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OpenCV 相机校正

最编程 2024-04-07 21:03:42
...
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob

def plot_contrast_imgs(origin_img, converted_img, origin_img_title="origin_img", converted_img_title="converted_img", converted_img_gray=False):
"""
用于对比显示两幅图像
"""
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 20))
ax1.set_title(origin_img_title)
ax1.imshow(origin_img)
ax2.set_title(converted_img_title)
if converted_img_gray==True:
ax2.imshow(converted_img, cmap="gray")
else:
ax2.imshow(converted_img)
plt.show()

# 1. 参数设定:定义棋盘横向和纵向的角点个数并指定校正图像的位置
nx = 9
ny = 6
file_paths = glob.glob("./camera_cal/calibration*.jpg")
# 2. 计算相机的内外参数及畸变系数
def cal_calibrate_params(file_paths):
object_points = [] # 三维空间中的点：3D
image_points = [] # 图像空间中的点：2d
# 2.1 生成真实的交点坐标：类似(0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)的三维点
objp = np.zeros((nx * ny, 3), np.float32)
objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
# 2.2 检测每幅图像角点坐标
for file_path in file_paths:
img = cv2.imread(file_path)
# 将图像转换为灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 自动检测棋盘格内4个棋盘格的角点（2白2黑的交点）
rect, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# 若检测到角点，则将其存储到object_points和image_points
if rect == True:
object_points.append(objp)
image_points.append(corners)
# 2.3 获取相机参数
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, gray.shape[::-1], None, None)
return ret, mtx, dist, rvecs, tvecs


def img_undistort(img, mtx, dist):
"""
图像去畸变
"""
return cv2.undistort(img, mtx, dist, None, mtx)

# 测试去畸变函数的效果
file_paths = glob.glob("./camera_cal/calibration*.jpg")
ret, mtx, dist, rvecs, tvecs = cal_calibrate_params(file_paths)
if mtx.any() != None: # a.any() or a.all()
img = mpimg.imread("./camera_cal/calibration1.jpg")
undistort_img = img_undistort(img, mtx, dist)
plot_contrast_imgs(img, undistort_img)
print("done!")
else:
print("failed")

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