A system and method for calculating calibration point spatial distance based on image midpoint attribute data set

Abstract

The invention discloses a system and method for calculating a calibration point space distance based on an image midpoint attribute data set. The method comprises the following steps of making an image and / or an image set capable of calibrating relevant parameters of recognizable points; focusing and calibrating recognizable points, recording attribute parameters of each point with the attribute parameters including the position and angle of a camera, the focal length and image distance parameters of a lens, the image distance of each focus of an image, coordinate position parameters in the image and the like, forming an image point attribute data set, particularly a space attribute data set, and calculating the space distance of calibrated image points by using the data set, calibrating afocusing point in the image as an identifiable point and recording attribute parameters; converting the image distance and the lens focal length corresponding to each point into the distance from thefocusing point cross section to the light spot, namely the object distance; for an established optical system, calibrating and correcting the correlation and errors between the object distance and the focal distance and between the object distance and the image distance, calculating the spatial coordinates of the points by adding data sets such as the position coordinates of the points in the image, and acquiring the spatial distances between the points according to the spatial coordinates of the points.

Description

technical field [0001] The present invention calibrates the point attributes of a digital image, forms an image point attribute data set, uses the data set to calculate and calibrate the spatial distance of image points, and relates to the fields of optics, image photography, digital images, data sets, three-dimensional space positioning, measurement and calculation, and computer technology. In particular, it relates to a system and method for calculating the spatial distance of calibration points based on a point attribute data set in an image. Background technique [0002] The current methods for calibrating the distance between two points are: 1. ruler measurement; 2. laser (infrared, sound wave, ultrasonic, etc.) distance measurement; 3. visual inertial measurement system VIO in the video system. The above three methods are for real-time measurement of real objects. At present, the ARkit ruler in Apple’s iOS12 is based on SLAM synchronous positioning and mapping technolo...

AI Technical Summary

Problems solved by technology

[0003] The above methods still cannot solve the calibration of the space distance between points that can be identified in the static two-dimensional image after shooting. The ARkit ruler realizes the calibration of the straight line length using the image, but there are three shortcomings: the measurement operation process still needs to be directed against the real object. It cannot be separated from the real object; it can get the straight-line distance between two points, and if the curve is artificially divided into straight-line segments for calibration; the effective distance is short, and it is invalid for a slightly longer distance

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