Method for determining depth value of image, image processor and module

Abstract

A method for determining the depth value of an image, an image processor (1000) and a module, the method for determining the depth value of an image comprising: acquiring a first depth image from a TOF module (11) and acquiring a second depth image (S401) from a binocular vision module (12) for an object in a target image; and performing weighted calculation using a preset weighted algorithm based on the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image and the accuracy value corresponding to the depth value of the second depth image, to obtain the depth value of the target image (S402), thereby improving the accuracy of the depth value of the image.

Description

Technical Field

[0001] Embodiments of the present application relate to a depth image synthesis technology, and more particularly to a method for determining a depth value of an image, an image processor, and a module. Background Art

[0002] The Time of Flight (TOF) module includes a modulated light transmitter, a modulated light receiver, and a depth image synthesis processor, wherein the modulated light transmitter continuously sends modulated light signals to the target, and uses a receiver to receive the modulated light signals returned from the object. In the depth image synthesis processor, the distance of the target object is obtained by detecting the round-trip time of the modulated light signal, thereby obtaining the depth signal of the object. The TOF module has good depth perception characteristics for objects at medium and short distances, but it cannot obtain accurate depth values ​​for objects at long and ultra-long distances, and when the TOF module is affected by external noise and ambient light, its depth image imaging accuracy is greatly affected.

[0003] The binocular vision module imitates the characteristics of the human eye, matches the feature points of the object in the left and right images, uses the parallax caused by the dual viewpoints to image the same object, and obtains the depth signal of the object through geometric calculation. In practical applications, the inaccurate matching of the feature points of the object becomes the biggest obstacle for the binocular vision module to obtain high-precision depth values.

[0004] It can be seen that the TOF module is affected by ambient light or noise, and the depth image obtained is not accurate. The TOF module is only suitable for close and medium distances, while the binocular vision module causes calculation errors of the depth image due to inaccurate feature point matching; that is to say, the depth image obtained by the existing TOF module or binocular vision module has low accuracy. Summary of the invention

[0005] The embodiments of the present application provide a method for determining the depth value of an image, an image processor, and a module, which can improve the accuracy of the depth value of the image.

[0006] The technical solution of the embodiment of the present application can be implemented as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for determining a depth value of an image, comprising:

[0008] For the object in the target image, a first depth image is obtained from the TOF module, and a second depth image is obtained from the binocular vision module; wherein the first depth image and the second depth image respectively further include accuracy values ​​corresponding to their respective depth values, and the accuracy values ​​are used to characterize the accuracy of the corresponding depth values;

[0009] A depth value of the target image is obtained by performing weighted calculation based on the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image, and the accuracy value corresponding to the depth value of the second depth image using a preset weighted algorithm.

[0010] In a second aspect, an embodiment of the present application provides a method for determining a depth value of an image, comprising:

[0011] Get the depth value of the target image;

[0012] According to the depth value of the target image, from the corresponding relationship between the preset depth value and the accuracy value, find the accuracy value corresponding to the depth value of the target image;

[0013] forming a first depth image using the depth value of the target image and the accuracy value corresponding to the depth value of the target image;

[0014] The first depth image is sent to an image processor so that the image processor determines a depth value of the target image.

[0015] In a third aspect, an embodiment of the present application provides a method for determining a depth value of an image, comprising:

[0016] After acquiring the depth value of the target image and the binocular vision image of the target image, respectively determining feature points of a first visual image in the binocular vision image and feature points of a second visual image in the binocular vision image;

[0017] Matching the feature points of the first visual image with the feature points of the second visual image to obtain a matching result of the target image;

[0018] According to the matching result of the target image, the accuracy value corresponding to the matching result of the target image is found from the preset correspondence relationship between the matching result and the accuracy value;

[0019] forming a second depth image using the depth value of the target image and the accuracy value corresponding to the matching result of the target image;

[0020] The second depth image is sent to an image processor so that the image processor determines a depth value of the target image.

[0021] In a fourth aspect, an embodiment of the present application provides an image processor, including:

[0022] A first acquisition module is configured to acquire a first depth image from the TOF module and a second depth image from the binocular vision module for an object in a target image; wherein the first depth image and the second depth image respectively further include accuracy values ​​corresponding to their respective depth values, and the accuracy values ​​are used to characterize the accuracy of the corresponding depth values;

[0023] The weighting module is configured to perform weighted calculation using a preset weighting algorithm according to the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image, and the accuracy value corresponding to the depth value of the second depth image, so as to obtain the depth value of the target image.

[0024] In a fifth aspect, an embodiment of the present application provides a TOF module, including:

[0025] A second acquisition module is configured to acquire a depth value of a target image;

[0026] A first search module is configured to search, according to the depth value of the target image, for an accuracy value corresponding to the depth value of the target image from a preset correspondence relationship between the depth value and the accuracy value;

[0027] A first forming module, configured to form a first depth image using the depth value of the target image and the accuracy value corresponding to the depth value of the target image;

[0028] The first sending module is configured to send the first depth image to an image processor, so that the image processor determines a depth value of the target image.

[0029] In a sixth aspect, an embodiment of the present application provides a binocular vision module, including:

[0030] A determination module, configured to obtain a depth value of a target image and a binocular vision image of the target image, and then respectively determine a feature point of a first visual image in the binocular vision image and a feature point of a second visual image in the binocular vision image;

[0031] a matching module, configured to match feature points of the first visual image with feature points of the second visual image to obtain a matching result of the target image;

[0032] A second search module is configured to search, according to the matching result of the target image, the accuracy value corresponding to the matching result of the target image from a preset correspondence relationship between the matching result and the accuracy value;

[0033] A second forming module, configured to form a second depth image using the depth value of the target image and the accuracy value corresponding to the depth value of the target image;

[0034] The second sending module is configured to send the second depth image to an image processor, so that the image processor determines a depth value of the target image.

[0035] In a seventh aspect, an embodiment of the present application provides an image processor, the image processor comprising:

[0036] A processor and a storage medium storing instructions executable by the processor, wherein the storage medium relies on the processor to perform operations via a communication bus, and when the instructions are executed by the processor, the method for determining the depth value of the image executed by the image processor described in one or more of the above embodiments is executed.

[0037] In an eighth aspect, an embodiment of the present application provides a TOF module, the TOF module comprising:

[0038] A processor and a storage medium storing instructions executable by the processor, wherein the storage medium relies on the processor to perform operations via a communication bus, and when the instructions are executed by the processor, the method for determining the depth value of the image executed by the TOF module described in one or more of the above embodiments is executed.

[0039] In a ninth aspect, an embodiment of the present application provides a binocular vision module, the binocular vision module comprising:

[0040] A processor and a storage medium storing instructions executable by the processor, wherein the storage medium relies on the processor to perform operations via a communication bus, and when the instructions are executed by the processor, the method for determining the depth value of the image performed by the binocular vision module described in one or more of the above embodiments is executed.

[0041] In the tenth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores executable instructions. When the executable instructions are executed by one or more processors, the processor executes the method for determining the depth value of the image executed by the image processor of one or more of the above-mentioned embodiments, or the method for determining the depth value of the image executed by the TOF module, or the method for determining the depth value of the image executed by the binocular vision module.

[0042] The embodiment of the present application provides a method for determining the depth value of an image, an image processor, and a module. For an object in a target image, a first depth image is obtained from a TOF module, and a second depth image is obtained from a binocular vision module, wherein the first depth image and the second depth image also include accuracy values ​​corresponding to their respective depth values, and the accuracy values ​​are used to characterize the accuracy of the corresponding depth values. According to a preset resolution of the target image, a weighted calculation is performed using a preset weighted algorithm according to the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image, and the accuracy value corresponding to the depth value of the second depth image, to obtain the depth value of the target image; that is, in the embodiment of the present application, the first depth image and the second depth image are obtained from the TOF module and the binocular vision module, and then a weighted calculation is performed according to the depth value of the first depth image and the corresponding accuracy value, and the depth value of the second depth image and the corresponding accuracy value. In this way, in the process of determining the depth value of the target image, the accuracy of the depth value of the first depth image and the accuracy of the depth value of the second depth image are taken into account, so that the determined depth value of the target image is more accurate, which is conducive to capturing a more realistic image, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the structure of an optional image processing system provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of an interactive process of an optional method for determining a depth value of an image provided in an embodiment of the present application;

[0045] Figure 3 A schematic flow chart of an example of an optional method for determining a depth value of an image provided in an embodiment of the present application;

[0046] Figure 4 A schematic flow chart of an optional method for determining a depth value of an image provided in an embodiment of the present application;

[0047] Figure 5 A schematic flow chart of another optional method for determining a depth value of an image provided in an embodiment of the present application;

[0048] Figure 6 A schematic flow chart of another optional method for determining a depth value of an image provided in an embodiment of the present application;

[0049] Figure 7 A schematic diagram of the structure of an optional image processor provided in an embodiment of the present application;

[0050] Figure 8A schematic diagram of the structure of an optional TOF module provided in an embodiment of the present application;

[0051] Fig. 9 A schematic diagram of the structure of an optional binocular vision module provided in an embodiment of the present application;

[0052] Fig.10 A schematic diagram of the structure of another optional image processor provided in an embodiment of the present application;

[0053] Fig.11 A schematic diagram of the structure of another optional TOF module provided in an embodiment of the present application;

[0054] Fig.12 A schematic diagram of the structure of another optional binocular vision module provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. It is understood that the specific embodiments described herein are only used to explain the related applications, rather than to limit the applications. It should also be noted that, for the convenience of description, only the parts related to the related applications are shown in the accompanying drawings.

[0056] The present application provides a method for determining a depth value of an image, the method being applied to an image processing system. Figure 1 A schematic diagram of the structure of an optional image processing system provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the image processing system includes: a TOF module 11, a binocular vision module 12 and an image processor 13, wherein a communication connection is established between the TOF module 11 and the image processor 13, and a communication connection is established between the binocular vision module 12 and the image processor 13.

[0057] Among them, in the image processing system, the TOF module 11 can obtain the distance between itself and the object in the target image, that is, the depth value, by detecting the round-trip time of the modulated light signal. However, the depth value determined by this method has good depth perception characteristics for objects at medium and short distances, but cannot obtain accurate depth values ​​for objects at long or ultra-long distances, and the TOF module 11 is easily affected by external noise and ambient light, thereby affecting the accuracy of the depth value.

[0058] In the image processing system, the binocular vision module 12 can match the feature points of the objects in the left and right images, use the parallax caused by imaging the same object from two viewpoints, and obtain the distance between itself and the object in the target image, that is, the depth value, through geometric calculation. However, in actual applications, if the feature points are not matched accurately, the accuracy of the depth value will be affected.

[0059] Here, the image processor can be used to collect depth values ​​from the TOF module 11 and the binocular vision module 12 respectively, and process the collected multiple depth values ​​to determine a more accurate depth value of the target image.

[0060] In order to overcome the error of the TOF module 11 or the binocular vision module 12 in determining the depth value, the embodiment of the present application provides a method for determining the depth value of an image, which is applied to the above-mentioned image processing system. Figure 2 A schematic diagram of an interactive process of an optional method for determining a depth value of an image provided in an embodiment of the present application, such as Figure 2 As shown, the method may include:

[0061] S201: The TOF module 11 determines a first depth image of a target image;

[0062] In order to overcome the fact that the TOF module 11 cannot obtain accurate depth values ​​for objects at a long distance or a very long distance, and the influence of inaccurate feature point matching of the binocular vision module 12 on the accuracy of the depth value, here, the TOF module 11 is used to determine the first depth image of the object in the target image, and the binocular vision module 12 is used to determine the second depth image of the target image, and then the image processor 13 is enabled to combine the first depth image and the second depth image to determine the depth value of the target image.

[0063] In a specific implementation process, in order to determine the first depth image of the target image, the TOF module 11 may include:

[0064] S2011: The TOF module 11 obtains a depth value of the target image;

[0065] Specifically, the TOF module 11 obtains the distance between itself and the object in the target image by detecting the round trip time of the modulated light signal, that is, the depth value of the target image can be obtained. The depth value of the target image is the depth value of each pixel position of the target image.

[0066] S2012: The TOF module 11 searches for the accuracy value corresponding to the depth value of the target image from a preset correspondence relationship between the depth value and the accuracy value according to the depth value of the target image;

[0067] After the TOF module 11 obtains the depth value of the target image in S2011, since the correspondence between the preset depth value and the accuracy value is pre-stored in the TOF module 11, after the depth value is determined, the accuracy value corresponding to the depth value of the target image can be found from the correspondence.

[0068] In practical applications, the effective distance of the TOF module 11 is used for determination. According to the time point of the laser modulation signal transmission and reception, the transmission time of the signal is calculated to obtain the distance between the TOF module 11 and the object in the target image, that is, the depth value. The accuracy can be set to the highest within the effective distance, or it can be set from near to far, the accuracy gradually decreases or other situations; outside the effective distance, the accuracy is the lowest. For example, the range of the accuracy value can be between 0 and 1, so that the corresponding relationship between the depth value and the accuracy value is established to determine the accuracy value.

[0069] S2013: The TOF module 11 forms a first depth image using the depth value of the target image and the accuracy value corresponding to the depth value of the target image.

[0070] The accuracy value is used to represent the accuracy of the depth value.

[0071] That is to say, the first depth image determined by the TOF module 11 includes not only the depth value of the target image, but also the accuracy value corresponding to the depth value of the target image. The accuracy value means the credibility of the depth value corresponding to the area.

[0072] S202: The TOF module 11 sends a first depth image to an image processor;

[0073] In S202, the TOF module 11 sends a first depth image including a depth value and an accuracy value to the image processor, so that the image processor 13 can obtain the depth value and the accuracy information of the depth value collected by the TOF module 11, which is beneficial for the image processor 13 to more accurately determine the depth value of the target image.

[0074] S203: The binocular vision module 12 determines a second depth image of the target image;

[0075] In a specific implementation process, in order to determine the second depth image of the target image, the binocular vision module 12 may include:

[0076] S2031: After the binocular vision module 12 obtains the depth value of the target image and the binocular vision image of the target image, it determines feature points of the first binocular vision image in the binocular vision image and feature points of the second binocular vision image in the binocular vision image respectively;

[0077] Specifically, the binocular vision module 12 matches the feature points of the objects in the left and right images, uses the parallax caused by imaging the same object from two viewpoints, and obtains the distance between itself and the target image through geometric calculation, that is, the depth value of the target image.

[0078] After obtaining the depth value of the target image, the binocular vision module 12 needs to first determine the feature points of each binocular vision image and the feature points of the second binocular vision image. The feature points can reflect the essential characteristics of the image and can identify the target object in the image. Image matching can be completed by matching the feature points.

[0079] S2032: The binocular vision module 12 matches the feature points of the first visual image with the feature points of the second visual image to obtain a matching result of the target image;

[0080] Here, the binocular vision module 12 matches the feature points of the first visual image with the feature points of the second visual image, and the obtained matching results include successful matching and failed matching. Successful matching indicates that the feature points of the first visual image and the second visual image are the same or similar, which can be represented by a matching degree, wherein, in practical applications, the matching degree can be the sum of the absolute values ​​of the differences of the same feature points, and the embodiments of the present application are not specifically limited here. Failed matching indicates that the feature points of the first visual image and the second visual image are completely different, that is, there are no feature points that can be matched.

[0081] S2033: the binocular vision module 12 searches for the accuracy value corresponding to the matching result of the target image from the preset correspondence relationship between the matching result and the accuracy value according to the matching result;

[0082] After the binocular vision module 11 obtains the depth value of the target image in S2032, since the corresponding relationship between the preset matching results and the accuracy values ​​is pre-stored in the binocular vision module 12, when the matching result is determined, the accuracy value corresponding to the determined matching result can be found from the corresponding relationship.

[0083] In practical applications, based on feature point matching, pixel-level traversal is performed in the left and right images with a specified step size to find the feature points of the two images for matching. If there is no matching feature point in a certain area, the accuracy of the position is the lowest. If there is a matching feature point, the accuracy is proportional to the matching degree. The higher the matching degree, the higher the accuracy. The matching degree can be expressed as the sum of the absolute values ​​of the differences of the same feature point in the two images, or in other forms. For example, the accuracy value can range from 0 to 1. In this way, a corresponding relationship between the matching degree and the accuracy value is established to determine the accuracy value.

[0084] S2034: The binocular vision module 12 forms a second depth image using the depth value of the target image and the accuracy value corresponding to the matching result of the target image.

[0085] That is to say, the second depth image determined by the binocular vision module 12 includes not only the depth value of the target image, but also the accuracy value corresponding to the matching result of the target image.

[0086] The accuracy value of the first depth image and the accuracy value of the second depth image may be different in size and expressed in different units, which may be pixel level, block level, row level or frame level.

[0087] S204: The binocular vision module 12 sends the second depth image to the image processor 13;

[0088] In S203, the binocular vision module 12 sends a second depth image including a depth value and an accuracy value to the image processor 13, so that the image processor 13 can obtain the depth value and the accuracy information of the depth value collected by the binocular vision module 12, which is beneficial for the image processor 13 to more accurately determine the depth value of the target image.

[0089] Here, it should be noted that the process of determining the first depth image by the TOF module 11 and the process of determining the second depth image by the binocular vision module 12 can be executed in parallel or successively. When executed successively, the process of determining the first depth image by the TOF module 11 can precede the process of determining the second depth image by the binocular vision module 12, or the process of determining the second depth image by the binocular vision module 12 can precede the process of determining the first depth image by the TOF module 11. Here, the embodiment of the present application does not make any specific limitation on this.

[0090] S205: The image processor 13 determines the depth value of the target image.

[0091] Here, it should be noted that when the resolution of the target image is the same as the resolution of the first depth image and the resolution of the second depth image, S205 may include:

[0092] The image processor 13 performs weighted calculation using a preset weighting algorithm according to the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image, and the accuracy value corresponding to the depth value of the second depth image to obtain the depth value of the target image.

[0093] However, when the resolution of the target image is different from the resolution of the first depth image and the resolution of the second depth image, S205 may include:

[0094] S2051: the image processor 13 performs scaling processing on the first depth image and the second depth image respectively according to a preset resolution of the target image to obtain a processed first depth image and a processed second depth image;

[0095] S2052: The image processor 13 performs weighted calculation using a preset weighting algorithm according to the depth value of the processed first depth image, the accuracy value corresponding to the depth value of the processed first depth image, the depth value of the processed second depth image, and the accuracy value corresponding to the depth value of the processed second depth image to obtain the depth value of the target image.

[0096] That is to say, when the resolution of the acquired first depth image is different from the resolution of the preset target image, and the resolution of the second depth image is different from the resolution of the preset target image, it is necessary to scale the first depth image and the second depth image so that the resolution of the first depth image and the resolution of the second depth image are the same as the resolution of the preset target image, thereby obtaining the processed first depth image and the second depth image.

[0097] Here, in order to determine a more accurate depth value for the target image, the image processor 13 inputs the depth value of the processed first depth image, the accuracy value corresponding to the depth value of the processed first depth image, the depth value of the second depth image, and the accuracy value corresponding to the depth value of the processed second depth image into a preset weighted algorithm for calculation, so as to obtain the depth value of the target image.

[0098] Specifically, let the row and column coordinates be i and j, the depth value of the first depth image generated by the TOF module 11 is represented by P_TOF, the accuracy value of the first depth image is represented by A_TOF, the depth value of the second depth image generated by the binocular vision module 12 is represented by P_BV, the accuracy value of the second depth image is represented by A_BV, and the depth value TMP of the target image in the i-th row and j-th column generated by the image processor 13 is i，j The calculation is as follows:

[0099]

[0100] Among them, K is the pixel compensation value of the accuracy value of the TOF module 11, T is the pixel compensation value of the accuracy value of the binocular vision module 12, K and T can be set according to actual debugging, F is the overall compensation value, which can be configured according to the device and scene, a and b are trust values, and different values ​​can be set according to the depth values ​​at different distances.

[0101] In practical applications, the depth value of the processed first depth image, the accuracy value of the processed first depth image, the depth value of the second depth image and the accuracy value of the processed second depth image are substituted into the above formula (1) for calculation to obtain the depth value of the target image.

[0102] In addition, it should be noted that, in the process of determining the depth value of the target image, the image processor 13 may filter the depth image after scaling, or may filter the depth value of the target image after determining the depth value of the target image, or may filter the depth image after scaling and also filter the depth value of the target image after determining the depth value of the target image. Here, the embodiments of the present application do not make specific limitations on this.

[0103] In an optional embodiment, S2051 may include:

[0104] The image processor 13 performs scaling processing on the first depth image and the second depth image according to a preset resolution of the target image to obtain a scaled first depth image and a scaled second depth image;

[0105] The image processor 13 performs filtering processing on the scaled first depth image and the scaled second depth image respectively to obtain a processed first depth image and a processed second depth image.

[0106] Here, the depth image is filtered after the scaling process. Specifically, after scaling the first depth image and the second depth image, the image processor 13 performs filtering on the scaled first depth image and the scaled second depth image respectively, wherein Gaussian filtering, smoothing filtering, or filtering with configurable coefficients may be performed, and the embodiments of the present application do not specifically limit this.

[0107] In an optional embodiment, after S205, the method may further include:

[0108] The image processor 13 performs filtering processing on the depth value of the target image to obtain the depth value of the target image after filtering processing.

[0109] Here, after determining the depth value of the target image, the depth value of the target image is filtered. Specifically, after determining the depth value of the target image, the image processor 13 filters the depth value of the target image to obtain a filtered depth value, which can improve the accuracy of the depth value. Among them, it can be a Gaussian filter, a smoothing filter, or a filter with a configurable coefficient. The embodiment of the present application does not make any specific limitation on this.

[0110] The following example is used to illustrate the method for determining the depth value of the image described in one or more of the above embodiments.

[0111] Example 1: First, the image processor scales the first depth image and the second depth image obtained from the TOF module and the binocular vision module to the same resolution as the final output target image, and generates a new first depth image and a new second depth image.

[0112] Secondly, let the row and column coordinates be i and j, the depth value of the generated new first depth image is P_TOF, the accuracy value corresponding to the depth value of the new first depth image is A_TOF, the depth value of the new second depth image is P_BV, the accuracy value corresponding to the depth value of the new second depth image is A_BV, and the depth value of the target image is calculated using the above formula (1).

[0113] Finally, the image processor performs Gaussian, smoothing or configurable coefficient filtering on the depth value of the target image to obtain the final filtered depth value.

[0114] Example 2: The image processor scales the first depth image and the second depth image obtained from the TOF module and the binocular vision module to the same resolution as the final output target image, and generates a new first depth image and a new second depth image.

[0115] After the image processor performs smoothing or filtering with a configurable coefficient on the new first depth image and the new second depth image, respectively, a depth value P_TOF of the new first depth image and an accuracy value A_TOF corresponding to the depth value of the new first depth image, a depth value P_BV of the new second depth image and an accuracy value A_BV corresponding to the depth value of the new second depth image are obtained; assuming that the row and column coordinates are i and j, the depth value of the target image is calculated using the above formula (1).

[0116] Example 3: First, the image processor scales the first depth image and the second depth image obtained from the TOF module and the binocular vision module to the same resolution as the final output target image, and generates a new first depth image and a new second depth image.

[0117] Secondly, the image processor smoothes or filters the new first depth image and the new second depth image with a configurable coefficient, respectively, to obtain the depth value P_TOF of the new first depth image and the accuracy value A_TOF corresponding to the depth value of the new first depth image, the depth value P_BV of the new second depth image and the accuracy value A_BV corresponding to the depth value of the new second depth image; assuming that the row and column coordinates are i and j, the depth value of the target image is calculated using the above formula (1).

[0118] Finally, the image processor performs Gaussian, smoothing or configurable coefficient filtering on the depth value of the target image to obtain the final filtered depth value.

[0119] Example 4: Figure 3A flowchart of an example of an optional method for determining a depth value of an image provided in an embodiment of the present application is shown as follows: Figure 3 As shown in the figure, object A is a near-field object, and object B is a far-field object. According to the depth judgment of object A by the TOF module and the binocular vision module, the depth of object B in the binocular vision module is corrected from b1 to b2 based on the TOF module. During the final synthesis, the near-field object adopts the depth value of the TOF module, and the far-field object adopts the depth value of the binocular vision module.

[0120] Depend on Figure 3 It can be seen that in the blurred depth of field photography, the effective distance of the TOF module is limited, and it cannot obtain the depth information of objects at long distances or ultra-long distances. The embodiment of the present application adopts a binocular vision module to compensate for this feature. The depth value of objects at long distances and ultra-long distances is judged by the binocular vision module, and the depth value of the final synthesized image can obtain a depth value with higher accuracy and richer depth details for blurred depth of field photography.

[0121] Through the above example, the first depth image and the second depth image obtained by the TOF module and the binocular vision module are synthesized, which can not only effectively avoid the shortcomings of the two technologies, improve the robustness and accuracy of the depth image of close objects, but also enhance each other, so that a more accurate depth image can be obtained regardless of the distance of the object.

[0122] For example, when shooting portraits in rainy and foggy weather, the TOF module and the binocular vision module can be used in conjunction. For close-up portraits, the low accuracy of depth image acquisition caused by noise can be effectively avoided. For distant scenes, more depth information can be obtained by relying on the binocular vision module.

[0123] For example, when shooting an object within the effective distance range of the TOF module, the TOF module is first used to obtain an accurate low-resolution depth image, and then based on the depth image, binocular vision is used to expand the depth details to achieve a higher-resolution depth image.

[0124] The method for determining the depth value of an image provided in the embodiment of the present application adopts TOF technology and binocular vision technology for joint processing, and obtains a more accurate depth value through the depth values ​​and corresponding accuracy values ​​of the two depth images by means of weighting and filtering. It not only utilizes the respective advantages of TOF technology and binocular vision technology to obtain higher-precision depth information at medium and short distances, but also can obtain depth values ​​at long distances and ultra-long distances, ultimately realizing the perception and acquisition of depth values ​​at any viewing distance.

[0125] The embodiment of the present application provides a method for determining the depth value of an image. For an object in a target image, a first depth image is obtained from a TOF module, and a second depth image is obtained from a binocular vision module, wherein the first depth image and the second depth image also include accuracy values ​​corresponding to their respective depth values, and the accuracy values ​​are used to characterize the accuracy of the corresponding depth values. A preset weighted algorithm is used to perform weighted calculation according to the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image, and the accuracy value corresponding to the depth value of the second depth image, to obtain the depth value of the target image; that is, in the embodiment of the present application, the first depth image and the second depth image are obtained from the TOF module and the binocular vision module, and then a weighted calculation is performed according to the depth value of the first depth image and the corresponding accuracy value, and the depth value of the second depth image and the corresponding accuracy value. In this way, in the process of determining the depth value of the target image, the accuracy of the depth value of the first depth image and the accuracy of the depth value of the second depth image are taken into account, so that the determined depth value of the target image is more accurate, which is conducive to capturing a more realistic image, thereby improving the user experience.

[0126] The following describes a method for determining the depth value of the above image using each device deployed in the image processing system.

[0127] First, a method for determining a depth value of an image on the image processor side is described.

[0128] Figure 4 A flowchart of an optional method for determining the depth value of an image provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the method for determining the depth value of the image may include:

[0129] S401: For an object in a target image, obtaining a first depth image from a TOF module and obtaining a second depth image from a binocular vision module;

[0130] The first depth image and the second depth image also include accuracy values ​​corresponding to their respective depth values, and the accuracy values ​​are used to characterize the accuracy of the corresponding depth values;

[0131] S402: Perform weighted calculation using a preset weighting algorithm according to the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image, and the accuracy value corresponding to the depth value of the second depth image to obtain the depth value of the target image.

[0132] In an optional embodiment, S402 may include:

[0133] According to a preset resolution of the target image, the first depth image and the second depth image are respectively scaled to obtain a processed first depth image and a processed second depth image;

[0134] A depth value of the target image is obtained by performing weighted calculation based on the depth value of the processed first depth image, the accuracy value corresponding to the depth value of the processed first depth image, the depth value of the processed second depth image, and the accuracy value corresponding to the depth value of the processed second depth image using a preset weighted algorithm.

[0135] In an optional embodiment, after performing weighted calculation using a preset weighted algorithm according to the depth value of the processed first depth image, the accuracy value corresponding to the depth value of the processed first depth image, the depth value of the processed second depth image, and the accuracy value corresponding to the depth value of the processed second depth image to obtain the depth value of the target image, the method may include:

[0136] The depth value of the target image is filtered to obtain the depth value of the target image after filtering.

[0137] In an optional embodiment, scaling the first depth image and the second depth image is performed respectively according to a preset resolution of the target image to obtain a processed first depth image and a processed second depth image, including:

[0138] According to a preset resolution of the target image, the first depth image and the second depth image are respectively scaled to obtain a scaled first depth image and a scaled second depth image;

[0139] The scaled first depth image and the scaled second depth image are respectively subjected to filtering processing to obtain a processed first depth image and a processed second depth image.

[0140] Next, a method for determining the depth value of the above image on the TOF module side is described.

[0141] Figure 5 A flowchart of another optional method for determining the depth value of an image provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the method may also include:

[0142] S501: Obtaining a depth value of a target image;

[0143] S502: according to the depth value of the target image, find the accuracy value corresponding to the depth value of the target image from the corresponding relationship between the preset depth value and the accuracy value;

[0144] S503: forming a first depth image using the depth value of the target image and the accuracy value corresponding to the depth value of the target image;

[0145] S504: Send the first depth image to an image processor, so that the image processor determines a depth value of the target image.

[0146] Again, the method for determining the depth value of the above image on the binocular vision module side is described.

[0147] Figure 6 A flowchart of another optional method for determining the depth value of an image provided in an embodiment of the present application is shown as follows: Figure 6 As shown, the method may also include:

[0148] S601: After acquiring the depth value of the target image and the binocular vision image of the target image, respectively determine the feature points of the first visual image in the binocular vision image and the feature points of the second visual image in the binocular vision image;

[0149] S602: Matching feature points of the first visual image with feature points of the second visual image to obtain a matching result of a target image;

[0150] S603: according to the matching result of the target image, find the accuracy value corresponding to the matching result of the target image from the preset correspondence relationship between the matching result and the accuracy value;

[0151] S604: forming a second depth image using the depth value of the target image and the accuracy value corresponding to the matching result of the target image;

[0152] S605: Send the second depth image to the image processor, so that the image processor determines the depth value of the target image.

[0153] Based on the same inventive concept, an embodiment of the present application provides an image processor, Figure 7 A schematic diagram of the structure of an optional image processor provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, the image processor may include: a first acquisition module 71 and a weighting module 72; wherein,

[0154] The first acquisition module 71 is configured to acquire a first depth image from the TOF module and a second depth image from the binocular vision module for the object in the target image; wherein the first depth image and the second depth image respectively further include accuracy values ​​corresponding to their respective depth values, and the accuracy values ​​are used to characterize the accuracy of the corresponding depth values;

[0155] The weighting module 72 is configured to perform weighted calculation using a preset weighting algorithm according to the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image, and the accuracy value corresponding to the depth value of the second depth image to obtain the depth value of the target image.

[0156] In an optional embodiment, the weighting module 72 includes:

[0157] A scaling submodule, configured to scale the first depth image and the second depth image according to a preset resolution of the target image, to obtain a processed first depth image and a processed second depth image;

[0158] The weighting submodule is configured to perform weighted calculation using a preset weighting algorithm according to the depth value of the processed first depth image, the accuracy value corresponding to the depth value of the processed first depth image, the depth value of the processed second depth image, and the accuracy value corresponding to the depth value of the processed second depth image, so as to obtain the depth value of the target image.

[0159] In an optional embodiment, the image processor is further configured as:

[0160] After performing weighted calculation using a preset weighted algorithm based on the depth value of the processed first depth image, the accuracy value corresponding to the depth value of the processed first depth image, the depth value of the processed second depth image, and the accuracy value corresponding to the depth value of the processed second depth image, to obtain the depth value of the target image, the depth value of the target image is filtered to obtain the depth value of the target image after filtering.

[0161] In an optional embodiment, the scaling submodule is specifically configured as follows:

[0162] According to a preset resolution of the target image, the first depth image and the second depth image are respectively scaled to obtain a scaled first depth image and a scaled second depth image;

[0163] The scaled first depth image and the scaled second depth image are respectively subjected to filtering processing to obtain a processed first depth image and a processed second depth image.

[0164] In practical applications, the above-mentioned first acquisition module 71, weighting module 72, scaling submodule and weighting submodule can be implemented by a processor located on the image processor, specifically a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA).

[0165] Based on the same inventive concept, the embodiment of the present application provides a TOF module. Figure 8 A schematic diagram of the structure of an optional TOF module provided in an embodiment of the present application, such as Figure 8 As shown, the TOF module may include: a second acquisition module 81, a first search module 82, a first formation module 83 and a first sending module 84; wherein,

[0166] A second acquisition module 81 is configured to acquire a depth value of a target image;

[0167] A first search module 82 is configured to search, according to the depth value of the target image, for an accuracy value corresponding to the depth value of the target image from a preset correspondence relationship between the depth value and the accuracy value;

[0168] A first forming module 83, configured to form a first depth image using the depth value of the target image and the accuracy value corresponding to the depth value of the target image;

[0169] The first sending module 84 is configured to send the first depth image to the image processor, so that the image processor determines the depth value of the target image.

[0170] In practical applications, the second acquisition module 81, the first search module 82, the first formation module 83 and the first sending module 84 can be implemented by a processor located on the TOF module, specifically a CPU, an MPU, a DSP or an FPGA.

[0171] Based on the same inventive concept, the embodiment of the present application provides a binocular vision module. Fig. 9 A schematic diagram of the structure of an optional binocular vision module provided in an embodiment of the present application, such as Fig. 9 As shown, the binocular vision module may include: a determination module 91, a matching module 92, a second search module 93, a second formation module 94 and a second sending module 95; wherein,

[0172] A determination module 91 is configured to obtain a depth value of a target image and a binocular vision image of the target image, and then determine feature points of a first visual image in the binocular vision image and feature points of a second visual image in the binocular vision image;

[0173] A matching module 92, configured to match the feature points of the first visual image with the feature points of the second visual image to obtain a matching result of the target image;

[0174] The second search module 93 is configured to search for the accuracy value corresponding to the matching result of the target image from the preset correspondence relationship between the matching result and the accuracy value according to the matching result of the target image;

[0175] A second forming module 94 is configured to form a second depth image using the depth value of the target image and the accuracy value corresponding to the matching result of the target image;

[0176] The second sending module 95 is configured to send the second depth image to the image processor, so that the image processor determines the depth value of the target image.

[0177] In practical applications, the above-mentioned determination module 91, matching module 92, second search module 93, second formation module 94 and second sending module 95 can be implemented by a processor located on the binocular vision module, specifically a CPU, MPU, DSP or FPGA.

[0178] Fig.10 A schematic diagram of the structure of another optional image processor provided in an embodiment of the present application, such as Fig.10 As shown, the embodiment of the present application provides an image processor 1000, including:

[0179] A processor 101 and a storage medium 102 storing executable instructions of the processor 101, wherein the storage medium 102 relies on the processor 101 to perform operations through a communication bus 103, and when the instructions are executed by the processor 101, the method for determining the depth value of the image executed by the above-mentioned image processor is executed.

[0180] It should be noted that, in actual application, the various components in the terminal are coupled together via the communication bus 103. It is understandable that the communication bus 103 is used to realize the connection and communication between these components. In addition to the data bus, the communication bus 103 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Fig.10 Various buses are labeled as communication buses 103.

[0181] Fig.11 A schematic diagram of the structure of another optional TOF module provided in the embodiment of the present application, such as Fig.11As shown, the embodiment of the present application provides a TOF module 1100, including:

[0182] A processor 111 and a storage medium 112 storing executable instructions of the processor 111, wherein the storage medium 112 relies on the processor 111 to perform operations through a communication bus 113, and when the instructions are executed by the processor 111, the method for determining the depth value of the image executed by the above-mentioned TOF module is executed.

[0183] It should be noted that in actual application, the various components in the terminal are coupled together through the communication bus 113. It is understandable that the communication bus 113 is used to realize the connection and communication between these components. In addition to the data bus, the communication bus 113 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Fig.11 Various buses are labeled as communication buses 113.

[0184] Fig.12 A schematic diagram of the structure of another optional binocular vision module provided in an embodiment of the present application, such as Fig.12 As shown, the embodiment of the present application provides a binocular vision module 1200, including:

[0185] A processor 121 and a storage medium 122 storing executable instructions of the processor 121, wherein the storage medium 122 relies on the processor 121 to perform operations through a communication bus 123, and when the instructions are executed by the processor 121, the method for determining the depth value of the image performed by the above-mentioned binocular vision module is executed.

[0186] It should be noted that in actual application, the various components in the terminal are coupled together through the communication bus 123. It is understandable that the communication bus 123 is used to realize the connection and communication between these components. In addition to the data bus, the communication bus 123 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Fig.12 Various buses are labeled as communication buses 123.

[0187] An embodiment of the present application provides a computer storage medium storing executable instructions. When the executable instructions are executed by one or more processors, the processor executes the method for determining the depth value of an image executed by the image processor in one or more of the above-mentioned embodiments, or the method for determining the depth value of an image executed by the TOF module in one or more embodiments, or the method for determining the depth value of an image executed by the binocular vision module in one or more embodiments.

[0188] It can be understood that the memory in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0189] The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0190] It is understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application, or a combination thereof.

[0191] For software implementation, the techniques described herein can be implemented by modules (e.g., procedures, functions, etc.) that perform the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0192] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0193] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0194] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0195] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0196] Industrial Applicability

[0197] An embodiment of the present application provides a method for determining the depth value of an image, an image processor, and a module. For an object in a target image, a first depth image is obtained from a TOF module, and a second depth image is obtained from a binocular vision module; wherein the first depth image and the second depth image respectively further include accuracy values ​​corresponding to their respective depth values, and the accuracy values ​​are used to characterize the accuracy of the corresponding depth values. According to the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image, and the accuracy value corresponding to the depth value of the second depth image, a weighted calculation is performed using a preset weighting algorithm to obtain the depth value of the target image, thereby improving the accuracy of the depth value of the image.

Claims

1. A method for determining a depth value of an image, comprising: For the object in the target image, a first depth image is obtained from the TOF module, and a second depth image is obtained from the binocular vision module; wherein the first depth image and the second depth image respectively further include accuracy values ​​corresponding to their respective depth values, and the accuracy values ​​are used to characterize the accuracy of the corresponding depth values; When the resolution of the target image is the same as the resolution of the first depth image and the resolution of the second depth image, a depth value of the target image is obtained by performing weighted calculation using a preset weighting algorithm according to the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image and the accuracy value corresponding to the depth value of the second depth image; When the resolution of the target image is different from the resolution of the first depth image and the resolution of the second depth image, scaling the first depth image and the second depth image are performed according to the preset resolution of the target image to obtain a processed first depth image and a processed second depth image; A depth value of the target image is obtained by performing a weighted calculation based on the depth value of the processed first depth image, the accuracy value corresponding to the depth value of the processed first depth image, the depth value of the processed second depth image, and the accuracy value corresponding to the depth value of the processed second depth image.

2. The method according to claim 1, wherein: After obtaining the depth value of the target image by performing weighted calculation using a preset weighting algorithm according to the depth value of the processed first depth image, the accuracy value corresponding to the depth value of the processed first depth image, the depth value of the processed second depth image, and the accuracy value corresponding to the depth value of the processed second depth image, the method further includes: The depth value of the target image is filtered to obtain the depth value of the target image after filtering.

3. The method according to claim 1 or 2, wherein: The step of performing scaling processing on the first depth image and the second depth image according to a preset resolution of the target image to obtain a processed first depth image and a processed second depth image includes: According to a preset resolution of the target image, scaling the first depth image and the second depth image are performed respectively to obtain a scaled first depth image and a scaled second depth image; The scaled first depth image and the scaled second depth image are respectively subjected to filtering processing to obtain the processed first depth image and the processed second depth image.

4. A method for determining a depth value of an image, comprising: Get the depth value of the target image; According to the depth value of the target image, from the correspondence relationship between the preset depth value and the accuracy value, find the accuracy value corresponding to the depth value of the target image; forming a first depth image using the depth value of the target image and the accuracy value corresponding to the depth value of the target image; The first depth image is sent to an image processor, so that the image processor determines the depth value of the target image according to the method according to any one of claims 1 to 3.

5. A method for determining a depth value of an image, comprising: After acquiring the depth value of the target image and the binocular vision image of the target image, respectively determining feature points of a first visual image in the binocular vision image and feature points of a second visual image in the binocular vision image; Matching the feature points of the first visual image with the feature points of the second visual image to obtain a matching result of the target image; According to the matching result of the target image, the accuracy value corresponding to the matching result of the target image is found from a preset correspondence relationship between the matching result and the accuracy value; forming a second depth image using the depth value of the target image and the accuracy value corresponding to the matching result of the target image; The second depth image is sent to an image processor, so that the image processor determines the depth value of the target image according to the method according to any one of claims 1 to 3.

6. An image processor, comprising: A first acquisition module is configured to acquire a first depth image from the TOF module and a second depth image from the binocular vision module for an object in a target image; wherein the first depth image and the second depth image respectively further include accuracy values ​​corresponding to their respective depth values, and the accuracy values ​​are used to characterize the accuracy of the corresponding depth values; a weighting module configured to, when the resolution of the target image is the same as the resolution of the first depth image and the resolution of the second depth image, perform weighted calculation according to the depth value of the first depth image, the accuracy value corresponding to the depth value of the first depth image, the depth value of the second depth image and the accuracy value corresponding to the depth value of the second depth image using a preset weighting algorithm to obtain the depth value of the target image; The weighting module includes a scaling submodule, which is configured to scale the first depth image and the second depth image according to a preset resolution of the target image to obtain a processed first depth image and a processed second depth image when the resolution of the target image is different from the resolution of the first depth image and the resolution of the second depth image; The weighting module also includes a weighting sub-module, which is configured to perform weighted calculation using a preset weighting algorithm according to the depth value of the processed first depth image, the accuracy value corresponding to the depth value of the processed first depth image, the depth value of the processed second depth image, and the accuracy value corresponding to the depth value of the processed second depth image, so as to obtain the depth value of the target image.

7. A TOF module, comprising: A second acquisition module is configured to acquire a depth value of a target image; A first search module is configured to search, according to the depth value of the target image, for an accuracy value corresponding to the depth value of the target image from a preset correspondence relationship between the depth value and the accuracy value; A first forming module, configured to form a first depth image using the depth value of the target image and the accuracy value corresponding to the depth value of the target image; The first sending module is configured to send the first depth image to the image processor according to claim 6, so that the image processor determines the depth value of the target image.

8. A binocular vision module, comprising: A determination module, configured to obtain a depth value of a target image and a binocular vision image of the target image, and then respectively determine a feature point of a first visual image in the binocular vision image and a feature point of a second visual image in the binocular vision image; a matching module, configured to match feature points of the first visual image with feature points of the second visual image to obtain a matching result of the target image; A second search module is configured to search, according to the matching result of the target image, the accuracy value corresponding to the matching result of the target image from a preset correspondence relationship between the matching result and the accuracy value; A second forming module is configured to form a second depth image using the depth value of the target image and the accuracy value corresponding to the matching result of the target image; The second sending module is configured to send the second depth image to the image processor according to claim 6, so that the image processor determines the depth value of the target image.

9. An image processor, wherein: The image processor comprises: A processor and a storage medium storing instructions executable by the processor, wherein the storage medium relies on the processor to perform operations through a communication bus, and when the instructions are executed by the processor, the method for determining the depth value of the image described in any one of claims 1 to 3 is executed.

10. A TOF module, wherein: The TOF module includes: A processor and a storage medium storing instructions executable by the processor, wherein the storage medium relies on the processor to perform operations through a communication bus, and when the instructions are executed by the processor, the method for determining the depth value of the image described in claim 4 is executed.

11. A binocular vision module, wherein: The binocular vision module comprises: A processor and a storage medium storing instructions executable by the processor, wherein the storage medium relies on the processor to perform operations through a communication bus, and when the instructions are executed by the processor, the method for determining the depth value of the image described in claim 5 is executed.

12. A computer-readable storage medium, wherein: Executable instructions are stored, and when the executable instructions are executed by one or more processors, the processors execute the method for determining the depth value of the image described in any one of claims 1 to 3, or the method for determining the depth value of the image described in claim 4, or the method for determining the depth value of the image described in claim 5.

Images