TWI778735B - Image processing method, computer device, and storage medium - Google Patents

Abstract

The present application provides an image processing method, a computer device, and a storage medium. The image processing method includes: determining a text area and a background area of a flawless image to obtain a first image of each text; removing the background area from the first image to obtain a second image; processing the second image to obtain a third image, N fourth images, and N fifth images corresponding to the fourth image one-to-one; calculating a similarity value between each fifth image and the corresponding third image, and determining a defect threshold; determining whether the fourth image corresponding to the fifth image is a defective image; when the fourth image is a flawed image, performing a shading processing on an intercepted background image, and synthesizing the flawed image and the intercepted background image after the shading processing, and obtaining a composite image. This application can assist in generating flawed training samples and reduce labor costs.

Description

Image processing method, computer device and storage medium

The present invention relates to the field of image processing, and in particular, to an image processing method, a computer device and a storage medium.

Due to the high yield rate in the current industrial production process, it is difficult to obtain a large number of defective printing image samples, which are used as training samples to train the defect detection model. The existing method of manually synthesizing defective samples requires manual labeling of the defective data after generating the simulated data, or training another deep learning model for classification, which consumes a lot of manpower and time, and requires manual labeling. Errors are also prone to occur in the process. How to efficiently solve the problem of insufficient training data under the premise of ensuring accuracy has always been a major issue in the application of deep learning.

In view of the above, it is necessary to provide an image processing method, a computer device and a storage medium, which can automatically generate a large number of defective printing images and reduce the cost of manual synthesis and marking.

The image processing method includes: acquiring a flawless image, determining a text area and a background area of the flawless image, and determining the position of each character in the text area; The text area is divided to obtain the first image of each character; the background is removed from the first image of each character to obtain the second image of each character; according to the preset first image The image processing method processes the second image of each character to obtain a third image of each character; processes the second image of each character according to a preset second image processing method to obtain each character N fourth images and N fifth images, the N fourth images of each character and the N fifth images are in one-to-one correspondence, and N is a positive integer greater than 1; Describe the similarity measure between each of the N fifth images of each character and the third image of each character, obtain N similarity measures corresponding to each character, and Associating the N fifth images of each character with the N similarity measures corresponding to each character respectively, and determining a defect threshold according to the N similarity measurements corresponding to all characters in the character area; compare The size of each similarity measure in the N similarity measures corresponding to each character and the defect threshold, wherein, when any similarity measure corresponding to any character is greater than the defect threshold, it is determined to be the same as the defect threshold. The fourth image corresponding to the fifth image associated with the similarity measure is a defective image; when any fourth image is determined to be a defective image, a background image is intercepted from the background area , the size of the intercepted background image is equal to the size of the defective image; and performing brightness processing on the intercepted background image, and synthesizing the defective image and the background after the brightness processing image to obtain a composite image.

Optionally, the first image processing method includes image binarization and contour extraction; the second image processing method includes erasure processing, image binarization, and contour extraction.

Optionally, removing the background from the first image of each character, and obtaining the second image of each character includes method 1: using the Otsu algorithm to determine the first threshold of the first image, according to obtaining the mask of the first image by the first threshold, and performing a bitwise AND operation on the mask and the first image to obtain a foreground text image of the first image; and The second image is obtained by softening the edge of the foreground text image by using the Gaussian blurring technique.

Optionally, removing the background from the first image of each character and obtaining the second image of each character includes method 2: removing the dots in the first image by using Fourier transform, Perform binarization on the first image after removing the dots; and perform edge softening processing on the binarized first image by using Gaussian blur technology to obtain the second image.

Optionally, the first image processing method and the second image processing method perform image binarization according to the first threshold, and perform contour extraction by using a Fourier descriptor algorithm or an invariant moment algorithm. .

Optionally, the second image of each character is processed according to the preset second image processing method to obtain N fourth images and N fifth images of each character, each There is a one-to-one correspondence between the N fourth images of the characters and the N fifth images, including: randomly erasing the second images of each character N times to obtain N fourth images of each character image; perform image binarization on the N fourth images of each character to obtain N black and white images; perform contour extraction on the N black and white images to obtain the N fifth images picture.

Optionally, the similarity measure refers to the Euclidean distance between each fifth image of each character and the third image of each character; The N similarity measures corresponding to all the texts respectively, and determining a defect threshold includes: according to the number of images of the fifth image corresponding to the value of each similarity measure, making the similarity measure and the a polyline relationship graph of the number of images; and the first local minimum value of the similarity measure in the polyline relationship graph as the defect threshold.

Optionally, performing brightness processing on the captured background image, synthesizing the defective image and the background image after brightness processing, and obtaining a composite image includes: processing the captured background image. The image is subjected to multiple brightness adjustment to obtain multiple adjusted images, and the multiple adjusted images are respectively combined with the defective image to obtain multiple synthesized images.

The computer-readable storage medium stores at least one instruction, and the at least one instruction implements the image processing method when executed by the processor.

The computer device includes a memory and at least one processor, the memory stores at least one instruction, and the at least one instruction implements the image processing method when executed by the at least one processor.

Compared with the prior art, the image processing method, computer device and storage medium can automatically generate a large number of defective printing images without training an additional deep learning model. Automatically label the training samples in it, reducing the cost of artificial synthesis and labeling.

3: Computer device

30: Image processing system

31: Storage

32: Processor

S1~S11: Steps

In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present application. For those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without any creative effort.

FIG. 1 is a flowchart of an image processing method provided by an embodiment of the present application.

FIG. 2 is a structural diagram of a computer device provided by an embodiment of the present application.

FIG. 3 is an example diagram of step S1 and step S2 provided by an embodiment of the present application.

FIG. 4 is an example diagram of a process for obtaining a second image provided by an embodiment of the present application.

FIG. 5 is an example diagram of a process for obtaining a defective image provided by an embodiment of the present application.

FIG. 6 is an example diagram of a polyline relationship diagram provided by an embodiment of the present application.

FIG. 7 is an exemplary diagram of a process for obtaining a composite image provided by an embodiment of the present application.

In order to more clearly understand the above objects, features and advantages of the present application, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments may be combined with each other in the case of no conflict.

In the following description, many specific details are set forth to facilitate a full understanding of the present application, and the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

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

Referring to FIG. 1 , it is a flowchart of an image processing method according to a preferred embodiment of the present application.

In this embodiment, the image processing method can be applied to a computer device (for example, the computer device 3 shown in FIG. 2 ). For a computer device that needs to perform image processing, the method of the present application can be directly integrated on the computer device. The provided functions for image processing, or run on a computer device in the form of a software development kit (Software Development Kit, SDK).

As shown in FIG. 1 , the image processing method specifically includes the following steps. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

In step S1, the computer device acquires a flawless image, determines a text area and a background area of the flawless image, and determines the position of each text in the text area.

In one embodiment, the computer device may acquire a flawless image in response to user input. The flawless image may also be pre-stored in the memory of the computer device, or pre-stored in other devices communicatively connected to the computer device. In this embodiment, the flawless image may be a standard sample (Golden Sample) image of a printed matter produced by a factory, and the flawless image contains characters (eg, Chinese characters, numbers, English letters, etc.). It should be noted that the position of the standard sample image does not need to be corrected, and the arrangement direction of the characters also does not need to be corrected.

In one embodiment, the computer device can use Optical Character Recognition (OCR) technology to recognize the text of the flawless image, and then confirm the text area and background area of the flawless image, and determine the text area and the background area of the flawless image. The position of each text in the description text area. The text area refers to an area containing text, and the computer device can use the Region Of Interest (ROI) technology to outline the text area. For example, as shown in FIG. The frame (solid line) encloses all the text in it, and frames the text area. It should be noted that the dotted frame around the large rectangular frame in FIG. 3 is only used to indicate the large rectangular frame; the background area is Refers to the area that does not contain text, that is, the area outside the text area in the flawless image, such as the area indicated by hatching in FIG. 3 .

In step S2, the computer device divides the character area according to the position of each character to obtain a first image of each character.

In one embodiment, the computer device can use the character cutting function of the OCR software to segment the text area, segment the area where each text is located, and obtain the first image of each text, the The first image contains an image of the complete text outline of the text. For example, the large rectangular frame (solid line) in Figure 3 can be segmented, and according to the small rectangular frame (solid line) in Figure 3, a rectangular image of the area where the text "0" is located can be obtained, as shown in Figure 4 In the first image 3B1 of the character “0” shown in FIG. 3 , it should be noted that the dotted frame around the small rectangular frame in FIG. 3 is only used to indicate the small rectangular frame.

In step S3, the computer device removes the background of the first image of each character to obtain a second image of each character.

In one embodiment, removing the background from the first image of each character to obtain the second image of each character includes method 1: a computer device determines the first image by using OTSU Thresholding The first threshold of the image, obtain the mask of the first image according to the first threshold, and perform a bitwise AND (Bitwise AND) operation on the mask and the first image , obtaining a foreground text image of the first image; and using a Gaussian Blur technology to perform Soft Edge processing on the foreground text image to obtain the second image.

In one embodiment, the first method is suitable for the first image with low background complexity, for example, the first image is a silk screen printing (Silk Screen Process Printing) image. The Otsu algorithm may determine the first threshold of the first image, the first threshold comprising an image binarization (Thresholding) optimal segmentation threshold (eg, 30) of the first image ; and binarize the first image according to the first threshold to obtain the mask of the first image. For example, in FIG. 4 , the mask image 3B2 of the first image 3B1 is obtained by using the Otsu algorithm.

In one embodiment, the bitwise AND operation may convert the foreground in the first image The text outline image is separated from the background that does not contain the text outline, and the foreground text image and the background image of the first image are obtained, and the background of the foreground text image is transparent. For example, in FIG. 4 , the foreground text image 3B3 and the background image 3B4 of the image 3B1 are obtained according to the image 3B2.

In one embodiment, as shown in the foreground text image 3B3 in FIG. 4 , since the foreground text image may contain jagged edges, the Gaussian blur technology can be used to soften the edges, and the obtained first The two images are shown as the second image 3B5 in FIG. 4 .

In one embodiment, removing the background from the first image of each character to obtain the second image of each character includes method 2: a computer device removes the first image by using Fourier Transform (Fourier Transform) For the dots in an image, binarize the first image after removing the dots; and use Gaussian blur technology to soften the edge of the binarized first image to obtain the first image. Second image.

In one embodiment, the second method is applicable to the first image with high background complexity including halftone dots (for example, square halftone dots, circular halftone dots, etc.), for example, the first image is color printing image. The Fourier transform can change the frequency (Frequency) of the first image, so as to achieve the effect of removing the dots; the binarization performed on the first image after removing the dots can be the method described above. The Otsu algorithm in one; the Gaussian blurring technique is the same as that used in method one.

Step S4, the computer device processes the second image of each character according to the preset first image processing method to obtain a third image of each character.

In one embodiment, the first image processing method includes image binarization and contour extraction. The computer device performs image binarization on the second image according to the first threshold, for example, when the pixel value at any position in the second image is greater than or equal to the first When the threshold is set, the image element at any position is binarized to 255; when the value of the image element at any position in the second image is less than the first threshold, the image at any position is Meta binarization to 0. For example, as shown in FIG. 5 , an image 4A1 is obtained after the second image 3B5 is binarized.

In one embodiment, the computer device utilizes Fourier descriptors Descriptors) algorithm or Invariant Moments algorithm to extract the outline of the text in the second image after image binarization; the Fourier descriptor algorithm can identify the second image The closed edge of the text outline in the second image after valueization is reconstructed, so as to extract the text outline; the invariant moment algorithm uses translation invariance, scale invariance and rotation invariance, etc. The property describes the overall characteristics of the second image after the image binarization, so as to extract the text outline in the second image after the image binarization. For example, as shown in FIG. 5 , after performing contour extraction on the image 4A1, a third image 4A2 with the character "0" is obtained. It should be noted that, each character in the flawless image has a third image corresponding to one of them, and when the flawless image contains M characters, a total of M pieces of the third image are obtained. image, where M represents a positive integer greater than 0.

Step S5, the computer device processes the second image of each character according to the preset second image processing method, and obtains N fourth images and N fifth images of each character. The N fourth images of , and N fifth images are in one-to-one correspondence, and N is a positive integer greater than 1.

In one embodiment, the computer device may use an image erasing tool (eg, an eraser tool in Photoshop) to randomly erase the second image of each character N times to obtain N images of each character For the fourth image, for example, as shown in FIG. 5 , after randomly erasing the second image 3B5 of the character “0” twice, two fourth images of the character “0” are obtained: the fourth image 4B1 and the second image 3B5 of the character “0”. Four images 4C1.

In one embodiment, the computer device performs image binarization on the N fourth images of each character to obtain N black and white images, and the image binarization is the same as the method used in step S4. Similarly, for example, as shown in FIG. 5 , image 4B2 is obtained after image binarization is performed on the fourth image 4B1 , and image 4C2 is obtained after image binarization is performed on the fourth image 4C1 .

In one embodiment, the computer device performs contour extraction on the N black-and-white images to obtain the N fifth images, and the contour extraction is the same as that used in step S4, for example, as shown in FIG. 5 , The fifth image 4B3 with the character "0" is obtained after the contour extraction is performed on the image 4B2; the fifth image 4C3 with the character "0" is obtained after the contour extraction is performed on the image 4C2.

It should be noted that, when the flawless image contains M characters, after N fourth images of each character are obtained, M*N fourth images of all characters are obtained in total, and the M*N fifth images.

Step S6, the computer device calculates the similarity measure (Similarity Measurement) between each fifth image in the N fifth images of each character and the third image of each character, and obtains each N similarity measures corresponding to each text, and the N fifth images of each text are associated with the N similarity measures corresponding to each text, respectively. The N similarity measures determine a flaw threshold.

In one embodiment, the similarity measure refers to the Euclidean Distance (Euclidean Distance) between each fifth image of each character and the third image of each character. The computer device calculates the Euclidean distance between each of the fifth images of each character and the third image of each character, thereby obtaining each of the fifth images of each character and each character The value of the corresponding similarity measure. For example, as shown in FIG. 5 , for the text “0” with two fifth images, the Euclidean distance between the fifth image 4B3 of the text “0” and the third image 4A2 is 0.76, The Euclidean distance between the fifth image 4C3 of the text "0" and the third image 4A2 is 0.23. It should be noted that, when the flawless image contains M characters, after N fourth images of each character are obtained, M*N fourth images of all characters are obtained in total, and the M*N fifth images, and then M*N similarity measure values are obtained.

In one embodiment, the determining a defect threshold according to the N similarity measures corresponding to all the texts in the text area comprises: according to the value of each similarity measure corresponding to the image of the fifth image According to the number of images, a graph of the relationship between the similarity measure and the number of images is made.

Specifically, when the flawless image contains M characters, after obtaining M*N similarity metric values, the computer device will count the number of the fifth image corresponding to each similarity metric value. and the number of the fifth images is taken as the horizontal axis and the number of the fifth images is taken as the vertical axis, and the polyline relationship graph is made. For example, the value of the fifth image corresponding to the similarity measure of 0.01 The number is 36, the number of fifth images corresponding to the similarity metric value of 0.35 is 0, the number of fifth images corresponding to the similarity metric value of 0.82 is 52, etc., the obtained polyline The relationship diagram is shown in Figure 6.

In one embodiment, the computer device uses the first local minimum of the similarity measure in the polyline relationship graph as the defect threshold. For example, as shown in FIG. 6 , the first local minimum value of the similarity measure in the line graph is 0.35, then the defect threshold is 0.35. It should be noted that the dotted line in FIG. 6 is only For example the first local minimum is 0.35.

Step S7, the computer device compares the size of each similarity measure in the N similarity measures corresponding to each character with the flaw threshold, and when any similarity measure corresponding to any character is less than or equal to the flaw threshold, Step S8 is performed, and when any similarity measure corresponding to any character is greater than the flaw threshold, step S9 is performed.

Step S8, the computer device determines that the fourth image corresponding to the fifth image associated with any one of the similarity metrics is a flawless image.

For example, the Euclidean distance between the fifth image 4C3 of the character "0" and the third image 4A2 is 0.23, which is smaller than the defect threshold of 0.35, and the computer device can mark the fourth image 4C1 as a defect-free image .

Step S9, the computer device determines that the fourth image corresponding to the fifth image associated with any one of the similarity metrics is a defective image; when any one of the fourth images is determined to be defective When taking an image, a background image is intercepted from the background area, and the size of the intercepted background image is equal to the size of the defective image; A composite image is obtained from the flawed image and the background image after shading processing.

For example, as shown in FIG. 5 , the Euclidean distance between the fifth image 4B3 of the character "0" and the third image 4A2 is 0.76, which is greater than the defect threshold of 0.35, and the computer device can convert the fourth image 4B1 Labeled as defective image 4B1.

In one embodiment, since the text area and the background area may have slightly different brightness, the background image may be adjusted for multiple times (for example, four times) to increase the exposure to ambient light sources. to obtain a plurality of shading-adjusted images (in order to clearly illustrate the present invention, the shading-adjusted images are referred to as “adjusted images” here); The defective images are synthesized to obtain a plurality of synthesized images. For example, as shown in FIG. 7 , the computer device intercepts a background image 6A with the same size as the defective image 4B1 from the background area, adjusts the brightness and darkness of the background image 6A four times, and converts the defective image The image 4B1 is combined with the background image 6A after each shading process to obtain a combined image 6B, a combined image 6C, a combined image 6D, and a combined image 6E.

In one embodiment, the synthetic image may be used as a defective sample to train a neural network to obtain a defect detection model; after obtaining the synthetic image of any character, steps S5 to S9 may be repeated to obtain the any character more (eg, 80) defective samples.

In one embodiment, when the flawless image includes M characters, after the computer device obtains N fourth images of each character, a total of M*N fourth images are obtained; the computer device determines M *K fourth images among the N fourth images are defective images, and K background images corresponding to each of the defective images are cut out from the background area; computer device Perform L times of brightness adjustment on each image, and then synthesize the background image after each brightness adjustment and the corresponding defective image to obtain K*L composite images, where K is less than M*N is a positive integer, and L is a positive integer greater than one.

In step S10, the computer device determines the magnitude relationship between the number of the composite images and the preset second threshold, and when the number of the composite images is less than the preset second threshold, step S1 is performed; When the number of the composite images is greater than or equal to the preset second threshold, step S11 is performed.

In one embodiment, the preset second threshold may be 100,000.

In step S11, the computer device uses the synthesized image to train a neural network to obtain a defect detection model.

The above-mentioned FIG. 1 describes the image processing method of the present application in detail. The following describes the hardware device architecture for implementing the image processing method with reference to FIG. 2 .

It should be understood that the embodiments are only used for illustration, and are not limited by this structure in the scope of the patent application.

Referring to FIG. 2 , it is a schematic structural diagram of a computer device according to a preferred embodiment of the present application. In a preferred embodiment of the present application, the computer device 3 includes a storage 31 and at least one processor 32 . Those skilled in the art should understand that the structure of the computer device shown in FIG. 2 does not constitute a limitation of the embodiments of the present application. More or less other hardware or software, or different component arrangements are shown.

In some embodiments, the computer device 3 includes a terminal capable of automatically performing numerical calculations and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, dedicated integrated circuits, Programmable gate arrays, digital signal processors and embedded devices, etc.

It should be noted that the computer device 3 is only an example, and other existing or future electronic products, if applicable to the present application, should also be included within the protection scope of the present application, and are incorporated herein by reference.

In some embodiments, the storage 31 is used to store code and various data. For example, the storage 31 can be used to store flawless images, and can also store the image processing system 30 installed in the computer device 3 , and can realize high-speed and automatic completion of programs or programs during the operation of the computer device 3 . access to data. The storage 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory) -Only Memory, EPROM), One-time Programmable Read-Only Memory (OTPROM), Electronically-Erasable Programmable Read-Only Memory (EEPROM) ,only Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage, magnetic tape storage, or any other computer-readable storage medium that can be used to carry or store data.

In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, may be composed of a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions , including one or more central processing units (Central Processing Units, CPUs), microprocessors, digital signal processing chips, graphics processors and combinations of various control chips. The at least one processor 32 is the control core (Control Unit) of the computer device 3, and uses various interfaces and lines to connect the various components of the entire computer device 3, by running or executing the program stored in the storage 31 or module, and call the data stored in the storage 31 to execute various functions of the computer device 3 and process data, such as the function of image processing.

In some embodiments, the image processing system 30 runs in the computer device 3 . The image processing system 30 may include a plurality of functional modules composed of program code segments. The code of each program segment in the image processing system 30 can be stored in the memory 31 of the computer device 3 and executed by at least one processor 32 to realize the image processing function shown in FIG. 1 .

In this embodiment, the image processing system 30 can be divided into a plurality of functional modules according to the functions performed by the image processing system 30 . The module referred to in this application refers to a series of computer program segments that can be executed by at least one processor and can perform fixed functions, and are stored in a memory.

Although not shown, the computer device 3 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 32 through the power management device, so as to realize the realization through the power management device. Manage charging, discharging, and power management functions. The power supply may also include one or more of a DC or AC power source, a recharging device, a power failure detection circuit, a power converter or inverter, a power supply status indicator, or any other element. The computer device 3 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., It is not repeated here.

It should be understood that the embodiments are only used for illustration, and are not limited by this structure in the scope of the patent application.

The above-mentioned integrated units implemented in the form of software function modules can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, and includes several instructions for causing a computer device (which may be a server, a personal computer, etc.) or a processor (processor) to execute parts of the methods described in the various embodiments of the present application.

In a further embodiment, referring to FIG. 2 , the at least one processor 32 can execute the operating system of the computer device 3 and various installed application programs (such as the image processing system 30 ), program codes, etc., For example, the above-mentioned modules.

The storage 31 stores program codes, and the at least one processor 32 can call the program codes stored in the storage 31 to execute related functions. The program codes stored in the storage 31 can be executed by the at least one processor 32, so as to realize the functions of the various modules to achieve the purpose of image processing.

In one embodiment of the present application, the storage 31 stores one or more instructions (ie, at least one instruction), and the at least one instruction is executed by the at least one processor 32 to realize the image shown in FIG. 1 . the purpose of processing.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and other division methods may be used in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple networks. on the unit. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated in one processing In a unit, each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.

It will be apparent to those skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, but that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Accordingly, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to fall within the scope of the claims. All changes within the meaning and scope of the equivalents of , are included in this application. Any reference sign in a claim should not be construed as limiting the claim to which it relates. Furthermore, it is clear that the word "comprising" does not exclude other units or, and the singular does not exclude the plural. Multiple units or means stated in the device claim may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limitations. Although the present application has been described in detail with reference to the above preferred embodiments, those of ordinary skill in the art should The technical solutions can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

S1~S11: Steps

Claims (9)

An image processing method, wherein the method includes: acquiring a flawless image, determining a text area and a background area of the flawless image, and determining the position of each character in the text area; The position of the text divides the text area to obtain a first image of each text; removes the background from the first image of each text to obtain a second image of each text; An image processing method processes the second image of each character to obtain a third image of each character, wherein the first image processing method includes image binarization and contour extraction; The two-image processing method processes the second images of each character to obtain N fourth images and N fifth images of each character, and N fourth images and N images of each character The fifth images are in one-to-one correspondence, and N is a positive integer greater than 1, wherein the second image processing method includes erasing processing, image binarization, and contour extraction; calculating the N fifth images of each character The similarity measure between each fifth image in the image and the third image of each word, N similarity measures corresponding to each word are obtained, and the N pictures of each word The fifth image is respectively associated with the N similarity measures corresponding to each text, and a defect threshold is determined according to the N similarity measures corresponding to all the texts in the text area; the N corresponding to each text are compared. The size of each similarity measure in the similarity measure and the flaw threshold, wherein when any one of the similarity measures corresponding to any character is greater than the flaw threshold, determine the fifth similarity measure associated with the any one of the similarity measures The fourth image corresponding to the image is a defective image; when any fourth image is determined to be a defective image, a background image is intercepted from the background area, and the intercepted background image The size of the image is equal to the size of the flawed image; and performing shading processing on the intercepted background image, synthesizing the flawed image and the background image after shading processing to obtain a composite image . The image processing method according to claim 1, wherein each of the characters The background of the first image is removed, and the second image of each character is obtained by method 1: use the Otsu algorithm to determine the first threshold of the first image, and obtain the first image according to the first threshold. and perform a bitwise AND operation on the mask and the first image to obtain a foreground text image of the first image; Soft edges are processed to obtain the second image. The image processing method according to claim 1, wherein removing the background from the first image of each character to obtain the second image of each character includes method 2: removing the background by using Fourier transform For the dots in the first image, binarize the first image after removing the dots; and use Gaussian blur technology to soften the edge of the binarized first image to obtain the second image. The image processing method according to claim 2, wherein the first image processing method and the second image processing method perform image binarization according to the first threshold, and use a Fourier descriptor algorithm Or invariant moment algorithm for contour extraction. The image processing method according to claim 1, wherein the second image of each character is processed according to a preset second image processing method to obtain N fourth images and N images of each character The fifth image, the N fourth images of each character and the N fifth images are in one-to-one correspondence, including: randomly erasing the second image of each character N times, obtaining N fourth images of each character; performing image binarization on the N fourth images of each character to obtain N black-and-white images; performing image binarization on the N black-and-white images Contour extraction is performed to obtain the N fifth images. The image processing method according to claim 1, wherein the similarity measure refers to the Euclidean distance between each fifth image of each character and the third image of each character ; Wherein, determining a defect threshold according to the N similarity measures corresponding to all the characters in the text area includes: the number of images of the fifth image corresponding to the value of each similarity measure, Making a polyline relationship graph of the similarity measure and the number of images; and taking the first local minimum value of the similarity measure in the polyline relationship map as the defect threshold. The image processing method according to claim 1, wherein the intercepted background image is subjected to shading processing, and the defective image and the background image after shading processing are synthesized to obtain a composite image The image includes: performing multiple brightness adjustment on the intercepted background image to obtain multiple adjusted images, and synthesizing the multiple adjusted images with the defective image respectively to obtain multiple synthesized images . A computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, implements the image processing method according to any one of claim 1 to 7. A computer device, wherein the computer device includes a memory and at least one processor, the memory stores at least one instruction, and when the at least one instruction is executed by the at least one processor, the implementation of claim 1 to The image processing method according to any one of 7.

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