Open jamalknight opened 3 years ago
Reformatted the output to match the example template, I calibrated 190 frames, not sure if I need that many ...
[
{
'id': 0, // optional
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[8.7083773 ],[ 0.99808634],[26.78350405],
[8.99775191],[ 1.6715963 ],[26.59178926],
[9.31553826],[ 2.36271304],[26.44402531],
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[10.01555132],[ 3.74444359],[26.30238494],
[10.38053438],[ 4.41315337],[26.31505714],
[7.96496956],[ 4.32552467],[26.10934223],
[5.55337945],[ 4.24160465],[25.89788475],
[3.14450296],[ 4.16227988],[25.67948139],
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'dist': [[ 6.95929106e-06 -2.00836991e-04 8.42048176e-06 2.98005924e-05
3.04891387e-04 ]]
重新格式化输出以匹配示例模板,我校准了190帧,不确定是否需要那么多...
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-5.40178763],[ 21.82415022], [-13.31384693],[ -4.88532471],[ 21.86746617], [-13.52362252],[ -4.35715022],[ 21.96370236], [-13.70484116],[ -3.82201743],[ 22.08985974], [-13.92132371],[ -2.78021789],[ 22.3136256 ], [-13.95009631],[ -2.2983199 ],[ 22.33882235], [-13.94280444],[ -1.84919091],[ 22.261118 ], [-13.91903533],[ -1.42394983],[ 22.04756372], [-11.79453727],[ -1.47107728],[ 22.63867602], [-9.66568976],[-1.51303156],[23.23007915], [-7.53010553],[-1.55010946],[23.81938766], [-5.38987144],[-1.58169641],[24.4091039 ], [-3.24411048],[-1.60860179],[24.99742874], [-1.09313656],[-1.62943417],[25.58429844], [1.06220894],[-1.64436314],[26.1689715 ], [3.22223862],[-1.65381828],[26.75259768], [7.55601901],[-1.65507476],[27.91239821], [7.77308603],[-1.25227671],[27.70949359], [7.98698812],[-0.77924244],[27.48111848], [8.20808877],[-0.24035137],[27.24021905], [8.44652258],[ 0.35604804],[27.0034321 ], [8.7083773 ],[ 0.99808634],[26.78350405], [8.99775191],[ 1.6715963 ],[26.59178926], [9.31553826],[ 2.36271304],[26.44402531], [9.65800996],[ 3.05785712],[26.34759193], [10.01555132],[ 3.74444359],[26.30238494], [10.38053438],[ 4.41315337],[26.31505714], [7.96496956],[ 4.32552467],[26.10934223], [5.55337945],[ 4.24160465],[25.89788475], [3.14450296],[ 4.16227988],[25.67948139], [0.73877632],[ 4.08528555],[25.45708993], [-1.66356045],[ 4.0113828 ],[25.23507107], [-4.06372185],[ 3.93822602],[25.01122381], [-6.4620782 ],[ 3.8657905 ],[24.79089856], [-8.85981245],[ 3.79288322],[24.57929644], [-11.25755087],[ 3.71702485],[ 24.37258417], [-13.65489903],[ 3.63767404],[ 24.17225827] ], 'dist': [[ 6.95929106e-06 -2.00836991e-04 8.42048176e-06 2.98005924e-05 3.04891387e-04 ]]
Hello, I also encountered the same problem. May I ask how did you generate the R and T in the camera parameters later
hi @liken95
Below is the code for camera calibration, which will print outputs for R, k,t and dist. Be sure to adjust your path where your images are in line 24.
#!/usr/bin/env python
import cv2
import numpy as np
import os
import glob
# Defining the dimensions of checkerboard
CHECKERBOARD = (9,6)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# Creating vector to store vectors of 3D points for each checkerboard image
objpoints = []
# Creating vector to store vectors of 2D points for each checkerboard image
imgpoints = []
# Defining the world coordinates for 3D points
objp = np.zeros((1, CHECKERBOARD[0]*CHECKERBOARD[1], 3), np.float32)
objp[0,:,:2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
prev_img_shape = None
# Extracting path of individual image stored in a given directory
images = glob.glob('./images/camera1/*.jpg')
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chess board corners
# If desired number of corners are found in the image then ret = true
ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH+
cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
"""
If desired number of corner are detected,
we refine the pixel coordinates and display
them on the images of checker board
"""
if ret == True:
objpoints.append(objp)
# refining pixel coordinates for given 2d points.
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners2)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2,ret)
cv2.imshow('img',img)
cv2.waitKey(1)
cv2.destroyAllWindows()
h,w = img.shape[:2]
"""
Performing camera calibration by
passing the value of known 3D points (objpoints)
and corresponding pixel coordinates of the
detected corners (imgpoints)
"""
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
# print("Camera matrix : \n")
# print(mtx)
# print("dist : \n")
# print(dist)
# print("rvecs : \n")
# print(rvecs)
# print("tvecs : \n")
# print(tvecs)
# Obtain R,k,t
rotation_mat = np.zeros(shape=(3, 3))
R, _ = cv2.Rodrigues(rvecs[0], rotation_mat)
t = tvecs[0]
k = mtx
# Put in a npz
np.savez("camera1data.npz", R=R, dist=dist, k=k, t=t)
print("R:", R)
print("k:", k)
print("t:", t)
print("dist:", dist)
Hi, I am trying to collect the correct camera calibration data for my own dataset in this format:
However, when using the OpenCV method of camera calibration the output is in this format (below) The Rotation matrix and translation matrix are in a different format (vectors?) How do I get the correct format? Thanks