opencv python中的椭圆检测

Question

我的图片在这里：

我正在寻找更好的解决方案或算法来检测这张照片中的椭圆部分（盘），并在Opencv中的另一张照片中对其进行遮罩。你能给我一些建议或解决方案吗？我的代码是：

 circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1.2, 1, param1=128, minRadius=200, maxRadius=600)
    # draw detected circles on image
    circles = circles.tolist()
    for cir in circles:
        for x, y, r in cir:
            x, y, r = int(x), int(y), int(r)
            cv2.circle(img, (x, y), r, (0, 255, 0), 4)

    # show the output image
    cv2.imshow("output", cv2.resize(img, (500, 500)))

Answer 1

skimage的另一种选择是由Xie, Yonghong, and Qiang Ji…

“一种新的有效的椭圆检测方法。” 模式识别，2002年。会议记录。第16届国际会议。卷 2. IEEE，2002年。

他们的椭圆检测代码相对较慢，此示例大约需要70秒；相比网站声称“ 28秒”。

如果您有conda或pip：“名称”，请安装scikit-image并试一试…

他们的代码可以在这里找到，也可以在下面复制/粘贴：

import matplotlib.pyplot as plt

from skimage import data, color, img_as_ubyte
from skimage.feature import canny
from skimage.transform import hough_ellipse
from skimage.draw import ellipse_perimeter

# Load picture, convert to grayscale and detect edges
image_rgb = data.coffee()[0:220, 160:420]
image_gray = color.rgb2gray(image_rgb)
edges = canny(image_gray, sigma=2.0,
              low_threshold=0.55, high_threshold=0.8)

# Perform a Hough Transform
# The accuracy corresponds to the bin size of a major axis.
# The value is chosen in order to get a single high accumulator.
# The threshold eliminates low accumulators
result = hough_ellipse(edges, accuracy=20, threshold=250,
                       min_size=100, max_size=120)
result.sort(order='accumulator')

# Estimated parameters for the ellipse
best = list(result[-1])
yc, xc, a, b = [int(round(x)) for x in best[1:5]]
orientation = best[5]

# Draw the ellipse on the original image
cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
image_rgb[cy, cx] = (0, 0, 255)
# Draw the edge (white) and the resulting ellipse (red)
edges = color.gray2rgb(img_as_ubyte(edges))
edges[cy, cx] = (250, 0, 0)

fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True,
                                sharey=True,
                                subplot_kw={'adjustable':'box-forced'})

ax1.set_title('Original picture')
ax1.imshow(image_rgb)

ax2.set_title('Edge (white) and result (red)')
ax2.imshow(edges)

plt.show()