如何从 numpy 数组中提取任意一行值？
- 2025-03-20 08:47:00
- admin 原创
- 43
问题描述：
我有一个包含一些图像数据的 numpy 数组。我想绘制横跨图像的横断面的“轮廓”。最简单的情况是轮廓与图像边缘平行，因此如果图像数组是imdat，则选定点的轮廓(r,c)只是imdat[r]（水平）或imdat[:,c]（垂直）。
现在，我想将两个点(r1,c1)和作为输入(r2,c2)，它们都位于内imdat。我想绘制沿连接这两点的线的值的分布图。
沿着这样的线从 numpy 数组中获取值的最佳方法是什么？更一般地说，沿着路径/多边形？
我以前使用过切片和索引，但对于连续切片元素不在同一行或同一列的情况，我似乎无法找到一个优雅的解决方案。谢谢你的帮助。
解决方案 1：
@Sven 的答案很简单，但对于大型数组来说效率很低。如果您处理的是相对较小的数组，则不会注意到差异，如果您想要从大型数组（例如 >50 MB）中获取配置文件，您可能需要尝试其他几种方法。不过，您需要在“像素”坐标中工作，因此会增加一层复杂性。
还有两种更节省内存的方法。1）scipy.ndimage.map_coordinates如果您需要双线性或三次插值，请使用。2）如果您只想要最近邻采样，那么直接使用索引即可。
以第一个例子为例：
import numpy as np
import scipy.ndimage
import matplotlib.pyplot as plt
#-- Generate some data...
x, y = np.mgrid[-5:5:0.1, -5:5:0.1]
z = np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2)
#-- Extract the line...
# Make a line with "num" points...
x0, y0 = 5, 4.5 # These are in _pixel_ coordinates!!
x1, y1 = 60, 75
num = 1000
x, y = np.linspace(x0, x1, num), np.linspace(y0, y1, num)
# Extract the values along the line, using cubic interpolation
zi = scipy.ndimage.map_coordinates(z, np.vstack((x,y)))
#-- Plot...
fig, axes = plt.subplots(nrows=2)
axes[0].imshow(z)
axes[0].plot([x0, x1], [y0, y1], 'ro-')
axes[0].axis('image')
axes[1].plot(zi)
plt.show()
使用最近邻插值的等效方法看起来像这样：
import numpy as np
import matplotlib.pyplot as plt
#-- Generate some data...
x, y = np.mgrid[-5:5:0.1, -5:5:0.1]
z = np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2)
#-- Extract the line...
# Make a line with "num" points...
x0, y0 = 5, 4.5 # These are in _pixel_ coordinates!!
x1, y1 = 60, 75
num = 1000
x, y = np.linspace(x0, x1, num), np.linspace(y0, y1, num)
# Extract the values along the line
zi = z[x.astype(np.int), y.astype(np.int)]
#-- Plot...
fig, axes = plt.subplots(nrows=2)
axes[0].imshow(z)
axes[0].plot([x0, x1], [y0, y1], 'ro-')
axes[0].axis('image')
axes[1].plot(zi)
plt.show()
但是，如果您使用最近邻，您可能只需要在每个像素上进行采样，因此您可能会做更像这样的事情......
import numpy as np
import matplotlib.pyplot as plt
#-- Generate some data...
x, y = np.mgrid[-5:5:0.1, -5:5:0.1]
z = np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2)
#-- Extract the line...
# Make a line with "num" points...
x0, y0 = 5, 4.5 # These are in _pixel_ coordinates!!
x1, y1 = 60, 75
length = int(np.hypot(x1-x0, y1-y0))
x, y = np.linspace(x0, x1, length), np.linspace(y0, y1, length)
# Extract the values along the line
zi = z[x.astype(np.int), y.astype(np.int)]
#-- Plot...
fig, axes = plt.subplots(nrows=2)
axes[0].imshow(z)
axes[0].plot([x0, x1], [y0, y1], 'ro-')
axes[0].axis('image')
axes[1].plot(zi)
plt.show()
解决方案 2：
我一直在用星系图像测试上述例程，并认为我发现了一个小错误。我认为需要在 Joe 提供的其他很棒的解决方案中添加转置。这是他的代码的一个略微修改的版本，它揭示了错误。如果您在没有转置的情况下运行它，您会发现配置文件不匹配；使用转置后它看起来没问题。这在 Joe 的解决方案中并不明显，因为他使用的是对称图像。
import numpy as np
import scipy.ndimage
import matplotlib.pyplot as plt
import scipy.misc # ADDED THIS LINE
#-- Generate some data...
x, y = np.mgrid[-5:5:0.1, -5:5:0.1]
z = np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2)
lena = scipy.misc.lena() # ADDED THIS ASYMMETRIC IMAGE
z = lena[320:420,330:430] # ADDED THIS ASYMMETRIC IMAGE
#-- Extract the line...
# Make a line with "num" points...
x0, y0 = 5, 4.5 # These are in _pixel_ coordinates!!
x1, y1 = 60, 75
num = 500
x, y = np.linspace(x0, x1, num), np.linspace(y0, y1, num)
# Extract the values along the line, using cubic interpolation
zi = scipy.ndimage.map_coordinates(z, np.vstack((x,y))) # THIS DOESN'T WORK CORRECTLY
zi = scipy.ndimage.map_coordinates(np.transpose(z), np.vstack((x,y))) # THIS SEEMS TO WORK CORRECTLY
#-- Plot...
fig, axes = plt.subplots(nrows=2)
axes[0].imshow(z)
axes[0].plot([x0, x1], [y0, y1], 'ro-')
axes[0].axis('image')
axes[1].plot(zi)
plt.show()
这是没有转置的版本。请注意，根据图像，左侧只有一小部分应该是明亮的，但图表显示几乎一半的图表都是明亮的。
这是转置后的版本。在此图像中，图似乎与您从图像中的红线所预期的非常吻合。
解决方案 3：
可能最简单的方法是使用scipy.interpolate.interp2d()：
# construct interpolation function
# (assuming your data is in the 2-d array "data")
x = numpy.arange(data.shape[1])
y = numpy.arange(data.shape[0])
f = scipy.interpolate.interp2d(x, y, data)
# extract values on line from r1, c1 to r2, c2
num_points = 100
xvalues = numpy.linspace(c1, c2, num_points)
yvalues = numpy.linspace(r1, r2, num_points)
zvalues = f(xvalues, yvalues)
解决方案 4：
为得到固定的解决方案，请查看scikit-image的measure.profile_line功能。
它建立在@Joe的答案scipy.ndimage.map_coordinates之上，并且内置了一些额外有用的功能。
解决方案 5：
将此答案与MPL 文档中的事件处理示例相结合，以下是允许基于 GUI 的拖动来绘制/更新切片的代码，通过拖动绘图数据（这是为 pcolormesh 图编码的）：
import numpy as np
import matplotlib.pyplot as plt
# Handle mouse clicks on the plot:
class LineSlice:
'''Allow user to drag a line on a pcolor/pcolormesh plot, and plot the Z values from that line on a separate axis.
Example
-------
fig, (ax1, ax2) = plt.subplots( nrows=2 ) # one figure, two axes
img = ax1.pcolormesh( x, y, Z ) # pcolormesh on the 1st axis
lntr = LineSlice( img, ax2 ) # Connect the handler, plot LineSlice onto 2nd axis
Arguments
---------
img: the pcolormesh plot to extract data from and that the User's clicks will be recorded for.
ax2: the axis on which to plot the data values from the dragged line.
'''
def __init__(self, img, ax):
'''
img: the pcolormesh instance to get data from/that user should click on
ax: the axis to plot the line slice on
'''
self.img = img
self.ax = ax
self.data = img.get_array().reshape(img._meshWidth, img._meshHeight)
# register the event handlers:
self.cidclick = img.figure.canvas.mpl_connect('button_press_event', self)
self.cidrelease = img.figure.canvas.mpl_connect('button_release_event', self)
self.markers, self.arrow = None, None # the lineslice indicators on the pcolormesh plot
self.line = None # the lineslice values plotted in a line
#end __init__
def __call__(self, event):
'''Matplotlib will run this function whenever the user triggers an event on our figure'''
if event.inaxes != self.img.axes: return # exit if clicks weren't within the `img` axes
if self.img.figure.canvas.manager.toolbar._active is not None: return # exit if pyplot toolbar (zooming etc.) is active
if event.name == 'button_press_event':
self.p1 = (event.xdata, event.ydata) # save 1st point
elif event.name == 'button_release_event':
self.p2 = (event.xdata, event.ydata) # save 2nd point
self.drawLineSlice() # draw the Line Slice position & data
#end __call__
def drawLineSlice( self ):
''' Draw the region along which the Line Slice will be extracted, onto the original self.img pcolormesh plot. Also update the self.axis plot to show the line slice data.'''
'''Uses code from these hints:
http://stackoverflow.com/questions/7878398/how-to-extract-an-arbitrary-line-of-values-from-a-numpy-array
http://stackoverflow.com/questions/34840366/matplotlib-pcolor-get-array-returns-flattened-array-how-to-get-2d-data-ba
'''
x0,y0 = self.p1[0], self.p1[1] # get user's selected coordinates
x1,y1 = self.p2[0], self.p2[1]
length = int( np.hypot(x1-x0, y1-y0) )
x, y = np.linspace(x0, x1, length), np.linspace(y0, y1, length)
# Extract the values along the line with nearest-neighbor pixel value:
# get temp. data from the pcolor plot
zi = self.data[x.astype(np.int), y.astype(np.int)]
# Extract the values along the line, using cubic interpolation:
#import scipy.ndimage
#zi = scipy.ndimage.map_coordinates(self.data, np.vstack((x,y)))
# if plots exist, delete them:
if self.markers != None:
if isinstance(self.markers, list):
self.markers[0].remove()
else:
self.markers.remove()
if self.arrow != None:
self.arrow.remove()
# plot the endpoints
self.markers = self.img.axes.plot([x0, x1], [y0, y1], 'wo')
# plot an arrow:
self.arrow = self.img.axes.annotate("",
xy=(x0, y0), # start point
xycoords='data',
xytext=(x1, y1), # end point
textcoords='data',
arrowprops=dict(
arrowstyle="<-",
connectionstyle="arc3",
color='white',
alpha=0.7,
linewidth=3
),
)
# plot the data along the line on provided `ax`:
if self.line != None:
self.line[0].remove() # delete the plot
self.line = self.ax.plot(zi)
#end drawLineSlice()
#end class LineTrace
# load the data:
D = np.genfromtxt(DataFilePath, ...)
fig, ax1, ax2 = plt.subplots(nrows=2, ncols=1)
# plot the data
img = ax1.pcolormesh( np.arange( len(D[0,:]) ), np.arange(len(D[:,0])), D )
# register the event handler:
LnTr = LineSlice(img, ax2) # args: the pcolor plot (img) & the axis to plot the values on (ax2)
在 pcolor 图上拖动后，结果如下（添加轴标签等之后）：
解决方案 6：
这是一个不使用 scipy 包的方法。它应该运行得更快，而且很容易理解。基本上，点 1 (pt1) 和点 2 (pt2) 之间的任何一对坐标都可以转换为 x 和 y 像素整数，因此我们不需要任何插值。
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
def euclideanDistance(coord1,coord2):
return np.sqrt((coord1[0]-coord2[0])**2+(coord1[1]-coord2[1])**2)
def getLinecut(image,X,Y,pt1,pt2):
row_col_1, row_col_2 = getRowCol(pt1,X,Y), getRowCol(pt2,X,Y)
row1,col1 = np.asarray(row_col_1).astype(float)
row2,col2 = np.asarray(row_col_2).astype(float)
dist = np.sqrt((pt1[0]-pt2[0])**2+(pt1[1]-pt2[1])**2)
N = int(euclideanDistance(row_col_1,row_col_2))#int(np.sqrt((row1-row2)**2+(col1-col2)**2))
rowList = [int(row1 + (row2-row1)/N*ind) for ind in range(N)]
colList = [int(col1 + (col2-col1)/N*ind) for ind in range(N)]
distList = [dist/N*ind for ind in range(N)]
return distList,image[rowList,colList]#rowList,colList
def getRowCol(pt,X,Y):
if X.min()<=pt[0]<=X.max() and Y.min()<=pt[1]<=Y.max():
pass
else:
raise ValueError('The input center is not within the given scope.')
center_coord_rowCol = (np.argmin(abs(Y-pt[1])),np.argmin(abs(X-pt[0])))
return center_coord_rowCol
image = np.asarray(Image.open('./Picture1.png'))[:,:,1]
image_copy = image.copy().astype(float)
X = np.linspace(-27,27,np.shape(image)[1])#[::-1]
Y = np.linspace(-15,15,np.shape(image)[0])[::-1]
pt1, pt2 = (-12,-14), (20,13)
distList, linecut = getLinecut(image_copy,X,Y,pt1,pt2)
plt.plot(distList, linecut)
plt.figure()
plt.pcolormesh(X,Y,image_copy)
plt.plot([pt1[0],pt2[0]],[pt1[1],pt2[1]],color='red')
plt.gca().set_aspect(1)
Picture1.png 所用图片：
更多详情请参见此处：
https://github.com/xuejianma/fastLinecut_radialLinecut
该代码还有另一个功能：对几条角度均匀分布的线取平均值。
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