Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #5790 from bastelflp:py3compatibility

Browse Source
pull/5798/head
Vadim Pisarevsky 9 years ago
parent 5cdf0e3e89 4ed2d6328b
commit 4c1c0b108d
23 changed files with 218 additions and 131 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 23
      samples/python2/asift.py
  2. 16
      samples/python2/contours.py
  3. 28
      samples/python2/digits.py
  4. 39
      samples/python2/digits_adjust.py
  5. 5
      samples/python2/digits_video.py
  6. 3
      samples/python2/edge.py
  7. 5
      samples/python2/feature_homography.py
  8. 27
      samples/python2/find_obj.py
  9. 26
      samples/python2/gaussian_mix.py
  10. 29
      samples/python2/hist.py
  11. 10
      samples/python2/kmeans.py
  12. 2
      samples/python2/lappyr.py
  13. 43
      samples/python2/letter_recog.py
  14. 10
      samples/python2/mouse_and_match.py
  15. 11
      samples/python2/opt_flow.py
  16. 4
      samples/python2/peopledetect.py
  17. 5
      samples/python2/plane_ar.py
  18. 17
      samples/python2/plane_tracker.py
  19. 17
      samples/python2/stereo_match.py
  20. 7
      samples/python2/texture_flow.py
  21. 2
      samples/python2/video.py
  22. 9
      samples/python2/video_v4l2.py
  23. 11
      samples/python2/watershed.py

23
samples/python2/asift.py
Unescape View File

@ -19,6 +19,9 @@ USAGE
Press left mouse button on a feature point to see its matching point. 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 numpy as np
import cv2 import cv2
@ -96,15 +99,15 @@ def affine_detect(detector, img, mask=None, pool=None):
ires = pool.imap(f, params) ires = pool.imap(f, params)
for i, (k, d) in enumerate(ires): 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) keypoints.extend(k)
descrs.extend(d) descrs.extend(d)
print print()
return keypoints, np.array(descrs) return keypoints, np.array(descrs)
if __name__ == '__main__': if __name__ == '__main__':
print __doc__ print(__doc__)
import sys, getopt import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature=']) opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
@ -121,23 +124,23 @@ if __name__ == '__main__':
detector, matcher = init_feature(feature_name) detector, matcher = init_feature(feature_name)
if img1 is None: if img1 is None:
print 'Failed to load fn1:', fn1 print('Failed to load fn1:', fn1)
sys.exit(1) sys.exit(1)
if img2 is None: if img2 is None:
print 'Failed to load fn2:', fn2 print('Failed to load fn2:', fn2)
sys.exit(1) sys.exit(1)
if detector is None: if detector is None:
print 'unknown feature:', feature_name print('unknown feature:', feature_name)
sys.exit(1) sys.exit(1)
print 'using', feature_name print('using', feature_name)
pool=ThreadPool(processes = cv2.getNumberOfCPUs()) pool=ThreadPool(processes = cv2.getNumberOfCPUs())
kp1, desc1 = affine_detect(detector, img1, pool=pool) kp1, desc1 = affine_detect(detector, img1, pool=pool)
kp2, desc2 = affine_detect(detector, img2, 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): def match_and_draw(win):
with Timer('matching'): with Timer('matching'):
@ -145,12 +148,12 @@ if __name__ == '__main__':
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches) p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4: if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0) 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) # do not draw outliers (there will be a lot of them)
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag] kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
else: else:
H, status = None, None 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) vis = explore_match(win, img1, img2, kp_pairs, None, H)

16
samples/python2/contours.py
Unescape View File

@ -9,6 +9,14 @@ Usage:
A trackbar is put up which controls the contour level from -3 to 3 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 numpy as np
import cv2 import cv2
@ -16,8 +24,8 @@ def make_image():
img = np.zeros((500, 500), np.uint8) img = np.zeros((500, 500), np.uint8)
black, white = 0, 255 black, white = 0, 255
for i in xrange(6): for i in xrange(6):
dx = (i%2)*250 - 30 dx = int((i%2)*250 - 30)
dy = (i/2)*150 dy = int((i/2.)*150)
if i == 0: if i == 0:
for j in xrange(11): for j in xrange(11):
@ -41,7 +49,7 @@ def make_image():
return img return img
if __name__ == '__main__': if __name__ == '__main__':
print __doc__ print(__doc__)
img = make_image() img = make_image()
h, w = img.shape[:2] h, w = img.shape[:2]
@ -52,7 +60,7 @@ if __name__ == '__main__':
def update(levels): def update(levels):
vis = np.zeros((h, w, 3), np.uint8) vis = np.zeros((h, w, 3), np.uint8)
levels = levels - 3 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) ) 3, cv2.LINE_AA, hierarchy, abs(levels) )
cv2.imshow('contours', vis) cv2.imshow('contours', vis)
update(3) update(3)

28
samples/python2/digits.py
Unescape View File

@ -23,6 +23,10 @@ Usage:
digits.py digits.py
''' '''
# Python 2/3 compatibility
from __future__ import print_function
# built-in modules # built-in modules
from multiprocessing.pool import ThreadPool from multiprocessing.pool import ThreadPool
@ -50,7 +54,7 @@ def split2d(img, cell_size, flatten=True):
return cells return cells
def load_digits(fn): def load_digits(fn):
print 'loading "%s" ...' % fn print('loading "%s" ...' % fn)
digits_img = cv2.imread(fn, 0) digits_img = cv2.imread(fn, 0)
digits = split2d(digits_img, (SZ, SZ)) digits = split2d(digits_img, (SZ, SZ))
labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N) labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
@ -67,7 +71,7 @@ def deskew(img):
class StatModel(object): class StatModel(object):
def load(self, fn): 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): def save(self, fn):
self.model.save(fn) self.model.save(fn)
@ -101,14 +105,14 @@ class SVM(StatModel):
def evaluate_model(model, digits, samples, labels): def evaluate_model(model, digits, samples, labels):
resp = model.predict(samples) resp = model.predict(samples)
err = (labels != resp).mean() err = (labels != resp).mean()
print 'error: %.2f %%' % (err*100) print('error: %.2f %%' % (err*100))
confusion = np.zeros((10, 10), np.int32) confusion = np.zeros((10, 10), np.int32)
for i, j in zip(labels, resp): for i, j in zip(labels, resp):
confusion[i, j] += 1 confusion[i, j] += 1
print 'confusion matrix:' print('confusion matrix:')
print confusion print(confusion)
print print()
vis = [] vis = []
for img, flag in zip(digits, resp == labels): for img, flag in zip(digits, resp == labels):
@ -145,17 +149,17 @@ def preprocess_hog(digits):
if __name__ == '__main__': if __name__ == '__main__':
print __doc__ print(__doc__)
digits, labels = load_digits(DIGITS_FN) digits, labels = load_digits(DIGITS_FN)
print 'preprocessing...' print('preprocessing...')
# shuffle digits # shuffle digits
rand = np.random.RandomState(321) rand = np.random.RandomState(321)
shuffle = rand.permutation(len(digits)) shuffle = rand.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle] digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits) digits2 = list(map(deskew, digits))
samples = preprocess_hog(digits2) samples = preprocess_hog(digits2)
train_n = int(0.9*len(samples)) train_n = int(0.9*len(samples))
@ -165,18 +169,18 @@ if __name__ == '__main__':
labels_train, labels_test = np.split(labels, [train_n]) labels_train, labels_test = np.split(labels, [train_n])
print 'training KNearest...' print('training KNearest...')
model = KNearest(k=4) model = KNearest(k=4)
model.train(samples_train, labels_train) model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test) vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('KNearest test', vis) cv2.imshow('KNearest test', vis)
print 'training SVM...' print('training SVM...')
model = SVM(C=2.67, gamma=5.383) model = SVM(C=2.67, gamma=5.383)
model.train(samples_train, labels_train) model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test) vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('SVM test', vis) cv2.imshow('SVM test', vis)
print 'saving SVM as "digits_svm.dat"...' print('saving SVM as "digits_svm.dat"...')
model.save('digits_svm.dat') model.save('digits_svm.dat')
cv2.waitKey(0) cv2.waitKey(0)

39
samples/python2/digits_adjust.py
Unescape View File

@ -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 numpy as np
import cv2 import cv2
from multiprocessing.pool import ThreadPool 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) model.train(train_samples, train_labels)
resp = model.predict(test_samples) resp = model.predict(test_samples)
score = (resp != test_labels).mean() score = (resp != test_labels).mean()
print ".", print(".", end='')
return score return score
if pool is None: if pool is None:
scores = map(f, xrange(kfold)) scores = list(map(f, xrange(kfold)))
else: else:
scores = pool.map(f, xrange(kfold)) scores = pool.map(f, xrange(kfold))
return np.mean(scores) return np.mean(scores)
@ -50,7 +58,7 @@ class App(object):
digits, labels = load_digits(DIGITS_FN) digits, labels = load_digits(DIGITS_FN)
shuffle = np.random.permutation(len(digits)) shuffle = np.random.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle] digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits) digits2 = list(map(deskew, digits))
samples = preprocess_hog(digits2) samples = preprocess_hog(digits2)
return samples, labels return samples, labels
@ -68,7 +76,7 @@ class App(object):
scores = np.zeros((len(Cs), len(gammas))) scores = np.zeros((len(Cs), len(gammas)))
scores[:] = np.nan scores[:] = np.nan
print 'adjusting SVM (may take a long time) ...' print('adjusting SVM (may take a long time) ...')
def f(job): def f(job):
i, j = job i, j = job
samples, labels = self.get_dataset() samples, labels = self.get_dataset()
@ -79,20 +87,21 @@ class App(object):
ires = self.run_jobs(f, np.ndindex(*scores.shape)) ires = self.run_jobs(f, np.ndindex(*scores.shape))
for count, (i, j, score) in enumerate(ires): for count, (i, j, score) in enumerate(ires):
scores[i, j] = score scores[i, j] = score
print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100) print('%d / %d (best error: %.2f %%, last: %.2f %%)' %
print scores (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) np.savez('svm_scores.npz', scores=scores, Cs=Cs, gammas=gammas)
i, j = np.unravel_index(scores.argmin(), scores.shape) i, j = np.unravel_index(scores.argmin(), scores.shape)
best_params = dict(C = Cs[i], gamma=gammas[j]) best_params = dict(C = Cs[i], gamma=gammas[j])
print 'best params:', best_params print('best params:', best_params)
print 'best error: %.2f %%' % (scores.min()*100) print('best error: %.2f %%' % (scores.min()*100))
return best_params return best_params
def adjust_KNearest(self): def adjust_KNearest(self):
print 'adjusting KNearest ...' print('adjusting KNearest ...')
def f(k): def f(k):
samples, labels = self.get_dataset() samples, labels = self.get_dataset()
err = cross_validate(KNearest, dict(k=k), samples, labels) 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)): for k, err in self.run_jobs(f, xrange(1, 9)):
if err < best_err: if err < best_err:
best_err, best_k = err, k 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) 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 return best_params
@ -111,14 +120,14 @@ if __name__ == '__main__':
import getopt import getopt
import sys import sys
print __doc__ print(__doc__)
args, _ = getopt.getopt(sys.argv[1:], '', ['model=']) args, _ = getopt.getopt(sys.argv[1:], '', ['model='])
args = dict(args) args = dict(args)
args.setdefault('--model', 'svm') args.setdefault('--model', 'svm')
args.setdefault('--env', '') args.setdefault('--env', '')
if args['--model'] not in ['svm', 'knearest']: if args['--model'] not in ['svm', 'knearest']:
print 'unknown model "%s"' % args['--model'] print('unknown model "%s"' % args['--model'])
sys.exit(1) sys.exit(1)
t = clock() t = clock()
@ -127,4 +136,4 @@ if __name__ == '__main__':
app.adjust_KNearest() app.adjust_KNearest()
else: else:
app.adjust_SVM() app.adjust_SVM()
print 'work time: %f s' % (clock() - t) print('work time: %f s' % (clock() - t))

5
samples/python2/digits_video.py
Unescape View File

@ -1,5 +1,8 @@
#!/usr/bin/env python #!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np import numpy as np
import cv2 import cv2
@ -22,7 +25,7 @@ def main():
classifier_fn = 'digits_svm.dat' classifier_fn = 'digits_svm.dat'
if not os.path.exists(classifier_fn): 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 return
model = SVM() model = SVM()
model.load(classifier_fn) model.load(classifier_fn)

3
samples/python2/edge.py
Unescape View File

@ -14,6 +14,7 @@ Usage:
from __future__ import print_function from __future__ import print_function
import cv2 import cv2
import numpy as np
# relative module # relative module
import video import video
@ -45,7 +46,7 @@ if __name__ == '__main__':
thrs2 = cv2.getTrackbarPos('thrs2', 'edge') thrs2 = cv2.getTrackbarPos('thrs2', 'edge')
edge = cv2.Canny(gray, thrs1, thrs2, apertureSize=5) edge = cv2.Canny(gray, thrs1, thrs2, apertureSize=5)
vis = img.copy() vis = img.copy()
vis /= 2 vis = np.uint8(vis/2.)
vis[edge != 0] = (0, 255, 0) vis[edge != 0] = (0, 255, 0)
cv2.imshow('edge', vis) cv2.imshow('edge', vis)
ch = cv2.waitKey(5) & 0xFF ch = cv2.waitKey(5) & 0xFF

5
samples/python2/feature_homography.py
Unescape View File

@ -22,6 +22,9 @@ Keys:
Select a textured planar object to track by drawing a box with a mouse. 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 numpy as np
import cv2 import cv2
@ -84,7 +87,7 @@ class App:
if __name__ == '__main__': if __name__ == '__main__':
print __doc__ print(__doc__)
import sys import sys
try: try:

27
samples/python2/find_obj.py
Unescape View File

@ -14,6 +14,9 @@ USAGE
Press left mouse button on a feature point to see its matching point. 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 numpy as np
import cv2 import cv2
from common import anorm, getsize 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: if status is None:
status = np.ones(len(kp_pairs), np.bool_) status = np.ones(len(kp_pairs), np.bool_)
p1 = np.int32([kpp[0].pt for kpp in kp_pairs]) p1, p2 = [], [] # python 2 / python 3 change of zip unpacking
p2 = np.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0) 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) green = (0, 255, 0)
red = (0, 0, 255) red = (0, 0, 255)
@ -133,7 +138,7 @@ def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
if __name__ == '__main__': if __name__ == '__main__':
print __doc__ print(__doc__)
import sys, getopt import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature=']) opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
@ -150,33 +155,33 @@ if __name__ == '__main__':
detector, matcher = init_feature(feature_name) detector, matcher = init_feature(feature_name)
if img1 is None: if img1 is None:
print 'Failed to load fn1:', fn1 print('Failed to load fn1:', fn1)
sys.exit(1) sys.exit(1)
if img2 is None: if img2 is None:
print 'Failed to load fn2:', fn2 print('Failed to load fn2:', fn2)
sys.exit(1) sys.exit(1)
if detector is None: if detector is None:
print 'unknown feature:', feature_name print('unknown feature:', feature_name)
sys.exit(1) sys.exit(1)
print 'using', feature_name print('using', feature_name)
kp1, desc1 = detector.detectAndCompute(img1, None) kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, 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): def match_and_draw(win):
print 'matching...' print('matching...')
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2 raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches) p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4: if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0) 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: else:
H, status = None, None 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) vis = explore_match(win, img1, img2, kp_pairs, status, H)

26
samples/python2/gaussian_mix.py
Unescape View File

@ -1,5 +1,13 @@
#!/usr/bin/env python #!/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 import numpy as np
from numpy import random from numpy import random
import cv2 import cv2
@ -30,19 +38,21 @@ if __name__ == '__main__':
cluster_n = 5 cluster_n = 5
img_size = 512 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: while True:
print 'sampling distributions...' print('sampling distributions...')
points, ref_distrs = make_gaussians(cluster_n, img_size) points, ref_distrs = make_gaussians(cluster_n, img_size)
print 'EM (opencv) ...' print('EM (opencv) ...')
em = cv2.EM(cluster_n, cv2.EM_COV_MAT_GENERIC) em = cv2.ml.EM_create()
em.train(points) em.setClustersNumber(cluster_n)
means = em.getMat('means') em.setCovarianceMatrixType(cv2.ml.EM_COV_MAT_GENERIC)
covs = em.getMatVector('covs') em.trainEM(points)
means = em.getMeans()
covs = em.getCovs() # Known bug: https://github.com/Itseez/opencv/pull/4232
found_distrs = zip(means, covs) found_distrs = zip(means, covs)
print 'ready!\n' print('ready!\n')
img = np.zeros((img_size, img_size, 3), np.uint8) img = np.zeros((img_size, img_size, 3), np.uint8)
for x, y in np.int32(points): for x, y in np.int32(points):

29
samples/python2/hist.py
Unescape View File

@ -15,6 +15,9 @@ Usage : python hist.py <image_file>
Abid Rahman 3/14/12 debug Gary Bradski Abid Rahman 3/14/12 debug Gary Bradski
''' '''
# Python 2/3 compatibility
from __future__ import print_function
import cv2 import cv2
import numpy as np import numpy as np
@ -38,8 +41,8 @@ def hist_curve(im):
def hist_lines(im): def hist_lines(im):
h = np.zeros((300,256,3)) h = np.zeros((300,256,3))
if len(im.shape)!=2: if len(im.shape)!=2:
print "hist_lines applicable only for grayscale images" print("hist_lines applicable only for grayscale images")
#print "so converting image to grayscale for representation" #print("so converting image to grayscale for representation"
im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
hist_item = cv2.calcHist([im],[0],None,[256],[0,256]) hist_item = cv2.calcHist([im],[0],None,[256],[0,256])
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX) cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
@ -58,18 +61,18 @@ if __name__ == '__main__':
fname = sys.argv[1] fname = sys.argv[1]
else : else :
fname = '../data/lena.jpg' fname = '../data/lena.jpg'
print "usage : python hist.py <image_file>" print("usage : python hist.py <image_file>")
im = cv2.imread(fname) im = cv2.imread(fname)
if im is None: if im is None:
print 'Failed to load image file:', fname print('Failed to load image file:', fname)
sys.exit(1) sys.exit(1)
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
print ''' Histogram plotting \n print(''' Histogram plotting \n
Keymap :\n Keymap :\n
a - show histogram for color image in curve mode \n a - show histogram for color image in curve mode \n
b - show histogram in bin 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 d - show histogram for color image in curve mode \n
e - show histogram for a normalized image in curve mode \n e - show histogram for a normalized image in curve mode \n
Esc - exit \n Esc - exit \n
''' ''')
cv2.imshow('image',im) cv2.imshow('image',im)
while True: while True:
@ -86,31 +89,31 @@ if __name__ == '__main__':
curve = hist_curve(im) curve = hist_curve(im)
cv2.imshow('histogram',curve) cv2.imshow('histogram',curve)
cv2.imshow('image',im) cv2.imshow('image',im)
print 'a' print('a')
elif k == ord('b'): elif k == ord('b'):
print 'b' print('b')
lines = hist_lines(im) lines = hist_lines(im)
cv2.imshow('histogram',lines) cv2.imshow('histogram',lines)
cv2.imshow('image',gray) cv2.imshow('image',gray)
elif k == ord('c'): elif k == ord('c'):
print 'c' print('c')
equ = cv2.equalizeHist(gray) equ = cv2.equalizeHist(gray)
lines = hist_lines(equ) lines = hist_lines(equ)
cv2.imshow('histogram',lines) cv2.imshow('histogram',lines)
cv2.imshow('image',equ) cv2.imshow('image',equ)
elif k == ord('d'): elif k == ord('d'):
print 'd' print('d')
curve = hist_curve(gray) curve = hist_curve(gray)
cv2.imshow('histogram',curve) cv2.imshow('histogram',curve)
cv2.imshow('image',gray) cv2.imshow('image',gray)
elif k == ord('e'): elif k == ord('e'):
print 'e' print('e')
norm = cv2.normalize(gray,alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX) norm = cv2.normalize(gray, gray, alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX)
lines = hist_lines(norm) lines = hist_lines(norm)
cv2.imshow('histogram',lines) cv2.imshow('histogram',lines)
cv2.imshow('image',norm) cv2.imshow('image',norm)
elif k == 27: elif k == 27:
print 'ESC' print('ESC')
cv2.destroyAllWindows() cv2.destroyAllWindows()
break break
cv2.destroyAllWindows() cv2.destroyAllWindows()

10
samples/python2/kmeans.py
Unescape View File

@ -10,6 +10,9 @@ Keyboard shortcuts:
space - generate new distribution space - generate new distribution
''' '''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np import numpy as np
import cv2 import cv2
@ -19,7 +22,7 @@ if __name__ == '__main__':
cluster_n = 5 cluster_n = 5
img_size = 512 img_size = 512
print __doc__ print(__doc__)
# generating bright palette # generating bright palette
colors = np.zeros((1, cluster_n, 3), np.uint8) colors = np.zeros((1, cluster_n, 3), np.uint8)
@ -28,7 +31,7 @@ if __name__ == '__main__':
colors = cv2.cvtColor(colors, cv2.COLOR_HSV2BGR)[0] colors = cv2.cvtColor(colors, cv2.COLOR_HSV2BGR)[0]
while True: while True:
print 'sampling distributions...' print('sampling distributions...')
points, _ = make_gaussians(cluster_n, img_size) points, _ = make_gaussians(cluster_n, img_size)
term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1) 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) img = np.zeros((img_size, img_size, 3), np.uint8)
for (x, y), label in zip(np.int32(points), labels.ravel()): 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.circle(img, (x, y), 1, c, -1)
cv2.imshow('gaussian mixture', img) cv2.imshow('gaussian mixture', img)

2
samples/python2/lappyr.py
Unescape View File

@ -64,7 +64,7 @@ if __name__ == '__main__':
pyr = build_lappyr(frame, leveln) pyr = build_lappyr(frame, leveln)
for i in xrange(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 pyr[i] *= v
res = merge_lappyr(pyr) res = merge_lappyr(pyr)

43
samples/python2/letter_recog.py
Unescape View File

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

10
samples/python2/mouse_and_match.py
Unescape View File

@ -1,11 +1,13 @@
#!/usr/bin/env python #!/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: Demonstrate using a mouse to interact with an image:
Read in the images in a directory one by one Read in the images in a directory one by one
Allow the user to select parts of an image with a mouse 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. When they let go of the mouse, it correlates (using matchTemplate) that patch with the image.
SPACE for next image
ESC to exit ESC to exit
''' '''
@ -54,8 +56,10 @@ def onmouse(event, x, y, flags, param):
drag_start = None drag_start = None
if __name__ == '__main__': if __name__ == '__main__':
print(__doc__)
parser = argparse.ArgumentParser(description='Demonstrate mouse interaction with images') 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() args = parser.parse_args()
path = args.input path = args.input
@ -68,7 +72,7 @@ if __name__ == '__main__':
print(infile) print(infile)
img=cv2.imread(infile,1) img=cv2.imread(infile,1)
if img == None: if img is None:
continue continue
sel = (0,0,0,0) sel = (0,0,0,0)
drag_start = None drag_start = None

11
samples/python2/opt_flow.py
Unescape View File

@ -1,5 +1,8 @@
#!/usr/bin/env python #!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np import numpy as np
import cv2 import cv2
import video import video
@ -15,7 +18,7 @@ Keys:
def draw_flow(img, flow, step=16): def draw_flow(img, flow, step=16):
h, w = img.shape[:2] 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 fx, fy = flow[y,x].T
lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2) lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
lines = np.int32(lines + 0.5) lines = np.int32(lines + 0.5)
@ -47,7 +50,7 @@ def warp_flow(img, flow):
if __name__ == '__main__': if __name__ == '__main__':
import sys import sys
print help_message print(help_message)
try: try:
fn = sys.argv[1] fn = sys.argv[1]
except: except:
@ -78,10 +81,10 @@ if __name__ == '__main__':
break break
if ch == ord('1'): if ch == ord('1'):
show_hsv = not show_hsv 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'): if ch == ord('2'):
show_glitch = not show_glitch show_glitch = not show_glitch
if show_glitch: if show_glitch:
cur_glitch = img.copy() cur_glitch = img.copy()
print 'glitch is', ['off', 'on'][show_glitch] print('glitch is', ['off', 'on'][show_glitch])
cv2.destroyAllWindows() cv2.destroyAllWindows()

4
samples/python2/peopledetect.py
Unescape View File

@ -35,7 +35,9 @@ if __name__ == '__main__':
hog = cv2.HOGDescriptor() hog = cv2.HOGDescriptor()
hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() ) 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, ' - ',) print(fn, ' - ',)
try: try:
img = cv2.imread(fn) img = cv2.imread(fn)

5
samples/python2/plane_ar.py
Unescape View File

@ -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. 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 numpy as np
import cv2 import cv2
import video import video
@ -97,7 +100,7 @@ class App:
if __name__ == '__main__': if __name__ == '__main__':
print __doc__ print(__doc__)
import sys import sys
try: try:

17
samples/python2/plane_tracker.py
Unescape View File

@ -21,6 +21,14 @@ Keys:
Select a textured planar object to track by drawing a box with a mouse. 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 numpy as np
import cv2 import cv2
@ -64,6 +72,7 @@ class PlaneTracker:
self.detector = cv2.ORB_create( nfeatures = 1000 ) self.detector = cv2.ORB_create( nfeatures = 1000 )
self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
self.targets = [] self.targets = []
self.frame_points = []
def add_target(self, image, rect, data=None): def add_target(self, image, rect, data=None):
'''Add a new tracking target.''' '''Add a new tracking target.'''
@ -87,8 +96,8 @@ class PlaneTracker:
def track(self, frame): def track(self, frame):
'''Returns a list of detected TrackedTarget objects''' '''Returns a list of detected TrackedTarget objects'''
frame_points, frame_descrs = self.detect_features(frame) self.frame_points, frame_descrs = self.detect_features(frame)
if len(frame_points) < MIN_MATCH_COUNT: if len(self.frame_points) < MIN_MATCH_COUNT:
return [] return []
matches = self.matcher.knnMatch(frame_descrs, k = 2) 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] 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 continue
target = self.targets[imgIdx] target = self.targets[imgIdx]
p0 = [target.keypoints[m.trainIdx].pt for m in matches] 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)) p0, p1 = np.float32((p0, p1))
H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0) H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0)
status = status.ravel() != 0 status = status.ravel() != 0
@ -169,7 +178,7 @@ class App:
break break
if __name__ == '__main__': if __name__ == '__main__':
print __doc__ print(__doc__)
import sys import sys
try: try:

17
samples/python2/stereo_match.py
Unescape View File

@ -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/ ) 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 numpy as np
import cv2 import cv2
@ -25,13 +28,13 @@ def write_ply(fn, verts, colors):
verts = verts.reshape(-1, 3) verts = verts.reshape(-1, 3)
colors = colors.reshape(-1, 3) colors = colors.reshape(-1, 3)
verts = np.hstack([verts, colors]) verts = np.hstack([verts, colors])
with open(fn, 'w') as f: with open(fn, 'wb') as f:
f.write(ply_header % dict(vert_num=len(verts))) f.write((ply_header % dict(vert_num=len(verts))).encode('utf-8'))
np.savetxt(f, verts, '%f %f %f %d %d %d') np.savetxt(f, verts, fmt='%f %f %f %d %d %d ')
if __name__ == '__main__': if __name__ == '__main__':
print 'loading images...' print('loading images...')
imgL = cv2.pyrDown( cv2.imread('../data/aloeL.jpg') ) # downscale images for faster processing imgL = cv2.pyrDown( cv2.imread('../data/aloeL.jpg') ) # downscale images for faster processing
imgR = cv2.pyrDown( cv2.imread('../data/aloeR.jpg') ) imgR = cv2.pyrDown( cv2.imread('../data/aloeR.jpg') )
@ -50,10 +53,10 @@ if __name__ == '__main__':
speckleRange = 32 speckleRange = 32
) )
print 'computing disparity...' print('computing disparity...')
disp = stereo.compute(imgL, imgR).astype(np.float32) / 16.0 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] h, w = imgL.shape[:2]
f = 0.8*w # guess for focal length f = 0.8*w # guess for focal length
Q = np.float32([[1, 0, 0, -0.5*w], Q = np.float32([[1, 0, 0, -0.5*w],
@ -67,7 +70,7 @@ if __name__ == '__main__':
out_colors = colors[mask] out_colors = colors[mask]
out_fn = 'out.ply' out_fn = 'out.ply'
write_ply('out.ply', out_points, out_colors) write_ply('out.ply', out_points, out_colors)
print '%s saved' % 'out.ply' print('%s saved' % 'out.ply')
cv2.imshow('left', imgL) cv2.imshow('left', imgL)
cv2.imshow('disparity', (disp-min_disp)/num_disp) cv2.imshow('disparity', (disp-min_disp)/num_disp)

7
samples/python2/texture_flow.py
Unescape View File

@ -10,6 +10,9 @@ Usage:
texture_flow.py [<image>] texture_flow.py [<image>]
''' '''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np import numpy as np
import cv2 import cv2
@ -22,7 +25,7 @@ if __name__ == '__main__':
img = cv2.imread(fn) img = cv2.imread(fn)
if img is None: if img is None:
print 'Failed to load image file:', fn print('Failed to load image file:', fn)
sys.exit(1) sys.exit(1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
@ -36,7 +39,7 @@ if __name__ == '__main__':
vis[:] = (192 + np.uint32(vis)) / 2 vis[:] = (192 + np.uint32(vis)) / 2
d = 12 d = 12
points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2) 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) 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.line(vis, (x-vx, y-vy), (x+vx, y+vy), (0, 0, 0), 1, cv2.LINE_AA)
cv2.imshow('input', img) cv2.imshow('input', img)

2
samples/python2/video.py
Unescape View File

@ -182,7 +182,7 @@ if __name__ == '__main__':
if len(sources) == 0: if len(sources) == 0:
sources = [ 0 ] sources = [ 0 ]
caps = map(create_capture, sources) caps = list(map(create_capture, sources))
shot_idx = 0 shot_idx = 0
while True: while True:
imgs = [] imgs = []

9
samples/python2/video_v4l2.py
Unescape View File

@ -14,6 +14,9 @@ Keys:
''' '''
# Python 2/3 compatibility
from __future__ import print_function
import cv2 import cv2
def decode_fourcc(v): def decode_fourcc(v):
@ -24,13 +27,13 @@ font = cv2.FONT_HERSHEY_SIMPLEX
color = (0, 255, 0) color = (0, 255, 0)
cap = cv2.VideoCapture(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") cv2.namedWindow("Video")
convert_rgb = True convert_rgb = True
fps = int(cap.get(cv2.CAP_PROP_FPS)) 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("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)) 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.putText(img, "FPS: {}".format(fps), (15, 80), font, 1.0, color)
cv2.imshow("Video", img) cv2.imshow("Video", img)
k = cv2.waitKey(1) k = 0xFF & cv2.waitKey(1)
if k == 27: if k == 27:
break break

11
samples/python2/watershed.py
Unescape View File

@ -22,7 +22,8 @@ Keys
''' '''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np import numpy as np
import cv2 import cv2
@ -44,7 +45,7 @@ class App:
self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors) self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors)
def get_colors(self): 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): def watershed(self):
m = self.markers.copy() m = self.markers.copy()
@ -60,13 +61,13 @@ class App:
break break
if ch >= ord('1') and ch <= ord('7'): if ch >= ord('1') and ch <= ord('7'):
self.cur_marker = ch - ord('0') 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): if ch == ord(' ') or (self.sketch.dirty and self.auto_update):
self.watershed() self.watershed()
self.sketch.dirty = False self.sketch.dirty = False
if ch in [ord('a'), ord('A')]: if ch in [ord('a'), ord('A')]:
self.auto_update = not self.auto_update 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')]: if ch in [ord('r'), ord('R')]:
self.markers[:] = 0 self.markers[:] = 0
self.markers_vis[:] = self.img self.markers_vis[:] = self.img
@ -80,5 +81,5 @@ if __name__ == '__main__':
fn = sys.argv[1] fn = sys.argv[1]
except: except:
fn = '../data/fruits.jpg' fn = '../data/fruits.jpg'
print __doc__ print(__doc__)
App(fn).run() App(fn).run()

Write Preview
Loading…
Cancel
Save