CN108109158B

CN108109158B - Video crossing processing method and device based on self-adaptive threshold segmentation

Info

Publication number: CN108109158B
Application number: CN201711377297.3A
Authority: CN
Inventors: 赵鑫; 邱学侃; 颜水成
Original assignee: Beijing Qihoo Technology Co Ltd
Current assignee: Beijing Qihoo Technology Co Ltd
Priority date: 2017-12-19
Filing date: 2017-12-19
Publication date: 2021-06-15
Anticipated expiration: 2037-12-19
Also published as: CN108109158A

Abstract

The invention discloses a video crossing processing method, a device, computing equipment and a storage medium based on self-adaptive threshold segmentation, wherein the method comprises the following steps: acquiring a current frame image containing a specific object and a time processing parameter in a video in real time; performing scene segmentation processing on a current frame image to obtain foreground probability information aiming at a specific object, determining a foreground region proportion according to the foreground probability information, and performing mapping processing on the foreground probability information according to the foreground region proportion to obtain an image segmentation result; determining a processed foreground image according to the image segmentation result, and determining a region to be processed according to the processed foreground image; drawing a traversing effect map according to the time processing parameters; fusing the crossing effect map and the processed foreground image to obtain a frame processing image; covering the frame processing image on the current frame image to obtain processed video data; and displaying the processed video data. The scheme can accurately add the crossing effect to the frame image.

Description

Video crossing processing method and device based on self-adaptive threshold segmentation

Technical Field

The invention relates to the technical field of image processing, in particular to a video crossing processing method and device based on adaptive threshold segmentation, computing equipment and a computer storage medium.

Background

In the prior art, when a user needs to perform personalized processing on a video, for example, the video is processed to have a display effect of passing through the video for 20 years, an image segmentation method is often used to perform scene segmentation processing on a frame image in the video, wherein a pixel-level segmentation effect can be achieved by using the image segmentation method based on deep learning. However, when the existing image segmentation method is used for scene segmentation processing, the proportion of the foreground image in the frame image is not considered, so when the proportion of the foreground image in the frame image is small, the existing image segmentation method is used for easily dividing the pixel points which actually belong to the edge of the foreground image into the background image, and the obtained image segmentation result has low segmentation precision and poor segmentation effect. Therefore, the image segmentation method in the prior art has a problem that the segmentation precision of image scene segmentation is low, so that the crossing effect cannot be well and accurately added to the frame image in the video by using the obtained image segmentation result, and the display effect of the obtained processed video data is poor.

Disclosure of Invention

In view of the above, the present invention has been made to provide a video traversal processing method, apparatus, computing device and computer storage medium based on adaptive threshold segmentation that overcome or at least partially address the above-mentioned problems.

According to an aspect of the present invention, there is provided a video crossing processing method based on adaptive threshold segmentation, the method comprising:

acquiring a current frame image containing a specific object and time processing parameters in a video shot and/or recorded by image acquisition equipment in real time;

performing scene segmentation processing on a current frame image to obtain foreground probability information aiming at a specific object, determining a foreground region proportion according to the foreground probability information, and performing mapping processing on the foreground probability information according to the foreground region proportion to obtain an image segmentation result corresponding to the current frame image;

determining a processed foreground image according to the image segmentation result, and determining a region to be processed in the processed foreground image according to the processed foreground image;

drawing a crossing effect map corresponding to the region to be processed according to the time processing parameters;

fusing the crossing effect map and the processed foreground image to obtain a frame processing image;

covering the frame processing image on the current frame image to obtain processed video data;

and displaying the processed video data.

Further, the foreground probability information records the probability that each pixel point in the current frame image belongs to the foreground image.

Further, the pass-through effect map comprises one or more of the following maps: a dress effect map, a decorative effect map, a texture effect map, and a face decorating effect map.

Further, according to the time processing parameter, drawing a crossing effect map corresponding to the region to be processed further comprises:

extracting key information of the area to be processed from the area to be processed;

and drawing a crossing effect map corresponding to the area to be processed according to the time processing parameters and the key information of the area to be processed.

Further, the key information is key point information;

according to the time processing parameter and the key information of the area to be processed, drawing a traversing effect map corresponding to the area to be processed further comprises:

searching a basic traversing effect map matched with the key point information according to the time processing parameters;

calculating position information between at least two key points with a symmetrical relation according to the key point information;

and processing the basic crossing effect map according to the position information to obtain a crossing effect map.

Further, processing the basic crossing effect map according to the position information, and obtaining the crossing effect map further comprises:

zooming the basic crossing effect map according to the distance information in the position information; and/or performing rotation processing on the basic crossing effect map according to the rotation angle information in the position information.

Further, the fusion processing of the crossing effect map and the processed foreground image to obtain a frame processing image further comprises:

and fusing the traversing effect mapping, the processed foreground image and the processed background image determined according to the image segmentation result to obtain a frame processing image.

Further, according to the foreground probability information, determining the foreground region proportion further includes:

determining pixel points belonging to the foreground image according to the foreground probability information;

and calculating the proportion of the pixel points belonging to the foreground image in all the pixel points in the current frame image, and determining the proportion as the foreground area ratio.

Further, according to the foreground probability information, determining pixel points belonging to the foreground image further includes:

and determining the pixel points with the probability higher than a preset probability threshold in the foreground probability information as the pixel points belonging to the foreground image.

Further, mapping the foreground probability information according to the foreground region ratio to obtain an image segmentation result corresponding to the current frame image further includes:

adjusting parameters of the mapping function according to the ratio of the foreground area;

mapping the foreground probability information by using the adjusted mapping function to obtain a mapping result;

and obtaining an image segmentation result corresponding to the current frame image according to the mapping result.

Further, the slope of the mapping function in the preset defined interval is greater than a preset slope threshold.

Further, displaying the processed video data further comprises: displaying the processed video data in real time;

the method further comprises the following steps: and uploading the processed video data to a cloud server.

Further, uploading the processed video data to a cloud server further comprises:

and uploading the processed video data to a cloud video platform server so that the cloud video platform server can display the video data on a cloud video platform.

and uploading the processed video data to a cloud live broadcast server so that the cloud live broadcast server can push the video data to a client of a watching user in real time.

and uploading the processed video data to a cloud public server so that the cloud public server pushes the video data to a public attention client.

According to another aspect of the present invention, there is provided a video crossing processing apparatus based on adaptive threshold segmentation, the apparatus comprising:

the acquisition module is suitable for acquiring a current frame image containing a specific object and time processing parameters in a video shot and/or recorded by image acquisition equipment in real time;

the segmentation module is suitable for carrying out scene segmentation processing on the current frame image to obtain foreground probability information aiming at a specific object, determining the foreground region proportion according to the foreground probability information, and carrying out mapping processing on the foreground probability information according to the foreground region proportion to obtain an image segmentation result corresponding to the current frame image;

the determining module is suitable for determining the processed foreground image according to the image segmentation result and determining a region to be processed in the processed foreground image according to the processed foreground image;

the drawing module is suitable for drawing a crossing effect map corresponding to the area to be processed according to the time processing parameters;

the fusion processing module is suitable for carrying out fusion processing on the crossing effect map and the processed foreground image to obtain a frame processing image;

the covering module is suitable for covering the frame processing image with the current frame image to obtain processed video data;

and the display module is suitable for displaying the processed video data.

Further, the rendering module is further adapted to:

Further, the key information is key point information;

the rendering module is further adapted to:

Further, the rendering module is further adapted to:

Further, the fusion processing module is further adapted to:

and fusing the traversing effect mapping, the processed foreground image and the background image determined according to the image segmentation result to obtain a frame processing image.

Further, the segmentation module is further adapted to:

Further, the display module is further adapted to: displaying the processed video data in real time;

the device also includes: and the uploading module is suitable for uploading the processed video data to the cloud server.

Further, the upload module is further adapted to:

According to yet another aspect of the present invention, there is provided a computing device comprising: the processor, the memory and the communication interface complete mutual communication through the communication bus;

the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the video crossing processing method based on the adaptive threshold segmentation.

According to still another aspect of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the adaptive threshold segmentation based video traversal processing method as described above.

According to the technical scheme provided by the invention, the foreground probability information aiming at the specific object is mapped according to the foreground area proportion, the self-adaptive mapping of the foreground probability information is realized, the image segmentation result corresponding to the frame image can be quickly and accurately obtained by utilizing the mapped foreground probability information, the segmentation precision and the processing efficiency of image scene segmentation are effectively improved, the image scene segmentation processing mode is optimized, the crossing effect can be more accurately and quickly added to the area to be processed of the frame image based on the obtained image segmentation result, the video with the crossing effect is obtained, the video data display effect is beautified, and the video data processing efficiency is improved.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

FIG. 1 is a flow diagram illustrating a method for adaptive threshold segmentation based video traversal processing according to an embodiment of the present invention;

FIG. 2 is a flow diagram illustrating a video traversal processing method based on adaptive threshold segmentation according to another embodiment of the present invention;

FIG. 3 is a block diagram of an adaptive threshold segmentation based video traversal processing apparatus according to an embodiment of the present invention;

FIG. 4 shows a schematic structural diagram of a computing device according to an embodiment of the invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

Fig. 1 is a flowchart illustrating a video crossing processing method based on adaptive threshold segmentation according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:

step S100, acquiring a current frame image containing a specific object and time processing parameters in a video shot and/or recorded by an image acquisition device in real time.

In this embodiment, the image capturing device takes a camera used by the terminal device as an example for description. The method comprises the steps of acquiring a current frame image of a camera of the terminal equipment when shooting a video or recording the video in real time. Since the specific object is processed by the method, only the current frame image containing the specific object is acquired when the current frame image is acquired. Wherein, the specific object can be a human body and the like. The specific object can be set by those skilled in the art according to actual needs, and is not limited herein.

Step S101, performing scene segmentation processing on a current frame image to obtain foreground probability information aiming at a specific object, determining a foreground region ratio according to the foreground probability information, and performing mapping processing on the foreground probability information according to the foreground region ratio to obtain an image segmentation result corresponding to the current frame image.

When the current frame image is subjected to scene segmentation processing, a depth learning method can be utilized. Deep learning is a method based on characterization learning of data in machine learning. An observation (e.g., an image) may be represented using a number of ways, such as a vector of intensity values for each pixel, or more abstractly as a series of edges, a specially shaped region, etc. And tasks are easier to learn from the examples using some specific representation methods. Scene segmentation processing can be carried out on the current frame image by utilizing a segmentation method of deep learning, and foreground probability information of the current frame image aiming at a specific object is obtained. Specifically, a scene segmentation network obtained by a deep learning method and the like may be used to perform scene segmentation processing on the current frame image to obtain foreground probability information of the current frame image for a specific object, where the foreground probability information records a probability that each pixel in the current frame image belongs to the foreground image, and specifically, a value range of the probability that each pixel belongs to the foreground image may be [0, 1 ].

In the present invention, the foreground image may only contain a specific object, and the background image is an image other than the foreground image in the current frame image. According to the foreground probability information, which pixel points in the current frame image belong to the foreground image, which pixel points belong to the background image, and which pixel points may belong to both the foreground image and the background image. For example, if the foreground probability information corresponding to a certain pixel point is close to 0, it is indicated that the pixel point belongs to a background image; if the foreground probability information corresponding to a certain pixel point is close to 1, the pixel point is indicated to belong to a foreground image; if the foreground probability information corresponding to a certain pixel point is close to 0.5, it is indicated that the pixel point may belong to both the foreground image and the background image.

After the foreground probability information is obtained, which pixel points in the current frame image belong to the foreground image can be determined according to the foreground probability information, so that the foreground area ratio is determined. The foreground area ratio is used for reflecting the ratio of the occupied area of the foreground image in the current frame image. Performing adaptive mapping processing on the foreground probability information according to the foreground region ratio, for example, when the foreground region ratio is smaller, for example, the foreground region ratio is 0.2, which indicates that the area occupied by the foreground image in the current frame image is smaller, the foreground probability information can be subjected to mapping processing, the smaller probability in the foreground probability information is adaptively mapped to a larger probability, and the larger probability in the foreground probability information is adaptively mapped to a smoother probability; for another example, when the foreground region occupancy is large, for example, the foreground region occupancy is 0.8, which indicates that the area occupied by the foreground image in the current frame image is large, the foreground probability information may be mapped, and the probability in the foreground probability information is adaptively mapped to be a smoother probability. After the foreground probability information is mapped, the image segmentation result corresponding to the current frame image is obtained according to the mapped foreground probability information.

And S102, determining the processed foreground image according to the image segmentation result, and determining the region to be processed in the processed foreground image according to the processed foreground image.

And clearly determining which pixel points in the current frame image belong to the foreground image and which pixel points belong to the background image according to the image segmentation result so as to determine the processed foreground image, and then identifying the processed foreground image so as to determine the region to be processed in the processed foreground image. Specifically, the image recognition method in the prior art can be used for recognizing the processed foreground image, and the trained recognition network can be used for recognizing the region to be processed in the processed foreground image. Because the recognition network is trained, the processed foreground image is input into the recognition network, and the region to be processed in the processed foreground image can be conveniently obtained. Taking a specific object as an example of a human body, the region to be processed may include regions such as a limb region and a face region of the human body, where the face region may specifically include a five sense organs region and regions corresponding to parts such as a cheek, a forehead, and a chin, and the like, where the five sense organs region may generally refer to regions of each part such as an eyebrow in the face region, and specifically, the five sense organs region may include: eyebrow, eyes, ears, nose and mouth.

And step S103, drawing a crossing effect map corresponding to the area to be processed according to the time processing parameters.

And after the area to be processed is determined, drawing a corresponding crossing effect map for the area to be processed according to the time processing parameter. A person skilled in the art can set a crossing effect map for the region to be processed according to actual needs, which is not limited herein. Wherein, the crossing effect map can comprise one or more of the following maps: a dress effect map, a decorative effect map, a texture effect map, and a face decorating effect map. Specifically, the clothing effect map refers to an effect map corresponding to a worn clothing, the decoration effect map may include effect maps corresponding to decorations such as jewelry, watches, ornaments and the like, the texture effect map includes maps with different texture effects, and the face decorating effect map may include effect maps corresponding to eye shadow, lip gloss, blush and the like. The crossing effect map may also include other effect maps, and those skilled in the art may set the effect maps according to actual needs, which is not limited herein.

For example, when the specific object is a human body, the obtained time processing parameters are time parameters corresponding to the era, and the region to be processed includes a body region and a face region of the human body, then a clothing effect map and a decoration effect map corresponding to the era may be drawn for the body region, a face decorating effect map corresponding to the era may be drawn for the face region, and the like according to the time processing parameters. For another example, when the specific object is a human body, the obtained time processing parameter is a time parameter corresponding to 10 years ago, and the region to be processed is a body region of the human body, then a clothing effect map and a decoration effect map corresponding to 10 years ago can be drawn for the body region according to the time processing parameter. For another example, when the specific object is a human body, the acquired time processing parameter is a time parameter corresponding to 20 years later, and the region to be processed is a facial region of the human body, a facial makeup effect map corresponding to 20 years later may be drawn for the facial region according to the time parameter, and the facial makeup effect map may have a wrinkle effect or the like.

And step S104, fusing the crossing effect map and the processed foreground image to obtain a frame processing image.

After the crossing effect map is obtained through drawing, the crossing effect map and the processed foreground image are fused, so that the crossing effect map can be truly and accurately fused with a to-be-processed area of a specific object in the processed foreground image, and a frame processing image is obtained.

Step S105, covering the frame processing image on the current frame image to obtain processed video data.

The original current frame image is directly covered by the frame processing image, and the processed video data can be directly obtained. Meanwhile, the recorded user can also directly see the frame processing image.

When the frame processing image is obtained, the frame processing image is directly covered on the original current frame image. The covering is faster, and is generally completed within 1/24 seconds. For the user, since the time of the overlay processing is relatively short, the human eye does not perceive the process of overlaying the original current frame image in the video data. Therefore, when the processed video data is subsequently displayed, the processed video data is displayed in real time while the video data is shot and/or recorded and/or played, and a user cannot feel the display effect of covering the frame image in the video data.

And step S106, displaying the processed video data.

After the processed video data is obtained, the processed video data can be displayed in real time, and a user can directly see the display effect of the processed video data.

According to the video crossing processing method based on the adaptive threshold segmentation provided by the embodiment, the foreground probability information aiming at a specific object is mapped according to the foreground region proportion, the adaptive mapping of the foreground probability information is realized, the image segmentation result corresponding to the frame image can be quickly and accurately obtained by using the mapped foreground probability information, the segmentation precision and the processing efficiency of image scene segmentation are effectively improved, the image scene segmentation processing mode is optimized, the crossing effect can be more accurately and quickly added to the region to be processed of the frame image based on the obtained image segmentation result, the video with the crossing effect is obtained, the video data display effect is beautified, and the video data processing efficiency is improved.

Fig. 2 is a flowchart illustrating a video crossing processing method based on adaptive threshold segmentation according to another embodiment of the present invention, as shown in fig. 2, the method includes the following steps:

step S200, acquiring a current frame image containing a specific object and time processing parameters in a video shot and/or recorded by the image acquisition equipment in real time.

Step S201, performing scene segmentation processing on the current frame image to obtain foreground probability information aiming at a specific object, and determining the foreground area ratio according to the foreground probability information.

The method comprises the steps of determining pixel points belonging to a foreground image according to foreground probability information, then calculating the proportion of the pixel points belonging to the foreground image in all the pixel points in a current frame image, and determining the proportion as a foreground area ratio. Specifically, the foreground probability information records a probability for reflecting that each pixel in the current frame image belongs to the foreground image, and a value range of the probability for each pixel to belong to the foreground image may be [0, 1], so that a pixel with a probability higher than a preset probability threshold in the foreground probability information may be determined as a pixel belonging to the foreground image. The skilled person can set the preset probability threshold according to actual needs, and the setting is not limited herein. For example, when the preset probability threshold is 0.7, the pixel point with foreground probability information higher than 0.7 may be determined as the pixel point belonging to the foreground image. After the pixels belonging to the foreground image are determined, the number of the pixels belonging to the foreground image and the number of all pixels in the current frame image can be calculated, and the ratio of the number of the pixels belonging to the foreground image to the number of all pixels is the foreground region ratio.

And step S202, adjusting parameters of the mapping function according to the foreground area ratio, and performing mapping processing on the foreground probability information by using the adjusted mapping function to obtain a mapping result.

The mapping function may be used to map the foreground probability information, and a person skilled in the art may set the mapping function according to actual needs, which is not limited herein. For example, the mapping function may be a piecewise linear transformation function or a non-linear transformation function. And for different foreground area ratios, the parameters of the corresponding mapping functions are different. After the mapping function is adjusted, the foreground probability information can be used as an independent variable of the adjusted mapping function, and the obtained function value is the mapping result.

Specifically, when the foreground region occupies a smaller area, it indicates that the area occupied by the foreground image in the current frame image is smaller, and then in step S202, the parameters of the mapping function are adjusted according to the foreground region occupation ratio, so that when the foreground probability information is mapped by using the adjusted mapping function, the smaller probability in the foreground probability information can be adaptively mapped to a larger probability, and the larger probability in the foreground probability information can be adaptively mapped to a smoother probability; when the foreground region accounts for a relatively large area, which indicates that the area of the foreground image in the current frame image is relatively large, in step S202, the parameters of the mapping function are adjusted according to the foreground region accounts, so that when the adjusted mapping function is used to map the foreground probability information, the probability in the foreground probability information can be adaptively mapped to a relatively smooth probability.

And the slope of the mapping function in the preset defined interval is greater than a preset slope threshold value. A person skilled in the art may set the preset definition interval and the preset slope threshold according to actual needs, which is not limited herein, for example, when the preset definition interval is (0, 0.5) and the preset slope threshold is 1, the slope of the mapping function in the definition interval (0, 0.5) is greater than 1, so that a smaller probability in the foreground probability information can be adaptively mapped to a larger probability, for example, 0.1 is mapped to 0.3.

Taking the mapping function as a non-linear transformation function as an example, in a specific embodiment, the specific formula may be as follows:

y＝1/(1+exp(-(k*x-a)))

the foreground region proportion is a foreground region proportion, k is a first parameter, a is a second parameter, specifically, the first parameter is a parameter which needs to be adjusted according to the foreground region proportion, and the second parameter is a preset fixed parameter. Assuming that the foreground region occupancy is represented by the parameter r, k may be set to 2/r and a may be set to 4, so that the corresponding value of k may be different for different foreground region occupancies.

Step S203, according to the mapping result, obtaining the image segmentation result corresponding to the current frame image.

After the mapping result is obtained, an image segmentation result corresponding to the current frame image can be obtained according to the mapping result. Compared with the prior art, the image segmentation result corresponding to the current frame image obtained according to the mapping result has higher segmentation precision and smoother segmentation edge.

And step S204, determining the processed foreground image according to the image segmentation result, and determining the region to be processed in the processed foreground image according to the processed foreground image.

In step S205, key information of the region to be processed is extracted from the region to be processed.

The key information may be key point information, key area information, and/or key line information. The embodiment of the present invention is described by taking the key information as the key point information as an example, but the key information of the present invention is not limited to the key point information. The processing speed and efficiency of drawing the traversing effect map according to the key point information can be improved by using the key point information, the traversing effect map can be directly drawn according to the key point information, and complex operations such as subsequent calculation, analysis and the like on the key information are not needed. Meanwhile, the key point information is convenient to extract and accurate in extraction, so that the effect of drawing the crossing effect map is more accurate. Specifically, the key point information of the edge of the region to be processed may be extracted from the region to be processed.

And step S206, drawing a crossing effect map corresponding to the area to be processed according to the time processing parameters and the key information of the area to be processed.

In order to draw the crossing effect map conveniently and quickly, a plurality of basic crossing effect maps can be drawn in advance, so that when the crossing effect map corresponding to the area to be processed is drawn, the corresponding basic crossing effect map can be found firstly, and then the basic crossing effect map is processed, so that the crossing effect map can be obtained quickly. In addition, in order to manage the basic traversing effect maps, an effect map library may be established and stored in the effect map library.

Specifically, taking the key information as the key point information as an example, after the key point information of the region to be processed is extracted from the region to be processed, the basic crossing effect map matched with the key point information can be searched according to the time processing parameter, then the position information between at least two key points with a symmetrical relationship is calculated according to the key point information, and then the basic crossing effect map is processed according to the position information to obtain the crossing effect map. Through the method, the traversing effect map can be accurately drawn.

The method can automatically search a basic crossing effect map matched with key point information from an effect map library according to time processing parameters and the extracted key point information, takes a region to be processed as a body region image as an example, the time processing parameters are time parameters corresponding to the Qing Dynasty, the extracted key point information is the key point information of the body of a human body, and then searches the basic crossing effect map matched with the key point information of the body from the effect map library according to the time processing parameters, namely, the basic crossing effect map is equivalent to the search of a clothing effect map corresponding to the Qing Dynasty. In addition, in practical application, in order to facilitate the use of the user and better meet the personalized requirements of the user, the basic crossing effect map corresponding to the time processing parameter and contained in the effect map library can be displayed to the user, and the user can select the basic crossing effect map according to the preference of the user, so that the method can obtain the basic crossing effect map corresponding to the operation selected by the user.

The position information may include distance information and rotation angle information, and specifically, the basic crossing effect map may be scaled according to the distance information in the position information, and/or the basic crossing effect map may be rotated according to the rotation angle information in the position information, so as to obtain a crossing effect map corresponding to the to-be-processed region.

And step S207, fusing the crossing effect mapping, the processed foreground image and the processed background image determined according to the image segmentation result to obtain a frame processing image.

Specifically, fusion position information corresponding to the crossing effect map can be determined according to the key information of the region to be processed, and then the crossing effect map, the processed foreground image and the processed background image (i.e., the original background image of the current frame image) determined according to the image segmentation result are fused according to the fusion position information to obtain a frame processing image.

Step S208, covering the frame processing image on the current frame image to obtain the processed video data.

In step S209, the processed video data is displayed.

And step S210, uploading the processed video data to a cloud server.

The processed video data can be directly uploaded to a cloud server, and specifically, the processed video data can be uploaded to one or more cloud video platform servers, such as a cloud video platform server for love art, Youkou, fast video and the like, so that the cloud video platform servers can display the video data on a cloud video platform. Or the processed video data can be uploaded to a cloud live broadcast server, and when a user at a live broadcast watching end enters the cloud live broadcast server to watch, the video data can be pushed to a watching user client in real time by the cloud live broadcast server. Or the processed video data can be uploaded to a cloud public server, and when a user pays attention to the public, the cloud public server pushes the video data to a public client; further, the cloud public number server can push video data conforming to user habits to the public number attention client according to the watching habits of users paying attention to the public numbers.

According to the video crossing processing method based on adaptive threshold segmentation provided by the embodiment, parameters of mapping functions can be adjusted according to the ratio of foreground regions, so that when the ratio of foreground regions is different, the parameters of corresponding mapping functions are different, and adaptive mapping of foreground probability information according to the ratio of foreground regions is realized; the image segmentation result corresponding to the frame image can be quickly and accurately obtained by utilizing the mapping result, so that the segmentation precision and the processing efficiency of image scene segmentation are effectively improved, and the segmentation edge is smoother; moreover, based on the obtained image segmentation result, a crossing effect can be added to the to-be-processed area of the frame image more accurately and quickly, a video with the crossing effect is obtained, and the video data display effect is beautified; in addition, the crossing effect map can be accurately zoomed and rotated according to the extracted key information of the to-be-processed area, so that the crossing effect map is more suitable for a specific object, and the video data display effect is further improved.

Fig. 3 is a block diagram illustrating a structure of an adaptive threshold segmentation based video crossing processing apparatus according to an embodiment of the present invention, as shown in fig. 3, the apparatus including: an acquisition module 310, a segmentation module 320, a determination module 330, a rendering module 340, a fusion processing module 350, an overlay module 360, and a display module 370.

The acquisition module 310 is adapted to: the method comprises the steps of acquiring a current frame image and time processing parameters of a specific object in a video shot and/or recorded by image acquisition equipment in real time.

The segmentation module 320 is adapted to: the method comprises the steps of carrying out scene segmentation processing on a current frame image to obtain foreground probability information aiming at a specific object, determining a foreground region proportion according to the foreground probability information, and carrying out mapping processing on the foreground probability information according to the foreground region proportion to obtain an image segmentation result corresponding to the current frame image.

The foreground probability information records the probability of each pixel point in the current frame image belonging to the foreground image. The segmentation module 320 is further adapted to: determining pixel points belonging to the foreground image according to the foreground probability information; and calculating the proportion of the pixel points belonging to the foreground image in all the pixel points in the current frame image, and determining the proportion as the foreground area ratio. Specifically, the segmentation module 320 determines the pixel points with the probability higher than the preset probability threshold in the foreground probability information as the pixel points belonging to the foreground image.

Optionally, the segmentation module 320 is further adapted to: adjusting parameters of the mapping function according to the ratio of the foreground area; mapping the foreground probability information by using the adjusted mapping function to obtain a mapping result; and obtaining an image segmentation result corresponding to the current frame image according to the mapping result. And the slope of the mapping function in the preset defined interval is greater than a preset slope threshold value.

The determination module 330 is adapted to: and determining the processed foreground image according to the image segmentation result, and determining a region to be processed in the processed foreground image according to the processed foreground image.

The rendering module 340 is adapted to: and drawing a through effect map corresponding to the region to be processed according to the time processing parameters.

Wherein the crossing effect map comprises one or more of the following maps: a dress effect map, a decorative effect map, a texture effect map, and a face decorating effect map. Optionally, the rendering module 340 is further adapted to: extracting key information of the area to be processed from the area to be processed; and drawing a crossing effect map corresponding to the area to be processed according to the time processing parameters and the key information of the area to be processed.

The key information may specifically be key point information, key area information, and/or key line information. The embodiment of the present invention is described by taking key information as key point information as an example. The rendering module 350 is further adapted to: searching a basic traversing effect map matched with the key point information according to the time processing parameters; calculating position information between at least two key points with a symmetrical relation according to the key point information; and processing the basic crossing effect map according to the position information to obtain a crossing effect map.

The rendering module 350 is further adapted to: zooming the basic crossing effect map according to the distance information in the position information; and/or performing rotation processing on the basic crossing effect map according to the rotation angle information in the position information.

The fusion processing module 350 is adapted to: and carrying out fusion processing on the crossing effect map and the processed foreground image to obtain a frame processing image.

Wherein the fusion processing module 350 is further adapted to: and fusing the traversing effect mapping, the processed foreground image and the background image determined according to the image segmentation result to obtain a frame processing image.

The overlay module 360 is adapted to: and covering the frame processing image on the current frame image to obtain processed video data.

The display module 370 is adapted to: and displaying the processed video data.

The apparatus may further comprise: and the uploading module 380 is adapted to upload the processed video data to the cloud server.

The uploading module 380 can directly upload the processed video data to a cloud server, specifically, the uploading module 380 can upload the processed video data to one or more cloud video platform servers, such as a cloud video platform server for love art, super and cool, fast video and the like, so that the cloud video platform servers can display the video data on a cloud video platform. Or the uploading module 380 can also upload the processed video data to the cloud live broadcast server, and when a user at a live broadcast watching end enters the cloud live broadcast server to watch, the cloud live broadcast server can push the video data to a watching user client in real time. Or the uploading module 380 can also upload the processed video data to a cloud public server, and when a user pays attention to the public, the cloud public server pushes the video data to a public client; further, the cloud public number server can push video data conforming to user habits to the public number attention client according to the watching habits of users paying attention to the public numbers.

According to the video crossing processing device based on the adaptive threshold segmentation provided by the embodiment, the foreground probability information aiming at a specific object is mapped according to the foreground region proportion, the adaptive mapping of the foreground probability information is realized, the image segmentation result corresponding to the frame image can be quickly and accurately obtained by using the mapped foreground probability information, the segmentation precision and the processing efficiency of image scene segmentation are effectively improved, the image scene segmentation processing mode is optimized, the crossing effect can be more accurately and quickly added to the region to be processed of the frame image based on the obtained image segmentation result, the video with the crossing effect is obtained, the video data display effect is beautified, and the video data processing efficiency is improved.

The invention also provides a nonvolatile computer storage medium, and the computer storage medium stores at least one executable instruction, and the executable instruction can execute the video crossing processing method based on adaptive threshold segmentation in any method embodiment.

Fig. 4 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.

As shown in fig. 4, the computing device may include: a processor (processor)402, a Communications Interface 404, a memory 406, and a Communications bus 408.

Wherein:

the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408.

A communication interface 404 for communicating with network elements of other devices, such as clients or other servers.

The processor 402 is configured to execute the program 410, and may specifically execute the relevant steps in the above-described video traversal processing method embodiment based on adaptive threshold segmentation.

In particular, program 410 may include program code comprising computer operating instructions.

The processor 402 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.

And a memory 406 for storing a program 410. Memory 406 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

The program 410 may be specifically configured to enable the processor 402 to execute the video traversal processing method based on adaptive threshold segmentation in any of the method embodiments described above. For specific implementation of each step in the program 410, reference may be made to corresponding steps and corresponding descriptions in units in the foregoing video traversal processing embodiment based on adaptive threshold segmentation, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.

The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components in accordance with embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

Claims

1. A method of video traversal processing based on adaptive threshold segmentation, the method comprising:

performing scene segmentation processing on the current frame image to obtain foreground probability information for a specific object, determining foreground region proportion according to the foreground probability information, and performing mapping processing on the foreground probability information according to the foreground region proportion to obtain an image segmentation result corresponding to the current frame image;

drawing a crossing effect map corresponding to the area to be processed according to the time processing parameter;

displaying the processed video data;

wherein, the mapping the foreground probability information according to the foreground region ratio to obtain the image segmentation result corresponding to the current frame image further comprises:

adjusting parameters of a mapping function according to the foreground area ratio;

2. The method of claim 1, wherein the foreground probability information records a probability for reflecting that each pixel point in the current frame image belongs to a foreground image.

3. The method of claim 1, wherein the traversal effect map comprises one or more of the following maps: a dress effect map, a decorative effect map, a texture effect map, and a face decorating effect map.

4. The method according to any one of claims 1-3, wherein the drawing a traversal effect map corresponding to the region to be processed according to the time processing parameter further comprises:

and drawing a crossing effect map corresponding to the area to be processed according to the time processing parameter and the key information of the area to be processed.

5. The method of claim 4, wherein the key information is key point information;

the step of drawing a crossing effect map corresponding to the to-be-processed area according to the time processing parameter and the key information of the to-be-processed area further comprises the following steps:

searching a basic traversing effect map matched with the key point information according to the time processing parameter;

6. The method of claim 5, wherein the processing the base traversal effect map according to the location information to obtain a traversal effect map further comprises:

7. The method according to any one of claims 1-3, wherein the fusing the traversal effect map with the processed foreground image to obtain a frame processing image further comprises:

and carrying out fusion processing on the crossing effect map, the processed foreground image and the processed background image determined according to the image segmentation result to obtain a frame processing image.

8. The method of any of claims 1-3, wherein the determining a foreground region proportion from the foreground probability information further comprises:

9. The method of claim 8, wherein said determining pixel points belonging to a foreground image according to the foreground probability information further comprises:

10. The method of claim 1, wherein a slope of the mapping function within a preset defined interval is greater than a preset slope threshold.

11. The method of any of claims 1-3, wherein the displaying the processed video data further comprises: displaying the processed video data in real time;

12. The method of claim 11, wherein the uploading the processed video data to a cloud server further comprises:

13. The method of claim 11, wherein the uploading the processed video data to a cloud server further comprises:

14. The method of claim 11, wherein the uploading the processed video data to a cloud server further comprises:

15. A video crossing processing device based on adaptive threshold segmentation, the device comprising:

the segmentation module is suitable for carrying out scene segmentation processing on the current frame image to obtain foreground probability information aiming at a specific object, determining foreground region proportion according to the foreground probability information, and carrying out mapping processing on the foreground probability information according to the foreground region proportion to obtain an image segmentation result corresponding to the current frame image;

the determining module is suitable for determining a processed foreground image according to the image segmentation result and determining a region to be processed in the processed foreground image according to the processed foreground image;

the covering module is suitable for covering the frame processing image on the current frame image to obtain processed video data;

the display module is suitable for displaying the processed video data;

wherein the segmentation module is further adapted to:

16. The apparatus of claim 15, wherein the foreground probability information records a probability for reflecting that each pixel in the current frame image belongs to a foreground image.

17. The apparatus of claim 15, wherein the ride-through effect map comprises one or more of the following maps: a dress effect map, a decorative effect map, a texture effect map, and a face decorating effect map.

18. The apparatus of any one of claims 15-17, wherein the rendering module is further adapted to:

19. The apparatus of claim 18, wherein the key information is key point information;

the rendering module is further adapted to:

20. The apparatus of claim 19, wherein the rendering module is further adapted to:

21. The apparatus according to any one of claims 15-17, wherein the fusion processing module is further adapted to:

and fusing the crossing effect map, the processed foreground image and a background image determined according to the image segmentation result to obtain a frame processing image.

22. The apparatus of any one of claims 15-17, wherein the segmentation module is further adapted to:

23. The apparatus of claim 22, wherein the segmentation module is further adapted to:

24. The apparatus of claim 15, wherein a slope of the mapping function within a preset defined interval is greater than a preset slope threshold.

25. The apparatus of any one of claims 15-17, wherein the display module is further adapted to: displaying the processed video data in real time;

the device further comprises: and the uploading module is suitable for uploading the processed video data to the cloud server.

26. The apparatus of claim 25, wherein the upload module is further adapted to:

27. The apparatus of claim 25, wherein the upload module is further adapted to:

28. The apparatus of claim 25, wherein the upload module is further adapted to:

29. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;

the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the adaptive threshold segmentation based video traversal processing method of any of claims 1-14.

30. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the adaptive threshold segmentation based video traversal processing method as claimed in any one of claims 1-14.