关于python：OpenCV – 如何使用棋盘作为参考从2D图像获得真实世界距离

OpenCV - How to get real world distance from a 2D image using a chessboard as reference

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在检查了几段代码之后，我拍了几张照片，找到棋盘的角，并用它们来获得相机矩阵、畸变系数、旋转和平移矢量。现在，有人能告诉我需要哪个python opencv函数来计算现实世界中与二维图像之间的距离吗？项目要点？例如，使用棋盘作为参考(见图)，如果瓷砖尺寸为5厘米，则4块瓷砖的距离应为20厘米。我看到了一些函数，如投影点、findhomography、solvepnp，但我不确定需要哪一个函数来解决我的问题，并得到相机世界和棋盘世界之间的转换矩阵。1个单摄像机，所有情况下摄像机的位置相同，但不完全在棋盘上，棋盘放在平面物体(桌子)上。

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# 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)