Heat map generation method based on block

Abstract

The present invention relates to a block-based heat map generation method, and in particular to the field of image processing. The longitude and latitude in image data are converted into image data plane coordinates by using Mercator projection, and the image data plane coordinates are reduced to a specified range by using minimum-maximum normalization, so as to improve the efficiency and convenience of data transmission, determine the size and shape of a grid area and each grid unit, and use a kernel function to measure the influence on the density of image data. For each image data, the contribution to the density of the grid unit is calculated according to the weight of the grid unit where the grid unit is located, and the final density value of the grid unit is obtained. The density value of each square is converted into a color value, and the corresponding color of the grid unit is allocated according to the mapping relationship between the final density value and the color value of the grid unit, and the color of each grid unit is filled into a corresponding position by using a drawing tool to form a visualization effect of a heat map, so that the color distribution of the heat map is more obvious and eye-catching.

Description

Technical Field

[0001] The present invention relates to the field of image processing, and more specifically, to a block-based thermal map generation method. Background Art

[0002] Existing heat map technology faces multiple challenges in terms of resolution and computational complexity. In terms of resolution, the kernel density estimation method lacks the correct selection of kernel functions, and different parameter selections lead to different results. For heat map layers and grid-based methods, the choice of pixel size also affects the accuracy of the heat map. In terms of computational complexity, high-resolution heat map calculations for large-scale image data will cause performance issues.

[0003] Based on the resolution and computational complexity of existing heat maps, a block-based heat map generation method can greatly reduce the computational complexity, improve performance and density accuracy, and use the Mercator projection to convert the longitude and latitude in the image data into image data plane coordinates. Drawing tools are used to fill the color of each grid cell to the corresponding position to form a visualization effect of the heat map. Summary of the invention

[0004] In order to solve the technical problems existing in the prior art, the present invention provides a block-based heat map generation method, which transmits the processed image data to a database through Mercator projection and normalization processing by wireless transmission, determines the grid area through the image data and projects it to the grid, measures the influence of the image data by using a Gaussian kernel function, and intuitively displays the density distribution of the image data by forming a visualization effect of the heat map, so as to solve the problems raised in the above background technology.

[0005] The technical solution of the present invention to solve the above technical problem is as follows: a block-based heat map generation method includes the following steps:

[0006] S101: converting the longitude and latitude in the image data into image data plane coordinates using Mercator projection, checking whether the image data plane coordinates have errors and inconsistent information, reducing the image data plane coordinates to a specified range using minimum-maximum normalization, and mapping them to the same scale;

[0007] S102: calling the processed image data in the database to determine the gridded area and the size and shape of each grid unit, and performing statistics according to the image data plane coordinate area in the grid unit to which the image data belongs and the image data discrete data points;

[0008] S103: using a kernel function to measure the influence on the density of the image data, for each image data, calculating the contribution to the density of the grid unit according to the weight of the grid unit where it is located, so as to obtain a final density value of the grid unit;

[0009] S104: converting the density value of each square into a color value, assigning a corresponding color to the grid unit according to a mapping relationship between the final density value and the color value of the grid unit, and using a drawing tool to fill the color of each grid unit to a corresponding position to form a visualization effect of a heat map;

[0010] In a preferred embodiment, in S101, the Mercator projection is used with the 0° meridian as the central meridian, and the longitude and latitude in the image data are converted into the image data plane coordinates, and the specific conversion formula is:

[0011] ;

[0012] in represents the horizontal and vertical coordinates in the image data plane coordinates, represents the image scale factor, represents the longitude in the image data, Indicates the latitude in the image data, checks whether there are errors and inconsistent information in the image data plane coordinates, and prepares preliminary image data items. When there are errors and inconsistent information in the image data plane coordinates, fill them with the preliminary image data items, reduce the image data plane coordinates to the specified range using minimum-maximum normalization, and map them to the same scale. The minimum-maximum normalization formula is:

[0013] ;

[0014] in Represents the value of the normalized image data plane coordinates, Represents the value of the current image data plane coordinates, Indicates the lowest value of the image data plane coordinates, The top value of the plane coordinates of the image data is represented, and the processed image data is transmitted to the database using a wireless transmission method.

[0015] In a preferred embodiment, in S102, the processed image data in the database is called to determine the gridded area, including the image data plane coordinate area and the image data discrete data points, and a rectangle with a length and width of 15 cm is used to determine the size and shape of each grid unit. Each grid unit represents a spatial area with a width of 1 m, which is used to represent the grid resolution. The image data is projected onto the grid, and the grid unit to which it belongs is determined. Statistics are performed based on the image data plane coordinate area and the image data discrete data points in the grid unit to which the image data belongs.

[0016] In a preferred embodiment, in S103, the weights of the grid cells where all the image data are located are set to a fixed value of 1, and a kernel function is used to measure the impact on the density of the image data. The kernel function is a Gaussian kernel function, and its specific formula is:

[0017] ;

[0018] in Indicates the influence of image data on the kernel function, the distance mean The farther away, the smaller the impact. Represents the distance from the image data to the center of the kernel function, represents the mean of the kernel function, Represents the standard deviation of the kernel function. For each image data, the contribution to the grid cell density is calculated according to the weight of the grid cell where it is located. The specific formula is:

[0019] ;

[0020] in Indicates the contribution of the weight of the grid cell to the grid cell density, It represents the weight of the grid cell where each image data is located, and the contribution of all image data in the grid cell is weighted summed to obtain the final density value of the grid cell.

[0021] In a preferred embodiment, in S104, the density value of each square is converted into a color value, wherein the final density value of the low grid cell is mapped to light yellow, and the final density value of the high grid cell is mapped to orange according to the range of the final density value of the grid cell. According to the mapping relationship between the final density value and the color value of the grid cell, the corresponding color of the grid cell is assigned, and the color of each grid cell is filled to the corresponding position using a drawing tool to form a visualization effect of a heat map, and the density change of the image data is displayed according to the plane coordinates of the image data and the color filling position.

[0022] The beneficial effects of the present invention are as follows: the longitude and latitude in the image data are converted into the plane coordinates of the image data by using the Mercator projection, and the image data plane coordinates are checked for errors and inconsistent information, the image data plane coordinates are reduced to a specified range by using minimum-maximum normalization, and mapped to the same scale to ensure the scale consistency of different image data, the processed image data are transmitted to the database by wireless transmission, the efficiency and convenience of data transmission are improved, the grid area is determined by the image data and projected to the grid, the calculation complexity is reduced, the performance and density accuracy are improved, the kernel function is used to measure the impact on the density of the image data, for each image data, the contribution to the density of the grid unit is calculated according to the weight of the grid unit where it is located, and the final density value of the grid unit is obtained, so as to better adapt to and reflect the distribution of different image data, and intuitively display the density distribution of the image data by forming a visualization effect of a heat map, so that the color distribution of the heat map is more obvious and eye-catching. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of the method of the present invention; DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0025] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0026] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0027] This embodiment provides Figure 1 The block-based heat map generation method shown in the figure specifically includes the following steps:

[0028] S101: converting the longitude and latitude in the image data into image data plane coordinates using Mercator projection, checking whether the image data plane coordinates have errors and inconsistent information, reducing the image data plane coordinates to a specified range using minimum-maximum normalization, and mapping them to the same scale;

[0029] Furthermore, the Mercator projection is used with the 0° meridian as the central meridian, and the longitude and latitude in the image data are converted to the image data plane coordinates. The specific conversion formula is:

[0030] ;

[0031] in represents the horizontal and vertical coordinates in the image data plane coordinates, represents the image scale factor, represents the longitude in the image data, Indicates the latitude in the image data, checks whether there are errors and inconsistent information in the image data plane coordinates, and prepares preliminary image data items. When there are errors and inconsistent information in the image data plane coordinates, fill them with the preliminary image data items, reduce the image data plane coordinates to the specified range using minimum-maximum normalization, and map them to the same scale. The minimum-maximum normalization formula is:

[0032] ;

[0033] in Represents the value of the normalized image data plane coordinates, Represents the value of the current image data plane coordinates, Indicates the lowest value of the image data plane coordinates, The top value of the plane coordinates of the image data is represented, and the processed image data is transmitted to the database using a wireless transmission method.

[0034] S102: calling the processed image data in the database to determine the gridded area and the size and shape of each grid unit, and performing statistics according to the image data plane coordinate area in the grid unit to which the image data belongs and the image data discrete data points;

[0035] Furthermore, the processed image data in the database are called to determine the gridded area, including the image data plane coordinate area and the image data discrete data points. A rectangle with a length and width of 15 cm is used to determine the size and shape of each grid unit. Each grid unit represents a spatial area with a width of 1 m, which is used to represent the grid resolution. The image data is projected onto the grid, and the grid unit to which it belongs is determined. Statistics are performed based on the image data plane coordinate area and the image data discrete data points in the grid unit to which the image data belongs.

[0036] S103: using a kernel function to measure the influence on the density of the image data, for each image data, calculating the contribution to the density of the grid unit according to the weight of the grid unit where it is located, so as to obtain a final density value of the grid unit;

[0037] Furthermore, the weights of the grid cells where all the image data are located are set to a fixed value of 1, and the kernel function is used to measure the impact on the density of the image data. The kernel function is a Gaussian kernel function, and its specific formula is:

[0038] ;

[0039] in Indicates the influence of image data on the kernel function, the distance mean The farther away, the smaller the impact. Represents the distance from the image data to the center of the kernel function, represents the mean of the kernel function, Represents the standard deviation of the kernel function. For each image data, the contribution to the grid cell density is calculated according to the weight of the grid cell where it is located. The specific formula is:

[0040] ;

[0041] in Indicates the contribution of the weight of the grid cell to the grid cell density, It represents the weight of the grid cell where each image data is located, and the contribution of all image data in the grid cell is weighted summed to obtain the final density value of the grid cell.

[0042] S104: converting the density value of each square into a color value, assigning a corresponding color to the grid unit according to a mapping relationship between the final density value and the color value of the grid unit, and using a drawing tool to fill the color of each grid unit to a corresponding position to form a visualization effect of a heat map;

[0043] Furthermore, the density value of each square is converted into a color value, wherein the final density value of the low grid cell is mapped to light yellow, and the final density value of the high grid cell is mapped to orange according to the range of the final density value of the grid cell. According to the mapping relationship between the final density value and the color value of the grid cell, the corresponding color of the grid cell is assigned, and the color of each grid cell is filled to the corresponding position using a drawing tool to form a visualization effect of the heat map, and the density change of the image data is displayed according to the plane coordinates of the image data and the color filling position.

[0044] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0045] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0046] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0047] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0048] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0049] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0050] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A block-based heat map generation method, characterized in that: The specific steps include: S101: converting the longitude and latitude in the image data into image data plane coordinates using Mercator projection, checking whether the image data plane coordinates have errors and inconsistent information, reducing the image data plane coordinates to a specified range using minimum-maximum normalization, and mapping them to the same scale; The Mercator projection is used with the 0° meridian as the central meridian, and the longitude and latitude in the image data are converted into image data plane coordinates, and the image data plane coordinates are checked for errors and inconsistent information, and preliminary image data items are prepared. When the image data plane coordinates have errors and inconsistent information, the preliminary image data items are used to fill them, and the image data plane coordinates are reduced to a specified range using minimum-maximum normalization, and mapped to the same scale; S102: calling the processed image data in the database to determine the gridded area and the size and shape of each grid unit, and performing statistics according to the image data plane coordinate area in the grid unit to which the image data belongs and the image data discrete data points; Call the processed image data in the database to determine the gridded area, including the image data plane coordinate area and the image data discrete data points, use a rectangle with a length and width of 15 cm to determine the size and shape of each grid unit, each grid unit represents a spatial area with a width of 1m, project the image data onto the grid, and determine the grid unit to which it belongs, and perform statistics based on the image data plane coordinate area and the image data discrete data points in the grid unit to which the image data belongs; S103: using a kernel function to measure the influence on the density of the image data, for each image data, calculating the contribution to the density of the grid unit according to the weight of the grid unit where it is located, so as to obtain a final density value of the grid unit; The weights of the grid cells where all image data are located are set to a fixed value of 1, and the kernel function is used to measure the impact on the density of image data. For each image data, the contribution to the density of the grid cell is calculated according to the weight of the grid cell where it is located, and the contribution of all image data in the grid cell is weighted summed; S104: converting the density value of each square into a color value, assigning a corresponding color to the grid unit according to a mapping relationship between the final density value and the color value of the grid unit, and using a drawing tool to fill the color of each grid unit to a corresponding position to form a visualization effect of a heat map; The density value of each square is converted into a color value. According to the range of the final density value of the grid cell, the final density value of the low grid cell is mapped to light yellow, and the final density value of the high grid cell is mapped to orange. According to the mapping relationship between the final density value and the color value of the grid cell, the corresponding color of the grid cell is assigned. The color of each grid cell is filled to the corresponding position using the drawing tool. The density change of the image data is displayed according to the image data plane coordinates and the color filling position.

2. The block-based heat map generation method according to claim 1 is characterized in that: The specific formula for converting the longitude and latitude in the image data into the plane coordinates of the image data is: ； in represents the horizontal and vertical coordinates in the image data plane coordinates, represents the image scale factor, represents the longitude in the image data, Represents latitude in image data.

3. The block-based heat map generation method according to claim 1 is characterized in that: The minimum-maximum normalization formula is: ； in Represents the value of the normalized image data plane coordinates, Represents the value of the current image data plane coordinates, Indicates the lowest value of the image data plane coordinates, Indicates the top value of the plane coordinates of the image data.

4. The block-based heat map generation method according to claim 1 is characterized in that: The kernel function is a Gaussian kernel function, and its specific formula is: ； in Indicates the influence of image data on the kernel function, the distance mean The farther away, the smaller the impact. Represents the distance from the image data to the center of the kernel function, represents the mean of the kernel function, Represents the standard deviation of the kernel function. The specific formula for calculating the contribution to the grid cell density based on the weight of the grid cell is: ； in Indicates the contribution of the weight of the grid cell to the grid cell density, Represents the weight of the grid cell where each image data is located.

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