mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-10 19:28:36 +08:00
changed how the doc decorator works. now it only adds the url.
This commit is contained in:
sccolbert
@@ -6,15 +6,10 @@ import numpy as np
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# the doc decorator
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class cvdoc(object):
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''' a doc decorator which adds the docs for the opencv functions.
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It should be self-explanatory. See how the arguments are passed in e.g.
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opencv_cv.pyx
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It primarily serves to append the appropriate opencv doc url
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to each function.
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'''
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SIGNATURE = '''Signature\n---------\n'''
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PARAMETERS = '''Parameters\n----------\n'''
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RETURNS = '''Returns\n-------\n'''
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NOTES = '''Notes\n-----\n'''
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EXAMPLES = '''Examples\n--------\n'''
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base_url = 'http://opencv.willowgarage.com/documentation/'
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branch_urls = {'cv': {'image': 'image_processing',
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'structural': 'structural_analysis',
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@@ -24,27 +19,14 @@ class cvdoc(object):
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'highgui': {}
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}
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def __init__(self, description='', signature='', parameters='', returns='',
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notes='', examples='', package='', group=''):
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self.description = str(description)
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self.signature = str(signature)
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self.parameters = str(parameters)
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self.returns = str(returns)
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self.notes = str(notes)
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self.examples = str(examples)
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def __init__(self, package='', group='', doc=''):
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self.package = str(package)
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self.group = str(group)
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self.doc = ''''''
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self.doc = str(doc)
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def __call__(self, func):
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# if key errors occur, fail silently
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try:
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self._add_description()
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self._add_signature()
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self._add_parameters()
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self._add_returns()
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self._add_notes()
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self._add_examples()
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self._add_url(func)
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np.add_docstring(func, self.doc)
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return func
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@@ -52,30 +34,6 @@ class cvdoc(object):
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except KeyError:
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return func
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def _add_description(self):
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if self.description != '':
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self.doc += self.description + '\n\n'
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def _add_signature(self):
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if self.signature != '':
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self.doc += self.SIGNATURE + self.signature + '\n\n'
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def _add_parameters(self):
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if self.parameters != '':
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self.doc += self.PARAMETERS + self.parameters + '\n\n'
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def _add_returns(self):
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if self.returns != '':
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self.doc += self.RETURNS + self.returns + '\n\n'
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def _add_notes(self):
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if self.notes != '':
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self.doc += self.NOTES + self.notes + '\n\n'
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def _add_examples(self):
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if self.examples != '':
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self.doc += self.EXAMPLES + self.examples + '\n\n'
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def _add_url(self, func):
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name = func.__name__
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full_url = (self.base_url +
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@@ -85,5 +43,5 @@ class cvdoc(object):
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The OpenCV documentation for this fuction can
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be found at the following url:''')
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self.doc += message + '\n\n' + full_url + '\n'
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self.doc += '\n\n' + message + '\n\n' + full_url + '\n'
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+1879
-2447
File diff suppressed because it is too large
Load Diff
+177
-155
@@ -285,26 +285,29 @@ c_cvDrawChessboardCorners = (<cvDrawChessboardCornersPtr*><size_t>
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# cvSobel
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#--------
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@cvdoc(
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description = \
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'''Apply the Sobel operator to the input image.''',
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signature = \
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'''cvSobel(src, xorder=1, yorder=0, aperture_size=3)''',
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parameters = \
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'''src : ndarray, 2D, dtype=[uint8, int8, float32]
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@cvdoc(package='cv', group='image', doc=\
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'''Apply the Sobel operator to the input image.
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Signature
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---------
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cvSobel(src, xorder=1, yorder=0, aperture_size=3)
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Parameters
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----------
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src : ndarray, 2D, dtype=[uint8, int8, float32]
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The source image.
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xorder : integer
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The x order of the Sobel operator.
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yorder : integer
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The y order of the Sobel operator.
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aperture_size : integer=[3, 5, 7]
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The size of the Sobel kernel.''',
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returns = \
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'''out : ndarray
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The size of the Sobel kernel.
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Returns
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-------
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out : ndarray
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A new which is the result of applying the Sobel
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operator to src.''',
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package = 'cv',
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group = 'image')
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operator to src.''')
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def cvSobel(np.ndarray src, int xorder=1, int yorder=0,
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int aperture_size=3):
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@@ -337,22 +340,25 @@ def cvSobel(np.ndarray src, int xorder=1, int yorder=0,
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# cvLaplace
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#----------
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@cvdoc(
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description = \
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'''Apply the Laplace operator to the input image.''',
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signature = \
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'''cvLaplace(src, aperture_size=3)''',
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parameters = \
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'''src : ndarray, 2D, dtype=[uint8, int8, float32]
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@cvdoc(package='cv', group='image', doc=\
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'''Apply the Laplace operator to the input image.
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Signature
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---------
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cvLaplace(src, aperture_size=3)
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Parameters
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----------
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src : ndarray, 2D, dtype=[uint8, int8, float32]
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The source image.
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aperture_size : integer=[3, 5, 7]
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The size of the Sobel kernel.''',
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returns = \
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'''out : ndarray
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The size of the Sobel kernel.
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Returns
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-------
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out : ndarray
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A new which is the result of applying the Laplace
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operator to src.''',
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package = 'cv',
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group = 'image')
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operator to src.''')
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def cvLaplace(np.ndarray src, int aperture_size=3):
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validate_array(src)
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@@ -384,26 +390,29 @@ def cvLaplace(np.ndarray src, int aperture_size=3):
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# cvCanny
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#--------
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@cvdoc(
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description = \
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'''Apply Canny edge detection to the input image.''',
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signature = \
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'''cvCanny(src, threshold1=10, threshold2=50, aperture_size=3)''',
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parameters = \
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'''src : ndarray, 2D, dtype=[uint8]
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@cvdoc(package='cv', group='image', doc=\
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'''Apply Canny edge detection to the input image.
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Signature
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---------
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cvCanny(src, threshold1=10, threshold2=50, aperture_size=3)
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Parameters
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----------
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src : ndarray, 2D, dtype=[uint8]
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The source image.
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threshold1 : float
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The lower threshold used for edge linking.
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threshold2 : float
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The upper threshold used to find strong edges.
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aperture_size : integer=[3, 5, 7]
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The size of the Sobel kernel.''',
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returns = \
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'''out : ndarray
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The size of the Sobel kernel.
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Returns
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-------
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out : ndarray
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A new which is the result of applying Canny
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edge detection to src.''',
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package = 'cv',
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group = 'image')
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edge detection to src.''')
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def cvCanny(np.ndarray src, double threshold1=10, double threshold2=50,
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int aperture_size=3):
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@@ -431,21 +440,24 @@ def cvCanny(np.ndarray src, double threshold1=10, double threshold2=50,
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# cvPreCornerDetect
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#------------------
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@cvdoc(
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description = \
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'''Calculate the feature map for corner detection.''',
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signature = \
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'''cvPreCornerDetect(src, aperture_size=3)''',
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parameters = \
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'''src : ndarray, 2D, dtype=[uint8, float32]
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@cvdoc(package='cv', group='image', doc=\
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'''Calculate the feature map for corner detection.
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Signature
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---------
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cvPreCornerDetect(src, aperture_size=3)
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Parameters
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----------
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src : ndarray, 2D, dtype=[uint8, float32]
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The source image.
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aperture_size : integer=[3, 5, 7]
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The size of the Sobel kernel.''',
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returns = \
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'''out : ndarray
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A new array of the corner candidates.''',
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package = 'cv',
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group = 'image')
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The size of the Sobel kernel.
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Returns
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-------
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out : ndarray
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A new array of the corner candidates.''')
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def cvPreCornerDetect(np.ndarray src, int aperture_size=3):
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validate_array(src)
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@@ -472,28 +484,31 @@ def cvPreCornerDetect(np.ndarray src, int aperture_size=3):
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# cvCornerEigenValsAndVecs
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#-------------------------
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@cvdoc(
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description = \
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@cvdoc(package='cv', group='image', doc=\
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'''Calculates the eigenvalues and eigenvectors of image
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blocks for corner detection.''',
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signature = \
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'''cvCornerEigenValsAndVecs(src, block_size=3, aperture_size=3)''',
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parameters = \
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'''src : ndarray, 2D, dtype=[uint8, float32]
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blocks for corner detection.
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Signature
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---------
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cvCornerEigenValsAndVecs(src, block_size=3, aperture_size=3)
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Parameters
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----------
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src : ndarray, 2D, dtype=[uint8, float32]
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The source image.
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block_size : integer
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The size of the neighborhood in which to calculate
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the eigenvalues and eigenvectors.
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aperture_size : integer=[3, 5, 7]
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The size of the Sobel kernel.''',
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returns = \
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'''out : ndarray
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The size of the Sobel kernel.
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Returns
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-------
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out : ndarray
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A new array of the eigenvalues and eigenvectors.
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The shape of this array is (height, width, 6),
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Where height and width are the same as that
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of src.''',
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package = 'cv',
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group = 'image')
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of src.''')
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def cvCornerEigenValsAndVecs(np.ndarray src, int block_size=3,
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int aperture_size=3):
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@@ -525,25 +540,28 @@ def cvCornerEigenValsAndVecs(np.ndarray src, int block_size=3,
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# cvCornerMinEigenVal
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#--------------------
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@cvdoc(
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description = \
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@cvdoc(package='cv', group='image', doc=\
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'''Calculates the minimum eigenvalues of gradient matrices
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for corner detection.''',
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signature = \
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'''cvCornerMinEigenVal(src, block_size=3, aperture_size=3)''',
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parameters = \
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'''src : ndarray, 2D, dtype=[uint8, float32]
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for corner detection.
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Signature
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---------
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cvCornerMinEigenVal(src, block_size=3, aperture_size=3)
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Parameters
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----------
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src : ndarray, 2D, dtype=[uint8, float32]
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The source image.
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block_size : integer
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The size of the neighborhood in which to calculate
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the eigenvalues.
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aperture_size : integer=[3, 5, 7]
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The size of the Sobel kernel.''',
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returns = \
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'''out : ndarray
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A new array of the eigenvalues.''',
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package = 'cv',
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group = 'image')
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The size of the Sobel kernel.
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Returns
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-------
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out : ndarray
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A new array of the eigenvalues.''')
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def cvCornerMinEigenVal(np.ndarray src, int block_size=3,
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int aperture_size=3):
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@@ -571,33 +589,38 @@ def cvCornerMinEigenVal(np.ndarray src, int block_size=3,
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# cvCornerHarris
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#---------------
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@cvdoc(
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description = \
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'''Applies the Harris edge detector to the input image.''',
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signature = \
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'''cvCornerHarris(src, block_size=3, aperture_size=3, k=0.04)''',
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parameters = \
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'''src : ndarray, 2D, dtype=[uint8, float32]
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@cvdoc(package='cv', group='image', doc=\
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'''Applies the Harris edge detector to the input image.
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Signature
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---------
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cvCornerHarris(src, block_size=3, aperture_size=3, k=0.04)
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Parameters
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----------
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src : ndarray, 2D, dtype=[uint8, float32]
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The source image.
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block_size : integer
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The size of the neighborhood in which to apply the detector.
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aperture_size : integer=[3, 5, 7]
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The size of the Sobel kernel.
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k : float
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Harris detector free parameter. See Notes.''',
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returns = \
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'''out : ndarray
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A new array of the Harris corners.''',
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notes = \
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'''The function cvCornerHarris() runs the Harris edge
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Harris detector free parameter. See Notes.
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Returns
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-------
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out : ndarray
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A new array of the Harris corners.
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Notes
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-----
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The function cvCornerHarris() runs the Harris edge
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detector on the image. Similarly to cvCornerMinEigenVal()
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and cvCornerEigenValsAndVecs(), for each pixel it calculates
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a gradient covariation matrix M over a block_size X block_size
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neighborhood. Then, it stores det(M) - k * trace(M)**2
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to the output image. Corners in the image can be found as the
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local maxima of the output image.''',
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package = 'cv',
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group = 'image')
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local maxima of the output image.''')
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def cvCornerHarris(np.ndarray src, int block_size=3, int aperture_size=3,
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double k=0.04):
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@@ -625,14 +648,17 @@ def cvCornerHarris(np.ndarray src, int block_size=3, int aperture_size=3,
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# cvFindCornerSubPix
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#-------------------
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@cvdoc(
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description = \
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'''Refines corner locations to sub-pixel accuracy.''',
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signature = \
|
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'''cvFindCornerSubPix(src, corners, win, zero_zone=(-1, -1),
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iterations=0, epsilon=1e-5)''',
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parameters = \
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'''src : ndarray, 2D, dtype=[uint8]
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@cvdoc(package='cv', group='image', doc=\
|
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'''Refines corner locations to sub-pixel accuracy.
|
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Signature
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---------
|
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cvFindCornerSubPix(src, corners, win, zero_zone=(-1, -1),
|
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iterations=0, epsilon=1e-5)
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Parameters
|
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----------
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src : ndarray, 2D, dtype=[uint8]
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The source image.
|
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corners : ndarray, shape=(N x 2)
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An initial approximation of the corners in the image.
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@@ -651,11 +677,11 @@ iterations : integer
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the function iterates until the error is less than epsilon.
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epsilon : float
|
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The epsilon error, below which the function terminates.
|
||||
Can be used in combination with iterations.''',
|
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returns = \
|
||||
'''None. The array corners is modified in place.''',
|
||||
package = 'cv',
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group = 'image')
|
||||
Can be used in combination with iterations.
|
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|
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Returns
|
||||
-------
|
||||
None. The array 'corners' is modified in place.''')
|
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def cvFindCornerSubPix(np.ndarray src, np.ndarray corners, win,
|
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zero_zone=(-1, -1), int iterations=0,
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double epsilon=1e-5):
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@@ -701,15 +727,18 @@ def cvFindCornerSubPix(np.ndarray src, np.ndarray corners, win,
|
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# cvGoodFeaturesToTrack
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#----------------------
|
||||
|
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@cvdoc(
|
||||
description = \
|
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'''Determines strong corners in an image.''',
|
||||
signature = \
|
||||
'''cvGoodFeaturesToTrack(src, corner_count, quality_level,
|
||||
min_distance, block_size=3,
|
||||
use_harris=0, k=0.04)''',
|
||||
parameters = \
|
||||
'''src : ndarray, 2D, dtype=[uint8, float32]
|
||||
@cvdoc(package='cv', group='image', doc=\
|
||||
'''Determines strong corners in an image.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvGoodFeaturesToTrack(src, corner_count, quality_level,
|
||||
min_distance, block_size=3,
|
||||
use_harris=0, k=0.04)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, 2D, dtype=[uint8, float32]
|
||||
The source image.
|
||||
corner_count : int
|
||||
The maximum number of corners to find.
|
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@@ -729,17 +758,19 @@ use_harris : integer
|
||||
is used instead of default cvCornerMinEigenVal()
|
||||
k : float
|
||||
Harris detector free parameter.
|
||||
Used only if use_harris != 0.''',
|
||||
returns = \
|
||||
'''out : ndarray
|
||||
The locations of the found corners in the image.''',
|
||||
notes = \
|
||||
'''This function finds distinct and strong corners
|
||||
Used only if use_harris != 0.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
The locations of the found corners in the image.
|
||||
|
||||
Notes
|
||||
-----
|
||||
This function finds distinct and strong corners
|
||||
in an image which can be used as features in a tracking
|
||||
algorithm. It also insures that features are distanced
|
||||
from one another by at least min_distance.''',
|
||||
package = 'cv',
|
||||
group = 'image')
|
||||
from one another by at least min_distance.''')
|
||||
def cvGoodFeaturesToTrack(np.ndarray src, int corner_count,
|
||||
double quality_level, double min_distance,
|
||||
int block_size=3, int use_harris=0, double k=0.04):
|
||||
@@ -781,49 +812,40 @@ def cvGoodFeaturesToTrack(np.ndarray src, int corner_count,
|
||||
return out[:ncorners_found]
|
||||
|
||||
|
||||
'''
|
||||
Paramters:
|
||||
src - source image.
|
||||
size - two tuple (height, width) of rectangle (ints)
|
||||
center - two tuple (x, y) of rectangle center (floats)
|
||||
|
||||
the center must lie within the image, but the rectangle
|
||||
may extend beyond the bounds of the image, at which point
|
||||
the border is replicated.
|
||||
|
||||
Returns:
|
||||
A new image of the extracted rectangle. The same dtype as the src image.
|
||||
'''
|
||||
|
||||
#----------------
|
||||
# cvGetRectSubPix
|
||||
#----------------
|
||||
|
||||
@cvdoc(
|
||||
description = \
|
||||
@cvdoc(package='cv', group='image', doc=\
|
||||
'''Retrieves the pixel rectangle from an image with
|
||||
sub-pixel accuracy.''',
|
||||
signature = \
|
||||
'''cvGetRectSubPix(src, size, center)''',
|
||||
parameters = \
|
||||
'''src : ndarray
|
||||
sub-pixel accuracy.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvGetRectSubPix(src, size, center)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray
|
||||
The source image.
|
||||
size : two tuple, integers, (height, width)
|
||||
The size of the rectangle to extract.
|
||||
center : two tuple, floats, (x, y)
|
||||
The center location of the rectangle.
|
||||
The center must lie within the image, but the
|
||||
rectangle may extend beyond the bounds of the image.''',
|
||||
returns = \
|
||||
'''out : ndarray
|
||||
The extracted rectangle of the image.''',
|
||||
notes = \
|
||||
'''The center of the specified rectangle must
|
||||
rectangle may extend beyond the bounds of the image.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
The extracted rectangle of the image.
|
||||
|
||||
Notes
|
||||
-----
|
||||
The center of the specified rectangle must
|
||||
lie within the image, but the bounds of the rectangle
|
||||
may extend beyond the image. Border replication is used
|
||||
to fill in missing pixels.''',
|
||||
package = 'cv',
|
||||
group = 'image')
|
||||
to fill in missing pixels.''')
|
||||
def cvGetRectSubPix(np.ndarray src, size, center):
|
||||
|
||||
validate_array(src)
|
||||
|
||||
Reference in New Issue
Block a user