--- a/python/calibration-translation.py	Mon Sep 12 16:38:47 2011 -0400
+++ b/python/calibration-translation.py	Mon Sep 19 16:43:28 2011 -0400
@@ -12,16 +12,12 @@
 import cvutils
 
 # development for the data collected and stabilized by Paul in Summer 2011
-# todo test other features
+# todo write help, add options to control the parameters for matching (n points and distance)
 
 options = utils.parseCLIOptions('Program to re-calibrate an initial calibration based on point correspondences by adjusting the points to slightly different viewpoints, where all the points are still visible\n\nUsage: ', ['ref_video=', 'ref_points='], sys.argv, ['mask_img='])
-#, 'ref_homography='
 
-referenceVideoFilename=options['--ref_video']#'1440-1459_Mercalli.avi'
-#referenceHomographyFilename=options['--ref_homography']#'1440-1459_Mercalli-homography.txt'
-points = np.loadtxt(options['--ref_points'], dtype=np.float32) # '1440-1459_Mercalli-point-correspondences.txt'
-wldPts = points[:2,:].T
-imgPts = points[2:,:].T
+referenceVideoFilename=options['--ref_video']
+wldPts, imgPts = cvutils.loadPointCorrespondences(options['--ref_points'])
 
 def translatePoints(points, t):
     'points is Nx2, t is [x,y]'
@@ -32,19 +28,18 @@
 
 filenames = [f for f in utils.listfiles('.','avi')] # directory to examine should be current directory
 
-#referenceHomography = np.loadtxt(referenceHomographyFilename)
 referenceVideoIndex = filenames.index(referenceVideoFilename)
 indices = set(range(len(filenames)))
 indices.discard(referenceVideoIndex)
 
 images = {}
-#features = {}
 captures = {}
 
 captures[referenceVideoFilename] = cv2.VideoCapture(referenceVideoFilename)
 (ret, img) = captures[referenceVideoFilename].read()
 images[referenceVideoFilename] = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
 
+# load a mask image to compute the translation
 if '--mask_img' in options.keys():
     maskImg = cv2.imread('mask.png', cv2.CV_LOAD_IMAGE_GRAYSCALE) # todo add possibility to look in the whole image if not providing mask
 else:
@@ -57,7 +52,8 @@
     cv2.putText(displayRef, str(j+1), tuple(p), cv2.FONT_HERSHEY_PLAIN, 1, (255,0,0))
 cv2.imshow('Reference',displayRef)
 
-for f in filenames: # get suitable image references for each video
+# get suitable image references for each video
+for f in filenames: 
     captures[f] = cv2.VideoCapture(f)
     frameFilename = utils.removeExtension(f)+'-frame.png' # TODO if frame image already exists, no need to search for it again
     if not os.path.exists(frameFilename):
@@ -84,6 +80,7 @@
 
 plt.close('all')
 
+# validate or input point correspondences and compute homography
 for i in indices:
     t = cvutils.computeTranslation(images[filenames[referenceVideoIndex]], images[filenames[i]], referenceFeatures, 100, 10)
     print filenames[i],t
@@ -103,8 +100,6 @@
         print('No translation could be found automatically. You will have to manually input world reference points.')
 
     if t==None or key != cvutils.cvKeyNumbers['y']:# if no translation could computed or it is not satisfactory
-        # image should be right to get points
-        # todo save image
         print('Select the corresponding points in the same order as in the reference image')
         plt.figure(1)
         plt.imshow(displayRef)

--- a/python/compute-homography.py	Mon Sep 12 16:38:47 2011 -0400
+++ b/python/compute-homography.py	Mon Sep 19 16:43:28 2011 -0400
@@ -21,9 +21,7 @@
     print('Usage: {0} --help|-h [--video_frame <video frame filename>] [<point_correspondences.txt>]'.format(sys.argv[0]))
     sys.exit()
 
-points = np.loadtxt(args[0], dtype=np.float32)
-srcPts = points[2:,:].T
-dstPts = points[:2,:].T
+dstPts, srcPts = cvutils.loadPointCorrespondences(args[0])
 homography, mask = cv2.findHomography(srcPts, dstPts) # method=0, ransacReprojThreshold=3
 np.savetxt(utils.removeExtension(sys.argv[1])+'-homography.txt',homography)
 

--- a/python/cvutils.py	Mon Sep 12 16:38:47 2011 -0400
+++ b/python/cvutils.py	Mon Sep 19 16:43:28 2011 -0400
@@ -52,6 +52,13 @@
     points = loadtxt(filename, delimiter=',')
     savetxt(utils.removeExtension(filename)+'-point-correspondences.txt',append(points[:,:2].T, points[:,3:].T, axis=0))
 
+def loadPointCorrespondences(filename):
+    '''Loads and returns the corresponding points in world (first 2 lines) and image spaces (last 2 lines)'''
+    from numpy.lib.io import loadtxt
+    from numpy import float32
+    points = loadtxt(filename, dtype=float32)
+    return  (points[:2,:].T, points[2:,:].T) # (world points, image points)
+
 def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=0.0):
     '''Returns the homography matrix mapping from srcPoints to dstPoints (dimension Nx2)'''
     #cvSrcPoints = arrayToCvMat(srcPoints);