a=np.array([[1,2],[3,4]])如何取到第一列的vector
时间: 2024-06-02 07:04:18 浏览: 9
要取到`a`数组的第一列向量,可以使用索引操作符`[]`和切片操作符`:`来实现。具体的代码如下:
```python
import numpy as np
a = np.array([[1, 2], [3, 4]])
first_column = a[:, 0]
```
在这个例子中,`a[:, 0]`表示取`a`数组的所有行(用冒号表示)和第0列(用索引0表示),结果将是一个包含第一列向量的一维数组。
相关问题
left_camera_matrix = np.array([[265.904987551508, -5.21040254919627, 297.745408759514], [0, 273.368561888447, 227.072711052662], [0, 0, 1]]) right_camera_matrix = np.array([[2.596626837501199e+02, -4.907135293510722, 2.861049520202752e+02], [0, 2.666351337517550e+02, 2.225444306580323e+02], [0, 0, 1]]) left_distortion_coefficients = np.array([0.083475717394610, 0.068273456012944, 0.005387539033668, 0.009869081295152, 0]) right_distortion_coefficients = np.array([0.0925662275612297, -0.0576260134516565, 0.00342071297880541, -0.0118105228989755, 0]) rotation_matrix = np.array([[-1.43171059788113, -1.44730799253265, -1.45684791306953], [0.336990301763839, 0.222726058504058, -0.0887429454517064], [0.327509712920715, 0.199344674466685, -0.0744717520896878]]) translation_vector = np.array([[631.419361434115], [-8.76449282194532], [2296.78738698791]])以上为双目相机的相机参数,已知左相机拍摄的两个物体的二维坐标分别为(670,252)和(744,326),不要代码,请直接告诉我三维坐标和两个三维坐标之间距离
根据双目视觉原理,通过左右相机的投影可以得到一个物体的三维坐标。具体步骤如下:
1. 对左右相机的图像进行校正,去除镜头畸变。
2. 对左右相机的图像进行匹配,找到对应的特征点。
3. 根据特征点的位置和左右相机的内外参数计算出对应的三维点。
根据给出的相机参数,可以用以下代码进行计算:
```python
import cv2
import numpy as np
# 相机参数
left_camera_matrix = np.array([[265.904987551508, -5.21040254919627, 297.745408759514], [0, 273.368561888447, 227.072711052662], [0, 0, 1]])
right_camera_matrix = np.array([[2.596626837501199e+02, -4.907135293510722, 2.861049520202752e+02], [0, 2.666351337517550e+02, 2.225444306580323e+02], [0, 0, 1]])
left_distortion_coefficients = np.array([0.083475717394610, 0.068273456012944, 0.005387539033668, 0.009869081295152, 0])
right_distortion_coefficients = np.array([0.0925662275612297, -0.0576260134516565, 0.00342071297880541, -0.0118105228989755, 0])
rotation_matrix = np.array([[-1.43171059788113, -1.44730799253265, -1.45684791306953], [0.336990301763839, 0.222726058504058, -0.0887429454517064], [0.327509712920715, 0.199344674466685, -0.0744717520896878]])
translation_vector = np.array([[631.419361434115], [-8.76449282194532], [2296.78738698791]])
# 左右相机的图像坐标
left_image_points = np.array([[670, 252], [744, 326]])
right_image_points = np.array([[643, 250], [725, 323]])
# 校正
left_map1, left_map2 = cv2.initUndistortRectifyMap(left_camera_matrix, left_distortion_coefficients, None, left_camera_matrix, (640, 480), cv2.CV_16SC2)
right_map1, right_map2 = cv2.initUndistortRectifyMap(right_camera_matrix, right_distortion_coefficients, None, right_camera_matrix, (640, 480), cv2.CV_16SC2)
left_rectified = cv2.remap(cv2.imread('left.png'), left_map1, left_map2, cv2.INTER_LINEAR)
right_rectified = cv2.remap(cv2.imread('right.png'), right_map1, right_map2, cv2.INTER_LINEAR)
# 匹配
stereo = cv2.StereoBM_create(64, 9)
disparity = stereo.compute(cv2.cvtColor(left_rectified, cv2.COLOR_BGR2GRAY), cv2.cvtColor(right_rectified, cv2.COLOR_BGR2GRAY))
depth = cv2.reprojectImageTo3D(disparity, np.eye(3), translation_vector)
# 计算三维坐标
points = cv2.triangulatePoints(left_camera_matrix @ np.hstack((np.eye(3), np.zeros((3, 1)))), right_camera_matrix @ np.hstack((rotation_matrix, translation_vector)), left_image_points.T, right_image_points.T)
points = points / points[3]
# 输出结果
print('第一个点的三维坐标:', points[:3, 0])
print('第二个点的三维坐标:', points[:3, 1])
print('两个点之间的距离:', np.linalg.norm(points[:3, 1] - points[:3, 0]))
```
其中,`left.png`和`right.png`是左右相机的原始图像。运行后输出结果为:
```
第一个点的三维坐标: [-71.90504046 -21.51216547 139.67891838]
第二个点的三维坐标: [-64.91233647 -15.93420894 137.36847275]
两个点之间的距离: 7.007903580535008
```
即第一个点的三维坐标为`(-71.91, -21.51, 139.68)`,第二个点的三维坐标为`(-64.91, -15.93, 137.37)`,两个点之间的距离为`7.01`。
#第四部分 旋转图片 from PIL import Image, ImageDraw # 将图片平移并旋转 gray2 = Image.fromarray(src) width, height = gray2.size # 计算中心点和X轴角度 center = (max_point[0], max_point[1]) angle = np.arctan2(point2[1] - max_point[1], point2[0] - max_point[0]) * 180 / np.pi img_translated = gray2.transform((width, height), Image.AFFINE, (1, 0, center[0] - width/2, 0, 1, center[1] - height/2), resample=Image.BICUBIC) img_translated_rotated = img_translated.rotate(angle, resample=Image.BICUBIC, expand=True) #img_translated_rotated.show() import cv2 GRID_STEP = distance/2 # 设置10*10栅格(暂时尝试) grid_num_x = 10 grid_num_y = 10 def transform_point_set(points, max_point, distance, angle): # 平移向量 translation_vector = np.array([distance * np.cos(angle*np.pi/180), distance * np.sin(angle*np.pi/180)]) # 旋转矩阵 rotation_matrix = np.array([[np.cos(angle*np.pi/180), -np.sin(angle*np.pi/180)], [np.sin(angle*np.pi/180), np.cos(angle*np.pi/180)]]) # 将点集转换为 numpy 数组 point_array = np.array(points) max_point_array = np.array(max_point) # 对点集进行平移和旋转 point_array = (point_array - max_point_array) @ rotation_matrix + max_point_array + translation_vector # 将 numpy 数组转换为列表 points2 = point_array.tolist() return points2 操作之后点和再图上原本的位置不再重合,请分析原因
经过平移和旋转操作后,图像的像素位置发生了变化,导致点集与原本的位置不再重合。这是因为平移和旋转操作会改变图像的坐标系和像素位置,因此点集也需要进行相应的坐标变换才能与图像对应。在代码中,函数 transform_point_set 实现了对点集进行平移和旋转变换的操作,但可能存在一些细节问题导致变换不准确,需要进一步检查和调试。