def hanbe(imagefilename): #判別分析法処理関数 #imagefilename :処理画像ファイル名 #返り値:二値化結果の二次元画素配列 pgm = pgmdat() pgm.readimg(imagefilename) img = pgm.img width = pgm.width height = pgm.height N = width * height print height, width,pgm.depth t = 0 fix = np.zeros([height,width],dtype = int) histdata = [0] * (pgm.depth+1) T = 0 tes1 = 0 #ヒストグラム作成 for x in range(height): for y in range(width): n = img[x][y] histdata[n] += 1 Ph = [double(p)/N for p in histdata] U = 0 for j in range(255): U += j*Ph[j] Ramda = 0 ramda = 0 #クラス間分散ramdaを求めしきい値Tを最適化する for t in range(255): w = 0 u = 0 for k in range(t+1): w += Ph[k] u += k*Ph[k] if w > 0: ramda = (U*w - u)**2/(w*(1-w)) if ramda > Ramda: Ramda = ramda T = t print T #求めたしきい値Tを使って二値化 for H in range(height): for W in range(width): if img[H][W] < T: fix[H][W] = 0 else: fix[H][W] = 255 return fix,img
#coding:utf-8 """[課題２]ノイズ除去""" import sys import numpy as np from pylab import * from pypgm import pgmdat import cv2 import matplotlib.pyplot as plt argvs = sys.argv if (len(argvs) != 2): print "miss" quit() imagefilename = argvs[1] pgm = pgmdat() pgm.readimg(imagefilename) img = pgm.img width = pgm.width height = pgm.height fix = np.zeros([height+1,width+1],dtype=int) fix2 = np.zeros([height+1,width+1],dtype=int) #先鋭化フィルタの宣言 matrix = np.array([[0,-1,0], [-1,5,-1], [0,-1,0]]) #平均値フィルタの宣言 matrix2 = np.array([[1.0/9,1.0/9,1.0/9], [1.0/9,1.0/9,1.0/9], [1.0/9,1.0/9,1.0/9]])
