Flying animal shot image processing method and system

Abstract

The invention is suitable for the technical field of image processing, and particularly relates to a flying animal shot image processing method and system, and the method comprises the steps: reading an image containing a flying animal, constructing a double linked list, marking a head node and a tail node, carrying out the linking through a main link and an auxiliary link, and inputting the image into the head node; and in the head node, preprocessing the image, determining the contour of the flying animal, creating a mask, covering the contour, delimiting a mask area, defining the mask area as a main image by using a pre-constructed edge detection algorithm, defining the part except the mask area in the image as an auxiliary image, and transferring the main image into a main link. According to the method, the compression area can be delimited, redundant information can be removed and the compression efficiency can be ensured by calculating the discrete value, and the visual efficiency can be improved by updating the intermediate image, so that the detail features of the flying animals in the main image are highlighted while the auxiliary image is compressed.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method and system for processing images shot of flying animals. Background Art

[0002] Flying animal imaging refers to the use of high-tech photographic equipment, such as drones or high-resolution cameras, to capture the dynamic performance of flying animals in their natural habitats in order to study their behavior, ecological characteristics, and adaptability to different environmental conditions.

[0003] Images of flying animals generally have delicate details, rich color levels and clear image quality, and can remain clear after large-format printing or enlargement; however, the size of these images themselves is also large, which may reach tens of MB or more, which requires more storage space and computing resources when processing and editing these photos.

[0004] Therefore, “how to compress the area except the flying animals in the image of the flying animals” is the technical problem that the present invention needs to solve. Summary of the invention

[0005] The object of the present invention is to provide a method and system for processing images of flying animals, so as to solve the problem of "how to compress the area except the flying animals in the images of flying animals" raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for processing images of flying animals, the method comprising: Reading an image containing a flying animal, constructing a double linked list, marking a head node and a tail node, and linking them using a main link and an auxiliary link, and inputting the image into the head node; In the head node, the image is preprocessed, and the outline of the flying animal is determined using a pre-built edge detection algorithm, a mask is created to cover the outline, a mask area is defined, the mask area is defined as a main image, and the portion of the image outside the mask area is defined as an auxiliary image, and the main image is transferred to the main link, and the auxiliary image is transferred to the auxiliary link; In the auxiliary link, the pixel points in the auxiliary image are located and the RGB values ​​are determined. The brightness value of each pixel point is calculated using a preset brightness formula. A brightness curve is drawn with the pixel point as the horizontal coordinate and the brightness value as the vertical coordinate. The brightness curve is subjected to discrete cosine transformation to determine the discrete value. Based on the discrete value, the pixel points are clustered to obtain a number of blocks, and the blocks whose discrete values ​​are greater than a threshold are compressed. In the tail node, the main image and the compressed auxiliary image are integrated to generate an intermediate image, the maximum and minimum brightness values ​​in the intermediate image are traversed, the intermediate image is updated using a preset stretching formula, and the updated intermediate image is pushed to a preset terminal.

[0007] Further, the step of "linking using the main link and the auxiliary link" includes: In the auxiliary link, multiple processing nodes are configured and a processing order is determined; The processing nodes are triggered in sequence, and the auxiliary graph is processed according to the processing order.

[0008] Furthermore, the step of "using a pre-built edge detection algorithm to determine the outline of the flying animal" includes: Using a preset convolutional layer, the gradients of the auxiliary image in the horizontal and vertical directions are calculated; The gradient change between two adjacent pixel points is compared, and the contour is corrected using the change.

[0009] Furthermore, the step of "creating a mask, covering the outline, and defining a mask area" includes: Create a binary mask and use preset logical operations to extract the mask area; The positions of the pixels in the mask area are counted, and labels are generated using the positions, and the labels are inserted into the mask area.

[0010] Furthermore, the step of “transferring the main graph into the main link” includes: Integrating a side chain into the main chain; When the main link receives the main image, it synchronizes the main image to the side chain, records and backs up the version of the main image, and generates a feature data set.

[0011] Furthermore, the step of "drawing a brightness curve" also includes: Extract the RGB value of each pixel and calculate the brightness value using a preset brightness formula; The preset brightness formula is: Brightness = 0.299 × R + 0.587 × G + 0.114 × B; R, G, and B represent the color components of each channel; The auxiliary image is divided into lines, and a number of each pixel is configured. A brightness curve is drawn with the number of the pixel as the horizontal coordinate and the brightness value as the vertical coordinate.

[0012] Furthermore, the step of “performing discrete cosine transformation on the brightness curve to determine discrete values, and clustering the pixel points based on the discrete values ​​to obtain a plurality of blocks” further includes: Using a basis function, discrete cosine transform is performed on the brightness curve, and the obtained weighting coefficient is defined as a discrete value; Dividing the discrete values ​​into a plurality of levels, and establishing a corresponding relationship between the discrete values, the levels and the pixels; Based on the corresponding relationship, pixels corresponding to the same level are merged to obtain a plurality of blocks.

[0013] Furthermore, the system comprises: An input module is used to read images containing flying animals, construct a double linked list, mark the head node and the tail node, and link them using the main link and the auxiliary link to input the image into the head node; A transfer module is used to pre-process the image in the head node, determine the outline of the flying animal using a pre-built edge detection algorithm, create a mask to cover the outline, define the mask area, define the mask area as the main image, define the part of the image outside the mask area as the auxiliary image, and transfer the main image to the main link and transfer the auxiliary image to the auxiliary link; A compression module is used to locate the pixel points in the auxiliary image in the auxiliary link and determine the RGB value, calculate the brightness value of each pixel point by using a preset brightness formula, draw a brightness curve with the pixel point as the horizontal coordinate and the brightness value as the vertical coordinate, and perform discrete cosine transformation on the brightness curve to determine the discrete value, cluster the pixel points based on the discrete value to obtain a plurality of blocks, and compress the blocks whose discrete values ​​are greater than a threshold; The push module is used to integrate the main image and the compressed auxiliary image in the tail node to generate an intermediate image, traverse the maximum and minimum brightness values ​​in the intermediate image, use a preset stretching formula to update the intermediate image, and push the updated intermediate image to a preset terminal.

[0014] Furthermore, the input module includes: A configuration unit, configured to configure a plurality of processing nodes in the auxiliary link and determine a processing order; A processing unit, used to trigger the processing nodes in sequence and process the auxiliary graph according to the processing order; A calculation unit, used to calculate the gradient of the auxiliary image in the horizontal and vertical directions using a preset convolution layer; A correction unit, used for comparing the gradient change between two adjacent pixel points, and correcting the contour using the change; A creation unit, used for creating a binary mask and extracting a mask area using a preset logical operation; The inserting unit is used to count the positions of the pixels in the mask area, generate labels using the positions, and insert the labels into the mask area.

[0015] Furthermore, the transfer module includes: An integration unit, used for integrating a side chain into the main link; The generating unit is used to synchronize the main graph to the side chain after the main link receives the main graph, record and back up the version of the main graph, and generate a feature data set.

[0016] Compared with the prior art, the present invention has the following beneficial effects: By constructing a head node, the image can be segmented, which improves the processing efficiency of images taken of flying animals. By constructing main links and auxiliary links, the main image and auxiliary image can be processed in parallel, further improving the processing efficiency. By creating a mask, the main image can be highlighted, and the auxiliary image can be processed and compressed, thereby saving storage space and increasing loading speed while retaining the features of flying animals in the main image, making it easier to share and download. By calculating discrete values, the compression area can be delineated, redundant information can be removed, and compression efficiency can be guaranteed. By updating the intermediate image, the visual efficiency can be improved, thereby compressing the auxiliary image while highlighting the detailed features of the flying animals in the main image. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for processing images of flying animals provided by an embodiment of the present invention; Figure 2 A first sub-flow chart of the method for processing images of flying animals provided in an embodiment of the present invention; Figure 3 A second sub-flow chart of the method for processing images of flying animals provided by an embodiment of the present invention; Figure 4 A third sub-flow chart of the method for processing flying animal images provided by an embodiment of the present invention; Figure 5 A block diagram of a flying animal image processing system provided by an embodiment of the present invention; Figure 6 A block diagram of the composition of an input module in a flying animal image processing system provided by an embodiment of the present invention; Figure 7 A block diagram of the composition of the transfer module in the flying animal image processing system provided by an embodiment of the present invention; Figure 8This is a block diagram of the composition of a compression module in the flying animal image processing system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In Example 1, Figure 1 The implementation process of the method for processing flying animal images provided by the embodiment of the present invention is shown and described in detail below: S100: Read an image containing flying animals, construct a double linked list, mark the head node and the tail node, and link them using the main link and the auxiliary link, and input the image into the head node.

[0020] Acquire an image containing flying animals; construct a double linked list, wherein the two ends of the double linked list are a head node and a tail node respectively, the head node and the tail node are linked by two links, the two links are a main link and an auxiliary link, and each link is composed of a number of processing nodes; input the image into the head node.

[0021] S200: In the head node, the image is preprocessed, and the outline of the flying animal is determined using a pre-built edge detection algorithm. A mask is created to cover the outline, and a mask area is defined. The mask area is defined as a main image, and the portion of the image outside the mask area is defined as an auxiliary image. The main image is transferred to the main link, and the auxiliary image is transferred to the auxiliary link.

[0022] In the head node, the image is preprocessed, where the preprocessing includes noise reduction, illumination compensation and sharpening, etc., and the Canny edge detection algorithm in the prior art is used to determine the outline of the flying animal, separate the outline of the flying animal from the background area, and generate a clear contour line; a mask is created using this contour information, and the mask is the same size as the outline; under normal circumstances, the mask area is generally white or black, and the outline of the flying animal corresponding to the mask area is defined as the main image, and the rest of the image is defined as the auxiliary image. The main image is transferred to the main link, and the auxiliary image is transferred to the auxiliary link. The main link and the auxiliary link are used to process the main image and the auxiliary image in parallel.

[0023] S300: In the auxiliary link, the pixel points in the auxiliary image are located and the RGB values ​​are determined. The brightness value of each pixel point is calculated using a preset brightness formula. A brightness curve is drawn with the pixel point as the horizontal coordinate and the brightness value as the vertical coordinate. The brightness curve is discretely cosine transformed to determine the discrete value. Based on the discrete value, the pixel points are clustered to obtain a number of blocks, and the blocks whose discrete values ​​are greater than a threshold are compressed.

[0024] Locate the pixels in the auxiliary image, determine the RGB value of each pixel, and calculate the brightness value of each pixel; draw the brightness curve of each row of pixels in units of "rows", and perform discrete cosine transformation on the brightness curve to obtain discrete values, where discrete values ​​are used to characterize changes in brightness. The larger the discrete value, the greater the brightness change; merge the pixels according to the discrete values, for example, the discrete values ​​can be divided into several levels, and if there are adjacent pixels in each level, these adjacent pixels are merged; after clustering is completed, divide the image into several blocks, and compress the blocks whose discrete values ​​are greater than the threshold.

[0025] The human eye is less sensitive to high-frequency information. Discarding the blocks corresponding to high-frequency discrete values ​​will not significantly affect the overall quality of the image, but it will reduce the amount of data storage.

[0026] S400: In the tail node, the main image and the compressed auxiliary image are integrated to generate an intermediate image, the maximum and minimum brightness values ​​in the intermediate image are traversed, the intermediate image is updated using a preset stretching formula, and the updated intermediate image is pushed to a preset terminal.

[0027] The main image and the compressed auxiliary image are integrated to generate an intermediate image, and the maximum and minimum brightness values ​​are found in the intermediate image. The intermediate image is updated using a preset stretching formula to increase the brightness difference of the intermediate image, and the updated intermediate image is pushed to a preset terminal, where the preset terminal can be a user terminal.

[0028] The preset stretching formula is: ; In Example 2, Figure 2 The implementation process of the flying animal image processing method provided by the embodiment of the present invention is shown. The steps of "linking using the main link and the auxiliary link" are described in detail as follows: S101: In the auxiliary link, multiple processing nodes are configured and a processing order is determined.

[0029] S102: triggering the processing nodes in sequence, and processing the auxiliary graph according to the processing order.

[0030] In the auxiliary link, multiple processing nodes are configured, and the auxiliary graph is processed in sequence according to the processing order.

[0031] In Example 3, Figure 2 The implementation process of the method for processing flying animal images provided by an embodiment of the present invention is shown. The step of "using a pre-built edge detection algorithm to determine the outline of the flying animal" is described in detail as follows: S103: Using a preset convolutional layer, calculate the gradients of the auxiliary image in the horizontal and vertical directions.

[0032] The convolution layer in the prior art is optimized for the horizontal and vertical directions respectively, and the optimized convolution layer is used to convolve each pixel to calculate the gradient of the auxiliary image in the horizontal and vertical directions.

[0033] S104: Compare the gradient change between two adjacent pixel points, and use the change to correct the contour.

[0034] Correct the contour by changing the gradient between two adjacent pixels.

[0035] If the gradient change between two adjacent pixels is greater than a preset threshold, it means that the contour line may pass through these two pixels.

[0036] In Example 4, Figure 2 The implementation process of the flying animal image processing method provided by the embodiment of the present invention is shown. The steps of "creating a mask, covering the outline, and defining the mask area" are described in detail as follows: S105: Create a binary mask and extract the mask area using a preset logical operation.

[0037] Create a binary mask, in which the pixel value 1 (or 255) represents the area that needs to be masked, and the pixel value 0 represents the area that does not need to be masked; use logical operations such as AND, OR, and NOT to combine the auxiliary image and the binary mask, extract the area covered by the mask, and obtain the mask area.

[0038] S106: Counting the positions of the pixels in the mask area, generating labels using the positions, and inserting the labels into the mask area.

[0039] The pixels in the mask area are counted, and labels are generated according to the positions of these pixels, and the labels are inserted into the mask area; the advantage of this is that it is convenient for the later integration of the main image and the auxiliary image.

[0040] In Example 5, Figure 3The implementation process of the method for processing flying animal images provided by an embodiment of the present invention is shown. The step of "transferring the main image to the main link" is described in detail as follows: S201: Integrate a side chain into the main link.

[0041] S202: After the main link receives the main graph, the main graph is synchronized to the side chain, a version record is made for the main graph, and a back-up is performed to generate a feature data set.

[0042] Insert a side chain into the main link. When the main link receives the main image, in addition to using the main link to process the main image, the side chain can also be used to back up the main image and use the backed-up main image to generate a feature data set, where the feature data set is a collection of images of the outlines of flying animals.

[0043] In Example 6, Figure 4 The implementation process of the flying animal image processing method provided by the embodiment of the present invention is shown. The step of "drawing a brightness curve" is described in detail as follows: S301: extracting the RGB value of each pixel, and calculating the brightness value using a preset brightness formula; The preset brightness formula is: Brightness = 0.299 × R + 0.587 × G + 0.114 × B; R, G, and B represent the color components of each channel respectively.

[0044] Use the brightness formula to calculate the brightness value of each pixel.

[0045] S302: Slice the auxiliary image into lines, assign a number to each pixel, and draw a brightness curve using the pixel number as the horizontal coordinate and the brightness value as the vertical coordinate.

[0046] The auxiliary image is segmented, the number of each pixel is determined, and a brightness curve is drawn according to the brightness of each pixel.

[0047] In Example 7, Figure 4 The implementation process of the flying animal image processing method provided by the embodiment of the present invention is shown. The steps of "performing discrete cosine transformation on the brightness curve to determine discrete values, clustering the pixel points based on the discrete values ​​to obtain a plurality of blocks" are described in detail as follows: S303: Using a basis function, discrete cosine transform is performed on the brightness curve, and the obtained weighting coefficient is defined as a discrete value.

[0048] Taking the brightness curve as the input signal, after discrete cosine transform, a set of discrete values, namely weighting coefficients, will be generated. Each coefficient corresponds to one or more specific frequency basis functions; the frequency reflects the intensity of the brightness of the pixel at that frequency, and the weighting coefficient is determined as a discrete value.

[0049] S304: Divide the discrete values ​​into several levels, and establish a corresponding relationship between the discrete values, levels and pixels.

[0050] S305: Based on the corresponding relationship, pixels corresponding to the same level are merged to obtain a plurality of blocks.

[0051] Find the pixels whose discrete values ​​are in the same level, and merge the adjacent pixels among these pixels to obtain blocks. If the pixels in the same level exist alone, they are also treated as blocks alone.

[0052] Figure 5 The structure block diagram of the flying animal image processing system provided by the embodiment of the present invention is shown. The flying animal image processing system 1 comprises: An input module 11 is used to read images containing flying animals, construct a double linked list, mark the head node and the tail node, and link them using the main link and the auxiliary link to input the image into the head node; The transfer module 12 is used to pre-process the image in the head node, determine the outline of the flying animal using a pre-built edge detection algorithm, create a mask to cover the outline, define the mask area, define the mask area as the main image, define the part of the image outside the mask area as the auxiliary image, and transfer the main image to the main link and transfer the auxiliary image to the auxiliary link; The compression module 13 is used to locate the pixel points in the auxiliary image in the auxiliary link and determine the RGB value, calculate the brightness value of each pixel point by using a preset brightness formula, draw a brightness curve with the pixel point as the horizontal coordinate and the brightness value as the vertical coordinate, and perform discrete cosine transformation on the brightness curve to determine the discrete value, cluster the pixel points based on the discrete value to obtain a plurality of blocks, and compress the blocks whose discrete values ​​are less than a threshold value; The push module 14 is used to integrate the main image and the compressed auxiliary image in the tail node to generate an intermediate image, traverse the maximum and minimum brightness values ​​in the intermediate image, use a preset stretching formula to update the intermediate image, and push the updated intermediate image to a preset terminal.

[0053] Figure 6 The structure block diagram of the flying animal image processing system provided by the embodiment of the present invention is shown, and the input module 11 includes: A configuration unit 111, configured to configure a plurality of processing nodes in the auxiliary link and determine a processing order; A processing unit 112, configured to trigger the processing nodes in sequence and process the auxiliary graph in accordance with the processing order; A calculation unit 113, used to calculate the gradient of the auxiliary image in the horizontal and vertical directions using a preset convolution layer; A correction unit 114, used to compare the gradient change between two adjacent pixel points, and use the change to correct the contour; A creating unit 115, used to create a binary mask and extract the mask area using a preset logical operation; The insertion unit 116 is used to count the positions of the pixels in the mask area, generate labels based on the positions, and insert the labels into the mask area. Figure 7 The structure block diagram of the flying animal image processing system provided by the embodiment of the present invention is shown, and the transfer module 12 includes: An integration unit 121, configured to integrate a side chain into the main link; The generating unit 122 is used for synchronizing the main image to the side chain after the main link receives the main image, recording and backing up the version of the main image, and generating a feature data set.

[0054] Figure 8 The structure block diagram of the flying animal image processing system provided by the embodiment of the present invention is shown, and the compression module 13 includes: The extraction unit 131 is used to extract the RGB value of each pixel and calculate the brightness value using a preset brightness formula; The preset brightness formula is: Brightness = 0.299 × R + 0.587 × G + 0.114 × B; R, G, and B represent the color components of each channel; A drawing unit 132, configured to divide the auxiliary image into lines, assign a number to each pixel, and draw a brightness curve with the pixel number as the horizontal coordinate and the brightness value as the vertical coordinate; A definition unit 133, configured to perform discrete cosine transformation on the brightness curve using a basis function, and define the obtained weighting coefficient as a discrete value; A corresponding unit 134, used to divide the discrete values ​​into a plurality of levels and establish a corresponding relationship between the discrete values, the levels and the pixels; The obtaining unit 135 is used to merge the pixels corresponding to the same level according to the corresponding relationship to obtain a plurality of blocks.

[0055] The input module 11 is mainly used to complete step S100, the transfer module 12 is mainly used to complete step S200, the compression module 13 is mainly used to complete step S300, and the push module 14 is mainly used to complete step S400; The configuration unit 111 is mainly used to complete step S101, the processing unit 112 is mainly used to complete step S102, the calculation unit 113 is mainly used to complete step S103, the correction unit 114 is mainly used to complete step S104, the creation unit 115 is mainly used to complete step S105, and the insertion unit 116 is mainly used to complete step S106; The integration unit 121 is mainly used to complete step S201, and the generation unit 122 is mainly used to complete step S202; The extraction unit 131 is mainly used to complete step S301, the drawing unit 132 is mainly used to complete step S302, the definition unit 133 is mainly used to complete step S303, the corresponding unit 134 is mainly used to complete step S304, and the obtaining unit 135 is mainly used to complete step S305.

[0056] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for processing images of flying animals, characterized in that: The method comprises: Reading an image containing a flying animal, constructing a double linked list, marking a head node and a tail node, and linking them using a main link and an auxiliary link, and inputting the image into the head node; In the head node, the image is preprocessed, and the outline of the flying animal is determined using a pre-built edge detection algorithm, a mask is created to cover the outline, a mask area is defined, the mask area is defined as a main image, and the portion of the image outside the mask area is defined as an auxiliary image, and the main image is transferred to the main link, and the auxiliary image is transferred to the auxiliary link; In the auxiliary link, the pixel points in the auxiliary image are located and the RGB values ​​are determined. The brightness value of each pixel point is calculated using a preset brightness formula. A brightness curve is drawn with the pixel point as the horizontal coordinate and the brightness value as the vertical coordinate. The brightness curve is subjected to discrete cosine transformation to determine the discrete value. Based on the discrete value, the pixel points are clustered to obtain a number of blocks, and the blocks whose discrete values ​​are greater than a threshold are compressed. In the tail node, the main image and the compressed auxiliary image are integrated to generate an intermediate image, the maximum and minimum brightness values ​​in the intermediate image are traversed, the intermediate image is updated using a preset stretching formula, and the updated intermediate image is pushed to a preset terminal.

2. The flying animal image processing method according to claim 1, characterized in that: The step of linking using the main link and the auxiliary link comprises: In the auxiliary link, multiple processing nodes are configured and a processing order is determined; The processing nodes are triggered in sequence, and the auxiliary graph is processed according to the processing order.

3. The flying animal image processing method according to claim 2, characterized in that: The step of determining the outline of the flying animal using the pre-built edge detection algorithm comprises: Using a preset convolutional layer, the gradients of the auxiliary image in the horizontal and vertical directions are calculated; The gradient change between two adjacent pixel points is compared, and the contour is corrected using the change.

4. The flying animal image processing method according to claim 1, characterized in that: The steps of creating a mask, covering the outline, and defining the mask area include: Create a binary mask and use preset logical operations to extract the mask area; The positions of the pixels in the mask area are counted, and labels are generated using the positions, and the labels are inserted into the mask area.

5. The flying animal image processing method according to claim 1, characterized in that: The step of transferring the main image into the main link comprises: Integrating a side chain into the main chain; When the main link receives the main image, it synchronizes the main image to the side chain, records and backs up the version of the main image, and generates a feature data set.

6. The flying animal image processing method according to claim 4, characterized in that: The step of drawing a brightness curve also includes: Extract the RGB value of each pixel and calculate the brightness value using a preset brightness formula; The preset brightness formula is: Brightness = 0.299 × R + 0.587 × G + 0.114 × B R, G, and B represent the color components of each channel; The auxiliary image is divided into lines, and a number of each pixel is configured. A brightness curve is drawn with the number of the pixel as the horizontal coordinate and the brightness value as the vertical coordinate.

7. The flying animal image processing method according to claim 6, characterized in that: The step of performing discrete cosine transformation on the brightness curve to determine discrete values, and clustering the pixel points based on the discrete values ​​to obtain a plurality of blocks further includes: Using a basis function, discrete cosine transform is performed on the brightness curve, and the obtained weighting coefficient is defined as a discrete value; Dividing the discrete values ​​into a plurality of levels, and establishing a corresponding relationship between the discrete values, the levels and the pixels; Based on the corresponding relationship, pixels corresponding to the same level are merged to obtain a plurality of blocks.

8. A flying animal image processing system, characterized in that: The system comprises: An input module is used to read images containing flying animals, construct a double linked list, mark the head node and the tail node, and link them using the main link and the auxiliary link to input the image into the head node; A transfer module is used to pre-process the image in the head node, determine the outline of the flying animal using a pre-built edge detection algorithm, create a mask to cover the outline, define the mask area, define the mask area as the main image, define the part of the image outside the mask area as the auxiliary image, and transfer the main image to the main link and transfer the auxiliary image to the auxiliary link; A compression module is used to locate the pixel points in the auxiliary image in the auxiliary link and determine the RGB value, calculate the brightness value of each pixel point by using a preset brightness formula, draw a brightness curve with the pixel point as the horizontal coordinate and the brightness value as the vertical coordinate, and perform discrete cosine transformation on the brightness curve to determine the discrete value, cluster the pixel points based on the discrete value to obtain a plurality of blocks, and compress the blocks whose discrete values ​​are greater than a threshold; The push module is used to integrate the main image and the compressed auxiliary image in the tail node to generate an intermediate image, traverse the maximum and minimum brightness values ​​in the intermediate image, use a preset stretching formula to update the intermediate image, and push the updated intermediate image to a preset terminal.

9. The flying animal image processing system according to claim 8, characterized in that: The input module comprises: A configuration unit, configured to configure a plurality of processing nodes in the auxiliary link and determine a processing order; A processing unit, used to trigger the processing nodes in sequence and process the auxiliary graph according to the processing order; A calculation unit, used to calculate the gradient of the auxiliary image in the horizontal and vertical directions using a preset convolution layer; A correction unit, used for comparing the gradient change between two adjacent pixel points, and correcting the contour using the change; A creation unit, used for creating a binary mask and extracting a mask area using a preset logical operation; The inserting unit is used to count the positions of the pixels in the mask area, generate labels using the positions, and insert the labels into the mask area.

10. The flying animal image processing system according to claim 8, characterized in that: The transfer module comprises: An integration unit, used for integrating a side chain into the main link; The generation unit is used to synchronize the main image to the side chain after the main link receives the main image, record and back up the version of the main image, and generate a feature data set.

Images