switched from argparse to getopt for compatibility with Python 2.6

samples/python2/edge.py

@@ -3,7 +3,7 @@ import video
 import sys
 
 try: fn = sys.argv[1]
-except: fn = 'synth:bg=../cpp/lena.jpg:noise=0.1'
+except: fn = video.presets['lena']
 
 def nothing(*arg):
     pass

samples/python2/letter_recog.py

@@ -91,31 +91,34 @@ class SVM(LetterStatModel):
 
 
 if __name__ == '__main__':
-    import argparse
+    import getopt
+    import sys
 
     models = [RTrees, KNearest, Boost, SVM] # MLP, NBayes
     models = dict( [(cls.__name__.lower(), cls) for cls in models] )
+
+    print 'USAGE: letter_recog.py [--model <model>] [--data <data fn>] [--load <model fn>] [--save <model fn>]'
+    print 'Models: ', ', '.join(models)
+    print
     
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-model', default='rtrees', choices=models.keys())
-    parser.add_argument('-data', nargs=1, default='../cpp/letter-recognition.data')
-    parser.add_argument('-load', nargs=1)
-    parser.add_argument('-save', nargs=1)
-    args = parser.parse_args()
-
-    print 'loading data %s ...' % args.data
-    samples, responses = load_base(args.data)
-    Model = models[args.model]
+    args, dummy = getopt.getopt(sys.argv[1:], '', ['model=', 'data=', 'load=', 'save='])
+    args = dict(args)
+    args.setdefault('--model', 'rtrees')
+    args.setdefault('--data', '../cpp/letter-recognition.data')
+
+    print 'loading data %s ...' % args['--data']
+    samples, responses = load_base(args['--data'])
+    Model = models[args['--model']]
     model = Model()
 
     train_n = int(len(samples)*model.train_ratio)
-    if args.load is None:
-        print 'training %s ...' % Model.__name__
-        model.train(samples[:train_n], responses[:train_n])
-    else:
-        fn = args.load[0]
+    if '--load' in args:
+        fn = args['--load']
         print 'loading model from %s ...' % fn
         model.load(fn)
+    else:
+        print 'training %s ...' % Model.__name__
+        model.train(samples[:train_n], responses[:train_n])
 
     print 'testing...'
     train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n])
@@ -123,7 +126,7 @@ if __name__ == '__main__':
 
     print 'train rate: %f  test rate: %f' % (train_rate*100, test_rate*100)
 
-    if args.save is not None:
-        fn = args.save[0]
+    if '--save' in args:
+        fn = args['--save']
         print 'saving model to %s ...' % fn
         model.save(fn)

samples/python2/video.py

@@ -47,19 +47,27 @@ def create_capture(source):
     return cv2.VideoCapture(source)
 
 
+presets = dict(
+    lena = 'synth:bg=../cpp/lena.jpg:noise=0.1'
+)
+
 if __name__ == '__main__':
     import sys
-    import argparse
-    
-    parser = argparse.ArgumentParser()
-    parser.add_argument('sources', nargs='*', default=['synth:bg=../cpp/lena.jpg:noise=0.1'])
-    parser.add_argument('-shotdir', nargs=1, default='.')
-    args = parser.parse_args()
-    print args
+    import getopt
+
+    print 'USAGE: video.py [--shotdir <dir>] [source0] [source1] ...'
+    print "source: '<int>' or '<filename>' or 'synth:<params>'"
+    print
+
+    args, sources = getopt.getopt(sys.argv[1:], '', 'shotdir=')
+    args = dict(args)
+    shotdir = args.get('--shotdir', '.')
+    if len(sources) == 0:
+        sources = [ presets['lena'] ]
 
     print 'Press SPACE to save current frame'
 
-    caps = map(create_capture, args.sources)
+    caps = map(create_capture, sources)
     shot_idx = 0
     while True:
         imgs = []
@@ -72,7 +80,7 @@ if __name__ == '__main__':
             break
         if ch == ord(' '):
             for i, img in enumerate(imgs):
-                fn = '%s/shot_%d_%03d.bmp' % (args.shotdir[0], i, shot_idx)
+                fn = '%s/shot_%d_%03d.bmp' % (shotdir, i, shot_idx)
                 cv2.imwrite(fn, img)
                 print fn, 'saved'
             shot_idx += 1