Ang*_*elo 3 python opencv fisheye calibration camera-calibration
信息：
我已经校准了相机，发现相机的本征矩阵 (K) 及其畸变系数 (d) 如下：
import numpy as np
K = np.asarray([[556.3834638575809,0,955.3259939726225],[0,556.2366649196925,547.3011305411478],[0,0,1]])
d = np.asarray([[-0.05165940570900624],[0.0031093602070252167],[-0.0034036648250202746],[0.0003390345044343793]])
从这里，我可以使用以下三行来消除图像的扭曲：
final_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(K, d, (1920, 1080), np.eye(3), balance=1.0)
map_1, map_2 = cv2.fisheye.initUndistortRectifyMap(K, d, np.eye(3), final_K, (1920, 1080), cv2.CV_32FC1)
undistorted_image = cv2.remap(image, map_1, map_2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
生成的未失真图像似乎是正确的，左图像扭曲，右图像未扭曲，但是当我尝试使用点不映射cv2.remap()到与图像中相应像素相同的位置来不扭曲图像点时。我使用以下方法检测了左图中的校准板点
ret, corners = cv2.findChessboardCorners(gray, (6,8),cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
corners2 = cv2.cornerSubPix(gray, corners, (3,3), (-1,-1), (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1))
然后通过以下方式重新映射这些点：
remapped_points = []
for corner in corners2:
remapped_points.append(
(map_1[int(corner[0][1]), int(corner[0][0])], map_2[int(corner[0][1]), int(corner[0][0])])
)
在这些水平连接的图像中，左图像显示在失真图像中检测到的点，而右图像显示右图像中点的重新映射位置。
另外，我无法使用cv2.fisheye.undistortPoints(). 我有以下功能来消除点扭曲：
def undistort_list_of_points(point_list, in_K, in_d):
K = np.asarray(in_K)
d = np.asarray(in_d)
# Input can be list of bbox coords, poly coords, etc.
# TODO -- Check if point behind camera?
points_2d = np.asarray(point_list)
points_2d = points_2d[:, 0:2].astype('float32')
points2d_undist = np.empty_like(points_2d)
points_2d = np.expand_dims(points_2d, axis=1)
result = np.squeeze(cv2.fisheye.undistortPoints(points_2d, K, d))
fx = K[0, 0]
fy = K[1, 1]
cx = K[0, 2]
cy = K[1, 2]
for i, (px, py) in enumerate(result):
points2d_undist[i, 0] = px * fx + cx
points2d_undist[i, 1] = py * fy + cy
return points2d_undist
该图显示了使用上述函数进行去畸变时的结果。
（这一切都在 Python 3.6.8 的 Ubuntu 18.04 上的 OpenCV 4.2.0 中运行）
问题
为什么图像坐标的重新映射不能正常工作？我使用map_1不map_2正确吗？
为什么使用and的结果cv2.fisheye.undistortPoints()与使用 and 不同？map_1map_2
小智 5
您没有正确使用map_1和map_2。
cv2.fisheye.initUn DistortRectifyMap函数生成的map应该是目标图像的像素位置到源图像的像素位置的映射，即dst(x,y)=src(mapx(x,y),mapy (x,y))。请参阅OpenCV 中的重新映射。
代码中，map_1用于x方向像素映射，map_2用于y方向像素映射。例如， (X_un Distorted，Y_unDistored)是未扭曲图像中的像素位置。map_1[Y_un Distorted, X_unDistored]给出该像素应映射到扭曲图像中的X坐标的位置，而Map_2将为你提供相应的Y坐标。
因此，map_1和map_2对于从失真图像构造未失真图像很有用，但并不真正适合相反的过程。
remapped_points = []
for corner in corners2:
remapped_points.append(
(map_1[int(corner[0][1]), int(corner[0][0])], map_2[int(corner[0][1]), int(corner[0][0])]))
此代码查找角点的未失真像素位置是不正确的。您将需要使用unactorPoints函数。
映射和不失真是不同的。
您可以将映射视为基于未失真图像中的像素位置与像素图构建未失真图像，而未失真是使用镜头失真模型使用原始像素位置找到未失真像素位置。
为了找到未失真图像中角点的正确像素位置。您需要使用新估计的 K 将未失真点的归一化坐标转换回像素坐标，在您的情况下，它是 Final_K ，因为未失真图像可以被视为由具有 Final_K 的相机拍摄而没有失真（有一个小缩放效果）。
这是修改后的不失真函数：
def undistort_list_of_points(point_list, in_K, in_d, in_K_new):
K = np.asarray(in_K)
d = np.asarray(in_d)
# Input can be list of bbox coords, poly coords, etc.
# TODO -- Check if point behind camera?
points_2d = np.asarray(point_list)
points_2d = points_2d[:, 0:2].astype('float32')
points2d_undist = np.empty_like(points_2d)
points_2d = np.expand_dims(points_2d, axis=1)
result = np.squeeze(cv2.fisheye.undistortPoints(points_2d, K, d))
K_new = np.asarray(in_K_new)
fx = K_new[0, 0]
fy = K_new[1, 1]
cx = K_new[0, 2]
cy = K_new[1, 2]
for i, (px, py) in enumerate(result):
points2d_undist[i, 0] = px * fx + cx
points2d_undist[i, 1] = py * fy + cy
return points2d_undist
import cv2
import numpy as np
import matplotlib.pyplot as plt
K = np.asarray([[556.3834638575809,0,955.3259939726225],[0,556.2366649196925,547.3011305411478],[0,0,1]])
D = np.asarray([[-0.05165940570900624],[0.0031093602070252167],[-0.0034036648250202746],[0.0003390345044343793]])
print("K:\n", K)
print("D:\n", D.ravel())
# read image and get the original image on the left
image_path = "sample.jpg"
image = cv2.imread(image_path)
image = image[:, :image.shape[1]//2, :]
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
fig = plt.figure()
plt.imshow(image_gray, "gray")
H_in, W_in = image_gray.shape
print("Grayscale Image Dimension:\n", (W_in, H_in))
scale_factor = 1.0
balance = 1.0
img_dim_out =(int(W_in*scale_factor), int(H_in*scale_factor))
if scale_factor != 1.0:
K_out = K*scale_factor
K_out[2,2] = 1.0
K_new = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(K_out, D, img_dim_out, np.eye(3), balance=balance)
print("Newly estimated K:\n", K_new)
map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, np.eye(3), K_new, img_dim_out, cv2.CV_32FC1)
print("Rectify Map1 Dimension:\n", map1.shape)
print("Rectify Map2 Dimension:\n", map2.shape)
undistorted_image_gray = cv2.remap(image_gray, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
fig = plt.figure()
plt.imshow(undistorted_image_gray, "gray")
ret, corners = cv2.findChessboardCorners(image_gray, (6,8),cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
corners_subpix = cv2.cornerSubPix(image_gray, corners, (3,3), (-1,-1), (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1))
undistorted_corners = cv2.fisheye.undistortPoints(corners_subpix, K, D)
undistorted_corners = undistorted_corners.reshape(-1,2)
fx = K_new[0,0]
fy = K_new[1,1]
cx = K_new[0,2]
cy = K_new[1,2]
undistorted_corners_pixel = np.zeros_like(undistorted_corners)
for i, (x, y) in enumerate(undistorted_corners):
px = x*fx + cx
py = y*fy + cy
undistorted_corners_pixel[i,0] = px
undistorted_corners_pixel[i,1] = py
undistorted_image_show = cv2.cvtColor(undistorted_image_gray, cv2.COLOR_GRAY2BGR)
for corner in undistorted_corners_pixel:
image_corners = cv2.circle(np.zeros_like(undistorted_image_show), (int(corner[0]),int(corner[1])), 15, [0, 255, 0], -1)
undistorted_image_show = cv2.add(undistorted_image_show, image_corners)
fig = plt.figure()
plt.imshow(undistorted_image_show, "gray")