CN114125312A - Isp fast ae convergence method, apparatus, device and storage medium - Google Patents
Isp fast ae convergence method, apparatus, device and storage medium Download PDFInfo
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Abstract
The application relates to an isp fast ae convergence method, a device, equipment and a storage medium, wherein the method comprises the steps of acquiring a photosensitive value and a current ae parameter value when a network camera is powered on and operated; judging whether the photosensitive value is in a mapping interval of a preset mapping table or not; when the current ae parameter value is located in the mapping interval of the mapping table, judging whether the current ae parameter value is in a convergence stage or a stabilization stage; if the current ae parameter value is in a convergence stage, storing the ae parameter value mapped with the photosensitive value in the mapping table; and taking the stored ae parameter value as a preset current convergence value of the automatic ae algorithm, and starting from the current convergence value to enable the automatic ae algorithm to converge until the network camera acquires the first frame of stable image. The problem that the matched ae parameters are difficult to rapidly acquire by the isp in a low-light environment is solved. The method has the effect of rapidly acquiring the matched ae parameters under the low-light environment.
Description
Technical Field
The present application relates to the field of camera device technologies, and in particular, to a method, an apparatus, a device, and a storage medium for isp fast ae convergence.
Background
At present, the demand of a battery ipc (ip camera) for quick start of image capture is higher and higher. A cmos sensor (image sensor of complementary metal oxide semiconductor material) module applied to the scene gradually appears in the market, and the main working principle of the cmos sensor module is to enter a small window mode, i.e. a high frame rate and small resolution mode, strive for calculating stable exposure gain in a short time, and then linearly convert the stable exposure gain into ae parameters with normal resolution, so as to achieve the effects of fast plotting and stable ae convergence.
However, the method is difficult to rapidly acquire the matched ae parameters in a low-light environment, and the ipc has a general expression effect.
In view of the above-mentioned related technologies, the inventor believes that there is a defect that it is difficult to quickly obtain matched ae parameters in a low-light environment in the existing isp.
Disclosure of Invention
In order to rapidly acquire matched ae parameters in a low-light environment, the application provides an isp rapid ae convergence method, device, equipment and storage medium.
In a first aspect, the application provides an isp fast ae convergence method, which has the characteristic of fast obtaining matched ae parameters in a low-light environment.
The application is realized by the following technical scheme:
an isp fast ae convergence method comprises the following steps:
when the network camera is powered on and operated, acquiring the numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value;
judging whether the value of the photosensitive digital-to-analog conversion module is in the mapping interval of a preset mapping table or not;
when the value of the photosensitive digital-to-analog conversion module is positioned in the mapping interval of the mapping table, judging whether the current ae parameter value is in a convergence stage or a stabilization stage;
if the current ae parameter value is in a convergence stage, storing the ae parameter value mapped with the value of the photosensitive digital-to-analog conversion module in the mapping table;
and taking the stored ae parameter value as a current convergence value of a preset automatic ae algorithm, and starting from the current convergence value, converging the automatic ae algorithm until the network camera acquires a first frame of stable image.
By adopting the technical scheme, when the network camera is powered on and operated, the image signal processing unit (isp) acquires the numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value to serve as the data basis of the processing flow; judging whether the value of the photosensitive digital-to-analog conversion module is in the mapping interval of a preset mapping table or not so as to obtain the required data from the mapping interval of the mapping table; when the environment brightness is darkest, the simulation gain and the exposure time are maximum values, namely the current ae parameter value is in a stable stage, and the current ae parameter value can be adjusted without; when the ambient brightness gradually becomes dark, namely the current ae parameter value is in a convergence stage, the current ae parameter needs to be adjusted; judging whether the current ae parameter value is in a convergence stage or a stabilization stage by judging to judge the brightness condition of the current environment, and adjusting the ae parameter based on the actual environment brightness condition; when the photosensitive value is located in the mapping interval of the mapping table and the current ae parameter value is in the convergence stage, the ae parameter value mapped with the photosensitive value in the mapping table is obtained, so that the ae parameter value close to the target brightness is quickly and directly obtained from the mapping table, the stored ae parameter value is made to be as close to the target brightness as possible, and quick adjustment is facilitated; the stored ae parameter values are used as the current convergence values of the preset automatic ae algorithm, the automatic ae algorithm is converged from the current convergence values until the network camera acquires a first frame of stable image, so that the current convergence values are adjusted to be close to the target brightness, the purpose that the network camera can basically acquire the stable image within one to two frames after configuration is achieved, and meanwhile, the performance effect of isp in a low-light environment is improved; therefore, the isp fast ae convergence method can enable the isp to quickly acquire matched ae parameters and improve the performance effect of the isp in a low-light environment.
The present application may be further configured in a preferred example to: after the step of judging whether the current ae parameter value is in a convergence stage or a stabilization stage, the method further comprises the following steps:
and if the current ae parameter value is in the stable stage, storing the current ae parameter value.
By adopting the technical scheme, if the current ae parameter value is in a stable stage, namely the current environment brightness is the darkest, the simulation gain and the exposure time are the maximum values, the current ae parameter value is stored, the current ae parameter value does not need to be adjusted and processed, the ae parameter value close to the target brightness can be acquired more quickly, and the rapid adjustment is facilitated.
The present application may be further configured in a preferred example to: further comprising the steps of:
and when the network camera is in power-off dormancy, automatically learning based on the acquired numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value, and updating the mapping table.
By adopting the technical scheme, when the network camera is in power-off dormancy, the mapping table is automatically learned and updated based on the acquired value of the photosensitive digital-to-analog conversion module and the current ae parameter value, so that the content of the preset mapping table is more comprehensive, more photosensitive values can be positioned in the mapping interval of the mapping table, the influence of each environmental change can be adapted, the applicability is stronger, and the popularization is facilitated; meanwhile, the content data in the preset mapping table is more accurate, and the obtained ae parameter value can be closer to the ae parameter value of the target brightness, so that the rapid adjustment is facilitated; and the network camera is learned and updated when the network camera is in power-off dormancy, so that the working performance of the network camera during power-on operation is not influenced.
The present application may be further configured in a preferred example to: the step of automatically learning based on the acquired value of the photosensitive digital-to-analog conversion module and the current ae parameter value comprises the following steps:
judging whether the ae parameter value of the automatic ae algorithm is in a stable state;
if the ae parameter value is stable, acquiring the current ae parameter value;
judging whether the value of the photosensitive digital-to-analog conversion module in the current environment is in the mapping interval of a preset mapping table;
and when the value of the photosensitive digital-to-analog conversion module is positioned in the mapping interval of the mapping table, storing the acquired ae parameter value into the corresponding mapping interval of the mapping table.
By adopting the technical scheme, whether the ae parameter value of the automatic ae algorithm is in a stable state is judged so as to judge whether the ae parameter value at the moment can improve the performance effect of isp in a low-light environment; if the ae parameter value is stable, acquiring the current ae parameter value as a storage value of the mapping table; judging whether the photosensitive value of the current environment is in a mapping interval of a preset mapping table or not so as to judge whether a mapping interval needs to be newly created or not; when the photosensitive value is located in the mapping interval of the mapping table, the acquired ae parameter value is stored in the corresponding mapping interval of the mapping table, so that the ae parameter value of the mapping table can be closer to the ae parameter value of the target brightness, the ae parameter value of the mapping table is more accurate and more accords with the actual photosensitive value condition, and quick adjustment is facilitated.
The present application may be further configured in a preferred example to: after the step of judging whether the value of the photosensitive digital-to-analog conversion module in the current environment is in the mapping interval of the preset mapping table, the method further comprises the following steps:
and when the numerical value of the photosensitive digital-to-analog conversion module at the moment is positioned outside the mapping interval of the mapping table, newly creating a mapping interval, and storing the acquired ae parameter value and the numerical value of the photosensitive digital-to-analog conversion module in the current environment into the newly created mapping interval.
By adopting the technical scheme, when the photosensitive value is positioned outside the mapping interval of the mapping table, a mapping interval is newly created, and the acquired ae parameter value and the photosensitive value of the current environment are stored in the newly created mapping interval, so that the content of the preset mapping table is more comprehensive, more photosensitive values can be positioned in the mapping interval of the mapping table, the applicability is stronger, and the popularization is facilitated.
The present application may be further configured in a preferred example to: the ae parameter values include analog gain and exposure time.
Through adopting above-mentioned technical scheme, general ae parameter value is by analog gain, exposure time, the inside digital gain of isp and total digital gain control, and the ae parameter of this application only adjusts and controls network camera through two parameter values of analog gain and exposure time for the regulation speed of ae parameter further accelerates, is favorable to obtaining the ae parameter of matching more fast under the low-light environment.
The present application may be further configured in a preferred example to: and the automatic ae algorithm calculates the difference value between the current environment brightness and the target brightness according to the brightness component of the pixel data, so that the current convergence value is adjusted to be close to the target brightness, and the ae parameter value is dynamically updated.
By adopting the technical scheme, the automatic ae algorithm calculates the difference value between the current environment brightness and the target brightness according to the brightness component of the pixel data, so that the current convergence value is adjusted to be close to the target brightness, the ae parameter value is dynamically updated, and the performance effect of isp under a low-light environment is improved.
The present application may be further configured in a preferred example to: the step of adjusting the current convergence value to approach the target brightness comprises:
when the ambient brightness becomes dark gradually, the analog gain is made to become smaller gradually by multiple;
when the analog gain is reduced to the minimum value, if the ambient brightness is continuously darkened, the exposure time is adjusted to be continuously reduced in a stepping mode;
when the exposure time is less than the minimum value, the exposure time is decremented by the size of a bit.
By adopting the technical scheme, the analog gain and the exposure time are adjusted according to the darkening degree of the environment, so that the current convergence value is adjusted to be close to the target brightness, the ae parameter value is dynamically updated, and the performance effect of the isp under the low-light environment is improved.
In a second aspect, the present application provides an isp fast ae convergence device having the feature of fast obtaining matched ae parameters under low-light environment.
The application is realized by the following technical scheme:
an isp fast ae convergence device comprising:
the data acquisition module is used for acquiring the numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value when the network camera is powered on and operates;
the first judgment module is used for judging whether the numerical value of the photosensitive digital-to-analog conversion module at the moment is in the mapping interval of the preset mapping table;
the second judgment module is used for judging whether the current ae parameter value is in a convergence stage or a stabilization stage when the value of the photosensitive digital-to-analog conversion module is positioned in the mapping interval of the mapping table at the moment;
the parameter module is used for storing the ae parameter value mapped with the value of the photosensitive digital-to-analog conversion module in the mapping table when the current ae parameter value is in a convergence stage;
and the convergence module is used for taking the stored ae parameter value as a current convergence value of a preset automatic ae algorithm, and starting from the current convergence value, the automatic ae algorithm is converged until the network camera acquires a first frame stable image.
In a third aspect, the present application provides a computer device having a feature of rapidly obtaining matched ae parameters in a low-light environment.
The application is realized by the following technical scheme:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the isp fast ae convergence method described above when executing the computer program.
In a fourth aspect, the present application provides a computer readable storage medium featuring fast acquisition of matched ae parameters in low-light environments.
The application is realized by the following technical scheme:
a computer-readable storage medium, storing a computer program which, when executed by a processor, implements the steps of the above-described isp fast ae convergence method.
In summary, the present application includes at least one of the following beneficial technical effects:
1. an isp fast ae convergence method can enable isp to quickly acquire matched ae parameters in a low-light environment, and simultaneously improve the performance effect of the isp;
2. if the current environment brightness is darkest, the current ae parameter does not need to be adjusted and processed, so that the ae parameter value close to the target brightness can be obtained more quickly, and the adjustment can be more quickly carried out;
3. when the network camera is in power-off dormancy, the automatic learning is carried out based on the acquired numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value, so that the content of a preset mapping table is more comprehensive, the applicability is stronger, and the popularization is facilitated; meanwhile, the obtained ae parameter value can be closer to the ae parameter value of the target brightness, so that the rapid adjustment is facilitated; the working performance of the network camera during power-on operation is not influenced;
4. the ae parameters are regulated and controlled only by simulating two parameter values of gain and exposure time, so that the regulation speed of the ae parameters is further accelerated, and the matched ae parameters can be obtained more quickly in a low-light environment;
5. the automatic ae algorithm adjusts the analog gain and the exposure time according to the dimming degree of the environment, dynamically updates ae parameter values, and adjusts the current convergence value to be close to the target brightness so as to improve the performance effect of isp in a low-light environment.
Drawings
Fig. 1 is a flowchart illustrating an isp fast ae convergence method according to an embodiment of the present application.
Fig. 2 is a flow chart of ae parameter learning at sleep.
Fig. 3 is a graph showing the trend of the light sensitivity value with the change of the ambient brightness value.
Fig. 4 is a graph of the trend of exposure time and analog gain as a function of light sensitivity value.
Fig. 5 is a first frame substantially stabilized image acquired by the present application.
Fig. 6 is a block diagram of an isp fast ae convergence device according to an embodiment of the present application.
Detailed Description
The present embodiment is only for explaining the present application, and it is not limited to the present application, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present application.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.
With the demand of the battery ipc for quick start of the grab image becoming higher and higher, by using a special quick start cmos sensor scheme, firstly, when the main control and the cmos sensor are powered on for the first time, the small window ae is started to converge quickly, and the initial configuration of the cmos sensor of the small window is written; then, polling a stability flag bit of the small window ae, and judging whether the adc value in the cmos sensor is in a stable state; when the adc value in the cmos sensor is in a stable state, linearly converting the exposure time and the gain value into the exposure time and the gain multiple value of a 1080P window, reading the exposure time and the gain value after linear conversion, and writing the exposure time and the gain value into a register; finally, writing the read exposure time and the gain value, opening a large window to make the cmos sensor graph, and powering off the master control; so as to achieve the effects of rapid mapping and ae convergence stabilization.
However, the above scheme has the disadvantages that when the ae stable flag bit is polled, the ae stable flag bit is blocked for a long time when the light is dark, about dozens to hundreds of milliseconds, and in a low-light environment, after the quick start parameter is correspondingly effective, the effect is not ideal and is darker, the performance is general, and the matched ae parameter is difficult to obtain; meanwhile, the model of the quick-start cmos sensor device is special, the cost is much higher than that of the common cmos sensor, and the model selection range is limited.
Therefore, the application provides a new ipc-based isp ae fast convergence starting scheme, according to an adc value read by a peripheral photosensitive adc (digital-to-analog conversion module) device, and according to a corresponding learning algorithm during each dormancy, a mapping table of a photosensitive adc value of a current environment, a-gain (analog gain) of the current environment and exp _ time (exposure time) is generated; meanwhile, stable ae parameters are obtained according to the mapping table during each starting, and the stable ae parameters are configured into a register of the cmos sensor and an automatic ae algorithm preset in an isp algorithm library, so that the isp can quickly obtain matched ae parameters in a low-light environment, and the performance of the isp can be improved.
The embodiments of the present application will be described in further detail with reference to the drawings attached hereto.
Referring to fig. 1, an embodiment of the present application provides an isp fast ae convergence method, and main steps of the method are described as follows.
S11, when the network camera is powered on and operated, the numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value are obtained;
s12, judging whether the value of the photosensitive D/A conversion module is in the mapping interval of the preset mapping table;
s13, when the value of the photosensitive D/A conversion module is in the mapping interval of the mapping table, judging whether the current ae parameter value is in the convergence stage or the stabilization stage;
s141, if the current ae parameter value is in a convergence stage, storing the ae parameter value mapped with the value of the photosensitive digital-to-analog conversion module in the mapping table;
and S15, taking the stored ae parameter value as the current convergence value of the preset automatic ae algorithm, and starting from the current convergence value to enable the automatic ae algorithm to converge until the network camera acquires the first frame of stable image.
Wherein, S13, after the step of judging whether the current ae parameter value is in the convergence stage or the stabilization stage, the method also comprises the following steps:
and S142, if the current ae parameter value is in a stable stage, storing the current ae parameter value. The current ae parameter value is now in the non-convergent phase.
In this application, the ae parameter values are the analog gain and the exposure time. The values of the ae parameters are typically controlled by a-gain, exp _ time, isp _ d _ gainisp (internal digital gain) and d-gain (total digital gain). According to the method and the device, the ae parameters of the network camera are regulated and controlled only through two parameter values of the analog gain and the exposure time, so that the regulation speed of the ae parameters is further accelerated, and the matched ae parameters can be acquired more quickly in a low-light environment.
In the actual setting process, exp _ time and again are enabled to play a main control role for preferential configuration, isp _ d _ gain plays a smoothing role in a convergence process and can not be configured firstly, dgain can not be configured firstly in a quick start, and the minimum gain multiple 1x is set by default.
Specifically, when the network camera is powered on and operates, the isp fast ae convergence method firstly obtains the numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value to serve as a data basis of a processing flow.
And then judging whether the value of the photosensitive digital-to-analog conversion module is in the preset mapping interval of the mapping table, so as to conveniently acquire the required data from the mapping interval of the mapping table, and directly and quickly.
An example of the preset adc-ae value mapping table is shown in table 1:
TABLE 1
Therefore, the mapping relationship of the mapping table is as follows: and calculating a current exp value and an a-gain value according to the adc multiple relation between the actual adc value and the mapping interval threshold, and writing the current exp value and the a-gain value into a register to generate a mapping table of the photosensitive adc value of the current environment and the current environment again value and exp value.
When the environment brightness is darkest, the simulation gain and the exposure time are maximum values, namely the current ae parameter value is in a stable stage, and the current ae parameter value can be adjusted without; when the ambient brightness gradually becomes dark, namely the current ae parameter value is in a convergence stage, the current ae parameter needs to be adjusted; namely, the brightness condition of the current environment is judged by judging whether the current ae parameter value is in a convergence stage or a stabilization stage, and the ae parameter is adjusted based on the actual environment brightness condition.
When the photosensitive value is located in the mapping interval of the mapping table and the current ae parameter value is in the convergence stage, the ae parameter value mapped with the photosensitive value in the mapping table is obtained, so that the ae parameter value close to the target brightness is quickly and directly obtained from the mapping table, the stored ae parameter value is made to be as close to the target brightness as possible, and quick adjustment is facilitated.
When the photosensitive value is located in the mapping interval of the mapping table and the current ae parameter value is in a stable stage, namely the current environment brightness is darkest, the simulation gain and the exposure time are maximum values, the current ae parameter value is stored, the current ae parameter value does not need to be adjusted and processed, the ae parameter value close to the target brightness can be obtained more quickly, and the rapid adjustment is facilitated.
And finally, taking the stored ae parameter value as a current convergence value of a preset automatic ae algorithm, starting from the current convergence value, converging the automatic ae algorithm until the network camera acquires a first frame of stable image, so that the current convergence value is adjusted to be close to the target brightness, the purpose that the network camera can basically acquire the stable image within one to two configured frames is achieved, and meanwhile, the performance effect of isp under a low-light environment is improved.
The preset automatic ae algorithm is located in the isp 3a algorithm library, and the automatic ae algorithm is also called as an automatic exposure gain algorithm.
Specifically, S15, the preset automatic ae algorithm calculates the difference between the current environment brightness and the target brightness according to the brightness component of the pixel data, so that the current convergence value is adjusted to be close to the target brightness, and the ae parameter value is dynamically updated.
Further, the step of adjusting the current convergence value to approach the target brightness comprises:
when the ambient brightness becomes dark gradually, the analog gain is made to become smaller gradually by multiple;
when the analog gain is reduced to the minimum value, if the ambient brightness is continuously darkened, the exposure time is adjusted to continuously reduce the exposure time in a stepping mode;
when the exposure time is less than the minimum value, the exposure time is decremented by the size of a bit.
Specifically, when the brightness becomes gradually darker, the a _ gain is adjusted to be gradually smaller and reduced by a multiple of 256; when decreasing to the minimum 256, if the brightness continues to dim, the exp _ time is adjusted instead; the exp _ time is adjusted by step, which is determined by anti-stroboscopic, and exp _ step is maxEXP frame rate/100, wherein maxEXP refers to the maximum exposure value; when the brightness is dark, the exp _ time is continuously reduced by the step size; when the exp _ time is less than the minimum step, the particle that the exp _ time is decremented by is changed to one bit.
Specifically, taking a 50HZ power supply as an example:
50Hz alternating current energy cycle: t ═ 1/(50 × 2);
the time T _ frame used by the sensor to expose one frame is 1/FPS;
the time T _ row taken for the sensor to expose one Line is T _ frame/Line _ max (Line _ max is Exp _ max);
in order to avoid flickers, the energy acquired by each row is integral multiple of the energy period of the alternating current, otherwise, the acquired energy is inconsistent, and the water ripple phenomenon is caused. Namely:
T_row*Step＝n*T；
therefore, the method comprises the following steps: step ═ (n × T)/T _ row ═ n (FPS × Exp _ max)/100; and (n is a positive integer) n is 1, and the exposure time adjustment step length corresponding to the calculated anti-stroboscopic effect is calculated.
If the brightness changes from dark to bright, the change trend is opposite, and exp _ time is increased first, and then a _ gain is increased.
Whether the brightness changes from dark to bright or from bright to dark, only one parameter of the agian and the exp _ time changes at the same time, so that when the ae parameters are mapped through the photosensitive adc, whether the exp and the again mapped at present are in a stable stage or not can be judged, namely whether the exp and the again mapped at present are in a maximum value or a minimum value or a gain multiple; if the current is in the stable stage, writing the maximum minimum value or gain multiple of the current; and if the convergence stage is reached, writing the mapped ae value.
In the present application, the current convergence value is adjusted to approach the target brightness, that is, the current convergence value is considered to approach the target brightness when the current convergence value approaches the preset brightness range, as the target brightness, through a preset brightness range, for example, the brightness in the range of targetluminea ± stabledrange, and at this time, AE adjustment is not performed any more.
Therefore, the automatic ae algorithm adjusts the analog gain and the exposure time according to the dimming degree of the environment, dynamically updates the ae parameter value, and adjusts the current convergence value to be close to the target brightness so as to improve the performance effect of the isp under the low-light environment.
Referring to FIG. 2, S21, when the network camera is powered off and sleeps, the mapping table is updated based on the acquired value of the photosensitive D/A conversion module and the current ae parameter value.
The automatic learning step based on the acquired value of the photosensitive digital-to-analog conversion module and the current ae parameter value comprises the following steps of:
s211, judging whether the ae parameter value of the automatic ae algorithm is in a stable state;
s212, if the ae parameter value is stable, acquiring the current ae parameter value;
s213, judging whether the value of the photosensitive digital-to-analog conversion module in the current environment is in the mapping interval of the preset mapping table;
s2141, when the value of the photosensitive digital-to-analog conversion module is in the mapping interval of the mapping table, storing the acquired ae parameter value into the corresponding mapping interval of the mapping table.
Further, S2142, when the value of the photosensitive digital-to-analog conversion module at this time is outside the mapping interval of the mapping table, newly creating a mapping interval, and storing the obtained ae parameter value and the value of the photosensitive digital-to-analog conversion module in the current environment into the newly created mapping interval.
Specifically, whether the ae parameter value of the automatic ae algorithm is in a stable state is judged so as to judge whether the ae parameter value at the moment can improve the performance effect of isp in a low-light environment; and if the ae parameter value is stable, acquiring the current ae parameter value as a storage numerical value of the mapping table.
And judging whether the photosensitive value of the current environment is in the mapping interval of the preset mapping table or not so as to judge whether the mapping interval needs to be newly created or not.
When the photosensitive value is located in the mapping interval of the mapping table, the mapping interval does not need to be created, and the obtained ae parameter value is stored in the corresponding mapping interval of the mapping table, so that the ae parameter value of the mapping table can be closer to the ae parameter value of the target brightness, the ae parameter value of the mapping table is more accurate and more accords with the actual photosensitive value condition, and the quick adjustment is facilitated.
When the photosensitive value is located outside the mapping interval of the mapping table, a new mapping interval needs to be created, and the acquired ae parameter value and the photosensitive value of the current environment are stored in the newly created mapping interval, so that the content of the preset mapping table is more comprehensive, more photosensitive values can be located in the mapping interval of the mapping table, the applicability is stronger, and the popularization is facilitated.
In the power-off dormancy process, the adc-ae value can be adaptive to a learning algorithm according to the current environment, so that parameter adaptation of various different environments is met, the content of a preset mapping table is more comprehensive, more photosensitive values can be located in the mapping interval of the mapping table, the influence of change of each environment can be adapted, the applicability is stronger, and the popularization is facilitated; meanwhile, the content data in the preset mapping table is more accurate, and the obtained ae parameter value can be closer to the ae parameter value of the target brightness, so that the rapid adjustment is facilitated; and the network camera is learned and updated when the network camera is in power-off dormancy, so that the working performance of the network camera during power-on operation is not influenced.
Refer to the trend graph of the change trend of the adc value of the photosensitive device of fig. 3 with the change of the ambient lx illuminance value, and refer to the trend graph of the ae parameter with the change of the adc parameter of fig. 4.
Assuming that a certain time is electrified, reading the value of adc as the ae parameter corresponding to the interval (a, b), writing the value into the isp algorithm library, and observing the convergence process of the subsequent frames of the sensor.
Specifically, the first frame:
cmos _ updata _ exp _ time: the interval exposure time parameter (immediate configuration takes effect);
cmos _ updata _ a _ gain: the interval analog gain multiple (immediate configuration takes effect);
and a third frame: (the sensor interval frame adjustment ae);
fine-tuning the exposure time and the analog gain multiple of the interval;
the third frame is several frames later, which does not trigger ae adjustment, and shows that ae has reached a steady state.
And storing the image captured by the application and observing whether the picture is stable.
Referring to fig. 5, a first frame of substantially stable image acquired by applying the present application is shown.
In conclusion, according to the method for photosensitive mapping ae parameters by adc, hardware isp can be quickly used for automatically ae to reach a stable state, and meanwhile, due to the addition of the adaptive learning algorithm: the automatic ae algorithm can adapt to the influence of each environment change, the environment adaptability is strong, and the first frame effect is good; and the ae parameter is configured immediately after the start of the isp acquisition, namely the isp interruption is triggered after the system initialization is completed, the automatic 3a algorithm is started, and meanwhile, a plurality of applied processes can be omitted, such as the step of initializing the sensor, the time for taking frames by applying is greatly advanced, and the first frame is fast in plotting speed.
The application only needs to adapt the central control software of the network camera, does not need to newly add peripheral devices, even co-processes a chip single chip microcomputer, and is low in cost.
The application of the peripheral equipment of all cmos sensors in the current market can be met, the special sensor with the quick starting function is not required to be specially used, and the portability is high.
Therefore, the speed of acquiring the first frame of stable image in the Internet of things camera scheme is optimized by the isp fast ae convergence method, namely the speed of performing the isp 3a algorithm until the exposure gain is stable after the internet of things camera is powered on is met, the requirement of snapshotting the first frame of stable image 200ms after the internet of things camera is powered on is met, the isp can fast acquire matched ae parameters in a low-light environment, and the isp performance effect is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Referring to fig. 6, an embodiment of the present application further provides an isp fast ae convergence device, where the isp fast ae convergence device corresponds to the isp fast ae convergence method in the foregoing embodiment one to one. The isp fast ae convergence device comprises:
the data acquisition module is used for acquiring the numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value when the network camera is powered on and operates;
the first judgment module is used for judging whether the numerical value of the photosensitive digital-to-analog conversion module at the moment is in the mapping interval of the preset mapping table;
the second judgment module is used for judging whether the current ae parameter value is in a convergence stage or a stabilization stage when the value of the photosensitive digital-to-analog conversion module is positioned in the mapping interval of the mapping table at the moment;
the parameter module is used for storing the ae parameter value mapped with the value of the photosensitive digital-to-analog conversion module in the mapping table when the current ae parameter value is in a convergence stage;
and the convergence module is used for taking the stored ae parameter value as a current convergence value of the preset automatic ae algorithm, and starting from the current convergence value, the automatic ae algorithm is converged until the network camera acquires the first frame of stable image.
For the specific definition of the isp fast ae convergence device, refer to the above definition of an isp fast ae convergence method, which is not described herein again. The modules in the above-mentioned isp fast ae convergence apparatus can be implemented in whole or in part by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an isp fast ae convergence method.
In one embodiment, a computer-readable storage medium is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
s11, when the network camera is powered on and operated, the numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value are obtained;
s12, judging whether the value of the photosensitive D/A conversion module is in the mapping interval of the preset mapping table;
s13, when the value of the photosensitive D/A conversion module is in the mapping interval of the mapping table, judging whether the current ae parameter value is in the convergence stage or the stabilization stage;
s141, if the current ae parameter value is in a convergence stage, storing the ae parameter value mapped with the value of the photosensitive digital-to-analog conversion module in the mapping table;
and S15, taking the stored ae parameter value as the current convergence value of the preset automatic ae algorithm, and starting from the current convergence value to enable the automatic ae algorithm to converge until the network camera acquires the first frame of stable image.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to perform all or part of the above-mentioned functions.
Claims (11)
1. An isp fast ae convergence method is characterized by comprising the following steps:
when the network camera is powered on and operated, acquiring the numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value;
judging whether the value of the photosensitive digital-to-analog conversion module is in the mapping interval of a preset mapping table or not;
when the value of the photosensitive digital-to-analog conversion module is positioned in the mapping interval of the mapping table, judging whether the current ae parameter value is in a convergence stage or a stabilization stage;
if the current ae parameter value is in a convergence stage, storing the ae parameter value mapped with the value of the photosensitive digital-to-analog conversion module in the mapping table;
and taking the stored ae parameter value as a current convergence value of a preset automatic ae algorithm, and starting from the current convergence value, converging the automatic ae algorithm until the network camera acquires a first frame of stable image.
2. The isp fast ae convergence method of claim 1, wherein after the step of determining whether the current ae parameter value is in the convergence stage or the stable stage, the method further comprises the steps of:
and if the current ae parameter value is in the stable stage, storing the current ae parameter value.
3. The isp fast ae convergence method of claim 1, further comprising the steps of:
and when the network camera is in power-off dormancy, automatically learning based on the acquired numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value, and updating the mapping table.
4. The isp fast ae convergence method of claim 3, wherein the step of automatically learning based on the obtained values of the photosensitive digital-to-analog conversion module and the current ae parameter values comprises:
judging whether the ae parameter value of the automatic ae algorithm is in a stable state;
if the ae parameter value is stable, acquiring the current ae parameter value;
judging whether the value of the photosensitive digital-to-analog conversion module in the current environment is in the mapping interval of a preset mapping table;
and when the value of the photosensitive digital-to-analog conversion module is positioned in the mapping interval of the mapping table, storing the acquired ae parameter value into the corresponding mapping interval of the mapping table.
5. The isp fast ae convergence method of claim 4, wherein after the step of determining whether the value of the photosensitive dac module in the current environment is within the mapping interval of the preset mapping table, the method further comprises the following steps:
and when the numerical value of the photosensitive digital-to-analog conversion module at the moment is positioned outside the mapping interval of the mapping table, newly creating a mapping interval, and storing the acquired ae parameter value and the numerical value of the photosensitive digital-to-analog conversion module in the current environment into the newly created mapping interval.
6. The isp fast ae convergence method of claim 1, wherein the ae parameter values comprise analog gain and exposure time.
7. The isp fast ae convergence method according to any one of claims 1 to 6, wherein the automatic ae algorithm calculates a difference between the current ambient brightness and the target brightness according to the brightness component of the pixel data, so that the current convergence value is adjusted to be close to the target brightness, and ae parameter values are dynamically updated.
8. The isp fast ae convergence method of claim 7, wherein the step of adjusting the current convergence value to approach the target brightness comprises:
when the ambient brightness becomes dark gradually, the analog gain is made to become smaller gradually by multiple;
when the analog gain is reduced to the minimum value, if the ambient brightness is continuously darkened, the exposure time is adjusted to be continuously reduced in a stepping mode;
when the exposure time is less than the minimum value, the exposure time is decremented by the size of a bit.
9. An isp fast ae convergence device, comprising:
the data acquisition module is used for acquiring the numerical value of the photosensitive digital-to-analog conversion module and the current ae parameter value when the network camera is powered on and operates;
the first judgment module is used for judging whether the numerical value of the photosensitive digital-to-analog conversion module at the moment is in the mapping interval of the preset mapping table;
the second judgment module is used for judging whether the current ae parameter value is in a convergence stage or a stabilization stage when the value of the photosensitive digital-to-analog conversion module is positioned in the mapping interval of the mapping table at the moment;
the parameter module is used for storing the ae parameter value mapped with the value of the photosensitive digital-to-analog conversion module in the mapping table when the current ae parameter value is in a convergence stage;
and the convergence module is used for taking the stored ae parameter value as a current convergence value of a preset automatic ae algorithm, and starting from the current convergence value, the automatic ae algorithm is converged until the network camera acquires a first frame stable image.
10. A computer device comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to perform the steps of the method of any one of claims 1 to 8.
11. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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