Cooking device and control method thereof

Abstract

Disclosed are a cooking apparatus and a control method thereof. The cooking apparatus according to the present disclosure may: acquire a first setting value by adjusting a setting value of at least one parameter related to a camera if a first event related to acquiring an image of a food raw material disposed in the cooking apparatus is recognized; acquiring, by the camera, at least one image of a food raw material arranged in the cooking device based on the first set value; if a second event is recognized during the acquisition of the at least one image, acquiring the first set value; acquiring a first image of the food raw materials based on a second set value; providing the first image to a user; acquiring a second image, for which the second set value for acquiring the first image is changed to the first set value; inputting the second image into an object recognition model used for recognizing food raw materials; and acquiring information related to the food ingredients included in the second image.

Description

Technical Field

[0001] The present disclosure relates to a cooking device and a control method thereof, and more particularly, to a cooking device capable of recognizing an image of food ingredients and a control method thereof. Background Art

[0002] Recently, functions of cooking devices such as electric ovens, electric ranges, electric stoves, etc. are becoming diversified. Specifically, a technology for acquiring an image of food materials arranged in a cooking device by using a camera, recognizing the food materials included in the image by using an object recognition model, and controlling the operation of the cooking device based on the recognition result is being developed.

[0003] However, as the cooking process progresses, the color of the food raw materials may change, and in this case, if an image of the food raw materials is acquired by automatically adjusting the setting values ​​of camera-related parameters according to the changed color, there is a problem that the recognition rate of the food raw materials in the acquired image may be reduced.

[0004] Even if the color of the food material changes, the image of the food material can be obtained by fixing the setting value of the parameter related to the camera to the initial value without changing it. However, if the image thus obtained is provided to the user, there is a problem that the quality of the image provided to the user may be reduced. Summary of the invention

[0005] A cooking apparatus is provided which is capable of acquiring an image of food raw materials while a cooking process is being performed and performing recognition of the food raw materials at a high recognition rate, and simultaneously providing a high-quality image including information on the food raw materials to a user.

[0006] According to one aspect of the present disclosure, a cooking device includes: a camera; at least one memory storing at least one instruction; and at least one processor configured to execute the at least one instruction to: adjust a setting value of at least one parameter related to the camera and acquire a first setting value based on identifying a first event related to acquiring an image of food ingredients arranged in the cooking device; acquire at least one image of the food ingredients arranged in the cooking device through the camera and based on the first setting value; acquire a second setting value based on identifying a second event when the at least one image is being acquired, acquire a first image of the food ingredients based on the second setting value, and provide the first image to a user; acquire a second image, wherein the second setting value used when acquiring the first image is changed to the first setting value; and input the second image into an object recognition model configured to recognize the food ingredients, and acquire information about the food ingredients based on the second image.

[0007] The at least one processor may be further configured to execute the at least one instruction to acquire the second image by changing the second setting value to the first setting value in data of the first image.

[0008] The at least one processor may be further configured to execute the at least one instruction to acquire the second image by inputting the first setting value, the second setting value, and the first image into a trained image acquisition model.

[0009] The cooking device may further include at least one sensor, and the first event may include at least one of: receiving user input for performing a cooking operation of the cooking device; detecting, by the at least one sensor, that a door of the cooking device is closed; or detecting, by the at least one sensor, that the food ingredients are placed in the cooking device.

[0010] The second event may include at least one of: identifying a change in color of the food material while a parameter stabilization operation is being performed; detecting a change in illumination around the cooking device by the at least one sensor while the parameter stabilization operation is being performed; or receiving a user input requesting an image of the food material while the parameter stabilization operation is not being performed.

[0011] The cooking device may further include a communicator, and the at least one processor may be further configured to execute the at least one instruction to: based on acquiring the first image, control the communicator to send the first image to a user terminal of the user.

[0012] The cooking device may further include a display, and the at least one processor may be further configured to execute the at least one instruction to: based on acquiring the first image, control the display to display the first image.

[0013] The cooking device may further include a light emitter, and the at least one processor may be further configured to execute the at least one instruction to control at least one of a color or brightness of light emitted by the light emitter based on a color of the food ingredients included in the first image.

[0014] According to one aspect of the present disclosure, a method for controlling a cooking device includes: based on identifying a first event related to acquiring an image of food raw materials arranged in the cooking device, adjusting a setting value of at least one parameter related to a camera of the cooking device and acquiring a first setting value; acquiring at least one image of the food raw materials arranged in the cooking device through the camera and based on the first setting value; based on identifying a second event when acquiring the at least one image, acquiring a second setting value, acquiring a first image of the food raw materials based on the second setting value, and providing the first image to a user; acquiring a second image, wherein the second setting value used when acquiring the first image is changed to the first setting value; and inputting the second image into an object recognition model configured to recognize the food raw materials, and acquiring information about the food raw materials based on the second image.

[0015] Acquiring the second image may further include acquiring the second image by changing the second setting value to the first setting value in data of the first image.

[0016] Acquiring the second image may further include acquiring the second image by inputting the first setting value, the second setting value, and the first image into a trained image acquisition model.

[0017] The first event may include at least one of: receiving a user input for performing a cooking operation of the cooking device; detecting, by the at least one sensor, that a door of the cooking device is closed; or detecting, by the at least one sensor, that the food ingredients are arranged in the cooking device.

[0018] The second event may include at least one of: identifying a change in color of the food material while a parameter stabilization operation is being performed; detecting a change in illumination around the cooking device by the at least one sensor while the parameter stabilization operation is being performed; or receiving a user input requesting an image of the food material while the parameter stabilization operation is not being performed.

[0019] The method may further include: based on acquiring the first image, sending the first image to a user terminal of the user.

[0020] The method may further include: displaying the first image based on acquiring the first image.

[0021] According to one aspect of the present disclosure, a non-transitory computer-readable medium has instructions stored therein, which, when executed by at least one processor, cause the at least one processor to perform a method for controlling a cooking device, the method comprising: adjusting a setting value of at least one parameter related to a camera of the cooking device and acquiring a first setting value based on identifying a first event related to acquiring an image of food ingredients arranged in the cooking device; acquiring at least one image of the food ingredients arranged in the cooking device through the camera and based on the first setting value; acquiring a second setting value based on identifying a second event when the at least one image is being acquired, acquiring a first image of the food ingredients based on the second setting value, and providing the first image to a user; acquiring a second image, wherein the second setting value used when acquiring the first image is changed to the first setting value; and inputting the second image into an object recognition model configured to recognize the food ingredients, and acquiring information about the food ingredients based on the second image.

[0022] Acquiring the second image may further include acquiring the second image by changing the second setting value to the first setting value in data of the first image.

[0023] Acquiring the second image may further include acquiring the second image by inputting the first setting value, the second setting value, and the first image into a trained image acquisition model.

[0024] The first event may include at least one of: receiving a user input for performing a cooking operation of the cooking device; detecting, by at least one sensor, that a door of the cooking device is closed; or detecting, by the at least one sensor, that the food ingredients are arranged in the cooking device.

[0025] The second event may include at least one of: identifying a change in color of the food material while a parameter stabilization operation is being performed; detecting a change in illumination around the cooking device by the at least one sensor while the parameter stabilization operation is being performed; or receiving a user input requesting an image of the food material while the parameter stabilization operation is not being performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other aspects, features and advantages of certain embodiments of the present disclosure will become more apparent from the following description in conjunction with the accompanying drawings, in which:

[0027] Figure 1 is a diagram showing a cooking device according to one or more embodiments of the present disclosure;

[0028] Figure 2is a flow chart showing a control method of a cooking device according to one or more embodiments of the present disclosure;

[0029] Figure 3 is a flowchart for illustrating an embodiment of evaluating whether a parameter stabilization operation is activated according to the present disclosure;

[0030] Figure 4 is a block diagram schematically illustrating a configuration of a cooking device according to one or more embodiments of the present disclosure;

[0031] Figure 5 is a block diagram illustrating a plurality of modules according to one or more embodiments of the present disclosure;

[0032] Figure 6 is a block diagram illustrating in detail the configuration of a cooking device according to one or more embodiments of the present disclosure; and

[0033] Figure 7 is a block diagram illustrating a system according to one or more embodiments of the present disclosure. DETAILED DESCRIPTION

[0034] Various modifications may be made to the embodiments of the present disclosure, and various types of embodiments may exist. Therefore, specific embodiments will be shown in the drawings, and the embodiments will be described in detail in the specific embodiments. However, it should be noted that the various embodiments are not intended to limit the scope of the present disclosure to specific embodiments, but they should be interpreted as including various modifications, equivalents and / or substitutes of the embodiments of the present disclosure. In addition, with respect to the detailed description of the drawings, similar components may be designated by similar reference numerals.

[0035] Furthermore, in describing the present disclosure, in the case where it is determined that a detailed explanation of a related known function or feature may unnecessarily obscure the gist of the present disclosure, the detailed explanation will be omitted.

[0036] In addition, the following embodiments can be modified into several different forms, and the scope of the technical ideas of the present disclosure is not limited to the following embodiments. On the contrary, these embodiments are provided to make the present disclosure more complete and complete, and to fully convey the technical ideas of the present disclosure to those skilled in the art.

[0037] In addition, the terms used in the present disclosure are only used to explain specific embodiments and are not intended to limit the scope of the present disclosure.In addition, a singular expression includes a plural expression unless it is clearly defined differently in the context.

[0038] Furthermore, in the present disclosure, expressions such as “having”, “may have”, “including”, and “may include” indicate the existence of such characteristics (for example, elements such as numbers, functions, operations, and components), and do not exclude the existence of additional characteristics.

[0039] Furthermore, in the present disclosure, the expressions "A or B", "at least one of A and / or B", or "one or more of A and / or B", etc. may include all possible combinations of the listed items. For example, "A or B", "at least one of A and B", or "at least one of A or B" may refer to all of the following: (1) only A, (2) only B, or (3) both A and B.

[0040] In addition, the expressions "first", "second", etc. used in the present disclosure may describe various elements, regardless of any order and / or degree of importance. In addition, such expressions are only used to distinguish one element from another element and are not intended to limit the elements.

[0041] In addition, the description in the present disclosure that one element (e.g., a first element) is “(operably or communicably) coupled” to / “(operably or communicably) coupled to” another element (e.g., the second element) or is “connected to” another element (e.g., the second element) should be interpreted as including the case where the one element is directly coupled to the other element and the case where the one element is coupled to the other element through another element (e.g., a third element).

[0042] Conversely, a description that one element (e.g., a first element) is “directly coupled” or “directly connected” to another element (e.g., a second element) may be interpreted to mean that there is no further element (e.g., a third element) between the one element and the other element.

[0043] Furthermore, the expression "configured to" used in the present disclosure may be used interchangeably with other expressions such as "suitable for," "capable of," "designed to," "suitable for," "manufactured to," and "capable of," depending on the circumstances. The term "configured to" does not necessarily mean that a device is "specifically designed to" in terms of hardware.

[0044] Conversely, in some cases, the expression "a device configured to..." may mean that the device is "capable" of performing an operation together with another device or component. For example, the phrase "a processor configured to perform A, B, and C" may mean a dedicated processor (e.g., an embedded processor) for performing the corresponding operations, or a general-purpose processor (e.g., a CPU or an application processor) that can perform the corresponding operations by executing one or more software programs stored in a memory device.

[0045] In addition, in the embodiments of the present disclosure, a "module" or "component" may perform at least one function or operation, and may be implemented as hardware or software, or as a combination of hardware and software. In addition, in addition to a "module" or "component" that needs to be implemented as specific hardware, multiple "modules" or "components" may be integrated into at least one module and implemented as at least one processor.

[0046] In addition, various elements and regions in the drawings are schematically shown. Therefore, the technical concept of the present disclosure is not limited by the relative sizes or intervals shown in the drawings.

[0047] Hereinafter, embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings to the extent that a person having ordinary skill in the art to which the present disclosure pertains can easily perform the embodiments.

[0048] Figure 1 is a diagram illustrating a cooking device 100 according to one or more embodiments of the present disclosure.

[0049] The cooking device 100 according to the present disclosure refers to a device that can cook food materials by providing heat to the food materials using gas, electricity, steam, etc. For example, the cooking device 100 according to the present disclosure may be as follows: Figure 1 The electric oven shown, and besides this, any device that can cook food ingredients, such as a stove and an electric range, etc., may fall under the cooking device 100 according to the present disclosure without limiting the type.

[0050] Specifically, the cooking device 100 according to the present disclosure can acquire an image of food raw materials through the camera 110 and provide the acquired image to the user. In addition, the cooking device 100 can acquire information about the food raw materials by analyzing the image acquired through the camera 110, and control the cooking operation based on the information about the food raw materials. For example, if the color change of the surface of the food raw materials while the cooking process is in progress is identified as a result of analyzing the image, the cooking device 100 can control the cooking operation based on the information about the color change of the surface of the food raw materials, provide a guidance message to the user, or further provide the user with information about a recommended recipe.

[0051] In other words, the cooking device 100 can perform recognition of food ingredients by acquiring an image of the food ingredients while the cooking process is in progress, and simultaneously provide the image including information about the food ingredients to the user. Figures 2 to 7 Various embodiments are described.

[0052] Figure 2 is a flowchart illustrating a control method of the cooking device 100 according to one or more embodiments of the present disclosure.

[0053] refer to Figure 2 , in operation S210, the cooking apparatus 100 according to the present disclosure may identify whether a first event for photographing food raw materials arranged in the cooking apparatus 100 occurs. In the description of the present disclosure, "first event" is used as a term generally referring to an event that becomes a condition for starting to photograph food raw materials arranged in the cooking apparatus 100. For example, the first event may include at least one of the following cases: a case where a user input for performing a cooking operation of the cooking apparatus 100 is received, a case where a door included in the cooking apparatus 100 is detected to be closed through at least one sensor 150 included in the cooking apparatus 100, or a case where food raw materials are detected to be arranged in the cooking apparatus 100 through at least one sensor 150.

[0054] If the first event-No is not recognized in operation S210, the cooking device 100 may maintain the current operation and recognize again whether the first event occurs in operation S210 until the first event occurs. On the contrary, if the first event-Yes is recognized in operation S210, the cooking device 100 may adjust the setting value of at least one parameter related to the camera 110 to be suitable for photographing food ingredients in operation S220, and obtain the first setting value.

[0055] In the description of the present disclosure, "at least one parameter" is used as a term for generally referring to parameters related to photographing by the camera 110. For example, the at least one parameter may include focus, exposure, and white balance of the camera 110, but is not limited thereto.

[0056] Adjusting the set value of at least one parameter to a feature suitable for photographing food raw materials may mean adjusting the set value of at least one parameter to a set value within a threshold range that enables stable photographing of food raw materials based on various factors such as the color of the food raw materials, the focal distance from the food raw materials, the illuminance around the cooking device 100, etc. For example, the cooking device 100 may adjust the set value of exposure based on the illuminance around the cooking device 100, so that the brightness of the image acquired as a result of photographing may be adjusted to become a brightness within a predetermined threshold range. The threshold range of the set value of each parameter that enables stable photographing may be changed according to the setting of the developer or the user.

[0057] In particular, in the case where at least one parameter includes focus, exposure, and white balance, the operation of automatically adjusting the set values ​​of focus, exposure, and white balance may be referred to as a so-called "3A (auto focus, auto exposure, auto white balance) stabilization operation". Hereinafter, the operation of adjusting the set value of at least one parameter to be suitable for photographing food ingredients will be referred to as a "parameter stabilization operation" for short.

[0058] Furthermore, in the present disclosure, a first setting value of at least one parameter means a setting value initially acquired by performing a parameter stabilization operation, that is, an initial setting value.

[0059] The cooking device 100 may perform a cooking operation in response to the occurrence of the first event. In addition, the cooking device 100 may perform a cooking operation based on a user input that is separately input before the first event occurs or after the first event occurs. For example, in the case where the first event is the reception of a user input for performing a cooking operation of the cooking device 100, the cooking device 100 may perform a predetermined cooking operation in response to the recognition of the first event. In addition, in the case where the first event is the detection that a door included in the cooking device 100 is closed, the cooking device 100 may perform a predetermined cooking operation based on a separate user input for performing a cooking operation.

[0060] When the first setting value is acquired, in operation S230, the cooking apparatus 100 may perform photographing based on the first setting value through the camera 110. That is, if the first setting value is acquired when the parameter stabilization operation according to the present disclosure is completed, the cooking apparatus 100 may start photographing food raw materials in a state where the setting value of at least one parameter is adjusted to be suitable for photographing food raw materials.

[0061] In addition, when the first setting value is acquired, the cooking apparatus 100 may store the acquired first setting value in the memory 120. This is to acquire the second image by post-processing the first image using the first setting value, as will be described below.

[0062] When photographing is being performed based on the first setting value, in operation S240, the cooking device 100 may identify whether a second event for changing the first setting value to the second setting value has occurred. Here, the second event is a term used to generally refer to a situation where the setting value of at least one parameter needs to be adjusted to be suitable for photographing food raw materials. That is, when the cooking operation is being performed, various factors such as the color of the food raw materials, the focal distance from the food raw materials, the illumination around the cooking device 100, etc. may change, and in this case, the cooking device 100 may obtain a new setting value according to the parameter stabilization operation.

[0063] For example, the second event may include at least one of the following situations: a situation in which a color change of food raw materials is recognized while a parameter stabilization operation according to the present disclosure is being performed, a situation in which a change in illumination around the cooking device 100 is detected by at least one sensor 150 while a parameter stabilization operation is being performed, or a situation in which a user input requesting an image of food raw materials is received while a parameter stabilization operation is not being performed.

[0064] If the second event-No is not recognized in operation S240, the cooking device 100 may maintain the current operation and recognize again whether the second event occurs in operation S240. On the contrary, if the second event-Yes is recognized in operation S240, the cooking device 100 may acquire a first image of the food material based on the second setting value in operation S250 and provide the image to the user.

[0065] In the present disclosure, the second setting value of at least one parameter means a setting value acquired as a result of performing a parameter stabilization operation again after acquiring a first setting value as an initial setting value by performing the parameter stabilization operation, that is, a current setting value.

[0066] The cooking device 100 may acquire a first image of the food material based on a second setting value, wherein the second setting value is adjusted from a setting value of at least one parameter to a setting value suitable for photographing the current food material. In the present disclosure, the first image is an image acquired based on the second setting value suitable for photographing the current food material, and it may be an image suitable for providing information about the current cooking state to the user.

[0067] In operation S260, the cooking device 100 may acquire a second image, wherein the second setting value used when acquiring the first image is changed to the first setting value. Specifically, the cooking device 100 may acquire the second image by performing post-processing on the first image, or by using a neural network model for post-processing of the image.

[0068] In the present disclosure, the second image is an image obtained by changing the setting value of the first image to the first setting value as the initial setting value, and refers to an image for being input into the object recognition model. That is, in object recognition, in terms of object recognition rate, it will be easy to use the second image with the first setting value as the initial setting value instead of the first image that can clearly indicate the state of the current food raw material, so the cooking device 100 may not input the first image into the object recognition model, but input the second image with the initial setting value into the object recognition model.

[0069] In one or more embodiments, the cooking device 100 may acquire the second image by changing the second setting value to the first setting value in the data of the first image. For example, in a case where the first setting value includes a feature that the r gain value of the white balance is 20 and the second setting value includes a feature that the r gain value of the white balance is 23, the cooking device 100 may subtract the r gain value of the second setting value by up to 3 from the data of the first image, thereby acquiring the second image in which the second setting value is changed to the first setting value in the data of the first image.

[0070] In addition, in one or more embodiments, the cooking device 100 may input the first setting value, the second setting value, and the first image into a trained image acquisition model and acquire the second image. Here, the image acquisition model refers to a neural network model, wherein the neural network model is trained to output a new image with the initial setting value if the initial setting value of at least one parameter, the original image, and the current setting value of the original image are input. For example, if the first setting value as the initial setting value, the first image, and the second setting value used when acquiring the first image are input, the image acquisition model may output the second image with the first setting value. The image acquisition model may be implemented not only on a device in the cooking device 100 itself, but also in an external device such as a server.

[0071] When the second image is acquired, the cooking device 100 may input the second image into an object recognition model for recognizing food raw materials, and acquire information about the food raw materials included in the second image in operation S270. Subsequently, when the information about the food raw materials included in the second image is acquired, the cooking device 100 may perform various operations, such as controlling a cooking operation based on the information about the food raw materials, providing a guide message to the user, or further providing information about a recommended recipe to the user.

[0072] The object recognition model according to the present disclosure refers to a neural network model that can acquire information about an object included in an image when an image is input. Specifically, the object recognition model can be trained to output information about the type of food raw materials included in the image and information about the color of the food raw materials, etc. The object recognition model can be implemented not only as an on-device part of the cooking device 100 itself, but also in an external device such as a server.

[0073] According to the above reference Figure 2 In the described embodiment, the cooking device 100 can acquire a first image based on the second setting value as the current setting value suitable for photographing the current food raw material and provide the image to the user, and at the same time, acquire a second image based on the first setting value as the initial setting value stabilized according to the parameter and perform object recognition. Therefore, the cooking device 100 can acquire an image of the food raw material while the cooking process is being performed and perform recognition of the food raw material with a high recognition rate, and at the same time provide the image including clear information about the food raw material to the user.

[0074] Figure 3 is a flowchart for illustrating an embodiment of evaluating whether a parameter stabilization operation is activated according to the present disclosure.

[0075] refer to Figure 3In operation S310, the cooking apparatus 100 according to the present disclosure may identify whether a first event for photographing food materials arranged in the cooking apparatus 100 occurs. Subsequently, if the first event-yes is identified in operation S310, the cooking apparatus 100 may adjust the setting value of at least one parameter related to the camera 110 to be suitable for photographing food materials and obtain the first setting value in operation S315. Figure 2 The process of identifying whether the first event occurs and the parameter stabilization operation are described, and thus repeated explanation on the same contents will be omitted.

[0076] When the first setting value is acquired, the cooking device 100 may fix the setting value of at least one parameter to the first setting value in operation S320. Subsequently, in operation S325, the cooking device 100 may perform photographing based on the first setting value through the camera 110. In other words, if the first setting value as the initial setting value is acquired as a result of performing the parameter stabilization operation, the cooking device 100 may stop the parameter stabilization operation and perform photographing based on the first setting value.

[0077] In operation S330, the cooking device 100 may perform different operations depending on whether a user input requesting an image of food raw materials is received. Specifically, if a user input requesting an image of food raw materials-yes is received in operation S330, the cooking device 100 may release the fixation of the set value of at least one parameter in operation S335. Subsequently, in operation S340, the cooking device 100 may adjust the set value of at least one parameter again to be suitable for photographing food raw materials, and obtain a second set value, and in operation S345, obtain a first image of the food raw materials based on the second set value, and provide the image to the user.

[0078] In other words, after stopping the parameter stabilization operation and while performing shooting based on the first setting value, in the case of receiving a user input requesting an image of food ingredients, it would be inappropriate to provide the user with an image acquired based on the first setting value instead of the current setting value. Therefore, the cooking device 100 may perform the parameter stabilization operation again and acquire the second setting value, and provide the user with an image acquired based on the second setting value.

[0079] When the first image acquired based on the second setting value is provided to the user, the cooking apparatus 100 may stop the parameter stabilization operation again, and perform photographing of the food ingredients based on the first setting value in operation S325.

[0080] If the user input requesting the image of the food material is not received in operation S330-NO, the cooking apparatus 100 may acquire a second image of the food material based on the first setting value in operation S350. Subsequently, in operation S355, the cooking apparatus 100 may input the second image into an object recognition model for recognizing the food material, and acquire information about the food material included in the second image.

[0081] In other words, after stopping the parameter stabilization operation and when shooting is performed based on the first setting value, unless a situation occurs in which the parameter stabilization operation is performed again (such as a situation in which a user input requesting an image of the food raw material is received), the cooking device 100 may acquire a second image of the food raw material based on the fixed first setting value in a state in which the parameter stabilization operation is stopped, and perform object recognition by using the second image.

[0082] According to the above reference Figure 3 In the described embodiment, the cooking device 100 can perform recognition of food ingredients with a high recognition rate by fixing the set value according to the parameter stabilization operation when performing the cooking process, and at the same time, obtain the set value again according to the parameter stabilization operation when obtaining the image to be provided to the user, so that high-quality images of the food ingredients can be provided to the user.

[0083] Figure 4 is a block diagram schematically showing a configuration of a cooking device 100 according to one or more embodiments of the present disclosure. Figure 5 is a block diagram illustrating multiple modules according to one or more embodiments of the present disclosure.

[0084] like Figure 4 As shown, the cooking device 100 according to one or more embodiments of the present disclosure may include a camera 110 , a memory 120 , and a processor 130 .

[0085] The camera 110 may acquire an image of at least one object. Specifically, the camera 110 may include an image sensor, and the image sensor may convert light entering through a lens into an electrical image signal.

[0086] Specifically, in various embodiments according to the present disclosure, the processor 130 may acquire an image of the food material through the camera 110. Specifically, the processor 130 may acquire a second image of the food material based on a first setting value of at least one parameter. In addition, the processor 130 may acquire a first image of the food material based on a second setting value of at least one parameter.

[0087] In the memory 120, at least one instruction regarding the cooking device 100 may be stored. In addition, in the memory 120, an operating system (O / S) for driving the cooking device 100 may be stored. In addition, in the memory 120, various software programs or applications for the cooking device 100 to operate according to various embodiments of the present disclosure may be stored. In addition, the memory 120 may include a semiconductor memory such as a flash memory or a magnetic storage medium such as a hard disk.

[0088] Specifically, in the memory 120, various software modules for the cooking device 100 to operate according to various embodiments of the present disclosure may be stored, and the processor 130 may control the operation of the cooking device 100 by executing the various software modules stored in the memory 120. That is, the memory 120 may be accessed by the processor 130, and reading / recording / correction / deletion / update of data, etc., by the processor 130 may be performed.

[0089] In the present disclosure, the term memory 120 may be used as a meaning including the memory 120 , a ROM and a RAM inside the processor 130 , or a memory card (eg, a micro SD card, a memory stick) mounted on the cooking device 100 .

[0090] Specifically, in various embodiments according to the present disclosure, images of food raw materials including the first image and the second image according to the present disclosure may be stored. In addition, in the memory 120, a setting value of at least one parameter according to the present disclosure, that is, a first setting value, a second setting value, etc. may be stored. In addition, in the memory 120, information about a neural network model including an image acquisition model and an object recognition model according to the present disclosure may be stored. Moreover, in the memory 120, information indicating what the first event and the second event according to the present disclosure are may be stored.

[0091] In addition to the above, various necessary information within the scope for achieving the purpose of the present disclosure may be stored in the memory 120, and the information stored in the memory 120 may be updated when it is received from an external device or input by a user.

[0092] The processor 130 controls the overall operation of the cooking device 100. Specifically, the processor 130 may be connected with components of the cooking device 100 including the camera 110 and the memory 120, and may control the overall operation of the cooking device 100 by executing at least one instruction stored in the memory 120 as described above.

[0093] The processor 130 may be implemented in various ways. For example, the processor 130 may be implemented as at least one of an application specific integrated circuit (ASIC), an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), or a digital signal processor (DSP). In addition, in the present disclosure, the term processor 130 may be used as a meaning including a central processing unit (CPU), a graphics processing unit (GPU), and a microprocessor unit (MPU), etc.

[0094] Specifically, the processor 130 can use Figure 5 The various embodiments according to the present disclosure are implemented by the multiple modules shown in . Here, the multiple modules may include a parameter stabilization module 131, an image acquisition module 132, an image post-processing module 133, and an object recognition module 134, and may be implemented as a software module or a hardware module. Hereinafter, the explanation will be described on the premise that the multiple modules according to the present disclosure are implemented by the processor 130, but according to an embodiment, some of the multiple modules may be implemented in the form of being included in a separate processor 130 for image processing.

[0095] The processor 130 may identify whether a first event for photographing food ingredients arranged in the cooking device 100 occurs. Then, if the first event is identified, the processor 130 may acquire a first setting value through the parameter stabilization module 131.

[0096] The parameter stabilization module 131 refers to a module that can obtain a set value by adjusting the set value of at least one parameter related to the camera 110 to be suitable for photographing food raw materials. Specifically, the parameter stabilization module 131 can adjust the set value of at least one parameter to a set value within a threshold range that can stabilize the photographing of food raw materials by executing a predefined algorithm based on the amount of light currently exposed to the camera 110, the distance from the object (e.g., food raw materials), the color of the object, etc. Therefore, according to the present disclosure, the parameter stabilization module 131 can obtain a first set value and a second set value. In addition, the parameter stabilization module 131 can store the acquired set value in the memory 120, and also send the set value to the image acquisition module 132.

[0097] When the first setting value is acquired, the processor 130 may perform photographing based on the first setting value through the camera 110. In addition, the processor 130 may identify whether a second event for changing the first setting value to the second setting value occurs while photographing is being performed based on the first setting value.

[0098] If the second event is recognized, the processor 130 may acquire the first image by using the image acquisition module 132. The image acquisition module 132 refers to a module that can acquire an image of the food material through the camera 110. Specifically, the image acquisition module 132 may acquire the image of the food material based on the set value received from the parameter stabilization module 131. For example, the image acquisition module 132 may acquire the first image of the food material based on the second set value and provide the image to the user. In addition, the image acquisition module 132 may store the acquired image in the memory 120, and also send the image to the image post-processing module 133.

[0099] The processor 130 may acquire the second image by using the image post-processing module 133. The image post-processing module 133 refers to a module that can acquire the second image by performing post-processing on the first image. Specifically, the image post-processing module 133 may acquire the second image, wherein the second setting value used when acquiring the first image is changed to the first setting value. In one or more embodiments, the image post-processing module 133 may acquire the second image by changing the second setting value to the first setting value in the data of the first image, or by inputting the first setting value, the second setting value, and the first image into a trained image acquisition model. In addition, the image post-processing module 133 may send the second image acquired as a result of the post-processing of the first image to the object recognition module 134.

[0100] When the second image is acquired, the processor 130 may acquire information about the food material included in the second image by using the object recognition module 134. The object recognition module 134 refers to a module that can recognize the food material included in the image, and specifically, it may be implemented in the form of an object recognition model trained to recognize objects included in the input image. Specifically, when the second image is received from the image post-processing module 133, the object recognition module 134 may perform object recognition on the second image and acquire information about the food material included in the second image.

[0101] In addition to the above, the above reference Figures 1 to 3 Various embodiments according to the present disclosure based on the control by the processor 130 are described, and thus repeated explanations will be omitted.

[0102] Figure 6 is a block diagram illustrating in detail the configuration of the cooking apparatus 100 according to one or more embodiments of the present disclosure.

[0103] like Figure 6 As shown, the cooking device 100 according to one or more embodiments of the present disclosure may further include a communicator 140, at least one sensor 150, a heat generating component 160, a light emitter 170, an input device 180, and an output device 190. Figure 4 and Figure 6 The components shown in the figure are merely exemplary components, and when carrying out the present disclosure, it will be apparent that, in addition to the components shown in the figure, Figure 4 and Figure 6 In addition to the components shown in , new components may be added, or some components may be omitted.

[0104] The communicator 140 may include a circuit and perform communication with an external device. Specifically, the processor 130 may receive various data or information from an external device connected through the communicator 140 and transmit various data or information to the external device.

[0105] The communicator 140 may include at least one of a WiFi module, a Bluetooth module, a wireless communication module, an NFC module, or an ultra-wideband (UWB) module. Specifically, the WiFi module and the Bluetooth module may perform communication by a WiFi method and a Bluetooth method, respectively. In the case of using a WiFi module or a Bluetooth module, various types of connection information such as SSID, etc. are first sent and received, and a connection for communication is performed by using the information, and various types of information may be sent and received thereafter.

[0106] In addition, the wireless communication module can perform communication according to various communication standards such as IEEE, Zigbee, third generation (3G), third generation partnership project (3GPP), long term evolution (LTE), fifth generation (5G), etc. In addition, the NFC module can perform communication by using a near field communication (NFC) method of the 13.56MHz band among various RF-ID bands such as 135kHz, 13.56MHz, 433MHz, 860-960MHz and 2.45GHz. In addition, the UWB module can correctly measure the time of arrival (ToA) and the angle of arrival (AoA) through communication between UWB antennas (wherein the time of arrival (ToA) is the time when the pulse reaches the target, and the angle of arrival (AoA) is the angle of arrival of the pulse in the transmission device), and therefore, the UWB module can perform correct distance and position recognition within an error range of several centimeters indoors.

[0107] Specifically, in various embodiments according to the present disclosure, if the first image is acquired, the processor 130 may control the communicator 140 to send the first image to the user terminal of the user. In addition, the processor 130 may receive a user input for performing a cooking operation through the communicator 140. Subsequently, the processor 130 may control the communicator 140 to transmit input data of the neural network model to a server including a neural network model (e.g., an image acquisition model, an object recognition model) according to the present disclosure, and receive output data of the neural network model through the communicator 140.

[0108] At least one sensor 150 may detect various information inside and outside the cooking device 100. In particular, the at least one sensor 150 may include a door sensor, a weight detection sensor, and an illumination sensor. In addition, the at least one sensor 150 may include at least one of a global positioning system (GPS) sensor, a gyroscope sensor (gyroscope), an acceleration sensor (accelerometer), a light detection and ranging sensor, an inertial sensor (e.g., an inertial measurement unit (IMU)), or a motion sensor. In addition, the at least one sensor 150 may include various types of sensors, such as a temperature sensor, a humidity sensor, an infrared sensor, a biosensor, etc.

[0109] Specifically, in various embodiments according to the present disclosure, the processor 130 may detect, through a door sensor, the opening or closing of a door included in the cooking device 100. Subsequently, if it is detected through the door sensor that the door is closed, the processor 130 may recognize that the first event has occurred, and if it is detected through the door sensor that the door is opened, the processor 130 may detect that the cooking operation is stopped.

[0110] In addition, the processor 130 may detect, through the weight detection sensor, whether the food ingredients are disposed in the cooking apparatus 100. Then, if it is detected through the weight detection sensor that the food ingredients are disposed in the cooking apparatus 100, the processor 130 may recognize that the first event occurs.

[0111] In addition, the processor 130 may detect the illuminance around the cooking device 100 through the illuminance sensor while the parameter stabilization operation is being performed. Subsequently, if the illuminance around the cooking device 100 is detected to be changed through the illuminance sensor, the processor 130 may change the setting value of at least one parameter based on the changed illuminance.

[0112] The heat generating component 160 refers to a component capable of heating food. Specifically, in the case where the cooking device 100 is implemented as an electric oven or an electric range, the heat generating component 160 can generate heat by supplying power, and the heat source in this case can be a radiation heater or an induction heater, etc. Operating the heat generating component 160 or the heat generating temperature and heat generating time of the heat generating component 160 can be performed based on user input, and can also be performed by the processor 130 based on information about food materials included in the image.

[0113] The light emitter 170 refers to a component that includes a light source and can irradiate light to the inside of the cooking device 100 through the light source. Specifically, the light emitter 170 may include a plurality of light sources, and the processor 130 may irradiate light of various colors to the inside of the cooking device 100 according to each of the plurality of light sources or a combination of the plurality of light sources. For example, the light emitter 170 may include a plurality of light emitting diode (LED) light sources, but is not limited thereto.

[0114] Specifically, the processor 130 may control at least one of the color or brightness of the light irradiated by the light emitter 170 based on the color of the food material included in the first image. For example, in the case where the food material is a croissant, a cold daylight color may be irradiated at the time point of starting the cooking operation so that the croissant in the dough state can be clearly seen, and when the croissant becomes baked while the cooking operation is in progress, yellow light may be irradiated, and thus the croissant can be seen to be more yellow. Therefore, the cooking device 100 can provide a better image to the user by using an illumination suitable for the type of food material and the cooking state.

[0115] The inputter 180 may include a circuit, and the processor 130 may receive a user instruction for controlling the operation of the cooking device 100 through the inputter 180. Specifically, the inputter 180 may include components such as a microphone 182, a camera 110, a remote control signal receiver, etc. In addition, the inputter 180 may be implemented in the form of being included in the display 191 as a touch screen. Specifically, the microphone 182 may receive a voice signal and convert the received voice signal into an electrical signal.

[0116] Specifically, in various embodiments according to the present disclosure, the processor 130 may receive, through the input 180, user input for performing a cooking operation, user input for setting a time to perform a cooking operation, user input for completing a cooking operation, user input for requesting an image of food ingredients, and the like.

[0117] The outputter 190 may include a circuit, and the processor 130 may output various functions executable by the cooking device 100 through the outputter 190. In addition, the outputter 190 may include at least one of a display 191, a speaker 192, or an indicator.

[0118] The display 191 may output image data by the control of the processor 130. Specifically, the display 191 may output an image pre-stored in the memory 120 by the control of the processor 130.

[0119] Specifically, the display 191 according to one or more embodiments of the present disclosure may display a user interface stored in the memory 120. The display 191 may be implemented as a liquid crystal display (LCD) panel, an organic light emitting diode (OLED), etc. In addition, the display 191 may be implemented as a flexible display, a transparent display, etc., depending on circumstances. However, the display 191 according to the present disclosure is not limited to a specific type. The speaker 192 may output audio data by the control of the processor 130, and the indicator may be turned on by the control of the processor 130.

[0120] Specifically, in various embodiments according to the present disclosure, if the first image according to the present disclosure is acquired, the processor 130 may control the display 191 to display the first image. In addition to the above, the processor 130 may display information indicating the progress of the cooking operation, a user interface for receiving user input, etc.

[0121] Figure 7 is a block diagram illustrating a system according to one or more embodiments of the present disclosure.

[0122] like Figure 7 As shown, the system according to one or more embodiments of the present disclosure may include a cooking device 100 , a server 200 , and a user terminal 300 .

[0123] In the above, the explanation is described on the premise of a case where various embodiments according to the present disclosure are implemented by the cooking apparatus 100 , but some processes in the above-described embodiments may be performed by the server 200 , and some processes may be performed by the user terminal 300 .

[0124] In the present disclosure, the server 200 refers to a device in which the neural network model according to the present disclosure is implemented. Specifically, the server 200 may include a neural network model including an image acquisition model and an object recognition model according to the present disclosure.

[0125] In one or more embodiments, the server 200 may include an image acquisition model. In this case, the cooking device 100 may control the communicator to send the first setting value as the initial setting value of at least one parameter, the second setting value as the current setting value, and the data of the first image to the server 200. When receiving the data of the first setting value, the second setting value, and the first image from the cooking device 100, the server 200 may input the data of the first setting value, the second setting value, and the first image into the trained image acquisition model, acquire the second image (wherein the second setting value used when acquiring the first image is changed to the first setting value), and send the image to the cooking device 100.

[0126] In addition, in one or more embodiments, the server 200 may include an object recognition model. The cooking device 100 may control the communicator to transmit the data of the second image to the server 200. When receiving the data of the second image from the cooking device 100, the server 200 may input the data of the second image into the object recognition model and acquire information about the food material included in the second image.

[0127] In addition, in the case where the server 200 includes an image acquisition model and an object recognition model, the server 200 can acquire a second image by using the image acquisition model, acquire information about food raw materials included in the second image by using the object recognition model, and then send the information about food raw materials included in the second image together with data of the second image to the cooking device 100.

[0128] In the present disclosure, the user terminal 300 refers to a device that can receive information about the situation of performing a cooking operation and display the information. For example, the user terminal device 300 can be implemented as a smart phone, a tablet PC, a smart watch, etc., but is not limited thereto.

[0129] In one or more embodiments, if the first image is acquired, the cooking device 100 may control the communicator to transmit the first image to the user terminal 300. Subsequently, when the first image is received, the user terminal 300 may output the first image through the display of the user terminal 300.

[0130] The user terminal 300 may receive user input for performing a cooking operation, user input for setting a time to perform a cooking operation, user input for completing a cooking operation, user input for requesting an image of food ingredients, etc., and send the received user input to the cooking device 100.

[0131] The control method of the cooking apparatus 100 according to the aforementioned embodiment may be implemented as a program and provided to the cooking apparatus 100. Specifically, the program including the control method of the cooking apparatus 100 may be provided while being stored in a non-transitory computer-readable medium.

[0132] Specifically, in a non-transitory computer-readable recording medium including a program for executing a control method for the cooking device 100, the control method for the cooking device 100 includes the following operations: based on identifying a first event for photographing food raw materials arranged in the cooking device 100, adjusting a setting value of at least one parameter related to the camera of the cooking device 100 to be suitable for photographing food raw materials and acquiring a first setting value, based on identifying a second event for changing the first setting value to a second setting value when photographing is being performed by the camera based on the first setting value, acquiring a first image of the food raw materials based on the second setting value and providing the image to a user, acquiring a second image (wherein the second setting value used when acquiring the first image is changed to the first setting value), and inputting the second image into an object recognition model for identifying food raw materials, and acquiring information about the food raw materials included in the second image.

[0133] In the above, the control method of the cooking device 100 and the computer-readable recording medium including a program for executing the control method of the cooking device 100 are briefly described, but this is only to omit repeated explanations, and various embodiments of the cooking device 100 can be applied to the control method of the cooking device 100 and the computer-readable recording medium including a program for executing the control method of the cooking device 100.

[0134] According to various embodiments of the present disclosure as described above, the cooking apparatus 100 may acquire an image of food raw materials while a cooking process is being performed and perform recognition of the food raw materials at a high recognition rate, and simultaneously provide the image including clear information about the food raw materials to a user.

[0135] Functions related to artificial intelligence according to the present disclosure are operated by the processor 130 and the memory of the cooking apparatus 100 .

[0136] The processor 130 may include one or more processors 130. Here, the one or more processors 130 may include at least one of a central processing unit (CPU), a graphics processing unit (GPU), or a neural processing unit (NPU), but is not limited to the aforementioned examples of the processor 130.

[0137] The CPU is a general-purpose processor 130 that can perform not only general operations but also artificial intelligence operations, and it can efficiently execute complex programs through a multi-layer cache structure. The CPU is advantageous for a serial processing method that can systematically link a previous calculation result and a next calculation result through sequential calculation. However, except for the case where the general-purpose processor 130 is designated as the aforementioned CPU, the general-purpose processor 130 is not limited to the aforementioned example.

[0138] The GPU is a processor 130 for large-scale operations such as floating-point operations for graphic processing, etc., and it can perform large-scale operations in parallel by large-scale integrated cores. In particular, the GPU may be advantageous for parallel processing methods such as convolution operations, etc., compared to the CPU. In addition, the GPU may be used as a co-processor 130 for supplementing the functions of the CPU. However, except for the case where the processor 130 for large-scale operations is specified as the aforementioned GPU, the processor 130 for large-scale operations is not limited to the aforementioned example.

[0139] The NPU is a processor 130 dedicated to artificial intelligence operations using artificial neural networks, and it can implement each layer that constitutes the artificial neural network as hardware (e.g., silicon). Here, the NPU is designed to be specialized according to the required specifications of the company, so the NPU has a lower degree of freedom compared to the CPU or GPU, but the NPU can effectively handle the artificial intelligence operations required by the company. In addition, as a processor 130 dedicated to artificial intelligence operations, the NPU can be implemented in various forms such as a tensor processing unit (TPU), an intelligent processing unit (IPU), a visual processing unit (VPU), etc. However, except for the case where the artificial intelligence processor 130 is designated as the aforementioned NPU, the artificial intelligence processor 130 is not limited to the aforementioned example.

[0140] In addition, the one or more processors 130 may be implemented as a system on chip (SoC). Here, the SoC may further include a memory and a network interface such as a bus for data communication between the processor 130 and the memory other than the one or more processors 130.

[0141] In the case where a plurality of processors 130 are included in a system on chip (SoC) included in the cooking device 100, the cooking device 100 may perform operations related to artificial intelligence (e.g., operations related to learning or inference of an artificial intelligence model) by using some of the plurality of processors 130. For example, the cooking device 100 may perform operations related to artificial intelligence by using at least one of a GPU, an NPU, a VPU, a TPU, or a hardware accelerator dedicated to artificial intelligence operations such as a convolution operation, a matrix product operation, etc. among the plurality of processors 130. However, this is merely an example, and the cooking device 100 may process operations related to artificial intelligence by using a general-purpose processor 130 such as a CPU, etc.

[0142] In addition, the cooking device 100 can perform artificial intelligence-related operations by using multiple cores (e.g., dual cores, quad cores, etc.) included in one processor 130. Specifically, the cooking device 100 can perform artificial intelligence operations such as convolution operations, matrix product operations, etc. in parallel by using multiple cores included in the processor 130.

[0143] The one or more processors 130 execute control to process input data according to predefined operation rules or artificial intelligence models stored in the memory. The predefined operation rules or artificial intelligence models are characterized in that they are generated through learning.

[0144] Here, generated by learning means that a predefined operating rule or an artificial intelligence model with desired characteristics is generated by applying a learning algorithm to a plurality of learning data. Such learning can be performed in the device for performing artificial intelligence according to the present disclosure itself, or by a separate server / system.

[0145] The artificial intelligence model may include multiple neural network layers. At least one layer has at least one weight value, and the operation of the layer is performed by the operation result of the previous layer and at least one defined operation. As examples of neural networks, there are convolutional neural networks (CNNs), deep neural networks (DNNs), recursive neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recursive deep neural networks (BRDNNs), deep Q networks, and transformers, and the neural networks in the present disclosure are not limited to the aforementioned examples except for explicitly specified cases.

[0146] A learning algorithm is a method of training a specific object device (e.g., a robot) by using a plurality of learning data, thereby enabling the specific object device to make decisions or predictions on its own. As examples of learning algorithms, there are supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but the learning algorithm in the present disclosure is not limited to the aforementioned examples except for explicitly specified cases.

[0147] The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, the term "non-transitory storage medium" means only that the storage medium is a tangible device and does not include signals (e.g., electromagnetic waves), and the term does not distinguish between a case where data is semi-permanently stored in the storage medium and a case where data is temporarily stored in the storage medium. For example, a "non-transitory storage medium" may include a buffer in which data is temporarily stored.

[0148] According to one or more embodiments of the present disclosure, the methods according to the various embodiments described in the present disclosure may be provided in the case of being included in a computer program product. A computer program product refers to a product, and it can be traded between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read-only memory (CD-ROM)), or may be distributed directly between two user devices (e.g., smartphones) and distributed through an application store (e.g., PlayStore). TM ) Online distribution (e.g., downloading or uploading). In the case of online distribution, at least a portion of the computer program product (e.g., a downloadable application) may be at least temporarily stored in a machine-readable storage medium (such as a memory of a manufacturer's server, an application store's server, and a relay server), or may be temporarily generated.

[0149] In addition, each component (e.g., module or program) according to the aforementioned various embodiments of the present disclosure may include a single object or multiple objects. In addition, in the aforementioned corresponding subcomponents, some subcomponents may be omitted, or other subcomponents may be further included in various embodiments. Alternatively or additionally, some components (e.g., modules or programs) may be integrated as objects, and the functions performed by each component before integration are performed identically or in a similar manner.

[0150] In addition, the operations performed by the modules, programs or other components according to various embodiments may be performed sequentially, in parallel, repeatedly or heuristically. Alternatively, at least some of the operations may be performed in a different order or omitted, or other operations may be added.

[0151] The term "component" or "module" used in the present disclosure may include a unit implemented as hardware, software or firmware, and may be used interchangeably with terms such as logic, logic block, component or circuit, for example. In addition, a "component" or "module" may be a component configured as an integrated body or its smallest unit or part that performs one or more functions. For example, a module may be configured as an application specific integrated circuit (ASIC).

[0152] In addition, various embodiments of the present disclosure may be implemented as software including instructions stored in a machine-readable storage medium, wherein the instructions can be read by a machine (e.g., a computer). A machine refers to a device that calls instructions stored in a storage medium and can operate according to the called instructions, and the device may include an electronic device according to the aforementioned embodiment (e.g., the cooking device 100).

[0153] In the case where the instructions are executed by a processor, the processor may perform the function corresponding to the instruction by itself, or perform the function corresponding to the instruction by using other components under its control. The instructions may include codes generated or executed by a compiler or an interpreter.

[0154] Furthermore, although the embodiments of the present disclosure have been shown and described, the present disclosure is not limited to the aforementioned specific embodiments, and it is apparent that various modifications may be made by a person of ordinary skill in the art to which the present disclosure belongs without departing from the gist of the present disclosure as claimed in the appended claims. Furthermore, it is intended that such modifications are not to be interpreted independently of the technical ideas or prospects of the present disclosure.

Claims

1. A cooking device, comprising: camera; at least one memory storing at least one instruction; as well as at least one processor configured to execute the at least one instruction to: adjusting a setting of at least one parameter associated with the camera and acquiring a first setting based on identifying a first event associated with acquiring an image of food ingredients arranged in the cooking device, acquiring, by the camera and based on the first setting value, at least one image of the food ingredients arranged in the cooking device, acquiring a second setting value based on recognizing a second event while the at least one image is being acquired, acquiring a first image of the food stuff based on the second setting value, and providing the first image to a user, acquiring a second image, wherein the second setting value used when acquiring the first image is changed to the first setting value, and The second image is input into an object recognition model configured to recognize the food stuff, and information about the food stuff is obtained based on the second image.

2. The cooking device according to claim 1, wherein: The at least one processor is further configured to execute the at least one instruction to: The second image is acquired by changing the second setting value to the first setting value in the data of the first image.

3. The cooking device according to claim 1, wherein: The at least one processor is further configured to execute the at least one instruction to: The second image is acquired by inputting the first setting value, the second setting value, and the first image into a trained image acquisition model.

4. The cooking device according to claim 1, further comprising: at least one sensor, The first event includes at least one of the following items: receiving a user input for performing a cooking operation of the cooking device; detecting through the at least one sensor that a door of the cooking device is closed; or detecting through the at least one sensor that the food ingredients are arranged in the cooking device.

5. The cooking device according to claim 4, wherein: The second event includes at least one of: identifying a color change of the food stuff while a parameter stabilization operation is being performed; detecting, by the at least one sensor, a change in illumination around the cooking device while the parameter stabilization operation is being performed; Or a user input requesting an image of the food ingredient is received while the parameter stabilization operation is not being performed.

6. The cooking device according to claim 1, further comprising: communicator, Wherein, the at least one processor is further configured to execute the at least one instruction to: Based on acquiring the first image, the communicator is controlled to send the first image to a user terminal of the user.

7. The cooking device according to claim 1, further comprising: monitor, Wherein, the at least one processor is further configured to execute the at least one instruction to: Based on acquiring the first image, the display is controlled to display the first image.

8. The cooking device according to claim 1, further comprising: Light transmitter, Wherein, the at least one processor is further configured to execute the at least one instruction to: At least one of a color or a brightness of light emitted by the light emitter is controlled based on a color of the food stuff included in the first image.

9. A method for controlling a cooking device, the method comprising: adjusting a setting of at least one parameter associated with a camera of the cooking device and acquiring a first setting based on identifying a first event associated with acquiring an image of food ingredients arranged in the cooking device; acquiring, by the camera and based on the first setting value, at least one image of the food ingredients arranged in the cooking device; acquiring a second setting value based on recognizing a second event while the at least one image is being acquired, acquiring a first image of the food stuff based on the second setting value, and providing the first image to a user; acquiring a second image, wherein the second setting value used when acquiring the first image is changed to the first setting value; as well as The second image is input into an object recognition model configured to recognize the food stuff, and information about the food stuff is obtained based on the second image.

10. The method of claim 9, wherein: Acquiring the second image further includes: The second image is acquired by changing the second setting value to the first setting value in the data of the first image.

11. The method of claim 9, wherein: Acquiring the second image further includes: The second image is acquired by inputting the first setting value, the second setting value, and the first image into a trained image acquisition model.

12. The method of claim 9, wherein: The first event includes at least one of the following: receiving a user input for performing a cooking operation of the cooking device; It is detected by at least one sensor that the door of the cooking device is closed; or it is detected by the at least one sensor that the food ingredients are arranged in the cooking device.

13. The method of claim 12, wherein: The second event includes at least one of: identifying a color change of the food stuff while a parameter stabilization operation is being performed; detecting, by the at least one sensor, a change in illumination around the cooking device while the parameter stabilization operation is being performed; Or a user input requesting an image of the food ingredient is received while the parameter stabilization operation is not being performed.

14. The method of claim 9, further comprising: Based on acquiring the first image, the first image is sent to a user terminal of the user.

15. The method of claim 9, further comprising: Based on acquiring the first image, the first image is displayed.

Images