Cooking appliance

Abstract

To provide a cooking appliance capable of accurately determining browning of food.SOLUTION: A cooking appliance comprises an imaging unit that generates image data representing an image including an image of food, and a processing unit that acquires color difference of the image in an L*a*b* color system from the image data and detects browning of the food based on the color difference.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a cooking appliance. [Background technology]

[0002] Patent Document 1 discloses a cooking device that estimates the state of an object to be cooked based on an image of the object to be cooked captured in a captured image. The cooking area containing the image of the object to be cooked is extracted, the brightness of the extracted cooking area is calculated, and the browning corresponding to the calculated brightness is reported (paragraphs 0033, 0054, 0056, and 0062). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7178622 Summary of the Invention [Problem to be solved by the invention]

[0004] The cooking device disclosed in Patent Document 1 is unable to accurately estimate the state of the object to be cooked, and is unable to accurately notify the degree of browning.

[0005] In view of this problem, an aspect of the present disclosure provides a cooking device that can accurately determine the browning of ingredients, for example. [Means for solving the problem]

[0006] The cooking device according to one aspect of the present disclosure includes an imaging unit that generates image data representing an image including an image of an ingredient, and an L * a * b * and a processing unit that acquires the color difference of the image in a color system and detects the browning of the food material based on the color difference. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a block diagram of a cooking device according to a first embodiment. [Figure 2] 3 is a diagram showing the contents of processing performed by an imaging unit and a processing unit provided in the cooking device of the first embodiment. FIG. [Figure 3] This is a graph showing an example of changes in lightness L*, chromaticity a*, and chromaticity b* of food from the start to the end of grilling in a three-dimensional Cartesian coordinate system defined by the L* axis, a* axis, and b* axis. [Figure 4] This is a diagram showing the lightness L*1, chromaticity a*1, and chromaticity b*1 of an ingredient at the beginning of cooking, and the lightness L*2, chromaticity a*2, and chromaticity b*2 of the ingredient at the end of cooking, in a color difference diagram in the L*a*b* color space. [Figure 5] 3A to 3C are diagrams schematically showing examples of images represented by image data generated by an imaging section provided in the cooking device of the first embodiment. [Figure 6] 4 is a flowchart showing a flow of processing performed by a processing unit provided in the cooking device of the first embodiment. [Figure 7] 4 is a flowchart showing a flow of processing performed by a processing unit provided in the cooking device of the first embodiment. [Figure 8] 1 is a graph showing an example of changes over time in lightness L*, chromaticity a*, and chromaticity b* of white bread during toasting. [Figure 9] 10 is a graph showing an example of the color difference ΔE* of white bread during toasting and the change over time in the image. [Figure 10] 1 is a graph showing an example of changes in lightness L*, chromaticity a*, and chromaticity b* of white bread and rye bread during toasting in a three-dimensional Cartesian coordinate system defined by the L* axis, a* axis, and b* axis. [Figure 11] 1 is a graph showing an example of changes in chromaticity a* and chromaticity b* of white bread and rye bread during toasting in a two-dimensional Cartesian coordinate system defined by the a* axis and the b* axis. [Figure 12]1 is a graph showing an example of changes in lightness L* and chromaticity a* of white bread and rye bread during toasting in a two-dimensional orthogonal coordinate system defined by the L* axis and the a* axis. [Figure 13] 1 is a graph showing an example of the change over time in the absolute value E* of white bread and rye bread during toasting. [Figure 14] 1 is a graph showing an example of the change over time in the relative value (color difference) ΔE* of white bread and rye bread during toasting. [Figure 15] 10 is a table showing images of white bread and rye bread when the absolute values ​​E* of white bread and rye bread during toasting are "255", "245", and "220". [Figure 16] This is a table showing images of white bread and rye bread when the relative values ​​(color differences) ΔE* of white bread and rye bread during toasting become "17", "78", "89", and "120". [Figure 17] 1 is a graph showing an example of changes over time in lightness L*, chromaticity a*, and chromaticity b* of a salmon fillet during heating. [Figure 18] 10 is a flowchart showing a flow of processing performed by a processing unit provided in a cooking device of a second embodiment. [Figure 19] 10 is a flowchart showing a flow of processing performed by a processing unit provided in a cooking device of a second embodiment. [Figure 20] 10 is a flowchart showing a flow of processing performed by a processing unit provided in a cooking device of a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the drawings, the same or equivalent elements are designated by the same reference numerals, and redundant description will be omitted.

[0009] 1. First embodiment 1.1 Cooker FIG. 1 is a block diagram of a cooking device according to a first embodiment.

[0010] The cooking device 1 of the first embodiment shown in Fig. 1 heats food ingredients 10 to cook them. While heating the food ingredients 10, the cooking device 1 detects the browning of the food ingredients 10 and controls the heating of the food ingredients 10 based on the detected browning of the food ingredients 10. The cooking device 1 can appropriately detect the browning of the food ingredients 10 and appropriately control the heating of the food ingredients 10. This makes it possible to prevent the food ingredients 10 from being overheated or underheated.

[0011] As shown in FIG. 1, the cooking device 1 includes a heating chamber 21, a heating unit 22, an imaging unit 23, and a processing unit 24.

[0012] An internal space 21a is formed in the heating chamber 21. The internal space 21a accommodates the food material 10.

[0013] Heating section 22 heats food material 10 accommodated in interior space 21a of heating chamber 21. Heating section 22 heats food material 10 by dielectric heating, induction heating, infrared heating, combustion heating, steam heating, or the like.

[0014] The imaging unit 23 captures an image of the interior space 21a of the heating chamber 21. As a result, the imaging unit 23 generates image data 31 that represents an image including an image of the food ingredients 10 contained in the interior space 21a.

[0015] Processing unit 24 controls heating unit 22 and imaging unit 23. Processing unit 24 controls heating unit 22 to cause heating unit 22 to heat food ingredient 10. While heating unit 22 is heating food ingredient 10, processing unit 24 controls imaging unit 23 to cause imaging unit 23 to generate image data 31. Processing unit 24 detects the browning of food ingredient 10 from the generated image data 31 and controls heating unit 22 based on the detected browning of food ingredient 10. For example, processing unit 24 controls the intensity of heating by heating unit 22, the heating time by heating unit 22, the timing to end heating by heating unit 22, etc., based on the browning of food ingredient 10. Detecting the browning of food ingredient 10 includes detecting the degree of browning of food ingredient 10.

[0016] The processing unit 24 includes a microcontroller and peripheral circuits. The microcontroller includes a processor and memory. The processor executes a program stored in the memory to cause the microcontroller and peripheral circuits to operate as the processing unit 24. All or part of the processing performed by the processing unit 24 may be performed by electronic circuits.

[0017] 1.2 Browning detection FIG. 2 is a diagram showing the contents of the processing performed by the imaging unit and the processing unit provided in the cooking device of the first embodiment.

[0018] 2, the imaging unit 23 generates image data 31 in the RGB color system. The image data 31 includes tristimulus values ​​R, G, and B for each of a plurality of pixels.

[0019] The processing unit 24 outputs the generated image data 31 to the L * a * b * The image data 32 is converted into image data 32 in a color system. The image data 32 is a lightness L * , chromaticity a * and chromaticity b * Includes: L * a * b * The color system has characteristics that are close to human vision.

[0020] The processing unit 24 extracts the L from the image data 32 obtained by the conversion. * a * b * The color difference (ΔE * )33 is obtained.

[0021] The processing unit 24 measures the color difference (ΔE * ) 33, the browning 34 of the food material 10 is detected.

[0022] L * a * b * Lightness L of the image of food ingredient 10 in the color system *The correlation between the browning of food 10 and the L * a * b * Lightness L of the image of food ingredient 10 in the color system * The browning 34 of the food material 10 cannot be accurately detected from the L * a * b * The color difference (ΔE * ) 33 and the browning of the food 10 34 are strongly correlated. * a * b * The color difference (ΔE * ) 33, the browning 34 of the food material 10 can be accurately detected.

[0023] 1.3 Relationship between color difference of food image and browning of food Lightness L * 1, chromaticity a * 1 and chromaticity b * The first color and lightness L are represented by 1 * 2, chromaticity a * 2 and chromaticity b * The color difference ΔE between the first color and the second color is expressed by * is expressed by equation (1).

[0024]

number

[0025] Figure 3 shows the L * axis, a * axis and b * The lightness L of the food from the beginning to the end of cooking in a three-dimensional Cartesian coordinate system defined by the axes * , chromaticity a * and chromaticity b * 4 is a graph showing an example of the change in L * a * b * The lightness L of the food at the beginning of cooking in the color difference diagram of the color space * 1, chromaticity a * 1 and chromaticity b *1 and the brightness of the food at the end of cooking * 2, chromaticity a * 2 and chromaticity b * 2 is a diagram showing

[0026] As shown in FIGS. 3 and 4, the food material 10 has a lightness L * 1, chromaticity a * 1 and chromaticity b * 1, and at the end of baking, the color has a first color represented by a value of L * 2, chromaticity a * 2 and chromaticity b * 2. The color of the food item 10 changes from the first color to the second color while being baked. At that time, the lightness L of the food item 10 * , chromaticity a * and chromaticity b * All of this changes.

[0027] Therefore, the lightness L of the ingredient 10 * If the browning 34 of the food material 10 is determined based on the brightness L of the food material 10, the browning 34 of the food material 10 cannot be accurately determined. * , chromaticity a * and chromaticity b * When the browning 34 of the food material 10 is determined based on the brightness L of the food material 10, the browning 34 of the food material 10 can be accurately determined. * , chromaticity a * and chromaticity b * Reflecting the color difference ΔE of 10 * If the browning 34 of the food material 10 is determined based on the above, the browning 34 of the food material 10 can be accurately determined. * ) 33 accurately detects the browning 34 of the food 10.

[0028] 1.4 Reference time for color difference of food images The processing unit 24 measures the color difference (ΔE * ) 33 is obtained. Therefore, the color difference (ΔE* ) 33 is the brightness L of the image of the food material 10 when the heating unit 22 starts heating the food material 10. * 1, chromaticity a * 1 and chromaticity b * 1 and the brightness L of the image of the food material 10 at that particular time * 2, chromaticity a * 2 and chromaticity b * 2 according to formula (1). The point in time when the heating unit 22 starts heating the food material 10 is taken as the reference point, and the lightness L * This is suitable for ingredients that do not grow large.

[0029] 1.5 Obtaining color differences in food images FIG. 5 is a diagram schematically showing an example of an image represented by image data generated by an imaging section provided in the cooking device of the first embodiment.

[0030] 5, the image 41 represented by the image data 31 includes a plurality of regions 51. The plurality of regions 51 are arranged in a matrix. The plurality of regions 51 do not have to be arranged in a matrix.

[0031] As shown in FIG. 2, the processing unit 24 extracts the L * a * b * Lightness (L * )35.

[0032] The processing unit 24 calculates the brightness (L * 5 from the plurality of regions 51 shown in Fig. 5 based on the image 35. The processing unit 24 selects the selection region 53 shown in Fig. 5 so that the selection region 53 exists within the image 55 of the food material 10.

[0033] The processing unit 24 is * a * b * Color difference ΔE in the selected area 53 of the color system * The color difference ΔE of the selected area 53 is obtained.* is the color difference (ΔE * )33.

[0034] 1.6 Selecting a Selection Area When selecting the selection area 53, the processing unit 24 performs the following processing.

[0035] The processing unit 24 calculates the brightness (L * )35 Maximum brightness L * and from the plurality of regions 51, the identified maximum brightness L * Brightness L more than double the setting * The processor 24 selects regions A, B, C, D, and E having the above-mentioned features. This makes it easier to select regions that exist within the image 55 of the food ingredient 10, and harder to select regions that exist outside the image 55 of the food ingredient 10. This makes it easier to select regions that are likely to be usable for detecting the browning 34 of the food ingredient 10, and harder to select regions that are unlikely to be usable for detecting the browning 34 of the food ingredient 10. The selected regions A, B, C, D, and E are candidate regions 52 that are candidates for the selection region 53. Therefore, the processor 24 includes parts of the selected regions A, B, C, D, and E in the selection region 53. The processor 24 may also include all of the selected regions A, B, C, D, and E in the selection region 53. The set magnification is, for example, 0.8.

[0036] The processing unit 24 detects a brightness L equal to or greater than the standard. * Region A having an intra-region variance of is excluded from selection region 53. This makes it possible to exclude from selection region 53 regions that exist both inside image 55 of food ingredient 10 and outside image 55 of food ingredient 10. This makes it possible to exclude from selection region 53 regions that are not suitable for detecting browning 34 of food ingredient 10. The criterion is, for example, 800.

[0037] Processing unit 24 excludes region B at the end of the row or column direction of the matrix array of multiple regions 51 from selection region 53. This makes it possible to exclude from selection region 53 regions that are likely to move from within image 55 of food ingredient 10 to outside image 55 of food ingredient 10 when food ingredient 10 is heated and shrinks. This makes it possible to exclude from selection region 53 regions that are not suitable for detecting browning 34 of food ingredient 10.

[0038] The processing unit 24 calculates the brightness L of the remaining two or more regions C, D, and E. * The average value of the remaining two or more areas C, D, and E is calculated, and the brightness L that deviates from the average value by more than the set value is calculated. * The area C having the value .gtoreq. 100 is excluded from the selection area 53. This allows the area where an abnormality has occurred slowly to be excluded from the selection area 53. For example, an area where a shadow formed by a part of the food material 10 has fallen in, an area that has become a shadow when the food material 10 is heated and deformed, etc. can be excluded from the selection area 53. The set value is, for example, 70. The brightness L used in excluding the area C is * is the brightness L in past frames. * The number of past frames is, for example, 5. The brightness L used in excluding the region C * is the brightness L in one frame. * may be.

[0039] The processing unit 24 detects the brightness L * The region D having a time change of 0.001 / s is excluded from the selection region 53. This makes it possible to exclude the region where an abnormality has occurred from the selection region 53. For example, it is possible to exclude from the selection region 53 a region where the brightness changes suddenly and the change in brightness is not due to sudden noise. For example, when the food material 10 is heated and expands, it is possible to exclude from the selection region 53 a region that changes from one of a shadow region and a non-shadow region to the other. The set time change is, for example, 20 / second. The brightness L used in excluding the region D is * is the brightness L in past frames. *The number of past frames is, for example, 5. The brightness L used in excluding the region D * is the brightness L in one frame. * may be.

[0040] The processing unit 24 sets the remaining region E as the selected region 53 .

[0041] 1.7 Processing flow 6 and 7 are flowcharts showing the flow of processing performed by a processing unit provided in the cooking device of the first embodiment.

[0042] The processing unit 24 executes steps S101 to S112 shown in FIGS.

[0043] In step S101, the processing unit 24 causes the imaging unit 23 to generate image data 31.

[0044] In the next step S102, the processing unit 24 calculates the brightness (L * At this time, the processing unit 24 converts the image data 31 in the RGB color system into L * a * b * Convert the image data into the color system 32, and * a * b * The lightness (L * )35.

[0045] In the next step S103, the processing unit 24 calculates the brightness (L * ) 35 of the plurality of regions 51. At this time, the processing unit 24 selects a candidate region 52 from the plurality of regions 51 based on the lightness (L * )35 Maximum brightness L * Identify the maximum brightness L from multiple areas 51 * Brightness L more than double the setting * Regions A, B, C, D and E having the following are selected as candidate regions 52:

[0046] In the next step S104, the processing unit 24 selects a selection area 53 from the candidate area 52. At this time, the processing unit 24 selects a selection area 53 from the candidate area 52. * Region A has an intra-region variance of , region B is at the end of the row or column direction, and the brightness L deviates from the average value by more than the set value. * and the area C has a brightness L that changes faster than the set time. * The processing unit 24 then excludes the region D having the time change of 100% from the selected region 53. As a result, the processing unit 24 selects the remaining region E as the selected region 53.

[0047] In the next step S105, the processing unit 24 calculates the luminance L of the selected region 53 from the image data 31. * 1, chromaticity a * 1 and chromaticity b * At this time, the processing unit 24 converts the image data 31 in the RGB color system into L * a * b * Convert the image data into the color system 32, and * a * b * The lightness L of the selected area 53 from the image data 32 in the color system * 1, chromaticity a * 1 and chromaticity b * 1. As a result, the processing unit 24 obtains the brightness L of the image 55 of the food material 10 at the reference time. * 1, chromaticity a * 1 and chromaticity b * Get 1.

[0048] In the following step S106, the processing unit 24 causes the heating unit 22 to start heating the food material 10.

[0049] In the following step S107, the processing unit 24 causes the imaging unit 23 to generate image data 31.

[0050] In the next step S108, the processing unit 24 calculates the luminosity L of the selected region 53 from the image data 31. * 2, chromaticity a * 2 and chromaticity b* At this time, the processing unit 42 converts the image data 31 in the RGB color system into L * a * b * Convert the image data into the color system 32, and * a * b * The lightness L of the selected area 53 from the image data 32 in the color system * 2, chromaticity a * 2 and chromaticity b * Get 2.

[0051] In the next step S109, the processing unit 24 calculates the brightness L of the selected area 53. * 1, chromaticity a * 1 and chromaticity b * 1 and the brightness L of the selected area 53 * 2, chromaticity a * 2 and chromaticity b * The color difference (ΔE * )33 is obtained.

[0052] In the next step S110, the processing unit 24 calculates the color difference (ΔE * ) 33, the browning 34 of the food material 10 is determined, and the heating unit 22 is controlled based on the browning 34 of the food material 10.

[0053] In the following step S111, the processing unit 24 determines whether or not the end timing has arrived. If the processing unit 24 determines that the end timing has arrived, it executes step S112. If the processing unit 24 determines that the end timing has not arrived, it executes step S107 again.

[0054] In step S112, the processing unit 24 causes the heating unit 22 to stop heating the food material 10.

[0055] Through steps S106 to S112, the processing unit 24 calculates the color difference (ΔE *) 33 is repeatedly acquired, the browning 34 of the food material 10 is repeatedly determined, and the heating unit 22 is controlled based on the browning 34 of the food material 10.

[0056] 1.8 Lightness of white bread during toasting L * , chromaticity a * and chromaticity b * Example of time change Figure 8 shows the lightness L of white bread during toasting. * , chromaticity a * and chromaticity b * 10 is a graph showing an example of a change over time.

[0057] In the graph of Figure 8, the horizontal axis represents the time elapsed since the start of toasting the white bread, and the lightness L * , chromaticity a * and chromaticity b * is taken on the vertical axis.

[0058] As shown in the graph in Figure 8, the lightness of white bread, L * The chromaticity of white bread a does not change over time in the period from 0 seconds to about 300 seconds, but decreases over time in the period from about 300 seconds to about 900 seconds. * and chromaticity b * Generally, the chromaticity a of white bread does not change over time in the period from 0 seconds to about 300 seconds, increases over time in the period from about 300 seconds to about 400 seconds, and decreases over time in the period from about 400 seconds to about 900 seconds. * and chromaticity b * may have the same value even though the white bread has a different brown color. * and chromaticity b * It is not possible to accurately determine the browning of white bread.

[0059] 1.9 Color difference ΔE of white bread during toasting * Example of time change Figure 9 shows the color difference ΔE of white bread during toasting. * 10 is a graph showing an example of a change in an image over time.

[0060] As shown in Figure 9, the color difference ΔE of white bread * Generally, in the period from 0 seconds to about 300 seconds, the color difference ΔE * This allows accurate determination of the browning of white bread.

[0061] Taking this into consideration, the cooking device 1 calculates the ΔE * The browning 34 of the food material 10 is determined from the above.

[0062] 1.10 Lightness of white and rye bread during toasting L * , chromaticity a * and chromaticity b * Examples of changes Figure 10 shows the L * axis, a * axis and b * The lightness L of white bread and rye bread during toasting in a three-dimensional Cartesian coordinate system defined by the axes * , chromaticity a * and chromaticity b * 11 is a graph showing an example of the change in * axis and b * Chromaticity of toasted white and rye bread in a two-dimensional Cartesian coordinate system defined by the axes a * and chromaticity b * FIG. 12 is a graph showing an example of the change in L * axis and a * The lightness L of white bread and rye bread during toasting in a two-dimensional Cartesian coordinate system defined by the axes * and chromaticity a * 10 is a graph showing an example of a change in

[0063] As shown in Figures 10, 11 and 12, the lightness L * , chromaticity a * and chromaticity b * and the lightness of rye bread L * , chromaticity a * and chromaticity b *changes in the same way. Therefore, whether the food material 10 is white bread or rye bread, the detection of browning 34 of the food material 10 by the heat cooker 1 has the same accuracy.

[0064] 1.11 Absolute value E of white toasted bread and rye bread * and relative value (color difference) ΔE * Example of time change In the following, we will use the absolute value E * , and the relative value (color difference) ΔE indicating the difference between the two colors, expressed by equation (1) * The difference is explained.

[0065]

number

[0066] Figure 13 shows the absolute values ​​of E for white and rye bread during toasting. * 10 is a graph showing an example of a change over time.

[0067] In the graph of FIG. 13, the horizontal axis represents the time elapsed since the start of toasting white bread and rye bread, and the absolute values ​​E * is taken on the vertical axis.

[0068] As shown in Figure 13, the absolute value E * Generally, after a period without significant change, the absolute value E of white bread increases and then decreases. * has the same value at both times, as shown by the dashed line. Therefore, the absolute value E * Therefore, it is difficult to accurately detect the browning of white bread.

[0069] Similarly, the absolute value of rye bread, E * Generally, the absolute value E of rye bread increases and then decreases after a period without significant change. *The absolute value of the rye bread, E, has the same value at both times, as shown by the dashed-dotted line. * Therefore, it is difficult to accurately detect the browning of rye bread.

[0070] FIG. 14 shows the relative color difference ΔE between white bread and rye bread during toasting. * 10 is a graph showing an example of a change over time.

[0071] In the graph of FIG. 14, the horizontal axis represents the time elapsed since the start of toasting white bread and rye bread, and the relative values ​​(color differences) ΔE * is taken on the vertical axis.

[0072] As shown in Figure 14, the relative value (color difference) ΔE * Generally, after a period without significant change, the relative value (color difference) ΔE of white bread increases monotonically as time passes. * Therefore, the relative value (color difference) ΔE of white bread does not have the problem of having the same value at two times. * This allows accurate detection of the browning of white bread.

[0073] Similarly, the relative value (color difference) ΔE of rye bread * Generally, after a period without any significant change, the relative value (color difference) ΔE of rye bread increases monotonically as time passes. * Therefore, the relative value (color difference) ΔE of rye bread is * This allows accurate detection of the browning of rye bread.

[0074] Figure 15 shows the absolute value of E for white bread and rye bread during toasting. * 10 is a table showing images of white bread and rye bread when the value of the color space is "255", "245", and "220".

[0075] As shown in FIG. 15, the toasted colors of the white breads in images 61, 62, 63, and 64 are different from one another. However, the absolute values ​​E * have the same value "255". This also shows that the absolute value E * Therefore, it can be seen that it is difficult to accurately detect the browning of white bread.

[0076] The toasted color of the rye bread in images 71, 72, 73, 74, and 75 is different from each other. However, the absolute values ​​E * The absolute values ​​E of the rye bread in images 73 and 74 are the same. * The absolute value of rye bread, E * Therefore, it can be seen that it is difficult to accurately detect the browning of rye bread.

[0077] Figure 16 shows the relative color difference ΔE between white bread and rye bread during toasting. * 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48

[0078] As shown in Figure 16, the toasted colors of the white breads in images 81, 82, 83, and 84 are different from one another. * are different from each other and do not have the same value. This also indicates that the relative value (color difference) ΔE * It can be seen that the browning of white bread can be accurately detected.

[0079] The toasted colors of the rye breads in images 91, 92, 93, and 94 are different from one another. * are different from each other and do not have the same value. This also indicates that the relative value (color difference) ΔE *It can be seen that the browning of rye bread can be accurately detected.

[0080] 2. Second embodiment The following describes the differences between the second embodiment and the first embodiment. For points that are not described, the second embodiment also employs the same configuration as that employed in the first embodiment.

[0081] In the first embodiment, the processing unit 24 calculates the color difference (ΔE * )33 is obtained.

[0082] In contrast, in the second embodiment, the processing unit 24 extracts L * a * b * Lightness L of image 55 of food ingredient 10 in the color system * While the heating unit 22 is heating the food material 10, the brightness L of the image 55 of the food material 10 is * The color difference (ΔE * ) 33 is obtained. Therefore, the color difference (ΔE * ) 33 is the brightness L of the image 55 of the food material 10 while the heating unit 22 is heating the food material 10. * The brightness L of the image 55 of the food ingredient 10 when * 1, chromaticity a * 1 and chromaticity b * 1 and the brightness L of the image 55 of the food material 10 at that particular time * 2, chromaticity a * 2 and chromaticity b * 2 according to formula (1). While the food material 10 is being heated by the heating unit 22, the brightness L of the image 55 of the food material 10 is calculated. * The reference time is the time when the brightness L * This is suitable for ingredients that grow large once they are cooked.

[0083] Figure 17 shows the lightness L of salmon fillets during heating.* , chromaticity a * and chromaticity b * 10 is a graph showing an example of a change over time.

[0084] In the graph of Figure 17, the horizontal axis represents the time elapsed since the start of heating the salmon fillet, and the lightness L * , chromaticity a * and chromaticity b * is taken on the vertical axis.

[0085] As shown in the graph in Figure 17, the lightness L of the salmon fillet * Generally, in the period from 0 seconds to about 3 seconds, the lightness L of the salmon fillet increases as time passes, and in the period from about 3 seconds to about 53 seconds, the lightness L of the salmon fillet decreases as time passes. * This indicates that the brightness L of the image 55 of the food material 10 increases as the heating unit 22 heats the food material 10. * It can be seen that setting the time when the temperature is maximum as the reference time is particularly suitable when the food material 10 is a fillet of salmon.

[0086] 18, 19 and 20 are flowcharts showing the flow of processing performed by a processing unit provided in the cooking device of the second embodiment.

[0087] In the second embodiment, the processing unit 24 executes steps S201 to S215 shown in FIGS.

[0088] In steps S201 to S204, the processing unit 24 performs the same processes as those performed in steps S101 to S104 shown in FIG.

[0089] In the following step S205, the processing unit 24 causes the heating unit 22 to start heating the food material 10.

[0090] In the following step S206, the processing unit 24 causes the imaging unit 23 to generate image data 31.

[0091] In the next step S207, the processing unit 24 calculates the brightness L of the selected area 53 from the image data 31. * At this time, the processing unit 24 converts the image data 31 in the RGB color system into L * a * b * Convert the image data into the color system 32, and * a * b * The lightness L of the selected area 53 from the image data 32 in the color system * Get.

[0092] In the next step S208, the processing unit 24 calculates the brightness L of the selected region 53 that has been acquired so far. * to maximum brightness L * The processing unit 24 determines whether the maximum brightness L * If it is determined that the maximum lightness L can be determined, the processing unit 24 executes step S209. * If it is determined that the value cannot be determined, step S206 is executed again.

[0093] In step S209, the processing unit 24 calculates the maximum brightness L * The brightness L of the selected region 53 is calculated from the acquired image data 31. * 1, chromaticity a * 1 and chromaticity b * 1. As a result, the processing unit 24 obtains the brightness L of the image 55 of the food material 10 at the reference time. * 1, chromaticity a * 1 and chromaticity b * Get 1.

[0094] By steps S205 to S209, the processing unit 24 heats the food material 10 at the maximum brightness L * The lightness L of the selected area 53 can be determined * Repeatedly obtain the maximum brightness L * It is repeatedly determined whether it is possible to determine the maximum brightness L *After determining the maximum lightness L * The brightness L of the image 55 of the food material 10 at the reference time is calculated from the acquired image data 31. * 1, chromaticity a * 1 and chromaticity b * Get 1.

[0095] In the following steps S210 to S213, the processing unit 24 performs the same processes as those performed in steps S107 to S112 shown in FIG.

[0096] The present disclosure is not limited to the above-described embodiments, and may be replaced with a configuration that is substantially the same as the configuration shown in the above-described embodiments, a configuration that has the same effect, or a configuration that can achieve the same purpose. [Explanation of symbols]

[0097] 1 Cooker 10 ingredients 21 Heating storage 21a Interior space 22 Heating section 23 Imaging unit 24 Processing section 31 Image data 32 Image data 33 Color difference of food images (ΔE * ) 34 Browning ingredients 35 Lightness of multiple areas (L * ) 41 images 51 areas 52 Candidate area 53 Selection Area 55 Food Statue 61,62,63,64,71,72,73,74,75,81,82,83,84,91,92,93,94 Images

Claims

1. an imaging unit that generates image data representing an image including an image of the food material; From the image data ＊ a ＊ b ＊ a processing unit that acquires a color difference of the image in a color system and detects the browning of the food material based on the color difference; A heating cooker comprising:

2. A heating unit for heating the food material is provided, The processing unit extracts the L ＊ a ＊ b ＊ The brightness of the image in the color system is acquired, and the color difference is acquired using the time when the brightness of the image is maximized as a reference time while the heating unit is heating the food material. The cooking device according to claim 1 .

3. The processing unit extracts the L ＊ a ＊ b ＊ determining brightnesses of a plurality of regions included in the image in a color system, and selecting a selection region from the plurality of regions based on the brightnesses of the plurality of regions; Determining the color difference of the image includes determining the color difference of the selected region. The cooking device according to claim 1 or 2.

4. Selecting the selected area from the plurality of areas based on the brightness of the plurality of areas includes including in the selected area a part or all of an area having a brightness equal to or greater than a set multiple of the maximum brightness included in the brightness of the plurality of areas. The cooking device according to claim 3.

5. Selecting the selected region from the plurality of regions based on the brightness of the plurality of regions includes excluding regions having a brightness variance within the region equal to or greater than a reference value from the selected region. The cooking device according to claim 4.

6. the plurality of regions are arranged in rows and columns, Selecting the selected region from the plurality of regions based on the brightness of the plurality of regions includes excluding regions at the ends of rows or columns of the matrix arrangement of the plurality of regions from the selected region. The cooking device according to claim 4.

7. Selecting the selection area from the plurality of areas based on the brightness of the plurality of areas includes obtaining an average value of brightness of two or more areas, and excluding from the selection area any area included in the two or more areas that has a brightness that deviates from the average value by a set value or more. The cooking device according to claim 4.

8. Selecting the selected region from the plurality of regions based on the brightness of the plurality of regions includes excluding from the selected region any region having a time change in brightness faster than a set time change. The cooking device according to claim 4.

Images