Image processing method, device, equipment and medium

Abstract

The disclosed embodiments relate to an image processing method, device, equipment and medium, wherein the method comprises: obtaining a line drawing and initial color prompt information from a user; coloring the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing; obtaining color modification information associated with the initial colored image from the user; generating target color prompt information based on the initial color prompt information, the color modification information and the initial colored image; and coloring the line drawing based on the target color prompt information to generate a target colored image of the line drawing. Thus, the line drawing is automatically colored according to the color prompt information provided by the user. If the user modifies the coloring effect, the line drawing can be further re-colored according to the user's modification, thereby satisfying the user's personalized coloring needs and improving the coloring efficiency while ensuring the coloring effect.

Description

Technical Field

[0001] The present disclosure relates to the field of computer application technology, and in particular to an image processing method, device, equipment and medium. Background Art

[0002] Coloring line drawings is a common image processing method. For example, when creating a two-dimensional character in a game, coloring the two-dimensional character is a common requirement for creating game characters.

[0003] In the related art, after receiving the line drawing image, the related technicians use the coloring function of the related application to colorize it based on their personal experience and the coloring requirement document. If the user is not satisfied with the coloring result, the corresponding color needs to be erased and re-colored.

[0004] However, in the above-mentioned process of coloring the line drawings, it depends on the manual coloring by the user. When the coloring of the line drawings is not satisfactory, it depends on the manual modification by the user, resulting in low coloring efficiency. Summary of the invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides an image processing method, device, equipment and medium, which automatically colorize the line drawing according to the color prompt information provided by the user. If the user modifies the coloring effect, the line drawing can be further re-colored according to the user's modification, thereby meeting the user's personalized coloring needs and improving the coloring efficiency while ensuring the coloring effect.

[0006] An embodiment of the present disclosure provides an image processing method, the method comprising: obtaining a line drawing and initial color prompt information from a user; coloring the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing; obtaining color modification information associated with the initial colored image from the user; generating target color prompt information based on the initial color prompt information, the color modification information and the initial colored image; and coloring the line drawing based on the target color prompt information to generate a target colored image of the line drawing.

[0007] The disclosed embodiment also provides an image processing device, which includes: a first acquisition module, used to acquire a line drawing and initial color prompt information from a user; a first generation module, used to color the line drawing based on the initial color prompt information, and generate an initial colored image of the line drawing; a second acquisition module, used to acquire color modification information associated with the initial colored image from the user; a second generation module, used to generate target color prompt information based on the initial color prompt information, the color modification information and the initial colored image; and a third generation module, used to color the line drawing based on the target color prompt information, and generate a target colored image of the line drawing.

[0008] An embodiment of the present disclosure also provides an electronic device, which includes: a processor; a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the image processing method as in the embodiment of the present disclosure.

[0009] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the image processing method according to the embodiment of the present disclosure.

[0010] Compared with the prior art, the technical solution of the disclosed embodiment has the following advantages:

[0011] The image processing scheme of the disclosed embodiment combines the line drawing and initial color information provided by the user, first preliminarily colors the line drawing to obtain an initial colored image, then obtains the associated color modification information, generates target color prompt information based on the color modification information and the initial color prompt information, and then colors the line drawing based on the target color prompt information to generate a target colored image of the line drawing. Thus, the line drawing is automatically colored according to the color prompt information provided by the user. If the user modifies the coloring effect, the line drawing can be further re-colored according to the user's modification, which meets the user's personalized coloring needs and improves the coloring efficiency while ensuring the coloring effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0013] Figure 1 A schematic diagram of a flow chart of an image processing method according to an embodiment of the present disclosure;

[0014] Figure 2A schematic diagram of an image processing scenario according to an embodiment of the present disclosure;

[0015] Figure 3 A schematic diagram of another image processing scenario according to an embodiment of the present disclosure;

[0016] Figure 4 A schematic diagram of another image processing scenario according to an embodiment of the present disclosure;

[0017] Figure 5 A schematic diagram of another image processing scenario according to an embodiment of the present disclosure;

[0018] Figure 6 A flowchart of another image processing method according to an embodiment of the present disclosure;

[0019] Figure 7 A schematic diagram of another image processing scenario according to an embodiment of the present disclosure;

[0020] Figure 8 A schematic diagram of another image processing scenario according to an embodiment of the present disclosure;

[0021] Fig. 9 A schematic diagram of another image processing scenario according to an embodiment of the present disclosure;

[0022] Fig.10 A flowchart of another image processing method according to an embodiment of the present disclosure;

[0023] Fig.11 A flowchart of another image processing method according to an embodiment of the present disclosure;

[0024] Fig.12 is a structural schematic diagram of an image processing device according to an embodiment of the present disclosure;

[0025] Fig.13 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0027] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0028] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0029] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0030] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0031] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0032] In order to solve the above problems, the embodiments of the present disclosure provide an image processing method, in which the user only needs to provide simple color prompt information to generate exquisite line drawing coloring results, and can also perform fine color modifications on the colored image. It not only provides intelligent coloring processing, but also supports fine color modification, which meets the user's personalized coloring needs and improves the coloring efficiency while ensuring the coloring effect.

[0033] The method is introduced below in conjunction with specific embodiments.

[0034] Figure 1 FIG. 1 is a flow chart of an image processing method according to an embodiment of the present disclosure. The method may be executed by an image processing device, wherein the device may be implemented by software and / or hardware and may generally be integrated in an electronic device. Figure 1 As shown, the method includes:

[0035] Step 101, obtaining a line drawing and initial color prompt information from a user.

[0036] Among them, the line drawing can be understood as a line drawing that only contains outline information, and the line drawing does not contain color filling.

[0037] In addition, the initial color prompt information is used to indicate the coloring of the line drawing. In different application scenarios, the initial color prompt information is different. The examples are as follows:

[0038] In one embodiment of the present disclosure, the initial color prompt information indicates one or more initial regions in the line drawing specified by the user, and the corresponding initial colors to be colored for the one or more regions, wherein, Figure 2 As shown, the initial indication information in this embodiment is in the form of an image, the size of the image is the same as the line drawing, and a plurality of color identification blocks are distributed in the image (different colors are identified by different grayscale values ​​in the figure). In actual application, the line drawing can be semantically segmented, and the initial line drawing can be divided into different areas according to the semantic recognition result. The color identification block corresponding to each area in the image corresponding to the initial indication information is identified, and the color of the corresponding color identification block is used as the initial color for coloring the area.

[0039] In another embodiment of the present disclosure, the name of each part of the line drawing can be obtained according to semantic recognition, a part name list containing all parts can be displayed, and initial color prompt information input by the user according to the part name list can be obtained, wherein the initial color prompt information can be in text form or in the form of selected color blocks, etc. For example, the displayed part names are "hair", "cheeks", "eyes" and "mouth", etc., and the user determines the initial colors of parts such as "hair", "cheeks", "eyes" and "mouth" by selecting color blocks or entering text.

[0040] In another embodiment of the present disclosure, the user can directly select a color reference image without understanding the name of each color, etc., and identify the reference color of each part by performing semantic recognition on the color reference image. The color reference image is semantically matched with the line drawing, and the reference color of the successfully matched color reference image is used as the initial color of the corresponding part of the line drawing.

[0041] Step 102 , coloring the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing.

[0042] It is easy to understand that the initial color prompt information reflects the user's initial coloring personalized needs. At this time, the line drawing is colored based on the initial color prompt information to generate an initial colored image of the line drawing. Among them, the initial colored image is filled with color according to the initial color prompt information.

[0043] Among them, in different application scenarios, the line drawing is colored based on the initial color prompt information, and the method of generating the initial colored image of the line drawing is different, as described as follows:

[0044] In one embodiment of the present disclosure, when the initial color prompt information indicates one or more initial areas in a line drawing specified by the user and the corresponding initial colors to be used to color the one or more areas, the one or more initial areas, the corresponding initial colors and the line drawing are input into a first model, wherein the first model can be understood as a pre-trained model for coloring, and then, an initial colored image of the line drawing is obtained to obtain the initial colored image of the line drawing, wherein the color of one or more initial areas in the initial colored image is consistent with the initial color.

[0045] For example, Figure 3 As shown (the figure only shows the color change of the indicated area), the initial color prompt information indicates the initial color of the eye area in the line drawing specified by the user, and the initial color of the eye area and the line drawing are input into the first model, and the eye area in the initial colored image obtained is consistent with the initial color.

[0046] In another embodiment of the present disclosure, when the initial color prompt information includes the initial color corresponding to each area, the pixel area where each area is located is identified based on the semantic recognition segmentation algorithm, and the color value of the pixel point of the corresponding pixel area is changed according to the corresponding initial color to obtain the initial colored image.

[0047] For example, Figure 4 As shown, the initial color prompt information indicates the initial color of the eye area in the line drawing specified by the user, then the eye area in the line drawing is identified, and the color of the pixel points in the eye area is modified to the corresponding initial color to obtain an initial colored image, wherein the color of the eye area in the initial colored image is the corresponding initial color.

[0048] Among them, image areas that are not included in the initial color hint information can be skipped or automatically filled with colors by the pre-learned model.

[0049] Step 103: Obtain color modification information associated with the initial colored image from the user.

[0050] During the actual execution process, the user may not be satisfied with the initial colored image. In this embodiment, in order to better meet the user's personalized needs, the color of the initial colored image can be modified according to the user's needs to meet the user's local refined modification needs, thereby further improving the flexibility of coloring.

[0051] In this embodiment, color modification information associated with the initial colored image is obtained from the user, wherein the associated color modification information may correspond to a specific one or more regions in the initial colored image.

[0052] Step 104: Generate target color prompt information based on the initial color prompt information, the color modification information and the initial colored image.

[0053] In one embodiment of the present disclosure, after the initial color prompt information is obtained, target color prompt information is generated according to the initial color prompt information, the color modification information and the initial coloring image, and the target color prompt information reflects the area modified by the user in the current scene and the modified color. The target color prompt information indicates one or more target areas in the line drawing specified by the user and the corresponding target colors to be colored for the one or more areas.

[0054] The generation of target color prompt information based on the initial color prompt information, the color modification information and the initial colored image will be described in subsequent embodiments and will not be repeated here.

[0055] Step 105 , coloring the line drawing based on the target color prompt information to generate a target colored image of the line drawing.

[0056] In this embodiment, the line drawing is colored based on the target color prompt information to generate a target colored image of the line drawing, in which the color of the target area specified by the user is consistent with the corresponding target color for coloring one or more areas contained in the target color prompt information. The display form of the target color prompt information can also be an image form, a text form, etc. corresponding to the initial color prompt information mentioned above.

[0057] For the image area not included in the target color hint information, the original filling color in the initial colored image is maintained.

[0058] Similarly, in this embodiment, one or more target areas and corresponding target colors and line drawings can also be input into the first model to obtain a target colored image of the line drawing, wherein the color of one or more target areas in the target colored image is consistent with the target color.

[0059] Therefore, the image processing method of the embodiment of the present disclosure is divided into two stages. For example, in the first stage, referring to Figure 5 In this embodiment, when the line drawing and initial color prompt information A1 are obtained from the user, the line drawing is colored according to A1 to obtain the initial colored image C1, and C1 is fully colored, thereby improving the coloring efficiency.

[0060] If the user is not satisfied with the coloring effect of C1, the second stage is entered, and color modification information associated with the initial colored image is obtained from the user. The color modification information is local modification information. If the color of the hair part is modified, target color prompt information A2 is generated based on the initial color prompt information, the color modification information and the initial colored image. The target color prompt information is used to indicate the target color of the modified hair part. The line drawing is colored based on the target color prompt information A2 to generate a target colored image C2 of the line drawing, wherein the color of the hair part of C2 is locally modified, thereby meeting the user's refined local color needs.

[0061] In summary, the image processing method of the disclosed embodiment, in combination with the line drawing and initial color information provided by the user, first performs preliminary coloring on the line drawing to obtain an initial colored image, then obtains the associated color modification information, generates target color prompt information based on the color modification information and the initial color prompt information, and then performs coloring on the line drawing based on the target color prompt information to generate a target colored image of the line drawing. Thus, the line drawing is automatically colored according to the color prompt information provided by the user. If the user modifies the coloring effect, the line drawing can be further re-colored according to the user's modification, thereby satisfying the user's personalized coloring needs and improving the coloring efficiency while ensuring the coloring effect.

[0062] The following describes, in conjunction with a specific embodiment, how to obtain the color modification information associated with the initial colored image.

[0063] In one embodiment of the present disclosure, Figure 6 As shown, obtaining color modification information associated with the initial colored image includes:

[0064] Step 601 : in response to a color modification request, color block the initial colored image to generate an initial color block image of the initial colored image, wherein the color block image includes a plurality of color block areas.

[0065] In this embodiment, in response to a color modification request, which may be triggered by user voice or by triggering a preset modification control, etc., in order to facilitate the user to perform local refined modifications, in this embodiment, the initial colored image is block-coded to generate an initial color block image of the initial colored image, wherein the color block image includes multiple color block areas.

[0066] In some possible embodiments, the initial colored image can be input into a second model, which is pre-trained based on a large amount of sample data. The second model can obtain multiple color block areas and corresponding area boundaries based on the input initial colored image, and then obtain the color mean of the pixels in the corresponding color block area from the initial colored image based on the area boundary, and use the corresponding color mean to fill the corresponding areas in the multiple color block areas to obtain the initial color block image, thereby processing the initial colored image into color block granularity, which is convenient for the user to perform local color modification based on the color block later.

[0067] For example, Figure 7 As shown in the figure (different color block areas are identified by different grayscale values, and the corresponding color block areas are identified by areas composed of color blocks that do not contain line outlines), when the initial colored image obtained is T1, T1 is input into the corresponding second model to obtain the color-blocked initial color block image T2, thereby processing T1 into a color block dimension to facilitate subsequent local color modification.

[0068] In one embodiment of the present disclosure, semantic recognition can be performed on the initial colored image to obtain the area where each part in the initial colored image is located, and the average of all the accompanying single pixels of the area where each part is located is taken. The obtained average value is used as the filling color to fill the area where the corresponding part is located to obtain the initial color block image.

[0069] For example, Figure 8 As shown in the figure (different color block areas are identified by different grayscale values, and the corresponding color block areas are identified by areas composed of color blocks that do not contain line outlines), when the initial colored image obtained is T3, T3 is semantically recognized, and the corresponding parts in T3 are obtained as "eyes", "mouth", "hair", etc. The area where each part is located is color-blocked to obtain the initial color block image T4. Thus, T3 is processed into a color block dimension to facilitate subsequent local color modification.

[0070] Step 602: Obtain color modification information associated with one or more color block areas among the plurality of color block areas.

[0071] After the initial color block image is obtained, color modification information associated with one or more color block areas among the plurality of color block areas is obtained, the color modification information corresponding to the color modification of the part area in the initial color block image.

[0072] The user may trigger one or more color block areas among the multiple color block areas and input the modified color of the triggered color block area, or paint the corresponding color block area with other colors to determine the associated color modification information.

[0073] Further, after the color modification information is acquired, target color prompt information is generated based on the initial color prompt information, the color modification information and the initial colored image.

[0074] In different application scenarios, different methods are used to generate target color hint information based on the initial color hint information, the color modification information, and the initial colored image. Examples are as follows:

[0075] In one embodiment of the present disclosure, based on color modification information associated with one or more color block areas among a plurality of color block areas, a target color block image is obtained from an initial color block image, and the target color block image includes a plurality of color blocks. The target color block image is a color block image after the user modifies the color. Target color prompt information is generated based on the target color block image. For example, if the target color prompt information is an image containing a color block identifier, the center area and the boundary area of ​​the target color block image can be determined, and the pixel values ​​of the center area and the boundary area can be sampled to obtain the sampling values ​​of a plurality of pixels, and the target color prompt information is generated according to the average value of the sampling values ​​of the plurality of pixels.

[0076] For example, Fig. 9 As shown, based on the color modification information of the initial color block image T5, it is a color block area corresponding to the human eye, then based on the color corresponding to the color modification information, the color block area corresponding to the human eye is modified to obtain the target color block image T6, and based on the sampling value of the pixel point of each color block in the target color block image T6, the target color prompt information S is obtained, wherein S includes the color identification of each color block in the target color block image T6. Then, according to S, the line drawing can be colored to obtain the target colored image T7. For example, one or more target areas and the corresponding target colors and the line drawing are input into the first model to obtain the target colored image of the line drawing, wherein the color of one or more target areas in the target colored image is consistent with the target color.

[0077] In another embodiment of the present disclosure, when the initial color prompt information includes the initial color corresponding to each area, the pixel area where each color block area of ​​the initial color block image is located is identified based on the semantic recognition segmentation algorithm, and the color value of the pixel point of the corresponding pixel area is changed based on the color modification information associated with one or more color block areas of the multiple color block areas to obtain the target color block image. The color of each color block in the target color block image is identified according to a preset deep learning model to obtain the target color prompt information.

[0078] In summary, in the image processing method of the embodiment of the present invention, the color modification information associated with the initial colored image is flexibly obtained according to the scene requirements, and the line drawing is colored according to the target color prompt information to generate the target colored image of the line drawing, which greatly improves the flexibility of the coloring processing.

[0079] Based on the above embodiment, before using the first model for coloring, it is necessary to train the first model, wherein the first model can be regarded as a coloring model.

[0080] In one embodiment of the present disclosure, Fig.10 As shown, if the first model is a coloring prompt model, the coloring prompt model is trained by the following steps:

[0081] Step 1001, obtaining a first sample line drawing corresponding to a first sample image.

[0082] In this embodiment, the first sample image may be a color-filled image. In this embodiment, the contour of the first sample image may be identified by a contour recognition algorithm or the like to obtain a first sample line drawing.

[0083] Step 1002: Acquire initial sample color prompt information from the first sample image.

[0084] In this embodiment, the initial sample color prompt information is directly acquired from the first sample image, wherein the initial sample color information is used to indicate the initial color of each sample area in the first sample image.

[0085] Step 1003 , coloring the first sample line drawing according to the first model to be trained based on the initial sample color prompt information to generate an initial sample colored image of the first sample line drawing.

[0086] In this embodiment, a first model to be trained is pre-built, and the first sample line drawing is colored based on the initial sample color prompt information according to the first model to be trained to generate an initial sample colored image of the first sample line drawing, wherein the initial sample colored image includes a fill color.

[0087] Step 1004: Generate a first target loss function according to the initial sample colored image and the first sample image.

[0088] It should be understood that the theoretical coloring effect of the initial sample colored image should be consistent with the first sample image. Therefore, in order to determine whether the model parameters of the first model to be trained are trained, a first target loss function is generated based on the initial sample colored image and the first sample image.

[0089] In different application scenarios, different algorithms are used to calculate the first objective loss function. For example, one or more of the following algorithms may be used to calculate the first objective loss function:

[0090] In some possible embodiments, the mean absolute error of pixel color between each pixel in the initial sample colored image and each pixel in the first sample image is calculated to obtain a reconstruction loss function. For example, the mean of the mean absolute errors of all pixels can be used as the reconstruction loss function.

[0091] In some possible embodiments, the mean square error of the color value of each pixel in the initial sample colored image and each pixel in the first sample color image is calculated to obtain the style loss function. For example, the average mean square error of all pixels can be used as the style loss function.

[0092] In some possible embodiments, the initial sample colored image and the first sample color image are processed according to a preset discriminator model to obtain an adversarial loss function. The discriminator model may be a discriminator module in a generative adversarial network (GAN, Generative Adversarial Networks), etc.

[0093] Step 1005 , according to the initial sample colored image and the first sample image, and based on the back propagation of the first target loss function, the parameters of the first model are trained to generate a coloring hint model.

[0094] In this embodiment, according to the initial sample colored image and the first sample image, and based on the back propagation of the first target loss function, the parameters of the first model are trained to generate a coloring prompt model. When the loss value of the first target loss function obtained by the first model is less than the preset loss threshold, the training of the model parameters is completed.

[0095] Similarly, before using the second model for color block processing, the second model needs to be trained. In one embodiment of the present disclosure, the second model is an image block model, such as Fig.11 As shown in the figure, the image segmentation model is trained through the following steps:

[0096] Step 1101: Acquire a second sample image.

[0097] The second sample image may be a color image filled with color, etc.

[0098] Step 1102 , performing region segmentation on the second sample image, and marking a plurality of sample color block regions and corresponding region boundaries of the second sample image.

[0099] In this embodiment, the second sample image is segmented into regions, and multiple sample color block regions and corresponding region boundaries of the second sample image are marked. The second sample image can be segmented into regions based on a pre-trained region segmentation model, or semantic analysis can be performed on the second sample image, and the same part can be regionalized into a sample color block region according to the semantic recognition result.

[0100] Step 1103: Process the second sample image according to the second model to be trained to generate a reference color block area and a corresponding area boundary.

[0101] In this embodiment, a second model for color block segmentation is pre-constructed, and the second sample image is processed according to the second model to be trained to generate a reference color block region and a corresponding region boundary.

[0102] Step 1104 , generating a second target loss function according to the reference color block area and the corresponding area boundary and the sample color block area and the corresponding area boundary.

[0103] It is easy to understand that the reference color block area obtained in theory should be consistent with the sample color block area and the corresponding area boundary, because the reference color block area is also derived from the second sample image. In order to determine whether the model parameters of the second model are trained, in this embodiment, a second target loss function is generated based on the reference color block area and the corresponding area boundary and the sample color block area and the corresponding area boundary.

[0104] In different application scenarios, different algorithms are used to calculate the second objective loss function. For example, one or more of the following algorithms may be used to calculate the second objective loss function:

[0105] In some possible embodiments, the average absolute error of pixel color between each pixel in the reference color block area and each pixel in the corresponding sample color block area is calculated to obtain a first reconstruction loss function, the average absolute error between the position information of each pixel point of the corresponding area boundary of the reference color block area and the position information of each pixel point of the corresponding area boundary of the sample color block area is calculated to obtain a second reconstruction loss function, and a reconstruction loss function between corresponding color block areas is obtained based on the first reconstruction loss function and the second reconstruction loss function, for example, the reconstruction loss function between corresponding color block areas is obtained based on the mean of the first reconstruction loss function and the reconstruction loss function.

[0106] In some possible embodiments, the mean square error of pixel color between each pixel in the reference color block area and each pixel in the corresponding sample color block area is calculated to obtain a first style loss function, the mean square error between the position information of each pixel point at the corresponding area boundary of the reference color block area and the position information of each pixel point at the corresponding area boundary of the sample color block area is calculated to obtain a second style loss function, and a second style loss function between corresponding color block areas is obtained based on the first style loss function and the second style loss function, for example, the style loss function between corresponding color block areas is obtained based on the mean of the first style loss function and the second style loss function.

[0107] In some possible embodiments, the reference color block area, the sample color block area, and the corresponding area boundaries are processed according to a preset discriminator model to obtain an adversarial loss function. The discriminator model may be a discriminator module in a generative adversarial network, etc.

[0108] Step 1105 , according to the reference color block area and the sample color block area, and based on the back propagation of the second target loss function, the parameters of the second model are trained to generate an image block model.

[0109] In this embodiment, according to the reference color block area and the sample color block area, and based on the back propagation of the second target loss function, the parameters of the second model are trained to generate an image block model. When the loss value of the second target loss function obtained by the second model is less than the preset loss threshold, the training of the model parameters is completed.

[0110] In summary, the image processing method of the embodiment of the present invention trains the first model and the second model based on the model training method, so as to perform coloring according to the first model and perform image segmentation according to the second model, without the need for human intervention, thereby reducing the cost of coloring and improving the coloring efficiency.

[0111] In order to implement the above embodiments, the present disclosure also proposes an image processing device. Fig.12 FIG. 1 is a schematic diagram of the structure of an image processing device according to an embodiment of the present disclosure. The device can be implemented by software and / or hardware and can generally be integrated into an electronic device. Fig.12 As shown, the device includes: a first acquisition module 1210, a first generation module 1220, a second acquisition module 1230, a second generation module 1240, and a third generation module 1250, wherein:

[0112] The first acquisition module 1210 is used to acquire the line drawing and initial color prompt information from the user;

[0113] The first generating module 1220 is used to color the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing;

[0114] A second acquisition module 1230 is used to acquire color modification information associated with the initial colored image from the user;

[0115] A second generating module 1240 is used to generate target color prompt information based on the initial color prompt information, the color modification information and the initial colored image; and

[0116] The third generating module 1250 is used to color the line drawing based on the target color prompt information to generate a target colored image of the line drawing.

[0117] The image processing device provided in the embodiments of the present disclosure can execute the image processing method provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0118] In summary, the image processing device of the disclosed embodiment, in combination with the line drawing and initial color information provided by the user, first performs preliminary coloring on the line drawing to obtain an initial colored image, and then obtains the associated color modification information, generates target color prompt information based on the color modification information and the initial color prompt information, and then performs coloring on the line drawing based on the target color prompt information to generate a target colored image of the line drawing. Thus, the line drawing is automatically colored according to the color prompt information provided by the user. If the user modifies the coloring effect, the line drawing can be further re-colored according to the user's modification, thereby satisfying the user's personalized coloring needs and improving the coloring efficiency while ensuring the coloring effect.

[0119] In order to implement the above embodiments, the present disclosure further proposes a computer program product, including a computer program / instruction, which implements the image processing method in the above embodiments when executed by a processor.

[0120] Fig.13 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure.

[0121] The following specific reference Fig.13 , which shows a schematic diagram of the structure of an electronic device 1300 suitable for implementing the embodiment of the present disclosure. The electronic device 1300 in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.13 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0122] like Fig.13 As shown, the electronic device 1300 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 1301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1302 or a program loaded from a storage device 1308 to a random access memory (RAM) 1303. In the RAM 1303, various programs and data required for the operation of the electronic device 1300 are also stored. The processing device 1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An input / output (I / O) interface 1305 is also connected to the bus 1304.

[0123] Typically, the following devices may be connected to the I / O interface 1305: an input device 1306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1309. The communication device 1309 may allow the electronic device 1300 to communicate with other devices wirelessly or by wire to exchange data. Although Fig.13 The electronic device 1300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0124] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 1309, or installed from a storage device 1308, or installed from a ROM 1302. When the computer program is executed by the processing device 1301, the above-mentioned functions defined in the image processing method of the embodiment of the present disclosure are executed.

[0125] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0126] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0127] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0128] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: combines the line drawing and initial color information provided by the user, first preliminarily colors the line drawing to obtain an initial colored image, then obtains the associated color modification information, generates target color prompt information based on the color modification information and the initial color prompt information, and then colors the line drawing based on the target color prompt information to generate a target colored image of the line drawing. Thus, the line drawing is automatically colored according to the color prompt information provided by the user. If the user modifies the coloring effect, the line drawing can be further re-colored according to the user's modification, which meets the user's personalized coloring needs and improves the coloring efficiency while ensuring the coloring effect.

[0129] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0130] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0131] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0132] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0133] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0134] According to one or more embodiments of the present disclosure, the present disclosure provides an image processing method, including: obtaining a line drawing and initial color prompt information from a user;

[0135] Coloring the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing;

[0136] obtaining, from the user, color modification information associated with the initially colored image;

[0137] generating target color hint information based on the initial color hint information, the color modification information and the initial colored image; and

[0138] The line drawing is colored based on the target color prompt information to generate a target colored image of the line drawing.

[0139] According to one or more embodiments of the present disclosure, in the image processing method provided by the present disclosure, the initial color prompt information indicates one or more initial areas in the line drawing specified by the user and the corresponding initial colors to be used to color the one or more areas.

[0140] According to one or more embodiments of the present disclosure, coloring the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing includes:

[0141] The one or more initial regions and the corresponding initial colors and the line drawing are input into a first model to obtain an initial colored image of the line drawing, wherein the colors of the one or more initial regions in the initial colored image are consistent with the initial colors.

[0142] According to one or more embodiments of the present disclosure, the step of obtaining color modification information associated with the initial colored image includes:

[0143] In response to a color modification request, color-blocking the initial colored image to generate an initial color block image of the initial colored image, wherein the color block image includes a plurality of color block areas;

[0144] Color modification information associated with one or more color block areas of the plurality of color block areas is obtained.

[0145] According to one or more embodiments of the present disclosure, in response to the color modification request, color-blocking the initial colored image to generate an initial color block image of the initial colored image includes:

[0146] Inputting the initial colored image into a second model, and determining the plurality of color block regions and corresponding region boundaries of the initial colored image;

[0147] Acquire the color mean of pixels in the corresponding color block area from the initial colored image based on the area boundary;

[0148] The corresponding areas among the plurality of color block areas are filled with corresponding color averages to obtain an initial color block image.

[0149] According to one or more embodiments of the present disclosure, generating target color hint information based on the initial color hint information, the color modification information and the initial colored image includes:

[0150] acquiring a target color block image from an initial color block image based on color modification information associated with the one or more color block areas of the plurality of color block areas, the target color block image comprising a plurality of color blocks;

[0151] Generate target color prompt information based on the target color block image.

[0152] According to one or more embodiments of the present disclosure, the target color prompt information indicates one or more target areas in the line drawing specified by the user and corresponding target colors to be used to color the one or more areas.

[0153] According to one or more embodiments of the present disclosure, coloring the line drawing based on the target color prompt information to generate a target colored image of the line drawing includes:

[0154] The one or more target areas and the corresponding target colors and the line drawing are input into the first model to obtain a target colored image of the line drawing, wherein the colors of the one or more target areas in the target colored image are consistent with the target color.

[0155] According to one or more embodiments of the present disclosure, the first model is a coloring prompt model, and the coloring prompt model is trained by the following steps:

[0156] Obtaining a first sample line drawing corresponding to the first sample image;

[0157] Acquire initial sample color prompt information from the first sample image;

[0158] Coloring the first sample line drawing according to the first model to be trained based on the initial sample color prompt information to generate an initial sample colored image of the first sample line drawing;

[0159] Generating a first target loss function according to the initial sample colored image and the first sample image; and

[0160] According to the initial sample colored image and the first sample image, and based on back propagation of the first target loss function, the parameters of the first model are trained to generate the coloring hint model.

[0161] According to one or more embodiments of the present disclosure, the second model is an image block model, and the image block model is trained by the following steps:

[0162] acquiring a second sample image;

[0163] Performing region segmentation on the second sample image, and marking a plurality of sample color block regions and corresponding region boundaries of the second sample image;

[0164] Processing the second sample image according to the second model to be trained to generate a reference color block area and a corresponding area boundary;

[0165] Generate a second target loss function according to the reference color block area and the corresponding area boundary and the sample color block area and the corresponding area boundary; and

[0166] According to the reference color block area and the sample color block area, and based on the back propagation of the second target loss function, the parameters of the second model are trained to generate the image block model.

[0167] According to one or more embodiments of the present disclosure, the present disclosure provides an image processing device, including: a first acquisition module, configured to acquire a line drawing and initial color prompt information from a user;

[0168] A first generating module, configured to color the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing;

[0169] a second acquisition module, configured to acquire color modification information associated with the initial colored image from the user;

[0170] A second generating module, configured to generate target color prompt information based on the initial color prompt information, the color modification information and the initial colored image; and

[0171] The third generating module is used to color the line drawing based on the target color prompt information to generate a target colored image of the line drawing.

[0172] According to one or more embodiments of the present disclosure, the initial color prompt information indicates one or more initial regions in the line drawing specified by the user and corresponding initial colors to be used to color the one or more regions.

[0173] According to one or more embodiments of the present disclosure, the first generating module is specifically configured to:

[0174] The one or more initial regions and the corresponding initial colors and the line drawing are input into a first model to obtain an initial colored image of the line drawing, wherein the colors of the one or more initial regions in the initial colored image are consistent with the initial colors.

[0175] According to one or more embodiments of the present disclosure, the second acquisition module is specifically configured to:

[0176] In response to a color modification request, color-blocking the initial colored image to generate an initial color block image of the initial colored image, wherein the color block image includes a plurality of color block areas;

[0177] Color modification information associated with one or more color block areas of the plurality of color block areas is obtained.

[0178] According to one or more embodiments of the present disclosure, the second acquisition module is specifically used to: input the initial colored image into a second model, and determine the multiple color block areas and corresponding area boundaries of the initial colored image;

[0179] Acquire the color mean of pixels in the corresponding color block area from the initial colored image based on the area boundary;

[0180] The corresponding areas among the plurality of color block areas are filled with corresponding color averages to obtain an initial color block image.

[0181] According to one or more embodiments of the present disclosure, the second acquisition module is specifically configured to: acquire a target color block image from an initial color block image based on color modification information associated with the one or more color block areas among the multiple color block areas, the target color block image comprising multiple color blocks;

[0182] Generate target color prompt information based on the target color block image.

[0183] According to one or more embodiments of the present disclosure, the target color prompt information indicates one or more target areas in the line drawing specified by the user and corresponding target colors to be used to color the one or more areas.

[0184] According to one or more embodiments of the present disclosure, the third generating module is specifically configured to:

[0185] The one or more target areas and the corresponding target colors and the line drawing are input into the first model to obtain a target colored image of the line drawing, wherein the colors of the one or more target areas in the target colored image are consistent with the target color.

[0186] According to one or more embodiments of the present disclosure, the first model is a coloring prompt model, and the device further includes: a first training module, which is used to:

[0187] Obtaining a first sample line drawing corresponding to the first sample image;

[0188] Acquire initial sample color prompt information from the first sample image;

[0189] Coloring the first sample line drawing according to the first model to be trained based on the initial sample color prompt information to generate an initial sample colored image of the first sample line drawing;

[0190] Generating a first target loss function according to the initial sample colored image and the first sample image; and

[0191] According to the initial sample colored image and the first sample image, and based on back propagation of the first target loss function, the parameters of the first model are trained to generate the coloring hint model.

[0192] According to one or more embodiments of the present disclosure, the second model is an image block model, and the apparatus further includes: a second training module, configured to:

[0193] acquiring a second sample image;

[0194] Performing region segmentation on the second sample image, and marking a plurality of sample color block regions and corresponding region boundaries of the second sample image;

[0195] Processing the second sample image according to the second model to be trained to generate a reference color block area and a corresponding area boundary;

[0196] Generate a second target loss function according to the reference color block area and the corresponding area boundary and the sample color block area and the corresponding area boundary; and

[0197] According to the reference color block area and the sample color block area, and based on the back propagation of the second target loss function, the parameters of the second model are trained to generate the image block model.

[0198] According to one or more embodiments of the present disclosure, the present disclosure provides an electronic device, including:

[0199] processor;

[0200] a memory for storing instructions executable by the processor;

[0201] The processor is used to read the executable instructions from the memory and execute the instructions to implement any image processing method provided in the present disclosure.

[0202] According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute any image processing method as provided by the present disclosure.

[0203] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0204] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0205] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. An image processing method, characterized in that: The method is performed by an image processing device, comprising: Get line drawings and initial color hints from the user; Coloring the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing; obtaining, from the user, color modification information associated with the initially colored image; generating target color hint information based on the initial color hint information, the color modification information and the initial colored image; and Coloring the line drawing based on the target color prompt information to generate a target colored image of the line drawing; The step of obtaining color modification information associated with the initial colored image includes: Inputting the initial colored image into a second model, determining a plurality of color block regions and corresponding region boundaries of the initial colored image; Based on the region boundary, obtaining the color mean value of pixels in the corresponding color block region from the initial colored image; Fill corresponding areas of the plurality of color block areas with corresponding color means to obtain an initial color block image; Obtaining color modification information associated with one or more color block areas of the plurality of color block areas; The generating target color prompt information based on the initial color prompt information, the color modification information and the initial colored image comprises: acquiring a target color block image from an initial color block image of the initial colored image based on color modification information associated with one or more color block areas of the plurality of color block areas; Generate target color prompt information based on the target color block image.

2. The method according to claim 1, characterized in that The initial color prompt information indicates one or more initial regions in the line drawing specified by the user and corresponding initial colors to be used to color the one or more regions.

3. The method according to claim 2, characterized in that The step of coloring the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing includes: The one or more initial regions and the corresponding initial colors and the line drawing are input into a first model to obtain an initial colored image of the line drawing, wherein the colors of the one or more initial regions in the initial colored image are consistent with the initial colors.

4. The method according to claim 1, characterized in that: The target color block image includes a plurality of color blocks.

5. The method according to claim 4, characterized in that The target color prompt information indicates one or more target areas in the line drawing specified by the user and corresponding target colors to be used to color the one or more areas.

6. The method according to claim 5, characterized in that The step of coloring the line drawing based on the target color prompt information to generate a target colored image of the line drawing includes: The one or more target areas and the corresponding target colors and the line drawing are input into a first model to obtain a target colored image of the line drawing, wherein the colors of the one or more target areas in the target colored image are consistent with the target color.

7. The method according to claim 3 or 6, characterized in that: The first model is a coloring prompt model, and the coloring prompt model is trained by the following steps: Obtaining a first sample line drawing corresponding to the first sample image; Acquire initial sample color prompt information from the first sample image; Coloring the first sample line drawing according to the first model to be trained based on the initial sample color prompt information to generate an initial sample colored image of the first sample line drawing; Generate a first target loss function according to the initial sample colored image and the first sample image; as well as According to the initial sample colored image and the first sample image, and based on back propagation of the first target loss function, the parameters of the first model are trained to generate the coloring hint model.

8. The method according to any one of claims 5 to 6, characterized in that: The second model is an image block model, which is trained by the following steps: acquiring a second sample image; Performing region segmentation on the second sample image, and marking a plurality of sample color block regions and corresponding region boundaries of the second sample image; Processing the second sample image according to the second model to be trained to generate a reference color block area and a corresponding area boundary; Generate a second target loss function according to the reference color block area and the corresponding area boundary and the sample color block area and the corresponding area boundary; as well as According to the reference color block area and the sample color block area, and based on the back propagation of the second target loss function, the parameters of the second model are trained to generate the image block model.

9. An image processing device, characterized in that: include: The first acquisition module is used to obtain the line drawing and initial color prompt information from the user; A first generating module, configured to color the line drawing based on the initial color prompt information to generate an initial colored image of the line drawing; a second acquisition module, configured to acquire color modification information associated with the initial colored image from the user; A second generating module, used for generating target color prompt information based on the initial color prompt information, the color modification information and the initial colored image; as well as A third generating module is used to color the line drawing based on the target color prompt information to generate a target colored image of the line drawing; The second acquisition module is specifically used to input the initial colored image into a second model to determine a plurality of color block areas and corresponding area boundaries of the initial colored image; Based on the region boundary, obtaining the color mean value of pixels in the corresponding color block region from the initial colored image; Fill corresponding areas of the plurality of color block areas with corresponding color means to obtain an initial color block image; Obtaining color modification information associated with one or more color block areas of the plurality of color block areas; The second generating module is specifically configured to obtain a target color block image from an initial color block image of the initial colored image based on color modification information associated with one or more color block areas of the plurality of color block areas; Generate target color prompt information based on the target color block image.

10. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the image processing method described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the image processing method described in any one of claims 1 to 8.

Images