python opencv 进行简单几何变换

16-python opencv 进行简单几何变换

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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 cv2import numpy as np# read the originalimg = 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。

# expandrows, cols, channels = img.shapeimg_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。

# zoomimg_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：

[1001txty](4)

其中

$tx

和

ty

$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。

# transM = 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。

# RotationM=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 cv2import numpy as np# read the originalimg = cv2.imread('../test2.jpg')cv2.imshow('original', img)# expandrows, cols, channels = img.shapeimg_ex = cv2.resize(img, (2*cols, 2*rows), interpolation=cv2.INTER_CUBIC)cv2.imshow('expand', img_ex)# zoomimg_zo = cv2.resize(img, (cols/2, rows/2), interpolation=cv2.INTER_AREA)cv2.imshow('zoom', img_zo)# transM = np.array([[1, 0, 50],[0, 1, 50]], np.float32)img_tr =cv2.warpAffine(img, M, img.shape[:2])cv2.imshow('trans', img_tr)# RotationM=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 windowscv2.waitKey(0)cv2.destroyAllWindows()

也可以参考我的GitHub上的，点击这里。

运行结果

在命令行进入该源程序所在目录后，运行python main.py后即可显示结果。显示结果如下：

参考

• http://docs.opencv.org/2.4/modules/imgproc/doc/geometric_transformations.html
• OpenCV-Python-Toturial-中文版.pdf
• https://github.com/hlthu/Python/tree/master/Python_OpenCV/Geometric_Trans/
• http://hlthu.github.io/blogs/PythonOpenCV/16-Geometric_Trans.html