Abstract:

In this study, an image model has been developed aiming to predict and generate the appearance of denim fabric based on its production parameters without actual production. The prediction model has been created by considering the parameters that affect the final appearance of denim fabric in its production. After examining over 100 fabrics, the selected production parameters include warp yarn count, weft yarn count, weave type, weave direction, and raw weft density. The proposed model utilizes these parameters to generate realistic images of denim fabric, providing manufacturers and designers with the opportunity to visualize the predicted appearance of fabrics with specific visual characteristics before production.To better understand the relationship between production parameters and the final appearance of denim fabric, examinations have been conducted on both microscopic and regular images of the products. As a result of these examinations, it has been observed that the length of each visible weft yarn in the microscopic images representing the fabric's inner surface is equal to the weave type number of the adjacent warp yarn. Based on this information, pixel values have been determined for fabric images to create the woven texture of the fabric. However, an algorithm developed solely based on this information would overlook the entropy present in the actual weaving process. Therefore, mathematical studies have also been conducted to obtain natural images in order to enhance realism. Examinations have been performed on regular fabric images obtained from a scanner using the Python OpenCV library. Image cleaning and conversion to black and white have been applied, and the dark and light pixels in the fabrics have been detected and counted. Through these analyses, it has been determined how much the weft yarns are influenced by the color of the warp yarns during weaving. In artificial images, the light-colored pixels belonging to weft yarns have been darkened to create a realistic denim appearance.To prevent user errors, an interface has been created using Python libraries. This interface prevents erroneous data entry and allows the algorithm to run in the background and generate the predicted fabric image after the user completes the data entry. Tests have been conducted on known fabric types to evaluate the model, and successful results have been obtained by comparing the original images with the artificial images generated by the algorithm. In this study, the fabric images predicted using a mathematical model were compared with the originals in terms of average pixel value, number of dark pixels and number of light pixels, and it was found that there was a similarity of over 90%..