CNN

import os
import sys
import numpy as np
import cv2

from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten ,BatchNormalization
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

IMAGE_SIZE = 48

#按照指定影象大小調整尺寸
def resize_image(image, height = IMAGE_SIZE, width = IMAGE_SIZE):
    top, bottom, left, right = (0, 0, 0, 0)
    
    #獲取影象尺寸
    h, w, _ = image.shape
    
    #對於長寬不相等的圖片，找到最長的一邊
    longest_edge = max(h, w)    
    
    #計算短邊需要增加多上畫素寬度使其與長邊等長
    if h < longest_edge:
        dh = longest_edge - h
        top = dh // 2
        bottom = dh - top
    elif w < longest_edge:
        dw = longest_edge - w
        left = dw // 2
        right = dw - left
    else:
        pass 
    
    #RGB顏色
    BLACK = [0, 0, 0]
    
    #給影象增加邊界，是圖片長、寬等長，cv2.BORDER_CONSTANT指定邊界顏色由value指定
    constant = cv2.copyMakeBorder(image, top , bottom, left, right, cv2.BORDER_CONSTANT, value = BLACK)
    
    #調整影象大小並返回
    return cv2.resize(constant, (height, width))

#讀取訓練資料
fall = "C:\Harden_project\project4_fall\跌倒照片"
stand = ""
images = []
labels = []
dir_counts = 0
for i in os.listdir(face1):
    img1 = cv2.imread(face1+"/"+i)
    img1 = resize_image(img1, IMAGE_SIZE, IMAGE_SIZE)
    images.append(img1)
    labels.append(dir_counts)
    #a = np.array(images,dtype=np.float32)
    #print(a.shape)
for i in os.listdir(face):
    img2 = cv2.imread(face+"/"+i)
    img2 = resize_image(img2, IMAGE_SIZE, IMAGE_SIZE)
    images.append(img2)
    labels.append(dir_counts+1)

for i in os.listdir(face2):
    img3 = cv2.imread(face2+"/"+i)
    img3 = resize_image(img3, IMAGE_SIZE, IMAGE_SIZE)
    images.append(img3)
    labels.append(dir_counts+2)

for i in os.listdir(face3):
    img4 = cv2.imread(face3+"/"+i)
    img4 = resize_image(img4, IMAGE_SIZE, IMAGE_SIZE)
    images.append(img4)
    labels.append(dir_counts+3)
label = np.array(labels)
print(len(label))
print(label)
#labels = np.array([0 if label.endswith('face0') else 1 for label in labels])
#print(type(labels))

#########CNN############
#print(images)
X_train_img,X_test_img,y_train_label,y_test_label =  train_test_split(images, label,test_size=0.3,random_state=2 )#
X_train = np.array(X_train_img, dtype=np.float32)
X_test = np.array(X_test_img, dtype=np.float32)
print(X_train.shape)
x_train_std = X_train/255
x_test_std  =  X_test/255
y_trainOneHot = np_utils.to_categorical(y_train_label)
y_testOneHot = np_utils.to_categorical(y_test_label)
print(x_test_std.shape)
print(y_test_label)
#x_train_std = x_train_std.reshape(x_train_std.shape[0],-1)
#x_test_std = x_test_std.reshape(x_test_std.shape[0],-1)
print(len(x_train_std))


#建立模組
#cnn modle
model = Sequential()
#捲積層conv2D
model.add(Conv2D(filters = 48, kernel_size=(5,5), padding='same',input_shape=(48,48,3),activation = 'relu'))
model.add(Dropout(0.5))
model.add(Conv2D(filters = 48, kernel_size=(5,5), padding='same',input_shape=(48,48,3),activation = 'relu'))

#化池層MaxPooling2D
model.add(MaxPooling2D(pool_size=(2,2)))
#conv2D *2
model.add(Conv2D(filters=192, kernel_size=(3,3),padding ='same',activation='relu'))
model.add(Dropout(0.5))
model.add(Conv2D(filters=384, kernel_size=(3,3),padding ='same',activation='relu'))

#Maxpooling2D *2
model.add(MaxPooling2D(pool_size=(2,2)))
#捲積層conv2D*3
model.add(Conv2D(filters= 384,kernel_size=(3,3),activation='relu',padding='same'))
model.add(Dropout(0.5))
model.add(Conv2D(filters= 768,kernel_size=(3,3),activation='relu',padding='same'))
#Maxpooling2D *3
model.add(MaxPooling2D(pool_size=(2,2)))
#平坦層Flatten壓平變成一維 64X8X8=4096
model.add(Flatten())
#Drop
model.add(Dropout(0.6))
#隱藏layer 
model.add(Dense(3072,kernel_initializer='normal',activation='relu'))
#Drop
model.add(Dropout(0.5))
#model.add(Dropout(0.3))
#隱藏layer2
model.add(Dense(1536,kernel_initializer='normal',activation='relu'))
#Drop
model.add(Dropout(0.5))
#輸出layer
model.add(Dense(4,activation='softmax'))
#summary
print(model.summary())
with open('CNN3.h5', 'w') as fp: 
    pass
try: 
    model.load_weights("CNN3.h5")
    print("載入模型成功")
except:
    print("載入模型失敗")

model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.fit(x=x_train_std,y=y_trainOneHot,validation_split=0.2,epochs=50,batch_size=100,verbose=1)

scores = model.evaluate(x_test_std,y_testOneHot,verbose=1)
model.save("CNN4.h5")
print("Save mode to disk")
prediction= model.predict_classes(x_test_std)
print("幹",scores[1])

import pandas as pd 
def plot_image(image,labels,prediction,idx,num=10):  

    fig = plt.gcf() 

    fig.set_size_inches(12, 14) 

    if num>25: 

        num=25 

    for i in range(0, num): 

        ax = plt.subplot(5,5, 1+i) 

        ax.imshow(image[idx], cmap='binary') 

        title = "label=" +str(labels[idx]) 

        if len(prediction)>0: 

            title+=",perdict="+str(prediction[idx]) 

        ax.set_title(title,fontsize=10) 

        ax.set_xticks([]);ax.set_yticks([]) 

        idx+=1 

    plt.show() 

p =confusion_amtrix =pd.crosstab(np.array(y_test_label),prediction, rownames=['label'],colnames=['perdict'],margins = True)
print(p)
plot_image(x_test_std,y_test_label,prediction,idx=10)