我尝试用python和opencv进行相机校准,以找到相机矩阵。我从这个链接中使用了以下代码
https://automaticaddison.com/how-to-perform-camera-calibration-using-opencv/
import cv2 # Import the OpenCV library to enable computer vision
import numpy as np # Import the NumPy scientific computing library
import glob # Used to get retrieve files that have a specified pattern
# Path to the image that you want to undistort
distorted_img_filename = r'C:\Users\uid20832\3.jpg'
# Chessboard dimensions
number_of_squares_X = 10 # Number of chessboard squares along the x-axis
number_of_squares_Y = 7 # Number of chessboard squares along the y-axis
nX = number_of_squares_X - 1 # Number of interior corners along x-axis
nY = number_of_squares_Y - 1 # Number of interior corners along y-axis
# Store vectors of 3D points for all chessboard images (world coordinate frame)
object_points = []
# Store vectors of 2D points for all chessboard images (camera coordinate frame)
image_points = []
# Set termination criteria. We stop either when an accuracy is reached or when
# we have finished a certain number of iterations.
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# Define real world coordinates for points in the 3D coordinate frame
# Object points are (0,0,0), (1,0,0), (2,0,0) ...., (5,8,0)
object_points_3D = np.zeros((nX * nY, 3), np.float32)
# These are the x and y coordinates
object_points_3D[:,:2] = np.mgrid[0:nY, 0:nX].T.reshape(-1, 2)
def main():
# Get the file path for images in the current directory
images = glob.glob(r'C:\Users\Kalibrierung\*.jpg')
# Go through each chessboard image, one by one
for image_file in images:
# Load the image
image = cv2.imread(image_file)
# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Find the corners on the chessboard
success, corners = cv2.findChessboardCorners(gray, (nY, nX), None)
# If the corners are found by the algorithm, draw them
if success == True:
# Append object points
object_points.append(object_points_3D)
# Find more exact corner pixels
corners_2 = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)
# Append image points
image_points.append(corners)
# Draw the corners
cv2.drawChessboardCorners(image, (nY, nX), corners_2, success)
# Display the image. Used for testing.
#cv2.imshow("Image", image)
# Display the window for a short period. Used for testing.
#cv2.waitKey(200)
# Now take a distorted image and undistort it
distorted_image = cv2.imread(distorted_img_filename)
# Perform camera calibration to return the camera matrix, distortion coefficients, rotation and translation vectors etc
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(object_points,
image_points,
gray.shape[::-1],
None,
None)
但我想我总是得到错误的参数。从校准结果来看,我的焦距在x和y方向约为1750。我认为这不可能是正确的,它几乎是正确的。相机文件显示焦距在4-7毫米之间。但我不确定,为什么校准后它会这么高。这是我的一些校准照片。也许他们有问题。我把棋盘移到相机下面的不同方向、角度和高度
我还想知道,为什么我不需要代码中正方形的大小。有人能给我解释一下吗?还是我把这个输入忘在什么地方了
你的误解是关于“焦距”。这是一个超负荷的术语
1750
如果你有一张高分辨率的图片(全高清或其他什么),那么它可能是正确的计算结果如下:
(注意单位和前缀,1 mm=1000µm)
示例:具有1.40µm像素间距和4.38 mm焦距的像素4a手机的f=~3128.57(=fx=fy)
另一个示例:像素4a具有约77.7度的对角线视野,分辨率为4032 x 3024像素,因此对角线方向为5040像素。您可以计算:
这个计算可以应用于任意的相机,你知道它的视野和图片大小。如果对角线分辨率不明确,则使用水平视野和水平分辨率。如果传感器不是16:9,但您从中拍摄的视频被剪切到16:9,则可能发生这种情况。。。假设作物只垂直生长,而水平生长不受影响
在这个代码中,为什么不需要棋盘方块的大小?因为它只校准内部参数(摄像机矩阵和失真系数)。这些并不取决于到板或场景中任何其他对象的距离
如果要校准外部参数,即立体设置中摄像机的距离,则需要给出正方形的大小
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