Image processing method and device, electronic equipment and storage medium

Abstract

The application discloses an image processing method and device, electronic equipment and a storage medium. Embodiments of the application acquire a first image of the inside of a cooking device in a first temperature environment. The first image includes at least one target food material. First color data corresponding to the first image is determined. The first color data is compared with second color data of the at least one target food material in a second temperature environment to determine an initial color difference value corresponding to each target food material. A target color difference value is determined according to the initial color difference value of the at least one target food material and a coefficient corresponding to each target food material. Color correction is performed on the first image based on the target color difference value to obtain a target image. Embodiments of the application can improve the efficiency of image processing while optimizing the quality of a recorded cooking video.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of image processing, and particularly relates to an image processing method and device, electronic equipment and a storage medium. BACKGROUND

[0002] At present, when a cooking process is photographed, a camera is often exposed to a high-temperature environment for a long time, which not only tests the heat resistance of the camera, but also causes the photographed picture to be full of dense noise points caused by high temperature.

[0003] Although the existing noise reduction algorithm performs well in a conventional environment, the effect is often not satisfactory when facing the noise reduction task in a high-temperature environment because the processing time window in the time domain is extremely short. The reason is that a large number of high-frequency components that are difficult to remove are mixed in the signal in the high-temperature environment, which seriously damages the picture quality, thereby seriously affecting the final result and clarity of image processing. Overall, the current noise reduction algorithm still has great limitations in the application in a high-temperature environment, which reduces the effect of image processing. SUMMARY

[0004] The purpose of the embodiments of the application is to provide an image processing method and device, electronic equipment and a storage medium, which can solve the problem of poor image processing effect in the process of related art image processing.

[0005] In a first aspect, the embodiments of the application provide an image processing method, which comprises:

[0006] obtaining a first image of the inside of a cooking device in a first temperature environment; the first image comprises at least one target food material;

[0007] determining first color data corresponding to the first image;

[0008] comparing the first color data with second color data of the at least one target food material in a second temperature environment to determine an initial color difference value corresponding to each target food material;

[0009] determining a target color difference value according to the initial color difference value of the at least one target food material and a coefficient corresponding to each target food material;

[0010] performing color correction on the first image based on the target color difference value to obtain a target image. In a second aspect, the embodiments of the application provide an image processing device, which comprises:

[0011] a first obtaining module, configured to obtain a first image of the inside of a cooking device in a first temperature environment; the first image comprises at least one target food material;

[0012] The first determining module is configured to determine first color data corresponding to the first image; compare the first color data with second color data of the at least one target food material in a second temperature environment, to determine an initial color difference value corresponding to each target food material; and determine a target color difference value according to the initial color difference value of the at least one target food material and a coefficient corresponding to each target food material.

[0013] The obtaining module is configured to perform color correction on the first image based on the target color difference value, to obtain a target image.

[0014] In a third aspect, an embodiment of the present application provides an image processing system, which comprises a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete communication with each other through the communication bus; the memory is configured to store executable instructions, and the executable instructions cause the processor to execute the image processing method according to any one of the preceding aspects.

[0015] In a fourth aspect, an embodiment of the present application provides a readable storage medium, which stores a program or instructions, and the program or instructions are executed by a processor to implement the image processing method according to any one of the preceding aspects.

[0016] In the embodiment of the present application, an image processing method is provided, a first image of a cooking device in a first temperature environment is obtained; the first image comprises at least one target food material; first color data corresponding to the first image is determined; the first color data is compared with second color data of the at least one target food material in a second temperature environment, to determine an initial color difference value corresponding to each target food material; a target color difference value is determined according to the initial color difference value of the at least one target food material and a coefficient corresponding to each target food material; and the first image is color corrected based on the target color difference value, to obtain a target image. In the process of image processing, the color difference value between the first color data of the target food material in the first temperature environment and the second color data of the target food material in the second temperature environment can be taken as an initial color difference value, the initial color difference value is adjusted, the target color difference value is obtained, the noise of the first image is processed, further, the first image is color corrected based on the target color difference value, to obtain a target image, the color correction of the first image is processed, the quality of the recorded cooking video is optimized, and the efficiency of image processing is improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a step flow of an image processing method provided by an embodiment of the present application Figure 1 ;

[0018] Figure 2is a step flow of an image processing method provided by an embodiment of the present application Figure 2 ;

[0019] Figure 3 is a step flow of an image processing method provided by an embodiment of the present application Figure 3 ;

[0020] Figure 4 is a logic block diagram of an image processing device provided by an embodiment of the present application

[0021] Figure 5 is a structural schematic diagram of an electronic device provided by an embodiment of the present application DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0023] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually a class, and are not limited to the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / ", generally indicates that the front and rear associated objects are in an "or" relationship.

[0024] Method embodiments

[0025] The image processing method provided by the embodiments of the present application will be described in detail below with reference to the drawings, specific embodiments and application scenarios.

[0026] Referring to Figure 1 , a step flow of an image processing method provided by an embodiment of the present application is shown Figure 1 , as shown in Figure 1 , the method specifically includes steps 101 to 106:

[0027] Step 101, acquiring a first image of the inside of a cooking device in a first temperature environment; the first image includes at least one target food material.

[0028] In the embodiment of the present application, in the process of image processing, the image processing system can first acquire a first image in a first temperature environment; the first image includes a plurality of target food materials.

[0029] Exemplarily, the first image can include a plurality of vegetables or a plurality of meat food materials, such as tomatoes, broccoli, chicken breasts, beef, etc., which are not limited in the embodiment of the present application.

[0030] In this step, the first temperature environment can be a high-temperature environment, such as an environment with a temperature greater than or equal to 60 degrees Celsius, or an environment with a temperature greater than or equal to 70 degrees Celsius, etc., which are not limited in the embodiment of the present application.

[0031] In this step, the first image can be any frame of image in a cooking video acquired by a camera, and the first image can include a plurality of target food materials and a background area excluding the target food materials, etc., which are not limited in the embodiment of the present application.

[0032] In this step, the at least one target food material can include a plurality of different food materials, and in the process of cooking, a plurality of target food materials can be determined in advance according to a recipe, such as in the case of a recipe of fried eggs with tomatoes, the target food materials can include eggs, tomatoes, green onions, garlic, etc., which are not limited in the embodiment of the present application.

[0033] In the embodiment of the present application, in the process of image processing, the image processing system can first acquire a first image in a low-temperature environment.

[0034] Step 102, determining first color data corresponding to the first image.

[0035] The image processing method provided in the embodiment of the present application, after acquiring a first image of a cooking device in a first temperature environment; the first image includes at least one target food material, the image processing system can determine first color data corresponding to the first image.

[0036] In this step, the first color data corresponding to the first image can include first color data of a plurality of target food materials in the first image, and the first color data is acquired in a high-temperature environment.

[0037] In this step, in the process of image processing, the image processing system can first determine the first color data in the high-temperature environment based on the cooking video.

[0038] Step 103, comparing the first color data with second color data of the at least one target food material in a second temperature environment to determine an initial color difference value corresponding to each target food material.

[0039] The image processing method provided in the embodiments of the present application can compare the first color data corresponding to the first image with second color data of the at least one target food material in a second temperature environment, to determine an initial color difference value corresponding to each target food material.

[0040] In this step, the second temperature environment can be a low temperature environment, for example, an environment with a temperature less than 60 degrees Celsius, or an environment with a temperature less than 50 degrees Celsius, which is not limited in the embodiments of the present application.

[0041] In this step, the second color data of the at least one target food material in the second temperature environment can include second color data of a plurality of target food materials, and the second color data is obtained in a low temperature environment.

[0042] For example, in the embodiments of the present application, the second color data can be RGB data in a normal temperature environment, and the second color data will not be affected by high temperature noise.

[0043] For example, in this step, the initial color difference value corresponding to each target food material can include an initial color difference value of each food material determined according to the first color data in the high temperature environment and the second color data in the low temperature environment. The initial color difference value of each food material includes a difference value of three colors corresponding to RGB, for example, in the case of the target food material being a tomato, the color data of the R channel in the high temperature environment is 240, the color data of the G channel in the high temperature environment is 40, the color data of the B channel in the high temperature environment is 30, the color data of the R channel in the low temperature environment is 245, the color data of the G channel is 30, and the color data of the B channel is 35, and then the color difference values corresponding to RGB channels respectively can be obtained, which is not limited in the embodiments of the present application.

[0044] In this step, the image processing system can compare the first color data of the target food material with the second color data to determine the initial color difference value corresponding to the target food material. The initial color difference values of other target food materials in the first image are also determined by comparing the first color data in the high temperature environment with the second color data in the low temperature environment.

[0045] Further, in the step of comparing the first color data with the second color data of the at least one target food material in the second temperature environment to determine the initial color difference value corresponding to each target food material, the image processing system can determine, for the at least one target food material, a first initial color difference value of a first color channel corresponding to the target food material, a second initial color difference value of a second color channel corresponding to the target food material, and a third initial color difference value of a third color channel corresponding to the target food material according to the first color data and the second color data of the target food material; and determine the initial color difference value corresponding to the target food material based on the first initial color difference value, the second initial color difference value, and the third initial color difference value.

[0046] For example, the second color data of the target food material can be color data of any target food material in a low-temperature environment, for example, can be data corresponding to RGB of sweet potato in a low-temperature environment, which is not limited in the embodiments of the present application.

[0047] In this step, the first initial color difference value of the first color channel corresponding to the target food material can be an initial color difference value determined by any target food material in the R channel, for example, the initial color difference value in the R channel can be 5 or 10, etc., which is not limited in the embodiments of the present application.

[0048] In this step, the second initial color difference value of the second color channel corresponding to the target food material can be an initial color difference value determined by any target food material in the G channel, for example, the initial color difference value in the G channel can be 5 or 10, etc., which is not limited in the embodiments of the present application.

[0049] In this step, the third initial color difference value of the third color channel corresponding to the target food material can be an initial color difference value determined by any target food material in the B channel, for example, the initial color difference value in the B channel can be 5 or 10, etc., which is not limited in the embodiments of the present application.

[0050] In this step, the initial color difference value corresponding to the target food material can be determined by subtracting the first color data from the second color data.

[0051] Further, in the step of comparing the first color data with the second color data of the at least one target food material in the second temperature environment to determine the initial color difference value corresponding to each target food material, the image processing system can determine, for the at least one target food material, a first initial color difference value of a first color channel corresponding to the target food material, a second initial color difference value of a second color channel corresponding to the target food material, and a third initial color difference value of a third color channel corresponding to the target food material according to the first color data and the second color data of the target food material; and determine the initial color difference value corresponding to the target food material based on the first initial color difference value, the second initial color difference value, and the third initial color difference value.

[0052] Exemplarily, in this step, the color data of the first target food material can be color data of the tomatoes, or color data of the potatoes, etc., which are not limited in the embodiments of the present application.

[0053] Exemplarily, in this step, the marking process can be a process of adding a mark to the color data of the tomatoes, or a process of adding a mark to the color data of the bell peppers, etc., which are not limited in the embodiments of the present application.

[0054] In this step, the marked region can be a region obtained after the color data is marked, which can be irregular, and is not limited in the embodiments of the present application.

[0055] Exemplarily, in this step, downsampling, also known as decimation or down-sampling, is a multi-rate digital signal processing technique, or a process used to reduce the sampling rate of a signal in the field of digital signal processing.

[0056] In this step, the color region of the first target food material can be a color region of the tomatoes, or a color region of any other target food material, which are not limited in the embodiments of the present application.

[0057] In this step, in the process of image processing, the first image can be marked according to the first color data of each target food material, respectively, to determine the marked region corresponding to the target food material; the marked region is down-sampled to determine the color region of the target food material; and the color information of the color region is compared with the second color data of the at least one target food material in the second temperature environment to determine the initial color difference value corresponding to the target food material.

[0058] In this step, the initial color difference value corresponding to the target food material can be obtained after the first color data and the second color data of the target food material are compared.

[0059] Step 104, determining a target color difference value according to the initial color difference value of the at least one target food material and the coefficient corresponding to each target food material.

[0060] The image processing method provided by the embodiments of the present application, after comparing the first color data with the second color data of the at least one target food material in the second temperature environment to determine the initial color difference value corresponding to each target food material, the image processing system can further determine a target color difference value according to the initial color difference value of the at least one target food material and the coefficient corresponding to each target food material.

[0061] In this step, the coefficient corresponding to each target food material can be the weight coefficient corresponding to the RGB parameter of the target food material. For example, the weight coefficient of the R channel of the tomato is higher than the weight coefficients of the G channel and the B channel.

[0062] For example, in the embodiment of the present application, the weight coefficient of the R channel of the tomato can be 0.8, the weight coefficient of the G channel can be 0.1, and the weight coefficient of the B channel can be 0.1, which is not limited in the embodiment of the present application.

[0063] In this step, the target color difference value can be determined according to the initial color difference value of the at least one target food material and the coefficient corresponding to each target food material.

[0064] For example, in this step, the target color difference value of the R channel is determined according to the initial color difference value of the at least one target food material in the R channel and the coefficient corresponding to each target food material, which is not limited in the embodiment of the present application.

[0065] In this step, the image processing system can determine the target color difference value corresponding to the target food material according to the initial color difference value of the at least one target food material and the coefficient corresponding to each target food material.

[0066] Further, when determining the target color difference value according to the initial color difference value of the at least one target food material and the coefficient corresponding to each target food material, the image processing system can obtain first color deviation data corresponding to the first target food material in a preset time range according to the first color data and the second color data of the first target food material; wherein the first target food material is a food material whose color belongs to the first color channel among the at least one target food material; determine at least one second color deviation data corresponding to the first color channel according to the color data of at least one second target food material corresponding to the first color channel in the first temperature environment and the color data in the second temperature environment; the second target food material is other food material whose color belongs to the first color channel among the at least one target food material except the first target food material; determine the color deviation of the first color channel according to the first color deviation data and the at least one second color deviation data; correct the initial color difference value based on the color deviation to obtain the target color difference value.

[0067] In this step, the first color deviation data corresponding to the first target food material in the preset time range can be the difference value of the change rate of the color data of the R channel in the high-temperature environment and the low-temperature environment, or the difference value of the change rate of the color data of the G channel in the high-temperature environment and the low-temperature environment, or the difference value of the change rate of the color data of the B channel in the high-temperature environment and the low-temperature environment, which is not limited in the embodiment of the present application.

[0068] In this step, the food material whose color belongs to the first color channel in the at least one target food material can be a food material whose color belongs to any channel in RGB, for example, tomatoes and red peppers are food materials whose color belongs to the R channel, which is not limited in the embodiments of the present application.

[0069] In this step, when the first color channel is the G channel, the color data of the second target food material in the first temperature environment can be the color data of the cucumber in the high-temperature environment.

[0070] In this step, the color data in the second temperature environment can be color data in a low-temperature environment, for example, color data of potatoes in a low-temperature environment, and the like, which is not limited in the embodiments of the present application.

[0071] For example, in the present application, when the first color deviation data is the color deviation data in the red channel, the at least one second color deviation data corresponding to the first color channel can include the color deviation data of sweet potatoes, or the color deviation data of red-skinned radishes, and the like, which is not limited in the embodiments of the present application.

[0072] It should be noted that the second target food material can be a food material belonging to the same color channel, for example, a food material belonging to the red channel, or a food material belonging to the green channel, or a food material belonging to the blue channel, which is not limited in the embodiments of the present application.

[0073] For example, when the first color channel is the red channel, the other food materials in the first color channel can include tomatoes, beets, and the like, which is not limited in the embodiments of the present application.

[0074] In this step, the color deviation corresponding to the first color channel can be the sum of the color deviations of all food materials in the red channel, or the sum of the color deviations of all food materials in the green channel, or the sum of the color deviations of all food materials in the blue channel, which is not limited in the embodiments of the present application.

[0075] In this step, the process of determining the target color difference value can include a process of correcting the initial color difference value based on the color deviation.

[0076] In this step, the image processing system can correct the initial color difference value based on the color deviation corresponding to the RGB channel, and then obtain the target color difference value.

[0077] Further, in the process of determining the target color difference value according to the initial color difference value of at least one target food material and the coefficient corresponding to each target food material, the image processing system can further perform product processing based on the initial color difference value of the target food material and the coefficient corresponding to the target food material to determine a first color difference value corresponding to the target food material; determine a corrected first color difference value based on the first color difference value and the color offset; and perform normalization processing on the corrected first color difference value corresponding to the target food material to obtain the target color difference value corresponding to the target food material.

[0078] In this embodiment of the present application, the first color difference value corresponding to the target food material can be obtained by performing product processing on the initial color difference value of the target food material and the coefficient corresponding to the target food material. For example, in the case of an initial color difference value of 25 in the R channel, the weight coefficient of a tomato in the R channel is 0.7, and the first color difference value corresponding to the target food material can be obtained as 17.5.

[0079] It should be noted that the corrected first color difference value can be determined according to the first color difference value and the color offset. For example, the corrected first color difference value corresponding to the R channel can be obtained by calibrating the first color difference value based on the color offset.

[0080] For example, in the case of a target food material of a tomato, the weight proportion of the red channel is 0.8, and the weight proportions of the blue channel and the green channel are less. At this time, the normalized processing can be performed on the corrected first color difference value corresponding to the tomato to obtain the target color difference value corresponding to the tomato.

[0081] In this embodiment of the present application, the first color difference value corresponding to the target food material can be obtained by performing product processing on the initial color difference value of the target food material and the coefficient corresponding to the target food material. For example, in the case of an initial color difference value of 25 in the R channel, the weight coefficient of a tomato in the R channel is 0.7, and the first color difference value corresponding to the target food material can be obtained as 17.5.

[0082] Step 105: performing color correction on the first image based on the target color difference value to obtain a target image.

[0083] In this embodiment of the present application, the first color difference value corresponding to the target food material can be obtained by performing product processing on the initial color difference value of the target food material and the coefficient corresponding to the target food material. For example, in the case of an initial color difference value of 25 in the R channel, the weight coefficient of a tomato in the R channel is 0.7, and the first color difference value corresponding to the target food material can be obtained as 17.5.

[0084] In this step, the color correction process can be a process of processing the first image according to the color difference value.

[0085] In this step, the target image can be an image obtained after the first image in the high-temperature environment is denoised and color corrected.

[0086] Further, the image processing system can also extract a background region from the first image; perform a clipping mean filter processing on the background region to obtain a denoised background region; and perform image fusion on the background region and the target image to obtain a corrected first image.

[0087] The background region can be an image region in which the target food material does not exist, for example, the first image is a picture of tomatoes and eggs being stir-fried in a pot, and the image region of the tomatoes and eggs and other food materials is removed, and the remaining region in the first image is the background region.

[0088] For example, the clipping mean filter processing on the background region can obtain the denoised background region.

[0089] In this step, the denoised background region replaces the original background region in the first image, and image fusion is performed with the target image to obtain the corrected first image, that is, the corrected first image is obtained after the first image in the high-temperature environment is denoised and color corrected.

[0090] In summary, in the image processing process, the image processing system can first acquire a first image of the inside of the cooking device in a first temperature environment; the first image includes at least one target food material; determine first color data corresponding to the first image; compare the first color data with second color data of the at least one target food material in a second temperature environment to determine an initial color difference value corresponding to each target food material; determine a target color difference value according to the initial color difference value of the at least one target food material and a coefficient corresponding to each target food material; and perform color correction on the first image based on the target color difference value to obtain a target image. In the image processing process, the color difference value between the first color data corresponding to the target food material in the first temperature environment and the second color data corresponding to the target food material in the second temperature environment can be used as the initial color difference value, and the initial color difference value can be adjusted to obtain the target color difference value, to realize denoising of the first image. Further, the first image needs to be color corrected based on the target color difference value to obtain the target image, to realize color correction processing of the first image, to optimize the quality of the recorded cooking video, and to further improve the efficiency of image processing.

[0091] Referring to Figure 2 , a step flow of an image processing method provided by an embodiment of the present application is shown Figure 2 , for example,Figure 3 As shown, the method specifically includes steps 201 to 214:

[0092] Step 201: Obtain a first image of the inside of the cooking device in a first temperature environment; the first image includes at least one target food material.

[0093] In an embodiment of the present application, in the process of image processing, the image processing system can first obtain a first image in a first temperature environment; the first image includes a plurality of target food materials.

[0094] Step 202: Determine first color data corresponding to the first image.

[0095] The image processing method provided in the embodiments of the present application, after obtaining a first image of the inside of the cooking device in a first temperature environment; the first image includes at least one target food material, the image processing system can determine first color data corresponding to the first image.

[0096] In this step, in the process of image processing, the image processing system can first determine the first color data in the high-temperature environment based on the cooking video.

[0097] Step 203: For at least one target food material, according to the first color data and the second color data of the target food material, determine a first initial color difference value of a first color channel corresponding to the target food material, a second initial color difference value of a second color channel corresponding to the target food material, and a third initial color difference value of a third color channel corresponding to the target food material.

[0098] The image processing method provided in the embodiments of the present application, after determining the first color data corresponding to the first image, the image processing system can determine, for at least one target food material, a first initial color difference value of a first color channel corresponding to the target food material, a second initial color difference value of a second color channel corresponding to the target food material, and a third initial color difference value of a third color channel corresponding to the target food material according to the first color data and the second color data of the target food material.

[0099] In this step, the initial color difference value corresponding to the target food material can be determined according to the first color data and the second color data.

[0100] In this step, in the process of determining the initial color difference value corresponding to the target food material, it can be obtained after comparing the first color data and the second color data of the target food material.

[0101] Exemplarily, the RGB channel can determine the initial color difference value corresponding to its channel respectively.

[0102] Step 204, determining the initial color difference value corresponding to the target food material based on the first initial color difference value, the second initial color difference value, and the third initial color difference value.

[0103] The image processing method provided in the embodiments of the present application can determine the initial color difference value corresponding to the target food material based on the first initial color difference value, the second initial color difference value, and the third initial color difference value after determining the first initial color difference value of the first color channel corresponding to the target food material, the second initial color difference value of the second color channel corresponding to the target food material, and the third initial color difference value of the third color channel corresponding to the target food material according to the first color data and the second color data of the target food material.

[0104] For example, the image processing system can determine the initial color difference value corresponding to the target food material based on the first initial color difference value, the second initial color difference value, and the third initial color difference value.

[0105] Step 205, obtaining first color deviation data corresponding to the first target food material in a preset time range according to the first color data and the second color data of the first target food material; the first target food material is a food material whose color belongs to the first color channel among the at least one target food material.

[0106] The image processing method provided in the embodiments of the present application can obtain first color deviation data corresponding to the first target food material in a preset time range according to the first color data and the second color data of the first target food material after determining the initial color difference value corresponding to the target food material based on the first initial color difference value, the second initial color difference value, and the third initial color difference value; the first target food material is a food material whose color belongs to the first color channel among the at least one target food material.

[0107] In this step, the first color deviation data corresponding to the first target food material in the preset time range can be a change rate difference value of color data in the R channel under a high-temperature environment and a low-temperature environment, or a change rate difference value of color data in the G channel under a high-temperature environment and a low-temperature environment, or a change rate difference value of color data in the B channel under a high-temperature environment and a low-temperature environment, which is not limited in the embodiments of the present application.

[0108] Step 206, determining at least one second color deviation data corresponding to the first color channel according to color data of at least one second target food material in the first temperature environment and color data of the second target food material in the second temperature environment; the second target food material is a food material other than the first target food material and whose color belongs to the first color channel among the at least one target food material.

[0109] The image processing method provided in the embodiments of the present application comprises: obtaining first color deviation data corresponding to the first target food material in a preset time range according to first color data and second color data of the first target food material; wherein the first target food material is a food material whose color belongs to a first color channel among at least one target food material; and the image processing system can determine at least one second color deviation data corresponding to the first color channel according to color data of at least one second target food material corresponding to the first color channel in a first temperature environment and color data of the at least one second target food material in a second temperature environment.

[0110] Exemplarily, in the present application, when the first color deviation data is color deviation data in a red color channel, the at least one second color deviation data corresponding to the first color channel can comprise color deviation data of sweet potatoes or color deviation data of red-skinned radishes, and the like, which are not limited in the embodiments of the present application.

[0111] Step 207: determining color deviation corresponding to the first color channel according to the first color deviation data and the at least one second color deviation data.

[0112] The image processing method provided in the embodiments of the present application comprises: obtaining first color deviation data corresponding to the first target food material in a preset time range according to first color data and second color data of the first target food material; wherein the first target food material is a food material whose color belongs to a first color channel among at least one target food material; and the image processing system can determine at least one second color deviation data corresponding to the first color channel according to color data of at least one second target food material corresponding to the first color channel in a first temperature environment and color data of the at least one second target food material in a second temperature environment.

[0113] In this step, the color deviation corresponding to the first color channel can be a sum result of color deviations of all food materials in a red color channel, or a sum result of color deviations of all food materials in a green color channel, or a sum result of color deviations of all food materials in a blue color channel, which are not limited in the embodiments of the present application.

[0114] Step 208: determining first color difference corresponding to the target food material by performing product processing on an initial color difference of the target food material and a coefficient corresponding to the target food material.

[0115] The image processing method provided in the embodiments of the present application comprises: obtaining first color deviation data corresponding to the first target food material in a preset time range according to first color data and second color data of the first target food material; wherein the first target food material is a food material whose color belongs to a first color channel among at least one target food material; and the image processing system can determine at least one second color deviation data corresponding to the first color channel according to color data of at least one second target food material corresponding to the first color channel in a first temperature environment and color data of the at least one second target food material in a second temperature environment.

[0116] The first color difference value corresponding to the target food material can be obtained by multiplying the initial color difference value of the target food material and the coefficient corresponding to the target food material, for example, in the case of the initial color difference value of 25 in the R channel, the weight coefficient of the tomato in the R channel is 0.7, and the first color difference value corresponding to the target food material can be obtained as 17.5, which is not limited in the embodiments of the present application.

[0117] In step 209, a corrected first color difference value is determined based on the first color difference value and the color offset.

[0118] The image processing method provided in the embodiments of the present application can determine a corrected first color difference value based on the first color difference value and the color offset after multiplying the initial color difference value of the target food material and the coefficient corresponding to the target food material to determine the first color difference value corresponding to the target food material.

[0119] It should be noted that the corrected first color difference value can be determined according to the first color difference value and the color offset, for example, the corrected first color difference value corresponding to the R channel can be obtained by calibrating the first color difference value based on the color offset.

[0120] In step 210, the corrected first color difference value corresponding to the target food material is normalized to obtain a target color difference value corresponding to the target food material.

[0121] The image processing method provided in the embodiments of the present application can normalize the corrected first color difference value corresponding to the target food material to obtain a target color difference value corresponding to the target food material after determining the corrected first color difference value based on the first color difference value and the color offset.

[0122] In this step, in the process of determining the target color difference value according to the initial color difference value of at least one target food material and the coefficient corresponding to each target food material, the image processing system can multiply the initial color difference value of the target food material and the coefficient corresponding to the target food material to determine the first color difference value corresponding to the target food material; determine the corrected first color difference value based on the first color difference value and the color offset; and normalize the corrected first color difference value corresponding to the target food material to obtain the target color difference value corresponding to the target food material.

[0123] In step 211, the first image is color corrected based on the target color difference value to obtain a target image.

[0124] The image processing method provided in the embodiments of the present application can color correct the first image based on the target color difference value to obtain a target image after normalizing the corrected first color difference value corresponding to the target food material to obtain the target color difference value corresponding to the target food material.

[0125] In this step, the color correction process can be a process of processing the first image according to the color difference value.

[0126] In this step, the target image can be an image obtained after the first image in the high-temperature environment is denoised and color corrected.

[0127] Step 212, extract the background region from the first image.

[0128] The image processing method provided by the embodiment of the application can extract the background region from the first image after the target color difference value is used to correct the color of the first image to obtain the target image.

[0129] The background region can be an image region in which the target food material does not exist. For example, the first image is a picture of a tomato and an egg being stir-fried in a pot, and the region in the first image except the image region of the tomato and the egg is the background region.

[0130] Step 213, perform a clipping mean filter processing on the background region to obtain a denoised background region.

[0131] The image processing method provided by the embodiment of the application can perform a clipping mean filter processing on the background region to obtain a denoised background region after the background region is extracted from the first image.

[0132] Exemplarily, the clipping mean filter processing on the background region can obtain the denoised background region.

[0133] Step 214, perform image fusion on the background region and the target image to obtain a corrected first image.

[0134] The image processing method provided by the embodiment of the application can perform image fusion on the background region and the target image to obtain a corrected first image after the clipping mean filter processing on the background region obtains the denoised background region.

[0135] In this step, the denoised background region replaces the original background region in the first image, and image fusion is performed on the denoised background region and the target image to obtain the corrected first image, that is, the corrected first image is obtained after the first image in the high-temperature environment is denoised and color corrected.

[0136] It should be noted that in the embodiment of the application, the execution order of steps 212 to 214 is not limited to the execution order of steps 201 to 211.

[0137] In summary, in the process of image processing, the image processing system can first acquire a first image of the inside of the cooking device in a first temperature environment; the first image includes at least one target food material; determine the first color data corresponding to the first image; compare the first color data with the second color data of the at least one target food material in a second temperature environment to determine the initial color difference value corresponding to each target food material; determine the target color difference value according to the initial color difference value of the at least one target food material and the coefficient corresponding to each target food material; and perform color correction on the first image based on the target color difference value to obtain a target image. In the process of image processing, the color difference value between the first color data corresponding to the target food material in the first temperature environment and the second color data corresponding to the target food material in the second temperature environment can be taken as the initial color difference value, and the initial color difference value can be adjusted to obtain the target color difference value, so that the noise reduction processing of the first image is realized, and further, the color correction of the first image is realized based on the target color difference value to obtain the target image, the quality of the recorded cooking video is optimized, and the efficiency of image processing is improved.

[0138] Reference Figure 3 , shows the steps of an image processing method provided by the embodiments of the present application Figure 4 As shown in Figure 4 , the method specifically includes steps 301 to 308:

[0139] Step 301, record a video of the cooking process, and extract temperature data during recording the video.

[0140] In the process of noise reduction and color correction of the video in the cooking process, as the cooking time increases, the temperature environment of the camera position will gradually increase, and as the temperature of the environment increases, the noise in the picture output by the camera will gradually increase, and the picture output by the camera will also produce color difference. However, for the cooking process, the change of the picture in the steaming oven is relatively consistent. Therefore, the picture data of the high temperature stage can be corrected based on the data of the picture at a low temperature.

[0141] The image processing system can acquire the real-time temperature of the camera according to the temperature sensor, take the video data when the temperature of the camera is low as the reference, and perform noise reduction on the picture in the high temperature working interval of the camera based on time domain analysis of multiple frames of picture data. The cooking target object is subjected to food material recognition, and the food material type is extracted, and the color and white balance of the current picture are corrected based on the color change curve of the type of food material.

[0142] Step 302, divide the recorded picture into a low temperature zone, a temperature rising zone and a high temperature zone based on the temperature data during recording the video.

[0143] According to the acquired temperature data, the image processing system can divide the recorded picture into different temperature regions. For example, the recorded picture can be divided into a low-temperature region, a temperature-increasing region, and a high-temperature region.

[0144] Step 303, based on the picture data of the low-temperature region, the picture of the high-temperature region is processed in combination with the change value of the picture data of the temperature-increasing region.

[0145] When recording the video of the cooking process of the steaming oven, the temperature environment in which the camera works is acquired by reading the temperature sensor located in the camera. The working time of the camera is divided into low-temperature working time, temperature-increasing working time, and high-temperature working time. The picture data of the low-temperature working time is used to correct the picture worked in the high-temperature working time in combination with the change of the picture data of the temperature-increasing working time, so as to smooth the abnormally high brightness points of the picture in the high-temperature working interval.

[0146] Using professional image equipment, the images of different food materials in the steaming oven are collected to obtain the real color data of the food material as the reference color data, and a one-to-one corresponding database of characteristic food material-cooking time-color is made.

[0147] When the food material is put into the steaming oven, the characteristic food material is identified and extracted. The color change curve of the characteristic food material in different cooking processes is obtained through prior experiments, and the put-in food material is matched to obtain the original color of the food. Based on the original color of the food in the above database and the color of the current picture, the deviation value of the color channel of the picture can be obtained, and based on the deviation value, the color of the whole picture can be corrected.

[0148] The above is a method of reducing noise in the time domain. After the picture is processed in the time domain, the background region, i.e., the region without a shooting target, needs to be processed.

[0149] Step 304, the region without a shooting target and with low brightness is subjected to amplitude limiting mean filtering.

[0150] The picture of the camera is divided into a picture region with a photographed object and a picture region without a photographed object. For the picture region without a photographed object and the region with low brightness, the amplitude limiting mean filtering is performed again to process the abnormal white noise in the low-brightness picture region.

[0151] Exemplarily, in the process of eliminating abnormal noise of the dark background, the picture collected by the camera in a high temperature environment may have abnormal white noise that does not conform to the background, which is generally caused by the thermal noise of a complementary metal oxide semiconductor (CMOS).

[0152] Step 305, generating a denoised video.

[0153] After the above two-step processing, the abnormal noise generated by the operation in a high temperature environment can be effectively suppressed, and the effect of the recorded cooking process video can be improved.

[0154] Step 306, identifying the food material when starting cooking.

[0155] When starting cooking, the image processing system can identify the type of food material.

[0156] Exemplarily, after extracting the features of the food material, the type of the food material can be determined, and then the color of the food material is marked and the food material image is down-sampled to extract the approximate distribution of the color of the food material.

[0157] Exemplarily, when the food material is put into the steaming oven, the food material is identified, the feature food material is extracted, and the color change curve of the feature food material in different cooking processes is obtained through prior experiments, and the put-in food material is matched.

[0158] Step 307, extracting the feature food material and matching it with the food material in the database to obtain the color data and the color change curve in the cooking process.

[0159] Exemplarily, multiple food materials are used during cooking, such as stir-frying tomatoes with broccoli, and other side dishes such as onions and garlic are also extracted. The extracted food material is matched with the food material category in the database, and the tomatoes and broccoli can be matched to the corresponding color parameters in the database. Generally, the red channel and brightness channel data are used for tomatoes, and the blue channel and green channel data are used for broccoli.

[0160] Step 308, correcting the color of the cooking video based on the color data in the database.

[0161] Exemplarily, in the embodiment of the present application, in the process of correcting the color of the cooking video based on the color data in the database, for example, the red channel of a tomato can decrease by 2% every minute during the hot air barbecue process; in fact, if the actual image collected by the image has a 3% decrease in the tomato; thus it can be determined that the polynomial y (red color offset) = a x 1 (tomato color offset) + b x 2 (other vegetable color offset), and then the color difference value is corrected according to the color offset value of the RGB three channels to obtain the target color difference value.

[0162] Finally, the image processing system can perform color correction on the first image based on the target color difference value to obtain a target image.

[0163] Exemplarily, the correction process according to the deviation value can include adjusting the filter of the three color channels when synthesizing the image color.

[0164] Exemplarily, the common thermal noise of the CMOS sensor is generally characterized by abnormal high-frequency signals in the time domain. Taking the value of a certain channel (red channel) of a pixel point as an example, the occurrence of noise is generally characterized by an increase in the value of the channel to a very high value in a very short time, forming a sharp peak. The purpose of noise reduction is to eliminate this sharp peak.

[0165] Exemplarily, the obtained signal feature can include the frequency domain signal of the image, because the signals generated by thermal noise are mainly high-frequency signals, and there are also camera color data and image gray gradient data. The color channel data is collected by downsampling (which can be understood as the image is reduced to a 64*64 color block group). In the embodiment of the present application, it is not limited.

[0166] Specifically, when the temperature of the camera is in a high temperature range for a long time, the time window of the general time domain noise reduction algorithm is short, and the effect is poor in the high temperature environment noise reduction process (because the signal already contains a lot of high-frequency components at this time). At this time, the signal features obtained in the low temperature environment are used to optimize the processing algorithm in the high temperature environment.

[0167] Exemplarily, the premise assumption is that the color offset of the CMOS image is consistent throughout the frame.

[0168] It is known that the types of food materials in the image and the colors (original colors) of the corresponding types of food materials.

[0169] Obtain color data of the target food material, compare the color data of the target food material with data in the database, and obtain a difference value of the image data. Multiply the difference value by a coefficient of the food material and perform normalization processing. One cooking process uses multiple food materials, and the above processing is performed for each food material.

[0170] Output: color deviation value of CMOS, and adjust the filter of the synthesized image based on the deviation value.

[0171] The above is a method of reducing noise based on time domain. The image of the camera is divided into a picture area where the photographed object exists and a picture area where the photographed object does not exist. For the picture area where the photographed object does not exist and the area where the brightness of the picture is dark, re-amplitude mean filtering is performed to process abnormal white noise in the low brightness picture area.

[0172] In summary, in the process of repairing color difference, color difference may occur in high-temperature environment imaging, which can be considered as the offset of the CMOS imaging channel. This offset needs to be corrected. For the elimination of imaging noise, it can be considered that the gradient change of most pictures in the entire cooking process is uniform, but thermal noise may produce abnormal gradient data locally, which needs to be smoothed.

[0173] In the embodiments of the present application, the recorded video is mainly divided into different intervals based on time, and time domain noise reduction is performed on the high-temperature region based on the low-temperature region. In addition, filtering is performed on the region where the photographed object does not exist to achieve spatial domain noise reduction.

[0174] Device embodiment

[0175] As shown in Figure 1 , Figure 5 A logic block diagram of an image processing device provided by an embodiment of the present application is shown, which includes:

[0176] The first acquisition module 1001 is configured to acquire a first image of the inside of the cooking device under a first temperature environment; the first image includes at least one target food material;

[0177] The first determination module 1002 is configured to determine first color data corresponding to the first image; compare the first color data with second color data of the at least one target food material in a second temperature environment, determine an initial color difference value corresponding to each target food material, and determine a target color difference value according to the initial color difference value of the at least one target food material and the coefficient corresponding to each target food material.

[0178] The obtaining module 1003 is configured to perform color correction on the first image based on the target color difference value to obtain a target image.

[0179] Optionally, the first determining module comprises:

[0180] The second obtaining module is configured to obtain first color cast data corresponding to the first target food material in a preset time range according to the first color data and second color data of the first target food material, wherein the first target food material is a food material whose color belongs to the first color channel among the at least one target food material.

[0181] The second determining module is configured to determine at least one second color cast data corresponding to the first color channel according to color data of at least one second target food material corresponding to the first color channel in the first temperature environment and color data of the second target food material in the second temperature environment, wherein the second target food material is a food material other than the first target food material and whose color belongs to the first color channel among the at least one target food material; and determine the color cast offset corresponding to the first color channel according to the first color cast data and the at least one second color cast data.

[0182] The obtaining module is configured to correct the initial color difference value based on the color cast offset to obtain a target color difference value.

[0183] Optionally, the determining module further comprises:

[0184] The determining sub-module is configured to determine, for at least one target food material, a first initial color difference value of a first color channel corresponding to the target food material, a second initial color difference value of a second color channel corresponding to the target food material, and a third initial color difference value of a third color channel corresponding to the target food material according to the first color data and second color data of the target food material; and determine an initial color difference value corresponding to the target food material based on the first initial color difference value, the second initial color difference value, and the third initial color difference value.

[0185] Optionally, the apparatus further comprises:

[0186] The third determining module is configured to mark the color data of the first target food material in the first image to determine a marking region; perform down-sampling based on the marking region to determine a color region of the first target food material; and determine a target color difference value based on the color region and the color cast offset.

[0187] Optionally, the first determining module further comprises:

[0188] The first determining sub-module is configured to determine a first color difference value corresponding to a target food material by performing product processing on an initial color difference value of the target food material and a coefficient corresponding to the target food material; and determine a corrected first color difference value based on the first color difference value and the color cast offset.

[0189] A sub-module is obtained, which is configured to normalize the corrected first color difference value corresponding to the target food material to obtain a target color difference value corresponding to the target food material.

[0190] Optionally, the apparatus further comprises:

[0191] An extraction sub-module is configured to extract a background region from the first image.

[0192] A first obtaining sub-module is configured to perform amplitude limiting mean filtering on the background region to obtain a background region after noise reduction, and perform image fusion on the background region after noise reduction and the target image to obtain a corrected first image.

[0193] For the apparatus embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the related parts are described in the method embodiment.

[0194] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same and similar parts of each embodiment can be referred to each other.

[0195] The image processing apparatus in the embodiments of the present application can be an electronic device or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices. For example, the electronic device can be a GPU BOX, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a mobile Internet device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), and the like. The electronic device can also be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, and the like. The embodiments of the present application are not limited in this regard.

[0196] The image processing apparatus in the embodiments of the present application can be a device with an operating system. The operating system can be an Android operating system, a Linux operating system, a Windows operating system, or other possible operating systems. The embodiments of the present application are not limited in this regard.

[0197] The image processing apparatus provided by the embodiments of the present application can realize ​ The method embodiments realize various processes, and details are not repeated herein.

[0198] Optionally, as ​ shown, the embodiments of the present application further provide an electronic device 1100, which comprises a processor 1101 and a memory 1102, and the memory 1102 stores programs or instructions executable on the processor 1101, and the programs or instructions are executed by the processor 1101 to realize various steps of the above image processing method embodiments and achieve the same technical effects. Details are not repeated herein.

[0199] In the embodiments of the present application, the memory 1102 can be used to store software programs and various data. The memory 1102 can mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area can store an operating system, at least one application program or instruction required by a function (such as a sound playing function, an image playing function, etc.), etc. In addition, the memory 1102 can include a volatile memory or a non-volatile memory, or the memory 1102 can include both volatile and non-volatile memories. The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synch link DRAM (SLDRAM), and a Direct Rambus RAM (DRRAM). The memory 1102 in the embodiments of the present application includes but is not limited to these and any other suitable types of memories.

[0200] The processor 1101 can include one or more processing units; optionally, the processor 1101 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and an application program, and the modem processor mainly processes a wireless communication signal, such as a baseband processor. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 1101.

[0201] The embodiment of the present application further provides an image processing system, which comprises a processor, a memory, a communication interface, and a communication bus, the processor, the memory, and the communication interface complete communication with each other through the communication bus; the memory is used for storing executable instructions, the executable instructions enable the processor to execute various processes of the image processing method embodiment and achieve the same technical effects, and details are not repeated here to avoid repetition.

[0202] The embodiment of the present application further provides a readable storage medium, which stores a program or instructions, the program or instructions are executed by a processor to realize various processes of the above-mentioned image processing method embodiment and achieve the same technical effects, and details are not repeated here to avoid repetition.

[0203] The processor is the processor in the image processing system in the above-mentioned embodiment. The readable storage medium includes a computer readable storage medium, such as a computer readable memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0204] The embodiment of the present application further provides a chip, which comprises a processor and a communication interface, the communication interface and the processor are coupled, the processor is used for running a program or instructions to realize various processes of the above-mentioned image processing method embodiment and achieve the same technical effects, and details are not repeated here to avoid repetition.

[0205] It should be understood that the chip involved in the embodiment of the present application can also be referred to as a system-level chip, a system chip, a chip system, or a system-on-chip chip.

[0206] The embodiment of the present application provides a computer program product, which is stored in a storage medium, the program product is executed by at least one processor to realize various processes of the above-mentioned image processing method embodiment and achieve the same technical effects, and details are not repeated here to avoid repetition.

[0207] It should be noted that, in the present document, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element. Furthermore, it is to be understood that the method and apparatus of the present application can be carried out by more than one process, method, article, or apparatus either simultaneously, concurrently, or with intervening action that are carried out at the same time, either in a simultaneous fashion or in a fashion that is interleaved in time. For example, the described methods can be performed in a different order from that described, and / or various steps can be combined or omitted, and / or additional steps can be added, without departing from the scope of the described methods. Also, features described with respect to certain examples can be combined in other examples.

[0208] From the above description of the embodiments, it is apparent that the above-mentioned method can be realized by means of software and necessary universal hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solution of the present application or the part that contributes to the related art can be embodied in the form of computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disc, optical disc), and includes a plurality of instructions to make a terminal (which can be a mobile phone, computer, server, or network equipment, etc.) execute the method described in each embodiment of the present application.

[0209] The embodiments of the present application are described above in conjunction with the drawings, but the present application is not limited to the above-described specific embodiments, and the above-described specific embodiments are merely illustrative, rather than limiting, and those skilled in the art can make many forms under the inspiration of the present application without departing from the scope of the present application and the protection scope of the claims, which all belong to the protection scope of the present application.

Claims

1. An image processing method, characterized in that, Applied to an image processing system, the method includes: Acquire a first image of the interior of a cooking device under a first temperature environment; the first image includes at least one target ingredient; Determine the first color data corresponding to the first image; The first color data is compared with the second color data of the at least one target ingredient in the second temperature environment to determine the initial color difference value corresponding to each target ingredient; The target color difference is determined based on the initial color difference of at least one target ingredient and the coefficient corresponding to each target ingredient; The first image is color-corrected based on the target color difference to obtain the target image; The step of determining the target color difference based on the initial color difference of at least one target ingredient and the coefficient corresponding to each target ingredient includes: Based on the first color data and the second color data of the first target ingredient, obtain the first color deviation data corresponding to the first target ingredient within a preset time range; wherein, the first target ingredient is an ingredient whose color belongs to the first color channel among the at least one target ingredients; Based on the color data of at least one second target ingredient corresponding to the first color channel in the first temperature environment and the color data in the second temperature environment, at least one second color shift data corresponding to the first color channel is determined; the second target ingredient is other ingredients whose colors, other than the first target ingredient, belong to the first color channel among the at least one target ingredients. Based on the first color shift data and the at least one second color shift data, determine the color shift corresponding to the first color channel; The initial color difference is corrected based on the color shift to obtain the target color difference. The first color difference of the target ingredient is determined by multiplying the initial color difference of the target ingredient with the coefficient corresponding to the target ingredient. Based on the first color difference and the color shift, the corrected first color difference is determined; The corrected first color difference value corresponding to the target ingredient is normalized to obtain the target color difference value corresponding to the target ingredient.

2. The method according to claim 1, characterized in that, The step of comparing the first color data with the second color data of the at least one target ingredient in the second temperature environment to determine the initial color difference value corresponding to each target ingredient includes: For at least one target ingredient, based on the first color data and the second color data of the target ingredient, a first initial color difference value for the first color channel corresponding to the target ingredient, a second initial color difference value for the second color channel corresponding to the target ingredient, and a third initial color difference value for the third color channel corresponding to the target ingredient are determined. Based on the first initial color difference, the second initial color difference, and the third initial color difference, the initial color difference corresponding to the target ingredient is determined.

3. The method according to claim 1, characterized in that, The step of comparing the first color data with the second color data of the at least one target ingredient in the second temperature environment to determine the initial color difference value corresponding to each target ingredient includes: Based on the first color data, each target ingredient in the first image is marked to determine the marked area corresponding to the target ingredient; The marked area is downsampled to determine the color region of the target ingredient; The color information corresponding to the color region is compared with the second color data of the at least one target ingredient in the second temperature environment to determine the initial color difference corresponding to the target ingredient.

4. The method according to claim 1, characterized in that, The method further includes: Extract the background region from the first image; The background region is subjected to amplitude-limited mean filtering to obtain the noise-reduced background region; The denoised background region and the target image are fused to obtain the corrected first image.

5. An image processing apparatus, characterized in that, The device includes: The first acquisition module is used to acquire a first image of the interior of the cooking device under a first temperature environment; the first image includes at least one target ingredient; A first determining module is configured to: determine the first color data corresponding to the first image; compare the first color data with the second color data of the at least one target food ingredient in a second temperature environment to determine the initial color difference value corresponding to each target food ingredient; determine the target color difference value based on the initial color difference value of the at least one target food ingredient and the coefficient corresponding to each target food ingredient; obtain the first color deviation data corresponding to the first target food ingredient within a preset time range based on the first color data and the second color data of the first target food ingredient; wherein, the first target food ingredient is the food ingredient whose color belongs to the first color channel among the at least one target food ingredient; and determine the first color deviation data based on the color data of the at least one second target food ingredient corresponding to the first color channel in the first temperature environment and the color data in the second temperature environment. At least one second color shift data corresponding to a color channel; the second target ingredient is other ingredients among the at least one target ingredients whose colors belong to the first color channel, excluding the first target ingredient; based on the first color shift data and the at least one second color shift data, determine the color shift corresponding to the first color channel; based on the color shift, correct the initial color difference value to obtain a target color difference value; based on the initial color difference value of the target ingredient and the coefficient corresponding to the target ingredient, perform a product operation to determine the first color difference value corresponding to the target ingredient; based on the first color difference value and the color shift, determine the corrected first color difference value; normalize the corrected first color difference value corresponding to the target ingredient to obtain the target color difference value corresponding to the target ingredient; The module is used to perform color correction on the first image based on the target color difference to obtain the target image.

6. An image processing system, characterized in that, The image processing system includes a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface communicate with each other through the communication bus. The memory is used to store executable instructions that cause the processor to perform the image processing method as described in any one of claims 1 to 4.

7. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the image processing method of any one of claims 1 to 4.

Images