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Update to 1.5.0
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1. Brand new auto mode based on KNN similarity
2. Size adjustments on some elements
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@LevyHsu
LevyHsu committed Apr 1, 2020
1 parent 717dd38 commit 8160356
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206 changes: 133 additions & 73 deletions Momus.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,131 +3,191 @@

import os
import PySimpleGUI as sg
import numpy as np

import cv2
import random
import image_processing
from matplotlib import pyplot as plt
from PIL import Image

def img_processing_SIFT(original_img_path,counter,pixel_number,Salt_and_pepper_Noise_level,Random_Scalar_level,Random_Crop_Pixel):
def img_processing_SIFT(mode,auto_target_similarity,original_img_path,counter,pixel_number,Salt_and_pepper_Noise_level,Random_Scalar_level,Random_Crop_Pixel):
user_image = image_processing.usr_img(original_img_path)

shape = user_image.show_demo_1()
window_width = shape[1]
window_height = shape[0]

original_matches = user_image.show_demo_3()
#print("original_matches: "+ str(original_matches))
match_proportion = 100

sg.theme('DarkAmber')
layout = [
[sg.Multiline(default_text='开始图像处理\n', size=(window_width, 2),key = '-TEXT-',do_not_clear=True)],
[sg.Multiline(default_text='开始图像处理\n', size=(window_width, 2),key = '-TEXT-',do_not_clear=False)],
[sg.ProgressBar(100, orientation='h', size=(window_width, 20), key='progressbar')],
[sg.Image('demo.png',key = '-IMAGE-')],
]
window = sg.Window('Momus 图像反识别工具',layout, size=(window_width + 2, window_height + 30))
progress_bar = window['progressbar']
counter_current = 0

#Manual
if (mode == 0):
while counter_current < counter:
event, values = window.Read(timeout=1)
if event is None:
break

if(pixel_number>0):
user_image.keypoint_obscure(pixel_number)
if(Salt_and_pepper_Noise_level>0):
user_image.keypoint_white_black_salt(Salt_and_pepper_Noise_level)
if(Random_Scalar_level>0):
user_image.Random_Scalar_Draw(Random_Scalar_level,counter)

while counter_current < counter:
event, values = window.Read(timeout=1)
if event is None:
break
if(pixel_number>0):
user_image.keypoint_obscure(pixel_number)
if(Salt_and_pepper_Noise_level>0):
user_image.keypoint_white_black_salt(Salt_and_pepper_Noise_level)
if(Random_Scalar_level>0):
user_image.Random_Scalar_Draw(Random_Scalar_level,counter)
progress_bar.UpdateBar(counter_current/counter*100)
window.Element('-TEXT-').Update(value=("----------------------------\n第"+str(counter_current)+"次迭代\n"), append=False)
progress_bar.UpdateBar(counter_current/counter*100)
window.Element('-TEXT-').Update(value=("第"+str(counter_current)+"次迭代\n 匹配度: " + "%5.3f" % (match_proportion) + "%\n"), append=True)

if(counter_current % 3 == 0):
user_image.show_demo_1()
window.Element('-IMAGE-').Update('demo.png')
window.Element('-TEXT-').Update(value="原图对比\n", append=True)

if(counter_current % 3 == 1):
user_image.show_demo_2()
window.Element('-IMAGE-').Update('demo.png')
window.Element('-TEXT-').Update(value="更新特征值\n", append=True)
if(counter_current % 3 == 0):
user_image.show_demo_1()
window.Element('-IMAGE-').Update('demo.png')
window.Element('-TEXT-').Update(value="原图对比\n", append=True)

if(counter_current % 3 == 1):
user_image.show_demo_2()
window.Element('-IMAGE-').Update('demo.png')
window.Element('-TEXT-').Update(value="更新特征值\n", append=True)

if(counter_current % 3 == 2):
user_image.show_demo_3()
window.Element('-IMAGE-').Update('demo.png')
window.Element('-TEXT-').Update(value="KNN匹配\n", append=True)
if(counter_current % 3 == 2):
match_proportion = user_image.show_demo_3()/original_matches *100
#print("match_proportion: "+ str(match_proportion))
window.Element('-IMAGE-').Update('demo.png')
window.Element('-TEXT-').Update(value="KNN匹配\n", append=True)

counter_current += 1
#Auto
if (mode == 1):
while match_proportion > auto_target_similarity:
event, values = window.Read(timeout=1)
if event is None:
break
user_image.keypoint_obscure(1)
user_image.keypoint_white_black_salt(1)

window.Element('-TEXT-').Update(value=("第"+str(counter_current)+"次迭代\n 匹配度: " + "%5.3f" % (match_proportion) + "%\n"), append=True)

counter_current += 1
if(counter_current % 3 == 0):
user_image.show_demo_1()
window.Element('-IMAGE-').Update('demo.png')
window.Element('-TEXT-').Update(value="原图对比\n", append=True)

if(counter_current % 3 == 1):
user_image.show_demo_2()
window.Element('-IMAGE-').Update('demo.png')
window.Element('-TEXT-').Update(value="更新特征值\n", append=True)

if(counter_current % 3 == 2):
match_proportion = user_image.show_demo_3()/original_matches *100
#print("match_proportion: "+ str(match_proportion))
window.Element('-IMAGE-').Update('demo.png')
window.Element('-TEXT-').Update(value="KNN匹配\n", append=True)

counter_current += 1
window.close()

if(Random_Crop_Pixel>0):
if(Random_Crop_Pixel>0 and mode == 0):
user_image.Random_Crop(Random_Crop_Pixel)
if(mode == 1):
user_image.Random_Crop(random.randint(2,5))
user_image.output()
sg.Popup('完成')
sg.Popup('完成 文件：'+ user_image.output_name)

def mainwindow():

sg.theme('DarkAmber')
sg.theme('DarkAmber')
auto_layout = [
[sg.Text(' ')],
[sg.Text(' ')],
[sg.Text(' ')],
[sg.Text('目标相似度(Target Similarity)：')],
[sg.Text(' ')],
[sg.Text(' ')],
[sg.Slider(range=(10, 100), orientation='h', size=(40, 5), default_value=30)],
[sg.Text(' ')],
[sg.Text(' ')],
[sg.Text(' ')],
[sg.Text(' ')],
[sg.Text(' ')],
[sg.Text(' ')],
[sg.Submit(tooltip='Click to start',size=(20,1)), sg.Cancel(size=(20,1))],
]
manual_layout = [
[sg.Text('干扰手段:')],
[sg.Checkbox('近似值处理(Pixel Approximation) 密度:', size=(38,1),default=True)],
[sg.Slider(range=(1, 10), orientation='h', size=(40, 5), default_value=1)],
[sg.Checkbox('椒盐噪声(Salt-and-pepper Noise) 密度:', size=(38,1),default=True)],
[sg.Slider(range=(1, 10), orientation='h', size=(40, 5), default_value=1)],
[sg.Checkbox('随机线段(Random Scalar) 密度:', size=(38,1),default=True)],
[sg.Slider(range=(1, 10), orientation='h', size=(40, 5), default_value=1)],
[sg.Checkbox('随机裁剪(Random Crop) 裁剪像素:', size=(40,1),default=True)],
[sg.Slider(range=(1, 30), orientation='h', size=(40, 5), default_value=3)],
[sg.Text(' ')],
[sg.Text('迭代次数: (对于多次无法发送的图片 多迭代小干扰)')],
[sg.Slider(range=(1, 50), orientation='h', size=(40, 5), default_value=5)],
[sg.Submit(tooltip='Click to start',size=(20,1)), sg.Cancel(size=(20,1))],
]
layout = [
[sg.Text('要处理的图片:')],
[sg.Input(), sg.FileBrowse()],
[sg.Text(' ')],
[sg.Checkbox('反SIFT (Scale-Invariant Feature Transform) 匹配识别', size=(50,1),default=True)],
[sg.Checkbox('水印 (未开放)', size=(20,1),default=False)],
[sg.Text(' ')],
[sg.Text('干扰手段:')],
[sg.Checkbox('近似值处理(Pixel Approximation) 密度:', size=(38,1),default=True)],
[sg.Slider(range=(1, 10), orientation='h', size=(40, 5), default_value=1)],
[sg.Checkbox('椒盐噪声(Salt-and-pepper Noise) 密度:', size=(38,1),default=True)],
[sg.Slider(range=(1, 10), orientation='h', size=(40, 5), default_value=1)],
[sg.Checkbox('随机线段(Random Scalar) 密度:', size=(38,1),default=True)],
[sg.Slider(range=(1, 10), orientation='h', size=(40, 5), default_value=1)],
[sg.Checkbox('随机裁剪(Random Crop) 裁剪像素:', size=(40,1),default=True)],
[sg.Slider(range=(1, 30), orientation='h', size=(40, 5), default_value=3)],
[sg.Text(' ')],
[sg.Text('迭代次数: (对于多次无法发送的图片 多迭代小干扰)')],
[sg.Slider(range=(1, 50), orientation='h', size=(40, 5), default_value=5)],
[sg.Submit(tooltip='Click to submit this form'), sg.Cancel()],
[sg.TabGroup([[sg.Tab('自动(Auto)', auto_layout), sg.Tab('手动(Manual)', manual_layout)]])],
]


window = sg.Window('Momus 图像反识别工具', layout)
window = sg.Window('Momus 图像反识别工具', layout,size=(430,600))
event, values = window.read()
window.close()
return values
return values,event

def main():
parameter = mainwindow()
filepath = parameter[0]
filepath = parameter[0][0]

anti_SIFT = parameter[1]
watermark = parameter[2]
Gaussian_noise = parameter[3]
Gaussian_noise_level = parameter[4]
Salt_and_pepper_Noise = parameter[5]
Salt_and_pepper_Noise_level = parameter[6]
Random_Scalar = parameter[7]
Random_Scalar_level = parameter[8] * 8
Random_Crop = parameter[9]
Random_Crop_Pixel = parameter[10]
counter = parameter[11]

if(Gaussian_noise):
pass
else:
anti_SIFT = parameter[0][1]
watermark = parameter[0][2]
auto_target_similarity = parameter[0][3]
Gaussian_noise = parameter[0][4]
Gaussian_noise_level = parameter[0][5]
Salt_and_pepper_Noise = parameter[0][6]
Salt_and_pepper_Noise_level = parameter[0][7]
Random_Scalar = parameter[0][8]
Random_Scalar_level = parameter[0][9] * 8
Random_Crop = parameter[0][10]
Random_Crop_Pixel = parameter[0][11]
counter = parameter[0][12]

if(Gaussian_noise == False):
Gaussian_noise_level = 0
if(Salt_and_pepper_Noise):
pass
else:

if(Salt_and_pepper_Noise== False):
Salt_and_pepper_Noise_level = 0
if(Random_Scalar):
pass
else:

if(Random_Scalar== False):
Random_Scalar_level = 0
if(Random_Crop):
pass
else:

if(Random_Crop== False):
Random_Crop_Pixel = 0

if(parameter[1] == "Submit0"):
mode = 0
elif(parameter[1] == "Submit"):
mode = 1

if anti_SIFT :
img_processing_SIFT(filepath,counter,Gaussian_noise_level,Salt_and_pepper_Noise_level,Random_Scalar_level,Random_Crop_Pixel)
img_processing_SIFT(mode,auto_target_similarity,filepath,counter,Gaussian_noise_level,Salt_and_pepper_Noise_level,Random_Scalar_level,Random_Crop_Pixel)


if __name__ == "__main__":
Expand Down
9 changes: 6 additions & 3 deletions image_processing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ def __init__(self,path):
self.height = self.img.shape[1]

class usr_img(image):

output_name = ''
not_png = False
_img_path = ''

Expand All @@ -71,7 +71,8 @@ def __init__(self,path):
def output(self):
if (self.not_png):
os.remove(self.img_path)
cv2.imwrite(os.path.splitext(self.img_path)[0] + "_output.png", self.img)
self.output_name = os.path.splitext(self.img_path)[0] + "_output.png"
cv2.imwrite(self.output_name, self.img)
os.remove("demo.png")


Expand Down Expand Up @@ -169,11 +170,13 @@ def show_demo_3(self):
matches = bf.knnMatch(des1,des2, k=2)
good = []
for m,n in matches:
if m.distance < 0.3*n.distance:
if m.distance < 0.8*n.distance:
good.append([m])
imgC = cv2.drawMatchesKnn(cv2.imread(self.img_path),kp1,self.img,kp2,good[:10000],None,flags=2)
#print("Pair of matches: " + str(len(good)))
cv2.imwrite('demo.png', imgC)
img_resize_to_GUI('demo.png')

return len(good)


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