Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/skimage/io/sift.py

@@ -0,0 +1,81 @@
+import numpy as np
+
+__all__ = ['load_sift', 'load_surf']
+
+
+def _sift_read(filelike, mode='SIFT'):
+    """Read SIFT or SURF features from externally generated file.
+
+    This routine reads SIFT or SURF files generated by binary utilities from
+    http://people.cs.ubc.ca/~lowe/keypoints/ and
+    http://www.vision.ee.ethz.ch/~surf/.
+
+    This routine *does not* generate SIFT/SURF features from an image.  These
+    algorithms are patent encumbered.  Please use `skimage.feature.CENSURE`
+    instead.
+
+    Parameters
+    ----------
+    filelike : string or open file
+        Input file generated by the feature detectors from
+        http://people.cs.ubc.ca/~lowe/keypoints/ or
+        http://www.vision.ee.ethz.ch/~surf/ .
+    mode : {'SIFT', 'SURF'}, optional
+        Kind of descriptor used to generate `filelike`.
+
+    Returns
+    -------
+    data : record array with fields
+      - row: int
+          row position of feature
+      - column: int
+          column position of feature
+      - scale: float
+          feature scale
+      - orientation: float
+          feature orientation
+      - data: array
+          feature values
+
+    """
+    if isinstance(filelike, str):
+        f = open(filelike, 'r')
+        filelike_is_str = True
+    else:
+        f = filelike
+        filelike_is_str = False
+
+    if mode == 'SIFT':
+        nr_features, feature_len = map(int, f.readline().split())
+        datatype = np.dtype([('row', float), ('column', float),
+                             ('scale', float), ('orientation', float),
+                             ('data', (float, feature_len))])
+    else:
+        mode = 'SURF'
+        feature_len = int(f.readline()) - 1
+        nr_features = int(f.readline())
+        datatype = np.dtype([('column', float), ('row', float),
+                             ('second_moment', (float, 3)),
+                             ('sign', float), ('data', (float, feature_len))])
+
+    data = np.fromfile(f, sep=' ')
+    if data.size != nr_features * datatype.itemsize / np.dtype(float).itemsize:
+        raise IOError("Invalid {} feature file.".format(mode))
+
+    # If `filelike` is passed to the function as filename - close the file
+    if filelike_is_str:
+        f.close()
+
+    return data.view(datatype)
+
+
+def load_sift(f):
+    return _sift_read(f, mode='SIFT')
+
+
+def load_surf(f):
+    return _sift_read(f, mode='SURF')
+
+
+load_sift.__doc__ = _sift_read.__doc__
+load_surf.__doc__ = _sift_read.__doc__