使用开放 CV 进行畸变校正

Question

使用开放 CV 进行畸变校正

use*_*412 5 python opencv calibration distortion camera-calibration

我正在尝试使用开放式 CV 来校正图像失真。我试图纠正的失真理论是桶形和枕形失真的组合，如下所示：

我在这里不使用普通相机，而是使用检流计扫描系统（如下所示： http: //www.chinagalvo.com/Content/uploads/2019361893/201912161511117936592.gif），所以我不能只记录像这样的棋盘图案所有 OpenCV 指南都建议。

但我可以将扫描仪移动到目标位置并测量激光束在图像平面中的实际位置，例如绘制为：

所以我将这些值放入此脚本中的 OpenCVcalibrateCamera函数中：

import numpy as np
import cv2

targetPosX = np.array([-4., -2., 0., 2., 4., -4., -2., 0., 2., 4., -4., -2., 2., 4., -4., -2., 0., 2., 4., -4., -2., 0., 2., 4.])
targetPosY = np.array([-4., -4., -4., -4., -4., -2., -2., -2., -2., -2., 0., 0., 0., 0., 2., 2., 2., 2., 2., 4., 4., 4., 4., 4.])
actualPosX = np.array([-4.21765834, -2.14708042, -0.07755157, 1.9910175, 4.05941744, -4.17816164, -2.10614537, -0.03775821, 2.02883123, 4.09875409, -4.13937186, -2.07079973, 2.07072068, 4.1377518, -4.10200901, -2.03229052, 0.0367603, 2.10655379, 4.17627114, -4.06449305, -1.99426964, 0.07737988, 2.14365487, 4.21625359])
actualPosY = np.array([-4.04808315, -4.08681247, -4.12545265, -4.16807799, -4.20657896, -1.98568911, -2.0217478, -2.06356789, -2.10326313, -2.14456442, 0.07567631, 0.03889721, -0.04043382, -0.08069954, 2.14054726, 2.09940048, 2.05965315, 2.02167639, 1.9800822, 4.20167787, 4.16215278, 4.12334605, 4.08099448, 4.04376011])

scale = 100 # px / mm
height = 9 * scale # range of measured points is -4 to 4mm --> show area from -4.5 to 4.5 with 100 px / mm
width = 9 * scale

def scale_and_shift(array, scl, shift):
    array *= scl
    array += shift
    return array

# shift recorded positon into image coordinate system
targetPosX = scale_and_shift(targetPosX, scale, width / 2.)
targetPosY = scale_and_shift(targetPosY, scale, height / 2.)
actualPosX = scale_and_shift(actualPosX, scale, width / 2.)
actualPosY = scale_and_shift(actualPosY, scale, height / 2.)

# create images
target_image = np.full((height,width), 255)
combined_image = np.full((height,width), 255)
actual_image = np.full((height,width), 255) 
for i in range(len(targetPosX)):
    cv2.circle(target_image, (int(targetPosX[i]), int(targetPosY[i])), 20, 0, -1)
    
    # circle in combined image is target position, full point is actual position
    cv2.circle(combined_image, (int(targetPosX[i]), int(targetPosY[i])), 20, 0, 5)
    cv2.circle(combined_image, (int(actualPosX[i]), int(actualPosY[i])), 20, 0, -1)

    cv2.circle(actual_image, (int(actualPosX[i]), int(actualPosY[i])), 20, 0, -1)

cv2.imwrite("combined_before.png", combined_image)

# create point lists for calibrateCamera function. set 3rd dimension to zero.
targetPoints = np.array([np.vstack([targetPosX, targetPosY]).T]).astype("float32")
targetPoints_zero = np.array([np.vstack([targetPosX, targetPosY, list(np.zeros(len(targetPosX)))]).T]).astype("float32")
imagePoints = np.array([np.vstack([actualPosX, actualPosY]).T]).astype("float32")
imagePoints_zero = np.array([np.vstack([actualPosX, actualPosY, np.zeros(len(actualPosX))]).T]).astype("float32")

# read image to apply to
# saving and reading because just passing the actual_image somehow didn't work
cv2.imwrite("image.png", actual_image)
img = cv2.imread("image.png", cv2.IMREAD_GRAYSCALE)
h, w = img.shape[:2]

# calulate distortion matrix
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(targetPoints_zero, imagePoints, (h,w), None, None)

# refine distortion matrix to avoid cut-off
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))

# undistort
dst = cv2.undistort(img, mtx, dist, None, newcameramtx)

cv2.imwrite('calibresult.png', dst)
cv2.imwrite("correction.png", dst - actual_image)

for i in range(len(targetPosX)):
    # circle in combined image is target position, full point is actual position
    cv2.circle(dst, (int(targetPosX[i]), int(targetPosY[i])), 20, 0, 5)

cv2.imwrite('combined_result.png', dst)

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然而，结果并不符合预期 - 校正后的图像与目标图像不对齐（实心点 - 校正后的实际点。圆圈 - 目标点）：