如何在Open CV Python中消除这些噪音？

Question

hi what i want to happen is eliminate noises and only recognize the circle. I have these code so far:

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()

what it does is detect the brightest light and checks how circle it is. What i want to happen is eliminate the noise and detect the circle only. I hope you can help me with my code.

This is just an example what my program does is detects the brightest pixel. Here's the original image:

Answer 1

You could try to select your contour by filtering other contours that are not in your size range. U should understand that I also just started to learn python and opencv and there is probablly loads of better approaches. Code should be somthing like this:

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()

U have to change the range in for loop so that it suits your purpose.

Update:

Maybe even better...u can eliminate other contours (noises with circular criterion):

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'):
        break

Answer 2

You can do this using Hough circle transform .

This is the solution for this image:

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()

output: