我使用的是标准的640x480网络摄像头。我已经在Python 3中的OpenCV中完成了相机校准。这就是我正在使用的代码。代码正在工作，并成功地为我提供了相机矩阵和失真系数。现在，我如何找到场景图像中640像素中有多少毫米。我把网络摄像头水平地连接在桌子上方，桌子上放着一只机器人手臂。使用相机，我正在查找对象的质心。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，这样我就可以将毫米赋予机械臂来拾取该对象。我搜索过，但没有找到任何相关信息。请告诉我，有什么方程或方法可以解决这个问题。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
畸变系数：
0.34 -5.7 0 0 33.45
我实际上在想，你应该能够用一种简单的方式解决你的问题：
mm_per_pixel = real_mm_width : 640px
假设相机最初与要拾取的对象的平面平行移动[即固定距离]，则可以找到real_mm_width，测量与图片的640像素相对应的物理距离。举个例子，假设你找到了real_mm_width = 32cm = 320mm，那么你就得到了mm_per_pixel = 0.5mm/px。在固定距离下，此比率不会改变
这似乎也是官方文件中的建议：
这种考虑有助于我们只找到X，Y值。现在对于X，Y值，我们可以简单地将点传递为(0,0(，(1,0(，(2,0(。。。其表示点的位置。在这种情况下，我们得到的结果将按棋盘方格大小的比例排列。但如果我们知道正方形大小，(比如30mm(，我们可以将值传递为(0,0(，(30,0(，(60,0(。因此，我们得到了毫米的结果
然后您只需要使用将质心坐标(以像素为单位([例如(pixel_x_centroid, pixel_y_centroid) = (300px, 200px)]转换为mm
mm_x_centroid = pixel_x_centroid * mm_per_pixel
mm_y_centroid = pixel_y_centroid * mm_per_pixel
这将给你最后的答案：
(mm_x_centroid, mm_y_centroid) = (150mm, 100mm)
另一种观察相同情况的方法是这个比例，其中第一个成员是可测量/已知的比例：
real_mm_width : 640px = mm_x_centroid : pixel_x_centroid = mm_y_centroid = pixel_y_centroid