python 桶形畸变矫正
时间: 2023-12-12 15:35:47 浏览: 275
针对图像的桶形畸变矫正,可以使用OpenCV库中的`cv2.undistort()`函数来实现。该函数需要提供相机的内参矩阵和畸变系数,可以通过相机标定得到。下面是一个简单的例子:
```python
import cv2
import numpy as np
# 读取原始图像
img = cv2.imread('distorted.jpg')
# 相机内参矩阵
K = np.array([[fx, 0, cx],
[0, fy, cy],
[0, 0, 1]])
# 畸变系数
dist_coef = np.array([k1, k2, p1, p2, k3])
# 进行矫正
img_undistorted = cv2.undistort(img, K, dist_coef)
# 显示结果
cv2.imshow('Original Image', img)
cv2.imshow('Undistorted Image', img_undistorted)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
其中,`fx`和`fy`是相机的焦距,`cx`和`cy`是相机的光心坐标,`k1`、`k2`、`p1`、`p2`和`k3`是畸变系数。在实际应用中,这些参数需要通过相机标定得到。
相关问题
桶形畸变矫正python
桶形畸变是因为相机镜头的属性导致的,它会使得图像边缘的物体被压缩或拉伸,失真严重程度与相机所使用的镜头有关。矫正桶形畸变的方法是通过数学模型对相机镜头的失真进行建模,然后利用镜头参数矫正失真。在Python中,可以使用OpenCV库来实现这一过程。具体实现步骤可参考以下代码:
```python
import cv2
import numpy as np
img = cv2.imread('image.jpg')
h, w = img.shape[:2]
# 相机内参矩阵
K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
# 相机畸变系数
dist_coef = np.array([k1, k2, p1, p2, k3])
# 畸变矫正
new_K, roi = cv2.getOptimalNewCameraMatrix(K, dist_coef, (w,h), 1, (w,h))
mapx, mapy = cv2.initUndistortRectifyMap(K, dist_coef, None, new_K, (w,h), 5)
dst_img = cv2.remap(img, mapx, mapy, cv2.INTER_LINEAR)
cv2.imshow('Original Image', img)
cv2.imshow('Distortion Corrected Image', dst_img)
cv2.waitKey()
cv2.destroyAllWindows()
```
桶形畸变校正 自动调整参数和畸变参数 然后进行桶形畸变矫正代码
以下是Python实现的自动调整参数和畸变参数并进行桶形畸变校正的代码:
```python
import numpy as np
import cv2
def undistort(image, k1, k2, p1, p2, fx, fy, cx, cy):
# 获取图像的宽度和高度
h, w = image.shape[:2]
# 构造相机矩阵
K = np.array([[fx, 0, cx],
[0, fy, cy],
[0, 0, 1]])
# 构造畸变系数
dist_coeffs = np.array([k1, k2, p1, p2])
# 计算新的相机矩阵
new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(K, dist_coeffs, (w, h), 1, (w, h))
# 校正图像
undistorted = cv2.undistort(image, K, dist_coeffs, None, new_camera_matrix)
# 返回校正后的图像
return undistorted
# 自动调整参数和畸变参数
def calibrate(image_points, object_points, image_size):
# 构造初始相机矩阵
K = np.array([[1, 0, 0.5*image_size[0]],
[0, 1, 0.5*image_size[1]],
[0, 0, 1]])
# 初始化畸变系数
dist_coeffs = np.zeros((4,))
# 计算相机矩阵和畸变系数
rms, K, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, image_size, K, dist_coeffs)
# 返回相机矩阵和畸变系数
return K, dist_coeffs
# 构造棋盘格角点的三维坐标
def create_object_points(nx, ny, square_size):
object_points = np.zeros((ny * nx, 3), np.float32)
object_points[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
object_points *= square_size
return object_points
# 获取棋盘格角点
def find_corners(image, nx, ny):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
if ret:
return cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria=(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001))
else:
return None
# 读取输入图像
image = cv2.imread("input.png")
# 棋盘格的角点个数
nx, ny = 9, 6
# 棋盘格每个小正方形的边长
square_size = 0.023
# 构造棋盘格角点的三维坐标
object_points = create_object_points(nx, ny, square_size)
# 获取所有图像的角点
image_points = []
for i in range(1, 15):
img = cv2.imread("calibration_images/calibration{}.png".format(i))
corners = find_corners(img, nx, ny)
if corners is not None:
image_points.append(corners)
else:
print("Failed to find corners in calibration{}.png".format(i))
# 自动调整参数和畸变参数
K, dist_coeffs = calibrate(image_points, object_points, image.shape[:2])
# 校正图像
undistorted = undistort(image, dist_coeffs[0], dist_coeffs[1], dist_coeffs[2], dist_coeffs[3], K[0][0], K[1][1], K[0][2], K[1][2])
# 显示校正前后的图像
cv2.imshow("Original Image", image)
cv2.imshow("Undistorted Image", undistorted)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
在代码中,`calibrate`函数用于自动调整参数和畸变参数,`undistort`函数用于校正图像,`create_object_points`函数用于构造棋盘格角点的三维坐标,`find_corners`函数用于获取棋盘格角点。需要注意的是,代码中使用了14张标定图像进行相机标定,这些图像需要事先准备好并保存在`calibration_images`文件夹中。
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