Merge pull request #986 from vighneshbirodkar/blob_example

Blob detection example

.travis.yml

@@ -47,6 +47,7 @@ before_install:
     -   pip install --use-mirrors matplotlib;
     - fi
 
+    - pip install --use-mirrors pillow
     - pip install --use-mirrors cython
     - pip install --use-mirrors flake8
     - pip install --use-mirrors six

doc/examples/plot_blob.py

@@ -0,0 +1,73 @@
+"""
+==============
+Blob Detection
+==============
+
+Blobs are bright on dark or dark on bright regions in an image. In
+this example, blobs are detected using 3 algorithms. The image used
+in this case is the Hubble eXtreme Deep Field. Each bright dot in the
+image is a star or a galaxy.
+
+Laplacian of Gaussian (LoG)
+-----------------------------
+This is the most accurate and slowest approach. It computes the Laplacian
+of Gaussian images with successively increasing standard deviation and
+stacks them up in a cube. Blobs are local maximas in this cube. Detecting
+larger blobs is especially slower because of larger kernel sizes during
+convolution. Only bright blobs on dark backgrounds are detected. See
+:py:meth:`skimage.feature.blob_log` for usage.
+
+Difference of Gaussian (DoG)
+----------------------------
+This is a faster approximation of LoG approach. In this case the image is
+blurred with increasing standard deviations and the difference between
+two successively blurred images are stacked up in a cube. This method
+suffers from the same disadvantage as LoG approach for detecting larger
+blobs. Blobs are again assumed to be bright on dark. See
+:py:meth:`skimage.feature.blob_dog` for usage.
+
+Determinant of Hessian (DoH)
+----------------------------
+This is the fastest approach. It detects blobs by finding maximas in the
+matrix of the Determinant of Hessian of the image. The detection speed is
+independent of the size of blobs as internally the implementation uses
+box filters instead of convolutions. Bright on dark as well as dark on
+bright blobs are detected. The downside is that small blobs (<3px) are not
+detected accurately. See :py:meth:`skimage.feature.blob_doh` for usage.
+
+"""
+
+from matplotlib import pyplot as plt
+from skimage import data
+from skimage.feature import blob_dog, blob_log, blob_doh
+from math import sqrt
+from skimage.color import rgb2gray
+
+image = data.hubble_deep_field()[0:500, 0:500]
+image_gray = rgb2gray(image)
+
+blobs_log = blob_log(image_gray, max_sigma=30, num_sigma=10, threshold=.1)
+# Compute radii in the 3rd column.
+blobs_log[:, 2] = blobs_log[:, 2] * sqrt(2)
+
+blobs_dog = blob_dog(image_gray, max_sigma=30, threshold=.1)
+blobs_dog[:, 2] = blobs_dog[:, 2] * sqrt(2)
+
+blobs_doh = blob_doh(image_gray, max_sigma=30, threshold=.01)
+
+blobs_list = [blobs_log, blobs_dog, blobs_doh]
+colors = ['yellow', 'lime', 'red']
+titles = ['Laplacian of Gaussian', 'Difference of Gaussian',
+          'Determinant of Hessian']
+sequence = zip(blobs_list, colors, titles)
+
+for blobs, color, title in sequence:
+    fig, ax = plt.subplots(1, 1)
+    ax.set_title(title)
+    ax.imshow(image, interpolation='nearest')
+    for blob in blobs:
+        y, x, r = blob
+        c = plt.Circle((x, y), r, color=color, linewidth=2, fill=False)
+        ax.add_patch(c)
+
+plt.show()

skimage/data/__init__.py

@@ -24,7 +24,8 @@ __all__ = ['load',
            'clock',
            'immunohistochemistry',
            'chelsea',
-           'coffee']
+           'coffee',
+           'hubble_deep_field']
 
 
 def load(f):
@@ -200,3 +201,23 @@ def coffee():
 
     """
     return load("coffee.png")
+
+
+def hubble_deep_field():
+    """Hubble eXtreme Deep Field.
+
+    This photograph contains the Hubble Telescope's farthest ever view of
+    the universe. It can be useful as an example for multi-scale
+    detection.
+
+    Notes
+    -----
+    This image was downloaded from
+    `HubbleSite
+    <http://hubblesite.org/newscenter/archive/releases/2012/37/image/a/>`__.
+
+    The image was captured by NASA and `may be freely used in the
+    public domain <http://www.nasa.gov/audience/formedia/features/MP_Photo_Guidelines.html>`_.
+
+    """
+    return load("hubble_deep_field.jpg")