Data acquisition method and system for open field test based on three-dimensional space imaging principle

Abstract

The application provides an open field test data acquisition method and system based on three-dimensional space imaging principle, which comprises the following steps: placing two image acquisition devices with the same internal parameters beside a target open field, the two image acquisition devices being on the same horizontal plane and the optical axes of the two image acquisition devices being parallel; acquiring an open field three-dimensional image of the target open field through a 3D image imaging method and based on the configuration parameters of the two image acquisition devices; screening a biological image area of a test organism located in the target open field from the open field three-dimensional image; calculating a geometric centroid coordinate of the test organism according to the pixel coordinates and the total number of the area pixels in the biological image area; and acquiring open field test data based on the open field three-dimensional image and / or the geometric centroid coordinate. The application can quickly acquire open field test data and save manpower by automatically shooting images and calculating and analyzing image information.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of open field test, and particularly relates to an open field test data acquisition method and system based on a three-dimensional space imaging principle. BACKGROUND

[0002] Generally, the pharmacological action and drug efficacy of a to-be-tested agent can be analyzed through an open field test to help the development of pharmacology. In the open field test, a suitable target organism is first selected as a test organism, and a mouse or a rat is usually used as the test organism, and the animal is adaptively trained before the test starts to make it familiar with the open field environment. Then, the to-be-tested agent is prepared, and is prepared and adjusted in concentration according to the needs of the experimental design. The test organism is used with the to-be-tested agent, and the test organism is placed in the open field.

[0003] In the above prior art, the open field test data is mainly acquired through a video recording and observation analysis method, and the specific steps are mainly video recording of the animal behavior in the open field, then playing the recorded video, and calculating and analyzing the animal behavior data by manually observing and analyzing the played video, but the data acquisition method of manual calculation and analysis needs to consume a large amount of time cost and labor cost. SUMMARY

[0004] The application provides an open field test data acquisition method and system based on a three-dimensional space imaging principle to solve the problem that the data acquisition method of manual calculation and analysis needs to consume a large amount of time cost and labor cost.

[0005] In a first aspect, the application provides an open field test data acquisition method based on a three-dimensional space imaging principle, which comprises the following steps:

[0006] Two image acquisition devices with the same internal parameters are placed beside a target open field, the two image acquisition devices are on the same horizontal plane, and the optical axes of the two image acquisition devices are parallel;

[0007] An open field three-dimensional image of the target open field is acquired through a 3D image imaging method and based on the configuration parameters of the two image acquisition devices;

[0008] A biological image area of a test organism located in the target open field is screened from the open field three-dimensional image;

[0009] Pixel coordinates of region pixels in the biological image area are calculated by using a binocular parallax distance measurement method, and geometric centroid coordinates of the test organism are calculated in combination with the pixel coordinates and a total number of the region pixels;

[0010] Open field test data is acquired based on the open field three-dimensional image and / or the geometric centroid coordinates.

[0011] Optionally, the configuration parameters include device focal length, device coordinates, device distance and optical center distance, and the obtaining the open-pit three-dimensional image of the target open-pit based on the configuration parameters of the two image acquisition devices through the 3D image imaging method comprises the following steps:

[0012] obtaining the open-pit plane images of the target open-pit collected by the two image acquisition devices;

[0013] combining the device focal length, the device coordinates, the device distance and the open-pit plane images, and constructing the coordinate relationship between the two image acquisition devices according to the pinhole imaging principle;

[0014] calculating the open-pit three-dimensional coordinates of each pixel in the open-pit plane image in the target open-pit based on the coordinate relationship;

[0015] combining all the open-pit three-dimensional coordinates and the optical center distance to generate the open-pit three-dimensional image of the target open-pit.

[0016] Optionally, the open-pit plane images include a first open-pit plane image and a second open-pit plane image, the first open-pit plane image and the second open-pit plane image are respectively the open-pit plane images collected by the two image acquisition devices, the coordinate relationship includes a first coordinate relationship and a second coordinate relationship, and the combining the device focal length, the device coordinates, the device distance and the open-pit plane images, and constructing the coordinate relationship between the two image acquisition devices according to the pinhole imaging principle comprises the following steps:

[0017] combining the device focal length, the device coordinates, the first open-pit plane image and the second open-pit plane image to construct the first coordinate relationship, and the expression of the first coordinate relationship is:

[0018]

[0019] wherein f represents the device focal length, (x1, y1, z1) represents the device coordinates of one of the image acquisition devices, (x2, y2, z2) represents the device coordinates of the other image acquisition device, (u1, v1) represents the image point coordinates in the first open-pit plane image, and (u2, v2) represents the image point coordinates in the second open-pit plane image;

[0020] combining the device coordinates and the device distance to construct the second coordinate relationship, and the expression of the second coordinate relationship is:

[0021]

[0022] wherein b represents the device distance.

[0023] Optionally, the calculation formula of the three-dimensional coordinates of the open field is as follows:

[0024]

[0025] In the formula, (X, Y, Z) represents the three-dimensional coordinates of any point in the target open field.

[0026] Optionally, the calculation formula of the geometric center coordinates of the test organism is as follows:

[0027]

[0028] In the formula, (u, v, w) represents the geometric center coordinates of the test organism, (i, j, k) represents the pixel coordinates of the regional pixels in the biological image region, N represents the total number of pixels, and Ω represents the pixel set of the regional pixels in the biological image region.

[0029] Optionally, the open field test data includes the planar motion speed of the test organism, and the calculation formula of the planar motion speed based on the geometric center coordinates is as follows:

[0030]

[0031]

[0032]

[0033] In the formula, V t represents the planar motion speed of the test organism at time t, V u represents the horizontal velocity component of the test organism, and V v represents the vertical velocity component of the test organism.

[0034] Optionally, the open field test data includes the upright behavior data of the test organism, and the upright behavior data includes the number of uprights and the upright frequency, and the upright behavior data based on the geometric center coordinates includes the following steps:

[0035] Generating a w value curve based on the geometric center coordinates and according to a preset time interval;

[0036] Identifying the characteristic peaks in the w value curve by peak detection method, and taking the number of the characteristic peaks as the number of uprights;

[0037] Taking the ratio of the number of uprights to the time interval as the upright frequency.

[0038] Optionally, the open field test data comprises a number of feces and a volume of feces in the target open field, and the number of feces and the volume of feces are obtained based on the open field three-dimensional image, comprising the following steps:

[0039] The open field spatial volume of the target open field is calculated based on the open field three-dimensional image.

[0040] The open field three-dimensional image is preprocessed.

[0041] The feces image area in the preprocessed open field three-dimensional image is extracted by combining an image recognition method and an edge feature extraction method.

[0042] The number of regions of the feces image area is counted, and the number of regions is taken as the number of feces.

[0043] The feces pixel ratio is calculated by combining the total sum of pixels of the feces image pixels in all the feces image areas and the total sum of pixels of the open field image pixels in the open field three-dimensional image.

[0044] The volume of feces is calculated by combining the feces pixel ratio and the open field spatial volume.

[0045] Optionally, the biological image area of the test biological located in the target open field is screened from the open field three-dimensional image, comprising the following steps:

[0046] The open field three-dimensional image is subjected to noise reduction processing to obtain a noise reduction open field image.

[0047] The noise reduction open field image is subjected to grayscale processing to obtain a grayscale open field image.

[0048] The grayscale open field image is subjected to binaryzation processing to obtain an open field binaryzation image.

[0049] The open field binaryzation image is segmented by a threshold segmentation method to highlight the initial biological image area of the test biological located in the target open field in the open field binaryzation image.

[0050] The initial biological image area is extracted based on the open field binaryzation image after threshold segmentation and by a difference image method.

[0051] The initial biological image area is processed by a morphological operation to obtain a biological image area.

[0052] In a second aspect, the present application further provides an open field test data acquisition system based on three-dimensional space imaging principle, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the method as described in the first aspect.

[0053] The beneficial effects of the present application are:

[0054] The open field test data acquisition method based on the three-dimensional space imaging principle adopted by the present application comprises the following steps: placing two image acquisition devices with the same internal parameters beside the target open field, the two image acquisition devices being on the same horizontal plane, and the optical axes of the two image acquisition devices being parallel; acquiring an open field three-dimensional image of the target open field through a 3D image imaging method and based on the configuration parameters of the two image acquisition devices; screening out a biological image area of a test organism located in the target open field from the open field three-dimensional image; calculating the geometric centroid coordinates of the test organism according to the pixel coordinates and the total number of region pixels in the biological image area; and acquiring open field test data based on the open field three-dimensional image and / or the geometric centroid coordinates. According to the above steps, the three-dimensional image of the open field can be acquired in real time through the image acquisition device, and the open field test data can be acquired through further calculation and analysis of the three-dimensional image. Compared with manual image recording and manual data analysis, the open field test data acquisition has higher efficiency and can save manpower. On the other hand, it can also exclude the interference of manual data analysis, so that the acquired open field test data is also more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 It is a flowchart of the open field test data acquisition method based on the three-dimensional space imaging principle in the present application.

[0056] Figure 2 It is a demonstration schematic diagram of the binocular parallax ranging method in the present application.

[0057] Figure 3 It is a curve schematic diagram of generating a w value curve based on the geometric centroid coordinates in the present application. DETAILED DESCRIPTION

[0058] The present application discloses an open field test data acquisition method based on a three-dimensional space imaging principle.

[0059] Reference Figure 1 The open field test data acquisition method based on the three-dimensional space imaging principle specifically comprises the following steps:

[0060] S101. Place two image acquisition devices with the same internal parameters beside the target open field.

[0061] The image acquisition device can be a high-definition camera. The two image acquisition devices are on the same horizontal plane, and the optical axes of the two image acquisition devices are parallel. The two image acquisition devices have a pair of coordinate axes that are collinear, and the imaging planes of the two image acquisition devices are coplanar. The two image acquisition devices also need to be spaced apart by a device interval. The internal parameters mainly include the focal length, pixel size, aperture, and other parameters of the image acquisition device. The target open field is an open field area that needs to be tested. The target open field is usually a regular area, such as a cubic area or a cuboid area. The target open field is used to place test organisms for open field testing. Therefore, the target open field has a clear boundary that restricts the test organisms from leaving the target open field, and the open field boundary can be observed and measured, and has obvious length, width, and height properties.

[0062] S102. Obtain an open field three-dimensional image of the target open field by a 3D image imaging method and based on the configuration parameters of the two image acquisition devices.

[0063] The two image acquisition devices can be used to obtain planar images of the target open field. In the two planar images, corresponding feature points are found by a feature extraction and matching algorithm (such as SIFT, SURF, ORB, etc.). The feature points have a certain correspondence in the two images. The parallax (i.e., the pixel difference between the two cameras) of the feature points is calculated by a stereo matching algorithm (such as parallax calculation, stereo matching algorithm, etc.) using the correspondence of the feature points. The parallax value is related to the depth of the target object. According to the parallax value and the configuration parameters of the image acquisition device, the three-dimensional coordinates of the target object can be calculated using a triangulation method or a method based on geometric relationships. By fusing the angles of view of the two image acquisition devices, the open field three-dimensional image of the target open field can be obtained.

[0064] S103. Screen a biological image area of the test organism located in the target open field from the open field three-dimensional image.

[0065] The biological image area containing only the test organism can be screened out by using an edge extraction method based on a deep learning-based recognition model to recognize the test organism in the open field three-dimensional image.

[0066] S104. Calculate the pixel coordinates of the region pixels in the biological image area using a binocular parallax ranging method, and calculate the geometric centroid coordinates of the test organism based on the pixel coordinates and the total number of region pixels.

[0067] Referring to Figure 2, c1 and c2 represent the placement positions of the two image acquisition devices respectively, b represents the device interval between the two image acquisition devices, d represents the optical center distance fixed by the image acquisition device, f represents the focal length of the image acquisition device, and P represents the pixel point to be calculated. By constructing a world coordinate system between the two image acquisition devices and the pixel point, and combining the placement positions of the two image acquisition devices and various parameters, the pixel coordinates can be calculated by using the binocular disparity ranging method. After obtaining the pixel coordinates of all regional pixels in the biological image region, the coordinate values of all pixel points belonging to the test organism are added and averaged, and the average value is taken as the geometric centroid coordinate of the test organism.

[0068] S105. Obtain the open field test data based on the open field three-dimensional image and / or the geometric centroid coordinate.

[0069] The open field test data is data analyzed based on the behavior activities of the test organism, and mainly includes the planar movement speed of the test organism, the upright behavior data of the test organism, the feces related data of the test organism, the number of times of entering the central region of the target open field of the test organism, and the time of entering the central region of the target open field of the test organism. The planar movement speed of the test organism and the upright behavior data of the test organism can be calculated based on the geometric centroid coordinate, the feces related data of the test organism can be analyzed based on the open field three-dimensional image, and the number of times of entering the central region of the target open field of the test organism and the time of entering the central region of the target open field of the test organism need to be calculated and analyzed based on the open field three-dimensional image and the geometric centroid coordinate.

[0070] The implementation principle of the embodiment is as follows:

[0071] Two image acquisition devices with the same internal parameters are placed beside the target open field; the open field three-dimensional image of the target open field is obtained by the 3D image imaging method based on the configuration parameters of the two image acquisition devices; the biological image region of the test organism located in the target open field is selected from the open field three-dimensional image; the geometric centroid coordinate of the test organism is calculated according to the pixel coordinates and the total number of regional pixels in the biological image region; and the open field test data is obtained based on the open field three-dimensional image and / or the geometric centroid coordinate. According to the above steps, the three-dimensional image of the open field can be obtained in real time by the image acquisition device, and the open field test data can be obtained by further calculation and analysis of the three-dimensional image. Compared with manual image recording and manual data analysis, the open field test data can be obtained more efficiently and manpower can be saved. On the other hand, the interference of manual data analysis can be excluded, so that the obtained open field test data is also more accurate.

[0072] In one embodiment, the configuration parameters include device focal length, device coordinates, device distance and optical center distance, and the step S102 of obtaining the open-pit three-dimensional image of the target open-pit based on the configuration parameters of the two image acquisition devices by the 3D image imaging method specifically includes the following steps:

[0073] Obtaining the open-pit planar image of the target open-pit collected by the two image acquisition devices;

[0074] Combining the device focal length, device coordinates, device distance and open-pit planar image, and constructing the coordinate relationship between the two image acquisition devices according to the pinhole imaging principle;

[0075] Calculating the open-pit three-dimensional coordinates of each pixel in the open-pit planar image in the target open-pit based on the coordinate relationship;

[0076] Combining all the open-pit three-dimensional coordinates and the optical center distance to generate the open-pit three-dimensional image of the target open-pit.

[0077] In this embodiment, the open-pit planar image includes a first open-pit planar image and a second open-pit planar image, and the first open-pit planar image and the second open-pit planar image are respectively collected by the two image acquisition devices. The coordinate relationship includes a first coordinate relationship and a second coordinate relationship. Combining the device focal length, device coordinates, device distance and open-pit planar image, and constructing the coordinate relationship between the two image acquisition devices according to the pinhole imaging principle specifically includes the following steps:

[0078] Combining the device focal length, device coordinates, first open-pit planar image and second open-pit planar image to construct the first coordinate relationship, and the expression of the first coordinate relationship is:

[0079]

[0080] In the formula: f represents the device focal length, (x1, y1, z1) represents the device coordinates of one of the image acquisition devices, (x2, y2, z2) represents the device coordinates of the other image acquisition device, (u1, v1) represents the image point coordinates in the first open-pit planar image, and (u2, v2) represents the image point coordinates in the second open-pit planar image;

[0081] Combining the device coordinates and device distance to construct the second coordinate relationship, and the expression of the second coordinate relationship is:

[0082]

[0083] In the formula: b represents the device distance.

[0084] In this embodiment, the first coordinate relationship expression and the second coordinate relationship expression are combined to obtain:

[0085]

[0086] In the formula, z represents the z-axis coordinate value of any one of the two image acquisition devices.

[0087] The device interval expression can be further obtained according to the above simultaneous expressions:

[0088] Each image has pixel points, for example, a 1960*2100 photo, the left top pixel point has a coordinate (0, 0) on the picture, and the right bottom vertex coordinate is (1960, 2100), each pixel point represents a certain distance, and the geometric information of the related information in the picture can be obtained by calculating the pixel coordinate information. Assuming that (X, Y, Z) represents the open field three-dimensional coordinates of any point in the target open field, the expression of the second coordinate relationship can also be:

[0089]

[0090] Therefore, the first coordinate relationship expression and the device interval expression are combined, and the calculation formula of the open field three-dimensional coordinates is obtained, so that the open field three-dimensional coordinates are calculated. The calculation formula of the open field three-dimensional coordinates is:

[0091]

[0092] In one embodiment, the coordinate values of all pixel points belonging to the test organism can be added and averaged, and the average value is taken as the centroid of the test organism. The calculation formula of the geometric centroid coordinates of the test organism is:

[0093]

[0094] In the formula, (u, v, w) represents the geometric centroid coordinates of the test organism, (i, j, k) represents the pixel coordinates of the regional pixels in the biological image region, N represents the total number of pixels, and Ω represents the pixel set of the regional pixels in the biological image region.

[0095] In one embodiment, the open field test data includes the planar motion speed of the test organism, and in step S105, the calculation formula of the planar motion speed based on the geometric centroid coordinates is as follows:

[0096]

[0097]

[0098]

[0099] In the formula, V t represents the planar motion speed of the test organism at time t, and V udenotes the velocity component of the test organism in the horizontal direction, V v denotes the velocity component of the test organism in the vertical direction.

[0100] In one embodiment, the open field test data includes the rearing behavior data of the test organism, and the rearing behavior data includes the rearing times and the rearing frequency. In the open field test, the emotional state and the movement intensity of the test organism during the test can be understood by analyzing the rearing behavior of the test organism, and thus the rearing behavior data is relatively important data in the open field test data. In step S105, the rearing behavior data can be obtained based on the geometric center coordinates, and the specific steps are as follows:

[0101] generating a w value curve based on the geometric center coordinates and according to a preset time interval;

[0102] identifying the characteristic peaks in the w value curve by a peak detection method, and taking the number of the characteristic peaks as the rearing times;

[0103] taking the ratio of the rearing times to the time interval as the rearing frequency.

[0104] In the present embodiment, with reference to Figure 3 , Figure 3 is a w value curve generated in an open field test, and (u, v, w) denotes the geometric center coordinates of the test organism. The w value curve is a curve of the w value in the geometric center coordinates changing with time. Since a wave peak will be generated on the w value curve when the test organism produces rearing behavior, the characteristic peaks in the w value curve can be identified by a peak detection method. As shown in Figure 3 , the curve above the dotted line is the characteristic peak, and thus it can be seen that Figure 3 contains four characteristic peaks, and thus the rearing times of the test organism in the preset time interval are four. In addition, the rearing frequency of the test organism can be calculated by dividing the time interval by the rearing times.

[0105] In one embodiment, with reference to Figure 3 , the duration of each characteristic peak, i.e., Δt1, Δt2, Δt3 and Δt4 in Figure 3 , can also be counted. The total duration is obtained by adding the durations of all the characteristic peaks, and the behavior density of the rearing behavior of the test organism in the time interval is calculated by dividing the total duration by the time interval.

[0106] In one embodiment, the feces-related data of the test organisms in the open field test is also important data, and the feces-related data can be used to analyze the drug response of the test organisms to the open field test drug and the health status of the open field organisms. Therefore, in this embodiment, the open field test data includes the number and volume of feces in the target open field, and in step S105, the number and volume of feces can be obtained based on the open field three-dimensional image, and the specific steps are as follows:

[0107] The open field space volume of the target open field is calculated based on the open field three-dimensional image;

[0108] The open field three-dimensional image is preprocessed;

[0109] The feces image area in the preprocessed open field three-dimensional image is extracted by combining the image recognition method and the edge feature extraction method;

[0110] The number of regions of the feces image area is counted, and the number of regions is taken as the number of feces;

[0111] The feces pixel ratio is calculated by combining the total sum of feces image pixels in all feces image areas and the total sum of open field image pixels in the open field three-dimensional image;

[0112] The feces volume is calculated by combining the feces pixel ratio and the open field space volume.

[0113] In this embodiment, the length, width and height of the target open field can be obtained according to the coordinate values of the image pixels in the open field three-dimensional image, and the open field space volume can be calculated according to the length, width and height. The preprocessing methods of the open field three-dimensional image include noise reduction processing, sharpening processing, binarization processing, etc. For the preprocessed open field three-dimensional image, the image recognition method based on neural network model can be used to identify the test organism feces in the open field three-dimensional image, and the edge feature extraction method can be used to extract all feces image areas containing test organism feces.

[0114] In the data analysis and calculation process, the number of regions of the feces image area can be counted as the number of feces, and the total sum of feces image pixels in all feces image areas divided by the total sum of open field image pixels in the open field three-dimensional image can be calculated to obtain the feces pixel ratio, and the feces pixel ratio multiplied by the open field space volume can be calculated to obtain the feces volume.

[0115] In one embodiment, step S103, i.e., filtering the biological image area of the test organism located in the target open field from the open field three-dimensional image, specifically includes the following steps:

[0116] The open field three-dimensional image is subjected to noise reduction processing to obtain a noise reduction open field image;

[0117] The noise reduction open field image is subjected to gray scale processing to obtain a gray scale open field image.

[0118] The gray scale open field image is subjected to binarization processing to obtain an open field binarization image.

[0119] The open field binarization image is segmented by a threshold segmentation method to highlight an initial biological image region of the test biological located in the target open field in the open field binarization image.

[0120] The initial biological image region is extracted based on the open field binarization image after threshold segmentation and by a difference image method.

[0121] The initial biological image region is processed by morphological operations to obtain a biological image region.

[0122] In the present embodiment, a filter (such as a Gaussian filter) can be used to remove noise in the image to reduce interference in subsequent processing steps. Gray scale is a process of converting a color image into a gray scale image, that is, converting the RGB value of each pixel into a corresponding gray scale value. In a gray scale image, the gray scale value of each pixel represents the brightness information of the pixel, without color information. Binarization is a process of converting a gray scale image into a binary image, that is, the pixel values in the gray scale image are segmented according to a threshold, and the pixels greater than the threshold are set to white (255), and the pixels less than or equal to the threshold are set to black (0). In a binary image, each pixel has only two values, representing the foreground and background of the pixel. Gray scale and binarization processing of the image can facilitate better extraction of the biological image region in the subsequent steps.

[0123] The open field binarization image is segmented by a threshold segmentation method to highlight an initial biological image region of the test biological located in the target open field in the open field binarization image, and the initial biological image region is extracted by a difference image method. At this time, there may be a connection break in the initial biological image region, so morphological operations (such as erosion, dilation, opening operation, closing operation, etc.) can be used to remove small noise points or connection breaks in the biological image, and finally obtain the biological image region.

[0124] The present application also discloses an open field test data acquisition system based on a three-dimensional space imaging principle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the open field test data acquisition method based on the three-dimensional space imaging principle as disclosed in any of the above embodiments.

[0125] The implementation principle of the present embodiment is as follows:

[0126] The steps can be executed by calling a program as follows: placing two image acquisition devices with the same internal parameters beside the target open field, the two image acquisition devices being on the same horizontal plane and the optical axes of the two image acquisition devices being parallel; acquiring a three-dimensional image of the open field of the target open field by a 3D image imaging method and based on the configuration parameters of the two image acquisition devices; screening a biological image area of the test organism in the target open field from the three-dimensional image of the open field; calculating the geometric center coordinates of the test organism according to the pixel coordinates and the total number of the region pixels in the biological image area; and acquiring the open field test data based on the three-dimensional image of the open field and / or the geometric center coordinates. According to the above steps, the three-dimensional image of the open field can be acquired in real time by the image acquisition device, and the open field test data can be acquired by further calculation and analysis of the three-dimensional image. Compared with manual image recording and manual data analysis, the open field test data can be acquired more efficiently, and the manpower can be saved. On the other hand, the interference of manual data analysis can be excluded, so that the acquired open field test data is more accurate.

[0127] It should be understood by those of ordinary skill in the art that the above discussion of any embodiment is merely exemplary and is not intended to suggest that the scope of protection of the present application is limited to these examples; under the idea of the present application, the above embodiments or technical features in different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of one or more embodiments of the present application as described above. In order to be brief, they are not provided in details.

[0128] One or more embodiments of the present application are intended to cover all such alternatives, modifications and variations falling within the broad scope of the present application. Therefore, any omission, modification, equivalent replacement, improvement, etc. made in the spirit and principles of one or more embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A method for obtaining data of an open field test based on the principle of three-dimensional space imaging, characterized in that, The method comprises the following steps: placing two image acquisition devices with the same internal parameters beside the target open field, the two image acquisition devices being on the same horizontal plane and the optical axes of the two image acquisition devices being parallel; acquiring a three-dimensional image of the target open field by a 3D image imaging method and based on the configuration parameters of the two image acquisition devices; screening a biological image area of a test organism located in the target open field from the three-dimensional image of the open field; calculating pixel coordinates of region pixels in the biological image area by a binocular parallax distance measurement method and combining the pixel coordinates with the total number of the region pixels to calculate a geometric centroid coordinate of the test organism; acquiring open field test data based on the geometric centroid coordinate; the open field test data comprising upright behavior data of the test organism, the upright behavior data comprising the number of uprights and the upright frequency, and the upright behavior data being acquired based on the geometric centroid coordinate comprising the following steps: generating a value curve based on the geometric center coordinates and according to a preset time interval wherein the value curve is a curve of the geometric center coordinates changing over time with time. Identify the peak detection method. The characteristic peaks in the value curve are identified, and the number of these characteristic peaks is taken as the number of vertical peaks. taking the ratio of the number of uprights to the time interval as the upright frequency; acquiring the upright behavior data based on the geometric centroid coordinate further comprising: counting the duration of each characteristic peak; adding the durations of all the characteristic peaks to obtain a total duration, the total duration representing the total time during which the test organism remains in an upright state in the time interval, and dividing the total duration by the time interval to calculate the behavior density of the upright behavior of the test organism in the time interval; the calculation formula of the geometric centroid coordinate of the test organism being: wherein: represents the geometric centroid coordinates of the test organism, represents the pixel coordinates of the region pixels within the organism image region, represents the total number of pixels, represents the pixel set of the region pixels within the organism image region.

2. The method of claim 1, wherein the method is based on a three-dimensional space imaging principle. the configuration parameters comprising device focal length, device coordinates, device spacing and optical center distance, and the three-dimensional image of the target open field being acquired by the 3D image imaging method and based on the configuration parameters of the two image acquisition devices comprising the following steps: acquiring open field planar images of the target open field collected by the two image acquisition devices; combining the device focal length, the device coordinates, the device spacing and the open field planar images and constructing a coordinate relationship between the two image acquisition devices according to the pinhole imaging principle; calculating open field three-dimensional coordinates of each pixel in the open field planar images in the target open field based on the coordinate relationship; generating the three-dimensional image of the target open field by combining all the open field three-dimensional coordinates and the optical center distance.

3. The method according to claim 2, wherein the open field planar images comprising a first open field planar image and a second open field planar image, the first open field planar image and the second open field planar image being open field planar images collected by the two image acquisition devices respectively, the coordinate relationship comprising a first coordinate relationship and a second coordinate relationship, and the coordinate relationship between the two image acquisition devices being constructed by combining the device focal length, the device coordinates, the first open field planar image and the second open field planar image according to the pinhole imaging principle comprising the following steps: constructing the first coordinate relationship by combining the device focal length, the device coordinates, the first open field planar image and the second open field planar image, the expression of the first coordinate relationship being: wherein: denotes the device focal length of said device, denotes the device coordinates of one of said image acquisition devices, denotes the device coordinates of the other of said image acquisition devices, denotes the image point coordinates in said first free-space planar image, denotes the image point coordinates in said second free-space planar image; constructing the second coordinate relationship by combining the device coordinates and the device spacing, the expression of the second coordinate relationship being: In the formulae: denotes the device spacing.

4. The method according to claim 3, wherein, The calculation formula of the open field three-dimensional coordinates is: In the formula: denotes the open field three-dimensional coordinates of any point in the target open field.

5. The method of claim 1, wherein the method is based on a three-dimensional space imaging principle. The open field test data includes the planar motion speed of the test organism, and the calculation formula for obtaining the planar motion speed based on the geometric centroid coordinates is as follows: wherein: denotes the planar motion velocity of the test organism at the time instant denotes the planar motion velocity of the test organism at the time instant denotes the velocity component of the test organism in horizontal direction, denotes the velocity component of the test organism in vertical direction.

6. The method of claim 1, wherein the method is based on a three-dimensional space imaging principle. The open field test data includes the number and volume of feces in the target open field, and the steps for obtaining the number and volume of feces based on the open field three-dimensional image are as follows: The open field space volume of the target open field is calculated based on the open field three-dimensional image; The open field three-dimensional image is preprocessed; The feces image area in the preprocessed open field three-dimensional image is extracted by combining image recognition methods and edge feature extraction methods; The number of regions of the feces image area is counted, and the number of regions is taken as the number of feces; The feces pixel ratio is calculated by combining the total sum of pixels of feces image pixels in all feces image areas and the total sum of pixels of open field image pixels in the open field three-dimensional image; The feces volume is calculated by combining the feces pixel ratio and the open field space volume.

7. The method of claim 1, wherein the method is based on a three-dimensional space imaging principle. The steps for screening the biological image area of the test organism in the target open field from the open field three-dimensional image are as follows: The open field three-dimensional image is denoised to obtain a denoised open field image; The denoised open field image is grayed to obtain a gray open field image; The gray open field image is binarized to obtain an open field binarized image; The open field binarized image is segmented by a threshold segmentation method to highlight the initial biological image area of the test organism in the target open field in the open field binarized image; The initial biological image area is extracted based on the open field binarized image after threshold segmentation and by a difference image method; The initial biological image area is processed by morphological operations to obtain a biological image area.

8. An open field test data acquisition system based on three-dimensional space imaging principles, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method of any one of claims 1-7.

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