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https://github.com/wassname/scikit-image.git
synced 2026-07-15 11:25:53 +08:00
Added some more docs and fixed up a few warts in the process.
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@@ -871,20 +871,49 @@ def cvGetRectSubPix(np.ndarray src, size, center):
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return out
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#----------------------
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# cvGetQuadrangleSubPix
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#----------------------
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@cvdoc(package='cv', group='image', doc=\
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'''Retrieves the pixel quandrangle from an image with
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sub-pixel accuracy. In english: apply an affine transform to an image.
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Signature
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---------
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cvGetQuadrangleSubPix(src, warpmat, float_out=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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warpmat : ndarray, 2x3
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The affine transformation to apply to the src image.
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float_out : bool
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If True, the return array will have dtype np.float32.
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Otherwise, the return array will have the same dtype
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as the src array.
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If True, the src array MUST have dtype np.uint8
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Returns
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-------
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out : ndarray
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Warped image of same size as src.
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Notes
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-----
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The values of pixels at non-integer coordinates are retrieved
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using bilinear interpolation. When the function needs pixels
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outside of the image, it uses replication border mode to
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reconstruct the values. Every channel of multiple-channel
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images is processed independently.
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This function has less overhead than cvWarpAffine
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and should be used unless specific feature of that
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function are required.''')
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def cvGetQuadrangleSubPix(np.ndarray src, np.ndarray warpmat, float_out=False):
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''' Retrieves the pixel quandrangle from an image with
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sub-pixel accuracy. In english: apply and affine transform to an image.
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Parameters:
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src - input image
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warpmat - a 2x3 array which is an affine transform
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float_out - return a float32 array. If true, input must be
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uint8. If false, output is same type as input.
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Return:
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warped image of same size and dtype as src. Except when
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float_out == True (see above)
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'''
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validate_array(src)
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validate_array(warpmat)
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@@ -919,22 +948,51 @@ def cvGetQuadrangleSubPix(np.ndarray src, np.ndarray warpmat, float_out=False):
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return out
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def cvResize(np.ndarray src, height=None, width=None,
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int method=CV_INTER_LINEAR):
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"""
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better doc string needed.
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for now:
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http://opencv.willowgarage.com/documentation/cvreference.html
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"""
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#---------
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# cvResize
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#---------
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@cvdoc(package='cv', group='image', doc=\
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'''Resize an to the given size.
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Signature
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---------
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cvResize(src, size, method=CV_INTER_LINEAR)
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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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size : tuple, (height, width)
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The target resize size.
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method : integer
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The interpolation method used for resizing.
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Supported methods are:
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CV_INTER_NN
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CV_INTER_LINEAR
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CV_INTER_AREA
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CV_INTER_CUBIC
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Returns
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-------
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out : ndarray
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The resized image.''')
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def cvResize(np.ndarray src, size, int method=CV_INTER_LINEAR):
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validate_array(src)
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if not height or not width:
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raise ValueError('width and height must not be none')
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if len(size) != 2:
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raise ValueError('size must be a 2-tuple (height, width)')
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if method not in [CV_INTER_NN, CV_INTER_LINEAR, CV_INTER_AREA,
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CV_INTER_CUBIC]:
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raise ValueError('unsupported interpolation type')
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cdef int ndim = src.ndim
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cdef np.npy_intp* shape = clone_array_shape(src)
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shape[0] = <np.npy_intp>height
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shape[1] = <np.npy_intp>width
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shape[0] = <np.npy_intp>size[0]
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shape[1] = <np.npy_intp>size[1]
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cdef np.ndarray out = new_array(ndim, shape, src.dtype)
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validate_array(out)
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@@ -950,23 +1008,62 @@ def cvResize(np.ndarray src, height=None, width=None,
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return out
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#-------------
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# cvWarpAffine
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#-------------
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@cvdoc(package='cv', group='image', doc=\
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'''Applies an affine transformation to the image.
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Signature
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---------
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cvWarpAffine(src, warpmat, flag=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS
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fillval=(0., 0., 0., 0.))
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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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warpmat : ndarray, 2x3
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The affine transformation to apply to the src image.
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flag : integer
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A combination of interpolation and method flags.
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Supported flags are: (see notes)
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Interpolation:
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CV_INTER_NN
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CV_INTER_LINEAR
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CV_INTER_AREA
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CV_INTER_CUBIC
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Method:
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CV_WARP_FILL_OUTLIERS
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CV_WARP_INVERSE_MAP
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fillval : 4-tuple, (R, G, B, A)
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The color to fill in missing pixels. Defaults to black.
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For < 4 channel images, use 0.'s for the value.
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Returns
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-------
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out : ndarray
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The warped image of same size and dtype as src.
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Notes
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-----
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CV_WARP_FILL_OUTLIERS - fills all of the destination image pixels;
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if some of them correspond to outliers in the source image,
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they are set to fillval.
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CV_WARP_INVERSE_MAP - indicates that warpmat is inversely transformed
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from the destination image to the source and, thus, can be used
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directly for pixel interpolation. Otherwise, the function finds
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the inverse transform from warpmat.
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This function has a larger overhead than cvGetQuadrangleSubPix,
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and that function should be used instead, unless specific
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features of this function are needed.''')
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def cvWarpAffine(np.ndarray src, np.ndarray warpmat,
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int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS,
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int flag=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS,
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fillval=(0., 0., 0., 0.)):
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''' Applies an affine transformation to an image.
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Parameters:
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src - source image
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warpmat - 2x3 affine transformation
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flags - a combination of interpolation and method flags.
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see opencv documentation for more details
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fillval - a 4 tuple of a color to fill the background
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defaults to black.
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Returns:
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a warped image the same size and dtype as src
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'''
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validate_array(src)
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validate_array(warpmat)
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if len(fillval) != 4:
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@@ -977,6 +1074,10 @@ def cvWarpAffine(np.ndarray src, np.ndarray warpmat,
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if warpmat.shape[0] != 2 or warpmat.shape[1] != 3:
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raise ValueError('warpmat must be 2x3')
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valid_flags = [0, 1, 2, 3, 8, 16, 9, 17, 11, 19, 10, 18]
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if flag not in valid_flags:
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raise ValueError('unsupported flag combination')
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cdef np.ndarray out
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out = new_array_like(src)
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@@ -995,29 +1096,64 @@ def cvWarpAffine(np.ndarray src, np.ndarray warpmat,
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populate_iplimage(warpmat, &cvmat)
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cvmatptr = cvmat_ptr_from_iplimage(&cvmat)
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c_cvWarpAffine(&srcimg, &outimg, cvmatptr, flags, cvfill)
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c_cvWarpAffine(&srcimg, &outimg, cvmatptr, flag, cvfill)
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PyMem_Free(cvmatptr)
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return out
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#------------------
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# cvWarpPerspective
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#------------------
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@cvdoc(package='cv', group='image', doc=\
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'''Applies a perspective transformation to an image.
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Signature
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---------
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cvWarpPerspective(src, warpmat, flag=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS
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fillval=(0., 0., 0., 0.))
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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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warpmat : ndarray, 3x3
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The affine transformation to apply to the src image.
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flag : integer
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A combination of interpolation and method flags.
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Supported flags are: (see notes)
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Interpolation:
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CV_INTER_NN
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CV_INTER_LINEAR
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CV_INTER_AREA
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CV_INTER_CUBIC
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Method:
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CV_WARP_FILL_OUTLIERS
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CV_WARP_INVERSE_MAP
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fillval : 4-tuple, (R, G, B, A)
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The color to fill in missing pixels. Defaults to black.
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For < 4 channel images, use 0.'s for the value.
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Returns
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-------
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out : ndarray
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The warped image of same size and dtype as src.
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Notes
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-----
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CV_WARP_FILL_OUTLIERS - fills all of the destination image pixels;
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if some of them correspond to outliers in the source image,
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they are set to fillval.
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CV_WARP_INVERSE_MAP - indicates that warpmat is inversely transformed
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from the destination image to the source and, thus, can be used
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directly for pixel interpolation. Otherwise, the function finds
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the inverse transform from warpmat.''')
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def cvWarpPerspective(np.ndarray src, np.ndarray warpmat,
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int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS,
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int flag=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS,
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fillval=(0., 0., 0., 0.)):
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''' Applies a perspective transformation to an image.
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Parameters:
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src - source image
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warpmat - 3x3 perspective transformation
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flags - a combination of interpolation and method flags.
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see opencv documentation for more details
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fillval - a 4 tuple of a color to fill the background
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defaults to black.
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Returns:
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a warped image the same size and dtype as src
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'''
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validate_array(src)
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validate_array(warpmat)
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if len(fillval) != 4:
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@@ -1027,6 +1163,10 @@ def cvWarpPerspective(np.ndarray src, np.ndarray warpmat,
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if warpmat.shape[0] != 3 or warpmat.shape[1] != 3:
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raise ValueError('warpmat must be 3x3')
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valid_flags = [0, 1, 2, 3, 8, 16, 9, 17, 11, 19, 10, 18]
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if flag not in valid_flags:
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raise ValueError('unsupported flag combination')
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cdef np.ndarray out
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out = new_array_like(src)
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@@ -1044,19 +1184,72 @@ def cvWarpPerspective(np.ndarray src, np.ndarray warpmat,
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populate_iplimage(out, &outimg)
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populate_iplimage(warpmat, &cvmat)
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cvmatptr = cvmat_ptr_from_iplimage(&cvmat)
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c_cvWarpPerspective(&srcimg, &outimg, cvmatptr, flags, cvfill)
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c_cvWarpPerspective(&srcimg, &outimg, cvmatptr, flag, cvfill)
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PyMem_Free(cvmatptr)
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return out
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#-----------
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# cvLogPolar
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#-----------
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@cvdoc(package='cv', group='image', doc=\
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'''Remaps and image to Log-Polar space.
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Signature
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---------
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cvLogPolar(src, center, M, flag=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS)
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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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center : tuple, (x, y)
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The keypoint for the log polar transform.
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M : float
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The scale factor for the transform.
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(40 is a good starting point for a 256x256 image)
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flag : integer
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A combination of interpolation and method flags.
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Supported flags are: (see notes)
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Interpolation:
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CV_INTER_NN
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CV_INTER_LINEAR
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CV_INTER_AREA
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CV_INTER_CUBIC
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Method:
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CV_WARP_FILL_OUTLIERS
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CV_WARP_INVERSE_MAP
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Returns
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-------
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out : ndarray
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A transformed image the same size and dtype as src.
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Notes
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-----
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CV_WARP_FILL_OUTLIERS - fills all of the destination image pixels;
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if some of them correspond to outliers in the source image,
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they are set to zero.
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CV_WARP_INVERSE_MAP - assume that the source image is already
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in Log-Polar space, and transform back to cartesian space.
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The function emulates the human “foveal” vision and can be used
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for fast scale and rotation-invariant template matching,
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for object tracking and so forth.''')
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def cvLogPolar(np.ndarray src, center, double M,
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int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS):
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int flag=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS):
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validate_array(src)
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if len(center) != 2:
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raise ValueError('center must be a 2-tuple')
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valid_flags = [0, 16, 8, 24, 1, 17, 9, 25, 2, 18, 10, 26, 3, 19, 11, 27]
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if flag not in valid_flags:
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raise ValueError('unsupported flag combination')
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cdef np.ndarray out = new_array_like(src)
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cdef CvPoint2D32f cv_center
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@@ -1068,7 +1261,7 @@ def cvLogPolar(np.ndarray src, center, double M,
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populate_iplimage(src, &srcimg)
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populate_iplimage(out, &outimg)
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c_cvLogPolar(&srcimg, &outimg, cv_center, M, flags)
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c_cvLogPolar(&srcimg, &outimg, cv_center, M, flag)
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return out
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def cvErode(np.ndarray src, np.ndarray element=None, int iterations=1,
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@@ -104,8 +104,8 @@ class TestGetQuadrangleSubPix(OpenCVTest):
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class TestResize(OpenCVTest):
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@opencv_skip
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def test_cvResize(self):
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cvResize(self.lena_RGB_U8, height=50, width=50, method=CV_INTER_LINEAR)
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cvResize(self.lena_RGB_U8, height=200, width=200, method=CV_INTER_CUBIC)
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cvResize(self.lena_RGB_U8, (50, 50), method=CV_INTER_LINEAR)
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cvResize(self.lena_RGB_U8, (200, 200), method=CV_INTER_CUBIC)
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class TestWarpAffine(OpenCVTest):
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