前篇博文https://blog.csdn.net/xiao__run/article/details/89190112涉及到了使用四路魚眼圖像拼接並識別車位線，這個過程涉及到魚眼圖像的標定，矯正以及裁剪，這篇博文我就就這個工作詳細介紹一下，工作流程如下：
1採集圖像
首先採用opencv採集到棋盤格圖像，大約２０幾張，我的棋盤格是１１x8個角點，６０mm大小．如下：
2 標定圖像
將２０幾張圖像放入一個文件夾，標定得出內參與畸變參數：，代碼如下，opencv3版本與python
import cv2
import numpy as np
import os
import glob
import matplotlib.pylab as plt
def get_K_and_D(checkerboard, imgsPath):
CHECKERBOARD = checkerboard
subpix_criteria = (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
calibration_flags = cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC+cv2.fisheye.CALIB_CHECK_COND+cv2.fisheye.CALIB_FIX_SKEW
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)
_img_shape = None
objpoints = []
imgpoints = []
images = glob.glob(imgsPath + '/*.jpg')
for fname in images:
img = cv2.imread(fname)
plt.figure('original image')
plt.imshow(img[..., ::-1])
if _img_shape == None:
_img_shape = img.shape[:2]
else:
assert _img_shape == img.shape[:2], "All images must share the same size."
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
for c in corners:
plt.plot(c[0][0],c[0][1], 'r*')
# plt.show()
if ret == True:
objpoints.append(objp)
cv2.cornerSubPix(gray,corners,(3,3),(-1,-1),subpix_criteria)
imgpoints.append(corners)
N_OK = len(objpoints)
K = np.zeros((3, 3))
D = np.zeros((4, 1))
rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
rms, _, _, _, _ = \
cv2.fisheye.calibrate(
objpoints,
imgpoints,
gray.shape[::-1],
K,
D,
rvecs,
tvecs,
calibration_flags,
(cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6)
)
DIM = _img_shape[::-1]
print("Found " + str(N_OK) + " valid images for calibration")
print("DIM=" + str(_img_shape[::-1]))
print("K=np.array(" + str(K.tolist()) + ")")
print("D=np.array(" + str(D.tolist()) + ")")
return DIM, K, D
# 計算內參和矯正係數
'''
# checkerboard： 棋盤格的格點數目
# imgsPath: 存放魚眼圖片的路徑
'''
print(get_K_and_D((8,11), '/home/crj/Desktop/車位線檢測與跟蹤/fisheye/snapshot'))
3 矯正圖像
側面攝像頭矯正之前圖像
通過opencv3版本的fisheye.undistortImage 函數即可矯正圖像,代碼如下：
assert float(cv2.__version__.rsplit('.', 1)[0]) >= 3, 'OpenCV version 3 or newer required.'
'''
K = np.array([[689.21, 0., 1295.56],
[0., 690.48, 942.17],
[0., 0., 1.]])
'''
K = np.array([[271.40629437753046, 0.0, 329.829270787], [0.0, 271.00780962876536, 216.9546102473427], [0.0, 0.0, 1.0]])
# zero distortion coefficients work well for this image
# D = np.array([0., 0., 0., 0.])
D=np.array([-0.01515303912651211, 0.09148012072176424, -0.2080496385404225, 0.13211726263924817])
# use Knew to scale the output
Knew = K.copy()
Knew[(0,1), (0,1)] = 0.4 * Knew[(0,1), (0,1)]
img = cv2.imread('/home/lenovo/Desktop/a.jpg')
plt.figure('original image')
plt.imshow(img[...,::-1])
img_undistorted = cv2.fisheye.undistortImage(img, K, D=D, Knew=Knew)
cv2.imwrite('right_60_undistorted.jpg', img_undistorted)
plt.figure('undistored image')
plt.imshow(img_undistorted[..., ::-1])
４ 裁剪圖像
將中間的圖像部分裁剪出來，代碼如下：
def cal_img(img):
num_coor_row = []
num_coor_col = []
# 記錄每行有多少個黑色像素
for i in range(img.shape[0]):
num_row = 0
for j in range(img.shape[1]):
if sum(img[i][j]) == 0:
# print([i, j])
num_row = num_row + 1
num_coor_row.append(num_row)
# 記錄每列有多少個黑色像素
for k in range(img.shape[1]):
num_col = 0
for l in range(img.shape[0]):
if sum(img[l][k]) == 0:
# print([l, k])
num_col = num_col + 1
num_coor_col.append(num_col)
return num_coor_row, num_coor_col
def multi_index(alist, f): # 返回list中多個元素的索引
return [i for (i,v) in enumerate(alist)if v == f]
# 去除圖像黑色邊緣
num_coor_row, num_coor_col = cal_img(img_undistorted)
edge_index_row = multi_index(num_coor_row, img.shape[1])
edge_index_col = multi_index(num_coor_col, img.shape[0])
img_cropped = np.delete(img_undistorted, edge_index_row, axis=0)
img_cropped = np.delete(img_cropped, edge_index_col, axis=1)
plt.figure('cropped image')
plt.imshow(img_cropped[..., ::-1])
def crop_img(img_cropped):
# 截取上下感興趣區域
top_roi = img_cropped[:int(img_cropped.shape[0] / 2),
int(img_cropped.shape[1] / 2) - 50:int(img_cropped.shape[1] / 2) + 50, :]
bottom_roi = img_cropped[int(img_cropped.shape[0] / 2) + 1:,
int(img_cropped.shape[1] / 2) - 50:int(img_cropped.shape[1] / 2) + 50, :]
# 統計黑色像素個數
_, top_num_coor_row = cal_img(top_roi)
_, bottom_num_coor_row = cal_img(bottom_roi)
# 截取左右感興趣區域
left_roi = img_cropped[int(img_cropped.shape[0] / 2) - 50:int(img_cropped.shape[0] / 2) + 50,
:int(img_cropped.shape[1] / 2), :]
right_roi = img_cropped[int(img_cropped.shape[0] / 2) - 50:int(img_cropped.shape[0] / 2) + 50,
int(img_cropped.shape[1] / 2) + 1:, :]
# 統計黑色像素個數
left_num_coor_col, _ = cal_img(left_roi)
right_num_coor_col, _ = cal_img(right_roi)
# plt.figure('roi')
# plt.imshow(top_roi)
'''
print(top_num_coor_row)
print(bottom_num_coor_row)
print(left_num_coor_col)
print(right_num_coor_col)
'''
# 確定黑色像素最多的行和列的索引
top_max_row = max(top_num_coor_row)
top_max_row_index = multi_index(top_num_coor_row, top_max_row)
bottom_max_row = max(bottom_num_coor_row)
bottom_max_row_index = multi_index(bottom_num_coor_row, bottom_max_row)
left_max_col = max(left_num_coor_col)
left_max_col_index = multi_index(left_num_coor_col, left_max_col)
right_max_col = max(right_num_coor_col)
right_max_col_index = multi_index(right_num_coor_col, right_max_col)
'''
print(top_max_row_index)
print(bottom_max_row_index)
print(left_max_col_index)
print(right_max_col_index)
'''
# 取索引的中位數
top_col_index = int(np.median(top_max_row_index))
bottom_col_index = int(np.median(bottom_max_row_index))
left_row_index = int(np.median(left_max_col_index))
right_row_index = int(np.median(right_max_col_index))
print(top_col_index,bottom_col_index,left_row_index,right_row_index)
# 取中位數對應的列
top_col = top_roi[:, top_col_index, :]
bottom_col = bottom_roi[:, bottom_col_index, :]
# 該列第一個不爲零的像素爲校正後圖像內切矩形的上切點
for top_c in range(top_roi.shape[0]):
if sum(top_col[top_c]) != 0:
top = top_c
break
# 該列第一個爲零的像素爲校正後圖像內切矩形的下切點
for bottom_c in range(bottom_roi.shape[0]):
if sum(bottom_col[bottom_c+1]) == 0:
bottom = bottom_c
break
# 取中位數對應的行
left_row = left_roi[left_row_index, :, :]
right_row = right_roi[right_row_index, :, :]
# 該行第一個不爲零的像素爲校正後圖像內切矩形的上切點
for left_r in range(left_roi.shape[1]):
if sum(left_row[left_r]) != 0:
left = left_r
break
# 該行第一個不爲零的像素爲校正後圖像內切矩形的上切點
for right_r in range(int(img_cropped.shape[1] / 2)):
if sum(right_row[right_r+1]) == 0:
right = right_r
break
# 計算上下左右切點的真實索引
top = top
bottom = bottom + top_roi.shape[0]
left = left
right = right + left_roi.shape[1]
return top, bottom, left, right
top_f,bottom_f,left_f,right_f = crop_img(img_cropped)
print(top_f,bottom_f,left_f,right_f)
img_cropped_f = img_cropped[top_f:bottom_f, left_f:right_f, :]
plt.figure('cropped image')
plt.figure('final result')
plt.imshow(img_cropped_f[...,::-1])
plt.show()
cv2.imwrite('final_result.jpg', img_cropped_f)
最後裁剪完的圖像我貼出實際中拍的圖像吧：
５ 完整工程代碼
#-*- coding:utf-8 -*-
import cv2
import numpy as np
import matplotlib.pylab as plt
assert float(cv2.__version__.rsplit('.', 1)[0]) >= 3, 'OpenCV version 3 or newer required.'
'''
K = np.array([[689.21, 0., 1295.56],
[0., 690.48, 942.17],
[0., 0., 1.]])
'''
K = np.array([[271.40629437753046, 0.0, 329.829270787], [0.0, 271.00780962876536, 216.9546102473427], [0.0, 0.0, 1.0]])
# zero distortion coefficients work well for this image
# D = np.array([0., 0., 0., 0.])
D=np.array([-0.01515303912651211, 0.09148012072176424, -0.2080496385404225, 0.13211726263924817])
# use Knew to scale the output
Knew = K.copy()
Knew[(0,1), (0,1)] = 0.4 * Knew[(0,1), (0,1)]
img = cv2.imread('/home/lenovo/Desktop/a.jpg')
plt.figure('original image')
plt.imshow(img[...,::-1])
img_undistorted = cv2.fisheye.undistortImage(img, K, D=D, Knew=Knew)
cv2.imwrite('right_60_undistorted.jpg', img_undistorted)
plt.figure('undistored image')
plt.imshow(img_undistorted[..., ::-1])
# plt.show()
def cal_img(img):
num_coor_row = []
num_coor_col = []
# 記錄每行有多少個黑色像素
for i in range(img.shape[0]):
num_row = 0
for j in range(img.shape[1]):
if sum(img[i][j]) == 0:
# print([i, j])
num_row = num_row + 1
num_coor_row.append(num_row)
# 記錄每列有多少個黑色像素
for k in range(img.shape[1]):
num_col = 0
for l in range(img.shape[0]):
if sum(img[l][k]) == 0:
# print([l, k])
num_col = num_col + 1
num_coor_col.append(num_col)
return num_coor_row, num_coor_col
def multi_index(alist, f): # 返回list中多個元素的索引
return [i for (i,v) in enumerate(alist)if v == f]
# 去除圖像黑色邊緣
num_coor_row, num_coor_col = cal_img(img_undistorted)
edge_index_row = multi_index(num_coor_row, img.shape[1])
edge_index_col = multi_index(num_coor_col, img.shape[0])
img_cropped = np.delete(img_undistorted, edge_index_row, axis=0)
img_cropped = np.delete(img_cropped, edge_index_col, axis=1)
plt.figure('cropped image')
plt.imshow(img_cropped[..., ::-1])
def crop_img(img_cropped):
# 截取上下感興趣區域
top_roi = img_cropped[:int(img_cropped.shape[0] / 2),
int(img_cropped.shape[1] / 2) - 50:int(img_cropped.shape[1] / 2) + 50, :]
bottom_roi = img_cropped[int(img_cropped.shape[0] / 2) + 1:,
int(img_cropped.shape[1] / 2) - 50:int(img_cropped.shape[1] / 2) + 50, :]
# 統計黑色像素個數
_, top_num_coor_row = cal_img(top_roi)
_, bottom_num_coor_row = cal_img(bottom_roi)
# 截取左右感興趣區域
left_roi = img_cropped[int(img_cropped.shape[0] / 2) - 50:int(img_cropped.shape[0] / 2) + 50,
:int(img_cropped.shape[1] / 2), :]
right_roi = img_cropped[int(img_cropped.shape[0] / 2) - 50:int(img_cropped.shape[0] / 2) + 50,
int(img_cropped.shape[1] / 2) + 1:, :]
# 統計黑色像素個數
left_num_coor_col, _ = cal_img(left_roi)
right_num_coor_col, _ = cal_img(right_roi)
# plt.figure('roi')
# plt.imshow(top_roi)
'''
print(top_num_coor_row)
print(bottom_num_coor_row)
print(left_num_coor_col)
print(right_num_coor_col)
'''
# 確定黑色像素最多的行和列的索引
top_max_row = max(top_num_coor_row)
top_max_row_index = multi_index(top_num_coor_row, top_max_row)
bottom_max_row = max(bottom_num_coor_row)
bottom_max_row_index = multi_index(bottom_num_coor_row, bottom_max_row)
left_max_col = max(left_num_coor_col)
left_max_col_index = multi_index(left_num_coor_col, left_max_col)
right_max_col = max(right_num_coor_col)
right_max_col_index = multi_index(right_num_coor_col, right_max_col)
'''
print(top_max_row_index)
print(bottom_max_row_index)
print(left_max_col_index)
print(right_max_col_index)
'''
# 取索引的中位數
top_col_index = int(np.median(top_max_row_index))
bottom_col_index = int(np.median(bottom_max_row_index))
left_row_index = int(np.median(left_max_col_index))
right_row_index = int(np.median(right_max_col_index))
print(top_col_index,bottom_col_index,left_row_index,right_row_index)
# 取中位數對應的列
top_col = top_roi[:, top_col_index, :]
bottom_col = bottom_roi[:, bottom_col_index, :]
# 該列第一個不爲零的像素爲校正後圖像內切矩形的上切點
for top_c in range(top_roi.shape[0]):
if sum(top_col[top_c]) != 0:
top = top_c
break
# 該列第一個爲零的像素爲校正後圖像內切矩形的下切點
for bottom_c in range(bottom_roi.shape[0]):
if sum(bottom_col[bottom_c+1]) == 0:
bottom = bottom_c
break
# 取中位數對應的行
left_row = left_roi[left_row_index, :, :]
right_row = right_roi[right_row_index, :, :]
# 該行第一個不爲零的像素爲校正後圖像內切矩形的上切點
for left_r in range(left_roi.shape[1]):
if sum(left_row[left_r]) != 0:
left = left_r
break
# 該行第一個不爲零的像素爲校正後圖像內切矩形的上切點
for right_r in range(int(img_cropped.shape[1] / 2)):
if sum(right_row[right_r+1]) == 0:
right = right_r
break
# 計算上下左右切點的真實索引
top = top
bottom = bottom + top_roi.shape[0]
left = left
right = right + left_roi.shape[1]
return top, bottom, left, right
top_f,bottom_f,left_f,right_f = crop_img(img_cropped)
print(top_f,bottom_f,left_f,right_f)
img_cropped_f = img_cropped[top_f:bottom_f, left_f:right_f, :]
plt.figure('cropped image')
plt.figure('final result')
plt.imshow(img_cropped_f[...,::-1])
plt.show()
cv2.imwrite('final_result.jpg', img_cropped_f)
C++的兩種形式矯正代碼
remap與cv::fisheye::undistortImage函數，略有不同，博主爲了方便調參，做了一個參數調節按鈕。下面給出完整C++代碼
//
// Created by lenovo on 19-5-16.
//
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
using namespace fisheye;
Mat image;
Mat cameraMatrix1;
cv::Mat distCoeffs1(4, 1, cv::DataType<double>::type);
Mat UndistortImg;
Mat new_intrinsic_mat; //Mat new_intrinsic_mat(3, 3, CV_64FC1, Scalar(0))亦可，注意數據類型;
int fx_threshold_min=80;
int fy_threshold_min=80;
string winname="UndistortImg";
//TrackBar發生改變的回調函數
void onChangeTrackBar(int, void* );
int main()
{
string bar_name_1="fx";
string bar_name_2="fy";
image=imread("./2.bmp");
imwrite("image.jpg",image);
Mat dst;
cameraMatrix1=Mat::eye(3, 3, cv::DataType<double>::type);
distCoeffs1.at<double>(0,0) = 0.061439051;
distCoeffs1.at<double>(1,0) = 0.03187556;
distCoeffs1.at<double>(2,0) = -0.00726151;
distCoeffs1.at<double>(3,0) = -0.00111799;
//distCoeffs1.at<double>(4,0) = -0.00678974;
//Taken from Mastring OpenCV d
double fx = 328.61652824;
double fy = 328.56512516;
double cx = 629.80551148;
double cy = 340.5442837;
cameraMatrix1.at<double>(0, 0) = fx;
cameraMatrix1.at<double>(1, 1) = fy;
cameraMatrix1.at<double>(0, 2) = cx;
cameraMatrix1.at<double>(1, 2) = cy;
std::cout << "After calib cameraMatrix --- 1: " << cameraMatrix1 << std::endl;
std::cout << "After calib distCoeffs: --- 1" << distCoeffs1 << std::endl;
////////////////////////////////////////////////////////////////////
///// 魚眼攝像頭畸變矯正
////////////////////////////////////////////////////////////////////
Size image_size(1280,720);
Mat mapx = Mat(image_size, CV_32FC1);
Mat mapy = Mat(image_size, CV_32FC1);
Mat R = Mat::eye(3, 3, CV_32F);
Mat new_cameraMatrix=Mat::zeros(3,3,CV_32F);
new_cameraMatrix=getOptimalNewCameraMatrix(cameraMatrix1, distCoeffs1, image_size, 1, image_size, 0);
cout<<"new cameraMatrix: "<<new_cameraMatrix<<endl;
// cv::fisheye::initUndistortRectifyMap()
fisheye::initUndistortRectifyMap(cameraMatrix1,distCoeffs1, R,new_cameraMatrix,image_size,CV_16SC2,mapx,mapy);
//cout<<mapx;
double t1 = (double)getTickCount();
remap(image, dst, mapx, mapy, INTER_LINEAR);
////////////////////////////////////////////////////////////////////
///// 魚眼攝像頭畸變矯正
////////////////////////////////////////////////////////////////////
t1 = (double)getTickCount() - t1;
std::cout << "compute time :" << t1*1000.0 / cv::getTickFrequency() << " ms \n";
imshow("result",dst);
imwrite("result.jpg",dst);
//fx,fy變大（小），視場變小（大），裁剪較多（少），但細節清晰（模糊）；很關鍵，new_intrinsic_mat決定輸出的畸變校正圖像的範圍
//float max_lowThreshold=1.0;
//調整輸出校正圖的視場
namedWindow(winname,WINDOW_AUTOSIZE);
createTrackbar(bar_name_1, winname, &fx_threshold_min, 100,onChangeTrackBar,0);
createTrackbar(bar_name_2, winname, &fy_threshold_min, 100,onChangeTrackBar,0);
//onChangeTrackBar(0,0);
waitKey(0);
}
void onChangeTrackBar(int pos, void* )
{
cameraMatrix1.copyTo(new_intrinsic_mat);
new_intrinsic_mat.at<double>(0, 0) *= ((float)fx_threshold_min)/100.0; //注意數據類型，非常重要
new_intrinsic_mat.at<double>(1, 1) *= ((float)fy_threshold_min)/100.0;
//調整輸出校正圖的中心
new_intrinsic_mat.at<double>(0, 2) = 0.5*UndistortImg.cols;
new_intrinsic_mat.at<double>(1, 2) = 0.5 *UndistortImg.rows;
cv::fisheye::undistortImage(image,UndistortImg,cameraMatrix1,distCoeffs1,new_intrinsic_mat);
cv::imshow("UndistortImg", UndistortImg);
}
最後我們再看下效果吧
結合車位線檢測與車位線定位，即可作爲自動泊車視覺項目哦