如何实现OpenCV中的透视变换进行图像几何调整？

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本例展示了如何利用OpenCV进行图像透视变换。以下为关键代码及内容摘要：

1. 基本原理及透视变换（Perspective Transformation）透视变换的本质是将图像投影到一个新的视平面，从而改变图像的视角。通常应用于以下场景：- 3D模型到2D图像的投影- 图像尺寸调整- 图像旋转、倾斜等

具体步骤如下：

pythonimport cv2import numpy as np

读取图像image=cv2.imread('image.jpg')

定义透视变换矩阵src_points=np.float32([[x1, y1], [x2, y2], [x3, y3], [x4, y4]])dst_points=np.float32([[x1', y1'], [x2', y2'], [x3', y3'], [x4', y4']])

计算透视变换矩阵M=cv2.getPerspectiveTransform(src_points, dst_points)

应用透视变换transformed_image=cv2.warpPerspective(image, M, (width, height))

显示结果cv2.imshow('Transformed Image', transformed_image)cv2.waitKey(0)cv2.destroyAllWindows()

2. 大家参考- OpenCV官方文档：https://docs.opencv.org/- Python OpenCV教程：https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_image_processing/py_image_transforms/py_image_transforms.- 透视变换原理及实现：https://www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspectivetransform-example/

本文实例为大家分享了OpenCV图像几何变换之透视变换的具体代码，供大家参考，具体内容如下

1. 基本原理

透视变换（Perspective Transformation）的本质是将图像投影到一个新的视平面，其通用变换公式为：

（u，v）为原始图像像素坐标，（x=x'/w'，y=y'/w'）为变换之后的图像像素坐标。透视变换矩阵图解如下：

仿射变换（Affine Transformation）可以理解为透视变换的特殊形式。透视变换的数学表达式为：

所以，给定透视变换对应的四对像素点坐标，即可求得透视变换矩阵；反之，给定透视变换矩阵，即可对图像或像素点坐标完成透视变换，如下图所示：

2. OpenCV透视变换函数

Mat getPerspectiveTransform(const Point2f* src, const Point2f* dst) // Calculate a perspective transform from four pairs of the corresponding points. // src – Coordinates of quadrangle vertices in the source image. // dst – Coordinates of the corresponding quadrangle vertices in the destination image. void warpPerspective(InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar()) // Apply a perspective transform to an image. // src – Source image. // dst – Destination image that has the size dsize and the same type as src. // M – 3*3 transformation matrix. // dsize – Size of the destination image. // flags – Combination of interpolation methods and the optional flag WARP_INVERSE_MAP that means that M is the inverse transformation (dstsrc). // borderMode – Pixel extrapolation method. When borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image that corresponds to the “outliers” in the source image are not modified by the function. // borderValue – Value used in case of a constant border. By default, it is 0.

3. 程序

#include <iostream> #include "highgui.h" #include "opencv2/imgproc/imgproc.hpp" int main() { // get original image. cv::Mat originalImage = cv::imread("road.png"); // perspective image. cv::Mat perspectiveImage; // perspective transform cv::Point2f objectivePoints[4], imagePoints[4]; // original image points. imagePoints[0].x = 10.0; imagePoints[0].y = 457.0; imagePoints[1].x = 395.0; imagePoints[1].y = 291.0; imagePoints[2].x = 624.0; imagePoints[2].y = 291.0; imagePoints[3].x = 1000.0; imagePoints[3].y = 457.0; // objective points of perspective image. // move up the perspective image : objectivePoints.y - value . // move left the perspective image : objectivePoints.x - value. double moveValueX = 0.0; double moveValueY = 0.0; objectivePoints[0].x = 46.0 + moveValueX; objectivePoints[0].y = 920.0 + moveValueY; objectivePoints[1].x = 46.0 + moveValueX; objectivePoints[1].y = 100.0 + moveValueY; objectivePoints[2].x = 600.0 + moveValueX; objectivePoints[2].y = 100.0 + moveValueY; objectivePoints[3].x = 600.0 + moveValueX; objectivePoints[3].y = 920.0 + moveValueY; cv::Mat transform = cv::getPerspectiveTransform(objectivePoints, imagePoints); // perspective. cv::warpPerspective(originalImage, perspectiveImage, transform, cv::Size(originalImage.rows, originalImage.cols), cv::INTER_LINEAR | cv::WARP_INVERSE_MAP); // cv::imshow("perspective image", perspectiveImage); // cvWaitKey(0); cv::imwrite("perspectiveImage.png", perspectiveImage); return 0; }

原始图像及其透视变换结果：

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