如何解决如何使用相机矩阵找到以毫米为单位的图像中的点的位置?
我正在使用标准的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,如何找到场景图像中的 640像素内有多少毫米。我已将网络摄像头水平安装在桌子上方,在桌子上放置了机械手。我正在使用相机找到对象的质心。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,以便我可以将毫米数赋予机械臂以拾取该对象。 我已搜索但未找到任何相关信息。 请告诉我,有没有方程式或方法。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),(1,(2,0) ....,(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,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],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
解决方法
Ciao
我实际上是在想,您应该能够以一种简单的方式解决您的问题:
mm_per_pixel = real_mm_width : 640px
假设摄像机最初与要拾取的对象平行于平面移动[即固定距离],real_mm_width
可以测量与图片640
像素相对应的物理距离。举例来说,假设您找到了real_mm_width = 32cm = 320mm
,因此得到了mm_per_pixel = 0.5mm/px
。在固定距离下,该比率不会改变
似乎也来自official documentation的建议:
此考虑因素有助于我们仅查找X,Y值。现在为X,Y 值,我们可以简单地将点传递为(0,0),(1,0),(2,0),... 表示点的位置。在这种情况下,我们得到的结果 将以棋盘方形的大小为单位。但是如果我们知道 正方形尺寸(例如30毫米),我们可以将值传递为(0,0),(30,0), (60,0),...因此,我们得到的结果以毫米为单位
然后,您只需要将质心坐标转换为像素[例如(pixel_x_centroid,pixel_y_centroid) = (300px,200px)
]毫米使用:
mm_x_centroid = pixel_x_centroid * mm_per_pixel
mm_y_centroid = pixel_y_centroid * mm_per_pixel
这将为您提供最终答案:
(mm_x_centroid,mm_y_centroid) = (150mm,100mm)
查看同一件事的另一种方法是该比例,其中第一个成员是可测量/已知比例:
real_mm_width : 640px = mm_x_centroid : pixel_x_centroid = mm_y_centroid = pixel_y_centroid
祝你有美好的一天,
安东尼诺
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