CN110889801A - Decontamination optimization method for camera of smoke stove system and smoke stove system - Google Patents
Decontamination optimization method for camera of smoke stove system and smoke stove system Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 55
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- 238000005202 decontamination Methods 0.000 title claims abstract description 28
- 230000003588 decontaminative effect Effects 0.000 title claims abstract description 28
- 238000005457 optimization Methods 0.000 title claims abstract description 24
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- 231100000719 pollutant Toxicity 0.000 claims abstract description 51
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- 230000008569 process Effects 0.000 claims description 7
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- 230000002159 abnormal effect Effects 0.000 description 7
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- 238000012935 Averaging Methods 0.000 description 2
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- 238000001514 detection method Methods 0.000 description 2
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- 239000000463 material Substances 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
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Abstract
The invention discloses a decontamination optimization method for a camera of a smoke stove system, which comprises the following steps: a condition judging step, namely acquiring a current image after a camera is started, acquiring image data of the current image, judging whether the current image meets a preset condition or not, and entering a dirt degree determining step if the current image meets the preset condition; a step of determining the degree of contamination, which is to compare the image data with pre-acquired original data to evaluate the image quality of the current image and determine whether the degree of contamination of the camera lens exceeds a threshold value, and if not, enter the step of determining the degree of contamination; determining a pollutant, namely determining a dirty position of the pollutant on a camera lens; and a decontamination optimization step, namely processing the image of the area corresponding to the dirty position shot by the camera according to the dirty position to optimize the image quality. By adopting the method, the application problem of the camera in the smoke stove system can be solved, and a user is reminded to clean the camera in time or optimize the image quality through an algorithm.
Description
Technical Field
The invention relates to the field of kitchen ranges with a range hood, in particular to a decontamination optimization method for a camera of a kitchen range system with a range hood. The invention also discloses a smoke stove system.
Background
With the increasingly mature of intelligent technology, more and more household appliances begin to carry intelligent modules so as to expand the intelligent functions of the household appliances. In cigarette machine cooking utensils field, more typical intelligent object uses including the camera, not only can realize eating the discernment of material, culinary art state with the help of image recognition technology, can also pass through image information monitoring smog concentration, has promoted the intelligent degree of cigarette kitchen product greatly.
However, a large amount of oil smoke is easily generated in a kitchen environment, and the corresponding oil smoke adheres to a camera lens, thereby affecting the shooting performance of the camera. Simultaneously, in order to guarantee the normal work of camera, need often remind the user clearance, and the clearance too frequently will seriously influence the user experience of user to cigarette kitchen product.
Disclosure of Invention
The invention aims to solve the technical problems in the prior art, and provides a camera decontamination optimization method for a smoke stove system, which can intelligently detect whether a camera lens is contaminated or not, and remind a user of cleaning in time according to the contamination degree of the camera lens, or optimize the shooting quality through an image algorithm under the condition of no serious contamination. The invention also discloses a smoke stove system which is provided with the camera and can intelligently detect the dirt degree of the camera lens, so that a user is reminded to clean or automatically optimize the shooting quality, and the applicability of the camera in the smoke stove system is improved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a decontamination optimization method for a camera of a smoke stove system comprises the following steps: a condition judging step, namely acquiring a current image after a camera is started, acquiring image data of the current image, judging whether the current image meets a preset condition or not, and entering a dirt degree determining step if the current image meets the preset condition; a step of determining the degree of contamination, which is to compare the image data with pre-acquired original data to evaluate the image quality of the current image and determine whether the degree of contamination of the camera lens exceeds a threshold value, and if not, enter the step of determining the degree of contamination; determining a pollutant, namely determining a dirty position of the pollutant on a camera lens; and a decontamination optimization step, namely processing the image of the area corresponding to the dirty position shot by the camera according to the dirty position to optimize the image quality.
Further, the contaminant determination step includes: performing color space conversion on the current image, and dividing the converted image into M × N sub-area images through block processing; and respectively carrying out a first processing flow and a second processing flow on the M by N sub-region images.
Further, the first processing flow includes: and decomposing the sub-area images into three-channel gray images Yi, Ui and Vi respectively, performing DCT (discrete cosine transformation) on the gray images Yi, Ui and Vi, performing threshold correction on the transformed images, performing DCT inverse transformation on the corrected images Yi ', Ui ' and Vi ', and calculating to obtain an average value PSNR _ mean of the peak signal-to-noise ratio of the corresponding sub-area image i and an average value SSIM _ mean of the structural similarity, wherein i represents the ith sub-area image.
Further, the contaminant determination step further includes: the type of contaminant on the camera lens is determined.
Further, the second process includes: respectively carrying out color histogram calculation on the subarea images to obtain corresponding subarea histogram data; performing correlation calculation on the histogram data of the sub-regions and preset pollutant histogram data to respectively obtain similarity coefficients Ci of the corresponding sub-region image i and the corresponding pollutants; and determining the type and the position of the pollutant according to the similarity coefficient Ci and the corresponding sub-area image.
Further, the decontamination optimizing step comprises: determining whether the dirty position meets the conditions of PSNR _ mean < Tpsnr, SSIM _ mean < Tsisim and Ci > Tc according to the type of the pollutant and the dirty position; if so, recalculating the gray value of the corresponding pixel point of the corresponding region and replacing the image of the region through interpolation processing; if not, retaining the gray value of the corresponding pixel point in the corresponding area and judging that the area is not influenced by the pollutants; wherein Tpsnr is a preset peak signal-to-noise ratio threshold, Tssim is a preset structural similarity threshold, and Tc is a preset similarity coefficient threshold.
Further, the method further comprises: and an initialization step, wherein a single-frame image is collected as a reference image after the camera is started for the first time, the reference image is preprocessed to obtain and record original data containing a hearth region, and the first area of the hearth region in the reference image is S1.
Further, the preset conditions include that at least a part of the current image includes a hearth region, and a second area of the hearth region in the current image is S2, where S2 is equal to or less than α × S1, 0.5 is equal to or less than α and is equal to or less than 1, and α is a preset area occlusion coefficient.
Further, the pre-processing comprises: positioning treatment of the hearth region and extraction treatment of the hearth region.
The invention also discloses a smoke stove system:
the utility model provides a cigarette kitchen system, includes cigarette machine and cooking utensils, is provided with the camera in the cigarette kitchen system, reminds module and decontamination optimization module, and the camera is used for gathering the image of culinary art in-process, and decontamination optimization module is used for handling in order to optimize image quality to the image that the dirty position that the camera was shot corresponds the region, reminds the module to be used for sending camera unusual warning and/or the clean warning of camera to the user.
The technical scheme of the invention has the following beneficial effects:
the camera decontamination optimization method for the smoke stove system can identify the type and the dirty position of pollutants, remind a user of cleaning under the condition of serious pollution, and replace a dirty area by using a corresponding image algorithm under the condition of not serious pollution, so that the times of cleaning the camera by the user are reduced, and the shooting quality is improved. The invention also discloses a smoke stove system, which not only can remind a user of cleaning the camera in time, but also can optimize the shooting quality through the algorithm of the smoke stove system, and has higher intelligence compared with the current smoke stove system.
Drawings
FIG. 1 is a block diagram of the steps of one embodiment of the method of the present invention;
FIG. 2 is a schematic flow chart of one embodiment of the method of the present invention;
FIG. 3 is a schematic flow chart of yet another embodiment of the method of the present invention;
FIG. 4 is a schematic diagram of region image replacement in one embodiment of the method of the present invention;
fig. 5 is a block diagram of a smoke cooker system according to the present invention.
Detailed Description
The technical solution provided by the present invention is described in more detail by the following figures and specific embodiments:
fig. 1 discloses a block diagram of steps of one embodiment of the method, and the embodiment discloses a camera decontamination optimization method of a smoke stove system, which comprises the following steps:
step 101: a condition judging step, namely acquiring a current image after a camera is started, acquiring image data of the current image, judging whether the current image meets a preset condition or not, and entering a dirt degree determining step if the current image meets the preset condition;
step 102: a step of determining the degree of contamination, which is to compare the image data with pre-acquired original data to evaluate the image quality of the current image and determine whether the degree of contamination of the camera lens exceeds a threshold value, and if not, enter the step of determining the degree of contamination;
step 103: determining a pollutant, namely determining a dirty position of the pollutant on a camera lens;
step 104: and a decontamination optimization step, namely processing the image of the area corresponding to the dirty position shot by the camera according to the dirty position to optimize the image quality.
In this embodiment, a decontamination optimization method for a camera of a smoke stove system is disclosed, which includes a condition determination step, a contamination degree determination step, a pollutant determination step, and a decontamination optimization step. When the smoke stove system needs to work, the camera is started to acquire a current image, and whether abnormal conditions such as shielding or covering by a large amount of fog exist in the camera is determined through a condition judging step, so that the condition that the camera is in an abnormal working environment is eliminated; and through the step of determining the degree of contamination, whether the user needs to clean the camera can be judged, under the condition that the influence of the contamination is sufficiently eliminated through the algorithm, the step of determining the contamination and the step of optimizing the contamination are carried out, the shooting quality of the image is optimized through the algorithm, and therefore the times of cleaning the camera by the user are reduced.
In one embodiment of the method of the present invention, the step of determining the condition further includes: and if not, sending camera abnormity reminding to the user. In this embodiment, the camera may be shielded by a large area, or covered by a large amount of water mist (such as when the pot cover is uncovered during the cooking process), so that the camera cannot normally work, at this time, the user should be reminded that the camera is in an abnormal state, corresponding adverse factors cannot be eliminated through an algorithm, and the camera cannot judge whether the camera is dirty or not at this time, so that the user should be reminded to eliminate the influence of the working environment, so as to recover the normal working state. In this embodiment, can carry out intelligence to the user and remind, distinguish the camera unusually, avoid the erroneous judgement of the dirty degree of camera.
In one embodiment of the method of the present invention, the step of determining the degree of contamination further comprises: and if so, sending a camera cleaning prompt to the user. In this embodiment, if it is found through detection that the degree of contamination of the camera has exceeded the threshold, it is difficult to compensate for the influence of the contamination on image capturing through the algorithm, and at this time, the user should be timely reminded to clean the camera, so that the accuracy of capturing parameters and image recognition is prevented from being influenced. Compared with the prior art, the scheme in the embodiment can autonomously judge the contamination degree of the camera and timely remind a user of the contamination degree of the camera, so that the problem that the user cannot determine the contamination degree of the camera in the past is solved.
Fig. 2 is a schematic flow chart of one embodiment of the method according to the present invention. The embodiment discloses a specific process of the camera decontamination optimization method of the smoke stove system, which comprises the following steps:
The embodiment discloses a specific flow of the method, and the method in the embodiment not only can remind a user of eliminating environmental interference or cleaning a camera in time according to different situations, but also can replace the image at the position corresponding to the pollutant through an algorithm under the condition that the pollution degree is not serious, so that the times of cleaning the camera by the user can be reduced, and the image shot by the camera is optimized to avoid influencing the accuracy of image identification.
As an embodiment of the method of the present invention, the contaminant determination step includes:
performing color space conversion on the current image, and dividing the converted image into M × N sub-area images through block processing;
and respectively carrying out a first processing flow and a second processing flow on the M x N sub-region images.
In this embodiment, in order to determine the dirty position of the pollutant on the lens of the camera, color space conversion and segmentation need to be performed on the current image, and the dirty position of the corresponding pollutant can be obtained through the subsequent first processing flow and the subsequent second processing flow. Preferably, the current image is converted to YUV color space to further determine the type of contaminant in conjunction with a subsequent algorithm.
As an embodiment of the method of the present invention, the first processing flow includes:
and decomposing the sub-area images into three-channel gray images Yi, Ui and Vi respectively, performing DCT (discrete cosine transformation) transformation on the gray images Yi, Ui and Vi, performing threshold correction on the transformed images, performing DCT inverse transformation on the corrected images Yi ', Ui and Vi', and calculating to obtain an average value PSNR _ mean of the peak signal-to-noise ratio of the corresponding sub-area image i and an average value SSIM _ mean of the structural similarity, wherein i represents the ith sub-area image.
In the embodiment, each divided subarea image is respectively decomposed into three-channel gray images Yi, Ui and Vi, DCT transformation is carried out to obtain three DCT transformation images Yi ', Ui ' and Vi ' of the corresponding subarea image i, and threshold correction processing is respectively carried out on the three DCT transformation images of the subarea image i; performing inverse DCT transformation on the corrected Yi ', Ui ' and Vi ' to obtain three new images Yi1, Ui1 and Vi1 related to the subarea image i; respectively calculating PSNR (peak signal-to-noise ratio) parameters and SSIM (structural similarity) parameters of Yi1, PSNR parameters and SSIM parameters of Ui1 and PSNR parameters and SSIM parameters of Vi1, averaging three PSNR parameters corresponding to the sub-region image i to obtain an average value PSNR _ mean of the peak signal-to-noise ratio of the sub-region image i, averaging three SSIM parameters corresponding to the sub-region image i to obtain an average value SSIM _ mean of the peak signal-to-noise ratio of the sub-region image i, and using the average value SSIM _ mean for subsequent calculation and judgment.
In one embodiment of the method of the present invention, the contaminant determination step further comprises: determining a type of contaminant on the camera lens.
In this embodiment, the location of the contaminant can be determined approximately by comparing the image data with the raw data. In addition, in this embodiment, the pollutant determining step may determine not only a pollutant position of the pollutant on the lens of the camera, but also a type of the pollutant, so as to select a suitable algorithm and parameters according to the type of the pollutant to replace the image of the corresponding polluted area.
In one embodiment of the method of the present invention, the second process includes:
respectively carrying out color histogram calculation on the sub-region images to obtain corresponding sub-region histogram data;
performing correlation calculation on the histogram data of the sub-regions and preset pollutant histogram data to respectively obtain similarity coefficients Ci of the corresponding sub-region image i and the corresponding pollutants;
and determining the type and the position of the pollutant according to the similarity coefficient Ci and the corresponding subarea image.
In the embodiment, color histogram calculation is carried out on the subregion image i, and subregion histogram data of the subregion image i is obtained; performing correlation calculation on the sub-region histogram data of the corresponding sub-region image i and preset pollutant histogram data, comparing the similarity of the sub-region histogram data and the corresponding pollutant histogram data, and calculating to obtain a similarity coefficient Ci between the sub-region image i and the corresponding pollutant; the type and the dirty position of the pollutant can be determined according to the similarity coefficient Ci and the position of the corresponding sub-area image i. For example, after obtaining the histogram data of a sub-region image, performing correlation calculation on the histogram data of the sub-region and pre-measured histogram data of pollutants, wherein the pollutants comprise oil stain, smoke dust and/or water mist, and the like, respectively calculating and obtaining the similarity between the histogram data of the sub-region and the histogram data of the pollutants of each type, and taking the similarity with the highest similarity value as the similarity coefficient Ci between the image i of the sub-region and the corresponding pollutant, so as to determine the type of the pollutant corresponding to the position of the image i of the sub-region. For example, after calculation, in the sub-region image i, the oil stain histogram data with the highest similarity to the sub-region image i is taken as the similarity coefficient Ci corresponding to the similarity coefficient Ci.
In one embodiment of the method of the present invention, the decontamination optimizing step comprises:
determining whether a dirty position meets a condition based on the type of the contaminant and the dirty position
PSNR _ mean < Tpsnr, SSIM _ mean < Tssim, and Ci > Tc;
if so, recalculating the gray value of the corresponding pixel point of the corresponding region and replacing the image of the region through interpolation processing; if not, retaining the gray value of the corresponding pixel point in the corresponding area and judging that the area is not influenced by the pollutants; wherein Tpsnr is a preset peak signal-to-noise ratio threshold, Tssim is a preset structural similarity threshold, and Tc is a preset similarity coefficient threshold.
In this embodiment, the decontamination optimizing step further includes determining whether or not the corresponding image area at the corresponding sub-area image i needs to be replaced according to the condition, where the specific determination rule is
PSNR _ mean < Tpsnr, SSIM _ mean < Tssim, and Ci > Tc; wherein Tpsnr is a preset peak signal-to-noise ratio threshold, Tssim is a preset structural similarity threshold, and Tc is a preset similarity coefficient threshold. In this embodiment, the most likely type of the pollutant in the image i of the sub-region is determined according to Ci, and then the determination is performed by combining the above conditions, if the conditions are satisfied, it is indicated that the region is indeed a dirty position, and the type of the pollutant can also be determined by Ci, so that replacement is required. Because the different types of pollutants can cause the change of parameters such as image definition, gray scale and the like, the replacement of the image of the corresponding area at the polluted position can be specifically adjusted according to the type of the corresponding pollutant. In this embodiment, if the above conditions are all satisfied, it indicates that the image is indeed contaminated, and the image at that position needs to be replaced and optimized according to a specific contaminant type; if any of the images cannot be satisfied, the image is not polluted, replacement or optimization is not needed, and the original image is reserved and is not processed.
Fig. 3 is a schematic flow chart of another embodiment of the method of the present invention. In this embodiment, a camera decontamination optimization method for a smoke stove system is disclosed, which includes:
In this embodiment, steps 304 to 306 relate to a contamination level determining step, and through preset image parameters, the image data of the current image may be compared with the original data of the reference image, and it is checked whether the variation level of the corresponding image parameter of the current image exceeds a threshold value compared with that of the reference image, so as to characterize the contamination level of the lens of the camera, and preliminarily evaluate the image quality of the current image. Wherein the image parameters include, but are not limited to, sharpness, contrast, peak signal-to-noise ratio, and/or structural similarity of the image.
In this embodiment, step 308 involves a decontamination optimization step, wherein FIG. 4 is a schematic representation of a regional image replacement according to one embodiment of the method of the present invention. As shown in FIG. 4, after the areas with the dirty positions A0-A8 are determined and the type of the contaminants is greasy, the areas A0-A8 are replaced. In this embodiment, a linear interpolation replacement method is adopted to replace the image of the corresponding region at the dirty position, and specifically, the gray values of nine pixel points a0 to A8 need to be recalculated, where:
A0＝α0R1+β0R8，A1＝α1R2+β1R7，
A2＝α2R3+β2R6，A3＝α3R4+β3R5，
A4＝α4R2+β4R7+λR4+μR5，
A5＝α5R4+β5R5，A6＝α6R3+β6R6，
A7＝α7R2+β7R7，A8＝α8R1+β8R8；
ri is the mean gray level of the corresponding block, αi、βiAnd lambda and mu are linear coefficients related to the relative positions of the pixel points and the change rate of the gray value, and the gray value of the corresponding pixel point in the corresponding region at the dirty position is recalculated by the formula, so that the image of the corresponding region can be replaced under the condition that the dirt is not serious enough, and the influence of the pollutant is eliminated. For the uncontaminated block, only the gray values of the corresponding pixel points in the corresponding area need to be reserved, and replacement processing is not needed.
In one embodiment of the method of the present invention, the method further comprises:
and an initialization step, wherein a single-frame image is collected as a reference image after a camera is started for the first time, the reference image is preprocessed to obtain and record original data containing a hearth region, and the first area of the hearth region in the reference image is S1.
The method in this embodiment also involves an initialization step aimed at acquiring reference images, detecting the cooktop area and recording raw data for subsequent condition determination and fouling level determination. The reference image is acquired when the camera is started for the first time, the camera is generally used for starting a newly purchased smoke stove system for the first time, and the camera is not polluted yet, so that the reference image can contain a complete stove area, and corresponding original data is also most accurate and can be used for subsequent condition judgment and inspection. In this embodiment, the first area of the cooktop region in the reference image is S1.
In one embodiment of the method of the present invention, the preset conditions include:
the current image at least partially comprises the hearth region, the second area of the hearth region in the current image is S2, wherein S2 is not less than α is S1, 0.5 is not less than α is not less than 1, and α is a preset area occlusion coefficient.
The embodiment relates to a condition determining step, wherein after a user opens a smoke stove system again, whether a current image meets a preset condition can be determined according to a second area of a hearth region contained in the current image and a first area of the hearth region contained in the reference image, wherein the second area is equal to or less than α S1, the reason is that the first opening can theoretically shoot a complete hearth region, and in the case of opening again, if a camera is blocked or a large amount of water mist is suddenly formed on a lens, the camera cannot normally work, the area of the hearth region shot by the camera is necessarily less than S1, if only a small part of the hearth region is not detected, the subsequent steps can be continued, but if a large part of the hearth region is not detected, the camera is in an abnormal working state, and the user needs to be reminded to eliminate the influence of interference factors, so in the embodiment, whether the first area S and the second area S3825 are equal to or less than or than a preset coefficient 39595, and whether the camera is in a blocked working range of 39592.3875, wherein the preset working coefficient is set to be equal to be smaller than or less than 3.3.
In one embodiment of the method of the present invention, the preprocessing includes: positioning the cooktop area and extracting the cooktop area. The embodiment relates to an initialization step, wherein the preprocessing comprises positioning processing of a hearth region and extraction processing of the hearth region, namely the position of the hearth region in a reference image needs to be determined, the outline of the hearth region is determined to be extracted, and subsequent comparison is facilitated.
As shown in fig. 5, a block architecture diagram relating to a smoke cooker system according to the present invention. The invention also discloses a smoke stove system 500 which comprises a smoke machine and a stove, wherein a camera 501, a reminding module 502 and a decontamination optimizing module 503 are arranged in the smoke stove system, the camera 501 is used for collecting images in the cooking process, the decontamination optimizing module 503 is used for processing the images of the area corresponding to the dirty position shot by the camera to optimize the image quality, and the reminding module 502 is used for sending abnormal camera reminding and/or camera cleaning reminding to a user. In this embodiment, preferably, the camera 502 is disposed on the range hood to photograph the range top area downward, wherein the range top system is capable of recognizing the cooking food material by means of the camera 502 and determining the concentration of smoke generated by cooking according to the photographed image. Smoke stove system 500 in this embodiment can also send camera unusual when camera 502 is in abnormal operating condition and remind, also can send the clean warning of camera to the user when camera 502 needs artifical clearance simultaneously.
The above embodiments are merely illustrative of the design method of the present invention and are not intended to limit the scope of the present invention. The modifications and the conversion under the guidance of the technical scheme of the invention are all within the protection scope of the invention.
Claims (10)
1. A decontamination optimization method for a camera of a smoke stove system is characterized by comprising the following steps:
a condition judging step, namely acquiring a current image after a camera is started, acquiring image data of the current image, judging whether the current image meets a preset condition or not, and entering a dirt degree determining step if the current image meets the preset condition;
a step of determining the degree of contamination, which is to compare the image data with pre-acquired original data to evaluate the image quality of the current image and determine whether the degree of contamination of the camera lens exceeds a threshold value, and if not, enter the step of determining the degree of contamination;
determining a pollutant position of the pollutant on the camera lens;
and a decontamination optimization step, namely processing the image of the area corresponding to the dirty position shot by the camera according to the dirty position to optimize the image quality.
2. The method of claim 1, wherein the contaminant determination step comprises:
performing color space conversion on the current image, and dividing the converted image into M × N sub-area images through block processing;
and respectively carrying out a first processing flow and a second processing flow on the M x N sub-region images.
3. The method of claim 2, wherein the first process flow comprises:
and decomposing the sub-area images into three-channel gray images Yi, Ui and Vi respectively, performing DCT (discrete cosine transformation) transformation on the gray images Yi, Ui and Vi, performing threshold correction on the transformed images, performing DCT inverse transformation on the corrected images Yi ', Ui and Vi', and calculating to obtain an average value PSNR _ mean of the peak signal-to-noise ratio of the corresponding sub-area image i and an average value SSIM _ mean of the structural similarity, wherein i represents the ith sub-area image.
4. The method of claim 2 or 3, wherein the contaminant determination step further comprises: determining a type of contaminant on the camera lens.
5. The method of claim 4, wherein the second process comprises:
respectively carrying out color histogram calculation on the sub-region images to obtain corresponding sub-region histogram data;
performing correlation calculation on the histogram data of the sub-regions and preset pollutant histogram data to respectively obtain similarity coefficients Ci of the corresponding sub-region image i and the corresponding pollutants;
and determining the type and the position of the pollutant according to the similarity coefficient Ci and the corresponding subarea image.
6. The method of claim 5, wherein the decontamination optimization step comprises:
determining whether the dirty position meets the conditions of PSNR _ mean < Tpsnr, SSIM _ mean < Tsism and Ci > Tc according to the type of the pollutant and the dirty position;
if so, recalculating the gray value of the corresponding pixel point of the corresponding region and replacing the image of the region through interpolation processing; if not, retaining the gray value of the corresponding pixel point in the corresponding area and judging that the area is not influenced by the pollutants; wherein Tpsnr is a preset peak signal-to-noise ratio threshold, Tssim is a preset structural similarity threshold, and Tc is a preset similarity coefficient threshold.
7. The method of claim 1, further comprising:
and an initialization step, wherein a single-frame image is collected as a reference image after a camera is started for the first time, the reference image is preprocessed to obtain and record original data containing a hearth region, and the first area of the hearth region in the reference image is S1.
8. The method according to claim 7, wherein the preset conditions include:
the current image at least partially comprises the hearth region, the second area of the hearth region in the current image is S2, wherein S2 is not less than α is S1, 0.5 is not less than α is not less than 1, and α is a preset area occlusion coefficient.
9. The method according to claim 7 or 8, wherein the pre-processing comprises: positioning the cooktop area and extracting the cooktop area.
10. The utility model provides a cigarette kitchen system, includes cigarette machine and cooking utensils, its characterized in that, be provided with camera, warning module and decontamination optimization module in the cigarette kitchen system, the camera is used for gathering the image of culinary art in-process, decontamination optimization module is used for handling in order to optimize image quality to the image that the dirty position that the camera was shot corresponds the region, it is used for sending camera unusual warning and/or the clean warning of camera to remind to the user to remind the module.
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