Non-zero pixels correspond to areas that should be inpainted (i.e., fixed), while zero pixels are considered “normal” and do not need inpaintingĪn example of these images can be seen in Figure 2 above. This image should have the same spatial dimensions (width and height) as the input image. The mask image, which indicates where in the image the damage is.Presumably, this image is “damaged” in some manner, and we need to apply inpainting algorithms to fix it The input image we wish to inpaint and restore.When applying inpainting with OpenCV, we need to provide two images: How does inpainting work with OpenCV?įigure 2: Photograph restoration via OpenCV, Python, and image inpainting. In the rest of this tutorial you will learn how to apply both the cv2.INPAINT_TELEA and cv2.INPAINT_NS methods using OpenCV. Once they are obtained, color is filled to reduce minimum variance in that area. For this, some methods from fluid dynamics are used. It continues isophotes (lines joining points with same intensity, just like contours joins points with same elevation) while matching gradient vectors at the boundary of the inpainting region. It first travels along the edges from known regions to unknown regions (because edges are meant to be continuous). This algorithm is based on fluid dynamics and utilizes partial differential equations. The second method, Navier-Stokes, is based on fluid dynamics. FMM ensures those pixels near the known pixels are inpainted first, so that it just works like a manual heuristic operation. Once a pixel is inpainted, it moves to next nearest pixel using Fast Marching Method. More weightage is given to those pixels lying near to the point, near to the normal of the boundary and those lying on the boundary contours. Selection of the weights is an important matter. This pixel is replaced by normalized weighted sum of all the known pixels in the neighbourhood. It takes a small neighbourhood around the pixel on the neighbourhood to be inpainted. Algorithm starts from the boundary of this region and goes inside the region gradually filling everything in the boundary first. Consider a region in the image to be inpainted. To quote the OpenCV documentation, the Telea method: cv2.INPAINT_NS: Navier-stokes, Fluid dynamics, and image and video inpainting (Bertalmío et al., 2001).cv2.INPAINT_TELEA: An image inpainting technique based on the fast marching method (Telea, 2004).This model card was written by: Robin Rombach and Patrick Esser and is based on the DALL-E Mini model card. Resources for more information: GitHub Repository, Paper.Ĭite as: = , It is a Latent Diffusion Model that uses a fixed, pretrained text encoder ( CLIP ViT-L/14) as suggested in the Imagen paper. Model Description: This is a model that can be used to generate and modify images based on text prompts. See also the article about the BLOOM Open RAIL license on which our license is based. License: The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. Model type: Diffusion-based text-to-image generation model Download the weights sd-v1-5-inpainting.ckptĭeveloped by: Robin Rombach, Patrick Esser.Face of a yellow cat, high resolution, sitting on a park bench
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