def sart_tvm(tils,
angles,
tau=0.2,
round_num=5,
tvm_iteration=200,
relaxation=0.3):
import rof
# print("--start sart_tvm---")
denoise_TVM = None
for epoch in range(round_num):
# SART
if epoch == 0:
recon_SART = iradon_sart(tils,
theta=angles,
image=None,
relaxation=relaxation)
else:
recon_SART = iradon_sart(tils,
theta=angles,
image=denoise_TVM,
relaxation=relaxation)
unit_tvm = np.zeros(recon_SART.shape)
image = rof.denoise(recon_SART,
unit_tvm,
tau=tau,
Ngrad=tvm_iteration)[0]
return image
def test():
im = zeros((500, 500))
im[100:400, 100:400] = 128
im[200:300, 200:300] = 255
im = im + 30 * random.standard_normal((500, 500))
from scipy.misc import imsave
imsave('synth_orgin.jpg', im)
return
U, T = rof.denoise(im, im)
G = filters.gaussian_filter(im, 10)
imsave('synth_rof.jpg', U)
imsave('synth_gaussian.jpg', G)
def test():
im = zeros((500, 500))
im[100:400,100:400] = 128
im[200:300,200:300] = 255
im = im + 30*random.standard_normal((500, 500))
from scipy.misc import imsave
imsave('synth_orgin.jpg', im)
return
U,T = rof.denoise(im, im)
G = filters.gaussian_filter(im, 10)
imsave('synth_rof.jpg', U)
imsave('synth_gaussian.jpg', G)
from PIL import Image
import numpy
import rof
import stereo
im_l = numpy.array(Image.open('./data/ppm/scene1.row3.col3.ppm').convert('L'), 'f')
im_r = numpy.array(Image.open('./data/ppm/scene1.row3.col4.ppm').convert('L'), 'f')
steps = 12
start = 4
wid = 9
res = stereo.plane_sweep_ncc(im_l, im_r, start, steps, wid)
res, _ = rof.denoise(res, res, tv_weight=80/255.0, tolerance=0.01)
import scipy.misc
scipy.misc.imsave('./output/depth_rof.png', res)
import numpy as np
from scipy.ndimage import filters
import rof
from matplotlib import pyplot as plt
from PIL import Image
# now lets see how the ROF algorithm handles real images
im2 = np.array(Image.open('Image 4_z34.tif'))
U2, T2 = rof.denoise(im2, im2)
plt.subplot(121), plt.imshow(im2,'gray'), plt.title('Original Image')
plt.axis('equal'), plt.axis('off')
plt.subplot(122), plt.imshow(U2,'gray'), plt.title('ROF denoising')
plt.axis('equal'), plt.axis('off')
plt.show()
from pylab import * from scipy import misc import numpy import rof im = misc.lena() #im = im + 20 * numpy.random.standard_normal(im.shape) U, T = rof.denoise(im, im, tv_weight=30, tolerance=0.01) gray() subplot(1, 2, 1) imshow(im) subplot(1, 2, 2) imshow(U) show()
im2H = getHisteq(im2Quotient)
Image.fromarray(im2H).convert('RGB').save(
'Lab1/Histeq on Quotient gaussian blur of 2 gray.png')
im3H = getHisteq(im3Quotient)
Image.fromarray(im3H).convert('RGB').save(
'Lab1/Histeq on Quotient gaussian blur of 5 gray.png')
im4H = getHisteq(im4Quotient)
Image.fromarray(im4H).convert('RGB').save(
'Lab1/Histeq on Quotient gaussian blur of 10 gray.png')
temp = getHisteq(array(Image.open('dark.jpg').convert('L')))
temp = array(temp, 'uint8')
Image.fromarray(temp).convert('RGB').save(
'Lab1/Histeq on dark image sample.png')
temp = getHisteq(array(Image.open('bright.jpg').convert('L')))
temp = array(temp, 'uint8')
Image.fromarray(temp).convert('RGB').save(
'Lab1/Histeq on bright image sample.png')
#RUDIN OSHER FATEMI ON BLURRED WITH GAMMA 2,5,10 AND OTHER SAMPLE IMAGES (SALT AND PEPPER NOISE)
temp = rof.denoise(im2Gray, im2Gray)
Image.fromarray(temp).convert('RGB').save('Lab1/rof on gamma blur 2.png')
temp = rof.denoise(im3Gray, im3Gray)
Image.fromarray(temp).convert('RGB').save('Lab1/rof on gamma blur 5.png')
temp = rof.denoise(im4Gray, im4Gray)
Image.fromarray(temp).convert('RGB').save('Lab1/rof on gamma blur 10.png')
salted = array(Image.open('salted.jpg').convert('L'))
temp = rof.denoise(salted, salted)
Image.fromarray(temp).convert('RGB').save('Lab1/rof on salt noise.png')
peppered = array(Image.open('peppered.jpg').convert('L'))
temp = rof.denoise(peppered, peppered)
Image.fromarray(temp).convert('RGB').save('Lab1/rof on pepper noise.png')
def update(val): U, _ = rof.denoise(im, im, tv_weight=srof.val, tolerance=0.01) subplot(1, 2, 2) imshow(U) draw()
import rof
from pylab import *
from PIL import Image
# import scipy.misc
import imageio
from skimage import *
im1 = array(Image.open('../data/flower32_t0.png').convert("L"))
im2 = array(Image.open('../data/ceramic-houses_t0.png').convert("L"))
U1, T1 = rof.denoise(im1, im1, tolerance=0.001)
U2, T2 = rof.denoise(im2, im2, tolerance=0.001)
t1 = 0.8 # flower32_t0 threshold
t2 = 0.4 # ceramic-houses_t0 threshold
seg_im1 = img_as_uint(U1 < t1*U1.max())
seg_im2 = img_as_uint(U2 < t2*U2.max())
fig = figure()
gray()
subplot(231)
axis('off')
imshow(im1)
subplot(232)
axis('off')
imshow(U1)
subplot(233)
axis('off')
Created on Sun Aug 14 21:14:44 2016
@author: user
"""
from numpy import *
from numpy import random
from pylab import *
from scipy.ndimage import filters
import rof
# create synthetic image with noise
im = zeros((500,500))
im[100:400,100:400] = 128
im[200:300,200:300] = 255
'''add in noise'''
im = im + 30*random.standard_normal((500,500))
imshow(im)
U,T = rof.denoise(im,im)
G = filters.gaussian_filter(im,10)
im1 = array(Image.open('baby_1.jpg').convert('L'))
U,T = rof.denoise(im1,im1)
figure()
gray()
imshow(U)
axis('equal')
axis('off')
show()
# save the result
import scipy.misc
scipy.misc.imsave('synth_rof.pdf',U)
scipy.misc.imsave('synth_gaussian.pdf',G)
import ncut
from scipy.misc import imresize
from PIL import Image
from numpy import *
from pylab import *
import sys
sys.path.append('../ch1/')
import rof
im2 = array(Image.open('../pcv_data/data/C-uniform03.ppm'))
im1 = array(Image.open('../pcv_data/data/alcatraz1.jpg'))
im = array(Image.open('../pcv_data/data/ceramic-houses_t0.png').convert("L"))
U, T = rof.denoise(im, im, tolerance=0.001)
t = 0.4 #threshold
m, n = im.shape[:2]
# resize image to (wid,wid)
wid = 50
rim = imresize(im, (wid, wid), interp='bilinear')
rim = array(rim, 'f')
# create normalized cut matrix
A = ncut.ncut_graph_matrix(rim, sigma_d=1, sigma_g=1e-2)
# cluster
code, V = ncut.cluster(A, k=3, ndim=3)
# reshape to original image size
codeim = imresize(code.reshape(wid, wid), (m, n), interp='nearest')
# plot result
from PIL import Image, ImageFilter
from pylab import *
import rof
import cv2
import pytesseract
ims = Image.open('D:\\work\\tesseract\\testing\\scan009.jpg').convert('L')
ims = ims.filter(ImageFilter.SHARPEN)
thresh = 250
fn = lambda x: 255 if x > thresh else 0
ims.point(fn, mode='1')
im = array(ims)
#im = im.convert('L').point(fn, mode='1')
U, T1 = rof.denoise(im, im, 0.2, 0.125, 100)
figure()
gray()
imshow(U)
axis('equal')
axis('off')
show()
show()
#pil_im = Image.fromarray(U)
#pil_im.save("rof"+str(i*10)+".jpg")
nim = Image.fromarray(U)
print(
pytesseract.image_to_string(nim,
lang='lat+eng',
config='--dpi 200 --psm 4'))
def get_image(name):
na = 'E:/PycharmProjects/ver_image/image/'+name
enhancer = ImageEnhance.Contrast(Image.open(na).resize((189, 67)).convert("L"))
image_enhancer = enhancer.enhance(4)
im = array(image_enhancer)
bins = 256
imhist, bins = histogram(im.flatten(), bins)
cdf = imhist.cumsum()
cdf = 255 * cdf / cdf[-1]
im2 = interp(im.flatten(), bins[:-1], cdf)
U, T = rof.denoise(im2.reshape(im.shape), im2.reshape(im.shape))
arr = array(U)
for i in range(arr.shape[1]-1):
if arr[0][i] < 250:
arr[1][i] = 255
arr[2][i] = 255
arr[3][i] = 255
for i in range(arr.shape[0]-1):
arr[i][arr.shape[1]-1] = 255
arr[i-1][arr.shape[1]-1] = 255
arr[i-2][arr.shape[1]-1] = 255
arr[i][arr.shape[1]-2] = 255
arr[i-1][arr.shape[1]-2] = 255
arr[i-2][arr.shape[1]-2] = 255
for i in range(arr.shape[0]-6, arr.shape[0]-1):
for j in range(arr.shape[1]-1):
arr[i][j] = 255
for i in range(arr.shape[1]-5, arr.shape[1]-1):
for j in range(arr.shape[0]-1):
arr[j][i] = 255
for i in range(0, 5):
for j in range(arr.shape[0]-1):
arr[j][i] = 255
i = 4
j = 4
while i < array(U).shape[0]-6:
while j < array(U).shape[1]-6:
if arr[i][j] < 200 or arr[i][j-5] < 200 or arr[i][j+5] < 200 or arr[i+5][j] < 200 \
or arr[i-5][j] < 200 or arr[i+5][j+5] < 200 or arr[i-5][j-5] < 200 or arr[i-5][j+5] < 200:
for k in range(i-6, i+6):
for l in range(j-6, j+6):
arr[k][l] = 255
j += 1
i += 1
w = array(U).shape[0]-5
h = array(U).shape[1]-5
while w > 4:
while h > 4:
if (arr[i][j] < 200) or (arr[i-5][j-5] < 200) or (arr[i][j-5] < 200) or (arr[i+5][j-5] < 200) \
or arr[i-5][j] < 200 or arr[i+5][j] < 200 or arr[i-5][j+5] < 200 or arr[i+5][j+5] < 200\
or arr[i-3][j-3] < 200 or arr[i][j-3] < 200 or arr[i+3][j-3] < 200 \
or arr[i-3][j] < 200 or arr[i+3][j] < 200 or arr[i-3][j+3] < 200 or arr[i+3][j+3] < 200:
for k in range(i-6, i+6):
for l in range(j-6, j+6):
arr[k][l] = 255
h -= 1
w -= 1
while i < array(U).shape[0]-3:
while j < array(U).shape[1]-3:
if arr[i][j] < 200 or arr[i][j-3] < 200 or arr[i][j+3] < 200 or arr[i+3][j] < 200 or arr[i-3][j] < 200:
for k in range(i-3, i+3):
for l in range(j-3, j+3):
arr[k][l] = 255
j += 1
i += 1
pil_im = Image.fromarray(arr)
data = pil_im.getdata()
w, h = pil_im.size
black_point = 0
for x in xrange(1, w-1):
for y in xrange(1, h-1):
mid_pixel = data[w*y+x] # 中央像素点像素值
if mid_pixel < 150: # 找出上下左右四个方向像素点像素值
pil_im.putpixel((x, y), 255)
pil_im.putpixel((x-1, y-1), 255)
pil_im.putpixel((x, y-1), 255)
pil_im.putpixel((x+1, y-1), 255)
pil_im.putpixel((x-1, y), 255)
pil_im.putpixel((x+1, y), 255)
pil_im.putpixel((x-1, y+1), 255)
pil_im.putpixel((x, y+1), 255)
pil_im.putpixel((x+1, y+1), 255)
pil_im = array(arr - array(pil_im))
pil_im = Image.fromarray(uint8(pil_im))
data = pil_im.getdata()
w, h = pil_im.size
for x in xrange(1, w-1):
for y in xrange(1, h-1):
mid_pixel = data[w*y+x] # 中央像素点像素值
if mid_pixel == 0: # 找出上下左右四个方向像素点像素值
pil_im.putpixel((x, y), 255)
pil_im.putpixel((x, y-1), 255)
pil_im.putpixel((x-1, y), 255)
pil_im.putpixel((x+1, y), 255)
pil_im.putpixel((x, y+1), 255)
return pil_im.resize((80, 30))
from pylab import * from scipy.ndimage import filters import numpy import rof im = numpy.zeros((500, 500)) im[100:400, 100:400] = 128 im[200:300, 200:300] = 255 im_noise = im + 30 * numpy.random.standard_normal((500, 500)) U, T = rof.denoise(im_noise, im_noise, tv_weight=100) G = filters.gaussian_filter(im_noise, 10) gray() subplot(2, 2, 1) imshow(im) subplot(2, 2, 2) imshow(im_noise) subplot(2, 2, 3) imshow(U) subplot(2, 2, 4) imshow(G) show()
def test2():
im = array(Image.open('data/empire.jpg').convert('L'))
U,T = rof.denoise(im,im)
showim(U)
def update(val):
U, _ = rof.denoise(im, im, tv_weight=srof.val, tolerance=0.01)
subplot(1, 2, 2)
imshow(U)
draw()
# -*- coding: utf-8 -*-
"""
Created on Fri May 16 15:37:09 2014
@author: admin
"""
import rof
from numpy import *
im=array(Image.open('empire.jpg').convert('L'))
U,T=rof.denoise(im,im,tolerance=0.001)
t=0.4#threshold
import scipy.misc
scipy.misc.imsave('result.pdf',U<t*U.max())
from numpy import * from numpy import random from PIL import Image from scipy.ndimage import filters import rof # create synthetic image with noise im = zeros((500, 500)) im[100:400, 100:400] = 128 im[200:300, 200:300] = 255 im = im + 30 * random.standard_normal((500, 500)) U, T = rof.denoise(im, im) G = filters.gaussian_filter(im, 10) G = array(G, 'uint8') G = Image.fromarray(uint8(G)) G.show()
from numpy import *
from numpy import random
from scipy.ndimage import filters
import rof
from scipy.misc import imsave
im = zeros((500, 500))
im[100:400, 100:400] = 128
im[200:300, 200:300] = 255
im = im + 30 * random.standard_normal((500, 500))
imsave('synth_ori.pdf', im)
U, T = rof.denoise(im, im, 0.07)
G = filters.gaussian_filter(im, 10)
imsave('synth_rof.pdf', U)
imsave('synth_gaussian.pdf', G)
def test2():
im = array(Image.open('data/empire.jpg').convert('L'))
U, T = rof.denoise(im, im)
showim(U)
# 1.4.4.2 配列を画像として保存する
im = np.array(Image.open("empire.jpg")) # imに画像の配列を入れる
imsave("test.jpg", im) # 配列を直接画像ファイルに保存
lena = scipy.misc.lena()
plt.imshow(lena)
# 1.5 画像のノイズ除去
# ノイズを含む画像を合成する
im = np.zeros((500, 500))
im[100:400, 100:400] = 128
im[200:300, 200:300] = 255
im = im + 30 * random.standard_normal((500, 500))
U, T = rof.denoise(im, im)
G = filters.gaussian_filter(im, 10)
imsave("synth_rof.pdf", U) # 結果を保存
imsave("synth_gaussian.pdf", G)
plt.figure()
plt.gray()
plt.imshow(im)
plt.figure()
plt.gray()
plt.imshow(G)
plt.figure()
plt.gray()
plt.imshow(U)
from PIL import Image
import numpy
import rof
import stereo
im_l = numpy.array(Image.open('out_stereo1.ppm').convert('L'), 'f')
im_r = numpy.array(Image.open('out_stereo2.ppm').convert('L'), 'f')
steps = 12
start = 4
wid = 9
res = stereo.plane_sweep_ncc(im_l, im_r, start, steps, wid)
res, _ = rof.denoise(res, res, tv_weight=80 / 255.0, tolerance=0.01)
import scipy.misc
scipy.misc.imsave('out_depth_rof.png', res)
def btnDenoiseEvent(self, event):
"""使用ROF去噪模型对灰度图去噪"""
assert self.picName != None # 确保已经有图片打开
self.im, T = rof.denoise(self.im, self.im)
self.displayImage()
import numpy as np
from scipy.ndimage import filters
import rof
from matplotlib import pyplot as plt
from PIL import Image
# create a synthetic image with noise
im = np.zeros((500,500))
im[100:400 , 100:400] = 128
im[200:300, 200:300] = 255
im = np.array(im, 'uint8')
# add some noise
im = im + 30*np.random.standard_normal((500,500))
U, T = rof.denoise(im, im)
G = filters.gaussian_filter(im,10)
# plot result
plt.figure(1)
plt.subplot(131), plt.imshow(im,'gray'), plt.title('Original Image')
plt.axis('off')
plt.subplot(132), plt.imshow(G,'gray'), plt.title('Gaussian Filter')
plt.axis('off')
plt.subplot(133), plt.imshow(U,'gray'), plt.title('ROF denoising')
plt.axis('off')
# now lets see how the ROF algorithm handles real images
im2 = np.array(Image.open('building.jpg').convert('L'))
U2, T2 = rof.denoise(im2, im2)
