WO2021046793A1

WO2021046793A1 - Image acquisition method and apparatus, and storage medium

Info

Publication number: WO2021046793A1
Application number: PCT/CN2019/105582
Authority: WO
Inventors: 李明采; 王波
Original assignee: 深圳市汇顶科技股份有限公司
Priority date: 2019-09-12
Filing date: 2019-09-12
Publication date: 2021-03-18
Also published as: CN113228622A

Abstract

An image acquisition method and apparatus, and a storage medium. The image acquisition method comprises: determining a target image acquisition distance between a target object and the image acquisition apparatus (S101); determining, according to the target image acquisition distance and an exposure time determining model, a target exposure time used when the image acquisition apparatus performs image acquisition on the target object, brightness of the target object in an acquired image obtained when image acquisition is performed on the target object according to an image acquisition distance and an exposure time corresponding to each other in the exposure time determining model falling within a preset range (S102); and performing image acquisition on the target object according to the target image acquisition distance and the target exposure time (S103). Brightness of a target object in an acquired image obtained by performing image acquisition on the target object according to a target image acquisition distance and a target exposure time corresponding thereto is not too high or too low, thereby avoiding overexposure or underexposure.

Description

Image acquisition method, device and storage medium

Technical field

The embodiments of the present application relate to the field of image processing technology, and in particular to an image acquisition method, device, and storage medium.

Background technique

Because images can display information more intuitively, the development of image acquisition is becoming more and more important, especially for image acquisition of some specific objects. For example, face image collection, license plate image collection, etc. Among them, the quality of the image will be affected by the photosensitive device (English: sensor) of the camera, the difference of the lens lens, and the exposure time. Take face recognition as an example. Face recognition builds a face model by acquiring face images. When actually acquiring images, the distance from which the images are collected, ambient light and other influencing factors are uncertain, and using a fixed exposure time often cannot guarantee the image. The quality of the image will be over-exposed or under-exposed, and the details of the image will not be obvious.

Summary of the invention

In view of this, one of the technical problems solved by the embodiments of the present application is to provide an image acquisition method, device, and storage medium to overcome the excessive or short exposure time caused by the distance of the image acquisition in the prior art. Defects in the quality of the captured image.

In the first aspect, an embodiment of the present application provides an image acquisition method, which includes:

Determine the target image capture distance between the target object and the image capture device;

According to the target image acquisition distance and exposure time determination model, the target exposure time used by the image acquisition device for image acquisition of the target object is determined, and the exposure time determination model is used to indicate the correspondence between at least one image acquisition distance and at least one exposure time. , According to the exposure time to determine the image acquisition distance and exposure time corresponding to each other in the model when image acquisition of the target object, the brightness of the target object in the acquired image is within the preset range;

According to the target image acquisition distance and target exposure time, image acquisition of the target object is performed.

Optionally, in an embodiment of the present application, the method further includes:

The exposure time corresponding to each image collection distance in the at least one image collection distance is determined, and an exposure time determination model is established according to the correspondence between the at least one image collection distance and the at least one exposure time.

Optionally, in an embodiment of the present application, determining the exposure time corresponding to each image collection distance in the at least one image collection distance includes:

Use the preset distance as the image collection distance to perform image collection on the target object according to at least one exposure time to obtain at least one sample image;

A sample image in at least one sample image whose brightness of the target object is within a preset range is determined as the target sample image, and the exposure time of the target sample image is determined as the exposure time corresponding to the preset distance.

Optionally, in an embodiment of the present application, using a preset distance as the image collection distance to perform image collection on the target object according to at least one exposure time to obtain at least one sample image includes:

Use the preset distance as the image collection distance to perform image collection on the target object according to the preset exposure time to obtain a sample image;

When the brightness of the target object in the sample image is less than the first threshold, increase the preset exposure time to perform image acquisition again on the target object; when the brightness of the target object in the sample image is greater than the second threshold, reduce the preset exposure time to restore the target object For image acquisition, the preset range includes a range greater than or equal to the first threshold and less than or equal to the second threshold.

Calculate the average value of the pixel brightness of the area where the target object in each sample image is located as the brightness of the target object in each sample image.

Optionally, in an embodiment of the present application, the target object is a human face, the preset range is [900, 1000], and the value range of at least one image collection distance is greater than or equal to 300 mm and less than or equal to 1200 mm .

Optionally, in an embodiment of the present application, determining the image capture distance between the target object and the image capture device includes:

The target image acquisition distance is determined by calculating the time difference or phase difference between the light signal emission and reflection between the target object and the image acquisition device.

In the second aspect, an embodiment of the present application provides an image acquisition device, including: a processor, a distance measurement component, and an image acquisition component; both the distance measurement component and the image acquisition component are electrically connected to the processor;

Among them, the distance measurement component is used to determine the target image acquisition distance between the target object and the image acquisition device;

The processor is configured to determine the target exposure time used by the image acquisition device for image acquisition of the target object according to the target image acquisition distance and the exposure time determination model, and the exposure time determination model is used to indicate one of the at least one image acquisition distance and the at least one exposure time Correspondence between the two, in which, when the image acquisition distance and exposure time corresponding to each other in the model are determined according to the exposure time, the brightness of the target object in the acquired image is within the preset range when image acquisition is performed on the target object;

The image acquisition component is used for image acquisition of the target object according to the target image acquisition distance and target exposure time.

Optionally, in an embodiment of the present application, the processor is further configured to determine the exposure time corresponding to each image acquisition distance in the at least one image acquisition distance, and according to the correspondence between the at least one image acquisition distance and the at least one exposure time The relationship establishes the exposure time to determine the model.

Optionally, in an embodiment of the present application, the processor is further configured to use a preset distance as the image acquisition distance to perform image acquisition of the target object according to at least one exposure time to obtain at least one sample image; A sample image whose brightness of the target object is within a preset range is determined as the target sample image, and the exposure time of the target sample image is determined as the exposure time corresponding to the preset distance.

Optionally, in an embodiment of the present application, the processor is further configured to use the preset distance as the image collection distance to perform image collection of the target object according to the preset exposure time to obtain a sample image; the brightness of the target object in the sample image When it is less than the first threshold, increase the preset exposure time to perform image acquisition again on the target object; when the brightness of the target object in the sample image is greater than the second threshold, reduce the preset exposure time to perform image acquisition again on the target object. The preset range includes The range is greater than or equal to the first threshold and less than or equal to the second threshold.

Optionally, in an embodiment of the present application, the processor is further configured to calculate the average value of the pixel brightness of the area where the target object is located in each sample image as the brightness of the target object in each sample image.

Optionally, in an embodiment of the present application, the distance measurement component is also used to determine the target image collection distance by calculating the time difference or phase difference between the emission and reflection of the optical signal between the target object and the image collection device.

Optionally, in an embodiment of the present application, the distance measurement component includes a time-of-flight ranging module, and the image acquisition component includes a structured light image acquisition component and/or an RGB image acquisition component.

In the third aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the implementation is as described in the first aspect or any one of the embodiments of the first aspect. Methods.

In the embodiment of the present application, the target exposure time corresponding to the target image acquisition distance between the target object and the image acquisition device is determined according to the exposure time determination model, and the target object is imaged according to the target image acquisition distance and the corresponding target exposure time. , The brightness of the target object in the acquired image will not be too bright or too dark, avoiding overexposure or underexposure, the display of the target object is clearer, improving the quality of the acquired image, and determining the model and target according to the exposure time The image collection distance directly determines the corresponding target exposure time, which can quickly determine the appropriate exposure time and improve efficiency.

Description of the drawings

Hereinafter, some specific embodiments of the embodiments of the present application will be described in detail in an exemplary but not restrictive manner with reference to the accompanying drawings. The same reference numerals in the drawings indicate the same or similar components or parts. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the attached picture:

FIG. 1 is a flowchart of an image acquisition method provided by an embodiment of the application;

2 is a schematic diagram of the relationship between exposure time and brightness provided by an embodiment of the application;

FIG. 3 is a structural diagram of a face recognition door lock provided by an embodiment of the application;

4 is a schematic diagram of brightness distribution of target objects at different distances according to an embodiment of the application;

FIG. 5 is a flowchart of a mapping establishment method provided by an embodiment of the application;

FIG. 6 is a logical block diagram of a method for collecting sample images according to an embodiment of the application;

FIG. 7 is a structural diagram of an image acquisition device provided by an embodiment of the application;

FIG. 8 is a structural diagram of an image acquisition device provided by an embodiment of the application.

detailed description

The implementation of any technical solution of the embodiments of the present application does not necessarily need to achieve all the above advantages at the same time.

In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the description The embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art should fall within the protection scope of the embodiments of the present application.

The specific implementation of the embodiments of the present application will be further described below in conjunction with the drawings of the embodiments of the present application.

Example one,

Figure 1 is a flowchart of an image acquisition method provided by an embodiment of the application. The image acquisition method can be applied to an image acquisition device. The image acquisition device can be an infrared camera, a digital camera, a smart phone tablet computer, etc., which has an image acquisition function. For electronic equipment, as shown in Fig. 1, the image acquisition method includes the following steps:

Step 101: Determine the target image capture distance between the target object and the image capture device.

In the embodiment of the present application, the target image acquisition distance is the distance between the target object and the image acquisition device. The target object can be a human face, a license plate, etc., which is not limited in this application. In this application, the term target is used to indicate a singular number and is not used for any limitation. The target object refers to a collection object. This application uses this collection object as an example to describe the process of implementation of the solution, and does not have any limiting effect. The target image collection distance also indicates any collection distance.

Optionally, in an embodiment of the present application, determining the image capture distance between the target object and the image capture device includes: calculating the time difference or phase difference between the light signal emission and reflection between the target object and the image capture device. Determine the target image collection distance.

For example, the image acquisition device sends a light signal (such as infrared light) to the target object through the sensor, and is received by the sensor after being reflected by the target object, and determines the target object and the image acquisition by calculating the time difference or phase difference between the light signal emission and reflection The distance between the devices. In some application scenarios, for example, the image acquisition device is an infrared camera, and the infrared camera itself has a sensor that receives infrared light, so there is no need to make much changes to the device itself, which is more convenient.

Step 102: Determine the target exposure time used by the image acquisition device for image acquisition of the target object according to the target image acquisition distance and exposure time determination model.

The exposure time determination model is used to indicate the correspondence between at least one image acquisition distance and at least one exposure time. The image collection distance refers to the distance between the image collection device and the collection object. In this application, the collection object is the target object. The target exposure time is the exposure time corresponding to the target image collection distance in the exposure time determination model. The target image collection distance belongs to at least one image collection distance. The corresponding image collection distance and exposure time in the model are determined according to the exposure time. During image acquisition, the brightness of the target object in the acquired image is within a preset range. The exposure time determination model may be a preset mapping, and the preset mapping may be expressed in the form of a list, a function, or an image, which is not limited in this application. For example, if the image acquisition distance is 500mm, the corresponding exposure time is 10ms, which means that the brightness of the target object in the acquired image obtained by the image acquisition of the target object according to the distance between the image acquisition device and the target object is 500mm and the exposure time is 10ms Within the preset range.

It should be noted that the greater the brightness of the target object, the clearer the target object will be displayed. However, if the brightness is too large, the target object may be unclear due to overexposure. The brightness can be determined by the DN value (English: Digital Number, remote sensing). Image pixel brightness value) or gray value representation. Optionally, in an embodiment of the present application, the target object may be a human face, the preset range is [900, 1000], and the value range of at least one image collection distance is greater than or equal to 300 mm and less than or equal to 1200 mm. Under normal circumstances, the effective distance for image collection of a human face is between 300 mm and 1200 mm. Of course, this is only an exemplary description. For the preset range, refer to FIG. 2. FIG. 2 is a schematic diagram of the relationship between exposure time and brightness provided by an embodiment of the application. In FIG. 2, when the exposure time is within 180ms, the brightness increases linearly with the increase of the exposure time. , After the exposure time exceeds 180ms, the brightness increases nonlinearly with the increase of the exposure time. When the exposure time is 180ms, the corresponding brightness is 900. Therefore, if the brightness of the target object in the captured image exceeds 900, increase the exposure time During image acquisition, the target object will be overexposed and the display is not clear. Therefore, the preset range is set between [900,1000] to ensure that the area where the target object is located in the image is fully exposed and displayed clearly, and it can also avoid overexposure. Exposure, of course, the preset range can also be flexibly adjusted. For example, it is possible to set the preset range between [800,900], or between [850,900], or between [900,950]. No restrictions.

Optionally, in an embodiment of the present application, the method further includes: determining the exposure time corresponding to each image acquisition distance in the at least one image acquisition distance, and according to the correspondence between the at least one image acquisition distance and the at least one exposure time Create a default mapping.

Step 103: Perform image collection on the target object according to the target image collection distance and the target exposure time.

With reference to the image acquisition method described in steps 101-103, here, a specific application scenario is taken as an example to illustrate the implementation process of the image acquisition method provided in the embodiment of the present application. This application scenario takes a face recognition door lock as an example. In this scenario, the target object is a face, and the image acquisition device may be a part or all of the face recognition door lock. Referring to Figure 3, Figure 3 is a structural diagram of a face recognition door lock provided by an embodiment of the application. In Figure 3, the face recognition door lock includes a processor, an infrared camera, a TOF distance sensor, and an infrared light supplement. As well as the door lock, the processor is respectively connected with an infrared camera, a TOF (English: Time of Flight, time of flight distance measurement method) distance sensor, and an infrared light supplementer to realize the control of these components. During use, the user needs to register on the face recognition door lock first, that is, enter his face image. After the registration is successful, the user can open the door lock through face recognition. Whether in the process of face registration or face recognition, face recognition door locks need to use an infrared camera to capture images of the user's face (that is, the target object).

In the image acquisition process, the processor controls the TOF distance sensor to measure the distance between the infrared camera and the user's face as the target image acquisition distance. The distance between the infrared camera and the user's face can represent the face recognition door lock to the user's person. For the distance of the face, the processor determines the target exposure time corresponding to the target image collection distance according to the preset mapping. The processor controls the infrared light supplementer to fill light according to the target exposure time, and controls the infrared camera to collect images of the user's face. The exposure time determined according to the preset mapping can make the brightness of the user's face area in the collected image reach 900, making the user's face display clearer. Referring to FIG. 4, FIG. 4 is a schematic diagram of the brightness distribution of a target object at different distances according to an embodiment of the application. In FIG. 4, the abscissa represents the distance and the ordinate represents the brightness. In FIG. 4, the preset range is ( 830,920) After automatic exposure according to the preset mapping, the brightness of the target object is between 830 and 920.

In the embodiment of the present application, the target exposure time corresponding to the target image acquisition distance between the target object and the image acquisition device is determined according to the preset mapping, and the target object is imaged according to the target image acquisition distance and the corresponding target exposure time. The brightness of the target object in the acquired captured image will not be too bright or too dark to avoid overexposure or underexposure, the display of the target object is clearer, and the quality of the captured image is improved. Moreover, according to the preset mapping and target image capture The distance directly determines the corresponding target exposure time, which can quickly determine the appropriate exposure time and improve efficiency.

Embodiment two

Based on the image acquisition method described in the first embodiment, the second embodiment of the present application provides a mapping establishment method. This embodiment uses a preset mapping as an example to illustrate the exposure time determination model, which does not mean that the application is limited to this. Referring to FIG. 5, FIG. 5 is a flowchart of a mapping establishment method provided by an embodiment of the application, and the method includes the following steps:

Step 501: Use the preset distance as the image collection distance to perform image collection on the target object according to at least one exposure time to obtain at least one sample image.

It should be noted that the preset distance may be any length of at least one image collection distance, and the value range of the at least one image collection distance may be any distance between 300 mm and 1200 mm.

Using the preset distance as the image collection distance, perform image collection of the target object according to the preset exposure time to obtain a sample image; when the brightness of the target object in the sample image is less than the first threshold, increase the preset exposure time to perform image collection again on the target object; When the brightness of the target object in the sample image is greater than the second threshold, reduce the preset exposure time to perform image acquisition again on the target object. The preset range includes a range greater than or equal to the first threshold and less than or equal to the second threshold. It should be noted that when increasing or decreasing the exposure time, it can be increased or decreased according to the preset step length. For example, the preset step length is 10ms. If the preset exposure time is 20ms, the target object will be imaged according to the exposure time of 10ms. In the acquired image, if the brightness of the target object is less than the first threshold, the exposure time is increased by 10ms, and the image is re-acquired according to the exposure time of 30ms. If the brightness of the target object is greater than the second threshold, the exposure time is reduced by 10ms, According to the exposure time of 10ms, the target object is imaged. Of course, this is only an exemplary description. The preset step length can also be 1ms, and the preset exposure time can be 8ms. The first threshold may be 800 or 900, and the second threshold may be 950 or 1000, which is not limited in this application.

Step 502: Calculate the average value of the pixel brightness of the area where the target object is located in each sample image as the brightness of the target object in each sample image.

The brightness can be represented by a DN value or a gray value, which is not limited in this application.

It should be noted that the maximum or minimum value of the pixel brightness of the area where the target object is located can also be used as the brightness of the target object in each sample image, which is not limited in this application.

Step 503: Determine a sample image in at least one sample image whose brightness of the target object is within a preset range as the target sample image.

Step 504: Determine the exposure time of the target sample image as the exposure time corresponding to the preset distance.

Steps 501 to 504 determine the exposure time corresponding to the preset distance according to at least one sample image at the preset distance. The preset mapping (ie, the exposure time determination model) indicates the correspondence between at least one image collection distance and at least one exposure time. In the at least one image collection distance, each image collection distance can be determined according to the method of step 501-step 504 The corresponding exposure time. For example, at least one image collection distance may include (300mm, 310mm, 320mm...1190mm, 1200mm), that is, increase from 300mm to 1200mm in steps of 10mm. Of course, this is only an exemplary description, and at least one image collection distance can also be Include more values.

Step 505: Establish a preset mapping according to the correspondence between at least one image collection distance and at least one exposure time.

Embodiment three

Based on the mapping establishment method described in step 501 to step 505 in the second embodiment, here, a specific example is cited to illustrate the collection process of the sample image, as shown in FIG. 6, which is a method provided by an embodiment of this application. The logical block diagram of the sample image acquisition method. Here, the target object is a human face as an example, the preset exposure time is 8ms, the step length of the exposure time is 1ms, the preset range is 900, and at least one value of the image acquisition distance The range is (300mm, 310mm, 320mm...1190mm, 1200mm). Referring to Figure 6, the sample image acquisition method of this example includes the following steps:

601. Set the image collection distance d to 300ms, that is, d=300ms;

602. Determine whether the image collection distance is less than or equal to 1200 ms;

If the image collection distance is less than or equal to 1200, step 603 is executed, otherwise, the method ends.

603. Acquire a frame of face image by using a preset exposure time.

It should be noted that the initial preset exposure time is 8ms.

604. Calculate the average value of the DN value of the face area (that is, the average value of the pixel brightness of the area where the target object is located).

605. Determine whether the average DN value is less than 900.

When the average DN value is less than 900, step 606 is executed; otherwise, step 607 is executed.

606. Increase the exposure time by 1 ms, and execute step 603.

607. Determine whether the average DN value is greater than 900.

When the average DN value is greater than 900, step 608 is executed; otherwise, step 609 is executed.

608. Reduce the exposure time by 1 ms, and execute step 603.

Collect a frame of face image (ie sample image) with 8ms as the exposure time, calculate the average DN value of the face area in the face image, determine whether the average DN value is less than 900, when the average DN value is less than 900, increase the exposure time 1ms, re-acquire the face image for judgment. When the average DN value is not less than 900, judge whether the average DN value is greater than 900. If the average DN value, record the exposure time when the DN value reaches 900 as the exposure time corresponding to 300mm; adjust the exposure Time makes the average DN value of the face area reach 900. Here, the preset range only includes the value of 900.

609. Record the mapping pair of the current image collection distance and exposure time.

610: Increase the image collection distance by 10 mm, and return to step 602.

Repeat steps 602-610 to determine the exposure time corresponding to each image collection distance, and establish a preset mapping.

Embodiment four

Based on the methods described in the first to third embodiments above, an embodiment of the present application provides an image acquisition device for executing the methods described in the first to third embodiments. As shown in FIG. 7, the image acquisition device 70 Including: a processor 701, a distance measurement component 702, and an image acquisition component 703, and both the distance measurement component 702 and the image acquisition component 703 are electrically connected to the processor 701;

Wherein, the distance measurement component 702 is used to determine the target image acquisition distance between the target object and the image acquisition device;

The processor 701 is configured to determine the target exposure time used by the image capture device for image capture of the target object according to the target image capture distance and a preset mapping, and the preset mapping is used to indicate a distance between at least one image capture distance and at least one exposure time Wherein, when the image acquisition distance and exposure time corresponding to each other in the model are determined according to the exposure time, when the target object is imaged, the brightness of the target object in the acquired image is within a preset range;

The image acquisition component 703 is used for image acquisition of the target object according to the target image acquisition distance and the target exposure time.

Optionally, in an embodiment of the present application, as shown in FIG. 8, the image acquisition device 70 may further include a memory 704, which is electrically connected to the processor 701, the memory 704 stores a computer program, and the processor 701 Executing the computer program implements the methods described in the first to third embodiments. Of course, this is only an exemplary description, and the computer program may also be stored on the processor 701, which is not limited in this application.

Optionally, in an embodiment of the present application, the processor 701 is further configured to determine the exposure time corresponding to each image acquisition distance in the at least one image acquisition distance, and determine the exposure time corresponding to the at least one image acquisition distance and the at least one exposure time. The corresponding relationship establishes a preset mapping, and the brightness of the target object is within the preset range in the images collected on the target object according to the corresponding image collection distance and exposure time.

Optionally, in an embodiment of the present application, the processor 701 is further configured to use a preset distance as the image collection distance to perform image collection of the target object according to at least one exposure time to obtain at least one sample image; The sample image in which the brightness of the target object is within the preset range is determined as the target sample image, and the exposure time of the target sample image is determined as the exposure time corresponding to the preset distance.

Optionally, in an embodiment of the present application, the processor 701 is further configured to use the preset distance as the image collection distance to perform image collection of the target object according to the preset exposure time to obtain a sample image; When the brightness is less than the first threshold, increase the preset exposure time to perform image acquisition again on the target object; when the brightness of the target object in the sample image is greater than the second threshold, reduce the preset exposure time to perform image acquisition again on the target object, the preset range Including the range greater than or equal to the first threshold and less than or equal to the second threshold.

Optionally, in an embodiment of the present application, the processor 701 is further configured to calculate the average value of the pixel brightness of the area where the target object is located in each sample image as the brightness of the target object in each sample image.

Optionally, in an embodiment of the present application, the distance measurement component 702 is also used to determine the target image collection distance by calculating the time difference or phase difference between the emission and reflection of the optical signal between the target object and the image collection device.

Optionally, in an embodiment of the present application, the distance measurement component 702 includes a time-of-flight ranging module, and the image acquisition component 703 includes a structured light image acquisition component and/or an RGB image acquisition component.

It should be noted that the time-of-flight ranging module may include a TOF distance sensor, and the structured light image acquisition component may include a speckle projector to obtain the depth information of the target object, RGB (English: Red Green Blue) The image acquisition component may include a camera for acquiring a two-dimensional image of the target object, and the complete three-dimensional image information of the target object can be acquired through the structured light image acquisition component and the RGB image acquisition component. Of course, this is only an exemplary description, and the image acquisition component 703 may also only include a camera, for example, an infrared camera, a common camera, etc., which is not limited in this application.

The image acquisition device 70 can be a smart phone, an infrared camera, a face recognition door lock and other equipment.

Embodiment five

Based on the methods described in the first to third embodiments above, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. The feature is that when the program is executed by a processor, the implementation is as in the first to third embodiments. The method described in the third embodiment.

The image acquisition device of the embodiment of the present application may exist in various forms, including but not limited to:

(1) Mobile communication equipment: This type of equipment is characterized by mobile communication functions, and its main goal is to provide voice and data communications. Such terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.

(2) Ultra-mobile personal computer equipment: This type of equipment belongs to the category of personal computers, has calculation and processing functions, and generally also has mobile Internet features. Such terminals include: PDA, MID and UMPC devices, such as iPad.

(3) Portable entertainment equipment: This type of equipment can display and play multimedia content. Such devices include: audio, video players (such as iPod), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

(4) Server: A device that provides computing services. The structure of a server includes a processor 810, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it is High requirements in terms of performance, reliability, security, scalability, and manageability.

(5) Other electronic devices with data interaction functions.

So far, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown in order to achieve the desired result. In certain embodiments, multitasking and parallel processing may be advantageous.

In the 1990s, the improvement of a technology can be clearly distinguished between hardware improvements (for example, improvements in circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements in method flow). However, with the development of technology, the improvement of many methods and processes of today can be regarded as a direct improvement of the hardware circuit structure. Designers almost always get the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be realized by the hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (for example, a Field Programmable Gate Array (Field Programmable Gate Array, FPGA)) is such an integrated circuit whose logic function is determined by the user's programming of the device. It is programmed by the designer to "integrate" a digital system on a PLD, without requiring the chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly realized with "logic compiler" software, which is similar to the software compiler used in program development and writing, but before compilation The original code must also be written in a specific programming language, which is called Hardware Description Language (HDL), and there is not only one type of HDL, but many types, such as ABEL (Advanced Boolean Expression Language) , AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description), etc., currently most commonly used It is VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. It should also be clear to those skilled in the art that only a little logic programming of the method flow in the above-mentioned hardware description languages and programming into an integrated circuit can easily obtain the hardware circuit that implements the logic method flow.

The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor, and a computer-readable medium storing computer-readable program codes (such as software or firmware) executable by the (micro)processor. , Logic gates, switches, application specific integrated circuits (ASICs), programmable logic controllers and embedded microcontrollers. Examples of controllers include but are not limited to the following microcontrollers: ARC625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicon Labs C8051F320, the memory controller can also be implemented as part of the memory control logic. Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code manner, it is entirely possible to program the method steps to make the controller use logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded The same function can be realized in the form of a microcontroller or the like. Therefore, such a controller can be regarded as a hardware component, and the devices included in it for realizing various functions can also be regarded as a structure within the hardware component. Or even, the device for realizing various functions can be regarded as both a software module for realizing the method and a structure within a hardware component.

The systems, devices, modules, or units explained in the above embodiments may be implemented by computer chips or entities, or implemented by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cell phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or Any combination of these devices.

For the convenience of description, when describing the above device, the functions are divided into various units and described separately. Of course, when implementing this application, the functions of each unit can be implemented in the same one or more software and/or hardware.

Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, this application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

This application is described with reference to flowcharts and/or block diagrams of methods, devices (systems), and computer program products according to embodiments of this application. It should be understood that each process and/or block in the flowchart and/or block diagram, and the combination of processes and/or blocks in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing equipment to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing equipment are generated It is a device that realizes the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.

These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device. The device implements the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.

These computer program instructions can also be loaded on a computer or other programmable data processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so as to execute on the computer or other programmable equipment. The instructions provide steps for implementing the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.

In a typical configuration, the computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include non-permanent memory in a computer readable medium, random access memory (RAM) and/or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer readable media.

Computer-readable media include permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, Magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices. According to the definition in this article, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

It should also be noted that the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or equipment including a series of elements not only includes those elements, but also includes Other elements that are not explicitly listed, or they also include elements inherent to such processes, methods, commodities, or equipment. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, commodity, or equipment that includes the element.

Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, this application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

This application may be described in the general context of computer-executable instructions executed by a computer, such as a program module. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific transactions or implement specific abstract data types. The present application can also be practiced in distributed computing environments. In these distributed computing environments, transactions are executed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment.

The foregoing descriptions are only examples of the present application, and are not used to limit the present application. For those skilled in the art, this application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included in the scope of the claims of this application.

Claims

An image acquisition method, characterized in that it is applied to an image acquisition device, and includes:

Determine the target image capture distance between the target object and the image capture device;

According to the target image acquisition distance and the exposure time determination model, the target exposure time used by the image acquisition device for image acquisition of the target object is determined, and the exposure time determination model is used to indicate at least one image acquisition distance and at least one Correspondence between exposure time, wherein, when the image acquisition distance and exposure time corresponding to each other in the model are determined according to the exposure time, the brightness of the target object in the acquired image is determined in advance. Set within

Image acquisition is performed on the target object according to the target image acquisition distance and the target exposure time.
The method according to claim 1, wherein the method further comprises:

The exposure time corresponding to each image collection distance in the at least one image collection distance is determined, and the exposure time determination model is established according to the corresponding relationship between the at least one image collection distance and the at least one exposure time.
The method according to claim 2, wherein determining the exposure time corresponding to each image acquisition distance in the at least one image acquisition distance comprises:

Use the preset distance as the image collection distance to perform image collection on the target object according to at least one exposure time to obtain at least one sample image;

Determine a sample image in the at least one sample image in which the brightness of the target object is within the preset range as a target sample image, and determine the exposure time of the target sample image as the exposure corresponding to the preset distance time.
The method according to claim 3, wherein the image acquisition of the target object according to at least one exposure time by using the preset distance as the image acquisition distance to obtain at least one sample image comprises:

Using the preset distance as the image collection distance to perform image collection on the target object according to the preset exposure time to obtain a sample image;

When the brightness of the target object in the sample image is less than the first threshold, increase the preset exposure time to re-image the target object; the brightness of the target object in the sample image is greater than the second When the threshold is set, the preset exposure time is reduced to perform image acquisition again on the target object, and the preset range includes a range greater than or equal to the first threshold and less than or equal to the second threshold.
The method according to claim 3, wherein the method further comprises:

The average value of the pixel brightness of the area where the target object is located in each sample image is calculated as the brightness of the target object in each sample image.
The method according to claim 1, wherein the target object is a human face, the preset range is [900, 1000], and the value range of the at least one image collection distance is greater than or equal to 300 mm, And less than or equal to 1200 mm.
The method according to any one of claims 1 to 6, wherein determining the target image collection distance between the target object and the image collection device comprises:

The target image acquisition distance is determined by calculating the time difference or phase difference between the emission and reflection of the optical signal between the target object and the image acquisition device.
An image acquisition device, characterized by comprising: a processor, a distance measurement component, and an image acquisition component; both the distance measurement component and the image acquisition component are electrically connected to the processor;

Wherein, the distance measurement component is used to determine the target image acquisition distance between the target object and the image acquisition device;

The processor is configured to determine the target exposure time used by the image acquisition device for image acquisition of the target object according to the target image acquisition distance and the exposure time determination model, and the exposure time determination model is used to indicate at least one image Correspondence between the acquisition distance and at least one exposure time, wherein, when the image acquisition distance and exposure time corresponding to each other in the model are determined according to the exposure time, the target object in the acquired image The brightness of the object is within the preset range;

The image acquisition component is used for image acquisition of the target object according to the target image acquisition distance and the target exposure time.
The device according to claim 8, wherein:

The processor is further configured to determine the exposure time corresponding to each image acquisition distance in the at least one image acquisition distance, and establish the exposure time determination model according to the corresponding relationship between the at least one image acquisition distance and the at least one exposure time.
The device according to claim 9, wherein:

The processor is further configured to use a preset distance as the image acquisition distance to perform image acquisition of the target object according to at least one exposure time to obtain at least one sample image; and to set the brightness of the target object in the at least one sample image at The sample image within the preset range is determined as the target sample image, and the exposure time of the target sample image is determined as the exposure time corresponding to the preset distance.
The device of claim 10, wherein:

The processor is further configured to use the preset distance as the image collection distance to perform image collection of the target object according to the preset exposure time to obtain a sample image; when the brightness of the target object in the sample image is less than a first threshold , Increase the preset exposure time to perform image acquisition again on the target object; when the brightness of the target object in the sample image is greater than a second threshold, reduce the preset exposure time to perform image acquisition again on the target object For image acquisition, the preset range includes a range greater than or equal to a first threshold and less than or equal to a second threshold.
The device of claim 10, wherein:

The processor is configured to calculate the average value of the pixel brightness of the area where the target object is located in each sample image as the brightness of the target object in each sample image.
The device according to claim 8, wherein:

The distance measurement component is also used to determine the target image acquisition distance by calculating the time difference or phase difference between the emission and reflection of the optical signal between the target object and the image acquisition device.
The device according to any one of claims 8-13, wherein the distance measurement component includes a time-of-flight ranging module, and the image acquisition component includes a structured light image acquisition component and/or an RGB image acquisition component.
A computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method according to any one of claims 1-7.