A method, device and system for efficiently and non-cutting massive framing image data

Abstract

The present application relates to a kind of mass framing image data efficient free slicing method, device and system.Its method part mainly includes: based on the coordinate system of framing image, first grouping is carried out, based on the closed figure number after first grouping, second grouping is carried out, based on the file number after second grouping, third grouping is carried out;For the framing image data after third grouping, virtual grid file after virtual merging is established, and is carried out to the rare sampling processing, generates the rare grid file after rarefying;The rare grid file after rarefying is as simplified data set, and the framing image file before grouping is as original data set;When rendering, if rendering level is lower than preset level, then through simplified data set, retrieval and rendering output are carried out;If rendering level is higher than preset level, then through original data set, retrieval and rendering output are carried out.The present application can greatly improve the rendering efficiency at low level, guarantee the rendering performance when low level rendering is carried out.

Description

Technical Field

[0001] This invention relates to the field of image data processing technology, and in particular to a method, apparatus and system for efficient, non-slicing method of massive image data. Background Technology

[0002] Image data is one of the main data outputs of satellite remote sensing and UAV aerial photography. Publishing image data as a space service is a common way to share and apply it. In order for applications to access and display image data smoothly, the image data is usually sliced ​​and converted into image tiles with a pyramid structure. However, due to the high precision and large volume of image data, traditional slicing usually takes several days, weeks, or even months. As applications have increasingly higher requirements for the timeliness of image data and need to update the image data frequently, the traditional process of publishing services using pre-sliced ​​images is increasingly unable to meet actual usage needs.

[0003] Based on practical needs, more and more methods for tile-free rendering of holographic image data have been proposed. However, in real-world scenarios, when dealing with massive holographic image data exceeding TB (such as holographic image data of a province), the large number of image data files involved in the rendering at low rendering levels can lead to excessively long rendering times, thus affecting performance.

[0004] In view of this, how to overcome the shortcomings of existing technologies and how to improve rendering efficiency and performance when rendering massive amounts of framed image data without tiling at low rendering levels have become important technical problems that the industry urgently needs to solve. Summary of the Invention

[0005] Addressing the shortcomings or improvement needs of existing technologies, this invention provides a method, apparatus, and system for efficient tile-free rendering of massive segmented image data at low rendering levels. This method preprocesses the massive segmented image data through segmented image grouping and merging, and data thinning. Then, different datasets are used for rendering at different rendering levels, thereby significantly improving rendering efficiency at low levels and ensuring rendering performance to meet the intended purpose. Simultaneously, it also guarantees rendering quality at higher rendering levels.

[0006] The present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides an efficient, non-slicing method for massive image data with multiple folds, comprising:

[0008] The first grouping is based on the coordinate system of the segmented image; the second grouping is based on the number of closed shapes after the first grouping; and the third grouping is based on the number of files after the second grouping.

[0009] A virtual raster file is created for the image data after the third grouping, and a thinning sampling process is performed to generate a thinned raster file. The thinned raster file is used as a simplified dataset, and the image data before grouping is used as the original dataset.

[0010] During rendering, if the rendering level is lower than the preset level, the simplified dataset is used for retrieval and rendering output; if the rendering level is higher than the preset level, the original dataset is used for retrieval and rendering output.

[0011] In some embodiments, the first grouping based on the coordinate system of the segmented image specifically includes:

[0012] Obtain the coordinate system information for all image frames;

[0013] The first grouping operation is performed based on the coordinate system of the image slab, dividing the image slab data with the same coordinate system into the same group.

[0014] In some embodiments, the second grouping based on the number of closed figures after the first grouping specifically includes:

[0015] Find the union of the outer rectangles of each group of image data after the first grouping;

[0016] The image data is grouped a second time based on the number of closed shapes in the union set, and the image data in the same closed shape are divided into the same group.

[0017] In some embodiments, the third grouping based on the number of files after the second grouping specifically includes:

[0018] Determine the preset total number of groups and the first preset number of files;

[0019] Based on the results of the second grouping, if the number of sectional images in a certain closed shape is greater than the number of the first preset files, then the sectional image data in the corresponding closed shape will be grouped a third time according to the preset total number of groups, so that the number of sectional images in each group is less than the number of the first preset files.

[0020] In some embodiments, the step of creating a virtual raster file after virtual merging of the image data after the third grouping, and performing thinning sampling processing to generate a thinned raster file specifically includes:

[0021] For the image data after the third grouping, the image data of each group is virtually merged to generate virtual raster files, and all virtual raster files are tiled.

[0022] All virtual raster files are subjected to bilinear interpolation for thinning sampling, and thinned raster files are generated.

[0023] In some embodiments, the step of performing retrieval and rendering output by simplifying the dataset if the rendering level is lower than a preset level, and by retrieving and rendering output using the original dataset if the rendering level is higher than the preset level, specifically includes:

[0024] Read the bounding box ranges and coordinate systems of all files in the simplified dataset and the original dataset respectively, specify a unified coordinate system for different coordinate systems, and perform virtual projection based on the unified coordinate system to create spatial indices for the simplified dataset and the original dataset respectively.

[0025] When rendering a tile with a specified row and column number, calculate the border of the tile to be rendered;

[0026] If the rendering level is lower than the preset level, the bounding box of the tile to be rendered is directly searched in the spatial index of the simplified dataset, and the matched data is read for rendering output.

[0027] If the rendering level is higher than the preset level, the bounding box of the tile to be rendered is searched in the spatial index of the original dataset based on the calculated bounding box. The number of hit files determines whether to read the hit data in the original dataset for rendering output.

[0028] In some embodiments, if the rendering level is higher than a preset level, then searching the spatial index of the original dataset based on the calculated border of the tile to be rendered, and determining whether to read the data that has been hit in the original dataset for rendering output based on the number of hit files, specifically includes:

[0029] Determine the second preset number of files;

[0030] If the number of files hit is lower than the second preset number of files after searching the spatial index of the original dataset, the hit data in the original dataset will be read directly for rendering output.

[0031] If the number of files matched after searching the spatial index of the original dataset is higher than the second preset number of files, then the search is performed again in the spatial index of the simplified dataset, and the matched data is read and rendered.

[0032] Secondly, the present invention also provides an apparatus for efficiently processing massive amounts of segmented image data without slicing, the apparatus comprising:

[0033] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor for performing the efficient, non-slicing method for massive sectional image data as described in the first aspect.

[0034] Thirdly, the present invention also provides a system for efficient, non-tiling of massive segmented image data, applying the method for efficient, non-tiling of massive segmented image data as described in the first aspect. The system includes an image grouping module, a dataset creation module, and a retrieval and rendering module, wherein:

[0035] The image grouping module is used to: perform a first grouping based on the coordinate system of the segmented images, perform a second grouping based on the number of closed shapes after the first grouping, and perform a third grouping based on the number of files after the second grouping;

[0036] The dataset creation module is used to: create a virtual raster file after virtual merging of the image data after the third grouping, and perform thinning sampling processing to generate a thinned raster file; use the thinned raster file as a simplified dataset, and use the image file before grouping as the original dataset;

[0037] The retrieval and rendering module is used to: when rendering, if the rendering level is lower than the preset level, retrieve and render the output using a simplified dataset; if the rendering level is higher than the preset level, retrieve and render the output using the original dataset.

[0038] Fourthly, the present invention also provides a non-volatile computer storage medium storing computer-executable instructions, which are executed by one or more processors to perform the efficient, non-slicing method for massive sectional image data described in the first aspect.

[0039] Compared with existing technologies, this invention provides a method, apparatus, and system for efficient, non-slicing of massive segmented image data. Its advantages are as follows: by preprocessing the massive segmented image data through segmented image grouping and merging and data thinning, and then using different datasets for rendering at different rendering levels, the rendering efficiency at low levels is greatly improved, ensuring rendering performance at low levels and thus meeting the purpose of generation and use; at the same time, it can also ensure the rendering effect at high levels. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0041] Figure 1 This is a flowchart of a method for efficient, non-slicing of massive image data provided in Embodiment 1 of the present invention;

[0042] Figure 2 This is a flowchart illustrating the first grouping process provided in Embodiment 1 of the present invention.

[0043] Figure 3 This is a flowchart illustrating the second grouping process provided in Embodiment 1 of the present invention.

[0044] Figure 4 This is a flowchart illustrating the third grouping process provided in Embodiment 1 of the present invention.

[0045] Figure 5 This is a flowchart illustrating the specific process of dilution sampling provided in Embodiment 1 of the present invention;

[0046] Figure 6 This is a flowchart illustrating the specific process of retrieval and rendering output based on rendering level, as provided in Embodiment 1 of the present invention.

[0047] Figure 7 This is a flowchart illustrating the specific process of searching and rendering output based on the number of hit files provided in Embodiment 1 of the present invention;

[0048] Figure 8 This is a schematic diagram of a system for efficient, tile-free processing of massive image data according to Embodiment 1 of the present invention;

[0049] Figure 9 This is a schematic diagram of data segmentation provided in Embodiment 2 of the present invention;

[0050] Figure 10 This is a schematic diagram of the results after the first grouping provided in Embodiment 2 of the present invention;

[0051] Figure 11 This is a schematic diagram of the results after the second grouping provided in Embodiment 2 of the present invention;

[0052] Figure 12 This is a schematic diagram of the results after the third grouping provided in Embodiment 2 of the present invention;

[0053] Figure 13 This is a schematic diagram of the virtual merging result provided in Embodiment 2 of the present invention;

[0054] Figure 14 This is a simplified and original data diagram provided in Embodiment 2 of the present invention;

[0055] Figure 15 This is a schematic diagram of the rendering area provided in Embodiment 2 of the present invention;

[0056] Figure 16 This is a schematic diagram of a device structure for efficient, non-slicing of massive image data provided in Embodiment 3 of the present invention. Detailed Implementation

[0057] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention. It should be noted that, unless otherwise specified, the various features in the embodiments of the present invention can be combined with each other, all within the protection scope of this application. Furthermore, although functional modules may be divided in the device schematic diagram, and a logical order may be shown in the flowchart, in some cases, the steps shown or described may be executed differently from the module division in the device or the order in the flowchart.

[0058] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.

[0059] Unless the context otherwise requires, throughout the specification and claims, the term "comprising" is interpreted as openly inclusive, meaning "including, but not limited to." In the description of the specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this disclosure. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics mentioned may be included in any suitable manner in any one or more embodiments or examples; that is, although they may be incorporated into embodiments or examples using the above terms for reasons such as order and position, it does not limit them to be incorporated in combination by a single embodiment or example.

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0061] Example 1:

[0062] like Figure 1 As shown in the figure, this embodiment of the invention provides an efficient method for avoiding slicing of massive sectional image data, which includes the following steps.

[0063] Step 101: Perform the first grouping based on the coordinate system of the image panels. Then, perform a second grouping based on the number of closed shapes after the first grouping. Finally, perform a third grouping based on the number of files after the second grouping. Grouping by coordinate system ensures that the image panel data in each group belongs to the same coordinate system. Grouping by the number of closed shapes ensures that the image panel data in each group belongs to the same closed shape. Grouping by the number of files ensures that the number of files in each group is not excessive. This grouping process reduces the complexity of subsequent retrieval and rendering, and improves the efficiency of retrieval and rendering.

[0064] Step 102: Create a virtual raster file after virtual merging of the image data after the third grouping, and perform thinning sampling to generate a thinned raster file. This process virtually merges the data from each group and reduces the amount of data through thinning sampling, simplifying the data and facilitating subsequent retrieval and rendering to reduce complexity and improve efficiency.

[0065] Step 103: Use the thinned raster files as the simplified dataset and the ungrouped image files as the original dataset. This step separates the simplified and original datasets so that the corresponding datasets can be selected for retrieval and rendering according to subsequent needs, balancing efficiency and quality.

[0066] Step 104: During rendering, if the rendering level is lower than the preset level, a simplified dataset is used for retrieval and rendering output; if the rendering level is higher than the preset level, the original dataset is used for retrieval and rendering output. In this step, when the rendering level is low, efficiency is the priority, so a simplified dataset is chosen for retrieval and rendering output to improve rendering efficiency. When the rendering level is high, quality is the priority, so the original dataset is chosen for retrieval and rendering output to ensure rendering quality.

[0067] Based on the above steps, this embodiment of the invention preprocesses massive segmented image data by grouping and merging segmented images and thinning data. Then, it uses different datasets for rendering at different rendering levels, thereby greatly improving the rendering efficiency at low levels and ensuring the rendering performance at low levels, thus achieving the purpose of generating and using the data. At the same time, it can also ensure the rendering effect at high levels.

[0068] refer to Figure 2 As shown, in some embodiments, the first grouping based on the coordinate system of the segmented image specifically includes the following steps.

[0069] Step 201: Obtain the boundaries and coordinate system information of all image frames. The boundaries refer to the four cardinal directions (north, south, east, and west). Based on the boundaries, the outer rectangular border representing the corresponding image frame can be determined.

[0070] Step 202: Perform the first grouping operation based on the coordinate system of the image frame, and group the image frame data with the same coordinate system into the same group.

[0071] refer to Figure 3 As shown, in some embodiments, the second grouping based on the number of closed shapes after the first grouping specifically includes the following steps.

[0072] Step 301: Find the union of the outer rectangles of each group of image data after the first grouping. The four boundaries of each image group can form the border of an outer rectangle, and the union refers to the large border area formed by these rectangular borders.

[0073] Step 302: Perform a second grouping operation on the image data based on the number of closed shapes in the union set, dividing the image data in the same closed shape into the same group. Because in the groups divided according to the coordinate system, the data in the same group are not necessarily all connected. It is possible that some data are connected here and some data are connected there, thus being distributed separately. Therefore, the data that can be connected to form closed shapes are further subdivided and grouped.

[0074] refer to Figure 4 As shown, in some embodiments, the third grouping based on the number of files after the second grouping specifically includes the following steps.

[0075] Step 401: Determine the total number of preset groups and the number of the first preset files. Generally, the total number of groups should not be too large, and the number of the first preset files should not be too many. The specific number can be determined according to the needs.

[0076] Step 402: Based on the results of the second grouping, if the number of sectional images in a closed shape is greater than the first preset number of files, then the sectional image data in the corresponding closed shape is grouped a third time according to the preset total number of groups, so that the number of sectional images in each group is less than the first preset number of files. Specifically, during the third grouping, the number of groups to be divided can be determined according to the total number of groups, and then the corresponding closed shape with a larger number of images is horizontally and / or vertically split to ensure that the number of files in each partition is not too large, that is, less than the first preset number of files.

[0077] refer to Figure 5 As shown, in some embodiments, the steps of creating a virtual raster file after virtual merging of the image data after the third grouping and performing thinning sampling to generate the thinned raster file specifically include the following steps.

[0078] Step 501: For the image data after the third grouping, virtually merge the image data of each group to generate virtual raster files, and then tile all the virtual raster files. This virtual merging step facilitates subsequent rapid retrieval and rendering.

[0079] Step 502: Apply bilinear interpolation to all virtual raster files for thinning sampling, and generate thinned raster files. This step reduces the data volume by 5-10 times through thinning sampling, further facilitating subsequent fast retrieval and rendering.

[0080] refer to Figure 6 As shown, in some embodiments, when rendering, if the rendering level is lower than a preset level, the retrieval and rendering output are performed using a simplified dataset; if the rendering level is higher than the preset level, the retrieval and rendering output are performed using the original dataset. Specifically, this includes the following steps.

[0081] Step 601: Read the bounding boxes and coordinate systems of all files in both the simplified and original datasets. Specify a unified coordinate system for the different coordinate systems and perform virtual projection based on this unified coordinate system to create spatial indices for both the simplified and original datasets. This step addresses the issue of different coordinate systems by specifying a unified coordinate system before virtual projection, thus obtaining usable spatial indices.

[0082] Step 602: When rendering tiles of specified row and column numbers, calculate the borders of the tiles to be rendered. This is to facilitate the subsequent selection of the dataset for rendering using two metrics: priority level and number of hit files. That is, to select data from either the simplified dataset or the original dataset for rendering output.

[0083] Step 603: If the rendering level is lower than the preset level, the calculated borders of the tiles to be rendered are directly searched in the spatial index of the simplified dataset, and the matched data is read for rendering output. The preset level in this step can be pre-set, for example, to level 6. If the level is lower than level 6, the simplified dataset's spatial index will be searched directly, and the matched data will be read for rendering output. This step greatly improves rendering efficiency at low levels, ensuring rendering performance during low-level rendering, thereby meeting the purpose of generation and use.

[0084] Step 604: If the rendering level is higher than the preset level, the spatial index of the original dataset is searched based on the calculated borders of the tiles to be rendered. The number of hit files determines whether to read the hit data from the original dataset for rendering output. For example, if the level is higher than 6, the search is performed in the spatial index of the original dataset. Furthermore, the number of hit files after searching the spatial index of the original dataset determines whether to read the hit data from the original dataset for rendering output or to search the spatial index of the simplified dataset again before rendering output; if the number of hit files is small, the former is chosen to improve rendering quality; if the number of hit files is large, the latter is chosen to improve rendering efficiency.

[0085] refer to Figure 7 As shown, in some embodiments, if the rendering level is higher than the preset level, the process of searching the spatial index of the original dataset based on the calculated border of the tile to be rendered, and determining whether to read the data that has been hit in the original dataset for rendering output based on the number of hit files, specifically includes the following steps.

[0086] Step 701: Determine the second preset file count. The second preset file count is the indicator that distinguishes the number of files hit; a count greater than the second preset file count indicates a large number of files hit, while a count less than the second preset file count indicates a small number of files hit.

[0087] Step 702: If the number of files matched after searching the spatial index of the original dataset is less than the second preset number of files, then the matched data in the original dataset is directly read for rendering output. For example, if the second preset number of files is set to 30, and the number of matched data files is less than 30, then the matched data in the original dataset is directly read for rendering output. This step ensures high-level rendering quality when the number of files is not too large.

[0088] Step 703: If the number of files matched after searching the spatial index of the original dataset exceeds the second preset number of files, then search the spatial index of the simplified dataset again, and read the matched data for rendering output. For example, if the number of matched data files exceeds 30, search the simplified dataset; and read the matched simplified dataset data for rendering output. This step ensures high-level rendering efficiency when the number of files is too large.

[0089] In summary, this invention provides an efficient, non-slicing method for massive image data. It preprocesses the massive image data through image grouping and merging, and data thinning. Then, it uses different datasets for rendering at different rendering levels, significantly improving rendering efficiency at lower levels and ensuring rendering performance to meet the intended purpose. Simultaneously, it balances rendering effect and efficiency at higher rendering levels based on the number of hit files.

[0090] Based on the above method, this embodiment also provides a system for efficient, tile-free processing of massive sectional image data, referencing... Figure 8 As shown, the system includes an image grouping module, a dataset creation module, and a retrieval and rendering module. The image grouping module performs the following: a first grouping based on the coordinate system of the segmented images; a second grouping based on the number of closed shapes after the first grouping; and a third grouping based on the number of files after the second grouping. The dataset creation module creates a virtual raster file after virtual merging for the segmented image data after the third grouping, performs thinning sampling processing, and generates a thinned raster file. The thinned raster file is used as a simplified dataset, and the segmented image files before grouping are used as the original dataset. The retrieval and rendering module performs the following: during rendering, if the rendering level is lower than a preset level, retrieval and rendering output are performed using the simplified dataset; if the rendering level is higher than the preset level, retrieval and rendering output are performed using the original dataset. The specific implementation process of each module can be found in the above method steps and will not be elaborated here.

[0091] Example 2:

[0092] Based on the efficient, non-slicing method for massive sectional image data provided in Embodiment 1 above, Embodiment 2 of the present invention provides a specific example for illustration.

[0093] Examples of the above solutions are as follows:

[0094] 1. Reference Figure 9 As shown, Figure 9 Each small cell in the image represents a frame of data, and the data is assumed to have two coordinate systems.

[0095] 2. After the data is first grouped based on the coordinate system, the resulting graph after grouping is shown in the reference image. Figure 10 As shown in the figure. The data in region A is in the same coordinate system, and the data in region B is in the same coordinate system.

[0096] 3. Figure 10 After grouping, region A contains two regions, and region B also contains two regions. Since the two regions of A and B do not share edges, meaning they are not spatially contiguous, a second grouping based on spatial spatial relationships is required. The grouping result is as follows: Figure 11 As shown. Figure 11 After grouping, there are a total of four regions: A, B, C, and D. As can be seen from the diagram, all four regions are closed.

[0097] 4. Assuming that the number of image frames in each region does not exceed 100, from... Figure 11 Looking at the data above, regions A and B each contain more than 100 items. Therefore, a third partitioning is performed based on the quantity, resulting in the following partitioning outcome: Figure 12 As shown.

[0098] 5. Since the data in each partition is continuous, a virtual merge can be performed based on spatial location and the size of each image frame to generate a virtual merge file. (See reference...) Figure 13 As shown.

[0099] The generated file example is as follows:

[0100]

[0101]

[0102] As shown in the file example, the new size after merging is calculated, and the SrcRect in each frame records the original size information, while the DstRect records the position information based on the mapping on the merged image.

[0103] The above 6 regions are merged sequentially to generate 6 virtual files that are tiled in the same way: A.rtv, B.rtv, C.rtv, D.rtv, E.rtv, and F.rtv.

[0104] 6. Perform thinning sampling on the 6 virtual files respectively to generate merged split image data A, B, C, D, E, and F.

[0105] 7. Load the two datasets separately to create simplified and original data. Refer to [reference needed]. Figure 14 As shown, the simplified data and the original data are consistent in spatial location.

[0106] 8. During rendering, after calculating the rendering area, if it does not exceed a specific rendering level, simplified data can be used for direct hits and rendering, improving rendering speed. If it exceeds a specific rendering level, the number of files hit by the layer and region can be used to select the corresponding files for rendering, such as... Figure 15 As shown, after exceeding a certain rendering level, rendering zone 1 hits 2 simplified data region files and 30 original data files. In order to improve rendering speed, simplified data can be used directly for rendering output, reducing the number of files involved in rendering. Rendering zone 2 hits 1 simplified data region file and 6 original data files, so it can directly provide higher-definition rendering results through the original data while ensuring rendering speed.

[0107] Example 3:

[0108] Based on the method for efficient, non-slicing of massive segmented image data provided in Embodiment 1 above, the present invention also provides an apparatus for efficient, non-slicing of massive segmented image data that can be used to implement the above method and system, such as... Figure 16 The diagram shown is a schematic representation of the device architecture according to an embodiment of the present invention. The device for efficiently processing massive, segmented image data without slicing, as described in this embodiment, includes one or more processors 21 and a memory 22. Figure 16 Take a processor 21 as an example.

[0109] Processor 21 and memory 22 can be connected via a bus or other means. Figure 16 Taking the example of a connection between China and Israel via a bus.

[0110] The memory 22, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the method for efficient tiling of massive segmented image data in Embodiment 1. The processor 21 executes various functional applications and data processing of the apparatus for efficient tiling of massive segmented image data by running the non-volatile software programs, instructions, and modules stored in the memory 22, thereby realizing the method for efficient tiling of massive segmented image data in Embodiment 1.

[0111] Memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 22 may optionally include memory remotely located relative to processor 21, which can be connected to processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0112] The program instructions / modules are stored in memory 22. When executed by one or more processors 21, they perform the efficient, non-slicing method for massive sectional image data described in Embodiment 1 above. For example, they perform the methods described above. Figures 1-7 The steps shown.

[0113] The above-described product can perform the methods provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in the embodiments of this application.

[0114] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software and a general-purpose hardware platform, or of course, using hardware. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; under the concept of the present invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above. For the sake of brevity, they are not provided in detail; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for efficiently avoiding slicing massive amounts of multi-frame image data, characterized in that, include: The first grouping is based on the coordinate system of the segmented image; the second grouping is based on the number of closed shapes after the first grouping; and the third grouping is based on the number of files after the second grouping. A virtual raster file is created for the image data after the third grouping, and a thinning sampling process is performed to generate a thinned raster file. The thinned raster file is used as a simplified dataset, and the image data before grouping is used as the original dataset. If the rendering level is lower than the preset level during rendering, the retrieval and rendering output will be performed by simplifying the dataset; If the rendering level is higher than the preset level, the original dataset will be used for retrieval and rendering output. This includes: calculating the border of the tile to be rendered when rendering a tile with a specified row and column number; If the rendering level is higher than the preset level, the spatial index of the original dataset is searched based on the border of the tile to be rendered. If the number of hit files is lower than the second preset number of files, the hit data in the original dataset is directly read and rendered. If the number of hit files is higher than the second preset number of files, the spatial index of the simplified dataset is searched again and the hit data is read and rendered.

2. The method for efficient, non-slicing-free processing of massive image data according to claim 1, characterized in that, The first grouping based on the coordinate system of the segmented image specifically includes: Obtain the coordinate system information for all image frames; The first grouping operation is performed based on the coordinate system of the image slab, dividing the image slab data with the same coordinate system into the same group.

3. The method for efficient, non-slicing-free processing of massive image data according to claim 2, characterized in that, The second grouping based on the number of closed figures after the first grouping specifically includes: Find the union of the bounding rectangles of the image data for each group after the first grouping; The image data is grouped a second time based on the number of closed shapes in the union set, and the image data in the same closed shape are divided into the same group.

4. The method for efficient, non-slicing-free processing of massive image data according to claim 3, characterized in that, The third grouping based on the number of files after the second grouping specifically includes: Determine the preset total number of groups and the first preset number of files; Based on the results of the second grouping, if the number of sectional images in a certain closed shape is greater than the number of the first preset files, then the sectional image data in the corresponding closed shape will be grouped a third time according to the preset total number of groups, so that the number of sectional images in each group is less than the number of the first preset files.

5. The method for efficient, tile-free processing of massive, segmented image data according to claim 1, characterized in that, The process of creating a virtual raster file from the third grouped image data and performing thinning sampling to generate the thinned raster file specifically includes: For the image data after the third grouping, the image data of each group is virtually merged to generate virtual raster files, and all virtual raster files are tiled. All virtual raster files are subjected to bilinear interpolation for thinning sampling, and thinned raster files are generated.

6. The method for efficient, non-slicing-free processing of massive image data according to claim 1, characterized in that, If the rendering level is lower than the preset level during rendering, the retrieval and rendering output are performed by simplifying the dataset. If the rendering level is higher than the preset level, the retrieval and rendering output will be performed using the original dataset, specifically including: Read the bounding box ranges and coordinate systems of all files in the simplified dataset and the original dataset respectively, specify a unified coordinate system for different coordinate systems, and perform virtual projection based on the unified coordinate system to create spatial indices for the simplified dataset and the original dataset respectively. If the rendering level is lower than the preset level, the bounding box of the tile to be rendered is directly retrieved from the spatial index of the simplified dataset based on the calculated bounding box, and the matched data is read for rendering output.

7. A device for efficiently processing massive amounts of segmented image data without slicing, characterized in that, The apparatus includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the processor for performing the efficient, non-slicing method for massive sectional image data as described in any one of claims 1-6.

8. A system for efficient, non-slicing of massive segmented image data, employing the method for efficient, non-slicing of massive segmented image data as described in any one of claims 1-6, characterized in that, The system includes an image grouping module, a dataset creation module, and a retrieval and rendering module, wherein: The image grouping module is used to: perform a first grouping based on the coordinate system of the segmented images, perform a second grouping based on the number of closed shapes after the first grouping, and perform a third grouping based on the number of files after the second grouping; The dataset creation module is used to: create a virtual raster file after virtual merging of the image data after the third grouping, and perform thinning sampling processing to generate a thinned raster file; use the thinned raster file as a simplified dataset, and use the image file before grouping as the original dataset; The retrieval and rendering module is used to: when rendering, if the rendering level is lower than the preset level, retrieve and render the output using a simplified dataset; if the rendering level is higher than the preset level, retrieve and render the output using the original dataset.

9. A non-volatile computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which are executed by one or more processors to perform the efficient, non-slicing method for massive sectional image data as described in any one of claims 1-6.

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