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(碟形)并在 Opencv 的另一张照片中将其遮盖。 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)))
最佳答案
在 Xie, Yonghong, and Qiang Ji
制作的 skimage 中有一个替代品并发布为...
“A new efficient ellipse detection method.” Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 2002.
他们的椭圆检测代码比较慢,例子大概需要70秒;与网站声称的“28 秒”相比。
如果您有 conda 或 pip: "name"安装 scikit-image 并试一试...
可以找到他们的代码here或复制/粘贴如下:
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'})
ax1.set_title('Original picture')
ax1.imshow(image_rgb)
ax2.set_title('Edge (white) and result (red)')
ax2.imshow(edges)
plt.show()
关于python - opencv python中的椭圆检测,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/42206042/