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Merge pull request #6548 from fnorf:master

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Vadim Pisarevsky 2016-05-25 13:16:07 +00:00
commit 90b844792d
1 changed files with 15 additions and 15 deletions
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doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown
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@ -121,21 +121,21 @@ images = glob.glob('*.jpg')
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (7,6),None)
ret, corners = cv2.findChessboardCorners(gray, (7,6), None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
cv2.cornerSubPix(gray,corners, (11,11), (-1,-1), criteria)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (7,6), corners2,ret)
cv2.imshow('img',img)
cv2.drawChessboardCorners(img, (7,6), corners, ret)
cv2.imshow('img', img)
cv2.waitKey(500)
cv2.destroyAllWindows()
@ -150,7 +150,7 @@ So now we have our object points and image points we are ready to go for calibra
use the function, **cv2.calibrateCamera()**. It returns the camera matrix, distortion coefficients,
rotation and translation vectors etc.
@code{.py}
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
@endcode
### Undistortion
@ -165,7 +165,7 @@ So we take a new image (left12.jpg in this case. That is the first image in this
@code{.py}
img = cv2.imread('left12.jpg')
h, w = img.shape[:2]
newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))
newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
@endcode
#### 1. Using **cv2.undistort()**
@ -175,9 +175,9 @@ This is the shortest path. Just call the function and use ROI obtained above to
dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
# crop the image
x,y,w,h = roi
x, y, w, h = roi
dst = dst[y:y+h, x:x+w]
cv2.imwrite('calibresult.png',dst)
cv2.imwrite('calibresult.png', dst)
@endcode
#### 2. Using **remapping**
@ -185,13 +185,13 @@ This is curved path. First find a mapping function from distorted image to undis
use the remap function.
@code{.py}
# undistort
mapx,mapy = cv2.initUndistortRectifyMap(mtx,dist,None,newcameramtx,(w,h),5)
dst = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
mapx, mapy = cv2.initUndistortRectifyMap(mtx, dist, None, newcameramtx, (w,h), 5)
dst = cv2.remap(img, mapx, mapy, cv2.INTER_LINEAR)
# crop the image
x,y,w,h = roi
x, y, w, h = roi
dst = dst[y:y+h, x:x+w]
cv2.imwrite('calibresult.png',dst)
cv2.imwrite('calibresult.png', dst)
@endcode
Both the methods give the same result. See the result below:
@ -215,8 +215,8 @@ calibration images.
mean_error = 0
for i in xrange(len(objpoints)):
imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
error = cv2.norm(imgpoints[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2)
tot_error += error
error = cv2.norm(imgpoints[i], imgpoints2, cv2.NORM_L2)/len(imgpoints2)
mean_error += error
print "total error: ", mean_error/len(objpoints)
@endcode