0x00. 使用 Canny 算法边缘识别
Canny 算法是一种多级边缘识别算法。
Canny边缘识别算法可以分为以下5个步骤:
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应用高斯滤波来平滑图像,目的是去除噪声。
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找寻图像的强度梯度(intensity gradients)。
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应用非最大抑制(non-maximum suppression)技术来消除边误检(本来不是但检测出来是)。
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应用双阈值的方法来决定可能的(潜在的)边界。
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利用滞后技术来跟踪边界。
具体原理性质的东西可以参考这里
读取本地视频处理代码示例:
12345678910111213141516171819202122 | import cv2.cv as cv capture = cv.CaptureFromFile(\’img/myvideo.avi\’) nbFrames = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_COUNT))fps = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FPS)wait = int(1/fps * 1000/1) dst = cv.CreateImage((int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH)), int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT))), 8, 1) for f in xrange( nbFrames ): frame = cv.QueryFrame(capture) cv.CvtColor(frame, dst, cv.CV_BGR2GRAY) cv.Canny(dst, dst, 125, 350) cv.Threshold(dst, dst, 128, 255, cv.CV_THRESH_BINARY_INV) cv.ShowImage(\”The Video\”, frame) cv.ShowImage(\”The Dst\”, dst) cv.WaitKey(wait) |
直接处理摄像头视频:
12345678910111213141516171819 | import cv2.cv as cv capture = cv.CaptureFromCAM(0) dst = cv.CreateImage((int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH)), int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT))), 8, 1) while True: frame = cv.QueryFrame(capture) cv.CvtColor(frame, dst, cv.CV_BGR2GRAY) cv.Canny(dst, dst, 125, 350) cv.Threshold(dst, dst, 128, 255, cv.CV_THRESH_BINARY_INV) cv.ShowImage(\”The Video\”, frame) cv.ShowImage(\”The Dst\”, dst) c = cv.WaitKey(1) if c == 27: #Esc on Windows break |
0x01. 人脸识别
使用OpenCV可以很简单的检测出视频中的人脸等:
1234567891011121314151617 | import cv2.cv as cv capture=cv.CaptureFromCAM(0) hc = cv.Load(\”haarcascades/haarcascade_frontalface_alt.xml\”) while True:frame=cv.QueryFrame(capture)faces = cv.HaarDetectObjects(frame, hc, cv.CreateMemStorage(), 1.2,2, cv.CV_HAAR_DO_CANNY_PRUNING, (0,0) ) for ((x,y,w,h),stub) in faces: cv.Rectangle(frame,(int(x),int(y)),(int(x)+w,int(y)+h),(0,255,0),2,0) cv.ShowImage(\”Window\”,frame) c=cv.WaitKey(1) if c==27 or c == 1048603: #If Esc entered break |