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Python samples adapted for Python3 compatibility

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Common fixes:
- print function
- int / float division
- map, zip iterators in py3 but lists in py2

Known bugs with opencv 3.0.0
- digits.py, called via digits_video.py: https://github.com/Itseez/opencv/issues/4969
- gaussian_mix.py: https://github.com/Itseez/opencv/pull/4232
- video_v4l2.py: https://github.com/Itseez/opencv/pull/5474

Not working:
- letter_recog.py due to changed ml_StatModel.train() signature
This commit is contained in:
flp
2015-12-13 02:43:58 +01:00
parent 5cdf0e3e89
commit 4ed2d6328b
23 changed files with 218 additions and 131 deletions
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39
samples/python2/digits_adjust.py
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@@ -13,6 +13,14 @@ Usage:
'''
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
if PY3:
xrange = range
import numpy as np
import cv2
from multiprocessing.pool import ThreadPool
@@ -33,10 +41,10 @@ def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None)
model.train(train_samples, train_labels)
resp = model.predict(test_samples)
score = (resp != test_labels).mean()
print ".",
print(".", end='')
return score
if pool is None:
scores = map(f, xrange(kfold))
scores = list(map(f, xrange(kfold)))
else:
scores = pool.map(f, xrange(kfold))
return np.mean(scores)
@@ -50,7 +58,7 @@ class App(object):
digits, labels = load_digits(DIGITS_FN)
shuffle = np.random.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits)
digits2 = list(map(deskew, digits))
samples = preprocess_hog(digits2)
return samples, labels
@@ -68,7 +76,7 @@ class App(object):
scores = np.zeros((len(Cs), len(gammas)))
scores[:] = np.nan
print 'adjusting SVM (may take a long time) ...'
print('adjusting SVM (may take a long time) ...')
def f(job):
i, j = job
samples, labels = self.get_dataset()
@@ -79,20 +87,21 @@ class App(object):
ires = self.run_jobs(f, np.ndindex(*scores.shape))
for count, (i, j, score) in enumerate(ires):
scores[i, j] = score
print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
print scores
print('%d / %d (best error: %.2f %%, last: %.2f %%)' %
(count+1, scores.size, np.nanmin(scores)*100, score*100))
print(scores)
print 'writing score table to "svm_scores.npz"'
print('writing score table to "svm_scores.npz"')
np.savez('svm_scores.npz', scores=scores, Cs=Cs, gammas=gammas)
i, j = np.unravel_index(scores.argmin(), scores.shape)
best_params = dict(C = Cs[i], gamma=gammas[j])
print 'best params:', best_params
print 'best error: %.2f %%' % (scores.min()*100)
print('best params:', best_params)
print('best error: %.2f %%' % (scores.min()*100))
return best_params
def adjust_KNearest(self):
print 'adjusting KNearest ...'
print('adjusting KNearest ...')
def f(k):
samples, labels = self.get_dataset()
err = cross_validate(KNearest, dict(k=k), samples, labels)
@@ -101,9 +110,9 @@ class App(object):
for k, err in self.run_jobs(f, xrange(1, 9)):
if err < best_err:
best_err, best_k = err, k
print 'k = %d, error: %.2f %%' % (k, err*100)
print('k = %d, error: %.2f %%' % (k, err*100))
best_params = dict(k=best_k)
print 'best params:', best_params, 'err: %.2f' % (best_err*100)
print('best params:', best_params, 'err: %.2f' % (best_err*100))
return best_params
@@ -111,14 +120,14 @@ if __name__ == '__main__':
import getopt
import sys
print __doc__
print(__doc__)
args, _ = getopt.getopt(sys.argv[1:], '', ['model='])
args = dict(args)
args.setdefault('--model', 'svm')
args.setdefault('--env', '')
if args['--model'] not in ['svm', 'knearest']:
print 'unknown model "%s"' % args['--model']
print('unknown model "%s"' % args['--model'])
sys.exit(1)
t = clock()
@@ -127,4 +136,4 @@ if __name__ == '__main__':
app.adjust_KNearest()
else:
app.adjust_SVM()
print 'work time: %f s' % (clock() - t)
print('work time: %f s' % (clock() - t))