CN101075350A - Assembly for converting two-dimensional cartoon into three-dimensional cartoon by dynamic outline technology - Google Patents

Abstract

The invention belongs to the technical field of image signal processing, and relates to a component for realizing conversion from two-dimensional animation to three-dimensional animation by using dynamic contour technology. The original animation film is collected into the computer memory frame by frame, and the overall image is processed by mathematical operators. The analysis method divides the overall image into several different regions according to the closed edge information of the image, and according to the change of each region in the continuous multi-frame images, the overall image is correspondingly divided into several images and stored in different document. When the animation is played, these files storing the images of each area are played simultaneously, and the images played by different files are mapped on screens of different layers, thereby realizing the conversion from two-dimensional animation to three-dimensional animation. The invention solves the film source problem of the three-dimensional animation film and reduces the price of the three-dimensional animation film.

Description

Components for converting 2D animation to 3D animation using dynamic contour technology

Technical field:

The invention belongs to the technical field of image signal processing, and relates to a component for realizing conversion from two-dimensional animation to three-dimensional animation by using dynamic contour technology.

Background technique:

3D animation film technology is widely used in museums, movie theaters and amusement parks, etc. However, most of the 3D animation film equipment in my country relies on imports, and 3D animation film films can only be purchased from foreign companies, with limited channels and high prices.

Invention content:

Aiming at the problem that most of the 3D animation film equipment in our country relies on imports, and the films of 3D animation films can only be purchased from foreign companies, the channels are limited, and the price is high. Aiming at the characteristics of 2D cartoons such as simple picture, clear outline, regular shape changes, and easy distinction between image background and foreground, the original animation film is collected into the computer memory frame by frame, and the two-dimensional image is extracted by means of image processing methods. Outline information of 3D animation films can be divided into regions, and each region is divided into foreground and background levels, and the original 2D cartoons in China are converted into 3D cartoons, which solves the source problem of 3D animation films and reduces the cost of 3D animation films. price.

The present invention includes:

A device that uses mathematical operators to analyze the overall image and search for the edge of the image;

A device for dividing the overall image into several different regions according to the closed edge information of the image;

According to the change of each area in the continuous multi-frame images, the overall image is correspondingly divided into several images and stored in different files.

When the animation is played, these files storing the images of each region are played simultaneously, and the images played by different files are mapped on screens of different layers. Usually, the document image corresponding to the area with little morphological change is mapped on the last layer of screen as the background area, and the document image corresponding to the area with the most drastic change is mapped on the front layer of the screen as the first foreground area, so as to achieve 2D animation to 3D animation conversion.

Beneficial effects: the present invention utilizes the dynamic outline technology to capture the original animation film into the computer memory frame by frame, aiming at the characteristics of simple two-dimensional cartoon picture, clear outline, regular shape change, and easy distinction between the image background and the foreground. The processing method extracts the contour information in the two-dimensional image, performs region segmentation, and divides each region into foreground and background layers, converts the original two-dimensional animation in my country into a three-dimensional animation, and solves the problem of the source of the three-dimensional film problem, reducing the price of 3D animation films.

Description of drawings

Fig. 1 is the procedure flow chart of the present invention.

Fig. 2 is a schematic diagram of the layering principle of the two-dimensional animation image of the present invention.

FIG. 3 is a schematic diagram of sector numbers in the present invention.

FIG. 4 is a schematic diagram of a 3×3 neighborhood in the present invention.

Detailed ways

The present invention adopts VC++6.0 programming, and the operating environment is a computer with PIII500 or above, a memory greater than 256MB, and a hard disk greater than 40GB.

The concrete realization method of the present invention is as follows:

Capture the original animation film frame by frame into the computer memory;

The canny operator is used to analyze the overall image and search for the edge of the image; the canny operator has good positioning accuracy and single edge response, and the edge detection accuracy is high.

According to the closed edge information of the image, the overall image is divided into several different regions (as shown in Figure 1, it is divided into 4 regions, wherein region 4 is the background);

According to the change of each region in the continuous multi-frame images, the overall image is correspondingly divided into 4 images and stored in 4 files respectively. When playing animation, 4 files are played at the same time. Usually, the file image corresponding to the region with little morphological change (region 4) is mapped on the last layer of screen as the background, and the file image corresponding to the region with the most drastic change (region 1) is mapped on the front as the first foreground layer on the screen. The file image corresponding to the area with relatively sharp change (area 2) is mapped on the second-layer screen as the second foreground, and the file image corresponding to the area with relatively slow change (area 3) is mapped on the third-layer screen as the third foreground.

Canny edge detection algorithm steps:

Step 1: Smooth the image with a Gaussian filter;

Step 2: Taking a certain point on the image as the base point, use the finite difference of the first-order partial derivative to calculate the magnitude and direction of the gradient of the detection point;

Step 3: Perform non-maximum suppression on the gradient magnitude;

Step 4: Detect and connect edges with a dual-threshold algorithm.

Gaussian smoothing function:

$\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="2"\; label="a"\; filenames="A20071005578400092.png"\; state="\{\{state\}\}">a</figure-callout></span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="2"\; label="b"\; filenames="A20071005578400092.png"\; state="\{\{state\}\}">b</figure-callout></span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}$

G(x,y)=f(x,y)*H(x,y)

Where f(x, y) is the gray value or color value of an image pixel; H(x, y) is the coefficient; G(x, y) is the smoothed gray value or color value; a, b , σ are parameters input by the computer in order to achieve the ideal smoothing effect.

First-order difference convolution template:

${\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\left|\begin{array}{cc}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right|<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="2"\; label="h"\; filenames="A20071005578400092.png"\; state="\{\{state\}\}">h</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\left|\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right|$

 ₁ (m, n) = f(m, n)*H ₁ (x, y)

 ₂ (m, n)=f(m, n)*H ₁ (m, n)

Among them, H1 and H2 are coefficients; f(m, n) is the gray value or color value of a certain image pixel;  ₁ (m, n),  ₂ (m, n) are convolution results; (m , n) is the magnitude of the gradient of the pixel point whose coordinates are (m, n); θ _ is the orientation angle of the pixel point.

Methods for non-maximum suppression:

Only obtaining the global gradient is not enough to determine the edge, so in order to determine the edge, the point with the largest local gradient must be retained and non-maximum values must be suppressed. Solution: Use the direction of the gradient. As shown in Figure 3 and Figure 4, the four sectors are labeled 0 to 3, corresponding to four possible combinations 0-1, 1-2, 2-3, 3-0 of the 3*3 neighborhood.

At each point, the center pixel M is compared to the neighbor two pixels along the gradient line. If the gradient value of M is not greater than the gradient values of two adjacent pixels along the gradient line, then the gradient value of M is equal to 0, otherwise the gradient value of M is equal to the gradient value of adjacent pixels. That is: N[i, j]=NMS(M[i, j], ξ[i, j]), where N[i, j] is the gradient value after non-maximum suppression, M[i, j ] is the gradient value of the central pixel M, and ξ[i, j] is the gradient value of adjacent pixels.

The specific method of detecting and connecting edges with the double threshold algorithm:

A typical way to reduce the number of false edge segments is to use a threshold on N[i,j]. Assign a value of zero to all values below the threshold. But the question is how to choose the threshold?

Solution: double threshold algorithm. The double-threshold algorithm has two thresholds T1 and T2 for non-maximum suppression images, and 2T1≈T2, so that two threshold edge images N1[i, j] and N2[i, j] can be obtained. Since N2[i, j] is obtained using a high threshold, it contains few false edges, but there are discontinuities (not closed). The double-threshold method needs to connect the edges into a contour in N2[i, j]. When the end point of the contour is reached, the algorithm searches for edges that can be connected to the contour at the 8 adjacent points of N1[i, j]. In this way, The algorithm keeps collecting edges in N1[i,j] until N2[i,j] is connected.

Claims (2)

1. A component that utilizes dynamic contour technology to realize conversion from two-dimensional animation to three-dimensional animation, characterized in that it comprises: A device that uses mathematical operators to analyze the overall image and search for the edge of the image; A device that divides the overall image into several different regions according to the closed edge information of the image; According to the change of each area in the continuous multi-frame images, the overall image is correspondingly divided into several images and stored in different files. 2. The component for realizing conversion from 2D animation to 3D animation using dynamic contour technology according to claim 1, characterized in that the mathematical operator is a canny operator.

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