OpenCV Python：在 GoodFeatureToDetect 中使用 Mask 参数时出错

Question

我试图在 Python 中制作一个结合了 Haar 级联分类和 Lucas Kanade 的面部检测程序。但是我说这样的话时出错：

错误：

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

我的程序如何工作：

我的程序使用 Haar Cascade 获取检测到的面部的坐标，复制由坐标创建的该区域中的任何内容（在本例中为面部），拍摄一张只有黑色的图像（所有像素都已设置）通过 numpy 归零），并将复制的面粘贴到黑色背景中。通过将黑色背景的新面孔设置到蒙版参数中，这将强制 Lucas Kanade (goodFeaturesToDetect) 在脸上创建特征点，这些点将被光流跟踪。

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('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()

谁能看到问题并告诉我如何解决？谢谢

Answer 1

我曾因使用不同大小的数组而导致此错误。

你有一个 for 循环动态分配值给 focused_face 但要跟踪的 good_features 使用静态大小（= 到 focused_face 的最后一个实例）。 Old_frame 看起来它使用了 focused_face 的第一个实例的形状。

确保您在 goodFeaturesToTrack 中使用相同形状的图像和蒙版数组。

OpenCV Python：在 GoodFeatureToDetect 中使用 Mask 参数时出错

OpenCV Python: Error with using Mask parameter in GoodFeatureToDetect

python

opencv

numpy

mask

computer-vision