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	<title>OpenCV Tutorials</title>
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	<link>https://talkera.org/opencv</link>
	<description>OpenCV Tutorials for C++ and Python</description>
	<lastBuildDate>Sat, 10 Jan 2015 19:38:55 +0000</lastBuildDate>
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		<title>Perspective transform with OpenCV</title>
		<link>https://talkera.org/opencv/perspective-transform-with-opencv/</link>
		<comments>https://talkera.org/opencv/perspective-transform-with-opencv/#comments</comments>
		<pubDate>Sat, 10 Jan 2015 19:38:55 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[OpenCV]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[perspective]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=76</guid>
		<description><![CDATA[We can change perspective in OpenCV using the functions cv2.getPerspectiveTransform(pts1,pts2) and cv2.warpPerspective(img,M,(640,480)). The cv2.getPerspectiveTransform() function calculates a perspective transform from four pairs of the corresponding points. The points pts1 and pts2 are two rectangles, where on is the source and the other the destination.  Putting it together you get something like this: [crayon-54b857ecb817d125769768/] Which you [&#8230;]]]></description>
		<wfw:commentRss>https://talkera.org/opencv/perspective-transform-with-opencv/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
<enclosure url="https://archive.org/download/bliptv-20131014-160024-Brigittedale-409WinkyHaHa665/bliptv-20131014-160024-Brigittedale-409WinkyHaHa665.mp4" length="0" type="video/mp4" />
		</item>
		<item>
		<title>Mixing OpenGL and OpenCV</title>
		<link>https://talkera.org/opencv/mixing-opengl-and-opencv/</link>
		<comments>https://talkera.org/opencv/mixing-opengl-and-opencv/#comments</comments>
		<pubDate>Sat, 10 Jan 2015 00:59:53 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[OpenCV]]></category>
		<category><![CDATA[animation]]></category>
		<category><![CDATA[augmented reality]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[opengl]]></category>
		<category><![CDATA[video]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=72</guid>
		<description><![CDATA[In this example we mix OpenGL (a computer graphics library) and OpenCV. We will play a video on a 3d rotating cube. (Yes, it will be animated!) We mix the code for video playing with code to render a 3d cube with PyOpenGL. If you do not have PyOpenGL installed install that first: [crayon-54b857ecb8d7f177808630/] If [&#8230;]]]></description>
		<wfw:commentRss>https://talkera.org/opencv/mixing-opengl-and-opencv/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
<enclosure url="https://archive.org/download/bliptv-20131014-102441-Brigittedale-WhatToTalkAbout167/bliptv-20131014-102441-Brigittedale-WhatToTalkAbout167.mp4" length="0" type="video/mp4" />
		</item>
		<item>
		<title>OpenCV play video from the web</title>
		<link>https://talkera.org/opencv/opencv-play-video-from-the-web/</link>
		<comments>https://talkera.org/opencv/opencv-play-video-from-the-web/#comments</comments>
		<pubDate>Fri, 09 Jan 2015 23:48:12 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[OpenCV]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[video]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=62</guid>
		<description><![CDATA[We can play video files directly from the worldwide web with OpenCV using the cv2.VideoCapture() function. OpenCV does not have default sound support as it is a computer vision library.  You can manipulate the frames directly from the stream, but if the frame manipulation is too computationally expensive the video will lag. [crayon-54b857ecb9248908583023/]]]></description>
		<wfw:commentRss>https://talkera.org/opencv/opencv-play-video-from-the-web/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
<enclosure url="https://archive.org/download/bliptv-20131014-102441-Brigittedale-WhatToTalkAbout167/bliptv-20131014-102441-Brigittedale-WhatToTalkAbout167.mp4" length="0" type="video/mp4" />
		</item>
		<item>
		<title>Image similarity with keypoints in Python</title>
		<link>https://talkera.org/opencv/image-similarity-with-keypoints-in-python/</link>
		<comments>https://talkera.org/opencv/image-similarity-with-keypoints-in-python/#comments</comments>
		<pubDate>Fri, 09 Jan 2015 01:08:30 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[detection]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[similarity]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=55</guid>
		<description><![CDATA[Other than template matching, there are other algorithms to find objects in images.  A group of algorithms based on keypoints is an example of that. Every keypoint must be a unique point in terms of contrast, edges etc.  Once these keypoints  are extracted the program can match them with keypoints of other images. Let us [&#8230;]]]></description>
		<wfw:commentRss>https://talkera.org/opencv/image-similarity-with-keypoints-in-python/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Image effects with Python</title>
		<link>https://talkera.org/opencv/image-effects-with-python/</link>
		<comments>https://talkera.org/opencv/image-effects-with-python/#comments</comments>
		<pubDate>Fri, 09 Jan 2015 00:25:31 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[OpenCV]]></category>
		<category><![CDATA[guassian]]></category>
		<category><![CDATA[laplacian]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[sobel]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=50</guid>
		<description><![CDATA[Image effects are created by applying a convolution matrix (also known as kernel) to the image. This is a small matrix that slides through the original image and does a mathematical operation on the pixels for each position of the sliding window. Depending on the convolution matrix you get a different image effect. OpenCV has [&#8230;]]]></description>
		<wfw:commentRss>https://talkera.org/opencv/image-effects-with-python/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Object detection using Template Matching</title>
		<link>https://talkera.org/opencv/object-detection-using-template-matching/</link>
		<comments>https://talkera.org/opencv/object-detection-using-template-matching/#comments</comments>
		<pubDate>Fri, 09 Jan 2015 00:04:09 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[object detection]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[template]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=40</guid>
		<description><![CDATA[We can use a technique called template matching to find a template  of size in an input image.  We use the function cv2.matchTemplate() which slides through the image and compares the overlapped patches of size against the input image using a statistical comparison method. The template image can be anywhere inside the input image but it [&#8230;]]]></description>
		<wfw:commentRss>https://talkera.org/opencv/object-detection-using-template-matching/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>OpenCV Face Detector</title>
		<link>https://talkera.org/opencv/opencv-face-detector/</link>
		<comments>https://talkera.org/opencv/opencv-face-detector/#comments</comments>
		<pubDate>Wed, 07 Jan 2015 14:04:24 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[OpenCV]]></category>
		<category><![CDATA[face]]></category>
		<category><![CDATA[opencv]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=33</guid>
		<description><![CDATA[Face detection can be achieved using Haar wavelets. The code below detects a face from an image using haar wavelets: Python code OpenCV Face detection [crayon-54b857ecba0ff825611658/] Output:]]></description>
		<wfw:commentRss>https://talkera.org/opencv/opencv-face-detector/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>OpenCV Keypoint Detector</title>
		<link>https://talkera.org/opencv/opencv-keypoint-detector/</link>
		<comments>https://talkera.org/opencv/opencv-keypoint-detector/#comments</comments>
		<pubDate>Wed, 07 Jan 2015 13:47:24 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[OpenCV]]></category>
		<category><![CDATA[keypoint]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[SURF]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=30</guid>
		<description><![CDATA[Keypoints are used for a variety of reasons, mainly object detection and classification. There are all kinds of keypoint detectoin algorithms inside OpenCV. Once these keypoints are detected, they can be matched to find similarity between images. Keypoints can be invariant to rotation, scale, translation amongst others, depending on the algorithm. OpenCV Keypoint detector and [&#8230;]]]></description>
		<wfw:commentRss>https://talkera.org/opencv/opencv-keypoint-detector/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>OpenCV Pixel Access Manipulation</title>
		<link>https://talkera.org/opencv/opencv-pixel-access-manipulation/</link>
		<comments>https://talkera.org/opencv/opencv-pixel-access-manipulation/#comments</comments>
		<pubDate>Wed, 07 Jan 2015 13:28:18 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[OpenCV]]></category>
		<category><![CDATA[Mat]]></category>
		<category><![CDATA[opencv]]></category>
		<category><![CDATA[pixels]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=26</guid>
		<description><![CDATA[To manipulate pixels of an OpenCV Image (Mat) , use the C++ code below: [crayon-54b857ecba82a382881872/] Output: Accessing pixels in Python is more easy: [crayon-54b857ecba837618217560/]]]></description>
		<wfw:commentRss>https://talkera.org/opencv/opencv-pixel-access-manipulation/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Histogram with OpenCV</title>
		<link>https://talkera.org/opencv/histogram-with-opencv/</link>
		<comments>https://talkera.org/opencv/histogram-with-opencv/#comments</comments>
		<pubDate>Wed, 07 Jan 2015 13:19:51 +0000</pubDate>
		<dc:creator><![CDATA[admin]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[histogram]]></category>
		<category><![CDATA[opencv]]></category>

		<guid isPermaLink="false">https://talkera.org/opencv/?p=22</guid>
		<description><![CDATA[Generating a histogram in OpenCV is very straightforward. C++ Generate histogram opencv [crayon-54b857ecbac36346829839/]  Draw histogram in Python: [crayon-54b857ecbac43860522063/]  Output:]]></description>
		<wfw:commentRss>https://talkera.org/opencv/histogram-with-opencv/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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