Day #211- OpenCV based Optical Flow Example

	#Modified and updated opencv example based on my requirements
	#https://docs.opencv.org/3.1.0/d7/d8b/tutorial_py_lucas_kanade.html
	import numpy as np
	import cv2
	cap = cv2.VideoCapture(r'E:\Optical_Flow\Demo.mp4')
	#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=(cv2.TERM_CRITERIA_EPS|cv2.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()
	image = cv2.resize(old_frame, (500, 400))
	old_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
	p0 = cv2.goodFeaturesToTrack(old_gray,mask=None,**feature_params)
	#create mask for drawing purpose
	mask = np.zeros_like(image)
	final_frame = np.zeros_like(image)
	while(1):
	flag, frame1 = cap.read()
	if flag:
	frame = cv2.resize(frame1, (500, 400))
	frame_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
	#calculate optical flow
	p1,st,err = cv2.calcOpticalFlowPyrLK(old_gray,frame_gray,p0,None,**lk_params)
	#select good points
	good_new = p1[st==1]
	good_old = p0[st==1]
	print(good_new)
	print(good_old)
	#draw the tracks
	for i,(new,old) in enumerate(zip(good_new,good_old)):
	a,b = new.ravel()
	c,d = old.ravel()
	mask = cv2.line(mask,(a,b),(c,d),color[i].tolist(),2)
	frame = cv2.circle(frame,(a,b),5,color[i].tolist())
	img = cv2.add(frame,mask)
	cv2.imshow('Intermediate frame',img)
	final_frame = img.copy()
	k = cv2.waitKey(30) & 0xff
	if k==27:
	break
	#now update previous frame and previous points
	old_gray = frame_gray.copy()
	p0 = good_new.reshape(-1,1,2)
	else:
	break
	cv2.destroyAllWindows()
	#Hough Transformation
	#Line Count
	gray = cv2.cvtColor(final_frame,cv2.COLOR_BGR2GRAY)
	img_gaussian = cv2.GaussianBlur(gray,(3,3),0)
	img_sobelx = cv2.Sobel(img_gaussian,cv2.CV_8U,1,0,ksize=5)
	img_sobely = cv2.Sobel(img_gaussian,cv2.CV_8U,0,1,ksize=5)
	#Compute lines using Hough Transformation
	xlines = cv2.HoughLines(img_sobelx,1,np.pi/180,200)
	ylines = cv2.HoughLines(img_sobely,1,np.pi/180,200)
	if(ylines is not None):
	if(xlines is not None):
	print('sobel - HoughLines')
	c = ylines.size/xlines.size
	print(c)
	print('X Line Counts')
	print(xlines)
	print('Y Line Counts')
	print(ylines)
	cv2.imshow("Final Frame",final_frame)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
	cap.release()

view raw Opticalflow_example.py hosted with ❤ by GitHub

Happy Mastering DL!!!