CN106218409A - A kind of can the bore hole 3D automobile instrument display packing of tracing of human eye and device - Google Patents

Abstract

The invention discloses a naked-eye 3D automobile instrument display method and device capable of human eye tracking, wherein the method includes: calibrating a binocular camera, acquiring a face image in real time, removing the human eye from the image, and calculating the relative instrument of the human eye The spatial position of the display screen, adjust the distance between the display panel and the lenticular lens according to the viewing area where the human eye is located, and change the outgoing light by adjusting the distance to achieve an ideal naked-eye 3D display. The above method can automatically and accurately detect the human eyes, and give the spatial position of the human eyes, so as to adjust the naked-eye 3D car instrument display device according to the spatial position of the human eyes, so that the current position of the human eyes is in the best viewing area.

Description

A naked-eye 3D automotive instrument display method and device that can be tracked by human eyes

technical field

The invention relates to a 3D automobile instrument display technology, in particular to a naked-eye 3D automobile instrument display method and device that can be tracked by human eyes.

Background technique

Compared with the traditional two-dimensional display instrument, the display of the 3D instrument matches the visual characteristics of the human being more closely, making people feel more three-dimensional and immersive when viewing the instrument, and can display road condition information and vehicle operating condition information more intuitively and in real time. The naked-eye 3D technology allows the driver to directly observe the three-dimensional vehicle data display with the naked eye without wearing glasses, without affecting driving safety. There are three main types of glasses-free 3D technologies at present, namely parallax barrier, lenticular lens and pointing light source. Because both the parallax barrier and the directional light source have the fatal disadvantage of relatively low screen brightness, the viewing experience is greatly reduced. So at present, lenticular lens technology is the most suitable. Its biggest advantage is that the brightness of the screen will not be reduced due to 3D. The principle of lenticular lens 3D technology is to add a layer of lenticular lens in front of the LCD screen, so that the image plane of the LCD screen is on the focal plane of the lens, and each pixel of the image on the LCD screen can be divided into several sub-pixels. In this way, the sub-pixels under the lens can be projected in different directions. When the lenticular lens and the pixel columns of the LCD screen form a certain angle, each group of sub-pixels can be repeatedly projected to the viewing area, that is, it can be viewed in several different viewing areas. to 3D images. However, the disadvantage of lenticular lens technology is that 3D images can only be viewed in these designated viewing areas, and the increase in sub-pixels will seriously reduce the image resolution and affect the viewing effect, so it is impossible to achieve multi-viewpoint 3D effects, and also That is to say, the optimal viewing distance and angle of the naked-eye 3D display are fixed after the production is completed, requiring users not to change the viewing distance at will, which greatly affects the viewing experience.

Contents of the invention

In order to solve the above problems, the present invention provides the following technical solutions.

A naked-eye 3D car instrument display method that can be tracked by human eyes, specifically comprising the following steps:

Step 1, install a binocular camera at the front of the vehicle cockpit, and calibrate the position of the binocular camera; use the binocular camera to collect two frames of left and right images at the same time, and correct the two frames of left and right images respectively to obtain the corrected image;

Step 2, extracting the range region of the face from the rectified image;

Step 3, extract the human eye area in the human face range area, and optimize the extracted human eye area, and detect the iris in the optimized human eye area;

Step 4, calculate the spatial position of the iris relative to the camera, and calculate the spatial position of the human eye relative to the instrument display by using the positional relationship between the camera and the instrument display and the spatial position of the iris relative to the camera.

Further, step 2 includes the following sub-steps:

Step 21, performing binarization processing on the corrected image to obtain a binarized image;

Step 22, take the horizontal direction of the binarized image as the X-axis, and take the direction perpendicular to the X-axis in the binarized image as the Y-axis to determine the starting point and the end point of the face area in the X-axis direction;

Step 23, determine the start point and end point of the face area in the Y-axis direction;

Step 24: Obtain a rectangle that frames the range of the human face through the start point and end point in the X-axis direction and the start point and end point in the Y-axis direction.

Further, the specific steps for determining the starting point and the end point of the face area in the X-axis direction described in step 22 are:

Step 221, setting X=b, b=0;

Step 222, when obtaining X=b, the number of white points in the binarized image is recorded as SumTempx and the number of all points Sum_p_c; set the threshold pLThresh1, if the ratio of SumTempx to Sum_p_c is greater than or equal to pLThresh1, then X=b As the starting point of the face area in the X-axis direction, it is the X coordinate of the left boundary of the face, denoted as x_L;

If the ratio of SumTempx and Sum_p_c is less than pLThresh1, then add step size x_p to b, and repeat step 222 until the left boundary X coordinate of the face is found;

Step 223, setting X=b, b=0;

Step 224, when obtaining X=b, the number of white points in the binarized image is recorded as SumTempx and the number of all points Sum_p_c; set the threshold pLThresh1, if the ratio of SumTempx to Sum_p_c is greater than or equal to pLThresh1, then X=b As the starting point of the face area in the X-axis direction, it is the X coordinate of the right boundary of the face, denoted as x_R;

If the ratio of SumTempx and Sum_p_c is less than pLThresh1, then add step-x_p to b, and repeat step 224 until the right boundary X coordinate of the face is found;

Further, the specific steps for determining the starting point and the end point of the face area in the Y-axis direction described in step 23 are:

Step 231, setting Y=c, c=0;

Step 232, when obtaining Y=c, the number of white points in the binarized image is recorded as SumTempy and the number Sum_p_r of all points; set the threshold pLThresh2, if the ratio of SumTempy and Sum_p_r is greater than or equal to pLThresh2, then Y=c As the starting point of the face area in the Y-axis direction, it is the Y coordinate of the left boundary of the face, denoted as y_U;

If the ratio of SumTempy and Sum_p_r is less than pLThresh2, then add step size y_p to c, repeat step 232, until finding the upper boundary Y coordinate of people's face;

Step 233, given x_R, x_L and y_U, the lower boundary coordinates of the face can be obtained by the following formula:

y_D=y_U-1.36×(x_R-x_L)

Further, in step 3, the method for extracting the human eye area from the human face range area is:

Among them, G(x, y) represents the gray value at coordinates (x, y) in the binarized image, and M _h (x) represents the gray value at coordinates (x, y) in the binarized image in [ The horizontal integral projection curve of x_L, x_R] area;

Find the trough corresponding to the human eye in the horizontal integral projection curve, and use the two peak points adjacent to the trough to find the Y-axis coordinates k_1 and k_2 corresponding to the two peak points;

Set y_1=k_2-3/5(k_2-k_1), y_2=k_2+3/5(k_2-k_1), to obtain the human eye area composed of y_1 and y_2.

Further, optimizing the extracted human eye region described in step 3 refers to:

Step 31, using Gaussian filtering to process the human eye area to obtain a smooth image;

Step 32, calculate the gradient magnitude and direction of the pixels in the smooth image, and then perform maximum value suppression to obtain a non-maximum value suppression image: the specific operation is as follows:

Select each pixel in the smooth image in turn as the current pixel, if the amplitude of the current pixel is greater than the amplitude of the two adjacent pixels in the gradient direction, then the current pixel is a local maximum; otherwise, it will be The gray value of the current pixel is set to 0; after removing all pixels with a gray value of 0 in the smooth image, a non-maximum suppressed image is formed;

Step 33, set two thresholds L and H, where L=1/2H, select a pixel in the non-maximum suppression image in turn as the current pixel, if the amplitude of the current pixel is greater than or equal to L , then the current pixel is a low threshold local maximum point, otherwise the gray value of the current pixel is set to 0; if the amplitude of the current pixel is greater than or equal to H, then the current pixel is a high threshold local maximum value point, otherwise the gray value of the current pixel point is set to 0;

Step 34, all low-threshold local maximum points form a low-threshold edge image; all high-threshold local maximum points form a high-threshold edge image;

Step 35, if there is a breakpoint on the edge of the high-threshold edge image, then find the coordinates of the breakpoint corresponding to the pixel in the low-threshold edge image, and find the eight neighborhood points of the pixel that can connect the breakpoint of the high-threshold edge image A pixel point, which is connected to the breakpoint of the high threshold edge image;

Step 36, repeat step 35 until the edge of the high-threshold edge image is closed, and the obtained high-threshold edge image at this time is the optimized human eye area.

Further, detecting iris in the optimized human eye area described in step 3 refers to:

Step 37, using the poles in the four directions of the upper, lower, left, and right directions of the edge of the human eye area obtained in step 36, using the minimum circumscribed rectangle method to estimate the center and radius of the human eye area, thereby obtaining the parameter equation of the human eye area;

Step 38, carry out Hough transform to described parametric equation within the range of radius to obtain a transform space, this transform space includes several circles with R as radius;

Step 39, selecting a circle in the transformation space as the current circle, traversing all the circles in the transformation space, counting the number of circles with the same coordinates as the center of the current circle as the number of the same center, and marking the current circle;

Step 310, repeat step 39 until all circles in the transformation space are marked as current circles;

Step 311 , find the current circle with the largest number of identical centers, and the coordinates of the centers of the current circle are the iris coordinates.

The present invention also designs a naked-eye 3D automotive instrument display device, which includes a display panel and a lenticular lens, and is characterized in that it also includes a microprocessor, and a hydraulic adjustment device is arranged between the display panel and the lenticular lens , the hydraulic regulating device is connected with the microprocessor.

Further, there are four hydraulic adjustment devices, which are distributed on the four corners of the bottom surface of the display panel in a double-circuit diagonal arrangement and connected with the lenticular lens.

Further, the display panel and the lenticular lens are connected through an annular elastic connecting piece.

Compared with the prior art, the present invention has the following technical effects:

1. The present invention can automatically and accurately detect the human eye, and give the spatial position of the human eye, so as to adjust the naked-eye 3D car instrument display device according to the spatial position of the human eye, so that the current human eye position is in the best viewing area .

2. The present invention has fast processing speed and high identification accuracy.

Description of drawings

Fig. 1 is a flow chart of a naked-eye 3D automobile instrument display method and system that can be tracked by human eyes according to the present invention;

Fig. 2 is a schematic diagram of determining the position of human eyes;

Fig. 3 is a one-dimensional transformation diagram of processing images;

Fig. 4 is a binary image;

Figure 5 is a face range extraction diagram;

Fig. 6 is the horizontal integral projection diagram of human face;

Figure 7 is a human eye extraction diagram;

Fig. 8 is an effect diagram of human eye recognition;

9 is a schematic diagram of a lenticular lens-type naked-eye 3D display device;

Fig. 10 is a schematic structural diagram of the present invention;

Figure 11 is a schematic diagram of the pipeline layout of the hydraulic device;

The symbols in the figure represent: 1—display panel, 2—elastic connector, 3—hydraulic adjustment device, 4—cylindrical lens.

detailed description

The present invention will be further described below in conjunction with drawings and embodiments.

A naked-eye 3D car meter display method that can be tracked by human eyes, the method utilizes a binocular camera installed at the front end of the vehicle cockpit to collect images to realize the car meter display that can be tracked by human eyes, and specifically includes the following steps:

Step 1, install the binocular camera, calibrate the position of the binocular camera, use the binocular camera to collect two left and right frames of images at the same time, and correct the left and right two frames of images to match the images;

The schematic diagram of binocular positioning shown in Figure 2, its specific implementation steps are:

Step 11, measure the relative position between the two cameras through calibration, that is, the three-dimensional translation t and rotation R parameters of the right camera relative to the left camera, and the external parameters of the relative positions of the binocular cameras C1 and C2 to the world coordinate system are rotation matrices R1 and R2 and translation vectors t1 and t2, the relative position of the binocular camera and the world coordinate system can be expressed as:

z _c1 =R ₁ z _w +t ₁ z _c2 =R ₂ z _w +t ₂

Get the positional relationship of the binocular camera:

z _c1 =R ₁ R ₂ ⁻¹ z _c2 +t ₁ −R ₁ R ₂ ⁻¹ t ₂ .

Thus the geometric relationship between the two cameras can be represented by R and t:

R＝R ₁ R ₂ ^-1 t＝t ₁ -R ₁ R ₂ ^-1 t ₂

Step 12, calculate the parallax formed by the target point on the left and right views. First, match the corresponding pixel points of the point on the left and right views. However, matching corresponding points in two-dimensional space is very time-consuming, so in order to reduce the matching For the search range, limit constraints are introduced to change the matching of corresponding points from two-dimensional to one-dimensional search, as shown in Figure 3.

Step 2, extracting the range region of the face from the rectified image;

Wherein, the specific method of extracting the range region of the human face in step 2 is:

Step 21, performing binarization processing on the corrected image, converting the image into a binary image composed of black points and white points;

Step 22, take the horizontal direction of the binarized image as the X-axis, and take the direction perpendicular to the X-axis in the black-and-white image as the Y-axis to determine the starting point and the end point of the face area in the X-axis direction:

Step 221, setting X=b, b=0;

Step 222, when obtaining X=b, the number of white points in the binarized image is recorded as SumTempx and the number of all points Sum_p_c; set the threshold pLThresh1, if the ratio of SumTempx to Sum_p_c is greater than or equal to pLThresh1, then X=b As the starting point of the face area in the X-axis direction, it is the X coordinate of the left boundary of the face, denoted as x_L;

If the ratio of SumTempx to Sum_p_c is less than pLThresh1, then add step size x_p to b, and repeat step (2-2-2), until the left boundary X coordinate of the face is found;

Step 223, setting X=b, b=0;

Step 224, when obtaining X=b, the number of white points in the binarized image is recorded as SumTempx and the number of all points Sum_p_c; set the threshold pLThresh1, if the ratio of SumTempx to Sum_p_c is greater than or equal to pLThresh1, then X=b As the starting point of the face area in the X-axis direction, it is the X coordinate of the right boundary of the face, denoted as x_R;

If the ratio of SumTempx to Sum_p_c is less than pLThresh1, then add the step size -x_p to b, and repeat steps (2-2-4), until the right boundary X coordinate of the face is found;

Step 23, determine the start point and end point of the face area in the Y-axis direction:

Step 231, setting Y=c, c=0;

Step 232, when obtaining Y=c, the number of white points in the binarized image is recorded as SumTempy and the number Sum_p_r of all points; set the threshold pLThresh2, if the ratio of SumTempy and Sum_p_r is greater than or equal to pLThresh2, then Y=c As the starting point of the face area in the Y-axis direction, it is the Y coordinate of the left boundary of the face, denoted as y_U;

If the ratio of SumTempy and Sum_p_r is less than pLThresh2, then add step size y_p to c, repeat step 232, until finding the upper boundary Y coordinate of people's face;

Step 233, x_R, x_L and y_U are known, obtained from a face conforming to aesthetic standards, the ratio of the distance from the hairline to the chin to the width of the face is about 1.36, so the coordinates of the lower boundary of the face are:

y_D=y_U-1.36×(x_R-x_L)

From x_R, x_L, y_U and y_D, the rectangle that frames the range of the face can be obtained.

Step 24, through the starting point and ending point of the X-axis direction obtained in Step 22, the starting point and the ending point of the Y-axis direction obtained in Step 23, to obtain a rectangle that frames the range of the human face

Step 3, extract the human eye area in the human face range area, and optimize the extracted human eye area, and detect the iris in the optimized human eye area;

Wherein, in step 3, the method for extracting the human eye area from the human face range area is:

Among them, G(x, y) represents the gray value of the binarized image at coordinates (x, y), and M _h (x) represents the gray value of the binarized image (x, y) in [x_L, x_R] area horizontal integral projection;

The horizontal projection curve of the binarized image obtained by formula 1 is shown in Figure 6. The obvious troughs in the curve correspond to the characteristics of the various organs of the face. If viewed from left to right, the most obvious ones are the four The trough, the first corresponds to the eyebrows, the second corresponds to the eyes, the third should be the nose, and the fourth corresponds to the mouth, so you need to locate the second and third peak points in this curve, find the two peak points in The corresponding Y-axis coordinates k_1 and k_2 in the binarized image;

Set y_1=k_2-3/5(k_2-k_1) and y_2=k_2+3/5(k_2-k_1), to obtain the human eye area composed of y_1 and y_2, as shown in FIG. 7 .

Among them, the method of optimizing the human eye area is:

Step 31, using Gaussian filtering to filter out the noise in the image of the human eye area, that is, smoothing;

Step 32, calculate the gradient magnitude and direction of the pixels in the smoothed image, and then perform maximum value suppression to obtain a non-maximum value suppressed image:

Select each pixel in the smoothed image in turn as the current pixel, if the magnitude of the current pixel is greater than the magnitude of the two adjacent pixels in the gradient direction, then the current pixel is a local maximum; Otherwise, the gray value of the current pixel is set to 0; after removing all pixels with a gray value of 0 in the smooth image, a non-maximum value suppressed image is formed;

Step 33, set two thresholds L and H, where L=1/2H, select a pixel in the non-maximum suppression image in turn as the current pixel, if the amplitude of the current pixel is greater than or equal to L , then the current pixel is a low threshold local maximum point, otherwise the gray value of the current pixel is set to 0; if the amplitude of the current pixel is greater than or equal to H, then the current pixel is a high threshold local maximum value point, otherwise the gray value of the current pixel point is set to 0;

Step 34, all low-threshold local maximum points form a low-threshold edge image; all high-threshold local maximum points form a high-threshold edge image;

Step 35, if there is a breakpoint on the edge of the high-threshold edge image, then find the coordinates of the breakpoint corresponding to the pixel in the low-threshold edge image, and find the eight neighborhood points of the pixel that can connect the breakpoint of the high-threshold edge image A pixel point, which is connected to the breakpoint of the high threshold edge image;

Step 36, repeat step 35 until the edge of the high-threshold edge image is closed, and the high-threshold edge image at this time is the optimized human eye area.

Wherein, the concrete method of detecting iris is:

Step 37, using the poles in the four directions of the upper, lower, left, and right directions of the edge of the human eye area obtained in step 36, using the minimum circumscribed rectangle method to estimate the center and radius of the human eye area, thereby obtaining the parameter equation of the human eye area;

Step 38, carry out Hough transform to described parametric equation within the range of radius to obtain a transform space, this transform space includes several circles with R as radius;

Step 39, selecting a circle in the transformation space as the current circle, traversing all the circles in the transformation space, counting the number of circles with the same coordinates as the center of the current circle as the number of the same center, and marking the current circle;

Step 310, repeat step 39 until all circles in the transformation space are marked as current circles;

Step 311 , find the current circle with the largest number of identical centers, and the coordinates of the centers of the current circle are the iris coordinates. Because when some coordinate points in the transformation space are equal, it means that these points are on the same circle, and the corresponding coordinate point when the same number of coordinate points has a peak value is the parameter of the circle, thus obtaining the iris.

Step 4, calculate the spatial position of the iris relative to the camera, and use the positional relationship between the camera and the instrument display to obtain the spatial position of the human eye relative to the instrument display.

The present invention also designs a naked-eye 3D automotive instrument display device, which includes a display panel 1, a lenticular lens 4 and a microprocessor, and a hydraulic adjustment device 3 is arranged between the display panel 1 and the lenticular lens 4, and the hydraulic adjustment device 3 The distance between the display panel 1 and the lenticular lens 4 is changed through the control of the microprocessor. The distance to be adjusted between the display panel 1 and the lenticular lens 4 is calculated according to the position of the human eye, so that the driver can watch it at any position. Perfect glasses-free 3D display effect.

As shown in Figure 10, there are four hydraulic adjustment devices, which are symmetrically distributed on the four corners of the bottom surface of the display panel 1 and connected to the lenticular lens 4. In order to facilitate the adjustment of the distance between the display panel 1 and the lenticular lens 4, The display panel 1 and the lenticular lens 4 are connected through an annular elastic connecting piece 2 .

As shown in FIG. 11 , the four hydraulic adjustment devices 3 adopt a double-circuit diagonal arrangement, which can effectively ensure the parallelism between the display panel 1 and the lenticular lens 4 with high reliability.

The above embodiments are only used to illustrate the present invention, but not to limit the present invention. Those of ordinary skill in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all Equivalent technical solutions also belong to the category of the present invention, and the scope of patent protection of the present invention should be defined by the claims.

Claims (10)

1. A naked-eye 3D automobile instrument display method that can be tracked by human eyes, is characterized in that, specifically comprises the following steps: Step 1, install a binocular camera at the front of the vehicle cockpit, and calibrate the position of the binocular camera; use the binocular camera to collect two frames of left and right images at the same time, and correct the two frames of left and right images respectively to obtain the corrected image; Step 2, extracting the range region of the face from the rectified image; Step 3, extract the human eye area in the human face range area, and optimize the extracted human eye area, and detect the iris in the optimized human eye area; Step 4, calculate the spatial position of the iris relative to the camera, and calculate the spatial position of the human eye relative to the instrument display by using the positional relationship between the camera and the instrument display and the spatial position of the iris relative to the camera. 2. A kind of naked-eye 3D automobile meter display method capable of human eye tracking as claimed in claim 1, wherein step 2 comprises the following sub-steps: Step 21, performing binarization processing on the corrected image to obtain a binarized image; Step 22, take the horizontal direction of the binarized image as the X-axis, and take the direction perpendicular to the X-axis in the binarized image as the Y-axis to determine the starting point and the end point of the face area in the X-axis direction; Step 23, determine the start point and end point of the face area in the Y-axis direction; Step 24: Obtain a rectangle that frames the range of the human face through the start point and end point in the X-axis direction and the start point and end point in the Y-axis direction. 3. A kind of naked-eye 3D car instrument display method capable of human eye tracking as claimed in claim 2, characterized in that, the specific steps of determining the starting point and the end point of the face area in the X-axis direction described in step 22 for: Step 221, setting X=b, b=0; Step 222, when obtaining X=b, the number of white points in the binarized image is recorded as SumTempx and the number of all points Sum_p_c; set the threshold pLThresh1, if the ratio of SumTempx to Sum_p_c is greater than or equal to pLThresh1, then X=b As the starting point of the face area in the X-axis direction, it is the X coordinate of the left boundary of the face, denoted as x_L; If the ratio of SumTempx and Sum_p_c is less than pLThresh1, then add step size x_p to b, and repeat step 222 until the left boundary X coordinate of the face is found; Step 223, setting X=b, b=0; Step 224, when obtaining X=b, the number of white points in the binarized image is recorded as SumTempx and the number of all points Sum_p_c; set the threshold pLThresh1, if the ratio of SumTempx to Sum_p_c is greater than or equal to pLThresh1, then X=b As the starting point of the face area in the X-axis direction, it is the X coordinate of the right boundary of the face, denoted as x_R; If the ratio of SumTempx to Sum_p_c is less than pLThresh1, then add step-x_p to b, and repeat step 224 until the right boundary X coordinate of the face is found. 4. A kind of naked-eye 3D automobile meter display method capable of human eye tracking as claimed in claim 3, characterized in that, the specific steps of determining the starting point and the ending point of the human face area in the Y-axis direction described in step 23 for: Step 231, setting Y=c, c=0; Step 232, when obtaining Y=c, the number of white points in the binarized image is recorded as SumTempy and the number Sum_p_r of all points; set the threshold pLThresh2, if the ratio of SumTempy and Sum_p_r is greater than or equal to pLThresh2, then Y=c As the starting point of the face area in the Y-axis direction, it is the Y coordinate of the left boundary of the face, denoted as y_U; If the ratio of SumTempy and Sum_p_r is less than pLThresh2, then add step size y_p to c, repeat step 232, until finding the upper boundary Y coordinate of people's face; Step 233, given x_R, x_L and y_U, the lower boundary coordinates of the face can be obtained by the following formula: y_D=y_U-1.36×(x_R-x_L). 5. a kind of naked-eye 3D automobile meter display method that can be tracked by human eyes as claimed in claim 4, is characterized in that, the method for extracting the human eye area from the human face range area in step 3 is: Among them, G(x, y) represents the gray value at coordinates (x, y) in the binarized image, and M _h (x) represents the gray value at coordinates (x, y) in the binarized image in [ The horizontal integral projection curve of x_L, x_R] area; Find the trough corresponding to the human eye in the horizontal integral projection curve, and use the two peak points adjacent to the trough to find the Y-axis coordinates k_1 and k_2 corresponding to the two peak points; Set y_1=k_2-3/5(k_2-k_1), y_2=k_2+3/5(k_2-k_1), to obtain the human eye area composed of y_1 and y_2. 6. A kind of naked-eye 3D car meter display method capable of human eye tracking as claimed in claim 1, wherein said optimizing the extracted human eye region in step 3 refers to: Step 31, using Gaussian filtering to process the human eye area to obtain a smooth image; Step 32, calculate the gradient magnitude and direction of the pixels in the smooth image, and then perform maximum value suppression to obtain a non-maximum value suppression image: the specific operation is as follows: Select each pixel in the smooth image in turn as the current pixel, if the amplitude of the current pixel is greater than the amplitude of the two adjacent pixels in the gradient direction, then the current pixel is a local maximum; otherwise, it will be The gray value of the current pixel is set to 0; after removing all pixels with a gray value of 0 in the smooth image, a non-maximum suppressed image is formed; Step 33, set two thresholds L and H, where L=1/2H, select a pixel in the non-maximum suppression image in turn as the current pixel, if the amplitude of the current pixel is greater than or equal to L , then the current pixel is a low threshold local maximum point, otherwise the gray value of the current pixel is set to 0; if the amplitude of the current pixel is greater than or equal to H, then the current pixel is a high threshold local maximum value point, otherwise the gray value of the current pixel point is set to 0; Step 34, all low-threshold local maximum points form a low-threshold edge image; all high-threshold local maximum points form a high-threshold edge image; Step 35, if there is a breakpoint on the edge of the high-threshold edge image, then find the coordinates of the breakpoint corresponding to the pixel in the low-threshold edge image, and find the eight neighborhood points of the pixel that can connect the breakpoint of the high-threshold edge image A pixel point, which is connected to the breakpoint of the high threshold edge image; Step 36, repeat step 35 until the edge of the high-threshold edge image is closed, and the obtained high-threshold edge image at this time is the optimized human eye area. 7. A kind of naked-eye 3D automobile meter display method and system capable of human eye tracking as claimed in claim 6, wherein the detection of iris in the optimized human eye area described in step 3 refers to: Step 37, using the poles in the four directions of the upper, lower, left, and right directions of the edge of the human eye area obtained in step 36, using the minimum circumscribed rectangle method to estimate the center and radius of the human eye area, thereby obtaining the parameter equation of the human eye area; Step 38, carry out Hough transform to described parametric equation within the range of radius to obtain a transform space, this transform space includes several circles with R as radius; Step 39, selecting a circle in the transformation space as the current circle, traversing all the circles in the transformation space, counting the number of circles with the same coordinates as the center of the current circle as the number of the same center, and marking the current circle; Step 310, repeat step 39 until all circles in the transformation space are marked as current circles; Step 311 , find the current circle with the largest number of identical centers, and the coordinates of the centers of the current circle are the iris coordinates. 8. A naked-eye 3D automotive instrument display device, said device comprising a display panel (1) and a lenticular lens (4), is characterized in that, also includes a microprocessor, said display panel (1) and lenticular lens ( 4) A hydraulic regulating device (3) is arranged between them, and the hydraulic regulating device (3) is connected with a microprocessor. 9. The naked-eye 3D automotive instrument display device as claimed in claim 8, characterized in that, said hydraulic adjustment device (3) is provided with four, and it adopts a double-circuit diagonal arrangement to be distributed on the display panel ( 1) On the four corners of the bottom surface, and connected with the lenticular lens (4). 10. The naked-eye 3D automotive instrument display device according to claim 8, wherein the display panel (1) and the lenticular lens (4) are connected by an annular elastic connector (2).