CN118803385A - Video slicing method and device, electronic device and storage medium - Google Patents

Abstract

The embodiments of the present application provide a video slicing method and device, an electronic device and a storage medium, which belong to the field of artificial intelligence technology. The method includes: obtaining a video file to be segmented; segmenting the video file according to a preset search time and multiple target I frames to obtain individual video slices, wherein the first frame of each video slice is a target I frame; saving the video data of each video slice to obtain each slice save file containing the corresponding target I frame. Based on this, the embodiments of the present application can not only segment the video into the desired length to ensure the real-time performance of the video segmentation processing, but also effectively save the information in the video, and there is no need to decode and re-encode the video, thereby saving a lot of computing power and speeding up the processing speed, especially in the field of artificial intelligence video processing, because the I frames in each video slice are fully preserved, the content of the segmented video frame can be completely restored without losing information, saving processing time and resource consumption.

Description

Video slicing method and device, electronic device and storage medium

Technical Field

The present application relates to the field of artificial intelligence technology, and in particular to a video slicing method and device, an electronic device, and a storage medium.

Background Art

In artificial intelligence video processing algorithms, a video is usually input into a neural network, which deframes the video and extracts features based on the information within the frame and the difference between frames for regression calculation to obtain the analysis results of the neural network. Generally speaking, the longer the video fed into the neural network, the longer the neural network processing time. In order to ensure the timeliness of the neural network results, it is usually necessary to limit the length of the video. In the current video processing algorithms, there are two commonly used methods for video interception and slicing:

(1) Specify the start time and end time and use re-encoding to extract the video;

During the slicing process, the video needs to be re-encoded in H264 strictly from the start time to the end time, and I frame information needs to be inserted as needed to achieve precise cropping.

This method can achieve precise cutting, but the encoding process requires a lot of time and resources.

(2) Use the copy method to achieve fast cutting without decoding;

During video time seeking, the seek operation would jump between I-frames and would not stop exactly at an I-frame. Since no re-encoding was done, video playback would be problematic before the first I-frame was reached.

Summary of the invention

The main purpose of the embodiments of the present application is to propose a video slicing method and device, an electronic device and a storage medium, which can not only cut the video into the desired length to ensure the real-time performance of the algorithm, but also effectively save the information in the video without the need to decode and re-encode the video, thereby saving a large amount of computing power and speeding up the processing speed.

To achieve the above object, a first aspect of an embodiment of the present application provides a video slicing method, the method comprising:

Get the video file to be segmented;

The video file is segmented according to a preset search duration and a plurality of target I frames to obtain video slices, wherein the first frame of each of the video slices is the target I frame;

The video data of each video slice is saved to obtain a save file of each slice including the corresponding target I frame.

In some embodiments, the video file is segmented according to a preset search duration and a plurality of target I frames to obtain video slices, including:

Searching each video frame of the video file starting from the first frame of the video file, reading the interval time between each video frame and the first frame, where the first frame is an I frame;

Traversing each video frame of the video file, and determining each target I frame within a preset search duration;

The video file is segmented according to the preset search duration, the interval duration and the target I frame to obtain various video slices.

In some embodiments, the segmenting of the video file according to the preset duration, the interval duration and the target I frame to obtain the video slices includes:

Determine the next target I frame closest to the previous target I frame according to the preset duration and the interval duration;

Writing the video frames from the last target I frame to the next target I frame to the corresponding slice saving file;

Each video frame of the video file is traversed and segmented until the last frame of the video file to obtain each video slice.

In some embodiments, determining the next target I frame closest to the previous target I frame according to the preset duration and the interval duration includes:

Determine a video frame within the interval time from the previous target I frame;

The next target I frame is searched from the video frames within the preset time length.

In some embodiments, searching for the next target I frame from the video frames within the preset duration includes:

Identify the identification position of the video frame within the preset time length to obtain an identification result;

When the identification result is AV_PKT_FLAG_KEY, the video frame is determined to be the next target I frame.

In some embodiments, the video data of each video slice is saved to obtain a saved file of each slice including the corresponding target I frame, including:

Create multiple slice save files;

Copying each of the segmented video slices;

The video data of each video slice is written into the corresponding slice saving file to obtain each slice saving file containing the corresponding target I frame.

In some embodiments, the video file includes a plurality of GOP groups of pictures, each of the GOP groups of pictures includes an I frame, at least one P frame and at least one B frame.

To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a video slicing device, the device comprising:

An acquisition module is used to acquire the video file to be segmented;

A slicing module, used to slice the video file according to a preset search duration and a target I frame to obtain various video slices, wherein the first frame of each of the video slices is the target I frame;

The saving module is used to save the video data of each video slice to obtain a saving file of each slice containing the corresponding target I frame.

To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

The video slicing method and device, electronic device and storage medium proposed in the present application obtain the video file to be segmented; segment the video file according to the preset search time and multiple target I frames to obtain each video slice, wherein the first frame of each video slice is the target I frame; save the video data of each video slice to obtain each slice save file containing the corresponding target I frame. The user can set the preset search time according to the desired video segmentation length, perform a video frame search for the video file to be segmented within the preset search time, and segment the video file based on the searched target I frame to obtain each video slice whose first frame is the target I frame. Therefore, each video slice obtained contains the target I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the video frame information, without the need to decode and re-encode the video data, saving a lot of calculation, thereby speeding up the processing speed. Based on this, the embodiments of the present invention can not only cut the video into the desired length to ensure the real-time performance of the video cutting process, but also effectively save the information in the video, and there is no need to decode and re-encode the video, thereby saving a lot of calculations and speeding up the processing speed, especially in the field of artificial intelligence video processing. Since the I frames in each video slice are fully preserved, the content of the cut video frame can be completely restored without losing information, so it is very suitable for artificial intelligence video algorithm processing. The entire video cutting process does not involve any information extraction and calculation within the video frame, but only quickly copying and saving after cutting. There is no need to re-encode the video after cutting, which saves processing time and resource consumption, thereby speeding up the video slicing processing speed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a video slicing method provided in an embodiment of the present application;

FIG2 is a flow chart of step S102 in FIG1 ;

FIG3 is a flow chart of step S203 in FIG2 ;

FIG4 is a flow chart of step S301 in FIG3 ;

FIG5 is a flow chart of step S402 in FIG4 ;

FIG6 is a flow chart of step S103 in FIG1 ;

FIG7 is a schematic diagram of the structure of a video slicing device provided in an embodiment of the present application;

FIG8 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

First, some nouns involved in this application are analyzed:

Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

H264 encoding: It is a new video compression encoding standard. This standard adopts a number of technical measures to improve image quality and increase compression ratio. It can be used for SDTV, HDTV and DVD, etc. H264 encoding saves more bit stream. H264 not only saves 50% of the bit rate compared with MPEG-4, but also has strong anti-error characteristics. It can adapt to video transmission in wireless channels with high packet loss rate and severe interference, so as to obtain smooth image quality. The H264 standard has brought motion image compression technology to a higher stage. Providing high-quality image transmission at a lower bandwidth is the application highlight of H.264.

GOP (Group of Pictures): Strategy affects encoding quality. The so-called GOP means group of pictures. A GOP is a group of continuous pictures. GOP is a set of pictures in a sequence, which is used to assist random access. The first picture of GOP must be an I frame, so that GOP can be decoded independently without referring to other pictures. MPEG encoding divides pictures (i.e. frames) into three types: I, P, and B. I is an internal coding frame, P is a forward prediction frame, and B is a bidirectional interpolation frame. Simply put, I frame is a key frame, which can be understood as a complete picture, while P frame and B frame record the changes relative to I frame. P frame indicates the difference with the previous frame, and B frame indicates the difference between the previous and next frames. Without I frame, P frame and B frame cannot be decoded. This is why it is difficult to edit accurately in MPEG format, and why we need to fine-tune the head and tail.

I frame: Also known as key frame or intra-coded frame, it is a complete image frame that exists independently of other frames. I frame can be decoded independently without relying on the information of other frames, similar to static images, and can be regarded as a reference point in the video sequence. Since I frame contains complete image information, its compression rate is relatively low, but it is the simplest to decode because it does not involve dependence on other frames.

P frame: a forward predictive coding frame (Pred ict ive Frame), which is generated based on the previous I frame or P frame. P frame stores the amount of change in the image compared to the previous frame, so its compression effect is usually better than I frame. When decoding a P frame, you need to first decode the I frame or P frame it depends on, and then reconstruct the current frame based on this information. The introduction of P frame effectively reduces redundancy in the time dimension and improves the compression efficiency of the video.

B frame: also known as Bidirectional Interpolated Prediction Frame, which needs to refer to the previous and next I frames or P frames to generate. B frame uses the information of the previous and next frames to predict the content of the current frame, thereby achieving a higher compression ratio. Since the decoding of B frame requires the information of the previous and next frames, it cannot be decoded independently and must be completed in combination with I frame and P frame in the decoding sequence.

In artificial intelligence video processing algorithms, a video is usually input into a neural network, which deframes the video and extracts features based on the information within the frame and the difference between frames, and performs regression calculations to obtain the analysis results of the neural network. Generally speaking, the longer the video fed into the neural network, the longer the neural network processing time. In order to ensure the timeliness of the neural network results, it is usually necessary to limit the length of the video.

In the current video processing algorithms, there are two commonly used video capture and slicing methods:

(1) Specify the start time and end time and use re-encoding to extract the video;

During the slicing process, the video needs to be re-encoded in H264 strictly from the start time to the end time, and I frame information needs to be inserted as needed to achieve precise cropping.

This method can achieve precise cutting, but the encoding process requires a lot of time and resources.

(2) Use the copy method to achieve fast cutting without decoding;

During video time seeking, the seek operation would jump between I-frames and would not stop exactly at an I-frame. Since no re-encoding was done, video playback would be problematic before the first I-frame was reached.

It is understandable that from the above-mentioned traditional slicing methods, it can be found that method (1) does not extract the I frame. Before slicing, the extracted content is usually decoded and H264 encoded to complete the generation of the slice file. However, encoding requires a lot of time and resources; method (2) will not stop accurately at the I frame during the video time search process. Since no re-encoding is performed, video playback will have problems before reaching the first I frame.

Based on this, the embodiments of the present application provide a video slicing method and device, an electronic device and a storage medium, which obtain a video file to be segmented; segment the video file according to a preset search time and multiple target I frames to obtain individual video slices, wherein the first frame of each of the video slices is the target I frame; save the video data of each video slice to obtain each slice save file containing the corresponding target I frame. The user can set the preset search time according to the desired video segmentation length, perform a video frame search for the video file to be segmented within the preset search time, and segment the video file based on the searched target I frame to obtain each video slice whose first frame is the target I frame. Therefore, each obtained video slice contains the target I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the video frame information, without the need to decode and re-encode the video data, saving a lot of calculation, thereby speeding up the processing speed. Based on this, the embodiments of the present invention can not only cut the video into the desired length to ensure the real-time performance of the video cutting process, but also effectively save the information in the video, and there is no need to decode and re-encode the video, thereby saving a lot of calculations and speeding up the processing speed, especially in the field of artificial intelligence video processing. Since the I frames in each video slice are fully preserved, the content of the cut video frame can be completely restored without losing information, so it is very suitable for artificial intelligence video algorithm processing. The entire video cutting process does not involve any information extraction and calculation within the video frame, but only quickly copying and saving after cutting. There is no need to re-encode the video after cutting, which saves processing time and resource consumption, thereby speeding up the video slicing processing speed.

The video slicing method and device, electronic device and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the video slicing method in the embodiments of the present application is described.

The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning/deep learning.

The video slicing method provided in the embodiment of the present application relates to the field of artificial intelligence technology. The video slicing method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the video slicing method, etc., but is not limited to the above forms.

The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

FIG1 is an optional flowchart of a video slicing method provided in an embodiment of the present application. The method in FIG1 may include but is not limited to steps S101 to S103.

Step S101, obtaining a video file to be segmented;

Step S102, dividing the video file according to a preset search duration and a plurality of target I frames to obtain video slices, wherein the first frame of each video slice is the target I frame;

Step S103, saving the video data of each video slice to obtain a save file of each slice including the corresponding target I frame.

In step S101 of some embodiments, a video file to be segmented is obtained. The video file to be uploaded may be a video file that has been shot or created by a user, and the encoding format of the video file includes but is not limited to H264 encoding. Taking a video file of an H264 code stream as an example, the video file contains multiple GOP image groups, and each GOP image group contains multiple video encoding frames. The division of the GOP image group is the image between two adjacent I frames. Each GOP image group contains an I frame, at least one P frame and at least one B frame. The decoding of P frames or B frames needs to rely on the previous or subsequent video frame images, but I frames can be decoded as a single frame. Therefore, if an I frame is missing in a GOP image group, the decoding of P frames and B frames will fail.

In step S102 of some embodiments, the video file is segmented according to a preset search duration and a plurality of target I frames to obtain various video slices, wherein the first frame of each video slice is the target I frame. The user can set the preset search duration according to the video segmentation length of the time limit, perform a video frame search on the video file to be segmented within the preset search duration, and segment the video file based on the searched target I frame to obtain various video slices whose first frame is the target I frame. Therefore, each obtained video slice contains the target I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the video frame information, without the need to decode and re-encode the video data, saving a large amount of calculation, thereby speeding up the processing speed.

In step S103 of some embodiments, the video data of each video slice is saved to obtain a save file of each slice containing the corresponding target I frame. For example, in the field of artificial intelligence video processing, since the I frames in each video slice are fully preserved, the content of the cut video frame can be completely restored without losing information, so it is very suitable for artificial intelligence video algorithm processing. The entire video segmentation process does not involve any information extraction and calculation within the video frame, but only quickly copying and saving after segmentation. After video segmentation, there is no need to re-encode the video, which saves processing time and resource consumption, thereby speeding up the processing of video slices.

It should be noted that the video slicing method of the embodiment of the present invention has the unique advantages of being fast and having a small amount of calculation, and can be applied to all algorithm scenarios that have requirements on the video length.

Steps S101 to S103 shown in the embodiment of the present application are to obtain the video file to be segmented; segment the video file according to the preset search time and multiple target I frames to obtain individual video slices, wherein the first frame of each of the video slices is the target I frame; save the video data of each video slice to obtain individual slice save files containing the corresponding target I frame. The user can set the preset search time according to the desired video segmentation length, perform a video frame search on the video file to be segmented within the preset search time, and segment the video file based on the searched target I frame to obtain individual video slices whose first frame is the target I frame. Therefore, each obtained video slice contains the target I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the video frame information. There is no need to decode and re-encode the video data, saving a lot of calculations, thereby speeding up the processing speed. Based on this, the embodiments of the present invention can not only cut the video into the desired length to ensure the real-time performance of the video cutting process, but also effectively save the information in the video, and there is no need to decode and re-encode the video, thereby saving a lot of calculations and speeding up the processing speed, especially in the field of artificial intelligence video processing. Since the I frames in each video slice are fully preserved, the content of the cut video frame can be completely restored without losing information, so it is very suitable for artificial intelligence video algorithm processing. The entire video cutting process does not involve any information extraction and calculation within the video frame, but only quickly copying and saving after cutting. There is no need to re-encode the video after cutting, which saves processing time and resource consumption, thereby speeding up the video slicing processing speed.

Please refer to FIG. 2 . In some embodiments, step S102 may include but is not limited to steps S201 to S203:

Step S201, searching each video frame of the video file starting from the first frame of the video file, reading the interval time between each video frame and the first frame, the first frame being an I frame;

Step S202, traversing each video frame of the video file, and determining each target I frame within a preset search duration;

Step S203, dividing the video file according to the preset search duration, interval duration and target I frame to obtain various video slices.

In some embodiments, taking the video file of H264 code stream as an example, the video file contains multiple GOP image groups, and each GOP image group contains multiple video encoding frames. The division of the GOP image group is the image between two adjacent I frames. Each GOP image group contains an I frame, at least one P frame and at least one B frame. The decoding of P frames or B frames needs to rely on the previous or subsequent video frame images, but I frames can be decoded as a single frame. Therefore, if an I frame is missing in a GOP image group, the decoding of P frames and B frames will fail. Assuming that the algorithm inputs a single video within 2.5s, it can ensure that the processing time of the video algorithm meets the business requirements, then the user can set the preset search time to 2.5s. The video file is segmented, with the length of the video file being 50s and the time of a single video segment being set to 2s. The video frames of the video file are searched starting from the first frame of the video file. The first frame of the video file is an I frame. Starting from the I frame, it can be searched backwards, and the interval time of each frame from the first frame is read. For example, when the video frame is searched to be 2s away from the first I frame, the nearest I frame is searched backwards, and the search time is within 2.5s. The data before the nearest I frame is searched is used as a segment to obtain a video slice, so as to facilitate subsequent writing and saving to the corresponding slice saving file. Then, starting from the above search to the nearest I frame, the above steps are repeated in sequence, and the video frames of the video file are traversed. Each target I frame is searched within the preset search time, and the video file is continued to be segmented based on this, until the last frame of the video file, thereby obtaining each video slice. The user can set the preset search time according to the desired video segmentation length, perform video frame search for the video file to be segmented within the preset search time, and segment the video file based on the searched target I frame, and obtain individual video slices whose first frame is the target I frame. Therefore, each obtained video slice contains the target I frame information. Based on the I frame, the subsequent video frame data can be completely decoded and the video frame information can be extracted. There is no need to decode and re-encode the video data, which saves a lot of calculations and speeds up the processing.

Please refer to FIG. 3 . In some embodiments, step S203 may include but is not limited to steps S301 to S303:

Step S301, determining the next target I frame closest to the previous target I frame according to the preset duration and the interval duration;

Step S302, writing the video frames from the previous target I frame to the next target I frame into the corresponding slice saving file;

Step S303, traverse each video frame of the video file to segment until the last frame of the video file, and obtain each video slice.

In some embodiments, the next target I frame closest to the previous target I frame is determined based on a preset duration and an interval duration. For example, assuming that the algorithm inputs a video within 2.5 seconds at a time, it can be ensured that the processing time of the video algorithm meets the business requirements, and the user can set the preset search duration to 2.5 seconds. The video file is segmented, with the video file length being 50 seconds and the single video segment time being set to 2 seconds. Starting from the first frame of the video file, each video frame of the video file is searched. The first frame of the video file is an I frame. Starting from the I frame, it can be searched backwards, and each frame reads the interval duration from the first frame. For example, when the searched video frame is 2 seconds away from the first I frame, the nearest next I frame is searched backwards, and the search duration is within 2.5 seconds. The data before the nearest next I frame is searched as a segment to obtain the first video slice, and the obtained first video slice is written and saved to the corresponding slice save file. Next, starting from the most recent I frame searched above as the previous I frame, the above steps are repeated to traverse each video frame of the video file, and the next target I frame is searched within the preset search time, and the video file is segmented based on this until the last frame of the video file, thereby obtaining each video slice. Based on this, the embodiment of the present invention can not only segment the video into the desired length, ensuring the real-time performance of the video segmentation process, but also effectively save the information in the video, and does not need to decode and re-encode the video, thereby saving a lot of calculation and speeding up the processing speed.

Please refer to FIG. 4 . In some embodiments, step S301 may include but is not limited to steps S401 to S402:

Step S401, determining video frames within different time intervals from the previous target I frame;

Step S402, searching for the next target I frame from the video frames within a preset duration.

In some embodiments, during the video segmentation process, the first frame of each video slice is the target I frame. For example, when the searched video frame is 2s away from the previous I frame, the nearest I frame is searched backward, and the search duration is within 2.5s. The data before the nearest next I frame is searched as a segment to obtain a video slice, and the obtained video slice is written and saved to the corresponding slice save file. Based on this, each obtained video slice contains I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the video frame information. There is no need to decode and re-encode the video data, saving a lot of calculations, thereby speeding up the processing speed.

Please refer to FIG. 5 . In some embodiments, step S402 may include but is not limited to steps S501 to S502:

Step S501, identifying the identification position of the video frame within a preset time length to obtain an identification result;

Step S502: When the identification result is AV_PKT_FLAG_KEY, the video frame is determined to be the next target I frame.

In some embodiments, an I frame exists in a video file. For example, in an H264-encoded video file, there are I frames, B frames, and P frames. Extracting an I frame requires identifying each H264-encoded video frame information, and determining the video frame type by identifying the video frame information. Specifically, this can be achieved through the flag corresponding to the flags. When the flag is determined to be AV_PKT_FLAG_KEY, it indicates that the video frame is an I frame.

Please refer to FIG. 6 . In some embodiments, step S103 may include but is not limited to steps S601 to S603:

Step S601, creating multiple slice saving files;

Step S602, copying each segmented video slice;

Step S603, writing the video data of each video slice into the corresponding slice saving file, and obtaining each slice saving file containing the corresponding target I frame.

In some embodiments, for each segmented video slice, a new slice save file needs to be created, each segmented video slice is copied, and then the video data of each video slice is written to the corresponding slice save file to obtain each slice save file containing the corresponding target I frame. It can be seen that the entire video segmentation process does not involve any information extraction and calculation within the video frame, but only quickly copying and saving after segmentation. Since each slice save file containing the corresponding target I frame is obtained, there is no need to re-encode the video after segmentation, saving processing time and resource consumption, thereby speeding up the video segmentation processing speed.

The video slicing method of the present application is further explained below in conjunction with specific embodiments.

Assuming that the algorithm inputs a video within 2.5 seconds at a time, the algorithm's processing time can be guaranteed to meet business requirements.

This method segments the video into 50 seconds long segments, with the duration of a single video segment set to 2 seconds.

Here are the steps:

(1) Search from the beginning of the video, the first frame is the I frame, and write the frame into the file.

(2) Start from the first I frame and search backwards, read the time from the first frame to each frame, and write all the frame data into the file.

(3) When the video frame is found to be 2 seconds away from the first I frame, search backward to the nearest I frame and write the data before the I frame into the file.

(4) Create a new file and start writing the file from the last I frame searched in (3), and repeat steps (2) and (3) in sequence.

(5) Repeat the steps in (4) until the end of the video.

Since the traditional video slicing method does not extract the I frame, before doing video slicing, the extracted content is usually decoded and H264 encoded to complete the generation of the slice file, but the encoding process requires a lot of time and resources. Compared with the traditional video slicing method, the video slicing method of the present invention reads data from the I frame and writes files. Therefore, the new slice file contains I frame information, and the subsequent video data can be completely decoded. Since there is no need to decode and re-encode the video data, a large amount of calculation and resource consumption are saved, thereby speeding up the processing speed.

It should be noted that the video slicing method of the embodiment of the present invention has the unique advantages of being fast and having a small amount of calculation, and can be applied to all algorithm scenarios that have requirements on the video length.

Based on this, the embodiment of the present application obtains the video file to be segmented; segments the video file according to the preset search time and multiple target I frames to obtain various video slices, wherein the first frame of each of the video slices is the target I frame; and saves the video data of each video slice to obtain various slice save files containing the corresponding target I frame. The user can set the preset search time according to the desired video segmentation length, perform a video frame search on the video file to be segmented within the preset search time, and segment the video file based on the searched target I frame to obtain various video slices whose first frame is the target I frame. Therefore, each obtained video slice contains the target I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the video frame information. There is no need to decode and re-encode the video data, saving a lot of calculations, thereby speeding up the processing speed. Based on this, the embodiments of the present invention can not only cut the video into the desired length to ensure the real-time performance of the video cutting process, but also effectively save the information in the video, and there is no need to decode and re-encode the video, thereby saving a lot of calculations and speeding up the processing speed, especially in the field of artificial intelligence video processing. Since the I frames in each video slice are fully preserved, the content of the cut video frame can be completely restored without losing information, so it is very suitable for artificial intelligence video algorithm processing. The entire video cutting process does not involve any information extraction and calculation within the video frame, but only quickly copying and saving after cutting. There is no need to re-encode the video after cutting, which saves processing time and resource consumption, thereby speeding up the video slicing processing speed.

Referring to FIG. 7 , an embodiment of the present application further provides a video slicing device, which can implement the above-mentioned video slicing method, and the device includes:

An acquisition module 710 is used to acquire a video file to be segmented;

The slicing module 720 is used to slice the video file according to the preset search time and the target I frame to obtain various video slices, wherein the first frame of each video slice is the target I frame;

The saving module 730 is used to save the video data of each video slice to obtain a saving file of each slice including a corresponding target I frame.

In some embodiments of the present application, the acquisition module 710 acquires the video file to be segmented; the slicing module 720 segments the video file according to the preset search time and the target I frame to obtain individual video slices, wherein the first frame of each video slice is the target I frame; the saving module 730 saves the video data of each video slice to obtain individual slice save files containing the corresponding target I frame.

In some embodiments of the present application, a video file to be segmented is obtained. The video file to be uploaded may be a video file that has been shot or created by a user, and the encoding format of the video file includes but is not limited to H264 encoding. Taking a video file of an H264 code stream as an example, the video file contains multiple GOP image groups, and each GOP image group contains multiple video encoding frames. The division of the GOP image group is the image between two adjacent I frames. Each GOP image group contains an I frame, at least one P frame and at least one B frame. The decoding of P frames or B frames needs to rely on the previous or subsequent video frame images, but I frames can be decoded as a single frame. Therefore, if an I frame is missing in a GOP image group, the decoding of P frames and B frames will fail.

In some embodiments of the present application, the video file is segmented according to a preset search duration and multiple target I frames to obtain various video slices, wherein the first frame of each video slice is the target I frame. The user can set the preset search duration according to the video segmentation length of the period, perform a video frame search on the video file to be segmented within the preset search duration, and segment the video file based on the searched target I frame to obtain various video slices whose first frame is the target I frame. Therefore, each obtained video slice contains the target I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the video frame information, without the need to decode and re-encode the video data, saving a lot of calculation, thereby speeding up the processing speed.

In some embodiments of the present application, the video data of each video slice is saved to obtain a save file of each slice containing the corresponding target I frame. For example, in the field of artificial intelligence video processing, since the I frames in each video slice are fully preserved, the content of the cut video frame can be completely restored without losing information, so it is very suitable for artificial intelligence video algorithm processing. The entire video segmentation process does not involve any information extraction and calculation within the video frame, but only quickly copying and saving after segmentation. After video segmentation, there is no need to re-encode the video, which saves processing time and resource consumption, thereby speeding up the processing speed of video slices.

Based on this, in the video slicing device of the embodiment of the present application, the acquisition module 710 acquires the video file to be segmented; the slicing module 720 segments the video file according to the preset search time and the target I frame to obtain each video slice, wherein the first frame of each video slice is the target I frame; the saving module 730 saves the video data of each video slice to obtain each slice saving file containing the corresponding target I frame. The present application obtains the video file to be segmented; segments the video file according to the preset search time and multiple target I frames to obtain each video slice, wherein the first frame of each video slice is the target I frame; saves the video data of each video slice to obtain each slice saving file containing the corresponding target I frame. The user can set the preset search time according to the desired video segmentation length, perform video frame search on the video file to be segmented within the preset search time, and segment the video file based on the searched target I frame, and obtain each video slice whose first frame is the target I frame. Therefore, each obtained video slice contains the target I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the information of the video frame. There is no need to decode and re-encode the video data, saving a lot of calculation, thereby speeding up the processing speed. Based on this, the embodiment of the present invention can not only segment the video into the desired length, ensure the real-time performance of the video segmentation processing, but also effectively save the information in the video, and there is no need to decode and re-encode the video, thereby saving a lot of calculation, speeding up the processing speed, especially in the field of artificial intelligence video processing, because the I frames in each video slice are fully preserved, the content of the cut video frame can be completely restored, and no information will be lost, so it is very suitable for artificial intelligence video algorithm processing. The entire video segmentation process does not involve any information extraction and calculation in the video frame, but only quickly copying and saving after segmentation. There is no need to re-encode the video after segmentation, which saves processing time and resource consumption, thereby speeding up the video segmentation processing.

The specific implementation of the video slicing device is basically the same as the specific implementation of the above-mentioned video slicing method, and will not be repeated here.

The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned video slicing method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

Please refer to FIG8 , which illustrates a hardware structure of an electronic device according to another embodiment. The electronic device includes:

The processor 801 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 is used to call and execute the video slicing method of the embodiment of the present application, that is, by obtaining a video file to be segmented; segmenting the video file according to a preset search duration and multiple target I frames to obtain each video slice, wherein the first frame of each video slice is the target I frame; and saving the video data of each video slice to obtain each slice save file containing the corresponding target I frame. The user can set the preset search time according to the desired video segmentation length, perform video frame search on the video file to be segmented within the preset search time, and segment the video file based on the searched target I frame, and obtain each video slice whose first frame is the target I frame. Therefore, each obtained video slice contains the target I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the information of the video frame. There is no need to decode and re-encode the video data, saving a lot of calculation, thereby speeding up the processing speed. Based on this, the embodiment of the present invention can not only segment the video into the desired length, ensure the real-time performance of the video segmentation processing, but also effectively save the information in the video, and there is no need to decode and re-encode the video, thereby saving a lot of calculation, speeding up the processing speed, especially in the field of artificial intelligence video processing, because the I frames in each video slice are fully preserved, the content of the cut video frame can be completely restored, and no information will be lost, so it is very suitable for artificial intelligence video algorithm processing. The entire video segmentation process does not involve any information extraction and calculation in the video frame, but only quickly copying and saving after segmentation. There is no need to re-encode the video after segmentation, which saves processing time and resource consumption, thereby speeding up the video segmentation processing.

The input/output interface 803 is used to implement information input and output.

The communication interface 804 is used to realize the communication interaction between this device and other devices. Communication can be realized through wired methods (such as USB, network cable, etc.) or wireless methods (such as mobile network, WIFI, Bluetooth, etc.).

The bus transmits information between various components of the device (eg, the processor 801 , the memory 802 , the input/output interface 803 , and the communication interface 804 ).

The processor 801 , the memory 802 , the input/output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus.

An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned video slicing method is implemented.

The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The video slicing method, video slicing device, electronic device and storage medium provided in the embodiment of the present application obtain the video file to be segmented; segment the video file according to the preset search time and multiple target I frames to obtain each video slice, wherein the first frame of each video slice is the target I frame; save the video data of each video slice to obtain each slice save file containing the corresponding target I frame. The user can set the preset search time according to the desired video segmentation length, perform a video frame search for the video file to be segmented within the preset search time, and segment the video file based on the searched target I frame to obtain each video slice whose first frame is the target I frame. Therefore, each obtained video slice contains the target I frame information, and the subsequent video frame data can be completely decoded based on the I frame to extract the video frame information, without the need to decode and re-encode the video data, saving a lot of calculation, thereby speeding up the processing speed. Based on this, the embodiments of the present invention can not only cut the video into the desired length to ensure the real-time performance of the video cutting process, but also effectively save the information in the video, and there is no need to decode and re-encode the video, thereby saving a lot of calculations and speeding up the processing speed, especially in the field of artificial intelligence video processing. Since the I frames in each video slice are fully preserved, the content of the cut video frame can be completely restored without losing information, so it is very suitable for artificial intelligence video algorithm processing. The entire video cutting process does not involve any information extraction and calculation within the video frame, but only quickly copying and saving after cutting. There is no need to re-encode the video after cutting, which saves processing time and resource consumption, thereby speeding up the video slicing processing speed.

It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as a computer-readable program, a data structure, a program module or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, disk storage or other magnetic storage device, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media generally contain computer-readable programs, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules/units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And/or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and/or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character "/" generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-On l y Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims (10)

1. A video slicing method, characterized in that the method comprises: Get the video file to be segmented; The video file is segmented according to a preset search duration and a plurality of target I frames to obtain video slices, wherein the first frame of each of the video slices is the target I frame; The video data of each video slice is saved to obtain a save file of each slice including the corresponding target I frame. 2. The method according to claim 1, characterized in that the step of segmenting the video file according to a preset search duration and a plurality of target I frames to obtain video slices comprises: Searching each video frame of the video file starting from the first frame of the video file, reading the interval time between each video frame and the first frame, where the first frame is an I frame; Traversing each video frame of the video file, and determining each target I frame within a preset search duration; The video file is segmented according to the preset search duration, the interval duration and the target I frame to obtain various video slices. 3. The method according to claim 2, characterized in that the segmenting of the video file according to the preset duration, the interval duration and the target I frame to obtain the respective video slices comprises: Determine the next target I frame closest to the previous target I frame according to the preset duration and the interval duration; Writing the video frames from the last target I frame to the next target I frame to the corresponding slice saving file; Each video frame of the video file is traversed and segmented until the last frame of the video file to obtain each video slice. 4. The method according to claim 3, characterized in that the step of determining the next target I frame closest to the previous target I frame according to the preset duration and the interval duration comprises: Determine a video frame within the interval time from the previous target I frame; The next target I frame is searched from the video frames within the preset time length. 5. The method according to claim 4, wherein searching for the next target I frame from the video frames within the preset duration comprises: Identify the identification position of the video frame within the preset time length to obtain an identification result; When the identification result is AV_PKT_FLAG_KEY, the video frame is determined to be the next target I frame. 6. The method according to claim 1, wherein the step of saving the video data of each video slice to obtain a saved file of each slice including the corresponding target I frame comprises: Create multiple slice save files; Copying each of the segmented video slices; The video data of each video slice is written into the corresponding slice saving file to obtain each slice saving file containing the corresponding target I frame. 7. The method according to any one of claims 1 to 6, characterized in that the video file includes multiple GOP image groups, each of which includes an I frame, at least one P frame and at least one B frame. 8. A video slicing device, characterized in that the device comprises: An acquisition module is used to acquire the video file to be segmented; A slicing module, used to slice the video file according to a preset search duration and a target I frame to obtain various video slices, wherein the first frame of each of the video slices is the target I frame; The saving module is used to save the video data of each video slice to obtain a saving file of each slice containing the corresponding target I frame. 9. An electronic device, characterized in that the electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the video slicing method described in any one of claims 1 to 7 when executing the computer program. 10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the video slicing method according to any one of claims 1 to 7.