merged sebastian and my work

David Mayerich
2 parents 6b2be991 90c935e3
Showing 2 changed files with 46 additions and 5 deletions Show diff stats
python/classify.py
python/envi.py
@@ -7,6 +7,8 @@ Created on Sun Jul 23 16:04:33 2017
 import numpy
 import colorsys
+import sklearn
+import sklearn.metrics
 from scipy import misc
 from envi import envi
@@ -89,15 +91,39 @@ def prob2class(prob_image):
     class_image = numpy.zeros_like(prob_image)
     #get nonzero indices
     nnz_idx = numpy.transpose(numpy.nonzero(numpy.sum(prob_image, axis=0)))
-
+    
     #set pixel corresponding to max probability to 1
     for idx in nnz_idx:
         idx_max_prob = numpy.argmax(prob_image[:, idx[0], idx[1]])
         class_image[idx_max_prob, idx[0], idx[1]] = 1
     return class_image
+#calculate an ROC curve given a probability image and mask of "True" values
+def image2roc(P, t_vals, mask=[]):
+    
+    if not P.shape == t_vals.shape:
+        print("ERROR: the probability and mask images must be the same shape")
+        return
+    
+    #if a mask image isn't provided, create one for the entire image
+    if mask == []:
+        mask = numpy.ones(t_vals.shape, dtype=numpy.bool)
+    
+    #create masks for the positive and negative probability scores
+    mask_p = t_vals
+    mask_n = mask - mask * t_vals
+    
+    #calculate the indices for the positive and negative scores
+    idx_p = numpy.nonzero(mask_p)
+    idx_n = numpy.nonzero(mask_n)
+    
+    Pp = P[idx_p]
+    Pn = P[idx_n]
-
-#create an ROC curve calculator
-#input: X x Y x C image giving the probability P(c | x,y)
-#output: ROC curve
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+    Lp = numpy.ones((Pp.shape), dtype=numpy.bool)
+    Ln = numpy.zeros((Pn.shape), dtype=numpy.bool)
+    
+    scores = numpy.concatenate((Pp, Pn))
+    labels = numpy.concatenate((Lp, Ln))
+    
+    return sklearn.metrics.roc_curve(labels, scores)
@@ -263,6 +263,21 @@ class envi:
                 p = p + i.shape[0]
                 bar.update(l+1)
         return M
+
+    def loadband(self, n):
+        X = self.header.samples
+        Y = self.header.lines
+        B = self.header.bands
+
+        band = numpy.zeros((Y, X), dtype=self.header.data_type)
+        type_bytes = numpy.dtype(self.header.data_type).itemsize
+
+        if self.header.interleave == "bsq":
+            self.file.seek(n * X * Y * type_bytes)
+            self.file.readinto(band)
+
+        return band
+
     def __del__(self):
         self.file.close()
 \ No newline at end of file