在检查了几个代码片段之后,我拍了几张照片,找到了棋盘的角点,并使用它们来获取相机矩阵、畸变系数、旋转和平移向量。现在,有人能告诉我需要哪个Python OpenCV函数来计算从2D图像到实际世界的距离吗?projectPoints?例如,使用棋盘作为参考(见图片),如果方块大小为5厘米,则4个方块的距离应为20厘米。我看到了一些函数,如projectPoints、findHomography、solvePnP,但我不确定我需要哪一个来解决我的问题并获取相机世界与棋盘世界之间的转换矩阵。
一个摄像头,相机位置在所有情况下都相同,但不完全位于棋盘上方,并且棋盘放置在一个平面物体(桌子)上。
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((nx * ny, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob(path.join(calib_images_dir, 'calibration*.jpg'))
print(images)
# Step through the list and search for chessboard corners
for filename in images:
img = cv2.imread(filename)
imgScale = 0.5
newX,newY = img.shape[1]*imgScale, img.shape[0]*imgScale
res = cv2.resize(img,(int(newX),int(newY)))
gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
pattern_found, corners = cv2.findChessboardCorners(gray, (nx,ny), None)
# If found, add object points, image points (after refining them)
if pattern_found is True:
objpoints.append(objp)
# Increase accuracy using subpixel corner refinement
cv2.cornerSubPix(gray,corners,(5,5),(-1,-1),(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1 ))
imgpoints.append(corners)
if verbose:
# Draw and display the corners
draw = cv2.drawChessboardCorners(res, (nx, ny), corners, pattern_found)
cv2.imshow('img',draw)
cv2.waitKey(500)
if verbose:
cv2.destroyAllWindows()
#Now we have our object points and image points, we are ready to go for calibration
# Get the camera matrix, distortion coefficients, rotation and translation vectors
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
print(mtx)
print(dist)
print('rvecs:', type(rvecs),' ',len(rvecs),' ',rvecs)
print('tvecs:', type(tvecs),' ',len(tvecs),' ',tvecs)
mean_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
error = cv2.norm(imgpoints[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2)
mean_error += error
print("total error: ", mean_error/len(objpoints))
imagePoints,jacobian = cv2.projectPoints(objpoints[0], rvecs[0], tvecs[0], mtx, dist)
print('Image points: ',imagePoints)