如何从图像中提取椭圆轮廓并保存到不同的变量中？

Question

我需要从图像中提取12个椭圆形，并将它们分别存储在变量1到12中。

原始图片如下：

输出图片如下：

有人能帮助我将所有这些椭圆形提取到不同的变量中吗？

我的代码是：

import cv2
import numpy as np

path = r'/home/parallels/Desktop/Opencv/data/test.JPG'
i = cv2.imread(path, -1)
img_rgb = cv2.resize(i, (1280,720))
cv2.namedWindow("Original Image",cv2.WINDOW_NORMAL)

img = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV)
img = cv2.bilateralFilter(img,9,105,105)

r,g,b=cv2.split(img)

equalize1= cv2.equalizeHist(r)
equalize2= cv2.equalizeHist(g)
equalize3= cv2.equalizeHist(b)
equalize=cv2.merge((r,g,b))
equalize = cv2.cvtColor(equalize,cv2.COLOR_RGB2GRAY)

ret,thresh_image = cv2.threshold(equalize,0,255,cv2.THRESH_OTSU+cv2.THRESH_BINARY)
equalize= cv2.equalizeHist(thresh_image)

canny_image = cv2.Canny(equalize,250,255)
canny_image = cv2.convertScaleAbs(canny_image)
kernel = np.ones((3,3), np.uint8)
dilated_image = cv2.dilate(canny_image,kernel,iterations=1)

contours, hierarchy = cv2.findContours(dilated_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours= sorted(contours, key = cv2.contourArea, reverse = True)[:10]
c=contours[0]
print(cv2.contourArea(c))

final = cv2.drawContours(img, [c], -1, (255,0, 0), 3)
mask = np.zeros(img_rgb.shape,np.uint8)

new_image = cv2.drawContours(mask,[c],0,255,-1,)
new_image = cv2.bitwise_and(img_rgb, img_rgb, mask=equalize)

cv2.namedWindow("new",cv2.WINDOW_NORMAL)
cv2.imshow("new",new_image)
cv2.waitKey(0)

- Pranav Warke

Answer 1

你走在正确的道路上。获得二进制图像后，可以执行轮廓面积+长宽比过滤。我们可以使用imutils.contours.sort_contours()按从左到右的顺序对轮廓进行排序。然后我们使用查找轮廓并使用cv2.contourArea和cv2.approxPolyDP+cv2.arcLength进行过滤。如果它们通过了这个过滤器，我们就绘制轮廓并将其附加到oval列表中以跟踪轮廓。以下是结果：

过滤掩码

结果

孤立的椭圆形

来自oval列表的输出

Oval contours: 12

代码

import cv2
import numpy as np
from imutils import contours

# Load image, resize, convert to HSV, bilaterial filter
image = cv2.imread('1.jpg')
resize = cv2.resize(image, (1280,720))
original = resize.copy()
mask = np.zeros(resize.shape[:2], dtype=np.uint8)
hsv = cv2.cvtColor(resize, cv2.COLOR_RGB2HSV)
hsv = cv2.bilateralFilter(hsv,9,105,105)

# Split into channels and equalize 
r,g,b=cv2.split(hsv)
equalize1 = cv2.equalizeHist(r)
equalize2 = cv2.equalizeHist(g)
equalize3 = cv2.equalizeHist(b)
equalize = cv2.merge((r,g,b))
equalize = cv2.cvtColor(equalize,cv2.COLOR_RGB2GRAY)

# Blur and threshold for binary image
blur = cv2.GaussianBlur(equalize, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Find contours, sort from left-to-right
# Filter using contour area and aspect ratio filtering
ovals = []
num = 0
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="left-to-right")
for c in cnts:
    area = cv2.contourArea(c)
    x,y,w,h = cv2.boundingRect(c)
    ar = w / float(h)
    if area > 1000 and ar < .8:
        cv2.drawContours(resize, [c], -1, (36,255,12), 3)
        cv2.drawContours(mask, [c], -1, (255,255,255), -1)
        cv2.putText(resize, str(num), (x,y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (36,55,12), 2)
        ovals.append(c)
        num += 1

result = cv2.bitwise_and(original, original, mask=mask)
result[mask==0] = (255,255,255)
print('Oval contours: {}'.format(len(ovals)))

cv2.imshow('equalize', equalize)
cv2.imshow('thresh', thresh)
cv2.imshow('resize', resize)
cv2.imshow('result', result)
cv2.imshow('mask', mask)
cv2.waitKey()

- nathancy