Dial generation method, device, electronic device and computer-readable storage medium

Abstract

The present application relates to a dial generation method, device, electronic device and computer-readable storage medium. The method includes obtaining a to-be-matched picture; extracting to-be-matched features of the to-be-matched picture; obtaining a reference picture and a reference feature of the reference picture; matching the to-be-matched features with the reference features of each reference picture respectively, and determining the similarity between the to-be-matched picture and each reference picture; determining a target picture from each reference picture based on the similarity between the to-be-matched picture and each reference picture; obtaining a time element, and generating a dial based on the time element and the target picture. The above-mentioned dial generation method, device, electronic device and computer-readable storage medium can generate a more accurate dial.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a dial generation method, device, electronic device and computer-readable storage medium. Background Art

[0002] With the development of mobile technology, many traditional electronic products have also begun to add mobile functions. For example, watches that could only be used to tell time in the past can now be connected to the Internet through smartphones or home networks to display incoming call information, social media chat information, news, weather information and other content.

[0003] On the display interface of electronic devices such as smart watches or smart bracelets, users can select their favorite patterns as watch faces. However, the traditional watch face generation method has the problem of inaccurate watch faces. Summary of the invention

[0004] The embodiments of the present application provide a dial generation method, device, electronic device, and computer-readable storage medium, which can improve the accuracy of the generated dial.

[0005] A method for generating a dial, comprising:

[0006] Get the image to be matched;

[0007] Extracting features to be matched from the picture to be matched;

[0008] Acquire a reference image and a reference feature of the reference image;

[0009] Matching the to-be-matched features with the reference features of each of the reference images respectively, and determining the similarity between the to-be-matched image and each of the reference images;

[0010] Determining a target picture from each of the reference pictures based on a similarity between the picture to be matched and each of the reference pictures;

[0011] A time element is obtained, and a watch face is generated based on the time element and the target image.

[0012] A method for generating a dial, comprising:

[0013] Get the image to be matched;

[0014] Extracting features to be matched from the picture to be matched;

[0015] Acquire a reference image and a reference feature of the reference image;

[0016] Matching the to-be-matched features with the reference features of each of the reference images respectively, and determining the similarity between the to-be-matched image and each of the reference images;

[0017] Determining a target picture from each of the reference pictures based on a similarity between the picture to be matched and each of the reference pictures;

[0018] The target image is sent to a wearable device; the target image is used to instruct the wearable device to obtain a time element, and to generate a watch face based on the time element and the target image.

[0019] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the dial generation method as described above.

[0020] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0021] The above-mentioned dial generation method, device, electronic device and computer-readable storage medium match the to-be-matched features of the to-be-matched picture with the reference features of the reference picture, so that based on the similarity between the to-be-matched picture and the reference picture, a more accurate target picture can be determined from each reference picture, and then the time element can be obtained. Based on the time element and the target picture, a more accurate dial can be generated.

[0022] A method for generating a dial, comprising:

[0023] Get the image to be matched;

[0024] Extracting features to be matched from the picture to be matched;

[0025] Acquire a reference image and a reference feature of the reference image;

[0026] Matching the to-be-matched features with the reference features of each of the reference images respectively, and determining the similarity between the to-be-matched image and each of the reference images;

[0027] Determining a target picture from each of the reference pictures based on a similarity between the picture to be matched and each of the reference pictures;

[0028] The target image is sent to a wearable device; the target image is used to instruct the wearable device to obtain a time element, and to generate a watch face based on the time element and the target image.

[0029] A dial generating device, comprising:

[0030] A module for obtaining images to be matched is used to obtain images to be matched;

[0031] A feature extraction module, used to extract the features to be matched of the image to be matched;

[0032] A reference picture and reference feature acquisition module, used to acquire a reference picture and reference features of the reference picture;

[0033] A matching module, used to match the to-be-matched features with the reference features of each of the reference images, respectively, to determine the similarity between the to-be-matched image and each of the reference images;

[0034] A target picture determination module, configured to determine a target picture from each of the reference pictures based on a similarity between the picture to be matched and each of the reference pictures;

[0035] A watch face generation module is used to send the target image to a wearable device; the target image is used to instruct the wearable device to obtain a time element, and to generate a watch face based on the time element and the target image.

[0036] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the dial generation method as described above.

[0037] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0038] The above-mentioned dial generation method, device, electronic device and computer-readable storage medium match the to-be-matched features of the to-be-matched picture with the reference features of the reference picture, so that based on the similarity between the to-be-matched picture and the reference picture, a more accurate target picture can be determined from each reference picture, and the target picture can be sent to the wearable device for the wearable device to generate a more accurate dial.

[0039] A dial generation method, applied to a wearable device, comprising:

[0040] Acquire a target image sent by an electronic device; the target image is determined by the electronic device from each reference image based on the similarity between the acquired image to be matched and each acquired reference image; the similarity between the image to be matched and each reference image is obtained by the electronic device matching the to-be-matched features of the image to be matched with the reference features of each reference image respectively; the to-be-matched features of the image to be matched are extracted by the electronic device from the image to be matched;

[0041] A time element is obtained, and a watch face is generated based on the time element and the target image.

[0042] A watch face generating device, applied to a wearable device, comprising:

[0043] The target image acquisition module is used to acquire a target image sent by the electronic device; the target image is determined by the electronic device from each reference image based on the similarity between the acquired image to be matched and each acquired reference image; the similarity between the image to be matched and each reference image is obtained by the electronic device matching the features to be matched of the image to be matched with the reference features of each reference image respectively; the features to be matched of the image to be matched are extracted by the electronic device from the image to be matched;

[0044] The dial generation module is used to obtain a time element and generate a dial based on the time element and the target image.

[0045] A wearable device comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the dial generation method as described above.

[0046] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0047] The above-mentioned dial generation method, apparatus, wearable device and computer-readable storage medium obtain a target picture sent by an electronic device, where the target picture is determined by the electronic device from various reference pictures based on the similarity between the picture to be matched and the reference picture, so that the determined target picture is more accurate; then a time element is obtained, and a more accurate dial is generated based on the time element and the target picture. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A diagram showing an application environment of a dial generation method in an embodiment;

[0050] Figure 2 is a flowchart of a method for generating a dial in one embodiment;

[0051] Figure 3A flowchart of the steps for determining a target image in one embodiment;

[0052] Figure 4 A flowchart of the steps for determining a region to be matched in an embodiment;

[0053] Figure 5 is a flowchart of a dial generation method in another embodiment;

[0054] Figure 6 is a structural block diagram of a dial generating device in an embodiment;

[0055] Figure 7 is a structural block diagram of a dial generating device in another embodiment;

[0056] Figure 8 is a structural block diagram of a dial generating device in another embodiment;

[0057] Fig. 9 FIG. 1 is a schematic diagram of the internal structure of an electronic device in an embodiment. DETAILED DESCRIPTION

[0058] 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.

[0059] Figure 1 FIG. 1 is a schematic diagram of an application environment of a dial generation method in an embodiment. Figure 1 As shown, the application environment includes a wearable device 102 and an electronic device 104, and the wearable device 102 and the electronic device 104 communicate through a network. The electronic device 104 obtains a picture to be matched; extracts features to be matched of the picture to be matched; obtains a reference picture and a reference feature of the reference picture; matches the features to be matched with the reference features of each reference picture respectively, and determines the similarity between the picture to be matched and each reference picture; determines a target picture from each reference picture based on the similarity between the picture to be matched and each reference picture; and sends the target picture to the wearable device 102 through the network. After receiving the target picture, the wearable device 102 obtains a time element and generates a dial based on the time element and the target picture.

[0060] Figure 2 FIG. 1 is a flow chart of a method for generating a dial in one embodiment. Figure 2 As shown, the dial generation method includes steps 202 to 212.

[0061] Step 202: Obtain the image to be matched.

[0062] The image to be matched refers to the image used for matching to generate the dial. The image to be matched can be one of RGB (Red, Green, Blue) images, grayscale images, etc. RGB images can be obtained by taking pictures with a color camera. Grayscale images can be obtained by taking pictures with a black and white camera. The image to be matched can be stored locally in the electronic device, stored in other devices, stored on the network, or taken in real time by the electronic device, but is not limited thereto.

[0063] Specifically, an ISP (Image Signal Processing) processor or a central processor of the electronic device may obtain the image to be matched from a local device or other devices, or obtain the image to be matched by photographing it with a camera.

[0064] Step 204: extract the features to be matched of the image to be matched.

[0065] The feature to be matched refers to the feature of the image to be matched. The feature to be matched may include at least one of the local feature and the global feature of the image to be matched. The local feature may be the texture feature, the contour feature, etc. of the image to be matched; the global feature may be the color feature, the contrast feature, etc. of the image to be matched.

[0066] Optionally, the to-be-matched features of the to-be-matched images may be represented by vectors.

[0067] The electronic device inputs the image to be matched into the feature extraction model, and extracts the matching features of the image to be matched through the trained feature extraction model. Among them, deep learning and metric learning are used to train the feature extraction model. The deep learning uses convolutional neural networks (CNN) for learning. Metric learning is a spatial mapping method that can learn a feature (Embedding) space in which all data is converted into a feature vector, and the distance between the feature vectors of similar samples is small, and the distance between the feature vectors of dissimilar samples is large, so as to distinguish the data.

[0068] The convolutional neural network in the feature extraction model is composed of multiple convolutional layers. The shallow convolutional layers can extract local details such as texture and contour in the image to be matched, and the high-level convolutional layers can extract global abstract features such as color and contrast. Finally, the entire convolutional neural network embeds the image to be matched into a high-dimensional vector (usually 128 dimensions, 256 dimensions, 512 dimensions, etc.) and outputs the high-dimensional vector. The high-dimensional vector is the feature to be matched of the image to be matched.

[0069] Furthermore, the electronic device can also perform denoising, wrinkle removal and other processing on the image to be matched, and then perform feature extraction on the processed image to be matched, so as to extract more accurate features to be matched.

[0070] Step 206, obtaining a reference picture and reference features of the reference picture.

[0071] The reference image refers to the image to be matched with the image to be matched. The reference feature refers to the feature of the reference image. Similarly, the reference feature may also include at least one of the local feature and the global feature of the reference image. The local feature may be a texture feature, a contour feature, etc. of the reference image; the global feature may be a color feature, a contrast feature, etc. of the reference image. Optionally, the reference feature of the reference image may be represented by a vector.

[0072] In one embodiment, the electronic device may extract reference features from the reference picture in advance. In another embodiment, the electronic device may also extract reference features from the reference picture after acquiring the reference picture.

[0073] The electronic device inputs the reference image into the feature extraction model, and extracts the reference features of the reference image through the trained feature extraction model. The feature extraction model is trained by deep learning and metric learning. The deep learning adopts convolutional neural networks (CNN) for learning. Metric learning is a spatial mapping method that can learn a feature (Embedding) space in which all data is converted into a feature vector, and the distance between the feature vectors of similar samples is small, and the distance between the feature vectors of dissimilar samples is large, so as to distinguish the data.

[0074] The convolutional neural network in the feature extraction model is composed of multiple convolutional layers. The shallow convolutional layers can extract local details such as texture and contour in the reference image, and the high-level convolutional layers can extract global abstract features such as color and contrast. Finally, the entire convolutional neural network embeds the reference image into a high-dimensional vector (usually 128 dimensions, 256 dimensions, 512 dimensions, etc.) and outputs the high-dimensional vector. The high-dimensional vector is the reference feature of the reference image.

[0075] Furthermore, the electronic device can also perform denoising, wrinkle removal and other processing on the reference image, and then perform feature extraction on the processed reference image to extract more accurate reference features.

[0076] Step 208 , matching the features to be matched with the reference features of each reference picture respectively, and determining the similarity between the picture to be matched and each reference picture.

[0077] It is understandable that similar pictures have similar feature expressions. The higher the similarity between the picture to be matched and the reference picture, the closer the to-be-matched feature of the picture to be matched is to the reference feature of the reference picture.

[0078] Specifically, the electronic device calculates the cosine distance between the feature to be matched and the reference feature, and uses the cosine distance as the similarity between the image to be matched and the reference image. The cosine distance, also known as cosine similarity, uses the cosine value of the angle between two vectors in a vector space as a measure of the size of the difference between two individuals.

[0079] Step 210 : determining a target picture from each reference picture based on the similarity between the picture to be matched and each reference picture.

[0080] Optionally, the number of the determined target pictures may be one or at least two.

[0081] In one embodiment, the electronic device may determine the reference picture with the highest similarity as the target picture. In another embodiment, the electronic device may determine the first two reference pictures with the highest similarity as the target pictures. In other embodiments, the electronic device may also determine other reference pictures with the highest similarity as the target pictures.

[0082] Furthermore, a weight factor of each reference picture is obtained, and based on the similarity between the picture to be matched and each reference picture and the weight factor of each reference picture, a target picture is determined from each reference picture.

[0083] For example, the similarity between the image to be matched and the reference image A is 60%, the similarity between the image to be matched and the reference image A is 85%, the weight factor of the reference image A is 1.5, and the weight factor of the reference image B is 1.0. The similarity of the reference image A is multiplied by the corresponding weight factor 1.5 to obtain 90%, and the similarity of the reference image B is multiplied by the corresponding weight factor 1.0 to obtain 85%. The target image is then determined based on the values ​​obtained for the reference images A and B. The electronic device may select the reference image A with a higher value as the target image, or may select the reference image B with a lower value as the target image.

[0084] Step 212, obtaining a time element, and generating a watch face based on the time element and the target image.

[0085] A time element refers to an element that includes time information. A time element may include a time scale, an hour hand, a minute hand, a second hand, etc. The style of a time element is not limited, such as a cartoon style, a landscape style, an object style, etc. The time information included in a time element may be running or static. For example, a time element may be a running clock or a sticker including a clock, and the clock in the sticker is static.

[0086] Specifically, the electronic device can use the target image as the background image and the time element as the foreground for overlay processing to generate a watch face.

[0087] The above-mentioned dial generation method matches the to-be-matched features of the to-be-matched picture with the reference features of the reference picture, so that based on the similarity between the to-be-matched picture and the reference picture, a more accurate target picture can be determined from each reference picture, and then the time element can be obtained. Based on the time element and the target picture, a more accurate dial can be generated.

[0088] Furthermore, through different to-be-matched pictures, different target pictures can be determined from the reference pictures, thereby generating various watch faces, which improves the richness of the watch faces. For example, the to-be-matched pictures are scenery, buildings, cars, etc. taken by electronic devices, thereby determining target pictures such as beautiful scenery, world famous buildings, famous cars, etc. from the reference pictures, thereby generating various watch faces.

[0089] In one embodiment, the above method for determining a target image can also be applied to image recommendation, shopping recommendation and other solutions.

[0090] In one embodiment, the method further includes: determining the category of the picture to be matched based on the features to be matched of the picture to be matched, determining a reference picture that matches the category of the picture to be matched, and using the reference picture that matches the category of the picture to be matched as an intermediate picture; matching the features to be matched with reference features of each reference picture respectively, and determining the similarity between the picture to be matched and each reference picture, including: matching the features to be matched with reference features of each intermediate picture respectively, and determining the similarity between the picture to be matched and each intermediate picture; determining a target picture from each reference picture based on the similarity between the picture to be matched and each reference picture, including: determining a target picture from each intermediate picture based on the similarity between the picture to be matched and each intermediate picture.

[0091] It is understandable that based on the to-be-matched features of the to-be-matched picture, the scene in the to-be-matched picture and information such as objects included in the to-be-matched picture can be identified, thereby determining the category of the to-be-matched picture.

[0092] The electronic device may classify the reference images in advance, and then use the reference images whose categories are consistent with the categories of the images to be matched as intermediate images.

[0093] In this embodiment, the middle picture is selected from the reference pictures, and then the features to be matched are matched with the reference features of the middle picture, thereby avoiding matching the features to be matched with the reference features of all reference pictures, thereby improving the efficiency of feature matching.

[0094] In one embodiment, Figure 3 As shown, after obtaining the image to be matched, it also includes:

[0095] Step 302: determine a region to be matched from the image to be matched, and obtain a sub-image according to the region to be matched.

[0096] The area to be matched refers to the area selected from the image to be matched. The shape of the area to be matched is not limited, and can be a circle, rectangle, triangle, or irregular shape.

[0097] A sub-image refers to an image generated based on the area to be matched. In one embodiment, the electronic device can use the area to be matched as a sub-image. In another embodiment, the electronic device can obtain a sub-image from the area to be matched. For example, the area to be matched is an irregular shape, and the largest rectangular area can be determined from the area to be matched as a sub-image. The specific implementation method of obtaining a sub-image based on the area to be matched is not limited and can be set according to user needs.

[0098] Extract the features to be matched of the image to be matched, including:

[0099] Step 304: extract sub-features of the sub-image.

[0100] Sub-features refer to features of a sub-image. Sub-features may include at least one of local features and global features of a sub-image. Local features include texture features and contour features of a sub-image; global features include color features and contrast features of a sub-image.

[0101] Optionally, sub-features of a sub-image may be represented by a vector.

[0102] The electronic device inputs the sub-image into the feature extraction model, and extracts the sub-features of the sub-image through the trained feature extraction model. The feature extraction model is trained by deep learning and metric learning. The deep learning adopts convolutional neural network (CNN) for learning. Metric learning is a spatial mapping method that can learn a feature (Embedding) space, in which all data are converted into a feature vector, and the distance between the feature vectors of similar samples is small, and the distance between the feature vectors of dissimilar samples is large, so as to distinguish the data.

[0103] The convolutional neural network in the feature extraction model is composed of multiple convolutional layers. The shallow convolutional layers can extract local details such as texture and contour in the sub-image, and the high-level convolutional layers can extract global abstract features such as color and contrast. Finally, the entire convolutional neural network embeds the sub-image into a high-dimensional vector (usually 128 dimensions, 256 dimensions, 512 dimensions, etc.) and outputs the high-dimensional vector. The high-dimensional vector is the sub-feature of the sub-image.

[0104] Furthermore, the electronic device can also perform denoising, wrinkle removal and other processing on the sub-image, and then perform feature extraction on the processed sub-image to extract more accurate sub-features.

[0105] Matching the features to be matched with the reference features of each reference image respectively, and determining the similarity between the image to be matched and each reference image, including:

[0106] Step 306 , matching the sub-features with the reference features of each reference picture respectively, and determining the similarity between the sub-picture and each reference picture.

[0107] The higher the similarity between the sub-image and the reference image, the closer the sub-features of the sub-image are to the reference features of the reference image.

[0108] Specifically, the electronic device calculates the cosine distance between the sub-feature and the reference feature, and uses the cosine distance as the similarity between the sub-image and the reference image. The cosine distance, also known as cosine similarity, uses the cosine value of the angle between two vectors in a vector space as a measure of the size of the difference between two individuals.

[0109] Based on the similarity between the image to be matched and each reference image, a target image is determined from each reference image, including:

[0110] Step 308: Determine a target picture from each reference picture based on the similarity between the sub-picture and each reference picture.

[0111] In this embodiment, a to-be-matched area is determined from the to-be-matched image, a sub-image is obtained based on the to-be-matched area, and sub-features of the sub-image are matched with reference features of the reference image, thereby avoiding obtaining features of all areas of the to-be-matched image and matching features of all areas of the to-be-matched image, saving resources of the electronic device, thereby improving the efficiency of feature matching, and determining the target image more quickly.

[0112] In one embodiment, extracting target features of a target area of ​​a to-be-matched image includes: obtaining a target scale; adjusting the size of a sub-image to the target scale; normalizing the pixel values ​​of each pixel in the sub-image of the target scale; and extracting features of the normalized sub-image to obtain target features of the sub-image.

[0113] It is understandable that the sub-image is obtained based on the area to be matched determined from the image to be matched, and the scale of the sub-image may be different from the scale of the reference image. Therefore, the size of the sub-image is adjusted to the target scale. The target scale can be set according to user needs. When the target scale is larger than the original scale of the sub-image, the sub-image is enlarged; when the target scale is smaller than the original scale of the sub-image, the sub-image is reduced.

[0114] For example, if the acquired target scale is (224×224 pixels), the size of the sub-image is adjusted to the target scale (224×224 pixels).

[0115] Normalization refers to mapping data to a range of 0 to 1, which can be processed more conveniently and quickly. Specifically, the pixel value of each pixel in the sub-image of the target scale is obtained, and the pixel value is mapped from 0-255 to 0-1.

[0116] In this embodiment, the size of the sub-image is adjusted to the target scale; and the pixel value of each pixel point in the sub-image of the target scale is normalized, which can facilitate subsequent processing of the normalized sub-image.

[0117] In one embodiment, before obtaining a reference image and a reference feature of the reference image, the method further includes: obtaining a reference image; adjusting the size of the reference image to a target scale; normalizing the pixel value of each pixel point in the reference image of the target scale; and extracting features of the normalized reference image to obtain a reference feature of the reference image.

[0118] It is understandable that by adjusting the sizes of the reference image and the sub-image to the target scale, the reference image and the sub-image can perform feature matching under the same conditions, and the similarity between the sub-image and the reference image can be more accurately obtained, thereby more accurately determining the target image from the reference image. In addition, the pixel values ​​of each pixel in the reference image are normalized to facilitate subsequent processing of the reference image.

[0119] In one embodiment, Figure 4 As shown, determining the area to be matched from the image to be matched includes:

[0120] Step 402: Generate a center weight map corresponding to the image to be matched, wherein the weight value represented by the center weight map gradually decreases from the center to the edge.

[0121] The center weight map refers to a map used to record the weight values ​​of each pixel in the image to be matched. The weight values ​​recorded in the center weight map gradually decrease from the center to the four sides, that is, the center weight is the largest and the weights gradually decrease toward the four sides. The center weight map represents that the weight values ​​of the center pixel of the image to be matched gradually decrease from the edge pixel of the image.

[0122] The ISP processor or the central processing unit can generate a corresponding center weight map according to the size of the image to be matched. The weight value represented by the center weight map gradually decreases from the center to the four sides. The center weight map can be generated using a Gaussian function, or a first-order equation, or a second-order equation. The Gaussian function can be a two-dimensional Gaussian function.

[0123] Step 404, input the to-be-matched picture and the center weight map into the subject detection model to obtain a subject area confidence map, wherein the subject detection model is a model pre-trained based on the to-be-matched picture, the center weight map and the corresponding annotated subject mask map of the same scene.

[0124] Among them, the subject detection model is obtained by pre-collecting a large amount of training data, and inputting the training data into the subject detection model containing the initial network weights for training. Each set of training data includes the to-be-matched picture, the center weight map and the labeled subject mask map corresponding to the same scene. Among them, the to-be-matched picture and the center weight map are used as the input of the trained subject detection model, and the labeled subject mask map is used as the ground truth expected to be output by the trained subject detection model. The subject mask map is an image filter template used to identify the subject in the picture, which can block other parts of the picture and filter out the subject in the picture. The subject detection model can be trained to recognize and detect various subjects, such as people, flowers, cats, dogs, backgrounds, etc.

[0125] Specifically, the ISP processor or the central processing unit can input the image to be matched and the center weight map into the subject detection model, and perform detection to obtain the subject area confidence map. The subject area confidence map is used to record the probability of the subject belonging to which identifiable subject, for example, the probability of a certain pixel belonging to a person is 0.8, the probability of a flower is 0.1, and the probability of a background is 0.1.

[0126] Step 406, determining the target subject in the to-be-matched image according to the subject region confidence map, and taking the region where the target subject is located as the to-be-matched region.

[0127] Specifically, the ISP processor or central processing unit can select the subject with the highest or second highest confidence as the subject in the image to be matched based on the subject area confidence map. If there is one subject, the subject is used as the target subject; if there are multiple subjects, one or more subjects can be selected as the target subject as needed.

[0128] In this embodiment, after generating a center weight map corresponding to the picture to be matched, the picture to be matched and the center weight map are input into the corresponding subject detection model for detection, and a subject area confidence map can be obtained. According to the subject area confidence map, the target subject in the picture to be matched can be determined. The center weight map can be used to make the object in the center of the image easier to detect. The subject detection model trained using the picture to be matched, the center weight map and the subject mask map can be used to more accurately identify the target subject in the picture to be matched, and the area where the target subject is located is used as the area to be matched, so as to more accurately determine the area to be matched in the picture to be matched.

[0129] In one embodiment, the above method also includes: dividing each reference picture into at least two reference categories; determining a target picture from each reference picture based on the similarity between the picture to be matched and each reference picture, including: determining the category of the picture to be matched based on the similarity between the picture to be matched and each reference picture; taking a reference category that matches the category of the picture to be matched as a target category, and determining the target picture from each reference picture included in the target category.

[0130] The electronic device obtains the labels of each reference image and classifies the reference images with the same label into the same reference category. For example, if the label of reference image A is "architecture", the label of reference image B is "flower", the label of reference image C is "flower", the label of reference image D is "architecture", and the label of reference image E is "architecture", then reference images A, D, and E are classified into the same reference category "architecture", and reference images B and C are classified into the same reference category "flower".

[0131] It can be understood that the higher the similarity between the to-be-matched picture and the reference picture, the closer the to-be-matched features of the to-be-matched picture are to the reference features of the reference picture, and the closer the categories of the to-be-matched picture are to the reference picture.

[0132] In one embodiment, the electronic device may use the reference category corresponding to the reference picture with the highest similarity as the category of the picture to be matched. In another embodiment, the electronic device may also obtain a preset number of reference pictures with the highest similarity, and use the reference category with the largest number among the preset number of reference pictures as the category of the picture to be matched. In other embodiments, the electronic device may also determine the category of the picture to be matched in other ways, which are not limited thereto.

[0133] The target category refers to the reference category that matches the category of the image to be matched. The number of target images can be one or at least two.

[0134] In this embodiment, the category of the picture to be matched is determined, and then the reference category that matches the category of the picture to be matched is used as the target category. The target picture is determined from the reference pictures included in the target category, thereby avoiding determining the target picture from all reference pictures. This can improve the efficiency of determining the target picture, and can also improve the accuracy of determining the target picture.

[0135] In one embodiment, Figure 5 As shown, the electronic device obtains a reference image 502; executes step 504 to classify the reference image 502 and divide the reference image 502 into at least two reference categories. The electronic device executes step 506 to denoise and de-wrinkle the reference image after classification to obtain a picture library 508. The electronic device executes step 510 to perform deep learning and metric learning on the reference images in the picture library 508, and can obtain reference features of each reference image in the picture library, thereby generating a picture feature library 512.

[0136] It should be noted that the execution process from 502 to 512 can be processed in advance or during the dial generation process, but is not limited thereto.

[0137] The electronic device obtains the image to be matched 514; executes step 516 to denoise and de-wrinkle the image to be matched 514; then executes step 518 to extract features from the image to be matched after denoising and de-wrinkling to obtain features to be matched. The electronic device executes step 520 to perform feature matching between the features to be matched and the reference features of the reference image to obtain the similarity between the image to be matched and each reference image; then based on the similarity between the image to be matched and each reference image, a target image 522 is determined from each reference image; a time element is obtained, and a dial 524 is generated based on the time element and the target image.

[0138] Furthermore, after obtaining the similarity between the picture to be matched and each reference picture, the electronic device can determine the category of the picture to be matched based on the similarity between the picture to be matched and each reference picture, take the reference category that matches the category of the picture to be matched as the target category, and determine the target picture 522 from the reference pictures included in the target category, thereby improving the efficiency of determining the target picture.

[0139] In one embodiment, a time element is obtained, and a watch dial is generated based on the time element and a target image, including: obtaining the time element, generating each candidate watch dial based on the time element and each target image determined in the target category, and displaying each candidate watch dial in a display interface; receiving a selection instruction for a candidate watch dial, and displaying the candidate watch dial selected by the selection instruction in the display interface to generate a watch dial.

[0140] When the number of determined target images is one, a candidate dial is generated based on the time element and the target image, and the electronic device can directly generate the candidate dial as a dial. When the number of determined target images is at least two, at least two candidate dials are generated based on the time element and the target image, and the at least two candidate dials are displayed on the display interface; when a selection instruction for a candidate dial is received, the candidate dial selected by the selection instruction is displayed on the display interface, thereby generating a dial.

[0141] In this embodiment, each candidate watch face is generated based on the time element and the target image determined in the target category, and one of the candidate watch faces can be selected from each candidate watch face to be displayed on the display interface, thereby generating a watch face, thereby improving the richness of the generated watch face.

[0142] In one embodiment, a time element is obtained, and a dial is generated based on the time element and a target image, including: obtaining a category of the target image; obtaining a corresponding target style based on the category of the target image; obtaining a time element of the target style, and generating a dial based on the target image and the time element of the target style.

[0143] In the electronic device, at least one style corresponding to each category may be pre-stored. When the electronic device obtains the category of the target image, the category of the target image is matched with each stored category, thereby obtaining the target style corresponding to the category of the target image. The target style is, for example, a cartoon style, a landscape style, an architectural style, and the like.

[0144] For example, if the category of the target image is "architecture", various styles of the category "architecture" are obtained from the memory of the electronic device, such as the "Guangzhou Tower" style, the "Window of the World" style, the "Yellow Crane Tower" style, and the like.

[0145] In this embodiment, the time element of the target style corresponding to the category of the target image is obtained. The time element is more matched with the target image and has a higher degree of fit. A more accurate dial can be generated based on the time elements of the target image and the target style.

[0146] In another embodiment, a method for generating a watch face is provided, including: obtaining a picture to be matched; extracting features to be matched of the picture to be matched; obtaining a reference picture, and reference features of the reference picture; matching the features to be matched with reference features of each reference picture respectively, and determining the similarity between the picture to be matched and each reference picture; determining a target picture from each reference picture based on the similarity between the picture to be matched and each reference picture; sending the target picture to a wearable device; the target picture is used to instruct the wearable device to obtain a time element, and generate a watch face based on the time element and the target picture.

[0147] Wearable devices such as smart watches, smart bracelets, etc.

[0148] It is understandable that time-consuming and labor-intensive tasks such as feature extraction and feature matching are performed in electronic devices, and the final target image is then sent to the wearable device. The wearable device only needs to obtain the time element, and then generate a dial based on the time element and the target image, thereby reducing the operating pressure of the wearable device and better realizing other functions of the wearable device.

[0149] In another embodiment, a watch face generation method is provided, which is applied to a wearable device, and includes: obtaining a target picture sent by an electronic device; the target picture is determined by the electronic device from each reference picture based on the similarity between the obtained picture to be matched and each obtained reference picture; the similarity between the picture to be matched and each reference picture is obtained by the electronic device matching the to-be-matched features of the picture to be matched with the reference features of each reference picture respectively; the to-be-matched features of the picture to be matched are extracted by the electronic device from the picture to be matched; and a time element is obtained, and a watch face is generated based on the time element and the target picture.

[0150] The process of generating a target image requires time-consuming and labor-intensive tasks such as feature extraction and feature matching, which are performed in electronic devices. The wearable device receives the target image sent by the electronic device, obtains the time element, and generates a watch face based on the time element and the target image, reducing the operating pressure of the wearable device and thus better realizing other functions of the wearable device.

[0151] It should be understood that although Figures 2 to 4The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figures 2 to 4 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0152] Figure 6 FIG. 4 is a structural block diagram of a dial generation device according to an embodiment. Figure 6 As shown, a dial generation device 600 is provided, comprising: a to-be-matched picture acquisition module 602, a feature extraction module 604, a reference picture and reference feature acquisition module 606, a matching module 608, a target picture determination module 610 and a dial generation module 612, wherein:

[0153] The to-be-matched picture acquisition module 602 is used to acquire the to-be-matched picture.

[0154] The feature extraction module 604 is used to extract the features to be matched of the image to be matched.

[0155] The reference picture and reference feature acquisition module 606 is used to acquire a reference picture and a reference feature of the reference picture.

[0156] The matching module 608 is used to match the features to be matched with the reference features of each reference picture respectively, and determine the similarity between the picture to be matched and each reference picture.

[0157] The target picture determination module 610 is used to determine the target picture from each reference picture based on the similarity between the picture to be matched and each reference picture.

[0158] The dial generation module 612 is used to obtain the time element and generate the dial based on the time element and the target image.

[0159] The above-mentioned dial generation device matches the to-be-matched features of the to-be-matched picture with the reference features of the reference picture, so that based on the similarity between the to-be-matched picture and the reference picture, a more accurate target picture can be determined from each reference picture, and then the time element can be obtained. Based on the time element and the target picture, a more accurate dial can be generated.

[0160] In one embodiment, the dial generation device 600 further includes an intermediate picture determination module, which is used to determine the category of the picture to be matched based on the features to be matched of the picture to be matched, determine a reference picture that matches the category of the picture to be matched, and use the reference picture that matches the category of the picture to be matched as the intermediate picture; the matching module 608 is also used to match the features to be matched with the reference features of each intermediate picture respectively, and determine the similarity between the picture to be matched and each intermediate picture; the target picture determination module 610 is also used to determine the target picture from each intermediate picture based on the similarity between the picture to be matched and each intermediate picture.

[0161] In one embodiment, the dial generation device 600 further includes a sub-image acquisition module, which is used to determine the area to be matched from the image to be matched, and obtain the sub-image based on the area to be matched; the feature extraction module 604 is also used to extract sub-features of the sub-image; the matching module 608 is also used to match the sub-features with the reference features of each reference image respectively, and determine the similarity between the sub-image and each reference image; the target image determination module 610 is also used to determine the target image from each reference image based on the similarity between the sub-image and each reference image.

[0162] In one embodiment, the feature extraction module 604 is further used to obtain a target scale; adjust the size of the sub-image to the target scale; normalize the pixel values ​​of each pixel in the sub-image of the target scale; and perform feature extraction on the normalized sub-image to obtain target features of the sub-image.

[0163] In one embodiment, the feature extraction module 604 is further used to obtain a reference image; adjust the size of the reference image to a target scale; normalize the pixel values ​​of each pixel in the reference image of the target scale; and perform feature extraction on the normalized reference image to obtain a reference feature of the reference image.

[0164] In one embodiment, the sub-image acquisition module is also used to generate a center weight map corresponding to the image to be matched, wherein the weight value represented by the center weight map gradually decreases from the center to the edge; the image to be matched and the center weight map are input into the subject detection model to obtain a subject area confidence map, wherein the subject detection model is a model pre-trained based on the image to be matched, the center weight map and the corresponding annotated subject mask map of the same scene; the target subject in the image to be matched is determined according to the subject area confidence map, and the area where the target subject is located is used as the area to be matched.

[0165] In one embodiment, the above-mentioned dial generation device also includes a classification module for dividing each reference image into at least two reference categories; the above-mentioned target image determination module 610 is also used to determine the category of the image to be matched based on the similarity between the image to be matched and each reference image; the reference category matching the category of the image to be matched is used as the target category, and the target image is determined from the reference images included in the target category.

[0166] In one embodiment, the above-mentioned dial generation module 612 is also used to obtain the time element, generate each candidate dial based on the time element and each target image determined in the target category, and display each candidate dial in the display interface; receive a selection instruction for the candidate dial, and display the candidate dial selected by the selection instruction in the display interface to generate a dial.

[0167] In one embodiment, the dial generation module 612 is also used to obtain the category of the target image; obtain the corresponding target style based on the category of the target image; obtain the time element of the target style, and generate a dial based on the target image and the time element of the target style.

[0168] Figure 7 FIG. 4 is a structural block diagram of a dial generating device according to another embodiment. Figure 7 As shown, a dial generation device 700 is provided, comprising: a to-be-matched picture acquisition module 702, a feature extraction module 704, a reference picture and reference feature acquisition module 706, a matching module 708, a target picture determination module 710 and a dial generation module 712, wherein:

[0169] The to-be-matched picture acquisition module 702 is used to acquire the to-be-matched picture.

[0170] The feature extraction module 704 is used to extract the features to be matched from the image to be matched.

[0171] The reference picture and reference feature acquisition module 706 is used to acquire a reference picture and a reference feature of the reference picture.

[0172] The matching module 708 is used to match the features to be matched with the reference features of each reference picture respectively, and determine the similarity between the picture to be matched and each reference picture.

[0173] The target picture determination module 710 is used to determine the target picture from each reference picture based on the similarity between the picture to be matched and each reference picture.

[0174] The dial generation module 712 is used to send the target image to the wearable device; the target image is used to instruct the wearable device to obtain the time element and generate the dial based on the time element and the target image.

[0175] The above-mentioned dial generation device matches the to-be-matched features of the to-be-matched picture with the reference features of the reference picture, so that based on the similarity between the to-be-matched picture and the reference picture, a more accurate target picture can be determined from each reference picture, and the target picture can be sent to the wearable device for the wearable device to generate a more accurate dial.

[0176] Figure 8 FIG. 4 is a structural block diagram of a dial generating device according to another embodiment. Figure 8 As shown, a dial generation device 800 is provided, including: a target image acquisition module 802 and a dial generation module 804, wherein:

[0177] The target image acquisition module 802 is used to acquire the target image sent by the electronic device; the target image is determined by the electronic device from each reference image based on the similarity between the acquired image to be matched and each acquired reference image; the similarity between the image to be matched and each reference image is obtained by the electronic device matching the features to be matched of the image to be matched with the reference features of each reference image respectively; the features to be matched of the image to be matched are extracted by the electronic device from the image to be matched.

[0178] The dial generation module 804 is used to obtain the time element and generate the dial based on the time element and the target image.

[0179] The above-mentioned dial generation device obtains the target picture sent by the electronic device. The target picture is determined by the electronic device from various reference pictures based on the similarity between the picture to be matched and the reference picture. Therefore, the determined target picture is more accurate; then the time element is obtained, and a more accurate dial is generated based on the time element and the target picture.

[0180] The division of the various modules in the above-mentioned dial generating device is only for illustration. In other embodiments, the dial generating device can be divided into different modules as needed to complete all or part of the functions of the above-mentioned dial generating device.

[0181] For the specific definition of the dial generation device, please refer to the definition of the dial generation method above, which will not be repeated here. Each module in the above-mentioned dial generation device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0182] Fig. 9 FIG. 1 is a schematic diagram of the internal structure of an electronic device in one embodiment. Fig. 9As shown, the electronic device includes a processor and a memory connected via a system bus. The processor is used to provide computing and control capabilities to support the operation of the entire electronic device. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by the processor to implement a dial generation method provided in each of the following embodiments. The internal memory provides a cached operating environment for the operating system computer program in the non-volatile storage medium. The electronic device can be any terminal device such as a mobile phone, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), a vehicle-mounted computer, a wearable device, etc.

[0183] The implementation of each module in the dial generation device provided in the embodiment of the present application can be in the form of a computer program. The computer program can be run on an electronic device. The program modules constituted by the computer program can be stored in the memory of the electronic device. When the computer program is executed by the processor, the steps of the method described in the embodiment of the present application are implemented.

[0184] The implementation of each module in the dial generation device provided in the embodiment of the present application can be in the form of a computer program. The computer program can be run on the wearable device. The program modules constituted by the computer program can be stored in the memory of the wearable device. When the computer program is executed by the processor, the steps of the method described in the embodiment of the present application are implemented.

[0185] The embodiment of the present application also provides a computer-readable storage medium, one or more non-volatile computer-readable storage media containing computer-executable instructions, when the computer-executable instructions are executed by one or more processors, the processors execute the steps of the dial generation method.

[0186] A computer program product comprising instructions, when executed on a computer, causes the computer to execute a dial generation method.

[0187] Any reference to memory, storage, database or other medium used in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM), which is used as an external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0188] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A dial generation method, characterized in that: include: Get the image to be matched; De-noising and de-wrinkling the to-be-matched picture, and extracting features from the to-be-matched picture after de-noising and de-wrinkling, to obtain the to-be-matched features of the to-be-matched picture; Get reference images; Classify the reference images to divide them into at least two reference categories, denoise and remove wrinkles on the classified reference images to obtain a picture library, perform deep learning and metric learning on the reference images in the picture library to obtain reference features of each reference image in the picture library; Calculating a cosine distance between the feature to be matched and a reference feature of the reference picture, and using the cosine distance as the similarity between the picture to be matched and the reference picture; Determining the category of the picture to be matched based on the similarity between the picture to be matched and each of the reference pictures; Taking a reference category that matches the category of the picture to be matched as a target category, and determining a target picture from each of the reference pictures included in the target category; Obtaining the category of the target image; Acquire a corresponding target style based on the category of the target image; The time element of the target style is obtained, and a watch face is generated based on the target image and the time element of the target style.

2. The method according to claim 1, characterized in that The method further comprises: The labels of the reference images are obtained, and the reference images with the same label are classified into the same reference category.

3. A dial generation method, characterized in that: include: Get the image to be matched; De-noising and de-wrinkling the to-be-matched picture, and extracting features from the to-be-matched picture after de-noising and de-wrinkling, to obtain the to-be-matched features of the to-be-matched picture; Get reference images; Classify the reference images to divide them into at least two reference categories, denoise and remove wrinkles on the classified reference images to obtain a picture library, perform deep learning and metric learning on the reference images in the picture library to obtain reference features of each reference image in the picture library; Calculating the cosine distance between the feature to be matched and the reference feature of the reference picture, and using the cosine distance as the similarity between the picture to be matched and each of the reference pictures; Determining the category of the picture to be matched based on the similarity between the picture to be matched and each of the reference pictures; Taking a reference category that matches the category of the picture to be matched as a target category, and determining a target picture from each of the reference pictures included in the target category; Sending the target image to a wearable device; The target image is used to instruct the wearable device to obtain the category of the target image; A corresponding target style is acquired based on the category of the target image; a time element of the target style is acquired, and a watch face is generated based on the target image and the time element of the target style.

4. A dial generation method, characterized in that: Applied to wearable devices, including: Acquire a target image sent by an electronic device; a method for determining the target image includes: the electronic device acquires a to-be-matched image, denoises and de-wrinkles the to-be-matched image, extracts features from the denoised and de-wrinkled image to obtain the to-be-matched features of the to-be-matched image, acquires a reference image, classifies the reference image, divides the reference image into at least two reference categories, denoises and de-wrinkles the classified reference image to obtain an image library, performs deep learning and metric learning on the reference images in the image library to obtain reference features of each reference image in the image library, calculates a cosine distance between the to-be-matched features and the reference features of the reference images, uses the cosine distance as the similarity between the to-be-matched image and the reference images, determines the category of the to-be-matched image based on the similarity between the to-be-matched image and each of the reference images, uses a reference category that matches the category of the to-be-matched image as a target category, and determines a target image from each of the reference images included in the target category; Obtaining the category of the target image; Acquire a corresponding target style based on the category of the target image; The time element of the target style is obtained, and a watch face is generated based on the target image and the time element of the target style.

5. A dial generation device, characterized in that: include: A module for obtaining images to be matched is used to obtain images to be matched; A feature extraction module is used to denoise and de-wrinkle the to-be-matched image, and extract features from the to-be-matched image after denoising and de-wrinkling, to obtain the to-be-matched features of the to-be-matched image; A reference picture and reference feature acquisition module is used to acquire reference pictures; classify the reference pictures into at least two reference categories, denoise and de-wrinkle the classified reference pictures to obtain a picture library, perform deep learning and metric learning on the reference pictures in the picture library to obtain reference features of each reference picture in the picture library; A matching module, used to calculate the cosine distance between the feature to be matched and the reference feature of the reference picture, and use the cosine distance as the similarity between the picture to be matched and each of the reference pictures; A target image determination module, configured to determine the category of the image to be matched based on the similarity between the image to be matched and each of the reference images; Taking a reference category that matches the category of the picture to be matched as a target category, and determining a target picture from each of the reference pictures included in the target category; A dial generation module, used to obtain the category of the target image; Acquire a corresponding target style based on the category of the target image; The time element of the target style is obtained, and a watch face is generated based on the target image and the time element of the target style.

6. A dial generation device, characterized in that: include: A module for obtaining images to be matched is used to obtain images to be matched; A feature extraction module is used to denoise and de-wrinkle the to-be-matched image, and extract features from the to-be-matched image after denoising and de-wrinkling, to obtain the to-be-matched features of the to-be-matched image; A reference picture and reference feature acquisition module is used to acquire reference pictures; classify the reference pictures into at least two reference categories, denoise and de-wrinkle the classified reference pictures to obtain a picture library, perform deep learning and metric learning on the reference pictures in the picture library to obtain reference features of each reference picture in the picture library; A matching module, used to calculate the cosine distance between the feature to be matched and the reference feature of the reference picture, and use the cosine distance as the similarity between the picture to be matched and each of the reference pictures; A target image determination module, configured to determine the category of the image to be matched based on the similarity between the image to be matched and each of the reference images; Taking a reference category that matches the category of the picture to be matched as a target category, and determining a target picture from each of the reference pictures included in the target category; A dial generation module, used for sending the target image to the wearable device; The target image is used to instruct the wearable device to obtain the category of the target image; A corresponding target style is acquired based on the category of the target image; a time element of the target style is acquired, and a watch face is generated based on the target image and the time element of the target style.

7. A dial generation device, characterized in that: Applied to wearable devices, including: The target image acquisition module is used to acquire the target image sent by the electronic device; the method for determining the target image includes: the electronic device acquires the image to be matched, denoises and de-wrinkles the image to be matched, extracts features from the denoised and de-wrinkled image to obtain the features to be matched of the image to be matched, acquires the reference image, classifies the reference image, divides the reference image into at least two reference categories, denoises and de-wrinkles the classified reference image to obtain an image library, performs deep learning and metric learning on the reference images in the image library to obtain reference features of each reference image in the image library, calculates the cosine distance between the features to be matched and the reference features of the reference images, uses the cosine distance as the similarity between the image to be matched and the reference images, determines the category of the image to be matched based on the similarity between the image to be matched and each of the reference images, uses the reference category that matches the category of the image to be matched as the target category, and determines the target image from each of the reference images included in the target category; A dial generation module is used to obtain the category of the target image; obtain the corresponding target style based on the category of the target image; obtain the time element of the target style, and generate a dial based on the target image and the time element of the target style.

8. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the computer program is executed by the processor, the processor is caused to perform the steps of the dial generation method according to any one of claims 1 to 3.

9. A wearable device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the computer program is executed by the processor, the processor is caused to perform the steps of the dial generating method according to claim 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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