Depth camera evaluation method, depth camera evaluation device, and storage medium

Abstract

This disclosure relates to a depth camera evaluation method, a depth camera evaluation device, and a storage medium. The depth camera evaluation method includes: arranging a depth camera to be tested and a target at a predetermined distance, wherein the target has multiple target objects of different sizes; identifying the target object imaged in the imaging window of the depth camera to be tested from among the multiple target objects, and identifying the smallest target object among the imaged target objects; determining the information content of the depth camera to be tested based on the imaging parameters of the smallest target object; and evaluating the depth camera to be tested based on the information content. Thus, the depth camera can be evaluated based on the information content, increasing the dimensionality of depth camera evaluation in related technologies.

Description

Technical Field

[0001] This disclosure relates to the field of camera technology, and in particular to a depth camera evaluation method, a depth camera evaluation device, and a storage medium. Background Technology

[0002] In some applications, image processing based on depth maps is required. In 3D pose recognition, depth maps assist in identifying the pose of a target. In Simultaneous Localization and Mapping (SLAM), depth maps are used to solve the problem of robot localization and map building when moving in unknown environments. It can be seen that the amount of information contained in a depth map is crucial for image analysis in various application scenarios. Depth maps are captured by depth cameras, and the amount of information contained in the resulting depth map is fixed. However, in the same scene, depth maps captured by different depth cameras contain different amounts of information. Therefore, it is necessary to evaluate the amount of information captured by different depth cameras. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a depth camera evaluation method, a depth camera evaluation device, and a storage medium.

[0004] According to a first aspect of the present disclosure, a depth camera evaluation method is provided, comprising:

[0005] A depth camera to be tested is positioned at a predetermined distance from a target, which has multiple target objects of different sizes. The target object that is imaged in the imaging window of the depth camera to be tested is identified from among the multiple target objects, and the smallest target object is identified from among the imaged target objects. The information content of the depth camera to be tested is determined based on the imaging parameters of the smallest target object. The depth camera to be tested is evaluated based on the information content.

[0006] In one implementation, determining the amount of information from the depth camera under test based on the imaging parameters of the smallest target object includes:

[0007] The size of the smallest target object and the number of pixels of the smallest target object are determined, and the diameter of the light spot emitted by the depth camera under test on the target, the size of the imaging window of the depth camera under test, and the number of light spots emitted by the depth camera under test are determined. Based on the size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot emitted by the depth camera under test on the target, the size of the imaging window, and the number of light spots, the information content of the depth camera under test is determined.

[0008] In one implementation, the amount of information from the depth camera under test is determined based on the size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot of the depth camera under test on the target, the size of the imaging window, and the number of light spots, including:

[0009] Based on the size of the smallest target object and the diameter of the light spot on the target from the depth camera under test, the ratio of the size of the smallest target object to the diameter of the light spot on the target from the depth camera under test is determined; based on the number of pixels of the smallest target object and the size of the imaging window, the ratio of the number of pixels of the smallest target object to the size of the imaging window is determined; the number of light spots is normalized; based on the ratio of the size of the smallest target object to the diameter of the light spot on the target from the depth camera under test, the ratio of the number of pixels of the smallest target object to the size of the imaging window, and the normalized number of light spots, the information content of the depth camera under test is determined.

[0010] In one embodiment, the size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot of the depth camera on the target, the size of the imaging window, and the number of light spots satisfy the following relationship:

[0011]

[0012] Where α represents the amount of information from the depth camera to be measured; size_object represents the size of the smallest target object; Diameter_Point represents the diameter of the light spot emitted by the depth camera on the target; W_object represents the number of pixels of the smallest target object; W_image represents the size of the imaging window of the depth camera to be measured; N represents the number of light spots emitted by the depth camera to be measured; and sigmoid(N) represents the normalization of the number of light spots emitted by the depth camera to be measured using the sigmoid function.

[0013] In one implementation, determining the size of the smallest target object and the number of pixels of the smallest target object, and determining the diameter of the light spot of the depth camera on the target, includes:

[0014] Determine the original resolution of the depth camera to be measured; at the original resolution, determine the size of the smallest target object and the number of pixels of the smallest target object, and determine the diameter of the light spot of the depth camera to be measured on the target.

[0015] According to a second aspect of the present disclosure, a depth camera evaluation apparatus is provided, comprising:

[0016] An information content determination unit is used to arrange the depth camera to be tested and a target at a predetermined distance, wherein the target is provided with multiple target objects of different sizes; determine the target object that is imaged in the imaging window of the depth camera to be tested among the multiple target objects, and determine the smallest target object among the imaged target objects; determine the information content of the depth camera to be tested based on the imaging parameters of the smallest target object; and an evaluation unit is used to evaluate the depth camera to be tested based on the information content.

[0017] In one implementation, the information content determination unit is used for:

[0018] The size of the smallest target object and the number of pixels of the smallest target object are determined, and the diameter of the light spot emitted by the depth camera under test on the target, the size of the imaging window of the depth camera under test, and the number of light spots emitted by the depth camera under test are determined. Based on the size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot emitted by the depth camera under test on the target, the size of the imaging window, and the number of light spots, the information content of the depth camera under test is determined.

[0019] In one implementation, the information content determination unit is used for:

[0020] Based on the size of the smallest target object and the diameter of the light spot on the target from the depth camera under test, the ratio of the size of the smallest target object to the diameter of the light spot on the target from the depth camera under test is determined; based on the number of pixels of the smallest target object and the size of the imaging window, the ratio of the number of pixels of the smallest target object to the size of the imaging window is determined; the number of light spots is normalized; based on the ratio of the size of the smallest target object to the diameter of the light spot on the target from the depth camera under test, the ratio of the number of pixels of the smallest target object to the size of the imaging window, and the normalized number of light spots, the information content of the depth camera under test is determined.

[0021] In one embodiment, the size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot of the depth camera on the target, the size of the imaging window, and the number of light spots satisfy the following relationship:

[0022]

[0023] Where α represents the amount of information from the depth camera to be measured; size_object represents the size of the smallest target object; Diameter_Point represents the diameter of the light spot emitted by the depth camera on the target; W_object represents the number of pixels of the smallest target object; W_image represents the size of the imaging window of the depth camera to be measured; N represents the number of light spots emitted by the depth camera to be measured; and sigmoid(N) represents the normalization of the number of light spots emitted by the depth camera to be measured using the sigmoid function.

[0024] In one implementation, the information content determination unit is used for:

[0025] Determine the original resolution of the depth camera to be measured; at the original resolution, determine the size of the smallest target object and the number of pixels of the smallest target object, and determine the diameter of the light spot of the depth camera to be measured on the target.

[0026] According to a third aspect of the present disclosure, a depth camera evaluation apparatus is provided, comprising:

[0027] A processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the depth camera evaluation method described in any embodiment of the first aspect.

[0028] According to a fourth aspect of the present disclosure, a storage medium is provided, the storage medium storing instructions that, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the depth camera evaluation method described in any embodiment of the first aspect.

[0029] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: A target object with the smallest imaging size is determined within the imaging window of the depth camera under test. The information content of the depth camera under test is determined based on the imaging parameters of the smallest target object. The depth camera under test is evaluated based on the information content. Thus, the depth camera can be evaluated based on the information content, increasing the dimensionality of depth camera evaluation in related technologies.

[0030] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0032] Figure 1This is a schematic diagram illustrating imaging using structured light technology according to an exemplary embodiment.

[0033] Figure 2 This is a schematic diagram illustrating imaging using ToF technology according to an exemplary embodiment.

[0034] Figure 3 This is a flowchart illustrating a depth camera evaluation method according to an exemplary embodiment.

[0035] Figure 4 This is a flowchart illustrating the determination of information from a depth camera according to an exemplary embodiment.

[0036] Figure 5 This is a block diagram of a depth camera evaluation device according to an exemplary embodiment.

[0037] Figure 6 This is a block diagram illustrating an apparatus for depth camera evaluation according to an exemplary embodiment. Detailed Implementation

[0038] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0039] Depth cameras in related technologies acquire depth maps using three techniques. These three techniques, based on their different depth measurement principles, are categorized into binocular stereo imaging, structured light, and time-of-flight (ToF) technology. This disclosure uses the imaging and processing of structured light as an example to illustrate the imaging process of obtaining a depth map using structured light technology. Structured light refers to the structuring of light; simple structuring includes point structured light, line structured light, and simple area structured light. Complex structuring involves encoding optical patterns. (See attached diagram.) Figure 1 This describes the process of obtaining a depth map by encoding light spots. Figure 1 This is a schematic diagram illustrating imaging using structured light technology according to an exemplary embodiment. Figure 1As shown, a depth camera emits an coded light spot. This coded light spot is projected onto the surface of the object under test and modulated by the object's height. The modulated coded light spot is then captured by the depth camera to obtain an initial speckle map. This initial speckle map is transmitted to a computing device for analysis and calculation to derive the object's 3D surface shape data. This analysis and calculation process can be called decoding the speckle to obtain the depth map. Depth maps obtained through structured light technology can be used for 3D facial recognition screen unlocking, facial payment, and 3D modeling, among other applications.

[0040] The embodiments disclosed herein will be described in conjunction with the appendix. Figure 2 Explain the process of obtaining a depth map based on Time-of-Flight (ToF) technology. Figure 2 This is a schematic diagram illustrating imaging using ToF technology according to an exemplary embodiment. Figure 2 As shown, a Time-of-Flight (ToF) sensor uses a tiny laser to emit infrared light, which is coded light. The generated infrared light bounces off any object and returns to the ToF sensor. The infrared light bouncing back into the ToF sensor generates an initial infrared radiation (IR) map. Based on the time difference between the emission of light and its return to the sensor after reflection, the ToF sensor can measure the distance between the object and the sensor, thus obtaining a depth map. This process of measuring the distance between the object and the sensor based on the time difference is called decoding. Depth maps obtained through ToF technology can be applied to motion sensing, gesture recognition, and environmental modeling.

[0041] pass Figure 1 and Figure 2 It can be seen that depth maps obtained from depth cameras are widely used in the field of image analysis and processing. The amount of information contained in a depth map is fixed. The more raw information a depth map contains, the more information it provides during image analysis and processing. Considering that different image analysis and processing methods will lose information to varying degrees during the processing of depth maps, for the same depth map, the more raw information it contains, the more processing methods can be used during image analysis and processing. For the same scene, depth maps obtained from different depth cameras capturing the same target object will contain different amounts of raw information.

[0042] Therefore, this disclosure provides a depth camera evaluation method to assess the raw information content of depth maps obtained from images of a target object captured by different depth cameras in the same test scene. The raw information content of the depth map is used as the raw information content of the corresponding depth camera under the corresponding test conditions in that test scene. The raw information content of the depth camera under the same test conditions is used as a metric parameter to evaluate the depth camera. For example, in the same shooting scene, a user has three options with different depth cameras to choose from. In this case, the information content of each depth camera can provide a reference for the user's selection. For example, within an acceptable cost range, the depth camera with the highest information content is selected. It should be noted that there are many reasons for the different information contents of different depth cameras, such as using different imaging technologies or selecting different sensor models.

[0043] The depth camera evaluation method provided in this disclosure quantifies the amount of information a depth camera generates from "emitting" a light spot to producing a "depth map." This method can be applied to selecting a depth camera that meets user needs from a variety of different depth cameras. The selection process includes determining the amount of information for each depth camera under uniform test conditions. By comparing the amount of information from different depth cameras and considering actual needs, a suitable depth camera can be selected. In other words, using the information content evaluation results obtained in this disclosure, the depth camera with the most information can be selected to capture the depth map in the same shooting scene. Furthermore, when processing the depth map, the image analysis and processing method with the least information loss can be selected based on the amount of information in the depth map. That is, the amount of information obtained in this disclosure can provide a basis for choosing which image analysis and processing method to use.

[0044] The following embodiments will illustrate the depth camera evaluation method in conjunction with the accompanying drawings.

[0045] Figure 3 This is a flowchart illustrating a depth camera evaluation method according to an exemplary embodiment. Figure 3 As shown, it includes the following steps.

[0046] In step S11, the depth camera to be measured is arranged at a predetermined distance from the target, and multiple target objects of different sizes are set on the target.

[0047] In this embodiment, the distance between the depth camera to be tested and the target is set according to actual needs. For example, multiple cameras to be tested need to be evaluated. This embodiment needs to determine the amount of information of each camera to be tested under the same evaluation scenario. The amount of information of each depth camera is then compared to determine the evaluation result of each camera to be tested. In other words, the distance between the position where the depth camera to be tested is placed and the target is preset, and each depth camera to be tested is placed at the position where the depth camera to be tested is placed for testing.

[0048] In this embodiment, to ensure accurate projection, the depth camera under test is kept parallel to the target. This avoids the issue of the depth camera emitting a circular light spot, which could be distorted due to the target's tilt. Setting the depth camera and target parallel ensures that the light spot projected onto the target is not distorted. In this disclosure, the target generally refers to a specific test object, such as a whiteboard for parallel testing. Multiple target objects of different sizes are placed on the target. In one embodiment, a sphere or a cube can be used as the target object.

[0049] In step S12, the target object that is imaged in the imaging window of the depth camera to be measured is determined among multiple target objects, and the smallest target object is determined among the imaged target objects.

[0050] Since the target has multiple target objects of different sizes, there is at least one imaged target object in the imaging window of the depth camera to be measured. Among the at least one imaged target object, the target object with the smallest image size is determined. In this embodiment, `size_object` is used to characterize the size of the smallest target object among the imaged target objects of the depth camera to be measured.

[0051] In step S13, the amount of information from the depth camera to be measured is determined based on the imaging parameters of the smallest target object.

[0052] In this embodiment of the disclosure, the amount of information from the depth camera to be measured is determined by the following steps.

[0053] In step S131, the size of the smallest target object and the number of pixels of the smallest target object are determined, and the diameter of the light spot of the depth camera under test on the target, the size of the imaging window of the depth camera under test, and the number of light spots emitted by the depth camera under test are determined.

[0054] In this embodiment of the disclosure, when the depth camera to be measured is fixed, the number of light spots emitted by the transmitting end of the depth camera to be measured is fixed.

[0055] In one example, the size of the imaging window can be provided by the manufacturer or obtained through measurement. In random speckle structured light imaging, the imaging window size of the depth camera under test refers to the window size selected and matched by the user. In coded structured light, the imaging window size of the depth camera under test refers to the size of the decoding window. In TOF imaging, the imaging window size of the depth camera under test is 1.0 by default. In this disclosure, W_image is used to characterize the imaging window size of the depth camera under test.

[0056] In this disclosure, the smallest imaging window size is selected. The smaller the imaging window and the higher the resolution of the depth camera, the smaller the target object can be seen. For example, if the target object is a needle tip, in Scheme 1, the original resolution is 5 megapixels, and the imaging window size is 11*11. In Scheme 2, the original resolution is 5 megapixels, and the imaging window size is 13*13. Therefore, the needle tip is more likely to be seen in the imaging window of Scheme 1.

[0057] In step S132, the amount of information of the depth camera under test is determined based on the size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot of the depth camera under test on the target, the size of the imaging window, and the number of light spots.

[0058] In step S14, the depth camera under test is evaluated based on the amount of information.

[0059] For multiple depth cameras to be tested, the amount of information corresponding to each depth camera is compared, and the depth cameras are evaluated based on the comparison results.

[0060] In related technologies, there is no technical solution for evaluating depth cameras based on information content. The embodiments of this disclosure provide a new reference index for evaluating depth cameras. Evaluating depth cameras based on information content fills the gap in related technologies that do not evaluate based on information content, making the dimensions of depth camera evaluation more comprehensive.

[0061] Combined with appendix Figure 4 This describes the process of determining the amount of information from the depth camera under test based on the size of the smallest target object, the number of pixels in the smallest target object, the diameter of the light spot of the depth camera under test on the target, the size of the imaging window, and the number of light spots. Figure 4 This is a flowchart illustrating the determination of information from a depth camera according to an exemplary embodiment. Figure 4 As shown, based on the size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot of the depth camera under test on the target, the size of the imaging window, and the number of light spots, the information content of the depth camera under test is determined according to the following steps.

[0062] In step S21, based on the size of the smallest target object and the diameter of the light spot of the depth camera on the target, the ratio of the size of the smallest target object to the diameter of the light spot of the depth camera on the target is determined.

[0063] In step S22, the ratio of the number of pixels of the smallest target object to the size of the imaging window is determined based on the number of pixels of the smallest target object and the size of the imaging window.

[0064] In step S23, the number of light spots is normalized.

[0065] This disclosure uses the sigmoid function to normalize the number of light spots emitted by the depth camera under test, so that the units of size_object, W_image, and N are consistent. This disclosure is not limited to using the sigmoid function for normalization. In addition to the sigmoid function, the ratio method can also be used, and this disclosure does not make a specific limitation.

[0066] In step S24, the amount of information of the depth camera to be measured is determined based on the ratio of the size of the smallest target object to the diameter of the light spot of the depth camera to be measured on the target, the ratio of the number of pixels of the smallest target object to the size of the imaging window, and the number of light spots after normalization.

[0067] In one implementation, the size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot of the depth camera on the target, the size of the imaging window, and the number of light spots satisfy the following relationship:

[0068]

[0069] Where α represents the amount of information from the depth camera to be measured; size_object represents the size of the smallest target object; Diameter_Point represents the diameter of the light spot emitted by the depth camera on the target; W_object represents the number of pixels of the smallest target object; W_image represents the size of the imaging window of the depth camera to be measured; N represents the number of light spots emitted by the depth camera to be measured; and sigmoid(N) represents the normalization of the number of light spots emitted by the depth camera to be measured using the sigmoid function.

[0070] This disclosure uses Diameter_Point to characterize the diameter of the light spot emitted by the depth camera under test after it is imaged on the target, with the unit being pixels. In the embodiments of this disclosure, any one light spot is selected from all the light spots imaged on the target by the depth camera under test, and its diameter is measured. Alternatively, multiple light spots are selected from all the light spots imaged on the target by the depth camera under test, their diameters are measured, and the average value is taken.

[0071] In one implementation, at the original resolution of the depth camera under test, the size of the smallest target object and the number of pixels of the smallest target object are determined, and the diameter of the light spot of the depth camera under test on the target is also determined. In this embodiment, the resolution used is the original resolution. It can be understood that manufacturers may exaggerate the resolution of the depth camera from different angles for marketing purposes. To ensure the accuracy of the test results, this embodiment obtains the size of the target observed in the imaging window size at the original resolution.

[0072] The depth camera evaluation method provided in this disclosure presents a mathematical model and evaluation method for the information content of the depth camera in a unified quantitative form. It can also be understood that this embodiment evaluates the depth camera by determining its information entropy.

[0073] Based on the same concept, this disclosure also provides a depth camera evaluation device.

[0074] It is understood that the depth camera evaluation device provided in this disclosure includes hardware structures and / or software modules corresponding to each function in order to achieve the above-mentioned functions. In conjunction with the units and algorithm steps of the various examples disclosed in this disclosure, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of this disclosure.

[0075] Figure 5 This is a block diagram illustrating a depth camera evaluation device according to an exemplary embodiment. (Refer to...) Figure 2 The device 100 includes an information quantity determination unit 101 and an evaluation unit 102.

[0076] Information content determination unit 101 is used to arrange the depth camera to be tested and the target at a predetermined distance, and the target is provided with multiple target objects of different sizes; determine the target object that is imaged in the imaging window of the depth camera to be tested among the multiple target objects, and determine the smallest target object among the imaged target objects; determine the information content of the depth camera to be tested based on the imaging parameters of the smallest target object; evaluation unit 102 is used to evaluate the depth camera to be tested based on the information content.

[0077] In one implementation, the information content determination unit 101 is used for:

[0078] The size and number of pixels of the smallest target object are determined, as well as the diameter of the light spot emitted by the depth camera on the target, the size of the imaging window of the depth camera, and the number of light spots emitted by the depth camera. Based on the size, number of pixels, diameter, imaging window, and number of light spots of the smallest target object, the amount of information of the depth camera is determined.

[0079] In one implementation, the information content determination unit 101 is used for:

[0080] Based on the size of the smallest target object and the diameter of the light spot on the target from the depth camera under test, the ratio of the size of the smallest target object to the diameter of the light spot on the target from the depth camera under test is determined; based on the number of pixels of the smallest target object and the size of the imaging window, the ratio of the number of pixels of the smallest target object to the size of the imaging window is determined; the number of light spots is normalized; based on the ratio of the size of the smallest target object to the diameter of the light spot on the target from the depth camera under test, the ratio of the number of pixels of the smallest target object to the size of the imaging window, and the number of light spots after normalization, the information content of the depth camera under test is determined.

[0081] In one implementation, the size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot of the depth camera on the target, the size of the imaging window, and the number of light spots satisfy the following relationship:

[0082]

[0083] Where α represents the amount of information from the depth camera to be measured; size_object represents the size of the smallest target object; Diameter_Point represents the diameter of the light spot emitted by the depth camera on the target; W_object represents the number of pixels of the smallest target object; W_image represents the size of the imaging window of the depth camera to be measured; N represents the number of light spots emitted by the depth camera to be measured; and sigmoid(N) represents the normalization of the number of light spots emitted by the depth camera to be measured using the sigmoid function.

[0084] In one implementation, the information content determination unit 101 is used for:

[0085] Determine the original resolution of the depth camera to be measured; at the original resolution, determine the size of the smallest target object and the number of pixels of the smallest target object, and determine the diameter of the light spot of the depth camera on the target.

[0086] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0087] Figure 6 This is a block diagram illustrating an apparatus 200 for depth camera evaluation according to an exemplary embodiment. For example, apparatus 200 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0088] Reference Figure 6 The device 200 may include one or more of the following components: processing component 202, memory 204, power component 206, multimedia component 208, audio component 210, input / output (I / O) interface 212, sensor component 214, and communication component 216.

[0089] Processing component 202 typically controls the overall operation of device 200, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 202 may include one or more processors 220 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 202 may include one or more modules to facilitate interaction between processing component 202 and other components. For example, processing component 202 may include a multimedia module to facilitate interaction between multimedia component 208 and processing component 202.

[0090] Memory 204 is configured to store various types of data to support the operation of device 200. Examples of such data include instructions for any application or method operating on device 200, contact data, phonebook data, messages, pictures, videos, etc. Memory 204 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0091] The power supply component 206 provides power to the various components of the device 200. The power supply component 206 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 200.

[0092] Multimedia component 208 includes a screen that provides an output interface between the device 200 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 208 includes a front-facing camera and / or a rear-facing camera. When the device 200 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0093] Audio component 210 is configured to output and / or input audio signals. For example, audio component 210 includes a microphone (MIC) configured to receive external audio signals when device 200 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 204 or transmitted via communication component 216. In some embodiments, audio component 210 also includes a speaker for outputting audio signals.

[0094] I / O interface 212 provides an interface between processing component 202 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0095] Sensor assembly 214 includes one or more sensors for providing status assessments of various aspects of device 200. For example, sensor assembly 214 may detect the on / off state of device 200, the relative positioning of components such as the display and keypad of device 200, changes in the position of device 200 or a component of device 200, the presence or absence of user contact with device 200, the orientation or acceleration / deceleration of device 200, and temperature changes of device 200. Sensor assembly 214 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 214 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 214 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0096] Communication component 216 is configured to facilitate wired or wireless communication between device 200 and other devices. Device 200 can access wireless networks based on communication standards, such as WiFi, 4G, or 5G, or combinations thereof. In one exemplary embodiment, communication component 216 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 216 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0097] In an exemplary embodiment, the apparatus 200 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0098] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 204 including instructions, which can be executed by a processor 220 of the device 200 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0099] It is understood that in this disclosure, "multiple" refers to two or more, and other quantifiers are similar. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. The singular forms "a," "the," and "the" are also intended to include the plural forms unless the context clearly indicates otherwise.

[0100] It is further understood that the terms "first," "second," etc., are used to describe various types of information, but this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another, and do not indicate a specific order or degree of importance. In fact, the expressions "first," "second," etc., are completely interchangeable. For example, without departing from the scope of this disclosure, first information can also be referred to as second information, and similarly, second information can also be referred to as first information.

[0101] It can be further understood that, unless otherwise specified, "connection" includes both direct connections where no other components exist between the two parties and indirect connections where other components exist between them.

[0102] It is further understood that although operations are described in a specific order in the accompanying drawings in the embodiments of this disclosure, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the shown operations to be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0103] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following scope of claims.

[0104] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for evaluating depth cameras, characterized in that, include: The depth camera to be measured is positioned at a predetermined distance from the target, and the target is provided with multiple target objects of different sizes. Identify the target object that is imaged in the imaging window of the depth camera under test among the plurality of target objects, and identify the smallest target object among the imaged target objects; The size of the smallest target object and the number of pixels of the smallest target object are determined, and the diameter of the light spot of the depth camera under test on the target, the size of the imaging window of the depth camera under test, and the number of light spots emitted by the depth camera under test are determined. Based on the size of the smallest target object and the diameter of the light spot of the depth camera on the target, determine the ratio of the size of the smallest target object to the diameter of the light spot of the depth camera on the target; Based on the number of pixels of the smallest target object and the size of the imaging window, determine the ratio of the number of pixels of the smallest target object to the size of the imaging window; The number of light spots is normalized. The amount of information of the depth camera under test is determined based on the ratio of the size of the smallest target object to the diameter of the light spot on the target of the depth camera under test, the ratio of the number of pixels of the smallest target object to the size of the imaging window, and the number of light spots after normalization. The depth camera under test is evaluated based on the amount of information provided.

2. The depth camera evaluation method according to claim 1, characterized in that, The size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot of the depth camera on the target, the size of the imaging window, and the number of light spots satisfy the following relationship: in, α The information content of the depth camera to be measured is represented by: size_object; Diameter_Point; W_object; W_image; N; and N represents the number of light spots emitted by the depth camera to be measured on the target. sigmoid (N) indicates the use of sigmoid The function normalizes the number of light spots emitted by the depth camera under test.

3. The depth camera evaluation method according to claim 2, characterized in that, Determining the size of the smallest target object and the number of pixels in the smallest target object, and determining the diameter of the light spot on the target by the depth camera to be measured, including: Determine the original resolution of the depth camera to be measured; At the original resolution, the size of the smallest target object and the number of pixels of the smallest target object are determined, and the diameter of the light spot of the depth camera under test on the target is determined.

4. A depth camera evaluation device, characterized in that, include: An information determination unit is used to arrange the depth camera to be measured and the target at a predetermined distance, wherein the target is provided with multiple target objects of different sizes; Identify the target object that is imaged in the imaging window of the depth camera under test among the plurality of target objects, and identify the smallest target object among the imaged target objects; The size of the smallest target object and the number of pixels of the smallest target object are determined, and the diameter of the light spot of the depth camera under test on the target, the size of the imaging window of the depth camera under test, and the number of light spots emitted by the depth camera under test are determined. Based on the size of the smallest target object and the diameter of the light spot of the depth camera on the target, determine the ratio of the size of the smallest target object to the diameter of the light spot of the depth camera on the target; Based on the number of pixels of the smallest target object and the size of the imaging window, determine the ratio of the number of pixels of the smallest target object to the size of the imaging window; The number of light spots is normalized. The amount of information of the depth camera under test is determined based on the ratio of the size of the smallest target object to the diameter of the light spot on the target of the depth camera under test, the ratio of the number of pixels of the smallest target object to the size of the imaging window, and the number of light spots after normalization. The evaluation unit is used to evaluate the depth camera under test based on the amount of information.

5. The depth camera evaluation device according to claim 4, characterized in that, The size of the smallest target object, the number of pixels of the smallest target object, the diameter of the light spot of the depth camera on the target, the size of the imaging window, and the number of light spots satisfy the following relationship: in, α The information content of the depth camera to be measured is represented by: size_object; Diameter_Point; W_object; W_image; N; and N represents the number of light spots emitted by the depth camera to be measured on the target. sigmoid (N) indicates the use of sigmoid The function normalizes the number of light spots emitted by the depth camera under test.

6. The depth camera evaluation device according to claim 4, characterized in that, Information content determination unit, used for: Determine the original resolution of the depth camera to be measured; At the original resolution, the size of the smallest target object and the number of pixels of the smallest target object are determined, and the diameter of the light spot of the depth camera under test on the target is determined.

7. A depth camera evaluation device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the depth camera evaluation method according to any one of claims 1 to 3.

8. A storage medium, characterized in that, The storage medium stores instructions that, when executed by the processor of the mobile terminal, enable the mobile terminal to perform the depth camera evaluation method according to any one of claims 1 to 3.

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