Support labeled segmented images

src/IceFloeTracker.jl

@@ -38,6 +38,7 @@ export readdlm,
     imsharpen,
     label_components,
     regionprops_table,
+    cropfloe,
     loadimg,
     matchcorr,
     centered,

src/bwtraceboundary.jl

@@ -54,7 +54,7 @@ julia> boundary[3]
 ```
 """
 function bwtraceboundary(
-    image::Union{Matrix{Int64},Matrix{Float64},T};
+    image::Union{Matrix{UInt8},Matrix{Int64},Matrix{Float64},T};
     P0::Union{Tuple{Int,Int},CartesianIndex{2},Nothing}=nothing,
     closed::Bool=true,
 ) where {T<:AbstractArray{Bool,2}}

src/regionprops.jl

@@ -180,16 +180,65 @@ function getbboxcolumns(props::DataFrame)
     return filter(col -> occursin(r"^bbox-\d$", col), names(props))
 end
 
+
+FloeLabelsImage = Union{BitMatrix, Matrix{<:Bool}, Matrix{<:Integer}}
+
 """
     cropfloe(floesimg, props, i)
 
 Crops the floe delimited by the bounding box data in `props` at index `i` from the floe image `floesimg`.
+
+If the dataframe has bounding box data `min_row`, `min_col`, `max_row`, `max_col`, but no `label`, then returns the largest contiguous component.
+
+If the dataframe has bounding box data `min_row`, `min_col`, `max_row`, `max_col`, and a `label`, then returns the component with the label. In this case, `floesimg` must be an Array{Int}.
+
+If the dataframe has only a `label` and no bounding box data, then returns the component with the label, padded by one cell of zeroes on all sides. In this case, `floesimg` must be an Array{Int}.
+
+
+"""
+function cropfloe(floesimg::FloeLabelsImage, props::DataFrame, i::Integer)
+    props_row = props[i, :]
+    colnames = Set(names(props_row))
+    bbox_column_names = Set(["min_row", "min_col", "max_row", "max_col"])
+    label_column_names = Set(["label"])
+    bbox_label_column_names = union(bbox_column_names, label_column_names)
+
+    if issubset(bbox_label_column_names, colnames)
+        return cropfloe(
+                floesimg,
+                props_row.min_row,
+                props_row.min_col,
+                props_row.max_row,
+                props_row.max_col,
+                props_row.label
+            )
+
+    elseif issubset(bbox_column_names, colnames)
+        floesimg_bitmatrix = floesimg .> 0
+        return cropfloe(
+            floesimg_bitmatrix,
+            props_row.min_row,
+            props_row.min_col,
+            props_row.max_row,
+            props_row.max_col
+        )
+
+    elseif issubset(label_column_names, colnames)
+        return cropfloe(floesimg, props_row.label)
+
+    end
+end
+
+"""
+    cropfloe(floesimg, min_row, min_col, max_row, max_col)
+
+Crops the floe delimited by `min_row`, `min_col`, `max_row`, `max_col`, from the floe image `floesimg`.
 """
-function cropfloe(floesimg::BitMatrix, props::DataFrame, i::Int64)
+function cropfloe(floesimg::BitMatrix, min_row::I, min_col::I, max_row::I, max_col::I) where {I<:Integer}
     #= 
     Crop the floe using bounding box data in props.
     Note: Using a view of the cropped floe was considered but if there were multiple components in the cropped floe, the source array with the floes would be modified. =#
-    prefloe = floesimg[props.min_row[i]:props.max_row[i], props.min_col[i]:props.max_col[i]]
+    prefloe = floesimg[min_row:max_row, min_col:max_col]
 
     #= Check if more than one component is present in the cropped image.
     If so, keep only the largest component by removing all on pixels not in the largest component =#
@@ -202,12 +251,35 @@ function cropfloe(floesimg::BitMatrix, props::DataFrame, i::Int64)
     return prefloe
 end
 
+"""
+    cropfloe(floesimg, min_row, min_col, max_row, max_col, label)
+
+Crops the floe from `floesimg` with the label `label`, returning the region bounded by `min_row`, `min_col`, `max_row`, `max_col`, and converting to a BitMatrix.
+"""
+function cropfloe(floesimg::Matrix{I}, min_row::J, min_col::J, max_row::J, max_col::J, label::I)  where {I<:Integer, J<:Integer}
+    #= 
+    Crop the floe using bounding box data in props.
+    Note: Using a view of the cropped floe was considered but if there were multiple components in the cropped floe, the source array with the floes would be modified. =#
+    prefloe = floesimg[min_row:max_row, min_col:max_col]
+    @debug "prefloe: $prefloe"
+
+    #= Remove any pixels not corresponding to that numbered floe 
+    (each segment has a different integer) =#
+    floe_area = prefloe .== label
+    @debug "mask: $floe_area"
+
+    return floe_area
+end
+
+
+
+
 """
     addfloearrays(props::DataFrame, floeimg::BitMatrix)
 
 Add a column to `props` called `floearray` containing the cropped floe masks from `floeimg`.
 """
-function addfloemasks!(props::DataFrame, floeimg::BitMatrix)
+function addfloemasks!(props::DataFrame, floeimg::FloeLabelsImage)
     props.mask = getfloemasks(props, floeimg)
     return nothing
 end
@@ -217,7 +289,7 @@ end
 
 Return a vector of cropped floe masks from `floeimg` using the bounding box data in `props`.
 """
-function getfloemasks(props::DataFrame, floeimg::BitMatrix)
+function getfloemasks(props::DataFrame, floeimg::FloeLabelsImage)
     return map(i -> cropfloe(floeimg, props, i), 1:nrow(props))
 end
 

src/tracker/tracker-funcs.jl

@@ -491,7 +491,7 @@ function addψs!(props::Vector{DataFrame})
     return nothing
 end
 
-function addfloemasks!(props, imgs)
+function addfloemasks!(props::Vector{DataFrame}, imgs::Vector{<:FloeLabelsImage})
     for (img, prop) in zip(imgs, props)
         IceFloeTracker.addfloemasks!(prop, img)
     end
@@ -507,13 +507,13 @@ Convert the centroid coordinates from row and column to latitude and longitude d
 """
 function convertcentroid!(propdf, latlondata, colstodrop)
     latitude, longitude = [
-        [latlondata[c][x, y] for
+        [latlondata[c][Int(round(x)), Int(round(y))] for
          (x, y) in zip(propdf.row_centroid, propdf.col_centroid)] for
         c in ["latitude", "longitude"]
     ]
 
     x, y = [
-        [latlondata[c][z] for z in V] for
+        [latlondata[c][Int(round(z))] for z in V] for
         (c, V) in zip(["Y", "X"], [propdf.row_centroid, propdf.col_centroid])
     ]
 

src/tracker/tracker.jl

@@ -45,7 +45,7 @@ function sort_floes_by_area!(props)
 end
 
 function _pairfloes(
-    segmented_imgs::Vector{BitMatrix},
+    segmented_imgs::Vector{<:FloeLabelsImage},
     props::Vector{DataFrame},
     passtimes::Vector{DateTime},
     condition_thresholds,
@@ -224,7 +224,7 @@ Returns a dataframe containing the following columns:
 - `corr`: psi-s shape correlation between the two floes in row_i and row_i+1
 """
 function pairfloes(
-    segmented_imgs::Vector{BitMatrix},
+    segmented_imgs::Vector{<:FloeLabelsImage},
     props::Vector{DataFrame},
     passtimes::Vector{DateTime},
     latlonrefimage::AbstractString,

test/runtests.jl

@@ -36,6 +36,7 @@ to_test = alltests # uncomment this line to run all tests or add individual file
 # "test-bwtraceboundary.jl",
 # "test-resample-boundary.jl",
 # "test-regionprops.jl",
+# "test-regionprops-labeled.jl",
 # "test-psi-s.jl",
 # "test-crosscorr.jl"
 # "test-bwperim.jl",

test/test-regionprops-labeled.jl

@@ -0,0 +1,170 @@
+@testset "regionprops-labeled.jl" begin
+    println("------------------------------------------------------------")
+    println("-------------- regionprops (labeled) Tests ----------------")
+
+    img1 = Int[
+        0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   # row  1
+        0 1 1 0 2 2 2 0 3 3 3 3 0 0 0 0   #      2
+        0 1 1 0 2 2 2 0 3 3 3 3 0 0 0 0   #      3
+        0 0 0 0 0 2 2 0 3 3 3 3 0 0 0 0   #      4
+        0 0 0 0 0 0 0 0 3 3 3 0 0 0 0 0   #      5
+        0 4 4 4 4 5 0 0 0 0 0 0 0 0 0 0   #      6
+        0 4 5 5 5 5 0 0 0 0 0 0 0 0 0 0   #      7
+        0 4 5 5 0 0 0 0 0 0 0 0 0 0 0 0   #      8
+        0 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0   #      9
+        0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   #     10
+        0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   #     11
+        0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   #     12
+        0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   #     13
+        0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   #     14
+        0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   #     15
+        0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0   #     16
+        #= col
+        1 2 3 4 5 6 7 8 9 a b c d e f g
+        =#
+
+    ]
+
+    @info "testing props with label"
+    props_with_label = DataFrame([
+        1 1 1 4 4
+        2 1 4 5 8
+        3 1 8 6 13
+        4 5 1 10 7
+        5 5 1 10 7
+    ], [:label, :min_row, :min_col, :max_row, :max_col])
+
+
+    @test cropfloe(img1, props_with_label, 1) == [
+        0 0 0 0
+        0 1 1 0
+        0 1 1 0
+        0 0 0 0
+    ]
+
+    @test cropfloe(img1, props_with_label, 2) == [
+        0 0 0 0 0
+        0 1 1 1 0
+        0 1 1 1 0
+        0 0 1 1 0
+        0 0 0 0 0
+    ]
+
+    @test cropfloe(img1, props_with_label, 3) == [
+        0 0 0 0 0 0
+        0 1 1 1 1 0
+        0 1 1 1 1 0
+        0 1 1 1 1 0
+        0 1 1 1 0 0
+        0 0 0 0 0 0
+    ]
+
+    @test cropfloe(img1, props_with_label, 4) == [
+        0 0 0 0 0 0 0
+        0 1 1 1 1 0 0
+        0 1 0 0 0 0 0
+        0 1 0 0 0 0 0
+        0 1 1 1 1 1 0
+        0 0 0 0 0 0 0
+    ]
+
+    @test cropfloe(img1, props_with_label, 5) == [
+        0 0 0 0 0 0 0
+        0 0 0 0 0 1 0
+        0 0 1 1 1 1 0
+        0 0 1 1 0 0 0
+        0 0 0 0 0 0 0
+        0 0 0 0 0 0 0
+    ]
+
+    @info "testing props without label"
+    props_without_label = DataFrame([
+        1 1 4 4
+        1 4 5 8
+        1 8 6 13
+        5 1 10 7
+        5 1 10 7
+    ], [:min_row, :min_col, :max_row, :max_col])
+
+    @test cropfloe(img1, props_without_label, 1) == [
+        0 0 0 0
+        0 1 1 0
+        0 1 1 0
+        0 0 0 0
+    ]
+
+    @test cropfloe(img1, props_without_label, 2) == [
+        0 0 0 0 0
+        0 1 1 1 0
+        0 1 1 1 0
+        0 0 1 1 0
+        0 0 0 0 0
+    ]
+
+    @test cropfloe(img1, props_without_label, 3) == [
+        0 0 0 0 0 0
+        0 1 1 1 1 0
+        0 1 1 1 1 0
+        0 1 1 1 1 0
+        0 1 1 1 0 0
+        0 0 0 0 0 0
+    ]
+
+    # This can't distinguish between the two interlocking regions, so it merges them
+    @test cropfloe(img1, props_without_label, 4) == [
+        0 0 0 0 0 0 0
+        0 1 1 1 1 1 0
+        0 1 1 1 1 1 0
+        0 1 1 1 0 0 0
+        0 1 1 1 1 1 0
+        0 0 0 0 0 0 0
+    ]
+
+    @test cropfloe(img1, props_without_label, 4) == cropfloe(img1, props_without_label, 5)
+
+
+    @info "testing values with label"
+    # cropfloe
+    @test cropfloe(img1, 1, 1, 4, 4, 1) == [
+        0 0 0 0
+        0 1 1 0
+        0 1 1 0
+        0 0 0 0
+    ]
+
+    @test cropfloe(img1, 1, 4, 5, 8, 2) == [
+        0 0 0 0 0
+        0 1 1 1 0
+        0 1 1 1 0
+        0 0 1 1 0
+        0 0 0 0 0
+    ]
+
+    @test cropfloe(img1, 1, 8, 6, 13, 3) == [
+        0 0 0 0 0 0
+        0 1 1 1 1 0
+        0 1 1 1 1 0
+        0 1 1 1 1 0
+        0 1 1 1 0 0
+        0 0 0 0 0 0
+    ]
+
+    @test cropfloe(img1, 5, 1, 10, 7, 4) == [
+        0 0 0 0 0 0 0
+        0 1 1 1 1 0 0
+        0 1 0 0 0 0 0
+        0 1 0 0 0 0 0
+        0 1 1 1 1 1 0
+        0 0 0 0 0 0 0
+    ]
+
+    @test cropfloe(img1, 5, 1, 10, 7, 5) == [
+        0 0 0 0 0 0 0
+        0 0 0 0 0 1 0
+        0 0 1 1 1 1 0
+        0 0 1 1 0 0 0
+        0 0 0 0 0 0 0
+        0 0 0 0 0 0 0
+    ]
+
+end