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？

问题描述

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