CN116977332A - Camera light filling lamp performance test method and device, electronic equipment and storage medium - Google Patents
Camera light filling lamp performance test method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application relates to the field of computer vision, and provides a method, a device, electronic equipment and a storage medium for testing performance of a camera light supplementing lamp, wherein the method comprises the following steps: obtaining N initial images through a camera with a light supplementing lamp, wherein N is an integer greater than or equal to 2; determining a reference attribute value of each initial image in the N Zhang Chushi image; determining a first target image from the N initial images based on the reference attribute values of the initial images; and obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image. The problem that performance test can not be carried out on the light supplementing lamp carried by the camera in the related art is solved, the accurate test on the performance of the light supplementing lamp of the camera is realized, and the shooting effect of the camera can be further improved.
Description
Technical Field
The application relates to the field of computer vision, in particular to a camera light supplementing lamp performance test method, a camera light supplementing lamp performance test device, electronic equipment and a storage medium.
Background
When taking a picture by adopting the camera with the light supplementing lamp, the performance of the light supplementing lamp directly influences the shooting effect of the camera. In general, if the light supplement lamp has poor performance, the sharpness of the photographed image may be low. If the performance of the light filling lamp is strong, the definition of the photographed image is relatively high. In the related art, the camera is detected, and only the photographing function of the camera is concerned, so that performance test can not be performed on the light supplementing lamp carried by the camera. How to realize the accurate test of the performance of the camera light supplementing lamp becomes a technical problem to be solved urgently.
Disclosure of Invention
The application provides a camera light supplementing lamp performance test method, a camera light supplementing lamp performance test device, electronic equipment and a storage medium, and aims to at least solve the technical problems in the prior art.
According to a first aspect of the present application, there is provided a camera light filling lamp performance test method, the method comprising:
obtaining N initial images through a camera with a light supplementing lamp, wherein N is an integer greater than or equal to 2;
determining a reference attribute value of each initial image in the N Zhang Chushi image;
determining a first target image from the N initial images based on the reference attribute values of the initial images;
and obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image.
In the above scheme, the reference attribute value includes an average brightness value;
the determining the reference attribute value of each initial image in the N Zhang Chushi image comprises the following steps:
acquiring the pixel number of each initial image and the brightness value of each pixel;
and obtaining the average brightness value of each initial image in the N initial images based on the pixel number of each initial image and the brightness value of each pixel.
In the above solution, the determining, based on the reference attribute value of each initial image, the first target image from the N initial images includes:
and taking the image with the largest reference attribute value in the N Zhang Chushi image as a first target image.
In the above solution, the obtaining a performance test result of the light compensating lamp based on a result of whether the target object can be identified from the first target image includes:
determining a second target image from the N initial images based on the reference attribute values of the initial images; wherein the reference attribute values of the first target image and the second target image are different;
determining a difference in the reference attribute value between the first target image and the second target image;
and when the difference meets a first condition, obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image.
In the above solution, the determining whether the target object can be identified from the first target image includes:
performing moving scanning of windows on the first target image, and extracting the characteristics of each window;
inputting the characteristics of each window into a target classifier to obtain a result of whether a target object can be identified from a first target image; the target classifier is obtained by training a classifier to be trained by a sample image with a normal sample label and a sample image with an abnormal sample label.
In the above scheme, the determining the difference between the first target image and the second target image in the reference attribute value includes:
acquiring the width and the height of a first target image and a second target image;
and obtaining the difference of the first target image and the second target image on the reference attribute value according to the width and the height and the gray value of each pixel in the first target image and the second target image.
In the above solution, the obtaining a performance test result of the light compensating lamp based on a result of whether the target object can be identified from the first target image includes:
determining that the performance test of the light supplementing lamp is passed when the target object can be identified from the first target image;
when the target object is not identified from the first target image, it is determined that the performance test for the light filling lamp is not passed.
According to a second aspect of the present application, there is provided a camera light filling lamp performance test device, the device comprising:
an acquisition unit for acquiring N initial images through a camera with a light supplementing lamp, wherein N is an integer greater than or equal to 2;
a first determining unit, configured to determine a reference attribute value of each initial image in the N Zhang Chushi image;
a second determining unit configured to determine a first target image from the N initial images based on the reference attribute values of the respective initial images;
and the test unit is used for obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image.
According to a third aspect of the present application, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the methods of the present application.
According to a fourth aspect of the present application there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of the present application.
According to the application, N initial images are obtained through a camera with a light supplementing lamp, the reference attribute value of each initial image in the N Zhang Chushi images is determined, a first target image is determined from the N initial images based on the reference attribute value of each initial image, and finally, a performance test result of the light supplementing lamp is obtained based on the result of whether a target object can be identified from the first target image. The accurate test of the performance of the camera light filling lamp is realized, and the shooting effect of the camera can be further improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
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The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present application will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present application are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Fig. 1 is a schematic diagram showing a shooting process of a camera when a light supplementing lamp is on and off respectively according to an embodiment of the application;
fig. 2 is a schematic implementation flow chart of a camera light compensating lamp performance test method according to an embodiment of the application;
FIG. 3 shows a schematic diagram of an application implementation flow of an embodiment of the application;
fig. 4 is a schematic diagram showing a composition structure of a camera light compensating lamp performance test device according to an embodiment of the application;
fig. 5 shows a schematic diagram of a composition structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, features and advantages of the present application more comprehensible, the technical solutions according to the embodiments of the present application will be clearly described in the following with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It can be understood that the light compensating lamp in the embodiment of the application is an infrared light compensating lamp. When some cameras with light supplementing lamps shoot objects, the performance of the light supplementing lamps directly influences the shooting effect of the cameras. Illustratively, as shown in fig. 1, it is assumed that in a scene in which face recognition is performed using a PC (Personal Computer ) terminal, a face is photographed with an infrared camera. If an LED (Light Emitting Diode ) lamp (used as a light supplementing lamp in the application) carried by the infrared camera has normal performance, the face shot by the LED lamp can reflect infrared light irradiated by the LED lamp when the LED lamp is on. When the LED lamp is not on, the camera only shoots the face, and the face part does not reflect any light. At this time, the face in the photo taken when the LED lamp is on is clearer than the face taken when the LED lamp is not on. Therefore, in order to ensure the shooting quality of the camera, the performance of the camera light filling lamp needs to be ensured. The performance of the light compensating lamp is often required to pass the test to reach the conclusion of good performance. Most of the related technologies only pay attention to testing the photographing function of the camera, and the performance test of the light supplementing lamp is rarely involved. Even if there are performance test schemes related to the light filling lamp, for example, related art adopts a Windows Hello (window authentication) function to identify a photographed object to determine whether the light filling lamp has normal performance. However, since a small amount of infrared light exists in the environment, the test scheme is adopted to mismeasure the performance of the light supplementing lamp, and the accuracy is low.
In the embodiment of the application, N initial images are obtained through a camera with a light supplementing lamp, the reference attribute value of each initial image in the N Zhang Chushi images is determined, a first target image is determined from the N initial images based on the reference attribute value of each initial image, and finally, a performance test result of the light supplementing lamp is obtained based on the result of whether a target object can be identified from the first target image. Compared with the related art, the method and the device for determining the first target image according to the reference attribute value determine whether the target object can be identified in the first target image or not, namely, the first target image determined according to the reference attribute value is analyzed, so that the performance of the light supplementing lamp of the camera is accurately tested, and the shooting effect of the camera can be further improved.
The following describes the method for testing the performance of the light supplementing lamp of the camera in detail.
The embodiment of the application provides a method for testing the performance of a camera light supplementing lamp, as shown in fig. 2, comprising the following steps:
s201: n initial images are obtained through a camera with a light supplementing lamp, and N is an integer greater than or equal to 2.
In this step, a plurality of images for a target object are continuously photographed by a camera, thereby obtaining N initial images. Illustratively, in the scene of face recognition, N initial images for the face are obtained by continuously capturing the face a plurality of times. It will be appreciated that in practical applications, in the N initial images, the faces captured in each image may be clear, the faces captured in the partial images may be clear, and the faces captured in each image may be unclear.
S202: the reference attribute values for each initial image in the N Zhang Chushi image are determined.
In this step, the reference attribute of each initial image may be any attribute parameter of the image, such as brightness, saturation, color, and the like. If the brightness is taken as a reference attribute parameter of the image, the reference attribute value of each initial image includes an average brightness value of each initial image. It can be understood that, under normal conditions, the light filling lamp is in a blinking state, and there is a possibility that average brightness values are different in a plurality of initial images continuously shot on the target object. According to the pixel number of each initial image and the brightness value of each pixel, the accurate reference attribute value of each initial image can be obtained. Through accurate reference attribute value, can guarantee follow-up accurate test to light filling lamp performance.
S203: a first target image is determined from the N initial images based on the reference attribute values of the respective initial images.
In this step, by calculating the reference attribute value of each initial image, an initial image having the largest reference attribute value is determined as the first target image from among N initial images continuously photographed.
S204: and obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image.
In this step, the first target image is used as the image with the largest reference attribute value in the N initial images, where the first target image may be the image with the largest brightness, the largest saturation or the brightest color, and if the target object cannot be identified in the first target image, it is indicated that the camera cannot shoot a clear or normal image with the aid of the light compensating lamp, and the light compensating lamp cannot meet the normal shooting requirement, and the performance test result should be that the test fails. And if the target object can be identified in the first target image, the performance test result of the light supplementing lamp is that the test is passed.
In the scheme shown in S201 to S204, N initial images are obtained through a camera with a light supplementing lamp, a reference attribute value of each initial image in the N Zhang Chushi images is determined, a first target image is determined from the N initial images based on the reference attribute value of each initial image, and finally a performance test result of the light supplementing lamp is obtained based on a result of whether a target object can be identified from the first target image. Compared with the related art, the method and the device for determining the first target image according to the reference attribute value determine whether the target object can be identified in the first target image or not, namely, the first target image determined according to the reference attribute value is analyzed, so that the performance of the light supplementing lamp of the camera is accurately tested, and the shooting effect of the camera can be further improved.
In an alternative, the reference attribute value comprises an average luminance value;
the determining the reference attribute value of each initial image in the N Zhang Chushi image comprises the following steps:
acquiring the pixel number of each initial image and the brightness value of each pixel;
and obtaining the average brightness value of each initial image in the N initial images based on the pixel number of each initial image and the brightness value of each pixel.
In the application, an average pixel value method is adopted as a brightness measurement method of each initial image, and the brightness of the initial image is estimated by calculating the average value of the brightness of all pixels in each initial image. The average luminance value of each initial image is calculated by the formula (1):
formula (1)
Wherein,,representing the average luminance value of the initial image, +.>Representing the number of pixels in the initial image, +.>Represents an integer from 1 to N, +.>Indicate->Luminance values of the individual pixels. />Representing the sum of all pixel luminance values in the original image.
In the application, the average brightness value of each initial image is calculated as the reference attribute value according to the pixel number of each initial image and the brightness value of each pixel, so that the calculation is simple and the implementation is easy. In addition, when the initial images of the application are all images with uniform brightness distribution, the average brightness value can better reflect the brightness of the whole initial image. And a data basis is provided for the follow-up accurate test of the performance of the light filling lamp.
In an alternative solution, the determining the first target image from the N initial images based on the reference attribute values of the initial images includes:
and taking the image with the largest reference attribute value in the N Zhang Chushi image as a first target image.
In the present application, an image having the largest reference attribute value is taken as a first target image. It will be appreciated that, in a plurality of initial images obtained by continuously photographing a target object, reference attribute values (such as average brightness values) of the respective initial images may differ to different extents. The initial image in which the reference attribute value is the largest is taken as the first target image, that is, the initial image having the highest average luminance value is taken as the first target image. The image with the largest average brightness value is used as the first target image, and the obtained test result is more accurate by identifying the target object in the brightest image, so that the method is simple and feasible in engineering, and unnecessary time can be saved.
In an optional solution, the obtaining a performance test result of the light compensating lamp based on a result of whether the target object can be identified from the first target image includes:
determining a second target image from the N initial images based on the reference attribute values of the initial images; wherein the reference attribute values of the first target image and the second target image are different;
determining a difference in the reference attribute value between the first target image and the second target image;
and when the difference meets a first condition, obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image.
In the present application, unlike the initial image in which the first target image is the initial image having the largest average luminance value among the N initial images, the first target image and the second target image differ in the reference attribute of the average luminance. The second target image is the initial image with the smallest average brightness value in the N initial images.
A difference threshold is preset. Whether the difference between the first target image and the second target image on the reference attribute value exceeds a difference threshold is judged. It will be appreciated that in the case of normal blinking of the light filling lamp, there will tend to be a difference in average brightness value between the initial images taken from one light to the next. If the first condition is defined as exceeding the difference threshold, the first condition is satisfied as: when the difference between the initial images having the largest average luminance value and the smallest average luminance value exceeds the difference threshold value, it is considered that this light-compensating lamp is likely to be a light-compensating lamp of normal performance. The first condition is not satisfied: when the difference between the initial images having the largest average brightness value and the smallest average brightness value does not exceed the difference threshold value, the light supplementing lamp is directly considered to be not a light supplementing lamp with normal performance, and the light supplementing lamp fails the test. And under the condition that the difference between the first target image and the second target image on the reference attribute value meets the first condition, continuously judging whether the target object can be identified in the first target image, and determining the performance test result of the light supplementing lamp according to the judgment result.
According to the application, whether the difference between the first target image and the second target image on the reference attribute value meets the first condition is judged, the performance test result of the light supplementing lamp can be continuously judged only when the difference meets the first condition, and the performance of the light supplementing lamp is directly determined to not pass the test when the difference does not meet the first condition. The performance test result of the light supplementing lamp can be directly determined when the performance test result is not met, and the test efficiency is improved. And when the first condition is met, whether the target object can be identified in the first target image is continuously judged, so that the performance test result of the light supplementing lamp is determined, and the accurate performance test of the light supplementing lamp is realized.
In an alternative solution, the determining whether the target object can be identified from the first target image includes:
performing moving scanning of windows on the first target image, and extracting the characteristics of each window;
inputting the characteristics of each window into a target classifier to obtain a result of whether a target object can be identified from a first target image; the target classifier is obtained by training a classifier to be trained by a sample image with a normal sample label and a sample image with an abnormal sample label.
In the application, a window with a fixed size is utilized to carry out mobile scanning in a first target image, and the characteristics of each window are extracted. In the present application, the feature of the window may be a Haar feature (Haar feature) of the window. And inputting the Haar characteristics of each window into a target classifier to classify, so as to obtain a classification result that the first target image can identify the target object or a classification result that the first target image cannot identify the target object. The target classifier is obtained by training a classifier to be trained through a sample image with a label. The object classifier may be derived by training one of a decision tree, a support vector machine, and/or a neural network. In the application, an AdaBoost algorithm (iterative algorithm) is adopted to select window characteristics and train a target classifier. Specifically, an AdaBoost algorithm is adopted to select the optimal Haar characteristic from the Haar characteristics of each window. For example, in the face recognition scenario, the best Haar features are the Haar features of the windows where the eyes, nose, mouth, etc. can jointly represent the main organs of the face, so as to exclude the non-face region. And judging whether the target object can be identified from the first target image according to the optimal Haar characteristic.
In the present application, whether or not the target object can be recognized from the first target image is determined, and whether or not the target object is clearly recognized from the first target image is referred to. This is because, in the captured initial image, there is an image in which the target object is not clearly captured. There are also cases where the target object may still not be clearly recognized in capturing a clear image of the target object.
According to the method, the scheme of classifying whether the target object can be identified in the first target image or not by utilizing the target classifier obtained through training is utilized, so that the accuracy of classification is ensured, and the classification efficiency is greatly improved. And the accurate test of the performance of the light supplementing lamp is also ensured.
In an alternative aspect, the determining the difference between the first target image and the second target image in the reference attribute value includes:
acquiring the width and the height of a first target image and a second target image;
and obtaining the difference of the first target image and the second target image on the reference attribute value according to the width and the height and the gray value of each pixel in the first target image and the second target image.
In the present application, it is preferable that the first target image and the second target image are images having the same size. That is, the first and second target images are identical in both height and width. The difference in the reference attribute value between the first target image and the second target image is calculated by the formula (2):
formula (2)
Wherein,,representing the difference in the reference attribute values between the first target image and the second target image. />Representing the width of the first target image/second target image. />Representing the length of the first target image/second target image. />Representing the total number of pixels in the first target image/the second target image, usually +.>Is equal to->And->Is a product of (a) and (b). />Representing a row in which a pixel in the first target image/second target image is located; />Representation ofThe columns where the pixels in the first target image/the second target image are located;representing the position in the first target image as +.>Gray value of the pixel of +.>Representing the position in the second target image as +.>Gray value of the pixel. />Representing the absolute value of the difference in gray values of the co-located pixels in the first target image and the second target image. />The sum of the differences of the gray values of the pixels representing all positions in the first target image and the second target image.
The gray level images of the first target image and the second target image can be obtained by carrying out gray level processing on the first target image and the second target image, so that gray level values of pixels can be conveniently read from the gray level images of the first target image and the second target image.
According to the application, the scheme of the difference of the first target image and the second target image on the reference attribute value is obtained through the width and the height of the first target image and the second target image and the gray value of each pixel in the first target image and the second target image, and is simple, easy to implement and easy to implement. And by calculating the difference between the first target image and the second target image on the reference attribute value, whether the difference exceeds the difference threshold value or not is conveniently and accurately judged, so that the performance of the light supplementing lamp is further and accurately tested.
In an optional solution, the obtaining a performance test result of the light compensating lamp based on a result of whether the target object can be identified from the first target image includes:
determining that the performance test of the light supplementing lamp is passed when the target object can be identified from the first target image;
when the target object is not identified from the first target image, it is determined that the performance test for the light filling lamp is not passed.
In the present application, the first target image may be an initial image having the largest average brightness value, that is, an initial image having the highest brightness, among the plurality of initial images. If the target object cannot be identified from the initial image (first target image) with the highest brightness, the light supplementing lamp does not play a role or plays a role too little in the process of taking a picture through the camera, and then the performance test result of the light supplementing lamp is determined to be failed. If the target object can be identified from the initial image (first target image) with the highest brightness, the performance test result of the light supplementing lamp is determined to pass the test.
According to the method and the device, the scheme of the performance test result of the light supplementing lamp is determined according to the result of whether the target object can be identified from the first target image, so that the method and the device are simple and feasible in engineering and easy to implement.
In a specific embodiment, taking a face recognition scenario as an example, the method for testing the performance of the light supplementing lamp of the camera is described.
As shown in fig. 3, 10 photographs (initial images) are continuously taken of an identification object (target object) by a camera, and the average luminance value of each of the 10 photographs is calculated by the aforementioned formula (1). And finding out the photo with the largest average brightness value as a first target image and the photo with the smallest average brightness value as a second target image. The difference in brightness (difference in reference attribute) between the first target image and the second target image is calculated by the foregoing formula (2). The difference is compared with a preset difference threshold. If the difference does not exceed the preset difference threshold, directly determining that the performance test result of the light supplementing lamp is failed. If the difference exceeds a preset difference threshold, judging whether the target object can be identified from the first target image, and if the target object can be identified, determining that the performance test result of the light supplementing lamp is passing. If the target object cannot be identified, determining that the performance test result of the light supplementing lamp is failed.
Compared with the prior art, the method and the device for determining the first target image according to the reference attribute value determine whether the target object can be identified in the first target image or not, namely, analyze the first target image determined according to the reference attribute value, judge whether the difference of the reference attribute of the first target image and the second target image meets the difference threshold before determining whether the target object can be identified in the first target image, so that the accurate test of the performance of the camera light supplementing lamp is realized, and the shooting effect of the camera can be further improved.
The foregoing describes the method for testing the performance of the light supplementing lamp of the camera according to the present application by taking the face recognition scene as an example, and when the scene is other, the scheme is understood, and will not be repeated.
An embodiment of the present application provides a device for testing performance of a light supplementing lamp of a camera, as shown in fig. 4, the device includes:
an acquisition unit 401 for acquiring N initial images by a camera having a light supplementing lamp, where N is an integer greater than or equal to 2;
a first determining unit 402, configured to determine a reference attribute value of each initial image in the N Zhang Chushi image;
a second determining unit 403 for determining a first target image from the N initial images based on the reference attribute values of the respective initial images;
and a test unit 404, configured to obtain a performance test result of the light filling lamp based on a result of whether the target object can be identified from the first target image.
In an alternative, the reference attribute value comprises an average luminance value; the first determining unit 402 is configured to obtain the number of pixels of each initial image and a luminance value of each pixel; and obtaining the average brightness value of each initial image in the N initial images based on the pixel number of each initial image and the brightness value of each pixel.
In an alternative solution, the second determining unit 403 is configured to take, as the first target image, an image with the largest reference attribute value in the N Zhang Chushi image.
In an alternative solution, the test unit 404 is configured to determine a second target image from the N initial images based on the reference attribute values of the initial images; wherein the reference attribute values of the first target image and the second target image are different; determining a difference in the reference attribute value between the first target image and the second target image; and when the difference meets a first condition, obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image.
In an alternative solution, the test unit 404 is configured to perform moving scanning of the windows on the first target image, and extract a feature of each window; inputting the characteristics of each window into a target classifier to obtain a result of whether a target object can be identified from a first target image; the target classifier is obtained by training a classifier to be trained by a sample image with a normal sample label and a sample image with an abnormal sample label.
In an alternative solution, the test unit 404 is configured to obtain the width and the height of the first target image and the second target image; and obtaining the difference of the first target image and the second target image on the reference attribute value according to the width and the height and the gray value of each pixel in the first target image and the second target image.
In an alternative solution, the test unit 404 is configured to determine that the performance test of the light compensating lamp is passed when the target object can be identified from the first target image; when the target object is not identified from the first target image, it is determined that the performance test for the light filling lamp is not passed.
It should be noted that, since the principle of the device for solving the problem is similar to the method for testing the performance of the light supplementing lamp of the camera, the implementation process, implementation principle and beneficial effect of the device can be referred to the description of the implementation process, implementation principle and beneficial effect of the method, and the repetition is omitted.
According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.
Fig. 5 shows a schematic block diagram of an example electronic device 500 that may be used to implement an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 5, the electronic device 500 includes a computing unit 501 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data required for the operation of the electronic device 500 may also be stored. The computing unit 501, ROM502, and RAM503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in electronic device 500 are connected to I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the respective methods and processes described above, such as the camera fill light performance test method. For example, in some embodiments, the camera light supplement lamp performance test method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 500 via the ROM502 and/or the communication unit 509. When the computer program is loaded into the RAM503 and executed by the computing unit 501, one or more steps of the camera light filling lamp performance test method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the camera light filling lamp performance test method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-a-chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution disclosed in the present application can be achieved, and are not limited herein.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. The method for testing the performance of the camera light supplementing lamp is characterized by comprising the following steps of:
obtaining N initial images through a camera with a light supplementing lamp, wherein N is an integer greater than or equal to 2;
determining a reference attribute value of each initial image in the N Zhang Chushi image;
determining a first target image from the N initial images based on the reference attribute values of the initial images;
and obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image.
2. The method of claim 1, wherein the reference attribute value comprises an average luminance value;
the determining the reference attribute value of each initial image in the N Zhang Chushi image comprises the following steps:
acquiring the pixel number of each initial image and the brightness value of each pixel;
and obtaining the average brightness value of each initial image in the N initial images based on the pixel number of each initial image and the brightness value of each pixel.
3. The method of claim 1, wherein determining the first target image from the N initial images based on the reference attribute values of the respective initial images comprises:
and taking the image with the largest reference attribute value in the N Zhang Chushi image as a first target image.
4. A method according to any one of claims 1 to 3, wherein the obtaining a performance test result for the light filling lamp based on a result of whether the target object can be identified from the first target image comprises:
determining a second target image from the N initial images based on the reference attribute values of the initial images; wherein the reference attribute values of the first target image and the second target image are different;
determining a difference in the reference attribute value between the first target image and the second target image;
and when the difference meets a first condition, obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image.
5. The method of claim 4, wherein the whether the target object can be identified from the first target image comprises:
performing moving scanning of windows on the first target image, and extracting the characteristics of each window;
inputting the characteristics of each window into a target classifier to obtain a result of whether a target object can be identified from a first target image; the target classifier is obtained by training a classifier to be trained by a sample image with a normal sample label and a sample image with an abnormal sample label.
6. The method of claim 4, wherein determining the difference in the reference attribute value between the first target image and the second target image comprises:
acquiring the width and the height of a first target image and a second target image;
and obtaining the difference of the first target image and the second target image on the reference attribute value according to the width and the height and the gray value of each pixel in the first target image and the second target image.
7. A method according to any one of claims 1 to 3, wherein the obtaining a performance test result for the light filling lamp based on a result of whether the target object can be identified from the first target image comprises:
determining that the performance test of the light supplementing lamp is passed when the target object can be identified from the first target image;
when the target object is not identified from the first target image, it is determined that the performance test for the light filling lamp is not passed.
8. A camera fill light performance testing device, the device comprising:
an acquisition unit for acquiring N initial images through a camera with a light supplementing lamp, wherein N is an integer greater than or equal to 2;
a first determining unit, configured to determine a reference attribute value of each initial image in the N Zhang Chushi image;
a second determining unit configured to determine a first target image from the N initial images based on the reference attribute values of the respective initial images;
and the test unit is used for obtaining a performance test result of the light supplementing lamp based on the result of whether the target object can be identified from the first target image.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-7.
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