CN112164142A - Building lighting simulation method based on smart phone - Google Patents
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Abstract
The invention discloses a building lighting simulation method based on a smart phone, which comprises the following steps: shooting a floor plan and simultaneously placing a reference object; identifying the position, contour and orientation of each building in the graph through an image identification module and a deep learning algorithm; inputting a drawing scale, the number of layers and the height of each building through an interactive interface of lighting simulation software to obtain height information of the building; calculating to obtain the actual size of each boundary on the building outline; the modeling module constructs a three-dimensional outline model of the building; acquiring geographic information and sunshine information; according to local sunshine information, combining a three-dimensional model, simulating and analyzing the illumination condition of each floor and each house type in each season; and (5) displaying the result. The user does not need professional equipment or professional knowledge, can just need smart mobile phone and building plane drawing before the actual house construction is accomplished, through intelligent software, conveniently, calculate the daylighting condition of optional position in the building throughout the year fast, purchase the room for the selection and improve effective reference information.
Description
Technical Field
The invention relates to the technical field of building lighting simulation, in particular to a building lighting simulation method based on a smart phone.
Background
The lighting quality of buildings is an important factor considered when people buy houses, and people pay attention to the lighting duration and lighting time of houses in one day. The house buyer can know the lighting condition through a field checking mode when selecting the house, but the mode is time-consuming, only can check the lighting condition in a short period, and not necessarily can know the lighting condition in winter which is most concerned by people. In addition, many new houses are sold in advance, the houses are not built when house buyers select the houses, only drawings and sand table models are used, and the lighting condition of the buildings is related to a plurality of factors such as local sunlight condition, building body layout, building height and the like. Since the ordinary house buyer does not have professional knowledge and professional measuring tools, the ordinary house buyer can hardly know the lighting quality of the house comprehensively, when buying the pre-sold house, the general lighting condition of the house can be known only through the introduction of house sales, the authenticity can not be ensured, and the detailed information such as the specific lighting time length and the lighting time length of a certain floor can not be obtained.
The existing real object lighting simulation system mostly adopts a form of matching parallel light sources on a guide rail and a cantilever to simulate sunlight, the whole system is large in size, heavy and not easy to disassemble and carry, and the lighting condition of a house can be simulated only by matching with a sand table model, so that a house buyer of the system cannot personally install and use the system.
The existing professional lighting calculation software can calculate various lighting data of a building through a computer, but when the software is used, a model of the building needs to be established, the operation is complex, the workload is large, special training or learning needs to be performed, and the software is usually professional software used by designers during building planning and design and is difficult to use by ordinary house purchasers.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the background art, the invention discloses a building lighting simulation method based on a smart phone, which mainly utilizes an image recognition technology, a deep learning technology and a lighting simulation algorithm, can conveniently calculate the lighting and shadow shielding conditions of a building through the smart phone, is convenient for an ordinary house buyer to quickly know the lighting quality of the house through a building drawing when watching the house, and provides reference for selecting the house.
The technical scheme is as follows: the invention relates to a building lighting simulation method based on a smart phone, which comprises the following steps:
s1, shooting a floor building plan by a mobile phone, and placing a reference object beside the drawing while shooting;
s2, identifying the boundary of the building outline and the boundary of the reference object in the graph through an image identification module and a deep learning algorithm, and obtaining the position, the outline and the orientation of each building;
s3, inputting a scale of a drawing by a user through an interactive interface of lighting simulation software, and inputting the floor number and floor height parameters of each building to obtain the height information of the building;
s4, calculating the actual size of each boundary on the building outline through a size calculation module;
s5, the modeling module constructs a three-dimensional outline model of the building through the plane information and the height information;
s6, acquiring geographic information and sunshine information;
s7, the lighting calculation and analysis module performs lighting and shadow calculation according to local sunshine information and by combining with a building three-dimensional model, and simulates and analyzes the lighting condition of each floor and each house type in each season;
and S8, displaying the result.
And S1, the right south of the drawing is downward, and a required standard direction is provided for lighting calculation through a fixed orientation mode.
Further, in S2, the image is subjected to recognition processing, including the steps of:
(1) and transmitting the shot image into a target detection model, and calibrating and framing the positions of the building and the reference object.
(2) Performing Gaussian filtering processing on a reference object image intercepted by target detection, and preliminarily filtering noise points and noises; then, opening the image once to reduce the interference of the background texture contour on the target contour; carrying out gray processing on the image to obtain a gray image, and then sharpening the image to obtain a clearer image; performing edge detection through a Canny operator, acquiring a corresponding binary image, performing contour detection on the basis to preliminarily obtain an image contour, solving the closed internal areas of all contours, deleting 15 contours with larger areas, and drawing the contours on an original image;
(3) performing four times of fuzzification processing on the image painted with the outline, wherein each time of fuzzification processing is to perform one time of Gaussian filtering, two times of corrosion and three times of expansion on the image to narrow or distort part of background texture, and finally performing one time of Gaussian filtering and one time of opening operation again to ensure the smoothness of the image;
(4) carrying out binarization processing on the image processed in the step (2) through a Canny operator, and simultaneously reducing the threshold value of the Canny operator step by step so as to identify the contour needing to be completed as much as possible, carrying out contour drawing on the image after reducing the threshold value each time, and keeping the contour and simultaneously carrying out Gaussian filtering and closing operation once so as to filter burrs generated by partial unnecessary contour identification due to the excessively low threshold value of the Canny operator; meanwhile, the width of the contour lines is properly increased in the process of each contour drawing, so that each section of contour line is easy to join, the contour is processed for three times, and the contours are basically overlapped; and deleting the contours once after each contour identification, and acquiring the first 15 contour data with larger area surrounded by the contours.
The outline data obtained by the last processing is used for obtaining the external approximate closed outlines of all the outlines, and meanwhile, the rectangular outlines are screened out, and as part of original card pattern or character outlines can also become rectangles after previous operation, all the rectangular outlines need to be further screened, the outlines with the area larger than 35000 and the unilateral size larger than 100 are taken as the target outlines of the actual reference object, and four corner coordinates are output, so that the number of pixels corresponding to the corresponding length or width, namely the size of the reference object in the drawing, can be obtained.
According to a target detection model and a target building plate calibrated by a user, intercepting the area of the target building plate by a target position coordinate point generated by target detection and the length and width of a corresponding framed rectangular frame, calculating the contour parameter of the target building plate by an original image processing mode to obtain four vertex coordinates of the target building plate so as to further calculate the width and the length of the corresponding building plate on a graph, combining a straight line where the orientation of the target building plate is located and the given length of the building plate, obtaining the distance of the building distance between the current building plate and the front and rear building plates on the graph by each target position coordinate obtained by the original target detection model, and subtracting the width of a building from the distance to obtain the distance on the graph of the front and rear building plates relative to the wall surface.
Further, in S6, the local latitude and longitude information is automatically obtained through the GPS function of the mobile phone; and calling local sunshine information from a sunshine database through longitude and latitude information, wherein the local sunshine information comprises the sunrise and sunset time of the four seasons, the solar altitude and the running track.
Further, S8 provides the longest and shortest lighting time of each suite in one year and the basic lighting information of the corresponding lighting time period to the user through the lighting simulation software, and displays the dynamic effect of the lighting of the whole building.
Has the advantages that: compared with the prior art, the invention has the advantages that: the user does not need professional equipment or professional knowledge, can just need smart mobile phone and building plane drawing before the actual house construction is accomplished, through intelligent software, conveniently, calculate the daylighting condition of optional position in the building throughout the year fast, purchase the room for the selection and improve effective reference information.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is an example of a floor plan taken in the practice of the present invention;
FIG. 3 is an example of a building planform outline generated by image recognition in the practice of the present invention;
FIG. 4 is an example of interactive input information in the practice of the present invention;
FIG. 5 is a system-computed dynamic shadow presentation effect in the practice of the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The method for simulating the lighting of the building based on the smart phone, as shown in fig. 1, comprises the following steps:
s1 photographic drawing
As shown in fig. 2, the floor building plan is photographed by a mobile phone, and two requirements are met during photographing: the method comprises the following steps of (1) enabling the right south of a drawing to face downwards, and providing a required standard direction for lighting calculation in a fixed orientation mode; an identity card or a bank card is placed at the upper left corner of the drawing as a standard reference object.
S2, image processing and recognition
The image recognition module performs image processing on the shot pictures, extracts the closed boundary in the pictures, and recognizes the building outline boundary and the identity card boundary at the upper left corner in the closed boundary through a deep learning algorithm, so as to obtain information such as the position, the outline, the orientation and the like of each building, as shown in fig. 3.
The image identification processing steps are as follows:
(1) and transmitting the shot image into a target detection model, and calibrating and framing the positions of the building and the reference object.
(2) Performing Gaussian filtering processing on a reference object image intercepted by target detection, and preliminarily filtering noise points and noises; then, opening the image once to reduce the interference of the background texture contour on the target contour; carrying out gray processing on the image to obtain a gray image, and then sharpening the image to obtain a clearer image; performing edge detection through a Canny operator, acquiring a corresponding binary image, performing contour detection on the basis to preliminarily obtain an image contour, solving the closed internal areas of all contours, deleting 15 contours with larger areas, and drawing the contours on an original image;
(3) performing four times of fuzzification processing on the image painted with the outline, wherein each time of fuzzification processing is to perform one time of Gaussian filtering, two times of corrosion and three times of expansion on the image to narrow or distort part of background texture, and finally performing one time of Gaussian filtering and one time of opening operation again to ensure the smoothness of the image;
(4) carrying out binarization processing on the image processed in the step (2) through a Canny operator, and simultaneously reducing the threshold value of the Canny operator step by step so as to identify the contour needing to be completed as much as possible, carrying out contour drawing on the image after reducing the threshold value each time, and keeping the contour and simultaneously carrying out Gaussian filtering and closing operation once so as to filter burrs generated by partial unnecessary contour identification due to the excessively low threshold value of the Canny operator; meanwhile, the width of the contour lines is properly increased in the process of each contour drawing, so that each section of contour line is easy to join, the contour is processed for three times, and the contours are basically overlapped; and deleting the contours once after each contour identification, and acquiring the first 15 contour data with larger area surrounded by the contours.
The outline data obtained by the last processing is used for obtaining the external approximate closed outlines of all the outlines, and meanwhile, the rectangular outlines are screened out, and as part of original card pattern or character outlines can also become rectangles after previous operation, all the rectangular outlines need to be further screened, the outlines with the area larger than 35000 and the unilateral size larger than 100 are taken as the target outlines of the actual reference object, and four corner coordinates are output, so that the number of pixels corresponding to the corresponding length or width, namely the size of the reference object in the drawing, can be obtained.
According to a target detection model and a target building plate calibrated by a user, intercepting the area of the target building plate by a target position coordinate point generated by target detection and the length and width of a corresponding framed rectangular frame, calculating the contour parameter of the target building plate by an original image processing mode to obtain four vertex coordinates of the target building plate so as to further calculate the width and the length of the corresponding building plate on a graph, combining a straight line where the orientation of the target building plate is located and the given length of the building plate, obtaining the distance of the building distance between the current building plate and the front and rear building plates on the graph by each target position coordinate obtained by the original target detection model, and subtracting the width of a building from the distance to obtain the distance on the graph of the front and rear building plates relative to the wall surface.
S3, manually inputting parameters
As shown in fig. 4, a user inputs a scale of a drawing through an interactive interface of lighting simulation software, and a necessary proportional relation is provided for calculating an actual size through images subsequently; the user inputs the parameters of the number of floors and the height of the floors of each building, thereby obtaining the height information of the building.
S4, calculating the plane size
And automatically calculating the size of the building outline by a size calculation module. Because the identity card and the bank card have a fixed standard size a, after shooting is completed, the size calculation module obtains the size b of the identity card in the picture, so that the proportional relation b: a between the picture and the drawing is obtained, then the proportional relation b: ac between the picture and the actual building is finally obtained by combining the manually input drawing proportional scale 1: c, and if the image size of a certain boundary in the picture is d, the actual size is acd/b, so that the actual size of each boundary on the building outline can be obtained.
S5, establishing a three-dimensional model
And the modeling module constructs a simple three-dimensional outline model of the building through the plane information and the height information.
S6, acquiring geographic information and sunshine information
Automatically acquiring local longitude and latitude information through a GPS positioning function of the mobile phone; and calling local sunshine information from a sunshine database through longitude and latitude information, wherein the local sunshine information comprises sunrise and sunset time, solar altitude and running track of the four seasons.
S7, lighting analysis and calculation
And the lighting calculation and analysis module performs lighting and shadow calculation according to local sunlight information and by combining with the three-dimensional building model, and simulates and analyzes the lighting condition of each floor and each house type in each season.
S8, displaying results
The lighting simulation software finally provides basic lighting information such as the longest and shortest lighting time of each suite in one year and corresponding lighting time, and can also show the dynamic effect of lighting of the whole building, as shown in fig. 5.
The conventional image contour recognition processing method sets a corresponding threshold value through a Canny operator at present, and performs corresponding contour recognition according to image gradient, but simultaneously faces a problem: the complete contour of the target to be identified for different images (high contrast and low contrast, strong light and weak light, presence or absence of obvious stripes in the background, etc.) needs to be adjusted to different thresholds.
The conventional method for processing the image to obtain the contour is to delete the contour satisfying the threshold by setting high and low thresholds through a Canny operator, and obtain a continuous contour because the high and low thresholds exist, so that a part of the contour between the low threshold and the high threshold and continuous with the high threshold is connected.
For the present embodiment, due to the uncertainty of lighting conditions; the target background has background contour caused by unevenness or self texture; and the texture, characters or patterns and the like of the upper part of the target are connected with the boundary, so that the contrast of the region is reduced, and the like. So that the Canny operator will identify unnecessary contours or discontinuities in the target contour due to a threshold that is too low. Although noise can be removed through filtering operation such as gaussian filtering, or part of unnecessary striped contour can be removed through proper opening operation, it is difficult to obtain the contour of the target through a single conventional image processing because the target itself has a certain reflective characteristic and the background has the possibility of thicker stripes. Meanwhile, although the complete contour can be obtained as far as possible by adjusting the threshold or adding the adaptive threshold on the basis of conventional operation, the former has no certain universality due to different thresholds for different images, and the latter can approach the optimal threshold through a certain algorithm but has a low fault-tolerant rate, which may cause a contour gap of a medium or small size (about 20-30 pixels) and further cannot obtain the closed minimum circumscribed rectangle contour on the basis of the contour again under the condition of not obtaining an accurate threshold, and further cannot determine the length and width parameters of the reference object.
Aiming at the problems, a blurring processing operation is added on the basis of the original processing method, namely, the recognized contour is drawn in the original image on the basis of the contour recognition (the contour is obtained under a high threshold value and is not continuous, but simultaneously, the background contour as few as possible is recognized and drawn), so that the gradient of the target contour is substantially increased, the target contour is ensured not to be easily distorted in the blurring process, then the image is subjected to the blurring process, most of the noise points in the forms of large-area stripes, spots and the like are removed through twice corrosion and three times of expansion, and the noise points are not removed through once corrosion and once expansion in the conventional opening operation because the conventional opening operation value can generate a better filtering effect aiming at smaller or thinner noise points and lines, however, the wood table top cannot be filtered by the texture similar to the wood table top, the striped pattern caused by the uneven plane of the target, or the spots caused by partial dirt. The interference of background textures with larger brightness is reduced through two times of corrosion, on the other hand, the gradient (the channels b, g and r are only increased in single value and are 255, and other channels are all 0) of the previously described contour is enhanced once, then noise points with lower brightness, which are expanded due to corrosion before, are properly inhibited through three times of expansion, and the width of the target contour is contracted to about 3 pixels, so that the error of contour recognition after the target contour is reduced.
The fuzzification processing solves the problem that the conventional filtering operation cannot filter noises such as large stripes and spots. Then, because the contrast ratio of a part of regions to the background environment is low, theoretically, according to the conventional operation, the Canny operator threshold is reduced to obtain a complete contour, but because the whole operation process is a cassette operation (the noise which cannot be manually adjusted from the outside and cannot be filtered is not determined, when the contour is obtained through a low threshold in the conventional operation, if the noise which cannot be sufficiently filtered in the fuzzification processing due to overlarge area is identified as noise by mistake, and if the noise is connected with the target contour, the influence is caused on the deletion of the target contour later), and the threshold cannot be accurately fed back, the method of directly obtaining the target contour through the low threshold is abandoned, the threshold is gradually reduced while the filtering and opening operation is assisted, three operations are carried out to ensure the fault tolerance ratio, and one Gaussian filtering and opening operation before each contour identification ensures that the background contour under the current threshold is identified as little as possible, the method comprises the steps of drawing a contour in an identified original image to increase the gradient of a target contour, setting the thicknesses of a first contour and a second contour to be 2 and 5 respectively, so that the similar discontinuous contours are connected as much as possible (the contours cannot be drawn in the original image in conventional operation, the contours under different thresholds are overlapped by repeated operation at the same time, the current contour is generally defaulted as the best contour directly, and the operation is mainly caused by the rotation of business The contour has to be identified, which is equivalent to a fault tolerance mechanism) is too small relative to the item).
The method comprises the steps of shooting a building plane graph according to specific requirements through a mobile phone camera, identifying a closed boundary in a picture by combining image processing and image identification technologies, and identifying and extracting a plane outline and an identity card outline of each building through a deep learning algorithm. Then, the identity card is used as a standard reference object to obtain an image proportional relation, information such as layout, orientation, appearance, size and the like of each building is automatically obtained, a large number of complex parameters do not need to be inquired and input, and only three simple and easily-obtained data such as a drawing scale, the number of layers and the layer height need to be input, so that a three-dimensional model of the building can be obtained, and the modeling process is convenient and simple.
Claims (5)
1. A building lighting simulation method based on a smart phone is characterized by comprising the following steps:
s1, shooting a floor building plan by a mobile phone, and placing a reference object beside the drawing while shooting;
s2, identifying the boundary of the building outline and the boundary of the reference object in the graph through an image identification module and a deep learning algorithm, and obtaining the position, the outline and the orientation of each building;
s3, inputting a scale of a drawing by a user through an interactive interface of lighting simulation software, and inputting the floor number and floor height parameters of each building to obtain the height information of the building;
s4, calculating the actual size of each boundary on the building outline through a size calculation module;
s5, the modeling module constructs a three-dimensional outline model of the building through the plane information and the height information;
s6, acquiring geographic information and sunshine information;
s7, the lighting calculation and analysis module performs lighting and shadow calculation according to local sunshine information and by combining with a building three-dimensional model, and simulates and analyzes the lighting condition of each floor and each house type in each season;
and S8, displaying the result.
2. The smartphone-based building lighting simulation method of claim 1, wherein: s1, the right south of the drawing is downward, and a standard direction required by lighting calculation is provided through a fixed orientation mode.
3. The smartphone-based building lighting simulation method of claim 1, wherein: in S2, the image is subjected to recognition processing, including the steps of:
(1) the shot images are transmitted into a target detection model, and the positions of the building and the reference object are calibrated and framed;
(2) performing Gaussian filtering processing on a reference object image intercepted by target detection, and preliminarily filtering noise points and noises; then, opening the image once to reduce the interference of the background texture contour on the target contour; carrying out gray processing on the image to obtain a gray image, and then sharpening the image to obtain a clearer image; performing edge detection through a Canny operator, acquiring a corresponding binary image, performing contour detection on the basis to preliminarily obtain an image contour, solving the closed internal areas of all contours, deleting 15 contours with larger areas, and drawing the contours on an original image;
(3) performing four times of fuzzification processing on the image painted with the outline, wherein each time of fuzzification processing is to perform one time of Gaussian filtering, two times of corrosion and three times of expansion on the image to narrow or distort part of background texture, and finally performing one time of Gaussian filtering and one time of opening operation again to ensure the smoothness of the image;
(4) carrying out binarization processing on the image processed in the step (2) through a Canny operator, and simultaneously reducing the threshold value of the Canny operator step by step so as to identify the contour needing to be completed as much as possible, carrying out contour drawing on the image after reducing the threshold value each time, and keeping the contour and simultaneously carrying out Gaussian filtering and closing operation once so as to filter burrs generated by partial unnecessary contour identification due to the excessively low threshold value of the Canny operator; meanwhile, the width of the contour lines is properly increased in the process of each contour drawing, so that each section of contour line is easy to join, the contour is processed for three times, and the contours are basically overlapped; deleting and selecting the contours once after each contour identification, and obtaining the first 15 contour data with larger area surrounded by the contours in the same way;
the method comprises the steps of obtaining external approximate closed outlines of all outlines by processing obtained outline data for the last time, screening out rectangular outlines, wherein part of original card pattern or character outlines may become rectangles after previous operation, further screening out all rectangular outlines, taking the outlines with the area larger than 35000 and the unilateral size larger than 100 as target outlines of actual reference objects, outputting coordinates of four corner points, and further obtaining the number of corresponding pixel points in the corresponding length or width, namely the size of the reference objects in the drawing;
according to a target detection model and a target building plate calibrated by a user, intercepting the area of the target building plate by a target position coordinate point generated by target detection and the length and width of a corresponding framed rectangular frame, calculating the contour parameter of the target building plate by an original image processing mode to obtain four vertex coordinates of the target building plate so as to further calculate the width and the length of the corresponding building plate on a graph, combining a straight line where the orientation of the target building plate is located and the given length of the building plate, obtaining the distance of the building distance between the current building plate and the front and rear building plates on the graph by each target position coordinate obtained by the original target detection model, and subtracting the width of a building from the distance to obtain the distance on the graph of the front and rear building plates relative to the wall surface.
4. The smartphone-based building lighting simulation method of claim 1, wherein: in S6, the local longitude and latitude information is automatically obtained through the GPS positioning function of the mobile phone; and calling local sunshine information from a sunshine database through longitude and latitude information, wherein the local sunshine information comprises the sunrise and sunset time of the four seasons, the solar altitude and the running track.
5. The smartphone-based building lighting simulation method of claim 1, wherein: s8, the lighting simulation software finally provides the longest and shortest lighting time of each suite in one year and the basic lighting information of the corresponding lighting time period for the users, and simultaneously displays the dynamic effect of the lighting of the whole building.
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| CN114283441A (en) * | 2021-12-18 | 2022-04-05 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Two-dimensional drawing recognition modeling method and device suitable for railway passenger station |
| CN116071514A (en) * | 2023-03-10 | 2023-05-05 | 成都欢聚堂科技有限公司 | Method, device and storage medium for automatically measuring building spacing |
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