WO2018223318A1

WO2018223318A1 - 360-degree panoramic image and video identification method and electronic device

Info

Publication number: WO2018223318A1
Application number: PCT/CN2017/087475
Authority: WO
Inventors: 卢启栋
Original assignee: 深圳市柔宇科技有限公司
Priority date: 2017-06-07
Filing date: 2017-06-07
Publication date: 2018-12-13
Also published as: CN108475432B; CN108475432A

Abstract

The present application discloses a 360-degree panoramic video identification method, comprising: acquiring a video file to be identified; extracting a plurality of representative image frames from the video file to be identified; determining whether each of the representative image frames is a 360-degree panoramic image; determining whether the proportion of representative image frames that are 360-degree panoramic images is greater than a preset proportion; and if so, determining that the video file is a 360-degree panoramic video, and if not, determining that the video file is not a 360-degree panoramic video. The present application further discloses a 360-degree panoramic image identification method and an electronic device. The electronic device and the 360-degree panoramic video and image identification method of the present application can more accurately and simply identify 360-degree panoramic videos or 360-degree panoramic images.

Description

360-degree panoramic image and 360-degree panoramic video identification method and electronic device

Technical field

The invention relates to a video image recognition method, in particular to a 360-degree panoramic image recognition method and a 360-degree panoramic video recognition method, and an electronic device applying the same.

Background technique

At present, 360-degree panoramic camera technology has been widely used, and 360-degree panoramic image or 360-degree panoramic video can be obtained through 360-degree panoramic photography technology. In some situations, when playing an image or video file, it is usually necessary to identify whether it is a 360-degree panoramic video to perform corresponding playback settings, reflecting the effect of 360-degree panoramic. The existing recognition method is usually based on the aspect ratio of the image frame in the image file or the video file and/or the RGB (red, green and blue primary colors) values for judgment and recognition, which is often not accurate enough.

Summary of the invention

The embodiment of the invention discloses a 360-degree panoramic image and a 360-degree panoramic video recognition method and an electronic device, which can accurately recognize a 360-degree panoramic image or a 360-degree panoramic video.

The method for identifying a 360-degree panoramic image disclosed by the embodiment of the present invention includes: acquiring an image file to be identified; determining a target image frame according to the image file to be identified; and determining pixels of the first column of the target image frame Whether the degree of matching between the gray value of the point and the gray value of the pixel of the last column is greater than or equal to the first threshold, and determining the difference between the gray values of all the pixels of the first line of the target image frame and Whether the difference between the gray values of all the pixels of the last row is less than or equal to the second threshold; if yes, determining that the image file to be identified is a 360-degree panoramic image; otherwise, determining that the image file to be identified is not 360 degree panoramic image.

The method for identifying a 360-degree panoramic video disclosed in the embodiment of the present invention includes: acquiring a video file to be identified; extracting a plurality of representative image frames from the video file to be identified; determining whether each representative image frame is a 360-degree panoramic image; whether the proportion of the representative image frame determined as the 360-degree panoramic image is greater than a preset ratio; if yes, determining that the video file is a 360-degree panoramic video, and if not, determining that the video file is not 360 Degree panoramic video.

The electronic device disclosed in the embodiment of the present invention includes a processor and a memory, wherein the memory stores a video file to be identified, the processor is configured to: acquire a video file to be identified; and extract a number of the video files to be identified Representing an image frame; determining whether each representative image frame is a 360-degree panoramic image; determining whether a proportion of the representative image frame of the 360-degree panoramic image is greater than a preset ratio; if yes, determining that the video file is a 360-degree panoramic video If not, it is determined that the video file is not a 360-degree panoramic video.

The electronic device disclosed in the embodiment of the present invention includes a processor and a memory, wherein the memory stores an image file to be identified, the processor is configured to: acquire an image file to be identified; and determine a target image frame according to the image file to be identified; Determining whether a matching degree between a gray value of a pixel of the first column of the target image frame and a gray value of a pixel of the last column is greater than or equal to a first threshold, and determining a first of the target image frame Whether the difference between the gray value of all the pixels of the row and the difference of the gray values of all the pixels of the last row are less than or equal to the second threshold; if yes, determining that the image file to be recognized is a 360-degree panoramic image, Otherwise, it is determined that the image file to be identified is not a 360-degree panoramic image.

The 360-degree panoramic image and the 360-degree panoramic video identification method and the electronic device of the present invention determine the target image frame, and then analyze the gray-scale difference between the pixel of the first column of the target image frame and the pixel of the last column. And determining a grayscale difference between pixels of the first row of the target image frame and a grayscale difference between the pixels of the last row to determine whether the target image frame is a 360-degree panoramic image frame, and then determining the corresponding image Whether the video is a 360-degree panoramic image or a 360-degree panoramic video can effectively improve the recognition accuracy.

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings to be used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without paying any creative work.

FIG. 1 is a flowchart of a method for recognizing a 360-degree panoramic image according to an embodiment of the present invention.

2 is a schematic diagram of a target image frame in an embodiment of the present invention.

FIG. 3 is a flowchart of a method for recognizing a 360-degree panoramic image according to another embodiment of the present invention.

4 is a sub-flow diagram of step S12 of FIG. 1 or step S102 of FIG. 3 in an embodiment of the present invention.

FIG. 5 is a sub-flow diagram of step S104 of FIG. 3 in an embodiment of the present invention.

FIG. 6 is a flowchart of a method for recognizing a 360-degree panoramic video according to an embodiment of the present invention.

Figure 7 is a sub-flow diagram of an embodiment in step S503 of Figure 6.

FIG. 8 is a structural block diagram of an electronic device according to an embodiment of the present invention.

detailed description

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

Please refer to FIG. 1 and FIG. 2. FIG. 1 is a flowchart of a method for recognizing a 360-degree panoramic image according to an embodiment of the present invention. 2 is a schematic diagram of a target image frame M1. The identification method includes the steps of:

S11: Acquire an image file to be identified.

S12: Determine the target image frame M1 according to the image file to be identified.

S13: determining whether the matching degree between the gray value of the pixel P1 of the first column L1 of the target image frame M1 and the gray value of the pixel of the last column Ln is greater than or equal to a first threshold, and determining the location Whether the difference between the gray value of all the pixel points P1 of the first line C1 of the target image frame M1 and the difference of the gray values of all the pixel points P1 of the last line C1 is less than or equal to the second threshold.

If yes, that is, the matching degree between the gray value of the pixel point P1 of the first column L1 and the gray value of the pixel point of the last column Ln is greater than or equal to the first threshold and all pixels of the first row C1 The difference between the gray value of the point P1 and the difference between the gray values of all the pixel points P1 of the last line Cm are less than or equal to the second threshold, then step S14 is performed, and if not, the pixel of the first column L1 The degree of matching between the gray value of the point P1 and the gray value of the pixel of the last column Ln is smaller than the first threshold, or The difference between the gray value of all the pixel points P1 of the first row C1 and the gray value of all the pixel points P1 of the last row C1 are greater than the second threshold, and then step S15 is performed.

S14: Determine that the image file to be identified is a 360-degree panoramic image.

S15: It is determined that the image file to be identified is not a 360-degree panoramic image.

Therefore, in the present application, it is determined by determining that the matching degree between the gradation value of the pixel point P1 of the first column L1 and the gradation value of the pixel point P1 of the last column L1 in the target image frame M1 is greater than the first threshold. Whether the gradation value of the pixel point P1 of the first column L1 of the target image frame is similar to the gradation value of the pixel point P1 of the last column Ln. And determining whether the difference between the gray value of the pixel point P1 of the first line C1 in the target image frame M1 and the difference between the gray values of the pixel point P1 of the last line C1 is within the second threshold to determine the target image frame M1 first Whether the gray value of the pixel point P1 of the line C1 is similar, and whether the gray value of the pixel point P1 of the last line Cm is similar, it is possible to accurately determine whether the target image frame M1 is a 360-degree panoramic image and then determine the image to be recognized. Whether the file is a 360-degree panoramic image.

Please refer to FIG. 3. FIG. 3 is a flowchart of a method for recognizing a 360-degree panoramic image according to another embodiment of the present invention. The method for identifying the 360-degree panoramic image is not limited to the following execution sequence. In another embodiment, the identifying method comprises:

S101: Acquire an image file to be identified.

S102: Determine a target image frame M1 according to the image file to be identified.

S103: Acquire a gray value of the pixel point P1 of the first column L1 of the target image frame M1 and a gray value of the pixel point of the last column. The target image frame M1 includes pixel points P1 arranged in a matrix. For example, as shown in FIG. 2, the target image frame M1 includes m*n pixel points P1, that is, m rows and n columns of pixel points P1, where m and n are positive integers greater than 1, and m and n may be equal or not equal.

S104: Calculate a matching degree between the gradation value of the pixel point P1 of the first column L1 and the gradation value of the pixel point of the last column Ln.

S105: Determine whether the matching degree is greater than or equal to a first threshold. If yes, step S106 is performed, and if no, step S110 is performed.

S106: Acquire gray values of all the pixel points P1 of the first row C1 of the target image frame M1 and gray values of all the pixel points P1 of the last row of Cm.

S107: Calculate a difference between the gradation values of all the pixel points P1 of the first row C1 and the gradation values of all the pixel points P1 of the last row Cm. In some embodiments, the step S107 Specifically, the method includes: calculating a variance of the gray values of all the pixel points P1 of the first row C1 to obtain a difference between the gray values of all the pixel points P1, and calculating a gray value of all the pixel points P1 of the last row Cm. The variance obtains the difference between the gray values of all the pixel points P1 of the last line Cm.

S108: Determine whether the difference between the gray value of all the pixel points P1 of the first row C1 and the difference of the gray values of all the pixel points P1 of the last row C1 are less than or equal to the second threshold. If yes, step S109 is performed, and if no, step S110 is performed. The second threshold may be a variance threshold of 20.

S109: Determine that the image file to be identified is a 360-degree panoramic image.

S110: Determine that the image file to be identified is not a 360-degree panoramic image.

The steps S103-S108 correspond to the step S13 in FIG. 1, and the more specific steps of step S13 are illustrated. It should be noted that when steps S103-S108 are taken as a more specific step flow of step S13, step S106 will be executed regardless of whether the determination result of S105 is YES or not.

Referring to FIG. 4, it is a sub-flowchart of step S12 of FIG. 1 or step S102 of FIG. 3 in an embodiment of the present invention. In an embodiment, the step S12 or the step S102 includes:

It is judged whether the image to be recognized is a three-dimensional image or a two-dimensional image (step S1021). If it is a three-dimensional image, step S1023 is performed, and if it is a two-dimensional image, step S1025 is performed.

If it is a three-dimensional image, the left-eye image or the right-eye image in the image to be recognized is extracted, and it is determined that the extracted left-eye image or right-eye image is the target image frame M1 (S1023).

If it is a two-dimensional image, it is determined that the image to be recognized is the target image frame M1 (S1025).

Since the three-dimensional image includes the left-eye image and the right-eye image, directly analyzing the three-dimensional image may result in inaccurate results. The present application determines whether the three-dimensional image is a three-dimensional image, and extracts a left-eye image or a right image when determining the three-dimensional image. The eye image is analyzed as a target image frame to improve the accuracy of the analysis result.

Referring to FIG. 5 together, it is a sub-flowchart of step S104 in an embodiment of the present invention. In an embodiment, the step S104 includes:

Step S1041: sequentially, each pixel point P1 of the first column L1 and/or the last column Ln is taken as a center point, and the pixel point P1 of the first column L1 and/or the last column Ln which is the current center point is obtained. The gray value of the preset pixel point P1 and the pixel value of the pixel point P1 that is currently the center point.

Step S1043: Calculate an average value of the obtained grayscale values of the plurality of pixel points P1 including the pixel point P1 that is currently the center point, and use the average value as the pixel point P1 of the current center point. The gradation value is corrected, and the corrected gradation value of each pixel point P1 of the first column L1 and the last column Ln is obtained one by one.

In some embodiments, the gray value of the preset pixel point P1 adjacent to the pixel point P1 of the first column L1 that is currently the center point and the pixel point P1 of the current center point may be acquired first. The pixel value is then calculated as the corrected gray value of the pixel point P1 which is currently the center point, and the corrected gray value of all the pixel points P1 of the first column L1 is obtained, and then the pixel point of the last column Ln which is the current center point is obtained. P1 the gray value of the preset pixel point P1 adjacent to the upper and lower sides and the pixel value of the pixel point P1 currently being the center point, and then calculating the corrected gray value of the pixel point P1 currently being the center point, and obtaining the last column The corrected gradation value of all the pixel points P1 of Ln is obtained, and the corrected gradation value of each pixel point P1 of the first column L1 and the last column Ln is obtained.

In other embodiments, the gray value of the preset pixel point P1 adjacent to the pixel point P1 of the first column L1 and the last column of the current column and the pixel point of the current center point may be simultaneously acquired. Pixel value of P1, and then simultaneously calculate the corrected gradation value of the pixel point P1 of the first column L1 and the last column of the current center point, and obtain each pixel point P1 of the first column L1 and the last column Ln Correct the gray value.

Step S1045: respectively calculating a difference between the corrected gradation value of each pixel point P1 of the first column L1 and the corrected gradation value of the pixel point P1 of the row corresponding to the last column Ln to obtain a plurality of gradation value difference values.

Step S1047: Calculate a proportion of the calculated gray value difference values that are less than or equal to the preset difference value among the calculated gray value difference values, and determine the ratio as the matching degree. For example, if the m is 1000, that is, the first column L1 and the last column Lm each include 1000 pixel points P1, 1000 gray value difference values will be calculated, assuming a gray value less than or equal to the preset difference value. The number of differences is 800, and the proportion is 0.8, which is 80%.

Corresponding to FIG. 5, in some embodiments, step S105 in FIG. 1 determines whether the matching degree is greater than or equal to a first threshold value in a first threshold value, for example, 0.75, that is, 75. %.

Please refer to FIG. 6 , which is a flowchart of a method for recognizing a 360-degree panoramic video according to an embodiment of the present invention. As shown in FIG. 6, the method for identifying the 360-degree panoramic video includes the following steps:

S501: Acquire a video file to be identified.

S502: Extract a plurality of representative image frames from the video file to be identified. In some embodiments, the representative image frame is selected based on at least one of the following selection principles: 1. Selecting a key frame in the video file as a representative image frame, and generally the key frame includes a complete frame image, other than Type frames have higher image quality. Key frames often include the start time of a camera lens. There is a big difference between adjacent key frames, which is beneficial to improve the diversity of recognition samples. 2. Select frames with rich picture changes. The image is used as a representative image frame; 3. a plurality of image frames whose time periods covered by the position of the time axis in the video file exceed a preset value are selected as a plurality of representative image frames, for example, the duration of the video file is 2 hours. Then, as many as possible, a plurality of image frames are intermittently selected as representative image frames from the beginning to the last time period, and the time span of the plurality of image frames may be 1 hour and 50 minutes or the like.

S503: Determine whether each representative image frame is a 360-degree panoramic image.

S504: Determine whether a proportion of the representative image frame of the 360-degree panoramic image is greater than a preset ratio. If yes, step S505 is performed, and if no, step S506 is performed.

S505: Determine that the video file is a 360-degree panoramic video.

S506: Determine that the video file is not a 360-degree panoramic video.

Please refer to FIG. 7, which is a sub-flowchart in step S503 in an embodiment. In some embodiments, the step S503 includes:

S531: Determine a target image frame corresponding to each representative image frame;

S532: Determine whether the matching degree between the gray value of the pixel of the first column of each target image frame and the gray value of the pixel of the last column is greater than or equal to a first threshold, and determine each target image frame. Whether the difference between the gray values of all the pixels of the first row and the gray value of all the pixels of the last row are less than or equal to the second threshold. If yes, go to step S533, if no, go to step S534.

S533: Determine that the corresponding representative image frame is a 360-degree panoramic image.

S534: Determine that the corresponding representative image frame is not a 360-degree panoramic image.

Thereby, it is determined one by one whether each representative image frame is a 360-degree panoramic image.

The steps S531-S534 correspond to steps S12-S15 in FIG. 1, respectively. The more specific steps of steps S531-S534 are the same as steps S12-S15 in FIG. 1, that is, any one of the representative image frames is equivalent to one image file, and any method for identifying whether the image frame is a 360-degree panoramic image in FIG. versus The method for identifying whether the image file is a 360-degree panoramic image is the same in FIG. 1, and further includes the corresponding method steps in FIGS. For example, step S531 includes determining whether the representative image frame is a three-dimensional image or a two-dimensional image. If it is a three-dimensional image, extracting a left eye image or a right eye image in the representative image frame, determining that the extracted left eye image or right eye image is a target image frame; if the representative image frame is a two-dimensional image, Determining that the representative image frame is the target image frame.

Correspondingly, the step S532 may specifically include the steps S103-S108 shown in FIG. 3 or further include the steps S1041-S1047 shown in FIG. 5.

Please refer to FIG. 8 , which is a structural block diagram of an electronic device 100 according to an embodiment of the invention. As shown in FIG. 1 , the electronic device 100 includes a processor 10 and a memory 20 .

The memory 20 stores image files and/or video files to be identified. The image files and/or video files stored in the memory 20 are pre-stored in the memory 20, and may also be temporarily downloaded from the server or received from other electronic devices 100.

In some embodiments, the electronic device 100 further includes a communication unit 30, which can establish a communication connection with a server or other electronic device through the communication unit 30 in advance, and receive an image to be recognized from a server or other electronic device. File and/or video files are stored in the memory 20. The communication unit 30 can be a wired or wireless communication module, and can be, for example, a wired network interface unit, a WIFI module, a Bluetooth module, or the like.

The processor 10 is configured to analyze an image file and/or a video file to be identified stored in the memory 20 to identify whether it is a 360-degree panoramic image and/or a 360-degree panoramic video.

The processor 10 is configured to perform any of the methods shown in FIG. 1 and FIG. 3-5 to identify whether the image file to be identified is a 360-degree panoramic image.

The processor 10 is further configured to perform any of the method steps shown in FIG. 6-7 and the related method steps shown in FIG. 3-5 to identify whether the video file to be identified is a 360-degree panoramic video.

In some embodiments, the memory 20 stores a plurality of program instructions, and after the processor 10 calls the execution of the plurality of program instructions, performing any of the methods shown in FIG. 1 and FIG. 3-5 to identify the to-be-identified Whether the image file is a 360-degree panoramic image, and/or performing any of the method steps shown in FIG. 6-7 and the related method steps shown in FIG. 3-5 to identify whether the video file to be recognized is a 360-degree panoramic video. .

For example, the processor 10 performs the following method to identify whether the image file to be recognized is 360 degrees. Panoramic image: acquiring an image file to be identified; determining a target image frame according to the image file to be identified; determining a match between a gray value of a pixel of the first column of the target image frame and a gray value of a pixel of the last column Whether the degree is greater than or equal to a first threshold, and whether the difference between the gray values of all the pixels of the first line of the target image frame and the difference of the gray values of all the pixels of the last line are less than or equal to the second threshold If yes, it is determined that the image file to be identified is a 360-degree panoramic image; if not, it is determined that the image file to be recognized is not a 360-degree panoramic image.

For another example, the processor 10 performs the following method: determining whether the video file to be identified is a 360-degree panoramic video: acquiring a video file to be identified; extracting a plurality of representative image frames from the video file to be identified; determining each representative Whether the image frame is a 360-degree panoramic image; whether the proportion of the representative image frame determined as the 360-degree panoramic image is greater than a preset ratio; if yes, determining that the video file is a 360-degree panoramic video, and if not, determining the video The file is not a 360 degree panoramic video.

The processor 40 can be a microcontroller, a microprocessor, a single chip, a digital signal processor, or the like.

The memory 20 can be any storage device that can store information such as a memory card, a solid state memory, a micro hard disk, an optical disk, or the like.

In some embodiments, the present invention further provides a computer readable storage medium having stored therein a plurality of program instructions, which are executed by the processor 10 for execution, and are executed in FIG. Any of the method steps 3-7, thereby identifying whether the image file is a 360-degree panoramic image and/or identifying whether the video file is a 360-degree panoramic video. In some embodiments, the computer storage medium is the memory 20, and may be any storage device that can store information such as a memory card, a solid state memory, a micro hard disk, an optical disk, or the like.

The electronic device 100 can be a mobile phone, a tablet computer, a notebook computer, a desktop computer, etc., and can also be a head-mounted device such as a smart helmet or smart glasses.

Thus, by the 360 degree panoramic image and the 360 degree panoramic video recognition method and the electronic device of the present invention, by determining the target image frame, and then analyzing the gray between the pixel of the first column of the target image frame and the pixel of the last column Determining whether the target image frame is a 360-degree panoramic image frame, and then determining whether the target image frame is a 360-degree panoramic image frame by analyzing the grayscale difference between the pixel points of the first row of the target image frame and the grayscale difference between the pixels of the last row. Whether the corresponding image or video is a 360-degree panoramic image or a 360-degree panoramic video can effectively improve the recognition accuracy.

The above is a preferred embodiment of the present invention, and it should be noted that those skilled in the art can also make several improvements and retouchings without departing from the principles of the present invention. It is the scope of protection of the present invention.

Claims

A method for recognizing a 360-degree panoramic image, characterized in that the identification method comprises:

Obtaining an image file to be identified;

Determining a target image frame according to the image file to be identified;

Determining whether a matching degree between a gray value of a pixel of the first column of the target image frame and a gray value of a pixel of the last column is greater than or equal to a first threshold, and determining a first of the target image frame Whether the difference between the gray values of all the pixels of the row and the gray value of all the pixels of the last row are less than or equal to the second threshold;

If yes, it is determined that the image file to be identified is a 360-degree panoramic image; otherwise, it is determined that the image file to be recognized is not a 360-degree panoramic image.
The identification method according to claim 1, wherein said step "determining a matching degree between a gray value of a pixel of the first column of the target image frame and a gray value of a pixel of the last column Whether it is greater than or equal to the first threshold, and determining whether the difference between the gray values of all the pixels of the first row of the target image frame and the difference of the gray values of all the pixels of the last row are less than or equal to the second threshold "include:

Obtaining a gray value of a pixel of the first column of the target image frame and a gray value of the pixel of the last column;

Calculating a matching degree between a gray value of a pixel of the first column and a gray value of a pixel of the last column;

Determining whether the matching degree is greater than or equal to a first threshold;

Obtaining a gray value of all pixels of the first row of the target image frame and a gray value of all pixels of the last row;

Calculating a difference between gray values of all pixels of the first row and a difference between gray values of all pixels of the last row;

Determining whether the difference between the gray values of all the pixels of the first row and the difference of the gray values of all the pixels of the last row are less than or equal to the second threshold.
The identification method according to claim 1, wherein the step of "determining a target image frame according to the image file to be recognized" comprises:

Determining whether the image to be identified is a three-dimensional image or a two-dimensional image;

If it is a three-dimensional image, extracting a left-eye image or a right-eye image in the image to be identified, and determining that the extracted left-eye image or right-eye image is a target image frame;

If it is a two-dimensional image, it is determined that the image to be recognized is the target image frame.
The identification method according to claim 2, wherein the step of "calculating the matching degree between the gray value of the pixel of the first column and the gray value of the pixel of the last column" includes:

Obtaining, according to each pixel point of the first column and/or the last column, the gray level of the preset pixel points adjacent to the pixel point of the current column of the first column and/or the last column a value and a pixel value of the pixel point currently being the center point;

Calculating an average value of the obtained grayscale values of the plurality of pixel points including the pixel point currently being the center point, and using the average value as the corrected gray value of the pixel point currently being the center point, This one obtains the corrected gray value of each pixel of the first column and the last column;

Calculating a difference between the corrected gray value of each pixel of the first column and the corrected gray value of the pixel of the corresponding row of the last column to obtain a plurality of gray value difference values;

Calculating a proportion of the gray value difference values of the plurality of gray value difference values that are less than or equal to the preset difference value, and determining the ratio as the matching degree.
The identification method according to claim 2, wherein said step "calculating a difference between gray values of all the pixels of said first line and a difference of gray values of all pixels of said last line" include:

Calculating a variance of gray values of all the pixels of the first row to obtain a difference of gray values of all the pixels, and calculating a variance of gray values of all the pixels of the last row to obtain all of the last row The difference between the gray values of the pixels.
A method for recognizing a 360-degree panoramic video, characterized in that the identification method comprises:

Obtain a video file to be identified;

Extracting a plurality of representative image frames from the video file to be identified;

Determining whether each representative image frame is a 360-degree panoramic image;

Whether the proportion of the representative image frame determined as the 360-degree panoramic image is greater than a preset ratio;

If so, it is determined that the video file is a 360-degree panoramic video, and if not, it is determined that the video file is not a 360-degree panoramic video.
The identification method according to claim 6, wherein said step of "determining whether each representative image frame is a 360-degree panoramic image" comprises:

Determining a target image frame corresponding to each representative image frame;

Determining whether a matching degree between a gray value of a pixel of the first column of each target image frame and a gray value of a pixel of the last column is greater than or equal to a first threshold, and determining a first of each target image frame Whether the difference between the gray values of all the pixels of the row and the gray value of all the pixels of the last row are less than or equal to the second threshold;

If yes, it is determined that the corresponding representative image frame is a 360-degree panoramic image; otherwise, it is determined that the corresponding representative image frame is not a 360-degree panoramic image.
The identification method according to claim 7, wherein said step "determining the degree of matching between the gray value of the pixel of the first column of each target image frame and the gray value of the pixel of the last column Whether it is greater than or equal to the first threshold, and whether the difference between the gray value of all the pixels of the first row of each target image frame and the difference of the gray values of all the pixels of the last row are less than or equal to the second threshold "include:

Obtaining a gray value of a pixel of the first column of the target image frame and a gray value of the pixel of the last column;

Calculating a matching degree between a gray value of a pixel of the first column and a gray value of a pixel of the last column;

Determining whether the matching degree is greater than or equal to a first threshold;

Obtaining a gray value of all pixels of the first row of the target image frame and a gray value of all pixels of the last row;

Calculating a difference between gray values of all pixels of the first row and a difference between gray values of all pixels of the last row;

Determining whether the difference between the gray values of all the pixels of the first row and the difference of the gray values of all the pixels of the last row are less than or equal to the second threshold.
The identification method according to claim 7, wherein the step of "determining a target image frame corresponding to each representative image frame" comprises:

Determining whether the representative image frame is a three-dimensional image or a two-dimensional image;

If it is a three-dimensional image, extracting a left-eye image or a right-eye image in the representative image frame, determining The extracted left eye image or right eye image is a target image frame;

If it is a two-dimensional image, it is determined that the representative image frame is the target image frame.
The identification method according to claim 8, wherein the step of "calculating the matching degree between the gray value of the pixel of the first column and the gray value of the pixel of the last column" includes:

Obtaining, according to each pixel point of the first column and/or the last column, the gray level of the preset pixel points adjacent to the pixel point of the current column of the first column and/or the last column a value and a pixel value of the pixel point currently being the center point;

Calculating an average value of the obtained grayscale values of the plurality of pixel points including the pixel point currently being the center point, and using the average value as the corrected gray value of the pixel point currently being the center point, This one obtains the corrected gray value of each pixel of the first column and the last column;

Calculating a difference between the corrected gray value of each pixel of the first column and the corrected gray value of the pixel of the corresponding row of the last column to obtain a plurality of gray value difference values;

Calculating a proportion of the gray value difference values of the plurality of gray value difference values that are less than or equal to the preset difference value, and determining the ratio as the matching degree.
The identification method according to claim 8, wherein said step "calculating a difference between gray values of all the pixels of said first line and a difference of gray values of all pixels of said last line" include:

Calculating a variance of gray values of all the pixels of the first row to obtain a difference of gray values of all the pixels, and calculating a variance of gray values of all the pixels of the last row to obtain all of the last row The difference between the gray values of the pixels.
The identification method according to claim 6, wherein the step of "extracting a plurality of representative image frames from the video file to be identified" comprises:

Selecting a plurality of representative image frames by selecting a key frame in the video file, or selecting a frame image rich in picture variation, or a time period in which the video file is overlaid according to a plurality of representative image frames exceeds a preset value.
An electronic device includes a processor and a memory, wherein the memory stores a video file to be identified, wherein the processor is configured to:

Obtain a video file to be identified;

Extracting a plurality of representative image frames from the video file to be identified;

Determining whether each representative image frame is a 360-degree panoramic image;

Whether the proportion of the representative image frame determined as the 360-degree panoramic image is greater than a preset ratio;

If so, it is determined that the video file is a 360-degree panoramic video, and if not, it is determined that the video file is not a 360-degree panoramic video.
The electronic device according to claim 13, wherein the processor determines whether each representative image frame is a 360-degree panoramic image, comprising:

Determining a target image frame corresponding to each representative image frame;

Determining whether a matching degree between a gray value of a pixel of the first column of each target image frame and a gray value of a pixel of the last column is greater than or equal to a first threshold, and determining a first of each target image frame Whether the difference between the gray values of all the pixels of the row and the gray value of all the pixels of the last row are less than or equal to the second threshold;

If yes, it is determined that the corresponding representative image frame is a 360-degree panoramic image; otherwise, it is determined that the corresponding representative image frame is not a 360-degree panoramic image.
The electronic device according to claim 14, wherein said processor determines a matching degree between a gray value of a pixel of the first column of each target image frame and a gray value of a pixel of the last column Whether it is greater than or equal to the first threshold, and whether the difference between the gray value of all the pixels of the first row of each target image frame and the difference of the gray values of all the pixels of the last row are less than or equal to the second threshold ,include:

Obtaining a gray value of a pixel of the first column of the target image frame and a gray value of the pixel of the last column;

Calculating a matching degree between a gray value of a pixel of the first column and a gray value of a pixel of the last column;

Determining whether the matching degree is greater than or equal to a first threshold;

Obtaining a gray value of all pixels of the first row of the target image frame and a gray value of all pixels of the last row;

Calculating a difference between gray values of all pixels of the first row and a difference between gray values of all pixels of the last row;

Determining whether the difference between the gray values of all the pixels of the first row and the difference of the gray values of all the pixels of the last row are less than or equal to the second threshold.
The electronic device of claim 14 wherein said processor determines each Representing the target image frame corresponding to the image frame, including:

Determining whether the representative image frame is a three-dimensional image or a two-dimensional image;

If it is a three-dimensional image, extracting a left eye image or a right eye image in the representative image frame, determining that the extracted left eye image or right eye image is a target image frame;

If it is a two-dimensional image, it is determined that the representative image frame is the target image frame.
The electronic device according to claim 15, wherein the processor calculates a matching degree between the gray value of the pixel of the first column and the gray value of the pixel of the last column, including:

Obtaining, according to each pixel point of the first column and/or the last column, the gray level of the preset pixel points adjacent to the pixel point of the current column of the first column and/or the last column a value and a pixel value of the pixel point currently being the center point;

Calculating an average value of the obtained grayscale values of the plurality of pixel points including the pixel point currently being the center point, and using the average value as the corrected gray value of the pixel point currently being the center point, This one obtains the corrected gray value of each pixel of the first column and the last column;

Calculating a difference between the corrected gray value of each pixel of the first column and the corrected gray value of the pixel of the corresponding row of the last column to obtain a plurality of gray value difference values;

Calculating a proportion of the gray value difference values of the plurality of gray value difference values that are less than or equal to the preset difference value, and determining the ratio as the matching degree.
The electronic device according to claim 15, wherein said processor calculates a difference between gray values of all pixels of said first line and a difference of gray values of all pixels of said last line, include:

Calculating a variance of gray values of all the pixels of the first row to obtain a difference of gray values of all the pixels, and calculating a variance of gray values of all the pixels of the last row to obtain all of the last row The difference between the gray values of the pixels.
The electronic device according to claim 15, wherein the processor selects a key frame in the video file as a representative image frame, or selects a frame image rich in picture variation as a representative image frame, or selects a cover video file. A plurality of image frames whose time period exceeds a preset range are used as the plurality of representative image frames.
An electronic device includes a processor and a memory, wherein the memory stores an image file to be identified, wherein the processor is configured to:

Obtaining an image file to be identified;

Determining a target image frame according to the image file to be identified;

Determining whether a matching degree between a gray value of a pixel of the first column of the target image frame and a gray value of a pixel of the last column is greater than or equal to a first threshold, and determining a first of the target image frame Whether the difference between the gray values of all the pixels of the row and the gray value of all the pixels of the last row are less than or equal to the second threshold;

If yes, it is determined that the image file to be identified is a 360-degree panoramic image; otherwise, it is determined that the image file to be recognized is not a 360-degree panoramic image.