Method, device and system for identifying projection position and storage medium

Abstract

The invention discloses a projection position identification method, device and system and a storage medium, and the method comprises the steps: obtaining a to-be-processed image which comprises a screen region and a projection region; selecting a point from the projection area as a ray starting point; searching along a plurality of preset directions based on the ray starting point to obtain a boundary point of the screen area and a boundary point of the projection area, a first preset angle being spaced between two adjacent preset directions; screening the boundary points to obtain near angular points; and obtaining the angular point of the screen area and the angular point of the projection area by using the near angular point, taking the angular point coordinate of the screen area as the position of the projection screen, and taking the angular point coordinate of the projection area as the position of the projection area. Through the above mode, the method can improve the recognition speed, is higher in anti-interference performance, and is higher in recognition accuracy.

Description

technical field [0001] The present application relates to the field of projection technology, and in particular to a method, device, system and storage medium for identifying a projection location. Background technique [0002] With the promotion and popularization of laser TVs in the market, there are problems such as the difficulty of installing laser TVs and the difficulty of adjusting the projection screen to match the screen completely; even professional installers spend a lot of time adjusting the position of the projector, and the work efficiency is low. High, and accidental bumps in life cause the projector to change its position and need to be readjusted, which greatly affects the user experience. [0003] To solve the above problems, common solutions include: first, use software to correct the projection screen at four points to fit the screen; second, project a specific type of test chart, measure the data by professionals, and manually input the data into the pro...

AI Technical Summary

Problems solved by technology

[0002] With the promotion and popularization of laser TVs in the market, there are problems such as the difficulty of installing laser TVs and the difficulty of adjusting the projection screen to match the screen completely; even professional installers spend a lot of time adjusting the position of the projector, and the work efficiency is low. High, and accidental bumps in life cause the projector position to change and need to be readjusted, which greatly affects the user experience

[0003] To solve the above problems, common solutions include: first, use software to correct the projection screen at four points to fit the screen; second, project a specific type of test chart, measure the data by professionals, and manually input the data into the projection Adjust the position of the projector and correct the position of the projector; thirdly, arrange the photoreceptor array at the four corners of the screen, the photoreceptor array senses the shape of the projection screen, matches it with various preset situations that need to be adjusted, and obtains the matching result, and then drives the Projector position adjustment equipment; the four-point correction scheme will affect the quality of the projection picture, resulting in a certain degree of picture distortion and affecting the long-term viewing effect; the scheme of inputting the projector position adjustment equipment by manual measurement data has the disadvantages of inaccurate measurement and measurement accuracy Low height, inconvenient operation, and poor usability; the arrangement of photoreceptor arrays at the four corners of the screen increases the cost and reduces the aesthetics; in addition, the existing commonly used corner recognition algorithm SUSAN (Smallest Univalue Segment Assimilating Nucleus, the smallest single value Segment similar kernel) is not suitable for processing large-scale images, it is not sensitive to local noise, but it is very sensitive to other objects in the image, and it is extremely difficult to filter in the later stage

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