nabu.estimation.tilt

Classes

CameraTilt — Alignment basic functions.

source class CameraTilt(vert_fft_width=False, horz_fft_width=False, verbose=False, logger=None, data_type=np.float32, extra_options=None)

Bases : CenterOfRotation

Alignment basic functions.

Parameters

vert_fft_width : boolean, optional — If True, restrict the vertical size to a power of 2:
```
new_v_dim = 2 ** math.floor(math.log2(v_dim))
```
horz_fft_width : boolean, optional — If True, restrict the horizontal size to a power of 2:
```
new_h_dim = 2 ** math.floor(math.log2(h_dim))
```
verbose : boolean, optional — When True it will produce verbose output, including plots.
data_type : numpy.<span class="mkapi-tooltip" title="numpy._core.float32">float32</span> — Computation data type.

Methods

compute_angle — Find the camera tilt, given two opposite images.

source method CameraTilt.compute_angle(img_1: np.ndarray, img_2: np.ndarray, method='1d-correlation', roi_yxhw=None, median_filt_shape=None, padding_mode=None, peak_fit_radius=1, high_pass=None, low_pass=None)

Find the camera tilt, given two opposite images.

This method finds the tilt between the camera pixel columns and the rotation axis, by performing a 1-dimensional correlation between two opposite images.

The output of this function, allows to compute motor movements for aligning the camera tilt.

Parameters

img_1 : numpy.ndarray — First image
img_2 : numpy.ndarray — Second image, it needs to have been flipped already (e.g. using numpy.fliplr).
method : str — Tilt angle computation method. Default is "1d-correlation" (traditional). All options are: - "1d-correlation": fastest, but works best for small tilts - "fft-polar": slower, but works well on all ranges of tilts
roi_yxhw : (2, ) or (4, ) numpy.ndarray, tuple, or array, optional — 4 elements vector containing: vertical and horizontal coordinates of first pixel, plus height and width of the Region of Interest (RoI). Or a 2 elements vector containing: plus height and width of the centered Region of Interest (RoI). Default is None -> deactivated.
median_filt_shape : (2, ) numpy.ndarray, tuple, or array, optional — Shape of the median filter window. Default is None -> deactivated.
padding_mode : str in numpy.pad's mode list, optional — Padding mode, which determines the type of convolution. If None or 'wrap' are passed, this resorts to the traditional circular convolution. If 'edge' or 'constant' are passed, it results in a linear convolution. Default is the circular convolution. All options are: None | 'constant' | 'edge' | 'linear_ramp' | 'maximum' | 'mean' | 'median' | 'minimum' | 'reflect' | 'symmetric' |'wrap'
peak_fit_radius : int, optional — Radius size around the max correlation pixel, for sub-pixel fitting. Minimum and default value is 1.
low_pass : float or sequence of two floats — Low-pass filter properties, as described in nabu.misc.fourier_filters
high_pass : float or sequence of two floats — High-pass filter properties, as described in nabu.misc.fourier_filters

Raises

ValueError — In case images are not 2-dimensional or have different sizes.

Returns

cor_offset_pix : float — Estimated center of rotation position from the center of the RoI in pixels.
tilt_deg : float — Estimated camera tilt angle in degrees.

Examples

The following code computes the center of rotation position for two given images in a tomography scan, where the second image is taken at 180 degrees from the first.

radio1 = data[0, :, :]
radio2 = np.fliplr(data[1, :, :])
tilt_calc = CameraTilt()
cor_offset, camera_tilt = tilt_calc.compute_angle(radio1, radio2)

Or for noisy images:

cor_offset, camera_tilt = tilt_calc.compute_angle(radio1, radio2, median_filt_shape=(3, 3))