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This commit is contained in:
Andrey Kamaev
2012-08-07 13:29:43 +04:00
parent a3527fc4d8
commit 5100ca7508
66 changed files with 1180 additions and 1305 deletions
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6
samples/python/chessboard.py
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@@ -15,7 +15,7 @@ if __name__ == "__main__":
im = cv.LoadImageM(fileName, False)
im3 = cv.LoadImageM(fileName, True)
except: # if local copy cannot be opened, try downloading it
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/left01.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/left01.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
@@ -23,12 +23,12 @@ if __name__ == "__main__":
im3 = cv.DecodeImageM(imagefiledata, cv.CV_LOAD_IMAGE_COLOR)
chessboard_dim = ( 9, 6 )
found_all, corners = cv.FindChessboardCorners( im, chessboard_dim )
print found_all, len(corners)
cv.DrawChessboardCorners( im3, chessboard_dim, corners, found_all )
cv.ShowImage("win", im3);
cv.WaitKey()
cv.DestroyAllWindows()

2
samples/python/cvutils.py
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@@ -9,7 +9,7 @@ def load_sample(name=None):
try:
img0 = cv.LoadImage(name, cv.CV_LOAD_IMAGE_COLOR)
except IOError:
urlbase = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/'
urlbase = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/'
file = name.split('/')[-1]
filedata = urllib2.urlopen(urlbase+file).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)

4
samples/python/demhist.py
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@@ -60,7 +60,7 @@ class DemHist:
cv.Rectangle(self.hist_image, (int(i * bin_w), self.hist_image.height),
(int((i + 1) * bin_w), self.hist_image.height - cv.Round(self.hist.bins[i])),
cv.ScalarAll(0), -1, 8, 0)
cv.ShowImage("histogram", self.hist_image)
if __name__ == "__main__":
@@ -68,7 +68,7 @@ if __name__ == "__main__":
if len(sys.argv) > 1:
src_image = cv.GetMat(cv.LoadImage(sys.argv[1], 0))
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/baboon.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/baboon.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))

14
samples/python/dft.py
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@@ -12,11 +12,11 @@ def cvShiftDFT(src_arr, dst_arr ):
dst_size = cv.GetSize(dst_arr)
if dst_size != size:
cv.Error( cv.CV_StsUnmatchedSizes, "cv.ShiftDFT", "Source and Destination arrays must have equal sizes", __FILE__, __LINE__ )
cv.Error( cv.CV_StsUnmatchedSizes, "cv.ShiftDFT", "Source and Destination arrays must have equal sizes", __FILE__, __LINE__ )
if(src_arr is dst_arr):
tmp = cv.CreateMat(size[1]/2, size[0]/2, cv.GetElemType(src_arr))
cx = size[0] / 2
cy = size[1] / 2 # image center
@@ -31,13 +31,13 @@ def cvShiftDFT(src_arr, dst_arr ):
if(src_arr is not dst_arr):
if( not cv.CV_ARE_TYPES_EQ( q1, d1 )):
cv.Error( cv.CV_StsUnmatchedFormats, "cv.ShiftDFT", "Source and Destination arrays must have the same format", __FILE__, __LINE__ )
cv.Error( cv.CV_StsUnmatchedFormats, "cv.ShiftDFT", "Source and Destination arrays must have the same format", __FILE__, __LINE__ )
cv.Copy(q3, d1)
cv.Copy(q4, d2)
cv.Copy(q1, d3)
cv.Copy(q2, d4)
else:
cv.Copy(q3, tmp)
cv.Copy(q1, q3)
@@ -47,11 +47,11 @@ def cvShiftDFT(src_arr, dst_arr ):
cv.Copy(tmp, q2)
if __name__ == "__main__":
if len(sys.argv) > 1:
im = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/baboon.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/baboon.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))

8
samples/python/distrans.py
View File

@@ -20,12 +20,12 @@ edge = 0
def on_trackbar(edge_thresh):
cv.Threshold(gray, edge, float(edge_thresh), float(edge_thresh), cv.CV_THRESH_BINARY)
#Distance transform
#Distance transform
cv.DistTransform(edge, dist, cv.CV_DIST_L2, cv.CV_DIST_MASK_5)
cv.ConvertScale(dist, dist, 5000.0, 0)
cv.Pow(dist, dist, 0.5)
cv.ConvertScale(dist, dist32s, 1.0, 0.5)
cv.AndS(dist32s, cv.ScalarAll(255), dist32s, None)
cv.ConvertScale(dist32s, dist8u1, 1, 0)
@@ -42,7 +42,7 @@ if __name__ == "__main__":
if len(sys.argv) > 1:
gray = cv.LoadImage(sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/stuff.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/stuff.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
@@ -61,7 +61,7 @@ if __name__ == "__main__":
# Create a window
cv.NamedWindow(wndname, 1)
# create a toolbar
# create a toolbar
cv.CreateTrackbar(tbarname, wndname, edge_thresh, 255, on_trackbar)
# Show the image

4
samples/python/edge.py
View File

@@ -24,7 +24,7 @@ def on_trackbar(position):
# copy edge points
cv.Copy(im, col_edge, edge)
# show the im
cv.ShowImage(win_name, col_edge)
@@ -32,7 +32,7 @@ if __name__ == '__main__':
if len(sys.argv) > 1:
im = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))

34
samples/python/ffilldemo.py
View File

@@ -44,36 +44,36 @@ def on_mouse( event, x, y, flags, param ):
if( is_mask ):
my_mask = mask
cv.Threshold( mask, mask, 1, 128, cv.CV_THRESH_BINARY );
if( is_color ):
color = cv.CV_RGB( r, g, b );
comp = cv.FloodFill( color_img, seed, color, cv.CV_RGB( lo, lo, lo ),
cv.CV_RGB( up, up, up ), flags, my_mask );
cv.ShowImage( "image", color_img );
else:
brightness = cv.RealScalar((r*2 + g*7 + b + 5)/10);
comp = cv.FloodFill( gray_img, seed, brightness, cv.RealScalar(lo),
cv.RealScalar(up), flags, my_mask );
cv.ShowImage( "image", gray_img );
print "%g pixels were repainted" % comp[0]
if( is_mask ):
cv.ShowImage( "mask", mask );
if __name__ == "__main__":
if len(sys.argv) > 1:
im = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
@@ -89,7 +89,7 @@ if __name__ == "__main__":
print "\tg - use gradient floodfill with floating(relative) range"
print "\t4 - use 4-connectivity mode"
print "\t8 - use 8-connectivity mode"
color_img = cv.CloneImage( im );
gray_img0 = cv.CreateImage( (color_img.width, color_img.height), 8, 1 );
cv.CvtColor( color_img, gray_img0, cv.CV_BGR2GRAY );
@@ -102,7 +102,7 @@ if __name__ == "__main__":
cv.SetMouseCallback( "image", on_mouse );
while True:
while True:
if( is_color ):
cv.ShowImage( "image", color_img );
else:
@@ -114,29 +114,29 @@ if __name__ == "__main__":
sys.exit(0)
elif c == ord('c'):
if( is_color ):
print("Grayscale mode is set");
cv.CvtColor( color_img, gray_img, cv.CV_BGR2GRAY );
is_color = 0;
else:
print("Color mode is set");
cv.Copy( im, color_img, None );
cv.Zero( mask );
is_color = 1;
elif c == ord('m'):
if( is_mask ):
cv.DestroyWindow( "mask" );
is_mask = 0;
else:
cv.NamedWindow( "mask", 0 );
cv.Zero( mask );
cv.ShowImage( "mask", mask );
is_mask = 1;
elif c == ord('r'):
print("Original image is restored");
cv.Copy( im, color_img, None );

16
samples/python/fitellipse.py
View File

@@ -27,12 +27,12 @@ class FitEllipse:
cv.CreateTrackbar("Threshold", "Result", slider_pos, 255, self.process_image)
self.process_image(slider_pos)
def process_image(self, slider_pos):
def process_image(self, slider_pos):
"""
This function finds contours, draws them and their approximation by ellipses.
"""
stor = cv.CreateMemStorage()
# Create the destination images
image02 = cv.CloneImage(self.source_image)
cv.Zero(image02)
@@ -56,18 +56,18 @@ class FitEllipse:
PointArray2D32f = cv.CreateMat(1, len(c), cv.CV_32FC2)
for (i, (x, y)) in enumerate(c):
PointArray2D32f[0, i] = (x, y)
# Draw the current contour in gray
gray = cv.CV_RGB(100, 100, 100)
cv.DrawContours(image04, c, gray, gray,0,1,8,(0,0))
# Fits ellipse to current contour.
(center, size, angle) = cv.FitEllipse2(PointArray2D32f)
# Convert ellipse data from float to integer representation.
center = (cv.Round(center[0]), cv.Round(center[1]))
size = (cv.Round(size[0] * 0.5), cv.Round(size[1] * 0.5))
# Draw ellipse in random color
color = cv.CV_RGB(random.randrange(256),random.randrange(256),random.randrange(256))
cv.Ellipse(image04, center, size,
@@ -82,12 +82,12 @@ if __name__ == '__main__':
if len(sys.argv) > 1:
source_image = cv.LoadImage(sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/stuff.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/stuff.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
source_image = cv.DecodeImage(imagefiledata, cv.CV_LOAD_IMAGE_GRAYSCALE)
# Create windows.
cv.NamedWindow("Source", 1)
cv.NamedWindow("Result", 1)

4
samples/python/houghlines.py
View File

@@ -14,7 +14,7 @@ if __name__ == "__main__":
filename = sys.argv[1]
src = cv.LoadImage(filename, cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/doc/pics/building.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/doc/pics/building.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
@@ -37,7 +37,7 @@ if __name__ == "__main__":
for (rho, theta) in lines[:100]:
a = cos(theta)
b = sin(theta)
x0 = a * rho
x0 = a * rho
y0 = b * rho
pt1 = (cv.Round(x0 + 1000*(-b)), cv.Round(y0 + 1000*(a)))
pt2 = (cv.Round(x0 - 1000*(-b)), cv.Round(y0 - 1000*(a)))

4
samples/python/inpaint.py
View File

@@ -27,7 +27,7 @@ if __name__=="__main__":
if len(sys.argv) > 1:
img0 = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
@@ -38,7 +38,7 @@ if __name__=="__main__":
print "\tr - restore the original image"
print "\ti or ENTER - run inpainting algorithm"
print "\t\t(before running it, paint something on the image)"
cv.NamedWindow("image", 1)
cv.NamedWindow("inpainted image", 1)

14
samples/python/logpolar.py
View File

@@ -19,27 +19,27 @@ def on_mouse(event, x, y, flags, param):
cv.ShowImage("inverse log-polar", src2)
if __name__ == "__main__":
if len(sys.argv) > 1:
src = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
src = cv.DecodeImage(imagefiledata, cv.CV_LOAD_IMAGE_COLOR)
cv.NamedWindow("original", 1)
cv.NamedWindow("log-polar", 1)
cv.NamedWindow("inverse log-polar", 1)
dst = cv.CreateImage((256, 256), 8, 3)
src2 = cv.CreateImage(cv.GetSize(src), 8, 3)
cv.SetMouseCallback("original", on_mouse)
on_mouse(cv.CV_EVENT_LBUTTONDOWN, src.width/2, src.height/2, None, None)
cv.ShowImage("original", src)
cv.WaitKey()
cv.DestroyAllWindows()

2
samples/python/morphology.py
View File

@@ -31,7 +31,7 @@ if __name__ == "__main__":
if len(sys.argv) > 1:
src = cv.LoadImage(sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))

2
samples/python/numpy_array.py
View File

@@ -22,7 +22,7 @@ if __name__ == "__main__":
if len(sys.argv) > 1:
img0 = cv.LoadImageM( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/lena.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/lena.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))

4
samples/python/watershed.py
View File

@@ -27,7 +27,7 @@ if __name__ == "__main__":
if len(sys.argv) > 1:
img0 = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
@@ -106,4 +106,4 @@ if __name__ == "__main__":
cv.AddWeighted(wshed, 0.5, img_gray, 0.5, 0, wshed)
cv.ShowImage("watershed transform", wshed)
cv.DestroyAllWindows()

20
samples/python2/digits.py
View File

@@ -1,9 +1,9 @@
'''
SVN and KNearest digit recognition.
SVM and KNearest digit recognition.
Sample loads a dataset of handwritten digits from 'digits.png'.
Then it trains a SVN and KNearest classifiers on it and evaluates
their accuracy.
Then it trains a SVM and KNearest classifiers on it and evaluates
their accuracy.
Following preprocessing is applied to the dataset:
- Moment-based image deskew (see deskew())
@@ -77,7 +77,7 @@ class KNearest(StatModel):
class SVM(StatModel):
def __init__(self, C = 1, gamma = 0.5):
self.params = dict( kernel_type = cv2.SVM_RBF,
self.params = dict( kernel_type = cv2.SVM_RBF,
svm_type = cv2.SVM_C_SVC,
C = C,
gamma = gamma )
@@ -95,7 +95,7 @@ def evaluate_model(model, digits, samples, labels):
resp = model.predict(samples)
err = (labels != resp).mean()
print 'error: %.2f %%' % (err*100)
confusion = np.zeros((10, 10), np.int32)
for i, j in zip(labels, resp):
confusion[i, j] += 1
@@ -128,7 +128,7 @@ def preprocess_hog(digits):
hist = np.hstack(hists)
# transform to Hellinger kernel
eps = 1e-7
eps = 1e-7
hist /= hist.sum() + eps
hist = np.sqrt(hist)
hist /= norm(hist) + eps
@@ -141,23 +141,23 @@ if __name__ == '__main__':
print __doc__
digits, labels = load_digits(DIGITS_FN)
print 'preprocessing...'
# shuffle digits
rand = np.random.RandomState(321)
shuffle = rand.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits)
samples = preprocess_hog(digits2)
train_n = int(0.9*len(samples))
cv2.imshow('test set', mosaic(25, digits[train_n:]))
digits_train, digits_test = np.split(digits2, [train_n])
samples_train, samples_test = np.split(samples, [train_n])
labels_train, labels_test = np.split(labels, [train_n])
print 'training KNearest...'
model = KNearest(k=4)
model.train(samples_train, labels_train)

20
samples/python2/digits_adjust.py
View File

@@ -1,15 +1,15 @@
'''
Digit recognition adjustment.
Grid search is used to find the best parameters for SVN and KNearest classifiers.
SVM adjustment follows the guidelines given in
Digit recognition adjustment.
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
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>]
--model {svm|knearest} - select the classifier (SVM is the default)
--cloud - use PiCloud computing platform
--env - cloud environment name
@@ -23,12 +23,12 @@ from multiprocessing.pool import ThreadPool
from digits import *
try:
try:
import cloud
have_cloud = True
except ImportError:
have_cloud = False
def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None):
@@ -93,7 +93,7 @@ class App(object):
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
ires = pool.imap_unordered(f, jobs)
return ires
def adjust_SVM(self):
Cs = np.logspace(0, 10, 15, base=2)
gammas = np.logspace(-7, 4, 15, base=2)
@@ -107,7 +107,7 @@ class App(object):
params = dict(C = Cs[i], gamma=gammas[j])
score = cross_validate(SVM, params, samples, labels)
return i, j, score
ires = self.run_jobs(f, np.ndindex(*scores.shape))
for count, (i, j, score) in enumerate(ires):
scores[i, j] = score
@@ -142,7 +142,7 @@ class App(object):
if __name__ == '__main__':
import getopt
import sys
print __doc__
args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])