CN110728235A - House type area marking method and device - Google Patents
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Abstract
The invention provides a house type area marking method and device, and relates to the technical field of area marking. The house type area marking method comprises the following steps: acquiring a family pattern to be identified from a family pattern database; sending the area division plan to a pre-established area identification model to obtain an identification result of a functional area in the household pattern to be identified; and marking the functional area in the user type graph to be identified according to the identification result of the functional area. According to the house type area marking method and device, the area division plane graph is sent to the pre-established area identification model, the identification result of the functional area in the house type graph to be identified is obtained, the functional area in the house type graph to be identified is marked according to the identification result, and the technical effects of improving the measurement accuracy and saving the labor cost are achieved.
Description
Technical Field
The invention relates to the technical field of area marking, in particular to a house type area marking method and device.
Background
Currently, in a home decoration design process, before a house type area in a house type diagram is subjected to decoration design, the house type area in the house type diagram needs to be acquired first, and the operation of acquiring the house type area is usually realized through manual measurement, so that the measurement accuracy is low and the labor cost is wasted.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for marking a residential area, so as to solve the technical problems of low measurement accuracy and labor cost waste.
In a first aspect, an embodiment of the present invention provides a method for marking a residential area, where the method includes the following steps:
acquiring a to-be-identified house type graph from a house type graph database, wherein the to-be-identified house type graph comprises a region division plan of each room in a plurality of rooms;
sending the area division plan to a pre-established area identification model to obtain an identification result of a functional area in the to-be-identified prototype, wherein the area identification model is a neural network model for identifying one functional area of the to-be-identified prototype, and the functional area comprises one of the following areas: dining rooms, bedrooms, living rooms, toilets;
and marking the functional area in the user type graph to be identified according to the identification result of the functional area.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where after obtaining a user type graph to be identified from a user type graph database, the method further includes:
sampling the area division plane graph contained in the house type graph to be identified to obtain a sampled area division plane graph;
and carrying out normalization processing on the sampled area division plane graph to obtain a normalized area division plane graph.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the method further includes:
acquiring a predetermined number of house type graphs marked with functional areas as samples, and dividing the samples into training samples and testing samples;
establishing a network structure of a convolutional neural network, wherein the network structure comprises a first sub-network and a second sub-network connected behind the first sub-network;
sending the training samples to the first sub-network and the second sub-network for training;
and sending the test sample to the trained first sub-network and the trained second sub-network, and testing and adjusting the first sub-network and the second sub-network to obtain the pre-established area identification model.
With reference to the second possible implementation manner of the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the first sub-network includes two convolutional layers connected to each other, and is configured to extract vertex information and side information of each room;
the second sub-network comprises a recursive sub-network layer, a convolutional layer and a full-connection layer which are connected in sequence; the recursive sub-network layer is configured to encode the vertex information and the side information as tensors; the convolutional layer and the fully-connected layer are used to map the tensor into a vector representing a functional region.
With reference to the second possible implementation manner of the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the sending the test sample to the trained first sub-network and second sub-network, and testing and tuning the first sub-network and the second sub-network to obtain the pre-established area identification model includes:
repeatedly sending the test sample to the trained first sub-network and the trained second sub-network, and correspondingly obtaining a plurality of test values;
averaging the plurality of test values as a test result;
when the accuracy of the test result does not meet the predetermined accuracy threshold, retraining, testing, and tuning the first sub-network and the second sub-network of the convolutional neural network until the accuracy of the test result meets the predetermined accuracy threshold.
In a second aspect, an embodiment of the present invention further provides a device for marking a residential area, where the device includes:
the device comprises a to-be-identified house type graph acquisition module, a to-be-identified house type graph generation module and a to-be-identified house type graph identification module, wherein the to-be-identified house type graph comprises a region division plan of each room in a plurality of rooms;
a sending module, configured to send the area division plan to a pre-established area identification model to obtain an identification result of a functional area in the to-be-identified floor plan, where the area identification model is a neural network model that identifies one of the functional areas in the to-be-identified floor plan, and the functional area includes one of: dining rooms, bedrooms, living rooms, toilets;
and the marking module is used for marking the functional area in the to-be-identified floor plan according to the identification result of the functional area.
With reference to the second aspect, an embodiment of the present invention provides a first possible implementation manner of the second aspect, where the apparatus further includes:
the sampling module is used for sampling the area division plan included in the house type image to be identified after the house type image to be identified acquisition module to obtain a sampled area division plan;
and the normalization module is used for performing normalization processing on the sampled area division plane graph to obtain a normalized area division plane graph.
With reference to the first possible implementation manner of the second aspect, an embodiment of the present invention provides a second possible implementation manner of the second aspect, where the apparatus further includes:
the device comprises a family pattern acquisition module, a training module and a testing module, wherein the family pattern acquisition module is used for acquiring a predetermined number of family patterns marked with functional areas as samples and dividing the samples into training samples and testing samples;
the network structure establishing module is used for establishing a network structure of the convolutional neural network, and the network structure comprises a first sub-network and a second sub-network connected behind the first sub-network;
the training module is used for sending the training samples to the first sub-network and the second sub-network for training;
and the testing and tuning module is used for sending the testing sample to the trained first sub-network and the trained second sub-network, and testing and tuning the first sub-network and the second sub-network to obtain the pre-established area identification model.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes: a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method described above.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to implement the method described above.
The embodiment of the invention has the following beneficial effects: according to the house type area marking method and device provided by the embodiment of the invention, the area division plane graph in the house type graph to be identified is firstly obtained from the house type graph database and sent to the pre-established area identification model, the identification result of the functional area in the house type graph to be identified is obtained, and the functional area in the house type graph to be identified is marked according to the identification result of the functional area, so that the technical effects of improving the measurement accuracy and saving the labor cost are achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for marking a residential area according to an embodiment of the present invention;
fig. 2 is a flowchart of another method for marking a residential area according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for obtaining a pre-established region identification model according to an embodiment of the present invention;
fig. 4 is a block diagram of a home zone tagging apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Now, in the home installation process, it is first necessary to perform area division on a house type diagram, and in general, the house type diagram mainly includes areas such as a restaurant, a bedroom, a living room, a toilet, and the like, and then function definition (as a restaurant, a bedroom, a living room, a toilet, and the like) is performed by measurement for each of the divided areas. In the house decoration design process, before the house type area in the house type graph is subjected to decoration design, the house type area in the house type graph needs to be acquired first, and the operation of acquiring the house type area is usually realized through manual measurement, so that the measurement accuracy is low and the labor cost is wasted. Accordingly, embodiments of the present invention provide a method and an apparatus for marking a residential area, so as to alleviate the above problems.
In order to facilitate understanding of the present embodiment, a method for marking a house type area disclosed in the present embodiment is first described in detail.
In one possible embodiment, the present invention provides a method for marking a residential zone. Fig. 1 is a flowchart of a method for marking a residential area, which includes the following steps:
step S102: and obtaining the user pattern graph to be identified from the user pattern graph database.
Wherein the house type graph to be identified comprises a region division plan of each room in a plurality of rooms.
Step S104: and sending the area division plan to a pre-established area identification model to obtain an identification result of the functional area in the to-be-identified household pattern.
The area identification model is a neural network model for identifying one functional area of the to-be-identified pattern, and the functional area comprises one of the following areas: dining room, bedroom, living room, bathroom.
It should be noted that the neural network model can identify functional areas, such as a restaurant area, a bathroom area, a living room area, a bedroom area, and the like.
Step S106: and marking the functional area in the user type graph to be identified according to the identification result of the functional area.
The embodiment of the invention has the following beneficial effects: according to the house type area marking method, the area division plane graph in the house type graph to be identified is obtained from the house type graph database and sent to the pre-established area identification model, the identification result of the functional area in the house type graph to be identified is obtained, and the functional area in the house type graph to be identified is marked according to the identification result of the functional area, so that the technical effects of improving the measurement accuracy and saving the labor cost are achieved.
In practical use, after acquiring the house type graph to be identified from the house type graph database, the area division plan graph included in the house type graph to be identified needs to be sequentially subjected to sampling processing and normalization processing to obtain a normalized area division plan graph, and therefore, in order to describe the sampling processing and the normalization processing in more detail, an embodiment of the present invention shows a flowchart of another house type area marking method in fig. 2, where the method includes the following steps:
step S202: and obtaining the user pattern graph to be identified from the user pattern graph database.
Step S204: and sampling the area division plane graph contained in the house type graph to be identified to obtain a sampled area division plane graph.
Wherein the region-divided plan is subjected to sampling processing to obtain a region-divided plan with a reduced pixel size.
Step S206: and carrying out normalization processing on the sampled area division plane graph to obtain a normalized area division plane graph.
The area division plane graph is subjected to normalization processing, and the pixel value of each pixel of the area division plane graph is normalized to be 1 in mean square error and 0 in mean value.
Further, in the embodiment of the present invention, a standard deviation normalization technique is used for performing normalization processing on the area division plane graph. The standard deviation normalization technique is a technique for normalizing each item of data of the area-division plan based on the mean square deviation and the mean of the area-division plan.
Step S208: and sending the area division plan to a pre-established area identification model to obtain an identification result of the functional area in the to-be-identified household pattern.
Step S210: and marking the functional area in the user type graph to be identified according to the identification result of the functional area.
In actual use, before sending the area division plan to the pre-established area identification model to obtain the identification result of the functional area in the home graph to be identified, the pre-established area identification model needs to be obtained, so in order to describe the process of obtaining the pre-established area identification model in more detail, fig. 3 shows a flowchart of a method for obtaining the pre-established area identification model provided by the embodiment of the present invention, and the method includes the following steps:
step S302: a predetermined number of house type graphs marked with functional areas are obtained as samples, and the samples are divided into training samples and testing samples.
For example, the house pattern marked with the functional area is randomly divided into 5 samples, 4 samples are used as training samples, and the remaining 1 sample is used as a test sample. It should be particularly noted that, in the embodiment of the present invention, the distribution of the number of copies of the training sample and the test sample is only one of the distribution cases of the number of copies, and there may be other cases as well, and the embodiment of the present invention does not limit this.
Step S304: a network structure of a convolutional neural network is established, which comprises a first subnetwork and a second subnetwork connected behind the first subnetwork.
Wherein the first sub-network includes two convolutional layers connected to each other for extracting vertex information and side information of each of the rooms; the second sub-network comprises a recursive sub-network layer, a convolutional layer and a full-connection layer which are connected in sequence; the recursive sub-network layer is configured to encode the vertex information and the side information as tensors; the convolutional layer and the fully-connected layer are used to map the tensor into a vector representing a functional region.
Step S306: and sending the training samples to the first sub-network and the second sub-network for training.
Step S308: and sending the test sample to the trained first sub-network and the trained second sub-network, and testing and adjusting the first sub-network and the second sub-network to obtain the pre-established area identification model.
The process of step S308 is implemented by the following steps:
1) repeatedly sending the test sample to the trained first sub-network and the trained second sub-network, and correspondingly obtaining a plurality of test values;
2) averaging the plurality of test values as a test result;
3) when the accuracy of the test result does not meet the predetermined accuracy threshold, retraining, testing, and tuning the first sub-network and the second sub-network of the convolutional neural network until the accuracy of the test result meets the predetermined accuracy threshold.
And when the accuracy of the test result meets a preset accuracy threshold, finishing the test and tuning operation to obtain a pre-established region identification model.
In summary, the house type area marking method according to the embodiment of the present invention obtains the recognition result of the functional area in the house type image to be recognized by sending the area division plan to the pre-established area recognition model, and marks the functional area in the house type image to be recognized according to the recognition result, thereby achieving the technical effects of improving the measurement accuracy and saving the labor cost.
In another possible implementation manner, corresponding to the method for marking a house type area provided in the foregoing implementation manner, an embodiment of the present invention further provides a house type area marking apparatus, and fig. 4 is a block diagram of a structure of the house type area marking apparatus provided in the embodiment of the present invention, as shown in fig. 4, the apparatus includes:
a to-be-identified house type graph obtaining module 401, configured to obtain a to-be-identified house type graph from a house type graph database, where the to-be-identified house type graph includes a region division plan of each room in a plurality of rooms;
a sending module 402, configured to send the area division plan to a pre-established area identification model to obtain an identification result of a functional area in the to-be-identified house type diagram, where the area identification model is a neural network model that identifies one functional area of the to-be-identified house type diagram, and the functional area includes one of the following: dining rooms, bedrooms, living rooms, toilets;
a marking module 403, configured to mark the functional area in the to-be-identified floor plan according to the identification result of the functional area.
In practical use, the apparatus further comprises:
the sampling module is used for sampling the area division plan included in the house type image to be identified after the house type image to be identified acquisition module to obtain a sampled area division plan;
and the normalization module is used for performing normalization processing on the sampled area division plane graph to obtain a normalized area division plane graph.
In practical use, the apparatus further comprises:
the device comprises a family pattern acquisition module, a training module and a testing module, wherein the family pattern acquisition module is used for acquiring a predetermined number of family patterns marked with functional areas as samples and dividing the samples into training samples and testing samples;
the network structure establishing module is used for establishing a network structure of the convolutional neural network, and the network structure comprises a first sub-network and a second sub-network connected behind the first sub-network;
the training module is used for sending the training samples to the first sub-network and the second sub-network for training;
and the testing and tuning module is used for sending the testing sample to the trained first sub-network and the trained second sub-network, and testing and tuning the first sub-network and the second sub-network to obtain the pre-established area identification model.
In yet another possible implementation manner, an embodiment of the present invention further provides a server, and fig. 5 shows a schematic structural diagram of the server provided in the embodiment of the present invention, and referring to fig. 5, the server includes: a processor 500, a memory 501, a bus 502 and a communication interface 503, wherein the processor 500, the memory 501, the communication interface 503 are connected through the bus 502; the processor 500 is used to execute executable modules, such as computer programs, stored in the memory 501.
Wherein the memory 501 stores computer-executable instructions that can be executed by the processor 500, the processor 500 executes the computer-executable instructions to implement the methods described above.
Further, the memory 501 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 503 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
Bus 502 can be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
The memory 501 is used for storing a program, and the processor 500 executes the program after receiving a program execution instruction, and the method for marking a user area disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 500, or implemented by the processor 500.
Further, processor 500 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 500. The Processor 500 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 501, and the processor 500 reads the information in the memory 501, and completes the steps of the method in combination with the hardware thereof.
In yet another possible implementation, the embodiment of the present invention further provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method described above.
The house type area marking device provided by the embodiment of the invention has the same technical characteristics as the house type area marking method provided by the embodiment, so that the same technical problems can be solved, and the same technical effects are achieved.
The computer program product of the method and the apparatus for marking a subscriber type area provided in the embodiment of the present invention includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, and will not be described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases for those skilled in the art.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a ReaD-Only Memory (ROM), a RanDom Access Memory (RAM), a magnetic disk, or an optical disk.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that the following embodiments are merely illustrative of the present invention, and not restrictive, and the scope of the present invention is not limited thereto: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for marking a residential area, the method comprising the steps of:
acquiring a to-be-identified house type graph from a house type graph database, wherein the to-be-identified house type graph comprises a region division plan of each room in a plurality of rooms;
sending the area division plan to a pre-established area identification model to obtain an identification result of a functional area in the to-be-identified prototype, wherein the area identification model is a neural network model for identifying one functional area of the to-be-identified prototype, and the functional area comprises one of the following areas: dining rooms, bedrooms, living rooms, toilets;
and marking the functional area in the user type graph to be identified according to the identification result of the functional area.
2. The method of claim 1, wherein after obtaining the layout to be identified from the layout database, the method further comprises:
sampling the area division plane graph contained in the house type graph to be identified to obtain a sampled area division plane graph;
and carrying out normalization processing on the sampled area division plane graph to obtain a normalized area division plane graph.
3. The method of claim 2, further comprising:
acquiring a predetermined number of house type graphs marked with functional areas as samples, and dividing the samples into training samples and testing samples;
establishing a network structure of a convolutional neural network, wherein the network structure comprises a first sub-network and a second sub-network connected behind the first sub-network;
sending the training samples to the first sub-network and the second sub-network for training;
and sending the test sample to the trained first sub-network and the trained second sub-network, and testing and adjusting the first sub-network and the second sub-network to obtain the pre-established area identification model.
4. The method of claim 3, wherein the first subnetwork comprises two convolutional layers connected to each other for extracting vertex information and side information for each of the rooms;
the second sub-network comprises a recursive sub-network layer, a convolutional layer and a full-connection layer which are connected in sequence; the recursive sub-network layer is configured to encode the vertex information and the side information as tensors; the convolutional layer and the fully-connected layer are used to map the tensor into a vector representing a functional region.
5. The method of claim 3, wherein the step of sending the test sample to the trained first and second sub-networks, testing and optimizing the first and second sub-networks, and obtaining the pre-established area identification model comprises:
repeatedly sending the test sample to the trained first sub-network and the trained second sub-network, and correspondingly obtaining a plurality of test values;
averaging the plurality of test values as a test result;
when the accuracy of the test result does not meet the predetermined accuracy threshold, retraining, testing, and tuning the first sub-network and the second sub-network of the convolutional neural network until the accuracy of the test result meets the predetermined accuracy threshold.
6. A home zone marking apparatus, comprising:
the device comprises a to-be-identified house type graph acquisition module, a to-be-identified house type graph generation module and a to-be-identified house type graph identification module, wherein the to-be-identified house type graph comprises a region division plan of each room in a plurality of rooms;
a sending module, configured to send the area division plan to a pre-established area identification model to obtain an identification result of a functional area in the to-be-identified floor plan, where the area identification model is a neural network model that identifies one of the functional areas in the to-be-identified floor plan, and the functional area includes one of: dining rooms, bedrooms, living rooms, toilets;
and the marking module is used for marking the functional area in the to-be-identified floor plan according to the identification result of the functional area.
7. The apparatus of claim 6, further comprising:
the sampling module is used for sampling the area division plan included in the house type image to be identified after the house type image to be identified acquisition module to obtain a sampled area division plan;
and the normalization module is used for performing normalization processing on the sampled area division plane graph to obtain a normalized area division plane graph.
8. The apparatus of claim 7, further comprising:
the device comprises a family pattern acquisition module, a training module and a testing module, wherein the family pattern acquisition module is used for acquiring a predetermined number of family patterns marked with functional areas as samples and dividing the samples into training samples and testing samples;
the network structure establishing module is used for establishing a network structure of the convolutional neural network, and the network structure comprises a first sub-network and a second sub-network connected behind the first sub-network;
the training module is used for sending the training samples to the first sub-network and the second sub-network for training;
and the testing and tuning module is used for sending the testing sample to the trained first sub-network and the trained second sub-network, and testing and tuning the first sub-network and the second sub-network to obtain the pre-established area identification model.
9. A server comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 5.
10. A computer-readable storage medium having stored thereon computer-executable instructions that, when invoked and executed by a processor, cause the processor to implement the method of any of claims 1 to 5.
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