Merge pull request #8809 from berak:fix_py_tut_braces_py3

doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown

@@ -218,7 +218,7 @@ for i in xrange(len(objpoints)):
     error = cv2.norm(imgpoints[i], imgpoints2, cv2.NORM_L2)/len(imgpoints2)
     mean_error += error
 
-print "total error: ", mean_error/len(objpoints)
+print( "total error: {}".format(mean_error/len(objpoints)) )
 @endcode
 Additional Resources
 --------------------

doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown

@@ -30,18 +30,18 @@ You can access a pixel value by its row and column coordinates. For BGR image, i
 of Blue, Green, Red values. For grayscale image, just corresponding intensity is returned.
 @code{.py}
 >>> px = img[100,100]
->>> print px
+>>> print( px )
 [157 166 200]
 
 # accessing only blue pixel
 >>> blue = img[100,100,0]
->>> print blue
+>>> print( blue )
 157
 @endcode
 You can modify the pixel values the same way.
 @code{.py}
 >>> img[100,100] = [255,255,255]
->>> print img[100,100]
+>>> print( img[100,100] )
 [255 255 255]
 @endcode
 
@@ -76,7 +76,7 @@ etc.
 Shape of image is accessed by img.shape. It returns a tuple of number of rows, columns and channels
 (if image is color):
 @code{.py}
->>> print img.shape
+>>> print( img.shape )
 (342, 548, 3)
 @endcode
 
@@ -85,12 +85,12 @@ good method to check if loaded image is grayscale or color image.
 
 Total number of pixels is accessed by `img.size`:
 @code{.py}
->>> print img.size
+>>> print( img.size )
 562248
 @endcode
 Image datatype is obtained by \`img.dtype\`:
 @code{.py}
->>> print img.dtype
+>>> print( img.dtype )
 uint8
 @endcode
 

doc/py_tutorials/py_core/py_image_arithmetics/py_image_arithmetics.markdown

@@ -23,10 +23,10 @@ For example, consider below sample:
 >>> x = np.uint8([250])
 >>> y = np.uint8([10])
 
->>> print cv2.add(x,y) # 250+10 = 260 => 255
+>>> print( cv2.add(x,y) ) # 250+10 = 260 => 255
 [[255]]
 
->>> print x+y          # 250+10 = 260 % 256 = 4
+>>> print( x+y )          # 250+10 = 260 % 256 = 4
 [4]
 @endcode
 It will be more visible when you add two images. OpenCV function will provide a better result. So

doc/py_tutorials/py_core/py_optimization/py_optimization.markdown

@@ -44,7 +44,7 @@ for i in xrange(5,49,2):
     img1 = cv2.medianBlur(img1,i)
 e2 = cv2.getTickCount()
 t = (e2 - e1)/cv2.getTickFrequency()
-print t
+print( t )
 
 # Result I got is 0.521107655 seconds
 @endcode

doc/py_tutorials/py_feature2d/py_brief/py_brief.markdown

@@ -69,8 +69,8 @@ kp = star.detect(img,None)
 # compute the descriptors with BRIEF
 kp, des = brief.compute(img, kp)
 
-print brief.descriptorSize()
+print( brief.descriptorSize() )
-print des.shape
+print( des.shape )
 @endcode
 The function brief.getDescriptorSize() gives the \f$n_d\f$ size used in bytes. By default it is 32. Next one
 is matching, which will be done in another chapter.

doc/py_tutorials/py_feature2d/py_fast/py_fast.markdown

@@ -108,10 +108,10 @@ kp = fast.detect(img,None)
 img2 = cv2.drawKeypoints(img, kp, None, color=(255,0,0))
 
 # Print all default params
-print "Threshold: ", fast.getThreshold()
+print( "Threshold: {}".format(fast.getThreshold()) )
-print "nonmaxSuppression: ", fast.getNonmaxSuppression()
+print( "nonmaxSuppression:{}".format(fast.getNonmaxSuppression()) )
-print "neighborhood: ", fast.getType()
+print( "neighborhood: {}".format(fast.getType()) )
-print "Total Keypoints with nonmaxSuppression: ", len(kp)
+print( "Total Keypoints with nonmaxSuppression: {}".format(len(kp)) )
 
 cv2.imwrite('fast_true.png',img2)
 
@@ -119,7 +119,7 @@ cv2.imwrite('fast_true.png',img2)
 fast.setNonmaxSuppression(0)
 kp = fast.detect(img,None)
 
-print "Total Keypoints without nonmaxSuppression: ", len(kp)
+print( "Total Keypoints without nonmaxSuppression: {}".format(len(kp)) )
 
 img3 = cv2.drawKeypoints(img, kp, None, color=(255,0,0))
 

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -85,7 +85,7 @@ if len(good)>MIN_MATCH_COUNT:
     img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
 
 else:
-    print "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)
+    print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
     matchesMask = None
 @endcode
 Finally we draw our inliers (if successfully found the object) or matching keypoints (if failed).

doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown

@@ -92,7 +92,7 @@ examples are shown in Python terminal since it is just same as SIFT only.
 While matching, we may need all those features, but not now. So we increase the Hessian Threshold.
 @code{.py}
 # Check present Hessian threshold
->>> print surf.getHessianThreshold()
+>>> print( surf.getHessianThreshold() )
 400.0
 
 # We set it to some 50000. Remember, it is just for representing in picture.
@@ -102,7 +102,7 @@ While matching, we may need all those features, but not now. So we increase the
 # Again compute keypoints and check its number.
 >>> kp, des = surf.detectAndCompute(img,None)
 
->>> print len(kp)
+>>> print( len(kp) )
 47
 @endcode
 It is less than 50. Let's draw it on the image.
@@ -119,7 +119,7 @@ on wings of butterfly. You can test it with other images.
 Now I want to apply U-SURF, so that it won't find the orientation.
 @code{.py}
 # Check upright flag, if it False, set it to True
->>> print surf.getUpright()
+>>> print( surf.getUpright() )
 False
 
 >>> surf.setUpright(True)
@@ -139,7 +139,7 @@ etc, this is better.
 Finally we check the descriptor size and change it to 128 if it is only 64-dim.
 @code{.py}
 # Find size of descriptor
->>> print surf.descriptorSize()
+>>> print( surf.descriptorSize() )
 64
 
 # That means flag, "extended" is False.
@@ -149,9 +149,9 @@ Finally we check the descriptor size and change it to 128 if it is only 64-dim.
 # So we make it to True to get 128-dim descriptors.
 >>> surf.extended = True
 >>> kp, des = surf.detectAndCompute(img,None)
->>> print surf.descriptorSize()
+>>> print( surf.descriptorSize() )
 128
->>> print des.shape
+>>> print( des.shape )
 (47, 128)
 @endcode
 Remaining part is matching which we will do in another chapter.

doc/py_tutorials/py_gui/py_mouse_handling/py_mouse_handling.markdown

@@ -21,7 +21,7 @@ in Python terminal:
 @code{.py}
 import cv2
 events = [i for i in dir(cv2) if 'EVENT' in i]
-print events
+print( events )
 @endcode
 Creating mouse callback function has a specific format which is same everywhere. It differs only in
 what the function does. So our mouse callback function does one thing, it draws a circle where we

doc/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.markdown

@@ -24,7 +24,7 @@ commands in your Python terminal :
 @code{.py}
 >>> import cv2
 >>> flags = [i for i in dir(cv2) if i.startswith('COLOR_')]
->>> print flags
+>>> print( flags )
 @endcode
 @note For HSV, Hue range is [0,179], Saturation range is [0,255] and Value range is [0,255].
 Different softwares use different scales. So if you are comparing OpenCV values with them, you need
@@ -96,7 +96,7 @@ terminal:
 @code{.py}
 >>> green = np.uint8([[[0,255,0 ]]])
 >>> hsv_green = cv2.cvtColor(green,cv2.COLOR_BGR2HSV)
->>> print hsv_green
+>>> print( hsv_green )
 [[[ 60 255 255]]]
 @endcode
 Now you take [H-10, 100,100] and [H+10, 255, 255] as lower bound and upper bound respectively. Apart

doc/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.markdown

@@ -27,7 +27,7 @@ im2,contours,hierarchy = cv2.findContours(thresh, 1, 2)
 
 cnt = contours[0]
 M = cv2.moments(cnt)
-print M
+print( M )
 @endcode
 From this moments, you can extract useful data like area, centroid etc. Centroid is given by the
 relations, \f$C_x = \frac{M_{10}}{M_{00}}\f$ and \f$C_y = \frac{M_{01}}{M_{00}}\f$. This can be done as

doc/py_tutorials/py_imgproc/py_contours/py_contours_more_functions/py_contours_more_functions.markdown

@@ -99,7 +99,7 @@ im2,contours,hierarchy = cv2.findContours(thresh2,2,1)
 cnt2 = contours[0]
 
 ret = cv2.matchShapes(cnt1,cnt2,1,0.0)
-print ret
+print( ret )
 @endcode
 I tried matching shapes with different shapes given below:
 

doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown

@@ -218,7 +218,7 @@ for i in xrange(1,256):
 
 # find otsu's threshold value with OpenCV function
 ret, otsu = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
-print thresh,ret
+print( "{} {}".format(thresh,ret) )
 @endcode
 *(Some of the functions may be new here, but we will cover them in coming chapters)*
 

doc/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.markdown

@@ -186,12 +186,12 @@ using IPython magic command %timeit.
 @code{.py}
 In [16]: img = cv2.imread('messi5.jpg',0)
 In [17]: rows,cols = img.shape
-In [18]: print rows,cols
+In [18]: print("{} {}".format(rows,cols))
 342 548
 
 In [19]: nrows = cv2.getOptimalDFTSize(rows)
 In [20]: ncols = cv2.getOptimalDFTSize(cols)
-In [21]: print nrows, ncols
+In [21]: print("{} {}".format(nrows,ncols))
 360 576
 @endcode
 See, the size (342,548) is modified to (360, 576). Now let's pad it with zeros (for OpenCV) and find

doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown

@@ -51,7 +51,7 @@ ret,result,neighbours,dist = knn.findNearest(test,k=5)
 matches = result==test_labels
 correct = np.count_nonzero(matches)
 accuracy = correct*100.0/result.size
-print accuracy
+print( accuracy )
 @endcode
 So our basic OCR app is ready. This particular example gave me an accuracy of 91%. One option
 improve accuracy is to add more data for training, especially the wrong ones. So instead of finding
@@ -64,7 +64,7 @@ np.savez('knn_data.npz',train=train, train_labels=train_labels)
 
 # Now load the data
 with np.load('knn_data.npz') as data:
-    print data.files
+    print( data.files )
     train = data['train']
     train_labels = data['train_labels']
 @endcode
@@ -109,7 +109,7 @@ ret, result, neighbours, dist = knn.findNearest(testData, k=5)
 
 correct = np.count_nonzero(result == labels)
 accuracy = correct*100.0/10000
-print accuracy
+print( accuracy )
 @endcode
 It gives me an accuracy of 93.22%. Again, if you want to increase accuracy, you can iteratively add
 error data in each level.

doc/py_tutorials/py_ml/py_knn/py_knn_understanding/py_knn_understanding.markdown

@@ -118,9 +118,9 @@ knn = cv2.ml.KNearest_create()
 knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
 ret, results, neighbours ,dist = knn.findNearest(newcomer, 3)
 
-print "result: ", results,"\n"
+print( "result:  {}\n".format(results) )
-print "neighbours: ", neighbours,"\n"
+print( "neighbours:  {}\n".format(neighbours) )
-print "distance: ", dist
+print( "distance:  {}\n".format(dist) )
 
 plt.show()
 @endcode

doc/py_tutorials/py_setup/py_setup_in_fedora/py_setup_in_fedora.markdown

@@ -30,7 +30,7 @@ $ yum install numpy opencv*
 Open Python IDLE (or IPython) and type following codes in Python terminal.
 @code{.py}
 >>> import cv2
->>> print cv2.__version__
+>>> print( cv2.__version__ )
 @endcode
 If the results are printed out without any errors, congratulations !!! You have installed
 OpenCV-Python successfully.
@@ -218,7 +218,7 @@ Installation is over. All files are installed in /usr/local/ folder. But to use
 should be able to find OpenCV module. You have two options for that.
 
 -#  **Move the module to any folder in Python Path** : Python path can be found out by entering
-    import sys;print sys.path in Python terminal. It will print out many locations. Move
+    `import sys; print(sys.path)` in Python terminal. It will print out many locations. Move
     /usr/local/lib/python2.7/site-packages/cv2.so to any of this folder. For example,
     @code{.sh}
     su mv /usr/local/lib/python2.7/site-packages/cv2.so /usr/lib/python2.7/site-packages

doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.markdown

@@ -36,7 +36,7 @@ Installing OpenCV from prebuilt binaries
 -#  Open Python IDLE and type following codes in Python terminal.
     @code
         >>> import cv2
-        >>> print cv2.__version__
+        >>> print( cv2.__version__ )
     @endcode
 
 If the results are printed out without any errors, congratulations !!! You have installed