A visual effect presentation method of urban green landscape based on greenness rate

Abstract

The application discloses a kind of urban green landscape visual effect presentation methods based on green feeling rate, including main steps are using active panoramic camera equipment, capture the complete panoramic image of urban green space from the perspective of pedestrian, then build interactive virtual reality environment of different types of urban green space, record the visual attention distribution data of pedestrian by eye tracking device, then calculate the correlation between y and x in visual attention distribution data using linear regression module, obtain the urban green landscape design scheme data to be presented and establish three-dimensional model, subsequently the model is imported into virtual reality engine and builds virtual reality scene, then select panoramic angle picture in the scene, then the picture is imported into picture processing software, the percentage of fixation point number in each Euclidean distance interval is calculated, and each Euclidean distance interval is labeled with different colors according to the percentage value, to realize the visualization of green feeling rate in the module.

Description

Technical Field

[0001] This invention relates to the field of urban green landscape planning and design, and in particular to a method for presenting the visual effects of urban green landscapes. Background Technology

[0002] Current technologies often use indicators such as green coverage rate, green space ratio, normalized difference vegetation index (NDE), and per capita green space area to measure the level or effect of urban greening. However, these indicators mainly reflect the greening situation on a two-dimensional plane, ignoring the greening resources and quality in three-dimensional space, and cannot objectively reflect pedestrians' feelings in the urban green environment. Green View Index (GVI) refers to the proportion of green in the field of vision, and it can better reflect the effect of greening in three-dimensional space than traditional two-dimensional greening indicators. Although GVI focuses more on two-dimensional space than indicators such as green space ratio, it still cannot truly reflect pedestrians' dynamic perception of the real environment from a three-dimensional perspective. Specifically, its presentation approach is based on measuring the vegetation ratio in a two-dimensional, static image plane. However, in reality, many factors such as the pedestrian's observation angle, different types of greening modules, and the openness of the space affect pedestrians' perception, and these influencing factors are not considered in the quantitative approach of GVI. Therefore, the presentation method cannot accurately and scientifically reflect the public's visual experience in urban green spaces.

[0003] Therefore, existing technologies urgently need a visual presentation method that can more scientifically reflect the pedestrian's perspective and their dynamic changes in the perception of urban green landscape. Summary of the Invention

[0004] To address one of the problems with traditional solutions, this invention provides a method for presenting the visual effects of urban green landscapes based on green sensitivity ratio, comprising the following steps:

[0005] S1: Using active panoramic camera equipment, capture complete panoramic images of urban green spaces from the perspective of pedestrians. The urban green spaces include M*N types, where M represents M scenes and N represents N types of greening.

[0006] S2: Using the images obtained in step S1, construct interactive virtual reality environments for different types of urban green spaces. Record the visual attention distribution data of pedestrians in the interactive virtual reality environment using an eye-tracking device. The visual attention distribution data includes the coordinates of each gaze point and the number of gaze points.

[0007] S3: Calculate the correlation between y and x in the visual attention distribution data of different types of urban green spaces in the interactive virtual reality environment obtained in step S2 using the linear regression module:

[0008] y = f(x)

[0009] Where x is the Euclidean distance from the fixation point to the center of the image, and y is the number of fixation points falling within the Euclidean distance range; x is calculated based on the coordinates of the fixation point.

[0010] S4: Obtain the urban green landscape design scheme data to be presented and build a 3D model, then import the model into the virtual reality engine and build a virtual reality scene;

[0011] S5: Select a single or continuous sampling point in the scene obtained in step S4 as the panoramic observation angle scene from the static or dynamic pedestrian perspective, and obtain panoramic angle images of all sampling points.

[0012] S6: Import the image obtained in step S5 into the image processing software, and select the green area boundary of different types of urban green spaces. Name the area within the continuous boundary as a module.

[0013] S7: Within the green area boundary of each module, take n Euclidean distance intervals based on the distance from the center point. Calculate the percentage of gaze points within each Euclidean distance interval based on the correlation between y and x of the urban green space type corresponding to the module, y = f(x). Then, mark each Euclidean distance interval with a different color based on this percentage value to visualize the green perception rate within the module.

[0014] The green sensitivity rate refers to the percentage of fixations within a certain Euclidean distance range of a module out of the total number of fixations within that module.

[0015] Furthermore, M is 2, and the scenes are either park scenes or street scenes; N is 5, and the greening types are either single tree type, tree type in a combination of trees and shrubs, shrub type in a combination of trees and shrubs, tree type in a combination of trees and grassland, and grassland type in a combination of trees and grassland.

[0016] Furthermore, step S2 includes the following sub-steps.

[0017] S21: Import the images obtained in step S1 into the Tobii Pro Lab platform, use the design module to create an interactive virtual reality environment, and set the observation parameters of each image to be consistent.

[0018] S22: Select participants of different genders and ages, and use the record module of the Tobii Pro Lab platform to record the visual attention distribution data of each participant.

[0019] S23: Record all visual attention distribution data on the Tobii Pro Lab platform;

[0020] S24: Export all visual attention distribution data.

[0021] Furthermore, the correlation between y and x in the visual attention distribution data of different types of urban green spaces in S3 is as follows: y = f(x), which shows a trend of change of the logarithmic function y = a·ln(x) + b.

[0022] Furthermore, step S4 specifically includes the following sub-steps:

[0023] S41: Obtain the greening design layout of the urban green landscape plan to be presented through urban design methods, and perform 3D scene modeling using Rhino;

[0024] S42: Import the model obtained in step S41 into the virtual reality engine, set the environment parameters, and build a virtual reality scene.

[0025] Furthermore, step S5 specifically includes the following sub-steps:

[0026] S51: Determine whether to present statically or dynamically as needed;

[0027] S52: Determine the simulated height of the pedestrian's perspective as 1.67m above the horizon. In the street scene, the simulated angle of the pedestrian's perspective is that the pedestrian is walking straight to the right, parallel to the right side of the road. In the park scene, the simulated angle of the pedestrian's perspective is that the pedestrian is facing the landscape directly.

[0028] S53: If step S51 determines to perform static presentation, then select a single sampling method according to the scene type; the sampling method for street scenes is to determine sampling points along the forward path of the pedestrian walking straight to the right, and the sampling method for park scenes is to determine sampling points along the forward path of the pedestrian facing the landscape.

[0029] If step S51 determines to perform dynamic presentation, then select the continuous sampling method according to the scene type; the sampling method for street scenes is to determine a sampling point every 0.5m along the path of the pedestrian walking straight to the right; the sampling method for park scenes is to determine a sampling point every 0.5m along the path of the pedestrian facing the landscape.

[0030] Furthermore, it also includes the following step S8: comparing the multiple urban green landscape design schemes presented from the perspective of green perception rate;

[0031] Furthermore, the comparison method in step S8 specifically refers to: comparison within a scheme or comparison between multiple schemes.

[0032] Furthermore, the comparison within the scheme specifically includes comparisons based on the same perspective and comparisons based on the same module combination;

[0033] The specific method for comparison based on the same perspective is as follows:

[0034] Within the boundaries of the green area of ​​each module, annotations are completed according to the same Euclidean distance range. The visualization results of the green sensitivity rate of each module are presented in the same panoramic image and then compared.

[0035] The specific method for comparison based on the same module combination is as follows:

[0036] Multiple panoramic images from the pedestrian's perspective are selected using the same module combination. The visualization results of each image are presented separately and then compared.

[0037] Furthermore, the comparison between the multiple schemes specifically includes comparisons based on the same green area and comparisons based on the same module combination;

[0038] The specific method for comparison based on the same green area is as follows: select multiple panoramic images from the perspective of pedestrians with the same green area, present the visualization results of each image separately, and then compare them;

[0039] The specific method for comparison based on the same module combination is as follows: select multiple panoramic images of pedestrians from the same module combination, present the visualization results of each image separately, and then compare them.

[0040] The present invention achieves the following technical effects: This method uses active devices to acquire and analyze complete panoramic images from a pedestrian's perspective; it introduces VR technology and designs eye-tracking experiments for panoramic images to simulate pedestrian experiences; and it obtains multiple real panoramic images from a pedestrian's perspective through continuous sampling, thereby accurately capturing the dynamic perception of different green spaces from a pedestrian's point of view. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0042] Figure 1 This is a schematic diagram illustrating the specific process of visually presenting urban green landscapes based on green sensitivity rate.

[0043] Figure 2 This is a schematic diagram illustrating the correlation between y and x in visual attention distribution data.

[0044] Figure 3 This is a schematic diagram simulating the situation from the perspective of a pedestrian in a street scene.

[0045] Figure 4 This is a schematic diagram simulating the situation from the perspective of pedestrians in the garden.

[0046] Figure 5 This is a schematic diagram of five types of greening. Detailed Implementation

[0047] The structure and operation of this invention will be further described in detail below with reference to the accompanying drawings. Obviously, the drawings are provided only for a better understanding of this invention and should not be construed as limiting it. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0048] In the description of this invention, it should be noted that terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0049] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0050] Example 1

[0051] This embodiment discloses a method for presenting the visual effects of urban green landscapes based on green sensitivity ratio, including the following steps:

[0052] S1: Using active panoramic camera equipment, capture complete panoramic images of urban green spaces from the perspective of pedestrians. The urban green spaces include M*N types, where M represents M scenes and N represents N types of greening.

[0053] S2: Using the images obtained in step S1, construct interactive virtual reality environments for different types of urban green spaces. Record the visual attention distribution data of pedestrians in the interactive virtual reality environment using an eye-tracking device. The visual attention distribution data includes the coordinates of each gaze point and the number of gaze points.

[0054] S3: Calculate the correlation between y and x in the visual attention distribution data of different types of urban green spaces in the interactive virtual reality environment obtained in step S2 using the linear regression module: y = f(x), where x is the Euclidean distance of the gaze point from the center of the screen, and y is the number of gaze points falling within the Euclidean distance interval; x is calculated based on the coordinates of the gaze point.

[0055] S4: Obtain the urban green landscape design scheme data to be presented and build a 3D model, then import the model into the virtual reality engine and build a virtual reality scene;

[0056] S5: Select a single or continuous sampling point in the scene obtained in step S4 as the panoramic observation angle scene from the static or dynamic pedestrian perspective, and obtain panoramic angle images of all sampling points.

[0057] S6: Import the image obtained in step S5 into the image processing software, and select the green area boundary of different types of urban green spaces. Name the area within the continuous boundary as a module.

[0058] S7: Within the green area boundary of each module, take n Euclidean distance intervals based on the distance from the center point. Calculate the percentage of gaze points within each Euclidean distance interval based on the correlation between y and x of the urban green space type corresponding to the module, y = f(x). Then, mark each Euclidean distance interval with a different color based on this percentage value to visualize the green perception rate within the module.

[0059] The green sensitivity rate refers to the percentage of fixations within a certain Euclidean distance range of a module, relative to the total number of fixations within that module.

[0060] Figure 1 The diagram illustrates the process of implementing the above method. By directly presenting the green view rate effect of urban green landscape design schemes, this method enables professionals to more accurately and scientifically assess the public's visual experience in urban green spaces and propose effective improvement suggestions, facilitating the iteration and modification of urban green landscape design schemes.

[0061] Specifically, M is 2, and the scenes are either park scenes or street scenes; N is 5, and the greening types are respectively: single tree type, tree type in a combination of trees and shrubs, shrub type in a combination of trees and shrubs, tree type in a combination of trees and grassland, and grassland type in a combination of trees and grassland. Schematic diagrams of the 5 typical greening types are shown below. Figure 5 As shown.

[0062] Table 1 shows the greening characteristics in different scenarios and the common types of greening modules. Table 2 shows the greening characteristics and specific forms of different greening types.

[0063] Table 1

[0064]

[0065]

[0066] Table 2

[0067]

[0068] Specifically, step S2 includes the following sub-steps.

[0069] S21: Import the images obtained in step S1 into the Tobii Pro Lab platform, use the design module to create an interactive virtual reality environment, and set the observation parameters for each image to be consistent; for example, the total gaze duration can be set to 30 seconds, and the recording frequency can be 50 times / millisecond.

[0070] S22: Select participants of different genders and ages, and use the record module of the Tobii Pro Lab platform to record the visual attention distribution data of each participant.

[0071] S23: Record all visual attention distribution data on the Tobii Pro Lab platform; in specific implementations, the AOI tool can be used to select the greening elements in the combination separately and name them in the form of AB combination-A;

[0072] S24: Export all visual attention distribution data. This data may include fixation duration, fixation point coordinates, etc.

[0073] The Tobii Pro Lab platform is a one-stop eye-tracking research software platform in the current technology. The design module, record module, and other functional modules are provided by it. The AOI tool refers to the Area of ​​Interest (AOI) editing tool, which is mainly used to define, edit, and analyze specific regions in related research or experiments.

[0074] Specifically, in step S3, the correlation between y and x in the visual attention distribution data of different types of urban green spaces in the interactive virtual reality environment is as follows: y = f(x) exhibits a trend of change of the logarithmic function y = alan(x) + b, where x is the Euclidean distance from the gaze point to the center of the screen, y is the number of gaze points falling within the Euclidean distance interval, and x is calculated based on the coordinates of the gaze point. For details... Figure 2 As shown, this was obtained by using SPSS's linear regression module to calculate the attenuation of the fixation point from the center of the image to the edge across all groups.

[0075] In specific scenarios and types of greening, y = f(x) has different coefficients:

[0076] Street scene with a single tree type: y = -363.305ln(x) + 2193.909;

[0077] The type of tree in a street scene with a combination of trees and shrubs: y = -210.844ln(x) + 1327.118;

[0078] Street scene with a combination of trees and shrubs: y = -275.112ln(x) + 1665.598;

[0079] The type of tree in a street scene combining trees and grass is: y = -270.748ln(x) + 1637.454;

[0080] Grassland type in a street scene with a combination of trees and grass: y = -101.474ln(x) + 623.371;

[0081] Park scene with a single tree type: y = -272.076ln(x) + 1689.316;

[0082] The type of tree in a street scene with a combination of trees and shrubs: y = 186.699ln(x) + 1143.815;

[0083] Street scene with a combination of trees and shrubs: y = -154.301ln(x) + 911.857;

[0084] The type of trees in a street scene combining trees and grass is: y = -213.500ln(x) + 1285.262;

[0085] The grass type in the street scene and the combination of trees and grass is: y = -170.388ln(x) + 992.622;

[0086] Specifically, step S4 includes the following sub-steps:

[0087] S41: Obtain the greening design layout of the urban green landscape plan to be presented through urban design methods, and perform three-dimensional scene modeling using Rhino; Rhino refers to the professional 3D modeling software developed by Robert McNeel & Assoc in the United States.

[0088] S42: Import the model obtained in step S41 into the virtual reality engine, set the environment parameters, and construct the virtual reality scene. In specific embodiments, the virtual reality engine includes software such as Unity 3D or Unreal Engine.

[0089] Specifically, step S5 includes the following sub-steps:

[0090] S51: Determine whether to present statically or dynamically as needed;

[0091] S52: Determine the simulated height of the pedestrian's perspective as 1.67m above the horizon. In the street scene, the simulated angle of the pedestrian's perspective is that the pedestrian is walking straight to the right, parallel to the right side of the road. In the park scene, the simulated angle of the pedestrian's perspective is facing the landscape directly. The pedestrian's perspective in these two scenes is as follows: Figure 3 and Figure 4 As shown.

[0092] S53: If step S51 determines to perform static presentation, then select a single sampling method according to the scene type; the sampling method for street scenes is to determine sampling points along the forward path of the pedestrian walking straight to the right, and the sampling method for park scenes is to determine sampling points along the forward path of the pedestrian facing the landscape.

[0093] If step S51 determines to perform dynamic presentation, then select the continuous sampling method according to the scene type; the sampling method for street scenes is to determine a sampling point every 0.5m along the path of the pedestrian walking straight to the right; the sampling method for park scenes is to determine a sampling point every 0.5m along the path of the pedestrian facing the landscape.

[0094] Specifically, it also includes the following step S8: comparing the multiple urban green landscape design schemes presented;

[0095] Specifically, the comparison method in step S8 is: comparison within a scheme or comparison between multiple schemes.

[0096] Specifically, the comparison within the scheme includes comparisons based on the same perspective and comparisons based on the same module combination;

[0097] The specific method for comparison based on the same perspective is as follows:

[0098] Within the boundaries of the green area of ​​each module, annotations are completed according to the same Euclidean distance range. The visualization results of the green sensitivity rate of each module are presented in the same panoramic image and then compared.

[0099] The specific method for comparison based on the same module combination is as follows:

[0100] Multiple panoramic images from the pedestrian's perspective are selected using the same module combination. The visualization results of each image are presented separately and then compared.

[0101] Specifically, the comparison between the multiple schemes includes comparisons based on the same green area and comparisons based on the same module combination;

[0102] The specific method for comparison based on the same green area is as follows: select multiple panoramic images from the perspective of pedestrians with the same green area, present the visualization results of each image separately, and then compare them;

[0103] The specific method for comparison based on the same module combination is as follows: select multiple panoramic images of pedestrian perspectives with the same module combination, present the visualization results of each image separately, and then compare them.

[0104] The method disclosed in this invention achieves analysis by combining active devices to acquire complete panoramic images from a pedestrian's perspective; it introduces VR technology and designs eye-tracking experiments for panoramic images to simulate pedestrian experiences; and it obtains multiple real panoramic images from a pedestrian's perspective through continuous sampling to accurately capture the dynamic perception of different green spaces from a pedestrian's point of view.

[0105] The above embodiments are only used to illustrate the present invention patent. The structure, connection method and manufacturing process of each component can be varied. Any equivalent transformations and improvements made on the basis of this technical solution should not be excluded from the protection scope of the present invention patent.

Claims

1. A method for visually presenting urban green landscapes based on green sensitivity ratio, characterized in that: Includes the following steps: S1: Using active panoramic camera equipment, capture complete panoramic images of urban green spaces from the perspective of pedestrians. The urban green spaces include M*N types, where M represents M scenes and N represents N types of greening. S2: Using the images obtained in step S1, construct interactive virtual reality environments for different types of urban green spaces. Record the visual attention distribution data of pedestrians in the interactive virtual reality environment using an eye-tracking device. The visual attention distribution data includes the coordinates of each gaze point and the number of gaze points. S3: Calculate the correlation between y and x in the visual attention distribution data of different types of urban green spaces in the interactive virtual reality environment obtained in step S2 using the linear regression module: y = f(x) Where x is the Euclidean distance from the fixation point to the center of the image, and y is the number of fixation points falling within the Euclidean distance range; x is calculated based on the coordinates of the fixation point. S4: Obtain the urban green landscape design scheme data to be presented and build a 3D model, then import the model into the virtual reality engine and build a virtual reality scene; S5: Select a single or continuous sampling point in the scene obtained in step S4 as the panoramic observation angle scene from the static or dynamic pedestrian perspective, and obtain panoramic angle images of all sampling points. S6: Import the image obtained in step S5 into the image processing software, and select the green area boundary of different types of urban green spaces. Name the area within the continuous boundary as a module. S7: Within the green area boundary of each module, take n Euclidean distance intervals based on the distance from the center point. Calculate the percentage of gaze points within each Euclidean distance interval based on the correlation between y and x of the urban green space type corresponding to the module, y = f(x). Then, mark each Euclidean distance interval with a different color based on this percentage value to visualize the green perception rate within the module. The green sensitivity rate refers to the percentage of fixations within a certain Euclidean distance range of a module out of the total number of fixations within that module.

2. The method according to claim 1, characterized in that: M is 2, and the scenes are either park scenes or street scenes; N is 5, and the greening types are either single tree type, tree type in combination of trees and shrubs, shrub type in combination of trees and shrubs, tree type in combination of trees and grassland, and grassland type in combination of trees and grassland.

3. The method according to claim 1, characterized in that: Step S2 includes the following sub-steps: S21: Import the images obtained in step S1 into the Tobii Pro Lab platform, use the design module to create an interactive virtual reality environment, and set the observation parameters of each image to be consistent. S22: Select participants of different genders and ages, and use the record module of the Tobii Pro Lab platform to record the visual attention distribution data of each participant. S23: Record all visual attention distribution data on the Tobii Pro Lab platform; S24: Export all visual attention distribution data.

4. The method according to claim 1, characterized in that: The correlation between y and x in the visual attention distribution data of different types of urban green spaces in S3 is as follows: y = f(x), which shows the trend of the logarithmic function y = a·ln(x) + b.

5. The method according to claim 1, characterized in that: Step S4 specifically includes the following sub-steps: S41: Obtain the greening design layout of the urban green landscape plan to be presented through urban design methods, and perform 3D scene modeling using Rhino; S42: Import the model obtained in step S41 into the virtual reality engine, set the environment parameters, and build a virtual reality scene.

6. The method according to claim 1, characterized in that: Step S5 specifically includes the following sub-steps: S51: Determine whether to present statically or dynamically as needed; S52: Determine the simulated height of the pedestrian's perspective as 1.67m above the horizon. In the street scene, the simulated angle of the pedestrian's perspective is that the pedestrian is walking straight to the right, parallel to the right side of the road. In the park scene, the simulated angle of the pedestrian's perspective is that the pedestrian is facing the landscape directly. S53: If step S51 determines to perform static presentation, then select a single sampling method according to the scene type; the sampling method for street scenes is to determine sampling points along the forward path of the pedestrian walking straight to the right, and the sampling method for park scenes is to determine sampling points along the forward path of the pedestrian facing the landscape. If step S51 determines to perform dynamic presentation, then select the continuous sampling method according to the scene type; the sampling method for street scenes is to determine a sampling point every 0.5m along the path of the pedestrian walking straight to the right; the sampling method for park scenes is to determine a sampling point every 0.5m along the path of the pedestrian facing the landscape.

7. The method according to claim 1, characterized in that: It also includes the following step S8: comparing the multiple urban green landscape design schemes presented.

8. The method according to claim 7, characterized in that: The comparison method in step S8 is specifically: comparison within a scheme or comparison between multiple schemes.

9. The method according to claim 8, characterized in that: The comparison within the scheme specifically includes comparisons based on the same perspective and comparisons based on the same module combination; The specific method for comparison based on the same perspective is as follows: Within the boundaries of the green area of ​​each module, annotations are completed according to the same Euclidean distance range. The visualization results of the green sensitivity rate of each module are presented in the same panoramic image and then compared. The specific method for comparison based on the same module combination is as follows: Multiple panoramic images from the pedestrian's perspective are selected using the same module combination. The visualization results of each image are presented separately and then compared.

10. The method according to claim 8, characterized in that: The comparison between the multiple schemes specifically includes comparisons based on the same green area and comparisons based on the same module combination; The specific method for comparison based on the same green area is as follows: select multiple panoramic images from the perspective of pedestrians with the same green area, present the visualization results of each image separately, and then compare them; The specific method for comparison based on the same module combination is as follows: select multiple panoramic images of pedestrians from the same module combination, present the visualization results of each image separately, and then compare them.

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