Vehicle-Anti-Theft-Face-Recognition-System/venv/Lib/site-packages/skimage/feature/tests/test_blob.py

import numpy as np
from skimage.draw import disk
from skimage.draw.draw3d import ellipsoid
from skimage.feature import blob_dog, blob_log, blob_doh
from skimage.feature.blob import _blob_overlap
import math
from numpy.testing import assert_almost_equal


def test_blob_dog():
    r2 = math.sqrt(2)
    img = np.ones((512, 512))

    xs, ys = disk((400, 130), 5)
    img[xs, ys] = 255

    xs, ys = disk((100, 300), 25)
    img[xs, ys] = 255

    xs, ys = disk((200, 350), 45)
    img[xs, ys] = 255

    blobs = blob_dog(img, min_sigma=5, max_sigma=50)
    radius = lambda x: r2 * x[2]
    s = sorted(blobs, key=radius)
    thresh = 5

    b = s[0]
    assert abs(b[0] - 400) <= thresh
    assert abs(b[1] - 130) <= thresh
    assert abs(radius(b) - 5) <= thresh

    b = s[1]
    assert abs(b[0] - 100) <= thresh
    assert abs(b[1] - 300) <= thresh
    assert abs(radius(b) - 25) <= thresh

    b = s[2]
    assert abs(b[0] - 200) <= thresh
    assert abs(b[1] - 350) <= thresh
    assert abs(radius(b) - 45) <= thresh

    # Testing no peaks
    img_empty = np.zeros((100,100))
    assert blob_dog(img_empty).size == 0

    # Testing 3D
    r = 10
    pad = 10
    im3 = ellipsoid(r, r, r)
    im3 = np.pad(im3, pad, mode='constant')

    blobs = blob_dog(im3, min_sigma=3, max_sigma=10,
                          sigma_ratio=1.2, threshold=0.1)
    b = blobs[0]

    assert b.shape == (4,)
    assert b[0] == r + pad + 1
    assert b[1] == r + pad + 1
    assert b[2] == r + pad + 1
    assert abs(math.sqrt(3) * b[3] - r) < 1

    # Testing 3D anisotropic
    r = 10
    pad = 10
    im3 = ellipsoid(r / 2, r, r)
    im3 = np.pad(im3, pad, mode='constant')

    blobs = blob_dog(
        im3,
        min_sigma=[1.5, 3, 3],
        max_sigma=[5, 10, 10],
        sigma_ratio=1.2,
        threshold=0.1
    )
    b = blobs[0]

    assert b.shape == (6,)
    assert b[0] == r / 2 + pad + 1
    assert b[1] == r + pad + 1
    assert b[2] == r + pad + 1
    assert abs(math.sqrt(3) * b[3] - r / 2) < 1
    assert abs(math.sqrt(3) * b[4] - r) < 1
    assert abs(math.sqrt(3) * b[5] - r) < 1

    # Testing exclude border

    # image where blob is 5 px from borders, radius 5
    img = np.ones((512, 512))
    xs, ys = disk((5, 5), 5)
    img[xs, ys] = 255


def test_blob_dog_excl_border():
    img = np.ones((512, 512))
    xs, ys = disk((5, 5), 5)
    img[xs, ys] = 255
    blobs = blob_dog(
        img,
        min_sigma=1.5,
        max_sigma=5,
        sigma_ratio=1.2,
    )
    assert blobs.shape[0] == 1
    b = blobs[0]
    assert b[0] == b[1] == 5, "blob should be 5 px from x and y borders"

    blobs = blob_dog(
        img,
        min_sigma=1.5,
        max_sigma=5,
        sigma_ratio=1.2,
        exclude_border=6,
    )
    msg = "zero blobs should be detected, as only blob is 5 px from border"
    assert blobs.shape[0] == 0, msg


def test_blob_log():
    r2 = math.sqrt(2)
    img = np.ones((256, 256))

    xs, ys = disk((200, 65), 5)
    img[xs, ys] = 255

    xs, ys = disk((80, 25), 15)
    img[xs, ys] = 255

    xs, ys = disk((50, 150), 25)
    img[xs, ys] = 255

    xs, ys = disk((100, 175), 30)
    img[xs, ys] = 255

    blobs = blob_log(img, min_sigma=5, max_sigma=20, threshold=1)

    radius = lambda x: r2 * x[2]
    s = sorted(blobs, key=radius)
    thresh = 3

    b = s[0]
    assert abs(b[0] - 200) <= thresh
    assert abs(b[1] - 65) <= thresh
    assert abs(radius(b) - 5) <= thresh

    b = s[1]
    assert abs(b[0] - 80) <= thresh
    assert abs(b[1] - 25) <= thresh
    assert abs(radius(b) - 15) <= thresh

    b = s[2]
    assert abs(b[0] - 50) <= thresh
    assert abs(b[1] - 150) <= thresh
    assert abs(radius(b) - 25) <= thresh

    b = s[3]
    assert abs(b[0] - 100) <= thresh
    assert abs(b[1] - 175) <= thresh
    assert abs(radius(b) - 30) <= thresh

    # Testing log scale
    blobs = blob_log(
        img,
        min_sigma=5,
        max_sigma=20,
        threshold=1,
        log_scale=True)

    b = s[0]
    assert abs(b[0] - 200) <= thresh
    assert abs(b[1] - 65) <= thresh
    assert abs(radius(b) - 5) <= thresh

    b = s[1]
    assert abs(b[0] - 80) <= thresh
    assert abs(b[1] - 25) <= thresh
    assert abs(radius(b) - 15) <= thresh

    b = s[2]
    assert abs(b[0] - 50) <= thresh
    assert abs(b[1] - 150) <= thresh
    assert abs(radius(b) - 25) <= thresh

    b = s[3]
    assert abs(b[0] - 100) <= thresh
    assert abs(b[1] - 175) <= thresh
    assert abs(radius(b) - 30) <= thresh

    # Testing no peaks
    img_empty = np.zeros((100,100))
    assert blob_log(img_empty).size == 0


def test_blob_log_3d():
    # Testing 3D
    r = 6
    pad = 10
    im3 = ellipsoid(r, r, r)
    im3 = np.pad(im3, pad, mode='constant')

    blobs = blob_log(im3, min_sigma=3, max_sigma=10)
    b = blobs[0]

    assert b.shape == (4,)
    assert b[0] == r + pad + 1
    assert b[1] == r + pad + 1
    assert b[2] == r + pad + 1
    assert abs(math.sqrt(3) * b[3] - r) < 1


def test_blob_log_3d_anisotropic():
    # Testing 3D anisotropic
    r = 6
    pad = 10
    im3 = ellipsoid(r / 2, r, r)
    im3 = np.pad(im3, pad, mode='constant')

    blobs = blob_log(
        im3,
        min_sigma=[1, 2, 2],
        max_sigma=[5, 10, 10],
    )

    b = blobs[0]
    assert b.shape == (6,)
    assert b[0] == r / 2 + pad + 1
    assert b[1] == r + pad + 1
    assert b[2] == r + pad + 1
    assert abs(math.sqrt(3) * b[3] - r / 2) < 1
    assert abs(math.sqrt(3) * b[4] - r) < 1
    assert abs(math.sqrt(3) * b[5] - r) < 1


def test_blob_log_exclude_border():
    # image where blob is 5 px from borders, radius 5
    img = np.ones((512, 512))
    xs, ys = disk((5, 5), 5)
    img[xs, ys] = 255

    blobs = blob_log(
        img,
        min_sigma=1.5,
        max_sigma=5,
    )
    assert blobs.shape[0] == 1
    b = blobs[0]
    assert b[0] == b[1] == 5, "blob should be 5 px from x and y borders"

    blobs = blob_log(
        img,
        min_sigma=1.5,
        max_sigma=5,
        exclude_border=6,
    )
    msg = "zero blobs should be detected, as only blob is 5 px from border"
    assert blobs.shape[0] == 0, msg


def test_blob_doh():
    img = np.ones((512, 512), dtype=np.uint8)

    xs, ys = disk((400, 130), 20)
    img[xs, ys] = 255

    xs, ys = disk((460, 50), 30)
    img[xs, ys] = 255

    xs, ys = disk((100, 300), 40)
    img[xs, ys] = 255

    xs, ys = disk((200, 350), 50)
    img[xs, ys] = 255

    blobs = blob_doh(
        img,
        min_sigma=1,
        max_sigma=60,
        num_sigma=10,
        threshold=.05)

    radius = lambda x: x[2]
    s = sorted(blobs, key=radius)
    thresh = 4

    b = s[0]
    assert abs(b[0] - 400) <= thresh
    assert abs(b[1] - 130) <= thresh
    assert abs(radius(b) - 20) <= thresh

    b = s[1]
    assert abs(b[0] - 460) <= thresh
    assert abs(b[1] - 50) <= thresh
    assert abs(radius(b) - 30) <= thresh

    b = s[2]
    assert abs(b[0] - 100) <= thresh
    assert abs(b[1] - 300) <= thresh
    assert abs(radius(b) - 40) <= thresh

    b = s[3]
    assert abs(b[0] - 200) <= thresh
    assert abs(b[1] - 350) <= thresh
    assert abs(radius(b) - 50) <= thresh


def test_blob_doh_log_scale():
    img = np.ones((512, 512), dtype=np.uint8)

    xs, ys = disk((400, 130), 20)
    img[xs, ys] = 255

    xs, ys = disk((460, 50), 30)
    img[xs, ys] = 255

    xs, ys = disk((100, 300), 40)
    img[xs, ys] = 255

    xs, ys = disk((200, 350), 50)
    img[xs, ys] = 255

    blobs = blob_doh(
        img,
        min_sigma=1,
        max_sigma=60,
        num_sigma=10,
        log_scale=True,
        threshold=.05)

    radius = lambda x: x[2]
    s = sorted(blobs, key=radius)
    thresh = 10

    b = s[0]
    assert abs(b[0] - 400) <= thresh
    assert abs(b[1] - 130) <= thresh
    assert abs(radius(b) - 20) <= thresh

    b = s[2]
    assert abs(b[0] - 460) <= thresh
    assert abs(b[1] - 50) <= thresh
    assert abs(radius(b) - 30) <= thresh

    b = s[1]
    assert abs(b[0] - 100) <= thresh
    assert abs(b[1] - 300) <= thresh
    assert abs(radius(b) - 40) <= thresh

    b = s[3]
    assert abs(b[0] - 200) <= thresh
    assert abs(b[1] - 350) <= thresh
    assert abs(radius(b) - 50) <= thresh


def test_blob_doh_no_peaks():
    # Testing no peaks
    img_empty = np.zeros((100,100))
    assert blob_doh(img_empty).size == 0


def test_blob_doh_overlap():
    img = np.ones((256, 256), dtype=np.uint8)

    xs, ys = disk((100, 100), 20)
    img[xs, ys] = 255

    xs, ys = disk((120, 100), 30)
    img[xs, ys] = 255

    blobs = blob_doh(
        img,
        min_sigma=1,
        max_sigma=60,
        num_sigma=10,
        threshold=.05
    )

    assert len(blobs) == 1


def test_blob_log_overlap_3d():
    r1, r2 = 7, 6
    pad1, pad2 = 11, 12
    blob1 = ellipsoid(r1, r1, r1)
    blob1 = np.pad(blob1, pad1, mode='constant')
    blob2 = ellipsoid(r2, r2, r2)
    blob2 = np.pad(blob2, [(pad2, pad2), (pad2 - 9, pad2 + 9),
                           (pad2, pad2)],
                   mode='constant')
    im3 = np.logical_or(blob1, blob2)

    blobs = blob_log(im3,  min_sigma=2, max_sigma=10, overlap=0.1)
    assert len(blobs) == 1


def test_blob_overlap_3d_anisotropic():
    # Two spheres with distance between centers equal to radius
    # One sphere is much smaller than the other so about half of it is within
    # the bigger sphere.
    s3 = math.sqrt(3)
    overlap = _blob_overlap(np.array([0, 0,  0, 2 / s3, 10 / s3, 10 / s3]),
                            np.array([0, 0, 10, 0.2 / s3, 1 / s3, 1 / s3]),
                            sigma_dim=3)
    assert_almost_equal(overlap, 0.48125)
    overlap = _blob_overlap(np.array([0, 0, 0, 2 / s3, 10 / s3, 10 / s3]),
                            np.array([2, 0, 0, 0.2 / s3, 1 / s3, 1 / s3]),
                            sigma_dim=3)
    assert_almost_equal(overlap, 0.48125)


def test_blob_log_anisotropic():
    image = np.zeros((50, 50))
    image[20, 10:20] = 1
    isotropic_blobs = blob_log(image, min_sigma=0.5, max_sigma=2, num_sigma=3)
    assert len(isotropic_blobs) > 1  # many small blobs found in line
    ani_blobs = blob_log(image, min_sigma=[0.5, 5], max_sigma=[2, 20],
                         num_sigma=3)  # 10x anisotropy, line is 1x10
    assert len(ani_blobs) == 1  # single anisotropic blob found


def test_blob_log_overlap_3d_anisotropic():
    r1, r2 = 7, 6
    pad1, pad2 = 11, 12
    blob1 = ellipsoid(r1, r1, r1)
    blob1 = np.pad(blob1, pad1, mode='constant')
    blob2 = ellipsoid(r2, r2, r2)
    blob2 = np.pad(blob2, [(pad2, pad2), (pad2 - 9, pad2 + 9),
                           (pad2, pad2)],
                   mode='constant')
    im3 = np.logical_or(blob1, blob2)

    blobs = blob_log(im3, min_sigma=[2, 2.01, 2.005],
                     max_sigma=10, overlap=0.1)
    assert len(blobs) == 1

    # Two circles with distance between centers equal to radius
    overlap = _blob_overlap(np.array([0, 0, 10 / math.sqrt(2)]),
                            np.array([0, 10, 10 / math.sqrt(2)]))
    assert_almost_equal(overlap,
                        1./math.pi * (2 * math.acos(1./2) - math.sqrt(3)/2.))


def test_no_blob():
    im = np.zeros((10, 10))
    blobs = blob_log(im,  min_sigma=2, max_sigma=5, num_sigma=4)
    assert len(blobs) == 0