Image processing method, device, computer equipment and storage medium

Abstract

The embodiments of the present disclosure provide an image processing method, apparatus, computer device, and storage medium, which relate to the field of computers and can solve the problems of low image processing efficiency and low image processing accuracy. The image processing method includes: in response to an instruction for processing an image, screening out multiple target images from all acquired images; determining a first eigenvalue and a second eigenvalue of each pixel in a target area of ​​each target image, determining the similarity between the first eigenvalues ​​of the target areas in each two target images based on all the determined first eigenvalues, and determining the similarity between the second eigenvalues ​​of the target areas in each two target images based on all the determined second eigenvalues; if the first target similarity is within a first preset range and the second target similarity is within a second preset range, determining that the first image and the second image belong to the same image set.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, and in particular to an image processing method, apparatus, computer equipment, and storage medium. Background Art

[0002] During the display panel backplane manufacturing process, AOI (automated optical inspection) is often used to monitor each process step to detect defects. The output data from AOI is an image, such as a camera capturing a defect image. Deep learning techniques are then used to perform image recognition on these defect images, identifying their characteristic information to improve the quality and yield of subsequent display panel production.

[0003] Deep learning technology uses neural network algorithms. These algorithms typically require a large number of high-quality images as samples to train the neural network model, ensuring the model converges in the correct direction. High-quality images can be obtained through image data processing.

[0004] Currently, image data processing primarily relies on manual processing. However, manual processing relies on human operators to judge large amounts of image data, which is complex and time-consuming. Furthermore, different operators can lead to inconsistent processing results. Summary of the Invention

[0005] The embodiments of the present disclosure provide an image processing method, apparatus, computer device, and storage medium, which can solve the problems of low image processing efficiency and low image processing accuracy.

[0006] To achieve the above objectives, the embodiments of the present disclosure adopt the following technical solutions:

[0007] In a first aspect, a method for processing an image is provided, which includes: an image processing device, in response to an instruction for processing an image, screening out multiple target images from all acquired images, where the target image is an image including a target area, wherein the target area includes an area where information of pixel points in the target image is aperiodic; then the image processing device determines a first eigenvalue and a second eigenvalue of each pixel in the target area of ​​each target image; based on all the determined first eigenvalues, determines the similarity between the first eigenvalues ​​of the target areas in each two target images, and based on all the determined second eigenvalues, determines the similarity between the second eigenvalues ​​of the target areas in each two target images; if the first target similarity is within a first preset range and the second target similarity is within a second preset range, it is determined that the first image and the second image belong to the same image set; the first target similarity is the similarity between the first eigenvalues ​​of the target areas in the first image and the second image, and the second target similarity is the similarity between the second eigenvalues ​​of the target areas in the first image and the second image.

[0008] Based on the first aspect, after receiving the processing instruction, the image processing device screens out multiple target images and determines the first eigenvalue and the second eigenvalue of each pixel in the target area of ​​each target image, and then determines the similarity between the first eigenvalues ​​of the target areas in each two target images based on all the first eigenvalues, and determines the similarity between the second eigenvalues ​​of the target areas in each two target images based on all the determined second eigenvalues. If the first target similarity is within a first preset range and the second target similarity is within a second preset range, it is determined that the first image and the second image belong to the same set. It can be seen that the image processing method provided by the embodiment of the present disclosure replaces the manual processing method, thereby improving the accuracy of image processing and improving the efficiency of image processing.

[0009] Optionally, the image processing method further includes: determining an identifier of the image set.

[0010] Based on this optional approach, the image processing device can identify the image sets, which can not only distinguish each image set, but also intuitively display the features of the target images in each image set through the identification.

[0011] Optionally, multiple target images are screened out from all acquired images, including: an image processing device acquires information of each pixel in each image; determines interference fringes of the image based on the information of the pixel points, and filters out the interference fringes to obtain a filtered image; if the information of the pixel points in the filtered image is non-periodic, the image is determined as the target image.

[0012] Based on this optional method, the image processing device can determine the interference fringes according to the information of each pixel point in each image, and filter out the interference fringes to obtain a filtered image. If the information of the pixel points in the filtered image is non-periodic, that is, there is a defective area in the filtered image, then the image is determined as the target image. Since the area where the information of the pixel points in the image is non-periodic can be clearly seen after filtering out the interference fringes, the influence of the interference fringes on the filtered target image is avoided, which is beneficial to improving the accuracy of the filtered target image.

[0013] Optionally, the image processing apparatus determines, based on all the determined first eigenvalues, the similarity between the first eigenvalues ​​of the target regions in each two target images, including: for image A and image B, determining, based on the first eigenvalue of the target region in image A and the first eigenvalue of the target region in image B, that the similarity between the first eigenvalues ​​of the target regions in image A and image B satisfies the following formula:

[0014]

[0015] Among them, A i represents the first eigenvalue of image A, B j represents the j-th first eigenvalue of image B, 1≤i≤z, 1≤j≤z; represents the sum of the z first eigenvalues ​​of image A and the z first eigenvalues ​​of image B; represents the arithmetic square root of the sum of the z first eigenvalues ​​of image A, Represents the arithmetic square root of the sum of the z first eigenvalues ​​of image B, S α is the similarity between the first eigenvalues ​​of the target area in image A and image B.

[0016] Based on this optional approach, since the similarity between the first eigenvalues ​​of the target areas in every two target images satisfies the above formula, the calculated similarity between the first eigenvalues ​​is more accurate.

[0017] Optionally, the image processing apparatus determines the similarity between the second eigenvalues ​​of the target areas in each two target images based on all the determined second eigenvalues, including: for images A and B, determining, based on the second eigenvalue of the target area in image A and the second eigenvalue of the target area in image B, that the similarity between the second eigenvalues ​​of the target areas in images A and B satisfies the following formula:

[0018]

[0019] Among them, A n Indicates the nth second eigenvalue of image A, B mrepresents the mth second eigenvalue of image B, 1≤n≤k, 1≤m≤k; represents the sum of the k second eigenvalues ​​of image A and the k second eigenvalues ​​of image B; represents the arithmetic square root of the sum of the k second eigenvalues ​​of image A, represents the arithmetic square root of the sum of the k second eigenvalues ​​of image B, S β is the similarity between the second eigenvalues ​​of the target area in image A and image B.

[0020] Based on this optional approach, since the similarity between the second eigenvalues ​​of the target areas in every two target images satisfies the above formula, the calculated similarity between the second eigenvalues ​​is more accurate.

[0021] In a second aspect, an image processing device is provided. The image processing device can implement the functions performed by the image processing device in the first aspect or the optional method of the first aspect. The functions can be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, such as a response module and a processing module. Specifically, the response module is configured to respond to a processing instruction for an image; the processing module is configured to filter out multiple target images from all acquired images; wherein the target image is an image including a target area; the target area includes an area where information of pixels in the target image is non-periodic; the processing module is also configured to determine the first eigenvalue and the second eigenvalue of each pixel in the target area of ​​each target image; the processing module is also configured to determine the similarity between the first eigenvalues ​​of the target areas in each two target images based on all the determined first eigenvalues, and determine the similarity between the second eigenvalues ​​of the target areas in each two target images based on all the determined second eigenvalues; the processing module is also configured to determine whether the first image and the second image belong to the same image set; specifically, if the first target similarity is within a first preset range and the second target similarity is within a second preset range, it is determined that the first image and the second image belong to the same image set; the first target similarity is the similarity between the first eigenvalues ​​of the target areas in the first image and the second image, and the second target similarity is the similarity between the second eigenvalues ​​of the target areas in the first image and the second image.

[0022] Optionally, the image processing device further includes a determination module; the determination module is configured to determine an identifier of the image set.

[0023] Optionally, the processing module is specifically configured to obtain information of each pixel in each image; determine the interference fringes of the image based on the information of the pixel points, and filter out the interference fringes to obtain a filtered image; if the information of the pixel points in the filtered image is non-periodic, the image is determined as the target image.

[0024] Optionally, the processing module is specifically configured to, for picture A and picture B, determine, based on the first eigenvalue of the target area in picture A and the first eigenvalue of the target area in picture B, whether the similarity between the first eigenvalues ​​of the target area in picture A and the first eigenvalue of the target area in picture B satisfies the following formula:

[0025]

[0026] Among them, A i represents the first eigenvalue of image A, B j represents the j-th first eigenvalue of image B, 1≤i≤z, 1≤j≤z; represents the sum of the z first eigenvalues ​​of image A and the z first eigenvalues ​​of image B; represents the arithmetic square root of the sum of the z first eigenvalues ​​of image A, Represents the arithmetic square root of the sum of the z first eigenvalues ​​of image B, S α is the similarity between the first eigenvalues ​​of the target area in image A and image B.

[0027] Optionally, the processing module is specifically configured to, for picture A and picture B, determine, based on the second eigenvalue of the target area in picture A and the second eigenvalue of the target area in picture B, whether the similarity between the second eigenvalues ​​of the target area in picture A and the second eigenvalue of the target area in picture B satisfies the following formula:

[0028]

[0029] Among them, A n Indicates the nth second eigenvalue of image A, B m represents the mth second eigenvalue of image B, 1≤n≤k, 1≤m≤k; represents the sum of the k second eigenvalues ​​of image A and the k second eigenvalues ​​of image B; represents the arithmetic square root of the sum of the k second eigenvalues ​​of image A, represents the arithmetic square root of the sum of the k second eigenvalues ​​of image B, S β is the similarity between the second eigenvalues ​​of the target area in image A and image B.

[0030] In a third aspect, a computer device is provided, which may be a computer device or a chip or system-on-chip in a computer device. The computer device can implement the functions performed by the image processing device in the above aspects or optional methods, and the functions can be implemented through hardware and software.

[0031] Optionally, the computer device may include: a processor and a memory; the processor and the memory are coupled. The memory is configured to store computer program code, the computer program code comprising computer instructions. When the processor executes the computer instructions, the computer device performs the image processing method according to the first aspect and any optional aspect thereof.

[0032] In a fourth aspect, a computer-readable storage medium is provided, which stores computer instructions or programs. When the instructions in the computer-readable storage medium are executed by a processor of an electronic computer device, the computer device is enabled to execute the image processing method as described in the first aspect or any optional method of the first aspect.

[0033] In a fifth aspect, a computer program product is provided, which includes computer instructions. When the computer instructions are run on a computer device, the computer device implements the image processing method of the first aspect or any optional image processing method of the first aspect when executing the computer device.

[0034] In a sixth aspect, a picture processing system is provided, comprising the picture processing device as described in the second aspect above.

[0035] The technical solutions provided by the embodiments of the present disclosure bring at least the following beneficial effects:

[0036] Based on any of the above aspects, in the present disclosure, after receiving a processing instruction, the image processing device screens out multiple target images, and determines the first eigenvalue and the second eigenvalue of each pixel in the target area of ​​each target image, and then determines the similarity between the first eigenvalues ​​of the target areas in each two target images based on all the first eigenvalues, and determines the similarity between the second eigenvalues ​​of the target areas in each two target images based on all the determined second eigenvalues. If the first target similarity is within a first preset range and the second target similarity is within a second preset range, it is determined that the first image and the second image belong to the same set. It can be seen that the image processing method provided by the embodiment of the present disclosure replaces the manual processing method, thereby improving the accuracy of image processing and improving the efficiency of image processing.

[0037] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 A picture processing system according to an exemplary embodiment is shown;

[0040] Figure 2 FIG1 is a schematic diagram showing the composition of a computer device according to an exemplary embodiment;

[0041] Figure 3 A flowchart of a method for processing an image according to an exemplary embodiment is shown. Figure 1 ;

[0042] Figure 4 is a schematic diagram showing a target image according to an exemplary embodiment;

[0043] Figure 5 is a schematic diagram showing a target image obtained by filtering an original image according to an exemplary embodiment;

[0044] Figure 6 A flowchart of a method for processing an image according to an exemplary embodiment is shown. Figure 2 ;

[0045] Figure 7 The figure is a schematic structural diagram of a picture processing device according to an exemplary embodiment. DETAILED DESCRIPTION

[0046] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0047] In the description of the present disclosure, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present disclosure.

[0048] Unless the context requires otherwise, throughout the specification and claims, the term "comprise" and its other forms, such as the third person singular form "comprises" and the present participle form "comprising", are to be interpreted as open and inclusive, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "example", "specific example" or "some examples" are intended to indicate that the particular features, structures, materials or characteristics associated with the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representation of the above terms does not necessarily refer to the same embodiment or example. In addition, the particular features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner.

[0049] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, "plurality" means two or more.

[0050] When describing some embodiments, the expressions "coupled" and "connected" and their derivatives may be used. For example, when describing some embodiments, the term "connected" may be used to indicate that two or more components are in direct physical or electrical contact with each other. For another example, when describing some embodiments, the term "coupled" may be used to indicate that two or more components are in direct physical or electrical contact. However, the term "coupled" or "communicatively coupled" may also refer to two or more components that are not in direct contact with each other, but still cooperate or interact with each other. The embodiments disclosed herein are not necessarily limited to the contents of this document.

[0051] “At least one of A, B and C” has the same meaning as “at least one of A, B or C” and both include the following combinations of A, B and C: A only, B only, C only, the combination of A and B, the combination of A and C, the combination of B and C, and the combination of A, B and C.

[0052] “A and / or B” includes the following three combinations: A only, B only, and a combination of A and B.

[0053] "Plurality" means at least two.

[0054] The use of "adapted to" or "configured to" herein is intended to be open and inclusive language that does not exclude devices adapted or configured to perform additional tasks or steps.

[0055] Additionally, the use of “based on” is meant to be open and inclusive, as a process, step, calculation, or other action “based on” one or more stated conditions or values ​​may, in practice, be based on additional conditions or values ​​beyond those stated.

[0056] As used herein, "about" or "approximately" includes the stated value and the average value that is within an acceptable range of deviation from the particular value, where the acceptable range of deviation is determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0057] Exemplary embodiments are described herein with reference to cross-sectional and / or plan views that are idealized exemplary drawings. In the drawings, the thicknesses of layers and regions are exaggerated for clarity. Therefore, variations in shape relative to the drawings due to, for example, manufacturing techniques and / or tolerances are contemplated. Therefore, the exemplary embodiments should not be construed as limited to the shapes of the regions shown herein, but rather include deviations in shape due to, for example, manufacturing. For example, an etched region shown as a rectangle will typically have curved features. Therefore, the regions shown in the drawings are schematic in nature, and their shapes are not intended to illustrate the actual shape of regions of the device and are not intended to limit the scope of the exemplary embodiments.

[0058] The image processing methods provided by related technologies mainly use manual processing, which often lacks a unified and standard processing process. This leads to the following main problems in manual processing:

[0059] 1. Manual processing relies on operators to judge the data, and further processing is required after the judgment, which makes image processing time-consuming.

[0060] 2. When operators process a large amount of image data, they may miss some images for some reasons, resulting in some images not being processed.

[0061] 3. The image processing results may be inconsistent due to different operators.

[0062] 4. In order to ensure the accuracy of the processing, after the image processing is completed, the processing results need to be re-checked, which is time-consuming and labor-intensive.

[0063] In order to solve the above problems, an embodiment of the present disclosure provides an image processing method, which is applied to a computer device. The processing method replaces the manual processing method, thereby improving the accuracy of image processing and enhancing the efficiency of image processing.

[0064] First, the application scenario of the image processing method provided by the embodiments of the present disclosure is described: During the production of display substrates, it is necessary to inspect the products obtained from some processes (at least one process) to determine whether the display substrate has defects and the type of defects. In a specific implementation, the inspection involves obtaining an image after completing at least one process in the production process of the display substrate, and then processing the image to determine information such as the type of defect in the display substrate.

[0065] In the embodiment of the present disclosure, the display substrate may be a product related to the display device, that is, a product obtained after completing at least one process in the process of manufacturing the display device.

[0066] The display device may be an LCD (Liquid Crystal Display) device; it may also be a self-luminous display device, such as an OLED (Organic Light Emitting Diode) display device, a QLED (Quantum Dot Light Emitting Diodes) display device, or a Micro LED (Micro Light-Emitting Diode). For example, the display device includes an array substrate. In an LCD, the array substrate may be a TFT array substrate; in an OLED, QLED, Micro LED, etc., the array substrate may be a driver backplane including pixel circuits.

[0067] On this basis, illustratively, in an LCD display device, the display substrate may be a TFT array substrate or a cell substrate; in a self-luminous display device, the display substrate may be a driving backplane.

[0068] In the embodiments of the present disclosure, an image refers to an image of a product obtained after completing at least one process in the production of a display substrate; specifically, it may refer to a substrate obtained by forming at least one pattern layer on a base substrate. The pattern layer refers to a pattern formed on the base substrate through a patterning process.

[0069] For example, the process of preparing a display substrate included in an LCD may include: sequentially forming a gate metal pattern layer (including: gate lines and TFT gates), an active layer and a source-drain metal pattern layer (including: TFT source, TFT drain and data lines) on a base substrate.

[0070] The image processing method provided by the embodiment of the present disclosure can be applied to an image processing system. Figure 1 FIG2 is a schematic diagram of a structure of an image processing system applicable to the technical method provided in the embodiment of the present disclosure. The image processing system includes an image acquisition device 10 and an image processing device 20; wherein the image acquisition device 10 and the image processing device 20 are connected via a network.

[0071] The image acquisition device 10 can be any device for taking images, such as an AOI inspection device. It is understood that the AOI inspection device includes a camera or a camera for taking images, so that images can be acquired.

[0072] Optionally, in the embodiments of the present disclosure, the images captured by the AOI inspection equipment can be images captured during the production process of the display panel backplane. For example, images of any patterned layer, such as a buffer layer, passivation layer, or planar layer; or images of the active layer, source, drain, or gate of a thin-film transistor; or images of traces, electrodes, and connection holes in a driver circuit. The images can be captured as needed and are not specifically limited in the embodiments of the present disclosure.

[0073] The image processing device 20 can be a computer device such as a terminal device or a server. The terminal device can be a palmtop computer, a laptop computer, a smart phone, a tablet computer, or a desktop computer. The server can be a single server, a server cluster consisting of multiple servers, or a cloud computing service center.

[0074] In actual implementation, the image acquisition device 10 and the image processing device 20 can be integrated into a single computer device or can be two independent devices. The present disclosure does not impose any restrictions on the relationship between the image acquisition device 10 and the image processing device 20. The following description is based on the example that the image acquisition device 10 and the image processing device 20 are two independent devices.

[0075] The above-mentioned image acquisition device 10 and image processing device 20 can both adopt Figure 2 Schematic diagram of the composition of computer equipment shown. Figure 2 A schematic diagram of the composition of a computer device 100 provided in an embodiment of the present disclosure is shown as follows: Figure 2 As shown, the computer device 100 includes but is not limited to: a processor 101 , a memory 102 , a communication interface 103 and a communication bus 104 .

[0076] The processor 101 , the memory 102 and the communication interface 103 may be connected via a communication line 104 .

[0077] Processor 101 may be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. Processor 201 may also be other devices with processing capabilities, such as circuits, devices, or software modules, without limitation.

[0078] The memory 102 is used to store instructions, where the instructions may be computer programs.

[0079] The memory 102 may be a read-only memory (ROM) or other type of static storage device that can store static information and / or instructions, or a random access memory (RAM) or other type of dynamic storage device that can store information and / or instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, etc., without limitation.

[0080] It should be noted that the memory 102 can exist independently of the processor 101 or can be integrated with the processor 101. The memory 102 can be used to store instructions, program code, or some data. The memory 102 can be located within the computer device 100 or outside the computer device 100, and the embodiments of the present disclosure are not limited thereto. The processor 101 is used to execute the instructions stored in the memory 102 to implement the image processing method provided in the following embodiments of the present disclosure.

[0081] Communication interface 103 , using any transceiver or other device, is used to communicate with other devices or communication networks, such as a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), a terminal, or the cloud. Communication interface 103 may include a receiving unit to implement a receiving function and a sending unit to implement a sending function.

[0082] The communication bus 104 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0083] In one example, the processor 101 may include one or more CPUs, such as Figure 2 CPU0 and CPU1 in.

[0084] As an optional implementation, the computer device 100 includes multiple processors, for example, Figure 3 In addition to the processor 101, the processor 105 may also be included.

[0085] In addition, the actions and terms involved in the various embodiments of the present disclosure may refer to each other without limitation. The message names or reference names in the messages exchanged between the various devices in the embodiments of the present disclosure are only examples, and other names may be used in specific implementations without limitation.

[0086] The following combination Figure 1 The image processing system shown is used to describe in detail the image processing method provided by the embodiment of the present disclosure.

[0087] Before describing the embodiments of the present disclosure, the scenarios involved in the embodiments of the present disclosure are first described. Specifically, the picture acquisition device acquires pictures, and sends the pictures acquired by itself to the picture processing device, so that the picture processing device acquires all pictures. In one possible design, before the picture processing device responds to the processing instruction for the picture, the picture acquisition device acquires pictures, and sends the pictures acquired by itself to the picture processing device; then, the picture processing device responds to the processing instruction for the picture and processes all pictures. In another possible design, after the picture processing device responds to the processing instruction for the picture, the picture acquisition device acquires pictures, and sends the pictures acquired by itself to the picture processing device, so that the picture processing device processes all pictures.

[0088] In the embodiment of the present disclosure, the picture acquisition device first obtains the picture, and then sends the picture to the picture processing device. When the picture processing device responds to the processing instruction for the picture, it executes the following steps: Figure 3 The steps of image processing are shown.

[0089] Figure 3 A flowchart of a method for processing an image provided by an embodiment of the present disclosure is shown in FIG. Figure 3 As shown, the image processing method includes:

[0090] S200: The picture acquisition device acquires pictures.

[0091] Exemplarily, the image acquisition device may be an AOI detection device; wherein the AOI detection device includes a camera, for example, a camera may be used to acquire images.

[0092] S201: The image acquisition device sends an image to the image processing device.

[0093] For example, a picture is input into a first virtual three-dimensional space in a picture processing device, and the first virtual three-dimensional space processes the picture and converts the picture information into three-dimensional data so that the picture processing device can perform subsequent steps.

[0094] S202: The image processing apparatus responds to an instruction for processing the image.

[0095] Optionally, the processing instruction may be a click operation, a double-click operation, a sliding operation, a timer switch, or a switch manually triggered by an operator. The specific operation may be determined according to actual needs and is not specifically limited in the embodiments of the present disclosure.

[0096] For example, taking the processing instruction as a click operation as an example, the processing instruction may be an operator clicking on a picture.

[0097] S203: The image processing device selects a plurality of target images from all acquired images.

[0098] The target image is an image including a target area; the target area includes an area in which information of pixel points in the target image is aperiodic.

[0099] Specifically, the target image includes a plurality of identical and periodically arranged patterns. Exemplarily, the pattern is a pixel pattern, which is composed of a plurality of pixel points.

[0100] The pixel pattern is the minimum repeating units distributed along the row and column directions in the display area (Active Area, AA) of the display substrate. If the display substrate is an array substrate included in an LCD, each minimum repeating unit includes a pixel electrode and a thin-film transistor connected to the pixel electrode. If the display substrate is a driving backplane included in a self-luminous display device (such as an OLED), each minimum repeating unit includes a pixel circuit that provides a driving signal to each light-emitting unit.

[0101] In addition, a pixel is the smallest light-emitting unit for displaying an image in a display device, and is composed of at least red, green, and blue pixel units.

[0102] like Figure 4 As shown, the target image includes multiple identical and periodically arranged patterns, which can be understood as follows: along the row direction, the black area of ​​each minimal repeating unit and the white area of ​​the gate and data lines connected to each minimal repeating unit are repeatedly arranged. In other words, along the row direction, the black area of ​​a minimal repeating unit and the white area of ​​the gate and data lines connected to the minimal repeating unit are the same as the black areas of other minimal repeating units and the white areas of the gate and data lines connected to the minimal repeating unit.

[0103] A pixel pattern is composed of multiple pixel points. By expressing the position, color, brightness and other information of the pixel points, a pixel pattern is formed. Multiple pixel patterns arranged together can form an image, that is, the target image of the present invention. Based on this, when the information of the pixel points included in each pixel pattern changes periodically, the multiple pixel patterns included in the target image will be the same and arranged periodically.

[0104] It should be noted that the pixel information includes the grayscale value, transmittance value, etc. of the pixel.

[0105] Based on this, it can be understood that if the target image includes an area where the information of pixel points is non-periodic (i.e., does not change periodically), then the area is determined to be the target area; the target area is the area where the target image has defects (e.g., there are missing parts, foreign matter, etc.).

[0106] Optionally, the image processing device selects multiple target images from all acquired images, including: the image processing device acquires information (such as grayscale value, transmittance value, etc.) of each pixel in each image, determines interference fringes of the image based on the pixel information, and filters out the interference fringes to obtain a filtered image; if the pixel information in the filtered image is non-periodic, the image is determined as the target image. Exemplarily, the image processing device uses a pixel recognition algorithm to acquire information of each pixel in each image, and then uses the similarity principle to compare the acquired information of each pixel in each image, determines interference fringes, and filters out the interference fringes.

[0107] It should be noted that in the embodiments of the present disclosure, the images captured by the AOI inspection equipment have interference fringes (also known as interference factors). Interference fringes are caused by light passing through a grid composed of tiny liquid crystal pixels, so the captured images appear to have distorted fringes.

[0108] Furthermore, those skilled in the art will appreciate that interference fringes are light and dark stripes that exhibit periodicity, and that interference fringes within the same image can include multiple similar horizontal fringes and multiple similar vertical fringes. In this disclosure, comparison based on the similarity principle refers to determining the interference fringes within an image based on the similarity of the horizontal and vertical fringes formed by the information of each pixel.

[0109] For example, Figure 5 As shown, Figure 5 Figure (a) is the original image captured by the AOI inspection equipment. As can be seen from Figure (a), the original image includes interference fringes. After filtering the interference fringes, the target image is obtained, as shown in Figure 2. Figure 5 As shown in (b), the area within the “circle” is the target area included in the target image.

[0110] It should be noted that in the embodiment of the present disclosure, filtering means that the image processing device does not process the area outside the "circle" during the subsequent processing of the target image, but only processes the target area (i.e., the area within the "circle"), rather than removing the interference fringes in the image, that is, the target image actually still has interference fringes.

[0111] In addition, it can be understood that each picture has multiple "horizontal" stripes and multiple "vertical" stripes. Figure 5 (a) is only used as an example of a picture with two "vertical" stripes, and the specific method can be determined according to actual conditions.

[0112] For example, the selected target images may be stored in a second virtual three-dimensional space, which processes the target images and converts the target image information into three-dimensional data, so that the image processing device can perform subsequent steps.

[0113] S204 : The image processing apparatus determines a first eigenvalue and a second eigenvalue of each pixel in a target area of ​​each target image.

[0114] Specifically, the target area of ​​each target image includes multiple pixels, each pixel corresponds to a first eigenvalue and a second eigenvalue, and thus the target area of ​​each target image includes multiple first eigenvalues ​​and multiple second eigenvalues. Exemplarily, the first eigenvalue is, for example, a plane eigenvalue of the target area, and the second eigenvalue is, for example, a boundary eigenvalue of the target area.

[0115] Optionally, the first eigenvalue and the second eigenvalue may be a grayscale value, a transmittance value, etc. of a pixel, which is not specifically limited in the embodiment of the present disclosure.

[0116] In the case where the first eigenvalue is a plane eigenvalue, optionally, a pixel recognition algorithm is used to obtain information of each pixel in the target area, such as a grayscale value, a transmittance value, and the like.

[0117] For example, the information of the determined first eigenvalue may be stored in a third virtual three-dimensional space, and the third virtual three-dimensional space converts the information of the first eigenvalue into three-dimensional data so that the image processing device can perform subsequent steps.

[0118] When the second eigenvalue is a boundary eigenvalue, optionally, a Canny edge detection algorithm is used to obtain the boundary eigenvalue of the target region. The Canny edge detection algorithm includes: first performing Gaussian smoothing, such as smoothing the image using a Gaussian filter; then calculating the gradient magnitude and direction using finite differences of first-order partial derivatives; then performing non-maximum suppression on the gradient magnitude; and finally detecting and connecting edges using a dual threshold algorithm.

[0119] It should be noted that the specific implementation process of the embodiment of the present disclosure in obtaining boundary feature values ​​using the Canny edge detection algorithm can refer to related technologies and will not be described in detail here.

[0120] For example, the information of the determined second eigenvalue may be stored in a fourth virtual three-dimensional space, and the fourth virtual three-dimensional space converts the information of the second eigenvalue into three-dimensional data so that the image processing device can perform subsequent steps.

[0121] S205a: The image processing apparatus determines the similarity between the first eigenvalues ​​of the target areas in every two target images based on all the determined first eigenvalues.

[0122] Optionally, assume that all target pictures include picture A, picture B, picture C, and picture D, so the similarity includes the similarity between the first eigenvalues ​​of the target areas in picture A and picture B, the similarity between the first eigenvalues ​​of the target areas in picture A and picture C, the similarity between the first eigenvalues ​​of the target areas in picture A and picture D, the similarity between the first eigenvalues ​​of the target areas in picture B and picture C, the similarity between the first eigenvalues ​​of the target areas in picture B and picture D, and the similarity between the first eigenvalues ​​of the target areas in picture C and picture D.

[0123] The above is only illustrated by taking all target pictures including picture A, picture B, picture C, and picture D as an example. It can be understood that when all target pictures include N, based on all the determined first eigenvalues, the similarity between the first eigenvalues ​​of the target areas in every two target pictures is determined to be N×(N-1) / 2.

[0124] Taking the target images including image A and image B as an example, in the third virtual three-dimensional space, based on the first eigenvalue of the target area in image A and the first eigenvalue of the target area in image B, it is determined that the similarity between the first eigenvalues ​​of the target area in image A and the first eigenvalue of the target area in image B satisfies the following formula:

[0125]

[0126] Among them, A i represents the first eigenvalue of image A, B j represents the j-th first eigenvalue of image B, 1≤i≤z, 1≤j≤z; represents the sum of the z first eigenvalues ​​of image A and the z first eigenvalues ​​of image B; represents the arithmetic square root of the sum of the z first eigenvalues ​​of image A, Represents the arithmetic square root of the sum of the z first eigenvalues ​​of image B, S α is the similarity between the first eigenvalues ​​of the target area in image A and image B.

[0127] S205b: The image processing apparatus determines the similarity between the second eigenvalues ​​of the target areas in every two target images based on all the determined second eigenvalues.

[0128] Optionally, assume that all target pictures include picture A, picture B, picture C, and picture D, so the similarity includes the similarity between the second eigenvalues ​​of the target area in picture A and picture B, the similarity between the second eigenvalues ​​of the target area in picture A and picture C, the similarity between the second eigenvalues ​​of the target area in picture A and picture D, the similarity between the second eigenvalues ​​of the target area in picture B and picture C, the similarity between the second eigenvalues ​​of the target area in picture B and picture D, and the similarity between the second eigenvalues ​​of the target area in picture C and picture D.

[0129] The above is only illustrated by taking all target pictures including picture A, picture B, picture C, and picture D as an example. It can be understood that when all target pictures include N, based on all the determined second eigenvalues, the similarity between the second eigenvalues ​​of the target areas in every two target pictures is determined to be N×(N-1) / 2.

[0130] Taking the target images including image A and image B as an example, in the fourth virtual three-dimensional space, based on the second eigenvalue of the target area in image A and the second eigenvalue of the target area in image B, it is determined that the similarity between the second eigenvalues ​​of the target area in image A and the second eigenvalue of the target area in image B satisfies the following formula:

[0131]

[0132] Among them, A n Indicates the nth second eigenvalue of image A, B m represents the mth second eigenvalue of image B, 1≤n≤k, 1≤m≤k; represents the sum of the k second eigenvalues ​​of image A and the k second eigenvalues ​​of image B; represents the arithmetic square root of the sum of the k second eigenvalues ​​of image A, represents the arithmetic square root of the sum of the k second eigenvalues ​​of image B, S β is the similarity between the second eigenvalues ​​of the target area in image A and image B.

[0133] S206: The picture processing apparatus determines that the first picture and the second picture belong to the same picture set.

[0134] Specifically, if the first target similarity is within a first preset range and the second target similarity is within a second preset range, the image processing device determines that the first image and the second image belong to the same image set; the first target similarity is the similarity between the first eigenvalues ​​of the target areas in the first image and the second image, and the second target similarity is the similarity between the second eigenvalues ​​of the target areas in the first image and the second image.

[0135] Optionally, the first picture is any picture among all target pictures, the second picture is any picture among all target pictures, and the first picture and the second picture are different. For example, the first picture is picture A in the above embodiment, and the second picture is picture B in the above embodiment.

[0136] It should be noted that the first preset range and the second preset range can be set according to specific needs; in addition, the first preset range and the second preset range can be the same or different, which is not limited in the embodiments of the present disclosure.

[0137] Optional, Figure 6 A flowchart of a method for processing an image provided by an embodiment of the present disclosure is shown in FIG. Figure 6 As shown, the image processing method further includes:

[0138] S207: The image processing apparatus determines an identifier of the image set.

[0139] Specifically, the identifier of the picture set is used to represent feature information of all target pictures in the picture set.

[0140] In one example, the image processing apparatus determines an identifier of the image set in response to a triggering operation on the image set.

[0141] Optionally, the trigger operation may be a click operation, a double-click operation, a sliding operation, etc., which may be determined according to actual needs and is not specifically limited in the embodiments of the present disclosure.

[0142] For example, taking the trigger operation as a click operation as an example, the trigger operation may be an operator clicking on a picture set to determine the identifier of the picture set.

[0143] It should be noted that the picture collection in the embodiment of the present disclosure may be represented in the form of a folder, or may be represented in other appropriate ways, and the embodiment of the present disclosure does not impose any specific limitation.

[0144] Optional, such as Figure 4 As shown, Figure 4 For example, if the target image included in the same image collection is a partial image of a circuit board, it can be seen that the target image has a defect within the "circle," and this defect is caused by a crack in the passivation layer during fabrication. Based on this, the image collection can be identified as PVXOpen. For example, if the image collection is a folder, the folder can be named PVXOpen.

[0145] The above mainly introduces the solution provided by the embodiment of the present disclosure from the perspective of interaction between devices. It is understandable that, in order to implement the above functions, each device includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present disclosure.

[0146] The embodiments of the present disclosure can divide the functional modules of each device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present disclosure is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0147] In the case of dividing each functional module into corresponding functional modules, Figure 7 The schematic diagram of the structure of an image processing device 300 is shown, which may include a response module 301 and a processing module 302. For example, the image processing device 300 may be a server, or a chip used in a server, or other combined devices or components having the above-mentioned server functions.

[0148] Specifically, the response module 301 is configured to respond to the processing instruction of the picture. Figure 3 As shown, the response module 301 can be used to execute S202.

[0149] The processing module 302 is configured to filter out multiple target images from all acquired images. Figure 3 As shown, the processing module 302 can be used to execute S203.

[0150] The target image is an image including a target area; the target area includes an area where information of pixel points in the target image is aperiodic.

[0151] The processing module 302 is further configured to determine a first feature value and a second feature value for each pixel in the target area of ​​each target image. Figure 3 As shown, the processing module 302 can be used to execute S204.

[0152] The processing module 302 is further configured to determine the similarity between the first feature values ​​of the target areas in each two target images based on all the determined first feature values, and to determine the similarity between the second feature values ​​of the target areas in each two target images based on all the determined second feature values. Figure 3 As shown, the processing module 302 can be used to execute S205a-S205b.

[0153] The processing module 302 is further configured to determine that the first picture and the second picture belong to the same picture set. Figure 3 As shown, the processing module 302 can be used to execute S206.

[0154] Specifically, if the first target similarity is within a first preset range and the second target similarity is within a second preset range, it is determined that the first image and the second image belong to the same image set; the first target similarity is the similarity between the first eigenvalues ​​of the target areas in the first image and the second image, and the second target similarity is the similarity between the second eigenvalues ​​of the target areas in the first image and the second image.

[0155] Optionally, the image processing apparatus further includes a determination module 303; the determination module 303 is configured to determine the identifier of the image set. Figure 6 As shown, the determination module 303 can be used to execute S207.

[0156] Optionally, the processing module is specifically configured to obtain information of each pixel in each image; determine the interference fringes of the image based on the information of the pixel points, and filter out the interference fringes to obtain a filtered image; if the information of the pixel points in the filtered image is non-periodic, the image is determined as the target image.

[0157] Optionally, the processing module 302 is specifically configured to, for picture A and picture B, determine, based on the first eigenvalue of the target area in picture A and the first eigenvalue of the target area in picture B, whether the similarity between the first eigenvalues ​​of the target area in picture A and the first eigenvalue of the target area in picture B satisfies the following formula:

[0158]

[0159] Among them, A i represents the first eigenvalue of image A, B j represents the j-th first eigenvalue of image B, 1≤i≤z, 1≤j≤z; represents the sum of the z first eigenvalues ​​of image A and the z first eigenvalues ​​of image B; represents the arithmetic square root of the sum of the z first eigenvalues ​​of image A, Represents the arithmetic square root of the sum of the z first eigenvalues ​​of image B, S α is the similarity between the first eigenvalues ​​of the target area in image A and image B.

[0160] Optionally, the processing module 302 is specifically configured to, for picture A and picture B, determine, based on the second eigenvalue of the target area in picture A and the second eigenvalue of the target area in picture B, whether the similarity between the second eigenvalues ​​of the target area in picture A and the second eigenvalue of the target area in picture B satisfies the following formula:

[0161]

[0162] Among them, A n Indicates the nth second eigenvalue of image A, B m represents the mth second eigenvalue of image B, 1≤n≤k, 1≤m≤k; represents the sum of the k second eigenvalues ​​of image A and the k second eigenvalues ​​of image B; represents the arithmetic square root of the sum of the k second eigenvalues ​​of image A, represents the arithmetic square root of the sum of the k second eigenvalues ​​of image B, S β is the similarity between the second eigenvalues ​​of the target area in image A and image B.

[0163] It should be noted that Figure 7For examples of functions implemented by various modules of the picture processing device 300 and the beneficial effects produced, reference can be made to the examples and beneficial effects of the picture processing method in the aforementioned embodiment, which will not be repeated here.

[0164] In actual implementation, the response module 301, the processing module 302 and the determination module 303 can be composed of Figure 2 The processor 101 shown calls the program code in the memory 102 to implement the process. Figure 3 、 Figure 6 The description of the image processing method shown in the figure will not be repeated here.

[0165] The embodiments of the present disclosure also provide a computer-readable storage medium. All or part of the processes in the above-mentioned method embodiments can be completed by a computer program to instruct the relevant hardware. The program can be stored in the above-mentioned computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The computer-readable storage medium can be an internal storage unit of the image processing device (including the data sending end and / or the data receiving end) of any of the above-mentioned embodiments, such as the hard disk or memory of the image processing device. The above-mentioned computer-readable storage medium can also be an external storage device of the above-mentioned image processing device, such as a plug-in hard disk, smart memory card (SMC), secure digital (SD) card, flash card, etc. equipped on the above-mentioned image processing device. Furthermore, the above-mentioned computer-readable storage medium can also include both the internal storage unit of the above-mentioned communication device and an external storage device. The above-mentioned computer-readable storage medium is used to store the above-mentioned computer program and other programs and data required by the above-mentioned terminal. The above-mentioned computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0166] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0167] In the several embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0168] The units described as separate components may or may not be physically separate, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0170] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present disclosure is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk.

[0171] The present disclosure also provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to perform any of the methods described in the above embodiments. The computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present disclosure are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0172] It should be noted that the above-mentioned devices for storing computer instructions or computer programs provided in the embodiments of the present disclosure, such as but not limited to the above-mentioned memories, computer-readable storage media and chips, etc., all have non-transitory properties.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present disclosure.

Claims

1. A method for image processing, characterized in that: Applied to a computer device, the image processing method includes: responding to an instruction for processing the image; Filtering a plurality of target images from all acquired images, wherein the target images are images including a target area; the target area includes an area in which pixel information in the target image is aperiodic; Determine a first eigenvalue and a second eigenvalue of each pixel in a target area of ​​each target image, wherein the first eigenvalue is a plane eigenvalue of the target area, and the second eigenvalue is a boundary eigenvalue of the target area; Determining the similarity between the first eigenvalues ​​of the target regions in each two target images based on all the determined first eigenvalues, and determining the similarity between the second eigenvalues ​​of the target regions in each two target images based on all the determined second eigenvalues; If the first target similarity is within a first preset range and the second target similarity is within a second preset range, then it is determined that the first image and the second image belong to the same image set; the first target similarity is the similarity between the first feature values ​​of the target areas in the first image and the second image, and the second target similarity is the similarity between the second feature values ​​of the target areas in the first image and the second image; Determining the first eigenvalue and the second eigenvalue of each pixel in the target area of ​​each target image includes: storing the selected target images in a virtual three-dimensional space, and processing the target images through the virtual three-dimensional space to convert information of the target images into three-dimensional data; Determining a first eigenvalue and a second eigenvalue of each pixel in a target area of ​​each target image according to the three-dimensional data of each target image information; The step of selecting multiple target images from all acquired images includes: Get the information of each pixel in each picture; determining interference fringes of the image based on information of the pixel points, and filtering out the interference fringes to obtain a filtered image; If the information of the pixel points in the filtered picture is non-periodic, the picture is determined as the target picture.

2. The image processing method according to claim 1, wherein: The image processing method further includes: An identifier of the picture set is determined.

3. The image processing method according to claim 1, wherein: Determining the similarity between the first eigenvalues ​​of the target areas in each two target images based on all the determined first eigenvalues ​​includes: For picture A and picture B, based on the first eigenvalue of the target area in picture A and the first eigenvalue of the target area in picture B, it is determined that the similarity between the first eigenvalues ​​of the target area in picture A and picture B satisfies the following formula: Among them, A i represents the first eigenvalue of image A, B j represents the j-th first eigenvalue of image B, 1≤i≤z, 1≤j≤z; represents the sum of the z first eigenvalues ​​of image A and the z first eigenvalues ​​of image B; represents the arithmetic square root of the sum of the z first eigenvalues ​​of image A, Represents the arithmetic square root of the sum of the z first eigenvalues ​​of image B, S α is the similarity between the first eigenvalues ​​of the target area in image A and image B.

4. The image processing method according to claim 1, wherein: Determining the similarity between the second eigenvalues ​​of the target areas in each two target images based on all the determined second eigenvalues ​​includes: For picture A and picture B, based on the second eigenvalue of the target area in picture A and the second eigenvalue of the target area in picture B, it is determined that the similarity between the second eigenvalues ​​of the target area in picture A and picture B satisfies the following formula: Among them, A n Indicates the nth second eigenvalue of image A, B m represents the mth second eigenvalue of image B, 1≤n≤k, 1≤m≤k; represents the sum of the k second eigenvalues ​​of image A and the k second eigenvalues ​​of image B; represents the arithmetic square root of the sum of the k second eigenvalues ​​of image A, represents the arithmetic square root of the sum of the k second eigenvalues ​​of image B, S β is the similarity between the second eigenvalues ​​of the target area in image A and image B.

5. A picture processing device, characterized in that: Applied to computer equipment, the image processing device includes: A response module, configured to respond to an instruction for processing the image; a processing module configured to filter out a plurality of target images from all acquired images, wherein the target images are images including a target area; the target area includes an area in which pixel information in the target image is aperiodic; The processing module is further configured to determine a first eigenvalue and a second eigenvalue of each pixel in a target area of ​​each target image; the first eigenvalue is a plane eigenvalue of the target area, and the second eigenvalue is a boundary eigenvalue of the target area; The processing module is further configured to determine the similarity between the first eigenvalues ​​of the target areas in every two target images based on all the determined first eigenvalues, and to determine the similarity between the second eigenvalues ​​of the target areas in every two target images based on all the determined second eigenvalues; The processing module is further configured to determine that the first image and the second image belong to the same image set if the first target similarity is within a first preset range and the second target similarity is within a second preset range; the first target similarity is the similarity between first feature values ​​of target areas in the first image and the second image, and the second target similarity is the similarity between second feature values ​​of target areas in the first image and the second image; The processing module is further configured to determine a first eigenvalue and a second eigenvalue of each pixel in a target area of ​​each target image, including: The processing module is further configured to: store the selected target images in a virtual three-dimensional space, process the target images in the virtual three-dimensional space to convert information of the target images into three-dimensional data; and determine a first eigenvalue and a second eigenvalue of each pixel in a target area of ​​each target image based on the three-dimensional data of the information of each target image; The processing module is configured to filter out multiple target images from all acquired images, including: The processing module is specifically configured to: obtain information of each pixel in each image; determine the interference fringes of the image based on the information of the pixel points, and filter out the interference fringes to obtain a filtered image; if the information of the pixel points in the filtered image is non-periodic, determine the image as the target image.

6. The image processing device according to claim 5, characterized in that: The image processing device further includes: a determination module; The determining module is configured to determine an identifier of the picture set.

7. The image processing device according to claim 5, wherein: The processing module is specifically configured to, for picture A and picture B, determine, based on the first eigenvalue of the target area in picture A and the first eigenvalue of the target area in picture B, that the similarity between the first eigenvalues ​​of the target area in picture A and the first eigenvalue of the target area in picture B satisfies the following formula: Among them, A i represents the first eigenvalue of image A, B j represents the j-th first eigenvalue of image B, 1≤i≤z, 1≤j≤z; represents the sum of the z first eigenvalues ​​of image A and the z first eigenvalues ​​of image B; represents the arithmetic square root of the sum of the z first eigenvalues ​​of image A, Represents the arithmetic square root of the sum of the z first eigenvalues ​​of image B, S α is the similarity between the first eigenvalues ​​of the target area in image A and image B.

8. The image processing device according to claim 5, wherein: The processing module is specifically configured to, for picture A and picture B, determine, based on the second eigenvalue of the target area in picture A and the second eigenvalue of the target area in picture B, that the similarity between the second eigenvalues ​​of the target area in picture A and the second eigenvalue of the target area in picture B satisfies the following formula: Among them, A n Indicates the nth second eigenvalue of image A, B m represents the mth second eigenvalue of image B, 1≤n≤k, 1≤m≤k; represents the sum of the k second eigenvalues ​​of image A and the k second eigenvalues ​​of image B; represents the arithmetic square root of the sum of the k second eigenvalues ​​of image A, represents the arithmetic square root of the sum of the k second eigenvalues ​​of image B, S β is the similarity between the second eigenvalues ​​of the target area in image A and image B.

9. A computer device, characterized in that: include: One or more processors and a memory; the processor and the memory are coupled; the memory is used to store computer program code, the computer program code including computer executable instructions; When the processor executes the computer-executable instruction, the computer device executes the image processing method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions or programs, and when the computer instructions or programs are executed on a computer, the computer executes the image processing method according to any one of claims 1 to 4.

11. A computer program product, characterized in that The computer program product includes computer instructions, and when the computer instructions are executed on a computer device, the computer device is caused to execute the image processing method according to any one of claims 1 to 4.

12. A picture processing system, characterized in that: The image processing system includes the image processing device according to claim 5.

Images