Loading autocnet/matcher/mutual_information.py +38 −34 Original line number Diff line number Diff line Loading @@ -2,41 +2,8 @@ from math import floor import numpy as np def mi(t1, t2, **kwargs): """ Computes the correlation coefficient between two images using a histogram comparison (Mutual information for joint histograms). The corr_map coefficient will be between 0 and 4 Parameters ---------- t1 : ndarray First image to use in the histogram comparison t2 : ndarray Second image to use in the histogram comparison Returns ------- : float Correlation coefficient computed between the two images being compared between 0 and 4 """ hgram, x_edges, y_edges = np.histogram2d(t1.ravel(),t2.ravel(), **kwargs) # Convert bins counts to probability values pxy = hgram / float(np.sum(hgram)) px = np.sum(pxy, axis=1) # marginal for x over y py = np.sum(pxy, axis=0) # marginal for y over x px_py = px[:, None] * py[None, :] # Broadcast to multiply marginals # Now we can do the calculation using the pxy, px_py 2D arrays nzs = pxy > 0 # Only non-zero pxy values contribute to the sum return np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs])) def mutual_information(d_template, s_image, subpixel_size=3, max_scaler=0.2, bins=100, func=mi): bins=100, func=None): """ Applys the mutual information matcher function over a search image using a defined template. Where the search area is 2x the size of the template image Loading Loading @@ -69,6 +36,43 @@ def mutual_information(d_template, s_image, subpixel_size=3, max_scaler=0.2, Map of corrilation coefficients when comparing the template to locations within the search area """ def mutual_information_match(t1, t2, **kwargs): """ Computes the correlation coefficient between two images using a histogram comparison (Mutual information for joint histograms). The corr_map coefficient will be between 0 and 4 Parameters ---------- t1 : ndarray First image to use in the histogram comparison t2 : ndarray Second image to use in the histogram comparison Returns ------- : float Correlation coefficient computed between the two images being compared between 0 and 4 """ hgram, x_edges, y_edges = np.histogram2d(t1.ravel(),t2.ravel(), **kwargs) # Convert bins counts to probability values pxy = hgram / float(np.sum(hgram)) px = np.sum(pxy, axis=1) # marginal for x over y py = np.sum(pxy, axis=0) # marginal for y over x px_py = px[:, None] * py[None, :] # Broadcast to multiply marginals # Now we can do the calculation using the pxy, px_py 2D arrays nzs = pxy > 0 # Only non-zero pxy values contribute to the sum return np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs])) if func == None: func = mutual_information_match image_size = s_image.shape template_size = d_template.shape Loading Loading
autocnet/matcher/mutual_information.py +38 −34 Original line number Diff line number Diff line Loading @@ -2,41 +2,8 @@ from math import floor import numpy as np def mi(t1, t2, **kwargs): """ Computes the correlation coefficient between two images using a histogram comparison (Mutual information for joint histograms). The corr_map coefficient will be between 0 and 4 Parameters ---------- t1 : ndarray First image to use in the histogram comparison t2 : ndarray Second image to use in the histogram comparison Returns ------- : float Correlation coefficient computed between the two images being compared between 0 and 4 """ hgram, x_edges, y_edges = np.histogram2d(t1.ravel(),t2.ravel(), **kwargs) # Convert bins counts to probability values pxy = hgram / float(np.sum(hgram)) px = np.sum(pxy, axis=1) # marginal for x over y py = np.sum(pxy, axis=0) # marginal for y over x px_py = px[:, None] * py[None, :] # Broadcast to multiply marginals # Now we can do the calculation using the pxy, px_py 2D arrays nzs = pxy > 0 # Only non-zero pxy values contribute to the sum return np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs])) def mutual_information(d_template, s_image, subpixel_size=3, max_scaler=0.2, bins=100, func=mi): bins=100, func=None): """ Applys the mutual information matcher function over a search image using a defined template. Where the search area is 2x the size of the template image Loading Loading @@ -69,6 +36,43 @@ def mutual_information(d_template, s_image, subpixel_size=3, max_scaler=0.2, Map of corrilation coefficients when comparing the template to locations within the search area """ def mutual_information_match(t1, t2, **kwargs): """ Computes the correlation coefficient between two images using a histogram comparison (Mutual information for joint histograms). The corr_map coefficient will be between 0 and 4 Parameters ---------- t1 : ndarray First image to use in the histogram comparison t2 : ndarray Second image to use in the histogram comparison Returns ------- : float Correlation coefficient computed between the two images being compared between 0 and 4 """ hgram, x_edges, y_edges = np.histogram2d(t1.ravel(),t2.ravel(), **kwargs) # Convert bins counts to probability values pxy = hgram / float(np.sum(hgram)) px = np.sum(pxy, axis=1) # marginal for x over y py = np.sum(pxy, axis=0) # marginal for y over x px_py = px[:, None] * py[None, :] # Broadcast to multiply marginals # Now we can do the calculation using the pxy, px_py 2D arrays nzs = pxy > 0 # Only non-zero pxy values contribute to the sum return np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs])) if func == None: func = mutual_information_match image_size = s_image.shape template_size = d_template.shape Loading
