour;
selecting key pixel points in the suspected defect area, carrying out binarization on the key pixel points based on a binarization threshold value, and obtaining a detection value of a defect index according to a binarization result of the key pixel points;
and comparing the detection value of the defect index with the corresponding defect index to obtain a detection result.
In accordance with another aspect of the present application, there is provided a screen detecting apparatus including:
the image acquisition unit is used for acquiring an original image of a screen to be detected, removing interference information in the original image and obtaining a detection image containing a screen outline;
the area determining unit is used for extracting one or more edge areas of the detection image according to the screen outline; determining lines in the edge region and the positions of line center points by using a line extraction algorithm, taking the screen contour as a boundary, and selecting an area block containing a plurality of pixel points where the line center points are located as a suspected defect region when the positions of the line center points are within the screen contour;
the binarization unit is used for selecting key pixel points in the suspected defect area, binarizing the key pixel points based on a binarization threshold value, and obtaining a detection value of the defect index according to a binarization result of the key pixel points;
and the judging unit is used for comparing the detection value of the defect index with the corresponding defect index to obtain a detection result.
According to yet another aspect of the present application, there is provided a head mounted display device comprising a screen detection apparatus as another aspect of the present application.
According to the technical scheme, the original image of the screen to be detected is obtained, the interference information in the original image is removed, the detection image containing the screen outline is obtained, and the interference information in the image is removed, so that the efficiency and the accuracy of the screen detection scheme are improved. Moreover, the embodiment of the application utilizes a line extraction algorithm to detect lines and line center points in the edge region, on the basis, defective points are extracted and screened based on binarization, the detection values of defect indexes (such as contrast, area and the like) of edge defects (such as edge black lines and edge scratches) are calculated, and the detection values are compared with corresponding defect indexes to obtain detection results.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart illustrating a screen detection method according to an embodiment of the present application;
FIG. 2 shows a schematic diagram of an original image of one embodiment of the present application;
FIG. 3 is a schematic diagram of a binary image after binarization of the original image shown in FIG. 2;
FIG. 4 is a schematic diagram illustrating the original image of FIG. 2 after screen profiling;
FIG. 5 shows a schematic diagram of a test image of one embodiment of the present application;
FIG. 6 is a diagram illustrating a result of edge region extraction performed on the detected image shown in FIG. 5 according to an embodiment of the present application;
FIG. 7a shows a schematic view of an edge region in an embodiment of the present application;
FIG. 7b shows a schematic view of an edge defect contained in the edge region shown in FIG. 7 a;
FIG. 7c is an enlarged view of the location of the edge defect shown in FIG. 7 b;
FIG. 8a is a schematic diagram of a suspected defect area extracted according to one embodiment of the present application;
FIG. 8b is a diagram illustrating a binarized suspected defect area shown in FIG. 8 a;
FIG. 9 shows a block diagram of a screen detection apparatus of an embodiment of the present application;
fig. 10 shows a schematic structural diagram of a head-mounted display device according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
At present, for assembled VR (Virtual Reality) products, human eyes are generally adopted to check whether the VR products are flawless through lenses (lenses), and the specific mode is as follows: control VR screen output different colours's picture card, people's eye looks over under the different picture cards whether have the defect through the lens, for example, this defect shows as green screen dim spot, green screen bright spot, red screen dim spot, black screen bright spot, white screen dim spot, mar, edge black line etc..
There is also a scheme that detection and differentiation are performed through a Halcon machine vision algorithm, and due to the characteristics that edge defects are located at the edge of an image effective area, distortion is large, adhesion with a background is caused, and the like, edge defects such as edge black lines and edge scratches are difficult to distinguish by the Halcon vision algorithm, that is, the accuracy of edge defect detection is low.
In contrast, in the embodiment of the present application, a screen contour is used as a boundary, a line center point detected by a line extraction algorithm is used as a reference, a defect point in a suspected defect area is extracted in a binarization manner, and then defect index detection values such as an area and a contrast of the suspected defect area are calculated, and according to a comparison result between the defect index detection value and a standard threshold, whether the defect meets a defect standard is determined.
When the line center point is detected, a Halcon visual algorithm can be used as a line extraction algorithm, and the line _ gauss function is called to detect the line center point.
Fig. 1 is a schematic flowchart illustrating a screen detection method according to an embodiment of the present application, and referring to fig. 1, the screen detection method according to the embodiment of the present application includes:
step S110, obtaining an original image of a screen to be detected, removing interference information in the original image, and obtaining a detection image containing a screen outline.
The original image is, for example, an image acquired by photographing an image output from a screen to be inspected. The interference information affects not only the accuracy of detection but also the detection efficiency, and therefore, the embodiment of the application removes the interference information in the original image.
Step S120, one or more edge regions of the detected image are extracted according to the screen contour.
Step S130, detecting and determining lines in the edge area and the position of a line central point by using a line extraction algorithm, taking the screen contour as a boundary, and selecting an area block containing a plurality of pixel points where the line central point is located as a suspected defect area when the position of the line central point is within the screen contour.
And step S140, selecting key pixel points in the suspected defect area, binarizing the key pixel points based on a binarization threshold value, and obtaining a detection value of the defect index according to the binarization result of the key pixel points.
And S150, comparing the detection value of the defect index with the corresponding defect index to obtain a detection result.
As shown in fig. 1, in the screen detection method according to the embodiment of the present application, a screen contour is used as a boundary, a line of an edge region and a position of a line center point are detected and determined by a line extraction algorithm, on this basis, a defect point in a suspected defect region is extracted by a binarization method, a detection value of a defect index is further calculated, and the detection value of the defect index is compared with a corresponding defect index, so as to obtain a detection result of whether the suspected edge defect point meets a defect standard. The technical problem that the edge defect cannot be accurately detected due to the fact that the edge defect and the boundary background are adhered together is solved, the detection values of defect indexes such as contrast, area and the like of the edge defect can be conveniently and accurately calculated through a Halcon visual algorithm, then screen detection is rapidly and accurately achieved, and the actual application requirements are met.
In order to better understand the implementation steps of the screen detection method according to the embodiment of the present application, the following description is made with reference to specific embodiments.
Fig. 2 is a schematic diagram of an original image according to an embodiment of the present application, and a screen inspection method of the present application first obtains an original image of a screen to be inspected, as shown in fig. 2.
Before the screen detection is carried out, the detected effective area is extracted to eliminate the interference. That is, the interference information in the original image is removed, and a detected image is obtained. The method specifically comprises the following steps: carrying out binarization processing on the original image, and converting the original image into a binary image; searching a screen outline on the binary image, correspondingly drawing the screen outline on the original image according to the searched screen outline, and determining a minimum circumscribed rectangle of the screen outline; and determining an effective area on the original image based on the minimum circumscribed rectangle, and extracting the effective area to obtain a detection image.
It should be understood that the process of removing the interference information in the original image is the process of extracting the effective area in the image. The manner of extracting the effective region includes, for example, the following steps:
step one, invoking an adaptive threshold () function in a processor to perform binarization processing on the original image shown in fig. 2, and obtaining a binary image, where the obtained binary image is shown in fig. 3.
Step two, on the basis of the binary image shown in fig. 3, finding out the screen contour by contour search, and correspondingly drawing the screen contour on the original image shown in fig. 2 to obtain fig. 4. The positions indicated by white arrows in fig. 4 are part of the located contours. Note: the contour search or contour search may be implemented in the prior art, for example, using an OpenCV (Open Source Computer Vision Library) platform.
And step three, determining the minimum circumscribed rectangle of the screen outline according to the screen outline, and intercepting the effective area to obtain an effective area image, namely a detection image. The width of the detected image is denoted as width, the height is denoted as height, and the detected image is shown in fig. 5.
Thus, a detection image including the screen outline is obtained. After the detection image containing the screen outline is obtained, the Halcon algorithm is used for detecting the defect of the edge area in the embodiment of the application.
Because the image resolution is high, if Halcon is used for detecting defects on the whole image, a long time is needed, so in order to improve the algorithm efficiency, the embodiment of the application adopts a mode of extracting edge regions, namely, one or more edge regions of the detected image are extracted according to the screen contour.
Illustratively, according to the embodiment of the application, a first line segment, a second line segment, a third line segment and a fourth line segment are selected from a detected image according to a screen contour; the length of the first line segment and the length of the second line segment are equal to the width value of the detection image, the distance from the first line segment to the upper side of the detection image and the distance from the second line segment to the upper side of the detection image are both determined according to the height value of the detection image, and the distance from the second line segment to the upper side of the detection image is larger than the distance from the first line segment to the upper side of the detection image; the length of the third line segment and the length of the fourth line segment are equal to the height value of the detection image, the distance from the third line segment to the left of the detection image and the distance from the fourth line segment to the left of the detection image are both determined according to the width value of the detection image, and the distance from the fourth line segment to the left of the detection image is larger than the distance from the third line segment to the left of the detection image.
The four edge regions of the detection image are extracted from the four line segments, specifically, a region which is more than the first line segment and less than the upper edge of the detection image is extracted as a first edge region, a region which is less than the second line segment and more than the lower edge of the detection image is extracted as a second edge region, a region which is more than the left side of the detection image and more than the left side of the third line segment is extracted as a third edge region, and a region which is more than the right side of the fourth line segment and more than the left side of the detection image and more than the right side of the.
In conjunction with fig. 6, the point at the upper left corner of the detected image in fig. 6, that is, the origin of the detected image is point0, and the coordinates of point0 are (0, 0). According to the embodiment of the application, a first line segment 610, a second line segment 620, a third line segment 630 and a fourth line segment 640 are selected from a detection image according to a screen outline, and four edge areas, namely, an upper edge area, a lower edge area, a left edge area and a right edge area of the detection image are extracted according to the four line segments so as to detect edge defects. The coordinates of the foregoing four edge regions are respectively expressed as follows:
first edge area _ top (0,0, width, height/6), where (0,0) represents the pixel position of the top left vertex of the first edge area, width represents the width of the first edge area (i.e., the width of the first edge area is equal to the width value in the horizontal direction of the detection image), and height/6 represents the height of the first edge area, i.e., the height of the first edge area is equal to the height value of the detection image shown in fig. 5 divided by 6. For example, if the height of the detected image is 30 pixels, the height of the first edge region is equal to: 30/6-5 pixels.
Referring to fig. 6, a second edge region area _ bottom (0, height 5/6, width, height/6), which is a region opposite to the top and bottom positions of the first edge region, where (0, height 5/6) represents the pixel position of the top left corner of the second edge region, width represents the width of the second edge region (i.e., the width of the second edge region is equal to the width value of the detected image), and height/6 represents the height of the second edge region (i.e., the height of the second edge region is equal to the height value of the detected image divided by 6 in fig. 5).
Referring to fig. 6, a third edge region area _ left (0,0, width/6, height), where (0,0) represents the pixel position of the top left corner vertex of the third edge region, width/6 represents the width of the third edge region (i.e., the width of the third edge region is equal to the width value of the detected image divided by 6), and height represents the height of the third edge region (i.e., the height of the third edge region is equal to the height value of the detected image).
The fourth edge region area _ right (width 5/6,0, width/6, height). Referring to fig. 6, the fourth edge region is a region right and left opposite to the position of the third edge region, where (width 5/6,0) represents the pixel position of the top left corner vertex of the fourth edge region, width/6 represents the width of the fourth edge region (i.e., the width of the fourth edge region is equal to the width value of the detection image divided by 6), and height represents the height of the fourth edge region (i.e., the height of the fourth edge region is equal to the height value of the detection image).
In the embodiment of the present invention, since the edge regions are partitioned by the maximum screen outline inscribed rectangle according to the detected image (rectangle), and the partitioned edge regions are also rectangles, the width value of the first edge region is equal to the width value of the second edge region, and the height values of the third edge region and the fourth edge region are equal.
And after the four edge areas are divided, carrying out edge defect detection on the edge areas. Since the defect detection processes of the four edge regions are the same, for the sake of brevity, only the second edge region of the four edge regions is taken as an example for description.
Referring to fig. 7a, for the edge region shown in fig. 7a, the embodiment of the present application first determines a line (or a line) in the current edge region and a position of a line center point by using a line extraction algorithm. It should be noted that the line extraction algorithm is the aforementioned Halcon algorithm, and specifically uses the lines _ gauss function of the Halcon algorithm, and the lines _ gauss function is used to detect lines and their widths in the image. Under the parameter with contrast, lines _ glass can return more accurate line positions, lines in the current edge region can be detected through lines _ gauss, and the pixel position of the center point of the line can be determined.
Extraction results referring to fig. 7b and 7c, two lines of edge defects are illustrated in a circle surrounded by a white dotted line in fig. 7b, fig. 7c is an enlarged schematic view of an image around the white circle in fig. 7b, and as is clear from fig. 7c, two intersecting lines with partial overlap exist at the screen boundary of the edge region.
Because the distance between the edge defect lines and the image boundary is short, accurate detection is difficult generally, for example, key indexes such as the area of a defect area and the contrast of a defect point are difficult to obtain, so that the accuracy of judging the defect is low. In contrast, in the embodiment of the application, the screen contour is used as a boundary, when the position of the line center point is within the screen contour, the area block containing the plurality of pixel points where the line center point is located is selected as the suspected defect area, and key pixel points in the suspected defect area are binarized, so that the detection value of the defect index is calculated.
During specific implementation, the distance between the line central point and the corresponding screen contour can be calculated according to the position of the line central point, and if the distance is smaller than a preset distance threshold value, the position of the line central point is considered to be within the screen contour. The calculation formula of the distance between the line center point and the corresponding screen contour is as follows:
Distance＝pointPolygonTest(vec,point(x,y),false)。
considering that when the Distance between the defective pixel point of the edge part and the screen contour line is too close, the defect is easily affected by the screen boundary and cannot be distinguished, therefore, in the embodiment of the application, when the Distance between the line center point and the corresponding screen contour line is greater than the threshold value, the region block containing a plurality of pixel points where the line center point is located is selected as the suspected defect region, and binarization processing is performed. For example, a 50 × 50 area where the central point of the line is located is selected as a suspected defect area, and an image containing the suspected defect area is shown in fig. 8 a.
Next, the suspected defect area shown in fig. 8a is binarized.
The binarization processing selects the key pixel points first, because the probability that the pixel points within the screen outline in the suspected defect area belong to the defect is higher, the key pixel points are selected first to improve the detection efficiency and accuracy.
The process of selecting the key pixel point is as follows: calculating the distance from each pixel point to the corresponding screen outline for each pixel point in the suspected defect area; and taking the pixel points with the distance greater than the preset distance threshold value as key pixel points.
For example, whether a certain pixel point in the suspected defect area is within the screen contour line is calculated by the following formula:
areain＝pointPolygonTest(vec,point(x,y),true)
wherein, areain1 denotes in the outline, areainAnd-1 is outside the contour.
For the pixel points within the screen contour line, namely the key pixel points, the gray value of the key pixel points is obtained, and binarization is performed on the key pixel points based on the binarization threshold value.
Specifically, the gray value of the key pixel point is compared with the binarization threshold, for different edge areas, the size of the suspected defect area in the edge area is the same or different, and the binarization threshold corresponding to the suspected defect area is the same or different. In consideration of the influence of the image brightness and the like in each edge region, the sizes of the suspected defect regions in each edge region may be the same (for example, the size of each region block is 50 × 50), or may be different, and accordingly, the binarization thresholds corresponding to the suspected defect regions are the same or different. That is to say, the binarization threshold is not fixed, and can be set according to the gray level characteristics of the image of the suspected defect area, so that the self-adaptive capability and the detection accuracy of the screen detection algorithm are improved.
The method and the device for obtaining the key pixel point binarization result have the advantages that the grey value of the key pixel point is obtained, the grey value of the key pixel point with the grey value larger than or equal to the binarization threshold value is set to be a first numerical value, the grey value of the key pixel point with the grey value smaller than the binarization threshold value is set to be a second numerical value, and the binarization result of the key pixel point is obtained.
Illustratively, the binarization formula is as follows:
pixel _ gray in the binarization formula represents the gray value of the key pixel point, 255 is a first numerical value, and 0 is a second numerical value. That is, if the gray value of the key pixel is greater than or equal to the binarization threshold, the gray value is set to 255, the gray value of the key pixel is less than the binarization threshold, and the gray value is set to 0.
After the above binarization is performed on the suspected defect area shown in fig. 8a, the result is shown in fig. 8b, and the white bright portion in fig. 8b is the result of binarization of the defect point (i.e. the key pixel point) in the suspected defect area.
After the binarization result of the key pixel points is obtained, the detection value of the defect index is obtained according to the binarization result of the key pixel points, specifically, the number of the key pixel points with the gray value as a first numerical value is counted, and the counted number is used as the detection value of the defect area index; or, according to the position of the key pixel point on the detection image and the binarization result of the key pixel point, determining the corresponding pixel point of the target pixel point, calculating a gray average value according to the gray value of each corresponding pixel point, and calculating to obtain a detection value of the contrast index according to the gray average value and the gray value of each corresponding pixel point, wherein the target pixel point is the key pixel point of which the gray value is the first numerical value after binarization processing.
It should be noted that, the corresponding pixel point here refers to a pixel point on the detected image corresponding to the position of the target pixel point, and since the suspected defect area is extracted and the image (as shown in fig. 8a) containing the suspected defect area is binarized in the embodiment of the present application, the gray value of the pixel point in the suspected defect area is indicated on the binarization result image (as shown in fig. 8b), and in order to find the edge defect point and further calculate the edge defect index detection value, the target pixel point (the key pixel point whose gray value is the first value) shown in fig. 8b needs to be corresponded to the detected image, that is, the pixel point corresponding to the position of the target pixel point is located on the detected image.
In practical application, the embodiment of the present application may determine whether the suspected defect point meets the defect standard only according to the area index of the defect area, or determine whether the suspected defect point meets the defect standard only according to the contrast index.
Further, the detected value of the defect index is compared with the corresponding defect index to obtain a detected result, for example, in the embodiment, the detected value of the defect area index is compared with the defect area index, and if the detected value of the defect area index satisfies the defect area index, the suspected defect area is determined to be an edge defect area. Alternatively, in the application example, the detection value of the contrast index is compared with the contrast index, and if the detection value of the contrast index satisfies the contrast index, it is determined that the suspected defect area is the edge defect area. Alternatively, in the application example, the detection value of the defect region area index is compared with the defect region area index, the detection value of the contrast index is compared with the contrast index, and if the detection value of the defect region area index satisfies the defect region area index and the detection value of the contrast index satisfies the contrast index, it is determined that the suspected defect region is an edge defect region.
For example, the number of key pixel points with a first gray value is counted, for example, the counted number is 8, that is, the detection value of the defect area index is 8 pixels, the detection value (8 pixels) of the defect area index is compared with the defect area index, if the content of the defect area index is that the detection value is greater than 7 pixels, it is determined that the edge defect region is determined, after the comparison, the current detection value satisfies the defect area index, and it is determined that the currently detected suspected defect region is the edge defect region.
Therefore, by calculating the detection value of the defect index, the detection value of the defect index is compared with the corresponding defect index to obtain the detection result of whether the suspected edge defect point meets the defect standard, the technical problem that the edge defect cannot be accurately detected due to the fact that the edge defect and the boundary background are adhered together is solved, the detection values of the defect indexes such as the contrast of the edge defect, the area size and the like can be conveniently and accurately calculated through a Halcon visual algorithm, and then the rapid and accurate screen detection is realized, the accuracy of the screen detection is improved, and the actual application requirements are met.
Fig. 9 shows a block diagram of a screen detecting apparatus according to an embodiment of the present application, and referring to fig. 9, the screen detecting apparatus 900 includes:
an image obtaining unit 910, configured to obtain an original image of a screen to be detected, remove interference information in the original image, and obtain a detection image including a screen contour;
an area determination unit 920, configured to extract one or more edge areas of the detected image according to the screen contour; determining lines in the edge region and the positions of line center points by using a line extraction algorithm, taking the screen contour as a boundary, and selecting an area block containing a plurality of pixel points where the line center points are located as a suspected defect region when the positions of the line center points are within the screen contour;
a binarization unit 930, configured to select a key pixel point in the suspected defect region, binarize the key pixel point based on a binarization threshold, and obtain a detection value of the defect index according to a binarization result of the key pixel point;
and a determining unit 940, configured to compare the detection value of the defect index with the corresponding defect index to obtain a detection result.
In an embodiment of the present application, the area determining unit 920 is specifically configured to select a first line segment, a second line segment, a third line segment, and a fourth line segment in the detected image according to the screen contour; the length of the first line segment and the length of the second line segment are equal to the width value of the detection image, the distance from the first line segment to the upper side of the detection image and the distance from the second line segment to the upper side of the detection image are both determined according to the height value of the detection image, and the distance from the second line segment to the upper side of the detection image is larger than the distance from the first line segment to the upper side of the detection image; the length of the third line segment and the length of the fourth line segment are equal to the height value of the detection image, the distance from the third line segment to the left of the detection image and the distance from the fourth line segment to the left of the detection image are both determined according to the width value of the detection image, and the distance from the fourth line segment to the left of the detection image is larger than the distance from the third line segment to the left of the detection image; the four edge regions of the detection image are extracted from the four line segments, specifically, a region which is more than the first line segment and less than the upper edge of the detection image is extracted as a first edge region, a region which is less than the second line segment and more than the lower edge of the detection image is extracted as a second edge region, a region which is more than the left side of the detection image and more than the left side of the third line segment is extracted as a third edge region, and a region which is more than the right side of the fourth line segment and more than the left side of the detection image and more than the right side of the.
In an embodiment of the present application, the binarization unit 930 is specifically configured to calculate, for each pixel point in the suspected defect area, a distance from each pixel point to a corresponding screen contour; and taking the pixel points with the distance greater than the preset distance threshold value as key pixel points.
In an embodiment of the present application, the binarization unit 930 is specifically configured to obtain a gray value of a key pixel, compare the gray value of the key pixel with a binarization threshold, and for different edge areas, the size of a suspected defect area in the edge area is the same or different, and the binarization thresholds corresponding to the suspected defect area are the same or different; and setting the gray value of the key pixel point with the gray value greater than or equal to the binarization threshold value as a first numerical value, and setting the gray value of the key pixel point with the gray value smaller than the binarization threshold value as a second numerical value to obtain the binarization result of the key pixel point.
In an embodiment of the present application, the binarization unit 930 is specifically configured to count the number of key pixel points with a first gray value, and use the counted number as a detection value of the defect area index; or, according to the position of the key pixel point on the detection image and the binarization result of the key pixel point, determining the corresponding pixel point of the target pixel point, calculating a gray average value according to the gray value of each corresponding pixel point, and calculating to obtain a detection value of the contrast index according to the gray average value and the gray value of each corresponding pixel point, wherein the target pixel point is the key pixel point of which the gray value is the first numerical value after binarization processing.
In an embodiment of the present application, the determining unit 940 is configured to compare the detected value of the defect area index with the defect area index, and determine that the suspected defect area is an edge defect area if the detected value of the defect area index satisfies the defect area index; or comparing the detection value of the contrast index with the contrast index, and if the detection value of the contrast index meets the contrast index, determining that the suspected defect area is the edge defect area; or, comparing the detected value of the defect region area index with the defect region area index, comparing the detected value of the contrast index with the contrast index, and determining that the suspected defect region is an edge defect region if the detected value of the defect region area index satisfies the defect region area index and the detected value of the contrast index satisfies the contrast index, respectively.
In an embodiment of the present application, the image obtaining unit 910 is specifically configured to perform binarization processing on an original image, and convert the original image into a binary image; searching a screen outline on the binary image, correspondingly drawing the screen outline on the original image according to the searched screen outline, and determining a minimum circumscribed rectangle of the screen outline; and determining an effective area on the original image based on the minimum circumscribed rectangle, and extracting the effective area to obtain a detection image.
It should be noted that, the screen detecting apparatus can implement the steps of the screen detecting method executed by the head-mounted display device provided in the foregoing embodiment, and the related explanations about the screen detecting method are applicable to the screen detecting apparatus, and are not described herein again.
It should be noted that: fig. 10 illustrates a schematic structural diagram of a head-mounted display device. Referring to fig. 10, at a hardware level, the head-mounted display device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the head mounted display device may also include hardware needed for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 10, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the screen detection device on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring an original image of a screen to be detected, removing interference information in the original image, and obtaining a detection image containing a screen outline;
extracting one or more edge regions of the detected image according to the screen outline;
detecting and determining lines in the edge region and the positions of line central points by using a line extraction algorithm, taking the screen contour as a boundary, and selecting an area block containing a plurality of pixel points where the line central points are located as a suspected defect area when the positions of the line central points are within the screen contour;
selecting key pixel points in the suspected defect area, carrying out binarization on the key pixel points based on a binarization threshold value, and obtaining a detection value of a defect index according to a binarization result of the key pixel points;
and comparing the detection value of the defect index with the corresponding defect index to obtain a detection result.
The functions performed by the screen detecting apparatus according to the embodiment shown in fig. 9 of the present application may be implemented in or by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The head-mounted display device may further perform the steps performed by the screen detection method in fig. 1, and implement the functions of the screen detection method in the embodiment shown in fig. 1, which are not described herein again in this embodiment of the application.
An embodiment of the present application further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by a head-mounted display device including a plurality of application programs, enable the head-mounted display device to perform the screen detection method in the embodiment shown in fig. 1, and are specifically configured to perform:
acquiring an original image of a screen to be detected, removing interference information in the original image, and obtaining a detection image containing a screen outline; extracting one or more edge regions of the detected image according to the screen outline; detecting and determining lines in the edge region and the positions of line central points by using a line extraction algorithm, taking the screen contour as a boundary, and selecting an area block containing a plurality of pixel points where the line central points are located as a suspected defect area when the positions of the line central points are within the screen contour; selecting key pixel points in the suspected defect area, carrying out binarization on the key pixel points based on a binarization threshold value, and obtaining a detection value of a defect index according to a binarization result of the key pixel points; and comparing the detection value of the defect index with the corresponding defect index to obtain a detection result.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) characterized by computer-usable program code.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) characterized by computer-usable program code.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A screen detection method, comprising:
acquiring an original image of a screen to be detected, removing interference information in the original image, and obtaining a detection image containing a screen outline;
extracting one or more edge regions of the detection image according to the screen outline;
detecting and determining lines in the edge region and the positions of line central points by using a line extraction algorithm, taking the screen contour as a boundary, and selecting an area block containing a plurality of pixel points where the line central points are located as a suspected defect area when the positions of the line central points are within the screen contour;
selecting key pixel points in the suspected defect area, carrying out binarization on the key pixel points based on a binarization threshold value, and obtaining a detection value of a defect index according to a binarization result of the key pixel points;
and comparing the detection value of the defect index with the corresponding defect index to obtain a detection result.
2. The method of claim 1, wherein said extracting one or more edge regions of the detected image from the screen profile comprises:
selecting a first line segment, a second line segment, a third line segment and a fourth line segment in the detection image according to the screen outline; the length of the first line segment and the length of the second line segment are both equal to the width value of the detection image, the distance from the first line segment to the upper side of the detection image and the distance from the second line segment to the upper side of the detection image are both determined according to the height value of the detection image, and the distance from the second line segment to the upper side of the detection image is larger than the distance from the first line segment to the upper side of the detection image; the length of the third line segment and the length of the fourth line segment are both equal to the height value of the detection image, the distance from the third line segment to the left of the detection image and the distance from the fourth line segment to the left of the detection image are both determined according to the width value of the detection image, and the distance from the fourth line segment to the left of the detection image is greater than the distance from the third line segment to the left of the detection image;
the four edge regions of the detection image are extracted according to the four line segments, specifically, a region which is more than the first line segment and less than the upper edge of the detection image is extracted as a first edge region, a region which is less than the second line segment and more than the lower edge of the detection image is extracted as a second edge region, a region which is more than the third line segment and more than the left side of the detection image is extracted as a third edge region, and a region which is more than the fourth line segment and more than the right side of the detection image is extracted as a fourth edge region.
3. The method of claim 1, wherein said selecting key pixels in the suspected defect area comprises:
calculating the distance from each pixel point to the corresponding screen contour for each pixel point in the suspected defect area;
and taking the pixel points with the distance greater than the preset distance threshold value as key pixel points.
4. The method of claim 3, wherein the binarizing key pixels within the suspected defect region based on a binarization threshold comprises:
acquiring a gray value of a key pixel point; comparing the gray value of the key pixel point with the size of the binarization threshold, wherein for different edge areas, the size of suspected defect areas in the edge areas is the same or different, and the binarization thresholds corresponding to the suspected defect areas are the same or different;
and setting the gray value of the key pixel point with the gray value greater than or equal to the binarization threshold value as a first numerical value, and setting the gray value of the key pixel point with the gray value smaller than the binarization threshold value as a second numerical value to obtain the binarization result of the key pixel point.
5. The method as claimed in claim 4, wherein the obtaining the detection value of the defect index according to the binarization result of the key pixel point comprises:
counting the number of key pixel points with gray values as first values, and taking the counted number as a detection value of the defect area index;
or the like, or, alternatively,
determining corresponding pixel points of target pixel points according to the positions of the key pixel points on the detection image and the binarization results of the key pixel points, calculating a gray average value according to the gray values of the corresponding pixel points, and calculating to obtain a detection value of a contrast index according to the gray average value and the gray values of the corresponding pixel points, wherein the target pixel points are the key pixel points of which the gray values are first numerical values after binarization processing.
6. The method of claim 5, wherein comparing the detected values of the defect indicators with the corresponding defect indicators to obtain the detected results comprises:
comparing the detection value of the defect area index with the defect area index, and if the detection value of the defect area index meets the defect area index, determining that the suspected defect area is an edge defect area;
or the like, or, alternatively,
comparing the detection value of the contrast index with the contrast index, and if the detection value of the contrast index meets the contrast index, determining that the suspected defect area is an edge defect area;
or the like, or, alternatively,
and comparing the detected value of the defect region area index with the defect region area index, comparing the detected value of the contrast index with the contrast index, and determining that the suspected defect region is the edge defect region if the detected value of the defect region area index meets the defect region area index and the detected value of the contrast index meets the contrast index.
7. The method of any one of claims 1-6, wherein removing the interference information from the original image to obtain a detected image containing the screen outline comprises:
carrying out binarization processing on the original image, and converting the original image into a binary image;
searching the screen outline on the binary image, correspondingly drawing the screen outline on the original image according to the searched screen outline, and determining the minimum circumscribed rectangle of the screen outline;
and determining an effective area on the original image based on the minimum circumscribed rectangle, and extracting the effective area to obtain the detection image.
8. A screen detecting apparatus, comprising:
the image acquisition unit is used for acquiring an original image of a screen to be detected, removing interference information in the original image and obtaining a detection image containing a screen outline;
the area determining unit is used for extracting one or more edge areas of the detection image according to the screen outline; determining lines in the edge region and the positions of line center points by using a line extraction algorithm, taking the screen contour as a boundary, and selecting an area block containing a plurality of pixel points where the line center points are located as a suspected defect region when the positions of the line center points are within the screen contour;
the binarization unit is used for selecting key pixel points in the suspected defect area, binarizing the key pixel points based on a binarization threshold value, and obtaining a detection value of the defect index according to a binarization result of the key pixel points;
and the judging unit is used for comparing the detection value of the defect index with the corresponding defect index to obtain a detection result.
9. The apparatus of claim 8,
the area determining unit is specifically used for selecting a first line segment, a second line segment, a third line segment and a fourth line segment in the detection image according to the screen contour; the length of the first line segment and the length of the second line segment are both equal to the width value of the detection image, the distance from the first line segment to the upper side of the detection image and the distance from the second line segment to the upper side of the detection image are both determined according to the height value of the detection image, and the distance from the second line segment to the upper side of the detection image is larger than the distance from the first line segment to the upper side of the detection image; the length of the third line segment and the length of the fourth line segment are both equal to the height value of the detection image, the distance from the third line segment to the left of the detection image and the distance from the fourth line segment to the left of the detection image are both determined according to the width value of the detection image, and the distance from the fourth line segment to the left of the detection image is greater than the distance from the third line segment to the left of the detection image;
the four edge regions of the detection image are extracted according to the four line segments, specifically, a region which is more than the first line segment and less than the upper edge of the detection image is extracted as a first edge region, a region which is less than the second line segment and more than the lower edge of the detection image is extracted as a second edge region, a region which is more than the third line segment and more than the left side of the detection image is extracted as a third edge region, and a region which is more than the fourth line segment and more than the right side of the detection image is extracted as a fourth edge region.
10. A head-mounted display device characterized in that it comprises the screen detecting apparatus according to claim 8 or 9.
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