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Python samples adapted for Python3 compatibility

Browse Source 
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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43
samples/python2/letter_recog.py
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@@ -25,6 +25,9 @@ USAGE:
Models: RTrees, KNearest, Boost, SVM, MLP
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
@@ -58,22 +61,22 @@ class LetterStatModel(object):
class RTrees(LetterStatModel):
def __init__(self):
self.model = cv2.RTrees()
self.model = cv2.ml.RTrees_create()
def train(self, samples, responses):
sample_n, var_n = samples.shape
var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], np.uint8)
var_types = np.array([cv2.ml.VAR_NUMERICAL] * var_n + [cv2.ml.VAR_CATEGORICAL], np.uint8)
#CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
params = dict(max_depth=10 )
self.model.train(samples, cv2.CV_ROW_SAMPLE, responses, varType = var_types, params = params)
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses, varType = var_types, params = params)
def predict(self, samples):
return np.float32( [self.model.predict(s) for s in samples] )
return [self.model.predict(s) for s in samples]
class KNearest(LetterStatModel):
def __init__(self):
self.model = cv2.KNearest()
self.model = cv2.ml.KNearest_create()
def train(self, samples, responses):
self.model.train(samples, responses)
@@ -85,16 +88,16 @@ class KNearest(LetterStatModel):
class Boost(LetterStatModel):
def __init__(self):
self.model = cv2.Boost()
self.model = cv2.ml.Boost_create()
def train(self, samples, responses):
sample_n, var_n = samples.shape
new_samples = self.unroll_samples(samples)
new_responses = self.unroll_responses(responses)
var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], np.uint8)
var_types = np.array([cv2.ml.VAR_NUMERICAL] * var_n + [cv2.ml.VAR_CATEGORICAL, cv2.ml.VAR_CATEGORICAL], np.uint8)
#CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 )
params = dict(max_depth=5) #, use_surrogates=False)
self.model.train(new_samples, cv2.CV_ROW_SAMPLE, new_responses, varType = var_types, params=params)
self.model.train(new_samples, cv2.ml.ROW_SAMPLE, new_responses, varType = var_types, params=params)
def predict(self, samples):
new_samples = self.unroll_samples(samples)
@@ -105,11 +108,11 @@ class Boost(LetterStatModel):
class SVM(LetterStatModel):
def __init__(self):
self.model = cv2.SVM()
self.model = cv2.ml.SVM_create()
def train(self, samples, responses):
params = dict( kernel_type = cv2.SVM_LINEAR,
svm_type = cv2.SVM_C_SVC,
params = dict( kernel_type = cv2.ml.SVM_LINEAR,
svm_type = cv2.ml.SVM_C_SVC,
C = 1 )
self.model.train(samples, responses, params = params)
@@ -119,7 +122,7 @@ class SVM(LetterStatModel):
class MLP(LetterStatModel):
def __init__(self):
self.model = cv2.ANN_MLP()
self.model = cv2.ml.ANN_MLP_create()
def train(self, samples, responses):
sample_n, var_n = samples.shape
@@ -130,7 +133,7 @@ class MLP(LetterStatModel):
# CvANN_MLP_TrainParams::BACKPROP,0.001
params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
train_method = cv2.ml.ANN_MLP_TRAIN_PARAMS_BACKPROP,
bp_dw_scale = 0.001,
bp_moment_scale = 0.0 )
self.model.train(samples, np.float32(new_responses), None, params = params)
@@ -144,7 +147,7 @@ if __name__ == '__main__':
import getopt
import sys
print __doc__
print(__doc__)
models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
models = dict( [(cls.__name__.lower(), cls) for cls in models] )
@@ -155,7 +158,7 @@ if __name__ == '__main__':
args.setdefault('--model', 'rtrees')
args.setdefault('--data', '../data/letter-recognition.data')
print 'loading data %s ...' % args['--data']
print('loading data %s ...' % args['--data'])
samples, responses = load_base(args['--data'])
Model = models[args['--model']]
model = Model()
@@ -163,20 +166,20 @@ if __name__ == '__main__':
train_n = int(len(samples)*model.train_ratio)
if '--load' in args:
fn = args['--load']
print 'loading model from %s ...' % fn
print('loading model from %s ...' % fn)
model.load(fn)
else:
print 'training %s ...' % Model.__name__
print('training %s ...' % Model.__name__)
model.train(samples[:train_n], responses[:train_n])
print 'testing...'
print('testing...')
train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n])
test_rate = np.mean(model.predict(samples[train_n:]) == responses[train_n:])
print 'train rate: %f test rate: %f' % (train_rate*100, test_rate*100)
print('train rate: %f test rate: %f' % (train_rate*100, test_rate*100))
if '--save' in args:
fn = args['--save']
print 'saving model to %s ...' % fn
print('saving model to %s ...' % fn)
model.save(fn)
cv2.destroyAllWindows()