Pythonでの色収差の評価

Question

こんにちは、このコードは、赤と緑のチャンネル間、および青と緑のチャンネル間の歪みの中心（歪みの中心）と歪みの大きさ（アルファ）を​​与えることによって画像の色収差を推定します。すべてのヘルプは大歓迎に感謝しますあなたを - 私はWarpRegion機能でエラー以下

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' 

は完全なコードである必要があります。 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

DSMは正しく、これはscipy.interp2dで見ることができinterp2dの正しい呼び出し構文ではありません述べています。呼び出し構文とエラーメッセージ（またはモジュール自体のどちらかが好きな方）をもう一度読むと、辞書をインデックスとして使用して自然に例外がスローされることがわかります。

あなたがしようとしているのは、新しい位置xrampf1、yrampf1の配列xrampf、yrampfによって与えられたグリッドの補間です。 scipyのドキュメントには、コードに次のように変換するまったく同じ使用例もあります。

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

私はそれがあなたの意図だったことを願っています。

種類について