OpenCV: Lucas Kanade applied within certain area (to detect facial features) - opencv

Question

I am trying to preform face tracking with the Lucas Kanade algorithm with Haar Cascade Classification. The Lucas Kanade is successful and can track the user, but unfortunately, some of the good features to detect points are wasted on corners in the background. I wish to use Haar Cascade's ability to detect the fact to get coordinates of detected face and apply Lucas Kanade to only within that restricted area.
Basically, I want to use Haar Cascade to detect fact, get x, y, w, and h values, and use those coordinates to apply Lucas Kanade within that restricted area (so that none are wasted on assigning good features to the background and only facial features are detected)
The line of code that is doing the Lucas Kanade algorithm is this code:
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
How do I do that?
Code:
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()

Answer 1

Here is the code snippet:
p0 = np.array([[[x,y]], [[x0,y0]]], np.float32)
just replace p0 in original code and and assign x,x0... with your desired points
- make sure its a 2d array
- and the type is float 32 for single precision