2
我試圖在Python中製作一個結合了Haar Cascade Classification和Lucas Kanade的面部檢測程序。但是我得到錯誤說是這樣的：OpenCV Python：在GoodFeatureToDetect中使用Mask參數出錯
錯誤：
Traceback (most recent call last):
File "/home/anthony/Documents/Programming/Python/Computer-Vision/OpenCV-Doc/optical-flow-and-haar-detection-test.py", line 80, in <module>
corners_t = cv2.goodFeaturesToTrack(gray, mask = mask_use, **feature_params)
error: /build/buildd/opencv-2.4.8+dfsg1/modules/imgproc/src/featureselect.cpp:63: error: (-215) mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) in function goodFeaturesToTrack
我的程序是如何工作的：
我的程序使用哈爾級聯得到一個檢測面部的座標，複製無論是在由座標（在這種情況下爲臉部）創建的區域，以除黑色之外的其他圖像（通過numpy將所有像素設置爲零）拍攝圖像，並將複製的臉部粘貼到黑色背景中。通過將具有黑色背景的新臉部設置爲遮罩參數，這將強制Lucas Kanade（goodFeaturesToDetect）在臉部上創建特徵點，該特徵點將通過光流跟蹤。
代碼：
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('video/sample.mov')
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()
#old_frame = cv2.imread('images/webcam-first-frame-two.png')
######Adding my code###
cv2.imshow('Old_Frame', old_frame)
cv2.waitKey(0)
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
restart = True
#while 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.rectangle(old_frame, (x,y), (x+w, y+h), (0,255,0),2)
#initalize all pixels to zero (picture completely black)
mask_use = np.zeros(old_frame.shape,np.uint8)
#Crop old_frame coordinates and paste it on the black mask)
mask_use[y:y+h,x:x+w] = old_frame[y:y+h,x:x+w]
height, width, depth = mask_use.shape
print "Height: ", height
print "Width: ", width
print "Depth: ", depth
height, width, depth = old_frame.shape
print "Height: ", height
print "Width: ", width
print "Depth: ", depth
cv2.imshow('Stuff', mask_use)
cv2.imshow('Old_Frame', old_frame)
#cv2.imshow('Zoom in', focused_face)
face_gray = cv2.cvtColor(old_frame,cv2.COLOR_BGR2GRAY)
gray = cv2.cvtColor(focused_face,cv2.COLOR_BGR2GRAY)
corners_t = cv2.goodFeaturesToTrack(gray, mask = mask_use, **feature_params)
corners = np.int0(corners_t)
#print corners
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()
"""
print "X: ", x
print "Y: ", y
print "W: ", w
print "H: ", h
#face_array = [x,y,w,h]
"""
#############################
p0 = cv2.goodFeaturesToTrack(old_gray, mask = old_gray, **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]
###print "Good_New"
###print good_new
good_old = p0[st==1]
# draw the tracks
for i,(new,old) in enumerate(zip(good_new,good_old)):
#print i
#print color[i]
a,b = new.ravel()
c,d = old.ravel()
cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
cv2.circle(frame,(a, b),5,color[i].tolist(),-1)
if i == 99:
break
#For circle, maybe replace (a,b) with (c,d)?
#img = cv2.add(frame,mask)
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()
任何人都可以看到的問題，並告訴我如何解決它？謝謝。