# -*- coding: utf-8 -*-
"""
Created on Mon Oct 1 09:32:37 2018 @author: Manuel
""" import numpy as np
from tkinter import *
#import tkinter
from PIL import Image, ImageTk
from scipy.misc import imread
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from time import gmtime, strftime
import cv2 #逐帧获取图片
videoFile = 'py5.mp4'
cap = cv2.VideoCapture(videoFile)
numF=cap.get(cv2.CAP_PROP_FRAME_COUNT)
fps=cap.get(cv2.CAP_PROP_FPS)
#cap.set(cv2.CAP_PROP_FRAME_WIDTH,640)
#cap.set(cv2.CAP_PROP_FRAME_HEIGHT,480)
class SVM_Classifier(Frame):
def __init__(self, master=None):
self.root = Tk()#tkinter.TK()
Frame.__init__(self, master)
Pack.config(self)
self.menus()
self.createWidgets()
self.after(10, self.callback)
self.state=0
global X_list
X_list=[]
global y_list
y_list=[]
def menus(self):
allmenu = Menu(self.root)#tkinter.Menu
# 添加子菜单
menu1 = Menu(allmenu, tearoff=0)
menu2=Menu(allmenu, tearoff=0)
# 添加选项卡
menu1.add_command(label='前景', command=self.target)
menu1.add_command(label='背景', command=self.background)
allmenu.add_cascade(label='样本标注', menu=menu1)
menu2.add_command(label='SVM学习并显示结果', command=self.processing)
allmenu.add_cascade(label='分析处理', menu=menu2)
self.root.config(menu=allmenu)
def target(self):
self.state=1
def background(self):
self.state=2
def processing(self):
self.state=0
X=np.array(X_list)
y=np.array(y_list)
print(X)
print(y)
#将X,Y写入txt文件
# np_X=[]
# np_y=[]
# np_X.append(X)
# np_y.append(y)
# svm_x='svm_x.txt'
# svm_y='svm_y.txt'
# X1_string = '\n'.join(str(x) for x in X)
# with open(svm_x,'w') as svm_file:
# svm_file.write(X1_string)
# y1_string = '\n'.join(str(x) for x in y)
# with open(svm_y,'w') as svm_file:
# svm_file.write(y1_string) #支持向量机学习
clf=SVC(kernel="linear", C=0.025)#SVC(gamma=2, C=1)
clf.fit(X, y)#SVM学习
score = clf.score(X,y)
print('score=',score) while(True):
ret, frame = cap.read()
if ret ==True:
image = frame
#img=rotate(frame,-90)
#img=np.rot90(frame)
#img=np.rot90(img)
#img=np.rot90(img)
cv2.imshow('my', image)
f = strftime("%Y%m%d%H%M%S.jpg", gmtime())
cv2.imwrite(f, image)
# image = imread("test.jpg")#fruits.png
image=imread(f)
XX=[]
for i in range(image.shape[0]):
for j in range(image.shape[1]):
XX.append([image[i,j,0],image[i,j,1],image[i,j,2]])
Z=clf.decision_function(XX)
ZZ=np.array(Z)
ZZ=ZZ.reshape(image.shape[0],image.shape[1])
for i in range(image.shape[0]):
for j in range(image.shape[1]):
if ZZ[i,j]<0:
image[i,j,0]=0
image[i,j,1]=0
image[i,j,2]=0
image= image[...,::-1]
_,image=cv2.threshold(image,10,255,cv2.THRESH_BINARY)
# image= cv2.adaptiveThreshold(image,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
# _,image=cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# cv2.imshow("image CR", cr1)
cv2.imshow("Skin", image) # img0 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#将图片转换为灰度图片
f = strftime("%Y%m%d%H%M%S_.jpg", gmtime())
cv2.imwrite('2/'+ f, image)
#if img.size == 0:
# break if cv2.waitKey(200) & 0xFF == ord('q'):
break
cap.release
cv2.destroyAllWindows() # for i in range(image.shape[0]):
# for j in range(image.shape[1]):
# Z = clf.decision_function([[image[i,j,0],image[i,j,1],image[i,j,2]]])
# if Z[0]<0:
# image[i,j,0]=0
# image[i,j,1]=0
# image[i,j,2]=0
# plt.imshow(image)
# plt.show() def createWidgets(self):
## The playing field
self.draw = Canvas(self, width=640, height=480)
self.im=Image.open('20190104030935.jpg')#fruits.png
self.tkimg=ImageTk.PhotoImage(self.im)
self.myImg=self.draw.create_image(10,10,anchor=NW,image=self.tkimg) self.draw.pack(side=LEFT)
def mouse_pick(self,event):
rgb=self.im.getpixel((event.x-10,event.y-10))
print("clicked at:x=", event.x-10,'y=',event.y-10,' r=',rgb[0],'g=',rgb[1],'b=',rgb[2])
X_list.append([np.uint8(rgb[0]),np.uint8(rgb[1]),np.uint8(rgb[2])])
if self.state==1:
self.pick_points = self.draw.create_oval((event.x - 2),(event.y - 2),(event.x + 2),(event.y + 2),fill="red")
y_list.append(1)#添加入前景标签
if self.state==2:
self.pick_points = self.draw.create_oval((event.x - 2),(event.y - 2),(event.x + 2),(event.y + 2),fill="green")
y=y_list.append(-1)#添加入背景标签
def callback(self, *args):
self.draw.tag_bind(self.myImg, "<Button-1>", self.mouse_pick) game = SVM_Classifier()
game.mainloop()