Merge pull request #85 from OpenPIV/py36

2to3 and py.test revealed the bugs

Author: alexlib

openpiv/junk.py

@@ -1,3 +1,5 @@
+from six import iteritems
+
 class ProcessParameters( UserDict ):
     def __init__ (self, config_file='' ):
         """
@@ -39,7 +41,7 @@ def __init__ (self, config_file='' ):
         # cast parameters to the right type.
         # this trick is necessary because the ConfigParser
         # class only understands strings
-        for k, v in self.iteritems():
+        for k, v in iteritems(self):
             try:
                 self[k] = int(v)
             except ValueError:
@@ -52,8 +54,8 @@ def pretty_print ( self ):
         """
         Pretty print all the processing parameters.
         """
-        for k, v in self.iteritems():
-            print("%s = %s" % ( k.rjust(30), repr(v).ljust(30) ));
+        for k, v in iteritems(self):
+            print(("%s = %s" % ( k.rjust(30), repr(v).ljust(30) )));
 
 class Hdf5Database( ):
     """

openpiv/pyprocess.py

@@ -25,6 +25,7 @@
 from numpy import ma
 from scipy.signal import convolve2d
 from numpy import log
+import matplotlib.pyplot as plt
 
 
 WINSIZE = 32
@@ -531,23 +532,11 @@ def piv(frame_a, frame_b,
         raise(BaseException,'window size cannot be larger than the image')
 
     # get field shape
-    n_rows, n_cols = get_field_shape(frame_a.shape, window_size, overlap )
+    n_rows, n_cols = get_field_shape(frame_a.shape, search_size, overlap )
 
     u = np.zeros((n_rows, n_cols))
     v = np.zeros((n_rows, n_cols))
-    sig2noise = np.zeros((n_rows, n_cols))
-    
-    if search_size > window_size:
-        frame_b = np.pad(frame_b, ((search_size-window_size)/2,
-                               (search_size-window_size)/2),'constant',
-    constant_values = (0,0))
-    
-#    print frame_a.shape, frame_a.dtype, frame_b.shape, frame_b.dtype
-#    fig,ax = plt.subplots(1,2)
-#    ax[0].imshow(frame_a,cmap=plt.cm.gray)
-#    ax[1].imshow(frame_b,cmap=plt.cm.gray)
-#    plt.show()
-#    'constant',constant_values = 0)    
+    sig2noise = np.zeros((n_rows, n_cols)) 
 
     u, v = np.zeros((n_rows, n_cols)), np.zeros((n_rows, n_cols))
 
@@ -563,62 +552,44 @@ def piv(frame_a, frame_b,
         for m in range(n_cols):
             # range(search_size/2, frame_a.shape[1] - search_size/2 , window_size - overlap ):
 
-            i = k*(window_size-overlap) + window_size/2
-            j = m*(window_size-overlap) + window_size/2
-
-#            print 'center', i,j
-
 
-            il = i - window_size/2 
-            jt = j - window_size/2 
-            ir = i + window_size/2 
-            jb = j + window_size/2 
+            # Select first the largest window, work like usual from the top left corner
+            # the left edge goes as: 
+            # e.g. 0, (search_size - overlap), 2*(search_size - overlap),....
 
-#            print 'a', il,ir,jt,jb
-
-#            il = il if il > 0 else 0
-#            ir = ir if ir < frame_a.shape[0] else frame_a.shape[0]
-#            jt = jt if jt > 0 else 0
-#            jb = jb if jb < frame_a.shape[1] else frame_a.shape[1]            
-    
-            window_a = frame_a[il:ir, jt:jb]
+            il = k*(search_size - overlap)
+            ir = il + search_size
 
-            # now shift the second image center accordingly
-            i += (search_size - window_size)/2
-            j += (search_size - window_size)/2
-                   
-            il = i - search_size/2
-            ir = i + search_size/2 
-            jt = j - search_size/2
-            jb = j + search_size/2 
+            # same for top-bottom
+            jt = m*(search_size - overlap)
+            jb = jt + search_size
 
-#            print 'b', il, ir, jt, jb
-
-#            il = il if il > 0 else 0
-#            ir = ir if ir < frame_a.shape[0] else frame_a.shape[0]
-#            jt = jt if jt > 0 else 0
-#            jb = jb if jb < frame_a.shape[1] else frame_a.shape[1]
-            
-            window_b = frame_b[il:ir, jt:jb]
-            
-#            fig,ax = plt.subplots(1,2)
-#            ax[0].imshow(window_a,cmap=plt.cm.gray)
-#            ax[1].imshow(window_b,cmap=plt.cm.gray)
-#            plt.show()
+            # pick up the window in the second image
+            window_b = frame_b[il:ir, jt:jb]            
 
+            # now shift the left corner of the smaller window inside the larger one
+            il += (search_size - window_size)//2
+            # and it's right side is just a window_size apart
+            ir = il + window_size
+            # same same
+            jt += (search_size - window_size)//2
+            jb =  jt + window_size
+
+            window_a = frame_a[il:ir, jt:jb]
+
             if np.any(window_a):
                 corr = correlate_windows(window_a, window_b,
                                          corr_method=corr_method, 
                                          nfftx=nfftx, nffty=nffty)
-#                plt.figure()
-#                plt.contourf(corr)
-#                plt.show()
+#                 plt.figure()
+#                 plt.contourf(corr)
+#                 plt.show()
                 # get subpixel approximation for peak position row and column index
-                row, col = find_subpixel_peak_position(
-                                                       corr, subpixel_method=subpixel_method)
+                row, col = find_subpixel_peak_position(corr, 
+                                                        subpixel_method=subpixel_method)
 
-                row -=  (search_size + window_size - 1)/2
-                col -=  (search_size + window_size - 1)/2
+                row -=  (search_size + window_size - 1)//2
+                col -=  (search_size + window_size - 1)//2
 
                 # get displacements, apply coordinate system definition
                 u[k,m],v[k,m] = -col, row

openpiv/tools.py

@@ -31,6 +31,7 @@
 import matplotlib.image as mpltimg
 from scipy import ndimage
 from skimage import filters, io
+from builtins import range
 
 
 def display_vector_field( filename, on_img=False, image_name='None', window_size=32, scaling_factor=1, **kw):
@@ -165,7 +166,7 @@ def mark_background(threshold, list_img, filename):
     mark = np.zeros(list_frame[0].shape, dtype=np.int32)
     background = np.zeros(list_frame[0].shape, dtype=np.int32)
     for I in range(mark.shape[0]):
-        print(" row ", I , " / " , mark.shape[0]);
+        print((" row ", I , " / " , mark.shape[0]));
         for J in range(mark.shape[1]):
             sum1 = 0
             for K in range(len(list_frame)):
@@ -187,7 +188,7 @@ def mark_background2(list_img, filename):
         list_frame.append(imread(list_img[I]))
     background = np.zeros(list_frame[0].shape, dtype=np.int32)
     for I in range(background.shape[0]):
-        print(" row ", I , " / " , background.shape[0]);
+        print((" row ", I , " / " , background.shape[0]));
         for J in range(background.shape[1]):
             min_1 = 255
             for K in range(len(list_frame)):
@@ -207,7 +208,7 @@ def find_reflexions(list_img, filename):
     background = mark_background2(list_img, filename)
     reflexion = np.zeros(background.shape, dtype=np.int32)
     for I in range(background.shape[0]):
-        print(" row ", I , " / " , background.shape[0]);
+        print((" row ", I , " / " , background.shape[0]));
         for J in range(background.shape[1]):
             if background[I,J] > 253:
                 reflexion[I,J] = 255
@@ -226,15 +227,15 @@ def find_boundaries(threshold, list_img1, list_img2, filename, picname):
     print("mark1..");
     mark1 = mark_background(threshold, list_img1, "mark1.bmp")
     print("[DONE]");
-    print(mark1.shape);
+    print((mark1.shape));
     print("mark2..");
     mark2 = mark_background(threshold, list_img2, "mark2.bmp")
     print("[DONE]");
     print("computing boundary");
-    print(mark2.shape);
+    print((mark2.shape));
     list_bound = np.zeros(mark1.shape, dtype=np.int32)
     for I in range(list_bound.shape[0]):
-        print( "bound row ", I , " / " , mark1.shape[0]);
+        print(( "bound row ", I , " / " , mark1.shape[0]));
         for J in range(list_bound.shape[1]):
             list_bound[I,J]=0
             if mark1[I,J]==0:
@@ -387,7 +388,7 @@ def run( self, func, n_cpus=1 ):
         """
 
         # create a list of tasks to be executed.
-        image_pairs = [ (file_a, file_b, i) for file_a, file_b, i in zip( self.files_a, self.files_b, xrange(self.n_files) ) ]
+        image_pairs = [ (file_a, file_b, i) for file_a, file_b, i in zip( self.files_a, self.files_b, range(self.n_files) ) ]
 
         # for debugging purposes always use n_cpus = 1,
         # since it is difficult to debug multiprocessing stuff.