mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-24 13:20:43 +08:00
migrated from area to sigma
This commit is contained in:
Vighnesh Birodkar
+58
-64
@@ -67,9 +67,9 @@ def _prune_blobs(blobs_array, overlap):
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Parameters
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----------
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blobs_array : ndarray
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a 2d array with each row representing 3 values, the ``(y,x,sigma)``
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where ``(y,x)`` are coordinates of the blob and sigma is the standard
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deviation of the Gaussian kernel which detected the blob.
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A 2d array with each row representing 3 values, ``(y,x,sigma)``
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where ``(y,x)`` are coordinates of the blob and ``sigma`` is the
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standard deviation of the Gaussian kernel which detected the blob.
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overlap : float
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A value between 0 and 1. If the fraction of area overlapping for 2
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blobs is greater than `overlap` the smaller blob is eliminated.
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@@ -126,8 +126,9 @@ def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0,
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Returns
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-------
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A : (n, 3) ndarray
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A 2d array with each row containing the Y-Coordinate , the
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X-Coordinate and the estimated area of the blob respectively.
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A 2d array with each row representing 3 values, ``(y,x,sigma)``
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where ``(y,x)`` are coordinates of the blob and ``sigma`` is the
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standard deviation of the Gaussian kernel which detected the blob.
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References
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----------
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@@ -137,31 +138,34 @@ def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0,
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--------
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>>> from skimage import data, feature
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>>> feature.blob_dog(data.coins(),threshold=.5,max_sigma=40)
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array([[ 45, 336, 1608],
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[ 52, 155, 1608],
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[ 52, 216, 1608],
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[ 54, 42, 1608],
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[ 54, 276, 628],
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[ 58, 100, 628],
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[ 120, 272, 1608],
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[ 124, 337, 628],
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[ 125, 45, 1608],
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[ 125, 208, 628],
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[ 127, 102, 628],
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[ 128, 154, 628],
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[ 185, 347, 1608],
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[ 193, 213, 1608],
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[ 194, 277, 1608],
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[ 195, 102, 1608],
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[ 196, 43, 628],
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[ 198, 155, 628],
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[ 260, 46, 1608],
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[ 261, 173, 1608],
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[ 263, 245, 1608],
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[ 263, 302, 1608],
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[ 267, 115, 628],
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[ 267, 359, 1608]])
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array([[ 45, 336, 16],
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[ 52, 155, 16],
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[ 52, 216, 16],
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[ 54, 42, 16],
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[ 54, 276, 10],
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[ 58, 100, 10],
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[120, 272, 16],
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[124, 337, 10],
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[125, 45, 16],
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[125, 208, 10],
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[127, 102, 10],
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[128, 154, 10],
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[185, 347, 16],
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[193, 213, 16],
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[194, 277, 16],
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[195, 102, 16],
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[196, 43, 10],
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[198, 155, 10],
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[260, 46, 16],
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[261, 173, 16],
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[263, 245, 16],
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[263, 302, 16],
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[267, 115, 10],
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[267, 359, 16]])
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Notes
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-----
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The radius of each blob is approximately :math:`\sqrt{2}sigma`.
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"""
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if image.ndim != 2:
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@@ -192,14 +196,7 @@ def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0,
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# Convert the last index to its corresponding scale value
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local_maxima[:, 2] = sigma_list[local_maxima[:, 2]]
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ret_val = _prune_blobs(local_maxima, overlap)
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if len(ret_val) > 0:
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ret_val[:, 2] = math.pi * \
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((ret_val[:, 2] * math.sqrt(2)) ** 2).astype(int)
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return ret_val
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else:
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return []
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return _prune_blobs(local_maxima, overlap)
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def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
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@@ -238,8 +235,9 @@ def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
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Returns
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-------
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A : (n, 3) ndarray
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A 2d array with each row containing the Y-Coordinate , the
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X-Coordinate and the estimated area of the blob respectively.
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A 2d array with each row representing 3 values, ``(y,x,sigma)``
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where ``(y,x)`` are coordinates of the blob and ``sigma`` is the
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standard deviation of the Gaussian kernel which detected the blob.
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References
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----------
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@@ -251,24 +249,27 @@ def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
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>>> img = data.coins()
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>>> img = exposure.equalize_hist(img) # improves detection
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>>> feature.blob_log(img,threshold = .3)
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array([[ 113, 323, 6],
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[ 121, 272, 1815],
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[ 124, 336, 760],
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[ 126, 46, 760],
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[ 126, 208, 760],
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[ 127, 102, 760],
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[ 128, 154, 760],
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[ 185, 344, 1815],
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[ 194, 213, 1815],
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[ 194, 276, 1815],
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[ 197, 44, 760],
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[ 198, 103, 760],
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[ 198, 155, 760],
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[ 260, 174, 1815],
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[ 263, 244, 1815],
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[ 263, 302, 1815],
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[ 266, 115, 760]])
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array([[113, 323, 1],
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[121, 272, 17],
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[124, 336, 11],
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[126, 46, 11],
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[126, 208, 11],
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[127, 102, 11],
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[128, 154, 11],
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[185, 344, 17],
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[194, 213, 17],
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[194, 276, 17],
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[197, 44, 11],
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[198, 103, 11],
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[198, 155, 11],
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[260, 174, 17],
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[263, 244, 17],
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[263, 302, 17],
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[266, 115, 11]])
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Notes
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-----
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The radius of each blob is approximately :math:`\sqrt{2}sigma`.
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"""
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if image.ndim != 2:
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@@ -294,11 +295,4 @@ def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
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# Convert the last index to its corresponding scale value
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local_maxima[:, 2] = sigma_list[local_maxima[:, 2]]
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ret_val = _prune_blobs(local_maxima, overlap)
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if len(ret_val) > 0:
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ret_val[:, 2] = math.pi * \
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((ret_val[:, 2] * math.sqrt(2)) ** 2).astype(int)
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return ret_val
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else:
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return []
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return _prune_blobs(local_maxima, overlap)
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@@ -5,6 +5,7 @@ import math
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def test_blob_dog():
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r2 = math.sqrt(2)
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img = np.ones((512, 512))
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xs, ys = circle(400, 130, 5)
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@@ -17,29 +18,29 @@ def test_blob_dog():
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img[xs, ys] = 255
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blobs = blob_dog(img, min_sigma=5, max_sigma=50)
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area = lambda x: x[2]
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radius = lambda x: math.sqrt(x / math.pi)
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s = sorted(blobs, key=area)
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radius = lambda x: r2*x[2]
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s = sorted(blobs, key=radius)
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thresh = 5
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b = s[0]
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assert abs(b[0] - 400) <= thresh
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assert abs(b[1] - 130) <= thresh
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assert abs(radius(b[2]) - 5) <= thresh
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assert abs(radius(b) - 5) <= thresh
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b = s[1]
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print abs(radius(b))
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assert abs(b[0] - 100) <= thresh
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assert abs(b[1] - 300) <= thresh
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assert abs(radius(b[2]) - 25) <= thresh
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assert abs(radius(b) - 25) <= thresh
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b = s[2]
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assert abs(b[0] - 200) <= thresh
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assert abs(b[1] - 350) <= thresh
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assert abs(radius(b[2]) - 45) <= thresh
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assert abs(radius(b) - 45) <= thresh
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def test_blob_log():
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r2 = math.sqrt(2)
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img = np.ones((512, 512))
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xs, ys = circle(400, 130, 5)
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@@ -56,27 +57,26 @@ def test_blob_log():
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blobs = blob_log(img, min_sigma=5, max_sigma=20, threshold=1)
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area = lambda x: x[2]
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radius = lambda x: math.sqrt(x / math.pi)
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s = sorted(blobs, key=area)
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radius = lambda x: r2*x[2]
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s = sorted(blobs, key=radius)
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thresh = 3
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b = s[0]
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assert abs(b[0] - 400) <= thresh
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assert abs(b[1] - 130) <= thresh
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assert abs(radius(b[2]) - 5) <= thresh
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assert abs(radius(b) - 5) <= thresh
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b = s[1]
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assert abs(b[0] - 160) <= thresh
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assert abs(b[1] - 50) <= thresh
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assert abs(radius(b[2]) - 15) <= thresh
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assert abs(radius(b) - 15) <= thresh
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b = s[2]
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assert abs(b[0] - 100) <= thresh
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assert abs(b[1] - 300) <= thresh
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assert abs(radius(b[2]) - 25) <= thresh
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assert abs(radius(b) - 25) <= thresh
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b = s[3]
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assert abs(b[0] - 200) <= thresh
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assert abs(b[1] - 350) <= thresh
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assert abs(radius(b[2]) - 30) <= thresh
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assert abs(radius(b) - 30) <= thresh
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