Question

此代码通过给出红色和绿色通道之间以及蓝色和绿色通道之间的失真（x，y）和失真（α）的大小来估计图像中的色差。我在WarpRegion函数中有错误

File "CAfeb.py", line 217, in warpRegion
    reg_w = sp.interpolate.interp2d(yrampf,xrampf,Cwarp, yramp1f, xramp1f,'cubic');
  File "/usr/lib/python2.7/dist-packages/scipy/interpolate/interpolate.py", line 109, in __init__
    'quintic' : 5}[kind]
TypeError: unhashable type: 'numpy.ndarray'

以下是完整的代码 - 非常感谢任何帮助 - 谢谢。 Areej

import math
from PIL import Image 
import numpy as np
from decimal import Decimal
import scipy as sp
from scipy import interpolate
from scitools.std import ndgrid
from scipy import ogrid, sin, mgrid, ndimage, array


def ldimage():
    #load image
    global im
    im = Image.open("/home/areej/Desktop/mandril_color.tif") 

def analyzeCA(mode, im):
    n_regions = 10;
    reg_size = [300, 300];
    overlap = 0.5;


    levels = 9;
    steps = 2;
    edge_width = 10;
    hist_sz = 128;

   # alpha_1 and alpha_2 are assumed to be between these values
    w_data = [0.9985, 1.0015];

    reg_list=[]

   #creating an array of pixels so that we can access them
    pix=im.load()

#
#Analyze full image

    if mode=='full':
        print "Doing a full analysis"
        # mx_shift is the third argument in 'full' mode
        mx_shift = n_regions;
            # [ydim,xdim,zdim]= size(im);
        ydim=im.size[0]
        xdim=im.size[1]
        zdim=3

        print "Image dimensions: [ydim, xdim, zdim]= "+str([ydim,xdim,zdim])


        global alpha_mx, alpha_mn
        alpha_mx = 1 + 4*mx_shift / math.sqrt( xdim*xdim + ydim*ydim );
        alpha_mn = 1.0/alpha_mx;

        print "alpha_mx= "+str(alpha_mx)
        print "alpha_mn= "+str(alpha_mn)
        #recompute alpha_1 and alpha_2 to be between
        #these new values
        w_data = [alpha_mn, alpha_mx];
        ew = edge_width;

        #take the image minus a ew-wide edge
        roi = [ew+1, xdim-ew, ew+1, ydim-ew];

        print "edge_width= "+str(ew)
        print "roi= "+str(roi)

        #Analyze blue to green chromatic aberration
        bg_params = parameterSearch( im, [3, 2], roi, ew, hist_sz, w_data);

        # Analyze red to green chromatic aberration
        rg_params = parameterSearch( im, [1, 2], roi, ew, hist_sz, w_data );
    elif mode=='reg':
      print "we should do a regional analysis here"

    else:
     print "unsupported call"
#def estimateCARegions( im, [3, 2], reg_list, settings ):
def parameterSearch( im, colour_space, roi, ew, hist_sz, w_data):

    #levels is number of iterations 
    levels = 8;
    steps = 2;

    #[ydim,xdim,zdim] = size(im);
    ydim=im.size[0]
    xdim=im.size[1]
    zdim= 3


    x_data = [1, xdim];
    y_data = [1, ydim];

    xlim = x_data;
    ylim = y_data;
    zlim = w_data;

#work out which of height and width is the bigger
    dim = max(xdim,ydim)

    print "The highest dimension is : "+str(dim)

#check that roi falls within expected boundries
    if ((roi[0] <= ew) or (roi[1] > xdim-ew) or (roi[2] <= ew) or (roi[3] > ydim-ew)):
        print "ROI is too close to image edges"
        return -1 # TODO: terminate here with an error
        #Get image regions

    source = im.split()
    Cfixed = source[2]
    Cwarp  = source[1]
    #[ydim,xdim,zdim] = size(im);
    ydimCwarp=Cwarp.size[0]
    xdimCwarp=Cwarp.size[1]
    print 'xdimCwarp'+str(xdimCwarp)

    roi_pad = [roi[0]-ew, roi[1]+ew, roi[2]-ew, roi[3]+ew];
    for levels in range(1,8):
    #Guess at a center and then compute best warp
    #user defined function linear_space used to generate linearly spaced vectors
        x_coords = np.linspace(0,511,steps+2)
        y_coords = np.linspace(0,511,steps+2)
        z_coords = np.linspace(alpha_mn,alpha_mx,steps+2)
        step_x=(xlim[1]-xlim[0])/(steps+1)
        start_x=xlim[0]+step_x
        end_x=xlim[1]-step_x+0.5
        step_y=(ylim[1]-ylim[0])/(steps+1)
        start_y=ylim[0]+step_y
        end_y=ylim[1]-step_y+0.5
        step_z=(zlim[1]-zlim[0])/(steps+1)
        start_z=zlim[0]+step_z
        fudge_z=step_z/2.0
        end_z=zlim[1]-step_z+fudge_z
        #Do not include end points in search;
        centers_x, centers_y, warps= np.mgrid[start_x:end_x:step_x,start_y:end_y:step_y,start_z:end_z:step_z]
        centers_x=centers_x.flatten()
        centers_y=centers_y.flatten()
        warps=warps.flatten()
        mi = np.zeros(centers_x.size)

        for k in range(0,centers_x.size):
           cx = centers_x[k]
           cy = centers_y[k]
           wz = warps[k]       
           #Warp the region 
           temp_im = warpRegion(Cwarp, roi_pad, [cx, cy, wz])
                #correlation
           mi[k] = np.corrcoef(Cfixed, temp_im)
               #Now pick the best quadrant
        v, max_ix = math.max(mi)
        ix, jx, kx = arrayInd(mi.size, max_ix);
        ##The coordinates of err are off by 1 from x_coords and y_coords because
        ##we did not include the end point
    xlim = x_coords([jx, jx+2]);
    ylim = y_coords([ix, ix+2]);
    zlim = z_coords([kx, kx+2]);

    cx = math.mean(xlim);
    cy = math.mean(ylim);
    wz = math.mean(zlim);

    print "x= "+str(cx)
    print "y= "+str(cy)
    print "z= "+str(wz)
def warpRegion(Cwarp, roi_pad, (cx, cy, wz)):
#Unpack region indices
    sx, ex, sy, ey = roi_pad

    xramp, yramp = np.mgrid[sx:ex+1, sy:ey+1]

    xrampc = xramp - cx;
    yrampc = yramp - cy;
    xramp1 = 1/wz*xrampc;
    yramp1 = 1/wz*yrampc;
    xrampf = xrampc.flatten()
    yrampf = yrampc.flatten()
    xramp1f = xramp1.flatten()
    yramp1f = yramp1.flatten()
    reg_w = sp.interpolate.interp2d(yrampf,xrampf,Cwarp, yramp1f, xramp1f,'cubic');

ldimage()
analyzeCA('full', im)

Answer 1

正如DSM所说，这不是interp2d的正确调用语法，可以在scipy.interp2d查看。如果您再次阅读调用语法然后再读取错误消息（或者您喜欢的模块本身），您会发现您正在尝试使用数组作为字典的索引，这将自然地引发异常。

我认为你要做的是在新位置xrampf1，yrampf1处对数组xrampf，yrampf给出的网格进行插值。 scipy文档还提供了一个完全相同的用法示例，它将代码转换为以下代码：

interp_func = sp.interpolate.interp2d(yrampf, xrampf, Cwarp, kind='cubic')
reg_w = interp_func(yramp1f, xramp1f)

我希望你有意这样做。

亲切的问候

蟒蛇中的色差估计

1 个答案: