bnf.py

#!/usr/bin/python
import numpy as np

class BNF:
    'Bilateral Noise Filtering'

    def __init__(self, img, dw, rw, rthres, clip):
        self.img = img
        self.dw = dw
        self.rw = rw
        self.rthres = rthres
        self.clip = clip

    def padding(self):
        img_pad = np.pad(self.img, (2, 2), 'reflect')
        return img_pad

    def clipping(self):
        np.clip(self.img, 0, self.clip, out=self.img)
        return self.img

    def execute(self):
        img_pad = self.padding()
        img_pad = img_pad.astype(np.uint16)
        raw_h = self.img.shape[0]
        raw_w = self.img.shape[1]
        bnf_img = np.empty((raw_h, raw_w), np.uint16)
        rdiff = np.zeros((5,5), dtype='uint16')
        for y in range(img_pad.shape[0] - 4):
            for x in range(img_pad.shape[1] - 4):
                print("[x,y]:["+str(x)+','+str(y)+']')
                for i in range(5):
                    for j in range(5):
                        rdiff[i,j] = abs(img_pad[y+i,x+j].astype(int) - img_pad[y+2, x+2].astype(int))
                        # rdiff[i,j] = abs(img_pad[y+i,x+j] - img_pad[y+2, x+2])
                        if rdiff[i,j] >= self.rthres[0]:
                            rdiff[i,j] = self.rw[0]
                        elif rdiff[i,j] < self.rthres[0] and rdiff[i,j] >= self.rthres[1]:
                            rdiff[i,j] = self.rw[1]
                        elif rdiff[i,j] < self.rthres[1] and rdiff[i,j] >= self.rthres[2]:
                            rdiff[i,j] = self.rw[2]
                        elif rdiff[i,j] < self.rthres[2]:
                            rdiff[i,j] = self.rw[3]
                weights = np.multiply(rdiff, self.dw)
                bnf_img[y,x] = np.sum(np.multiply(img_pad[y:y+5,x:x+5], weights[:,:])) / np.sum(weights)
        self.img = bnf_img
        return self.clipping()