发布于 2015-12-21 21:54:24 | 1184 次阅读 | 评论: 0 | 来源: PHPERZ
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:
应用高斯滤波来平滑图像,目的是去除噪声。
找寻图像的强度梯度(intensity gradients)。
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)。
应用双阈值的方法来决定可能的(潜在的)边界。
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:
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:
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
使用OpenCV可以很简单的检测出视频中的人脸等:
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