114 lines
2.9 KiB
ReStructuredText
114 lines
2.9 KiB
ReStructuredText
Introduction
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============
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Cookbook
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--------
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Here is a small collection of code fragments demonstrating some features
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of the OpenCV Python bindings.
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Convert an image from png to jpg
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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::
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import cv
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cv.SaveImage("foo.png", cv.LoadImage("foo.jpg"))
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Compute the Laplacian
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^^^^^^^^^^^^^^^^^^^^^
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::
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im = cv.LoadImage("foo.png", 1)
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dst = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 3);
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laplace = cv.Laplace(im, dst)
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cv.SaveImage("foo-laplace.png", dst)
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Using cvGoodFeaturesToTrack
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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::
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img = cv.LoadImage("foo.jpg")
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eig_image = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_32F, 1)
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temp_image = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_32F, 1)
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# Find up to 300 corners using Harris
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for (x,y) in cv.GoodFeaturesToTrack(img, eig_image, temp_image, 300, None, 1.0, use_harris = True):
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print "good feature at", x,y
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Using GetSubRect
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^^^^^^^^^^^^^^^^
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GetSubRect returns a rectangular part of another image. It does this without copying any data.
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::
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img = cv.LoadImage("foo.jpg")
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sub = cv.GetSubRect(img, (0, 0, 32, 32)) # sub is 32x32 patch from img top-left
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cv.SetZero(sub) # clear sub to zero, which also clears 32x32 pixels in img
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Using CreateMat, and accessing an element
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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::
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mat = cv.CreateMat(5, 5, cv.CV_32FC1)
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mat[3,2] += 0.787
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ROS image message to OpenCV
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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See this tutorial: http://www.ros.org/wiki/cv_bridge/Tutorials/UsingCvBridgeToConvertBetweenROSImagesAndOpenCVImages
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PIL Image to OpenCV
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^^^^^^^^^^^^^^^^^^^
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(For details on PIL see the `PIL manual <http://www.pythonware.com/library/pil/handbook/image.htm>`_).
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::
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import Image
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import cv
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pi = Image.open('foo.png') # PIL image
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cv_im = cv.CreateImageHeader(pi.size, cv.IPL_DEPTH_8U, 1)
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cv.SetData(cv_im, pi.tostring())
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OpenCV to PIL Image
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^^^^^^^^^^^^^^^^^^^
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::
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cv_im = cv.CreateImage((320,200), cv.IPL_DEPTH_8U, 1)
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pi = Image.fromstring("L", cv.GetSize(cv_im), cv_im.tostring())
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NumPy and OpenCV
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^^^^^^^^^^^^^^^^
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Using the `array interface <http://docs.scipy.org/doc/numpy/reference/arrays.interface.html>`_, to use an OpenCV CvMat in NumPy::
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import cv
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import numpy
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mat = cv.CreateMat(5, 5, cv.CV_32FC1)
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a = numpy.asarray(mat)
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and to use a NumPy array in OpenCV::
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a = numpy.ones((640, 480))
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mat = cv.fromarray(a)
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even easier, most OpenCV functions can work on NumPy arrays directly, for example::
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picture = numpy.ones((640, 480))
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cv.Smooth(picture, picture, cv.CV_GAUSSIAN, 15, 15)
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Given a 2D array,
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the fromarray function (or the implicit version shown above)
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returns a single-channel CvMat of the same size.
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For a 3D array of size :math:`j \times k \times l`, it returns a
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CvMat sized :math:`j \times k` with :math:`l` channels.
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Alternatively, use fromarray with the allowND option to always return a cvMatND.
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