generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge pull request #5102 from nzjrs:fix-python-digits

Browse Source
This commit is contained in:
Vadim Pisarevsky 2015-08-03 05:40:23 +00:00
commit 02906bf23a
2 changed files with 8 additions and 44 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
samples/python2/digits.py
View File

@ -77,7 +77,6 @@ class KNearest(StatModel):
self.model = cv2.ml.KNearest_create()
def train(self, samples, responses):
self.model = cv2.ml.KNearest_create()
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
def predict(self, samples):
@ -93,7 +92,6 @@ class SVM(StatModel):
self.model.setType(cv2.ml.SVM_C_SVC)
def train(self, samples, responses):
self.model = cv2.ml.SVM_create()
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
def predict(self, samples):

50
samples/python2/digits_adjust.py
View File

@ -6,18 +6,12 @@ Grid search is used to find the best parameters for SVM and KNearest classifiers
SVM adjustment follows the guidelines given in
http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Threading or cloud computing (with http://www.picloud.com/)) may be used
to speedup the computation.
Usage:
digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]
digits_adjust.py [--model {svm|knearest}]
--model {svm|knearest} - select the classifier (SVM is the default)
--cloud - use PiCloud computing platform
--env - cloud environment name
'''
# TODO cloud env setup tutorial
import numpy as np
import cv2
@ -25,14 +19,6 @@ from multiprocessing.pool import ThreadPool
from digits import *
try:
import cloud
have_cloud = True
except ImportError:
have_cloud = False
def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None):
n = len(samples)
folds = np.array_split(np.arange(n), kfold)
@ -57,23 +43,10 @@ def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None)
class App(object):
def __init__(self, usecloud=False, cloud_env=''):
if usecloud and not have_cloud:
print 'warning: cloud module is not installed, running locally'
usecloud = False
self.usecloud = usecloud
self.cloud_env = cloud_env
if self.usecloud:
print 'uploading dataset to cloud...'
cloud.files.put(DIGITS_FN)
self.preprocess_job = cloud.call(self.preprocess, _env=self.cloud_env)
else:
self._samples, self._labels = self.preprocess()
def __init__(self):
self._samples, self._labels = self.preprocess()
def preprocess(self):
if self.usecloud:
cloud.files.get(DIGITS_FN)
digits, labels = load_digits(DIGITS_FN)
shuffle = np.random.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle]
@ -82,18 +55,11 @@ class App(object):
return samples, labels
def get_dataset(self):
if self.usecloud:
return cloud.result(self.preprocess_job)
else:
return self._samples, self._labels
return self._samples, self._labels
def run_jobs(self, f, jobs):
if self.usecloud:
jids = cloud.map(f, jobs, _env=self.cloud_env, _profile=True, _depends_on=self.preprocess_job)
ires = cloud.iresult(jids)
else:
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
ires = pool.imap_unordered(f, jobs)
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
ires = pool.imap_unordered(f, jobs)
return ires
def adjust_SVM(self):
@ -147,7 +113,7 @@ if __name__ == '__main__':
print __doc__
args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])
args, _ = getopt.getopt(sys.argv[1:], '', ['model='])
args = dict(args)
args.setdefault('--model', 'svm')
args.setdefault('--env', '')
@ -156,7 +122,7 @@ if __name__ == '__main__':
sys.exit(1)
t = clock()
app = App(usecloud='--cloud' in args, cloud_env = args['--env'])
app = App()
if args['--model'] == 'knearest':
app.adjust_KNearest()
else: