changed around demo names, added dim mismatch bug fix to sig detectors

Author: Yutai

README.md

@@ -1,2 +1,4 @@
 # hsi_toolkit_py
 GatorSense Hyperspectral Image Analysis Toolkit - Python Implementation
+
+Hyperspectral image format: height x width x bands

anomaly_detectors/anomaly_det_demo.py renamed to anomaly_detectors/demo_anomaly_detector.py

@@ -21,6 +21,9 @@ def abd_detector(hsi_img, tgt_sig, ems, mask = None):
 	6/2/2018 - Edited by Alina Zare
 	12/2018 - Python Implementation by Yutai Zhou
 	"""
+	if tgt_sig.ndim == 1:
+		tgt_sig = tgt_sig[:, np.newaxis]
+
 	abd_out, kwargsout = img_det(abd_helper,hsi_img,tgt_sig,mask,ems = ems);
 
 	return abd_out

classifiers/classification_demo.py renamed to classifiers/demo_classification.py

@@ -4,44 +4,47 @@
 import numpy as np
 
 def ace_detector(hsi_img, tgt_sig, mask = None, mu = None, sig_inv = None):
-	"""
-	Squared Adaptive Cosine/Coherence Estimator
-
-	Inputs:
-		hsi_image - n_row x n_col x n_band hyperspectral image
-		tgt_sig - target signature (n_band x 1 - column vector)
-		mask - binary image limiting detector operation to pixels where mask is true
-		       if not present or empty, no mask restrictions are used
-		mu - background mean (n_band x 1 column vector)
-		sig_inv - background inverse covariance (n_band x n_band matrix)
-
-	Outputs:
-		ace_out - detector image
-		mu - mean of input data
-		sig_inv - inverse covariance of input data
-
-	 8/8/2012 - Taylor C. Glenn
-	 6/2/2018 - Edited by Alina Zare
-	 10/2018 - Python Implementation by Yutai Zhou
-	 """
-	ace_out, kwargsout = img_det(ace_det_helper, hsi_img, tgt_sig, mask, mu = mu, sig_inv = sig_inv)
-	return ace_out, kwargsout['mu'], kwargsout['sig_inv']
+    """
+    Squared Adaptive Cosine/Coherence Estimator
+
+    Inputs:
+        hsi_image - n_row x n_col x n_band hyperspectral image
+        tgt_sig - target signature (n_band x 1 - column vector)
+        mask - binary image limiting detector operation to pixels where mask is true
+               if not present or empty, no mask restrictions are used
+        mu - background mean (n_band x 1 column vector)
+        sig_inv - background inverse covariance (n_band x n_band matrix)
+
+    Outputs:
+        ace_out - detector image
+        mu - mean of input data
+        sig_inv - inverse covariance of input data
+
+     8/8/2012 - Taylor C. Glenn
+     6/2/2018 - Edited by Alina Zare
+     10/2018 - Python Implementation by Yutai Zhou
+     """
+    if tgt_sig.ndim == 1:
+        tgt_sig = tgt_sig[:, np.newaxis]
+
+    ace_out, kwargsout = img_det(ace_det_helper, hsi_img, tgt_sig, mask, mu = mu, sig_inv = sig_inv)
+    return ace_out, kwargsout['mu'], kwargsout['sig_inv']
 
 def ace_det_helper(hsi_data, tgt_sig, kwargs):
-	mu = np.mean(hsi_data, axis = 1) if kwargs['mu'] is None else kwargs['mu']
-	sig_inv = np.linalg.pinv(np.cov(hsi_data.T, rowvar = False)) if kwargs['sig_inv'] is None else kwargs['sig_inv']
+    mu = np.mean(hsi_data, axis = 1) if kwargs['mu'] is None else kwargs['mu']
+    sig_inv = np.linalg.pinv(np.cov(hsi_data.T, rowvar = False)) if kwargs['sig_inv'] is None else kwargs['sig_inv']
 
-	mu = np.reshape(mu, (len(mu), 1), order='F')
-	s = tgt_sig - mu
-	z = hsi_data - mu
+    mu = np.reshape(mu, (len(mu), 1), order='F')
+    s = tgt_sig - mu
+    z = hsi_data - mu
 
-	st_sig_inv = s.T @ sig_inv
-	st_sig_inv_s = s.T @ sig_inv @ s
+    st_sig_inv = s.T @ sig_inv
+    st_sig_inv_s = s.T @ sig_inv @ s
 
-	A = np.sum(st_sig_inv @ z, 0)
-	B = st_sig_inv_s
-	C = np.sum(z * (sig_inv @ z), 0)
+    A = np.sum(st_sig_inv @ z, 0)
+    B = st_sig_inv_s
+    C = np.sum(z * (sig_inv @ z), 0)
 
-	ace_data = A * A / (B * C)
+    ace_data = A * A / (B * C)
 
-	return ace_data.T.squeeze(), {'mu':mu, 'sig_inv': sig_inv}
+    return ace_data.T.squeeze(), {'mu':mu, 'sig_inv': sig_inv}

dim_reduction/dim_reduction_demo.py renamed to dim_reduction/demo_dim_reduction.py

@@ -27,6 +27,10 @@ def ace_local_detector(hsi_img, tgt_sig, mask = None, guard_win = 2, bg_win = 4,
 	mask = np.ones([n_row, n_col]) if mask is None else mask
 	reg = beta * np.eye(n_band)
 
+	if tgt_sig.ndim == 1:
+		tgt_sig = tgt_sig[:, np.newaxis]
+
+
 	out, kwargsout = rx_det(ace_local_helper, hsi_img, tgt_sig, mask = mask, guard_win = guard_win, bg_win = bg_win, reg = reg)
 	return out, kwargsout
 

signature_detectors/abd_detector.py

@@ -24,6 +24,9 @@ def ace_rt_detector(hsi_img, tgt_sig, mask = None, mu = None, sig_inv = None):
 	6/2/2018 - Edited by Alina Zare
 	11/2018 - Python Implementation by Yutai Zhou
 	"""
+	if tgt_sig.ndim == 1:
+		tgt_sig = tgt_sig[:, np.newaxis]
+
 	ace_rt_out, kwargsout = img_det(ace_rt_helper, hsi_img, tgt_sig, mask, mu = mu, sig_inv = sig_inv)
 	return ace_rt_out, kwargsout['mu'], kwargsout['sig_inv']
 

signature_detectors/ace_detector.py

@@ -25,6 +25,9 @@ def ace_rt_max_detector(hsi_img, tgt_sig, mask = None, mu = None, sig_inv = None
 	6/2/2018 - Edited by Alina Zare
 	11/2018 - Python Implementation by Yutai Zhou
 	"""
+	if tgt_sig.ndim == 1:
+		tgt_sig = tgt_sig[:, np.newaxis]
+
 	ace_rt_max_out, kwargsout = img_det(ace_rt_max_helper, hsi_img, tgt_sig, mask, mu = mu, sig_inv = sig_inv)
 	return ace_rt_max_out, kwargsout['mu'], kwargsout['sig_inv']
 

signature_detectors/ace_local_detector.py

@@ -20,6 +20,9 @@ def ace_ss_detector(hsi_img, tgt_sig, mask = None, mu = None, sig_inv = None):
 	6/2/2018 - Edited by Alina Zare
 	11/2018 - Python Implementation by Yutai Zhou
 	"""
+	if tgt_sig.ndim == 1:
+		tgt_sig = tgt_sig[:, np.newaxis]
+
 	ace_ss_out, kwargsout = img_det(ace_ss_helper, hsi_img, tgt_sig, mask, mu = mu, sig_inv = sig_inv)
 	return ace_ss_out