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我正在尝试使用带有 Haar Cascade 分类的 Lucas Kanade 算法进行人脸跟踪。Lucas Kanade 是成功的，可以跟踪用户，但不幸的是，一些检测点的好功能被浪费在了后台的角落。我希望使用 Haar Cascade 的能力来检测事实以获取检测到的人脸的坐标，并将 Lucas Kanade 应用于该限制区域内。

基本上，我想使用 Haar Cascade 来检测事实，获取 x、y、w 和 h 值，并使用这些坐标在该受限区域内应用 Lucas Kanade（这样就不会浪费在为背景分配好的特征上，而且只有检测到面部特征）

执行 Lucas Kanade 算法的代码行是以下代码：

p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

我怎么做？

代码：

from matplotlib import pyplot as plt
import numpy as np

import cv2

rectangle_x = 0

face_classifier = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')

cap = cv2.VideoCapture(0)


# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 200,
                       qualityLevel = 0.01,
                       minDistance = 10,
                       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()

cv2.imshow('Old_Frame', old_frame)
cv2.waitKey(0)
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
restart = True
face = face_classifier.detectMultiScale(old_gray, 1.2, 4)

if len(face) == 0:
    print "This is empty"

for (x,y,w,h) in face:
    focused_face = old_frame[y: y+h, x: x+w]



cv2.imshow('Old_Frame', old_frame)

face_gray = cv2.cvtColor(old_frame,cv2.COLOR_BGR2GRAY)

gray = cv2.cvtColor(focused_face,cv2.COLOR_BGR2GRAY)

corners_t = cv2.goodFeaturesToTrack(gray, mask = None, **feature_params)
corners = np.int0(corners_t)




for i in corners:
    ix,iy = i.ravel()
    cv2.circle(focused_face,(ix,iy),3,255,-1)
    cv2.circle(old_frame,(x+ix,y+iy),3,255,-1)

    print ix, " ", iy

plt.imshow(old_frame),plt.show()


##########

#############################
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
#############################
# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)

print "X: ", x
print "Y: ", y

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 circles
    for i,(new,old) in enumerate(zip(good_new,good_old)):
        a,b = new.ravel()
        c,d = old.ravel()
        cv2.circle(frame,(a, b),5,color[i].tolist(),-1)
        if i == 99:
            break

    cv2.imshow('frame',frame)
    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()
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1 回答 1

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这是代码片段：

p0 = np.array([[[x,y]], [[x0,y0]]], np.float32)

只需替换原始代码中的 p0 并用您想要的点分配 x,x0... - 确保它是一个二维数组 - 并且类型是 float 32 单精度

于 2016-09-25T20:58:33.863 回答