所以我遇到的问题是这个代码：
import cv2
import numpy as np
from sklearn.metrics import pairwise #distance calculations for later
background= None
accumulated_weight= 0.5
roi_top=30
roi_bottom= 300
roi_left= 600
roi_right= 300
#function to find average background value
def calc_accum_avg(frame, accumulated_weight):
global background
if background is None:
background= frame.copy().astype('float')
cv2.accumulateWeighted(frame, background, accumulated_weight)
#Segmenting the contour
def segment(frame, threshold_min=25):
diff= cv2.absdiff(background.astype('uint8'), frame)
ret, thresholded= cv2.threshold(diff, threshold_min, 255, cv2.THRESH_BINARY)
contours, hierarchy= cv2.findContours(thresholded.copy(),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) ==0:
return None
else:
#Assuming that the biggest contour in ROI(Region of Interest) is Hand itself
hand_segment= max(contours, key= cv2.contourArea)
return(thresholded, hand_segment)
def count_fingers(thresholded, hand_segment):
conv_hull= cv2.convexHull(hand_segment)
top= tuple(conv_hull[conv_hull[:,:,1].argmin()][0])
bottom= tuple(conv_hull[conv_hull[:,:,1].argmax()][0])
left= tuple(conv_hull[conv_hull[:,:,0].argmin()][0])
right= tuple(conv_hull[conv_hull[:,:,0].argmax()][0])
cX= (left[0]+ right[0])//2
cY=(top[1]+ bottom[1])//2
distance= pairwise.euclidean_distances([cX, cY], Y= [left, right, top, bottom])[0]
max_distance= distance.max()
radius= int(0.9*max_distance)
circumference= (2*np.pi*radius)
circular_roi= np.zeros_like(thresholded[:2], dtype= 'uint8')
cv2.circle(circular_roi, (cX, cY), radius, 255, 10)
circular_roi= cv2.bitwise_and(thresholded, thresholded, mask= circular_roi)
contours, hierarchy= cv2.findContours(circular_roi.copy(),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
count=0
for cnt in count:
(x,y, w,h)= cv2.boundingRect(cnt)
out_of_wrist= (cY+ (cY*0.25))>(y+h)
limit_points= ((circumference*0.25)> cnt.shape[0])
if out_of_wrist and limit_points:
count +=1
return count
cam= cv2.VideoCapture(0)
num_frames=0
while True:
ret, frame= cam.read()
frame_copy= frame.copy()
roi= frame[roi_top: roi_bottom, roi_right:roi_left]
gray= cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
gray= cv2.GaussianBlur(gray,(7,7),0)
if num_frames<60 :
calc_accum_avg(gray, accumulated_weight)
if num_frames<=59:
cv2.putText(frame_copy, 'WAIT, GETTING BACKGROUND',(200,300), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255),2)
cv2.imshow('Finger Count', frame_copy)
else:
hand= segment(gray)
if hand is not None:
thresholded, hand_segment= hand
#Draw contours
cv2.drawContours(frame_copy, [hand_segment+(roi_right, roi_top)],-1, (255,0,0), 5)
fingers= count_fingers(thresholded, hand_segment)
cv2.putText(frame_copy, str(fingers), (70,50), cv2.FONT_HERSHEY_COMPLEX, 1,(0,0,255),2)
cv2.imshow('Thresholded', thresholded)
cv2.rectangle(frame_copy, (roi_left, roi_top), (roi_right, roi_bottom), (0,0,255),5)
num_frames+=1
cv2.imshow('Finger count', frame_copy)
k= cv2.waitKey(1) & 0xFF
if k==27:
break
cam.release()
cv2.destroyAllWindows()
哪个提供了错误：
错误消息
这个错误似乎在多米诺骨牌效应中标记，它提供了1个问题，在正在使用的另一个模块中提供了另一个问题，该模块使用了程序中另一个函数的信息，等等。我所附的图像中有我正在努力解决的错误，不知道如何继续。
我相信它没有不起作用的原因，但我可以说，当我的网络摄像头关闭时(摄像头返回黑屏(，它确实起作用了。当它接收到现场视频时，它就会分崩离析(换句话说，当襟翼被抬起，相机看到一切时(，并发出显著的"；1D阵列，当它预期2D阵列误差时；大约
问题出在这一行：
distance= pairwise.euclidean_distances([cX, cY], Y=[left, right, top, bottom])[0]
如果你把它改成：，它就会起作用
distance= pairwise.euclidean_distances([(cX, cY)], Y=[left, right, top, bottom])[0]