import numpy as np
import cv2
from lucaskanade import PyrLK
import time

cap = cv2.VideoCapture('eee.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 = 0,
                  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()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)
time0 = time.time()
while(1):
    ret, frame = cap.read()
    if not ret:
        break
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # calculate optical flow
    # p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
    p1, st, err = PyrLK(old_gray, frame_gray, p0, maxLevel=3, window=15)
    # import pdb
    # pdb.set_trace()
    # Select feature that is not ill conditioned edge or corner
    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()
        a, b, c, d = int(a), int(b), int(c), int(d)
        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(20) & 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)

print(time.time() - time0)
cv2.destroyAllWindows()
cap.release()
import numpy as np
import cv2
from lucaskanade import PyrLK
import time

cap = cv2.VideoCapture('eee.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 = 0,
                  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()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)
time0 = time.time()
while(1):
    ret, frame = cap.read()
    if not ret:
        break
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # calculate optical flow
    # p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
    p1, st, err = PyrLK(old_gray, frame_gray, p0, maxLevel=3, window=15)
    # import pdb
    # pdb.set_trace()
    # Select feature that is not ill conditioned edge or corner
    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()
        a, b, c, d = int(a), int(b), int(c), int(d)
        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(20) & 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)

print(time.time() - time0)
cv2.destroyAllWindows()
cap.release()
# params for ShiTomasi corner detection # Parameters for lucas kanade optical flow # Create some random colors # Take first frame and find corners in it # Create a mask image for drawing purposes # calculate optical flow # p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params) # import pdb # pdb.set_trace() # Select feature that is not ill conditioned edge or corner # draw the tracks # Now update the previous frame and previous points