Python OpenCV: Optical Flow with Lucas-Kanade method

09 Mar, 2020
Prerequisites: OpenCV
OpenCV is a huge open-source library for computer vision, machine learning, and image processing. OpenCV supports a wide variety of programming languages like Python, C++, Java, etc. It can process images and videos to identify objects, faces, or even the handwriting of a human. When it is integrated with various libraries, such as Numpy which is a highly optimized library for numerical operations, then the number of weapons increases in your Arsenal i.e whatever operations one can do in Numpy can be combined with OpenCV.
In this article, we will be learning how to apply the Lucas-Kanade method to track some points on a video. To track the points, first, we need to find the points to be tracked. For finding the points, we’ll use cv2.goodFeaturesToTrack(). Now, we will capture the first frame and detect some corner points. These points will be tracked using the Lucas-Kanade Algorithm provided by OpenCV, i.e, cv2.calcOpticalFlowPyrLK().
Syntax: cv2.calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts[, winSize[, maxLevel[, criteria]]])
Parameters:prevImg – first 8-bit input imagenextImg – second input imageprevPts – vector of 2D points for which the flow needs to be found.winSize – size of the search window at each pyramid level.maxLevel – 0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on.criteria – parameter, specifying the termination criteria of the iterative search algorithm.
Return:nextPts – output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.status – output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0.err – output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn’t found then the error is not defined (use the status parameter to find such cases).
Example:
import numpy as npimport cv2  cap = cv2.VideoCapture('sample.mp4')  # params for corner detectionfeature_params = dict( maxCorners = 100,                       qualityLevel = 0.3,                       minDistance = 7,                       blockSize = 7 )  # Parameters for lucas kanade optical flowlk_params = dict( winSize = (15, 15),                  maxLevel = 2,                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,                              10, 0.03))  # Create some random colorscolor = np.random.randint(0, 255, (100, 3))  # Take first frame and find corners in itret, old_frame = cap.read()old_gray = cv2.cvtColor(old_frame,                        cv2.COLOR_BGR2GRAY)p0 = cv2.goodFeaturesToTrack(old_gray, mask = None,                             **feature_params)  # Create a mask image for drawing purposesmask = np.zeros_like(old_frame)  while(1):          ret, frame = cap.read()    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]      # 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(), -1)              img = cv2.add(frame, mask)      cv2.imshow('frame', img)          k = cv2.waitKey(25)    if k == 27:        break      # Updating Previous frame and points     old_gray = frame_gray.copy()    p0 = good_new.reshape(-1, 1, 2)  cv2.destroyAllWindows()cap.release()
Output:
Python-OpenCV
Python
Writing code in comment?
Please use ide.geeksforgeeks.org,
generate link and share the link here.