if mod is None:

mod = init_mxnet() # 初始化mxnet

subfolders = [folder for folder in os.listdir(

path) if os.path.isdir(os.path.join(path, folder))]

# 循环扫描大类文件夹

tilesPerImage = splite_num

for imgDir in subfolders:

splits_resamples(facescrub_root=os.path.join(path, imgDir),

tilesPerImage=tilesPerImage,

t_time = time.time()

# 设置大类文件夹

fold = facescrub_root

# 获得所有色卡文件夹，并执行

subfolders = [folder for folder in os.listdir(

fold) if os.path.isdir(os.path.join(fold, folder))]

temp_Process(subfolders, fold, mod)

# 设置增强多少张

tilesPerImage = splite_num

# 循环所有色卡

for subfolder in subfolders:

# 获取图片文件列表（以.JPG为结尾的文件）

imgsfiles = [os.path.join(fold, subfolder, img)

for img in os.listdir(os.path.join(fold, subfolder)) if img.endswith('.JPG')]

temp_list = []

''' 旧策略

# 检测该文件夹色卡文件夹是否存在npy_name， 有则不训练该文件夹数据

if not_double and os.path.exists(os.path.join(fold, subfolder, npy_name)):

logging.info('Has %s' % os.path.join(

fold, subfolder, npy_name))

continue

'''

#''' 新策略

# 检测该文件夹色卡文件夹是否存在npy_name， 有则不训练该文件夹数据

if os.path.exists(os.path.join(fold, subfolder, npy_name)):

try:

if len(np.load(os.path.join(fold, subfolder, npy_name))) / tilesPerImage == len(imgsfiles):

continue

else:

logging.info('Has new images insert, Then remove file' +

os.path.join(fold, subfolder, npy_name))

removeFile(os.path.join(fold, subfolder, npy_name))

except Exception:

logging.error('npy_name: %s' %

os.path.join(fold, subfolder, npy_name))

removeFile(os.path.join(fold, subfolder, npy_name))

#'''

temp_time = time.time()

# 循环遍历所有的图片文件

for imgfile in imgsfiles:

if os.path.exists(imgfile) == False:

logging.error('Bad Image: %s' % imgfile)

continue

try:

# 打开图片

im = Image.open(imgfile)

# 获得原始图片大小

w, h = im.size

# 变换形状224， 224

temp_im = cv2.resize(np.array(im), (224, 224))

# 增加原始图片

temp_list.append(

[getFeaturesOfSplite(temp_im, mod), subfolder, imgfile])

# 删除图片上下尺子的影响

im = im.crop((0, int(h * 0.1), w, int(h * 0.9)))

dx = dy = 224

# 将图片增强tilesPerImage份

for i in range(1, tilesPerImage + 1):

# 获得图片大小

# 获取图片截取大小

if i < (tilesPerImage / 360) * 100 and w > 300:

dx = 224

if (tilesPerImage / 360) * 100 < i < (tilesPerImage / 360) * 200 and w > 500:

dx = 320

if (tilesPerImage / 360) * 200 < i < (tilesPerImage / 360) * 300 and w > 800:

dx = 640

if i < (tilesPerImage / 360) * 100 and h > 300:

dy = 224

if (tilesPerImage / 360) * 100 < i < (tilesPerImage / 360) * 200 and h > 500:

dy = 320

if (tilesPerImage / 360) * 200 < i < (tilesPerImage / 360) * 300 and h > 800:

dy = 640

# 随机获得图片区域图片

x = random.randint(0, w - dx - 1)

y = random.randint(0, h - dy - 1)

# 将图片截取指定大小

im_cropped = im.crop((x, y, x + dx + 1, y + dy + 1))

if i % 2 == 0: # 将图片旋转 90\180

im_cropped = im_cropped.transpose(

random.choice(rotateAction))

if i % 2 == 0 and i > (tilesPerImage / 360) * 300: # 将图片旋转1-45角度

roate_drgree = random.choice(rotate45degree)

im_cropped = im_crotate_image_square(

im_cropped, roate_drgree)

# 将处理后的图片转为224、224

im_cropped = cv2.resize(

np.array(im_cropped), (224, 224))

# 将处理后的图片按照，图片特征， 色卡id，图片地址进行存入数组

temp_list.append(

[getFeaturesOfSplite(im_cropped, mod), subfolder, imgfile])

except Exception:

logging.error('Bad Image: %s' % imgfile)

continue

#break;

logging.info('Save %s SpeedTime: %0.2f' % (os.path.join(

fold, subfolder, npy_name), (time.time() - temp_time)))

#break;

# 将处理后得到的特征数组存到色卡文件夹下的npy_name

np.save(os.path.join(fold, subfolder, npy_name), temp_list)

import time

import random

# 获得大类文件夹

subfolders = [folder for folder in os.listdir(

path) if os.path.isdir(os.path.join(path, folder))]

tt = time.time()

main_imgArray = [] # 存取读取到的数组

# 临时变量

per = 0

testNum = 0

num = 0

# 遍历所有大类文件夹

for file in subfolders:

# 获得大类文件夹完整路径

filepath = os.path.join(path, file)

logging.info('Start Merge Npy %s' % filepath)

# 获得所有色卡文件夹

subfolders2 = [folder for folder in os.listdir(

filepath) if os.path.isdir(os.path.join(filepath, folder))]

# 遍历所有色卡文件夹

for file2 in subfolders2:

try:

t1 = time.time()

# 获得色卡文件夹完整路径

filepath2 = os.path.join(filepath, file2)

# 判断是否存在npy_name文件

if os.path.exists(os.path.join(filepath2, npy_name)):

imgArray = np.load(os.path.join(

filepath2, npy_name))

else:

continue

# 记录数组占用大小

num += sys.getsizeof(imgArray)

# 如果读取到的imgArray数组为0，则npy_name所在文件夹损坏

if len(imgArray) == 0:

logging.error('Bad Npy: %s' %

os.path.join(filepath2, npy_name))

continue

t_time = time.time()

j = 0 # 记录当前循环得到了几个数组

n = 0 # 记录已经获取了几个图片

# 遍历所有数组

for i in range(0, len(imgArray) / splite_num):

# 将获取的数组打乱顺序

tempList = imgArray[splite_num * i: splite_num * (i + 1)]

random.shuffle(tempList)

j = 0

for img in tempList:

if j <= max_img:

#print('Load %d ' % len(img[0]))

main_imgArray.append(img.copy())

j += 1

if j >= splite_num:

break

# 将多余的数组删除

del tempList

# 将多余的数组删除

del imgArray

# 刷新，无用指针清理

gc.collect()

#break

except EOFError:

logging.error('Bad Folder ' + file + '_' + file2)

logging.info('End Merge Npy: %d %f s' %

(len(main_imgArray), (time.time() - tt)))

logging.info('Good Job')

img=cv2.imread(img, 1)

if img is not None:

img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

img=cv2.resize(img, (224, 224))

img=np.swapaxes(img, 0, 2)

img=np.swapaxes(img, 1, 2)

img=img[np.newaxis, :]

return img

else:

img=getImage(img)

if img is not None:

f=f_mod.predict(img)

f=np.ravel(f)

return f

else:

img = np.swapaxes(img, 0, 2)

img = np.swapaxes(img, 1, 2)

img = img[np.newaxis, :]

img = getImageOfSplite(img)

f = f_mod.predict(img)

f = np.ravel(f)

# 获得数组

train=np.array(load_all_beOne(path))

# 获取数组数量

trainNum=len(train)

# 获得默认参数

p=falconn.get_default_parameters(trainNum, dim)

t=falconn.LSHIndex(p)

dataset=[np.ravel(x[0]).astype(np.float32) for x in train]

dataset=np.array(dataset)

# 生成hash

logging.info('Start Hash setup')

t.setup(dataset)

if is_pool:

q=t.construct_query_pool()

else:

q=t.construct_query_object()

import mxnet as mx

model=mx.model.FeedForward.load(

prefix, num_round, ctx = mx.gpu(GPUid), numpy_batch_size = 1)

internals=model.symbol.get_internals()

fea_symbol=internals[layer]

feature_extractor=mx.model.FeedForward(ctx = mx.gpu(GPUid), symbol = fea_symbol, numpy_batch_size = 1,

arg_params = model.arg_params, aux_params = model.aux_params, allow_extra_params = True)

init_mod=feature_extractor

up=np.sum(np.multiply(f, t))

ff=np.sqrt(np.sum(np.multiply(f, f)))

tt=np.sqrt(np.sum(np.multiply(t, t)))

down=ff * tt

fal=getFeatures(img, f_mod = mod)

if fal is not None:

tList=train[np.array(q.find_k_nearest_neighbors(fal, k))]

return fal, tList

else:

try:

while True:

msg=conn.recv()

logging.info(msg)

opts, args=getopt.getopt(msg, 'f:zt:k:sp', ['help', 'train'])

img=None

k=20

img_type=0

is_save=True

msg=[]

filepath=None

is_pcl=False

for op, value in opts:

if op == '-f':

img=value

elif op == '-z':

return 'Close'

elif op == '-k':

k=int(value)

elif op == '-s':

is_save=False

elif op == '--train':

return "train"

elif op == '-p':

is_pcl=True

elif op == '-t':

img_type=value

elif op == '--help':

msg.append(' ')

msg.append('Usage:')

msg.append(' Client [options]')

msg.append(' ')

msg.append('General Options:')

msg.append('-f <path>\t\t Set test image path')

msg.append('-z \t\t\t Close server')

msg.append('-k <number>\t\t Set rank')

msg.append('-s \t\t\t No Save image of rank K')

msg.append(

'-t <number>\t\t Set image type if you want to know test type')

return msg

if img is None:

msg.append('Must set Image Path use -f')

return msg

ti_time= time.time()

fal, tList = getTest(img, mod, train, q, k =k * max_img)

if is_pcl: # 是否为批处理

is_Right= False

if tList is None:

msg.append('Bad Img Path')

return msg

else:

ti_time2= time.time()

ti= str(int(time.time()))

ti= ti + '_' + img

imgList= {}

nk= 400

for i in tList:

if is_save:

gailv= int(getDistOfCos(fal, i[0]) * 100)

if imgList.has_key(i[1]):

if imgList[i[1]][0] < gailv:

imgList[i[1]]= [gailv, i[2]]

else:

imgList[i[1]]= [gailv, i[2]]

if len(imgList) >= 400:

break

if img_type != 0 and img_type == i[1]:

nk= len(imgList)

is_Right= True

break

msg.append('Next')

msg.append([imgList, nk])

msg.append('Test Image Spend Time: %.2lf s' %

(time.time() - ti_time))

else:

msg.append('Next')

msg.append([fal, tList])

msg.append('Test Image Spend Time: %.2lf s' %

(time.time() - ti_time))

return msg

except EOFError:

logging.info('Connection closed')

serv = Listener(address, authkey =authkey)

while True:

try:

client= serv.accept()

msg= make_work(client, mod, q, train)

if msg == 'Close': # 关闭监听

serv.close()

return "Close"

else:

client.send(msg)

if msg == 'train': # 开始训练，训练期间将关闭监听

serv.close()

spliteAllOfPath(mod);

q, train= initWithoutMod()

return "train"

except Exception:

traceback.print_exc()