視頻的處理和圖片的處理類似，只不過視頻處理需要連續(xù)處理一系列圖片。

一般有兩種視頻源，一種是直接從硬盤加載視頻，另一種是獲取攝像頭視頻。

0x00. 本地讀取視頻

核心函數(shù):

cv.CaptureFromFile()

代碼示例：

import cv2.cv as cvcapture = cv.CaptureFromFile('myvideo.avi')nbFrames = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_COUNT))#CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream #CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video streamfps = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FPS)wait = int(1/fps * 1000/1)duration = (nbFrames * fps) / 1000print 'Num. Frames = ', nbFrames print 'Frame Rate = ', fps, 'fps' print 'Duration = ', duration, 'sec'for f in xrange( nbFrames ):frameImg = cv.QueryFrame(capture)print cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_POS_FRAMES)cv.ShowImage("The Video", frameImg)cv.WaitKey(wait)

cv2

import numpy as np import cv2cap = cv2.VideoCapture('vtest.avi')while(cap.isOpened()):ret, frame = cap.read()gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)cv2.imshow('frame',gray)if cv2.waitKey(1) & 0xFF == ord('q'):breakcap.release() cv2.destroyAllWindows()

0x01. 攝像頭視頻讀取

核心函數(shù)：

cv.CaptureFromCAM()

示例代碼：

import cv2.cv as cvcapture = cv.CaptureFromCAM(0)while True:frame = cv.QueryFrame(capture)cv.ShowImage("Webcam", frame)c = cv.WaitKey(1)if c == 27: #Esc on Windowsbreak

cv2

import numpy as np import cv2cap = cv2.VideoCapture(0)while(True):# Capture frame-by-frameret, frame = cap.read()# Our operations on the frame come heregray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# Display the resulting framecv2.imshow('frame',gray)if cv2.waitKey(1) & 0xFF == ord('q'):break# When everything done, release the capture cap.release() cv2.destroyAllWindows()

0x02. 寫入視頻

攝像頭錄制視頻

import cv2.cv as cvcapture=cv.CaptureFromCAM(0) temp=cv.QueryFrame(capture) writer=cv.CreateVideoWriter("output.avi", cv.CV_FOURCC("D", "I", "B", " "), 5, cv.GetSize(temp), 1) #On linux I used to take "M","J","P","G" as fourcccount=0 while count<50:print countimage=cv.QueryFrame(capture)cv.WriteFrame(writer, image)cv.ShowImage('Image_Window',image)cv.WaitKey(1)count+=1

從文件中讀取視頻并保存

import cv2.cv as cv capture = cv.CaptureFromFile('img/mic.avi')nbFrames = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_COUNT)) width = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH)) height = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT)) fps = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FPS) codec = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FOURCC)wait = int(1/fps * 1000/1) #Compute the time to wait between each frame queryduration = (nbFrames * fps) / 1000 #Compute durationprint 'Num. Frames = ', nbFrames print 'Frame Rate = ', fps, 'fps'writer=cv.CreateVideoWriter("img/new.avi", int(codec), int(fps), (width,height), 1) #Create writer with same parameterscv.SetCaptureProperty(capture, cv.CV_CAP_PROP_POS_FRAMES,80) #Set the number of framesfor f in xrange( nbFrames - 80 ): #Just recorded the 80 first frames of the videoframe = cv.QueryFrame(capture)print cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_POS_FRAMES)cv.WriteFrame(writer, frame)cv.WaitKey(wait)

cv2

import numpy as np import cv2cap = cv2.VideoCapture(0)# Define the codec and create VideoWriter object fourcc = cv2.VideoWriter_fourcc(*'XVID') out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480))while(cap.isOpened()):ret, frame = cap.read()if ret==True:frame = cv2.flip(frame,0)# write the flipped frameout.write(frame)cv2.imshow('frame',frame)if cv2.waitKey(1) & 0xFF == ord('q'):breakelse:break# Release everything if job is finished cap.release() out.release()

cv2.destroyAllWindows()

————————————————————————————————————喵星人說這是分割線————————————————————————————————————

0x00. 使用 Canny 算法邊緣識別

Canny 算法是一種多級邊緣識別算法。

Canny邊緣識別算法可以分為以下5個步驟：

應(yīng)用高斯濾波來平滑圖像，目的是去除噪聲。

找尋圖像的強度梯度（intensity gradients）。

應(yīng)用非最大抑制（non-maximum suppression）技術(shù)來消除邊誤檢（本來不是但檢測出來是）。

應(yīng)用雙閾值的方法來決定可能的（潛在的）邊界。

利用滯后技術(shù)來跟蹤邊界。

具體原理性質(zhì)的東西可以參考這里

讀取本地視頻處理代碼示例:

import cv2.cv as cvcapture = 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)

直接處理攝像頭視頻：

import cv2.cv as cvcapture = 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 Windowsbreak

0x01. 人臉識別

使用OpenCV可以很簡單的檢測出視頻中的人臉等：

import cv2.cv as cvcapture=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 enteredbreak

from: https://segmentfault.com/a/1190000003804797

https://segmentfault.com/a/1190000003804807

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