如何在Open CV Python中消除这些噪声？
如何在Open CV Python中消除这些噪声？
提问于 2018-06-09 13:09:22
嗨，我想要做的是消除噪音，只识别圆圈。到目前为止，我已经有了这些代码：
import cv2
import numpy as np
import math
cap = cv2.VideoCapture(0)
while True:
try:
ret, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) #convert from bgr to
hsv color space
lower = np.array([0,0,255])
upper = np.array([255, 255, 255])
mask = cv2.inRange(hsv, lower, upper)
im2, contours, hierarchy =
cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
area = sorted(contours, key=cv2.contourArea, reverse=True)
contour = area[0]
(x,y),radius = cv2.minEnclosingCircle(contour)
radius = int(radius)
area = cv2.contourArea(contour)
circ = 4*area/(math.pi*(radius*2)**2)
print(circ)
except:
pass
cv2.imshow('mask', mask)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()它所做的是检测最亮的光，并检查它有多圆。我想要做的是消除噪声，只检测圆圈。我希望你能帮助我完成我的代码。
这只是一个例子，我的程序所做的是检测最亮的像素。原图如下：
回答 2
Stack Overflow用户
回答已采纳
发布于 2018-06-09 17:24:25
您可以尝试通过过滤超出您的尺寸范围的其他轮廓来选择您的轮廓。你应该明白，我也刚刚开始学习python和opencv，可能有很多更好的方法。代码应该是这样的：
import cv2
import numpy as np
import math
cap = cv2.VideoCapture(0)
while True:
try:
ret, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) #convert from bgr to
lower = np.array([0,0,255])
upper = np.array([255, 255, 255])
mask = cv2.inRange(hsv, lower, upper)
im2, contours, hierarchy = cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
area = sorted(contours, key=cv2.contourArea, reverse=True)
for number in range(0, len(area)):
cnts = area[number]
if 40 < len(cnts) < 80:
contour = area[number]
break
(x,y),radius = cv2.minEnclosingCircle(contour)
radius = int(radius)
area2 = cv2.contourArea(contour)
circ = 4*area2/(math.pi*(radius*2)**2)
print(circ)
except:
pass
cv2.imshow('mask', mask)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()您必须更改for循环中的范围，以使其符合您的目的。
更新：
也许更好的是...你可以消除其他轮廓(圆形标准的噪声)：
import cv2
import numpy as np
import math
cap = cv2.VideoCapture(0)
while True:
try:
ret, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) #convert from bgr to
lower = np.array([0,0,255])
upper = np.array([255, 255, 255])
mask = cv2.inRange(hsv, lower, upper)
im2, contours, hierarchy = cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
area = sorted(contours, key=cv2.contourArea, reverse=True)
for number in range(0, len(area)):
cnts = area[number]
if 40 < len(cnts) < 80:
contour = area[number]
(x,y),radius = cv2.minEnclosingCircle(contour)
radius = int(radius)
area2 = cv2.contourArea(contour)
circ = 4*area2/(math.pi*(radius*2)**2)
if 0.8 < circ < 1.5:
rect = cv2.boundingRect(contour)
x,y,w,h = rect
cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
cv2.putText(frame,'Laser point detected',(x+w+10,y+h),0,0.5,(0,255,0))
print(circ)
break
except:
pass
cv2.imshow('mask', mask)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
breakStack Overflow用户
发布于 2018-06-09 13:32:15
您可以使用Hough circle transform来完成此操作。
这是this镜像的解决方案：
import cv2
import numpy as np
img = cv2.imread('stack.jpg',0)
img = cv2.medianBlur(img,5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
black = np.zeros_like(cimg)
circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,
param1=50,param2=30,minRadius=0,maxRadius=0)
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
# draw the outer circle
cv2.circle(black,(i[0],i[1]),i[2],(0,255,0),1)
# draw the center of the circle
cv2.circle(black,(i[0],i[1]),2,(0,0,255),1)
cv2.imshow('detected circles',black)
cv2.waitKey(0)
cv2.destroyAllWindows()输出：
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原文链接：
https://stackoverflow.com/questions/50771103
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