Dense DAISY feature description

The DAISY local image descriptor is based on gradient orientation histograms similar to the SIFT descriptor. It is formulated in a way that allows for fast dense extraction which is useful for e.g. bag-of-features image representations.

In this example a limited number of DAISY descriptors are extracted at a large scale for illustrative purposes.

Traceback (most recent call last):
  File "/build/skimage-Lp2Zl4/skimage-0.16.2/doc/examples/features_detection/plot_daisy.py", line 1
    ===============================
    ^
SyntaxError: invalid syntax

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Dense DAISY feature description
===============================

The DAISY local image descriptor is based on gradient orientation histograms
similar to the SIFT descriptor. It is formulated in a way that allows for fast
dense extraction which is useful for e.g. bag-of-features image
representations.

In this example a limited number of DAISY descriptors are extracted at a large
scale for illustrative purposes.
"""
from skimage.feature import daisy
from skimage import data
import matplotlib.pyplot as plt


img = data.camera()
descs, descs_img = daisy(img, step=180, radius=58, rings=2, histograms=6,
                         orientations=8, visualize=True)

fig, ax = plt.subplots()
ax.axis('off')
ax.imshow(descs_img)
descs_num = descs.shape[0] * descs.shape[1]
ax.set_title('%i DAISY descriptors extracted:' % descs_num)
plt.show()

Total running time of the script: ( 0 minutes 0.000 seconds)