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Merge branch 4.x

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Alexander Alekhin
2023-01-09 11:08:02 +00:00
parent 8a62b03761 2ab46d9553
commit 593a376566
880 changed files with 83958 additions and 9368 deletions
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doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown
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@@ -127,7 +127,7 @@ for fname in images:
objpoints.append(objp)
corners2 = cv.cornerSubPix(gray,corners, (11,11), (-1,-1), criteria)
imgpoints.append(corners)
imgpoints.append(corners2)
# Draw and display the corners
cv.drawChessboardCorners(img, (7,6), corners2, ret)
doc/py_tutorials/py_calib3d/py_pose/py_pose.markdown
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@@ -59,7 +59,7 @@ pixels. Then to calculate the rotation and translation, we use the function,
**cv.solvePnPRansac()**. Once we those transformation matrices, we use them to project our **axis
points** to the image plane. In simple words, we find the points on image plane corresponding to
each of (3,0,0),(0,3,0),(0,0,3) in 3D space. Once we get them, we draw lines from the first corner
to each of these points using our draw() function. Done !!!
to each of these points using our generateImage() function. Done !!!
@code{.py}
for fname in glob.glob('left*.jpg'):
img = cv.imread(fname)
@@ -89,9 +89,9 @@ See some results below. Notice that each axis is 3 squares long.:
### Render a Cube
If you want to draw a cube, modify the draw() function and axis points as follows.
If you want to draw a cube, modify the generateImage() function and axis points as follows.
Modified draw() function:
Modified generateImage() function:
@code{.py}
def draw(img, corners, imgpts):
imgpts = np.int32(imgpts).reshape(-1,2)
doc/py_tutorials/py_core/py_optimization/py_optimization.markdown
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@@ -14,7 +14,7 @@ So in this chapter, you will learn:
Apart from OpenCV, Python also provides a module **time** which is helpful in measuring the time of
execution. Another module **profile** helps to get a detailed report on the code, like how much time
each function in the code took, how many times the function was called, etc. But, if you are using
IPython, all these features are integrated in an user-friendly manner. We will see some important
IPython, all these features are integrated in a user-friendly manner. We will see some important
ones, and for more details, check links in the **Additional Resources** section.
Measuring Performance with OpenCV
doc/py_tutorials/py_imgproc/py_gradients/py_gradients.markdown
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@@ -17,7 +17,7 @@ We will see each one of them.
### 1. Sobel and Scharr Derivatives
Sobel operators is a joint Gausssian smoothing plus differentiation operation, so it is more
Sobel operators is a joint Gaussian smoothing plus differentiation operation, so it is more
resistant to noise. You can specify the direction of derivatives to be taken, vertical or horizontal
(by the arguments, yorder and xorder respectively). You can also specify the size of kernel by the
argument ksize. If ksize = -1, a 3x3 Scharr filter is used which gives better results than 3x3 Sobel