def test_selem(self):
"""Test results if selem is given."""
selem_cross = np.array(
[[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=np.bool)
result_selem_cross = extrema.local_maxima(
self.image, selem=selem_cross)
assert result_selem_cross.dtype == np.bool
assert_equal(result_selem_cross, self.expected_cross)
for selem in [
((True,) * 3,) * 3,
np.ones((3, 3), dtype=np.float64),
np.ones((3, 3), dtype=np.uint8),
np.ones((3, 3), dtype=np.bool),
]:
# Test different dtypes for selem which expects a boolean array but
# will accept and convert other types if possible
result_selem_square = extrema.local_maxima(self.image, selem=selem)
assert result_selem_square.dtype == np.bool
assert_equal(result_selem_square, self.expected_default)
selem_x = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]], dtype=np.bool)
expected_selem_x = np.array(
[[1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0]],
dtype=np.bool
)
result_selem_x = extrema.local_maxima(self.image, selem=selem_x)
assert result_selem_x.dtype == np.bool
assert_equal(result_selem_x, expected_selem_x)
def test_nd(self):
"""Test one- and three-dimensional case."""
# One-dimension
x_1d = np.array([1, 1, 0, 1, 2, 3, 0, 2, 1, 2, 0])
expected_1d = np.array([1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0],
dtype=np.bool)
result_1d = extrema.local_maxima(x_1d)
assert result_1d.dtype == np.bool
assert_equal(result_1d, expected_1d)
# 3-dimensions (adapted from old unit test)
x_3d = np.zeros((8, 8, 8), dtype=np.uint8)
expected_3d = np.zeros((8, 8, 8), dtype=np.bool)
# first maximum: only one pixel
x_3d[1, 1:3, 1:3] = 100
x_3d[2, 2, 2] = 200
x_3d[3, 1:3, 1:3] = 100
expected_3d[2, 2, 2] = 1
# second maximum: three pixels in z-direction
x_3d[5:8, 1, 1] = 200
expected_3d[5:8, 1, 1] = 1
# third: two maxima in 0 and 3.
x_3d[0, 5:8, 5:8] = 200
x_3d[1, 6, 6] = 100
x_3d[2, 5:7, 5:7] = 200
x_3d[0:3, 5:8, 5:8] += 50
expected_3d[0, 5:8, 5:8] = 1
expected_3d[2, 5:7, 5:7] = 1
# four : one maximum in the corner of the square
x_3d[6:8, 6:8, 6:8] = 200
x_3d[7, 7, 7] = 255
expected_3d[7, 7, 7] = 1
result_3d = extrema.local_maxima(x_3d)
assert result_3d.dtype == np.bool
assert_equal(result_3d, expected_3d)
def test_footprint(self):
"""Test results if footprint is given."""
footprint_cross = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]],
dtype=bool)
result_footprint_cross = extrema.local_maxima(
self.image, footprint=footprint_cross)
assert result_footprint_cross.dtype == bool
assert_equal(result_footprint_cross, self.expected_cross)
for footprint in [
((True, ) * 3, ) * 3,
np.ones((3, 3), dtype=np.float64),
np.ones((3, 3), dtype=np.uint8),
np.ones((3, 3), dtype=bool),
]:
# Test different dtypes for footprint which expects a boolean array
# but will accept and convert other types if possible
result_footprint_square = extrema.local_maxima(self.image,
footprint=footprint)
assert result_footprint_square.dtype == bool
assert_equal(result_footprint_square, self.expected_default)
footprint_x = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]], dtype=bool)
expected_footprint_x = np.array(
[[1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0]],
dtype=bool)
result_footprint_x = extrema.local_maxima(self.image,
footprint=footprint_x)
assert result_footprint_x.dtype == bool
assert_equal(result_footprint_x, expected_footprint_x)
def test_nd(self):
"""Test one- and three-dimensional case."""
# One-dimension
x_1d = np.array([1, 1, 0, 1, 2, 3, 0, 2, 1, 2, 0])
expected_1d = np.array([1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0],
dtype=np.bool)
result_1d = extrema.local_maxima(x_1d)
assert result_1d.dtype == np.bool
assert_equal(result_1d, expected_1d)
# 3-dimensions (adapted from old unit test)
x_3d = np.zeros((8, 8, 8), dtype=np.uint8)
expected_3d = np.zeros((8, 8, 8), dtype=np.bool)
# first maximum: only one pixel
x_3d[1, 1:3, 1:3] = 100
x_3d[2, 2, 2] = 200
x_3d[3, 1:3, 1:3] = 100
expected_3d[2, 2, 2] = 1
# second maximum: three pixels in z-direction
x_3d[5:8, 1, 1] = 200
expected_3d[5:8, 1, 1] = 1
# third: two maxima in 0 and 3.
x_3d[0, 5:8, 5:8] = 200
x_3d[1, 6, 6] = 100
x_3d[2, 5:7, 5:7] = 200
x_3d[0:3, 5:8, 5:8] += 50
expected_3d[0, 5:8, 5:8] = 1
expected_3d[2, 5:7, 5:7] = 1
# four : one maximum in the corner of the square
x_3d[6:8, 6:8, 6:8] = 200
x_3d[7, 7, 7] = 255
expected_3d[7, 7, 7] = 1
result_3d = extrema.local_maxima(x_3d)
assert result_3d.dtype == np.bool
assert_equal(result_3d, expected_3d)
def test_selem(self):
"""Test results if selem is given."""
selem_cross = np.array(
[[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=np.bool)
result_selem_cross = extrema.local_maxima(
self.image, selem=selem_cross)
assert result_selem_cross.dtype == np.bool
assert_equal(result_selem_cross, self.expected_cross)
for selem in [
((True,) * 3,) * 3,
np.ones((3, 3), dtype=np.float64),
np.ones((3, 3), dtype=np.uint8),
np.ones((3, 3), dtype=np.bool),
]:
# Test different dtypes for selem which expects a boolean array but
# will accept and convert other types if possible
result_selem_square = extrema.local_maxima(self.image, selem=selem)
assert result_selem_square.dtype == np.bool
assert_equal(result_selem_square, self.expected_default)
selem_x = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]], dtype=np.bool)
expected_selem_x = np.array(
[[1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0]],
dtype=np.bool
)
result_selem_x = extrema.local_maxima(self.image, selem=selem_x)
assert result_selem_x.dtype == np.bool
assert_equal(result_selem_x, expected_selem_x)
def test_empty(self):
"""Test result with empty image."""
result = extrema.local_maxima(np.array([]), indices=False)
assert result.size == 0
assert result.dtype == np.uint8
result = extrema.local_maxima(np.array([]), indices=True)
assert result.size == 0
assert result.dtype == np.intp
def test_indices(self):
"""Test output if indices of peaks are desired."""
# Connectivity 1
expected_conn1 = np.nonzero(self.expected_cross)
result_conn1 = extrema.local_maxima(self.image, connectivity=1,
indices=True)
assert_equal(result_conn1, expected_conn1)
# Connectivity 2
expected_conn2 = np.nonzero(self.expected_default)
result_conn2 = extrema.local_maxima(self.image, connectivity=2,
indices=True)
assert_equal(result_conn2, expected_conn2)
def test_indices(self):
"""Test output if indices of peaks are desired."""
# Connectivity 1
expected_conn1 = np.nonzero(self.expected_cross)
result_conn1 = extrema.local_maxima(self.image, connectivity=1,
indices=True)
assert_equal(result_conn1, expected_conn1)
# Connectivity 2
expected_conn2 = np.nonzero(self.expected_default)
result_conn2 = extrema.local_maxima(self.image, connectivity=2,
indices=True)
assert_equal(result_conn2, expected_conn2)
def test_connectivity(self):
"""Test results if selem is a scalar."""
# Connectivity 1: generates cross shaped structuring element
result_conn1 = extrema.local_maxima(self.image, connectivity=1)
assert_equal(result_conn1, self.expected_cross)
# Connectivity 2: generates square shaped structuring element
result_conn2 = extrema.local_maxima(self.image, connectivity=2)
assert_equal(result_conn2, self.expected_default)
# Connectivity 3: generates square shaped structuring element
result_conn3 = extrema.local_maxima(self.image, connectivity=3)
assert_equal(result_conn3, self.expected_default)
def test_connectivity(self):
"""Test results if footprint is a scalar."""
# Connectivity 1: generates cross shaped footprint
result_conn1 = extrema.local_maxima(self.image, connectivity=1)
assert result_conn1.dtype == bool
assert_equal(result_conn1, self.expected_cross)
# Connectivity 2: generates square shaped footprint
result_conn2 = extrema.local_maxima(self.image, connectivity=2)
assert result_conn2.dtype == bool
assert_equal(result_conn2, self.expected_default)
# Connectivity 3: generates square shaped footprint
result_conn3 = extrema.local_maxima(self.image, connectivity=3)
assert result_conn3.dtype == bool
assert_equal(result_conn3, self.expected_default)
def test_local_maxima(self):
"local maxima for various data types"
data = np.array([[10, 11, 13, 14, 14, 15, 14, 14, 13, 11],
[11, 13, 15, 16, 16, 16, 16, 16, 15, 13],
[13, 15, 40, 40, 18, 18, 18, 60, 60, 15],
[14, 16, 40, 40, 19, 19, 19, 60, 60, 16],
[14, 16, 18, 19, 19, 19, 19, 19, 18, 16],
[15, 16, 18, 19, 19, 20, 19, 19, 18, 16],
[14, 16, 18, 19, 19, 19, 19, 19, 18, 16],
[14, 16, 80, 80, 19, 19, 19, 100, 100, 16],
[13, 15, 80, 80, 18, 18, 18, 100, 100, 15],
[11, 13, 15, 16, 16, 16, 16, 16, 15, 13]],
dtype=np.uint8)
expected_result = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8)
for dtype in [np.uint8, np.uint64, np.int8, np.int64]:
test_data = data.astype(dtype)
out = extrema.local_maxima(test_data)
error = diff(expected_result, out)
assert error < eps
assert out.dtype == expected_result.dtype
def get_maxima_points(boundary_points, img, sigma=0.3 ):
width = img.shape[0]
height = img.shape[1]
canvas = Image.new('L', (width, height), 0)
ImageDraw.Draw(canvas).polygon(boundary_points, outline=1, fill=1)
mask = np.array(canvas)
transformed_image = ndimage.distance_transform_edt(mask)
normalized_ti =transformed_image/np.max(transformed_image)
laplacian2 = ndimage.gaussian_laplace(normalized_ti, sigma=sigma)
plt.figure(figsize=(10,10))
plt.imshow(laplacian2)
plt.colorbar()
plt.show()
local_maxima = extrema.local_maxima(1-laplacian2)
label_maxima = label(local_maxima)
plt.figure(figsize=(10,10))
plt.imshow(label_maxima)
plt.colorbar()
plt.show()
np.unique(label_maxima)
all_inside_y, all_inside_x = np.where(label_maxima>1)
all_points =[]
for x,y in zip(all_inside_x, all_inside_y):
all_points.append(tuple((x,y)))
return all_points
def test_exceptions(self):
"""Test if input validation triggers correct exceptions."""
# Mismatching number of dimensions
with raises(ValueError, match="number of dimensions"):
extrema.local_maxima(self.image, selem=np.ones((3, 3, 3)))
with raises(ValueError, match="number of dimensions"):
extrema.local_maxima(self.image, selem=np.ones((3,)))
# All dimensions in selem must be of size 3
with raises(ValueError, match="dimension size"):
extrema.local_maxima(self.image, selem=np.ones((2, 3)))
with raises(ValueError, match="dimension size"):
extrema.local_maxima(self.image, selem=np.ones((5, 5)))
with raises(TypeError, match="float16 which is not supported"):
extrema.local_maxima(np.empty(1, dtype=np.float16))
def test_dtypes_old(self):
"""
Test results with default configuration and data copied from old unit
tests for all supported dtypes.
"""
data = np.array(
[[10, 11, 13, 14, 14, 15, 14, 14, 13, 11],
[11, 13, 15, 16, 16, 16, 16, 16, 15, 13],
[13, 15, 40, 40, 18, 18, 18, 60, 60, 15],
[14, 16, 40, 40, 19, 19, 19, 60, 60, 16],
[14, 16, 18, 19, 19, 19, 19, 19, 18, 16],
[15, 16, 18, 19, 19, 20, 19, 19, 18, 16],
[14, 16, 18, 19, 19, 19, 19, 19, 18, 16],
[14, 16, 80, 80, 19, 19, 19, 100, 100, 16],
[13, 15, 80, 80, 18, 18, 18, 100, 100, 15],
[11, 13, 15, 16, 16, 16, 16, 16, 15, 13]],
dtype=np.uint8
)
expected = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8
)
for dtype in self.supported_dtypes:
image = data.astype(dtype)
result = extrema.local_maxima(image)
assert_equal(result, expected)
def test_extrema_float(self):
"""Specific tests for float type."""
# Copied from old unit test for local_maxma
image = np.array(
[[0.10, 0.11, 0.13, 0.14, 0.14, 0.15, 0.14, 0.14, 0.13, 0.11],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13],
[0.13, 0.15, 0.40, 0.40, 0.18, 0.18, 0.18, 0.60, 0.60, 0.15],
[0.14, 0.16, 0.40, 0.40, 0.19, 0.19, 0.19, 0.60, 0.60, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16],
[0.15, 0.182, 0.18, 0.19, 0.204, 0.20, 0.19, 0.19, 0.18, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16],
[0.14, 0.16, 0.80, 0.80, 0.19, 0.19, 0.19, 1.0, 1.0, 0.16],
[0.13, 0.15, 0.80, 0.80, 0.18, 0.18, 0.18, 1.0, 1.0, 0.15],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13]],
dtype=np.float32)
inverted_image = 1.0 - image
expected_result = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.bool)
# Test for local maxima with automatic step calculation
result = extrema.local_maxima(image)
assert result.dtype == np.bool
assert_equal(result, expected_result)
# Test for local minima with automatic step calculation
result = extrema.local_minima(inverted_image)
assert result.dtype == np.bool
assert_equal(result, expected_result)
def test_3d(self):
"""tests the detection of maxima in 3D."""
img = np.zeros((8, 8, 8), dtype=np.uint8)
local_maxima = np.zeros((8, 8, 8), dtype=np.uint8)
# first maximum: only one pixel
img[1, 1:3, 1:3] = 100
img[2, 2, 2] = 200
img[3, 1:3, 1:3] = 100
local_maxima[2, 2, 2] = 1
# second maximum: three pixels in z-direction
img[5:8, 1, 1] = 200
local_maxima[5:8, 1, 1] = 1
# third: two maxima in 0 and 3.
img[0, 5:8, 5:8] = 200
img[1, 6, 6] = 100
img[2, 5:7, 5:7] = 200
img[0:3, 5:8, 5:8] += 50
local_maxima[0, 5:8, 5:8] = 1
local_maxima[2, 5:7, 5:7] = 1
# four : one maximum in the corner of the square
img[6:8, 6:8, 6:8] = 200
img[7, 7, 7] = 255
local_maxima[7, 7, 7] = 1
se = ndi.generate_binary_structure(3, 1)
out = extrema.local_maxima(img, se)
error = diff(local_maxima, out)
assert error < eps
def test_empty(self):
"""Test result with empty image."""
result = extrema.local_maxima(np.array([[]]), indices=False)
assert result.size == 0
assert result.dtype == np.bool
assert result.shape == (1, 0)
result = extrema.local_maxima(np.array([]), indices=True)
assert isinstance(result, tuple)
assert len(result) == 1
assert result[0].size == 0
assert result[0].dtype == np.intp
result = extrema.local_maxima(np.array([[]]), indices=True)
assert isinstance(result, tuple)
assert len(result) == 2
assert result[0].size == 0
assert result[0].dtype == np.intp
assert result[1].size == 0
assert result[1].dtype == np.intp
def test_empty(self):
"""Test result with empty image."""
result = extrema.local_maxima(np.array([[]]), indices=False)
assert result.size == 0
assert result.dtype == np.bool
assert result.shape == (1, 0)
result = extrema.local_maxima(np.array([]), indices=True)
assert isinstance(result, tuple)
assert len(result) == 1
assert result[0].size == 0
assert result[0].dtype == np.intp
result = extrema.local_maxima(np.array([[]]), indices=True)
assert isinstance(result, tuple)
assert len(result) == 2
assert result[0].size == 0
assert result[0].dtype == np.intp
assert result[1].size == 0
assert result[1].dtype == np.intp
def test_allow_borders(self):
"""Test maxima detection at the image border."""
# Use connectivity 1 to allow many maxima, only filtering at border is
# of interest
result_with_boder = extrema.local_maxima(
self.image, connectivity=1, allow_borders=True)
assert_equal(result_with_boder, self.expected_cross)
expected_without_border = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0],
[0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8
)
result_without_border = extrema.local_maxima(
self.image, connectivity=1, allow_borders=False)
assert_equal(result_without_border, expected_without_border)
def test_constant(self):
"""Test behaviour for 'flat' images."""
const_image = np.full((7, 6), 42, dtype=np.uint8)
expected = np.zeros((7, 6), dtype=np.uint8)
for dtype in self.supported_dtypes:
const_image = const_image.astype(dtype)
# test for local maxima
result = extrema.local_maxima(const_image)
assert_equal(result, expected)
# test for local minima
result = extrema.local_minima(const_image)
assert_equal(result, expected)
def test_small_array(self):
"""Test output for arrays with dimension smaller 3.
If any dimension of an array is smaller than 3 and `allow_borders` is
false a structuring element, which has at least 3 elements in each
dimension, can't be applied. This is an implementation detail so
`local_maxima` should still return valid output (see gh-3261).
If `allow_borders` is true the array is padded internally and there is
no problem.
"""
warning_msg = "maxima can't exist .* any dimension smaller 3 .*"
x = np.array([0, 1])
extrema.local_maxima(x, allow_borders=True) # no warning
with warns(UserWarning, match=warning_msg):
result = extrema.local_maxima(x, allow_borders=False)
assert_equal(result, [0, 0])
assert result.dtype == np.bool
x = np.array([[1, 2], [2, 2]])
extrema.local_maxima(x, allow_borders=True, indices=True) # no warning
with warns(UserWarning, match=warning_msg):
result = extrema.local_maxima(x, allow_borders=False, indices=True)
assert_equal(result, np.zeros((2, 0), dtype=np.intp))
assert result[0].dtype == np.intp
assert result[1].dtype == np.intp
def test_small_array(self):
"""Test output for arrays with dimension smaller 3.
If any dimension of an array is smaller than 3 and `allow_borders` is
false a structuring element, which has at least 3 elements in each
dimension, can't be applied. This is an implementation detail so
`local_maxima` should still return valid output (see gh-3261).
If `allow_borders` is true the array is padded internally and there is
no problem.
"""
warning_msg = "maxima can't exist .* any dimension smaller 3 .*"
x = np.array([0, 1])
extrema.local_maxima(x, allow_borders=True) # no warning
with warns(UserWarning, match=warning_msg):
result = extrema.local_maxima(x, allow_borders=False)
assert_equal(result, [0, 0])
assert result.dtype == np.bool
x = np.array([[1, 2], [2, 2]])
extrema.local_maxima(x, allow_borders=True, indices=True) # no warning
with warns(UserWarning, match=warning_msg):
result = extrema.local_maxima(x, allow_borders=False, indices=True)
assert_equal(result, np.zeros((2, 0), dtype=np.intp))
assert result[0].dtype == np.intp
assert result[1].dtype == np.intp
def test_selem(self):
"""Test results if selem is an array."""
selem_cross = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
result_selem_cross = extrema.local_maxima(self.image,
selem=selem_cross)
assert_equal(result_selem_cross, self.expected_cross)
selem_square = np.ones((3, 3), dtype=np.uint8)
result_selem_square = extrema.local_maxima(self.image,
selem=selem_square)
assert_equal(result_selem_square, self.expected_default)
selem_x = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
expected_selem_x = np.array(
[[1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0]],
dtype=np.uint8)
result_selem_x = extrema.local_maxima(self.image, selem=selem_x)
assert_equal(result_selem_x, expected_selem_x)
def test_extrema_float(self):
"specific tests for float type"
data = np.array(
[[0.10, 0.11, 0.13, 0.14, 0.14, 0.15, 0.14, 0.14, 0.13, 0.11],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13],
[0.13, 0.15, 0.40, 0.40, 0.18, 0.18, 0.18, 0.60, 0.60, 0.15],
[0.14, 0.16, 0.40, 0.40, 0.19, 0.19, 0.19, 0.60, 0.60, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16],
[0.15, 0.182, 0.18, 0.19, 0.204, 0.20, 0.19, 0.19, 0.18, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16],
[0.14, 0.16, 0.80, 0.80, 0.19, 0.19, 0.19, 1.0, 1.0, 0.16],
[0.13, 0.15, 0.80, 0.80, 0.18, 0.18, 0.18, 1.0, 1.0, 0.15],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13]],
dtype=np.float32)
inverted_data = 1.0 - data
expected_result = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8)
# test for local maxima with automatic step calculation
out = extrema.local_maxima(data)
error = diff(expected_result, out)
assert error < eps
# test for local minima with automatic step calculation
out = extrema.local_minima(inverted_data)
error = diff(expected_result, out)
assert error < eps
out = extrema.h_maxima(data, 0.003)
expected_result = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8)
error = diff(expected_result, out)
assert error < eps
out = extrema.h_minima(inverted_data, 0.003)
error = diff(expected_result, out)
assert error < eps
def test_selem(self):
"""Test results if selem is an array."""
selem_cross = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
result_selem_cross = extrema.local_maxima(
self.image, selem=selem_cross)
assert_equal(result_selem_cross, self.expected_cross)
selem_square = np.ones((3, 3), dtype=np.uint8)
result_selem_square = extrema.local_maxima(
self.image, selem=selem_square)
assert_equal(result_selem_square, self.expected_default)
selem_x = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
expected_selem_x = np.array(
[[1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0]],
dtype=np.uint8
)
result_selem_x = extrema.local_maxima(self.image, selem=selem_x)
assert_equal(result_selem_x, expected_selem_x)
def test_local_extrema_uniform(self):
"local extrema tests for uniform arrays with various data types"
data = np.full((7, 6), 42, dtype=np.uint8)
expected_result = np.zeros((7, 6), dtype=np.uint8)
for dtype in [np.uint8, np.uint64, np.int8, np.int64]:
data = data.astype(dtype)
# test for local maxima
out = extrema.local_maxima(data)
error = diff(expected_result, out)
assert error < eps
assert out.dtype == expected_result.dtype
# test for local minima
out = extrema.local_minima(data)
error = diff(expected_result, out)
assert error < eps
assert out.dtype == expected_result.dtype
def test_extrema_float(self):
"""Specific tests for float type."""
# Copied from old unit test for local_maxma
image = np.array(
[[0.10, 0.11, 0.13, 0.14, 0.14, 0.15, 0.14, 0.14, 0.13, 0.11],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13],
[0.13, 0.15, 0.40, 0.40, 0.18, 0.18, 0.18, 0.60, 0.60, 0.15],
[0.14, 0.16, 0.40, 0.40, 0.19, 0.19, 0.19, 0.60, 0.60, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16],
[0.15, 0.182, 0.18, 0.19, 0.204, 0.20, 0.19, 0.19, 0.18, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16],
[0.14, 0.16, 0.80, 0.80, 0.19, 0.19, 0.19, 1.0, 1.0, 0.16],
[0.13, 0.15, 0.80, 0.80, 0.18, 0.18, 0.18, 1.0, 1.0, 0.15],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13]],
dtype=np.float32
)
inverted_image = 1.0 - image
expected_result = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.bool
)
# Test for local maxima with automatic step calculation
result = extrema.local_maxima(image)
assert result.dtype == np.bool
assert_equal(result, expected_result)
# Test for local minima with automatic step calculation
result = extrema.local_minima(inverted_image)
assert result.dtype == np.bool
assert_equal(result, expected_result)
def distance_transform_seeds(image_data):
"""Create seed locations maximally distant from thresholded raw image.
Parameters
----------
image_data : ndarray
Returns
-------
list of ndarray
"""
seeds = []
if image_data.dtype == np.bool:
#assuming membrane zero and cells one labeled
thresh = image_data
else:
# otsu thresholding
thresh = image_data < filters.threshold_otsu(image_data)
transform = ndimage.distance_transform_cdt(thresh)
skmax = extrema.local_maxima(transform)
seeds = np.transpose(np.nonzero(skmax))
return seeds
def localMaximaBlobDetection(image_path: str):
image = io.imread(image_path)
local_maxima = extrema.local_maxima(image)
def test_extrema_float(self):
"specific tests for float type"
data = np.array([[0.10, 0.11, 0.13, 0.14, 0.14, 0.15, 0.14,
0.14, 0.13, 0.11],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16,
0.16, 0.15, 0.13],
[0.13, 0.15, 0.40, 0.40, 0.18, 0.18, 0.18,
0.60, 0.60, 0.15],
[0.14, 0.16, 0.40, 0.40, 0.19, 0.19, 0.19,
0.60, 0.60, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19,
0.19, 0.18, 0.16],
[0.15, 0.182, 0.18, 0.19, 0.204, 0.20, 0.19,
0.19, 0.18, 0.16],
[0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19,
0.19, 0.18, 0.16],
[0.14, 0.16, 0.80, 0.80, 0.19, 0.19, 0.19,
1.0, 1.0, 0.16],
[0.13, 0.15, 0.80, 0.80, 0.18, 0.18, 0.18,
1.0, 1.0, 0.15],
[0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16,
0.16, 0.15, 0.13]],
dtype=np.float32)
inverted_data = 1.0 - data
expected_result = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8)
# test for local maxima with automatic step calculation
out = extrema.local_maxima(data)
error = diff(expected_result, out)
assert error < eps
# test for local minima with automatic step calculation
out = extrema.local_minima(inverted_data)
error = diff(expected_result, out)
assert error < eps
out = extrema.h_maxima(data, 0.003)
expected_result = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8)
error = diff(expected_result, out)
assert error < eps
out = extrema.h_minima(inverted_data, 0.003)
error = diff(expected_result, out)
assert error < eps
def buttonSave(arg):
copy = arg[1]
imageTemp = arg[2]
filterTry = filterVar.get()
if (filterTry == 1):
copy = cv2.GaussianBlur(copy, (5, 5), 0)
elif (filterTry == 2):
copy = cv2.Canny(copy, 100, 150)
elif (filterTry == 3):
copy = filters.roberts(imageTemp)
elif (filterTry == 4):
copy = filters.sato(imageTemp)
elif (filterTry == 5):
copy = filters.scharr(imageTemp)
elif (filterTry == 6):
copy = filters.sobel(imageTemp)
elif (filterTry == 7):
copy = filters.unsharp_mask(copy, radius=30, amount=3)
elif (filterTry == 8):
#copy = filters.median(imageTemp, disk(5))
b, g, r = cv2.split(copy)
b = filters.median(b, disk(5))
g = filters.median(g, disk(5))
r = filters.median(r, disk(5))
copy = cv2.merge((b, g, r))
elif (filterTry == 9):
copy = filters.prewitt(imageTemp)
elif (filterTry == 10):
copy = filters.rank.modal(imageTemp, disk(5))
flag = 0
if (np.ndim(copy) == 2):
flag = 0
else:
flag = 1
if (hEsitleme.get() or hGrafik.get()):
if (flag):
copy = cv2.cvtColor(copy, cv2.COLOR_BGR2GRAY)
if (hGrafik.get()):
plt.hist(copy.ravel(), 256, [0, 256])
plt.show()
if (hEsitleme.get()):
copy = cv2.equalizeHist(copy)
if (uzaysalVars[0].get()):
reScaleRatio = float(uzaysalVarsInputs[0].get())
if (np.ndim(copy) == 3):
b, g, r = cv2.split(copy)
b = transform.rescale(b, reScaleRatio)
g = transform.rescale(g, reScaleRatio)
r = transform.rescale(r, reScaleRatio)
copy = cv2.merge((b, g, r))
else:
copy = transform.rescale(copy, reScaleRatio)
if (uzaysalVars[1].get()):
resizeY = float(uzaysalVarsInputs[1].get())
resizeX = float(uzaysalVarsInputs[2].get())
if (np.ndim(copy) == 3):
b, g, r = cv2.split(copy)
b = transform.resize(
b, (b.shape[0] // resizeX, b.shape[1] // resizeY),
anti_aliasing=True)
g = transform.resize(
g, (g.shape[0] // resizeX, g.shape[1] // resizeY),
anti_aliasing=True)
r = transform.resize(
r, (r.shape[0] // resizeX, r.shape[1] // resizeY),
anti_aliasing=True)
copy = cv2.merge((b, g, r))
else:
copy = transform.resize(
copy, (copy.shape[0] // resizeX, copy.shape[1] // resizeY),
anti_aliasing=True)
if (uzaysalVars[2].get()):
copy = transform.swirl(copy, rotation=0, strength=10, radius=120)
if (uzaysalVars[3].get()):
copy = transform.rotate(copy,
int(uzaysalVarsInputs[3].get()),
resize=True)
if (uzaysalVars[4].get()):
copy = copy[:, ::-1]
if (yogunlukVars[0].get() or yogunlukVars[1].get()):
if (yogunlukVars[0].get()):
startINX = int(yogunlukVars[2].get())
finishINX = int(yogunlukVars[3].get())
copy = exposure.rescale_intensity(copy,
in_range=(startINX,
finishINX))
if (yogunlukVars[1].get()):
startOUTX = int(yogunlukVars[4].get())
finishOUTX = int(yogunlukVars[5].get())
copy = exposure.rescale_intensity(copy,
out_range=(startOUTX,
finishOUTX))
morfoTry = morfVar.get()
morfoGirisN = 0
if (np.ndim(copy) == 3):
morfoGirisN = 1
if (morfoTry == 1):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.area_closing(b, 128, 9)
g = morphology.area_closing(g, 128, 9)
r = morphology.area_closing(r, 128, 9)
copy = cv2.merge((b, g, r))
else:
copy = morphology.area_closing(copy)
elif (morfoTry == 2):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.area_opening(b, 128, 9)
g = morphology.area_opening(g, 128, 9)
r = morphology.area_opening(r, 128, 9)
copy = cv2.merge((b, g, r))
else:
copy = morphology.area_opening(copy)
elif (morfoTry == 3):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.erosion(b, disk(6))
g = morphology.erosion(g, disk(6))
r = morphology.erosion(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.erosion(copy, disk(6))
elif (morfoTry == 4):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.dilation(b, disk(6))
g = morphology.dilation(g, disk(6))
r = morphology.dilation(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.dilation(copy, disk(6))
elif (morfoTry == 5):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.opening(b, disk(6))
g = morphology.opening(g, disk(6))
r = morphology.opening(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.opening(copy, disk(6))
elif (morfoTry == 6):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.closing(b, disk(6))
g = morphology.opening(g, disk(6))
r = morphology.opening(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.opening(copy, disk(6))
elif (morfoTry == 7):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.white_tophat(b, disk(6))
g = morphology.white_tophat(g, disk(6))
r = morphology.white_tophat(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.white_tophat(copy, disk(6))
elif (morfoTry == 8):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.black_tophat(b, disk(6))
g = morphology.black_tophat(g, disk(6))
r = morphology.black_tophat(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.black_tophat(copy, disk(6))
elif (morfoTry == 10):
if (morfoGirisN):
copy = cv2.cvtColor(copy, cv2.COLOR_BGR2GRAY)
copy = exposure.rescale_intensity(copy)
local_maxima = extrema.local_maxima(copy)
label_maxima = measure.label(local_maxima)
copy = color.label2rgb(label_maxima,
copy,
alpha=0.7,
bg_label=0,
bg_color=None,
colors=[(1, 0, 0)])
elif (morfoTry == 9):
if (morfoGirisN):
copy = cv2.cvtColor(copy, cv2.COLOR_BGR2GRAY)
copy = exposure.rescale_intensity(copy)
h = 0.05
h_maxima = extrema.h_maxima(copy, h)
label_h_maxima = measure.label(h_maxima)
copy = color.label2rgb(label_h_maxima,
copy,
alpha=0.7,
bg_label=0,
bg_color=None,
colors=[(1, 0, 0)])
arg[1] = copy
arg[2] = imageTemp
cv2.imshow("org", copy)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
img = color.rgb2gray(
io.imread('E:/OwnWork/Leaf/TestImage/Deliveryimage/1.jpg'))
# the rescaling is done only for visualization purpose.
# the algorithms would work identically in an unscaled version of the
# image. However, the parameter h needs to be adapted to the scale.
img = exposure.rescale_intensity(img)
##############################################################
# MAXIMA DETECTION
# Maxima in the galaxy image are detected by mathematical morphology.
# There is no a priori constraint on the density.
# We find all local maxima
local_maxima = extrema.local_maxima(img)
label_maxima = label(local_maxima)
overlay = color.label2rgb(label_maxima,
img,
alpha=0.7,
bg_label=0,
bg_color=None,
colors=[(1, 0, 0)])
# We observed in the previous image, that there are many local maxima
# that are caused by the noise in the image.
# For this, we find all local maxima with a height of h.
# This height is the gray level value by which we need to descent
# in order to reach a higher maximum and it can be seen as a local
# contrast measurement.
# The value of h scales with the dynamic range of the image, i.e.
def test_dtypes(self):
"""Test results with default configuration for all supported dtypes."""
for dtype in self.supported_dtypes:
result = extrema.local_maxima(self.image.astype(dtype))
assert result.dtype == np.bool
assert_equal(result, self.expected_default)
def test_dtypes(self):
"""Test results with default configuration for all supported dtypes."""
for dtype in self.supported_dtypes:
result = extrema.local_maxima(self.image.astype(dtype))
assert result.dtype == np.bool
assert_equal(result, self.expected_default)
from PIL import Image
from skimage.feature import peak_local_max
# %%
img = Image.open('C:\\Users\\Dell\\Desktop\\od_zacatku\\train_img\\train4.tiff')
imarray = np.array(img)
h = 0.3
x_0 = 70
y_0 = 354
width = 256
height = 256
imarray = exposure.rescale_intensity(imarray)
local_maxima = extrema.local_maxima(imarray)
label_maxima = label(local_maxima)
print(label_maxima.shape)
print(imarray.shape)
print(label_maxima.dtype)
print(imarray.dtype)
overlay = color.label2rgb(label_maxima, imarray, alpha=0.3, bg_label=0,
bg_color=None, colors=[(1, 0, 0)])
h_maxima = extrema.h_maxima(imarray, h)
label_h_maxima = label(h_maxima)
overlay_h = color.label2rgb(label_h_maxima, imarray, alpha=0.3, bg_label=0,
bg_color=None, colors=[(1, 0, 0)])
img = color.rgb2gray(color_image)[y_0:(y_0 + height), x_0:(x_0 + width)]
# the rescaling is done only for visualization purpose.
# the algorithms would work identically in an unscaled version of the
# image. However, the parameter h needs to be adapted to the scale.
img = exposure.rescale_intensity(img)
##############################################################
# MAXIMA DETECTION
# Maxima in the galaxy image are detected by mathematical morphology.
# There is no a priori constraint on the density.
# We find all local maxima
local_maxima = extrema.local_maxima(img)
label_maxima = label(local_maxima)
overlay = color.label2rgb(label_maxima, img, alpha=0.7, bg_label=0,
bg_color=None, colors=[(1, 0, 0)])
# We observed in the previous image, that there are many local maxima
# that are caused by the noise in the image.
# For this, we find all local maxima with a height of h.
# This height is the gray level value by which we need to descent
# in order to reach a higher maximum and it can be seen as a local
# contrast measurement.
# The value of h scales with the dynamic range of the image, i.e.
# if we multiply the image with a constant, we need to multiply
# the value of h with the same constant in order to achieve the same result.
h = 0.05
h_maxima = extrema.h_maxima(img, h)
def MPE(img, folder, original_img, YN_binary, nbOfRowPerPlot,
nbOfColumnPerPlot, globalOrientation, noise, field_image, aff,
y_offset, x_offset):
''' Identifies and crop the columns and the rows of the field.
12 inputs:
img: array of lists
Binary image of the original image read in openCV
folder: path
Absolute path to the folder in which all is saved
original_img: array of lists
Original image read in openCV
YN_binary: bolean
Equals True if the original image is a binary
nbOfRowPerPlot: int
Number of row per microplot
nbOfColumnPerPlot: int
Number of column per microplot
globalOrientation: str ('V' or 'H')
Global orientation of the column as inputted by the user
noise: int
Noise removal value
field_image: Path object
absolute path to the original image
aff: Affine object OR int (0)
Affine transformation matrix associated with the original image
georeferencement, project the calculated points into its CRS
Equals zero if the original image is not georeferenced
y_offset: int
The vertical distance that has been cropped and should be considered
for the coordinates calculation
x_offset: int
The horizontal distance that has been cropped and should be considered
for the coordinates calculation
Outputs: none
Returns number if there is an error which will trigger a pop up displaying
a message.
Returns 'OK' if the end has been reached.
'''
#######################################################################
####################### ROTATE THE BINARY IMAGE #######################
#######################################################################
# get all coordinates of the white pixels i.e. plants
coords_angle = np.column_stack(np.where(img > 0))
# use these coordinates to compute a rotated bounding box that contains all
# coordinates
angle = cv2.minAreaRect(coords_angle)[-1]
# the 'cv2.minAreaRect' function returns values in the
# range [-90, 0[ ; we want it positive
if angle < -45:
angle = -(90 + angle)
# otherwise, just take the inverse of the angle to make
# it positive
else:
angle = -angle
# if the rows are orginally horizontal, we want them to become vertical
if globalOrientation == 'H':
angle += 90
print('ANGLE: ' + str(angle))
img_binary = img.copy()
img_rotated, add_y, add_x = rotate_bound(img.astype(np.uint8),
angle,
change_bigger=True)
cv2.imwrite(str(folder / 'Binary_straight.jpg'), img_rotated)
# make a skeleton of the binary image so that clusters will be in the
# middle of the rows, then erode and dilate to erase weeds and connect
# broken lines
# parameters (horizontal array)
param_binaryErosion = np.ones((1, 20))
param_skeletonErosion = np.ones((1, 5))
param_skeletonDil = np.ones((1, 100))
# application
binary_erode = morphology.binary_erosion(img_rotated,
selem=param_binaryErosion)
skeleton = morphology.skeletonize(
(binary_erode * 1).astype(np.uint8)) * 255
cv2.imwrite(str(folder / 'Skeleton_original.jpg'), skeleton)
skeleton = morphology.binary_erosion(skeleton, selem=param_skeletonErosion)
skeleton = morphology.binary_dilation(skeleton,
selem=param_skeletonDil) * 255
cv2.imwrite(str(folder / 'Skeleton_manipulated.jpg'), skeleton)
#######################################################################
########################### GET THE COLUMNS ###########################
#######################################################################
# get local maxima on the whole binary picture
local_maxima = extrema.local_maxima(skeleton)
# sum the number of local maxima into a one-line array (i.e. number of
# maxima in each column)
sum_maxima = np.sum(local_maxima, axis=0).astype(float)
# make a copy
sum_maxima_nan = sum_maxima.copy()
# replace 0 with NaN
sum_maxima_nan[sum_maxima == 0] = np.nan
# get the average number of maxmima for a column
mean_line = np.nanmean(sum_maxima_nan)
# erase all the values smaller than 1/3 of the average
sum_maxima[sum_maxima < mean_line / 3] = 0
# draw the columns and save the corner points of each column area
img, cut_points, col_w, img_core_col = draw_separation_lines(
sum_maxima, rows_img=img_rotated, col=True)
# if no points have been detected, it means the code is not working as it is
if cut_points == []:
return ('1')
# save the image with the columns delimited
cv2.imwrite(str(folder / 'Binary_columns_straight.jpg'), img)
cv2.imwrite(str(folder / 'Binary_core_columns.jpg'), img_core_col)
## rotate the points of the separation lines and get the lines equations
center = (img_binary.shape[0] / 2, img_binary.shape[1] / 2)
cut_points = np.array(cut_points)
# subtraction of the black pixels which had been added for the rotation
cut_points[:, :, 0] = cut_points[:, :, 0] - add_x
cut_points[:, :, 1] = cut_points[:, :, 1] - add_y
# rotate the points back into the original angle
cut_points = rotate(center, cut_points, math.radians(angle))
# get the line equations from points
columns_a, columns_b = get_equations(cut_points)
# rotate back the core column image
img_core_col, _, _ = rotate_bound(img_core_col, -1 * angle)
y = int((img_core_col.shape[0] - img_binary.shape[0]) / 2)
x = int((img_core_col.shape[1] - img_binary.shape[1]) / 2)
img_core_col = img_core_col[y:-y, x:-x]
img_core_col = cv2.resize(img_core_col, img_binary.shape[::-1])
cv2.imwrite(str(folder / 'Binary_core.jpg'), img_core_col)
## cut the columns
# create all the needed directories / check if existent / delete if necessary
if YN_binary == False:
sub_folder_columnOriginal = folder / str('Plot_columns_original')
sub_folder_columnBinary = folder / str('Plot_columns_binary')
sub_folder_columnCoreBinary = folder / str('Plot_columns_core')
if not (sub_folder_columnBinary.is_dir()):
if YN_binary == False:
sub_folder_columnOriginal.mkdir()
sub_folder_columnBinary.mkdir()
sub_folder_columnCoreBinary.mkdir()
# initialization
maxY, maxX = img_binary.shape
c = str(0).zfill(2) # only exists for saving names
# for every detected column, with a step defined by the inputted number
# nbOfColumnPerPlot in the GUI
for k in range(0, len(columns_a), nbOfColumnPerPlot):
# try first as it is (i.e. with that step value)
try:
n = nbOfColumnPerPlot - 1 #bc we want the end line
pt1 = ((0 - columns_b[k][0]) / columns_a[k][0], 0)
pt2 = ((0 - columns_b[k + n][1]) / columns_a[k + n][1], 0
) #could have done [k+n+1][0] -> equivalent
pt3 = ((maxY - columns_b[k + n][1]) / columns_a[k + n][1], maxY)
pt4 = ((maxY - columns_b[k][0]) / columns_a[k][0], maxY)
# all 4 points of one column, counter clock wise
roi_corners = np.array([[pt1, pt2, pt3, pt4]], dtype=np.int32)
# make a mask and crop out the current column
mask = np.zeros(img_binary.shape, dtype=np.uint8)
cv2.fillPoly(mask, roi_corners, (255, ))
if YN_binary == False:
column_original = cv2.bitwise_and(original_img,
original_img,
mask=mask)
cv2.imwrite(
str(sub_folder_columnOriginal /
str('Plot_column_' + c + '_cropped.jpg')),
column_original)
# apply the mask and save the images
column_binary = cv2.bitwise_and(img_binary, img_binary, mask=mask)
cv2.imwrite(
str(sub_folder_columnBinary /
str('Plot_column_' + c + '_cropped.jpg')), column_binary)
column_core = cv2.bitwise_and(img_core_col,
img_core_col,
mask=mask)
cv2.imwrite(
str(sub_folder_columnCoreBinary /
str('Plot_column_' + c + '_cropped.jpg')), column_core)
c = str(int(c) + 1).zfill(2)
# if the number inputed is not proportional to the detected number of columns
# then do one by one for what is left
except IndexError:
# same as before but one detected column at a time
for i in range(k, len(columns_a)):
pt1 = ((0 - columns_b[i][0]) / columns_a[i][0], 0)
pt2 = ((0 - columns_b[i][1]) / columns_a[i][1], 0)
pt3 = ((maxY - columns_b[i][1]) / columns_a[i][1], maxY)
pt4 = ((maxY - columns_b[i][0]) / columns_a[i][0], maxY)
# 4 column points, counter clockwise
roi_corners = np.array([[pt1, pt2, pt3, pt4]], dtype=np.int32)
# mask making
mask = np.zeros(img_binary.shape, dtype=np.uint8)
cv2.fillPoly(mask, roi_corners, (255, ))
if YN_binary == False:
column_original = cv2.bitwise_and(original_img,
original_img,
mask=mask)
cv2.imwrite(
str(sub_folder_columnOriginal /
str('Plot_column_' + str(c) + '_cropped.jpg')),
column_original)
# applying mask and saving images
column_binary = cv2.bitwise_and(img_binary, mask)
cv2.imwrite(
str(sub_folder_columnBinary /
str('Plot_column_' + str(c) + '_cropped.jpg')),
column_binary)
column_core = cv2.bitwise_and(img_core_col,
img_core_col,
mask=mask)
cv2.imwrite(
str(sub_folder_columnCoreBinary /
str('Plot_column_' + c + '_cropped.jpg')), column_core)
c = str(int(c) + 1).zfill(2)
#######################################################################
############################# GET THE ROWS ############################
#######################################################################
# new angle to get the ROWS in a vertical state (i.e. columns are
# oriented horizontally)
angle_horiz = angle + 90
print('Horizontal angle : ' + str(angle_horiz))
# get all the files from the previous part, i.e. *binary* columns
files = list(sub_folder_columnCoreBinary.glob('*.jpg'))
# make all the necessary folders etc
sub_folder_horizontalColumn = folder / 'Horizontal_columns'
if YN_binary == False:
sub_folder_rowOriginal = folder / str('Plot_rows_original')
sub_folder_rowOriginalWhole = folder / str('Plot_rows_original_whole')
else:
sub_folder_rowOriginalWhole = folder / str('Plot_rows_binary_whole')
sub_folder_rowBinary = folder / str('Plot_rows_binary')
sub_folder_SHP = folder / str('SHP_files')
if not (sub_folder_horizontalColumn.is_dir()):
sub_folder_horizontalColumn.mkdir()
if YN_binary == False:
sub_folder_rowOriginal.mkdir()
sub_folder_rowOriginalWhole.mkdir()
sub_folder_rowBinary.mkdir()
sub_folder_SHP.mkdir()
# initialization
maxY, maxX = img.shape
nbOfRowPerColumn = 0
intersection, intersection_geo = [], []
nbOfColumn = len(columns_a)
# for every file in the column file
for nb in range(0, len(files)):
# get the column number
nb_column = str(nb).zfill(2)
## identify the rows
# read the column file
file_img = cv2.imread(str(files[nb]), 0)
# rotate it until the column is horizontally oriented
column_binary_rotate, add_y, add_x = rotate_bound(file_img,
angle_horiz,
change_bigger=True)
# make the sum of all white pixels on one line
sum_rows = np.sum(column_binary_rotate, axis=0).astype(float)
# make a copy and replace 0 by nan to get the mean value of the sum without 0
sum_rows_nan = sum_rows.copy()
sum_rows_nan[sum_rows == 0] = np.nan
mean_line = np.nanmean(sum_rows_nan)
# erase the values smaller than half of the mean
sum_rows[sum_rows < mean_line / 2] = 0
# identify the rows with the changing pattern
_, cut_points, w = draw_separation_lines(np.array(sum_rows),
rows_img=column_binary_rotate)
# if not separation lines has been detected, the code is not working
# as it is
if cut_points == []:
# save the output image with separation lines for rows
return ('2')
cv2.imwrite(
str(sub_folder_horizontalColumn /
str('Rows_horizontal_delimited_column_' + str(nb_column) +
'.jpg')), column_binary_rotate)
## get the points in the original angle
# original center
center = (img_binary.shape[0] / 2, img_binary.shape[1] / 2)
cut_points = np.array(cut_points)
# points without the added black border (in rotate_bound)
cut_points[:, :, 0] = cut_points[:, :, 0] - add_x
cut_points[:, :, 1] = cut_points[:, :, 1] - add_y
# rotate the points
cut_points = rotate(center, cut_points, math.radians(angle_horiz))
# get equations based on the points
rows_a, rows_b = get_equations(cut_points)
# initialization of various counts
c = str(0).zfill(2) # for file names
nbStartColumn = nb # start detected column number
nbEndColumn = int(
nbStartColumn) + nbOfColumnPerPlot - 1 # end detected column
print('Column nb: ' + str(nb_column))
### cut the rows
# read the original column image
current_column_binary = cv2.imread(
str(sub_folder_columnBinary / files[nb].name), 0)
if YN_binary == False:
current_column_original = cv2.imread(
str(sub_folder_columnOriginal / files[nb].name))
else:
current_column_original = cv2.imread(
str(sub_folder_columnBinary / files[nb].name), 0)
# get the number of rows in this column
nbOfRowPerColumn += len(rows_a)
# for every row in that column, with a step defined by the inputted
# number nbOfRowPerPlot in the GUI
for k in range(0, len(rows_a), nbOfRowPerPlot):
# try with this step first
try:
### get the cut points of the row
n = nbOfRowPerPlot - 1 # bc we want the end line of the end row
pt1 = ((0 - rows_b[k][0]) / rows_a[k][0], 0)
pt2 = ((0 - rows_b[k + n][1]) / rows_a[k + n][1], 0
) #[k+n+1][0] would NOT be equivalent
pt3 = ((maxY - rows_b[k + n][1]) / rows_a[k + n][1], maxY)
pt4 = ((maxY - rows_b[k][0]) / rows_a[k][0], maxY)
# get all the points counter-clockwise
roi_corners = np.array([[pt1, pt2, pt3, pt4]], dtype=np.int32)
# fill a mask
mask = np.zeros(file_img.shape, dtype=np.uint8)
cv2.fillPoly(mask, roi_corners, (255, ))
# apply the mask to the column images (binary)
row_binary = cv2.bitwise_and(current_column_binary, mask)
cv2.imwrite(
str(sub_folder_rowBinary /
str('Plot_column_' + str(nb_column) + '_row_' +
str(c) + '_cropped.jpg')), row_binary)
# use a bounding box to only get the wanted part of the original
# image i.e. the row and not all the black pixels around
row_original = cv2.bitwise_and(current_column_original,
current_column_original,
mask=mask)
if YN_binary == False:
# get the coords for which pixel value =/= 0
coords = np.argwhere(row_original)
# get the first pixels
x0, y0, z = coords.min(axis=0)
# get the last pixels
x1, y1, z = coords.max(axis=0) + 1
# crop
row_original_cropped = row_original[x0:x1, y0:y1]
# save
cv2.imwrite(
str(sub_folder_rowOriginal /
str('Plot_column_' + str(nb_column) + '_row_' +
str(c) + '_cropped.jpg')),
row_original_cropped)
cv2.imwrite(
str(sub_folder_rowOriginalWhole /
str('Plot_column_' + str(nb_column) + '_row_' +
str(c) + '_whole_pic.jpg')), row_original)
# get the intersection of the lines between the considered
# column and row (i.e. the 4 cropping points)
inter, inter_geo = line_intersection(
columns_a[nbStartColumn], columns_a[nbEndColumn],
columns_b[nbStartColumn], columns_b[nbEndColumn],
rows_a[k], rows_a[k + n], rows_b[k], rows_b[k + n], aff,
y_offset, x_offset)
# if the georeferenced points exist
if type(inter_geo) != type(None):
# make the shp files out of the 4 corner points
make_shp(sub_folder_SHP, nb_column, c, inter_geo)
# prepare the array to be saved
inter_geo = [
item for sublist in inter_geo for item in sublist
]
inter_geo.insert(0, c)
inter_geo.insert(0, nb_column)
intersection_geo.append(inter_geo)
# if not georeference, use pixel values
else:
# make the shp files out of the 4 corner point
make_shp(sub_folder_SHP, nb_column, c, inter)
# get the points in a 8-elements list (no more tuples)
inter = [item for sublist in inter for item in sublist]
# insert row number at the begining
inter.insert(0, c)
# insert column number at the beginning
inter.insert(0, nb_column)
# "save" the list into "intersection" list
intersection.append(inter)
# add up 1 element for the next row
c = str(int(c) + 1).zfill(2)
# if the number inputed is not proportional to the detected
# number of columns, then do one by one for what is left
except IndexError:
for i in range(k, len(rows_a)):
pt1 = ((0 - rows_b[i][0]) / rows_a[i][0], 0)
pt2 = ((0 - rows_b[i][1]) / rows_a[i][1], 0)
pt3 = ((maxY - rows_b[i][1]) / rows_a[i][1], maxY)
pt4 = ((maxY - rows_b[i][0]) / rows_a[i][0], maxY)
roi_corners = np.array([[pt1, pt2, pt3, pt4]],
dtype=np.int32)
mask = np.zeros(img_binary.shape, dtype=np.uint8)
cv2.fillPoly(mask, roi_corners, (255, ))
row_binary = cv2.bitwise_and(current_column_binary,
current_column_binary,
mask=mask)
cv2.imwrite(
str(sub_folder_rowBinary /
str('Plot_column_' + str(nb_column) + '_row_' +
str(c) + '_cropped.jpg')), row_binary)
row_original = cv2.bitwise_and(current_column_original,
current_column_original,
mask=mask)
if YN_binary == False:
coords = np.argwhere(row_original)
x0, y0, z = coords.min(axis=0)
x1, y1, z = coords.max(
axis=0) + 1 # slices are exclusive at the top
row_original_cropped = row_original[x0:x1, y0:y1]
cv2.imwrite(
str(sub_folder_rowOriginal /
str('Plot_column_' + str(nb_column) + '_row_' +
str(c) + '_cropped.jpg')),
row_original_cropped)
cv2.imwrite(
str(sub_folder_rowOriginalWhole /
str('Plot_column_' + str(nb_column) + '_row_' +
str(c) + '_whole_pic.jpg')), row_original)
# get the intersection between column and row considered
inter, inter_geo = line_intersection(
columns_a[nbStartColumn], columns_a[nbEndColumn],
columns_b[nbStartColumn], columns_b[nbEndColumn],
rows_a[i], rows_a[i], rows_b[i], rows_b[i], aff,
y_offset, x_offset)
if type(inter_geo) != type(None):
# make the shp files out of the 4 corner points
make_shp(sub_folder_SHP, nb_column, c, inter_geo)
# prepare the array to be saved
inter_geo = [
item for sublist in inter_geo for item in sublist
]
inter_geo.insert(0, c)
inter_geo.insert(0, nb_column)
intersection_geo.append(inter_geo)
# if not georeference, use pixel values
else:
# make the shp files out of the 4 corner point
make_shp(sub_folder_SHP, nb_column, c, inter)
inter = [item for sublist in inter for item in sublist]
inter.insert(0, c)
inter.insert(0, nb_column)
intersection.append(inter)
c = str(int(c) + 1).zfill(2)
### make a shp file with all individual shp merged
# get all the shp absolute paths
individual_shp_files = os.listdir(sub_folder_SHP)
individual_shp_files = [
str(sub_folder_SHP / f) for f in individual_shp_files if '.shp' in f
]
# make a shp file with the meta of the individual shp
meta = fiona.open(individual_shp_files[0]).meta
# write all the individual shp into the merging file
with fiona.open(folder / 'All_plots.shp', 'w', **meta) as output:
for k in individual_shp_files:
with fiona.open(k) as f:
for features in f:
output.write(features)
# get the average number of row per column
nbOfRowPerColumn = nbOfRowPerColumn / nbOfColumn
# save the metadata
metadata(folder, field_image, noise, nbOfColumnPerPlot, nbOfRowPerPlot,
globalOrientation, sub_folder_rowBinary, sub_folder_SHP, angle,
nbOfColumn, nbOfRowPerColumn, intersection, aff, intersection_geo)
return ('OK')
from skimage.morphology import watershed
from skimage.feature import peak_local_max
from skimage.morphology import extrema
# load dtm image
image = color.rgb2gray(io.imread('2092.jpg'))
xsize=image.shape[0]
ysize=image.shape[1]
#img=resize(img, (int(img.shape[0] / 6), int(img.shape[1] / 6)))
# k-means clustering of the image (population of pixels intensities)
#image = image.reshape((-1, 1))
distance = ndi.distance_transform_edt(image)
local_maxi = extrema.local_maxima(distance, image, np.ones((3, 3)))
markers = ndi.label(local_maxi)[0]
labels = watershed(-distance, markers, mask=image)
##distance = ndi.distance_transform_edt(image)
##local_maxi = peak_local_max(
## distance, indices=False, footprint=np.ones((3, 3)), labels=image)
##markers = measure.label(local_maxi)
##labels_ws = watershed(-distance, markers, mask=image)
print(labels.shape)
X_clustered=labels
X_clustered.shape = image.shape
ax[1].axis('off')
ax[1].set_title('Peak local max')
fig.tight_layout()
plt.show()
###########################################
#prepare image for h_maxima and local_maxima functions
img = color.rgb2gray(Color_Masked)
img = 255 - img
img = exposure.rescale_intensity(img)
#extract local maxima
local_maxima = extrema.local_maxima(img_gs_inv)
label_maxima = label(local_maxima)
overlay = color.label2rgb(label_maxima,
img_gs_inv,
alpha=0.7,
bg_label=0,
bg_color=None,
colors=[(1, 0, 0)])
#local maxima with certrain contrast
h = 0.05
ding = morphology.selem.disk(radius=3)
h_maxima = extrema.h_maxima(img, h, selem=ding)
label_h_maxima = label(h_maxima)
overlay_h = color.label2rgb(label_h_maxima,
img,