OpenCVではじめようディープラーニングによる画像認識 ch3

Mix.install(
  [
    {:evision, "~> 0.1.19"},
    {:exla, "~> 0.4.0"},
    {:kino, "~> 0.7.0"}
  ],
  config: [
    nx: [default_backend: EXLA.Backend]
  ]
)

ch3/3.1/blank_gray_image

setup

alias Evision, as: Ev
path = "/Users/shou/livebook_samples/images/yorkie.png"

width = 200
height = 100
value = 128.0

Ev.Mat.zeros({height, width}, :u8)

Nx.broadcast(value, {height, width})
|> Ev.Mat.from_nx()

ch3/3.1/blank_color_image

width = 200
height = 100
channel = 3
value = [0.0, 0.0, 255.0]

Ev.Mat.zeros({height, width}, :u8)

Nx.tensor(value)
|> Nx.broadcast({height, width, channel})
|> Evision.Mat.from_nx_2d()

ch3/3.1/read_pixel_value

x = 200
y = 100
channel = 0

img = Ev.imread(path)

img[[y, x]]
|> Ev.Mat.to_nx()
|> Nx.to_flat_list()
|> IO.inspect()

img[[y, x, channel]]
|> Ev.Mat.to_nx()
|> Nx.to_flat_list()
|> IO.inspect(charlists: :as_lists)

ch3/3.1/change_pixel_value

img = Ev.imread(path)
x = 200
y = 100
channel = 0

before = img[[y, x]] |> Ev.Mat.to_nx() |> Nx.to_flat_list() |> Enum.join(",")
IO.puts("before bgr_val(#{x}, #{y}) = #{before}")

# Evision Backendはslice系が未実装のためExla Backendに変換
img
|> Ev.Mat.to_nx(EXLA.Backend)
|> Nx.indexed_put(Nx.tensor([[y, x, 0], [y, x, 1], [y, x, 2]]), Nx.tensor([255, 255, 255]))
|> then(&IO.inspect(&1[y][x]))

before = img[[y, x, channel]] |> Ev.Mat.to_nx() |> Nx.to_flat_list() |> Enum.join(",")
IO.puts("before b_val(#{x}, #{y}) = #{before}")

img
|> Ev.Mat.to_nx(EXLA.Backend)
|> Nx.indexed_put(Nx.tensor([[y, x, channel]]), Nx.tensor([0]))
|> then(&IO.inspect(&1[y][x]))

ch3/3.1/image_shape_as_color

img = Ev.imread(path)

Ev.Mat.shape(img) |> IO.inspect()
Ev.Mat.type(img) |> IO.inspect()

ch3/3.1/image_shape_as_gray

img = Ev.imread(path, flags: Ev.cv_IMREAD_GRAYSCALE())

Ev.Mat.shape(img) |> IO.inspect()
Ev.Mat.type(img) |> IO.inspect()

ch3/3.1/image_size

img = Ev.imread(path)

{height, width, channel} = Ev.Mat.shape(img)

ch3/3.1/image_roi

img = Ev.imread(path)

img_roi = Ev.Mat.roi(img, [135..319, 150..290])

ch3/3.2/add1

x = Nx.tensor([250], type: :u8)
y = Nx.tensor([5], type: :u8)
z = Ev.add(x, y) |> Ev.Mat.to_nx()

ch3/3.2/add2

x = Nx.tensor([250], type: :u8)
y = Nx.tensor([10], type: :u8)
z = Ev.add(x, y) |> Ev.Mat.to_nx()

ch3/3.2/subtract1

x = Nx.tensor([10], type: :u8)
y = Nx.tensor([5], type: :u8)
z = Ev.subtract(x, y) |> Ev.Mat.to_nx()

ch3/3.2/subtract2

x = Nx.tensor([10], type: :u8)
y = Nx.tensor([20], type: :u8)
z = Ev.subtract(x, y) |> Ev.Mat.to_nx()

ch3/3.2/bitwise_and

src1 = Ev.imread(path)

src2 =
  Nx.tensor([0, 0, 0], type: :u8)
  |> Nx.broadcast(Ev.Mat.shape(src1))
  |> Ev.Mat.from_nx_2d()
  |> Ev.rectangle({150, 135}, {290, 315}, {255, 255, 255}, thickness: -1)

dst = Ev.Mat.bitwise_and(src1, src2)

ch3/3.2/bitwise_or

src1 = Ev.imread(path)

src2 =
  Nx.tensor([0, 0, 0], type: :u8)
  |> Nx.broadcast(Ev.Mat.shape(src1))
  |> Ev.Mat.from_nx_2d()
  |> Ev.rectangle({150, 135}, {290, 315}, {255, 255, 255}, thickness: -1)

dst = Ev.Mat.bitwise_or(src1, src2)

ch3/3.2/addWeighted

aloe_l = "/Users/shou/livebook_samples/images/aloeL.png"
aloe_r = "/Users/shou/livebook_samples/images/aloeR.png"

src1 = Ev.imread(aloe_l)
src2 = Ev.imread(aloe_r)

alpha = 0.5
beta = 0.5
gamma = 0.0

dat = Ev.addWeighted(src1, alpha, src2, beta, gamma)

ch3/3.2/absdiff

img1 = Nx.tensor([[1, 2, 3, 4, 5]], type: :u8) |> Ev.Mat.from_nx()
img2 = Nx.tensor([[5, 4, 3, 2, 1]], type: :u8) |> Ev.Mat.from_nx()

diff = Ev.absdiff(img1, img2)

ch3/3.2/copy_image

org_img = Ev.imread(path)
{w, h, ch} = Ev.Mat.shape(org_img)
mask = Ev.Mat.full({w, h}, 255, :u8)
cv_copy_img = Ev.copyTo(org_img, mask)

ch3/3.2/flip_image

src = Ev.imread(path)

data = Ev.flip(src, 0)

ch3/3.2/rotate_image

src = Ev.imread(path)

rotated = Ev.rotate(src, 0)

ch3/3.2/clipping

img = Nx.tensor([[0, 1, 2, 3, 4, 5, 6]], type: :u8) |> Ev.Mat.from_nx()
clip1 = Ev.Mat.clip(img, 0, 5)
# 挙動がおかしい

ch3/3.3/min_max

img = Nx.tensor([[1, 2, 3, 4, 5]], type: :u8)
Nx.reduce_max(img) |> IO.inspect()
Nx.reduce_min(img) |> IO.inspect()

ch3/3.3/minMaxLoc

img = Nx.tensor([[1, 2, 3], [4, 5, 6]], type: :u8) |> Ev.Mat.from_nx_2d()

{min_val, max_val, min_loc, max_loc} = Ev.minMaxLoc(img)

ch3/3.3/mean_value

img =
  Nx.tensor([[1, 2, 3, 4, 5]])
  |> Nx.mean()

ch3/3.3/sum_pixcel_value

img = Nx.tensor([[1, 2, 3, 4, 5]]) |> Nx.sum()

ch3/3.3/countNonZero

img = Nx.tensor([[1, 0, 0, 1, 1]], type: :u8) |> Ev.Mat.from_nx()
count = Ev.countNonZero(img)

ch3/3.3/check_same_image

is_same_image = fn img1, img2 ->
  case Ev.absdiff(img1, img2) |> Ev.countNonZero() do
    0 -> true
    _ -> false
  end
end

width = 200
height = 100
value1 = 120
value2 = 255

img1 = Ev.Mat.full({height, width}, value1, :u8)
img2 = Ev.Mat.full({height, width}, value2, :u8)

is_same_image.(img1, img1) |> IO.inspect()
is_same_image.(img1, img2) |> IO.inspect()

ch3/3.4/split_plane

img = Ev.imread(path)

[b_plane, g_plane, r_plane] = Ev.split(img)
b_plane

g_plane

b_plane

ch3/3.4/merge_plane

Ev.merge([b_plane, g_plane, r_plane])

ch3/3.4/hconcat

img1 = Ev.imread(path)
img2 = Ev.flip(img1, 0)

hconcat_img = Ev.hconcat([img1, img2])

ch3/3.4/hconcat_invalid

width1 = 200
height1 = 100
img1 = Ev.Mat.zeros({height1, width1}, :u8)

concat1 = Ev.hconcat([img1, img1])
IO.inspect(Ev.Mat.shape(concat1))

width2 = 200
height2 = 200
img2 = Ev.Mat.zeros({height2, width2}, :u8)

{:error, msg} = Ev.hconcat([img1, img2])
IO.puts(msg)

ch3/3.4/vconcat

img1 = Ev.imread(path)
img2 = Ev.flip(img1, 0)

vconcat_img = Ev.vconcat([img1, img2])

ch3/3.5/getBuildInformation

Ev.getBuildInformation() |> IO.puts()

ch3/3.5/measurement_time_once

img = Ev.imread(path)
timer = Ev.TickMeter.tickMeter()

Ev.TickMeter.start(timer)

gray = Ev.cvtColor(img, Ev.cv_COLOR_BGR2GRAY())

Ev.TickMeter.stop(timer)
measurement_time = Ev.TickMeter.getTimeMilli(timer)

ch3/3.5/measurement_time

img = Ev.imread(path)

timer = Ev.TickMeter.tickMeter()

for _i <- 1..5 do
  Ev.TickMeter.reset(timer)
  Ev.TickMeter.start(timer)
  _gray = Ev.cvtColor(img, Ev.cv_COLOR_BGR2GRAY())
  Ev.TickMeter.stop(timer)
  IO.inspect(Ev.TickMeter.getTimeMilli(timer))
end

ch3/3.5/write_xml

filename = "output.xml"

fs = Ev.FileStorage.fileStorage(filename, 0)
# うまく動かない

ch3/3.5/read_xml

# skip

ch3/3.5/useOptimized

Ev.useOptimized()

ch3/3.5/setUseOptimized

Ev.setUseOptimized(true)
Ev.useOptimized() |> IO.inspect()
Ev.setUseOptimized(false)
Ev.useOptimized() |> IO.inspect()
Ev.setUseOptimized(true)