Merge branch 4.x

2026-07-31 00:03:03 +04:00 · 2021-10-15 15:59:36 +00:00
parent 91a7a4523a 0e86e292e4
commit 7ba26ada12
537 changed files with 39768 additions and 10712 deletions
@@ -60,6 +60,14 @@ of C++.

 So this is the basic version of how OpenCV-Python bindings are generated.

+@note There is no 1:1 mapping of numpy.ndarray on cv::Mat. For example, cv::Mat has channels field,
+which is emulated as last dimension of numpy.ndarray and implicitly converted.
+However, such implicit conversion has problem with passing of 3D numpy arrays into C++ code
+(the last dimension is implicitly reinterpreted as number of channels).
+Refer to the [issue](https://github.com/opencv/opencv/issues/19091) for workarounds if you need to process 3D arrays or ND-arrays with channels.
+OpenCV 4.5.4+ has `cv.Mat` wrapper derived from `numpy.ndarray` to explicitly handle the channels behavior.
+
+
 How to extend new modules to Python?
 ------------------------------------

@@ -98,7 +98,7 @@ import numpy as np
 import cv2 as cv
 from matplotlib import pyplot as plt

-img = cv.imread('simple.jpg',0)
+img = cv.imread('blox.jpg',0) # `<opencv_root>/samples/data/blox.jpg`

 # Initiate FAST object with default values
 fast = cv.FastFeatureDetector_create()
@@ -113,17 +113,17 @@ print( "nonmaxSuppression:{}".format(fast.getNonmaxSuppression()) )
 print( "neighborhood: {}".format(fast.getType()) )
 print( "Total Keypoints with nonmaxSuppression: {}".format(len(kp)) )

-cv.imwrite('fast_true.png',img2)
+cv.imwrite('fast_true.png', img2)

 # Disable nonmaxSuppression
 fast.setNonmaxSuppression(0)
-kp = fast.detect(img,None)
+kp = fast.detect(img, None)

 print( "Total Keypoints without nonmaxSuppression: {}".format(len(kp)) )

 img3 = cv.drawKeypoints(img, kp, None, color=(255,0,0))

-cv.imwrite('fast_false.png',img3)
+cv.imwrite('fast_false.png', img3)
@endcode
 See the results. First image shows FAST with nonmaxSuppression and second one without
 nonmaxSuppression:
@@ -74,7 +74,7 @@ Canny Edge Detection in OpenCV

 OpenCV puts all the above in single function, **cv.Canny()**. We will see how to use it. First
 argument is our input image. Second and third arguments are our minVal and maxVal respectively.
-Third argument is aperture_size. It is the size of Sobel kernel used for find image gradients. By
+Fourth argument is aperture_size. It is the size of Sobel kernel used for find image gradients. By
 default it is 3. Last argument is L2gradient which specifies the equation for finding gradient
 magnitude. If it is True, it uses the equation mentioned above which is more accurate, otherwise it
 uses this function: \f$Edge\_Gradient \; (G) = |G_x| + |G_y|\f$. By default, it is False.
@@ -1,6 +1,9 @@
 Contour Features {#tutorial_py_contour_features}
 ================

+@prev_tutorial{tutorial_py_contours_begin}
+@next_tutorial{tutorial_py_contour_properties}
+
 Goal
 ----

@@ -91,7 +94,7 @@ convexity defects, which are the local maximum deviations of hull from contours.

 There is a little bit things to discuss about it its syntax:
@code{.py}
-hull = cv.convexHull(points[, hull[, clockwise[, returnPoints]]
+hull = cv.convexHull(points[, hull[, clockwise[, returnPoints]]])
@endcode
 Arguments details:

@@ -1,6 +1,9 @@
 Contour Properties {#tutorial_py_contour_properties}
 ==================

+@prev_tutorial{tutorial_py_contour_features}
+@next_tutorial{tutorial_py_contours_more_functions}
+
 Here we will learn to extract some frequently used properties of objects like Solidity, Equivalent
 Diameter, Mask image, Mean Intensity etc. More features can be found at [Matlab regionprops
 documentation](http://www.mathworks.in/help/images/ref/regionprops.html).
@@ -1,6 +1,8 @@
 Contours : Getting Started {#tutorial_py_contours_begin}
 ==========================

+@next_tutorial{tutorial_py_contour_features}
+
 Goal
 ----

@@ -1,6 +1,8 @@
 Contours Hierarchy {#tutorial_py_contours_hierarchy}
 ==================

+@prev_tutorial{tutorial_py_contours_more_functions}
+
 Goal
 ----

@@ -1,6 +1,10 @@
 Contours : More Functions {#tutorial_py_contours_more_functions}
 =========================

+@prev_tutorial{tutorial_py_contour_properties}
+@next_tutorial{tutorial_py_contours_hierarchy}
+
+
 Goal
 ----

@@ -117,7 +117,7 @@ for i in range(5,0,-1):
 LS = []
 for la,lb in zip(lpA,lpB):
    rows,cols,dpt = la.shape
-    ls = np.hstack((la[:,0:cols/2], lb[:,cols/2:]))
+    ls = np.hstack((la[:,0:cols//2], lb[:,cols//2:]))
    LS.append(ls)

 # now reconstruct
@@ -127,7 +127,7 @@ for i in range(1,6):
    ls_ = cv.add(ls_, LS[i])

 # image with direct connecting each half
-real = np.hstack((A[:,:cols/2],B[:,cols/2:]))
+real = np.hstack((A[:,:cols//2],B[:,cols//2:]))

 cv.imwrite('Pyramid_blending2.jpg',ls_)
 cv.imwrite('Direct_blending.jpg',real)