如何检测图像中的圆形区域并用Python对其进行中心化？

Question

我有一个数字火焰的形式如下：

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我试图检测相机的外部边缘和中心的图形，这样圆形的火焰视图正是在中心的情节。因为圆圈的位置可能会随着图像捕获日期的变化而改变。有时可能是上半身，有时是下半身，等等。

Python中是否有任何模块可以检测视图并将其中心化？

可复制代码

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img=mpimg.imread('flame.png')
lum_img = img[:,:,0]
img_plot = plt.imshow(lum_img)
img_plot.set_cmap('jet')
plt.axis('Off')
plt.show()

Answer 1

改编自this answer，进行边缘检测，并使用RANSAC可靠地将一个圆圈与轮廓相匹配：

from __future__ import print_function
from skimage import io, feature, color, measure, draw, img_as_float
import numpy as np

image = img_as_float(color.rgb2gray(io.imread('flame.png')))
edges = feature.canny(image)
coords = np.column_stack(np.nonzero(edges))

model, inliers = measure.ransac(coords, measure.CircleModel,
                                min_samples=3, residual_threshold=1,
                                max_trials=1000)

print(model.params)

rr, cc = draw.circle_perimeter(int(model.params[0]),
                               int(model.params[1]),
                               int(model.params[2]),
                               shape=image.shape)

image[rr, cc] = 1

import matplotlib.pyplot as plt
plt.imshow(image, cmap='gray')
plt.scatter(model.params[1], model.params[0], s=50, c='red')
plt.axis('off')
plt.savefig('/tmp/flame_center.png', bbox_inches='tight')
plt.show()

这产生了：

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Answer 2

我觉得你有很多选择。两个简单的方法，在我的脑海中将是阈值你的输入图像的低强度值，这将给你一个白色的圆圈。然后你可以在上面运行Hough变换来找到中心。

或者您可以使用阈值白像素的距离变换，并取此距离变换的最大值：

# code derived from watershed example of scikit-image
# http://scikit-image.org/docs/dev/auto_examples/plot_watershed.html

import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi

from skimage.morphology import watershed
from skimage.feature import peak_local_max
from skimage.color import rgb2gray
from skimage.io import imread

img = imread('flame.png')
image = rgb2gray(img) > 0.01

# Now we want to separate the two objects in image
# Generate the markers as local maxima of the distance to the background
distance = ndi.distance_transform_edt(image)

# get global maximum like described in 
# http://stackoverflow.com/a/3584260/2156909
max_loc = unravel_index(distance.argmax(), distance.shape)

fig, axes = plt.subplots(ncols=4, figsize=(10, 2.7))
ax0, ax1, ax2, ax3 = axes

ax0.imshow(img,interpolation='nearest')
ax0.set_title('Image')
ax1.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
ax1.set_title('Thresholded')
ax2.imshow(-distance, cmap=plt.cm.jet, interpolation='nearest')
ax2.set_title('Distances')
ax3.imshow(rgb2gray(img), cmap=plt.cm.gray, interpolation='nearest')
ax3.set_title('Detected centre')
ax3.scatter(max_loc[1], max_loc[0], color='red')

for ax in axes:
    ax.axis('off')

fig.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
                    right=1)
plt.show()

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为了让您知道这个方法有多健壮，如果我选择一个非常糟糕的阈值(image = rgb2gray(img) > 0.001 --太低了，无法得到一个很好的圆圈)，结果几乎是一样的：

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