python opencv 进行简单几何变换

最新推荐文章于 2025-04-26 19:52:15 发布

原创最新推荐文章于 2025-04-26 19:52:15 发布 · 1.7w 阅读

11 ·

CC 4.0 BY-SA版权

文章标签：

#opencv #python

Py-OpenCV 专栏收录该内容

16 篇文章

订阅专栏

本文介绍如何使用Python结合OpenCV库实现图像的放大、缩小、平移及旋转等基本几何变换。通过具体实例演示了每种变换的操作步骤及所需函数。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

16-python opencv 进行简单几何变换

16-python opencv 进行简单几何变换

概述

本节实现的是使用OpenCV里自带的函数，对图像进行简单的几何变换。

放大
缩小
平移
旋转

实现过程

读取原图并显示

不再赘述。

import cv2
import numpy as np

# read the original
img = cv2.imread('../test2.jpg')
cv2.imshow('original', img)

放大

利用OpenCV自带的resize()函数实现放大与缩小。其声明为：

cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) → dst

其中各个参数的意义如下：

src – input image.
dst – output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize –output image size; if it equals zero, it is computed as:
dsize = Size(round(fx × src.cols), round(fy × src.rows))
fx –scale factor along the horizontal axis; when it equals 0, it is computed as
fy –scale factor along the vertical axis; when it equals 0, it is computed as
interpolation –interpolation method:

参数	意义
INTER_NEAREST	a nearest-neighbor interpolation
INTER_LINEAR	a bilinear interpolation (used by default)
INTER_AREA	resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method.
INTER_CUBIC	a bicubic interpolation over 4x4 pixel neighborhood
INTER_LANCZOS4	a Lanczos interpolation over 8x8 pixel neighborhood

本文将原图放大至原来的2倍。

# expand
rows, cols, channels = img.shape
img_ex = cv2.resize(img, (2*cols, 2*rows), interpolation=cv2.INTER_CUBIC)
cv2.imshow('expand', img_ex)

缩小

这里将原图缩小为原来的一半。

# zoom
img_zo = cv2.resize(img, (cols/2, rows/2), interpolation=cv2.INTER_AREA)
cv2.imshow('zoom', img_zo)

平移

平移可以由平移矩阵描述：

[1001 t x t y] (4)

$\begin{bmatrix} 1 & 0 & t_x \\ 0 & 1 & t_y \end{bmatrix} \tag{4}$

其中

$ t x

$$t_x$

和和 $和$

t_{y}

$t_y$ $分别为向右和向下平移的距离。这里我们利用np.array()创建这个矩阵，然后调用warpAffine来实现这个变换，并保持图像的大小不变。

# trans
M = np.array([[1, 0, 50],[0, 1, 50]], np.float32)
img_tr =cv2.warpAffine(img, M, img.shape[:2])
cv2.imshow('trans', img_tr)

其中warpAffine()的声明如下：

cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) → dst

各个参数的意义如下：

src – input image.
dst – output image that has the size dsize and the same type as src .
M – 2 × 3 transformation matrix.
dsize – size of the output image.
flags – combination of interpolation methods (see resize() ) and the optional flag WARP_INVERSE_MAP that means that M is the inverse transformation.
borderMode – pixel extrapolation method (see borderInterpolate()); when borderMode=BORDER_TRANSPARENT , it means that the pixels in the destination image corresponding 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.

旋转

利用getRotationMatrix2D()获得旋转矩阵，其声明为

cv2.getRotationMatrix2D(center, angle, scale) → retval

各个参数的意义：

center – Center of the rotation in the source image.
angle – Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner).
scale – Isotropic scale factor.
retval – The output affine transformation, 2x3 floating-point matrix.

然后再利用warpAffine()函数进行变换。

# Rotation
M=cv2.getRotationMatrix2D((cols/2,rows/2), 45, 1)
img_ro =cv2.warpAffine(img, M, img.shape[:2])
cv2.imshow('rotation', img_ro)

源代码

程序的源代码如下：

# created by Huang Lu
# 2016/8/26 17:35
# Department of EE, Tsinghua Univ.

import cv2
import numpy as np

# read the original
img = cv2.imread('../test2.jpg')
cv2.imshow('original', img)

# expand
rows, cols, channels = img.shape
img_ex = cv2.resize(img, (2*cols, 2*rows), interpolation=cv2.INTER_CUBIC)
cv2.imshow('expand', img_ex)

# zoom
img_zo = cv2.resize(img, (cols/2, rows/2), interpolation=cv2.INTER_AREA)
cv2.imshow('zoom', img_zo)

# trans
M = np.array([[1, 0, 50],[0, 1, 50]], np.float32)
img_tr =cv2.warpAffine(img, M, img.shape[:2])
cv2.imshow('trans', img_tr)

# Rotation
M=cv2.getRotationMatrix2D((cols/2,rows/2), 45, 1)
img_ro =cv2.warpAffine(img, M, img.shape[:2])
cv2.imshow('rotation', img_ro)

# wait the key and close windows
cv2.waitKey(0)
cv2.destroyAllWindows()