A thin wrapper for installing and using segment-anything in Julia.
It provides SamPredictor and a predict function and
SamAutomaticMaskGenerator and generate, as in segment-anything.
predict and generate return the raw outputs of their namesake python methods.
ImageMask provides a more julia wrapper for SamPredictor and predict.
using SegmentAnything, GLMakie, FileIO
# Load the predictor model
predictor = SamPredictor()
# Download an image
using Downloads
url = "https://upload.wikimedia.org/wikipedia/commons/a/a1/Beagle_and_sleeping_black_and_white_kitty-01.jpg"
Downloads.download(url, "pic.jpg")
img = load("pic.jpg")
# Plot it
p = Makie.image(rotr90(img); transparency=true)
p.axis.autolimitaspect = 1
hidedecorations!(p.axis)
# Mask the kitten, and plot
mask1 = ImageMask(predictor, img;
point_coords=[(400, 800)],
point_labels=[true],
)
m1 = rotr90(map(mask1.masks[2, :, :]) do x
x ? RGBAf(1, 0, 0, 1) : RGBAf(0, 0, 0, 0)
end)
image!(p.axis, m1; transparency=true)
# Now mask the beagle, and plot
mask2 = ImageMask(predictor, img;
point_coords=[(400, 600)],
point_labels=[true],
)
m2 = rotr90(map(mask2.masks[2, :, :]) do x
x ? RGBAf(0, 0.0, 1.0, 1) : RGBAf(0, 0, 0, 0)
end)
image!(p.axis, m2; transparency=true)
save("beagle_and_kitten.png", p.figure)
We can also use the automatic mask generator:
using SegmentAnything, GLMakie, FileIO, PythonCall
p = Makie.image(rotr90(img); transparency=true)
# Load the predictor model
generator = SamAutomaticMaskGenerator()
out = generate(generator, img)
# Create an array of zeros to write into
segments = rotr90(PythonCall.pyconvert(Array, out[1]["segmentation"])) .* 0
# Loop over the returned objects, which is an array of python dictionaries.
for (n, x) in enumerate(out)
A = rotr90(PythonCall.pyconvert(Array, x["segmentation"]))
for i in eachindex(A)
if A[i]
segments[i] = n
end
end
end
Makie.image!(p.axis, segments; transparency=true, colormap=(:tableau_20, 2.0))
save("auto_segmentation.png", p.figure)