那些遇到过的问题

分段仿射变换+扭曲输出看起来很奇怪

Joe*_*lip 5 python numpy affinetransform python-2.7 scikit-image

我有一个图像，我试图使用skimage.PiecewiseAffineTransform和来扭曲skimage.warp。我有一组控制点 ( true) 映射到一组新的控制点 ( ideal)，但扭曲没有返回我期望的结果。

在这个例子中，我有一个简单的波长梯度，我试图将其“拉直”成列。（您可能会问为什么我要查找轮廓和插值，但那是因为我实际上将此代码应用于更复杂的用例。我只是想重现这个简单示例的所有代码，这会导致相同的奇怪输出。）

为什么我的输出图像只是将输入图像扭曲成正方形和插图？我正在使用 Python 2.7.12 和 matplotlib 1.5.1。这是代码。

import matplotlib.pyplot as plt
import numpy as np
from skimage import measure, transform

true = np.array([range(i,i+10) for i in range(20)])
ideal = np.array([range(10)]*20)

# Find contours of ideal and true images and create list of control points for warp
true_control_pts = []
ideal_control_pts = []

for lam in ideal[0]:
    try:
        # Get the isowavelength contour in the true and ideal images
        tc = measure.find_contours(true, lam)[0]
        ic = measure.find_contours(ideal, lam)[0]
        nc = np.ones(ic.shape)

        # Use the y coordinates of the ideal contour
        nc[:, 0] = ic[:, 0]

        # Interpolate true contour onto ideal contour y axis so there are the same number of points
        nc[:, 1] = np.interp(ic[:, 0], tc[:, 0], tc[:, 1])

        # Add the control points to the appropriate list
        true_control_pts.append(nc.tolist())
        ideal_control_pts.append(ic.tolist())

    except (IndexError,AttributeError):
        pass

true_control_pts = np.array(true_control_pts)
ideal_control_pts = np.array(ideal_control_pts)

length = len(true_control_pts.flatten())/2
true_control_pts = true_control_pts.reshape(length,2)
ideal_control_pts = ideal_control_pts.reshape(length,2)

# Plot the original image
image = np.array([range(i,i+10) for i in range(20)]).astype(np.int32)
plt.figure()
plt.imshow(image, origin='lower', interpolation='none')
plt.title('Input image')

# Warp the actual image given the transformation between the true and ideal wavelength maps
tform = transform.PiecewiseAffineTransform()
tform.estimate(true_control_pts, ideal_control_pts)
out = transform.warp(image, tform)

# Plot the warped image!
fig, ax = plt.subplots()
ax.imshow(out, origin='lower', interpolation='none')
plt.title('Should be parallel lines')
Run Code Online (Sandbox Code Playgroud)

其输出如下所示：

在此输入图像描述

任何帮助将不胜感激！

cxw*_*cxw 4

我会给你我所拥有的。我认为我无法通过给出的测试数据来确定这一点。在如此小的图像中将 45 度角映射到直线意味着需要发生大量运动，但没有大量数据作为基础。我确实发现了一些在下面的代码中修复的特定错误，并进行了标记/* */（因为这不是您通常在 Python 文件中看到的内容，所以该标记应该突出:)）。

请在您的真实数据上尝试此代码，并让我知道它是否有效！对于此输入数据集，有一些非零输出。

最大的问题是：

  • interp数据需要排序
  • 许多控制点都是无意义的，因为该数据集中没有足够的数据（例如，将大跨度的列映射到单个点）
  • 图像中的值范围关闭（因此您的原始输出实际上几乎全部为 0 - 色块的值约为 1e-9）。

为了您将来的编码，我添加的最重要的事情是一个 3D 绘图，显示“真实”控制点如何映射到“理想”控制点。这为您提供了一个调试工具，可以显示您的控制点映射是否符合您的预期。这个情节导致了我的问题interp。

顺便说一下，请使用“before”和“after”之类的名称，而不是idealand true:) 。试图记住哪一个至少让我绊倒过一次。

第一次尝试

import pdb
import matplotlib.pyplot as plt
import numpy as np
from skimage import measure, transform, img_as_float

from mpl_toolkits.mplot3d import Axes3D # /**/

#/**/
# From /sf/answers/1014374161/ by
# /sf/users/94865501/
def flatten_list(L):
    for item in L:
        try:
            for i in flatten_list(item): yield i
        except TypeError:
            yield item
#end flatten_list

true_input = np.array([range(i,i+10) for i in range(20)])  # /** != True **/
ideal = np.array([range(10)]*20)

#pdb.set_trace()
# Find contours of ideal and true_input images and create list of control points for warp
true_control_pts = []
ideal_control_pts = []
OLD_true=[]     # /**/ for debugging
OLD_ideal=[]

for lam in [x+0.5 for x in ideal[0]]:   # I tried the 0.5 offset just to see,
    try:                                # but it didn't make much difference

        # Get the isowavelength contour in the true_input and ideal images
        tc = measure.find_contours(true_input, lam)[0]
        ic = measure.find_contours(ideal, lam)[0]
        nc = np.zeros(ic.shape) # /** don't need ones() **/

        # Use the y /** X? **/ coordinates of the ideal contour
        nc[:, 0] = ic[:, 0]

        # Interpolate true contour onto ideal contour y axis so there are the same number of points

        # /** Have to sort first - https://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.html#numpy-interp **/
        tc_sorted = tc[tc[:,0].argsort()]
            # /** Thanks to /sf/answers/197968501/ by
            # /sf/users/14583761/ **/

        nc[:, 1] = np.interp(ic[:, 0], tc_sorted[:, 0], tc_sorted[:, 1],
            left=np.nan, right=np.nan)
            # /** nan: If the interpolation is out of range, we're not getting
            #     useful data.  Therefore, flag it with a nan. **/

        # /** Filter out the NaNs **/
        # Thanks to /sf/answers/801726481/ by
        # /sf/users/31461461/
        #pdb.set_trace()
        indices = ~np.isnan(nc).any(axis=1)
        nc_nonan = nc[indices]
        ic_nonan = ic[indices]

        # Add the control points to the appropriate list.
        # /** Flattening here since otherwise I wound up with dtype=object
        #     in the numpy arrays. **/
        true_control_pts.append(nc_nonan.flatten().tolist())
        ideal_control_pts.append(ic_nonan.flatten().tolist())

        OLD_true.append(nc)     # /** for debug **/
        OLD_ideal.append(ic)

    except (IndexError,AttributeError):
        pass

#pdb.set_trace()
# /** Make vectors of all the control points. **/
true_flat = list(flatten_list(true_control_pts))
ideal_flat = list(flatten_list(ideal_control_pts))
true_control_pts = np.array(true_flat)
ideal_control_pts = np.array(ideal_flat)

# Make the vectors 2d
length = len(true_control_pts)/2
true_control_pts = true_control_pts.reshape(length,2)
ideal_control_pts = ideal_control_pts.reshape(length,2)

#pdb.set_trace()

# Plot the original image
image = np.array([range(i,i+10) for i in range(20)]) / 30.0 # /**.astype(np.int32)**/
    # /** You don't want int32 images!  See
    #     http://scikit-image.org/docs/dev/user_guide/data_types.html .
    #     Manually rescale the image to [0.0,1.0] by dividing by 30. **/
image_float = img_as_float(image) #/** make sure skimage is happy */ 
fig = plt.figure()
plt.imshow(image_float, origin='lower', interpolation='none')
plt.title('Input image')
fig.show()  # /** I needed this on my test system **/

# Warp the actual image given the transformation between the true and ideal wavelength maps
tform = transform.PiecewiseAffineTransform()
tform.estimate(true_control_pts, ideal_control_pts)
out = transform.warp(image, tform)
    # /** since we started with float, and this is float, too, the two are
    #     comparable. **/

pdb.set_trace()

# Plot the warped image!
fig, ax = plt.subplots()
ax.imshow(out, origin='lower', interpolation='none')    # /**note: float**/
plt.title('Should be parallel lines')
fig.show()

# /** Show the control points.
#     The z=0 plane will be the "true" control points (before), and the
#     z=1 plane will be the "ideal" control points (after). **/
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
fig.show()

for rowidx in range(length):
    ax.plot([true_control_pts[rowidx,0], ideal_control_pts[rowidx,0]],
            [true_control_pts[rowidx,1], ideal_control_pts[rowidx,1]],
            [0,1])

input() # /** because I was running from the command line **/
Run Code Online (Sandbox Code Playgroud)

第二次尝试

越来越近：

在此输入图像描述

这是看起来更有希望的控制点映射视图：

在此输入图像描述

您可以看到它正在尝试稍微旋转图像，这正是我对该数据集的期望。

代码

import pdb
import matplotlib.pyplot as plt
import numpy as np
from skimage import measure, transform, img_as_float

from mpl_toolkits.mplot3d import Axes3D # /**/

#/**/
# From /sf/answers/1014374161/ by
# /sf/users/94865501/
def flatten_list(L):
    for item in L:
        try:
            for i in flatten_list(item): yield i
        except TypeError:
            yield item
#end flatten_list

#/**/
# Modified from /sf/answers/1338545281/ by
# /sf/users/181174731/
def equispace(data, npts):
    x,y = data.T
    xd =np.diff(x)
    yd = np.diff(y)
    dist = np.sqrt(xd**2+yd**2)
    u = np.cumsum(dist)
    u = np.hstack([[0],u])

    t = np.linspace(0,u.max(),npts)
    xn = np.interp(t, u, x)
    yn = np.interp(t, u, y)
    return np.column_stack((xn, yn))

true_input = np.array([range(i,i+10) for i in range(20)])  # /** != True **/
ideal = np.array([range(10)]*20)

#pdb.set_trace()
# Find contours of ideal and true_input images and create list of control points for warp
true_control_pts = []
ideal_control_pts = []
OLD_true=[]     # /**/ for debugging
OLD_ideal=[]

for lam in [x+0.5 for x in ideal[0]]:   # I tried the 0.5 offset just to see,
    try:                                # but it didn't make much difference

        # Get the isowavelength contour in the true_input and ideal images
        tc = measure.find_contours(true_input, lam)[0]
            # /** So this might not have very many numbers in it. **/
        ic = measure.find_contours(ideal, lam)[0]
            # /** CAUTION: this is assuming the contours are going the same
            #       direction.  If not, you'll need to make it so. **/
        #nc = np.zeros(ic.shape) # /** don't need ones() **/

        # /** We just want to find points on _tc_ to match _ic_.  That's
        #       interpolation _within_ a curve. **/
        #pdb.set_trace()
        nc_by_t = equispace(tc,ic.shape[0])
        ic_by_t = equispace(ic,ic.shape[0])


        ### /** Not this **/
        ## Use the y /** X? **/ coordinates of the ideal contour
        #nc[:, 0] = ic[:, 0]
        #
        ## Interpolate true contour onto ideal contour y axis so there are the same number of points
        #
        ## /** Have to sort first - https://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.html#numpy-interp **/
        #tc_sorted = tc[tc[:,0].argsort()]
        #    # /** Thanks to /sf/answers/197968501/ by
        #    # /sf/users/14583761/ **/
        #
        #nc[:, 1] = np.interp(ic[:, 0], tc_sorted[:, 0], tc_sorted[:, 1],
        #    left=np.nan, right=np.nan)
        #    # /** nan: If the interpolation is out of range, we're not getting
        #    #     useful data.  Therefore, flag it with a nan. **/

        # /** Filter out the NaNs **/
        # Thanks to /sf/answers/801726481/ by
        # /sf/users/31461461/
        #pdb.set_trace()
        #indices = ~np.isnan(nc).any(axis=1)
        #nc_nonan = nc[indices]
        #ic_nonan = ic[indices]
        #

        # Add the control points to the appropriate list.
        ## /** Flattening here since otherwise I wound up with dtype=object
        ##     in the numpy arrays. **/
        #true_control_pts.append(nc_nonan.flatten().tolist())
        #ideal_control_pts.append(ic_nonan.flatten().tolist())

        #OLD_true.append(nc)     # /** for debug **/
        #OLD_ideal.append(ic)

        true_control_pts.append(nc_by_t)
        ideal_control_pts.append(ic_by_t)

    except (IndexError,AttributeError):
        pass

pdb.set_trace()
# /** Make vectors of all the control points. **/
true_flat = list(flatten_list(true_control_pts))
ideal_flat = list(flatten_list(ideal_control_pts))
true_control_pts = np.array(true_flat)
ideal_control_pts = np.array(ideal_flat)

# Make the vectors 2d
length = len(true_control_pts)/2
true_control_pts = true_control_pts.reshape(length,2)
ideal_control_pts = ideal_control_pts.reshape(length,2)

#pdb.set_trace()

# Plot the original image
image = np.array([range(i,i+10) for i in range(20)]) / 30.0 # /**.astype(np.int32)**/
    # /** You don't want int32 images!  See
    #     http://scikit-image.org/docs/dev/user_guide/data_types.html .
    #     Manually rescale the image to [0.0,1.0] by dividing by 30. **/
image_float = img_as_float(image) #/** make sure skimage is happy */ 
fig = plt.figure()
plt.imshow(image_float, origin='lower', interpolation='none')
plt.title('Input image')
fig.show()  # /** I needed this on my test system **/

# Warp the actual image given the transformation between the true and ideal wavelength maps
tform = transform.PiecewiseAffineTransform()
tform.estimate(true_control_pts, ideal_control_pts)
out = transform.warp(image, tform)
    # /** since we started with float, and this is float, too, the two are
    #     comparable. **/

pdb.set_trace()

# Plot the warped image!
fig, ax = plt.subplots()
ax.imshow(out, origin='lower', interpolation='none')    # /**note: float**/
plt.title('Should be parallel lines')
fig.show()

# /** Show the control points.
#     The z=0 plane will be the "true" control points (before), and the
#     z=1 plane will be the "ideal" control points (after). **/
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
fig.show()

for rowidx in range(length):
    ax.plot([true_control_pts[rowidx,0], ideal_control_pts[rowidx,0]],
            [true_control_pts[rowidx,1], ideal_control_pts[rowidx,1]],
            [0,1])

input() # /** because I was running from the command line **/
Run Code Online (Sandbox Code Playgroud)

解释

以不同的方式思考插值：X 和 Y 坐标实际上并不是您想要映射的，是吗？我认为你想要映射的是沿轮廓的距离，以便对角线轮廓被拉伸为垂直轮廓。这就是这些行的作用：

nc_by_t = equispace(tc,ic.shape[0])
ic_by_t = equispace(ic,ic.shape[0])
Run Code Online (Sandbox Code Playgroud)

我们ic.shape[0]沿着每个轮廓制作许多等距的点，然后将这些点相互映射。 equispace是根据这个答案修改的。因此，这会拉伸较短的轮廓以适应较长的轮廓，无论哪种方式，其点数由 中的轮廓长度定义ic。实际上，您可以使用此技术使用任意数量的点；我刚刚测试了100点，得到了基本相同的结果。

再次使用您的实际数据进行测试。考虑一下您的插值参考到底是什么。它真的是X坐标还是Y坐标吗？是沿着轮廓的距离吗？轮廓的百分比（如上所述）？

好的，还有一个想法

对于这个特定的测试用例，更多数据会有帮助吗？是的！

在此输入图像描述

我使用较大的图像来确定控制点，然后在该区域的中心映射较小的图像。

代码

（此时完全是一团糟 - 请参阅===========标记）

import pdb
import matplotlib.pyplot as plt
import numpy as np
from skimage import measure, transform, img_as_float

from mpl_toolkits.mplot3d import Axes3D # /**/

#/**/
def test_figure(data,title):
    image_float = img_as_float(data) #/** make sure skimage is happy */ 
    fig = plt.figure()
    plt.imshow(image_float, origin='lower', interpolation='none')
    plt.title(title)
    fig.show()

#/**/
# From /sf/answers/1014374161/ by
# /sf/users/94865501/
def flatten_list(L):
    for item in L:
        try:
            for i in flatten_list(item): yield i
        except TypeError:
            yield item
#end flatten_list

#/**/
# Modified from /sf/answers/1338545281/ by
# /sf/users/181174731/
def equispace(data, npts):
    x,y = data.T
    xd =np.diff(x)
    yd = np.diff(y)
    dist = np.sqrt(xd**2+yd**2)
    u = np.cumsum(dist)
    u = np.hstack([[0],u])

    t = np.linspace(0,u.max(),npts)
    xn = np.interp(t, u, x)
    yn = np.interp(t, u, y)
    return np.column_stack((xn, yn))

# ======================  BIGGER
true_input = np.array([range(i,i+20) for i in range(30)])  # /** != True **/
ideal = np.array([range(20)]*30)
# ======================    
test_figure(true_input / 50.0, 'true_input')
test_figure(ideal / 20.0, 'ideal')

#pdb.set_trace()
# Find contours of ideal and true_input images and create list of control points for warp
true_control_pts = []
ideal_control_pts = []
OLD_true=[]     # /**/ for debugging
OLD_ideal=[]

for lam in [x+0.5 for x in ideal[0]]:   # I tried the 0.5 offset just to see,
    try:                                # but it didn't make much difference

        # Get the isowavelength contour in the true_input and ideal images
        tc = measure.find_contours(true_input, lam)[0]
            # /** So this might not have very many numbers in it. **/
        ic = measure.find_contours(ideal, lam)[0]
            # /** CAUTION: this is assuming the contours are going the same
            #       direction.  If not, you'll need to make it so. **/
        #nc = np.zeros(ic.shape) # /** don't need ones() **/

        # /** We just want to find points on _tc_ to match _ic_.  That's
        #       interpolation _within_ a curve. **/
        #pdb.set_trace()
        nc_by_t = equispace(tc,ic.shape[0])
        ic_by_t = equispace(ic,ic.shape[0])


        ### /** Not this **/
        ## Use the y /** X? **/ coordinates of the ideal contour
        #nc[:, 0] = ic[:, 0]
        #
        ## Interpolate true contour onto ideal contour y axis so there are the same number of points
        #
        ## /** Have to sort first - https://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.html#numpy-interp **/
        #tc_sorted = tc[tc[:,0].argsort()]
        #    # /** Thanks to /sf/answers/197968501/ by
        #    # /sf/users/14583761/ **/
        #
        #nc[:, 1] = np.interp(ic[:, 0], tc_sorted[:, 0], tc_sorted[:, 1],
        #    left=np.nan, right=np.nan)
        #    # /** nan: If the interpolation is out of range, we're not getting
        #    #     useful data.  Therefore, flag it with a nan. **/

        # /** Filter out the NaNs **/
        # Thanks to /sf/answers/801726481/ by
        # /sf/users/31461461/
        #pdb.set_trace()
        #indices = ~np.isnan(nc).any(axis=1)
        #nc_nonan = nc[indices]
        #ic_nonan = ic[indices]
        #

        # Add the control points to the appropriate list.
        ## /** Flattening here since otherwise I wound up with dtype=object
        ##     in the numpy arrays. **/
        #true_control_pts.append(nc_nonan.flatten().tolist())
        #ideal_control_pts.append(ic_nonan.flatten().tolist())

        #OLD_true.append(nc)     # /** for debug **/
        #OLD_ideal.append(ic)

        true_control_pts.append(nc_by_t)
        ideal_control_pts.append(ic_by_t)

    except (IndexError,AttributeError):
        pass

#pdb.set_trace()
# /** Make vectors of all the control points. **/
true_flat = list(flatten_list(true_control_pts))
ideal_flat = list(flatten_list(ideal_control_pts))
true_control_pts = np.array(true_flat)
ideal_control_pts = np.array(ideal_flat)

# Make the vectors 2d
length = len(true_control_pts)/2
true_control_pts = true_control_pts.reshape(length,2)
ideal_control_pts = ideal_control_pts.reshape(length,2)

#pdb.set_trace()

# Plot the original image
image = np.array([range(i,i+10) for i in range(20)]) / 30.0 # /**.astype(np.int32)**/
    # /** You don't want int32 images!  See
    #     http://scikit-image.org/docs/dev/user_guide/data_types.html .
    #     Manually rescale the image to [0.0,1.0] by dividing by 30. **/

# ======================    
# /** Pad from 10x20 to 20x30 just for grins **/
#pdb.set_trace()
image = np.concatenate( (np.zeros((20,5)),image,np.zeros((20,5))), 1)
    # now it's 20x20
image = np.concatenate( (np.zeros((5,20)),image,np.zeros((5,20))), 0)
# ======================    

#Plot it
image_float = img_as_float(image) #/** make sure skimage is happy */ 
fig = plt.figure()
plt.imshow(image_float, origin='lower', interpolation='none')
plt.title('Input image')
fig.show()  # /** I needed this on my test system **/

# Warp the actual image given the transformation between the true and ideal wavelength maps
tform = transform.PiecewiseAffineTransform()
tform.estimate(true_control_pts, ideal_control_pts)
out = transform.warp(image, tform)
    # /** since we started with float, and this is float, too, the two are
    #     comparable. **/

pdb.set_trace()

# Plot the warped image!
fig, ax = plt.subplots()
ax.imshow(out, origin='lower', interpolation='none')    # /**note: float**/
plt.title('Should be parallel lines')
fig.show()

# /** Show the control points.
#     The z=0 plane will be the "true" control points (before), and the
#     z=1 plane will be the "ideal" control points (after). **/
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
fig.show()

for rowidx in range(length):
    ax.plot([true_control_pts[rowidx,0], ideal_control_pts[rowidx,0]],
            [true_control_pts[rowidx,1], ideal_control_pts[rowidx,1]],
            [0,1])

input() # /** because I was running from the command line **/
Run Code Online (Sandbox Code Playgroud)

归档时间：

查看次数：

2916 次

最近记录：

8 年，11 月 前
压扁不规则的列表列表 412
按列对NumPy中的数组进行排序 295
如何删除包含非数字值的numpy.ndarray中的所有行 81
如何生成等间隔插值 12
更多相关链接
相关归档
通过Python中的属性比较对象实例是否相等 219
在Python中使用代码存储库时如何引用资源的相对路径 179
Python模块,名称中带有破折号或连字符( - ) 79
目标数据库不是最新的 56
在Windows上通过命令行运行jupyter 44
如何计算python中的百分比 9
在没有循环的3D阵列中计算沿着第三轴的2D阵列的逆 7
用于Point(x,y)类的Python中的__setitem__实现 4
如何在numpy中表示“：” 4
ImportError:libcusolver.so.8.0:无法打开共享对象文件:没有这样的文件或目录 3
难疑归档
在Python中调用外部命令 4553
ListView中的图像延迟加载 1881
如何在Bash中解析命令行参数？ 1764
如何基于通配符匹配以递归方式查找当前和子文件夹中的所有文件？ 1695
在JavaScript中解析JSON？ 1642
如何将字节数组转换为十六进制字符串,反之亦然？ 1313
为什么不从List <T>继承？ 1299
谁正在侦听Mac OS X上的给定TCP端口？ 1267
如何在SQL中使用JOIN执行UPDATE语句？ 1262
angular.service vs angular.factory 1061