API Reference¶
Computing the DT-CWT¶
These functions provide API-level compatibility with MATLAB.
Note
The functionality of dtwavexfm2b and dtwaveifm2b have been folded into dtwavexfm2 and dtwaveifm2. For convenience of porting MATLAB scripts, the original function names are available in the dtcwt module as aliases but they should not be used in new code.
- dtcwt.dtwavexfm(X, nlevels=3, biort='near_sym_a', qshift='qshift_a', include_scale=False)¶
Perform a n-level DTCWT decompostion on a 1D column vector X (or on the columns of a matrix X).
Parameters: Returns Yl: The real lowpass image from the final level
Returns Yh: A tuple containing the (N, M, 6) shape complex highpass subimages for each level.
Returns Yscale: If include_scale is True, a tuple containing real lowpass coefficients for every scale.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a 5-level transform on the real image X using the 13,19-tap # filters for level 1 and the Q-shift 14-tap filters for levels >= 2. Yl, Yh = dtwavexfm(X,5,'near_sym_b','qshift_b')
- dtcwt.dtwaveifm(Yl, Yh, biort='near_sym_a', qshift='qshift_a', gain_mask=None)¶
Perform an n-level dual-tree complex wavelet (DTCWT) 1D reconstruction.
Parameters: Returns Z: Reconstructed real array.
The l-th element of gain_mask is gain for wavelet subband at level l. If gain_mask[l] == 0, no computation is performed for band l. Default gain_mask is all ones. Note that l is 0-indexed.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a reconstruction from Yl,Yh using the 13,19-tap filters # for level 1 and the Q-shift 14-tap filters for levels >= 2. Z = dtwaveifm(Yl, Yh, 'near_sym_b', 'qshift_b')
- dtcwt.dtwavexfm2(X, nlevels=3, biort='near_sym_a', qshift='qshift_a', include_scale=False)¶
Perform a n-level DTCWT-2D decompostion on a 2D matrix X.
Parameters: Returns Yl: The real lowpass image from the final level
Returns Yh: A tuple containing the complex highpass subimages for each level.
Returns Yscale: If include_scale is True, a tuple containing real lowpass coefficients for every scale.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a 3-level transform on the real image X using the 13,19-tap # filters for level 1 and the Q-shift 14-tap filters for levels >= 2. Yl, Yh = dtwavexfm2(X, 3, 'near_sym_b', 'qshift_b')
- dtcwt.dtwaveifm2(Yl, Yh, biort='near_sym_a', qshift='qshift_a', gain_mask=None)¶
Perform an n-level dual-tree complex wavelet (DTCWT) 2D reconstruction.
Parameters: Returns Z: Reconstructed real array
The (d, l)-th element of gain_mask is gain for subband with direction d at level l. If gain_mask[d,l] == 0, no computation is performed for band (d,l). Default gain_mask is all ones. Note that both d and l are zero-indexed.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a 3-level reconstruction from Yl,Yh using the 13,19-tap # filters for level 1 and the Q-shift 14-tap filters for levels >= 2. Z = dtwaveifm2(Yl, Yh, 'near_sym_b', 'qshift_b')
- dtcwt.dtwavexfm2b(X, nlevels=3, biort='near_sym_a', qshift='qshift_a', include_scale=False)¶
Perform a n-level DTCWT-2D decompostion on a 2D matrix X.
Parameters: Returns Yl: The real lowpass image from the final level
Returns Yh: A tuple containing the complex highpass subimages for each level.
Returns Yscale: If include_scale is True, a tuple containing real lowpass coefficients for every scale.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a 3-level transform on the real image X using the 13,19-tap # filters for level 1 and the Q-shift 14-tap filters for levels >= 2. Yl, Yh = dtwavexfm2(X, 3, 'near_sym_b', 'qshift_b')
- dtcwt.dtwaveifm2b(Yl, Yh, biort='near_sym_a', qshift='qshift_a', gain_mask=None)¶
Perform an n-level dual-tree complex wavelet (DTCWT) 2D reconstruction.
Parameters: Returns Z: Reconstructed real array
The (d, l)-th element of gain_mask is gain for subband with direction d at level l. If gain_mask[d,l] == 0, no computation is performed for band (d,l). Default gain_mask is all ones. Note that both d and l are zero-indexed.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a 3-level reconstruction from Yl,Yh using the 13,19-tap # filters for level 1 and the Q-shift 14-tap filters for levels >= 2. Z = dtwaveifm2(Yl, Yh, 'near_sym_b', 'qshift_b')
- dtcwt.dtwavexfm3(X, nlevels=3, biort='near_sym_a', qshift='qshift_a', ext_mode=4, discard_level_1=False)¶
Perform a n-level DTCWT-3D decompostion on a 3D matrix X.
Parameters: Returns Yl: The real lowpass image from the final level
Returns Yh: A tuple containing the complex highpass subimages for each level.
Each element of Yh is a 4D complex array with the 4th dimension having size 28. The 3D slice Yh[l][:,:,:,d] corresponds to the complex higpass coefficients for direction d at level l where d and l are both 0-indexed.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
There are two values for ext_mode, either 4 or 8. If ext_mode = 4, check whether 1st level is divisible by 2 (if not we raise a ValueError). Also check whether from 2nd level onwards, the coefs can be divided by 4. If any dimension size is not a multiple of 4, append extra coefs by repeating the edges. If ext_mode = 8, check whether 1st level is divisible by 4 (if not we raise a ValueError). Also check whether from 2nd level onwards, the coeffs can be divided by 8. If any dimension size is not a multiple of 8, append extra coeffs by repeating the edges twice.
If discard_level_1 is True the highpass coefficients at level 1 will be discarded. (And, in fact, will never be calculated.) This turns the transform from being 8:1 redundant to being 1:1 redundant at the cost of no-longer allowing perfect reconstruction. If this option is selected then Yh[0] will be None. Note that dtwaveifm3() will accepts Yh[0] being None and will treat it as being zero.
Example:
# Performs a 3-level transform on the real 3D array X using the 13,19-tap # filters for level 1 and the Q-shift 14-tap filters for levels >= 2. Yl, Yh = dtwavexfm3(X, 3, 'near_sym_b', 'qshift_b')
- dtcwt.dtwaveifm3(Yl, Yh, biort='near_sym_a', qshift='qshift_a', ext_mode=4)¶
Perform an n-level dual-tree complex wavelet (DTCWT) 3D reconstruction.
Parameters: Returns Z: Reconstructed real image matrix.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
There are two values for ext_mode, either 4 or 8. If ext_mode = 4, check whether 1st level is divisible by 2 (if not we raise a ValueError). Also check whether from 2nd level onwards, the coefs can be divided by 4. If any dimension size is not a multiple of 4, append extra coefs by repeating the edges. If ext_mode = 8, check whether 1st level is divisible by 4 (if not we raise a ValueError). Also check whether from 2nd level onwards, the coeffs can be divided by 8. If any dimension size is not a multiple of 8, append extra coeffs by repeating the edges twice.
Example:
# Performs a 3-level reconstruction from Yl,Yh using the 13,19-tap # filters for level 1 and the Q-shift 14-tap filters for levels >= 2. Z = dtwaveifm3(Yl, Yh, 'near_sym_b', 'qshift_b')
- dtcwt.biort(name)¶
Load level 1 wavelet by name.
Parameters: name – a string specifying the wavelet family name Returns: a tuple of vectors giving filter coefficients Name Wavelet antonini Antonini 9,7 tap filters. legall LeGall 5,3 tap filters. near_sym_a Near-Symmetric 5,7 tap filters. near_sym_b Near-Symmetric 13,19 tap filters. near_sym_b_bp Near-Symmetric 13,19 tap filters + BP filter Return a tuple whose elements are a vector specifying the h0o, g0o, h1o and g1o coefficients.
See Rotational symmetry modified wavelet transform for an explanation of the near_sym_b_bp wavelet filters.
Raises: - IOError – if name does not correspond to a set of wavelets known to the library.
- ValueError – if name specifies a qshift() wavelet.
- dtcwt.qshift(name)¶
Load level >=2 wavelet by name,
Parameters: name – a string specifying the wavelet family name Returns: a tuple of vectors giving filter coefficients Name Wavelet qshift_06 Quarter Sample Shift Orthogonal (Q-Shift) 10,10 tap filters, (only 6,6 non-zero taps). qshift_a Q-shift 10,10 tap filters, (with 10,10 non-zero taps, unlike qshift_06). qshift_b Q-Shift 14,14 tap filters. qshift_c Q-Shift 16,16 tap filters. qshift_d Q-Shift 18,18 tap filters. qshift_b_bp Q-Shift 18,18 tap filters + BP Return a tuple whose elements are a vector specifying the h0a, h0b, g0a, g0b, h1a, h1b, g1a and g1b coefficients.
See Rotational symmetry modified wavelet transform for an explanation of the qshift_b_bp wavelet filters.
Raises: - IOError – if name does not correspond to a set of wavelets known to the library.
- ValueError – if name specifies a biort() wavelet.
Backends¶
Base classes¶
- class dtcwt.backend.base.ReconstructedSignal(value)¶
A representation of the reconstructed signal from the inverse transform. A backend is free to implement their own version of this class providing it corresponds to the interface documented.
- value¶
A NumPy-compatible array containing the reconstructed signal.
- class dtcwt.backend.base.Transform2d(biort='near_sym_a', qshift='qshift_a')¶
An implementation of a 2D DT-CWT transformation. Backends must provide a transform class which provides an interface compatible with this base class.
Parameters: - biort – Level 1 wavelets to use. See biort().
- qshift – Level >= 2 wavelets to use. See qshift().
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
In some cases the tuples may have more elements. This is used to represent the Rotational symmetry modified wavelet transform.
- forward(X, nlevels=3, include_scale=False)¶
Perform a n-level DTCWT-2D decompostion on a 2D matrix X.
Parameters: - X – 2D real array
- nlevels – Number of levels of wavelet decomposition
Returns: A dtcwt.backend.TransformDomainSignal compatible object representing the transform-domain signal
- inverse(td_signal, gain_mask=None)¶
Perform an n-level dual-tree complex wavelet (DTCWT) 2D reconstruction.
Parameters: - td_signal – A dtcwt.backend.TransformDomainSignal-like class holding the transform domain representation to invert.
- gain_mask – Gain to be applied to each subband.
Returns: A dtcwt.backend.ReconstructedSignal compatible instance with the reconstruction.
The (d, l)-th element of gain_mask is gain for subband with direction d at level l. If gain_mask[d,l] == 0, no computation is performed for band (d,l). Default gain_mask is all ones. Note that both d and l are zero-indexed.
- class dtcwt.backend.base.TransformDomainSignal(lowpass, subbands, scales=None)¶
A representation of a transform domain signal.
Backends are free to implement any class which respects this interface for storing transform-domain signals. The inverse transform may accept a backend-specific version of this class but should always accept any class which corresponds to this interface.
- lowpass¶
A NumPy-compatible array containing the coarsest scale lowpass signal.
- subbands¶
A tuple where each element is the complex subband coefficients for corresponding scales finest to coarsest.
- scales¶
(optional) A tuple where each element is a NumPy-compatible array containing the lowpass signal for corresponding scales finest to coarsest. This is not required for the inverse and may be None.
NumPy¶
A backend which uses NumPy to perform the filtering. This backend should always be available.
- class dtcwt.backend.backend_numpy.TransformDomainSignal(lowpass, subbands, scales=None)¶
A representation of a transform domain signal.
Backends are free to implement any class which respects this interface for storing transform-domain signals. The inverse transform may accept a backend-specific version of this class but should always accept any class which corresponds to this interface.
- lowpass¶
A NumPy-compatible array containing the coarsest scale lowpass signal.
- subbands¶
A tuple where each element is the complex subband coefficients for corresponding scales finest to coarsest.
- scales¶
(optional) A tuple where each element is a NumPy-compatible array containing the lowpass signal for corresponding scales finest to coarsest. This is not required for the inverse and may be None.
- class dtcwt.backend.backend_numpy.Transform2d(biort='near_sym_a', qshift='qshift_a')¶
An implementation of the 2D DT-CWT via NumPy. biort and qshift are the wavelets which parameterise the transform. Valid values are documented in dtcwt.dtwavexfm2().
- forward(X, nlevels=3, include_scale=False)¶
Perform a n-level DTCWT-2D decompostion on a 2D matrix X.
Parameters: - X – 2D real array
- nlevels – Number of levels of wavelet decomposition
Returns: A dtcwt.backend.TransformDomainSignal compatible object representing the transform-domain signal
- inverse(td_signal, gain_mask=None)¶
Perform an n-level dual-tree complex wavelet (DTCWT) 2D reconstruction.
Parameters: - td_signal – A dtcwt.backend.TransformDomainSignal-like class holding the transform domain representation to invert.
- gain_mask – Gain to be applied to each subband.
Returns: A dtcwt.backend.ReconstructedSignal compatible instance with the reconstruction.
The (d, l)-th element of gain_mask is gain for subband with direction d at level l. If gain_mask[d,l] == 0, no computation is performed for band (d,l). Default gain_mask is all ones. Note that both d and l are zero-indexed.
OpenCL¶
Provide low-level OpenCL accelerated operations. This backend requires that PyOpenCL be installed.
- class dtcwt.backend.backend_opencl.TransformDomainSignal(lowpass, subbands, scales=None)¶
An interface-compatible version of dtcwt.backend.TransformDomainSignal where the initialiser arguments are assumed to by pyopencl.array.Array instances.
The attributes defined in dtcwt.backend.TransformDomainSignal are implemented via properties. The original OpenCL arrays may be accessed via the cl_... attributes.
Note
The copy from device to host is performed once and then memoized. This makes repeated access to the host-side attributes efficient but will mean that any changes to the device-side arrays will not be reflected in the host-side attributes after their first access. You should not be modifying the arrays once you return an instance of this class anyway but if you do, beware!
- cl_lowpass¶
The CL array containing the lowpass image.
- cl_subbands¶
A tuple of CL arrays containing the subband images.
- cl_scales¶
(optional) Either None or a tuple of lowpass images for each scale.
- class dtcwt.backend.backend_opencl.Transform2d(biort='near_sym_a', qshift='qshift_a', queue=None)¶
An implementation of the 2D DT-CWT via OpenCL. biort and qshift are the wavelets which parameterise the transform. Valid values are documented in dtcwt.dtwavexfm2().
If queue is non-None it is an instance of pyopencl.CommandQueue which is used to compile and execute the OpenCL kernels which implement the transform. If it is None, the first available compute device is used.
Note
At the moment only the forward transform is accelerated. The inverse transform uses the NumPy backend.
- forward(X, nlevels=3, include_scale=False)¶
Perform a n-level DTCWT-2D decompostion on a 2D matrix X.
Parameters: - X – 2D real array
- nlevels – Number of levels of wavelet decomposition
Returns: A dtcwt.backend.TransformDomainSignal compatible object representing the transform-domain signal
Note
X may be a pyopencl.array.Array instance which has already been copied to the device. In which case, it must be 2D. (I.e. a vector will not be auto-promoted.)
- inverse(td_signal, gain_mask=None)¶
Perform an n-level dual-tree complex wavelet (DTCWT) 2D reconstruction.
Parameters: - td_signal – A dtcwt.backend.TransformDomainSignal-like class holding the transform domain representation to invert.
- gain_mask – Gain to be applied to each subband.
Returns: A dtcwt.backend.ReconstructedSignal compatible instance with the reconstruction.
The (d, l)-th element of gain_mask is gain for subband with direction d at level l. If gain_mask[d,l] == 0, no computation is performed for band (d,l). Default gain_mask is all ones. Note that both d and l are zero-indexed.
Keypoint analysis¶
- dtcwt.keypoint.find_keypoints(highpass_subbands, method=None, alpha=1.0, beta=0.4, kappa=0.16666666666666666, threshold=None, max_points=None, upsample_keypoint_energy=None, upsample_subbands=None, refine_positions=True, skip_levels=1)¶
Parameters: - highpass_subbands – (NxMx6) matrix of highpass subband images
- method – (optional) string specifying which keypoint energy method to use
- alpha – (optional) scale parameter for 'fauqueur' method
- beta – (optional) shape parameter for 'fauqueur' method
- kappa – (optiona) suppression parameter for 'kingsbury' method
- threshold – (optional) minimum keypoint energy of returned keypoints
- max_points – (optional) maximum number of keypoints to return
- upsample_keypoint_energy – is non-None, a string specifying a method used to upscale the keypoint energy map before finding keypoints
- upsample_subands – is non-None, a string specifying a method used to upscale the subband image before finding keypoints
- refine_positions – (optional) should the keypoint positions be refined to sub-pixel accuracy
- skip_levels – (optional) number of levels of the transform to ignore before looking for keypoints
Returns: (Px4) array of P keypoints in image co-ordinates
Warning
The interface and behaviour of this function is the subject of an open research project. It is provided in this release as a preview of forthcoming functionality but it is subject to change between releases.
The rows of the returned keypoint array give the x co-ordinate, y co-ordinate, scale and keypoint energy. The rows are sorted in order of decreasing keypoint energy.
If refine_positions is True then the positions (and energy) of the keypoints will be refined to sub-pixel accuracy by fitting a quadratic patch. If refine_positions is False then the keypoint locations will be those corresponding directly to pixel-wise maxima of the subband images.
The max_points and threshold parameters are cumulative: if both are specified then the max_points greatest energy keypoints with energy greater than threshold will be returned.
Usually the keypoint energies returned from the finest scale level are dominated by noise and so one usually wants to specify skip_levels to be 1 or 2. If skip_levels is 0 then all levels will be used to compute keypoint energy.
The upsample_subbands and upsample_keypoint_energy parameters are used to control whether the individual subband coefficients and/org the keypoint energy map are upscaled by 2 before finding keypoints. If these parameters are None then no corresponding upscaling is performed. If non-None they specify the upscale method as outlined in dtcwt.sampling.upsample().
If method is None, the default 'fauqueur' method is used.
Name Description Parameters used fauqueur Geometric mean of absolute values[1] alpha, beta bendale Minimum absolute value[2] none kingsbury Cross-product of orthogonal subbands kappa [1] Julien Fauqueur, Nick Kingsbury, and Ryan Anderson. Multiscale Keypoint Detection using the Dual-Tree Complex Wavelet Transform. 2006 International Conference on Image Processing, pages 1625-1628, October 2006. ISSN 1522-4880. doi: 10.1109/ICIP.2006.312656. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4106857.
[2] Pashmina Bendale, Bill Triggs, and Nick Kingsbury. Multiscale Keypoint Analysis based on Complex Wavelets. In British Machine Vision Con-ference (BMVC), 2010. http://www-sigproc.eng.cam.ac.uk/~pb397/publications/BTK_BMVC_2010_abstract.pdf.
Image sampling¶
This module contains function for rescaling and re-sampling high- and low-pass subbands.
Note
All of these functions take an integer co-ordinate (x, y) to be the centre of the corresponding pixel. Therefore the upper-left pixel notionally covers the interval (-0.5, 0.5) in x and y. An image with N rows and M columns, therefore, has an extent (-0.5, M-0.5) on the x-axis and an extent of (-0.5, N-0.5) on the y-axis. The rescale and upsample functions in this module will use this region as the extent of the image.
- dtcwt.sampling.sample(im, xs, ys, method=None)¶
Sample image at (x,y) given by elements of xs and ys. Both xs and ys must have identical shape and output will have this same shape. The location (x,y) refers to the centre of im[y,x]. Samples at fractional locations are calculated using the method specified by method (or 'lanczos' if method is None.)
Parameters: - im – array to sample from
- xs – x co-ordinates to sample
- ys – y co-ordinates to sample
- method – one of ‘bilinear’, ‘lanczos’ or ‘nearest’
Raises ValueError: if xs and ys have differing shapes
- dtcwt.sampling.sample_highpass(im, xs, ys, method=None)¶
As sample() except that the highpass image is first phase shifted to be centred on approximately DC.
- dtcwt.sampling.rescale(im, shape, method=None)¶
Return a resampled version of im scaled to shape.
Since the centre of pixel (x,y) has co-ordinate (x,y) the extent of im is actually \(x \in (-0.5, w-0.5]\) and \(y \in (-0.5, h-0.5]\) where (y,x) is im.shape. This returns a sampled version of im that has the same extent as a shape-sized array.
- dtcwt.sampling.rescale_highpass(im, shape, method=None)¶
As rescale() except that the highpass image is first phase shifted to be centred on approximately DC.
- dtcwt.sampling.upsample(image, method=None)¶
Specialised function to upsample an image by a factor of two using a specified sampling method. If image is an array of shape (NxMx...) then the output will have shape (2Nx2Mx...). Only rows and columns are upsampled, depth axes and greater are interpolated but are not upsampled.
Parameters: - image – an array containing the image to upsample
- method – if non-None, a string specifying the sampling method to use.
If method is None, the default sampling method 'lanczos' is used. The following sampling methods are supported:
Name Description nearest Nearest-neighbour sampling bilinear Bilinear sampling lanczos Lanczos sampling with window radius of 3
- dtcwt.sampling.upsample_highpass(im, method=None)¶
As upsample() except that the highpass image is first phase rolled so that the filter has approximate DC centre frequency. The upshot is that this is the function to use when re-sampling complex subband images.
Image registration¶
Note
This module is experimental. It’s API may change between versions.
This module implements function for DTCWT-based image registration as outlined in [1]. These functions are 2D-only for the moment.
The functions in the top-level dtcwt.registration module are imported as a convenience from dtcwt.numpybackend. You could also import dtcwt.numpybackend directly to explicitly select backend.
- dtcwt.registration.estimatereg(reference, target)¶
Estimate registration from reference image to target.
Parameters: - reference – transformed reference image
- target – transformed target image
The reference and transform parameters should support the same API as dtcwt.backend.base.TransformDomainSignal.
The local affine distortion is estimated at at 8x8 pixel scales. Return a NxMx6 array where the 6-element vector at (N,M) corresponds to the affine distortion parameters for the 8x8 block with index (N,M).
Use the velocityfield() function to convert the return value from this function into a velocity field.
- dtcwt.registration.velocityfield(avecs, shape, method=None)¶
Given the affine distortion parameters returned from estimatereg(), return a tuple of 2D arrays giving the x- and y- components of the velocity field. The shape of the velocity component field is shape. The velocities are measured in terms of normalised units where the image has width and height of unity.
The method parameter is interpreted as in dtcwt.sampling.rescale() and is the sampling method used to resize avecs to shape.
- dtcwt.registration.warp(I, avecs, method=None)¶
A convenience function to warp an image according to the velocity field implied by avecs.
- dtcwt.registration.warptransform(t, avecs, levels, method=None)¶
Return a warped version of a transformed image acting only on specified levels.
Parameters: - t – a transformed image
- avecs – an array of affine distortion parameters
- levels – a sequence of 0-based indices specifying which levels to act on
t should be a dtcwt.backend.base.TransformDomainSignal-compatible instance.
The method parameter is interpreted as in dtcwt.sampling.rescale() and is the sampling method used to resize avecs to shape.
Note
This function will clone the transform t but it is a shallow clone where possible. Only the levels specified in levels will be deep-copied and warped.
Miscellaneous and low-level support functions¶
A normal user should not need to call these functions but they are documented here just in case you do.
Useful utilities for testing the 2-D DTCWT with synthetic images
- dtcwt.utils.appropriate_complex_type_for(X)¶
Return an appropriate complex data type depending on the type of X. If X is already complex, return that, if it is floating point return a complex type of the appropriate size and if it is integer, choose an complex floating point type depending on the result of numpy.asfarray().
- dtcwt.utils.as_column_vector(v)¶
Return v as a column vector with shape (N,1).
- dtcwt.utils.asfarray(X)¶
Similar to numpy.asfarray() except that this function tries to preserve the original datatype of X if it is already a floating point type and will pass floating point arrays through directly without copying.
- dtcwt.utils.drawcirc(r, w, du, dv, N)¶
Generate an image of size N*N pels, containing a circle radius r pels and centred at du,dv relative to the centre of the image. The edge of the circle is a cosine shaped edge of width w (from 10 to 90% points).
Python implementation by S. C. Forshaw, November 2013.
- dtcwt.utils.drawedge(theta, r, w, N)¶
Generate an image of size N * N pels, of an edge going from 0 to 1 in height at theta degrees to the horizontal (top of image = 1 if angle = 0). r is a two-element vector, it is a coordinate in ij coords through which the step should pass. The shape of the intensity step is half a raised cosine w pels wide (w>=1).
T. E . Gale’s enhancement to drawedge() for MATLAB, transliterated to Python by S. C. Forshaw, Nov. 2013.
- dtcwt.utils.reflect(x, minx, maxx)¶
Reflect the values in matrix x about the scalar values minx and maxx. Hence a vector x containing a long linearly increasing series is converted into a waveform which ramps linearly up and down between minx and maxx. If x contains integers and minx and maxx are (integers + 0.5), the ramps will have repeated max and min samples.
- dtcwt.utils.stacked_2d_matrix_matrix_prod(mats1, mats2)¶
Interpret mats1 and mats2 as arrays of 2D matrices. I.e. mats1 has shape PxQxNxM and mats2 has shape PxQxMxR. The result is a PxQxNxR array equivalent to:
result[i,j,:,:] = mats1[i,j,:,:].dot(mats2[i,j,:,:])
for all valid row and column indices i and j.
- dtcwt.utils.stacked_2d_matrix_vector_prod(mats, vecs)¶
Interpret mats and vecs as arrays of 2D matrices and vectors. I.e. mats has shape PxQxNxM and vecs has shape PxQxM. The result is a PxQxN array equivalent to:
result[i,j,:] = mats[i,j,:,:].dot(vecs[i,j,:])
for all valid row and column indices i and j.
- dtcwt.utils.stacked_2d_vector_matrix_prod(vecs, mats)¶
Interpret mats and vecs as arrays of 2D matrices and vectors. I.e. mats has shape PxQxNxM and vecs has shape PxQxN. The result is a PxQxM array equivalent to:
result[i,j,:] = mats[i,j,:,:].T.dot(vecs[i,j,:])
for all valid row and column indices i and j.
- dtcwt.lowlevel.coldfilt(X, ha, hb)¶
Filter the columns of image X using the two filters ha and hb = reverse(ha). ha operates on the odd samples of X and hb on the even samples. Both filters should be even length, and h should be approx linear phase with a quarter sample advance from its mid pt (i.e. \(|h(m/2)| > |h(m/2 + 1)|\)).
ext top edge bottom edge ext Level 1: ! | ! | ! odd filt on . b b b b a a a a a a a a b b b b odd filt on . a a a a b b b b b b b b a a a a Level 2: ! | ! | ! +q filt on x b b a a a a b b -q filt on o a a b b b b a a
The output is decimated by two from the input sample rate and the results from the two filters, Ya and Yb, are interleaved to give Y. Symmetric extension with repeated end samples is used on the composite X columns before each filter is applied.
Raises ValueError if the number of rows in X is not a multiple of 4, the length of ha does not match hb or the lengths of ha or hb are non-even.
- dtcwt.lowlevel.colfilter(X, h)¶
Filter the columns of image X using filter vector h, without decimation. If len(h) is odd, each output sample is aligned with each input sample and Y is the same size as X. If len(h) is even, each output sample is aligned with the mid point of each pair of input samples, and Y.shape = X.shape + [1 0].
Parameters: - X – an image whose columns are to be filtered
- h – the filter coefficients.
Returns Y: the filtered image.
- dtcwt.lowlevel.colifilt(X, ha, hb)¶
Filter the columns of image X using the two filters ha and hb = reverse(ha). ha operates on the odd samples of X and hb on the even samples. Both filters should be even length, and h should be approx linear phase with a quarter sample advance from its mid pt (i.e :math:`|h(m/2)| > |h(m/2 + 1)|).
ext left edge right edge ext Level 2: ! | ! | ! +q filt on x b b a a a a b b -q filt on o a a b b b b a a Level 1: ! | ! | ! odd filt on . b b b b a a a a a a a a b b b b odd filt on . a a a a b b b b b b b b a a a a
The output is interpolated by two from the input sample rate and the results from the two filters, Ya and Yb, are interleaved to give Y. Symmetric extension with repeated end samples is used on the composite X columns before each filter is applied.