OpenCV Python图像处理，类型错误
我使用下面提到的代码来跟踪棕色磁珠。行OpenCV Python图像处理，类型错误,opencv,Opencv,我使用下面提到的代码来跟踪棕色磁珠。行cv2.圆（帧，中心，5，（0,0255），-1）给出了一个类型错误。有人能提供这方面的见解吗 import cv2 import numpy as np import matplotlib.pyplot as plt import imutils black_lower = (0,0,0) #this two-blacks corresponds to the colour range in hsv, which
cv2.圆（帧，中心，5，（0,0255），-1） import cv2
import numpy as np
import matplotlib.pyplot as plt
import imutils
black_lower = (0,0,0) #this two-blacks corresponds to the colour range in hsv, which we are looking in ants
black_upper = (130, 130, 128)
kernel_erode = np.ones((1,1),np.uint8) #these kernels are defined for eroding and dialating image
kernel_dialate = np.ones((3,3),np.uint8)
r,h,c,w = 600, 400, 100, 700 # i have to play with the size of the window for getting the roi (currently cutting defined roi)
track_window = (c,r,w,h)
vid = cv2.VideoCapture('/home/idaho/Desktop/opencv_files/brown_beads.mp4')
while (vid.isOpened()):
ret,frame = vid.read()
frame = imutils.resize(frame) #we have resized the frame, now we have to convert to hsv color space!
hsv_frame = cv2.cvtColor(frame,cv2.COLOR_RGB2HSV) # we have converted the frame in to hsv space!
mask1 = cv2.inRange(hsv_frame,black_lower,black_upper)#now we should define a mask for color ranges from black_lower to black_upper and remove other noices!
mask2 = cv2.erode(mask1,kernel_erode,iterations=1) #for increasing the object detection , we have to reduce the erode_kernel_size and increase the dialate_kernel_size
mask3 = cv2.dilate(mask2,kernel_dialate,iterations=2)
mask4 = cv2.GaussianBlur(mask3,(1,1),0)
x,y,w,h = track_window # these are for defining the roi in the video, will have to play with this!
cv2.rectangle(mask4,(x,y),(x+w,y+h),(255,0,0),2)
dst = np.zeros_like(mask4)
dst[y:y+h,x:x+w] = mask4[y:y+h,x:x+w]
cv2.imshow('gray',frame)
#this is finding the specific object in the video and drawing a contour against it
counts = cv2.findContours(mask4.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
center = None
if len(counts) > 0:
calculate = max(counts,key = cv2.contourArea)
((x,y),radius) = cv2.minEnclosingCircle(calculate)
M = cv2.moments(calculate)
center = (int(M["m10"]/M["m00"]),(M["m01"]/M["m00"]))
if radius > 1:
cv2.circle(frame, (int(x),int(y)),int(radius),(0,255,255),2)
cv2.circle(frame, center, 5 ,(0,0,255),-1)
pts.appendleft(center)
for i in xrange(1,len(pts)):
if pts[i-1] is None or pts[i] is None:
continue
thickness = int(np.sqrt(64/float(i+1))*2.5)
cv2.line(frame,pts[i-1],pts[i],(0,0,255),thickness)
cv2.imshow('frame',frame)
if cv2.waitKey(50) and 0xFF == ord('q'):
break
vid.release()
cv2.destroyAllWindows()
类型错误center=（int（M[“m10”]/M[“m00”]），（M[“m01”]/M[“m00”）center=（int（M[“m10”]/M[“m00”]），int（M[“m01”]/M[“m00”]）您没有将type
intcenter=（int（M[“m10”]/M[“m00”]，（M[“m01”]/M[“m00”]）int类型错误pts=np。append（center）