CN111104879B - Method and device for identifying house functions, readable storage medium and electronic equipment - Google Patents

Method and device for identifying house functions, readable storage medium and electronic equipment Download PDF

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CN111104879B
CN111104879B CN201911250311.2A CN201911250311A CN111104879B CN 111104879 B CN111104879 B CN 111104879B CN 201911250311 A CN201911250311 A CN 201911250311A CN 111104879 B CN111104879 B CN 111104879B
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room
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house structure
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CN111104879A (en
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董秋成
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Seashell Housing Beijing Technology Co Ltd
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Abstract

The embodiment of the disclosure discloses a house function room identification method and device, a readable storage medium and electronic equipment, wherein the method comprises the following steps: acquiring a house structure image based on the house structure coordinate information; the house structure image comprises at least one room; processing the house structure image by using a neural network to obtain a house characteristic diagram; determining a function of each room in the house structure image based on information of each feature point in the house feature map; according to the embodiment of the invention, the function of each room is automatically identified through the structure diagram of the house, so that the drawing time of the house type diagram is shortened, and the drawing difficulty is reduced.

Description

Method and device for identifying house functions, readable storage medium and electronic equipment
Technical Field
The present disclosure relates to image recognition technologies, and in particular, to a house function room recognition method and apparatus, a readable storage medium, and an electronic device.
Background
The current method for drawing house floor plan basically depends on manual field survey. For the setting of the function rooms, not only is there a problem that the function rooms of the blank room are difficult to identify, but also the original functions of the room may be changed due to the modification of the owner. The method completely relying on manual work has high requirements on professional skills of family drawing personnel, high working strength and low drawing efficiency, and is difficult to meet the current requirements of large-batch and high-efficiency family drawing.
Disclosure of Invention
The present disclosure is proposed to solve the above technical problems. The embodiment of the disclosure provides a house function room identification method and device, a readable storage medium and electronic equipment.
According to an aspect of an embodiment of the present disclosure, there is provided a method for identifying a house function room, including:
acquiring a house structure image based on the house structure coordinate information; the house structure image comprises at least one room;
processing the house structure image by using a neural network to obtain a house characteristic diagram;
determining a function of each room in the house structure image based on the information of each feature point in the house feature map.
Optionally, the house structure coordinate information includes at least coordinate information of walls constituting a house;
the obtaining of the house structure image based on the house coordinate information includes:
converting the coordinate information of the wall into a coordinate system of an image to obtain the image of the house structure; wherein, the house structure image represents walls by line segments, and the house structure image represents rooms by polygons formed by a plurality of line segments.
Optionally, the performing coordinate system conversion on the coordinate information of the wall, and converting the coordinate information of the wall into an image coordinate system to obtain the house structure image includes:
obtaining at least two pieces of corner point coordinate information based on the coordinate information of the wall, and determining the length of the longest side in the structure of the house based on the at least two pieces of corner point coordinate information;
and converting the coordinate information of the wall into an image coordinate system by combining the length of the longest edge in the house structure to obtain the house structure image.
Optionally, before the processing the house structure image by using the neural network to obtain the house feature map, the method further includes:
and training the neural network by using an annotated house image set, wherein the annotated house image set comprises a plurality of annotated house structure images and corresponding annotated feature maps.
Optionally, the training the neural network by using the labeled house image set includes:
inputting the marked house structure images in the marked house image set into the neural network to obtain a prediction characteristic diagram;
inputting the predicted feature map and an annotated feature map corresponding to the annotated house structure image into a discrimination network, and obtaining difference information between the predicted feature map and the annotated feature map based on the discrimination network;
obtaining a network loss based on the difference information, and alternately training the neural network and the discriminant network based on the network loss.
Optionally, the determining the function of each room in the house structure image based on the information of each feature point in the house feature map includes:
carrying out standardization processing on the pixel value of each feature point in the house feature map to obtain an updated house feature map;
determining the function of each room in the house structure image based on the updated house feature map and the house structure image.
Optionally, the determining a function of each room in the house structure image based on the updated house feature map and the house structure image includes:
performing connected domain analysis on the house structure image, and determining that each room included in the house structure image corresponds to a pixel area in the updated house feature map;
determining the function of each room based on the pixel area corresponding to each room included in the house structure image.
Optionally, the determining the function of each room based on the pixel area corresponding to each room included in the house structure image includes:
in response to a preset proportion or a preset number of pixel values in a pixel region corresponding to the room being equal to any one set pixel value, determining a function of the room based on the set pixel value; the functions comprise at least one, and each function corresponds to a set pixel value;
and in response to that the preset proportion or the preset number of pixel values in the pixel area corresponding to the room are not equal to any set pixel value, not determining the function of the room.
According to another aspect of the embodiments of the present disclosure, there is provided an apparatus for identifying a house function room, including:
the visualization module is used for obtaining a house structure image based on the house structure coordinate information; the house structure image comprises at least one room;
the image processing module is used for processing the house structure image by utilizing a neural network to obtain a house characteristic diagram;
and the function determining module is used for determining the function of each room in the house structure image based on the information of each feature point in the house feature map.
Optionally, the house coordinate information includes at least coordinate information of walls constituting a house;
the visualization module is specifically configured to perform coordinate system conversion on the coordinate information of the wall, convert the coordinate information of the wall into an image coordinate system, and obtain the house structure image; wherein, the house structure image represents walls by line segments, and the house structure image represents rooms by polygons formed by a plurality of line segments.
Optionally, the visualization module is specifically configured to obtain at least two pieces of corner point coordinate information based on the coordinate information of the wall, and determine the length of the longest side in the structure of the house based on the at least two pieces of corner point coordinate information; and converting the coordinate information of the wall into an image coordinate system by combining the length of the longest edge in the house structure to obtain the house structure image.
Optionally, the apparatus further comprises:
and the training module is used for training the neural network by utilizing an annotated house image set, wherein the annotated house image set comprises a plurality of annotated house structure images and corresponding annotated feature maps thereof.
Optionally, the training module is specifically configured to input the labeled house structure image in the labeled house image set into the neural network, so as to obtain a predicted feature map; inputting the predicted feature map and an annotated feature map corresponding to the annotated house structure image into a discrimination network, and obtaining difference information between the predicted feature map and the annotated feature map based on the discrimination network; obtaining a network loss based on the difference information, and alternately training the neural network and the discriminant network based on the network loss.
Optionally, the function determining module includes:
the normalization unit is used for normalizing the pixel value of each feature point in the house feature map to obtain an updated house feature map;
a room function unit, configured to determine a function of each room in the house structure image based on the updated house feature map and the house structure image.
Optionally, the room function unit is specifically configured to perform connected domain analysis on the house structure image, and determine that each room included in the house structure image corresponds to a pixel area in the updated house feature map; determining the function of each room based on the pixel area corresponding to each room included in the house structure image.
Optionally, the room function unit, when determining the function of each room included in the house structure image based on the pixel area corresponding to the each room, is configured to determine the function of the room based on a preset pixel value in response to a preset proportion or a preset number of pixel values in the pixel area corresponding to the room being equal to any one of the set pixel values; the functions comprise at least one, and each function corresponds to a set pixel value; and in response to that the preset proportion or the preset number of pixel values in the pixel area corresponding to the room are not equal to any set pixel value, not determining the function of the room.
According to still another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program for executing the method for identifying a house function room according to any one of the embodiments.
According to still another aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method for identifying a house function room according to any of the above embodiments.
Based on the identification method and device for the house function room, the readable storage medium and the electronic device, provided by the embodiment of the disclosure, the house structure image is obtained based on the house structure coordinate information; the house structure image comprises at least one room; processing the house structure image by using a neural network to obtain a house characteristic diagram; determining a function of each room in the house structure image based on information of each feature point in the house feature map; according to the embodiment of the invention, the function of each room is automatically identified through the structure diagram of the house, so that the drawing time of the house type diagram is shortened, and the drawing difficulty is reduced.
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in more detail embodiments of the present disclosure with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the principles of the disclosure and not to limit the disclosure. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 is a schematic flowchart of a house function room identification method according to an embodiment of the present disclosure.
Fig. 2 is a schematic structural diagram of a house structure image obtained in a house function room identification method provided in an embodiment of the present disclosure.
FIG. 3 is a schematic flow chart of step 102 in the embodiment shown in FIG. 1 of the present disclosure.
Fig. 4 is another schematic flow chart of a house function room identification method provided by the embodiment of the present disclosure.
Fig. 5 is a schematic diagram of a house characteristic diagram obtained by a house function room identification method provided in the embodiment of the present disclosure.
FIG. 6 is a schematic flow chart of step 106 in the embodiment shown in FIG. 1 of the present disclosure.
Fig. 7 is a schematic structural diagram of an identification device for a house function room according to an embodiment of the present disclosure.
Fig. 8 is a block diagram of an electronic device provided in an exemplary embodiment of the present disclosure.
Detailed Description
Hereinafter, example embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of the embodiments of the present disclosure and not all embodiments of the present disclosure, with the understanding that the present disclosure is not limited to the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present disclosure are used merely to distinguish one element from another, and are not intended to imply any particular technical meaning, nor is the necessary logical order between them.
It is also understood that in embodiments of the present disclosure, "a plurality" may refer to two or more and "at least one" may refer to one, two or more.
It is also to be understood that any reference to any component, data, or structure in the embodiments of the disclosure, may be generally understood as one or more, unless explicitly defined otherwise or stated otherwise.
In addition, the term "and/or" in the present disclosure is only one kind of association relationship describing an associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in the present disclosure generally indicates that the former and latter associated objects are in an "or" relationship.
It should also be understood that the description of the various embodiments of the present disclosure emphasizes the differences between the various embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The disclosed embodiments may be applied to electronic devices such as terminal devices, computer systems, servers, etc., which are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with electronic devices, such as terminal devices, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set top boxes, programmable consumer electronics, network pcs, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above systems, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
Summary of the application
In the process of implementing the present disclosure, the inventor finds that the method for drawing house floor plan in the prior art basically depends on manual field survey, but the technical scheme has at least the following problems: the requirement on professional skills of house type drawing personnel is high, the working intensity is high, and the drawing efficiency is low.
Exemplary method
Fig. 1 is a schematic flowchart of a house function room identification method according to an embodiment of the present disclosure. The embodiment can be applied to an electronic device, as shown in fig. 1, and includes the following steps:
and 102, acquiring a house structure image based on the house structure coordinate information.
Wherein the house structure image comprises at least one room.
The embodiment realizes visualization of a house structure, house coordinate information reflects coordinates of each structure constituting the house in the world coordinate system of the house entity, and through the processing of the embodiment, the house is drawn into an image with a set size, and the house structure, for example, each room included in the house, is represented in the image. The house coordinate information can be obtained through field collection and the like, and the embodiment of the disclosure does not limit the obtaining mode of the house coordinate information.
And 104, processing the house structure image by using the neural network to obtain a house characteristic diagram.
Optionally, the neural network in this embodiment is trained, and in an optional example, the neural network may be pix2pix, and the parameters of the network may be set as: netG — 128', ngf ndf — 32.
And step 106, determining the function of each room in the house structure image based on the information of each feature point in the house feature map.
Optionally, the information of each feature point in the house feature map includes a pixel value of the point, for example, a pixel value expressed in RGB form; different rooms in the house characteristic map can be divided into different colors through different pixel values, and the function of each room is represented by different colors.
Based on the identification method for the house function room provided by the embodiment of the disclosure, a house structure image is obtained based on house coordinate information; the house structure image comprises at least one room; processing the house structure image by using a neural network to obtain a house characteristic diagram; determining a function of each room in the house structure image based on information of each feature point in the house feature map; according to the embodiment of the invention, the function of each room is automatically identified through the structure diagram of the house, so that the drawing time of the house type diagram is shortened, and the drawing difficulty is reduced.
In some alternative embodiments, step 102 comprises:
and converting the coordinate information of the wall into an image coordinate system to obtain a house structure image.
The house structure image represents a wall by a line segment, and a room in the house structure image is represented by a polygon formed by a plurality of line segments.
In this embodiment, the house usually includes a plurality of rooms, and each room is surrounded by at least three walls, so the coordinate information of the house in this embodiment at least includes coordinate information of all the walls constituting the house, and all the walls can be displayed in the image by converting the coordinate information of all the walls, and the display form can be a line segment, for example, one wall is represented by one line segment. Fig. 2 is a schematic structural diagram of a house structure image obtained in a house function room identification method provided in an embodiment of the present disclosure. As shown in fig. 2, the wall in the house is indicated by a line segment, and the house is divided into a plurality of rooms by the wall.
In an alternative example, the house coordinate information includes an X-axis coordinate value and a Y-axis coordinate value of each point in the world coordinate system (since the constructed house structure image is a house structure top view, the Z-axis coordinate value is not involved in the present embodiment).
As shown in fig. 3, based on the embodiment shown in fig. 1, step 102 may include the following steps:
and step 1021, obtaining at least two corner point coordinate information based on the coordinate information of the wall, and determining the length of the longest edge in the structure of the house based on the at least two corner point coordinate information.
Wherein a corner point represents the junction of at least two walls.
In this embodiment, the position information of the two farthest corner points in the house structure can be obtained based on the coordinate information of all the corner points, so as to obtain the parameters of coordinate conversion.
Optionally, the minimum value of the x-axis coordinates according to the corner points of all walls in the building structure is xminMaximum value of xmaxThe minimum value of the y-axis coordinates of all points is yminMaximum value of ymaxAt this time, the length of the longest side in the structure of the house can be determined by the following formula (1):
Rangemax＝max((xmax-xmin)，(ymax-ymin) Equation (1)
Wherein, RangemaxIndicating the length of the longest side in the structure of the house.
And 1022, converting the coordinate information of the wall into an image coordinate system by combining the length of the longest edge in the structure of the house to obtain the house structure image.
Optionally, after obtaining the length of the longest side in the structure of the house, obtaining the coordinates of each coordinate in the image through coordinate system conversion to obtain a house structure image; for example, the coordinate information of all the walls in the house structure image can be converted by the following formula (2) and formula (3), where formula (2) represents the x-coordinate transformation of each point:
xnew＝256*(x-((xmax+xmin)-Rangemax)/2+0.05*Rangemax)/(1.1*Rangemax)
formula (2)
Where equation (3) represents the y-coordinate transformation of each point: :
ynew＝256*(y-((ymax+ymin)-Rangemax)/2+0.05*Rangemax)/(1.1*Rangemax)
formula (3)
256 in the above formula is an adjustable integer value, and the house structure can be converted into an image with 256 × 256 pixels based on the transformation of the formulas (2) and (3), and if the house structure needs to be converted into an image with other size, the value of 256 in the formula can be adjusted; after conversion based on the above formula, drawing line segments on all walls at corresponding positions in the image according to the corner positions of the walls after conversion, wherein the thickness of the line segments can be set to any pixel value, such as 4 pixels, and the line color can be adjusted as required, for example, the RGB value is set to (0, 128), and then the line color is displayed as blue.
Fig. 4 is another schematic flow chart of a house function room identification method provided by the embodiment of the present disclosure. The embodiment can be applied to an electronic device, as shown in fig. 4, and includes the following steps:
and step 402, obtaining a house structure image based on the house structure coordinate information.
Wherein the house structure image comprises at least one room.
And 403, training the neural network by using the marked house image set.
The marked house image set comprises a plurality of marked house structure images and corresponding marked feature graphs.
And step 404, processing the house structure image by using the neural network to obtain a house characteristic diagram.
In step 406, the function of each room in the house structure image is determined based on the information of each feature point in the house feature map.
In this embodiment, in order to obtain the house characteristic map, before using the neural network (which may be a generation network), the neural network needs to be trained, and optionally, the neural network (e.g., the generation network) and a discriminant network may be combined to form a generation countermeasure network for training. Processing the house structure image through the trained neural network, wherein each feature point in the obtained house feature map corresponds to a pixel value, the pixel value may be in an RGB format, at this time, the house feature map is displayed as an image displayed in multiple colors, optionally, different functions may be represented by different colors, for example, including 6 functions, and their names and corresponding color RGB values are shown in table 1:
name between functions Color (RGB value)
Bedroom (255,0,0)
Kitchen cabinet (0,255,0)
Balcony (255,255,0)
Toilet (0,0,255)
Parlor (255,0,255)
Corridor (W) (0,255,255)
TABLE 1 function names of rooms and their corresponding pixel values
The rooms not belonging to the above 6 functional rooms are filled with (0,0,0) to obtain the house characteristic maps filled with different colors, as shown in fig. 5, and fig. 5 is a schematic diagram of the house characteristic maps obtained by the identification method of the house functional rooms provided by the embodiment of the present disclosure.
Optionally, the training process of the neural network may include:
inputting the marked house structure images in the marked house image set into a neural network to obtain a prediction characteristic diagram;
inputting the prediction characteristic diagram and the labeled characteristic diagram corresponding to the labeled house structure image into a discrimination network, and acquiring difference information between the prediction characteristic diagram and the labeled characteristic diagram based on the discrimination network;
and obtaining network loss based on the difference information, and alternately training a neural network and a discriminant network based on the network loss.
The embodiment trains the neural network by combining a large amount of data with a discrimination network, wherein the discrimination network discriminates whether an input image is a real image or a prediction characteristic diagram output by the neural network, determines network loss based on a discrimination result output by the discrimination network, and trains the neural network and the discrimination network alternately by using the network loss to obtain the trained neural network.
As shown in fig. 6, based on the embodiment shown in fig. 1, step 106 may include the following steps:
step 1061, performing normalization processing on the pixel value of each feature point in the house feature map to obtain an updated house feature map.
Optionally, the house feature map may be displayed as an image in img format, at this time, a binarization process is performed, and R, G, and B values of each feature point pixel are respectively binarized, in this embodiment, by binarizing, 255 is set for each pixel in img where R, G, and B values are greater than 200, otherwise 0 is set, for example, a pixel value of one feature point pixel is (223,242,108), at this time, three values are respectively binarized to obtain (255, 0); the present embodiment normalizes the pixel values in the house characteristic map by normalization (e.g., binarization), and facilitates the function of identifying each room.
Step 1062, determining the function of each room in the house structure image based on the updated house feature map and the house structure image.
In this embodiment, the house feature map may be divided into at least one region according to the color of each region in the house feature map subjected to the binarization processing, each region with a different color corresponds to one room, and the function of the room corresponding to each region may be determined according to the set correspondence between the color and the function.
In some alternative embodiments, step 1062 may include:
performing connected domain analysis on the house structure image, and determining a pixel area in the house characteristic diagram, which is updated correspondingly to each room and is included in the house structure image;
the function of each room is determined based on the pixel region corresponding to each room included in the house structure image.
Connected Component (Connected Component) generally refers to an image area (Blob) composed of foreground pixels having the same pixel value and adjacent positions in an image. Connected Component Analysis (Connected Component Labeling) refers to finding and Labeling each Connected Component in an image.
In the embodiment, the house structure image and the house characteristic map are divided into at least one pixel area corresponding to each other based on the pixel value through connected component analysis, wherein the pixel value in each pixel area is the same, and the pixel value corresponding to each room is determined by combining the corresponding relationship between the pixel area and the room included in the house structure image, that is, the function of each room can be determined.
Optionally, determining the function of each room based on the pixel area corresponding to each room included in the house structure image includes:
determining a function of the room based on a set pixel value in response to a preset proportion or a preset number of pixel values in a pixel region corresponding to the room being equal to any one of the set pixel values; and in response to the fact that the preset proportion or the preset number of pixel values in the pixel area corresponding to the room are not equal to any set pixel value, the function of the room is not determined.
The functions comprise at least one, and each function corresponds to a set pixel value; for example, the correspondence as shown in table 1.
In this embodiment, for a pixel region corresponding to each room, an image corresponding to the region position in the house feature map is extracted, and the number or proportion of pixels in the pixel region, which are equal to RGB values corresponding to a plurality of functions, is counted respectively, for example, the number of pixels corresponding to each function is less than 20, and it is determined that the room does not belong to the 6 functions, otherwise, the function corresponding to the room is determined as the function corresponding to the room corresponding to the function with the largest number of pixels.
Any of the house function identification methods provided by the embodiments of the present disclosure may be performed by any suitable device having data processing capability, including but not limited to: terminal equipment, a server and the like. Alternatively, any of the house function and room identification methods provided by the embodiments of the present disclosure may be executed by a processor, for example, the processor may execute any of the house function and room identification methods mentioned in the embodiments of the present disclosure by calling a corresponding instruction stored in a memory. And will not be described in detail below.
Exemplary devices
Fig. 7 is a schematic structural diagram of an identification device for a house function room provided in an embodiment of the present disclosure. As shown in fig. 7, the apparatus of this embodiment includes:
and the visualization module 71 is used for obtaining a house structure image based on the house structure coordinate information.
Wherein the house structure image comprises at least one room.
And the image processing module 72 is configured to process the house structure image by using a neural network to obtain a house feature map.
And a function determining module 73, configured to determine a function of each room in the house structure image based on the information of each feature point in the house feature map.
Based on the identification device for the house function room provided by the embodiment of the disclosure, a house structure image is obtained based on house coordinate information; the house structure image comprises at least one room; processing the house structure image by using a neural network to obtain a house characteristic diagram; determining a function of each room in the house structure image based on information of each feature point in the house feature map; according to the embodiment of the invention, the function of each room is automatically identified through the structure diagram of the house, so that the drawing time of the house type diagram is shortened, and the drawing difficulty is reduced.
In some optional embodiments, the house coordinate information includes at least coordinate information of walls constituting the house;
the visualization module 71 is specifically configured to perform coordinate system conversion on the coordinate information of the wall, convert the coordinate information of the wall into an image coordinate system, and obtain a house structure image.
The house structure image represents a wall by a line segment, and a room in the house structure image is represented by a polygon formed by a plurality of line segments.
In this embodiment, the house usually includes a plurality of rooms, and each room is surrounded by at least three walls, so the coordinate information of the house in this embodiment at least includes coordinate information of all the walls constituting the house, and all the walls can be displayed in the image by converting the coordinate information of all the walls, and the display form can be a line segment, for example, one wall is represented by one line segment.
Optionally, the visualization module 71 is specifically configured to obtain at least two pieces of corner point coordinate information based on the coordinate information of the wall, and determine the length of the longest side in the structure of the house based on the at least two pieces of corner point coordinate information; and converting the coordinate information of the wall into an image coordinate system by combining the length of the longest edge in the structure of the house to obtain the house structure image.
In some optional embodiments, the apparatus provided in this embodiment further includes:
and the training module is used for training the neural network by utilizing the marked house image set.
The marked house image set comprises a plurality of marked house structure images and corresponding marked feature graphs.
In this embodiment, in order to obtain the house characteristic map, before the neural network is used, the neural network needs to be trained, and optionally, the neural network and a discriminant network may be formed into a generation countermeasure network for training.
Optionally, the training module is specifically configured to input the marked house structure image in the marked house image set into a neural network, so as to obtain a prediction feature map; inputting the prediction characteristic diagram and the labeled characteristic diagram corresponding to the labeled house structure image into a discrimination network, and acquiring difference information between the prediction characteristic diagram and the labeled characteristic diagram based on the discrimination network; and obtaining network loss based on the difference information, and alternately training a neural network and a discriminant network based on the network loss.
In some optional embodiments, the function determining module 73 includes:
the normalization unit is used for normalizing the pixel value of each feature point in the house feature map to obtain an updated house feature map;
and the room function unit is used for determining the function of each room in the house structure image based on the updated house characteristic diagram and the house structure image.
Optionally, the room function unit is specifically configured to perform connected domain analysis on the house structure image, and determine a pixel area corresponding to each room included in the house structure image; the function of each room is determined based on the pixel area in the updated house characteristic map corresponding to each room included in the house structure image.
Optionally, the room function unit, when determining the function of each room based on the pixel region corresponding to each room included in the house structure image, is configured to determine the function of the room based on a set pixel value in response to a preset ratio or a preset number of pixel values in the pixel region corresponding to the room being equal to any one set pixel value; and in response to the fact that the preset proportion or the preset number of pixel values in the pixel area corresponding to the room are not equal to any set pixel value, the function of the room is not determined.
The functions comprise at least one, and each function corresponds to a set pixel value.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present disclosure is described with reference to fig. 8. The electronic device may be either or both of the first device 100 and the second device 200, or a stand-alone device separate from them that may communicate with the first device and the second device to receive the collected input signals therefrom.
FIG. 8 illustrates a block diagram of an electronic device in accordance with an embodiment of the disclosure.
As shown in fig. 8, the electronic device 80 includes one or more processors 81 and memory 82.
The processor 81 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 80 to perform desired functions.
Memory 82 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 81 to implement the house function room identification method of the various embodiments of the present disclosure described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 80 may further include: an input device 83 and an output device 84, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
For example, when the electronic device is the first device 100 or the second device 200, the input device 83 may be a microphone or a microphone array as described above for capturing an input signal of a sound source. When the electronic device is a stand-alone device, the input means 83 may be a communication network connector for receiving the acquired input signals from the first device 100 and the second device 200.
The input device 83 may also include, for example, a keyboard, a mouse, and the like.
The output device 84 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 84 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.
Of course, for simplicity, only some of the components of the electronic device 80 relevant to the present disclosure are shown in fig. 8, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device 80 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method of identification between house functions according to various embodiments of the present disclosure described in the "exemplary methods" section of this specification above.
The computer program product may write program code for carrying out operations for embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method for identification between house functions according to various embodiments of the present disclosure described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The block diagrams of devices, apparatuses, systems referred to in this disclosure are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
It is also noted that in the devices, apparatuses, and methods of the present disclosure, each component or step can be decomposed and/or recombined. These decompositions and/or recombinations are to be considered equivalents of the present disclosure.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.

Claims (16)

1. A house function room identification method is characterized by comprising the following steps:
acquiring a house structure image based on the house structure coordinate information; the house structure image comprises at least one room;
processing the house structure image by using a neural network to obtain a house characteristic diagram; each feature point in the house feature map corresponds to a pixel value;
carrying out standardization processing on the pixel value of each feature point in the house feature map to obtain an updated house feature map;
determining the function of each room in the house structure image based on the updated house feature map and the house structure image.
2. The method according to claim 1, wherein the house structure coordinate information includes at least coordinate information of walls constituting a house;
based on the house structure coordinate information, obtaining a house structure image, including:
converting the coordinate information of the wall into a coordinate system of an image to obtain the image of the house structure; wherein, the house structure image represents walls by line segments, and the house structure image represents rooms by polygons formed by a plurality of line segments.
3. The method of claim 2, wherein the converting the coordinate information of the wall into a coordinate system and converting the coordinate information of the wall into an image coordinate system to obtain the image of the building structure comprises:
obtaining at least two pieces of corner point coordinate information based on the coordinate information of the wall, and determining the length of the longest side in the structure of the house based on the at least two pieces of corner point coordinate information;
and converting the coordinate information of the wall into an image coordinate system by combining the length of the longest edge in the house structure to obtain the house structure image.
4. The method of claim 1, wherein before processing the image of the house structure using the neural network to obtain the house feature map, further comprising:
and training the neural network by using an annotated house image set, wherein the annotated house image set comprises a plurality of annotated house structure images and corresponding annotated feature maps.
5. The method of claim 4, wherein training the neural network with the set of annotated house images comprises:
inputting the marked house structure images in the marked house image set into the neural network to obtain a prediction characteristic diagram;
inputting the predicted feature map and an annotated feature map corresponding to the annotated house structure image into a discrimination network, and obtaining difference information between the predicted feature map and the annotated feature map based on the discrimination network;
obtaining a network loss based on the difference information, and alternately training the neural network and the discriminant network based on the network loss.
6. The method according to any one of claims 1 to 5, wherein determining the function of each room in the house structural image based on the updated house characteristic map and the house structural image comprises:
performing connected domain analysis on the house structure image, and determining that each room included in the house structure image corresponds to a pixel area in the updated house feature map;
determining the function of each room based on the pixel area corresponding to each room included in the house structure image.
7. The method of claim 6, wherein determining the function of each room included in the image of the building structure based on the pixel region corresponding to the each room comprises:
in response to a preset proportion or a preset number of pixel values in a pixel region corresponding to the room being equal to any one set pixel value, determining a function of the room based on the set pixel value; the functions comprise at least one, and each function corresponds to a set pixel value;
and in response to that the preset proportion or the preset number of pixel values in the pixel area corresponding to the room are not equal to any set pixel value, not determining the function of the room.
8. An apparatus for identifying a room between house functions, comprising:
the visualization module is used for obtaining a house structure image based on the house structure coordinate information; the house structure image comprises at least one room;
the image processing module is used for processing the house structure image by utilizing a neural network to obtain a house characteristic diagram; each feature point in the house feature map corresponds to a pixel value;
a function determination module for determining a function of each room in the house structure image based on information of each feature point in the house feature map;
the function determination module includes:
the normalization unit is used for normalizing the pixel value of each feature point in the house feature map to obtain an updated house feature map;
a room function unit, configured to determine a function of each room in the house structure image based on the updated house feature map and the house structure image.
9. The apparatus according to claim 8, wherein the house coordinate information includes at least coordinate information of walls constituting a house;
the visualization module is specifically configured to perform coordinate system conversion on the coordinate information of the wall, convert the coordinate information of the wall into an image coordinate system, and obtain the house structure image; wherein, the house structure image represents walls by line segments, and the house structure image represents rooms by polygons formed by a plurality of line segments.
10. The apparatus according to claim 9, wherein the visualization module is specifically configured to obtain at least two corner point coordinate information based on the coordinate information of the wall, and determine a length of a longest side of the structure of the house based on the at least two corner point coordinate information; and converting the coordinate information of the wall into an image coordinate system by combining the length of the longest edge in the house structure to obtain the house structure image.
11. The apparatus of claim 8, further comprising:
and the training module is used for training the neural network by utilizing an annotated house image set, wherein the annotated house image set comprises a plurality of annotated house structure images and corresponding annotated feature maps thereof.
12. The apparatus according to claim 11, wherein the training module is specifically configured to input the labeled house structure image in the labeled house image set into the neural network to obtain a predicted feature map; inputting the predicted feature map and an annotated feature map corresponding to the annotated house structure image into a discrimination network, and obtaining difference information between the predicted feature map and the annotated feature map based on the discrimination network; obtaining a network loss based on the difference information, and alternately training the neural network and the discriminant network based on the network loss.
13. The apparatus according to any of the claims 8 to 12, wherein the room function unit is specifically configured to perform connected component analysis on the building structure image to determine that each room included in the building structure image corresponds to a pixel region in the updated building feature map; determining the function of each room based on the pixel area corresponding to each room included in the house structure image.
14. The apparatus according to claim 13, wherein the room function unit, when determining the function of each room included in the house structure image based on the pixel area corresponding to the each room, is configured to determine the function of the room based on any one of the set pixel values in response to a preset ratio or a preset number of pixel values in the pixel area corresponding to the room being equal to the set pixel value; the functions comprise at least one, and each function corresponds to a set pixel value; and in response to that the preset proportion or the preset number of pixel values in the pixel area corresponding to the room are not equal to any set pixel value, not determining the function of the room.
15. A computer-readable storage medium, characterized in that the storage medium stores a computer program for executing the method for identifying a house function room according to any one of claims 1 to 7.
16. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method for identifying a house function according to any one of claims 1 to 7.
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