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Copy pathProjTest.py
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132 lines (109 loc) · 3.24 KB
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#=================================================================
#
# Image Processing - Main
# Copyright(c) KazukiAmakawa, all right reserved.
#
#=================================================================
#This is the main function of the program, all program will begin from this program
#May cannot compile under the Windows System.
#import head
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import os
import os.path
import math
import matplotlib.patches as patches
from scipy import misc
from collections import deque
from PIL import ImageFilter
import cv2
import random
import pywt
from copy import deepcopy
import time
#import files
import Init
#Definition Constant
Beta = 0.1
Lambda = 0.1
Gamma = 0.04
TotalKase = 3
def Wavelet(img):
img1 = np.array([[np.float64(0.00) for n in range(len(img[0]))] for n in range(len(img))])
for i in range(0, len(img)):
for j in range(0, len(img[i])):
img1[i][j] = float(img[i][j])
coeffs = pywt.dwt2(img1, "haar")
#Init.ArrOutput(coeffs)
return pywt.idwt2(coeffs, "haar")
def Gauss(img):
Rem = [[0 for n in range(len(img[0]) + 20)] for n in range(len(img) + 20)]
img = cv2.GaussianBlur(img, (11, 11), 1.6)
for i in range(0, len(img)):
for j in range(0, len(img[i])):
rand = random.normalvariate(0, 1.4142135623730951)
img[i][j] += rand
Rem[i][j] += rand
Wavelet(img)
return img, Rem
def ADMM(img):
#Pre-treatment
img, Rem = Gauss(img)
Jud = [[0 for n in range(len(img[0]))] for n in range(len(img))]
#print(img)
#print(Rem)
for i in range(0, len(img)):
for j in range(0, len(img[i])):
img[i][j] = float(img[i][j])
Rem[i][j] = float(Rem[i][j])
#Definition Iterator vartiables
u = np.matrix(img)
y = [[0.00 for n in range(0, len(img[0]))] for n in range(len(img))]
y = np.matrix(pywt.dwt2(y, "haar"))
mu = [[0.00 for n in range(0, len(img[0]))] for n in range(len(img))]
mu = np.matrix(pywt.dwt2(mu, "haar"))
"""
for i in range(0, len(y)):
for j in range(0, len([i])):
print(y[i][j])
"""
#y = [np.matrix(y[0]), np.matrix(y[1]), np.matrix(y[2]), np.matrix(y[3])]
#mu = [np.matrix(mu[0]), np.matrix(mu[1]), np.matrix(mu[2]), np.matrix(mu[3])]
#Definition Constant
H = np.matrix(np.abs(np.fft.fftshift(np.fft.fft2(img).real)))
HT = H.transpose()
#Main Algorithm
for Kase in range(0, TotalKase):
if TotalKase == 0:
#print(TotalKase)
u1 = ((np.matrix(H.transpose() * H) + Beta).I) * (HT * u + Beta * np.matrix(pywt.idwt2([y[0] + mu/Beta, y[1] + mu/Beta, y[2] + mu/Beta, y[3] + mu/Beta], "haar")))
#print("SB!!")
#y1 = pywt.dwt2(u1) - mu/Beta
y1 = y
if Gamma < 1:
#print("Input Error")
time.sleep(5)
break
if np.abs(y1.all()) > 0.0001:
y1 -= Lambda / Beta
elif np.abs(y1.all()) < 0.0001:
y1 += Lambda / Beta
else:
y1 = np.matrix([[0.00 for n in range(0, len(img[0]))] for n in range(len(img))] for n in range(4))
mu1 = mu + Gamma*(y1 - pywt.dwt2(u1))
u = u1
y = y1
mu = mu1
else:
pass
for i in range(0, len(img)):
for j in range(0, len(img[i])):
Jud[i][j] = img[i][j] - 0.1*Rem[i][j]
#After treatment
for i in range(0, len(Jud)):
for j in range(0, len(Jud[i])):
Jud[i][j] = int(Jud[i][j])
Jud[i][j] = min(255, Jud[i][j])
Jud[i][j] = max(0, Jud[i][j])
return Jud