WO2018155087A1

WO2018155087A1 - Image processing device, image forming device and image processing method

Info

Publication number: WO2018155087A1
Application number: PCT/JP2018/002735
Authority: WO
Inventors: 田中　邦彦
Original assignee: 京セラドキュメントソリューションズ株式会社
Priority date: 2017-02-23
Filing date: 2018-01-29
Publication date: 2018-08-30
Also published as: JP6870728B2; JPWO2018155087A1

Abstract

The purpose of the present invention is to extract still image data by using temporal continuity that is a characteristic of moving image data. An image processing device (110) is provided with: a still image data generation unit (111) which generates, from moving image data, a plurality of pieces of still image data; and a continuous still image data extraction unit (114) which extracts, from the generated plurality of pieces of still image data, continuous still image data in which a movement of an identical subject satisfies a preset condition, as still image data which shows the identical subject and is temporally continuous in a prescribed period.

Description

Image processing apparatus, image forming apparatus, and image processing method

The present invention relates to an image processing technique for extracting still image data from moving image data, and more particularly to a technique that can be used for creating an album.

In recent years, with improvement in performance and image quality of video cameras and smartphones, still image data that can be extracted from moving image data has been generated.

Also, a technique has been proposed in which a desired still image is extracted from moving image data and an album of the extracted still image can be created. For example, Patent Document 1 discloses a technique for automatically creating an album by detecting an image of a predetermined person from a moving image and selecting a representative part from a plurality of frames in which the person's image is reflected. is suggesting.

JP 2009-88687 A

Incidentally, conventionally, the moving image data is simply regarded as a set of a plurality of frame image data. For this reason, sufficient studies have not been made in terms of extracting still image data by taking advantage of temporal continuity, which is a characteristic of the moving image data.

The present disclosure has been made in view of such a situation, and an object thereof is to provide a technique for extracting still image data by taking advantage of temporal continuity which is a characteristic of the moving image data.

The image processing apparatus according to one aspect of the present disclosure includes a still image data generation unit and a continuous still image data extraction unit. The still image data generation unit generates a plurality of still image data from moving image data. The continuous still image data extraction unit is still image data that includes images of the same subject from the plurality of generated still image data and that is temporally continuous in a predetermined cycle, and the same Continuous still image data in which the movement of the subject image satisfies a preset condition is extracted.

An image forming apparatus according to another aspect of the present disclosure includes the image processing apparatus and an image forming unit that forms an image on a print medium.

An image processing method according to another aspect of the present disclosure generates a plurality of still image data from moving image data, includes an image of the same subject from the plurality of generated still image data, and has a predetermined cycle. And extracting continuous still image data that is temporally continuous still image data and that satisfies the preset condition of the motion of the same subject image.

According to the present disclosure, it is possible to provide a technology that realizes the extraction of still image data that takes advantage of temporal continuity that is a characteristic of moving image data.

2 is a block diagram illustrating a functional configuration of an image forming apparatus 100 according to an embodiment of the present disclosure. It is a flowchart which shows the content of the still image acquisition process which concerns on one Embodiment. It is a flowchart which shows the content of the person registration process which concerns on one Embodiment. It is a data flow diagram which shows the content of the frame image data generation process which concerns on one Embodiment. It is explanatory drawing which shows the content of several frame image data containing the image of the same person. It is explanatory drawing which shows the content of two frame image data F1, F2 containing the image of the same person. It is explanatory drawing which shows the content of the frame image data which the to-be-photographed object is moving in the perspective direction.

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In addition, the said embodiment is a form for implementing this indication.

1 is a block diagram illustrating a functional configuration of an image forming apparatus 100 according to an embodiment of the present disclosure, as illustrated in FIG. The image forming apparatus 100 includes a control unit 110, an image forming unit 120, an operation display unit 130, a storage unit 140, and a communication interface unit 150. The communication interface unit 150 is also called a communication I / F unit.

The control unit 110, the image forming unit 120, the operation display unit 130, the storage unit 140, and the communication interface unit 150 are examples of an image processing device, a print processing device, an operation display device, a storage device, and a communication interface device, respectively.

The image forming apparatus 100 is connected to the smartphone 200 through short-range wireless communication via the communication interface unit 150. Thereby, the image forming apparatus 100 can receive the moving image data generated by imaging with the smartphone 200.

Near field communication uses CLASS 2 of BLUETOOTH (registered trademark) in this embodiment. CLASS 2 of BLUETOOTH (registered trademark) is a communication with an output of 2.5 mW, and is a short-range wireless communication that enables communication between the image forming apparatus 100 and the smartphone 200 within about 10 m.

The control unit 110 includes storage means such as RAM and ROM, and a processor such as an MPU (Micro Processing Unit) or a CPU (Central Processing Unit). The processor is an example of a control unit.

The control unit 110 also has controller functions related to various I / O, USB (Universal Serial Bus), bus, and other hardware interfaces. The control unit 110 controls the entire image forming apparatus 100.

The control unit 110 further includes a frame extraction unit 111, a person verification unit 112, a speed detection unit 113, a high-speed moving section extraction unit 114, a still image output unit 115, and a cycle setting unit 116. The person verification unit 112 includes a person registration unit 112a.

Note that the processor executes a program stored in the ROM or the like. Thereby, the control unit 110 functions as a frame extraction unit 111, a person verification unit 112, a speed detection unit 113, a high-speed moving section extraction unit 114, a still image output unit 115, and a period setting unit 116.

The frame extraction unit 111, the person verification unit 112, the speed detection unit 113, the high-speed movement section extraction unit 114, the still image output unit 115, and the period setting unit 116 are respectively a frame extraction device, a person verification device, a speed detection device, and a high-speed movement section It is an example of an extracting device, a still image output device, and a period setting device.

The image forming unit 120 forms an image on a sheet or other sheet-like print medium. The operation display unit 130 functions as a touch panel including an operation unit and a display unit. The operation display unit 130 displays various menu screens as input screens on the display unit, and receives user operation inputs through the operation unit.

The storage unit 140 is a storage device including a hard disk drive or flash memory which is a non-temporary recording medium.

The storage unit 140 stores a control program and data corresponding to processing executed by the control unit 110, respectively. The storage unit 140 includes a frame memory 141 for temporarily storing frame image data, a still image storage area 142, and a person registration data storage area 143. The storage area is a part of the data storage unit in the storage unit 140.

It is conceivable that the frame memory 141, the still image storage area 142, and the person registration data storage area 143 are part of a data storage unit in one storage device. On the other hand, the still image storage area 142 and the person registration data storage area 143 may be separate storage devices.

The control unit 110 can execute a still image acquisition process according to an embodiment. 2, S10, S20, S30,... Are identification codes of a plurality of steps in the still image acquisition process.

FIG. 2 is a flowchart showing the content of a still image acquisition process according to an embodiment, as shown in FIG. In step S10 of the still image acquisition process, the person verification unit 112 executes a person registration process that involves the use of the operation display unit 130 by the user. In the person registration process, the person registration unit 112a can register information about a person to be detected in a still image to be extracted as one of the conditions for extracting still image data from moving image data. .

3, S11, S12, S13,... Are identification codes of a plurality of steps in the still image acquisition process.

FIG. 3 is a flowchart showing the contents of person registration processing according to an embodiment, as shown in FIG. In step S11 of the person registration process, the person registration unit 112a executes a moving image data take-in process. In the moving image data capturing process, the person registration unit 112a selects the moving image data MD according to the operation of the operation display unit 130 by the user. Furthermore, the person registration unit 112a can capture the moving image data MD into the image forming apparatus 100 through, for example, a wireless communication device (not shown) or a portable storage medium (not shown).

In step S12, the person verification unit 112 of the control unit 110 executes a person detection process. In the person detection process, the person verification unit 112 generates frame image data from the moving image data MD. Furthermore, the person collation unit 112 extracts a person detection area, which is an image area having the characteristics of a person, from a still image represented by the frame image data file.

The person collation unit 112 can extract a person detection area using machine learning such as SVM (Support Vector Machine) based on, for example, HOG (Histograms of Oriented Gradients) features.

In step S13, the person verification unit 112 executes a person classification process. In the person classification process, the person verification unit 112 classifies the person in the person detection area by determining which of the family images registered in advance is the person image in the person detection area, for example. .

For example, family information includes information on father A, mother B, son C, and daughter D. The person registration unit 112a registers the family information in the storage unit 140 in advance in response to an operation on the operation display unit 130 by the user.

The person verification unit 112 selects frame image data including a face image having a size larger than a preset image size. Furthermore, the person verification unit 112 classifies the selected frame image data into a plurality of groups according to the classification result of the person classification process, and causes the operation display unit 130 to display the group image data.

Furthermore, the person verification unit 112 determines whether each of the plurality of groups corresponds to the father A, the mother B, the son C, the daughter D, or another person according to the input operation to the operation display unit 130 by the user. Can be corrected.

The user can perform an operation to correct a misrecognition that a still image of father A is included in the group of son C, for example. Thereby, the person collation part 112 can improve the precision of machine learning.

The person verification unit 112 generates a database using the father A, mother B, son C, and daughter D as records. In the database, HOG feature amounts of face images of father A, mother B, son C, and daughter D are registered.

In step S14, the person collation unit 112 executes a clothing selection process. In the clothing selection process, the person collation unit 112 extracts HOG feature values for the clothing worn by each of the father A, mother B, son C, and daughter D from the frame image data. Thereby, the person collation part 112 can specify a person using the HOG feature-value of the clothes image in addition to the HOG feature-value of the face image of father A, mother B, son C, and daughter D. This is because each person often wears the same clothes, and different persons tend to wear different clothes.

In step S15, the person registration unit 112a of the person verification unit 112 executes a database registration process. In the database registration process, the person registration unit 112 a stores a database for the father A, mother B, son C, and daughter D in the person registration data storage area 143 of the storage unit 140.

In the database, father A, mother B, son C, and daughter D are records. The database includes HOG feature values of face images, HOG feature values of clothes images, machine learning data of face images, machine learning data of clothes images, and height and other attribute data that can be input by the user.

Furthermore, the person registration unit 112a can also register the face image and clothing image data of each person using still image data captured by a digital camera in accordance with a user operation. The person registration unit 112a can generate the HOG feature value of the face image and the HOG feature value of the clothes image by using such image data, and register them in the database. In the present embodiment, it is assumed that the HOG feature amount is generated based on YUV image data with a small calculation load in image recognition.

As shown in FIG. 2, in step S <b> 20, the user sets a still image extraction mode via the operation display unit 130. The still image extraction mode includes, for example, a continuous photo mode in which lively continuous photos are extracted, in addition to various modes such as a mode for extracting a close-up photo of a person's face and a mode for extracting a group photo. Yes. In setting the still image extraction mode, the control unit 110 causes the operation display unit 130 to display a screen (not shown) that accepts selection of each mode.

For example, when the continuous photo mode is selected, the cycle setting unit 116 causes the operation display unit 130 to display an operation display screen (not shown) that receives a cycle setting input. The cycle setting input is an input operation for setting a cycle, which is a time interval between frame images. The period is used for setting an extraction period of a frame image as a still image. However, if no user input is made, 0.2 seconds is used as the initial setting.

In step S30, the person verification unit 112 selects a person through the operation of the operation display unit 130 by the user. In the selection of a person, the person verification unit 112 causes the operation display unit 130 to display a screen for accepting selection of father A, mother B, son C, and daughter D. In this example, it is assumed that son C is selected.

In step S40, the frame extraction unit 111 executes a frame image generation process. The frame extraction unit 111 is an example of an apparatus that functions as a still image data generation unit. In the frame image generation process, the frame extraction unit 111 generates frame image data having a predetermined cycle from moving image data MD having a frame rate of 30 fps, for example. The predetermined period is set in advance by the user, for example. For example, it is conceivable that the predetermined cycle of the initial setting is 0.2 seconds.

FIG. 4 is a data flow diagram showing the contents of frame image data generation processing according to an embodiment. In FIG. 4, a data flow diagram is shown on the upper side, and GOP (Group of Pictures) is shown on the lower side. The data flow diagram shows a flow from extraction of frame image data from moving image data MD to conversion and storage of the extracted data. The frame image data is configured as YUV image data. The frame image data generation process is a process for extracting a plurality of frame image data from the moving image data MD, and is executed by the frame extraction unit 111.

Frame image data generation processing includes, for example, MPEG-4 (ISO / IEC 14496) and H.264. H.264 is included. In the frame image data generation process, the frame extraction unit 111 generates frame image data from an I frame (Intra-coded Frame), a P frame (Predicted Frame), and a B frame (Bi-directional Predicted Frame).

An I frame is a frame that is encoded without using inter-frame prediction. The I frame is also called an intra frame or a key frame. The I frame constitutes a GOP together with a P frame (Predicted Frame) and a B frame (Bi-directional Predicted Frame).

It is possible to generate frame image data by subjecting the P frame to inter-frame processing with the I frame. Frame image data can be generated by subjecting the B frame to inter-frame processing with the I frame, P frame, and other B frames.

The moving image data is generated from a plurality of frame image data arranged in time series. A plurality of frame image data is often approximated between frames before and after time series. Inter-frame prediction is a process that uses such characteristics of moving image data. Inter-frame prediction is a process of predicting the current frame image from the previous frame image in time series.

Specifically, in the inter-frame prediction, the movement for each pixel block is estimated, and further, the difference of the pixel block between the frames after the movement is DCT transformed and quantized. Thereby, the compression rate per GOP increases. P frames are reconstructed from I frames by using motion vectors. The motion vector is a movement vector of each pixel block.

The frame extraction unit 111 generates frame image data as YUV image data including luminance data and color difference data by performing inverse discrete cosine transform (also called inverse DCT transform) on the I frame. The inverse DCT transform is executed for each 8 × 8 pixel or 16 × 16 pixel block, for example. The frame extraction unit 111 stores the reproduced frame image data in the frame memory 141.

The frame extraction unit 111 generates difference data by performing inverse discrete cosine transform on the P frame and the B frame. The frame extraction unit 111 generates frame image data by performing inter-frame processing using the difference data and the motion vector. The motion vector is data generated when the moving image data MD is encoded. This processing is performed using MPEG-4 or H.264. H.264 is a normal decoding process.

The frame extraction unit 111 executes frame image data generation processing based on the P frame and the B frame on the RAM (not shown) of the control unit 110. The frame extraction unit 111 stores frame image data having a preset period in the frame memory 141. Specifically, when the frame rate of the moving image data MD is 30 fps, the cycle is set to 0.2 seconds. In this case, the frame extraction unit 111 stores the frame image data in the frame memory 141 every six frames. On the other hand, the frame extraction unit 111 discards other frame image data. Thereby, the frame extraction unit 111 can reduce excessive consumption of the frame memory 141.

When the frame extraction unit 111 stores the frame image data in the frame memory 141, the frame extraction unit 111 stores the motion vector used in generating the frame image data together with each frame image data.

In step S50, the person verification unit 112 executes a person detection process. In the person detection process, the person verification unit 112 determines whether or not the person selected in step S30 is included for each of the plurality of frame image data stored in the frame memory 141.

For example, it is conceivable that the person selected in step S30 is the son C. In this case, the person verification unit 112 tries to detect the son C from the frame image data as YUV image data including luminance data and color difference data.

The person collation unit 112 can detect and specify a person using, for example, the well-known OpenCV (Open Source Computer Vision Library). First, the person verification unit 112 detects a person from the frame image data. Furthermore, the person verification unit 112 determines whether the face of the person detected using the HOG feature amount of the son C face image is the face of the son C.

When the accuracy of the determination as to whether or not the face is the son C's face is low, the person verification unit 112 uses the HOG feature value of the clothes image of the son C and determines whether or not the face of the person is the face of the son C. Determine whether. In particular, when the son C is photographed from the lateral direction and the size of the face image is small, the HOG feature amount of the clothes image can be used as an auxiliary. This is because, generally, when a continuous image with a dynamic feeling is taken, it is difficult to capture a face image stably.

In step S60, the speed detector 113 of the controller 110 detects the moving speed of the person who is the subject. In this example, the person is son C. The speed detection unit 113 specifies a temporal section in which the same subject image is included in a plurality of temporally continuous frame image data.

The speed detection unit 113 can estimate the speed of the subject image using the motion vector of the pixel block that forms the image area including the same subject image, that is, the same person image. This is because the pixel block constituting the image area including the image of the son C has a motion vector corresponding to the translational movement speed of the son C.

In the example shown in FIG. 5, images of nine frame image data F1 to F9 in which the son C is photographed as the same person are shown. The frame image data F1 to F9 represent a series of continuous images with a feeling of dynamism in which the son C is running against the background of three stationary trees.

FIG. 6 is an explanatory diagram showing the contents of two pieces of frame image data F1, F2 representing the same person. In the two pieces of frame image data F1 and F2 in FIG. 6, the image of the son C is translated to the right side in FIG. The translational movement amount of the image of the son C is a movement amount between the frames of the two pieces of frame image data F1 and F2. By dividing the translational movement amount by the period, the translational movement speed can be calculated. However, in the present embodiment, the translational movement speed is estimated from a motion vector assuming 30 fps.

In step S70, the high speed movement section extraction unit 114 of the control unit 110 determines whether or not the moving speed of the image of the son C is equal to or higher than a preset threshold value. If the moving speed of the son C image is equal to or higher than the threshold, the high-speed moving section extraction unit 114 proceeds to step S80. If the moving speed of the son C image is lower than the threshold, the high-speed moving section extraction unit 114 performs the process. Proceed to S90.

In step S80, the high-speed movement section extraction unit 114 executes a frame image data grouping process. The frame image data grouping process is frame image data that is temporally continuous in a predetermined cycle before and after the two pieces of frame image data F1 and F2, and includes the image of the son C.

FIG. 5 shows an example in which the photographing of the son C is started with the frame image data F1, and the photographing of the son C is continued until the frame image data F9.

The high-speed movement section extraction unit 114 groups the nine pieces of frame image data F1 to F9 and stores them in the still image storage area 142. As a result, the nine pieces of frame image data F1 to F9 can be managed and handled as one continuous image data file. In the still image storage area 142, nine frame image data F1 to F9 are DCT converted and stored as JPEG still image data.

Note that the high-speed moving section extraction unit 114 is an example of a continuous still image data extraction unit. Nine pieces of frame image data F1 to F9 are examples of continuous still image data.

In the present embodiment, the state in which the moving speed of the image of the subject (son C) is equal to or higher than the threshold may be instantaneous for one continuous image data file. That is, it is only necessary that the moving speed of the subject image is equal to or greater than the threshold value once in one continuous image data file. It is not necessary for the moving speed of the image to exceed the threshold in one continuous image data file. However, it is also conceivable that the high speed moving section extracting unit 114 determines that the moving speed exceeds a threshold in all sections of one continuous image data file as a necessary condition.

The control unit 110 repeatedly executes the processing from step S40 to step S80 for a plurality of frame image data until the final frame image data becomes a processing target (step S90).

In step S100, the still image output unit 115 executes frame image data output processing. In the frame image data output process, the still image output unit 115 displays one continuous image data file on the operation display unit 130 as nine thumbnail images. Here, one continuous image data file includes nine grouped frame image data F1 to F9.

The control unit 110 selects an arbitrary frame image as a processing target from the nine pieces of frame image data F1 to F9 according to an operation of touching a thumbnail image by the user, and processes all the frame images at once. It can also be selected as a target.

The still image output unit 115 controls the image forming unit 120 based on the selected frame image data in accordance with a user instruction to execute processing for forming an image on the print medium. Can be made.

The printed matter output by the image forming unit 120 can be used for an album or the like as a continuous photograph with a lively feeling. On the other hand, the still image output unit 115 can also transmit a continuous image data file to the smartphone 200 or a personal computer (not shown) via the communication interface unit 150 in response to an instruction operation by the user.

As described above, according to the image forming apparatus 100 according to the present embodiment, the temporal continuity that is the characteristic of the moving image data is utilized to obtain a continuous image (continuous photograph) with a dynamic feeling at a predetermined cycle. A plurality of still image data (frame image data) can be extracted.

The present disclosure can be implemented not only in the above-described embodiment but also in the following modifications.

[Modification 1]
In the above-described embodiment, frame image data in which the translational movement speed of an image of a person as a subject exceeds a threshold is extracted as a continuous image with a sense of dynamism. However, a continuous image with a feeling of dynamism is not limited to an image having a high translational movement speed.

Specifically, as shown in FIG. 7, for example, an image showing a scene in which a subject approaches in the perspective direction is also included in a continuous image having a dynamic feeling.

In the example shown in FIG. 7, the size of the area where the person as the detection target is detected is changed from the size of the detection area Fr1 to the size of the detection area Fr2. In step S70, it is conceivable that the speed detection unit 113 makes a determination based on whether or not the change rate of the image size of the subject is equal to or greater than a preset threshold value. The change rate of the image size of the subject is a reduction rate or an enlargement rate per unit time.

In the P frame or B frame, the motion vector associated with the pixel block constituting the image of the person as the subject has many components in the divergence direction when the person as the detection target approaches. On the other hand, the motion vector has many components in the convergence direction when the person to be detected moves away.

Translational movement, perspective movement, and combinations thereof can be detected by analyzing the translational component, the divergent component, and the convergence component of the motion vector. The speed detector 113 determines whether or not the image is a continuous image based on at least one of a translation component, a divergence component, and a convergence component of a motion vector associated with the moving speed of an image of a person as a subject. Can do.

[Modification 2]
In the above-described embodiment, frame image data is extracted as a continuous image with a sense of movement based on the moving speed of an image of a person as a subject. However, a continuous image with a feeling of dynamism is not limited to an image having a high moving speed.

Specifically, for example, a case where a person as a subject is doing gymnastics can be considered. In this case, the image of the person as the subject is an image when the person's limb is moving or rotating without moving. In such a case, the control unit 110 may extract a continuous image including an image of a person who is moving or rotating a limb as a continuous image having a sense of movement.

Thus, the case where the limb of the person as the detection target is moving or rotating may be considered. In this case, a motion vector associated with a pixel block constituting an image of a person as a subject may have a random direction and size. In this case, when the motion vector of the pixel block that forms the image of the person as the subject has a random direction and size, the speed detection unit 113 has a continuous image corresponding to the motion vector with a lively feeling. Can be determined.

[Modification 3]
In the above-described embodiment, a motion vector is used as data used for determining whether or not a continuous image is lively. However, the motion vector is not necessarily used, and other data may be adopted.

Specifically, the speed detection unit 113 may be configured to detect the movement speed of the person with reference to, for example, the position or size of the person area existing in each frame image. For example, the speed detection unit 113 calculates the difference in the position of the person region between temporally adjacent frame images as the movement distance, and if the distance is larger than a predetermined threshold, the person in the moving image moves at high speed. Can be determined. Here, the person area is an area including an image of a person.

Further, the speed detection unit 113 compares not only the position of the person area but also the size of the person area. Even when the difference in size is larger than a predetermined threshold, the image of the person in the moving image moves at high speed. You may judge.

The method using the motion vector in the above embodiment has an advantage that the calculation load is small and the processing can be speeded up. On the other hand, the method according to the present modification has an advantage that the degree of freedom in design of processing for determining whether or not a continuous image is lively is high.

Furthermore, for example, one of the difference in the position of the person area and the difference in the size of the person area may be mainly used, and the other may be used in a complementary manner. As described above, the speed detection unit 113 only needs to determine whether or not the movement of the image of the same subject satisfies a preset condition.

[Modification 4]
In the above embodiment, the present invention is applied to the image forming apparatus. However, the present invention can also be applied to an apparatus that functions as an image processing apparatus such as a smartphone or a personal computer.

Claims

A still image data generating unit that generates a plurality of still image data from moving image data;
From the generated still image data, the still image data includes the same subject image and is temporally continuous in a predetermined cycle, and the movement of the same subject image is preset. A continuous still image data extraction unit that extracts continuous still image data that satisfies the following conditions:
An image processing apparatus comprising:
The image processing apparatus according to claim 1,
When the continuous still image data extraction unit determines that the image of the subject is moving in translation at a speed exceeding a preset threshold, the condition that the movement of the same subject image is preset An image processing apparatus that determines that the above is satisfied.
The image processing apparatus according to claim 1,
The continuous still image data extraction unit sets a condition in which the movement of the same subject image is preset when it is determined that the rate of change in the image size of the subject is greater than or equal to a preset threshold value. An image processing apparatus that determines that the condition is satisfied.
The image processing apparatus according to claim 1,
The still image data generation unit generates the plurality of still image data by reproducing an image of a plurality of pixel blocks to which the motion vector is linked using a motion vector included in the moving image data. And
Whether the continuous still image data extraction unit satisfies a condition in which the motion of the subject is set in advance using the motion vector associated with at least one pixel block constituting the image of the subject An image processing apparatus that determines whether or not.
The image processing apparatus according to claim 1, further comprising:
An operation display that displays a screen and accepts input from the user;
A cycle setting unit that causes the operation display unit to display a screen that accepts a cycle setting input that is a setting of the predetermined cycle;
An image processing apparatus comprising:
An image processing apparatus according to claim 1;
An image forming unit that forms an image on a print medium;
An image forming apparatus comprising:
Generating a plurality of still image data from moving image data;
From the plurality of generated still image data, still image data including an image of the same subject and continuous in time with a predetermined cycle, and movement of the image of the same subject is preset. Extracting continuous still image data that satisfies the following conditions:
An image processing method including: