CN117452021A - Two-dimensional space-time image velocity measurement method and device for field river surface and storage medium - Google Patents

Abstract

The two-dimensional space-time image velocimetry method for the surface of the field river comprises the steps of firstly collecting a surface flow image at a section position to be measured of the field river, and then preprocessing the surface flow image; for each preprocessed image, firstly, carrying out grid division, then respectively constructing a rotation speed measuring line for each grid node by taking the grid node as a circle center, synthesizing a plurality of spatiotemporal images of each grid node according to texture change of the rotation speed measuring line, calculating autocorrelation coefficients of the spatiotemporal images, selecting the spatiotemporal image with the largest autocorrelation coefficient as a preferable spatiotemporal image of each grid node, constructing a rotation line segment by taking the center of the preferable spatiotemporal image as the circle center, adding the autocorrelation coefficients of each rotation line segment, taking the angle of the rotation line segment where the autocorrelation coefficient and the maximum value are positioned as a river flow velocity direction, and taking the tangent value of the river flow velocity direction as the river flow velocity. The method does not need to pre-designate the flow velocity direction of the measuring point, and solves the problem of flow velocity measurement on the surface of the field river with great change of the flow direction.

Description

Two-dimensional spatio-temporal image velocity measurement method, device and storage medium of wild river surface

Technical field

The present disclosure relates to the field of field river surface flow velocity measurement, and specifically relates to a two-dimensional spatiotemporal image velocity measurement method, device and storage medium of a field river surface.

Background technique

River flow velocity is an important hydrological parameter. Among the many current measurement methods, image methods are increasingly used in river measurement due to their advantages of non-contact and large-scale measurement. The traditional space-time image velocimetry (STIV) method requires artificially presetting the river flow direction and calculating the river surface velocity based on the movement of the river surface texture. Because rivers in the wild are generally curved under the influence of topography, climate, geology, Coriolis force and other factors, the flow direction changes greatly, making it difficult to judge the direction of the river, or even impossible to judge the direction of the river in advance. STIV has limitations when applied to field river measurements.

In addition, existing techniques for calculating river surface flow velocity using tracer particles require the presence of significant tracers on the river surface. However, deploying artificial tracers on large-scale river surfaces in the wild is time-consuming and labor-intensive, and difficult to spread evenly, making it basically impossible to use them in large-area measurements. The appearance time and location of natural tracers such as floating branches are highly random, making it difficult to guarantee their quantity and density. Therefore, there are great limitations in field applications.

Contents of the invention

The present disclosure aims to solve at least one of the technical problems existing in the prior art.

To this end, the first aspect of the present disclosure provides a two-dimensional spatio-temporal image velocity measurement method for a wild river surface without pre-specifying the flow velocity direction of the measurement point, solving the problem of flow velocity measurement on a wild river surface with large flow direction changes, and avoiding It is a time-consuming and labor-intensive problem to deploy artificial tracers on a large-scale river surface in the wild. It is necessary to simultaneously measure the flow velocity and direction of the river surface in the wild.

The two-dimensional spatiotemporal image velocity measurement method of the wild river surface provided by the first aspect of the present disclosure includes:

Step S1: Collect surface flow images at the cross-section location of the wild river to be measured to construct an original image sequence;

Step S2: Preprocess each original image in the original image sequence to obtain a preprocessed image sequence;

Step S3: For each image in the preprocessed image sequence, first divide the image into a grid, then set a horizontal speed measuring line on each grid node, and obtain the result by rotating the speed measuring line at equal intervals. The grid node is a series of tachometer lines with the center of the circle and within the range of [0,2π]. This series of tachometer lines is recorded as the rotation tachometer line of the grid node. According to the texture changes of the rotation tachometer line of each grid node, various tachometer lines are synthesized. Several spatio-temporal images of the grid node, calculate the autocorrelation coefficient of each spatio-temporal image, select the spatio-temporal image with the largest autocorrelation coefficient, and obtain the preferred spatio-temporal image of each grid node; for each of the preferred spatio-temporal images, set the midpoint at The line segment at the center of the preferred space-time image, the length of the line segment is the shorter length of the side lengths of the preferred space-time image, and is obtained by rotating the line segment at equal intervals with the center of the preferred space-time image as the center of the circle. And for a series of rotating line segments in the range of [0,2π], calculate the autocorrelation coefficient of each rotating line segment, add the autocorrelation coefficients of each rotating line segment, and combine the autocorrelation coefficient and the rotating line segment with the maximum value. The angle is taken as the river flow direction, and the tangent value of the river flow direction is taken as the river flow speed.

In some embodiments, in step S1, the location of the section to be measured should be selected as a river bank location that is convenient for placing landmark points and whose river surface has a stable flow state.

In some embodiments, in step S1, a drone carrying a high-definition camera is used to obtain a surface flow video at the location of the section of the river to be measured in the wild, and images are extracted frame by frame from the surface flow video to obtain a number of the surface flow images. .

In some embodiments, in step S2, the preprocessing performed on each original image includes performing image correction and image enhancement on each original image in sequence.

In some embodiments, the image correction includes the following steps:

Using the first original image in the original image sequence as a benchmark, select several non-moving feature areas on the shore in the original image;

Perform cross-correlation operations on the grayscale images in the feature areas of each of the remaining original images in the original image sequence and the first original image to obtain the rotation and displacement of each feature area in each original image;

According to the rotation and displacement of each feature area in each original image, the affine transformation matrix corresponding to each original image and the first original image is obtained;

Inverse transformation is performed on each original image according to the affine transformation matrix corresponding to each original image and the first original image, so that the corresponding points of each original image have the same coordinates, forming a corrected image sequence.

In some embodiments, in step S3, the angle between two adjacent speed measuring lines ranges from 0.1° to 10°.

In some embodiments, in step S3, the angle between two adjacent rotation line segments ranges from 0.1° to 10°.

In some embodiments, in step S3, the autocorrelation coefficient of the spatiotemporal image and the preferred spatiotemporal image is calculated according to the following steps:

Perform edge detection on the spatio-temporal image or preferably the spatio-temporal image to reduce noise in the image;

Calculate the characteristic gradient R(τ _x ,τ _y ) of the spatio-temporal image or preferred spatio-temporal image according to the following formula:

Among them, f (x, y) is the grayscale distribution of the pixel point with coordinates (x, y) in the space-time image or preferred space-time image, (τ _x , τ _y ) is the coordinate (x in the space-time image or preferred space-time image) , y) texture displacement parameters of pixels;

The characteristic gradient of the spatio-temporal image or the preferred spatio-temporal image is normalized to obtain the autocorrelation coefficient of the spatio-temporal image or the preferred spatio-temporal image.

The second aspect of the present disclosure provides a two-dimensional spatiotemporal image speed measurement device for the surface of a wild river, including:

The image acquisition module is used to collect surface flow images at the location of the cross-section to be measured in the wild river to construct the original image sequence;

An image preprocessing module, used to preprocess each original image in the original image sequence to obtain a preprocessed image sequence;

The flow velocity and flow direction calculation module is used to first grid-divide the image for each image in the pre-processed image sequence, and then set a horizontal speed measurement line on each grid node, and rotate the speed measurement line at equal intervals. In the form of lines, a series of speed measurement lines with the grid node as the center and within the range of [0,2π] are obtained. This series of speed measurement lines are recorded as the rotation speed measurement lines of the grid node. According to the rotation speed measurement line of each grid node Texture changes synthesize several spatio-temporal images of each grid node, calculate the autocorrelation coefficient of each spatio-temporal image, select the spatio-temporal image with the largest autocorrelation coefficient, and obtain the preferred spatio-temporal image of each grid node; for each of the preferred spatio-temporal images , set a line segment whose midpoint is located at the center of the preferred space-time image. The length of the line segment is the shorter of the side lengths of the preferred space-time image. The preferred space-time is obtained by rotating the line segment at equal intervals. The center of the image is a series of rotating line segments within the range of [0,2π]. Calculate the autocorrelation coefficient of each rotating line segment. Add the autocorrelation coefficients of each rotating line segment. Add the autocorrelation coefficient and the maximum value. The angle of the rotating line segment is used as the river flow direction, and the tangent value of the river flow direction is used as the river flow speed.

A third aspect of the present disclosure provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions. The computer instructions are used to cause the computer to operate in the field according to any embodiment of the first aspect of the present disclosure. Two-dimensional spatiotemporal image velocimetry method of river surface.

Compared with the prior art, the present disclosure has the following characteristics:

The embodiment of the present disclosure uses the correlation coefficient comparison method to identify the angle of the speed measuring line with the clearest image texture, and realizes the simultaneous measurement of the direction and size of the flow speed on the surface of the wild river. Specifically, by constructing the rotating speed measuring line/rotating line segment, when the speed measuring line/rotating line segment When it is consistent with the flow direction of the river, the texture in the spatio-temporal image formed is clear and the autocorrelation coefficient is the largest. According to the principle of maximum correlation coefficient, the direction of the velocity line is determined to represent the flow direction, and the flow velocity is obtained at the same time, thereby obtaining the two-dimensional flow velocity field of the wild river surface. .

Compared with the prior art, the present disclosure has the following beneficial effects:

1. The embodiment of the present disclosure uses large-scale light and dark changes produced by river surface ripples to replace traditional tracer particles for calculating river surface flow velocity, solving the problem that tracer particles and tracers cannot be artificially dispersed under extreme conditions such as floods and dam breaks. Problems such as poor particle followability.

2. The embodiment of the present disclosure does not need to predetermine the flow direction of the river surface, and can well solve the problem of flow velocity measurement on the wild river surface where the flow direction changes greatly.

3. The embodiment of the present disclosure can be used with a high-definition camera mounted on a drone to achieve remote measurement of the surface flow velocity field of a river in the wild.

Description of the drawings

Figure 1 is a flow chart of a two-dimensional spatiotemporal image velocity measurement method of a wild river surface provided by the first embodiment of the present disclosure.

FIG. 2 is a schematic diagram of a rotation speed measurement line generated by the two-dimensional spatiotemporal image speed measurement method provided by the first embodiment of the present disclosure.

Figure 3 is a space-time image at different angles synthesized by the two-dimensional space-time image velocity measurement method provided by the first embodiment of the present disclosure, in which the angles corresponding to (a) to (f) are 12.5°, 45°, 50°, and 102.5° respectively. , 120° and 150°.

Figure 4 is a spatio-temporal image at a certain angle synthesized by the two-dimensional spatio-temporal image velocity measurement method provided by the first embodiment of the present disclosure.

FIG. 5 is a schematic structural diagram of an electronic device provided by a third embodiment of the present disclosure.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described in further detail below in conjunction with embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

On the contrary, this application covers any alternatives, modifications, equivalent methods and solutions that fall within the spirit and scope of this application as defined by the claims. Furthermore, in order to enable the public to have a better understanding of the present application, some specific details are described in detail in the detailed description of the present application below. A person skilled in the art may fully understand the present application without these detailed descriptions.

Referring to Figure 1, the first embodiment of the present disclosure provides a two-dimensional spatiotemporal image speed measurement method of a wild river surface, including the following steps:

Step S1: Collect surface flow images at the cross-section location of the wild river to be measured to construct an original image sequence;

Step S2: Preprocess each original image in the constructed original image sequence to obtain a preprocessed image sequence;

Step S3: For each image in the preprocessed image sequence, first divide the image into a grid, then set a horizontal speed measuring line on each grid node, and obtain the grid by rotating the speed measuring line at equal intervals. A series of tachometer lines whose nodes are the center of the circle and within the range of [0,2π] are recorded as the rotation tachometer lines of the grid nodes, and each grid is synthesized according to the texture changes of the rotation tachometer lines of each grid node. Several spatio-temporal images of the node (each grid node will generate several corresponding spatio-temporal images), calculate the autocorrelation coefficient of each spatio-temporal image, select the spatio-temporal image with the largest autocorrelation coefficient, and obtain the optimal spatio-temporal image of each grid node (each grid nodes correspond to a preferred spatio-temporal image); for the preferred spatio-temporal image of each grid node, set a line segment with the midpoint located at the center of the preferred spatio-temporal image, and the length of this line segment is the shorter of the side lengths of the preferred spatio-temporal image. length, by rotating the line segments at equal intervals, a series of rotating line segments with the center of the optimal space-time image as the circle center and within the range of [0, 2π] are obtained, the autocorrelation coefficient of each rotating line segment is calculated, and the The autocorrelation coefficients are summed, and the angle of the rotation line segment where the autocorrelation coefficient and the maximum value is located is used as the river flow velocity direction corresponding to the optimal spatiotemporal image, and the tangent value of the river flow velocity direction is used as the river flow velocity corresponding to the optimal spatiotemporal image.

In some embodiments, in step S1, a drone carrying a high-definition camera is used to collect surface flow images of a wild river. At the same time, the drone can achieve wireless communication through its own satellite positioning module positioning and wireless transceiver module.

Furthermore, when collecting field river surface flow images, first select the location of the cross section of the wild river to be measured. The selection of the cross section should follow the requirements of the river bank position with convenient placement of landmark points and the river surface with stable flow state. Reflective tape should be laid on both sides of the bank to accurately cross the section. location, use drones to collect river flow videos at cross-section locations. The videos must be stable and contain enough effective information. Then, the collected river flow videos are extracted frame by frame to obtain several wild river surface flow images to construct the original image sequence.

In some embodiments, in step S2, due to the inevitable problems such as lens shake and noise interference when shooting videos with drones, the original video images collected cannot meet the computing requirements well and cannot be used directly. Therefore, it is necessary to construct The original image sequence is sequentially subjected to image correction and image enhancement to eliminate interference during the acquisition process, making subsequent flow velocity calculations more accurate.

Furthermore, the purpose of image correction is to make the landmark points in the world coordinates correspond to the coordinate points in the image and remain unchanged during the shooting process, so that the river in the processed image sequence can maintain a fixed position and eliminate image jitter. Error, the flow rate of the river can be measured more accurately. Specific steps are as follows:

Step S21: Based on the first original image collected by the drone, artificially select some non-moving feature areas on the shore. The selection principle of the feature areas is the position, shape, color, etc. that have distinctive characteristics and are present throughout the shooting process. constant;

Step S22: Perform cross-correlation operations on the grayscale images in the feature areas of each original image in the original image sequence except the first original image and the first original image to obtain the features of each original image. Rotation and displacement of areas;

Step S23: Based on the rotation and displacement of each feature area in each original image, obtain the affine transformation matrix corresponding to each original image and the first original image;

Step S24: Perform inverse transformation on each original image according to the affine transformation matrix corresponding to each original image and the first original image, so that the corresponding points of each original image have the same coordinates to form a stable image sequence.

Furthermore, image enhancement is to emphasize some features in the image, such as the contours and edges of the image, to enhance the area of interest, making it more convenient for human eye recognition and computer processing. The embodiment of the present disclosure uses a histogram equalization method to enhance the changes in image surface texture of each image after image correction, making it clearer and easier to identify. After completing the processing of image correction and image enhancement, the flow velocity and direction can be calculated.

In some embodiments, step S3 requires the same operation to calculate the flow velocity and direction for each image in the processed image sequence. Now, any one of the images will be used as an example for explanation. The specific steps are as follows:

Step S31: Grid any image in the processed image sequence. The size of the grid is determined according to the number of required flow velocity vectors. The more flow velocity vectors required, the denser the grid will be. In each grid A horizontal speed measuring line is set up on the node, and the length of the speed measuring line is determined by the number of consecutive images of the synthesized spatiotemporal image; as an embodiment of this application, the length of the horizontal speed measuring line is taken to be the length corresponding to 150 pixels, that is, it is composed of 150 The speed measuring lines at the same position in the image are synthesized;

Step S32: Rotate the set horizontal speed measurement line within the range of [0, 2π] with the grid node as the center and α as the step size, so that n=1+2π/α speed measurement lines can be formed on the grid node. line, α is preferably 0.1° to 10°, 2π should be divisible by α, and speed measurement lines at various angles passing through the grid node are obtained, ensuring that each speed measurement line contains the same amount of information, which will form a series of The speed measuring line is recorded as the rotating speed measuring line of the grid node, see Figure 2; in one embodiment of the present application, α=1° is taken, thereby generating n=361 speed measuring lines;

Step S33: For each speed measuring line of each grid node, the spatio-temporal image of each grid node is synthesized according to the texture changes of each speed measuring line in the image sequence. See (a) to (f) in Figure 3, which illustrates Spatio-temporal images from different angles, and the synthesized spatio-temporal images are all square. It will be found that the spatio-temporal images at some angles have clear textures, as shown in (a) in Figure 3, while the textures of the spatio-temporal images at some angles are messy and do not form corresponding clarity. The straight line is as shown in (d) in Figure 3;

Step S34: For each grid node, determine its corresponding preferred spatio-temporal image. The steps are as follows:

In order to judge the flow velocity and the clarity of the texture, edge detection is first performed on each spatio-temporal image, which significantly reduces the noise in the image and makes the texture more prominent. Then the characteristic gradient R(τ _x ,τ _y )：

Among them, f (x, y) is the grayscale distribution of the pixel point with coordinates (x, y) in the space-time image, (τ _x , τ _y ) represents the grayscale distribution of the pixel point with coordinates (x, y) in the space-time image. Texture displacement parameters;

Then, normalize the characteristic gradients of each spatio-temporal image obtained to obtain the autocorrelation coefficient of each spatio-temporal image;

Finally, the spatio-temporal image with the largest autocorrelation coefficient is selected as the preferred spatio-temporal image of the corresponding grid node;

Step S35: For each optimal spatio-temporal image, set the shorter length of the side lengths of the optimal spatio-temporal image as the line segment length, take the center of the optimal spatio-temporal image as the center of the circle, make the midpoint of the line segment pass through the center of the circle, and use β as the step length. of rotation line segments, β is preferably 0.1° to 10°, 2π should be divisible by β, in this embodiment β = 0.1°, obtain rotation line segments at various angles, and calculate the autocorrelation coefficient of each rotation line segment (specific calculation steps The steps for calculating the autocorrelation coefficient of the speed line are the same and will not be repeated here). Add the autocorrelation coefficients of each rotating line segment to obtain the sum of the correlation coefficients of each rotating straight line. Compare the autocorrelation coefficients of each space-time image. The maximum value of the sum, where the angle of the rotation line segment where the maximum value of the autocorrelation coefficient and the maximum value is located is the river flow velocity direction corresponding to the optimal spatio-temporal image, and the tangent value of the angle is the river flow velocity corresponding to the optimal spatio-temporal image.

Refer to Figure 4, which is a preferred spatio-temporal image synthesized from a certain angle in this embodiment. The vertical axis of the image represents the displacement Textures are formed in the image, and these textures are roughly parallel to each other, representing the displacement of each bright spot on the surface of the river over time. Therefore, the average of the tangent value of the angle θ between the straight lines generated by the bright spot and the t-axis is The average velocity of the river surface at the velocity line during this period. Most rivers in the wild are curved. By rotating the river surface velocity line, we can calculate the autocorrelation coefficient of the space-time image at various angles, solve the autocorrelation coefficient and the maximum angle, and obtain the mainstream direction when the river flow direction is unknown. At the same time, the corresponding The slope of the preferred spatiotemporal image is the surface flow velocity of the river.

Therefore, this embodiment uses the large-scale light and dark changes produced by the ripples on the river surface to replace traditional tracer particles for calculating the river surface flow velocity, solving the problem that tracer particles cannot be artificially dispersed under extreme conditions such as floods and dam breaks. Problems such as poor followability. In addition, there is no need to know the flow direction of the river surface in advance, and it can well solve the problem of flow velocity measurement on field river surfaces where the flow direction changes greatly.

The embodiment of the second aspect of the present disclosure provides a two-dimensional spatiotemporal image speed measurement device for the surface of a wild river, including:

The image acquisition module is used to collect field river surface flow images at the location of the section to be measured to construct the original image sequence;

The image preprocessing module is used to preprocess each original image in the constructed original image sequence to obtain a preprocessed image sequence;

The flow velocity and flow direction calculation module is used to first grid-divide the image for each image in the pre-processed image sequence, and then set a horizontal velocity line on each grid node, and rotate the velocity line at equal intervals. The method obtains a series of tachometer lines with the grid node as the center and within the range of [0,2π]. This series of tachometer lines is recorded as the rotation tachometer line of the grid node. According to the texture of the rotation tachometer line of each grid node Change and synthesize several spatio-temporal images of each grid node, calculate the autocorrelation coefficient of each spatio-temporal image, select the spatio-temporal image with the largest autocorrelation coefficient, and obtain the preferred spatio-temporal image of each grid node; for each of the preferred spatio-temporal images, set A line segment whose midpoint is located at the center of the preferred space-time image. The length of this line segment is the shorter of the side lengths of the preferred space-time image. By rotating the line segment at equal intervals, a circle with the center of the preferred space-time image as the center and in [0, 2π] range, calculate the autocorrelation coefficient of each rotating line segment, add the autocorrelation coefficient of each rotating line segment, and use the angle of the autocorrelation coefficient and the rotating line segment with the maximum value as the direction of river flow velocity. , taking the tangent value of the river flow direction as the river flow velocity.

It should be noted that the above-mentioned explanation of the embodiment of the two-dimensional spatio-temporal image velocity measurement method of the wild river surface is also applicable to the two-dimensional spatio-temporal image velocity measurement device of the wild river surface in this embodiment, and will not be described again here.

In order to implement the above embodiments, embodiments of the present disclosure also provide a computer-readable storage medium on which a computer program is stored. The program is executed by the processor and is used to perform the two-dimensional spatiotemporal image velocity measurement of the wild river surface in the above embodiments. method.

Referring now to FIG. 5 , a schematic structural diagram of an electronic device suitable for implementing embodiments of the present disclosure is shown. It should be noted that the electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet PCs), PMPs (Portable Multimedia Players). devices), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, servers, etc. The electronic device shown in FIG. 5 is only an example and should not impose any limitations on the functions and scope of use of the embodiments of the present disclosure.

As shown in FIG. 5 , the electronic device may include a processing device (eg, central processing unit, graphics processor, etc.) 101 that may be loaded into a random access memory according to a program stored in a read-only memory (ROM) 102 or from a storage device 108 (RAM) 103 to execute various appropriate actions and processes. In the RAM 103, various programs and data required for the operation of the electronic device are also stored. The processing device 101, the ROM 102 and the RAM 103 are connected to each other via a bus 104. An input/output (I/O) interface 105 is also connected to bus 104 .

Generally, the following devices may be connected to the I/O interface 105: input devices 106 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, etc.; output devices 107 including, for example, a liquid crystal display (LCD), speakers, vibrators, etc. ; Storage device 108 including, for example, magnetic tape, hard disk, etc.; and communication device 109. The communication device 109 may allow the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although FIG. 5 illustrates an electronic device having various means, it should be understood that implementation or availability of all illustrated means is not required. More or fewer means may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, this embodiment includes a computer program product including a computer program carried on a computer-readable medium, the computer program including program code for performing the method shown in the flowchart. In such embodiments, the computer program may be downloaded and installed from the network via the communication device 109, or from the storage device 108, or from the ROM 102. When the computer program is executed by the processing device 101, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. The computer-readable storage medium may be, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination thereof. More specific examples of computer readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard drive, random access memory (RAM), read only memory (ROM), removable Programmed read-only memory (EPROM or flash memory), fiber optics, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In this disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device . Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wire, optical cable, RF (radio frequency), etc., or any suitable combination of the above.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; it may also exist independently without being assembled into the electronic device.

The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device performs the two-dimensional spatiotemporal image velocity measurement method of the wild river surface.

Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, python, and A conventional procedural programming language—such as the "C-" language or similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In situations involving remote computers, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as an Internet service provider through Internet connection).

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "an example," "specific examples," or "some examples" or the like means that specific features are described in connection with the embodiment or example. , structures, materials or features are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.

In addition, the terms “first” and “second” are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of this application, "plurality" means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.

Any process or method descriptions in flowcharts or otherwise described herein may be understood to represent modules, segments, or portions of code that include one or more executable instructions for implementing the specified logical functions or steps of the process. , and the scope of the preferred embodiments of the present application includes additional implementations in which functions may be performed out of the order shown or discussed, including in a substantially simultaneous manner or in the reverse order, depending on the functionality involved, which shall It should be understood by those skilled in the technical field to which the embodiments of this application belong.

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered a sequenced list of executable instructions for implementing the logical functions, and may be embodied in any computer-readable medium, For use by, or in combination with, instruction execution systems, devices or devices (such as computer-based systems, systems including processors or other systems that can fetch instructions from and execute instructions from the instruction execution system, device or device) or equipment. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer readable media include the following: electrical connections with one or more wires (electronic device), portable computer disk cartridges (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium on which the program may be printed, as the program may be printed, for example, by optical scanning of the paper or other medium, followed by editing, interpretation, or in other suitable manner if necessary Processing to obtain a program electronically and then store it in computer memory.

It should be understood that various parts of the present application can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if it is implemented in hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a logic gate circuit with a logic gate circuit for implementing a logic function on a data signal. Discrete logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

Those of ordinary skill in the art can understand that all or part of the steps involved in implementing the methods of the above embodiments can be completed by instructing the relevant hardware through a program. The developed program can be stored in a computer-readable storage medium, and the program can be stored in a computer-readable storage medium. When executed, one of the steps of the method embodiment or a combination thereof is included.

In addition, each functional unit in various embodiments of the present application can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software function modules. Integrated modules can also be stored in a computer-readable storage medium if they are implemented in the form of software function modules and sold or used as independent products.

The storage media mentioned above can be read-only memory, magnetic disks or optical disks, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above-mentioned embodiments are illustrative and cannot be understood as limitations of the present application. Those of ordinary skill in the art can make modifications to the above-mentioned embodiments within the scope of the present application. The embodiments are subject to changes, modifications, substitutions and variations.

Claims (10)

1. A two-dimensional spatio-temporal image velocity measurement method on the surface of a wild river, which is characterized by including the following steps: Step S1: Collect surface flow images at the cross-section location of the wild river to be measured to construct an original image sequence; Step S2: Preprocess each original image in the original image sequence to obtain a preprocessed image sequence; Step S3: For each image in the preprocessed image sequence, first divide the image into a grid, then set a horizontal speed measuring line on each grid node, and obtain the result by rotating the speed measuring line at equal intervals. The grid node is a series of tachometer lines with the center of the circle and within the range of [0,2π]. This series of tachometer lines is recorded as the rotation tachometer line of the grid node. According to the texture changes of the rotation tachometer line of each grid node, various tachometer lines are synthesized. Several spatio-temporal images of the grid node, calculate the autocorrelation coefficient of each spatio-temporal image, select the spatio-temporal image with the largest autocorrelation coefficient, and obtain the preferred spatio-temporal image of each grid node; for each of the preferred spatio-temporal images, set the midpoint at The line segment at the center of the preferred space-time image, the length of the line segment is the shorter length of the side lengths of the preferred space-time image, and is obtained by rotating the line segment at equal intervals with the center of the preferred space-time image as the center of the circle. And for a series of rotating line segments in the range of [0,2π], calculate the autocorrelation coefficient of each rotating line segment, add the autocorrelation coefficients of each rotating line segment, and combine the autocorrelation coefficient and the rotating line segment with the maximum value. The angle is taken as the river flow direction, and the tangent value of the river flow direction is taken as the river flow speed. 2. The two-dimensional spatio-temporal image speed measurement method according to claim 1, characterized in that in step S1, the position of the section to be measured should be selected to be a river bank position that is convenient for placing landmark points and the river surface has a stable flow state. 3. The two-dimensional spatiotemporal image velocity measurement method according to claim 1, characterized in that, in step S1, a drone carrying a high-definition camera is used to obtain the surface flow video at the position of the section of the wild river to be measured, and the surface flow video is measured. The flow video extracts images frame by frame to obtain several surface flow images. 4. The two-dimensional spatiotemporal image speed measurement method according to claim 1, characterized in that in step S2, the preprocessing of each original image includes image correction and image enhancement of each original image in sequence. 5. The two-dimensional spatiotemporal image velocity measurement method according to claim 4, characterized in that the image correction includes the following steps: Using the first original image in the original image sequence as a benchmark, select several non-moving feature areas on the shore in the original image; Perform cross-correlation operations on the grayscale images in the feature areas of each of the remaining original images in the original image sequence and the first original image to obtain the rotation and displacement of each feature area in each original image; According to the rotation and displacement of each feature area in each original image, the affine transformation matrix corresponding to each original image and the first original image is obtained; Inverse transformation is performed on each original image according to the affine transformation matrix corresponding to each original image and the first original image, so that the corresponding points of each original image have the same coordinates, forming a corrected image sequence. 6. The two-dimensional spatio-temporal image speed measurement method according to claim 1, characterized in that in step S3, the angle between two adjacent speed measurement lines ranges from 0.1° to 10°. 7. The two-dimensional spatio-temporal image speed measurement method according to claim 1, characterized in that in step S3, the angle between two adjacent rotating line segments ranges from 0.1° to 10°. 8. The two-dimensional spatiotemporal image speed measurement method according to claim 1, characterized in that, in step S3, the autocorrelation coefficient of the spatiotemporal image and the preferred spatiotemporal image is calculated according to the following steps: Perform edge detection on the spatio-temporal image or preferably the spatio-temporal image to reduce noise in the image; Calculate the characteristic gradient R(τ _x ,τ _y ) of the spatio-temporal image or preferred spatio-temporal image according to the following formula: Among them, f (x, y) is the grayscale distribution of the pixel point with coordinates (x, y) in the space-time image or preferred space-time image, (τ _x , τ _y ) is the coordinate (x in the space-time image or preferred space-time image) , y) texture displacement parameters of pixels; The characteristic gradient of the spatio-temporal image or the preferred spatio-temporal image is normalized to obtain the autocorrelation coefficient of the spatio-temporal image or the preferred spatio-temporal image. 9. A two-dimensional spatio-temporal image speed measurement device on the surface of a wild river, which is characterized by including: The image acquisition module is used to collect surface flow images at the location of the cross-section to be measured in the wild river to construct the original image sequence; An image preprocessing module, used to preprocess each original image in the original image sequence to obtain a preprocessed image sequence; The flow velocity and flow direction calculation module is used to first grid-divide the image for each image in the pre-processed image sequence, and then set a horizontal speed measurement line on each grid node, and rotate the speed measurement line at equal intervals. In the form of lines, a series of speed measurement lines with the grid node as the center and within the range of [0,2π] are obtained. This series of speed measurement lines are recorded as the rotation speed measurement lines of the grid node. According to the rotation speed measurement line of each grid node Texture changes synthesize several spatio-temporal images of each grid node, calculate the autocorrelation coefficient of each spatio-temporal image, select the spatio-temporal image with the largest autocorrelation coefficient, and obtain the preferred spatio-temporal image of each grid node; for each of the preferred spatio-temporal images , set a line segment whose midpoint is located at the center of the preferred space-time image. The length of the line segment is the shorter of the side lengths of the preferred space-time image. The preferred space-time is obtained by rotating the line segment at equal intervals. The center of the image is a series of rotating line segments within the range of [0,2π]. Calculate the autocorrelation coefficient of each rotating line segment. Add the autocorrelation coefficients of each rotating line segment. Add the autocorrelation coefficient and the maximum value. The angle of the rotating line segment is used as the river flow direction, and the tangent value of the river flow direction is used as the river flow speed. 10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the wild river method according to any one of claims 1 to 8. Two-dimensional spatiotemporal image velocimetry method for surfaces.