我使用的是标准 640x480 网络摄像头。我已经在 Python 3 的 OpenCV 中完成了相机校准。这是我正在使用的代码。该代码正在运行，并成功为我提供了相机矩阵和失真系数。现在，我如何才能在我的场景图像中找到 640 像素中有多少毫米。我已将网络摄像头水平安装在桌子上方，并在桌子上放置了机械臂。使用相机我正在寻找物体的质心。使用相机矩阵我的目标是将该对象的位置（例如 300x200 像素）转换为毫米单位，以便我可以将毫米分配给机械臂以拾取该对象。我已经搜索但没有找到任何相关信息。请告诉我是否有任何方程式或方法。非常感谢！
import numpy as np
import cv2
import yaml
import os
# Parameters
#TODO : Read from file
n_row=4 #Checkerboard Rows
n_col=6 #Checkerboard Columns
n_min_img = 10 # number of images needed for calibration
square_size = 40 # size of each individual box on Checkerboard in mm
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # termination criteria
corner_accuracy = (11,11)
result_file = "./calibration.yaml" # Output file having camera matrix
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(n_row-1,n_col-1,0)
objp = np.zeros((n_row*n_col,3), np.float32)
objp[:,:2] = np.mgrid[0:n_row,0:n_col].T.reshape(-1,2) * square_size
# Intialize camera and window
camera = cv2.VideoCapture(0) #Supposed to be the only camera
if not camera.isOpened():
print("Camera not found!")
quit()
width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
cv2.namedWindow("Calibration")
# Usage
def usage():
print("Press on displayed window : \n")
print("[space] : take picture")
print("[c] : compute calibration")
print("[r] : reset program")
print("[ESC] : quit")
usage()
Initialization = True
while True:
if Initialization:
print("Initialize data structures ..")
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
n_img = 0
Initialization = False
tot_error=0
# Read from camera and display on windows
ret, img = camera.read()
cv2.imshow("Calibration", img)
if not ret:
print("Cannot read camera frame, exit from program!")
camera.release()
cv2.destroyAllWindows()
break
# Wait for instruction
k = cv2.waitKey(50)
# SPACE pressed to take picture
if k%256 == 32:
print("Adding image for calibration...")
imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(imgGray, (n_row,n_col),None)
# If found, add object points, image points (after refining them)
if not ret:
print("Cannot found Chessboard corners!")
else:
print("Chessboard corners successfully found.")
objpoints.append(objp)
n_img +=1
corners2 = cv2.cornerSubPix(imgGray,corners,corner_accuracy,(-1,-1),criteria)
imgpoints.append(corners2)
# Draw and display the corners
imgAugmnt = cv2.drawChessboardCorners(img, (n_row,n_col), corners2,ret)
cv2.imshow('Calibration',imgAugmnt)
cv2.waitKey(500)
# "c" pressed to compute calibration
elif k%256 == 99:
if n_img <= n_min_img:
print("Only ", n_img , " captured, ", " at least ", n_min_img , " images are needed")
else:
print("Computing calibration ...")
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, (width,height),None,None)
if not ret:
print("Cannot compute calibration!")
else:
print("Camera calibration successfully computed")
# Compute reprojection errors
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)
tot_error += error
print("Camera matrix: ", mtx)
print("Distortion coeffs: ", dist)
print("Total error: ", tot_error)
print("Mean error: ", np.mean(error))
# Saving calibration matrix
try:
os.remove(result_file) #Delete old file first
except Exception as e:
#print(e)
pass
print("Saving camera matrix .. in ",result_file)
data={"camera_matrix": mtx.tolist(), "dist_coeff": dist.tolist()}
with open(result_file, "w") as f:
yaml.dump(data, f, default_flow_style=False)
# ESC pressed to quit
elif k%256 == 27:
print("Escape hit, closing...")
camera.release()
cv2.destroyAllWindows()
break
# "r" pressed to reset
elif k%256 ==114:
print("Reset program...")
Initialization = True
这是相机矩阵：
818.6 0 324.4
0 819.1 237.9
0 0 1
失真系数：
0.34 -5.7 0 0 33.45