Merge branch 4.x

doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown

@@ -81,7 +81,7 @@ pass in terms of square size).
 
 So to find pattern in chess board, we can use the function, **cv.findChessboardCorners()**. We also
 need to pass what kind of pattern we are looking for, like 8x8 grid, 5x5 grid etc. In this example, we
-use 7x6 grid. (Normally a chess board has 8x8 squares and 7x7 internal corners). It returns the
+use 6x7 grid. (Normally a chess board has 8x8 squares and 7x7 internal corners). It returns the
 corner points and retval which will be True if pattern is obtained. These corners will be placed in
 an order (from left-to-right, top-to-bottom)
 
@@ -107,9 +107,11 @@ import glob
 # termination criteria
 criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
 
-# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
-objp = np.zeros((6*7,3), np.float32)
-objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
+# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(5,6,0)
+cols = 6
+rows = 7
+objp = np.zeros((cols*rows,3), np.float32)
+objp[:,:2] = np.mgrid[0:cols,0:rows].T.reshape(-1,2)
 
 # Arrays to store object points and image points from all the images.
 objpoints = [] # 3d point in real world space
@@ -122,7 +124,7 @@ for fname in images:
     gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
 
     # Find the chess board corners
-    ret, corners = cv.findChessboardCorners(gray, (7,6), None)
+    ret, corners = cv.findChessboardCorners(gray, (cols, rows), None)
 
     # If found, add object points, image points (after refining them)
     if ret == True:
@@ -132,7 +134,7 @@ for fname in images:
         imgpoints.append(corners2)
 
         # Draw and display the corners
-        cv.drawChessboardCorners(img, (7,6), corners2, ret)
+        cv.drawChessboardCorners(img, (cols, rows), corners2, ret)
         cv.imshow('img', img)
         cv.waitKey(500)
 

doc/py_tutorials/py_calib3d/py_pose/py_pose.markdown

@@ -50,12 +50,14 @@ our X axis is drawn from (0,0,0) to (3,0,0), so for Y axis. For Z axis, it is dr
 (0,0,-3). Negative denotes it is drawn towards the camera.
 @code{.py}
 criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
-objp = np.zeros((6*7,3), np.float32)
-objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
+cols = 6
+rows = 7
+objp = np.zeros((cols*rows,3), np.float32)
+objp[:,:2] = np.mgrid[0:cols,0:rows].T.reshape(-1,2)
 
 axis = np.float32([[3,0,0], [0,3,0], [0,0,-3]]).reshape(-1,3)
 @endcode
-Now, as usual, we load each image. Search for 7x6 grid. If found, we refine it with subcorner
+Now, as usual, we load each image. Search for 6x7 grid. If found, we refine it with subcorner
 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
@@ -65,7 +67,7 @@ to each of these points using our generateImage() function. Done !!!
 for fname in glob.glob('left*.jpg'):
     img = cv.imread(fname)
     gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
-    ret, corners = cv.findChessboardCorners(gray, (7,6),None)
+    ret, corners = cv.findChessboardCorners(gray, (cols, rows), None)
 
     if ret == True:
         corners2 = cv.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)