sk = socket.socket() # s.bind(address) 將套接字綁定到地址。在AF_INET下，以元組（host,port）的形式表示地址。 sk.bind(("172.29.25.11",8007)) # 開始監聽傳入連接。 sk.listen(True)  while True:  for i in range(100):   # 接受連接并返回（conn,address）,conn是新的套接字對象，可以用來接收和發送數據。address是連接客戶端的地址。   conn,address = sk.accept()    # 建立圖片存儲路徑   path = str(i+1) + '.jpg'    # 接收圖片大小（字節數）   size = conn.recv(1024)   size_str = str(size,encoding="utf-8")   size_str = size_str[2 :]   file_size = int(size_str)    # 響應接收完成   conn.sendall(bytes('finish', encoding="utf-8"))    # 已經接收數據大小 has_size   has_size = 0   # 創建圖片并寫入數據   f = open(path,"wb")   while True:    # 獲取    if file_size == has_size:     break    date = conn.recv(1024)    f.write(date)    has_size += len(date)   f.close()    # 圖片縮放   resize(path)   # cut_img(path)：圖片裁剪成功返回True；失敗返回False   if cut_img(path):    yuchuli()    result = test('test.jpg')    conn.sendall(bytes(result,encoding="utf-8"))   else:    print('falue')    conn.sendall(bytes('人眼檢測失敗，請保持圖片眼睛清晰',encoding="utf-8"))   conn.close() 

# 根據圖片大小等比例縮放圖片 def resize(path):  image=cv2.imread(path,0)  row,col = image.shape  if row >= 2500:   x,y = int(row/5),int(col/5)  elif row >= 2000:   x,y = int(row/4),int(col/4)  elif row >= 1500:   x,y = int(row/3),int(col/3)  elif row >= 1000:   x,y = int(row/2),int(col/2)  else:   x,y = row,col  # 縮放函數  res=cv2.resize(image,(y,x),interpolation=cv2.INTER_CUBIC)  cv2.imwrite(path,res) 

# 直方圖均衡化 eq = cv2.equalizeHist(img) # 中值濾波 lbimg=cv2.medianBlur(eq,3) 

# -*- coding: utf-8 -*- # 檢測人眼，返回眼睛數據  import numpy as np import cv2  def eye_test(path):  # 待檢測的人臉路徑  imagepath = path   # 獲取訓練好的人臉參數  eyeglasses_cascade = cv2.CascadeClassifier('haarcascade_eye_tree_eyeglasses.xml')   # 讀取圖片  img = cv2.imread(imagepath)  # 轉為灰度圖像  gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)   # 檢測并獲取人眼數據  eyeglasses = eyeglasses_cascade.detectMultiScale(gray)  # 人眼數為2時返回左右眼位置數據  if len(eyeglasses) == 2:   num = 0   for (e_gx,e_gy,e_gw,e_gh) in eyeglasses:    cv2.rectangle(img,(e_gx,e_gy),(e_gx+int(e_gw/2),e_gy+int(e_gh/2)),(0,0,255),2)    if num == 0:     x1,y1 = e_gx+int(e_gw/2),e_gy+int(e_gh/2)    else:     x2,y2 = e_gx+int(e_gw/2),e_gy+int(e_gh/2)    num += 1   print('eye_test')   return x1,y1,x2,y2  else:   return False 

# -*- coding: utf-8 -*- # 人眼對齊并裁剪  # 參數含義： # CropFace(image, eye_left, eye_right, offset_pct, dest_sz) # eye_left is the position of the left eye # eye_right is the position of the right eye # 比例的含義為：要保留的圖像靠近眼鏡的百分比， # offset_pct is the percent of the image you want to keep next to the eyes (horizontal, vertical direction) # 最后保留的圖像的大小。 # dest_sz is the size of the output image # import sys,math from PIL import Image from eye_test import eye_test   # 計算兩個坐標的距離 def Distance(p1,p2):  dx = p2[0]- p1[0]  dy = p2[1]- p1[1]  return math.sqrt(dx*dx+dy*dy)   # 根據參數，求仿射變換矩陣和變換后的圖像。 def ScaleRotateTranslate(image, angle, center =None, new_center =None, scale =None, resample=Image.BICUBIC):  if (scale is None)and (center is None):   return image.rotate(angle=angle, resample=resample)  nx,ny = x,y = center  sx=sy=1.0  if new_center:   (nx,ny) = new_center  if scale:   (sx,sy) = (scale, scale)  cosine = math.cos(angle)  sine = math.sin(angle)  a = cosine/sx  b = sine/sx  c = x-nx*a-ny*b  d =-sine/sy  e = cosine/sy  f = y-nx*d-ny*e  return image.transform(image.size, Image.AFFINE, (a,b,c,d,e,f), resample=resample)   # 根據所給的人臉圖像，眼睛坐標位置，偏移比例，輸出的大小，來進行裁剪。 def CropFace(image, eye_left=(0,0), eye_right=(0,0), offset_pct=(0.2,0.2), dest_sz = (70,70)):  # calculate offsets in original image 計算在原始圖像上的偏移。  offset_h = math.floor(float(offset_pct[0])*dest_sz[0])  offset_v = math.floor(float(offset_pct[1])*dest_sz[1])  # get the direction 計算眼睛的方向。  eye_direction = (eye_right[0]- eye_left[0], eye_right[1]- eye_left[1])  # calc rotation angle in radians 計算旋轉的方向弧度。  rotation =-math.atan2(float(eye_direction[1]),float(eye_direction[0]))  # distance between them # 計算兩眼之間的距離。  dist = Distance(eye_left, eye_right)  # calculate the reference eye-width 計算最后輸出的圖像兩只眼睛之間的距離。  reference = dest_sz[0]-2.0*offset_h  # scale factor # 計算尺度因子。  scale =float(dist)/float(reference)  # rotate original around the left eye # 原圖像繞著左眼的坐標旋轉。  image = ScaleRotateTranslate(image, center=eye_left, angle=rotation)  # crop the rotated image # 剪切  crop_xy = (eye_left[0]- scale*offset_h, eye_left[1]- scale*offset_v) # 起點  crop_size = (dest_sz[0]*scale, dest_sz[1]*scale) # 大小  image = image.crop((int(crop_xy[0]),int(crop_xy[1]),int(crop_xy[0]+crop_size[0]),int(crop_xy[1]+crop_size[1])))  # resize it 重置大小  image = image.resize(dest_sz, Image.ANTIALIAS)  return image  def cut_img(path):  image = Image.open(path)   # 人眼識別成功返回True；否則，返回False  if eye_test(path):   print('cut_img')   # 獲取人眼數據   leftx,lefty,rightx,righty = eye_test(path)    # 確定左眼和右眼位置   if leftx > rightx:    temp_x,temp_y = leftx,lefty    leftx,lefty = rightx,righty    rightx,righty = temp_x,temp_y    # 進行人眼對齊并保存截圖   CropFace(image, eye_left=(leftx,lefty), eye_right=(rightx,righty), offset_pct=(0.30,0.30), dest_sz=(92,112)).save('test.jpg')   return True  else:   print('falue')   return False 

# -*- coding: utf-8 -*-  from numpy import * import cv2 import tensorflow as tf  # 圖片大小 TYPE = 112*92 # 訓練人數 PEOPLENUM = 42 # 每人訓練圖片數 TRAINNUM = 15 #( train_face_num ) # 單人訓練人數加測試人數 EACH = 21 #( test_face_num + train_face_num )  # 2維=>1維 def img2vector1(filename):  img = cv2.imread(filename,0)  row,col = img.shape  vector1 = zeros((1,row*col))  vector1 = reshape(img,(1,row*col))  return vector1  # 獲取人臉數據 def ReadData(k):  path = 'face_flip/'  train_face = zeros((PEOPLENUM*k,TYPE),float32)  train_face_num = zeros((PEOPLENUM*k,PEOPLENUM))  test_face = zeros((PEOPLENUM*(EACH-k),TYPE),float32)  test_face_num = zeros((PEOPLENUM*(EACH-k),PEOPLENUM))   # 建立42個人的訓練人臉集和測試人臉集  for i in range(PEOPLENUM):   # 單前獲取人   people_num = i + 1   for j in range(k):    #獲取圖片路徑    filename = path + 's' + str(people_num) + '/' + str(j+1) + '.jpg'    #2維=>1維    img = img2vector1(filename)     #train_face:每一行為一幅圖的數據；train_face_num:儲存每幅圖片屬于哪個人    train_face[i*k+j,:] = img/255    train_face_num[i*k+j,people_num-1] = 1    for j in range(k,EACH):    #獲取圖片路徑    filename = path + 's' + str(people_num) + '/' + str(j+1) + '.jpg'     #2維=>1維    img = img2vector1(filename)     # test_face:每一行為一幅圖的數據；test_face_num:儲存每幅圖片屬于哪個人    test_face[i*(EACH-k)+(j-k),:] = img/255    test_face_num[i*(EACH-k)+(j-k),people_num-1] = 1   return train_face,train_face_num,test_face,test_face_num  # 獲取訓練和測試人臉集與對應lable train_face,train_face_num,test_face,test_face_num = ReadData(TRAINNUM)  # 計算測試集成功率 def compute_accuracy(v_xs, v_ys):  global prediction  y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})  correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})  return result  # 神經元權重 def weight_variable(shape):  initial = tf.truncated_normal(shape, stddev=0.1)  return tf.Variable(initial)  # 神經元偏置 def bias_variable(shape):  initial = tf.constant(0.1, shape=shape)  return tf.Variable(initial)  # 卷積 def conv2d(x, W):  # stride [1, x_movement, y_movement, 1]  # Must have strides[0] = strides[3] = 1  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')  # 最大池化，x,y步進值均為2 def max_pool_2x2(x):  # stride [1, x_movement, y_movement, 1]  return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')   # define placeholder for inputs to network xs = tf.placeholder(tf.float32, [None, 10304])/255. # 112*92 ys = tf.placeholder(tf.float32, [None, PEOPLENUM]) # 42個輸出 keep_prob = tf.placeholder(tf.float32) x_image = tf.reshape(xs, [-1, 112, 92, 1]) # print(x_image.shape) # [n_samples, 112,92,1]  # 第一層卷積層 W_conv1 = weight_variable([5,5, 1,32]) # patch 5x5, in size 1, out size 32 b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 112x92x32 h_pool1 = max_pool_2x2(h_conv1)       # output size 56x46x64   # 第二層卷積層 W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64 b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 56x46x64 h_pool2 = max_pool_2x2(h_conv2)       # output size 28x23x64   # 第一層神經網絡全連接層 W_fc1 = weight_variable([28*23*64, 1024]) b_fc1 = bias_variable([1024]) # [n_samples, 28, 23, 64] ->> [n_samples, 28*23*64] h_pool2_flat = tf.reshape(h_pool2, [-1, 28*23*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  # 第二層神經網絡全連接層 W_fc2 = weight_variable([1024, PEOPLENUM]) b_fc2 = bias_variable([PEOPLENUM]) prediction = tf.nn.softmax((tf.matmul(h_fc1_drop, W_fc2) + b_fc2))   # 交叉熵損失函數 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = tf.matmul(h_fc1_drop, W_fc2)+b_fc2, labels=ys)) regularizers = tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(b_fc1) +tf.nn.l2_loss(W_fc2) + tf.nn.l2_loss(b_fc2) # 將正則項加入損失函數 cost += 5e-4 * regularizers # 優化器優化誤差值 train_step = tf.train.AdamOptimizer(1e-4).minimize(cost)  sess = tf.Session() init = tf.global_variables_initializer() saver = tf.train.Saver() sess.run(init)  # 訓練1000次，每50次輸出測試集測試結果 for i in range(1000):  sess.run(train_step, feed_dict={xs: train_face, ys: train_face_num, keep_prob: 0.5})  if i % 50 == 0:   print(sess.run(prediction[0],feed_dict= {xs: test_face,ys: test_face_num,keep_prob: 1}))   print(compute_accuracy(test_face,test_face_num)) # 保存訓練數據 save_path = saver.save(sess,'my_data/save_net.ckpt') 

# -*- coding: utf-8 -*- # 兩層神經卷積網絡加兩層全連接神經網絡  from numpy import * import cv2 import tensorflow as tf  # 神經網絡最終輸出個數 PEOPLENUM = 42  # 2維=>1維 def img2vector1(img):  row,col = img.shape  vector1 = zeros((1,row*col),float32)  vector1 = reshape(img,(1,row*col))  return vector1  # 神經元權重 def weight_variable(shape):  initial = tf.truncated_normal(shape, stddev=0.1)  return tf.Variable(initial)  # 神經元偏置 def bias_variable(shape):  initial = tf.constant(0.1, shape=shape)  return tf.Variable(initial)  # 卷積 def conv2d(x, W):  # stride [1, x_movement, y_movement, 1]  # Must have strides[0] = strides[3] = 1  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')  # 最大池化，x,y步進值均為2 def max_pool_2x2(x):  # stride [1, x_movement, y_movement, 1]  return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')  # define placeholder for inputs to network xs = tf.placeholder(tf.float32, [None, 10304])/255. # 112*92 ys = tf.placeholder(tf.float32, [None, PEOPLENUM]) # 42個輸出 keep_prob = tf.placeholder(tf.float32) x_image = tf.reshape(xs, [-1, 112, 92, 1]) # print(x_image.shape) # [n_samples, 112,92,1]  # 第一層卷積層 W_conv1 = weight_variable([5,5, 1,32]) # patch 5x5, in size 1, out size 32 b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 112x92x32 h_pool1 = max_pool_2x2(h_conv1)       # output size 56x46x64   # 第二層卷積層 W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64 b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 56x46x64 h_pool2 = max_pool_2x2(h_conv2)       # output size 28x23x64   # 第一層神經網絡全連接層 W_fc1 = weight_variable([28*23*64, 1024]) b_fc1 = bias_variable([1024]) # [n_samples, 28, 23, 64] ->> [n_samples, 28*23*64] h_pool2_flat = tf.reshape(h_pool2, [-1, 28*23*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  # 第二層神經網絡全連接層 W_fc2 = weight_variable([1024, PEOPLENUM]) b_fc2 = bias_variable([PEOPLENUM]) prediction = tf.nn.softmax((tf.matmul(h_fc1_drop, W_fc2) + b_fc2))  sess = tf.Session() init = tf.global_variables_initializer()  # 下載訓練數據 saver = tf.train.Saver() saver.restore(sess,'my_data/save_net.ckpt')  # 返回簽到人名 def find_people(people_num):  if people_num == 41:   return '任童霖'  elif people_num == 42:   return 'LZT'  else:   return 'another people'  def test(path):  # 獲取處理后人臉  img = cv2.imread(path,0)/255  test_face = img2vector1(img)  print('true_test')   # 計算輸出比重最大的人及其所占比重  prediction1 = sess.run(prediction,feed_dict={xs:test_face,keep_prob:1})  prediction1 = prediction1[0].tolist()  people_num = prediction1.index(max(prediction1))+1  result = max(prediction1)/sum(prediction1)  print(result,find_people(people_num))   # 神經網絡輸出最大比重大于0.5則匹配成功  if result > 0.50:   # 保存簽到數據   qiandaobiao = load('save.npy')   qiandaobiao[people_num-1] = 1   save('save.npy',qiandaobiao)    # 返回 人名+簽到成功   print(find_people(people_num) + '已簽到')   result = find_people(people_num) + ' 簽到成功'  else:   result = '簽到失敗'  return result