WO2020192195A1 - Image processing method and apparatus, and electronic device - Google Patents

Abstract

Disclosed are an image processing method and apparatus, and an electronic device. The image processing method comprises: obtaining a video image from an image source, the video image comprising at least one target object; segmenting the target object from the video image; obtaining at least one first point on the target object; connecting identical first points in adjacent multi-frame image frames to calculate the trajectory of the first points; and obtaining an image processing parameter and processing the trajectory of the first points to obtain a processed trajectory. According to the image processing method in embodiments of the present invention, the movement trajectory of points on the target object is calculated, and a preset image processing approach is used for processing the trajectory to generate the special effect thereof, which improves the efficiency and flexibility in generating the special effect.

Description

Image processing method, device and electronic equipment

Cross references to related applications

This application claims the priority of the Chinese patent application filed on March 26, 2019 with the application number 201910230223.X and the invention title "Image processing method, device and electronic equipment". The full text of this application is incorporated into this by reference. Applying.

Technical field

The present disclosure relates to the field of image processing, and in particular to an image processing method, device and electronic equipment.

Background technique

With the development of computer technology, the application range of smart terminals has been extensively improved. For example, smart terminals can be used to listen to music, play games, chat online, and take photos. For the camera technology of the smart terminal, the camera pixel has reached more than 10 million pixels, with higher definition and the camera effect comparable to professional cameras.

At present, when using smart terminals to take pictures, not only can the camera software built-in at the factory be used to achieve the traditional functions of the camera, but also can be downloaded from the network application (Application, referred to as: APP) to achieve the camera effect with additional functions , Such as apps that can realize dark light detection, beauty camera, and super pixel functions. The beauty function of the smart terminal usually includes skin color adjustment, skin grinding, big eyes and face thinning, etc., which can perform the same degree of beauty processing on all faces that have been identified in the image. There are also APPs that can implement simple special effects.

However, the current special effects function can only pre-set the special effects and synthesize them into the video or image. If you need to modify the special effects, you need to recreate the special effects and then synthesize them into the video or image, making the generation of special effects very inflexible.

Summary of the invention

According to one aspect of the present disclosure, the following technical solutions are provided:

An image processing method, including:

Acquiring a video image from an image source, where the video image includes at least one target object;

Segmenting the target object from the video image;

Acquiring at least one first point on the target object;

Connecting the same first point in multiple adjacent image frames to calculate the trajectory of the first point;

Obtain image processing parameters and process the trajectory of the first point to obtain a processed trajectory.

Further, said acquiring a video image from an image source, wherein the video image includes at least one target object, including:

Obtain a video image from a video capture device, the video image includes multiple image frames, and the image frame includes at least one target object.

Further, the segmenting the target object from the video image includes:

Identifying the target object in the video image;

Segmenting out the contour of the target object;

The contour of the target object and the image within the contour are extracted from the video image.

Further, the acquiring at least one first point on the target object includes:

Acquire at least one corner point on the target object, where the corner point is an intersection of two edges on the contour of the target object.

Further, the acquiring at least one first point on the target object includes:

Randomly select at least one random point on the target object.

Further, the connecting the same first point in multiple adjacent image frames to calculate the trajectory of the first point includes:

Sort the first points according to their numbers;

Identify the first point of the same number in multiple frames of images;

Connecting the first points of the same number to generate the trajectory of the first point of the number.

Further, the acquiring image processing parameters and processing the trajectory of the first point to obtain the processed trajectory includes:

Obtain the image processing mode and image processing resources from the image processing configuration file;

The trajectory of the first point is processed according to the image processing mode and the image processing resource to obtain a processed trajectory.

Further, after the acquiring image processing parameters and processing the trajectory of the first point to obtain the processed trajectory, the method further includes:

Obtaining at least one second point on the target object after a predetermined number of image frames have passed;

Connecting the same second point in multiple adjacent image frames to calculate the trajectory of the second point;

Obtain image processing parameters and process the trajectory of the second point to obtain a processed trajectory.

Further, the acquiring image processing parameters and processing the trajectory of the first point to obtain the processed trajectory includes:

Obtain image processing modes and image processing resources with different numbers from image processing configuration files;

According to the number of the first point generating the trajectory, the trajectory of the numbered first point is processed by using the image processing mode and image processing resource corresponding to the number to obtain the processed trajectory.

According to another aspect of the present disclosure, the following technical solutions are also provided:

An image processing device including:

An image acquisition module for acquiring a video image from an image source, wherein the video image includes at least one target object;

A segmentation module for segmenting the target object from the video image;

A target object point acquisition module, configured to acquire at least one first point on the target object;

A trajectory calculation module, configured to connect the same first point in multiple adjacent image frames to calculate the trajectory of the first point;

The trajectory processing module is used to obtain image processing parameters and process the trajectory of the first point to obtain the processed trajectory.

Further, the image acquisition module is also used for:

Obtain a video image from a video capture device, the video image includes multiple image frames, and the image frame includes at least one target object.

Further, the segmentation module further includes:

The target object recognition module is used to recognize the target object in the video image;

A contour segmentation module for segmenting the contour of the target object;

The target object extraction module is used to extract the contour of the target object and the image within the contour from the video image.

Further, the target object point acquisition module further includes:

The corner point acquisition module is used to acquire at least one corner point on the target object, where the corner point is the intersection of two edges on the contour of the target object.

Further, the target object point acquisition module further includes:

The random point acquisition module is used to randomly select at least one random point on the target object.

Further, the trajectory calculation module further includes:

A sorting module, configured to sort the first points according to their numbers;

The first point recognition module is used to recognize the first point with the same number in multiple frames of images;

The connection module is used to connect the first points of the same number to generate the track of the first point of the same number.

Further, the trajectory processing module further includes:

The processing parameter acquisition module is used to acquire the image processing mode and image processing resources from the image processing configuration file;

The first point trajectory processing module is configured to process the trajectory of the first point according to the image processing mode and image processing resources to obtain a processed trajectory.

Further, the device further includes:

The second target object point acquiring module is configured to acquire at least one second point on the target object after a predetermined number of image frames have passed;

The second connection module is configured to connect the same second point in multiple adjacent image frames to calculate the trajectory of the second point;

The second point trajectory processing module is used to obtain image processing parameters and process the trajectory of the second point to obtain a processed trajectory.

Further, the trajectory processing module further includes:

The second processing parameter acquisition module is used to acquire image processing modes and image processing resources with different numbers from the image processing configuration file;

The multi-track processing module is configured to process the track of the first point of the number by using the image processing mode and image processing resources corresponding to the number according to the number of the first point of the generated track to obtain the processed track .

According to another aspect of the present disclosure, the following technical solutions are also provided:

An electronic device, comprising: a memory for storing non-transitory computer readable instructions; and a processor for running the computer readable instructions, so that when the processor is executed, the above-mentioned image processing method A step of.

According to another aspect of the present disclosure, the following technical solutions are also provided:

A computer-readable storage medium for storing non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are executed by a computer, the computer can execute the steps in any of the above methods.

The present disclosure discloses an image processing method, device and electronic equipment. Wherein, the image processing method includes: acquiring a video image from an image source, wherein the video image includes at least one target object; segmenting the target object from the video image; acquiring at least one first object on the target object One point; connecting the same first point in multiple adjacent image frames to calculate the trajectory of the first point; acquiring image processing parameters and processing the trajectory of the first point to obtain a processed trajectory. The image processing method of the embodiment of the present disclosure calculates the movement trajectory of a point on the target object, and uses a preset image processing method to process the trajectory to generate a special effect of the trajectory, thereby improving the efficiency and flexibility of generating special effects.

The above description is only an overview of the technical solutions of the present disclosure. In order to understand the technical means of the present disclosure more clearly, they can be implemented in accordance with the content of the specification, and to make the above and other objectives, features and advantages of the present disclosure more obvious and understandable. In the following, the preferred embodiments are cited in conjunction with the drawings, and the detailed description is as follows.

Description of the drawings

Fig. 1 is a schematic flowchart of an image processing method according to an embodiment of the present disclosure;

2 is a schematic diagram of corner detection of a target object according to an embodiment of the present disclosure;

3 is a schematic flowchart of an image processing method according to another embodiment of the present disclosure;

Fig. 4 is a schematic structural diagram of an image processing device according to an embodiment of the present disclosure;

Fig. 5 is a schematic structural diagram of an electronic device provided according to an embodiment of the present disclosure.

detailed description

The following describes the implementation of the present disclosure through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and the features in the embodiments can be combined with each other if there is no conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present disclosure.

It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and/or function described herein are only illustrative. Based on the present disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspects, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement devices and/or methods of practice. In addition, other structures and/or functionalities other than one or more of the aspects set forth herein may be used to implement this device and/or practice this method.

It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present disclosure in a schematic manner. The figures only show the components related to the present disclosure rather than the number, shape, and shape of the components in actual implementation. For size drawing, the type, quantity, and ratio of each component can be changed at will during actual implementation, and the component layout type may also be more complicated.

In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects may be practiced without these specific details.

The embodiment of the present disclosure provides an image processing method. The image processing method provided in this embodiment can be executed by a computing device, the computing device can be implemented as software, or as a combination of software and hardware, and the computing device can be integrated in a server, terminal device, etc. As shown in FIG. 1, the image processing method mainly includes the following steps S101 to S105. among them:

Step S101: Obtain a video image from an image source, where the video image includes at least one target object;

In the present disclosure, the image source is a local storage space or a network storage space. The obtaining of video images from an image source includes obtaining video images from a local storage space or obtaining video images from a network storage space, no matter where it is obtained. For video images, it is first necessary to obtain the storage address of the video image, and then obtain the video image from the storage address. The video image includes multiple image frames. The video image can be a video or a picture with dynamic effects, as long as it is Images with multiple frames may all be video images in the present disclosure.

In the present disclosure, the video source may be a video capture device, such as an image sensor, and the acquisition of a video image from the image source includes acquiring a video image from the video capture device. The image sensor refers to various devices that can collect images, and typical image sensors are video cameras, cameras, and cameras. In this embodiment, the image sensor may be a camera on a mobile terminal, such as a front or rear camera on a smart phone. The video image collected by the camera may be directly displayed on the display screen of the phone. In this step, Obtain the video taken by the image sensor as the image to be processed.

Since the technical solution of the present disclosure needs to recognize the target object, at least one target object is included in the video image. The multiple image frames of the video image include at least one target object. It is understandable that the video image may also include multiple target objects, which will not be repeated here.

Step S101: segment the target object from the video image;

In the present disclosure, the segmenting the target object from the video image includes: identifying the target object in the video image; segmenting the contour of the target object; and dividing the contour and the contour of the target object The images inside are extracted from the video image.

In this step, the video image is an image frame in the video image, and the image frame is a picture with a target object. The target object can be any object. In this embodiment, the target object is human body.

Image segmentation is generally divided into interactive image segmentation and automatic image segmentation. Traditional image processing generally uses interactive image segmentation, which requires human participation in image segmentation. In the present disclosure, automatic image segmentation is used. The following uses human body image segmentation as an example to describe the automatic image segmentation.

Generally speaking, automatic human body image segmentation methods can be divided into the following categories: (1) Model-based human body image segmentation methods. For this method, the face is first detected based on the prior knowledge of the face, and then the torso model is used to find The torso under the face, and then estimate the position of the lower body based on the segmented torso, and finally use the estimated torso and upper leg regions to provide seed points for image segmentation to complete the segmentation of the human body image; (2) based on the hierarchical tree Human body image segmentation method. For this method, the neighboring body parts are first modeled, and then the entire body pose is modeled. The different poses of the human body are modeled as the sum of nodes on different paths in the hierarchical detection tree. Different layers in the hierarchical detection tree correspond to different models of adjacent human body parts, and follow different paths on the hierarchical detection tree, corresponding to different human postures. When detecting, follow the root node of the tree to detect downward and divide along different paths. Different postures of the human body; (3) An independent component analysis human body image segmentation method based on reference signals. For this method, the face is first detected based on the prior knowledge of the face, and then the torso model is used to find the torso under the face. Then obtain the reference signal from the detected torso, and then use the independent component analysis method of the reference signal to highlight the torso from the image to complete the segmentation of the torso. The segmentation of other body parts is similar, and the segmentation of the entire human body image is finally completed; (4) The human body image segmentation method based on the expectation maximization algorithm. For this method, first use the pattern structure model to estimate the human body posture in the image to obtain the probability map of the human body posture, and then use the image segmentation method on the basis of the probability map to get The final human segmentation image. In the above segmentation method, no matter which method generally needs to recognize the human body or a part of the human body, the outer contour of the human body is estimated or determined, and finally the image within the outer contour is extracted to obtain the segmented image of the human body.

It is understandable that other human body image segmentation methods can also be used, which will not be repeated in this disclosure. Any image segmentation method can be introduced into this disclosure to segment the target object from the video image.

Step S103: Acquire at least one first point on the target object;

In the present disclosure, the acquiring at least one first point on the target object includes: acquiring at least one corner point on the target object, where the corner point is an intersection of two edges on the contour of the target object. Optionally, the target object is a human body, and the corner points are the corner points of the clothes on the human body, as shown in Figure 2, where 201 is the corner point of the clothes on the shoulders of the human body, and 202 is the corner points of the clothes on the elbows of the human body. The corner point, 203 is the corner point of the clothing at the human knee. To obtain corner points on the target object, corner detection is first required. In this disclosure, corner detection can use any corner detection method, such as Harris corner detection algorithm, Moravec corner detection algorithm, FAST corner detection Algorithms and so on. The present disclosure does not limit which corner detection algorithm to use, as long as it is an algorithm that can quickly detect corners, it can be applied to the technical solutions of the present disclosure. Generally speaking, the detected corner point is the intersection of two edges on the contour of the target object. The corner point 203 in FIG. 2 is the intersection of the contours of the human thigh and calf. The detected corner point may be one point or multiple points. When the corner point is acquired, one or more of the detected corner points may be selected. When the corner point is selected, the The order of said corner points selects the first n corner points, where n≥1; the corner points obtained can also be determined according to certain criteria, such as the color of the corner point, the brightness difference with other surrounding points, etc., here No longer.

In the present disclosure, the acquiring at least one first point on the target object includes: randomly selecting at least one random point on the target object. Optionally, the target object is a human body. In this embodiment, at least one point is randomly selected on or within the contour of the human body. The selection of at least one point can be achieved by randomly scattering points within the contour of the human body. Optionally, a random function can be used to generate a random point. If the point is within the contour of the human body, the point is regarded as a random point; if the point is outside the contour of the human body, the point is discarded; The image in the contour of the human body is cut into several triangles, the range of the triangle can be determined, and dots are randomly scattered within the range of the triangle; optionally, the image in the contour of the human body can be cut into multiple squares of equal size using a quad tree. Then use the center of the square as a random point. The present disclosure does not limit the method of generating random points. Any method can be used to generate one or more random points in the contour of the human body, and then one or more random points are selected as the first point. When selecting the random points, the first n random points can be selected according to the order in which the random points are generated, where n≥1; or according to certain criteria, such as random points whose positions are within a predetermined range, etc. Repeat it again.

It is understandable that the acquisition of the first point is not limited to the above two methods. As long as it is a point on or within the contour of the target object, it can be used as the first point. The first point in the present disclosure can be acquired by various methods. , I won’t repeat it here.

Step S104: Connect the same first point in multiple adjacent image frames to calculate the trajectory of the first point;

In the present disclosure, the connecting the same first point in multiple adjacent image frames to calculate the trajectory of the first point includes: sorting the first points according to the numbers of the first points; Identifying the first point of the same number in the multiple frames of images; connecting the first points of the same number to generate the trajectory of the first point of the number. In this embodiment, the first point has a number, and the number can be a pre-numbered number. For example, when detecting a corner point, three fixed corner points can be set for detection. As shown in FIG. Set fixed numbers for 3 corner points, for example, the number of corner point 201 is set to 001, the number of corner point 202 is set to 002, and the number of corner point 203 is set to 003; in multi-frame images, the corner points with the same number are identified Here, the streamer method can be used to calculate the position of the corner point in the first image frame in the subsequent image frame. The typical streamer calculation method is LK algorithm, dense optical flow method, pyramid optical flow method, etc., use The optical flow method can easily track the same point in multiple image frames. The point can be a feature point such as a corner point, a random point or any other custom point. After the points of the same number are identified in the multiple image frames, the points of the same number are connected to generate the trajectory of the first point of the number. When the image frame sampling is relatively dense, the trajectory can present a relatively smooth motion trajectory of the target object.

It is understandable that in this step, the method of tracking the same point in multiple image frames is not limited to the above method, and any method that can track the same point in multiple image frames to form a motion track can be applied to the present disclosure In, not repeat them here.

Step S105: Obtain image processing parameters and process the trajectory of the first point to obtain a processed trajectory.

In the present disclosure, the acquiring image processing parameters and processing the trajectory of the first point to obtain the processed trajectory includes: acquiring an image processing mode and image processing resources from an image processing configuration file; and processing according to the image The pattern and image processing resources process the trajectory of the first point to obtain a processed trajectory. Wherein, the image processing configuration file is used to save the processing types of the image processing, such as textures, deformations, filters, etc., and the addresses of resources required for the image processing, such as textures and textures required in the texture processing. The location of the texture, the type of deformation and the deformation parameters in the deformation processing, the color card and the filter processing range in the filter processing, etc.; then the trajectory of the first point is processed according to the acquired processing mode and image processing resources Obtain a trajectory with a certain effect. Optionally, the image processing is to add a texture to the first point on the trajectory, the texture includes a plurality of texture frames, and the brightness of each texture frame changes cyclically from small to large. In this case, from the image processing The address of the texture required for texture processing is obtained in the configuration file, and the corresponding texture frame is obtained, and then the position of the first point in the image frame is obtained, and the texture frame is rendered to the first point in each The position of the texture frame to achieve the effect of the first point flashing in the multi-frame image. Optionally, the image processing is to add a rainbow effect texture to the trajectory. At this time, the address of the texture required for texture processing is obtained from the image processing configuration file, and the texture here is a fixed size Paste the center line of the rectangular rainbow graph to the track, and then paste the rainbow graph to the entire track in turn to achieve the effect of the rainbow track. It is understandable that the image processing here can be any processing, as long as the image processing that can process the trajectory of the first point or the point on the trajectory can be applied to the present disclosure, the specific processing type and processing type The corresponding parameters can be pre-set in the image processing configuration file, so that by modifying the configuration file, the motion track can be rendered to achieve different effects, and the effect is rendered in real time to the motion track, without the need to record the video in advance and pass the post The production plus the effect is more convenient and quicker than traditional methods.

In the present disclosure, the acquiring image processing parameters and processing the trajectory of the first point to obtain the processed trajectory may further include: acquiring image processing modes and image processing resources with different numbers from an image processing configuration file; According to the number of the first point generating the trajectory, the trajectory of the numbered first point is processed by using the image processing mode and image processing resource corresponding to the number to obtain the processed trajectory. Wherein, the image processing configuration file includes multiple processing modes and resources corresponding to the processing modes, and the multiple processing modes are corresponded to the number of the first point in the image processing configuration file, so that for the first point of different numbers The trajectory generated by one point can be processed in different image processing modes, such as using flicker mapping for the trajectory generated by the corner point numbered 001, and using rainbow mapping for the trajectory generated by the corner point numbered 002. The trajectory can be processed in different ways. In addition, for the case where multiple target objects are recognized in the video image, it can also be pre-configured in the configuration file to realize the distinction processing of multiple target objects and multiple trajectories. The specific configuration method is similar to the above, only the target object and the target need to be distinguished. The different trajectories of objects will not be repeated here.

As shown in FIG. 3, it is a schematic flowchart of another embodiment of the present disclosure. In this embodiment, segmentation processing is performed on the video image, that is, the video image is divided into several stages, and different processing is performed on each stage. Specifically, after the step S105, it further includes:

Step S301: After a predetermined number of image frames have passed, acquire at least one second point on the target object;

Step S302: Connect the same second point in multiple adjacent image frames to calculate the trajectory of the second point;

Step S303: Obtain image processing parameters and process the trajectory of the second point to obtain a processed trajectory.

In this embodiment, a predetermined number of image frames can be preset as the number of processed frames, such as setting M frames as the number of processed frames, that is to say, each image processing is bounded by M frames, and the processing target is switched after M frames are exceeded. The trajectory and/or processing mode. Optionally, the second point can be the same as the first point. At this time, the same method as the first point can be used to continue to obtain the second point, and the second point is the same as the first point. The processing mode uses a different processing method than the previous M frames to process the trajectory generated by the same point, which presents a segmented trajectory effect. Optionally, the second point may be different from the first point. At this time, after M frames are exceeded, the second point is re-acquired to form a new trajectory. At this time, the effect of different trajectories with the same processing or different trajectories with different processing can be formed . For the specific processing configuration and processing method, please refer to the description in step S105, and the configuration method here is the same as it, and will not be repeated.

The present disclosure discloses an image processing method, device and electronic equipment. Wherein, the image processing method includes: acquiring a video image from an image source, wherein the video image includes at least one target object; segmenting the target object from the video image; acquiring at least one first object on the target object One point; connecting the same first point in multiple adjacent image frames to calculate the trajectory of the first point; acquiring image processing parameters and processing the trajectory of the first point to obtain a processed trajectory. The image processing method of the embodiment of the present disclosure calculates the movement trajectory of a point on the target object, and uses a preset image processing method to process the trajectory to generate a special effect of the trajectory, thereby improving the efficiency and flexibility of generating special effects.

In the above, although the steps in the above method embodiments are described in the above order, those skilled in the art should understand that the steps in the embodiments of the present disclosure are not necessarily executed in the above order, and they can also be reversed, parallel, or interleaved. Other steps are executed in other order, and on the basis of the above steps, those skilled in the art can also add other steps. These obvious modifications or equivalent substitutions should also be included in the protection scope of the present disclosure, and will not be repeated here. .

The following are device embodiments of the present disclosure. The device embodiments of the present disclosure can be used to perform the steps implemented by the method embodiments of the present disclosure. For ease of description, only the parts related to the embodiments of the present disclosure are shown. For specific technical details that are not disclosed, please Refer to the method embodiment of the present disclosure.

The embodiment of the present disclosure provides an image processing device. The device can execute the steps described in the above image processing method embodiment. As shown in FIG. 4, the device 400 mainly includes: an image acquisition module 401, a segmentation module 402, a target object point acquisition module 403, a trajectory calculation module 404, and a trajectory processing module 405. among them,

The image acquisition module 401 is configured to acquire a video image from an image source, where the video image includes at least one target object;

A segmentation module 402, configured to segment the target object from the video image;

The target object point acquiring module 403 is configured to acquire at least one first point on the target object;

The trajectory calculation module 404 is configured to connect the same first point in multiple adjacent image frames to calculate the trajectory of the first point;

The trajectory processing module 405 is configured to obtain image processing parameters and process the trajectory of the first point to obtain a processed trajectory.

Further, the image acquisition module 401 is also used for:

Obtain a video image from a video capture device, the video image includes multiple image frames, and the image frame includes at least one target object.

Further, the segmentation module 402 further includes:

The target object recognition module is used to recognize the target object in the video image;

A contour segmentation module for segmenting the contour of the target object;

The target object extraction module is used to extract the contour of the target object and the image within the contour from the video image.

Further, the target object point acquisition module 403 further includes:

The corner point acquisition module is used to acquire at least one corner point on the target object, where the corner point is the intersection of two edges on the contour of the target object.

Further, the target object point acquisition module 403 further includes:

The random point acquisition module is used to randomly select at least one random point on the target object.

Further, the trajectory calculation module 404 further includes:

A sorting module, configured to sort the first points according to their numbers;

The first point recognition module is used to recognize the first point with the same number in multiple frames of images;

The connection module is used to connect the first points of the same number to generate the track of the first point of the same number.

Further, the trajectory processing module 405 further includes:

The processing parameter acquisition module is used to acquire the image processing mode and image processing resources from the image processing configuration file;

The first point trajectory processing module is configured to process the trajectory of the first point according to the image processing mode and image processing resources to obtain a processed trajectory.

Further, the device 400 further includes:

The second target object point acquiring module is configured to acquire at least one second point on the target object after a predetermined number of image frames have passed;

The second connection module is configured to connect the same second point in multiple adjacent image frames to calculate the trajectory of the second point;

The second point trajectory processing module is used to obtain image processing parameters and process the trajectory of the second point to obtain a processed trajectory.

Further, the trajectory processing module 405 further includes:

The second processing parameter acquisition module is used to acquire image processing modes and image processing resources with different numbers from the image processing configuration file;

The multi-track processing module is configured to process the track of the first point of the number by using the image processing mode and image processing resources corresponding to the number according to the number of the first point of the generated track to obtain the processed track .

The device shown in FIG. 4 can execute the methods of the embodiments shown in FIG. 1 and FIG. 3. For parts that are not described in detail in this embodiment, please refer to the related descriptions of the embodiments shown in FIG. 1 and FIG. For the implementation process and technical effects of this technical solution, please refer to the description in the embodiment shown in FIG. 1 and FIG. 3, which will not be repeated here.

Referring now to FIG. 5, it shows a schematic structural diagram of an electronic device 500 suitable for implementing the embodiments of the present disclosure. Electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals (for example, Mobile terminals such as car navigation terminals) and fixed terminals such as digital TVs, desktop computers, etc. The electronic device shown in FIG. 5 is only an example, and should not bring any limitation to the function and scope of use of the embodiments of the present disclosure.

As shown in FIG. 5, the electronic device 500 may include a processing device (such as a central processing unit, a graphics processor, etc.) 501, which may be loaded into a random access device according to a program stored in a read-only memory (ROM) 502 or from a storage device 508. The program in the memory (RAM) 503 executes various appropriate actions and processing. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to the bus 504.

Generally, the following devices can be connected to the I/O interface 505: including input devices 506 such as touch screens, touch panels, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; including, for example, liquid crystal displays (LCD), speakers, An output device 507 such as a vibrator; a storage device 508 such as a magnetic tape and a hard disk; and a communication device 509. The communication device 509 may allow the electronic device 500 to perform wireless or wired communication with other devices to exchange data. Although FIG. 4 shows an electronic device 500 having various devices, it should be understood that it is not required to implement or have all the illustrated devices. It may alternatively be implemented or provided with more or fewer devices.

In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

It should be noted that the above-mentioned computer-readable medium in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as a part of a carrier wave, and a computer-readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium. The computer-readable signal medium may send, propagate, or transmit the program for use by or in combination with the instruction execution system, apparatus, or device . The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wire, optical cable, RF (Radio Frequency), etc., or any suitable combination of the above.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist alone without being assembled into the electronic device.

The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains a video image from an image source, wherein the video image includes at least one target object Segment the target object from the video image; obtain at least one first point on the target object; connect the same first point in multiple adjacent image frames to calculate the first point The trajectory; acquiring image processing parameters and processing the trajectory of the first point to obtain the processed trajectory.

The computer program code used to perform the operations of the present disclosure may be written in one or more programming languages or a combination thereof. The above-mentioned programming languages include object-oriented programming languages-such as Java, Smalltalk, C++, and also conventional Procedural programming language-such as "C" language or similar programming language. The program code can be executed entirely on the user's computer, partly on the user's computer, executed as an independent software package, partly on the user's computer and partly executed on a remote computer, or entirely executed on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to pass Internet connection).

The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions, and operations of the system, method, and computer program product according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram can represent a module, program segment, or part of code, and the module, program segment, or part of code contains one or more for realizing the specified logical function Executable instructions. It should also be noted that, in some alternative implementations, the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two blocks shown in succession can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations Or it can be realized by a combination of dedicated hardware and computer instructions.

The units involved in the embodiments described in the present disclosure may be implemented in a software manner, or may be implemented in a hardware manner. Among them, the name of the unit does not constitute a limitation on the unit itself under certain circumstances.

The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in this disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover the above technical features or technical solutions without departing from the above disclosed concept. Other technical solutions formed by any combination of its equivalent features. For example, the above-mentioned features and the technical features disclosed in the present disclosure (but not limited to) with similar functions are mutually replaced to form a technical solution.

Claims (12)

An image processing method, including: Acquiring a video image from an image source, where the video image includes at least one target object; Segmenting the target object from the video image; Acquiring at least one first point on the target object; Connecting the same first point in multiple adjacent image frames to calculate the trajectory of the first point; Obtain image processing parameters and process the trajectory of the first point to obtain a processed trajectory. 8. The image processing method according to claim 1, wherein said obtaining a video image from an image source, wherein said video image includes at least one target object, including: Obtain a video image from a video capture device, the video image includes multiple image frames, and the image frame includes at least one target object. 5. The image processing method of claim 1, wherein said segmenting said target object from said video image comprises: Identifying the target object in the video image; Segmenting out the contour of the target object; The contour of the target object and the image within the contour are extracted from the video image. 5. The image processing method of claim 1, wherein said acquiring at least one first point on said target object comprises: Acquire at least one corner point on the target object, where the corner point is an intersection of two edges on the contour of the target object. 5. The image processing method of claim 1, wherein said acquiring at least one first point on said target object comprises: Randomly select at least one random point on the target object. 5. The image processing method according to claim 1, wherein said connecting the same first point in multiple adjacent image frames to calculate the trajectory of the first point comprises: Sort the first points according to their numbers; Identify the first point of the same number in multiple frames of images; Connecting the first points of the same number to generate the trajectory of the first point of the number. 8. The image processing method according to claim 1, wherein said acquiring image processing parameters and processing the trajectory of said first point to obtain the processed trajectory comprises: Obtain the image processing mode and image processing resources from the image processing configuration file; The trajectory of the first point is processed according to the image processing mode and the image processing resource to obtain a processed trajectory. 5. The image processing method of claim 1, after said acquiring image processing parameters and processing the trajectory of the first point to obtain a processed trajectory, further comprising: Obtaining at least one second point on the target object after a predetermined number of image frames have passed; Connecting the same second point in multiple adjacent image frames to calculate the trajectory of the second point; Obtain image processing parameters and process the trajectory of the second point to obtain a processed trajectory. 7. The image processing method according to claim 6, wherein said acquiring image processing parameters and processing the trajectory of said first point to obtain the processed trajectory comprises: Obtain image processing modes and image processing resources with different numbers from the image processing configuration file; According to the number of the first point generating the trajectory, the trajectory of the numbered first point is processed by using the image processing mode and image processing resource corresponding to the number to obtain the processed trajectory. An image processing device including: An image acquisition module for acquiring a video image from an image source, wherein the video image includes at least one target object; A segmentation module for segmenting the target object from the video image; A target object point acquisition module, configured to acquire at least one first point on the target object; A trajectory calculation module, configured to connect the same first point in multiple adjacent image frames to calculate the trajectory of the first point; The trajectory processing module is used to obtain image processing parameters and process the trajectory of the first point to obtain the processed trajectory. An electronic device including: Memory for storing computer readable instructions; and The processor is configured to run the computer-readable instructions, so that the processor implements the image processing method according to any one of claims 1-9 when the processor is running. A non-transitory computer-readable storage medium for storing computer-readable instructions. When the computer-readable instructions are executed by a computer, the computer can execute the image processing according to any one of claims 1-9 method.

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