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Color Space Transformations
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RGB to YcrCb conversion
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OpenCV provides methods to convert between the most common color spaces. In this section only the conversion from RGB to YCrCb is demonstrated.

The following packages must be imported::

	import cv2
	import numpy as np
	from matplotlib import pyplot as plt

A RGB color image is imported. The returned numpy-array is 3-dimensional, i.e. one 2-dimensional array per color channel::

	fn3= 'tria.jpg'
	img1 = cv2.imread(fn3, cv2.CV_LOAD_IMAGE_UNCHANGED)

Note that in OpenCV RGB images are represented as BGR. Thus the first component *img[:,:,0]* is the blue channel and the last component *img[:,:,2]* is the red channel.
In order to display the color image using Pyplot's *imshow()* method, a copy of image *img1* is generated, which contains the color channels in the usual order RGB.::

	img2=np.zeros(img1.shape,np.uint8)
	img2[:,:,0]=img1[:,:,2]
	img2[:,:,1]=img1[:,:,1]
	img2[:,:,2]=img1[:,:,0]
	plt.imshow(img2)
	
.. figure:: tria.png
   :scale: 60 %
   :align: center
   
Next the BGR image is converted into a YCrCb image::

	transcol=cv2.cvtColor(img1, cv2.cv.CV_BGR2YCrCb)

The RGB channels and the YCrCb channels are plotted in a single pyplot figure as intensity images.::

	plt.figure()
	plt.subplot(2,3,1)
	plt.imshow(img1[:,:,2],cmap="gray")
	plt.title('Red')
	plt.subplot(2,3,2)
	plt.imshow(img1[:,:,1],cmap="gray")
	plt.title('Green')
	plt.subplot(2,3,3)
	plt.imshow(img1[:,:,0],cmap="gray")
	plt.title('Blue')

	plt.subplot(2,3,4)
	plt.imshow(transcol[:,:,0],cmap="gray")
	plt.title('Luminance Y')
	plt.subplot(2,3,5)
	plt.imshow(transcol[:,:,1],cmap="gray")
	plt.title('Chrominance Cr')
	plt.subplot(2,3,6)
	plt.imshow(transcol[:,:,2],cmap="gray")
	plt.title('Chrominance Cb')
	plt.show()

.. figure:: triaRGBYCrCb.png
   :scale: 60 %
   :align: center
   :alt: x
   
Subsampling of Chrominance Channels
====================================

The YCrCb color space is used, because the human eyes are less sensitive to chrominance changes than to luminance changes. Hence the chrominance channels can be subsampled in a lossy compression procedure. If the following code snippet is attached to the code described above, the chrominance channels are first filtered with a box filter of size *(2,2)*. The filtered subchannels are then subsampled by a factor of 2 in both, the vertical and the horicontal direction. This yields a compression of the entire image (all 3 channels) by a factor of 2.:: 

	crf=cv2.boxFilter(transcol[:,:,1],ddepth=-1,ksize=(2,2))
	cbf=cv2.boxFilter(transcol[:,:,2],ddepth=-1,ksize=(2,2))
	print type(crf[0,0])
	SSV=2
	SSH=2
	crsub=crf[::SSV,::SSH]
	cbsub=cbf[::SSV,::SSH]

The subsampled channels are plotted as follows::

	plt.figure()
	plt.subplot(1,3,1)
	plt.imshow(transcol[:,:,0],cmap="gray")
	plt.title('Luminance Y')
	plt.subplot(1,3,2)
	plt.imshow(crsub,cmap="gray")
	plt.title('Subsampled Chrominance Cr')
	plt.subplot(1,3,3)
	plt.imshow(cbsub,cmap="gray")
	plt.title('Subsampled Chrominance Cb')
	plt.show()

.. figure:: triasubsampled.png
   :scale: 60 %
   :align: center
   :alt: x