相关疑难解决方法(0)

我有一个带有5个油滴的视频,我正在尝试使用cv2.HoughCircles来查找它们.

这是我的代码:

import cv, cv2
import numpy as np

foreground1 = cv2.imread("foreground1.jpg")
vid = cv2.VideoCapture("NB14.avi")

cv2.namedWindow("video")
cv2.namedWindow("canny")
cv2.namedWindow("blur")

while True:
    ret, frame = vid.read()
    subtract1 = cv2.subtract( foreground1, frame)
    framegrey1 = cv2.cvtColor(subtract1, cv.CV_RGB2GRAY)
    blur = cv2.GaussianBlur(framegrey1, (0,0), 2)
    circles =  cv2.HoughCircles(blur, cv2.cv.CV_HOUGH_GRADIENT, 2, 10, np.array([]), 40, 80, 5, 100)
    if circles is not None:
            for c in circles[0]:
                    cv2.circle(frame, (c[0],c[1]), c[2], (0,255,0),2)
    edges = cv2.Canny( blur, 40, 80 )
    cv2.imshow("video", frame)
    cv2.imshow("canny", edges)
    cv2.imshow("blur", blur)
    key = cv2.waitKey(30)

我会说canny边缘检测器看起来非常好,而hough变换的结果非常不稳定,每帧都会提供不同的结果.

例: …

我正在尝试数一群蟋蟀（昆虫）的数量。我将使用 openCV 库的图像处理方法。当农民出售蟋蟀时，这将提供更准确的练习。这张照片是用智能手机拍摄的。不幸的是，结果并不如预期。由于大多数蟋蟀彼此重叠，我的代码无法将它们分开，从而导致计数不正确。

我应该采用什么方法来解决这个问题？我的代码有问题吗？

蟋蟀图像

这是我的代码。

import cv2
import numpy as np

img = cv2.imread("c1.jpg",1)

roi=img[0:1500,0:1100]  
gray = cv2.cvtColor(roi,cv2.COLOR_BGR2GRAY)
gray_blur=cv2.GaussianBlur(gray,(15,15),0)
thresh = cv2.adaptiveThreshold(gray_blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,1)
kernel=np.ones((1,1),np.uint8)
closing=cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel,iterations=10)

result_img=closing.copy()
contours,hierachy=cv2.findContours(result_img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
counter=0
for cnt in contours:
    area = cv2.contourArea(cnt)
    if area <  150  :
    #if area<  300 :
        continue
    counter+=1
    ellipse = cv2.fitEllipse(cnt)
    cv2.ellipse(roi,ellipse,(0,255,0),1)
        
  
cv2.putText(roi,"Crickets="+str(counter),(100,70),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,0),1,cv2.LINE_AA)
cv2.imshow('ImageOfCrickets',roi)
#cv2.imshow('ImageOfGray',gray)
#cv2.imshow('ImageOfGray_blur',gray_blur)
#cv2.imshow('ImageOfThreshold',thresh)
#cv2.imshow('ImageOfMorphology',closing)

print('Crickets = '+ str(counter))     

cv2.waitKey(0)
cv2.destroyAllWindows()

现在，我使用闭合形态学和轮廓层次结构来实现椭圆形状方法。

我有一个要处理的图像.我需要检测图像中的所有圆圈.这就是它.

这是我的代码.

import cv2
import cv2.cv as cv
img = cv2.imread(imgpath)
cv2.imshow("imgorg",img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow("gray",gray)
ret,thresh = cv2.threshold(gray, 199, 255, cv.CV_THRESH_BINARY_INV)
cv2.imshow("thresh",thresh)
cv2.waitKey(0)
cv2.destrotAllWindows()

然后,我得到了这样的图像.

我尝试使用侵蚀和扩张将它们分成单个.但它不起作用.我的问题是如何将这些接触的圆分成单个,所以我可以检测到它们.

根据@ Micka的想法,我尝试按照以下方式处理图像,这是我的代码.

import cv2
import cv2.cv as cv
import numpy as np

def findcircles(img,contours):
    minArea = 300;
    minCircleRatio = 0.5;
    for  contour  in contours:

        area = cv2.contourArea(contour)
        if area < minArea: 
            continue

        (x,y),radius = cv2.minEnclosingCircle(contour)
        center = (int(x),int(y))
        radius = int(radius)
        circleArea = radius*radius*cv.CV_PI;

        if area/circleArea < minCircleRatio:
             continue;
        cv2.circle(img, center, radius, (0, …