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67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
#!/usr/bin/python
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import urllib2
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import sys
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import cv2.cv as cv
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import numpy
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# SRGB-linear conversions using NumPy - see http://en.wikipedia.org/wiki/SRGB
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def srgb2lin(x):
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a = 0.055
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return numpy.where(x <= 0.04045,
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x * (1.0 / 12.92),
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numpy.power((x + a) * (1.0 / (1 + a)), 2.4))
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def lin2srgb(x):
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a = 0.055
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return numpy.where(x <= 0.0031308,
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x * 12.92,
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(1 + a) * numpy.power(x, 1 / 2.4) - a)
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if __name__ == "__main__":
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if len(sys.argv) > 1:
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img0 = cv.LoadImageM( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
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else:
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url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/lena.jpg'
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filedata = urllib2.urlopen(url).read()
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imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
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cv.SetData(imagefiledata, filedata, len(filedata))
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img0 = cv.DecodeImageM(imagefiledata, cv.CV_LOAD_IMAGE_COLOR)
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cv.NamedWindow("original", 1)
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cv.ShowImage("original", img0)
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# Image was originally bytes in range 0-255. Turn it into an array of floats in range 0.0 - 1.0
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n = numpy.asarray(img0) / 255.0
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# Use NumPy to do some transformations on the image
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# Negate the image by subtracting it from 1.0
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cv.NamedWindow("negative")
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cv.ShowImage("negative", cv.fromarray(1.0 - n))
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# Assume the image was sRGB, and compute the linear version.
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cv.NamedWindow("linear")
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cv.ShowImage("linear", cv.fromarray(srgb2lin(n)))
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# Look at a subwindow
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cv.NamedWindow("subwindow")
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cv.ShowImage("subwindow", cv.fromarray(n[200:300,200:400]))
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# Compute the grayscale image
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cv.NamedWindow("monochrome")
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ln = srgb2lin(n)
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red = ln[:,:,0]
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grn = ln[:,:,1]
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blu = ln[:,:,2]
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linear_mono = 0.3 * red + 0.59 * grn + 0.11 * blu
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cv.ShowImage("monochrome", cv.fromarray(lin2srgb(linear_mono)))
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# Apply a blur to the NumPy array using OpenCV
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cv.NamedWindow("gaussian")
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cv.Smooth(n, n, cv.CV_GAUSSIAN, 15, 15)
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cv.ShowImage("gaussian", cv.fromarray(n))
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cv.WaitKey(0)
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cv.DestroyAllWindows()
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