Image Processing Device

Abstract

An image processing device 300 comprises: an acquisition unit 321 which acquires image data; and a processing unit 322 which subjects a range, that is within a region corresponding to a person in the image data acquired by the acquisition unit 321 and that is determined according to a prescribed condition, to image processing for hiding information within the range.

Description

[Technical Field]

[0001] The present invention relates to an image processing device, an image processing method, and a recording medium. [Background technology]

[0002] A person appearing in image data may be subjected to predetermined image processing such as mosaic processing or masking processing.

[0003] For example, Patent Document 1 is a document that describes such image processing. Patent Document 1 describes an image processing device having a control unit and an image processing unit. According to Patent Document 1, the control unit identifies non-processing target subjects from the multiple subjects as non-processing targets that will not be subjected to predetermined image processing based on the positional relationship between multiple subject regions corresponding to each of multiple subjects in frames that make up a moving image and a specific region in the frame. Then, the image processing unit applies predetermined image processing to processing target regions corresponding to processing target subjects other than the non-processing target subjects among the multiple subjects. Patent Document 1 also discloses that the subject regions are face regions corresponding to the subjects. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-33573 Summary of the Invention [Problem to be solved by the invention]

[0005] Depending on conditions such as the purpose and content of image data, it may be desirable to perform image processing on areas other than the facial area. However, the technology described in Patent Document 1 only considers whether or not to perform image processing on the facial area. Therefore, it was unable to address the above-mentioned issues. Thus, there was a problem that it may not be possible to perform appropriate image processing according to the conditions.

[0006] SUMMARY OF THE INVENTION It is therefore an object of the present invention to provide an image processing device, an image processing method, and a recording medium that solve the problem that it may not be possible to perform appropriate image processing according to conditions. [Means for solving the problem]

[0007] In order to achieve this object, an image processing device according to one embodiment of the present disclosure comprises: an acquisition unit that acquires image data; a processing unit that performs image processing for hiding information within a range determined according to a predetermined condition within an area corresponding to a person in the image data acquired by the acquisition unit; have The structure is as follows.

[0008] Furthermore, an image processing method according to another aspect of the present disclosure includes: The information processing device Acquire image data, Image processing is performed on a range determined according to a predetermined condition within an area corresponding to a person in the acquired image data to hide information within the range. The structure is as follows.

[0009] Furthermore, a recording medium according to another aspect of the present disclosure includes: In the information processing device, Acquire image data, Image processing is performed on a range determined according to a predetermined condition within an area corresponding to a person in the acquired image data to hide information within the range. It is a computer-readable recording medium that records a program for implementing the processing. [Effects of the Invention]

[0010] According to the above-described configurations, it is possible to perform appropriate image processing according to the conditions. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating an overview of an image processing device according to a first embodiment of the present disclosure. [Figure 2] FIG. 1 is a block diagram illustrating an example of the configuration of an image processing device. [Figure 3] FIG. 10 is a diagram illustrating an example of information included in determination information. [Figure 4] FIG. 10 is a diagram illustrating an example of skeletal information. [Figure 5] FIG. 10 is a diagram illustrating an example of skeleton detection by a skeleton recognition unit. [Figure 6] FIG. 10 is a diagram illustrating an example of processing by a processing unit. [Figure 7] 10 is a flowchart illustrating an example of the operation of the image processing device. [Figure 8] FIG. 10 is a diagram illustrating an example of a hardware configuration of an image processing device according to a second embodiment of the present disclosure. [Figure 9] FIG. 1 is a block diagram illustrating an example of the configuration of an image processing device. DETAILED DESCRIPTION OF THE INVENTION

[0012] [First embodiment] A first embodiment of the present disclosure will be described with reference to Figs. 1 to 7. Fig. 1 is a diagram for explaining an overview of an image processing device 100. Fig. 2 is a block diagram showing an example of the configuration of the image processing device 100. Fig. 3 is a diagram showing an example of information included in determination information 142. Fig. 4 is a diagram showing an example of skeleton information 144. Fig. 5 is a diagram showing an example of skeleton detection by the skeleton recognition unit 152. Fig. 6 is a diagram showing an example of processing by the processing unit 155. Fig. 7 is a flowchart showing an example of the operation of the image processing device 100.

[0013] In the first embodiment of the present disclosure, an image processing device 100 will be described, which is an information processing device that performs image processing such as masking and mosaic processing on a predetermined region in acquired image data 200. As will be described later, when the image processing device 100 acquires image data 200, the image processing device 100 performs image processing on a predetermined range, such as a part determined according to predetermined conditions, of a region corresponding to a person depicted in the image data 200. For example, in the example illustrated in FIG. 1 , the image processing device 100 performs image processing on the "whole body" range of a region corresponding to person A in the image data 200. The image processing device 100 also performs image processing on the "head" range of a region corresponding to person B in the image data 200, and performs image processing on the "body" range of a region corresponding to person C in the image data 200. On the other hand, the image processing device 100 does not perform image processing on the region corresponding to person D in the image data 200. For example, in this manner, the image processing device 100 performs image processing such as masking on a range corresponding to each person in the image data 200 according to conditions based on the image data 200 and the person.

[0014] In addition, the image processing device 100 can be configured to determine the area or range to be subjected to image processing for each area corresponding to a person appearing in the image data 200, depending on conditions such as the purpose of the image data 200 and features calculated from the image data 200.

[0015] As will be described later, the image processing device 100 in this embodiment uses the results of skeleton detection by the skeleton recognition unit 152 to identify a predetermined range for image processing within the area corresponding to each person appearing in the image data 200. In other words, the image processing device 100 performs image processing on parts and ranges determined according to conditions such as the purpose of the image data 200 and calculated feature amounts, among the parts and ranges identified as a result of skeleton detection by the skeleton recognition unit 152.

[0016] Fig. 2 shows an example of the configuration of the image processing device 100. Referring to Fig. 2, the image processing device 100 has, as main components, for example, an operation input unit 110, a screen display unit 120, a communication I / F unit 130, a storage unit 140, and an arithmetic processing unit 150.

[0017] 2 illustrates an example in which the functions of the image processing device 100 are realized using one information processing device. However, the image processing device 100 may be realized using a plurality of information processing devices, for example, on the cloud. For example, the image processing device 100 may be realized by a plurality of information processing devices having some of the functions illustrated in FIG. 2. Furthermore, the image processing device 100 may not have some of the configurations illustrated above, such as not having the operation input unit 110, or may have configurations other than those illustrated above.

[0018] The operation input unit 110 is made up of operation input devices such as a keyboard, a mouse, etc. The operation input unit 110 detects operations of the person operating the image processing device 100 and outputs the operations to the arithmetic processing unit 150.

[0019] The screen display unit 120 is composed of a screen display device such as an LCD (Liquid Crystal Display). The screen display unit 120 can display various information stored in the storage unit 140 on the screen in response to instructions from the arithmetic processing unit 150.

[0020] The communication I / F unit 130 is made up of a data communication circuit and performs data communication with an external device such as an imaging device connected via a communication line.

[0021] The storage unit 140 is a storage device such as a hard disk or memory. The storage unit 140 stores processing information and a program 145 required for various processes in the arithmetic processing unit 150. The program 145 is read into the arithmetic processing unit 150 and executed to realize various processing units. The program 145 is read in advance from an external device or recording medium via a data input / output function such as the communication I / F unit 130, and is stored in the storage unit 140. Main information stored in the storage unit 140 includes, for example, a trained model 141, determination information 142, image information 143, and skeleton information 144.

[0022] The trained model 141 is a trained model used by the skeleton recognition unit 152 when detecting a skeleton. For example, the trained model 141 is trained in advance to output the coordinates of the skeleton in response to the input of image data 200. As an example, the trained model 141 can be trained in advance by performing machine learning using training data such as image data containing the coordinates of the skeleton in an external device or the like. For example, the trained model 141 is acquired from an external device or the like via the communication I / F unit 130 or the like and stored in the storage unit 140. The trained model 141 may be updated by a re-learning process using additional training data.

[0023] The determination information 142 includes information for determining the range in which image processing is to be performed. For example, the determination information 142 is acquired in advance from an external device or the like via the communication I / F unit 130 or the like, or is input in advance using the operation input unit 110 or the like, and stored in the storage unit 140.

[0024] Fig. 3 shows an example of information included in the determination information 142. Referring to Fig. 3, the determination information 142 can include a first condition, a second condition, a processing range, and the like.

[0025] Here, the first condition indicates a condition that can be identified without using features calculated based on the image data 200, such as the use of the image data 200. For example, the first condition can include uses such as person detection that detects a predetermined person by facial recognition using the image data 200, behavior detection that detects predetermined behavior such as loitering or staying based on the time-series image data 200, and motion analysis that indicates that the image data 200 will be used for analyzing a person's motion. The first condition may be a single one that is determined in advance, or may include conditions other than those exemplified above, such as analysis of running form and analysis of pitching form.

[0026] The second condition indicates a more detailed condition, such as various feature amounts calculated based on the image data 200, corresponding to the first condition. For example, the second condition may include various feature amounts corresponding to the first condition, such as facial feature amounts of a predetermined person, clothing feature amounts corresponding to the color and shape of clothing such as a uniform, and behavior feature amounts of a person calculated from a behavioral trajectory discernible from the time-series image data 200 or the time spent in a predetermined area. The second condition may be a so-called blacklist or whitelist that collects facial feature amounts of a predetermined person. The second condition may also indicate feature amounts of a predetermined part that can be identified based on skeletal coordinates, etc. The second condition may not include any condition, or may include conditions other than those exemplified above.

[0027] The processing range indicates the part or range where image processing is performed according to the corresponding first condition or second condition. For example, the processing range may include information indicating the part or range where image processing is performed within an area corresponding to a person, such as the whole body, face, body, or none. The processing range may also indicate more detailed parts or ranges corresponding to a person, such as both arms, lower body, or right hand. In the example shown in FIG. 3, the processing range includes information indicating the range when the second condition is satisfied and information indicating the range when the second condition is not satisfied. However, the processing range may also include more detailed information, such as information indicating the range for each second condition.

[0028] Note that Fig. 3 shows an example of the determination information 142, and the information included in the determination information 142 is not limited to the example shown in Fig. 3. For example, the determination information 142 may include only one of the first condition and the second condition.

[0029] The image information 143 includes image data 200 acquired by an external imaging device such as a camera. The image information 143 may also include time-series image data 200. For example, the image information 143 is acquired in advance from an external device or the like via the communication I / F unit 130 or the like, and is stored in the storage unit 140.

[0030] As an example, in the image information 143, identification information for identifying the image data 200 is associated with the image data 200. The image information 143 may include information other than the above examples, such as information indicating the date and time when the imaging device acquired the image data 200 and information indicating the use of the image data 200.

[0031] As will be described later, the image information 143 may include image data 200 before image processing and image data 200 after image processing. When the image information 143 includes image data 200 after image processing, the image data 200 included in the image information 143 may be configured so that the masking process can be removed, or may be configured so that the masking process cannot be removed.

[0032] The skeleton information 144 includes information indicating the coordinates (skeleton coordinates) of each part of a person in the image data 200 recognized by the skeleton recognition unit 152 through skeleton detection. The skeleton information 144 includes, for example, information indicating the coordinates of each part corresponding to each person in the image data 200 for each piece of image data 200. For example, the skeleton information 144 is generated and updated as a result of the skeleton detection process performed by the skeleton recognition unit 152.

[0033] Fig. 4 shows an example of skeletal information 144. Referring to Fig. 4, in skeletal information 144, for example, identification information is associated with position information of each body part. Here, the identification information is information corresponding to a person in image data 200, etc. Furthermore, the position information of each body part includes information indicating the skeletal coordinates of each body part in image data 200, such as the position of the pelvis.

[0034] The parts included in the position information of each part correspond to the trained model 141. For example, in FIG. 4, the parts are the pelvis, the center of the spine, ..., the right knee, the left knee, ..., the right ankle, the left ankle, ... are shown as examples. The position information of each part can include, for example, about 30 parts such as the right shoulder, ..., left elbow, ... (other parts may be included as well). The parts included in the position information of each part may be other than those exemplified in FIG. 4 etc.

[0035] The arithmetic processing unit 150 has an arithmetic device such as a CPU (Central Processing Unit) and its peripheral circuits. The arithmetic processing unit 150 reads and executes a program 145 from the storage unit 140, thereby causing the above hardware and the program 145 to work together to realize various processing units. Major processing units realized by the arithmetic processing unit 150 include, for example, an image acquisition unit 151, a skeleton recognition unit 152, a feature calculation unit 153, a determination unit 154, a processing unit 155, and an output unit 156.

[0036] The image acquisition unit 151 acquires image data 200 acquired by an imaging device or the like from an external device such as an imaging device via the communication I / F unit 130. The image acquisition unit 151 may acquire time-series image data 200. The image acquisition unit 151 also stores the acquired image data 200 in the storage unit 140 as image information 143.

[0037] Note that the image acquisition unit 151 may acquire information indicating the use of the image data 200 in addition to the image data 200. The image acquisition unit 151 can store the information indicating the use of the image data 200 together with the image data 200 as image information 143 in the storage unit 140.

[0038] The skeleton recognition unit 152 uses the trained model 141 to recognize the skeleton of a person in the image data 200. For example, by inputting the image data 200 to the trained model 141, the skeleton recognition unit 152 acquires the coordinates of each part of each person in the image data 200, such as the upper spine, right shoulder, left shoulder, right elbow, left elbow, right wrist, left wrist, right hand, left hand, ..., as illustrated in FIG. 5. Then, the skeleton recognition unit 152 associates the acquired results with identification information for identifying the person, etc., and stores them in the storage unit 140 as skeleton information 144.

[0039] The parts recognized by the skeleton recognition unit 152 correspond to the trained model 141. Therefore, the skeleton recognition unit 152 may recognize parts other than those exemplified above according to the trained model 141.

[0040] The feature amount calculation unit 153 extracts areas corresponding to people in the image data 200, and calculates feature amounts of the face, clothing, belongings, etc. For example, the feature amount calculation unit 153 calculates various feature amounts so that each person in the image data 200 can be identified, similar to the skeleton recognition unit 152. Note that the feature amount calculation unit 153 may calculate feature amounts using known means.

[0041] For example, the feature amount calculation unit 153 detects a facial region of a person in the image data 200, extracts feature points such as the eyes, nose, and mouth, and calculates facial feature amounts based on the extracted feature points. The feature amount calculation unit 153 can also calculate feature amounts such as clothing feature amounts and belonging feature amounts based on color information, shape, and the like of a predetermined part. The feature amount calculation unit 153 may calculate feature amounts for a predetermined part that can be specified by referring to the skeletal information 144. For example, the feature amount calculation unit 153 may specify a predetermined part such as the right shoulder by referring to the skeletal information 144, and calculate clothing feature amounts based on color information and the like of the specified part.

[0042] The feature amount calculation unit 153 may be configured to calculate only some of the feature amounts exemplified above, such as calculating only facial feature amounts, or may be configured to calculate multiple feature amounts. Furthermore, the feature amount calculation unit 153 may be configured to calculate feature amounts according to, for example, the purpose of the image data 200. The feature amount calculation unit 153 may calculate feature amounts other than those exemplified above.

[0043] The determination unit 154 determines the part or range on which image processing such as masking is performed based on predetermined conditions according to the intended use of the image data 200, the calculated feature amount, etc. For example, the determination unit 154 can determine the part or range according to the intended use of the image data 200 based on a first condition, etc. Furthermore, the determination unit 154 can determine the part or range according to the feature amount of each person calculated from the image data 200 based on a second condition, etc.

[0044] For example, the determination unit 154 refers to the determination information 142 and the image information 143. Then, the determination unit 154 determines the range that satisfies the first condition depending on the purpose of the image data 200. For example, if the purpose of the image data 200 is "motion analysis," the determination unit 154 can determine that the range for which image processing is to be performed is the "head" range.

[0045] Furthermore, when a second condition is set, the determination unit 154 can determine a range according to the second condition using the feature calculated by the feature calculation unit 153. For example, when the purpose of the image data 200 is "behavior detection," the determination unit 154 determines that image processing is to be performed on the "face" range for a region of a person whose calculated behavior feature satisfies the second condition. On the other hand, the determination unit 154 determines that image processing is to be performed on the "whole body" range for a region of a person whose calculated behavior feature does not satisfy the second condition. For example, as described above, the determination unit 154 can determine that image processing is to be performed on a range according to the second condition, etc. In other words, the determination unit 154 can determine a range for image processing for each person in the image data 200 based on the second condition and various feature amounts.

[0046] The determination unit 154 may be configured to perform only one of the determination based on the first condition and the determination based on the second condition, or may be configured to determine the condition to be used for the determination based on, for example, the determination information 142. The determination unit 154 may determine to perform image processing of the same range for all areas of a person that satisfy, for example, the second condition, or may determine to perform image processing of different ranges for each predetermined second condition that is set in advance.

[0047] Processing unit 155 performs image processing such as masking on the range determined by determination unit 154. Processing unit 155 then stores the processing result as image information 143 or the like in storage unit 140. Note that processing unit 155 may perform processing to hide information within a predetermined range, such as mosaic processing, instead of masking.

[0048] For example, processing unit 155 refers to the coordinates of each part indicated by skeleton information 144 and identifies the position or range in image data 200 that corresponds to the part or range determined by determination unit 154. Processing unit 155 then performs image processing such as masking on the identified position or range. In this way, determination unit 154 uses the recognition result by skeleton recognition unit 152 to perform image processing on the part and range determined by determination unit 154.

[0049] After image processing such as masking, processing unit 155 may store the results of the processing in storage unit 140 or the like so that they can be restored, or may store the results of the processing in storage unit 140 or the like so that they cannot be restored. For example, processing unit 155 may store image data 200 before masking and image data 200 after masking separately in storage unit 140, or may update the information in image information 143 with image data 200 after masking. Processing unit 155 may also be configured to select whether to store image data 200 so that it can be restored or so that it cannot be restored, depending on the purpose of the image data 200, etc.

[0050] The output unit 156 outputs the masked image data 200, etc. For example, the output unit 156 displays the masked image data 200, etc. on the screen display unit 120, or transmits it to an external device via the communication I / F unit 130. The output unit 156 may display or output information other than the above examples.

[0051] The above is an example of the configuration of the image processing device 100. Next, the operation of the image processing device 100 will be described with reference to FIG.

[0052] Fig. 7 is a flowchart showing an example of the operation of the image processing device 100. Referring to Fig. 7, the image acquisition unit 151 acquires image data 200 acquired by an external device such as an imaging device via the communication I / F unit 130 (step S101).

[0053] The skeleton recognition unit 152 uses the trained model 141 to recognize the skeleton of a person in the image data 200 (step S102). For example, by inputting the image data 200 to the trained model 141, the skeleton recognition unit 152 acquires the coordinates of each part of each person in the image data 200, such as the upper part of the spine, right shoulder, left shoulder, right elbow, left elbow, right wrist, left wrist, right hand, left hand, etc., as illustrated in FIG.

[0054] The determination unit 154 determines the part or range on which image processing such as masking is performed based on predetermined conditions according to the purpose of the image data 200, the calculated feature amount, etc. (Step S103). For example, the determination unit 154 determines the part or range according to the purpose of the image data 200 based on a first condition, etc. Furthermore, the determination unit 154 can determine the part or range according to the feature amount of each person calculated from the image data 200 based on a second condition, etc.

[0055] The process by the determination unit 154 may be performed before or together with the process of step S102. Furthermore, the feature calculation unit 153 may calculate various feature amounts before or after the process of step S102 or step S103.

[0056] The processing unit 155 performs image processing such as masking on the range determined by the determination unit 154 (step S104). In other words, the processing unit 155 performs image processing on the parts and ranges according to the conditions specified using the results of the skeleton recognition, based on the results of the determination by the determination unit 154.

[0057] The output unit 156 outputs the masked image data 200, etc. (step S105). For example, the output unit 156 displays the masked image data 200, etc. on the screen display unit 120, or transmits the masked image data 200, etc. to an external device via the communication I / F unit 130.

[0058] As described above, the image processing device 100 has the processing unit 155. With this configuration, the processing unit 155 can perform image processing on a predetermined range, such as a part determined according to predetermined conditions, within an area corresponding to a person appearing in the image data 200. As a result, it is possible to perform image processing appropriately on a range according to the conditions.

[0059] For example, according to the present invention, when acquiring image data 200 for behavior detection, it is possible to perform masking processing on areas other than those corresponding to the "body" used for behavior detection, and thus mask areas other than those for the intended purpose. As a result, for example, only the minimum necessary parts are hidden depending on the intended purpose of the image data 200, thereby more appropriately considering the privacy of people in the image data 200. Furthermore, according to the present invention, it is possible to hide unnecessary parts when analyzing the behavior of a person in a specific part or range. As a result, it is possible to perform analysis, etc., with unnecessary information about unnecessary parts eliminated.

[0060] The image processing device 100 also has a determination unit 154. With this configuration, the processing unit 155 can perform image processing on a part or range according to the result of the determination by the determination unit 154. As a result, it is possible to perform image processing on a range according to the conditions more appropriately.

[0061] [Second embodiment] Next, a second embodiment of the present disclosure will be described with reference to Fig. 8 and Fig. 9. In the second embodiment of the present disclosure, an overview of the configuration of an image processing device 300, which is an information processing device, will be described.

[0062] Fig. 8 shows an example of the hardware configuration of the image processing device 300. Referring to Fig. 8, the image processing device 300 has, as an example, the following hardware configuration. ·CPU(Central Processing Unit)301(Arithmetic unit) ROM (Read Only Memory) 302 (storage device) RAM (Random Access Memory) 303 (storage device) Programs 304 loaded into RAM 303 A storage device 305 for storing the program group 304 A drive device 306 that reads and writes data from a recording medium 310 outside the information processing device A communication interface 307 for connecting to a communication network 311 outside the information processing device Input / output interface 308 for inputting and outputting data Bus 309 connecting each component

[0063] 9 by the CPU 301 acquiring the program group 304 and executing it. The program group 304 is stored in advance in the storage device 305 or the ROM 302, for example, and is loaded into the RAM 303 or the like by the CPU 301 for execution as needed. The program group 304 may be supplied to the CPU 301 via the communication network 311, or may be stored in advance in the recording medium 310, with the drive device 306 reading out the programs and supplying them to the CPU 301.

[0064] 8 shows an example of the hardware configuration of the image processing device 300. The hardware configuration of the image processing device 300 is not limited to the above-described case. For example, the image processing device 300 may be configured with only a part of the above-described configuration, such as excluding the drive device 306.

[0065] The acquisition unit 321 acquires image data.

[0066] The processing unit 322 performs image processing for a range determined according to a predetermined condition within the area corresponding to the person in the acquired image data, in order to hide information within the range.

[0067] As described above, the image processing device 300 includes the processing unit 322. With this configuration, the processing unit 322 can perform image processing for hiding information within a range determined according to predetermined conditions within an area corresponding to a person in the acquired image data. As a result, it is possible to perform appropriate image processing according to the conditions.

[0068] Note that an information processing device such as the above-described image processing device 300 can be realized by incorporating a predetermined program into the information processing device. Specifically, the program according to another aspect of the present invention is a program for realizing processing in the information processing device, which program acquires image data and performs image processing for hiding information within a range determined according to predetermined conditions within an area corresponding to a person in the acquired image data.

[0069] In addition, the determination method executed by the above-mentioned information processing device is that the information processing device acquires image data and performs image processing to hide information within a range determined according to predetermined conditions within an area corresponding to a person in the acquired image data.

[0070] Inventions such as a program (or recording medium) or an image processing method having the above-described configuration have the same functions and effects as those described above, and can therefore achieve the above-described object of the present invention.

[0071] <Additional Notes> A part or all of the above-described embodiments can be described as follows: An image processing device and the like according to the present invention will be outlined below. However, the present invention is not limited to the following configuration.

[0072] (Appendix 1) an acquisition unit that acquires image data; a processing unit that performs image processing for hiding information within a range determined according to a predetermined condition within an area corresponding to a person in the image data acquired by the acquisition unit; have Image processing device. (Appendix 2) 10. The image processing device according to claim 1, The processing unit uses the result of the skeleton recognition to specify the range in which image processing is to be performed. Image processing device. (Appendix 3) 10. The image processing device according to claim 1, wherein: The processing unit performs image processing on a range determined depending on the use of the image data. Image processing device. (Appendix 4) 1. An image processing device according to claim 1, wherein: a determination unit that determines the range in which image processing is performed according to a predetermined condition, The processing unit performs image processing on the range according to the determination result of the determination unit. Image processing device. (Appendix 5) 5. The image processing device according to claim 4, The determination unit determines the range in which image processing is performed based on the purpose of the image data. Image processing device. (Appendix 6) 6. The image processing device according to claim 4 or 5, a calculation unit that calculates a predetermined feature amount based on image data; The determination unit determines the range in which image processing is performed based on the feature amount calculated by the calculation unit. Image processing device. (Appendix 7) 10. The image processing device according to claim 4, wherein: The determination unit determines the range in which image processing is to be performed for each person in the image data. Image processing device. (Appendix 8) The information processing device Acquire image data, Image processing is performed on a range determined according to a predetermined condition within an area corresponding to a person in the acquired image data to hide information within the range. Image processing methods. (Appendix 9) 9. The image processing method according to claim 8, further comprising: When performing image processing, the area in which image processing is to be performed is specified using the results of skeleton recognition, and image processing is performed on the specified area. Image processing methods. (Appendix 10) In the information processing device, Acquire image data, Image processing is performed on a range determined according to a predetermined condition within an area corresponding to a person in the acquired image data to hide information within the range. A program to realize the processing.

[0073] The programs described in the above embodiments and appendices may be stored in a storage device or a computer-readable recording medium, such as a portable medium such as a flexible disk, an optical disk, a magneto-optical disk, or a semiconductor memory.

[0074] Although the present invention has been described above with reference to the above-mentioned embodiments, the present invention is not limited to the above-mentioned embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0075] In addition, the present invention claims the benefit of priority based on patent application No. 2022-011104, filed in Japan on January 27, 2022, and all contents described in that patent application are incorporated herein by reference. [Explanation of symbols]

[0076] 100 Image processing device 110 Operation input section 120 Screen display section 130 Communication I / F section 140 Storage section 141 trained models 142 Judgment information 143 Image Information 144 Skeletal Information 145 Programs 150 Processing unit 151 Image acquisition unit 152 Skeleton Recognition Unit 153 Feature Calculation Unit 154 Judgment section 155 Processing section 156 Output section 200 image data 300 Image processing device 301 CPU 302 ROM 303 RAM 304 Programs 305 Storage device 306 Drive Device 307 Communication Interface 308 Input / Output Interface 309 Bus 310 Recording Media 311 Communication Network 321 Acquisition Department 322 Processing Section

Claims

1. an acquisition unit that acquires image data; a calculation unit that calculates a feature amount according to the intended use of the image data based on the image data; a determination unit that performs a determination according to a predetermined condition; a processing unit that performs image processing on the range determined by the determination unit to hide information within the range; and The determination unit determines the range in which the image processing is to be performed based on predetermined conditions corresponding to the use of the image data and the feature amount calculated by the calculation unit. Image processing device.

2. 2. The image processing device according to claim 1, The determination unit determines the range in which the image processing is performed for each person in the image data. Image processing device.

3. The information processing device Acquire image data, Calculating a feature amount according to the intended use of the image data based on the image data; Make a judgment based on the specified conditions, image processing is performed on the determined range to hide information within the range; When making the determination, the range in which the image processing is to be performed is determined based on predetermined conditions corresponding to the use of the image data and the calculated feature amount. Image processing methods.

4. In the information processing device, Acquire image data, Calculating a feature amount according to the intended use of the image data based on the image data; Make a judgment based on the specified conditions, Image processing is performed on the determined range to hide information within the range. Realize the processing, When making the determination, the range in which the image processing is to be performed is determined based on predetermined conditions corresponding to the use of the image data and the calculated feature amount. program.

Images