![]() I don't really know what Matte is doing in this instance so I'm happy to be enlightened on that front, but hopefully those looking for why inpainting doesn't work will be helped. ![]() Whether this is a bug or not I don't know. Reselect the element you wish to obliterate. Open the Edit menu and Inpaint is greyed out. ![]() Use the brush selection tool to selection tool to isolate an element you wish to remove.ģ. Right-click and copy an image from your browser window, then switch to Affinity Photo and choose File > New from clipboard.Ģ. Well, I've finally been able to replicate the problem and found a solution that seems to work to re-enable the inpainting functionality.ġ. Replies to this and other topics do not seem to acknowledge that this happens at all, instead implying that the user is doing something wrong. When this happens neither the inpainting brush or selecting the area and choosing Inpaint form the Edit menu will work. Result2 = cv2.inpaint(img,mask3,11,cv2.INPAINT_NS)Ĭv2.imwrite('airport_sign_mask.png', mask3)Ĭv2.imwrite('airport_sign_inpainted1.png', result1)Ĭv2.imwrite('airport_sign_inpainted2.Just in order to potentially save somebody else from this frustration, I too have found the Inpaint option to be greyed out for reasons I have not been able to determine. Result1 = cv2.inpaint(img,mask3,11,cv2.INPAINT_TELEA) Mask3 = cv2.morphologyEx(mask2, cv2.MORPH_DILATE, kernel) Kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)) Paint.NET is image and photo editing software for PCs that run Windows. ![]() #mask2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) Download Inpaint and remove unwanted objects from photos Inpaint reconstructs the selected image area from the pixels near the area boundary. #gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) Mask1 = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel) Kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (135,135)) Apply morphology dilate to enlarge it slightly and save as mask3 We believe in bringing custom gaming computers to the masses, thats why we only charge a small build fee and dont cut corners on quality.Threshold on the white in this new image and keep as mask2.Apply the mask to the image to blacken out the outside of the sign.Apply morphology to close it up and keep as mask1.So, to show you what I mean, here is my Python/OpenCV approach. So you are best to try to get the mask of just the letters (characters), not rectangular blocks for the words. However, the OpenCV methods do work here, I suspect, because you are filling with constant colors (green) and not texture. You really need an exemplar type method such as. They work best on thin (scratch-like) regions, not large blocks. Python/OpenCV inpaint methods, generally, are not appropriate to your type of image. Still I think it needs improvement, If I chose a different image the results are not so good. I did the inpainting algorithm 3 times, it basically the other times inverse the mask, because in some cases required mask is the inversed mask. After doing this I performed the masking process 3 different times with variable masks and inversions. I've managed to get this new better result by noticing that my threshold is the best mask I can get. Threshold Sub IMG part: Thresholded Image AI Photo Colorizer AI Image Upscaler Unblur. How do I prevent these BLOCKS of other colours to FORM on the IMAGE? Watermark Remover Remove Objects from Photo Inpaint Old Photos Retouch Your Skin. As we can see, it is making some BLOCKS OF DIFFERENT COLOR over the IMAGE, I want to prevent that, How do I achieve this? I see that mask images are not formed well many times, and in cases when the text is white the PREPROCESSING doesn't occur properly. When I run the above code, I here am the OUTPUT Image OUTPUT. Rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 3))ĭilation = cv2.dilate(thresh1, rect_kernel, iterations = 1)Ĭnts = cv2.findContours(py(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) Preprocess operations: ret,thresh1 = cv2.threshold(gray, 0, 255,cv2.THRESH_OTSU|cv2.THRESH_BINARY_INV) Img = cv2.inpaint(img,mask,7,cv2.INPAINT_TELEA) Mask = np.ones(img.shape, dtype="uint8") * 255 Inpaint photo restoration software reconstructs the selected image area from the pixels near the area boundary. I had been trying various methods, and eventually found that I can get the results through OCR and then using thresholding MASK THE IMAGE. I wanted to Remove all the texts USING INPAINTING from this IMAGE.
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