Package: magicinpaint-cpu
IntroductionMagicInpaintis image processing Python library for high quality image reconstruction and inpaint. It is available for the GPU and CPU. There is also and image processing tool using the library available here -https://github.com/antonmilev/MagicInpainter.To install MagicInpaint for GPU:pip install magicinpaint-gpuTo test if MagicInpaint is installed:importmagicinpaintasmimi.version()Normally, this should print the version and the application type:MagicInpaint 1.01 (gpu)To install MagicInpaint for CPU:pip install magicinpaint-cpu:warning:Please Note:For the GPU version to work you need to have NVIDIA GPU card and correctly installed CUDA driver (GTX or RTX)!Inpaint with MagicInpaintTo useMagicInpaintfor image files with cv2:importnumpyasnpimportmagicinpaintasmiimportos,sysimportcv2# check magicinpaint versionprint(mi.version())img_path='08_beach1.jpg'mask_path='08_beach1_mask.png'out_path='08_beach1_inpaint.png'img=cv2.imread(img_path)ifimgisNone:print('Can not open image:',img_path)exit()mask=cv2.imread(mask_path,cv2.IMREAD_GRAYSCALE)ifmaskisNone:print('Can not open mask:',mask_path)exit()mi.inpaint(img,mask,15,mi.InpaintGPUfast,verbose=True)cv2.imshow('dst',img)cv2.imwrite(out_path,img)cv2.waitKey(0)cv2.destroyAllWindows()Output:Noise Pixels:       20221
Image Pixels:       495650
Radius:             15
Inpaint Method:     GPU Fast
TFLOPS:             28.8950
Starting Inpaint.....
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Inpainting Completed
Inpaint time:       21375.00 ms
Saving output image....Use without OpenCV:There is also a method that allows to process image files directly (supported image extensions are JPG,BMP,PNG):importmagicinpaintasmi# check magicinpaint versionprint(mi.version())img_path='test/cloth1_red1.png'mask_path='test/cloth1_mask.png'out_path='results_mi/cloth1_inpaint_mi_GPUfast_r10.png'mi.inpaint_file(img_path,mask_path,out_path,30,mi.InpaintGPUfast,verbose=True)Desription of the function arguments:image_file- path to color image file, supported formats JPG,BMP,PNGmask_file- path to mask black-white image file, 0-valid pixels, 255- noise pixels to removeout_path- path to output image file, if None result is saved in image file with appended string "onpaint"rad- inpaint radius, limited to64method- inpaint method, see belowverbose- print details, by default isFalseFunction outputsTrueif sucessful, otherwiseFalse. The smaeInpaint with OpenCVTo use OpenCV inpaint seehttps://docs.opencv.org/3.4/df/d3d/tutorial_py_inpainting.html:Inpaint with cv2.INPAINT_NS:importnumpyasnpimportcv2img_path='test/08_beach1.png'mask_path='test/08_beach1_mask.png'out_path='results_cv2/08_beach1_inpaint_cv2_NS_r30.png'img=cv2.imread(img_path)ifimgisNone:print('can not open image:',img_path)exit()mask=cv2.imread(mask_path,cv2.IMREAD_GRAYSCALE)dst=cv2.inpaint(img,mask,30,cv2.INPAINT_NS)cv2.imshow('dst',dst)cv2.imwrite(out_path,dst)cv2.waitKey(0)cv2.destroyAllWindows()Inpaint with cv2.INPAINT_TELEA:importnumpyasnpimportcv2img_path='test/cloth1_red1.png'mask_path='test/cloth1_mask.bmp'out_path='results_cv2/cloth1_inpaint_cv2_Telea_r30.png'img=cv2.imread(img_path)ifimgisNone:print('can not open image:',img_path)exit()mask=cv2.imread(mask_path,cv2.IMREAD_GRAYSCALE)dst=cv2.inpaint(img,mask,30,cv2.INPAINT_TELEA)cv2.imshow('dst',dst)cv2.imwrite(out_path,dst)cv2.waitKey(0)cv2.destroyAllWindows()OpenCV inpaint methods are faster but works well only for small scratches and line defects.Inpaint methodsFollowing MagicInpaint methods are available:InpaintCPU- this is the default inpaint method if GPU with CUDA is missing. Results can be worse and this method would be too slow so is not recommended for images with high resolution and large noise area.InpaintGPU- this is the most precise GPU method, however for high resolution is also too slowInpaintGPUfast- this is the recomended GPU method for real-life photos, results can be very little worse but perfomance can rise more than 500 percentsLimitationsCurently the library is avaliable and tested only for Windows and Python versions Python3.6- Python3.12Inpaint radius is limited to64For very large image resolutions above 4K and large noise area inpaint can be too slowFor more details see:https://github.com/antonmilev/MagicInpainter.Future WorkIn comming releases there would be several improvements:GPU performance- several optimizations are possible for the GPU to work even faster, also search algorithms can be greately improvedImages with larger resolutionOptimization algorithms used in MagicInpainter 3.0 become sometimes too slow and unstable for images with high resolution (in these casesZoom In/Outbuttons can be used). One other limitation is that inpaint radius can not exceed64pixels.Processing several images and videoMagicInpainter 3.0 is currently limited to single photos.Use AI and deep learning to speed up algorithms and optimal inpaint radius selectionAssist inpaint algorithms with AI for classification, automation of algorithms type and optimal radius size selection. Used here inpaint algorithms can be combined with various neural networks models, especially in the cases when inpaint involves complicated features and big resolution, speed can be also significantly improved.