Image feature extraction method and system for sewage treatment

Abstract

The invention relates to an image feature extraction method and system for sewage treatment. The method comprises the following steps: acquiring a plurality of underwater images Pi of a continuously shot sewage pool; taking the image with the highest definition in the plurality of underwater images Pi as a target underwater image; s2, determining the distribution uniformity of the suspended particles in the target underwater image, and returning to the step S1 when the distribution uniformity of the suspended particles in the target underwater image is smaller than a preset threshold value; and when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to a preset threshold value, extracting a feature value in the target underwater image. According to the method, multiple underwater images are continuously shot, the image with the highest definition is subjected to distribution uniformity analysis, if the partial uniformity is greater than the threshold value, suspended particles in the image are uniformly distributed, and finally, the characteristics are extracted from the image, so that the characteristic value can accurately reflect the reaction condition of sewage. And a data basis is provided for adding control of the reaction agent.

Description

Technical Field

[0001] The invention relates to the technical field of image processing, and in particular to an image feature extraction method and system for sewage treatment. Background Art

[0002] Sewage treatment (wastewater treatment) is the process of purifying sewage to meet the water quality requirements for discharge into a water body or reuse. Sewage treatment is widely used in various fields such as construction, agriculture, transportation, energy, petrochemicals, environmental protection, urban landscape, medical treatment, catering, etc., and is increasingly entering the daily lives of ordinary people.

[0003] In the prior art, if automatic dosing is required during sewage treatment, the water quality in the sewage pool needs to be monitored, and the corresponding dosing amount is determined after obtaining water body information. However, in the prior art, the dosing amount is generally determined by testing the water quality, and the characteristics of the sediment in the sewage pool are not extracted. Therefore, the dosing amount cannot be adjusted according to the sediment in the sewage pool, which leads to inaccurate dosing amount. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide an image feature extraction method and system for sewage treatment, so as to solve the problem in the prior art that the dosage of the drug cannot be adjusted according to the sediment in the sewage pool, thereby resulting in inaccurate dosage of the drug.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention provides an image feature extraction method for sewage treatment, comprising:

[0007] S1, obtain multiple underwater images P of the sewage pool taken continuously i ;

[0008] S2, the multiple underwater images P i The image with the highest resolution is used as the target underwater image;

[0009] S3, determining the distribution uniformity of the suspended particles in the target underwater image, and when the distribution uniformity of the suspended particles in the target underwater image is less than a preset threshold, returning to step S1; when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to the preset threshold, extracting the characteristic values ​​in the target underwater image.

[0010] In one embodiment of the present application, the plurality of underwater images P i The highest definition image is used as the target underwater image, including:

[0011] Calculate each underwater image P i The clarity D(P i );

[0012] Determine the multiple underwater images P i The highest clarity value D max (P i ), and the highest definition value D max (P i ) is used as the target underwater image.

[0013] In one embodiment of the present application, each underwater image P is calculated i The clarity D(P i ),include:

[0014] Each underwater image P i Convert to grayscale image G i ;

[0015] Based on the segmentation grid, each grayscale image G i Divide into multiple areas;

[0016] Randomly select the target area from the multiple areas and calculate each grayscale image G i The variance of the pixel grayscale values ​​in the target area

[0017] The variance of the pixel gray value As the corresponding grayscale image G i The clarity D(P i ), where the variance of the pixel grayscale value The larger it is, the higher the clarity.

[0018] In one embodiment of the present application, before extracting the feature value in the target underwater image, the method further includes:

[0019] Performing centering processing on the target underwater image to obtain a first intermediate image;

[0020] Convert the first intermediate image into a grayscale image G t ;

[0021] For the grayscale image G t Performing median filtering to obtain a second intermediate image;

[0022] The brightness of the second intermediate image is adjusted to obtain a target underwater image, so that the outlines of suspended particles in the target underwater image are clearer.

[0023] In one embodiment of the present application, determining the distribution uniformity of suspended particles in the target underwater image includes:

[0024] Extracting contour features in the target underwater image;

[0025] When any contour feature meets the target conditions, the contour feature is used as the contour of the suspended particle, and the target conditions include: (1) the contour feature forms a closed figure; (2) the pixel value of the pixel point in the closed figure is within a preset pixel value range;

[0026] The target underwater image is divided into multiple regions, and the number of suspended particles C in each region is determined. n and the average size of the suspended particles S n , wherein the number of suspended particles is the number of outlines of suspended particles, and the size of suspended particles is the number of pixel points within the outline of suspended particles;

[0027] Based on the number of suspended particles C in each area n and the average size of the suspended particles S n The distribution uniformity U of the suspended particles in the target underwater image is calculated. The mathematical expression of the distribution uniformity U is:

[0028]

[0029] In the formula, α is the first weight factor, β is the second weight factor, C is the average value of the number of suspended particles in multiple regions, and S is the average value of the average size of the suspended particles in multiple regions.

[0030] In one embodiment of the present application, the number of suspended particles C in each area is determined. n and the average size of the suspended particles S n ,include:

[0031] Determine whether the outline of each suspended particle is located in only one area. If so, increase the number of suspended particles in the corresponding area by one, and associate the outline of the suspended particle with the corresponding area; if not, increase the number of the proportion of the outline of the suspended particle in the corresponding multiple areas, and associate the outline of the suspended particle with the corresponding multiple areas;

[0032] The number of suspended particles in each area is accumulated to obtain the number of suspended particles C in each area. n ; The average number of pixels in the outline of the suspended particles associated with each region is calculated to obtain the average size S of the suspended particles n .

[0033] In one embodiment of the present application, extracting feature values ​​from the target underwater image includes:

[0034] Calculate the average value A and standard deviation σ of the suspended particle size in the target underwater image;

[0035] Constructing a distribution range [(A-σ), (A+σ)] of the suspended particle size in the target underwater image based on the average value A and the standard deviation σ;

[0036] The characteristic value of the target underwater image is constructed based on the distribution range of the suspended particle size [(A-σ), (A+σ)] and the number of suspended particles.

[0037] The present application also provides an image feature extraction system for sewage treatment, comprising:

[0038] The acquisition module is used to execute step S1 to acquire a plurality of underwater images P of the sewage pool taken continuously. i ;

[0039] The screening module is used to execute step S2 to select the plurality of underwater images P i The image with the highest resolution is used as the target underwater image;

[0040] The extraction module is used to execute step S3 to determine the distribution uniformity of the suspended particles in the target underwater image. When the distribution uniformity of the suspended particles in the target underwater image is less than a preset threshold, the process returns to step S1; when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to the preset threshold, the feature value in the target underwater image is extracted.

[0041] The beneficial effects of the present invention are as follows: the image feature extraction method and system for sewage treatment of the present invention obtains a plurality of underwater images P of a sewage pool taken continuously. i ; Multiple underwater images P i The image with the highest definition is used as the target underwater image; the distribution uniformity of the suspended particles in the target underwater image is determined, and when the distribution uniformity of the suspended particles in the target underwater image is less than the preset threshold, the process returns to step S1; when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to the preset threshold, the characteristic value in the target underwater image is extracted. The present application continuously shoots multiple underwater images, and performs a distribution uniformity analysis on the image with the highest definition. If the partial uniformity is greater than the threshold, it means that the suspended particles in the image are evenly distributed. Finally, features are extracted from this image, thereby ensuring that the characteristic value can accurately reflect the reaction of the sewage. This provides a data basis for the addition control of the reaction agent. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0043] Figure 1This is an application scenario diagram of an image feature extraction method and system for sewage treatment in one embodiment of the present invention;

[0044] Figure 2 The structure diagram of an image feature extraction method and system for sewage treatment in one embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0046] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show the layers related to the present invention rather than being drawn according to the number, shape and size of the layers in actual implementation. In actual implementation, the type, quantity and proportion of each layer may be changed arbitrarily, and the layer layout may also be more complicated.

[0047] In the following description, numerous details are discussed to provide a more thorough explanation of embodiments of the present invention; however, it is apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details.

[0048] Figure 1 is a flow chart of an image feature extraction method for sewage treatment shown in an embodiment of the present application. Figure 1 Shown: Included:

[0049] S1, obtain multiple underwater images P of the sewage pool taken continuously i Among them, multiple underwater images are obtained through a camera preset in the sewage pool, and the camera transmits the underwater images to the computer host in the computer room through a data cable for storage.

[0050] S2, the multiple underwater images P i The image with the highest definition among the images is used as the target underwater image; the host computer screens the multiple underwater images to obtain the target underwater image with the highest definition.

[0051] In one embodiment of the present application, the plurality of underwater images P i The highest definition image is used as the target underwater image, including:

[0052] Calculate each underwater image P iThe clarity D(P i );

[0053] Determine the multiple underwater images P i The highest value of clarity D max (P i ), and the highest definition value D max (P i ) is used as the target underwater image.

[0054] In one embodiment of the present application, each underwater image P is calculated i The clarity D(P i ),include:

[0055] Each underwater image P i Convert to grayscale image G i ;

[0056] Based on the segmentation grid, each grayscale image G i Divide into multiple areas;

[0057] Randomly select the target area from the multiple areas and calculate each grayscale image G i The variance of the pixel grayscale values ​​in the target area

[0058] The variance of the pixel gray value As the corresponding grayscale image G i The clarity D(P i ), where the variance of the pixel grayscale value The larger it is, the higher the clarity.

[0059] This application uses the continuous shooting function of the camera to shoot multiple images each time, and then calculates the clarity of each image after converting it into a grayscale image. This application determines the clarity by calculating the variance of the grayscale value of the pixel. In general, the larger the grayscale value variance, the clearer the image as a whole. In order to increase the calculation speed, this application divides the image into 9 3*3 areas and calculates the grayscale value variance of the middle area, thereby simplifying the calculation process.

[0060] S3, determining the distribution uniformity of the suspended particles in the target underwater image, and when the distribution uniformity of the suspended particles in the target underwater image is less than a preset threshold, returning to step S1; when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to the preset threshold, extracting the characteristic values ​​in the target underwater image.

[0061] In one embodiment of the present application, before extracting the feature value in the target underwater image, the method further includes:

[0062] Performing centering processing on the target underwater image to obtain a first intermediate image;

[0063] Convert the first intermediate image into a grayscale image G t ;

[0064] For the grayscale image G t Performing median filtering to obtain a second intermediate image;

[0065] The brightness of the second intermediate image is adjusted to obtain a target underwater image, so that the outlines of suspended particles in the target underwater image are clearer.

[0066] Before feature extraction, the present application also performs a series of image preprocessing to make subsequent feature extraction more accurate and faster. The preprocessing methods include centering, grayscale conversion, median filtering and brightness adjustment.

[0067] In one embodiment of the present application, determining the distribution uniformity of suspended particles in the target underwater image includes:

[0068] Extracting contour features in the target underwater image;

[0069] When any contour feature meets the target conditions, the contour feature is used as the contour of the suspended particle, and the target conditions include: (1) the contour feature forms a closed figure; (2) the pixel value of the pixel point in the closed figure is within a preset pixel value range;

[0070] The target underwater image is divided into multiple regions, and the number of suspended particles C in each region is determined. n and the average size of the suspended particles S n , wherein the number of suspended particles is the number of outlines of suspended particles, and the size of suspended particles is the number of pixel points within the outline of suspended particles;

[0071] Based on the number of suspended particles C in each area n and the average size of the suspended particles S n The distribution uniformity U of the suspended particles in the target underwater image is calculated. The mathematical expression of the distribution uniformity U is:

[0072]

[0073] In the formula, α is the first weight factor, β is the second weight factor, C is the average value of the number of suspended particles in multiple regions, and S is the average value of the average size of the suspended particles in multiple regions.

[0074] This application sets target conditions to screen out suspended particles, then divides the image into multiple regions, calculates the number of suspended particles in each region and the average size of the suspended particles, and finally obtains the overall distribution uniformity U of the suspended particles through variance calculation.

[0075] In one embodiment of the present application, the number of suspended particles C in each area is determined. n and the average size of the suspended particles S n ,include:

[0076] Determine whether the outline of each suspended particle is located in only one area. If so, increase the number of suspended particles in the corresponding area by one, and associate the outline of the suspended particle with the corresponding area; if not, increase the number of the proportion of the outline of the suspended particle in the corresponding multiple areas, and associate the outline of the suspended particle with the corresponding multiple areas;

[0077] The number of suspended particles in each area is accumulated to obtain the number of suspended particles C in each area. n ; The average number of pixels in the outline of the suspended particles associated with each region is calculated to obtain the average size S of the suspended particles n .

[0078] In this application, the number of suspended particles in each area is determined based on the actual distribution of the suspended particles. For example, suspended particle A is distributed in both area 1 and area 2, where area 1 accounts for 0.4 and area 2 accounts for 0.6. When counting, the number of area 1 is accumulated by 0.4 and the number of area 2 is accumulated by 0.6. The average size of the suspended particles is determined based on the complete area of ​​the suspended particle contour. For example, if the area of ​​suspended particle A is S, then when calculating the average area of ​​suspended particles in area 1, the area corresponding to suspended particle A is S, and when calculating the average area of ​​suspended particles in area 2, the area corresponding to suspended particle A is also S.

[0079] In one embodiment of the present application, extracting feature values ​​from the target underwater image includes:

[0080] Calculate the average value A and standard deviation σ of the suspended particle size in the target underwater image;

[0081] Constructing a distribution range [(A-σ), (A+σ)] of the suspended particle size in the target underwater image based on the average value A and the standard deviation σ;

[0082] The characteristic value of the target underwater image is constructed based on the distribution range of the suspended particle size](A-σ),(A+σ)] and the number of suspended particles.

[0083] The more fully the particles in the sewage react with the reagent, the larger the suspended particles are, the fewer they are, and the more uniform their size is. The average value A and standard deviation σ of the suspended particle size are used to characterize the band and uniformity of the suspended particles. Combined with the number of suspended particles, a characteristic value that can reflect the degree of reaction between sewage and the reagent can be constructed.

[0084] The present invention provides an image feature extraction method for sewage treatment, by acquiring a plurality of underwater images P of a sewage pool taken continuously. i ; Multiple underwater images P i The image with the highest definition is used as the target underwater image; the distribution uniformity of the suspended particles in the target underwater image is determined, and when the distribution uniformity of the suspended particles in the target underwater image is less than the preset threshold, the process returns to step S1; when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to the preset threshold, the characteristic value in the target underwater image is extracted. The present application continuously shoots multiple underwater images, and performs a distribution uniformity analysis on the image with the highest definition. If the partial uniformity is greater than the threshold, it means that the suspended particles in the image are evenly distributed. Finally, features are extracted from this image, thereby ensuring that the characteristic value can accurately reflect the reaction of the sewage. This provides a data basis for the addition control of the reaction agent.

[0085] like Figure 2 As shown, the present application also provides an image feature extraction system for sewage treatment, comprising:

[0086] An acquisition module, used to execute step S1, to acquire a plurality of underwater images Pi of the sewage pool taken continuously;

[0087] The screening module is used to execute step S2 to select the plurality of underwater images P i The image with the highest resolution is used as the target underwater image;

[0088] The extraction module is used to execute step S3 to determine the distribution uniformity of the suspended particles in the target underwater image. When the distribution uniformity of the suspended particles in the target underwater image is less than a preset threshold, the process returns to step S1; when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to the preset threshold, the feature value in the target underwater image is extracted.

[0089] The image feature extraction system for sewage treatment of the present invention obtains a plurality of underwater images P of a sewage pool taken continuously. i ; Multiple underwater images P iThe image with the highest definition is used as the target underwater image; the distribution uniformity of the suspended particles in the target underwater image is determined, and when the distribution uniformity of the suspended particles in the target underwater image is less than the preset threshold, the process returns to step S1; when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to the preset threshold, the characteristic value in the target underwater image is extracted. The present application continuously shoots multiple underwater images, and performs a distribution uniformity analysis on the image with the highest definition. If the partial uniformity is greater than the threshold, it means that the suspended particles in the image are evenly distributed. Finally, features are extracted from this image, thereby ensuring that the characteristic value can accurately reflect the reaction of the sewage. This provides a data basis for the addition control of the reaction agent.

[0090] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented, wherein the method is the execution logic of this system.

[0091] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0092] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0093] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0094] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0095] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0096] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0097] In the above-mentioned embodiments, although the present invention has been described in conjunction with the specific embodiments of the present invention, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art according to the foregoing description. The embodiments of the present invention are intended to cover all such replacements, modifications and variations falling within the broad scope of the appended claims.

[0098] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. An image feature extraction method for sewage treatment, characterized in that: include: S1, obtain multiple underwater images P of the sewage pool taken continuously i ; S2, the multiple underwater images P i The image with the highest resolution is used as the target underwater image; S3, determining the distribution uniformity of the suspended particles in the target underwater image, and when the distribution uniformity of the suspended particles in the target underwater image is less than a preset threshold, returning to step S1; when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to the preset threshold, extracting the characteristic values ​​in the target underwater image.

2. The image feature extraction method for sewage treatment according to claim 1, characterized in that: The multiple underwater images P i The highest definition image is used as the target underwater image, including: Calculate each underwater image P i The clarity D(P i ); Determine the multiple underwater images P i The highest clarity value D max (P i ), and the highest definition value D max (P i ) is used as the target underwater image.

3. The image feature extraction method for sewage treatment according to claim 2, characterized in that: Calculate each underwater image P i The clarity D(P i ),include: Each underwater image P i Convert to grayscale image G i ; Based on the segmentation grid, each grayscale image G i Divide into multiple areas; Randomly select the target area from the multiple areas and calculate each grayscale image G i The variance of the pixel grayscale values ​​in the target area The variance of the pixel gray value As the corresponding grayscale image G i The clarity D(P i ), where the variance of the pixel grayscale value The larger it is, the higher the clarity.

4. The image feature extraction method for sewage treatment according to claim 1, characterized in that: Before extracting the characteristic value in the target underwater image, the method further includes: Performing centering processing on the target underwater image to obtain a first intermediate image; Convert the first intermediate image into a grayscale image G t ; For the grayscale image C t Performing median filtering to obtain a second intermediate image; The brightness of the second intermediate image is adjusted to obtain a target underwater image, so that the outlines of suspended particles in the target underwater image are clearer.

5. The image feature extraction method for sewage treatment according to claim 1, characterized in that: Determining the distribution uniformity of suspended particles in the target underwater image includes: Extracting contour features in the target underwater image; When any contour feature meets the target conditions, the contour feature is used as the contour of the suspended particle, and the target conditions include: (1) the contour feature forms a closed figure; (2) the pixel value of the pixel point in the closed figure is within a preset pixel value range; The target underwater image is divided into multiple regions, and the number of suspended particles C in each region is determined. n and the average size of the suspended particles S n , wherein the number of suspended particles is the number of outlines of suspended particles, and the size of suspended particles is the number of pixel points within the outline of suspended particles; Based on the number of suspended particles C in each area n and the average size of the suspended particles S n The distribution uniformity U of the suspended particles in the target underwater image is calculated. The mathematical expression of the distribution uniformity U is: In the formula, α is the first weight factor, β is the second weight factor, C is the average value of the number of suspended particles in multiple regions, and S is the average value of the average size of the suspended particles in multiple regions.

6. The image feature extraction method for sewage treatment according to claim 5, characterized in that: Determine the number of suspended particles C in each area n and the average size of the suspended particles S n ,include: Determine whether the outline of each suspended particle is located in only one area. If so, increase the number of suspended particles in the corresponding area by one, and associate the outline of the suspended particle with the corresponding area; if not, increase the number of the proportion of the outline of the suspended particle in the corresponding multiple areas, and associate the outline of the suspended particle with the corresponding multiple areas; The number of suspended particles in each area is accumulated to obtain the number of suspended particles C in each area. n ; The average number of pixels in the outline of the suspended particles associated with each region is calculated to obtain the average size S of the suspended particles n .

7. The image feature extraction method for sewage treatment according to claim 1, characterized in that: Extracting feature values ​​from the target underwater image includes: Calculate the average value A and standard deviation σ of the suspended particle size in the target underwater image; Constructing a distribution range [(A-σ), (A+σ)] of the suspended particle size in the target underwater image based on the average value A and the standard deviation σ; The characteristic value of the target underwater image is constructed based on the distribution range of the suspended particle size [(A-σ), (A+σ)] and the number of suspended particles.

8. An image feature extraction system for sewage treatment, characterized in that: include: The acquisition module is used to execute step S1 to acquire a plurality of underwater images P of the sewage pool taken continuously. i ; The screening module is used to execute step S2 to select the plurality of underwater images P i The image with the highest resolution is used as the target underwater image; The extraction module is used to execute step S3 to determine the distribution uniformity of the suspended particles in the target underwater image. When the distribution uniformity of the suspended particles in the target underwater image is less than a preset threshold, the process returns to step S1; when the distribution uniformity of the suspended particles in the target underwater image is greater than or equal to the preset threshold, the feature value in the target underwater image is extracted.

Images