Introduction

Cookbook

Here is a small collection of code fragments demonstrating some features of the OpenCV Python bindings.

Convert an image from png to jpg

import cv
cv.SaveImage("foo.png", cv.LoadImage("foo.jpg"))

Compute the Laplacian

im = cv.LoadImage("foo.png", 1)
dst = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 3);
laplace = cv.Laplace(im, dst)
cv.SaveImage("foo-laplace.png", dst)

Using cvGoodFeaturesToTrack

img = cv.LoadImage("foo.jpg")
eig_image = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_32F, 1)
temp_image = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_32F, 1)
# Find up to 300 corners using Harris
for (x,y) in cv.GoodFeaturesToTrack(img, eig_image, temp_image, 300, None, 1.0, use_harris = True):
    print "good feature at", x,y

Using GetSubRect

GetSubRect returns a rectangular part of another image. It does this without copying any data.

img = cv.LoadImage("foo.jpg")
sub = cv.GetSubRect(img, (0, 0, 32, 32))  # sub is 32x32 patch from img top-left
cv.SetZero(sub)                           # clear sub to zero, which also clears 32x32 pixels in img

Using CreateMat, and accessing an element

mat = cv.CreateMat(5, 5, cv.CV_32FC1)
mat[3,2] += 0.787

ROS image message to OpenCV

See this tutorial: http://www.ros.org/wiki/cv_bridge/Tutorials/UsingCvBridgeToConvertBetweenROSImagesAndOpenCVImages

PIL Image to OpenCV

(For details on PIL see the PIL manual).

import Image
import cv
pi = Image.open('foo.png')       # PIL image
cv_im = cv.CreateImageHeader(pi.size, cv.IPL_DEPTH_8U, 1)
cv.SetData(cv_im, pi.tostring())

OpenCV to PIL Image

cv_im = cv.CreateImage((320,200), cv.IPL_DEPTH_8U, 1)
pi = Image.fromstring("L", cv.GetSize(cv_im), cv_im.tostring())

NumPy and OpenCV

Using the array interface, to use an OpenCV CvMat in NumPy:

import cv
import numpy
mat = cv.CreateMat(5, 5, cv.CV_32FC1)
a = numpy.asarray(mat)

and to use a NumPy array in OpenCV:

a = numpy.ones((640, 480))
mat = cv.fromarray(a)

even easier, most OpenCV functions can work on NumPy arrays directly, for example:

picture = numpy.ones((640, 480))
cv.Smooth(picture, picture, cv.CV_GAUSSIAN, 15, 15)

Given a 2D array, the fromarray function (or the implicit version shown above) returns a single-channel CvMat of the same size. For a 3D array of size , it returns a CvMat sized with channels.

Alternatively, use fromarray with the allowND option to always return a cvMatND.