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	<title>opencv - 四号程序员</title>
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		<title>用opencv的背景差分做运动检测</title>
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		<pubDate>Fri, 19 Jan 2024 08:58:10 +0000</pubDate>
				<category><![CDATA[opencv]]></category>
		<category><![CDATA[背景差分]]></category>
		<category><![CDATA[运动检测]]></category>
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					<description><![CDATA[目前，许多运动检测技术都是基于简单的背景差分概念的，即假设摄像头（视频）的曝光和场景中的光照条件是稳定的，当摄像头捕捉到新的帧时，我们可以从参考图像中减去该帧，并取这个差的绝对值，以获得帧中每个像素位置的运动测量值。如果帧的任何区域与参考图像有很大的不同，我们就认为给定区域中是一个运动物体。 常用的有MOG2： fgbg = cv2.createBackgroundSubtractorMOG2( detectShadows=False, # disable shadow det[......] 继续阅读]]></description>
		
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		<title>opencv学习笔记</title>
		<link>https://www.coder4.com/archives/7895</link>
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		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Fri, 01 Sep 2023 02:08:33 +0000</pubDate>
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					<description><![CDATA[1 图片读取与通道 图片读取后，默认是个numpy的3维数组(row行数是height, col高度是图片宽度，3是BGR通道) import cv2 img1 = cv2.imread('./dog_backpack.png') img1.shape (1401, 934, 3) 注意上面通道顺序是BGR哦，反人类吧，需要做转换，才能正常显示图片，如下： img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB) import matplo[......] 继续阅读]]></description>
		
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		<title>用opencv检查是否为白纸</title>
		<link>https://www.coder4.com/archives/7852</link>
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		<pubDate>Thu, 27 Apr 2023 09:47:24 +0000</pubDate>
				<category><![CDATA[opencv]]></category>
		<category><![CDATA[白纸]]></category>
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					<description><![CDATA[def check_blank_image(file): img = cv2.imread(file) if img is None: return False # grary img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # threshold to two value image ret,thresh = cv2.threshold(img, 150, 255,cv2.THRESH_BINARY)[......] 继续阅读]]></description>
		
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		<title>opencv拼接图片和切分图片</title>
		<link>https://www.coder4.com/archives/7850</link>
					<comments>https://www.coder4.com/archives/7850#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Mon, 24 Apr 2023 01:32:09 +0000</pubDate>
				<category><![CDATA[opencv]]></category>
		<category><![CDATA[切分]]></category>
		<category><![CDATA[图片]]></category>
		<category><![CDATA[拼接]]></category>
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					<description><![CDATA[1 左右拼接(height要求一样) import cv2 from google.colab.patches import cv2_imshow img_left = cv2.imread("./left.jpg") img_right = cv2.imread("./right.jpg") img = cv2.hconcat([img_left, img_right]) cv2.imwrite("merge.jpg", img) 2 垂直拼接(width要求一样） i[......] 继续阅读]]></description>
		
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