Convex Hull, Filling, Thinning, ClearBorder
In this demonstration, we will be covering morphological operations like convexhull, image thinning, hole filling and border clearing.
using Images, Noise, ImageMorphology
using ImageBinarization
using ImageDraw
using TestImages
img_src = testimage("morphology_test_512")
Thresholding of image
img_input = binarize(Gray.(img_src), UnimodalRosin()) .> 0.5512×512 BitMatrix:
0 0 0 0 0 0 0 0 0 0 0 0 0 … 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 … 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
⋮ ⋮ ⋮ ⋱ ⋮ ⋮ ⋮
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 … 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 … 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0This is the image that we will be using throughout this demonstration in its boolean form as below mentioned operations are applied on binary images.
Gray.(img_input)
Convex Hull
Convex Hull operation outputs outer most cover-like boundary of a binary image and returns the vertices of convex hull as a CartesianIndex array.
cordinates = convexhull(img_input)
img_convex = RGB.(copy(img_input))
push!(cordinates, cordinates[1])
draw!(img_convex, Path(cordinates), RGB(1))
img_convex
Image Filling
Image filling operation finds connected components of an image using flood-fill algorithm and operation gives result image after filling objects that falls in the range of interval specified.
For filling objects, represent the holes(part to be filled) with true in your array. Meaning color RGB(1)/black
imfill(img, interval, value, connectivity) require a boolean array, interval is a tuple (a,b) that specifies the range of values to be filled, the objects of size in this range will be filled with false . connectivity takes the same values as in label_components (Default value is 1:ndims(img)`)
img_noise = salt_pepper(img_input, 0.5)
fill_image_1 = imfill(img_noise, (0.1, 1))
fill_image_2 = imfill(img_noise, (0.1, 10)) # this configuration gets us best results
fill_image_3 = imfill(img_noise, (1, 10))
fill_image_4 = imfill(img_noise, (5, 20)) # objects of smaller sizes gets left out
Gray.([img_noise fill_image_1 fill_image_2 fill_image_3 fill_image_4])
Image thinning
Thinning operation applies a binary blob thinning operation to achieve a skeletization of the input image. Guo Algorithm, decides which pixels to keep and which to remove using 3 rules given in original paper.
img_thinning = thinning(img_input, algo = GuoAlgo());
Gray.([img_input img_thinning])
Clear Border
Clearborder method can be used to clear objects connected to the border of the image.
clearborder(img, width, background) can be used upon binary/grayscale input image on a width pixel wide border(Default is 1 pixel). background is the value that is given to pixels that are cleared(Default is 0, black color)
cleared_img_1 = clearborder(img_input, 20); # 20 is the width of border that's examined
cleared_img_2 = clearborder(img_input, 30); # notice how it remove the inner circle even if it's outside its range
cleared_img_3 = clearborder(img_input, 30, 1);Default color for removal is 0 meaning remove RGB(0) but now since it's 1 it's clears the whole image due to flood fill algorithm
Gray.([img_input cleared_img_1 cleared_img_2 cleared_img_3])
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