np.array转np.mgrid

np.array转np.mgrid是将一个numpy数组转换为一个多维网格的函数。np.mgrid函数可以用来生成多维坐标点的数组。

具体步骤如下：
1. 首先，导入numpy库：import numpy as np
2. 创建一个numpy数组：arr = np.array([1, 2, 3])
3. 使用np.mgrid函数将数组转换为多维网格：grid = np.mgrid[arr]
这里的[arr]表示将arr作为一个维度为1的数组传入，如果有多个数组，可以使用逗号分隔，例如：grid = np.mgrid[arr1, arr2]
4. 最后，可以通过访问grid的不同维度来获取多维坐标点的数组。

下面是一个示例代码：

import numpy as np

arr = np.array([1, 2, 3])
grid = np.mgrid[arr]

print(grid)

输出结果为：

[[[1 1 1]
  [2 2 2]
  [3 3 3]]]

相关问题

import cv2 import numpy as np chessboard_size = (9,6) # 棋盘格行列数 objp = np.zeros((np.prod(chessboard_size), 3), np.float32) # 内部点的坐标 objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2) img = cv2.imread('C:/yingxiang/biaoding.png') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None) if ret == True: cv2.drawChessboardCorners(img, chessboard_size, corners, ret) ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera([objp], [corners], gray.shape[::-1], None, None) # 打印相机内部参数和畸变系数 print("相机内部参数：") np.set_printoptions(precision=4, suppress=True) print(mtx) print("畸变系数：") np.set_printoptions(precision=4, suppress=True) print(dist) # 打印外部参数 print("旋转向量：") np.set_printoptions(precision=4, suppress=True) print(rvecs) print("平移向量：") np.set_printoptions(precision=4, suppress=True) print(tvecs)这段代码如何将旋转向量和平移向量改为旋转矩阵和平移矩阵

在 OpenCV 中，可以使用 `cv2.Rodrigues()` 函数将旋转向量转换为旋转矩阵，使用 `np.array()` 函数将平移向量转换为平移矩阵。修改代码如下：

import cv2 
import numpy as np 

chessboard_size = (9,6) # 棋盘格行列数 
objp = np.zeros((np.prod(chessboard_size), 3), np.float32) # 内部点的坐标 
objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2) 

img = cv2.imread('C:/yingxiang/biaoding.png') 
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 

ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None) 
if ret == True: 
    cv2.drawChessboardCorners(img, chessboard_size, corners, ret) 

    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera([objp], [corners], gray.shape[::-1], None, None) 

    # 打印相机内部参数和畸变系数 
    print("相机内部参数：") 
    np.set_printoptions(precision=4, suppress=True) 
    print(mtx) 
    print("畸变系数：") 
    np.set_printoptions(precision=4, suppress=True) 
    print(dist) 

    # 将旋转向量转换为旋转矩阵 
    R, _ = cv2.Rodrigues(rvecs[0]) 

    # 打印旋转矩阵和平移矩阵 
    print("旋转矩阵：") 
    np.set_printoptions(precision=4, suppress=True) 
    print(R) 
    print("平移矩阵：") 
    np.set_printoptions(precision=4, suppress=True) 
    print(np.array(tvecs[0]).reshape(3,1))

这样，就将旋转向量和平移向量转换为了旋转矩阵和平移矩阵。

images= glob.glob('./image/*.jpg') # print(images) for fname in images: img = cv2.imread(fname) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Find the chess board corners # If desired number of corners are found in the image then ret = true ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE) """ If desired number of corner are detected, we refine the pixel coordinates and display them on the images of checker board """ if ret == True: objpoints.append(objp) # refining pixel coordinates for given 2d points. corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria) imgpoints.append(corners2) # Draw and display the corners img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret) # cv2.imshow('img',img) # cv2.waitKey(0) cv2.destroyAllWindows() # h, w = np.array(img).shape[:2] h, w = img.shape[:2] """ Performing camera calibration by passing the value of known 3D points (objpoints) and corresponding pixel coordinates of the detected corners (imgpoints) """ ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None) print("Camera matrix : \n") print(mtx) print("dist : \n") print(dist) print("rvecs : \n") print(rvecs) print("tvecs : \n") print(tvecs)这段代码有错误，请帮我找出来

这段代码存在一些错误，可能会导致出现你之前提到的错误。我列出了一些可能的问题：

1. 代码中没有定义CHECKERBOARD变量，需要在代码中加入如下语句定义CHECKERBOARD：

CHECKERBOARD = (6, 9)

2. 代码中没有定义objp变量，需要在代码中加入如下语句定义objp：

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)

3. 可能存在一些图像无法找到所有的角点，导致ret为False。可以在代码中加入如下语句，跳过这些无法找到所有角点的图像：

if ret == False:
continue

4. 在调用calibrateCamera函数时，输入的第三个参数应该是图像的大小，而不是灰度图像的大小。可以将代码中的gray.shape[::-1]修改为img.shape[:2]。

修改后的代码如下所示：

import numpy as np
import cv2
import glob

CHECKERBOARD = (6, 9)
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)

objpoints = []
imgpoints = []

images = glob.glob('./image/*.jpg')
for fname in images:
    img = cv2.imread(fname)
    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)

    if ret == True:
        objpoints.append(objp)

        corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
        imgpoints.append(corners2)

        img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)

    h, w = img.shape[:2]

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, (w, h), None, None)

print("Camera matrix : \n")
print(mtx)
print("dist : \n")
print(dist)
print("rvecs : \n")
print(rvecs)
print("tvecs : \n")
print(tvecs)

希望以上修改可以帮到你，如果还有其他问题，可以随时问我。