def startSearch0(var1, var2, var3, tree, x, root1): #所有子文件夹
count1 = 0
path = var2.get()
im1 = Image.open(var1.get())
#Const_Image_Format = [".jpg",".jpeg",".bmp",".png"]
for (root, dirs, files) in os.walk(path):
for filename in files:
if os.path.splitext(filename)[1] in Const_Image_Format: #筛选格式
sizePingheng = 51 #现在只改这个就好了
pingheng = 100 / (sizePingheng - 1)**2
im2 = Image.open(os.path.join(root, filename))
j = pingheng * ((sizePingheng - 1)**2 - _dhash.classfiy_dHash(
im1, im2, size=(sizePingheng, sizePingheng - 1)))
count1 += 1
x.set('已处理' + str(count1) + '张')
root1.update() #显示处理过程
if j >= int(var3.get()): #条件筛选
treePrint(
tree, #调用打印函数
round(j, 2),
os.path.join(root),
os.path.splitext(filename)[0],
os.path.splitext(filename)[1],
os.path.getsize(os.path.join(root, filename)),
im2,
os.path.getctime(os.path.join(root, filename)),
"")
def startSearch01(var1, var2, var3, tree, x, root1):
count1 = 0
path = var2.get()
im1 = Image.open(var1.get())
global len
len = str(len(os.listdir(path))) #显示总文件数量
#Const_Image_Format = [".jpg",".jpeg",".bmp",".png"]
for filename in os.listdir(path):
if os.path.splitext(filename)[1] in Const_Image_Format: #筛选格式
im2 = Image.open(os.path.join(path, filename))
j = 100 - 2.38 * _dhash.classfiy_dHash(im1, im2, size=(7, 6))
count1 += 1
x.set(len + '个文件中，处理了' + str(count1) + '张')
root1.update() #显示处理过程
if j >= int(var3.get()): #条件筛选
treePrint(
tree, #调用打印函数
round(j, 2),
os.path.join(path),
os.path.splitext(filename)[0],
os.path.splitext(filename)[1],
os.path.getsize(os.path.join(path, filename)),
im2,
os.path.getctime(os.path.join(path, filename)),
"")
def startSearch():
im1=Image.open( var1.get())
#遍历文件夹并传到列表
Const_Image_Format = [".jpg",".jpeg",".bmp",".png"]
path= var2.get()
list1=[]
for (root, dirs, files) in os.walk(path):
for filename in files:
if os.path.splitext(filename)[1] in Const_Image_Format :
#print(os.path.join(root,filename))
list1.append(os.path.join(root,filename))
#遍历比较
list2=[]
for i in list1:
im2=Image.open(i)
j=_dhash.classfiy_dHash(im1,im2,size=(9,8))
if j<=int(var3.get()):
print(j)
print(i)
list2.append(str(100-3*j)+'%')
list2.append(i)
#在listbox显示
lb.delete(0, END)
for item in list2:
lb.insert(END, item)
#====================================
load = Image.open(var1.get())
render=load.resize((300,200),Image.ANTIALIAS)
render_ed= ImageTk.PhotoImage(render)
canvas.delete('all')
canvas.create_image(0,0,anchor=NW,image=render_ed)
def comp_faces(faces1, faces2, size, part_size):
min_code = 100
for face1 in faces1:
for face2 in faces2:
code = _dhash.classfiy_dHash(face1, face2,
size=(11,
10)) #来自d哈希的对比~~~~~~~~~~~~~~
if code < min_code:
min_code = code
return min_code
def classify_passage(img1Path, img2Path):
image1 = Image.open(img1Path)
b1 = image1.split()[0] # rgb通道分离
g1 = image1.split()[1] # rgb通道分离
r1 = image1.split()[2] # rgb通道分离
image2 = Image.open(img2Path)
b2 = image2.split()[0] # rgb通道分离
g2 = image2.split()[1] # rgb通道分离
r2 = image2.split()[2] # rgb通道分离
result = 0
result1 = _dhash.classfiy_dHash(b1, b2, size=(11, 10))
result2 = _dhash.classfiy_dHash(g1, g2, size=(11, 10))
result3 = _dhash.classfiy_dHash(r1, r2, size=(11, 10))
#print(max(result3,result2,result1))
#采用最大值就是省去了其他未画的部分的对比差异
return max(result3, result2, result1)