（Python）如何旋转图像使特征变为垂直？

Question

我想旋转一个图像（如下图），使其特征之一（类似于直线）变为垂直。然而，我似乎找不到一种用Python编程的方法。 Example_Image

Answer 1

旋转本身可以通过scipy.ndimage.interpolation.rotate操作完成。在

下面的第一部分是解决原始问题中示例场景的问题（有一个拉长的数据blob），请参见下面的更一般（但较慢）的方法。希望这有帮助！在

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第一种方法：要找到轴和直线的角度，我建议对非零值使用PCA：

from scipy.ndimage.interpolation import rotate
#from skimage.transform import rotate ## Alternatively
from sklearn.decomposition.pca import PCA ## Or use its numpy variant
import numpy as np

def verticalize_img(img):
    """
    Method to rotate a greyscale image based on its principal axis.

    :param img: Two dimensional array-like object, values > 0 being interpreted as containing to a line
    :return rotated_img: 
    """# Get the coordinates of the points of interest:
    X = np.array(np.where(img > 0)).T
    # Perform a PCA and compute the angle of the first principal axes
    pca = PCA(n_components=2).fit(X)
    angle = np.arctan2(*pca.components_[0])
    # Rotate the image by the computed angle:
    rotated_img = rotate(img,angle/pi*180-90)
    return rotated_img

通常，此函数也可以写成一行：

^{pr2}$

下面是一个例子：

from matplotlib import pyplot as plt
# Example data:
img = np.array([[0,0,0,0,0,0,0],
                [0,1,0,0,0,0,0],
                [0,0,1,1,0,0,0],
                [0,0,0,1,1,0,0],
                [0,0,1,0,0,1,0],
                [0,0,0,0,0,0,1]])
# Or alternatively a straight line:
img = np.diag(ones(15))
img = np.around(rotate(img,25))

# Or a distorted blob:
from sklearn import cluster, datasets
X, y = datasets.make_blobs(n_samples=100, centers = [[0,0]])
distortion = [[0.6, -0.6], [-0.4, 0.8]]
theta = np.radians(20)
rotation = np.array(((cos(theta),-sin(theta)), (sin(theta), cos(theta))))
X =  np.dot(np.dot(X, distortion),rotation)
img = np.histogram2d(*X.T)[0] # > 0 ## uncomment for making the example binary

rotated_img = verticalize_img(img)
# Plot the results
plt.matshow(img)
plt.title('Original')
plt.matshow(rotated_img)
plt.title('Rotated'))

请注意，对于高噪声的数据或没有明确方向的图像，这种方法会产生任意旋转。在

下面是一个输出示例：

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第二种方法：确定在更复杂的设置中澄清实际任务后（见注释），这里是基于模板匹配的第二种方法：

from matplotlib import pyplot as plt
import numpy as np
import pandas
from scipy.ndimage.interpolation import rotate
from scipy.signal import correlate2d#, fftconvolve
# Data from CSV file:
img = pandas.read_csv('/home/casibus/testdata.csv')
# Create a template:
template = np.zeros_like(img.values)
template[:,int(len(template[0])*1./2)] = 1
suggested_angles = np.arange(0,180,1) # Change to any resolution you like
overlaps = [np.amax(correlate2d(rotate(img,alpha,reshape=False),template,mode='same')) for alpha in suggested_angles]
# Determine the angle resulting in maximal overlap and rotate:
rotated_img = rotate(img.values,-suggested_angles[np.argmax(overlaps)])
plt.matshow(rotated_img)
plt.matshow(template)