A computer system, method and medium

Abstract

The invention relates to the technical field of animation production, and discloses a computer system, comprising: a frame image extraction module, which is used to extract frame images from a three-dimensional animation video; a character recognition module, which is used to recognize characters from frame images and obtain character posture data; a key frame extraction module, which is used to screen and obtain a key frame sequence from the frame images; a posture sequence correction module, which first extracts a key frame sequence corresponding to a first character and then marks the key frames; a reference sequence correction module, which is used to mark the key frames of a key frame sequence of a third character; a key frame sequence synthesis module, which is used to extract a reference key frame sequence; each reference key frame sequence is synthesized with the key frame sequence of the first character respectively; the invention can map reference images to key frames that need to be hand-drawn in the process of converting 3D animation into 2D based on image processing and data mining, and provide a reference with a high matching degree for hand-drawing personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of animation production, and more specifically, to a computer system. Background Art

[0002] In the process of converting 3D animation to 2D, in addition to edge processing and color processing of images through deep learning, if you want to reflect the style of 2D animation, you also need to replace some frame images with frame images generated based on hand-drawn keyframes. This requires the hand-drawn personnel to be able to draw hyper-realistic character movements between real movements, which requires a high level of artistic creation ability from the hand-drawn personnel. Summary of the invention

[0003] The present invention provides a computer system to solve the technical problem of XXXXX in the related technology.

[0004] The present invention provides a computer system, comprising:

[0005] A frame image extraction module is used to extract a first frame image from a three-dimensional animation video and a second frame image from a two-dimensional animation video; a character recognition module is used to recognize a character from a frame image and obtain character posture data; the character information includes the character's gender, and the character posture data includes character posture parameters; a character matching module is used to select a first character extracted from the first frame image, and then match a second character extracted from the second frame image, the first character and the second character have the same information, and the matched second character is marked as a third character; a key frame extraction module is used to filter and obtain a key frame sequence from the frame image;

[0006] The posture sequence correction module first extracts the key frame sequence corresponding to the first character, and then marks the key frames. The mark value of the key frames that need to be redrawn is 0, and the mark values ​​of the remaining key frames are 1;

[0007] A reference sequence correction module, which is used to extract a key frame sequence corresponding to a third person, and then mark the key frames of the key frame sequence of the third person;

[0008] The key frame sequence synthesis module is used to calculate the reference distance between the key frame sequences of the first character and the second character, extract the first N key frame sequences of the second character with the smallest reference distance to the key frame sequence of the first character as reference key frame sequences; and synthesize each reference key frame sequence with the key frame sequence of the first character.

[0009] Furthermore, the method of selecting and obtaining key frames from frame images includes:

[0010] Step 101, extracting the character posture parameters of the selected character from each frame image to generate a character posture sequence, wherein each unit of the character posture sequence includes the character posture parameters of the selected character in a frame image;

[0011] Step 102, starting from the sequence unit of the character posture sequence and traversing backwards, until the first similarity between the traversed sequence unit and the sequence unit at the start of the traversal is less than a set first similarity threshold; marking the sequence unit at the end of the traversal as the first sequence unit;

[0012] Step 103, iteratively executing step 102 until all sequence units of the character posture sequence are traversed, starting from the first sequence unit of the character posture sequence when executing for the first time, and traversing backwards from the first sequence unit generated last time in each subsequent execution;

[0013] Step 104, extracting all generated first sequence units to generate a first character posture sequence;

[0014] Step 105 , extracting the frame images of the character posture parameter sources of the first sequence unit as key frames, and sorting the key frames according to time points to generate a key frame sequence.

[0015] Furthermore, the calculation formula of the first similarity is as follows:

[0016] Where k is the number of limbs in the human skeleton model, θ i1 is the angle between the i-th limb of the character posture parameter of the sequence unit of a character posture sequence and the i-th limb in the standard posture model; θ i2 is the angle between the i-th limb of the character posture parameter of the sequence unit of another character posture sequence and the i-th limb in the standard posture model.

[0017] Furthermore, the method for marking the key frames of the key frame sequence of the third character includes:

[0018] Define a two-dimensional random field, where the node set of the two-dimensional random field is represented as V, and the set of edges between the nodes is represented as E;

[0019] The energy function of this two-dimensional random field is:

[0020] Where X is the node set, θ p (x p ) is the potential function of node p, θ pq (x p ,x q ) is the potential function of the edge between nodes p and q, x p The label value of the image node mapped to node p, x qis the tag value of the key frame sequence mapped to node q; a tag value of 0 indicates that the character posture parameters corresponding to the key frame deviate greatly from the real character; a tag value of 1 indicates that the character posture parameters corresponding to the key frame are close to the real character;

[0021] in,

[0022] similar p,q Represents x p With x q a second similarity of the character pose parameters mapped by the mapped keyframes;

[0023] in,

[0024] S1 is the set mobile entropy threshold, S(x p ) is x p The motion entropy of the mapped key frame is calculated as follows:

[0025] S(x p )=∑ c∈K cosθ c1,c2 , where K is the set of limbs of the human skeleton model, and θ c1,c2 Represents x p The angle between the c-th limb of the character posture parameter of the mapped key frame and the c-th limb in the standard posture model;

[0026] When calculating the minimization of the energy function, the label value of each node of the two-dimensional random field is used as the label value of the key frame of the key frame sequence.

[0027] Furthermore, the calculation formula of the second similarity is as follows:

[0028] Among them, k is the number of limbs in the human skeleton model, θ ip is the angle between the ith limb of a character's posture parameter and the ith limb in the standard posture model; θ iq is the angle between the i-th limb of another character's posture parameter and the i-th limb in the standard posture model.

[0029] Furthermore, the key frame sequence synthesis module includes:

[0030] The distance matrix generation module establishes an m×n distance matrix, and the element in the i-th row and j-th column of the distance matrix is ​​represented as d(i, j);

[0031] The value of d(i, j) is d ij , d ij represents the second distance or the third distance between the i-th key frame of the key frame sequence Q and the j-th key frame of the key frame sequence C;

[0032] A first distance calculation module calculates the second distance and fills it into the distance matrix, then generates a path K from the element in the 1st row and the 1st column to the element in the mth row and the nth column on the distance matrix, sums the values ​​of the elements of the distance matrix on the path K as the path distance value, selects the path K with the smallest path distance value as the shortest path, and uses the path distance value of the shortest path as the first distance dist1;

[0033] a fourth distance calculation module, which calculates the third distance and fills it into the distance matrix, then generates a path K from the element in the 1st row and the 1st column to the element in the mth row and the nth column on the distance matrix, sums the values ​​of the elements of the distance matrix on the path K as the path distance value, selects the path K with the smallest path distance value as the shortest path, and uses the path distance value of the shortest path as the fourth distance dist4;

[0034] A reference distance calculation module is configured to calculate a reference distance based on the first distance and the fourth distance. c The calculation formula is as follows:

[0035]

[0036] Where t is the maximum number of key frames in two key frame sequences, and k is the number of limbs in the human skeleton model;

[0037] The synthesis module is used to map the key frame sequence of the first character and the key frame marked as 0 of the reference key frame sequence, and the third distance between the two key frames with a mapping relationship is less than a set third distance threshold.

[0038] Furthermore, the calculation formula of the second distance dist2 is as follows:

[0039] dist2=|Q i -C j |, where Q i is the label value of the i-th key frame in the key frame sequence Q, C j is the label value of the jth key frame in the key frame sequence C.

[0040] Furthermore, the calculation formula of the third distance is as follows:

[0041]

[0042] Among them, k is the number of limbs in the human skeleton model, θ ip is the angle between the ith limb of a character's posture parameter and the ith limb in the standard posture model; θ iq is the angle between the i-th limb of another character's posture parameter and the i-th limb in the standard posture model.

[0043] The present invention provides a method for processing an image by a computer, wherein the computer system described above is used to perform the following steps:

[0044] Step 201, extracting a first frame image from a 3D animation video, and extracting a second frame image from a 2D animation video;

[0045] Step 202, identifying a person from the frame image and obtaining the person's posture data;

[0046] Step 203, filtering and obtaining a key frame sequence from the frame image;

[0047] Step 204, extracting key frame sequences corresponding to the first character and the third character, and then marking the key frames;

[0048] Step 205, calculating the reference distance between the key frame sequences of the first character and the second character, and extracting the first N key frame sequences of the second character with the smallest reference distance to the key frame sequence of the first character as reference key frame sequences;

[0049] Step 206: synthesize each reference key frame sequence with the key frame sequence of the first character.

[0050] The present invention provides a computer storage medium for storing the above-mentioned computer system.

[0051] The beneficial effects of the present invention are:

[0052] The present invention can map reference images of key frames that need to be hand-drawn in the process of converting 3D animation into 2D based on image processing and data mining, provide hand-drawn personnel with a reference with a high matching degree, facilitate their imitation, and reduce the requirements for the artistic creation ability of hand-drawn personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a module schematic diagram of a computer system of the present invention;

[0054] Figure 2 It is a module schematic diagram of the key frame sequence synthesis module of the present invention;

[0055] Figure 3 It is a flow chart of a method for selecting and obtaining key frames from frame images of the present invention;

[0056] Figure 4 The present invention is a flowchart of a method for computer processing images.

[0057] In the figure: frame image extraction module 101, character recognition module 102, character matching module 103, key frame extraction module 104, posture sequence correction module 105, reference sequence correction module 106, key frame sequence synthesis module 107, distance matrix generation module 1071, first distance calculation module 1072, fourth distance calculation module 1073, reference distance calculation module 1074, synthesis module 1075. DETAILED DESCRIPTION

[0058] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0059] Embodiment 1

[0060] like Figure 1-Figure 3 As shown, a computer system comprises:

[0061] A frame image extraction module 101, which is used to extract a first frame image from a 3D animation video and a second frame image from a 2D animation video;

[0062] A person recognition module 102, which is used to recognize a person from a frame image and obtain person posture data;

[0063] The character information includes the character's gender, and the character posture data includes the character's posture parameters;

[0064] The current character's information can also include more content, such as age group, role type, etc. More content facilitates more accurate matching, but the required amount of character data is large enough, otherwise more content means more filtering items, and insufficient data will lead to insufficient data on the characters available for matching.

[0065] A character matching module 103 is used to select a first character extracted from the first frame image, and then match a second character extracted from the second frame image, the first character and the second character have the same information, and the matched second character is marked as a third character;

[0066] A key frame extraction module 104 is used to filter and obtain a key frame sequence from the frame image;

[0067] Methods for selecting key frames from frame images include:

[0068] Step 101, extracting character posture parameters of a selected character (the selected character is a first character or a third character) from each frame image, generating a character posture sequence, wherein each unit of the character posture sequence includes character posture parameters of a character selected in a frame image;

[0069] Step 102, starting from the sequence unit of the character posture sequence and traversing backwards, until the first similarity between the traversed sequence unit and the sequence unit at the start of the traversal is less than a set first similarity threshold; marking the sequence unit at the end of the traversal as the first sequence unit;

[0070] Step 103, iteratively executing step 102 until all sequence units of the character posture sequence are traversed, starting from the first sequence unit of the character posture sequence when executing for the first time, and traversing backwards from the first sequence unit generated last time in each subsequent execution;

[0071] Step 104, extracting all generated first sequence units to generate a first character posture sequence;

[0072] Step 105, extracting the frame images of the character posture parameter sources of the first sequence unit as key frames, and sorting the key frames according to time points to generate a key frame sequence;

[0073] In this step, the key frame sequence may express multiple actions with large intervals. The key frame sequence is intercepted by setting a time threshold based on the time difference between the time points of the key frames in the key frame sequence. The intercepted key frame sequence enters the posture sequence correction module 105, the reference sequence correction module 106, and the key frame sequence synthesis module 107 for processing.

[0074] The time difference can be set to 3 minutes. As the time difference increases, the number of key frame sequences intercepted will decrease;

[0075] The calculation formula of the first similarity is as follows:

[0076]

[0077] Where k is the number of limbs in the human skeleton model, θ i1 is the angle between the i-th limb of the character posture parameter of the sequence unit of a character posture sequence and the i-th limb in the standard posture model; θ i2 is the angle between the i-th limb of the character posture parameter of the sequence unit of another character posture sequence and the i-th limb in the standard posture model;

[0078] In one embodiment of the present invention, character pose parameters are generated from an image based on a character pose estimation algorithm such as the OpenPose algorithm.

[0079] The first similarity threshold can be adjusted according to the number of limbs of the human skeleton model, and is generally inversely proportional to the number of limbs of the human skeleton model.

[0080] The posture sequence correction module 105 first extracts the key frame sequence corresponding to the first character, and then marks the key frames, where the mark value of the key frames that need to be redrawn is 0, and the mark values ​​of the remaining key frames are 1;

[0081] A reference sequence correction module 106, which is used to extract a key frame sequence corresponding to the third character, and then mark the key frames of the key frame sequence of the third character;

[0082] The method for marking the key frames of the key frame sequence of the third character includes:

[0083] Define a two-dimensional random field, where the node set of the two-dimensional random field is represented as V, and the set of edges between the nodes is represented as E;

[0084] The energy function of this two-dimensional random field is:

[0085]

[0086] Where X is the node set, θ p (x p ) is the potential function of node p, θ pq (x p ,x q ) is the potential function of the edge between nodes p and q, x p The label value of the image node mapped to node p, x q is the tag value of the key frame sequence mapped to node q; a tag value of 0 indicates that the character posture parameters corresponding to the key frame deviate greatly from the real character; a tag value of 1 indicates that the character posture parameters corresponding to the key frame are close to the real character;

[0087] in,

[0088] similar p,q Represents x p With x q a second similarity of the character pose parameters mapped by the mapped keyframes;

[0089] The calculation formula of the second similarity is as follows:

[0090]

[0091] Among them, k is the number of limbs in the human skeleton model, θ ip is the angle between the ith limb of a character's posture parameter and the ith limb in the standard posture model; θ iqis the angle between the ith limb of another character's posture parameter and the ith limb in the standard posture model;

[0092] in,

[0093] S1 is the set mobile entropy threshold, S(x p ) is x p The motion entropy of the mapped key frame is calculated as follows:

[0094]

[0095] Where K is the set of limbs of the human skeleton model, and θ c1,c2 Represents x p The angle between the c-th limb of the character posture parameter of the mapped key frame and the c-th limb in the standard posture model;

[0096] The standard posture model is the limb angle parameters of the human skeleton model in a natural standing state with the hands hanging naturally.

[0097] When calculating the minimization of the energy function, the label value of each node of the two-dimensional random field is used as the label value of the key frame of the key frame sequence.

[0098] A key frame sequence synthesis module 107, which is used to calculate the reference distance between the key frame sequences of the first character and the second character, and extract the first N key frame sequences of the second character with the smallest reference distance to the key frame sequence of the first character as reference key frame sequences;

[0099] Each reference key frame sequence is synthesized with the key frame sequence of the first character.

[0100] The key frame sequence synthesis module 107 includes:

[0101] The distance matrix generation module 1071 establishes an m×n distance matrix, where the element in the i-th row and j-th column of the distance matrix is ​​represented as d(i, j);

[0102] The value of d(i, j) is d ij , d ij represents the second distance or the third distance between the i-th key frame of the key frame sequence Q and the j-th key frame of the key frame sequence C;

[0103] The first distance calculation module 1072 calculates the second distance and fills it into the distance matrix, then generates a path K from the element in the 1st row and 1st column to the element in the mth row and nth column on the distance matrix, sums the values ​​of the elements of the distance matrix on the path K as the path distance value, selects the path K with the smallest path distance value as the shortest path, and uses the path distance value of the shortest path as the first distance dist1.

[0104] The fourth distance calculation module 1073 calculates the third distance and fills it into the distance matrix, then generates a path K from the element in the 1st row and 1st column to the element in the mth row and nth column on the distance matrix, sums the values ​​of the elements of the distance matrix on path K as the path distance value, selects the path K with the smallest path distance value as the shortest path, and uses the path distance value of the shortest path as the fourth distance dist4.

[0105] The calculation formula for the second distance dist2 is as follows:

[0106] dist2=|Q i -C j |

[0107] Where Q i is the label value of the i-th key frame in the key frame sequence Q, C j is the label value of the jth key frame in the key frame sequence C.

[0108] The calculation formula for the third distance is as follows:

[0109]

[0110] Among them, k is the number of limbs in the human skeleton model, θ ip is the angle between the ith limb of a character's posture parameter and the ith limb in the standard posture model; θ iq is the angle between the i-th limb of another character's posture parameter and the i-th limb in the standard posture model.

[0111] A reference distance calculation module 1074 calculates a reference distance based on the first distance and the fourth distance, the reference distance dist c The calculation formula is as follows:

[0112]

[0113] Where t is the maximum number of key frames in two key frame sequences, and k is the number of limbs in the human skeleton model.

[0114] The synthesis module 1075 is used to map the key frame sequence of the first character and the key frame marked as 0 of the reference key frame sequence, and the third distance between the two key frames in the mapping relationship is less than a set third distance threshold.

[0115] like Figure 4 As shown, this embodiment provides a method for processing an image by a computer, which uses the above-mentioned computer system to perform the following steps:

[0116] Step 201, extracting a first frame image from a 3D animation video, and extracting a second frame image from a 2D animation video;

[0117] Step 202, identifying a person from the frame image and obtaining the person's posture data;

[0118] Step 203, filtering and obtaining a key frame sequence from the frame image;

[0119] Step 204, extracting key frame sequences corresponding to the first character and the third character, and then marking the key frames;

[0120] Step 205, calculating the reference distance between the key frame sequences of the first character and the second character, and extracting the first N key frame sequences of the second character with the smallest reference distance to the key frame sequence of the first character as reference key frame sequences;

[0121] Step 206: synthesize each reference key frame sequence with the key frame sequence of the first character.

[0122] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A computer system, characterized in that: include: A frame image extraction module, which is used to extract a first frame image from a three-dimensional animation video and a second frame image from a two-dimensional animation video; A person recognition module is used to recognize a person from a frame image and obtain person posture data; the person information includes the person's gender, and the person posture data includes person posture parameters; a person matching module is used to select a first person extracted from a first frame image, and then match a second person extracted from a second frame image, the first person and the second person have the same information, and the matched second person is marked as a third person; a key frame extraction module is used to filter and obtain a key frame sequence from the frame image; The posture sequence correction module first extracts the key frame sequence corresponding to the first character, and then marks the key frames. The mark value of the key frames that need to be redrawn is 0, and the mark values ​​of the remaining key frames are 1; A reference sequence correction module, which is used to extract a key frame sequence corresponding to a third person, and then mark the key frames of the key frame sequence of the third person; A key frame sequence synthesis module, which is used to calculate the reference distance between the key frame sequences of the first character and the second character, and extract the first N key frame sequences of the second character with the smallest reference distance to the key frame sequence of the first character as reference key frame sequences; synthesizing each reference key frame sequence with the key frame sequence of the first character; Methods for selecting key frames from frame images include: Step 101, extracting the character posture parameters of the selected character from each frame image to generate a character posture sequence, wherein each unit of the character posture sequence includes the character posture parameters of the selected character in a frame image; Step 102, starting from the sequence unit of the character posture sequence and traversing backwards, until the first similarity between the traversed sequence unit and the sequence unit at the start of the traversal is less than a set first similarity threshold; marking the sequence unit at the end of the traversal as the first sequence unit; Step 103, iteratively executing step 102 until all sequence units of the character posture sequence are traversed, starting from the first sequence unit of the character posture sequence when executing for the first time, and traversing backwards from the first sequence unit generated last time in each subsequent execution; Step 104, extracting all generated first sequence units to generate a first character posture sequence; Step 105, extracting the frame images of the character posture parameter sources of the first sequence unit as key frames, and sorting the key frames according to time points to generate a key frame sequence; The calculation formula of the first similarity is as follows: Where k is the number of limbs in the human skeleton model, θ I1 is the angle between the i-th limb of the character posture parameter of the sequence unit of a character posture sequence and the i-th limb in the standard posture model; θ l2 is the angle between the i-th limb of the character posture parameter of the sequence unit of another character posture sequence and the i-th limb in the standard posture model; The method for marking the key frames of the key frame sequence of the third character includes: Define a two-dimensional random field, where the node set of the two-dimensional random field is represented as V, and the set of edges between the nodes is represented as E; The energy function of this two-dimensional random field is: Where X is the node set, 0 p (x p ) is the potential function of node p, 0 pq (x p , x q ) is the potential function of the edge between nodes p and q, X p The label value of the image node mapped to node p, X q is the tag value of the key frame sequence mapped to node q; a tag value of 0 indicates that the character posture parameters corresponding to the key frame deviate greatly from the real character; a tag value of 1 indicates that the character posture parameters corresponding to the key frame are close to the real character; in, similar p，q Represents x p With x q a second similarity of the character pose parameters mapped by the mapped keyframes; in; S1 is the set mobile entropy threshold, S(x p ) is x p The motion entropy of the mapped key frame is calculated as follows: S(x p )=∑ c∈K cosθ c1，c2 , where K is the set of limbs of the human skeleton model Total, where 0 c1，c2 Represents x p The angle between the c-th limb of the character posture parameter of the mapped key frame and the c-th limb in the standard posture model; When calculating the minimization of the energy function, the label value of each node of the two-dimensional random field is used as the label value of the key frame of the key frame sequence; The calculation formula of the second similarity is as follows: Where k is the number of limbs in the human skeleton model, 0 lp is the angle between the ith limb of a character's posture parameter and the ith limb in the standard posture model; 0 pq is the angle between the ith limb of another character's posture parameter and the ith limb in the standard posture model; The key frame sequence synthesis module includes: The distance matrix generation module establishes an m×n distance matrix, and the element in the i-th row and j-th column of the distance matrix is ​​represented as d(i, j); The value of d(i, j) is d ij , d ij represents the second distance or the third distance between the i-th key frame of the key frame sequence Q and the j-th key frame of the key frame sequence C; A first distance calculation module calculates the second distance and fills it into the distance matrix, then generates a path K from the element in the 1st row and the 1st column to the element in the mth row and the nth column on the distance matrix, sums the values ​​of the elements of the distance matrix on the path K as the path distance value, selects the path K with the smallest path distance value as the shortest path, and uses the path distance value of the shortest path as the first distance disl1; a fourth distance calculation module, which calculates the third distance and fills it into the distance matrix, then generates a path K from the element in the 1st row and the 1st column to the element in the mth row and the nth column on the distance matrix, sums the values ​​of the elements of the distance matrix on the path K as the path distance value, selects the path K with the smallest path distance value as the shortest path, and uses the path distance value of the shortest path as the fourth distance dist4; A reference distance calculation module is configured to calculate a reference distance based on the first distance and the fourth distance. c The calculation formula is as follows: Where t is the maximum number of key frames in two key frame sequences, and k is the number of limbs in the human skeleton model; A synthesis module, which is used to map the key frame sequence of the first character and the key frame marked as 0 of the reference key frame sequence, and the third distance between the two key frames in the mapping relationship is less than a set third distance threshold; The calculation formula for the second distance dist2 is as follows: dist2=|Q i -C j |, where Q i is the label value of the i-th key frame in the key frame sequence Q, C j is the label value of the jth key frame in the key frame sequence C; The calculation formula for the third distance is as follows: Among them, k is the number of limbs in the human skeleton model, D ip is the angle between the ith limb of a character's posture parameter and the ith limb in the standard posture model; 0 iq is the angle between the ith limb of another character's posture parameter and the ith limb in the standard posture model; The computer system performs the following steps: Step 201, extracting a first frame image from a 3D animation video, and extracting a second frame image from a 2D animation video; Step 202, identifying a person from the frame image and obtaining the person's posture data; Step 203, filtering and obtaining a key frame sequence from the frame image; Step 204, extracting key frame sequences corresponding to the first character and the third character, and then marking the key frames; Step 205, calculating the reference distance between the key frame sequences of the first character and the second character, and extracting the first N key frame sequences of the second character with the smallest reference distance to the key frame sequence of the first character as reference key frame sequences; Step 206: synthesize each reference key frame sequence with the key frame sequence of the first character.

2. A computer storage medium, characterized in that: A computer system as claimed in claim 1 is stored.

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