OpenCV |鱼眼不失真时如何考虑相机方向？

Question

你好

我正在尝试从鱼眼扭曲的图像中获得向下的视图。

当前相机方向如下所示。如果从左视图看，相机向下看，

40deg

从前轴朝向下轴。如果从俯视图看，相机相对于前轴向左倾斜

30deg

。

我制作了一个python3脚本，但对输出不满意。我认为我对相机方向的解释有问题。

import cv2
import numpy as np

rollDeg = 50
pitchDeg = 30
# Camera calibration parameters
K = np.array([[413.4459003089536, 0.0, 625.4657090067509],
          [0.0, 414.5602416017697, 393.99252903112915],
          [0.0, 0.0, 1.0]])

D = np.array([[0.2422953376828772],
          [0.5059306403608751],
          [-0.48731143899233426],
          [0.17754105229960523]])

# Estimate a new camera matrix optimized for undistortion
new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(K, D, (1280, 720), np.eye(3), balance=1, new_size=(1280, 720), fov_scale=1)

# Initial values for new_K parameters
fx_init = 230
fy_init = 230
cx_init = 1280//2
cy_init = 720//2

# Function to update undistorted image
def update_image():
# Update new_K matrix based on trackbar values
rollDeg    = cv2.getTrackbarPos('roll', 'img')
pitchDeg      = cv2.getTrackbarPos('pitch', 'img')
new_K[0, 0] = cv2.getTrackbarPos('fx', 'img')
new_K[1, 1] = cv2.getTrackbarPos('fy', 'img')
new_K[0, 2] = cv2.getTrackbarPos('cx', 'img')
new_K[1, 2] = cv2.getTrackbarPos('cy', 'img')

roll = rollDeg * np.pi / 180
pitch = -pitchDeg * np.pi / 180

# ******** I am doubtful about these couple of lines **********  
# Rotation matrices for roll (rotX) and pitch (rotY)
rotX = np.array([[1, 0, 0],
                 [0, np.cos(roll), -np.sin(roll)],
                 [0, np.sin(roll), np.cos(roll)]])

rotY = np.array([[np.cos(pitch), 0, np.sin(pitch)],
                 [0, 1, 0],
                 [-np.sin(pitch), 0, np.cos(pitch)]])

# Combined rotation matrix
rotation = rotX @ rotY

# Calculate rotated intrinsic matrix
K_rotated = rotation @ K

# Generate undistortion map
mapx, mapy = cv2.fisheye.initUndistortRectifyMap(K_rotated, D, rotation, new_K, (1280, 720), cv2.CV_16SC2)
# ********         **********  

# Remap input image using undistortion map
undistorted_image = cv2.remap(input_image, mapx, mapy, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)

# Display undistorted image
cv2.imshow('img', undistorted_image)
#cv2.imwrite('undistortedImg.jpg', undistorted_image)

# Load input image
input_image = cv2.imread("distortedImg.png")

# Create named window and trackbars
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.createTrackbar('roll', 'img', int(rollDeg), 89, lambda x: update_image())
cv2.createTrackbar('pitch', 'img', int(pitchDeg), 89, lambda x: update_image())
cv2.createTrackbar('fx', 'img', fx_init, 500, lambda x: update_image())
cv2.createTrackbar('fy', 'img', fy_init, 500, lambda x: update_image())
cv2.createTrackbar('cx', 'img', cx_init, 1920, lambda x: update_image())
cv2.createTrackbar('cy', 'img', cy_init, 1080, lambda x: update_image())

# Initialize undistorted image
update_image()

# Wait for key press to exit
cv2.waitKey(0)
cv2.destroyAllWindows()

扭曲Img.png

未扭曲Img.png

Answer 1

import cv2
import numpy as np

rollDeg = 50
pitchDeg = 30
# Camera calibration parameters
K = np.array([[413.4459003089536, 0.0, 625.4657090067509],
              [0.0, 414.5602416017697, 393.99252903112915],
              [0.0, 0.0, 1.0]])

D = np.array([[0.2422953376828772],
              [0.5059306403608751],
              [-0.48731143899233426],
              [0.17754105229960523]])

# Estimate a new camera matrix optimized for undistortion
new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(K, D, (1280, 720), np.eye(3), balance=1, new_size=(1280, 720), fov_scale=1)

# Initial values for new_K parameters
fx_init = 82
fy_init = 82
cx_init = 1000
cy_init = 600

# Function to update undistorted image
def update_image():
    # Update new_K matrix based on trackbar values
    rollDeg    = cv2.getTrackbarPos('roll', 'img')
    pitchDeg      = cv2.getTrackbarPos('pitch', 'img')
    new_K[0, 0] = cv2.getTrackbarPos('fx', 'img')
    new_K[1, 1] = cv2.getTrackbarPos('fy', 'img')
    new_K[0, 2] = cv2.getTrackbarPos('cx', 'img')
    new_K[1, 2] = cv2.getTrackbarPos('cy', 'img')

    roll = rollDeg * np.pi / 180
    pitch = -pitchDeg * np.pi / 180

    # Rotation matrices for roll (rotX) and pitch (rotY)
    rotX = np.array([[1, 0, 0],
                     [0, np.cos(roll), -np.sin(roll)],
                     [0, np.sin(roll), np.cos(roll)]])

    # rotY = np.array([[np.cos(pitch), 0, np.sin(pitch)],
    #                  [0, 1, 0],
    #                  [-np.sin(pitch), 0, np.cos(pitch)]])

    rotZ = np.array([[np.cos(pitch), -np.sin(pitch), 0],
                     [np.sin(pitch),  np.cos(pitch), 0],
                     [0, 0, 1]])

    # Combined rotation matrix
    rotation = rotZ @ rotX

    # Calculate rotated intrinsic matrix
    # K_rotated = rotation @ K

    # Generate undistortion map
    mapx, mapy = cv2.fisheye.initUndistortRectifyMap(K, D, rotation, new_K, (1280, 720), cv2.CV_16SC2)

    # Remap input image using undistortion map
    undistorted_image = cv2.remap(input_image, mapx, mapy, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)

    # Display undistorted image
    cv2.imshow('img', undistorted_image)
    # cv2.imshow('img', cv2.rotate(undistorted_image, cv2.ROTATE_90_COUNTERCLOCKWISE))

# Load input image
input_image = cv2.imread("distortedImg.png")

# Create named window and trackbars
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.createTrackbar('roll', 'img', int(rollDeg), 89, lambda x: update_image())
cv2.createTrackbar('pitch', 'img', int(pitchDeg), 89, lambda x: update_image())
cv2.createTrackbar('fx', 'img', fx_init, 500, lambda x: update_image())
cv2.createTrackbar('fy', 'img', fy_init, 500, lambda x: update_image())
cv2.createTrackbar('cx', 'img', cx_init, 1920, lambda x: update_image())
cv2.createTrackbar('cy', 'img', cy_init, 1080, lambda x: update_image())

# Initialize undistorted image
update_image()

# Wait for key press to exit
cv2.waitKey(0)
cv2.destroyAllWindows()