import numpy as np import Image from mymeanfilter import mymeanfilter from mystdfilter import mystdfilter im_0 = np.asarray(Image.open('test3_1.jpg').convert('L'))/float(255) fig = pl.figure(figsize=(8,4)) # ax_1 ax_1 = fig.add_subplot(131) ax_1.imshow(im_0,cmap=cm.gray,norm=pl.Normalize(vmin=0,vmax=1)) ax_1.set_title('input image') ax_1.set_xticks([]) ax_1.set_yticks([]) # ax_2 ax_2 = fig.add_subplot(132) im_1 = mymeanfilter(im_0,[5,5]) ax_2.imshow(im_1,cmap=cm.gray,norm=pl.Normalize(vmin=0,vmax=1)) ax_2.set_title('my mean filter') ax_2.set_xticks([]) ax_2.set_yticks([]) # ax_3 ax_3 = fig.add_subplot(133) im_2 = mystdfilter(im_0,im_1,[5,5]) ax_3.imshow(im_2,cmap=cm.gray,norm=pl.Normalize(vmin=0,vmax=1)) ax_3.set_title('my std filter') ax_3.set_xticks([]) ax_3.set_yticks([]) fig.suptitle('exp_3_2') fig.savefig('exp_3_2.jpg') fig.savefig('exp_3_2.eps')
import Image from mywgn import wgn from mymeanfilter import mymeanfilter im_0 = np.asarray(Image.open('test3_1.jpg').convert('L'))/float(255) fig = pl.figure(figsize=(8,4)) # ax_1 ax_1 = fig.add_subplot(131) ax_1.imshow(im_0,cmap=cm.gray,norm=pl.Normalize(vmin=0,vmax=1)) ax_1.set_title('input image') ax_1.set_xticks([]) ax_1.set_yticks([]) # ax_2 ax_2 = fig.add_subplot(132) im_1 = im_0 + wgn(0,np.sqrt(0.02),im_0.shape) ax_2.imshow(im_1,cmap=cm.gray,norm=pl.Normalize(vmin=0,vmax=1)) ax_2.set_title('with WGN') ax_2.set_xticks([]) ax_2.set_yticks([]) # ax_3 ax_3 = fig.add_subplot(133) im_2 = mymeanfilter(im_1,[5,5]) ax_3.imshow(im_2,cmap=cm.gray,norm=pl.Normalize(vmin=0,vmax=1)) ax_3.set_title('after my mean filter') ax_3.set_xticks([]) ax_3.set_yticks([]) fig.suptitle('exp_3_3') fig.savefig('exp_3_3.jpg') fig.savefig('exp_3_3.eps')