python - 检测图像中重叠的嘈杂圆圈

Question

我尝试在下图中识别两个区域。内部区域和内部区域之间的区域-边界-用python openCV圈起来。



我尝试了不同的方法，例如:

Detecting circles images using opencv hough circles

Find and draw contours using opencv python

那不太适合。

使用经典图像处理甚至可以做到这一点，还是我需要一些神经网络？

编辑: Detecting circles images using opencv hough circles

# import the necessary packages
import numpy as np
import argparse
import cv2
from PIL import Image

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
args = vars(ap.parse_args())

# load the image, clone it for output, and then convert it to grayscale
image = cv2.imread(args["image"])
output = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# detect circles in the image
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2, 500)
# ensure at least some circles were found
if circles is not None:
    # convert the (x, y) coordinates and radius of the circles to integers
    circles = np.round(circles[0, :]).astype("int")
    # loop over the (x, y) coordinates and radius of the circles
    for (x, y, r) in circles:
        # draw the circle in the output image, then draw a rectangle
        # corresponding to the center of the circle
        cv2.circle(output, (x, y), r, (0, 255, 0), 4)
        cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
    # show the output image
    img = Image.fromarray(image)
    if img.height > 1500:
        imS = cv2.resize(np.hstack([image, output]), (round((img.width * 2) / 3), round(img.height / 3)))
    else:
        imS = np.hstack([image, output])
    # Resize image
    cv2.imshow("gray", gray)
    cv2.imshow("output", imS)
    cv2.waitKey(0)
else:
    print("No circle detected")

测试图片:

Answer 1

一般错误:当使用HoughCircles() ，参数应适当选择。我看到您只在代码中使用前4个参数。 Ypu可以检查here以获得关于这些参数的好主意。

经验丰富的想法:在使用HoughCircles时，我注意到如果2个2个圆的中心相同或几乎彼此靠近，则HoughCircles无法检测到它们。即使您将min_dist parameter分配为一个较小的值。在您的情况下，圆心也相同。

我的建议:我将在两个圆圈的代码中附加适当的参数。由于上述问题，我无法通过一个参数列表找到2个圆。我的建议是对同一张图片两次应用这两个参数，然后获取圆并获取结果。

对于外圆结果和参数包括的代码:

结果:

# import the necessary packages
import numpy as np
import argparse
import cv2
from PIL import Image

# load the image, clone it for output, and then convert it to grayscale
image = cv2.imread('image.jpg')
output = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

gray = cv2.medianBlur(gray,15)
rows = gray.shape[0]

# detect circles in the image
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT,1, rows / 8,
                               param1=100, param2=30,
                               minRadius=200, maxRadius=260)
# ensure at least some circles were found
if circles is not None:
    # convert the (x, y) coordinates and radius of the circles to integers
    circles = np.round(circles[0, :]).astype("int")
    # loop over the (x, y) coordinates and radius of the circles
    for (x, y, r) in circles:
        # draw the circle in the output image, then draw a rectangle
        # corresponding to the center of the circle
        cv2.circle(output, (x, y), r, (0, 255, 0), 4)
        cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
    # show the output image
    img = Image.fromarray(image)
    if img.height > 1500:
        imS = cv2.resize(np.hstack([image, output]), (round((img.width * 2) / 3), round(img.height / 3)))
    else:
        imS = np.hstack([image, output])
    # Resize image
    cv2.imshow("gray", gray)
    cv2.imshow("output", imS)
    cv2.waitKey(0)
else:
    print("No circle detected")

对于内圆，参数:

circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT,1, rows / 8,
                                   param1=100, param2=30,
                                   minRadius=100, maxRadius=200)

结果: