Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add a classification_proba() function. #77

Merged
merged 1 commit into from
May 19, 2024
Merged

Add a classification_proba() function. #77

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
77 changes: 66 additions & 11 deletions src/utils.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -785,23 +785,46 @@ end
when training the model with `train_raster`.
"""
function classify(cube::GItype, model; class_names::Union{String, Vector{String}}="")
nr, nc = size(cube)[1:2];
mat = Vector{UInt8}(undef, nr*nc);
t = permutedims(cube.z, (1,3,2));
i1 = 1; i2 = nr;
nr, nc = size(cube)[1:2]
mat = Vector{UInt8}(undef, nr*nc)
t = permutedims(cube.z, (1,3,2))
i1 = 1; i2 = nr
for k = 1:nc # Loop over columns
mat[i1:i2] = DecisionTree.predict(model, Float64.(t[:,:,k]))
i1 = i2 + 1
i2 = i1 + nr - 1
end
I = mat2img(reshape(mat, nc,nr), cube);
I = mat2img(reshape(mat, nr,nc), cube)
(class_names == "") && return I # No class names, no CPT
classes = isa(class_names, Vector) ? join(class_names, ",") : class_names
cpt = makecpt(cmap=:categorical, range=classes);
cpt = makecpt(cmap=:categorical, range=classes)
image_cpt!(I, cpt)
return I
end

# ----------------------------------------------------------------------------------------------------------
"""
I = classification_proba(cube::GItype, model; class_number=1) -> GMTimage

Returns an image with the assigned probabilities when classifying the class number `class_number`

- `cube`: The cube wtih band data to classify
- `model`: is the model obtained from the `train_raster` function
- `class_number`: is the class number to be classified
"""
function classification_proba(cube::GItype, model; class_number::Int=1)
nr, nc = size(cube)[1:2]
mat = Vector{UInt8}(undef, nr*nc)
t = permutedims(cube.z, (1,3,2))
i1 = 1; i2 = nr
for k = 1:nc # Loop over columns
mat[i1:i2] = round.(UInt8, predict_proba(model, Float64.(t[:,:,k]))[:,class_number] * 255)
i1 = i2 + 1
i2 = i1 + nr - 1
end
mat2img(reshape(mat, nc,nr), cube)
end

# ----------------------------------------------------------------------------------------------------------
"""
model, classes = train_raster(cube::GItype, train::Union{Vector{<:GMTdataset}, String}; np::Int=0, density=0.1)
Expand All @@ -816,15 +839,47 @@ end

Returns the trained model and the class names.
"""
function train_raster(cube::GItype, train::Union{Vector{<:GMTdataset}, String}; np::Int=0, density=0.1)
function train_raster(cube::GItype, train::Union{Vector{<:GMTdataset}, String}; np::Int=0, density=0.1, max_depth=3)
samples = isa(train, String) ? gmtread(train) : train
pts = randinpolygon(samples, np=np, density=density);
LCsamp = grdinterpolate(cube, S=pts, nocoords=true);
features = GMT.ds2ds(LCsamp);
pts = randinpolygon(samples, np=np, density=density)
LCsamp = grdinterpolate(cube, S=pts, nocoords=true)
features = GMT.ds2ds(LCsamp)
labels = parse.(UInt8, vcat([fill(LCsamp[k].attrib["id"], size(LCsamp[k],1)) for k=1:length(LCsamp)]...));

model = DecisionTree.DecisionTreeClassifier(max_depth=3);
model = DecisionTree.DecisionTreeClassifier(max_depth=max_depth)
DecisionTree.fit!(model, features, labels)
classes = join(unique(GMT.make_attrtbl(samples, false)[1][:,1]), ",")
return model, classes
end

# ----------------------------------------------------------------------------------------------------------
#=
From https://rspatial.org/rs/5-supclassification.html

TODO. Simplify this with a call to classify() and make it an example.

samp = gmtread("samples.shp.zip");
pts = randinpolygon(samp, density=0.02);
C = gdalread("LC08_L1TP_20210525_02_cube.tiff");
LCsamp = grdinterpolate(C, S=pts, nocoords=true);
features = GMT.ds2ds(LCsamp);
#labels = vcat([fill(LCsamp[k].attrib["class"], size(LCsamp[k],1)) for k=1:length(LCsamp)]...);
labels = parse.(UInt8, vcat([fill(LCsamp[k].attrib["id"], size(LCsamp[k],1)) for k=1:length(LCsamp)]...));

using DecisionTree
model = DecisionTreeClassifier(max_depth=3);
fit!(model, features, labels)
nr, nc = size(C)[1:2];
mat = Array{UInt8}(undef, nr*nc);
t = permutedims(C.z, (1,3,2));
i1 = 1; i2 = nr;
for k = 1:nc # Loop over columns
mat[i1:i2] = predict(model, t[:,:,k])
i1 = i2 + 1
i2 = i1 + nr - 1
end
I = mat2img(reshape(mat, nc,nr), C);
cpt = makecpt(cmap=:categorical, range="cropland,water,fallow,built,open");
image_cpt!(I, cpt)
viz(I, colorbar=true)
=#
Loading