CN106657803A - Automatic exposure method for high-speed camera applied to electro-optic theodolite - Google Patents

Abstract

The invention discloses an automatic exposure method for a high-speed camera applied to an electro-optic theodolite, relates to the field of photoelectric technologies, and solves the problem that the high-speed camera cannot be automatically dimmed in case of continuously changing target background illumination by using the automatic exposure methods in existing technologies. The automatic exposure method using an image histogram feature HF function provided by the invention is used for controlling automatic exposure of the high-speed camera in case of rapidly and greatly changing the background illumination. Compared with a method directly using image information entropy as an evaluation criterion, the method using the image histogram feature HF function can provide higher information entropy in a short time. A compensation direction and a step length are determined by the HF function, so that search time of the automatic exposure system is reduced. According to the automatic exposure method using the image histogram feature HF function provided by the invention can effectively improve accuracy and stability of automatic exposure of the high-speed camera, and provides more detailed image information for subsequent image recognition and image tracking.

Description

Automatic exposure method of high-speed camera for photoelectric theodolite

technical field

The invention relates to the field of photoelectric technology, in particular to an automatic exposure method of a high-speed camera for a photoelectric theodolite.

Background technique

With the rapid development of CMOS image sensor (CMOS Image Sensor, CIS) technology, CIS system has been widely used in military and civilian fields. A high-speed camera is a type of CIS system, and its frame rate is several times to thousands of times higher than that of a common CIS system (for example: NTSC 30fps or PAL 25fps). With this feature, high-speed cameras are widely used to record target movements The specific instantaneous state or the whole process in the process can obtain accurate time and space information, and provide a reliable basis for studying the law of motion of high-speed phenomena.

High-speed cameras generally use high-sensitivity image sensors, which have high requirements for the brightness of the observation target and background lighting. Early high-speed cameras are generally used in conditions where good lighting can be provided artificially, such as industrial inspection and observation of athletes' movement status. At present, with the wider demand for analyzing the motion characteristics of high-speed targets, high-speed cameras have begun to be used in natural light conditions, such as photoelectric theodolites, etc. However, the dynamic range of illumination in nature is much higher than that of CIS. Images captured by high-speed cameras are particularly prone to saturation and cause a lot of image details to be lost. Both subsequent human eye observation and image tracker identification of image features will be greatly affected. Thus affecting the tracking performance of photoelectric theodolite etc. Therefore, the present invention focuses on how the high-speed camera for the photoelectric theodolite can quickly exit the overexposure or underexposure state when performing tasks, and find a relatively accurate exposure value, so as to provide a good exposure level for follow-up focusing and target tracking. image.

Auto Exposure (AE) has become an important factor affecting the image quality of digital cameras. By automatically adjusting the exposure time of the camera, the automatic exposure system can effectively reduce the overexposure or underexposure of the camera, and maximize the detailed information of the obtained image.

At present, there are many researches at home and abroad on automatic exposure from algorithms such as average brightness value, image brightness histogram, information entropy, DCT transformation, mathematical iteration and image fusion. But most of them are only for digital cameras that take still pictures, or cameras that work at regular frequencies, and there are few researches on automatic dimming of high-speed cameras when the target background light is constantly changing.

Contents of the invention

The invention provides an automatic exposure method for a high-speed camera for a photoelectric theodolite in order to solve the problem that the automatic exposure method in the prior art cannot realize automatic light adjustment of a high-speed camera under the condition that the target background light is constantly changing.

The photoelectric theodolite uses a high-speed camera automatic exposure method, and the method is realized by the following steps:

Step 1, using the HF function to analyze the brightness of the input image; the specific analysis process is:

The HF function is defined as the sum of the image histogram normalization functions whose luminance value in the normalized image histogram is higher than the luminance threshold value th;

The HF function is:

Four parameters are used to analyze the image brightness captured by the high-speed camera. The four parameters are the HF function value H_mean when the brightness threshold value th is the average brightness value, and the HF function value when the brightness threshold value th is half the average brightness value. H_half, the brightness threshold value th is the function value H_twice of HF and the calculated value H_diff when the brightness threshold value th is twice the average brightness value, and the value of the calculated value H_diff is the contrast between the bright area and the dark area in the image obtained in response;

Step 2: Rough exposure adjustment and exposure fine adjustment to realize automatic exposure of high-speed camera;

The process of rough exposure adjustment is:

a. Determine whether the function value H_twice of HF is greater than or equal to the overexposure threshold α when the brightness threshold value th is twice the average brightness value, if yes, perform step b, if not, perform step c;

b. Reduce the exposure time and return to step a;

c. Determine whether the HF function value H_half is greater than or equal to the underexposure threshold value β when the brightness threshold value th is half of the average brightness value, if yes, increase the exposure time, and return to a, if not, execute step d;

d. Carry out fine adjustment of exposure;

The process of exposure fine-tuning is:

A. Determine whether H _R (k) is greater than or equal to the exposure point threshold value γ, if yes, use fuzzy rules to calculate the compensation step size C _p , if no, perform step B;

B. Judging whether H _m (k) is greater than 0, said H _m (k) is the difference between the H_half value of the k-th image and the H_half value of the k-1 image, said H _m (k)=H_half (k )-H_half(k-1);

If yes, go to step C, if no, go to step D;

C. Reduce the value of the compensation step C _p by N times, that is: C _p = C _p /N, and at the same time use the exposure compensation step of the k-2th image as a reference to compensate, expressed as: E(k) =E(k-2)×C _P ;

Determine whether C _p is less than or equal to θ, if yes, then keep the exposure compensation step of the k-2th image unchanged, that is: E(k)=E(k-2), if not, E(k)=E (k-1)×(1+C _P );

D. E(k)=E(k-2)×(1+C _P );

In the above step A,

HR (k) is the ratio of overexposed pixels between the kth image and the first image _{H O} (0) compensated by fuzzy rules; the estimation function of _HR (k) is:

Where H _O (k) is the sum of the overexposed pixels of the kth image;

The specific process of using fuzzy rules to calculate the compensation step size C _p is as follows:

Using the triangular membership function, the HF function value H_mean and the calculated value H_diff when the brightness threshold value th is the average brightness value are respectively summarized into five levels of triangular membership functions;

Set C(i,j) as the compensation value of the exposure time, the direction of exposure time adjustment is indicated by a plus or minus sign, and λ is the exposure time adjustment step;

Define u(ij) as the triangular membership degree of fuzzy rules, expressed as:

definition

In the formula, U(i) and U(j) are the membership function of the HF function value H_mean when the calculated value H_diff and the brightness threshold value th are the average brightness value respectively;

The compensation step size _Cp is obtained by the following formula:

Then the exposure time of the kth image is expressed as:

E(k)=E(k-1)×C _P .

Beneficial effects of the present invention: the present invention adopts the automatic exposure method of the image histogram feature (Histogram Feature, HF) function, which is used for automatic exposure control of the high-speed camera when the background light changes rapidly and in a large range.

The method of the present invention can complete the brightness measurement of a frame of image within 2ms and adjust the exposure time. Compared with methods such as the average brightness value method and the mathematical iteration method that directly use the brightness information of the image as the evaluation standard, the image histogram The graph feature function method can provide higher information entropy value in a short time; compared with the method of directly using image information entropy as the evaluation standard,

The method of the present invention can judge the compensation direction and the step size through the HF function and reduce the search time of the automatic exposure system. Experiments show that the automatic exposure method using image histogram feature function can effectively improve the accuracy and stability of high-speed camera automatic exposure, and provide more image detail information for subsequent image recognition and image tracking.

Description of drawings

Fig. 1 is the system structural diagram of high-speed camera automatic exposure method for photoelectric theodolite of the present invention;

Fig. 2 is the flowchart of the automatic exposure method of the high-speed camera for the photoelectric theodolite according to the present invention.

Fig. 3 is the principle diagram of the membership function of the HF function in the high-speed camera automatic exposure method for the photoelectric theodolite according to the present invention.

detailed description

The specific embodiment one, illustrate present embodiment in conjunction with Fig. 1 to Fig. 3, photoelectric theodolite is divided into two steps with high-speed camera automatic exposure method,

The first step is the measurement of image brightness: Figure 1 shows the overall process of the high-speed camera automatic exposure system. The specific process is:

The high-speed camera displays the image to the user through Camera Link. After the camera performs automatic exposure, in order to reduce the amount of system calculation, the currently acquired image first analyzes the brightness of the acquired image through the HF function.

Grayscale histogram (Histogram) is a function of grayscale, which indicates the number of pixels with each grayscale in the image, and reflects the frequency of each grayscale in the image. Set the input image as I(x, y), have xy pixels, the gray level is L, h(r) is the gray level histogram of I(x, y): the gray level histogram h(r ) is represented by the formula:

in,

Normalize the gray histogram h(r) to get:

norm(r)=h(r)/xy and

Although the grayscale histogram can accurately represent the distribution of all pixels in the image at each grayscale level, it is too sensitive to slight changes in brightness and noise in the image. When it is used for machine judgment of image brightness, it often leads to The evaluation function oscillates. In order to improve the brightness robustness of the measurement target and the background, the HF function is defined as the sum of the image histogram normalization functions whose brightness values in the normalized image histogram are higher than the brightness threshold value th;

The HF function is:

In this embodiment, four parameters are used to measure the image captured by the high-speed camera, among which three parameters obtained by the HF function are recorded as: H_mean, H_half and H_twice, which are respectively the HF when the brightness threshold value th is the average brightness value function value, the HF function value when the brightness threshold value th is half of the average brightness value, and the HF function value when the brightness threshold value th is twice the average brightness value. The fourth parameter H_diff is the calculated value, expressed as:

The second step is to adjust the exposure time. FIG. 2 shows that the automatic exposure method proposed in this embodiment is roughly divided into exposure rough adjustment and exposure fine adjustment.

In the stage of coarse exposure adjustment, the brightness of the obtained image is firstly extracted, and four HF function values are obtained. Then, use the following judgment method to trigger exposure coarse adjustment, the process of exposure coarse adjustment is:

a. Determine whether the function value H_twice of HF is greater than or equal to the overexposure threshold α when the brightness threshold value th is twice the average brightness value, if yes, perform step b, if not, perform step c;

b. Reduce the exposure time and return to step a;

c. Determine whether the HF function value H_half is greater than or equal to the underexposure threshold value β when the brightness threshold value th is half of the average brightness value, if yes, increase the exposure time, and return to a, if not, execute step d;

d. Carry out fine adjustment of exposure; the process of fine adjustment of exposure is:

In the exposure fine-tuning stage, HR (k) represents the ratio of overexposed pixels of the kth image to the first image _{H O} (0) after fuzzy rule compensation. The estimation function of H _R (k) is:

where H _O (k) represents the sum of overexposed pixels of the kth image. In this embodiment, an overexposed pixel point is defined as a pixel point exceeding 95% of the maximum brightness value, and the threshold value γ of the exposure point is preset as 0.2.

When the obtained image is the first image or _HR (k) exceeds the exposure point threshold γ,

The automatic exposure system of this embodiment judges the direction and compensation of exposure time compensation through fuzzy rules. Firstly, the triangular membership degree function is used to summarize H_mean and H_diff into five levels of VS, S, M, B, and VB respectively. For these membership function, 12 fuzzy rule pairs are proposed

The exposure value is compensated, C(i, j) represents the compensation value of the exposure time, the sign indicates the direction of the exposure time adjustment, and λ is the exposure time adjustment step.

Define u(i,j) as the triangular membership degree of fuzzy rules, expressed as follows:

U(i) and U(j) are the membership functions of H_diff and H_mean respectively.

The compensation step size can be obtained by the formula:

Then the exposure time of the Kth image can be obtained from the formula:

E(k)=E(k-1)×C _P (9)

Where E(k) represents the exposure time of the kth image.

When H _O (k) does not exceed the threshold value, continue to judge H _m (k).

H _m (k) represents the difference between the H_half value of the kth image and the minimum value of H_half of the k-1 image, and its function is shown in the formula:

H _m (k)=H_half(k)-H_half(k-1) (10)

When H _m (k)<0, the exposure time of the kth image is compensated with the exposure value of the k-1th image as a reference, as shown in formula (10).

When H _m (k)>0, the Cp value is reduced by N times, and the exposure value of the k-2th image is used as a reference, as shown in the formula:

E(k)=E(k-2)×C _P (12)

At the same time, if Cp is less than the threshold value θ, the exposure value of the k-2th image remains unchanged, as shown in the formula:

E(k)=E(k-2) (13)

In this embodiment, the five degrees of triangular membership functions are defined as: very small VS, small S, medium M, large B and very large VB. For the five degrees of triangular membership functions, the following 12 items are adopted. The fuzzy rules compensate the exposure time;

Rule 1: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is very small VS, the compensation value C(1,1) is -2λ;

Rule 2: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is small S, the compensation value C(1,2) is +2λ;

Rule 3: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is M, the compensation value C(1,3) is +4λ;

Rule 4: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is large B, the compensation value C(1,4) is +3λ;

Rule 5: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is very large VB, the compensation value C(1,5) is +λ;

Rule 6: When the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is very small VS, the compensation value C(2,1) is –λ;

Rule 7: When the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is small S, the compensation value C(2,2) is +λ;

Rule 8: When the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is medium M, the compensation value C(2,3) is +3λ;

Rule 9: When the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is large B, the compensation value C(2,4) is +2λ;

Rule 10: Set the compensation value C(2,5) to be λ when the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is very large VB;

Rule 11: When the value of the calculated value H_diff is small to medium M and when the brightness threshold value th is the average brightness value H_mean M, the compensation value C(3,3) is λ;

Rule 12: In other cases, the compensation is 0.

In the coarse exposure adjustment described in this embodiment, the range of decrease and increase of α, β and exposure time is a preset fixed value. At the same time, it is also necessary to judge whether the exposure time has reached the minimum or maximum exposure time of the high-speed camera. When the system detects that the camera's exposure time needs to exceed the minimum or maximum exposure time, it means that the automatic exposure system can no longer control the camera to image well under the current lighting conditions, and the automatic exposure control needs to be terminated.

The method described in this embodiment first adjusts the exposure time of the camera in a large range through rough exposure adjustment, and monitors in real time whether the background light intensity of the image changes beyond the preset range. The compensation value is measured and calculated. On the contrary, if the background light changes little, the exposure is fine-tuned by the method of variable step size to ensure the accuracy of high-speed camera light adjustment.

Claims (5)

1. The high-speed camera automatic exposure method for photoelectric theodolite is characterized in that the method is realized by the following steps: Step 1, using the HF function to analyze the brightness of the input image; the specific analysis process is: The HF function is defined as the sum of the image histogram normalization functions whose luminance value in the normalized image histogram is higher than the luminance threshold value th; The HF function is: $\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ In the formula, L is the gray level of the image, th is the brightness threshold, and four parameters are used to analyze the brightness of the image captured by the high-speed camera, and the four parameters are respectively the HF function when the brightness threshold th is the average brightness value value H_mean, the brightness threshold value th is the HF function value H_half when the average brightness value is half, the brightness threshold value th is the function value H_twice of HF when the average brightness value is doubled, and the calculated value H_diff, the value of the calculated value H_diff is the response Obtain the contrast between bright and dark areas in the image; Step 2: Rough exposure adjustment and exposure fine adjustment to realize automatic exposure of high-speed camera; The process of rough exposure adjustment is: a. Determine whether the function value H_twice of HF is greater than or equal to the overexposure threshold α when the brightness threshold value th is twice the average brightness value, if yes, perform step b, if not, perform step c; b. Reduce the exposure time and return to step a; c. Determine whether the HF function value H_half is greater than or equal to the underexposure threshold value β when the brightness threshold value th is half of the average brightness value, if yes, increase the exposure time, and return to a, if not, execute step d; d. Carry out fine adjustment of exposure; The process of exposure fine-tuning is: A. Determine whether H _R (k) is greater than or equal to the exposure point threshold value γ, if yes, use fuzzy rules to calculate the compensation step size C _p , if no, perform step B; B. Judging whether H _m (k) is greater than 0, said H _m (k) is the difference between the H_half value of the k-th image and the H_half value of the k-1 image, said H _m (k)=H_half (k )-H_half(k-1); If yes, go to step C, if no, go to step D; C. Reduce the value of the compensation step C _p by N times, that is: C _p = C _p /N, and at the same time use the exposure compensation step of the k-2th image as a reference to compensate, expressed as: E(k) =E(k-2)×C _P ; Determine whether C _p is less than or equal to θ, if yes, then keep the exposure compensation step of the k-2th image unchanged, that is: E(k)=E(k-2), if not, E(k)=E (k-1)×(1+C _P ); D. E(k)=E(k-2)×(1+C _P ); In the above step A, HR (k) is the ratio of overexposed pixels between the kth image and the first image _{H O} (0) compensated by fuzzy rules; the estimation function of _HR (k) is: ${\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; R</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span>}$ Where H _O (k) is the sum of the overexposed pixels of the kth image; The specific process of using fuzzy rules to calculate the compensation step size C _p is as follows: Using the triangular membership function, the HF function value H_mean and the calculated value H_diff when the brightness threshold value th is the average brightness value are respectively summarized into five levels of triangular membership functions; Set C(i,j) as the compensation value of the exposure time, the direction of exposure time adjustment is indicated by a plus or minus sign, and λ is the exposure time adjustment step; Define u(i,j) as the triangular membership degree of fuzzy rules, expressed as: definition In the formula, U(i) and U(j) are the membership function of the HF function value H_mean when the calculated value H_diff and the brightness threshold value th are the average brightness value respectively; The compensation step size _Cp is obtained by the following formula: ${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span>}$ Then the exposure time of the kth image is expressed as: E(k)=E(k-1)×C _P . 2. the photoelectric theodolite according to claim 1 uses high-speed camera automatic exposure method, it is characterized in that, in step 1, Set the input image as I(x, y), have xy pixels, and h(r) is the grayscale histogram of I(x, y): the grayscale histogram h(r) is expressed as: $\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ;</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; S</span>}}^{<span\; class="notranslate">\; +</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$ in, Normalize the gray histogram h(r) to get: norm(r)=h(r)/xy and 3. photoelectric theodolite according to claim 1 uses high-speed camera automatic exposure method, it is characterized in that, described calculation value H_diff is expressed as with formula: $\begin{array}{c}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; \{</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; ,</span>\\ <span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; .</span>\end{array}$ 4. photoelectric theodolite according to claim 1 uses high-speed camera automatic exposure method, it is characterized in that, The sum H _O (k) of the overexposed pixel points is the pixel point exceeding 95% of the maximum brightness value, and the threshold value γ of the overexposed point is defined as 0.2. 5. the photoelectric theodolite according to claim 1 uses high-speed camera automatic exposure method, it is characterized in that, five kinds of degrees of described definition triangular membership degree function are respectively: very small VS, small S, medium M, big B and Very large VB, for five degrees of triangular membership functions, the following 12 fuzzy rules are used to compensate the exposure time; Rule 1: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is very small VS, the compensation value C(1,1) is -2λ; Rule 2: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is small S, the compensation value C(1,2) is +2λ; Rule 3: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is M, the compensation value C(1,3) is +4λ; Rule 4: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is large B, the compensation value C(1,4) is +3λ; Rule 5: When the value of the calculated value H_diff is very small VS and when the brightness threshold value th is the average brightness value H_mean is very large VB, the compensation value C(1,5) is +λ; Rule 6: When the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is very small VS, the compensation value C(2,1) is –λ; Rule 7: When the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is small S, the compensation value C(2,2) is +λ; Rule 8: When the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is medium M, the compensation value C(2,3) is +3λ; Rule 9: When the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is large B, the compensation value C(2,4) is +2λ; Rule 10: Set the compensation value C(2,5) to be λ when the value of the calculated value H_diff is small S and when the brightness threshold value th is the average brightness value H_mean is very large VB; Rule 11: When the value of the calculated value H_diff is small to medium M and when the brightness threshold value th is the average brightness value H_mean M, the compensation value C(3,3) is λ; Rule 12: In other cases, the compensation is 0.