opencv-python视频分析

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如何去除前景

步骤：

使用cv::VideoCapture 读取同图像或视频数据
使用 cv::BackgroundSubtractor 创建和更新背景
使用cv::imshow展示结果

from __future__ import print_function
import cv2 as cv
import argparse
parser = argparse.ArgumentParser(description='This program shows how to use background subtraction methods provided by \
                                              OpenCV. You can process both videos and images.')
parser.add_argument('--input', type=str, help='Path to a video or a sequence of image.', default='vtest.avi')
parser.add_argument('--algo', type=str, help='Background subtraction method (KNN, MOG2).', default='MOG2')
args = parser.parse_args()
if args.algo == 'MOG2':
    backSub = cv.createBackgroundSubtractorMOG2()
else:
    backSub = cv.createBackgroundSubtractorKNN()
capture = cv.VideoCapture(cv.samples.findFileOrKeep(args.input))
if not capture.isOpened:
    print('Unable to open: ' + args.input)
    exit(0)
while True:
    ret, frame = capture.read()
    if frame is None:
        break
fgMask <span class="token operator">=</span> backSub<span class="token punctuation">.</span><span class="token builtin">apply</span><span class="token punctuation">(</span>frame<span class="token punctuation">)</span>


cv<span class="token punctuation">.</span>rectangle<span class="token punctuation">(</span>frame<span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">10</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">100</span><span class="token punctuation">,</span><span class="token number">20</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">255</span><span class="token punctuation">,</span><span class="token number">255</span><span class="token punctuation">,</span><span class="token number">255</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span>
cv<span class="token punctuation">.</span>putText<span class="token punctuation">(</span>frame<span class="token punctuation">,</span> <span class="token builtin">str</span><span class="token punctuation">(</span>capture<span class="token punctuation">.</span>get<span class="token punctuation">(</span>cv<span class="token punctuation">.</span>CAP_PROP_POS_FRAMES<span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">15</span><span class="token punctuation">,</span> <span class="token number">15</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
           cv<span class="token punctuation">.</span>FONT_HERSHEY_SIMPLEX<span class="token punctuation">,</span> <span class="token number">0.5</span> <span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">,</span><span class="token number">0</span><span class="token punctuation">,</span><span class="token number">0</span><span class="token punctuation">)</span><span class="token punctuation">)</span>


cv<span class="token punctuation">.</span>imshow<span class="token punctuation">(</span><span class="token string">'Frame'</span><span class="token punctuation">,</span> frame<span class="token punctuation">)</span>
cv<span class="token punctuation">.</span>imshow<span class="token punctuation">(</span><span class="token string">'FG Mask'</span><span class="token punctuation">,</span> fgMask<span class="token punctuation">)</span>

keyboard <span class="token operator">=</span> cv<span class="token punctuation">.</span>waitKey<span class="token punctuation">(</span><span class="token number">30</span><span class="token punctuation">)</span>
<span class="token keyword">if</span> keyboard <span class="token operator">==</span> <span class="token string">'q'</span> <span class="token operator">or</span> keyboard <span class="token operator">==</span> <span class="token number">27</span><span class="token punctuation">:</span>
    <span class="token keyword">break</span>

https://docs.opencv.org/3.4/d1/dc5/tutorial_background_subtraction.html

1 Meanshift and Camshift

1.1 Meanshift

作用：用来跟踪物体

import numpy as np
import cv2 as cv
import argparse
parser = argparse.ArgumentParser(description='This sample demonstrates the meanshift algorithm. \
                                              The example file can be downloaded from: \
                                              https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
parser.add_argument('image', type=str, help='path to image file')
args = parser.parse_args()
cap = cv.VideoCapture(args.image)
# take first frame of the video
ret,frame = cap.read()
# setup initial location of window
x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
track_window = (x, y, w, h)
# set up the ROI for tracking
roi = frame[y:y+h, x:x+w]
hsv_roi =  cv.cvtColor(roi, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
while(1):
    ret, frame = cap.read()
    if ret == True:
        hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
        dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
        # apply meanshift to get the new location
        ret, track_window = cv.meanShift(dst, track_window, term_crit)
        # Draw it on image
        x,y,w,h = track_window
        img2 = cv.rectangle(frame, (x,y), (x+w,y+h), 255,2)
        cv.imshow('img2',img2)
        k = cv.waitKey(30) & 0xff
        if k == 27:
            break
    else:
        break

1.2 Camshift

import numpy as np
import cv2 as cv
import argparse
parser = argparse.ArgumentParser(description='This sample demonstrates the camshift algorithm. \
                                              The example file can be downloaded from: \
                                              https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
parser.add_argument('image', type=str, help='path to image file')
args = parser.parse_args()
cap = cv.VideoCapture(args.image)
# take first frame of the video
ret,frame = cap.read()
# setup initial location of window
x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
track_window = (x, y, w, h)
# set up the ROI for tracking
roi = frame[y:y+h, x:x+w]
hsv_roi =  cv.cvtColor(roi, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
while(1):
    ret, frame = cap.read()
    if ret == True:
        hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
        dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
        # apply camshift to get the new location
        ret, track_window = cv.CamShift(dst, track_window, term_crit)
        # Draw it on image
        pts = cv.boxPoints(ret)
        pts = np.int0(pts)
        img2 = cv.polylines(frame,[pts],True, 255,2)
        cv.imshow('img2',img2)
        k = cv.waitKey(30) & 0xff
        if k == 27:
            break
    else:
        break

1.3小结

第二种方法比第一张跟踪效果更好

https://docs.opencv.org/3.4/d7/d00/tutorial_meanshift.html

2 Optical Flow视觉流

使用 cv.calcOpticalFlowPyrLK()跟踪视频中的对象
使用cv.calcOpticalFlowFarneback() 创建密度视觉流

2.1 Lucas-Kanade Optical Flow in OpenCV

作用：显示特征在视频移动的路径

import numpy as np
import cv2 as cv
import argparse
parser = argparse.ArgumentParser(description='This sample demonstrates Lucas-Kanade Optical Flow calculation. \
                                              The example file can be downloaded from: \
                                              https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
parser.add_argument('image', type=str, help='path to image file')
args = parser.parse_args()
cap = cv.VideoCapture(args.image)
# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )
# Parameters for lucas kanade optical flow
lk_params = dict( winSize  = (15,15),
                  maxLevel = 2,
                  criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
# Create some random colors
color = np.random.randint(0,255,(100,3))
# Take first frame and find corners in it
ret, old_frame = cap.read()
old_gray = cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY)
p0 = cv.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)
while(1):
    ret,frame = cap.read()
    frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
    # calculate optical flow
    p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
    # Select good points
    good_new = p1[st==1]
    good_old = p0[st==1]
    # draw the tracks
    for i,(new,old) in enumerate(zip(good_new, good_old)):
        a,b = new.ravel()
        c,d = old.ravel()
        mask = cv.line(mask, (a,b),(c,d), color[i].tolist(), 2)
        frame = cv.circle(frame,(a,b),5,color[i].tolist(),-1)
    img = cv.add(frame,mask)
    cv.imshow('frame',img)
    k = cv.waitKey(30) & 0xff
    if k == 27:
        break
    # Now update the previous frame and previous points
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1,1,2)

2.2 密度视觉流

import numpy as np
import cv2 as cv
cap = cv.VideoCapture(cv.samples.findFile("vtest.avi"))
ret, frame1 = cap.read()
prvs = cv.cvtColor(frame1,cv.COLOR_BGR2GRAY)
hsv = np.zeros_like(frame1)
hsv[...,1] = 255
while(1):
    ret, frame2 = cap.read()
    next = cv.cvtColor(frame2,cv.COLOR_BGR2GRAY)
    flow = cv.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
    mag, ang = cv.cartToPolar(flow[...,0], flow[...,1])
    hsv[...,0] = ang*180/np.pi/2
    hsv[...,2] = cv.normalize(mag,None,0,255,cv.NORM_MINMAX)
    bgr = cv.cvtColor(hsv,cv.COLOR_HSV2BGR)
    cv.imshow('frame2',bgr)
    k = cv.waitKey(30) & 0xff
    if k == 27:
        break
    elif k == ord('s'):
        cv.imwrite('opticalfb.png',frame2)
        cv.imwrite('opticalhsv.png',bgr)
    prvs = next

https://docs.opencv.org/3.4/d4/dee/tutorial_optical_flow.html