Food image provision system, food image provision method, and program

Abstract

This technology provides a way to simplify the selection of colorants and their amounts. [Solution] The food image provision system 1 includes a storage unit 11 that stores data D2 for displaying food images of colored foods using coloring agents, food image information D1 that associates the coloring agent added to the colored food with the amount of coloring agent added, a receiving unit 12 that can receive a specification of at least one of the following: food type, food shape, color classification of the coloring agent, coloring agent, and amount added, and an output unit 13 that can provide at least one food image corresponding to the specification received by the receiving unit 12. The output unit 13 is capable of performing the following processes: providing a first food image associated with the coloring agent and amount added, and providing a second food image that has the same food shape as the first food image associated with the coloring agent and amount added.

Description

Technical Field

[0001] The present disclosure relates to a technique for providing a food image of a colored food colored with a coloring agent.

Background Art

[0002] Various coloring agents are used to enhance the appearance of foods. Food developers need to select the coloring agent to be used and the amount of the coloring agent to be added from a variety of coloring agents in order to obtain a color that matches the concept of the food. Usually, a pamphlet (a color sample on paper) of the coloring agent is used to select the coloring agent to be used and the amount of the coloring agent to be added.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a need for a technique that facilitates the selection of the coloring agent and the amount added as compared with the case of using a pamphlet of the coloring agent.

[0005] The present disclosure provides a technique that facilitates the selection of the coloring agent and the amount added.

[0006] Since no patent document related to the above was found, although not related to the above, as Patent Document 1, a document that discloses a system for displaying food additives and cost calculations is described.

Means for Solving the Problems

[0008] [Figure 1] This is a block diagram showing a food image provisioning system according to the first embodiment. [Figure 2] This figure shows an example of the first screen provided by the food image provision system. [Figure 3] This figure shows an example of the second screen provided by the food image provision system. [Figure 4] This figure shows an example of the third screen provided by the food image provision system. [Figure 5] This figure shows an example of the fourth screen provided by the food image provision system. [Figure 6] This is a block diagram showing a food image provisioning system according to a second embodiment. [Figure 7] This is an explanatory diagram showing a color sample photograph and a predictive model (correlation formula). [Figure 8] This is an explanatory diagram regarding the base image and conversion data. [Figure 9] This is an explanatory diagram of a color sample photograph related to the modified form and a predictive model (correlation formula). [Figure 10]This figure compares a food image generated based on a predictive model constructed using a different type of milk and water as a color sample from the colored cookie used for image generation, with an image of a food that was actually produced, and a food image generated based on a predictive model constructed using the same type of colored cookie as the colored cookie used for image generation as a color sample. [Modes for carrying out the invention]

[0009] In this specification, "coloring agent" is not particularly limited as long as it is a material added for the purpose of coloring, and includes so-called natural coloring agents (e.g., anthocyanin pigments, gardenia pigments, flavonoid pigments, caramel, etc.), so-called synthetic coloring agents (e.g., tar dyes, synthetic carotenes, water-soluble annatto, copper chlorophyll, etc.), food ingredients (e.g., grape juice, purple sweet potato juice, etc.).

[0010] [First Embodiment] <Configuration of Food Image Provisioning System 1> Hereinafter, a first embodiment of this disclosure will be described with reference to the drawings. Figure 1 is a block diagram showing a food image provisioning system 1 of the first embodiment.

[0011] The food image provision system 1 of the first embodiment provides food images that match the specified conditions from food image information D1 based on a user's request. The food image provision system 1 can also provide (display) the food image along with the coloring agent used and the amount added. As shown in Figure 1, the food image provision system 1 has a storage unit 11 for storing food image information D1, a receiving unit 12, and an output unit 13. Each part (12, 13) constituting the food image provision system 1 is implemented by a computer and realized by one or more processors executing a predetermined program stored in memory.

[0012] As shown in Figure 1, the food image information D1 is information that associates data D2 for displaying a food image of a colored food using coloring agents, the coloring agents added to the colored food, and the amount of coloring agent added. The data D2 for displaying the food image can include the food image data itself, the URL of the food image, and, if the food image is generated in real time, the data necessary for generation (in the second embodiment shown in Figure 6, examples include a prediction model 14a, a base image 14b, conversion data 14c, and adjustment data 14d). In the first embodiment, the URL of the food image is stored in the food image information D1 as the data D2 for displaying the food image, but it is not limited to this. In the first embodiment, the coloring agent is stored in the food image information D1 as the pigment name and the product name of the coloring agent. In the first embodiment, the amount added is stored in the food image information D1 as a percentage (%). For example, if the ingredients of the colored food include powder, it can be stored as a percentage of the amount of powder relative to the material.

[0013] In addition, the food image information D1 further has metadata such as an ID, a food type (information for distinguishing the types of foods, for example, deep-fried foods, cookies, cheese, donuts, bread, beverages, rice crackers, etc. can be exemplified), information regarding the food shape (for example, a full photo, a sliced photo, etc. can be exemplified), the color classification of the coloring agent (a rough color classification of the coloring agent, for example, orange, blue, brown, yellow, red, green, etc. can be exemplified), and trial production conditions (for example, pH value, presence or absence of a sterilization process, etc. can be exemplified). Since the food image information D1 has a food type, it enables a narrowing-down search by specifying the food type. Since the food image information D1 has information regarding the food shape, it enables a narrowing-down search by specifying the food shape. Since the food image information D1 has the color classification of the coloring agent, it enables a narrowing-down search by specifying the color classification. The same applies to other trial production conditions. Since the food type may be determined by the food shape, the information regarding the food shape may include not only information regarding the shape but also information regarding the food type. Also, when information regarding the food shape is linked to the food type, a narrowing-down search by the food type is possible, and in some cases, a further narrowing-down search by the food shape within the food type becomes possible.

[0014] The reception unit 12 has a function of receiving requests from the user, and for example, can receive a specification of at least one of the coloring agent and the addition amount. Also, the reception unit 12 may be able to receive a specification of at least one of the food type, the food shape, the color classification of the coloring agent, the coloring agent, and the addition amount. Details will be described later together with the screen provided by the food image providing system 1.

[0015] The output unit 13 is configured to be able to provide at least one food image corresponding to the specification received by the reception unit 12 among the food image information D1 stored in the storage unit 11. Details will be described later together with the screen provided by the food image providing system 1. The reception unit 12 and the output unit 13 provide the user interface of the system.

[0016] <The screen displayed by the food image providing system 1> Figure 2 shows an example of the first screen provided by the food image provision system 1. As shown in Figure 2, the first screen is the initial screen. When a user terminal connected to the network accesses the food image provision system 1, the reception unit 12 receives a request from the user terminal with no conditions specified. The output unit 13 provides the first screen to a display unit such as a user terminal. As shown in Figure 2, the first screen includes a first area A1 for specifying the type of food, a second area A2 for specifying the trial production conditions, a third area A3 for specifying the coloring agent by pigment name, a fourth area A4 for specifying the coloring agent by product name, a fifth area A5 for specifying the color classification, a sixth area A6 for specifying the food shape, a seventh area A7 for specifying the amount to be added, and an eighth area A8 for displaying food images corresponding to the specifications. On the first screen, since no specifications have been made to the reception unit 12, one or more food images corresponding to the initial display conditions (no conditions specified, i.e., all food images) from the food image information D1 stored in the storage unit 11 are displayed in the eighth area A8. In the example in Figure 2, multiple food images G1 to G7 are displayed in area A8, associated with the amount of additive. Displaying (providing) in association with the amount of additive includes both a method of providing the food image with the amount of additive and a method of providing the food image by manipulating it. Specifically, in area A8 of Figures 2 to 5, the amount of additive is displayed along with the food image, and when the mouse hovers over, clicks, or taps on the food image, detailed information about the food image (coloring agent pigment name, product name, amount added, prototype conditions, etc.) is displayed. In some cases, the coloring agent and the amount added are displayed together with the food image.

[0017] In the example in Figure 2, in area A8, the following food images are displayed from left to right: G1 showing the entire dumpling (0.008% added), G2 showing the entire dumpling (0.008% added), G3 showing the entire bread (0.008% added), G4 showing sliced ​​bread (0.008% added), G5 showing the entire donut (0.008% added), G6 showing a sliced ​​donut (0.008% added), and G7 showing a beverage in a container. The amount of coloring added for each food image G1 to G7 is displayed next to each food image.

[0018] Please note that the food images in Figures 2 to 5 are actually color images, but have been converted to grayscale for the purposes of the application documents.

[0019] Figure 3 shows an example of the second screen provided by the food image provision system 1. As shown in Figure 3, the second screen is the screen that is output because "donut" was specified as the food type on the first screen. When the user selects "donut" as the food type on the first screen, the reception unit 12 receives the specification that the food type is "donut" from the user terminal. The output unit 13 provides a second screen that displays only a food image showing a donut in the eighth area A8. In the example in Figure 3, in area A8, the following images are displayed in order from left to right: food image G10 showing the entire donut colored in a red base color (addition amount 0.44533%), food image G11 showing a slice of the donut to the left (addition amount 0.44533%), food image G21 showing the entire donut colored in a blue base color (addition amount 0.46889%), food image G22 showing a slice of the donut to the left (addition amount 0.46889%), food image G12 showing the entire donut colored in a red base color (addition amount 0.5%), food image G13 showing a slice of the donut to the left (addition amount 0.5%), and food image G14 showing the entire donut colored in a green base color (addition amount 0.5%).

[0020] Figure 4 shows an example of the third screen provided by the food image provision system 1. As shown in Figure 4, the third screen is the screen that is output when the color category is further specified as "blue" on the second screen. When the food type is specified as "donut" on the second screen, and the user further specifies the color category as "blue", the reception unit 12 receives the specification of "donut" as the food type and the specification of "blue" as the color category from the user terminal. The output unit 13 provides a third screen in the eighth area A8 that displays only food images of donuts that use a coloring agent belonging to the blue color. In the example in Figure 4, in area A8, the following food images are displayed in order from left to right: G20 showing a sliced ​​donut colored with a blue base (addition amount 0.40333%), G21 showing the entire blue-colored donut (addition amount 0.46889%), G22 showing a sliced ​​donut to the left (addition amount 0.46889%), G23 showing the entire blue-colored donut (addition amount 0.53444%), G24 showing a sliced ​​donut to the left (addition amount 0.53444%), G25 showing the entire blue-colored donut (addition amount 0.6%), and G26 showing a sliced ​​donut to the left (addition amount 0.6%).

[0021] Figure 5 shows an example of the fourth screen provided by the food image provision system 1. As shown in Figure 5, the fourth screen is the screen output as a result of the food shape being further specified as "sliced" on the third screen. When the food type is specified as "donut" and the color category is specified as "blue" on the third screen, and the user further specifies the food shape as "sliced," the receiving unit 12 receives the specification of "donut" as the food type, the specification of "blue" as the color category, and the specification of "sliced" as the food shape from the user terminal. The output unit 13 provides the fourth screen, which displays only food images in the eighth area A8 that are donuts, use a coloring agent belonging to the blue color, and have a food shape of sliced. In the example in Figure 5, in area A8, the following food images are displayed sequentially from left to right: G31, a sliced ​​donut colored primarily in blue with an additive amount of 0.07556%; G32, the same as the one to its left but with an additive amount of 0.14111%; G33, the same donut but with an additive amount of 0.20667%; G34, the same as the one to its left but with an additive amount of 0.27222%; G35, the same as the one to its left but with an additive amount of 0.33778%; G20, the same as the one to its left but with an additive amount of 0.40333%; and G23, the same as the one to its left but with an additive amount of 0.46889%. The multiple food images shown in Figure 5 represent colored foods that use the same coloring agent, have the same food shape (sliced ​​donut), are the same food type (donut), but have different additive amounts. Each food image will display (provide) information about the coloring agents and their amounts.

[0022] To summarize screens 1 through 4, as shown in Figure 5, for example, a first process is performed to provide a first food image (G31) in which the coloring agent is added in a first amount (0.07556%), associated with the coloring agent and the amount added. A second process is also performed to provide a second food image (G32) in which the coloring agent and food shape are the same as the first food image (G31), but the amount of coloring agent added is different from the first amount (0.07556%), in a second amount (0.14111%), associated with the coloring agent and the amount added. In the example in Figure 5, the first and second processes are performed simultaneously, and the first food image (G31) and the second food image (G32) are displayed on a single screen.

[0023] As another example, as shown in Figure 3, multiple food images of the same food type (donut) but with different food shapes (whole, sliced) may be displayed using different coloring agents and varying amounts of each. As another example, as shown in Figure 4, multiple food images of the same food type (donuts), the same coloring agent, and different food shapes (whole, sliced) may be displayed with different amounts of the coloring agent added to each. As another example, multiple food images of the same food type (donuts) and the same color category may be displayed with different colorants and varying amounts of each. Alternatively, multiple food images of the same color category may be displayed with only the color category specified, but with differences in food type, food shape, and colorants.

[0024] In the first embodiment, not all food images provided are necessarily images of actual prototype foods. Some of the food images provided in the first embodiment are pre-generated based on predicted colors and stored as data D2 (see Figure 1) for displaying food images of colored foods using coloring agents. The technique for predicting colors based on coloring agents and amounts added, and generating food images of colored foods, will be described in the second embodiment.

[0025] As described above, it is possible to accept specifications for at least one of the following: food type, food shape, color classification of colorants, colorants, and amounts added. A first food image that matches the specifications and a second food image that has the same food shape as the first food image can be provided, along with the associated colorants and amounts added. The first and second food images can be categorized into at least the following two patterns: Pattern 1: The first and second food images use the same coloring agent, but with different amounts added. Pattern 2: The first food image and the second food image use different colorings. This makes it easier to access desired food images associated with colorants and additive amounts compared to conventional brochures, thus simplifying the selection process for colorants and additive amounts.

[0026] Traditional food coloring brochures typically feature images of prototype foods colored with each type of food coloring, for example, donuts or cookies. Specifically, for each food type (e.g., cookies), one food image is included for each food coloring, and if there are N types of food colorings (where N is a natural number greater than or equal to 2), then N food images (cookie images) are included. The food coloring used and the amount added are indicated for each food image. Often, only one food image of a prototype food is included per food coloring, and it is rare to see multiple food images of the same food coloring type with different amounts added. For example, there might be one prototype cookie image for food coloring A and one prototype cookie image for food coloring B. However, when an image of a cookie prototype made with 0.1% coloring agent A is included, and no images of prototype cookies made with other amounts of coloring agent A are included, it is unclear what color the cookies would be with amounts other than 0.1% of coloring agent A. Coloring agent brochures sometimes include multiple color samples of coloring agent A added to liquids such as milk, water, or mixtures thereof, rather than food products, with different amounts of the coloring agent added. However, since these are color samples of liquids without a specific food image, it is only possible to imagine what color cookies would be with amounts of coloring agent A other than 0.1%, and it cannot be said that this facilitates food developers in smoothly selecting the optimal type and amount of coloring agent.

[0027] To address this issue, in the first embodiment, the output unit 13 is capable of performing the following processes: providing a first food image in association with the coloring agent and the amount added, where the amount of coloring agent added is the same as the first food image, the coloring agent and food shape, and the amount of coloring agent added is a second amount different from the first amount, in association with the coloring agent and the amount added. As a result, multiple food images with the same food shape and the same coloring agent but different amounts of coloring agent added can be viewed, further simplifying the selection of coloring agents and amounts added.

[0028] In the first embodiment, it is possible to specify at least two of the following: food type, food shape, color classification of coloring agent, and coloring agent. In the first stage, when one of the first two of the above is specified, one or more food images matching the first specification are provided. Subsequently, in the second stage, when both of the first and second specifications are specified, one or more food images matching both the first and second specifications are provided. The second stage implements a so-called AND search. For example, first, the food type "donut" is narrowed down as the first specification, and food images of all donut colorants are displayed (provided) in the first stage. Then, the color category of the colorant "red category" is added as the second specification, and in the second stage, food images of donuts with red radish colorant and donuts with red cabbage colorant, which satisfy the AND condition of being both the first and second specification, are displayed (provided).

[0029] In the first embodiment, it is possible to specify at least one of the following: food type, food shape, color classification of coloring agent, and coloring agent. In the first stage, when a first condition is specified for at least one of the above, one or more food images matching the specification are provided. Subsequently, in the second stage, when a second condition of the same type as the first condition but different from the first condition is additionally specified, one or more food images matching either the specified first condition or second condition are provided. The second stage implements what is known as an OR search. For example, if red radish pigment is specified as the first condition for coloring, then images of foods using red radish pigment are displayed (provided) in the first stage. Subsequently, if red cabbage pigment is specified as the second condition for coloring, then images of foods using red radish pigment and images of foods using red cabbage pigment are displayed (provided) as food images that meet the OR condition of either the first or second condition.

[0030] [Second Embodiment] A second embodiment will now be described. Figure 6 is a block diagram showing the food image provision system 1 of the second embodiment. The food image provision system 1 of the second embodiment differs from the first embodiment in that it has an image generation unit 14, and the image generation unit 14 can generate food images in real time. The image generation unit 14 may generate a food image when there is no food image in the food image information D1 that matches the specification to the reception unit 12.

[0031] The color prediction and food image generation algorithm for colored foods executed by the image generation unit 14 shown in Figure 6 will be described below. As shown in Figure 6, the image generation unit 14 includes a prediction model 14a, a base image 14b, conversion data 14c, adjustment data 14d, and a generation unit 14e.

[0032] The prediction model 14a is a model that predicts pixel values ​​indicating color based on the amount of coloring agent added. The prediction model 14a in the second embodiment is a correlation formula that outputs pixel values ​​indicating color with the amount of additive as an argument, but is not limited to this. For example, it may be a model constructed using machine learning, or it may be a table-formatted data having pixel values ​​corresponding to each amount of additive. In the second embodiment, multiple color samples are used in which coloring agents are added to food and beverages other than the target colored food (e.g., donuts) (e.g., liquids without a specific shape, such as a mixture of milk and water, or just water). A process is performed to photograph the color samples (actual products made) with a camera to obtain photographic images of the color samples. Multiple color samples use the same coloring agent, but the amount of additive differs from one another. Figure 7 is an explanatory diagram relating to the photographs of the color samples and the prediction model 14a (correlation formula). In this embodiment, as shown in Figure 7, nine types of color samples were made by adding product A, which is a coloring agent, to 50% milk and water (milk:water = 1:1). The explanation assumes that the additive amounts for the color samples of product A are 0.01%, 0.02%, 0.04%, 0.07%, 0.1%, 0.2%, 0.3%, 0.4%, and 0.6%, respectively. The number of color sample types and additive amounts are examples for illustrative purposes only and are not limited to these. As shown in Figure 7, unnecessary parts such as containers are removed from a photograph of one color sample using image processing to leave only the color portion, and the average value of the pixel values ​​(sum of each pixel value / number of pixels) is calculated for all pixels in the color portion. The average value of the pixel values ​​is calculated separately for R, G, and B. In this way, three average values ​​for R, G, and B are obtained for one color sample, and 3 × 9 types = 27 pixel average values ​​are obtained. Next, based on the average values ​​of the nine R values, we create a correlation equation for R based on the amount added. R = F(amount added), where F is a function. Similarly, based on the average values ​​of the nine G values, we create a correlation equation for G based on the amount added. G = F(amount added). Similarly, based on the average values ​​of the nine B values, we create a correlation equation for B based on the amount added. B = F(amount added). This makes it possible to generate the predictive model 14a. The correlation equation can be exemplified by a quadratic function, for example, as follows: X represents the amount added. a1~a3, b1~b3, and c1~c3 are constants. R=a1·X 2 +b1·X+c1 G=a²·X 2+b2·X+c2 B = a³·X 2 +b3·X+c3 Furthermore, the range of additive amounts that can be predicted by prediction model 14a should be limited to the range of the color samples. In other words, there is a risk that the accuracy of predictions below the minimum additive amount and above the maximum additive amount among multiple color samples may not be sufficient. Furthermore, it is best to use color samples that correspond to the ingredients and shades of the colored food being predicted. Here, we use donuts as an example; since donuts are primarily white, we have adopted a color sample using white milk. For example, in the case of colored foods that are primarily transparent, such as gummies or sauces, it is possible to use a color sample using water.

[0033] Figure 8 is an explanatory diagram relating to the base image 14b and the converted data 14c. As shown in Figure 8, the base image 14b is the food image used as the base in the image generation process. In the base image 14b, areas other than those showing the color of the food are removed by applying transparency processing or other methods. In the example in Figure 8, a donut was prototyped using the same coloring agent (product A) as the color sample shown in Figure 7, with an additive amount of 0.4%, and the donut was sliced ​​and photographed to create the base image 14b. As shown in Figure 8, the base image 14b has the unique shape and color of the food (product), and the unique color of the food differs from that of the color sample. For example, the internal cross-section of the donut tends to be close to the color sample, but the surface is brown due to charring. Also, even inside the donut, the color differs between the bubble areas and the non-bubble areas. Therefore, the pixel values ​​obtained by the prediction model 14a cannot be directly used as the pixel values ​​of the donut food image.

[0034] Therefore, it is necessary to generate conversion data 14c. Conversion data 14c is data for converting pixel values ​​so that the pixel values ​​obtained by the prediction model 14a can be applied to the base image 14b to obtain a food image of the predicted color. In this embodiment, as shown in Figure 8, conversion data 14c is the ratio of the pixel values ​​of the base image 14b to the pixel values ​​predicted by the prediction model 14a. In the example in Figure 8, for the sake of simplicity, we will explain assuming that the number of pixels in the base image 14b is 1000. For each pixel in the base image 14b, the ratio of the pixel value to the predicted value (pixel value) of the prediction model 14a is calculated and this is used as the conversion data 14c. For example, the R value of pixel 1 in the base image 14b with a coloring agent addition amount of 0.4% is R ベース Assuming that the R value according to the predictive model 14a for an added colorant of 0.4% or the average R value of the color sample is R 予測 If so, the converted data 14c of the R value of pixel 1 is (R ベース / R 予測 ) is obtained. G and B are calculated similarly. The conversion data 14c is calculated not only for pixel 1, but for all pixels from pixel 2 to 1000. The conversion data 14c is not limited to the 0.4% additive amount used during generation, but can be provided for various additive amounts. This makes it possible to calculate conversion data 14c for each pixel of the base image 14b to convert the pixel values ​​predicted by the prediction model 14a into pixel values ​​representing donuts. In this way, since each pixel has conversion data 14c, it becomes possible to appropriately reproduce the color changes unique to food, such as the burning of donuts (a form in which the surface is burnt and the inside is close to being colored by the coloring agent). The conversion data 14c needs to be generated for each base image 14b and each coloring agent.

[0035] Adjustment data 14d is data used to adjust the base pixel values ​​of the entire images due to differences in shape between the target colored food such as donuts and the color sample, as well as differences in overall color (contrast, etc.) between the images. Specifically, it is the ratio of the average pixel value of base image 14b to the average pixel value of a color sample with the same coloring agent and amount as base image 14b. It consists of three values: the ratio of the average R value of base image 14b to the average R value of the color sample, the ratio of the average G value of base image 14b to the average G value of the color sample, and the ratio of the average B value of base image 14b to the average B value of the color sample.

[0036] The generation unit 14e generates a food image of a colored food colored by a coloring agent and its amount of addition, based on information about the coloring agent, its amount of addition, and the target food. The generation unit 14e generates a food image based on the pixel values ​​predicted by a prediction model 14a that can predict pixel values ​​indicating coloring by the amount of coloring agent to be predicted, and the base image 14b of the food. For example, the image generation unit 14 will be described as receiving a request to generate a food image of a sliced ​​donut with coloring agent product A added at an amount of 0.015%. In this case, the image generation unit 14 predicts the pixel values ​​for an amount of 0.015% of coloring agent product A using the prediction model 14a. The predicted values ​​will be three values ​​(R, G, and B). The image generation unit 14 obtains a base image 14b for generating a food image of a sliced ​​donut, and conversion data 14c and adjustment data 14d corresponding to the base image 14b. The generation unit 14e multiplies the predicted pixel value by the value of the conversion data 14c and the value of the adjustment data 14d for each image of the base image 14b, and sets the resulting pixel value to the value of the corresponding pixel in the base image 14b. This makes it possible to obtain a food image of a sliced ​​donut with a coloring agent added at a concentration of 0.015% in product A. Specifically, by multiplying the predicted pixel value by the value of the conversion data 14c, the pixel values ​​are converted from the color sample to the pixel values ​​of a sliced ​​donut on a pixel-by-pixel basis. Furthermore, by multiplying by the value of the adjustment data 14d, it becomes possible to balance the overall pixel values. In the first embodiment, part or all of the food image is an image generated based on the predicted pixel values ​​and the base image 14b of the food, with the prediction model 14a used to predict the pixel values ​​indicating coloring by the amount of coloring agent to be predicted.

[0037] [1] As in this embodiment, the food image provision system 1 includes a storage unit 11 that stores data D2 for displaying a food image of a colored food using a coloring agent, food image information D1 that associates the coloring agent added to the colored food with the amount of coloring agent added, a receiving unit 12 that can receive a specification of at least one of the following: food type, food shape, color classification of the coloring agent, coloring agent, and amount added, and an output unit 13 that can provide at least one food image corresponding to the specification received by the receiving unit 12. The output unit 13 may be capable of performing the following processes: providing a first food image associated with the coloring agent and amount added, and providing a second food image that has the same food shape as the first food image associated with the coloring agent and amount added. This configuration allows for the specification of at least one of the following: food type, food shape, color category of coloring agent, coloring agent, and amount added. Furthermore, it is possible to receive a first and second food image from among multiple food images, where the food shape corresponding to the specification is identical, thereby facilitating the selection of coloring agents and amounts added.

[0038] [2] The food image provision system 1 described in [1] above may be such that the first food image has a first amount of coloring added, and the second food image has the same coloring and food shape as the first food image, but has a second amount of coloring added that is different from the first amount. This configuration allows for simultaneous or delayed viewing of two food images, one with the same food shape and the same coloring agent, but with different amounts of the coloring agent added. This facilitates the selection of the coloring agent and its amount.

[0039] [3] The food image provision system 1 described in [1] or [2] above is also provided, wherein the output unit 13 is capable of displaying multiple food images in a predetermined display area (8th area A8) that have the same coloring agent, the same food shape, and different amounts of coloring agent added to each other, and the multiple food images displayed in the predetermined display area (8th area A8) are displayed in association with the respective added coloring agents and amounts. This configuration allows for simultaneous viewing of two food images, one with the same food shape and the same coloring agent, but with different amounts of the coloring agent added. Furthermore, it enables comparison between the two images, thus facilitating the selection of coloring agents and their amounts.

[0040] [4] The food image provision system 1 described in any of [1] to [3] above may be such that at least one of the food images is an image generated based on the predicted pixel values ​​and a base image 14b of the food, where the pixel values ​​indicating coloring due to the amount of coloring agent to be predicted are predicted using a prediction model 14a. This configuration eliminates the need to prototype colored foods for every possible combination of colorants and their amounts, thereby simplifying, reducing the cost, and saving time in the selection process for colorants and their amounts.

[0041] [5] A food image provision system 1 as described in any of [1] to [4] above, comprising an image generation unit 14 capable of generating food images, wherein the image generation unit 14 generates a food image based on pixel values ​​predicted by a prediction model 14a capable of predicting pixel values ​​indicating coloring due to the amount of coloring agent to be predicted, and a base image 14b of the food. This configuration eliminates the need to prototype colored foods for every possible combination of colorants and their amounts, thereby simplifying, reducing the cost, and saving time in the selection process for colorants and their amounts.

[0042] [6] As in this embodiment, the food image provision method is a method executed by one or more processors and includes the steps of: receiving a specification of at least one of the following: food type, food shape, color classification of coloring agent, coloring agent, and amount added; providing at least one food image corresponding to the received specification using data D2 for displaying a food image of a colored food using a coloring agent, and food image information D1 which associates the coloring agent added to the colored food and the amount of coloring agent added, wherein the step of providing at least one food image may include the steps of providing a first food image associated with the coloring agent and amount added, and providing a second food image which has the same food shape as the first food image and is associated with the coloring agent and amount added.

[0043] [7] As in this embodiment, the program may cause one or more processors to execute the method described in [6] above.

[0044] The computer-readable temporary recording medium according to this embodiment stores the above-mentioned program.

[0045] Although embodiments of this disclosure have been described above with reference to the drawings, it should be understood that the specific configurations are not limited to these embodiments. The scope of this disclosure is indicated not only by the description of the embodiments above but also by the claims, and further includes all modifications within the meaning and scope equivalent to the claims.

[0046] The structures adopted in each of the above embodiments can be adopted in any other embodiment. The specific configuration of each part is not limited to the embodiments described above, and various modifications are possible without departing from the spirit of this disclosure.

[0047] <Variation> (A) In the above embodiment, the food image information D1 includes, but is not limited to, an ID, food type, information about the food shape, color classification, and prototype conditions. These can be omitted as needed. For example, if only one type of food is being handled, the food type is not required.

[0048] (B) In the above embodiment, the food image provision system 1 is described as being implemented on a server or in the cloud, and users access the food image provision system 1 from a personal computer or smartphone via a network, but the embodiment is not limited to this. For example, the food image provision system 1 may be implemented as an application for a personal computer or smartphone. In this case, the food image provision system 1 may operate as a single application or as an application that cooperates with an external server.

[0049] (C) In the above embodiment, in the example shown in Figure 5, the first process and the second process are executed simultaneously, and the first food image (G31) and the second food image (G32) are displayed on one screen, but the embodiment is not limited to this. For example, the execution timing of the first process and the second process may be different. For example, the first food image (G31) may be displayed on the screen at the first timing, and the second food image (G32) may be displayed on the screen at a second timing different from the first timing. In other words, the first food image (G31) and the second food image (G32) do not have to be displayed on the same screen at the same timing.

[0050] (D) In ​​the above embodiment, some of the food images are food images of the colored food that was actually produced as a prototype, and some of the food images are predicted images generated based on predicted colors, but are not limited to this. For example, all of the food images may be food images of the colored food that was actually produced as a prototype, or all of the food images may be predicted images generated based on predicted colors.

[0051] (E) In the above embodiment, the image generation unit 14 is incorporated into the food image provision system 1, but is not limited thereto. For example, this disclosure can also be specified as an image generation system having an image generation unit 14, separate from the food image provision system 1. This image generation system can also be described as a system capable of predicting the color of colored food based on colorants and the amount added.

[0052] (F) In the second embodiment, the image generation unit 14 may further use correction data. The correction data is used to correct pixel values ​​in the calculation (multiplication) process of the generation unit 14e. For example, the correction data may be a correction coefficient to reduce or eliminate the difference when the RGB values ​​of the image generated from the four data (prediction model 14a, base image 14b, conversion data 14c, and adjustment data 14d) become brighter despite increasing the amount of coloring agent added, or when the image generated from the four data appears to be of a different color (yellow or blue) despite the coloring agent being predominantly red. The correction coefficient is multiplied, but it may also be added. That is, the image generation unit 14 may generate a food image based on the pixel values ​​predicted by the prediction model 14a, which can predict the pixel values ​​indicating coloring by the amount of coloring agent to be predicted, the base image 14b of the food, and the correction data.

[0053] (G) In the above embodiment, the base image 14b is an image showing a colored food product colored with a coloring agent, but is not limited to this. For example, the base image 14b may be an image of an uncolored food product.

[0054] (H) In the above embodiment, as shown in Figure 7, the prediction model 14a is constructed based on photographic images of multiple color samples obtained by adding colorants to food and beverages other than the colored food to be image-generated (e.g., donuts) (e.g., liquids without a specific shape, such as a mixture of milk and water or just water), but is not limited to this. As shown in Figure 9, it is preferable that the prediction model 14a is constructed based on photographic images of multiple color samples obtained by adding colorants to the same type of food as the colored food to be image-generated. Figure 9 is an explanatory diagram relating to a photograph of a color sample and the prediction model 14a (correlation formula) according to a modified example. In the example shown in Figure 9, the colored food to be image-generated is a cookie, and multiple color samples were created by adding the same product A as shown in Figure 7 to the same type of colored food (cookie) with different amounts (three color samples are shown in Figure 9). The amounts of product A added to the color samples shown are 0.07%, 0.3%, and 0.6%. Aside from adding coloring agents to the same type of food (cookies) as the colored food (cookies) used for image generation to create a color sample, the process is the same as described in the example shown in Figure 7. Figure 10 compares a food image generated based on a prediction model constructed using a different type of milk and water as a color sample than the colored cookie targeted for image generation, an actually produced food image, and a food image generated based on a prediction model constructed using the same type of colored cookie as the color sample. The actual food image shown in the middle of Figure 10 shows a stronger green component compared to the food image based on the milk and water color sample shown in the upper part of Figure 10. This can be inferred to be because the cookie contains butter, and the yellow component of the butter and the blue component of the coloring agent mix to produce the green component. On the other hand, the food image based on the colored cookie color sample shown in the lower part of Figure 10 has the same green tone as the actual food image shown in the middle of Figure 10. Therefore, it is considered more preferable for the prediction model 14a to be constructed based on photographic images of multiple color samples of the same type of food as the colored food targeted for image generation, with the coloring agent added to them.

[0055] (I) In the above embodiment, the prediction model 14a outputs pixel values ​​that indicate coloring according to the amount of one colorant added, but is not limited to this. For example, the prediction model 14a may be configured to output an image that indicates color according to the amount of each of multiple colorants added. For example, when the prediction model 14a predicts color mixing with two colorants, if the amount of the first colorant and the amount of the second colorant are input to the prediction model 14a, the prediction model 14a may output pixel values ​​that indicate coloring. In this case, one prediction model 14a is generated for each combination of two colorants. In the above embodiment, the food image of a single colored food is associated with one type of coloring agent and the amount added, but is not limited to this. For example, the food image of a single colored food may be associated with multiple types (e.g., two types) of coloring agents and the amount of each coloring agent added. This makes it possible to provide food images with mixed colors from multiple types of coloring agents. [Explanation of Symbols]

[0056] 1: Food image provision system 11: Storage section 12: Reception Department 13: Output section 14: Image generation unit 14a: Predictive Model 14b: Base image 14c: Conversion data 14d: Adjustment data D1: Food image information D2: Data for displaying food images A1: 1st area A2:Second area A3: 3rd area A4: 4th area A5: 5th area A6 :6th area A7 :7th area A8 :8th area

Claims

1. A storage unit stores data for displaying food images of colored foods using coloring agents, and food image information that associates the coloring agents added to the colored foods with the amount of coloring agents added. A receiving unit capable of accepting at least one specification from the following: food type, food shape, color classification of colorants, colorants, and amount added; The system includes an output unit capable of providing at least one food image corresponding to the designation received by the reception unit, The output unit is, The first step is to provide food images in association with colorants and their amounts, A process to provide a second food image, which has the same food shape as the first food image, in association with the coloring agent and the amount added; A food image provision system that is capable of performing this task.

2. The first food image above shows that the amount of coloring agent added is the first amount. The food image provisioning system according to claim 1, wherein the second food image is identical to the first food image in terms of coloring and food shape, and the amount of coloring added is a second amount different from the first amount.

3. The output unit is capable of displaying a plurality of food images in a predetermined display area that have the same coloring agent, the same food shape, and different amounts of coloring agent added to each, and the plurality of food images displayed in the predetermined display area are displayed in association with the respective added coloring agent and amount added, as described in claim 1 or 2.

4. The food image providing system according to claim 1 or 2, wherein at least one of the food images is an image generated based on the predicted pixel values ​​and a base image of the food, with the pixel values ​​indicating coloring by a predicted amount of coloring agent being added being predicted using a prediction model.

5. Equipped with an image generation unit capable of generating food images, The food image providing system according to claim 1 or 2, wherein the image generation unit generates a food image based on pixel values ​​predicted by a prediction model capable of predicting pixel values ​​indicating coloring due to the amount of colorant to be added, and a base image of the food.

6. A step of accepting the designation of at least one of the following: food type, food shape, color classification of colorants, colorants, and amount added, A step of providing at least one food image corresponding to a received specification, using data for displaying a food image of a colored food using a coloring agent, food image information that associates the coloring agent added to the colored food and the amount of coloring agent added, Includes, The step of providing at least one food image is: The first step is to provide a food image associated with coloring agents and their amounts, The steps include providing a second food image, which has the same food shape as the first food image, in relation to the coloring agent and the amount added, A method for providing food images, which includes one or more processors.

7. A program that causes one or more processors to execute the method described in claim 6.

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