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Freeshman · web-flow · commit 8390a5661621 · 2018-03-31T08:01:51.000+08:00
diff --git a/face_detection.py b/face_detection.py
@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jan 11 19:38:42 2018
+
+@author: hu-tom
+"""
+
+import cv2
+win_name='face'
+camera_id=1
+#def usbvideo(win_name,camera_id):
+pic_num=500
+num=0
+cv2.namedWindow(win_name)
+cap=cv2.VideoCapture(camera_id)
+classfier=cv2.CascadeClassifier(r'/PATH/TO/YOUR/OpenCV-tmp/opencv/data/haarcascades/haarcascade_frontalface_alt.xml')
+color=(0,255,0)
+if cap.isOpened():
+    print('open the cap')
+else:
+    print('open faild!')
+count=0
+while cap.isOpened():
+    count=count+1        
+    ok,frame=cap.read()
+    if not ok:
+        break
+    c=cv2.waitKey(1)
+    if c&0xff==ord('c') or count>130:
+        count=0
+        print("detecte face...")
+        gray=cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
+        faceRects=classfier.detectMultiScale(gray,1.2, 3, cv2.CASCADE_SCALE_IMAGE,(32,32))
+        if len(faceRects)>0:
+            for faceRect in faceRects:
+                x,y,w,h=faceRect           
+                img_name = './new/n%d.jpg'%(num)  
+                image = gray[y - 10: y + h + 10, x - 10: x + w + 10]  
+                cv2.imwrite(img_name, image)  
+                cv2.rectangle(frame,(x-10,y-10),(x+w+10,y+h+10),color,2)
+                num += 1  
+            if num > pic_num:   #如果超过指定最大保存数量退出循环  
+                break  
+    elif c&0xff==ord('q'):
+        break
+    cv2.imshow(win_name,frame)
+    
+cap.release()
+cv2.destroyAllWindows()
diff --git a/identifie.py b/identifie.py
@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Dec 29 20:24:18 2017
+
+@author: hu-tom
+"""
+import tensorflow as tf 
+import numpy as np
+import matplotlib.pyplot as plt
+import photo_data_generator
+import cv2
+from PIL import Image
+camera_id=0
+istrained=True
+model="save/model.ckpt"
+checkpoint_dir="save"
+def weight_variable(shape,name):
+    initial=tf.truncated_normal(shape,stddev=0.1)
+    return tf.Variable(initial,name=name)
+def bias_variable(shape,name):
+    initial=tf.constant(0.1,shape=shape)
+    return tf.Variable(initial,name=name)
+def conv2d(x,W):
+    #strides[0]=stirdes[3]=1
+    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
+def max_pool_2x2(x):
+    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
+#conv1 layer
+def compute_accuracy(v_xs,v_ys):
+	global prediction
+	y_pre = sess.run(prediction,feed_dict={xs:v_xs,keep_prob:0.5})
+	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:0.5})
+	return result
+
+#x_data = np.linspace(-1,1,300)[:,np.newaxis]
+#noise  = np.random.normal(0,0.05,x_data.shape)
+#y_data = np.square(x_data)-0.5+noise
+
+xs = tf.placeholder(tf.float32,[None,784])#28x28
+ys = tf.placeholder(tf.float32,[None,2])
+keep_prob=tf.placeholder(tf.float32)
+x_image=tf.reshape(xs,[-1,28,28,1])
+
+
+#print(x_image.shape)#[n_samples,28,28,1]
+
+#conv1 layer
+W_conv1=weight_variable([3,3,1,32],name='wc1')#patch 5x5,in size 1,out size 32     
+b_conv1=bias_variable([32],name='bc1')
+h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)#tf.nn.relu for nonlinear output size28x28x32 
+h_pool1=max_pool_2x2(h_conv1)#output size14x14x32
+
+#conv2 layer
+W_conv2=weight_variable([3,3,32,64],name='wc2')#patch 5x5,in size 32,out size 64     
+b_conv2=bias_variable([64],name='bc2')
+h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)#tf.nn.relu for nonlinear output size28x28x32 
+h_pool2=max_pool_2x2(h_conv2)#output size7x7x64
+
+#conv3 layer
+W_conv3=weight_variable([5,5,64,128],name='wc3')#patch 5x5,in size 32,out size 64     
+b_conv3=bias_variable([128],name='bc3')
+h_conv3=tf.nn.relu(conv2d(h_pool2,W_conv3)+b_conv3)#tf.nn.relu for nonlinear output size28x28x32 
+h_pool3=max_pool_2x2(h_conv3)#output size7x7x128
+#print(h_pool3)
+
+#conv4 layer
+W_conv4=weight_variable([5,5,128,256],name='wc4')#patch 5x5,in size 32,out size 64     
+b_conv4=bias_variable([256],name='bc4')
+h_conv4=tf.nn.relu(conv2d(h_pool3,W_conv4)+b_conv4)#tf.nn.relu for nonlinear output size28x28x32 
+h_pool4=max_pool_2x2(h_conv4)#output size7x7x128
+#print(h_pool4)
+
+##conv5 layer
+#W_conv5=weight_variable([5,5,256,512])#patch 5x5,in size 32,out size 64     
+#b_conv5=bias_variable([512])
+#h_conv5=tf.nn.relu(conv2d(h_pool4,W_conv5)+b_conv5)#tf.nn.relu for nonlinear output size28x28x32 
+#h_pool5=max_pool_2x2(h_conv5)#output size7x7x128
+#print(h_pool5)
+
+#func1 layer
+W_f1=weight_variable([2*2*256,1024],name='wf1')
+b_f1=bias_variable([1024],name='bf1')
+#[n_samples,7,7,64]->>[n_samples,7*7*64]
+h_pool4_flat=tf.reshape(h_pool4,[-1,2*2*256])
+h_f1=tf.nn.relu(tf.matmul(h_pool4_flat,W_f1)+b_f1)
+h_f1_drop=tf.nn.dropout(h_f1,keep_prob)
+
+#func2 layer
+W_f2=weight_variable([1024,2],name='wf2')
+b_f2=bias_variable([2],name='bf2')
+
+prediction=tf.nn.softmax(tf.matmul(h_f1_drop,W_f2)+b_f2)
+
+Cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(tf.clip_by_value(prediction,1e-8,1.0)),reduction_indices=[1]))
+train_step = tf.train.AdamOptimizer(1e-4).minimize(Cross_entropy)
+#train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(Cross_entropy)
+#cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=ys))
+#train_step = tf.train.AdamOptimizer(1e-5).minimize(cost_func)
+
+init = tf.global_variables_initializer()
+# Create a saver.
+saver = tf.train.Saver()
+sess = tf.Session()
+
+
+sess.run(init)
+if istrained:
+    saver=tf.train.import_meta_graph('save/model.ckpt.meta')
+    saver.restore(sess,model)      
+mnist=photo_data_generator.dataresize("./tr_data")
+for i in range(100):
+    batch_xs,bathc_ys = mnist[0][:][:]
+    sess.run(train_step,feed_dict={xs:batch_xs,ys:bathc_ys,keep_prob:0.5})
+    if i % 50 ==0:
+        accu=compute_accuracy(mnist[2][0],mnist[2][1])
+        print(accu)
+        if(accu>=0.98):
+            break
+        '''
+        for j in range(5):
+            im_test=mnist[0][0][j]
+            im_test=im_test.reshape(1,784)
+            print('pre=',prediction.eval(feed_dict={xs:im_test,keep_prob:0.5}, session=sess))
+            print('anser=',mnist[0][1][j])
+        '''
+        #print(sess.run(y_pre))
+saver_path = saver.save(sess,model)  # 将模型保存到save/model.ckpt文件
+print("Model saved in file:", saver_path)
+cap=cv2.VideoCapture(camera_id)
+classfier=cv2.CascadeClassifier(r'/PATH/TO/YOUR/OpenCV-tmp/opencv/data/haarcascades/haarcascade_frontalface_alt.xml')
+if cap.isOpened():
+    print('open the cap')
+else:
+    print('open faild!')
+while cap.isOpened():        
+    ok,frame=cap.read()
+    if not ok:
+        break
+    gray=cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
+    faceRects=classfier.detectMultiScale(gray,1.2, 3, cv2.CASCADE_SCALE_IMAGE,(32,32))
+    if len(faceRects)>0:
+        for faceRect in faceRects:
+            x,y,w,h=faceRect           
+            #img_name = './%d.jpg'%(num)  
+            img = frame[y : y + h , x : x + w ]
+            img = Image.fromarray(img)
+            img =img.convert('L').resize((28,28))
+            width,hight=img.size
+            img = np.asarray(img,dtype='float64')/256.   
+            img = img.reshape(1, hight*width)     
+            pre = prediction.eval(feed_dict={xs:img,keep_prob:0.5},session=sess)
+            if pre[0][1]>=0.7:  
+                color=(0,255,0)
+                cv2.rectangle(frame, (x, y), (x + w , y + h ), color, thickness = 2)
+                cv2.putText(frame,'Me',(x, y),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)                                  
+            else:  
+                color=(255,0,0)
+                cv2.rectangle(frame, (x , y ), (x + w , y + h ), color, thickness = 2)  
+                cv2.putText(frame,'Others',(x, y),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,0),2)  
+            #cv2.imwrite(img_name, image)  
+            #cv2.rectangle(frame,(x-10,y-10),(x+w+10,y+h+10),color,2)
+         #   num += 1  
+        #if num > pic_num:   #如果超过指定最大保存数量退出循环  
+         #   break  
+    cv2.imshow('identify',frame)
+    c=cv2.waitKey(1)
+    if c&0xff==ord('q'):
+        break
+cap.release()
+cv2.destroyAllWindows()
+#sess.close()
diff --git a/photo_data_generator.py b/photo_data_generator.py
@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jan 12 19:43:43 2018
+
+@author: hu-tom
+"""
+
+# -*-coding:utf-8-*-
+import numpy
+#import theano
+from PIL import Image
+from pylab import hstack
+import os
+#import theano.tensor as T
+import random
+
+def dataresize(path):
+    # test path
+    path_t ="./te_data"
+    datas = []
+    train_x= []
+    train_y= []
+    valid_x= []
+    valid_y= []
+    test_x= []
+    test_y= []
+    for dirs in os.listdir(path):
+        # print dirs
+        for filename in os.listdir(os.path.join(path,dirs)):
+            imgpath =os.path.join(os.path.join(path,dirs),filename)
+            img = Image.open(imgpath)
+            img =img.convert('L').resize((28,28))
+            width,hight=img.size
+            img = numpy.asarray(img,dtype='float64')/256.
+    
+            tmp = img.reshape(1, hight*width)[0]
+            if dirs=='0':
+                #print("dirs=0")
+                dirs2bin=[1,0]
+            else:
+                #print("dirs=1")
+                dirs2bin=[0,1]
+            tmp =hstack((dirs2bin,tmp))  # 在此将标签加在数据的前面。
+    
+            datas.append(tmp)
+       # datas.append(img.reshape(1, hight*width)[0])
+        #在此处取出第一行的数据否则在后面的转换的过程中会出现叠加的情况，在成在转换成矩阵时宝类型转换的错误
+    #将数据打乱顺序
+    random.shuffle(datas)
+    random.shuffle(datas)
+    random.shuffle(datas)
+    # 将数据和标签进行分离
+    label=[]
+    for num in range(len(datas)):
+        label.append((datas[num])[:2])
+        datas[num] =(datas[num])[2:]
+        #将数据的标签项去掉
+    tests = []
+        # #读取测试集
+    for dirs in os.listdir(path_t):
+        for filename in os.listdir(os.path.join(path_t,dirs)):
+            imgpath =os.path.join(os.path.join(path_t,dirs),filename)
+            img = Image.open(imgpath)
+            img =img.convert('L').resize((28,28))
+            width,hight=img.size
+            img = numpy.asarray(img,dtype='float64')/256.
+            tmp = img.reshape(1, hight*width)[0]
+            # 在此如果不是取出[0]的话在后面会发现其实其是一个多维的数据的叠加，
+            # 在后面使用theano中的cnn在调用时会出现数据的异常（转换的异常），
+            # 在此是跟原始的mnist的数据集的形式做了比较修改才发现的。。。
+            if dirs=='0':
+                #print("dirs=0")
+                dirs2bin=[1,0]
+            else:
+                #print("dirs=1")
+                dirs2bin=[0,1]
+            tmp =hstack((dirs2bin,tmp))
+            tests.append(tmp)
+    #将数据打乱顺序
+    random.shuffle(tests)
+    random.shuffle(tests)
+    random.shuffle(tests)
+    #  将数据和标签进行分离
+    label_t=[]
+    for num in range(len(tests)):
+        label_t.append((tests[num])[:2])
+        tests[num] =(tests[num])[2:]
+        #将数据的标签项去掉
+        '''    将数据进行打乱，拆分成train test valid    '''
+    for num in range(len(label)):
+        train_x.append(datas[num])
+        train_y.append(label[num])
+    
+    for num in range(len(tests)):
+        if num%2==0:
+            valid_x.append(tests[num])
+            valid_y.append(label_t[num])
+        if num%2==1:
+            test_x.append(tests[num])
+            test_y.append(label_t[num])
+    train_x=numpy.asarray(train_x,dtype='float64')
+    train_y=numpy.asarray(train_y,dtype='int64')
+    valid_x=numpy.asarray(valid_x,dtype='float64')
+    valid_y=numpy.asarray(valid_y,dtype='int64')
+    test_x=numpy.asarray(test_x,dtype='float64')
+    test_y=numpy.asarray(test_y,dtype='int64')
+    
+    rval = [(train_x, train_y), (valid_x, valid_y),(test_x, test_y)]
+    return rval
+rva=dataresize("./tr_data")
