Construction and validation of a parametric generation model for visual illusions fabric weave

To integrate the art of visual illusions into fabric weave design, a fabric weave model based on a parametric design for visual illusion effect generation was constructed, and a parameter interaction interface was developed using Python3.12 and PySide6 to realize the intelligent generation of a visual illusion fabric weave by adjusting the core parameters of the propagation gradient, gradient of the number of skipped numbers, and number of flips. Experiments show that five different high-visual-illusion fabric weaves can be designed when the oscillation trends of the propagation gradient and the skipped number gradient are the same or different: incremental gradient, oscillatory gradient, incremental followed by oscillatory, oscillatory followed by incremental, and skipped number phase difference gradients. To assess the degree of geometric distortion of fabric weave diagrams, edge detection and the Hough transform were used to calculate the standard deviation of straight-line angles in the weave diagrams to represent the coefficient of visual distortion of fabric weave diagrams. It was found that the average visual distortion coefficient of the aforementioned five types of high-visual-illusion fabric structures was 27.97, with a variance of 51.30. This suggests that different parameters lead to varying effects in the visual illusion fabric structures obtained. The proposed model opens new avenues for the digital design of textile fabrics.