Color data processing method and system for FDM color printing

Abstract

The present invention discloses a color data processing method and system for FDM color printing, the method comprising: obtaining 3D model data to be printed and historical printing records of a 3D printer ready to perform printing; identifying the color-deficient model part in the 3D model data based on a color-deficient recognition algorithm; predicting the color of each color-deficient model part according to the historical printing records and a preset color prediction algorithm to obtain a predicted color result; and color-completing the 3D model data according to the predicted color result to obtain the completed 3D model data and send it to the 3D printer for printing. It can be seen that the present invention can effectively improve the color-completing function and color printing effect of 3D printing, and realize smarter and better 3D full-color printing.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a color data processing method and system for FDM color printing. Background Art

[0002] In recent years, with the rapid development of 3D printing technology (including 3D printing hardware, software and materials), 3D printers have transformed from early novelties to an indispensable part of the design, engineering and manufacturing workflows in all walks of life. Among them, FDM (fused deposition modeling) is the most widely used 3D printing technology at the consumer level. Compared with photocuring printing, it has great advantages in printing speed, manufacturing size, material and cost. However, in the existing technology, FDM3D printing cannot recognize color missing for model data with complex colors when achieving full-color printing, and does not consider using a prediction algorithm model to predict the color of the color-missing position, so the printing color effect is poor. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a color data processing method and system for FDM color printing, which can effectively improve the color completion function and color printing effect of 3D printing, and realize smarter and better 3D full-color printing.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a color data processing method for FDM color printing, the method comprising:

[0005] Acquire the 3D model data to be printed and the historical printing records of the 3D printer to be used for printing;

[0006] Based on the color deficiency recognition algorithm, identifying the color deficiency model part in the 3D model data;

[0007] According to the historical printing records and a preset color prediction algorithm, the color of each color-deficient model part is predicted to obtain a predicted color result;

[0008] The 3D model data is color-completed according to the predicted color result, and the completed 3D model data is obtained and sent to the 3D printer for printing.

[0009] As an optional implementation, in the first aspect of the present invention, the step of identifying the color-deficient model part in the 3D model data based on the color-deficient recognition algorithm includes:

[0010] Framing the 3D model data based on multiple viewpoints and multiple angles to obtain multiple framing images;

[0011] Inputting each of the framing images into a preset low-saturation color recognition model to obtain a color-deficient portion of the image corresponding to each of the framing images;

[0012] According to the color-deficient image parts corresponding to all the viewfinder images, the color-deficient model parts in the 3D model data are identified.

[0013] As an optional implementation, in the first aspect of the present invention, the step of identifying the color-deficient model portion in the 3D model data according to the color-deficient image portions corresponding to all the viewfinder images includes:

[0014] Based on a preset positional relationship of the model parts, identifying a plurality of parts with a high possibility of missing in the 3D model data;

[0015] For each of the high missing possibility parts, calculating the first occurrence number of the image color missing parts corresponding to the high missing possibility part in all the viewfinder images;

[0016] If the first occurrence number is greater than the first number threshold, the high missing possibility portion is determined as a color-deficient model portion;

[0017] For any other part of the 3D model data except the part with high missing probability, calculating the second occurrence number of the image color missing part of the other part in all the viewfinder images;

[0018] If the second occurrence times is greater than a second number threshold, the other part is determined as a color-deficient model part; and the second number threshold is greater than the first number threshold.

[0019] As an optional embodiment, in the first aspect of the present invention, the portion with a high possibility of missing is a component connection portion, a non-visible display surface portion, or a low-visibility display surface portion.

[0020] As an optional implementation, in the first aspect of the present invention, predicting the color of each color-deficient model part according to the historical printing record and a preset color prediction algorithm to obtain a predicted color result includes:

[0021] For each of the color-deficient model parts, determining the colors of the neighboring regions of the plurality of neighboring regions corresponding to the color-deficient model part;

[0022] Inputting all the nearby area colors and corresponding area positions into a trained color prediction neural network to obtain probability values ​​of the color-deficient model parts belonging to different colors; the color prediction neural network is trained by a training data set including a combination of multiple training area colors and corresponding color-deficient part color annotations;

[0023] Determine a plurality of printing colors of the same type as the color-deficient model portion in the model printed by the 3D printer in the historical printing record;

[0024] According to the multiple printing colors and the probability values ​​belonging to different colors, a predicted color result corresponding to the color deficiency model part is determined.

[0025] As an optional implementation, in the first aspect of the present invention, determining the predicted color result corresponding to the color-deficient model part according to the multiple printed colors and the probability values ​​belonging to different colors includes:

[0026] For the probability value that the color-deficient model part belongs to each color, calculating the third occurrence number of the corresponding color in the plurality of printing colors;

[0027] Calculate the product of the probability weight proportional to the third number of occurrences and the probability value to obtain a corrected probability value of the color-deficient model part belonging to each color;

[0028] The color with the highest correction probability value is determined as the predicted color result corresponding to the color-deficient model part.

[0029] As an optional implementation, in the first aspect of the present invention, the color completion of the 3D model data according to the predicted color result includes:

[0030] For each color-deficient model part, filling the corresponding predicted color result into the model area of ​​the color-deficient model part;

[0031] Screening out at least one high color difference region whose color difference value between the region color and the predicted color result is greater than a color difference value threshold from a plurality of nearby partial regions of the color-deficient model portion;

[0032] For each regional boundary between the high color difference area and the model area, fill in the regional color of the high color difference area and the transition color corresponding to the predicted color result; the color value of the transition color is the weighted sum average of the regional color value of the high color difference area and the result color value of the predicted color result.

[0033] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0034] In the process of the 3D printer printing the completed 3D model data, a printing image of each layer of printing material is obtained in real time through an image acquisition module;

[0035] Inputting the image color corresponding to the printed image and the color data in the completed 3D model data into a trained color difference judgment neural network to obtain a color difference judgment result;

[0036] According to the color difference judgment result, a pigment output control instruction for the 3D printer is generated to reduce the color difference of 3D printing.

[0037] A second aspect of an embodiment of the present invention discloses a color data processing system for FDM color printing, the system comprising:

[0038] An acquisition module, used to acquire the 3D model data to be printed and the historical printing records of the 3D printer ready to perform printing;

[0039] A recognition module, used for identifying the color-deficient model part in the 3D model data based on a color-deficient recognition algorithm;

[0040] A prediction module, used to predict the color of each color-deficient model part according to the historical printing records and a preset color prediction algorithm to obtain a predicted color result;

[0041] The completion module is used to complete the color of the 3D model data according to the predicted color result, and obtain the completed 3D model data to send to the 3D printer for printing.

[0042] As an optional implementation, in the second aspect of the present invention, the specific manner in which the recognition module recognizes the color-deficient model part in the 3D model data based on the color-deficient recognition algorithm includes:

[0043] Framing the 3D model data based on multiple viewpoints and multiple angles to obtain multiple framing images;

[0044] Inputting each of the framing images into a preset low-saturation color recognition model to obtain a color-deficient portion of the image corresponding to each of the framing images;

[0045] According to the color-deficient image parts corresponding to all the viewfinder images, the color-deficient model parts in the 3D model data are identified.

[0046] As an optional implementation, in the second aspect of the present invention, the specific manner in which the recognition module recognizes the color-deficient model part in the 3D model data according to the color-deficient image parts corresponding to all the viewfinder images includes:

[0047] Based on a preset positional relationship of the model parts, identifying a plurality of parts with a high possibility of missing in the 3D model data;

[0048] For each of the high missing possibility parts, calculating the first occurrence number of the image color missing parts corresponding to the high missing possibility part in all the viewfinder images;

[0049] If the first occurrence number is greater than the first number threshold, the high missing possibility portion is determined as a color-deficient model portion;

[0050] For any other part of the 3D model data except the part with high missing probability, calculating the second occurrence number of the image color missing part of the other part in all the viewfinder images;

[0051] If the second occurrence times is greater than a second number threshold, the other part is determined as a color-deficient model part; and the second number threshold is greater than the first number threshold.

[0052] As an optional embodiment, in the second aspect of the present invention, the portion with a high possibility of missing is a component connection portion, a non-visible display surface portion, or a low-visibility display surface portion.

[0053] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the color of each color-deficient model part according to the historical printing record and a preset color prediction algorithm, and obtains a specific method of predicting the color result, including:

[0054] For each of the color-deficient model parts, determining the colors of the neighboring regions of the plurality of neighboring regions corresponding to the color-deficient model part;

[0055] Inputting all the nearby area colors and corresponding area positions into a trained color prediction neural network to obtain probability values ​​of the color-deficient model parts belonging to different colors; the color prediction neural network is trained by a training data set including a combination of multiple training area colors and corresponding color-deficient part color annotations;

[0056] Determine a plurality of printing colors of the same type as the color-deficient model portion in the model printed by the 3D printer in the historical printing record;

[0057] According to the multiple printing colors and the probability values ​​belonging to different colors, a predicted color result corresponding to the color deficiency model part is determined.

[0058] As an optional implementation, in the second aspect of the present invention, the prediction module determines the specific manner of the predicted color result corresponding to the color deficiency model part according to the multiple printing colors and the probability values ​​belonging to different colors, including:

[0059] For the probability value that the color-deficient model part belongs to each color, calculating the third occurrence number of the corresponding color in the plurality of printing colors;

[0060] Calculate the product of the probability weight proportional to the third number of occurrences and the probability value to obtain a corrected probability value of the color-deficient model part belonging to each color;

[0061] The color with the highest correction probability value is determined as the predicted color result corresponding to the color-deficient model part.

[0062] As an optional implementation, in the second aspect of the present invention, the specific manner in which the completion module performs color completion on the 3D model data according to the predicted color result includes:

[0063] For each color-deficient model part, filling the corresponding predicted color result into the model area of ​​the color-deficient model part;

[0064] Screening out at least one high color difference region whose color difference value between the region color and the predicted color result is greater than a color difference value threshold from a plurality of nearby partial regions of the color-deficient model portion;

[0065] For each regional boundary between the high color difference area and the model area, fill in the regional color of the high color difference area and the transition color corresponding to the predicted color result; the color value of the transition color is the weighted sum average of the regional color value of the high color difference area and the result color value of the predicted color result.

[0066] As an optional embodiment, in the second aspect of the present invention, the system is further configured to perform the following steps:

[0067] In the process of the 3D printer printing the completed 3D model data, a printing image of each layer of printing material is obtained in real time through an image acquisition module;

[0068] Inputting the image color corresponding to the printed image and the color data in the completed 3D model data into a trained color difference judgment neural network to obtain a color difference judgment result;

[0069] According to the color difference judgment result, a pigment output control instruction for the 3D printer is generated to reduce the color difference of 3D printing.

[0070] The third aspect of the present invention discloses another color data processing system for FDM color printing, the system comprising:

[0071] A memory storing executable program code;

[0072] a processor coupled to the memory;

[0073] The processor calls the executable program code stored in the memory to execute part or all of the steps in the color data processing method for FDM color printing disclosed in the first aspect of the present invention.

[0074] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the color data processing method for FDM color printing disclosed in the first aspect of the present invention.

[0075] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0076] The present invention can identify the color-deficient model part in the 3D model data through a color-deficient recognition algorithm, and then perform color prediction based on the printer's historical printing records and a preset color prediction algorithm to complete the model data to obtain a more complete color effect, thereby effectively improving the color completion function and color printing effect of 3D printing, and achieving smarter and better 3D full-color printing. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0078] Figure 1 The invention discloses a flow chart of a color data processing method for FDM color printing.

[0079] Figure 2 The invention discloses a color data processing system for FDM color printing.

[0080] Figure 3 It is a structural schematic diagram of another color data processing system for FDM color printing disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0083] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0084] The present invention discloses a color data processing method and system for FDM color printing, which can identify the color-deficient model part in 3D model data through a color-deficient recognition algorithm, and then perform color prediction based on the historical printing records of the printer and a preset color prediction algorithm to complete the model data with more complete color effects, thereby effectively improving the color completion function and color printing effect of 3D printing, and realizing smarter and better 3D full-color printing. The following are detailed descriptions.

[0085] Embodiment 1

[0086] See also Figure 1 , Figure 1 1 is a flow chart of a color data processing method for FDM color printing disclosed in an embodiment of the present invention. Figure 1 The color data processing method for FDM color printing described can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the color data processing method for FDM color printing may include the following operations:

[0087] 101. Obtain 3D model data to be printed and historical printing records of a 3D printer that is ready to perform printing.

[0088] 102. Based on the color deficiency recognition algorithm, the color deficiency model part in the 3D model data is identified.

[0089] 103. According to the historical printing records and the preset color prediction algorithm, the color of each color-deficient model part is predicted to obtain a predicted color result.

[0090] 104. Perform color completion on the 3D model data according to the predicted color result, and obtain the completed 3D model data to send to a 3D printer for printing.

[0091] It can be seen that the above-mentioned embodiment of the invention can identify the color-missing model part in the 3D model data through the color-missing recognition algorithm, and then perform color prediction based on the printer's historical printing records and the preset color prediction algorithm to complete the model data with more complete color effects, thereby effectively improving the color completion function and color printing effect of 3D printing, and realizing smarter and better 3D full-color printing.

[0092] As an optional embodiment, in the above step, based on the color deficiency recognition algorithm, identifying the color-deficient model part in the 3D model data includes:

[0093] Framing the 3D model data based on multiple viewpoints and multiple angles to obtain multiple framing images;

[0094] Input each framed image into a preset low-saturation color recognition model to obtain the color-deficient portion of the image corresponding to each framed image;

[0095] According to the color-deficient image parts corresponding to all the framing images, the color-deficient model parts in the 3D model data are identified.

[0096] It can be seen that through the above-mentioned optional embodiments, multiple framing images can be obtained through multi-angle and multi-viewpoint framing, and after low-saturation recognition of all framing images, the color-deficient model part in the 3D model data can be accurately identified, so as to perform more accurate color completion later, thereby assisting in improving the color completion function and color printing effect of 3D printing, and realizing smarter and better 3D full-color printing.

[0097] As an optional embodiment, in the above step, identifying the color-deficient model part in the 3D model data according to the color-deficient image parts corresponding to all the viewfinder images includes:

[0098] Based on the preset model part position relationship, multiple parts with high missing probability in the 3D model data are identified;

[0099] For each high missing possibility portion, calculate the first occurrence number of the image color missing portions corresponding to the high missing possibility portion in all the framed images;

[0100] If the first occurrence number is greater than the first number threshold, the high missing possibility part is determined as the color-deficient model part;

[0101] For any other part of the 3D model data except the part with high missing probability, calculate the second occurrence number of the image color missing part corresponding to the other part in all the framed images;

[0102] If the second number of occurrences is greater than the second number threshold, the other part is determined as a color-deficient model part; and the second number threshold is greater than the first number threshold.

[0103] It can be seen that through the above-mentioned optional embodiments, the color-missing model parts in the 3D model data can be accurately identified by calculating and screening the number of occurrences of model parts with different missing possibilities in the color missing results of the framing image, so as to perform more accurate color completion later, thereby assisting in improving the color completion function and color printing effect of 3D printing, and realizing smarter and better 3D full-color printing.

[0104] As an optional embodiment, in the above steps, the portion with a high possibility of missing is a component connection portion, an invisible display surface portion, or a low-visibility display surface portion.

[0105] It can be seen that through the above optional embodiments, the types of parts with high missing probability are limited, which facilitates the subsequent targeted calculation and screening of the number of occurrences of model parts with low versus high missing probability in the color missing results of the framing image, and accurately identifies the color-missing model parts in the 3D model data, so as to perform more accurate color completion later, thereby assisting in improving the color completion function and color printing effect of 3D printing, and achieving smarter and better 3D full-color printing.

[0106] As an optional embodiment, in the above steps, the color of each color-deficient model part is predicted according to the historical printing records and a preset color prediction algorithm to obtain a predicted color result, including:

[0107] For each color-deficient model portion, determining the colors of the neighboring regions of a plurality of neighboring regions corresponding to the color-deficient model portion;

[0108] Inputting all nearby area colors and corresponding area positions into a trained color prediction neural network to obtain probability values ​​of the color-deficient model parts belonging to different colors; optionally, the color prediction neural network is trained by a training data set including a combination of multiple training area colors and corresponding color annotations of the color-deficient parts;

[0109] Determine multiple printing colors of the same type as the color-deficient model part in the model printed by the 3D printer in the historical printing record;

[0110] According to multiple printing colors and probability values ​​belonging to different colors, a predicted color result corresponding to the color-deficient model part is determined.

[0111] It can be seen that through the above-mentioned optional embodiments, the color of the area near the color-deficient model part can be predicted through a neural network, and then combined with the colors in the historical printing records, the color of the color-deficient part can be accurately predicted to perform more accurate color completion, thereby assisting in improving the color completion function and color printing effect of 3D printing, and achieving smarter and better 3D full-color printing.

[0112] As an optional embodiment, in the above step, determining the predicted color result corresponding to the color-deficient model part according to multiple printing colors and probability values ​​belonging to different colors includes:

[0113] For the probability value that the color-deficient model part belongs to each color, calculating the third occurrence number of the corresponding color in the plurality of printed colors;

[0114] Calculate the product of the probability weight proportional to the third occurrence number and the probability value to obtain the corrected probability value of the color-deficient model part belonging to each color;

[0115] The color with the highest correction probability value is determined as the predicted color result corresponding to the color-deficient model part.

[0116] It can be seen that through the above-mentioned optional embodiments, the probability results predicted by the neural network can be corrected by the weights corresponding to the number of color appearances in the historical printing records, and then the color of the missing part can be accurately predicted in combination with the corrected probability to perform more accurate color completion, thereby assisting in improving the color completion function and color printing effect of 3D printing, and achieving smarter and better 3D full-color printing.

[0117] As an optional embodiment, in the above step, color completion of the 3D model data according to the predicted color result includes:

[0118] For each color-deficient model part, fill the corresponding predicted color result into the model area of ​​the color-deficient model part;

[0119] Screening out at least one high color difference region whose color difference value between the regional color and the predicted color result is greater than a color difference value threshold from a plurality of nearby partial regions of the color-deficient model portion;

[0120] For each regional boundary between a high color difference region and a model region, fill in the regional color of the high color difference region and the transition color corresponding to the predicted color result; the color value of the transition color is the weighted sum average of the regional color value of the high color difference region and the result color value of the predicted color result.

[0121] It can be seen that through the above-mentioned optional embodiments, each missing part can be completed according to the predicted color result, and the junction of the nearby parts with high color difference can be filled with transition color to perform more accurate color completion, thereby improving the color completion function and color printing effect of 3D printing, and realizing smarter and better 3D full-color printing.

[0122] As an optional embodiment, in the above steps, the method further includes:

[0123] When the 3D printer prints the completed 3D model data, the image acquisition module acquires the printing image of each layer of printing material in real time;

[0124] Input the image color corresponding to the printed image and the color data in the completed 3D model data into the trained color difference judgment neural network to obtain the color difference judgment result;

[0125] According to the color difference judgment result, a pigment output control instruction for the 3D printer is generated to reduce the color difference of 3D printing.

[0126] It can be seen that through the above optional embodiments, the pigment output of 3D printing can be controlled in real time during the 3D printing process through image acquisition and neural network color difference judgment to reduce the color difference of 3D printing, thereby improving the color printing effect of 3D printing and achieving smarter and better 3D full-color printing.

[0127] Embodiment 2

[0128] See also Figure 2 , Figure 2 1 is a schematic diagram of a color data processing system for FDM color printing disclosed in an embodiment of the present invention. Figure 2 The color data processing system for FDM color printing described can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the color data processing system for FDM color printing may include:

[0129] The acquisition module 201 is used to acquire the 3D model data to be printed and the historical printing records of the 3D printer that is ready to perform printing.

[0130] The recognition module 202 is used to recognize the color-deficient model part in the 3D model data based on the color-deficient recognition algorithm.

[0131] The prediction module 203 is used to predict the color of each color-deficient model part according to the historical printing records and a preset color prediction algorithm to obtain a predicted color result.

[0132] The completion module 204 is used to complete the color of the 3D model data according to the predicted color result, and obtain the completed 3D model data to send to the 3D printer for printing.

[0133] It can be seen that the above-mentioned embodiment of the invention can identify the color-missing model part in the 3D model data through the color-missing recognition algorithm, and then perform color prediction based on the printer's historical printing records and the preset color prediction algorithm to complete the model data with more complete color effects, thereby effectively improving the color completion function and color printing effect of 3D printing, and realizing smarter and better 3D full-color printing.

[0134] As an optional embodiment, the specific manner in which the recognition module recognizes the color-deficient model part in the 3D model data based on the color-deficient recognition algorithm includes:

[0135] Framing the 3D model data based on multiple viewpoints and multiple angles to obtain multiple framing images;

[0136] Input each framed image into a preset low-saturation color recognition model to obtain the color-deficient portion of the image corresponding to each framed image;

[0137] According to the color-deficient image parts corresponding to all the framing images, the color-deficient model parts in the 3D model data are identified.

[0138] It can be seen that through the above-mentioned optional embodiments, multiple framing images can be obtained through multi-angle and multi-viewpoint framing, and after low-saturation recognition of all framing images, the color-deficient model part in the 3D model data can be accurately identified, so as to perform more accurate color completion later, thereby assisting in improving the color completion function and color printing effect of 3D printing, and realizing smarter and better 3D full-color printing.

[0139] As an optional embodiment, the specific manner in which the recognition module recognizes the color-deficient model part in the 3D model data according to the color-deficient image parts corresponding to all the viewfinder images includes:

[0140] Based on the preset model part position relationship, multiple parts with high missing probability in the 3D model data are identified;

[0141] For each high missing possibility portion, calculate the first occurrence number of the image color missing portions corresponding to the high missing possibility portion in all the framed images;

[0142] If the first occurrence number is greater than the first number threshold, the high missing possibility part is determined as the color-deficient model part;

[0143] For any other part of the 3D model data except the part with high missing probability, calculate the second occurrence number of the image color missing part corresponding to the other part in all the framed images;

[0144] If the second number of occurrences is greater than the second number threshold, the other part is determined as a color-deficient model part; and the second number threshold is greater than the first number threshold.

[0145] It can be seen that through the above-mentioned optional embodiments, the color-missing model parts in the 3D model data can be accurately identified by calculating and screening the number of occurrences of model parts with different missing possibilities in the color missing results of the framing image, so as to perform more accurate color completion later, thereby assisting in improving the color completion function and color printing effect of 3D printing, and realizing smarter and better 3D full-color printing.

[0146] As an optional embodiment, the portion with a high possibility of missing is a component connection portion, a non-visible display surface portion, or a low-visibility display surface portion.

[0147] It can be seen that through the above optional embodiments, the types of parts with high missing probability are limited, which facilitates the subsequent targeted calculation and screening of the number of occurrences of model parts with low versus high missing probability in the color missing results of the framing image, and accurately identifies the color-missing model parts in the 3D model data, so as to perform more accurate color completion later, thereby assisting in improving the color completion function and color printing effect of 3D printing, and achieving smarter and better 3D full-color printing.

[0148] As an optional embodiment, the prediction module predicts the color of each color-deficient model part according to the historical printing records and the preset color prediction algorithm, and obtains the specific method of predicting the color result, including:

[0149] For each color-deficient model portion, determining the colors of the neighboring regions of a plurality of neighboring regions corresponding to the color-deficient model portion;

[0150] Inputting all nearby area colors and corresponding area positions into a trained color prediction neural network to obtain probability values ​​of the color-deficient model parts belonging to different colors; optionally, the color prediction neural network is trained by a training data set including a combination of multiple training area colors and corresponding color annotations of the color-deficient parts;

[0151] Determine multiple printing colors of the same type as the color-deficient model part in the model printed by the 3D printer in the historical printing record;

[0152] According to multiple printing colors and probability values ​​belonging to different colors, a predicted color result corresponding to the color-deficient model part is determined.

[0153] It can be seen that through the above-mentioned optional embodiments, the color of the area near the color-deficient model part can be predicted through a neural network, and then combined with the colors in the historical printing records, the color of the color-deficient part can be accurately predicted to perform more accurate color completion, thereby assisting in improving the color completion function and color printing effect of 3D printing, and achieving smarter and better 3D full-color printing.

[0154] As an optional embodiment, the prediction module determines the specific manner of the predicted color result corresponding to the color-deficient model part according to the multiple printing colors and the probability values ​​belonging to different colors, including:

[0155] For the probability value that the color-deficient model part belongs to each color, calculating the third occurrence number of the corresponding color in the plurality of printed colors;

[0156] Calculate the product of the probability weight proportional to the third occurrence number and the probability value to obtain the corrected probability value of the color-deficient model part belonging to each color;

[0157] The color with the highest correction probability value is determined as the predicted color result corresponding to the color-deficient model part.

[0158] It can be seen that through the above-mentioned optional embodiments, the probability results predicted by the neural network can be corrected by the weights corresponding to the number of color appearances in the historical printing records, and then the color of the missing part can be accurately predicted in combination with the corrected probability to perform more accurate color completion, thereby assisting in improving the color completion function and color printing effect of 3D printing, and achieving smarter and better 3D full-color printing.

[0159] As an optional embodiment, the specific manner in which the completion module completes the color of the 3D model data according to the predicted color result includes:

[0160] For each color-deficient model part, fill the corresponding predicted color result into the model area of ​​the color-deficient model part;

[0161] Screening out at least one high color difference region whose color difference value between the regional color and the predicted color result is greater than a color difference value threshold from a plurality of nearby partial regions of the color-deficient model portion;

[0162] For each regional boundary between a high color difference region and a model region, fill in the regional color of the high color difference region and the transition color corresponding to the predicted color result; the color value of the transition color is the weighted sum average of the regional color value of the high color difference region and the result color value of the predicted color result.

[0163] It can be seen that through the above-mentioned optional embodiments, each missing part can be completed according to the predicted color result, and the junction of the nearby parts with high color difference can be filled with transition color to perform more accurate color completion, thereby improving the color completion function and color printing effect of 3D printing, and realizing smarter and better 3D full-color printing.

[0164] As an optional embodiment, the system is further configured to perform the following steps:

[0165] When the 3D printer prints the completed 3D model data, the image acquisition module acquires the printing image of each layer of printing material in real time;

[0166] Input the image color corresponding to the printed image and the color data in the completed 3D model data into the trained color difference judgment neural network to obtain the color difference judgment result;

[0167] According to the color difference judgment result, a pigment output control instruction for the 3D printer is generated to reduce the color difference of 3D printing.

[0168] It can be seen that through the above optional embodiments, the pigment output of 3D printing can be controlled in real time during the 3D printing process through image acquisition and neural network color difference judgment to reduce the color difference of 3D printing, thereby improving the color printing effect of 3D printing and achieving smarter and better 3D full-color printing.

[0169] Embodiment 3

[0170] See also Figure 3 , Figure 3 It is another color data processing system for FDM color printing disclosed in an embodiment of the present invention. Figure 3 The color data processing system for FDM color printing described is applied in a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the color data processing system for FDM color printing may include:

[0171] A memory 301 storing executable program codes;

[0172] a processor 302 coupled to the memory 301;

[0173] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the color data processing method for FDM color printing described in the first embodiment.

[0174] Embodiment 4

[0175] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the color data processing method for FDM color printing described in the first embodiment.

[0176] Embodiment 5

[0177] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the color data processing method for FDM color printing described in the first embodiment.

[0178] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0179] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0180] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0181] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0182] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0183] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0184] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0185] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0186] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0187] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0188] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0189] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0190] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0191] Finally, it should be noted that the color data processing method and system for FDM color printing disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A color data processing method for FDM color printing, characterized in that: The method comprises: Acquire the 3D model data to be printed and the historical printing records of the 3D printer to be used for printing; Based on the color deficiency recognition algorithm, identifying the color deficiency model part in the 3D model data; According to the historical printing records and the preset color prediction algorithm, the color of each color-deficient model part is predicted to obtain a predicted color result, including: For each of the color-deficient model parts, determining the colors of the neighboring regions of the plurality of neighboring regions corresponding to the color-deficient model part; Inputting all the nearby area colors and corresponding area positions into a trained color prediction neural network to obtain probability values ​​of the color-deficient model parts belonging to different colors; the color prediction neural network is trained by a training data set including a combination of multiple training area colors and corresponding color-deficient part color annotations; Determine a plurality of printing colors of the same type as the color-deficient model portion in the model printed by the 3D printer in the historical printing record; For the probability value that the color-deficient model part belongs to each color, calculating the third occurrence number of the corresponding color in the plurality of printing colors; Calculate the product of the probability weight proportional to the third number of occurrences and the probability value to obtain a corrected probability value of the color-deficient model part belonging to each color; The color with the highest correction probability value is determined as the predicted color result corresponding to the color-deficient model part; The 3D model data is color-completed according to the predicted color result, and the completed 3D model data is obtained and sent to the 3D printer for printing.

2. The color data processing method for FDM color printing according to claim 1, characterized in that: The step of identifying the color-deficient model part in the 3D model data based on the color-deficient recognition algorithm includes: Framing the 3D model data based on multiple viewpoints and multiple angles to obtain multiple framing images; Inputting each of the framing images into a preset low-saturation color recognition model to obtain a color-deficient portion of the image corresponding to each of the framing images; According to the color-deficient image parts corresponding to all the viewfinder images, the color-deficient model parts in the 3D model data are identified.

3. The color data processing method for FDM color printing according to claim 2, characterized in that: The step of identifying the color-deficient model portion in the 3D model data according to the color-deficient image portions corresponding to all the viewfinder images includes: Based on a preset positional relationship of the model parts, identifying a plurality of parts with a high possibility of missing in the 3D model data; For each of the high missing possibility parts, calculating the first occurrence number of the image color missing parts corresponding to the high missing possibility part in all the viewfinder images; If the first occurrence number is greater than the first number threshold, the high missing possibility portion is determined as a color-deficient model portion; For any other part of the 3D model data except the part with high missing probability, calculating the second occurrence number of the image color missing part of the other part in all the viewfinder images; If the second occurrence times is greater than a second number threshold, the other part is determined as a color-deficient model part; and the second number threshold is greater than the first number threshold.

4. The color data processing method for FDM color printing according to claim 3, characterized in that: The portion with a high possibility of missing is a component connection portion, an invisible display surface portion or a low-visibility display surface portion.

5. The color data processing method for FDM color printing according to claim 1, characterized in that: The color completion of the 3D model data according to the predicted color result includes: For each color-deficient model part, filling the corresponding predicted color result into the model area of ​​the color-deficient model part; Screening out at least one high color difference region whose color difference value between the region color and the predicted color result is greater than a color difference value threshold from a plurality of nearby partial regions of the color-deficient model portion; For each regional boundary between the high color difference area and the model area, fill in the regional color of the high color difference area and the transition color corresponding to the predicted color result; the color value of the transition color is the weighted sum average of the regional color value of the high color difference area and the result color value of the predicted color result.

6. The color data processing method for FDM color printing according to claim 1, characterized in that: The method further comprises: In the process of the 3D printer printing the completed 3D model data, a printing image of each layer of printing material is obtained in real time through an image acquisition module; Inputting the image color corresponding to the printed image and the color data in the completed 3D model data into a trained color difference judgment neural network to obtain a color difference judgment result; According to the color difference judgment result, a pigment output control instruction for the 3D printer is generated to reduce the color difference of 3D printing.

7. A color data processing system for FDM color printing, characterized in that: The system comprises: An acquisition module, used to acquire the 3D model data to be printed and the historical printing records of the 3D printer ready to perform printing; A recognition module, used for identifying the color-deficient model part in the 3D model data based on a color-deficient recognition algorithm; A prediction module, used to predict the color of each color-deficient model part according to the historical printing records and a preset color prediction algorithm to obtain a predicted color result, including: For each of the color-deficient model parts, determining the colors of the neighboring regions of the plurality of neighboring regions corresponding to the color-deficient model part; Inputting all the nearby area colors and corresponding area positions into a trained color prediction neural network to obtain probability values ​​of the color-deficient model parts belonging to different colors; the color prediction neural network is trained by a training data set including a combination of multiple training area colors and corresponding color-deficient part color annotations; Determine a plurality of printing colors of the same type as the color-deficient model portion in the model printed by the 3D printer in the historical printing record; For the probability value that the color-deficient model part belongs to each color, calculating the third occurrence number of the corresponding color in the plurality of printing colors; Calculate the product of the probability weight proportional to the third number of occurrences and the probability value to obtain a corrected probability value of the color-deficient model part belonging to each color; The color with the highest correction probability value is determined as the predicted color result corresponding to the color-deficient model part; The completion module is used to complete the color of the 3D model data according to the predicted color result, and obtain the completed 3D model data to send to the 3D printer for printing.

8. A color data processing system for FDM color printing, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the color data processing method for FDM color printing as described in any one of claims 1-6.

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