CN112954229A - Method and equipment for adjusting light intensity of light supplementing lamp based on gray value and refrigerator - Google Patents

Method and equipment for adjusting light intensity of light supplementing lamp based on gray value and refrigerator Download PDF

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Publication number
CN112954229A
CN112954229A CN202110172098.9A CN202110172098A CN112954229A CN 112954229 A CN112954229 A CN 112954229A CN 202110172098 A CN202110172098 A CN 202110172098A CN 112954229 A CN112954229 A CN 112954229A
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gray value
gray
value
light
light intensity
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CN112954229B (en
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杨帆
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Qingdao Haier Refrigerator Co Ltd
Haier Smart Home Co Ltd
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Qingdao Haier Refrigerator Co Ltd
Haier Smart Home Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/70—Circuitry for compensating brightness variation in the scene
    • H04N23/74—Circuitry for compensating brightness variation in the scene by influencing the scene brightness using illuminating means
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03B—APPARATUS OR ARRANGEMENTS FOR TAKING PHOTOGRAPHS OR FOR PROJECTING OR VIEWING THEM; APPARATUS OR ARRANGEMENTS EMPLOYING ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ACCESSORIES THEREFOR
    • G03B15/00—Special procedures for taking photographs; Apparatus therefor
    • G03B15/02—Illuminating scene
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
    • H05B47/10—Controlling the light source
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
    • Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Cold Air Circulating Systems And Constructional Details In Refrigerators (AREA)
  • Devices That Are Associated With Refrigeration Equipment (AREA)

Abstract

The invention discloses a method and equipment for adjusting light intensity of a light supplementing lamp based on a gray value and a refrigerator, wherein the method comprises the following steps: training a relation curve E of a gray value difference Delta G of an ROI area of a shot image in a refrigerator and light intensity E of a light supplementing lamp to be supplemented with light to be obtained, wherein the gray value difference Delta G is a difference value between a gray value threshold G0 and an actual gray value G1 of the ROI area; acquiring an actual gray value G1 of the ROI of the image to be identified, and calculating a gray value difference delta G1 to G0-G1; and calculating the light intensity E1 of the light supplement lamp to be supplemented according to the delta G1 and the relation curve E ═ f (delta G), and adjusting the light intensity of the light supplement lamp according to the E1. Compared with the prior art, the method for adjusting the light intensity of the light supplement lamp based on the gray value can quickly adjust the light intensity of the light supplement lamp, so that the lighting in the refrigerator is sufficient, the quality of the shot image is improved, the identification rate of food materials in the shot image is improved, the identification result can be quickly fed back to a user, and the user experience is improved.

Description

Method and equipment for adjusting light intensity of light supplementing lamp based on gray value and refrigerator
Technical Field
The invention relates to the field of household appliances, in particular to a method and equipment for adjusting light intensity of a light supplementing lamp based on gray values and a refrigerator.
Background
With the development of science and technology, the intellectualization of the refrigerator also becomes a necessary trend. The intelligent refrigerator is a type of refrigerator which can intelligently control the refrigerator and intelligently manage food. Specifically, the mode of the refrigerator can be automatically changed, food can be always kept in the optimal storage state, a user can know the quantity and the fresh-keeping and quality-guaranteeing information of the food in the refrigerator at any time and any place through a mobile phone or a computer, a healthy recipe and nutrition taboo can be provided for the user, and the user can be reminded to supplement the food at regular time.
In order to achieve the above functions, it is an essential way to install a camera in the refrigerator to photograph the food material inside the refrigerator and to identify the food material image.
In image recognition, light is important when an image is shot, and for example, insufficient lighting can seriously affect the recognition rate of the image (a picture shot under the condition of insufficient lighting can show insufficient brightness, dark color, poor visual effect and even difficult recognition). The problem that lighting is insufficient in the image shot in the refrigerator can be specifically divided into various situations, for example, after the refrigerator door is closed, in order to save electricity, the light source in the refrigerator can be gradually weakened until the light source is completely darkened, or the light source is directly and quickly extinguished; even if the light source is normal, the situation that the lighting in the refrigerator is insufficient due to the fact that the food material blocks the light may occur.
Therefore, how to solve the problem that the recognition rate of the shot images is low due to insufficient lighting in the refrigerator is a problem to be solved at present.
Disclosure of Invention
The invention aims to provide a method and equipment for adjusting light intensity of a light supplementing lamp based on a gray value and a refrigerator.
In order to achieve one of the above objects, an embodiment of the present invention provides a method for adjusting light intensity of a fill-in lamp based on a gray-level value, the method including:
training a relation curve E of a gray value difference Delta G of an ROI area of a shot image in a refrigerator and light intensity E of a light supplementing lamp to be supplemented with light to be f (Delta G), wherein the gray value difference Delta G is a difference value of a gray value threshold G0 and an actual gray value G1 of the ROI area, and the shot image is an image shot from a fixed angle;
acquiring an actual gray value G1 of the ROI of the image to be identified, and calculating a gray value difference delta G1 to G0-G1;
and calculating the light intensity E1 of the light supplement lamp to be supplemented according to the delta G1 and the relation curve E ═ f (delta G), and adjusting the light intensity of the light supplement lamp according to the E1.
As a further improvement of an embodiment of the present invention, the method for acquiring the gray-level threshold value G0 includes:
the method comprises the steps of obtaining a plurality of shot images in the refrigerator under different lighting conditions, calculating the gray value of the ROI of each shot image and the identification rate of the corresponding image, obtaining a gray value interval corresponding to the image with the identification rate being larger than or equal to the identification rate threshold according to the identification rate threshold, and selecting a gray value from the gray value interval as the gray value threshold.
As a further improvement of an embodiment of the present invention, the "selecting a gray value from the gray value interval as the gray value threshold" includes:
and selecting the minimum value of the gray value interval as a gray value threshold value.
As a further improvement of an embodiment of the present invention, the "selecting a gray value from the gray value interval as the gray value threshold" includes:
and taking the gray value corresponding to the image with the highest identification rate as a gray value threshold value.
As a further improvement of an embodiment of the present invention, the method for calculating the actual gray value G1 includes:
calculating the gray value of each pixel point in the ROI area;
and calculating the average gray value of all the pixel points to obtain the actual gray value G1.
As a further improvement of an embodiment of the present invention, calculating the gray-level value of a pixel includes:
respectively acquiring gray values of the pixel points in R, G, B three channels;
the gray value of the pixel point is the average value of the gray values of the pixel point in the R, G, B three channels.
As a further improvement of an embodiment of the present invention, the method for calculating the actual gray value G1 includes:
selecting some pixel points at the edge and the center of the ROI area as target pixel points, and calculating the gray value of the target pixel points;
and calculating the average gray value of all target pixel points to obtain the actual gray value G1.
As a further improvement of an embodiment of the present invention, the relationship curve E ═ f (Δ G) includes the following constraints:
when Δ G < ═ 0, E ═ 0.
In order to achieve one of the above objects, an embodiment of the present invention provides an electronic device, which includes a memory and a processor, where the memory stores a computer program operable on the processor, and the processor implements any one of the steps in the method for adjusting the light intensity of a fill light based on a gray-level value when executing the program.
In order to achieve one of the above objects, an embodiment of the present invention provides a refrigerator, which includes the electronic device.
Compared with the prior art, the method for adjusting the light intensity of the light supplement lamp based on the gray value feeds back the lighting condition in the refrigerator through the gray value of the shot image, directly adjusts the light intensity of the light supplement lamp according to the relationship between the gray value difference value learned in advance and the light intensity of the light supplement lamp, enables the lighting condition in the refrigerator to be sufficient, improves the quality of the shot image and further improves the identification rate of food materials in the shot image. The method can quickly adjust the light intensity of the light supplement lamp, so that the recognition result can be quickly fed back to the user, and the user experience is improved.
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Fig. 1 is a schematic flow chart of a method for adjusting light intensity of a fill-in light based on gray scale values according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to specific embodiments shown in the drawings. These embodiments are not intended to limit the present invention, and structural, methodological, or functional changes made by those skilled in the art according to these embodiments are included in the scope of the present invention.
A special yoghourt area is arranged in the refrigerator, and yoghourt can be monitored in a centralized manner. Generally, a bottle seat is arranged in a special yoghourt area, and yoghourt is placed in the bottle seat. In the process of monitoring the yoghourt in the bottle seat, image shooting and identification are required to be carried out on a special yoghourt area. At present, pictures are generally taken and identified after the refrigerator door is closed, and insufficient lighting can occur due to various reasons: (1) the refrigerator door is closed and then the light source is closed; (2) the light source becomes dark gradually after the refrigerator door is closed; (3) after the refrigerator door is closed, although the light source is not closed, the light source is shielded by food materials. And insufficient lighting easily causes the reduction of the identification rate of food materials in the image.
In order to solve the problems, the invention provides a method for adjusting the light intensity of a light supplement lamp based on a gray value, which feeds back the lighting condition in a refrigerator through the gray value of a shot image, and directly adjusts the light intensity of the light supplement lamp according to the relationship between the gray value difference value learned in advance and the light intensity of the light supplement lamp, so that the lighting condition in the refrigerator is sufficient, the quality of the shot image is improved, and the identification rate of food materials in the shot image is further improved. The method can quickly adjust the light intensity of the light supplement lamp, so that the recognition result can be quickly fed back to the user, and the user experience is improved.
As shown in fig. 1, the method includes:
step S100: a relation curve E of a gray value difference Δ G of an ROI region of a shot image in the training refrigerator and a light intensity E of a fill-in light to be filled in is f (Δ G), wherein the gray value difference Δ G is a difference between a gray value threshold G0 and an actual gray value G1 of the ROI region, and the shot image is an image shot from a fixed angle.
Through the fixed angle of the fixed camera, image shooting and recognition are carried out on the yoghourt area of the refrigerator, so that the yoghourt area is monitored. In this process, the lighting condition in the refrigerator can be fed back by acquiring the gray value of an ROI (region of interest) region of the photographed image. For example, the influence of the gray value on the identification rate can be determined by acquiring a plurality of shot images in a refrigerator under different lighting conditions and calculating the gray value of the ROI region of each shot image and the identification rate of the corresponding image, and the influence of the current lighting level on the identification rate can be reflected by the gray value.
It should be noted that, for the selection of the ROI region, various factors need to be considered. For example, the difference of the food material types and the outer packages easily causes the deviation of the gray value, so that the bottle seat region cannot be selected as the ROI region, or the region which can reflect light cannot be selected as the ROI region, and the region which can reflect the food material cannot be selected as the ROI region. Therefore, a portion with relatively simple color brightness, no reflection and no reflection can be selected as the ROI area, and of course, some frosting treatment can be preferably performed on the ROI area.
The gray-level difference Δ G is a difference between the gray-level threshold G0 and the actual gray-level value G1 of the ROI region, i.e., Δ G — G0 to G1. The method for acquiring the gray value threshold G0 comprises the following steps:
the method comprises the steps of obtaining a plurality of shot images in the refrigerator under different lighting conditions, calculating the gray value of the ROI of each shot image and the identification rate of the corresponding image, and obtaining the gray value interval corresponding to the image with the identification rate larger than or equal to the identification rate threshold according to the identification rate threshold. Assuming that the threshold of the recognition rate is 98%, all high-recognition-rate images with the recognition rate greater than or equal to 98% are selected from the images shot in the front under different lighting conditions, and the gray values of the high-recognition-rate images are acquired to obtain a gray value interval. Then, a gray value is selected from the gray value interval as a gray value threshold G0, and the gray value threshold G0 has the following functions: when the gray value of the image to be recognized is larger than the gray value threshold value, the recognition rate of the image to be recognized is judged to meet the requirement (namely, the recognition rate is larger than or equal to the recognition rate threshold value).
Specifically, the method for selecting the gray value threshold G0 may include, but is not limited to, the following methods: (1) selecting the minimum value of the gray value interval as a gray value threshold; (2) and selecting the gray value corresponding to the image with the highest identification rate as a gray value threshold.
There are many methods for calculating the actual gray value G1 of the ROI region, and two methods with a relatively high calculation speed are listed below.
The method comprises the following steps: and calculating the gray value of each pixel point in the ROI area, and then averaging the gray values of all the pixel points to obtain the actual gray value G1.
In the invention, the gray value of one pixel point can be obtained by calculating the average value of the gray values of three color channels of red (R), green (G) and blue (B). Of course, other methods may be used.
The second method comprises the following steps: selecting some pixel points at the edge and the center of the ROI area as target pixel points, calculating the gray value of the target pixel points, and then averaging the gray values of all the target pixel points to obtain the actual gray value G1.
In actual tests, the time for calculating the actual gray value of the ROI region using method one is within several tens of milliseconds, but the time for using method two is negligible. Meanwhile, the accuracy of the actual gray value of the ROI calculated by the two methods is very small.
G0 and G1 can be obtained by the above method, and the difference Δ G between the gray-scale values G0 and G1 can be obtained.
According to the invention, the problem of insufficient lighting in the refrigerator is solved by adding the light supplement lamp. How can the light supplement lamp be rapidly adjusted, so that the lighting in the refrigerator can be sufficiently ensured to ensure the quality of the photographed image? The method is realized by establishing a relation curve E of the gray value difference value delta G and the light intensity E of the light supplementing lamp, wherein the relation curve E is f (delta G).
In an actual operation process, the relationship curve E ═ f (Δ G) can be obtained through a training mode. Specifically, under the condition of different gray value differences, the light intensity of the light supplement lamp which needs to be adjusted is obtained, a plurality of coordinate points which take the gray value difference as an x axis and the light intensity of the light supplement lamp as a y axis are obtained, curve fitting is performed on the coordinate points, and therefore the relation curve E is obtained as f (Δ G).
And f (delta G) is used for indicating that the light intensity E of the light supplementing lamp is adjusted through the delta G value, so that the lighting in the refrigerator is sufficient, and the recognition rate of the shot image reaches the recognition rate threshold value.
For example, when Δ G is Δ G1, E (Δ G1) is E1 obtained from the above-mentioned relation E (Δ G), and the light intensity of the fill-in lamp is adjusted to E1.
Note that, the relationship curve E ═ f (Δ G) includes the following limitation conditions:
when Δ G < ═ 0, E ═ 0.
When Δ G is G0-G1 is 0, that is, G0 is G1, it indicates that the lighting in the refrigerator is sufficient and light supplement is not necessary, and therefore, E is 0 at this time.
It should be noted that, in the present invention, it is preferable that both the camera and the light supplement lamp are disposed at the top of the special yogurt area and are located on the same horizontal plane, and the light supplement lamp is a plurality of LED lamps distributed around the camera according to a certain rule. Specifically, a plurality of light filling lamps are rectangular or circular and are distributed, and the camera is arranged in the middle of the rectangle or the circle. And a plurality of light filling lamps emit downward parallel light beams, so that shadow is reduced or prevented while light filling is performed.
Further, the distance between the light supplement lamp and the camera is preferably within the range of 20-40 cm.
Step S200: acquiring an actual gray value G1 of the ROI of the image to be identified, and calculating a gray value difference delta G1 which is G0-G1.
The actual gray value G1 of the ROI region of the image to be identified can be calculated by the method of calculating the actual gray value described above.
Step S300: and calculating the light intensity E1 of the light supplement lamp to be supplemented according to the delta G1 and the relation curve E ═ f (delta G), and adjusting the light intensity of the light supplement lamp according to the E1.
As can be seen from Δ G1 and the relationship E ═ f (Δ G): e1 ═ f (Δ G1), and then the light intensity of the fill light is adjusted according to E1.
It should be noted that the light intensity of the light supplement lamp refers to the light emitting intensity of the light supplement lamp, and the light intensity of the light supplement lamp can be adjusted by adjusting the power of the light supplement lamp.
After the light intensity of the light supplement lamp is adjusted, the image can be shot again for image recognition.
The method for adjusting the light intensity of the light supplementing lamp based on the gray value judges whether the light in the refrigerator is sufficient or not by selecting the gray value of an ROI (region of interest) of an image to be identified, and when the gray value (or the light) is lower than the minimum standard, the light intensity (brightness) of the light supplementing lamp is adjusted through a relation curve between the gray value and the light intensity of the light supplementing lamp learned in advance, so that the gray value (or the light) reaches the minimum standard. The method has the advantages that the speed of adjusting the light supplement lamp is very high, and the recognition precision of the picture can be improved, so that the recognition result can be fed back to the user rapidly, and the user experience is improved.
In a specific embodiment, a bottle seat area of a refrigerator door body is set as a special yoghourt area, a fixed camera is installed at the top of the special yoghourt area, and the fixed camera shoots the special yoghourt area at a fixed angle to obtain a shot image of the special yoghourt area. Because the camera is arranged at the top of the special yoghourt area, the shot images not only comprise the bottle seat area, but also comprise the areas around the bottle seat, therefore, the part with relatively simple bright color around the bottle seat, no reflection and no reflection is selected as the ROI area, and the actual gray value G1 of the area is calculated. The calculation method of the actual gray value G1 includes: the gray value of a pixel point is obtained by calculating the average value of the gray values of three color channels of red (R), green (G) and blue (B) of the pixel point, then some pixel points at the edge and the center of the ROI area are selected as target pixel points, the gray value of the target pixel points is calculated, and then the gray values of all the target pixel points are averaged to obtain the actual gray value of the ROI area.
Then, a gray value threshold G0 is obtained by learning, that is, when the actual gray value of the ROI region of the image is greater than or equal to the gray value threshold, the recognition rate of the image is determined to be greater than or equal to the recognition rate threshold. The learning method of the specific gray value threshold comprises the following steps: the method comprises the steps of obtaining a plurality of shot images in the refrigerator under different lighting conditions, calculating the gray value of the ROI of each shot image and the identification rate of the corresponding image, and obtaining the gray value interval corresponding to the image with the identification rate larger than or equal to the identification rate threshold according to the identification rate threshold. The minimum value is selected from this gray value interval as the gray value threshold G0.
Obtaining a gray-scale value difference Δ G of G0-G1 through the above G0 and G1, and training a relation curve E of the gray-scale value difference Δ G and a fill-in light intensity E to be filled in as f (Δ G), where the training method specifically includes: under the condition of different gray value differences, acquiring light intensity of a corresponding light supplement lamp needing to be adjusted, acquiring a plurality of coordinate points with the gray value difference as an x axis and the light intensity of the light supplement lamp as a y axis, and performing curve fitting on the coordinate points to obtain a relation curve E-f (delta G), wherein when the delta G <, the E-0.
When the refrigerator door is closed, the camera shoots a special yoghourt area to obtain an image A, the actual gray value of the ROI area of the image A is calculated to obtain a gray value difference delta G1, if delta G1 is larger than 0, the light intensity of the supplementary lighting lamp is directly adjusted to E1 according to a relation curve of E-f (delta G) f (delta G1) E1. Then, the camera shoots the yogurt special area again to obtain an image B, the food materials in the image B are identified, and finally the identification result is fed back to the terminal.
The invention further provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor executes the program to realize any one of the steps of the method for adjusting the light intensity of the supplementary lighting lamp based on the gray-scale value, namely, the steps in any one of the technical schemes of the method for adjusting the light intensity of the supplementary lighting lamp based on the gray-scale value.
The invention also provides a refrigerator comprising the electronic equipment.
Preferably, the refrigerator further comprises a special yoghourt area, the special yoghourt area is arranged in a bottle seat of the door body, a camera and a light supplement lamp are arranged at the top of the special yoghourt area, the camera and the light supplement lamp are on the same horizontal plane, the light supplement lamp is a plurality of LED lamps distributed around the camera according to a rectangle, and the camera is arranged in the middle of the rectangle. And a plurality of light filling lamps emit downward parallel light beams, so that shadow is reduced or prevented while light filling is performed.
It should be understood that although the present description refers to embodiments, not every embodiment contains only a single technical solution, and such description is for clarity only, and those skilled in the art should make the description as a whole, and the technical solutions in the embodiments can also be combined appropriately to form other embodiments understood by those skilled in the art.
The above-listed detailed description is only a specific description of a possible embodiment of the present invention, and they are not intended to limit the scope of the present invention, and equivalent embodiments or modifications made without departing from the technical spirit of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method for adjusting light intensity of a fill-in lamp based on gray scale values, the method comprising:
training a relation curve E of a gray value difference Delta G of an ROI area of a shot image in a refrigerator and light intensity E of a light supplementing lamp to be supplemented with light to be f (Delta G), wherein the gray value difference Delta G is a difference value of a gray value threshold G0 and an actual gray value G1 of the ROI area, and the shot image is an image shot from a fixed angle;
acquiring an actual gray value G1 of the ROI of the image to be identified, and calculating a gray value difference delta G1 to G0-G1;
and calculating the light intensity E1 of the light supplement lamp to be supplemented according to the delta G1 and the relation curve E ═ f (delta G), and adjusting the light intensity of the light supplement lamp according to the E1.
2. The method for adjusting the light intensity of a fill-in light based on a gray-scale value of claim 1, wherein the obtaining method of the gray-scale value threshold G0 comprises:
the method comprises the steps of obtaining a plurality of shot images in the refrigerator under different lighting conditions, calculating the gray value of the ROI of each shot image and the identification rate of the corresponding image, obtaining a gray value interval corresponding to the image with the identification rate being larger than or equal to the identification rate threshold according to the identification rate threshold, and selecting a gray value from the gray value interval as the gray value threshold.
3. The method for adjusting the light intensity of a fill-in light based on gray scale values as claimed in claim 2, wherein the selecting a gray scale value from the gray scale value interval as the gray scale value threshold comprises:
and selecting the minimum value of the gray value interval as a gray value threshold value.
4. The method for adjusting the light intensity of a fill-in light based on gray scale values as claimed in claim 2, wherein the selecting a gray scale value from the gray scale value interval as the gray scale value threshold comprises:
and taking the gray value corresponding to the image with the highest identification rate as a gray value threshold value.
5. The method as claimed in claim 1, wherein the calculating of the actual gray value G1 comprises:
calculating the gray value of each pixel point in the ROI area;
and calculating the average gray value of all the pixel points to obtain the actual gray value G1.
6. The method of claim 5, wherein calculating the gray-level value of a pixel comprises:
respectively acquiring gray values of the pixel points in R, G, B three channels;
the gray value of the pixel point is the average value of the gray values of the pixel point in the R, G, B three channels.
7. The method as claimed in claim 1, wherein the calculating of the actual gray value G1 comprises:
selecting some pixel points at the edge and the center of the ROI area as target pixel points, and calculating the gray value of the target pixel points;
and calculating the average gray value of all target pixel points to obtain the actual gray value G1.
8. The method as claimed in claim 1, wherein the relationship E ═ f (Δ G) includes the following constraints:
when Δ G < ═ 0, E ═ 0.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and wherein the processor executes the program to perform the steps of the method for adjusting light intensity of a fill lamp based on gray scale values of any one of claims 1-8.
10. A refrigerator characterized in that it contains an electronic apparatus as claimed in claim 9.
CN202110172098.9A 2021-02-08 2021-02-08 Method and device for adjusting light intensity of light supplementing lamp based on gray value and refrigerator Active CN112954229B (en)

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