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Open Source Computer Vision Library
https://opencv.org/
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113 lines
2.9 KiB
113 lines
2.9 KiB
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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