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Merge branch 4.x

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Alexander Alekhin
2021-12-30 16:23:27 +00:00
parent 80c5d18f9c 5f249a3e67
commit a0d5277e0d
951 changed files with 158314 additions and 152668 deletions
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doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown
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@@ -78,7 +78,7 @@ if len(good)>MIN_MATCH_COUNT:
M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0)
matchesMask = mask.ravel().tolist()
h,w,d = img1.shape
h,w = img1.shape
pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
dst = cv.perspectiveTransform(pts,M)
doc/py_tutorials/py_ml/py_svm/py_svm_opencv/py_svm_opencv.markdown
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@@ -36,7 +36,7 @@ gives us a feature vector containing 64 values. This is the feature vector we us
Finally, as in the previous case, we start by splitting our big dataset into individual cells. For
every digit, 250 cells are reserved for training data and remaining 250 data is reserved for
testing. Full code is given below, you also can download it from [here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py):
testing. Full code is given below, you also can download it from [here](https://github.com/opencv/opencv/tree/5.x/samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py):
@include samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py