Pythonでオプティカルフローのやり方・方法とマスク処理の追加【OpenCV】

pip install opencv-python

import numpy as np
import cv2

cap = cv2.VideoCapture('vtest.avi')

# 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()
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)

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()
        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(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)

cv2.destroyAllWindows()
cap.release()

import cv2
import numpy as np
cap = cv2.VideoCapture("vtest.avi")

ret, frame1 = cap.read()
prvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)
hsv = np.zeros_like(frame1)
hsv[...,1] = 255

while(1):
    ret, frame2 = cap.read()
    next = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)

    flow = cv2.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0)

    mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
    hsv[...,0] = ang*180/np.pi/2
    hsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)
    rgb = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)

    cv2.imshow('frame2',rgb)
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break
    elif k == ord('s'):
        cv2.imwrite('opticalfb.png',frame2)
        cv2.imwrite('opticalhsv.png',rgb)
    prvs = next

cap.release()
cv2.destroyAllWindows()

import cv2
import numpy as np

cap = cv2.VideoCapture('vtest.avi')
ret, frame1 = cap.read()

# 前のフレームとの差分をとるための変数を初期化
prvs = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
hsv_mask = np.zeros_like(frame1)

while True:
    ret, frame2 = cap.read()
    if not ret:
        break
    
    # 現在のフレームをグレースケールに変換
    next = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
    
    # オプティカルフローを計算
    flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
    
    # オプティカルフローの角度と大きさを計算
    mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
    
    # 色相の値を角度に、彩度の値を255に固定して、hsv画像を作成
    hsv_mask[..., 0] = ang * 180 / np.pi / 2
    hsv_mask[..., 1] = 255
    hsv_mask[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
    hsv_mask = cv2.cvtColor(hsv_mask,cv2.COLOR_HSV2BGR)
    
    # マスキング処理
    mask = cv2.inRange(hsv_mask, np.array([0, 0, 0]), np.array([30, 50, 50]))
    mask = 255 - mask
    masked_frame = cv2.bitwise_and(frame2, frame2, mask=mask)
    
    cv2.imshow('masked video', masked_frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    
    # 次のフレームの計算のために現在のフレームを保存
    prvs = next

cap.release()
cv2.destroyAllWindows()