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我是新来的，在编程方面有点新手。

我有一个问题。我有 Sun 的 bmp 文件和 16 位图片。图片看起来是黑色背景的白色圆圈。

在此处输入图像描述

我想找到一个圆并在 x,y 坐标中确定它的中心。

我有这个脚本

import cv
import numpy as np




orig = cv.LoadImage('sun0016.bmp')

grey_scale = cv.CreateImage(cv.GetSize(orig), 8, 1)
processed = cv.CreateImage(cv.GetSize(orig), 8, 1)

cv.Smooth(orig, orig, cv.CV_GAUSSIAN, 5, 5)
cv.CvtColor(orig, grey_scale, cv.CV_RGB2GRAY)
cv.Erode(grey_scale, processed, None, 10)
cv.Dilate(processed, processed, None, 10)
cv.Canny(processed, processed, 5, 70, 3)
cv.Smooth(processed, processed, cv.CV_GAUSSIAN, 15, 15)

storage = cv.CreateMat(orig.width, 1, cv.CV_32FC3)


cv.HoughCircles(processed, storage, cv.CV_HOUGH_GRADIENT, 1, 16.0, 10, 140)

for i in range(0, len(np.asarray(storage))):
    print "circle #%d" %i
    Radius = int(np.asarray(storage)[i][0][2])
    x = int(np.asarray(storage)[i][0][0])
    y = int(np.asarray(storage)[i][0][1])
    center = (x, y)
    print x,y

    cv.Circle(orig, center, 1, cv.CV_RGB(0, 255, 0), 1, 8, 0)
    cv.Circle(orig, center, Radius, cv.CV_RGB(255, 0, 0), 1, 8, 0)

    cv.Circle(processed, center, 1, cv.CV_RGB(0, 0, 0), -1, 8, 0)
    cv.Circle(processed, center, Radius, cv.CV_RGB(255, 0, 0), 3, 8, 0)

cv.ShowImage("sun0016", orig)
cv.ShowImage("processed", processed)
cv_key = cv.WaitKey(0)

当我运行这个时，我发现太阳的边缘是圆的，但非常不准确。请知道您设置参数 HoughCircles 模块以进行精确搜索圈。谢谢

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3 回答 3

1

这里的主要问题是为您的半径找到一个好的范围。您可能会看一下您的图片并猜测半径。

从您给出的图片中，我猜想 180 - 220 将是​​一个不错的范围。

您的代码如下所示：

cv.HoughCircles(processed, storage, cv.CV_HOUGH_GRADIENT, 1, 16.0, 180, 220)

只需尝试为minRadiusand找到好的值maxRadius，这应该可以正常工作。

于 2013-11-04T13:52:30.957 回答
1

这是我的问题的解决方案

import numpy as np
import cv2

im = cv2.imread('sun0016.bmp')
height, width, depth = im.shape
print height, width, depth
thresh = 132
imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(imgray,(5,5),0)
edges = cv2.Canny(blur,thresh,thresh*2)
contours, hierarchy = cv2.findContours(edges,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cnt = contours[0]
cv2.drawContours(im,contours,-1,(0,255,0),-1)

#centroid_x = M10/M00 and centroid_y = M01/M00
M = cv2.moments(cnt)
x = int(M['m10']/M['m00'])
y = int(M['m01']/M['m00'])
print x,y
print width/2.0,height/2.0
print width/2-x,height/2-y


cv2.circle(im,(x,y),1,(0,0,255),2)
cv2.putText(im,"center of Sun contour", (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255))
cv2.circle(im,(width/2,height/2),1,(255,0,0),2)
cv2.putText(im,"center of image", (width/2,height/2), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0))
cv2.imshow('contour',im)
cv2.waitKey(0)
于 2013-11-06T11:55:56.630 回答
1

以为我会提出替代解决方案，以防将来有人偶然发现这个问题。

以下函数使用cv2.inRange代替cv2.Canny，和cv2.minEnclosingCircle代替cv2.moments。它cv2.findContours通过测量候选者的最小包围圆的半径来选择最大的轮廓。此过滤有助于拒绝来自例如水印或灰尘的误报，但根据您的要求，您可能希望以不同的方式执行此步骤或完全省略它。

该函数返回 x,y 坐标以及检测到的磁盘的半径，这是我正在处理的项目的要求。

import cv2


def find_disk(img, threshold=10):
    """Finds the center and radius of a single solar disk present in the supplied image.

    Uses cv2.inRange, cv2.findContours and cv2.minEnclosingCircle to determine the centre and 
    radius of the solar disk present in the supplied image.

    Args:
        img (numpy.ndarray): greyscale image containing a solar disk against a background that is below `threshold`.
        threshold (int): threshold of min pixel value to consider as part of the solar disk

    Returns:
        tuple: center coordinates in x,y form (int) 
        int: radius
    """
    if img is None:
        raise TypeError("img argument is None - check that the path of the loaded image is correct.")

    if len(img.shape) > 2:
        raise TypeError("Expected single channel (grayscale) image.")

    blurred = cv2.GaussianBlur(img, (5, 5), 0)
    mask = cv2.inRange(blurred, threshold, 255)
    img_mod, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # Find and use the biggest contour
    r = 0
    for cnt in contours:
        (c_x, c_y), c_r = cv2.minEnclosingCircle(cnt)
        # cv2.circle(img, (round(c_x), round(c_y)), round(c_r), (255, 255, 255), 2)
        if c_r > r:
            x = c_x
            y = c_y
            r = c_r

    # print("Number of contours found: {}".format(len(contours)))
    # cv2.imwrite("mask.jpg", mask)
    # cv2.imwrite("circled_contours.jpg", img)

    if x is None:
        raise RuntimeError("No disks detected in the image.")

    return (round(x), round(y)), round(r)


if __name__ == "__main__":
    image = cv2.imread("path/to/your/image.jpg")
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    center, radius = find_disk(img=gray, threshold=20)

    print("circle x,y: {},{}".format(center[0], center[1]))
    print("circle radius: {}".format(radius))

    # Output the original image with the detected disk superimposed
    cv2.circle(image, center, radius, (0, 0, 255), 1)
    cv2.rectangle(image, (center[0] - 2, center[1] - 2), (center[0] + 2, center[1] + 2), (0, 0, 255), -1)
    cv2.imwrite("disk_superimposed.jpg", image)

我留下了一些带注释的调试语句，如果您发现需要进一步修改它，它们可能会派上用场。

如果您的图像包含大量眩光，您可能需要使用更高的阈值。

于 2017-04-20T22:12:40.527 回答