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 :math:`j \times k \times l`, it returns a CvMat sized :math:`j \times k` with :math:`l` channels. Alternatively, use fromarray with the allowND option to always return a cvMatND.