pivuq.lib
- pivuq.lib.construct_subpixel_position_map(im)[source]
Construct sub-pixel position map based on 3-point Gaussian fit stencil.
- Parameters
im (
np.ndarray) – Image of size \(N \times M\).- Returns
X_sub, Y_sub – Subpixel position map of size \(N \times M\).
- Return type
np.ndarray
- pivuq.lib.disparity_ensemble_statistics(D, c, weights, wr, grid_size, coeff, ROI)[source]
Numba accelerated loop for computing the disparity statistics inside a window of radius wr.
- Parameters
D (
np.ndarray) – Disparity map \(D\) of size \(2 \times N \times M\) defined by Eq. (2) 1.c (
np.ndarray) – Disparity weight map \(c\) of size \(N \times M\) defined by Eq. (3) 1.weights (
np.ndarray) – Windowing weights of size \(N \times M\) defined by Gaussian or tophat filter.wr (
int) – Window radius.ws (
int) – Disparity resolution size.coeff (
float) – Confidence interval coefficient.ROI (
tuple) – Row and column indices of the ROI: (i_min, i_max, j_min, j_max).
- Returns
delta (
np.ndarray) – Instantaneous error estimation map of size \(2 \times N \times M\) defined by Eq. (3) 1.N (
np.ndarray) – Number of peaks inside the window.mu (
np.ndarray) – Mean disparity map of size \(2 \times N \times M\) defined by Eq. (3) 1.sigma (
np.ndarray) – Standard deviation disparity map of size \(2 \times N \times M\) defined by Eq. (3) 1.
References
- pivuq.lib.disparity_vector_computation(warped_image_pair, radius=2.0, sliding_window_size=16)[source]
Python implementation of Sciacchitano-Wieneke-Scarano disparity vector computation algorithm for PIV Uncertainty Quantification by image matching 1.
- Parameters
warped_image_pair (
np.ndarray) – Warped image pair \(\hat{\mathbf{I}} = (\hat{I}_0, \hat{I}_1)^{\top}\) of size \(2 \times N \times M\).radius (
int, default:2) – Discrete particle position search radius from the centroid defined by \(\varphi\).sliding_window_size (
int, default:16) – Sliding window average subtraction window size.
- Returns
D (
np.ndarray) – Disparity map \(D\) of size \(2 \times N \times M\) defined by Eq. (2).c (
np.ndarray) – Disparity weight map \(c\) of size \(N \times M\) defined by Eq. (3).
- pivuq.lib.find_particle(im, ic, jc, radius=1)[source]
Particle peak position finder around the radius of centroid.
- Parameters
im (
np.ndarray) – Image array of size \(N \times M\).ic (
int) – Row and column index of centroid.jc (
int) – Row and column index of centroid.radius (
int) – Search radius of centroid.
- Returns
Row index and column index of peak.
- Return type
int,int