Привет, этот код оценивает хроматическую аберрацию в изображении, давая центр искажения (x, y) и величину искажения (альфа) между красным и зеленым каналами, а также между синим и зеленым каналами.У меня есть ошибка в функции WarpRegion
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'
Ниже приведен полный код. Любая помощь будет принята с благодарностью. Спасибо.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)