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小能豆

Matplotlib 轮廓图无法提取精确的轮廓

py

我在提取以下数据的精确轮廓时遇到了困难：（只需查看数据就可以看到轮廓）

data = np.array(
      [[ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277]])

如果我将其绘制出来：

plt.imshow(data)

1.png

但是，当我尝试使用以下方法提取轮廓时：

plt.contour(data, levels = np.unique(data))

2.png

如您所见，轮廓线并未遵循实际数据的尖角。如果我叠加两个图：

3.png

完整代码如下：

import numpy as np
import matplotlib.pyplot as plt

data = np.array([[ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277]])

plt.imshow(data)
plt.show()
plt.contour(data, levels=np.unique(data), cmap="jet")
plt.colorbar()

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2024-11-26

共1个答案

小能豆

使用行进平方算法计算轮廓位置来绘制轮廓，它在网格点之间进行插值。

也许您正在寻找离散区域边界：可以像这样检索这些边界：

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

data = np.array([[ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277]])


def region_borders(data, value, color, **kwargs):
    v = np.argwhere(np.diff((data == value).T, axis=0))
    vlines = np.array(list(zip(v + [.5, -.5], v + [.5, .5])))

    h = np.argwhere(np.diff((data == value).T, axis=1))
    hlines = np.array(list(zip(h + [-.5, .5], h + [.5, .5])))

    if len(vlines) and len(hlines):
        lines = np.vstack((vlines, hlines))
    elif len(vlines):
        lines = vlines
    else:
        lines = hlines
    return mpl.collections.LineCollection(lines, colors=color, **kwargs)


contours = np.unique(data)

fig, ax = plt.subplots(ncols=len(contours)+1, sharex=True, sharey=True, layout='constrained')
im = ax[0].matshow(data, cmap='jet', aspect='auto')
fig.colorbar(im, ax=ax[-1]

1.png

2024-11-26