mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 23:57:58 +08:00
Merge branch 'docs' of http://github.com/sccolbert/scikits.image
This commit is contained in:
Stefan van der Walt
@@ -13,7 +13,7 @@ class cvdoc(object):
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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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'calibration': 'camera_calibration_and_3D_reconstruction'
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'calibration': 'camera_calibration_and_3d_reconstruction'
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},
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'cxcore': {},
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'highgui': {}
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@@ -1,4 +1,4 @@
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/* Generated by Cython 0.11.3 on Fri Oct 30 22:11:01 2009 */
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/* Generated by Cython 0.11.3 on Sat Oct 31 20:19:15 2009 */
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#define PY_SSIZE_T_CLEAN
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#include "Python.h"
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+1871
-1076
File diff suppressed because it is too large
Load Diff
@@ -1267,6 +1267,42 @@ def cvLogPolar(np.ndarray src, center, double M,
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c_cvLogPolar(&srcimg, &outimg, cv_center, M, flag)
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return out
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#--------
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# cvErode
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#--------
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@cvdoc(package='cv', group='image', doc=\
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'''Erode the source image with the given element.
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Signature
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---------
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cvErode(src, element=None, iterations=1, anchor=None, in_place=False)
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Parameters
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----------
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src : ndarray
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The source image.
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element : ndarray, 2D
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The structuring element. Must be 2D. Non-zero elements
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indicate which pixels of the underlying image to include
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in the operation as the element is slid over the image.
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If None, a 3x3 block element is used.
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iterations : integer
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The number of times to perform the operation.
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anchor: 2-tuple, (x, y)
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The anchor of the structuring element. Must be
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FULLY inside the element. If None, the center of the
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element is used.
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in_place: bool
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If True, perform the operation in place.
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Otherwise, store the results in a new image.
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Returns
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-------
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out/None : ndarray or None
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An new array is returned only if in_place=False.
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Otherwise, this function returns None.''')
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def cvErode(np.ndarray src, np.ndarray element=None, int iterations=1,
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anchor=None, in_place=False):
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@@ -1299,6 +1335,42 @@ def cvErode(np.ndarray src, np.ndarray element=None, int iterations=1,
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else:
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return out
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#---------
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# cvDilate
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#---------
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@cvdoc(package='cv', group='image', doc=\
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'''Dilate the source image with the given element.
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Signature
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---------
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cvDilate(src, element=None, iterations=1, anchor=None, in_place=False)
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Parameters
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----------
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src : ndarray
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The source image.
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element : ndarray, 2D
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The structuring element. Must be 2D. Non-zero elements
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indicate which pixels of the underlying image to include
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in the operation as the element is slid over the image.
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If None, a 3x3 block element is used.
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iterations : integer
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The number of times to perform the operation.
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anchor: 2-tuple, (x, y)
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The anchor of the structuring element. Must be
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FULLY inside the element. If None, the center of the
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element is used.
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in_place: bool
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If True, perform the operation in place.
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Otherwise, store the results in a new image.
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Returns
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-------
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out/None : ndarray or None
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An new array is returned only if in_place=False.
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Otherwise, this function returns None.''')
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def cvDilate(np.ndarray src, np.ndarray element=None, int iterations=1,
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anchor=None, in_place=False):
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@@ -1331,6 +1403,50 @@ def cvDilate(np.ndarray src, np.ndarray element=None, int iterations=1,
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else:
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return out
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#---------------
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# cvMorphologyEx
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#---------------
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@cvdoc(package='cv', group='image', doc=\
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'''Apply a morphological operation to the image.
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Signature
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---------
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cvMorphologyEx(src, element, operation, iterations=1, anchor=None,
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in_place=False)
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||||
|
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Parameters
|
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----------
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src : ndarray
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The source image.
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element : ndarray, 2D
|
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The structuring element. Must be 2D. Non-zero elements
|
||||
indicate which pixels of the underlying image to include
|
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in the operation as the element is slid over the image.
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Cannot be None.
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operation : flag
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The morphology operation to perform. Must be one of:
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CV_MOP_OPEN
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CV_MOP_CLOSE
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CV_MOP_GRADIENT
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CV_MOP_TOPHAT
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CV_MOP_BLACKHAT
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iterations : integer
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The number of times to perform the operation.
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anchor: 2-tuple, (x, y)
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The anchor of the structuring element. Must be
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FULLY inside the element. If None, the center of the
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element is used.
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in_place: bool
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If True, perform the operation in place.
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Otherwise, store the results in a new image.
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|
||||
Returns
|
||||
-------
|
||||
out/None : ndarray or None
|
||||
An new array is returned only if in_place=False.
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||||
Otherwise, this function returns None.''')
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def cvMorphologyEx(np.ndarray src, np.ndarray element, int operation,
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int iterations=1, anchor=None, in_place=False):
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@@ -1380,14 +1496,96 @@ def cvMorphologyEx(np.ndarray src, np.ndarray element, int operation,
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else:
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return out
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#---------
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# cvSmooth
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#---------
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@cvdoc(package='cv', group='image', doc=\
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'''Smooth an image with the specified filter.
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Signature
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||||
---------
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cvSmooth(src, smoothtype=CV_GAUSSIAN, param1=3, param2=0, param3=0.,
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param4=0., in_place=False)
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Parameters
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----------
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src : ndarray
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The source image.
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smoothtype : integer
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The flag representing which smoothing operation to perfom.
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See notes on restrictions.
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Must be one of:
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CV_BLUR_NO_SCALE
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CV_BLUR
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CV_GAUSSIAN
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CV_MEDIAN
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CV_BILATERAL
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||||
param1 : integer
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||||
See notes.
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||||
param2 : integer
|
||||
See notes.
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||||
param3 : float
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||||
See notes.
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||||
param4 : float
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||||
See notes.
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in_place : bool
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||||
If True, perform the operation in place.
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||||
This is not supported for every combination of arguments.
|
||||
See notes.
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||||
Returns
|
||||
-------
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out/None : ndarray or None
|
||||
If in_place == True the function operates in place and returns None.
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||||
Otherwise, the operation returns a new array that is
|
||||
the result of the smoothing operation.
|
||||
|
||||
Notes
|
||||
-----
|
||||
The following details the restrictions and argument interpretaions
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||||
for each of the smoothing operations.
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CV_BLUR_NO_SCALE:
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Source image must be 2D and have dtype uint8, int8, or float32.
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param1 x param2 define the neighborhood over which the pixels
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are summed. If param2 is zero it is set equal to param1.
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param3 and param4 are ignored.
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in_place operation is not supported.
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||||
CV_BLUR:
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||||
Source image must have dtype uint8, int8, or float32.
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param1 x param2 define the neighborhood over which the pixels
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||||
are summed. If param2 is zero it is set equal to param1.
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||||
param3 and param4 are ignored.
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||||
CV_GAUSSIAN:
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Source image must have dtype uint8, int8, or float32.
|
||||
param1 x param2 defines the size of the gaussian kernel.
|
||||
If param2 is zero it is set equal to param1.
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||||
param3 is the standard deviation of the kernel.
|
||||
If param3 is zero, an optimum stddev is calculated based
|
||||
on the kernel size. If both param1 and param2 or zero,
|
||||
then an optimum kernel size is calculated based on
|
||||
param3.
|
||||
in_place operation is supported.
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||||
CV_MEDIAN:
|
||||
Source image must have dtype uint8, or int8.
|
||||
param1 x param1 define the neigborhood over which
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||||
to find the median.
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||||
param2, param3, and param4 are ignored.
|
||||
in_place operation is not supported.
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CV_BILATERAL:
|
||||
Source image must have dtype uint8, or int8.
|
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param1 x param2 define the neighborhood.
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param3 defines the color stddev.
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||||
param4 defines the space stddev.
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||||
in_place operation is not supported.
|
||||
|
||||
Using standard sigma for small kernels (3x3 to 7x7)
|
||||
gives better speed.''')
|
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def cvSmooth(np.ndarray src, int smoothtype=CV_GAUSSIAN, int param1=3,
|
||||
int param2=0, double param3=0, double param4=0,
|
||||
bool in_place=False):
|
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"""
|
||||
better doc string needed.
|
||||
for now:
|
||||
http://opencv.willowgarage.com/documentation/cvreference.html
|
||||
"""
|
||||
|
||||
validate_array(src)
|
||||
|
||||
@@ -1444,6 +1642,42 @@ def cvSmooth(np.ndarray src, int smoothtype=CV_GAUSSIAN, int param1=3,
|
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else:
|
||||
return out
|
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|
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|
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#-----------
|
||||
# cvFilter2D
|
||||
#-----------
|
||||
|
||||
@cvdoc(package='cv', group='image', doc=\
|
||||
'''Convolve an image with the given kernel.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvFilter2D(src, kernel, anchor=None, in_place=False)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray
|
||||
The source image.
|
||||
kernel : ndarray, 2D, dtype=float32
|
||||
The kernel with which to convolve the image.
|
||||
anchor : 2-tuple, (x, y)
|
||||
The kernel anchor.
|
||||
in_place : bool
|
||||
If True, perform the operation in_place.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out/None : ndarray or None
|
||||
If in_place is True, returns None.
|
||||
Otherwise a new array is returned which is the result
|
||||
of the convolution.
|
||||
|
||||
Notes
|
||||
-----
|
||||
This is a high performance function. OpenCV automatically
|
||||
determines, based on the size of the image and the kernel,
|
||||
whether it will faster to do the convolution in the spatial
|
||||
or the frequency domain, and behaves accordingly.''')
|
||||
def cvFilter2D(np.ndarray src, np.ndarray kernel, anchor=None, in_place=False):
|
||||
|
||||
validate_array(src)
|
||||
@@ -1490,6 +1724,35 @@ def cvFilter2D(np.ndarray src, np.ndarray kernel, anchor=None, in_place=False):
|
||||
else:
|
||||
return out
|
||||
|
||||
|
||||
#-----------
|
||||
# cvIntegral
|
||||
#-----------
|
||||
|
||||
@cvdoc(package='cv', group='image', doc=\
|
||||
'''Calculate the integral of an image.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvIntegral(src, square_sum=False, titled_sum=False)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, dtyp=[uint8, float32, float64]
|
||||
The source image.
|
||||
square_sum : bool
|
||||
If True, also returns the square sum.
|
||||
tilted_sum : bool
|
||||
If True, also returns the titled sum (45 degree tilt)
|
||||
|
||||
Returns
|
||||
-------
|
||||
[out1, out2, out3] : list of ndarray's
|
||||
Returns a list consisting at least of:
|
||||
out1: the integral image, and optionally:
|
||||
out2: the square sum image
|
||||
out3: the titled sum image,
|
||||
or any combination of these two.''')
|
||||
def cvIntegral(np.ndarray src, square_sum=False, tilted_sum=False):
|
||||
|
||||
validate_array(src)
|
||||
@@ -1544,6 +1807,56 @@ def cvIntegral(np.ndarray src, square_sum=False, tilted_sum=False):
|
||||
|
||||
return out
|
||||
|
||||
|
||||
#-----------
|
||||
# cvCvtColor
|
||||
#-----------
|
||||
|
||||
@cvdoc(package='cv', group='image', doc=\
|
||||
'''Convert an image to another color space.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvCvtColor(src, code)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, dtype=[uint8, uint16, float32]
|
||||
The source image.
|
||||
code : integer
|
||||
A flag representing which color conversion to perform.
|
||||
Valid flags are the following:
|
||||
CV_BGR2BGRA, CV_RGB2RGBA, CV_BGRA2BGR, CV_RGBA2RGB,
|
||||
CV_BGR2RGBA, CV_RGB2BGRA, CV_RGBA2BGR, CV_BGRA2RGB,
|
||||
CV_BGR2RGB, CV_RGB2BGR, CV_BGRA2RGBA, CV_RGBA2BGRA,
|
||||
CV_BGR2GRAY, CV_RGB2GRAY, CV_GRAY2BGR, CV_GRAY2RGB,
|
||||
CV_GRAY2BGRA, CV_GRAY2RGBA, CV_BGRA2GRAY, CV_RGBA2GRAY,
|
||||
CV_BGR2BGR565, CV_RGB2BGR565, CV_BGR5652BGR, CV_BGR5652RGB,
|
||||
CV_BGRA2BGR565, CV_RGBA2BGR565, CV_BGR5652BGRA, CV_BGR5652RGBA,
|
||||
CV_GRAY2BGR565, CV_BGR5652GRAY, CV_BGR2BGR555, CV_RGB2BGR555,
|
||||
CV_BGR5552BGR, CV_BGR5552RGB, CV_BGRA2BGR555, CV_RGBA2BGR555,
|
||||
CV_BGR5552BGRA, CV_BGR5552RGBA, CV_GRAY2BGR555, CV_BGR5552GRAY,
|
||||
CV_BGR2XYZ, CV_RGB2XYZ, CV_XYZ2BGR, CV_XYZ2RGB,
|
||||
CV_BGR2YCrCb, CV_RGB2YCrCb, CV_YCrCb2BGR, CV_YCrCb2RGB,
|
||||
CV_BGR2HSV, CV_RGB2HSV, CV_BGR2Lab, CV_RGB2Lab,
|
||||
CV_BayerBG2BGR, CV_BayerGB2BGR, CV_BayerRG2BGR, CV_BayerGR2BGR,
|
||||
CV_BayerBG2RGB, CV_BayerGB2RGB, CV_BayerRG2RGB, CV_BayerGR2RGB,
|
||||
CV_BGR2Luv, CV_RGB2Luv, CV_BGR2HLS, CV_RGB2HLS,
|
||||
CV_HSV2BGR, CV_HSV2RGB, CV_Lab2BGR, CV_Lab2RGB,
|
||||
CV_Luv2BGR, CV_Luv2RGB, CV_HLS2BGR, CV_HLS2RGB
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
A new image in the requested color-space, with
|
||||
an appropriate dtype.
|
||||
|
||||
Notes
|
||||
-----
|
||||
Not all conversion types support all dtypes.
|
||||
An exception will be raise if the dtype is not supported.
|
||||
See the OpenCV documentation for more details
|
||||
about the specific color conversions.''')
|
||||
def cvCvtColor(np.ndarray src, int code):
|
||||
|
||||
validate_array(src)
|
||||
@@ -1588,6 +1901,45 @@ def cvCvtColor(np.ndarray src, int code):
|
||||
|
||||
return out
|
||||
|
||||
|
||||
#------------
|
||||
# cvThreshold
|
||||
#------------
|
||||
|
||||
@cvdoc(package='cv', group='image', doc=\
|
||||
'''Threshold an image.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvThreshold(src, threshold, max_value=255, threshold_type=CV_THRESH_BINARY,
|
||||
use_otsu=False)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, 2D, dtype=[uint8, float32]
|
||||
threshold : float
|
||||
The threshold value. (decision value)
|
||||
max_value : float
|
||||
The maximum value.
|
||||
threshold_type : integer
|
||||
The flag representing which type of thresholding to apply.
|
||||
Valid flags are:
|
||||
CV_THRESH_BINARY (max_value if src(x,y) > threshold else 0)
|
||||
CV_THRESH_BINARY_INV (0 if src(x,y) > threshold else max_value)
|
||||
CV_THRESH_TRUNC (threshold if src(x,y) > threshold else src(x,y))
|
||||
CV_THRESH_TOZERO (src(x,y) if src(x,y) > threshold else 0)
|
||||
CV_THRESH_TOZERO_INV (0 if src(x,y) > threshold else src(x,y))
|
||||
use_otsu : bool
|
||||
If true, the optimum threshold is automatically computed
|
||||
and the passed in threshold value is ignored.
|
||||
Only implemented for uint8 source images.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out/(out, threshold) : ndarray or (ndarray, float)
|
||||
If use_otsu is True, then the computed threshold value is
|
||||
returned in addition to the thresholded image. Otherwise
|
||||
just the thresholded image is returned.''')
|
||||
def cvThreshold(np.ndarray src, double threshold, double max_value=255,
|
||||
int threshold_type=CV_THRESH_BINARY, use_otsu=False):
|
||||
|
||||
@@ -1614,6 +1966,45 @@ def cvThreshold(np.ndarray src, double threshold, double max_value=255,
|
||||
else:
|
||||
return out
|
||||
|
||||
|
||||
#--------------------
|
||||
# cvAdaptiveThreshold
|
||||
#--------------------
|
||||
|
||||
@cvdoc(package='cv', group='image', doc=\
|
||||
'''Apply an adaptive threshold to an image.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvAdaptiveThreshold(src, max_value,
|
||||
adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C,
|
||||
threshold_type=CV_THRESH_BINARY,
|
||||
block_size=3, param1=5)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, 2D, dtype=uint8
|
||||
max_value : float
|
||||
The maximum value.
|
||||
adaptive_method : integer
|
||||
The flag representing the adaptive method.
|
||||
Valid flags are:
|
||||
CV_ADAPTIVE_THRESH_MEAN_C (uses mean of the neighborhood)
|
||||
CV_ADAPTIVE_THRESH_GAUSSIAN_C (uses gaussian of the neighborhood)
|
||||
threshold_type : integer
|
||||
The flag representing which type of thresholding to apply.
|
||||
Valid flags are:
|
||||
CV_THRESH_BINARY (max_value if src(x,y) > threshold else 0)
|
||||
CV_THRESH_BINARY_INV (0 if src(x,y) > threshold else max_value)
|
||||
block_size : integer
|
||||
Defines a block_size x block_size neighborhood
|
||||
param1 : float
|
||||
The weight to be subtracted from the neighborhood computation.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
The thresholded image.''')
|
||||
def cvAdaptiveThreshold(np.ndarray src, double max_value,
|
||||
int adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C,
|
||||
int threshold_type=CV_THRESH_BINARY,
|
||||
@@ -1646,6 +2037,27 @@ def cvAdaptiveThreshold(np.ndarray src, double max_value,
|
||||
|
||||
return out
|
||||
|
||||
|
||||
#----------
|
||||
# cvPyrDown
|
||||
#----------
|
||||
|
||||
@cvdoc(package='cv', group='image', doc=\
|
||||
'''Downsample an image.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvPyrDown(src)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, dtype=[uint8, uint16, float32, float64]
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
Downsampled image half the size of the original
|
||||
in each dimension.''')
|
||||
def cvPyrDown(np.ndarray src):
|
||||
|
||||
validate_array(src)
|
||||
@@ -1669,6 +2081,27 @@ def cvPyrDown(np.ndarray src):
|
||||
|
||||
return out
|
||||
|
||||
|
||||
#--------
|
||||
# cvPyrUp
|
||||
#--------
|
||||
|
||||
@cvdoc(package='cv', group='image', doc=\
|
||||
'''Upsample an image.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvPyrUp(src)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, dtype=[uint8, uint16, float32, float64]
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
Upsampled image twice the size of the original
|
||||
in each dimension.''')
|
||||
def cvPyrUp(np.ndarray src):
|
||||
|
||||
validate_array(src)
|
||||
@@ -1692,6 +2125,37 @@ def cvPyrUp(np.ndarray src):
|
||||
|
||||
return out
|
||||
|
||||
|
||||
#-------------------
|
||||
# cvCalibrateCamera2
|
||||
#-------------------
|
||||
|
||||
@cvdoc(package='cv', group='calibration', doc=\
|
||||
'''Finds the intrinsic and extrinsic camera parameters
|
||||
using a calibration pattern.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvCalibrateCamera2(object_points, image_points, point_counts, image_size)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
object_points : ndarray, Nx3
|
||||
An array representing the (X, Y, Z) known coordinates of the
|
||||
calibration object.
|
||||
image_points : ndarry, Nx2
|
||||
An array representing the pixel image coordinate of the
|
||||
points in object_points.
|
||||
point_counts : ndarry, 1D, dtype=int32
|
||||
Vector containing the number of points in each particular view.
|
||||
image_size : 2-tuple, (height, width)
|
||||
The height and width of the images used.
|
||||
|
||||
Returns
|
||||
-------
|
||||
(intrinsics, distortion) : ndarray 3x3, ndarray 5-vector
|
||||
Intrinsics is the 3x3 camera instrinsics matrix.
|
||||
Distortion is the 5-vector of distortion coefficients.''')
|
||||
def cvCalibrateCamera2(np.ndarray object_points, np.ndarray image_points,
|
||||
np.ndarray point_counts, image_size):
|
||||
|
||||
@@ -1755,19 +2219,42 @@ def cvCalibrateCamera2(np.ndarray object_points, np.ndarray image_points,
|
||||
|
||||
return intrinsics, distortion
|
||||
|
||||
|
||||
#------------------------
|
||||
# cvFindChessboardCorners
|
||||
#------------------------
|
||||
|
||||
@cvdoc(package='cv', group='calibration', doc=\
|
||||
'''Finds the position of the internal corners of a chessboard.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvFindChessboardCorners(src, pattern_size, flag=CV_CALIB_CB_ADAPTIVE_THRESH)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, dtype=uint8
|
||||
Image to search for chessboard corners.
|
||||
pattern_size : 2-tuple of inner corners (h,w)
|
||||
flag : integer
|
||||
CV_CALIB_CB_ADAPTIVE_THRESH - use adaptive thresholding
|
||||
to convert the image to black and white,
|
||||
rather than a fixed threshold level
|
||||
(computed from the average image brightness).
|
||||
CV_CALIB_CB_NORMALIZE_IMAGE - normalize the image using
|
||||
cvNormalizeHist() before applying fixed or adaptive
|
||||
thresholding.
|
||||
CV_CALIB_CB_FILTER_QUADS - use additional criteria
|
||||
(like contour area, perimeter, square-like shape) to
|
||||
filter out false quads that are extracted at the contour
|
||||
retrieval stage.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray Nx2
|
||||
An nx2 array of the corners found.''')
|
||||
def cvFindChessboardCorners(np.ndarray src, pattern_size,
|
||||
int flags = CV_CALIB_CB_ADAPTIVE_THRESH):
|
||||
"""
|
||||
Wrapper around the OpenCV cvFindChessboardCorners function.
|
||||
|
||||
src - Image to search for chessboard corners
|
||||
pattern_size - Tuple of inner corners (h,w)
|
||||
flags - see appropriate flags in opencv docs
|
||||
http://opencv.willowgarage.com/documentation/cvreference.html
|
||||
|
||||
returns - an nx2 array of the corners found.
|
||||
|
||||
"""
|
||||
int flag=CV_CALIB_CB_ADAPTIVE_THRESH):
|
||||
|
||||
validate_array(src)
|
||||
|
||||
@@ -1791,27 +2278,44 @@ def cvFindChessboardCorners(np.ndarray src, pattern_size,
|
||||
|
||||
cdef int ncorners_found
|
||||
c_cvFindChessboardCorners(&srcimg, cvpattern_size, cvpoints,
|
||||
&ncorners_found, flags)
|
||||
&ncorners_found, flag)
|
||||
|
||||
return out[:ncorners_found]
|
||||
|
||||
|
||||
#------------------------
|
||||
# cvFindChessboardCorners
|
||||
#------------------------
|
||||
|
||||
@cvdoc(package='cv', group='calibration', doc=\
|
||||
'''Renders found chessboard corners into an image.
|
||||
|
||||
Signature
|
||||
---------
|
||||
cvDrawChessboardCorners(src, pattern_size, corners, in_place=False)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, dim 3, dtype: uint8
|
||||
Image to draw into.
|
||||
pattern_size : 2-tuple, (h, w)
|
||||
Number of inner corners (h,w)
|
||||
corners : ndarray, nx2, dtype=float32
|
||||
Corners found in the image. See cvFindChessboardCorners and
|
||||
cvFindCornerSubPix
|
||||
in_place: bool
|
||||
If true, perform the drawing on the submitted
|
||||
image. If false, a copy of the image will be made and drawn to.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out/None : ndarray or none
|
||||
If in_place is True, the function returns None.
|
||||
Otherwise, the function returns a new image with
|
||||
the corners drawn into it.''')
|
||||
def cvDrawChessboardCorners(np.ndarray src, pattern_size, np.ndarray corners,
|
||||
in_place=False):
|
||||
"""
|
||||
Wrapper around the OpenCV cvDrawChessboardCorners function.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
src : ndarray, dim 3, dtype: uint8
|
||||
Image to draw into.
|
||||
pattern_size : array_like, shape (2,)
|
||||
Number of inner corners (h,w)
|
||||
corners : ndarray, shape (n,2), dtype: float32
|
||||
Corners found in the image. See cvFindChessboardCorners and
|
||||
cvFindCornerSubPix
|
||||
in_place: True/False (default=True) perform the drawing on the submitted
|
||||
image. If false, a copy of the image will be made and drawn to.
|
||||
"""
|
||||
validate_array(src)
|
||||
|
||||
assert_nchannels(src, [3])
|
||||
|
||||
Reference in New Issue
Block a user