CN110895813A - Resident building data extraction mechanism - Google Patents
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- CN110895813A CN110895813A CN201910051449.3A CN201910051449A CN110895813A CN 110895813 A CN110895813 A CN 110895813A CN 201910051449 A CN201910051449 A CN 201910051449A CN 110895813 A CN110895813 A CN 110895813A
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- 230000000877 morphologic effect Effects 0.000 claims description 21
- 230000011218 segmentation Effects 0.000 claims description 15
- 238000003708 edge detection Methods 0.000 claims description 14
- 238000009826 distribution Methods 0.000 claims description 9
- 230000007797 corrosion Effects 0.000 claims description 6
- 238000005260 corrosion Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 6
- 230000003628 erosive effect Effects 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000003709 image segmentation Methods 0.000 claims description 3
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- G—PHYSICS
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- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
Abstract
The invention relates to a resident building data extraction mechanism, comprising: the wireless acquisition equipment is arranged in front of the monitored residential building and is used for acquiring image data of the residential building so as to acquire wireless acquisition images of the whole residential building; and the automatic filtering equipment is connected with the wireless acquisition equipment and is used for receiving the wireless acquisition image and equally dividing the wireless acquisition image into blocks with corresponding block sizes based on the distance between the signal-to-noise ratio grade of the wireless acquisition image and the preset lower limit signal-to-noise ratio grade. The residential building data extraction mechanism is convenient to operate and simple in design. On the basis of data processing of the acquired high-definition images, the lighting condition of a room at a certain moment can be accurately detected, so that the data deviation of the acquired vacancy rate is overcome, and important reference data is provided for the urban housing management department.
Description
Technical Field
The invention relates to the field of buildings, in particular to a resident building data extraction mechanism.
Background
Buildings can be classified into the following categories by nature of use:
residential building: it is a building for a family or an individual to live in for a long period of time, and can be divided into two types of houses and dormitories (the houses are divided into ordinary residential buildings, high-grade apartments and villas, and the dormitories are divided into single-worker dormitories and student dormitories).
Public buildings: it refers to non-productive buildings for people to shop, work, study, medical treatment, travel, sports, etc., such as office buildings, shops, hotels, movie theaters, gymnasiums, exhibition halls, hospitals, etc.
Industrial construction: it is a building for industrial production or directly serving industrial production, such as factory building, warehouse, etc.
Agricultural construction: refers to buildings for agricultural production or directly serving agricultural production, such as a storage bin, a farm and the like.
Disclosure of Invention
The invention aims to provide a residential building data extraction mechanism, which comprises: the wireless acquisition equipment is arranged in front of the monitored residential building and used for acquiring image data of the residential building so as to acquire wireless acquisition images of the whole residential building.
More specifically, in the residential building data extraction facility, the facility further includes: the automatic filtering equipment is connected with the wireless acquisition equipment and used for receiving the wireless acquisition image, equally dividing the wireless acquisition image into blocks with corresponding block sizes based on the distance between the signal-to-noise ratio grade of the wireless acquisition image and a preset lower limit signal-to-noise ratio grade, selecting corresponding filtering processing with different strengths for each block based on the noise degree of the block to obtain filtering blocks, and combining the obtained filtering blocks to obtain a combined filtering image; in the automatic filtering device, the farther the signal-to-noise ratio level of the wirelessly acquired image is from a preset lower signal-to-noise ratio level, the larger the corresponding block into which the wirelessly acquired image is equally divided, and in the automatic filtering device, the larger the noise degree of the block is for each block, the larger the filtering processing strength is selected.
More specifically, in the residential building data extraction facility, the facility further includes: and the edge detection equipment is connected with the automatic filtering equipment and used for receiving the combined filtering image, acquiring each gray value of each pixel point of the combined filtering image, and determining whether each pixel point is an edge pixel point or not based on the deviation degree from the gray value of each pixel point to the gray value mean value of each pixel point in the neighborhood.
More specifically, in the residential building data extraction facility, the facility further includes: the region dividing equipment is connected with the edge detection equipment and used for receiving each edge pixel point in the merged filtering image, forming an edge region in the merged filtering image based on each edge pixel point and each surrounding pixel point, the distance between each edge pixel point and each surrounding pixel point is less than or equal to the number of preset pixel points, and taking the merged filtering image after the edge region is stripped as a non-edge region; the distribution condition detection equipment is connected with the region division equipment and is used for receiving each edge region and each non-edge region in the merged filtering image, determining the number of noise pixel points in each edge region and the number of noise pixel points in each non-edge region, accumulating the number of noise pixel points in each edge region to obtain the total number of edge noise points, and accumulating the number of noise pixel points in each non-edge region to obtain the total number of non-edge noise points; and the processing trigger equipment is connected with the distribution condition detection equipment, calculates the total number of all pixel points forming each edge region in the combined filtering image to obtain the total number of edge pixel points, calculates the total number of all pixel points forming each non-edge region in the combined filtering image to obtain the total number of non-edge pixel points, divides the total number of edge noise points by the total number of edge pixel points to obtain an edge noise ratio, divides the total number of non-edge noise points by the total number of non-edge pixel points to obtain a non-edge noise ratio, sends an edge processing trigger signal when the edge noise ratio exceeds the non-edge noise ratio, and sends a content processing trigger signal when the edge noise ratio is smaller than the non-edge noise ratio.
The invention at least has the following four key inventions:
(1) on the basis of the targeted image processing, adopting an vacancy rate analysis device for determining the vacancy rate of the residential building based on the number of the plurality of light areas in the restored filtered image and the number of preset rooms of the residential building;
(2) by utilizing the characteristics that the performance of eliminating edge noise by corrosion expansion processing is superior to that of opening and closing operation processing and the detail of a protection target by the opening and closing operation processing is superior to that of the corrosion expansion processing, a morphological processing mechanism based on the image content characteristics is established on the basis of analyzing the noise distribution condition of the image;
(3) determining the sizes of image segmentation blocks which are in direct proportion to the maximum amplitude of the noise in the image to obtain the segmentation blocks with the same size;
(4) in order to save the amount of computation for image processing, four levels of shading of four corner image regions of an image are averaged to obtain the level of shading of the entire image, and whether to perform restoration filtering processing is determined based on the level of shading of the entire image to improve the usability of the image.
The residential building data extraction mechanism is convenient to operate and simple in design. On the basis of data processing of the acquired high-definition images, the lighting condition of a room at a certain moment can be accurately detected, so that the data deviation of the acquired vacancy rate is overcome, and important reference data is provided for the urban housing management department.
Drawings
Fig. 1 is a monitoring scene diagram of a residential building applied to the residential building data extraction mechanism of the present invention.
Detailed Description
The vacancy rate is the ratio of the vacant house area to the total house area at a certain time. According to the international common practice, the vacancy rate of the commodity house is between 5% and-10% which is a reasonable area, the supply and demand of the commodity house are balanced, and the healthy development of national economy is facilitated; the vacancy rate is between 10% and-20% and is a vacancy dangerous area, certain measures are taken to increase the sales force of the commodity house so as to ensure the normal development of the real estate market and the normal operation of national economy; the vacancy rate is more than 20 percent, which is a severe pressure accumulation area of the commodity house.
In the prior art, the vacancy rate of a residential building is one of important data concerned by an urban housing management department, and is generally performed in an artificial statistics mode, however, the artificial statistics mode is low in efficiency and poor in real-time performance, and cannot accurately detect the lighting condition of a room at a certain moment, so that a certain data deviation exists in the obtained vacancy rate.
In order to solve the defects, the invention provides a residential building data extraction mechanism which can effectively solve the corresponding technical problems.
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Fig. 1 is a monitoring scene diagram of a residential building applied to the residential building data extraction mechanism of the present invention.
A residential building data extraction mechanism, comprising:
the wireless acquisition equipment is arranged in front of the monitored residential building and used for acquiring image data of the residential building so as to acquire wireless acquisition images of the whole residential building.
Next, a detailed description will be given of a specific configuration of the residential building data extraction means according to the present invention.
The resident building data extraction mechanism can further comprise:
the automatic filtering equipment is connected with the wireless acquisition equipment and used for receiving the wireless acquisition image, equally dividing the wireless acquisition image into blocks with corresponding block sizes based on the distance between the signal-to-noise ratio grade of the wireless acquisition image and a preset lower limit signal-to-noise ratio grade, selecting corresponding filtering processing with different strengths for each block based on the noise degree of the block to obtain filtering blocks, and combining the obtained filtering blocks to obtain a combined filtering image; in the automatic filtering device, the farther the signal-to-noise ratio level of the wirelessly acquired image is from a preset lower signal-to-noise ratio level, the larger the corresponding block into which the wirelessly acquired image is equally divided, and in the automatic filtering device, the larger the noise degree of the block is for each block, the larger the filtering processing strength is selected.
The resident building data extraction mechanism can further comprise:
and the edge detection equipment is connected with the automatic filtering equipment and used for receiving the combined filtering image, acquiring each gray value of each pixel point of the combined filtering image, and determining whether each pixel point is an edge pixel point or not based on the deviation degree from the gray value of each pixel point to the gray value mean value of each pixel point in the neighborhood.
The resident building data extraction mechanism can further comprise:
the region dividing equipment is connected with the edge detection equipment and used for receiving each edge pixel point in the merged filtering image, forming an edge region in the merged filtering image based on each edge pixel point and each surrounding pixel point, the distance between each edge pixel point and each surrounding pixel point is less than or equal to the number of preset pixel points, and taking the merged filtering image after the edge region is stripped as a non-edge region;
the distribution condition detection equipment is connected with the region division equipment and is used for receiving each edge region and each non-edge region in the merged filtering image, determining the number of noise pixel points in each edge region and the number of noise pixel points in each non-edge region, accumulating the number of noise pixel points in each edge region to obtain the total number of edge noise points, and accumulating the number of noise pixel points in each non-edge region to obtain the total number of non-edge noise points;
a processing trigger device connected to the distribution condition detection device, for calculating the total number of pixels constituting each edge region in the merged filtered image to obtain the total number of edge pixels, calculating the total number of pixels constituting each non-edge region in the merged filtered image to obtain the total number of non-edge pixels, dividing the total number of edge noise points by the total number of edge pixels to obtain an edge noise ratio, dividing the total number of non-edge noise points by the total number of non-edge pixels to obtain a non-edge noise ratio, and when the edge noise ratio exceeds the non-edge noise ratio, sending an edge processing trigger signal, and when the edge noise ratio is smaller than the non-edge noise ratio, sending a content processing trigger signal;
the erosion expansion equipment is respectively connected with the edge detection equipment and the processing triggering equipment and is used for carrying out erosion-first expansion processing and then expansion processing on the combined filtering image when the edge processing triggering signal is received so as to obtain an erosion expansion image;
the opening and closing operation device is respectively connected with the edge detection device and the processing trigger device and is used for executing first opening operation and then closing operation processing on the combined filtering image when receiving the content processing trigger signal so as to obtain an opening and closing operation image;
the signal combining equipment is respectively connected with the corrosion expansion equipment and the opening and closing operation equipment and is used for taking the received corrosion expansion image or the received opening and closing operation image as a morphological processing image and outputting the morphological processing image;
the size selection device is connected with the signal combination device and used for receiving the morphological processing image, analyzing the amplitude of the noise in the morphological processing image to obtain the maximum amplitude in the morphological processing image, and determining the sizes of the image segmentation blocks which are in proportion to the maximum amplitude to obtain the segmentation blocks with the same sizes;
the area selection device is connected with the size selection device and used for receiving the segmentation blocks with the same size and selecting four segmentation blocks positioned at four corner positions in the morphological processing image from the segmentation blocks in the morphological processing image as four corner segmentation blocks;
the regional identification equipment is respectively connected with the size selection equipment and the regional selection equipment and is used for receiving the four segmentation blocks, acquiring the brightness of each corner segmentation block, and performing average calculation on the four brightness of the four corner image regions so as to output the acquired average as a target brightness;
the command starting device is connected with the partitioned area identification device and used for receiving the target brightness, sending a command with lower brightness when the target brightness is darker than a preset brightness value and sending a command with higher brightness when the target brightness is lighter than the preset brightness value;
the restoration filtering device is respectively connected with the sub-area identification device and the command starting device, and is used for executing restoration filtering processing on the morphological processing image to obtain a restoration filtering image when the command with the low brightness degree is received, and is also used for skipping executing the restoration filtering processing on the morphological processing image when the command with the high brightness degree is received, and outputting the morphological processing image as the restoration filtering image;
the object identification equipment is connected with the restoration filtering equipment and used for receiving the restoration filtering image, taking the pixel points with the luminance value exceeding the limit in the restoration filtering image as lighting pixel points, and acquiring a plurality of lighting areas in the restoration filtering image based on the distribution condition of each lighting pixel point in the restoration filtering image;
and the vacancy rate analysis equipment is connected with the object identification equipment and is used for determining the vacancy rate of the residential building based on the number of the plurality of light areas in the restored filtered image and the number of preset rooms of the residential building.
The resident building data extraction mechanism can further comprise:
and the signal-to-noise ratio improving device is connected with the automatic filtering device and is used for executing signal-to-noise ratio improving operation on the wireless acquisition image when the signal-to-noise ratio grade of the wireless acquisition image is smaller than the preset lower signal-to-noise ratio grade before the automatic filtering device executes automatic filtering on the wireless acquisition image, replacing the wireless acquisition image with the wireless acquisition image after executing the signal-to-noise ratio improving operation and inputting the wireless acquisition image into the automatic filtering device, and not executing the signal-to-noise ratio improving operation on the wireless acquisition image when the signal-to-noise ratio grade of the wireless acquisition image is larger than or equal to the preset lower signal-to-.
In the resident building data extraction mechanism:
the size selection device, the area selection device, the sub-area identification device, the command starting device and the restoration filtering device are respectively realized by different models of PAL devices.
The resident building data extraction mechanism can further comprise:
and the SDRAM storage device is connected with the automatic filtering device and is used for pre-storing the preset lower limit signal-to-noise ratio grade.
In the resident building data extraction mechanism:
and when the deviation degree from the gray value of the pixel point in the combined filtering image to the gray value mean value of each pixel point in the neighborhood exceeds a limit amount, the edge detection equipment determines that the pixel point is an edge pixel point.
In the resident building data extraction mechanism:
and when the deviation degree from the gray value of the pixel point in the combined filtering image to the gray value mean value of each pixel point in the neighborhood does not exceed the limit, the edge detection equipment determines that the pixel point is a non-edge pixel point.
In addition, the SDRAM: synchronous Dynamic Random Access Memory, wherein synchronization refers to that a Synchronous clock is required for Memory work, and internal command sending and data transmission are based on the Synchronous clock; dynamic means that the memory array needs to be refreshed continuously to ensure that data is not lost; random means that data are not stored linearly and sequentially, but data are read and written by freely appointing addresses. The clock frequency of the SDR SDRAM is the frequency of data storage. The operating voltage of the SDRAM is 3.3V.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A residential building data extraction mechanism, comprising:
the wireless acquisition equipment is arranged in front of the monitored residential building and used for acquiring image data of the residential building so as to acquire wireless acquisition images of the whole residential building.
2. The residential building data extraction mechanism as claimed in claim 1, further comprising:
the automatic filtering equipment is connected with the wireless acquisition equipment and used for receiving the wireless acquisition image, equally dividing the wireless acquisition image into blocks with corresponding block sizes based on the distance between the signal-to-noise ratio grade of the wireless acquisition image and a preset lower limit signal-to-noise ratio grade, selecting corresponding filtering processing with different strengths for each block based on the noise degree of the block to obtain filtering blocks, and combining the obtained filtering blocks to obtain a combined filtering image; in the automatic filtering device, the farther the signal-to-noise ratio level of the wirelessly acquired image is from a preset lower signal-to-noise ratio level, the larger the corresponding block into which the wirelessly acquired image is equally divided, and in the automatic filtering device, the larger the noise degree of the block is for each block, the larger the filtering processing strength is selected.
3. The residential building data extraction mechanism as claimed in claim 2, further comprising:
and the edge detection equipment is connected with the automatic filtering equipment and used for receiving the combined filtering image, acquiring each gray value of each pixel point of the combined filtering image, and determining whether each pixel point is an edge pixel point or not based on the deviation degree from the gray value of each pixel point to the gray value mean value of each pixel point in the neighborhood.
4. A residential building data extraction mechanism as claimed in claim 3, further comprising:
the region dividing equipment is connected with the edge detection equipment and used for receiving each edge pixel point in the merged filtering image, forming an edge region in the merged filtering image based on each edge pixel point and each surrounding pixel point, the distance between each edge pixel point and each surrounding pixel point is less than or equal to the number of preset pixel points, and taking the merged filtering image after the edge region is stripped as a non-edge region;
the distribution condition detection equipment is connected with the region division equipment and is used for receiving each edge region and each non-edge region in the merged filtering image, determining the number of noise pixel points in each edge region and the number of noise pixel points in each non-edge region, accumulating the number of noise pixel points in each edge region to obtain the total number of edge noise points, and accumulating the number of noise pixel points in each non-edge region to obtain the total number of non-edge noise points;
a processing trigger device connected to the distribution condition detection device, for calculating the total number of pixels constituting each edge region in the merged filtered image to obtain the total number of edge pixels, calculating the total number of pixels constituting each non-edge region in the merged filtered image to obtain the total number of non-edge pixels, dividing the total number of edge noise points by the total number of edge pixels to obtain an edge noise ratio, dividing the total number of non-edge noise points by the total number of non-edge pixels to obtain a non-edge noise ratio, and when the edge noise ratio exceeds the non-edge noise ratio, sending an edge processing trigger signal, and when the edge noise ratio is smaller than the non-edge noise ratio, sending a content processing trigger signal;
the erosion expansion equipment is respectively connected with the edge detection equipment and the processing triggering equipment and is used for carrying out erosion-first expansion processing and then expansion processing on the combined filtering image when the edge processing triggering signal is received so as to obtain an erosion expansion image;
the opening and closing operation device is respectively connected with the edge detection device and the processing trigger device and is used for executing first opening operation and then closing operation processing on the combined filtering image when receiving the content processing trigger signal so as to obtain an opening and closing operation image;
the signal combining equipment is respectively connected with the corrosion expansion equipment and the opening and closing operation equipment and is used for taking the received corrosion expansion image or the received opening and closing operation image as a morphological processing image and outputting the morphological processing image;
the size selection device is connected with the signal combination device and used for receiving the morphological processing image, analyzing the amplitude of the noise in the morphological processing image to obtain the maximum amplitude in the morphological processing image, and determining the sizes of the image segmentation blocks which are in proportion to the maximum amplitude to obtain the segmentation blocks with the same sizes;
the area selection device is connected with the size selection device and used for receiving the segmentation blocks with the same size and selecting four segmentation blocks positioned at four corner positions in the morphological processing image from the segmentation blocks in the morphological processing image as four corner segmentation blocks;
the regional identification equipment is respectively connected with the size selection equipment and the regional selection equipment and is used for receiving the four segmentation blocks, acquiring the brightness of each corner segmentation block, and performing average calculation on the four brightness of the four corner image regions so as to output the acquired average as a target brightness;
the command starting device is connected with the partitioned area identification device and used for receiving the target brightness, sending a command with lower brightness when the target brightness is darker than a preset brightness value and sending a command with higher brightness when the target brightness is lighter than the preset brightness value;
the restoration filtering device is respectively connected with the sub-area identification device and the command starting device, and is used for executing restoration filtering processing on the morphological processing image to obtain a restoration filtering image when the command with the low brightness degree is received, and is also used for skipping executing the restoration filtering processing on the morphological processing image when the command with the high brightness degree is received, and outputting the morphological processing image as the restoration filtering image;
the object identification equipment is connected with the restoration filtering equipment and used for receiving the restoration filtering image, taking the pixel points with the luminance value exceeding the limit in the restoration filtering image as lighting pixel points, and acquiring a plurality of lighting areas in the restoration filtering image based on the distribution condition of each lighting pixel point in the restoration filtering image;
and the vacancy rate analysis equipment is connected with the object identification equipment and is used for determining the vacancy rate of the residential building based on the number of the plurality of light areas in the restored filtered image and the number of preset rooms of the residential building.
5. The residential building data extraction mechanism as claimed in claim 4, further comprising:
and the signal-to-noise ratio improving device is connected with the automatic filtering device and is used for executing signal-to-noise ratio improving operation on the wireless acquisition image when the signal-to-noise ratio grade of the wireless acquisition image is smaller than the preset lower signal-to-noise ratio grade before the automatic filtering device executes automatic filtering on the wireless acquisition image, replacing the wireless acquisition image with the wireless acquisition image after executing the signal-to-noise ratio improving operation and inputting the wireless acquisition image into the automatic filtering device, and not executing the signal-to-noise ratio improving operation on the wireless acquisition image when the signal-to-noise ratio grade of the wireless acquisition image is larger than or equal to the preset lower signal-to-.
6. The residential building data extraction mechanism as claimed in claim 5, wherein:
the size selection device, the area selection device, the sub-area identification device, the command starting device and the restoration filtering device are respectively realized by different models of PAL devices.
7. The residential building data extraction mechanism as claimed in claim 6, further comprising:
and the SDRAM storage device is connected with the automatic filtering device and is used for pre-storing the preset lower limit signal-to-noise ratio grade.
8. The residential building data extraction mechanism as claimed in claim 7, wherein:
and when the deviation degree from the gray value of the pixel point in the combined filtering image to the gray value mean value of each pixel point in the neighborhood exceeds a limit amount, the edge detection equipment determines that the pixel point is an edge pixel point.
9. The residential building data extraction mechanism as claimed in claim 8, wherein:
and when the deviation degree from the gray value of the pixel point in the combined filtering image to the gray value mean value of each pixel point in the neighborhood does not exceed the limit, the edge detection equipment determines that the pixel point is a non-edge pixel point.
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Application publication date: 20200320 |