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Commit 4c1c0b10 authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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Merge pull request #5790 from bastelflp:py3compatibility

parents 5cdf0e3e 4ed2d632
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Showing with 218 additions and 131 deletions
samples/python2/asift.py
View file @ 4c1c0b10
......@@ -19,6 +19,9 @@ USAGE
Press left mouse button on a feature point to see its matching point.
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
......@@ -96,15 +99,15 @@ def affine_detect(detector, img, mask=None, pool=None):
ires = pool.imap(f, params)
for i, (k, d) in enumerate(ires):
print 'affine sampling: %d / %d\r' % (i+1, len(params)),
print('affine sampling: %d / %d\r' % (i+1, len(params)), end='')
keypoints.extend(k)
descrs.extend(d)
print
print()
return keypoints, np.array(descrs)
if __name__ == '__main__':
print __doc__
print(__doc__)
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
......@@ -121,23 +124,23 @@ if __name__ == '__main__':
detector, matcher = init_feature(feature_name)
if img1 is None:
print 'Failed to load fn1:', fn1
print('Failed to load fn1:', fn1)
sys.exit(1)
if img2 is None:
print 'Failed to load fn2:', fn2
print('Failed to load fn2:', fn2)
sys.exit(1)
if detector is None:
print 'unknown feature:', feature_name
print('unknown feature:', feature_name)
sys.exit(1)
print 'using', feature_name
print('using', feature_name)
pool=ThreadPool(processes = cv2.getNumberOfCPUs())
kp1, desc1 = affine_detect(detector, img1, pool=pool)
kp2, desc2 = affine_detect(detector, img2, pool=pool)
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2)))
def match_and_draw(win):
with Timer('matching'):
......@@ -145,12 +148,12 @@ if __name__ == '__main__':
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
print '%d / %d inliers/matched' % (np.sum(status), len(status))
print('%d / %d inliers/matched' % (np.sum(status), len(status)))
# do not draw outliers (there will be a lot of them)
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
else:
H, status = None, None
print '%d matches found, not enough for homography estimation' % len(p1)
print('%d matches found, not enough for homography estimation' % len(p1))
vis = explore_match(win, img1, img2, kp_pairs, None, H)
......
samples/python2/contours.py
View file @ 4c1c0b10
......@@ -9,6 +9,14 @@ Usage:
A trackbar is put up which controls the contour level from -3 to 3
'''
# 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
......@@ -16,8 +24,8 @@ def make_image():
img = np.zeros((500, 500), np.uint8)
black, white = 0, 255
for i in xrange(6):
dx = (i%2)*250 - 30
dy = (i/2)*150
dx = int((i%2)*250 - 30)
dy = int((i/2.)*150)
if i == 0:
for j in xrange(11):
......@@ -41,7 +49,7 @@ def make_image():
return img
if __name__ == '__main__':
print __doc__
print(__doc__)
img = make_image()
h, w = img.shape[:2]
......@@ -52,7 +60,7 @@ if __name__ == '__main__':
def update(levels):
vis = np.zeros((h, w, 3), np.uint8)
levels = levels - 3
cv2.drawContours( vis, contours, (-1, 3)[levels <= 0], (128,255,255),
cv2.drawContours( vis, contours, (-1, 2)[levels <= 0], (128,255,255),
3, cv2.LINE_AA, hierarchy, abs(levels) )
cv2.imshow('contours', vis)
update(3)
......
samples/python2/digits.py
View file @ 4c1c0b10
......@@ -23,6 +23,10 @@ Usage:
digits.py
'''
# Python 2/3 compatibility
from __future__ import print_function
# built-in modules
from multiprocessing.pool import ThreadPool
......@@ -50,7 +54,7 @@ def split2d(img, cell_size, flatten=True):
return cells
def load_digits(fn):
print 'loading "%s" ...' % fn
print('loading "%s" ...' % fn)
digits_img = cv2.imread(fn, 0)
digits = split2d(digits_img, (SZ, SZ))
labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
......@@ -67,7 +71,7 @@ def deskew(img):
class StatModel(object):
def load(self, fn):
self.model.load(fn)
self.model.load(fn) # Known bug: https://github.com/Itseez/opencv/issues/4969
def save(self, fn):
self.model.save(fn)
......@@ -101,14 +105,14 @@ class SVM(StatModel):
def evaluate_model(model, digits, samples, labels):
resp = model.predict(samples)
err = (labels != resp).mean()
print 'error: %.2f %%' % (err*100)
print('error: %.2f %%' % (err*100))
confusion = np.zeros((10, 10), np.int32)
for i, j in zip(labels, resp):
confusion[i, j] += 1
print 'confusion matrix:'
print confusion
print
print('confusion matrix:')
print(confusion)
print()
vis = []
for img, flag in zip(digits, resp == labels):
......@@ -145,17 +149,17 @@ def preprocess_hog(digits):
if __name__ == '__main__':
print __doc__
print(__doc__)
digits, labels = load_digits(DIGITS_FN)
print 'preprocessing...'
print('preprocessing...')
# shuffle digits
rand = np.random.RandomState(321)
shuffle = rand.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits)
digits2 = list(map(deskew, digits))
samples = preprocess_hog(digits2)
train_n = int(0.9*len(samples))
......@@ -165,18 +169,18 @@ if __name__ == '__main__':
labels_train, labels_test = np.split(labels, [train_n])
print 'training KNearest...'
print('training KNearest...')
model = KNearest(k=4)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('KNearest test', vis)
print 'training SVM...'
print('training SVM...')
model = SVM(C=2.67, gamma=5.383)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('SVM test', vis)
print 'saving SVM as "digits_svm.dat"...'
print('saving SVM as "digits_svm.dat"...')
model.save('digits_svm.dat')
cv2.waitKey(0)
samples/python2/digits_adjust.py
View file @ 4c1c0b10
......@@ -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))
samples/python2/digits_video.py
View file @ 4c1c0b10
#!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
......@@ -22,7 +25,7 @@ def main():
classifier_fn = 'digits_svm.dat'
if not os.path.exists(classifier_fn):
print '"%s" not found, run digits.py first' % classifier_fn
print('"%s" not found, run digits.py first' % classifier_fn)
return
model = SVM()
model.load(classifier_fn)
......
samples/python2/edge.py
View file @ 4c1c0b10
......@@ -14,6 +14,7 @@ Usage:
from __future__ import print_function
import cv2
import numpy as np
# relative module
import video
......@@ -45,7 +46,7 @@ if __name__ == '__main__':
thrs2 = cv2.getTrackbarPos('thrs2', 'edge')
edge = cv2.Canny(gray, thrs1, thrs2, apertureSize=5)
vis = img.copy()
vis /= 2
vis = np.uint8(vis/2.)
vis[edge != 0] = (0, 255, 0)
cv2.imshow('edge', vis)
ch = cv2.waitKey(5) & 0xFF
......
samples/python2/feature_homography.py
View file @ 4c1c0b10
......@@ -22,6 +22,9 @@ Keys:
Select a textured planar object to track by drawing a box with a mouse.
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
......@@ -84,7 +87,7 @@ class App:
if __name__ == '__main__':
print __doc__
print(__doc__)
import sys
try:
......
samples/python2/find_obj.py
View file @ 4c1c0b10
......@@ -14,6 +14,9 @@ USAGE
Press left mouse button on a feature point to see its matching point.
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
from common import anorm, getsize
......@@ -82,8 +85,10 @@ def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
if status is None:
status = np.ones(len(kp_pairs), np.bool_)
p1 = np.int32([kpp[0].pt for kpp in kp_pairs])
p2 = np.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0)
p1, p2 = [], [] # python 2 / python 3 change of zip unpacking
for kpp in kp_pairs:
p1.append(np.int32(kpp[0].pt))
p2.append(np.int32(np.array(kpp[1].pt) + [w1, 0]))
green = (0, 255, 0)
red = (0, 0, 255)
......@@ -133,7 +138,7 @@ def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
if __name__ == '__main__':
print __doc__
print(__doc__)
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
......@@ -150,33 +155,33 @@ if __name__ == '__main__':
detector, matcher = init_feature(feature_name)
if img1 is None:
print 'Failed to load fn1:', fn1
print('Failed to load fn1:', fn1)
sys.exit(1)
if img2 is None:
print 'Failed to load fn2:', fn2
print('Failed to load fn2:', fn2)
sys.exit(1)
if detector is None:
print 'unknown feature:', feature_name
print('unknown feature:', feature_name)
sys.exit(1)
print 'using', feature_name
print('using', feature_name)
kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, None)
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2)))
def match_and_draw(win):
print 'matching...'
print('matching...')
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
print '%d / %d inliers/matched' % (np.sum(status), len(status))
print('%d / %d inliers/matched' % (np.sum(status), len(status)))
else:
H, status = None, None
print '%d matches found, not enough for homography estimation' % len(p1)
print('%d matches found, not enough for homography estimation' % len(p1))
vis = explore_match(win, img1, img2, kp_pairs, status, H)
......
samples/python2/gaussian_mix.py
View file @ 4c1c0b10
#!/usr/bin/env python
# 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
from numpy import random
import cv2
......@@ -30,19 +38,21 @@ if __name__ == '__main__':
cluster_n = 5
img_size = 512
print 'press any key to update distributions, ESC - exit\n'
print('press any key to update distributions, ESC - exit\n')
while True:
print 'sampling distributions...'
print('sampling distributions...')
points, ref_distrs = make_gaussians(cluster_n, img_size)
print 'EM (opencv) ...'
em = cv2.EM(cluster_n, cv2.EM_COV_MAT_GENERIC)
em.train(points)
means = em.getMat('means')
covs = em.getMatVector('covs')
print('EM (opencv) ...')
em = cv2.ml.EM_create()
em.setClustersNumber(cluster_n)
em.setCovarianceMatrixType(cv2.ml.EM_COV_MAT_GENERIC)
em.trainEM(points)
means = em.getMeans()
covs = em.getCovs() # Known bug: https://github.com/Itseez/opencv/pull/4232
found_distrs = zip(means, covs)
print 'ready!\n'
print('ready!\n')
img = np.zeros((img_size, img_size, 3), np.uint8)
for x, y in np.int32(points):
......
samples/python2/hist.py
View file @ 4c1c0b10
......@@ -15,6 +15,9 @@ Usage : python hist.py <image_file>
Abid Rahman 3/14/12 debug Gary Bradski
'''
# Python 2/3 compatibility
from __future__ import print_function
import cv2
import numpy as np
......@@ -38,8 +41,8 @@ def hist_curve(im):
def hist_lines(im):
h = np.zeros((300,256,3))
if len(im.shape)!=2:
print "hist_lines applicable only for grayscale images"
#print "so converting image to grayscale for representation"
print("hist_lines applicable only for grayscale images")
#print("so converting image to grayscale for representation"
im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
hist_item = cv2.calcHist([im],[0],None,[256],[0,256])
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
......@@ -58,18 +61,18 @@ if __name__ == '__main__':
fname = sys.argv[1]
else :
fname = '../data/lena.jpg'
print "usage : python hist.py <image_file>"
print("usage : python hist.py <image_file>")
im = cv2.imread(fname)
if im is None:
print 'Failed to load image file:', fname
print('Failed to load image file:', fname)
sys.exit(1)
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
print ''' Histogram plotting \n
print(''' Histogram plotting \n
Keymap :\n
a - show histogram for color image in curve mode \n
b - show histogram in bin mode \n
......@@ -77,7 +80,7 @@ if __name__ == '__main__':
d - show histogram for color image in curve mode \n
e - show histogram for a normalized image in curve mode \n
Esc - exit \n
'''
''')
cv2.imshow('image',im)
while True:
......@@ -86,31 +89,31 @@ if __name__ == '__main__':
curve = hist_curve(im)
cv2.imshow('histogram',curve)
cv2.imshow('image',im)
print 'a'
print('a')
elif k == ord('b'):
print 'b'
print('b')
lines = hist_lines(im)
cv2.imshow('histogram',lines)
cv2.imshow('image',gray)
elif k == ord('c'):
print 'c'
print('c')
equ = cv2.equalizeHist(gray)
lines = hist_lines(equ)
cv2.imshow('histogram',lines)
cv2.imshow('image',equ)
elif k == ord('d'):
print 'd'
print('d')
curve = hist_curve(gray)
cv2.imshow('histogram',curve)
cv2.imshow('image',gray)
elif k == ord('e'):
print 'e'
norm = cv2.normalize(gray,alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX)
print('e')
norm = cv2.normalize(gray, gray, alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX)
lines = hist_lines(norm)
cv2.imshow('histogram',lines)
cv2.imshow('image',norm)
elif k == 27:
print 'ESC'
print('ESC')
cv2.destroyAllWindows()
break
cv2.destroyAllWindows()
samples/python2/kmeans.py
View file @ 4c1c0b10
......@@ -10,6 +10,9 @@ Keyboard shortcuts:
space - generate new distribution
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
......@@ -19,7 +22,7 @@ if __name__ == '__main__':
cluster_n = 5
img_size = 512
print __doc__
print(__doc__)
# generating bright palette
colors = np.zeros((1, cluster_n, 3), np.uint8)
......@@ -28,7 +31,7 @@ if __name__ == '__main__':
colors = cv2.cvtColor(colors, cv2.COLOR_HSV2BGR)[0]
while True:
print 'sampling distributions...'
print('sampling distributions...')
points, _ = make_gaussians(cluster_n, img_size)
term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
......@@ -36,7 +39,8 @@ if __name__ == '__main__':
img = np.zeros((img_size, img_size, 3), np.uint8)
for (x, y), label in zip(np.int32(points), labels.ravel()):
c = map(int, colors[label])
c = list(map(int, colors[label]))
cv2.circle(img, (x, y), 1, c, -1)
cv2.imshow('gaussian mixture', img)
......
samples/python2/lappyr.py
View file @ 4c1c0b10
......@@ -64,7 +64,7 @@ if __name__ == '__main__':
pyr = build_lappyr(frame, leveln)
for i in xrange(leveln):
v = cv2.getTrackbarPos('%d'%i, 'level control') / 5
v = int(cv2.getTrackbarPos('%d'%i, 'level control') / 5)
pyr[i] *= v
res = merge_lappyr(pyr)
......
samples/python2/letter_recog.py
View file @ 4c1c0b10
......@@ -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()
samples/python2/mouse_and_match.py
View file @ 4c1c0b10
#!/usr/bin/env python
'''
mouse_and_match.py [-i path | --input path: default ./]
mouse_and_match.py [-i path | --input path: default ../data/]
Demonstrate using a mouse to interact with an image:
Read in the images in a directory one by one
Allow the user to select parts of an image with a mouse
When they let go of the mouse, it correlates (using matchTemplate) that patch with the image.
SPACE for next image
ESC to exit
'''
......@@ -54,8 +56,10 @@ def onmouse(event, x, y, flags, param):
drag_start = None
if __name__ == '__main__':
print(__doc__)
parser = argparse.ArgumentParser(description='Demonstrate mouse interaction with images')
parser.add_argument("-i","--input", default='./', help="Input directory.")
parser.add_argument("-i","--input", default='../data/', help="Input directory.")
args = parser.parse_args()
path = args.input
......@@ -68,7 +72,7 @@ if __name__ == '__main__':
print(infile)
img=cv2.imread(infile,1)
if img == None:
if img is None:
continue
sel = (0,0,0,0)
drag_start = None
......
samples/python2/opt_flow.py
View file @ 4c1c0b10
#!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
import video
......@@ -15,7 +18,7 @@ Keys:
def draw_flow(img, flow, step=16):
h, w = img.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int)
fx, fy = flow[y,x].T
lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
lines = np.int32(lines + 0.5)
......@@ -47,7 +50,7 @@ def warp_flow(img, flow):
if __name__ == '__main__':
import sys
print help_message
print(help_message)
try:
fn = sys.argv[1]
except:
......@@ -78,10 +81,10 @@ if __name__ == '__main__':
break
if ch == ord('1'):
show_hsv = not show_hsv
print 'HSV flow visualization is', ['off', 'on'][show_hsv]
print('HSV flow visualization is', ['off', 'on'][show_hsv])
if ch == ord('2'):
show_glitch = not show_glitch
if show_glitch:
cur_glitch = img.copy()
print 'glitch is', ['off', 'on'][show_glitch]
print('glitch is', ['off', 'on'][show_glitch])
cv2.destroyAllWindows()
samples/python2/peopledetect.py
View file @ 4c1c0b10
......@@ -35,7 +35,9 @@ if __name__ == '__main__':
hog = cv2.HOGDescriptor()
hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )
for fn in it.chain(*map(glob, sys.argv[1:])):
default = ['../data/basketball2.png '] if len(sys.argv[1:]) == 0 else []
for fn in it.chain(*map(glob, default + sys.argv[1:])):
print(fn, ' - ',)
try:
img = cv2.imread(fn)
......
samples/python2/plane_ar.py
View file @ 4c1c0b10
......@@ -22,6 +22,9 @@ Select a textured planar object to track by drawing a box with a mouse.
Use 'focal' slider to adjust to camera focal length for proper video augmentation.
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
import video
......@@ -97,7 +100,7 @@ class App:
if __name__ == '__main__':
print __doc__
print(__doc__)
import sys
try:
......
samples/python2/plane_tracker.py
View file @ 4c1c0b10
......@@ -21,6 +21,14 @@ Keys:
Select a textured planar object to track by drawing a box with a mouse.
'''
# 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
......@@ -64,6 +72,7 @@ class PlaneTracker:
self.detector = cv2.ORB_create( nfeatures = 1000 )
self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
self.targets = []
self.frame_points = []
def add_target(self, image, rect, data=None):
'''Add a new tracking target.'''
......@@ -87,8 +96,8 @@ class PlaneTracker:
def track(self, frame):
'''Returns a list of detected TrackedTarget objects'''
frame_points, frame_descrs = self.detect_features(frame)
if len(frame_points) < MIN_MATCH_COUNT:
self.frame_points, frame_descrs = self.detect_features(frame)
if len(self.frame_points) < MIN_MATCH_COUNT:
return []
matches = self.matcher.knnMatch(frame_descrs, k = 2)
matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]
......@@ -103,7 +112,7 @@ class PlaneTracker:
continue
target = self.targets[imgIdx]
p0 = [target.keypoints[m.trainIdx].pt for m in matches]
p1 = [frame_points[m.queryIdx].pt for m in matches]
p1 = [self.frame_points[m.queryIdx].pt for m in matches]
p0, p1 = np.float32((p0, p1))
H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0)
status = status.ravel() != 0
......@@ -169,7 +178,7 @@ class App:
break
if __name__ == '__main__':
print __doc__
print(__doc__)
import sys
try:
......
samples/python2/stereo_match.py
View file @ 4c1c0b10
......@@ -6,6 +6,9 @@ Simple example of stereo image matching and point cloud generation.
Resulting .ply file cam be easily viewed using MeshLab ( http://meshlab.sourceforge.net/ )
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
......@@ -25,13 +28,13 @@ def write_ply(fn, verts, colors):
verts = verts.reshape(-1, 3)
colors = colors.reshape(-1, 3)
verts = np.hstack([verts, colors])
with open(fn, 'w') as f:
f.write(ply_header % dict(vert_num=len(verts)))
np.savetxt(f, verts, '%f %f %f %d %d %d')
with open(fn, 'wb') as f:
f.write((ply_header % dict(vert_num=len(verts))).encode('utf-8'))
np.savetxt(f, verts, fmt='%f %f %f %d %d %d ')
if __name__ == '__main__':
print 'loading images...'
print('loading images...')
imgL = cv2.pyrDown( cv2.imread('../data/aloeL.jpg') ) # downscale images for faster processing
imgR = cv2.pyrDown( cv2.imread('../data/aloeR.jpg') )
......@@ -50,10 +53,10 @@ if __name__ == '__main__':
speckleRange = 32
)
print 'computing disparity...'
print('computing disparity...')
disp = stereo.compute(imgL, imgR).astype(np.float32) / 16.0
print 'generating 3d point cloud...',
print('generating 3d point cloud...',)
h, w = imgL.shape[:2]
f = 0.8*w # guess for focal length
Q = np.float32([[1, 0, 0, -0.5*w],
......@@ -67,7 +70,7 @@ if __name__ == '__main__':
out_colors = colors[mask]
out_fn = 'out.ply'
write_ply('out.ply', out_points, out_colors)
print '%s saved' % 'out.ply'
print('%s saved' % 'out.ply')
cv2.imshow('left', imgL)
cv2.imshow('disparity', (disp-min_disp)/num_disp)
......
samples/python2/texture_flow.py
View file @ 4c1c0b10
......@@ -10,6 +10,9 @@ Usage:
texture_flow.py [<image>]
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
......@@ -22,7 +25,7 @@ if __name__ == '__main__':
img = cv2.imread(fn)
if img is None:
print 'Failed to load image file:', fn
print('Failed to load image file:', fn)
sys.exit(1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
......@@ -36,7 +39,7 @@ if __name__ == '__main__':
vis[:] = (192 + np.uint32(vis)) / 2
d = 12
points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
for x, y in points:
for x, y in np.int32(points):
vx, vy = np.int32(flow[y, x]*d)
cv2.line(vis, (x-vx, y-vy), (x+vx, y+vy), (0, 0, 0), 1, cv2.LINE_AA)
cv2.imshow('input', img)
......
samples/python2/video.py
View file @ 4c1c0b10
......@@ -182,7 +182,7 @@ if __name__ == '__main__':
if len(sources) == 0:
sources = [ 0 ]
caps = map(create_capture, sources)
caps = list(map(create_capture, sources))
shot_idx = 0
while True:
imgs = []
......
samples/python2/video_v4l2.py
View file @ 4c1c0b10
......@@ -14,6 +14,9 @@ Keys:
'''
# Python 2/3 compatibility
from __future__ import print_function
import cv2
def decode_fourcc(v):
......@@ -24,13 +27,13 @@ font = cv2.FONT_HERSHEY_SIMPLEX
color = (0, 255, 0)
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_AUTOFOCUS, False)
cap.set(cv2.CAP_PROP_AUTOFOCUS, False) # Known bug: https://github.com/Itseez/opencv/pull/5474
cv2.namedWindow("Video")
convert_rgb = True
fps = int(cap.get(cv2.CAP_PROP_FPS))
focus = int(cap.get(cv2.CAP_PROP_FOCUS)) * 100
focus = int(min(cap.get(cv2.CAP_PROP_FOCUS) * 100, 2**31-1)) # ceil focus to C_LONG as Python3 int can go to +inf
cv2.createTrackbar("FPS", "Video", fps, 30, lambda v: cap.set(cv2.CAP_PROP_FPS, v))
cv2.createTrackbar("Focus", "Video", focus, 100, lambda v: cap.set(cv2.CAP_PROP_FOCUS, v / 100))
......@@ -55,7 +58,7 @@ while True:
cv2.putText(img, "FPS: {}".format(fps), (15, 80), font, 1.0, color)
cv2.imshow("Video", img)
k = cv2.waitKey(1)
k = 0xFF & cv2.waitKey(1)
if k == 27:
break
......
samples/python2/watershed.py
View file @ 4c1c0b10
......@@ -22,7 +22,8 @@ Keys
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
......@@ -44,7 +45,7 @@ class App:
self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors)
def get_colors(self):
return map(int, self.colors[self.cur_marker]), self.cur_marker
return list(map(int, self.colors[self.cur_marker])), self.cur_marker
def watershed(self):
m = self.markers.copy()
......@@ -60,13 +61,13 @@ class App:
break
if ch >= ord('1') and ch <= ord('7'):
self.cur_marker = ch - ord('0')
print 'marker: ', self.cur_marker
print('marker: ', self.cur_marker)
if ch == ord(' ') or (self.sketch.dirty and self.auto_update):
self.watershed()
self.sketch.dirty = False
if ch in [ord('a'), ord('A')]:
self.auto_update = not self.auto_update
print 'auto_update if', ['off', 'on'][self.auto_update]
print('auto_update if', ['off', 'on'][self.auto_update])
if ch in [ord('r'), ord('R')]:
self.markers[:] = 0
self.markers_vis[:] = self.img
......@@ -80,5 +81,5 @@ if __name__ == '__main__':
fn = sys.argv[1]
except:
fn = '../data/fruits.jpg'
print __doc__
print(__doc__)
App(fn).run()
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