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@ -12,8 +12,10 @@ This model can either be planar, curved or mapped.
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# Prerequisites
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For now this is only viable for ubuntu gnu/linux machines.
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It should however be possible to run it in WSL and virtual machines of most linux distributions.
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For now this has only been tested on Ubuntu gnu/linux machines.
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It should however be possible to run it in on most distributions, WSL and virtual machines of most linux distributions.
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This guide is for Ubuntu machines only.
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Before cloning repository, you need these to succesfully use the application.
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* python version 3.10 is a requirement might work on earlier python 3 versions
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@ -148,47 +150,47 @@ There is also an option to save current command line setting as a preset using -
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python3 src/main.py res/examples/Palec_P4.tif res/examples/Palec_P4_from_cline.png 600 -d preset_gaussian gaussian sigma=1
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```
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List of all implemented filters and their parameters is described below.
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## Available filters with parameters
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Overview of all implemented filters and their parameters with descriptions is listed below.
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- median blur
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- ksize - Kernel size (int)
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- ksize (int) - Kernel size, determines how large of an area the filter processes.
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- gaussian blur
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- sigma - Gaussian kernel standart deviation (int)
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- sigma (int) - Gaussian kernel standart deviation, determines the weight of further pixels on the currently processed pixel.
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- bilateral blur
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- diameter - Diameter of pixel neighborhood used for filtering (int)
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- sigmaColor - Standard deviation for grayvalue/color distance (int)
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- sigmaSpace - Standard deviation for range distance in pixels (int)
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- diameter (int) - Diameter of pixel neighborhood used for filtering.
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- sigmaColor (int) - Determines the weight of pixels of different color.
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- sigmaSpace (int) - Determines the weight of further pixels.
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- bilateral_scikit
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- sigmaColor - Standard deviation for grayvalue/color distance (float)
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- sigmaSpace - Standard deviation for range distance in pixels (float)
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- sigmaColor (float) - Determines the weight of pixels of different color.
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- sigmaSpace (float) - Determines the weight of further pixels.
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- nlmeans (non-local means)
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- patch_size - Size of patches used for denoising (int)
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- patch_distance - Distance in pixels where to search for patches (int)
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- h - Cut-off distance, higher means more smoothed image (float)
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- patch_size (int) - Size of patches used for denoising.
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- patch_distance (int) - Distance in pixels where to search for patches.
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- h (float) - Cut-off distance, higher means more smoothed image.
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- total_variation
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- weight - Denoising weight. (float)
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- weight (float) - Denoising weight, determines how much the image will be denoised.
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- block_match
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- block_match
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- sigma - ? (?)
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- sigma (float)- Standart deviation
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- unsharp mask scikit
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- radius - Radius of the gaussian filter (int)
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- amount - Strength of the unsharp mask (float)
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- radius (int) - Radius of the gaussian filter.
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- amount (float) - Strength of the unsharp mask, determines how much of the mask will be used for filtering.
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- farid
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@ -198,7 +200,7 @@ List of all implemented filters and their parameters is described below.
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- hessian
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- sigmas - ? (float)
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- sigmas (float) - Standart deviations
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- invert
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@ -206,24 +208,22 @@ List of all implemented filters and their parameters is described below.
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- binarize
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- threshold - value to cut differentiate pixels (int)
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- maxval - maximal value (int) ??
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- type - ? (str)
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- threshold (int) - Value to cut differentiate pixels.
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- binarize_otsu
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- add_margin
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- margin - number of pixels to add to the sides of the image (int)
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- color - color value of newly added pixels (int)
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- margin (int) - Number of pixels to add to the sides of the image.
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- color (int) - Color value of newly added pixels.
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- erode
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- kernel - kernel shape (numpy.matrix)
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- kernel (numpy matrix) - Shape of the kernel used to erode image.
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- dilate
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- kernel - kernel shape (numpy.matrix)
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- kernel (numpy matrix)- Shape of the kernel used to dilate image.
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# Comparison
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