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painter.py
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painter.py
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'''
最开始的naive版本，不看懂的话也是会很坑的
'''
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import cv2
import numpy as np
import h5py
import os
from keras.utils import np_utils, conv_utils
from keras.models import Model
from keras.layers import Flatten, Dense, Input
from keras.optimizers import Adam
from keras.applications.resnet50 import ResNet50
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.applications.vgg19 import VGG19
from keras.applications.inception_v3 import InceptionV3
from keras.applications.densenet import DenseNet201
from keras import backend as K
#from past.builtins import xrange
from keras.layers import AveragePooling2D
def get_name_list(filepath): #获取各个类别的名字
pathDir = os.listdir(filepath)
out = []
for allDir in pathDir:
if os.path.isdir(os.path.join(filepath,allDir)):
#child = allDir.decode('gbk') # .decode('gbk')是解决中文显示乱码问题
out.append(allDir)
return out
def eachFile(filepath): #将目录内的文件名放入列表中
pathDir = os.listdir(filepath)
out = []
for allDir in pathDir:
#child = allDir.decode('gbk') # .decode('gbk')是解决中文显示乱码问题
out.append(allDir)
return out
def get_data(data_name,train_left=0.0,train_right=0.7,train_all=0.7,resize=True,data_format=None,t=''): #从文件夹中获取图像数据
file_name = os.path.join(pic_dir_out,data_name+t+'_'+str(train_left)+'_'+str(train_right)+'_'+str(Width)+"X"+str(Height)+".h5")
print (file_name)
if os.path.exists(file_name): #判断之前是否有存到文件中
f = h5py.File(file_name,'r')
if t=='train':
X_train = f['X_train'][:]
y_train = f['y_train'][:]
f.close()
return (X_train, y_train)
elif t=='test':
X_test = f['X_test'][:]
y_test = f['y_test'][:]
f.close()
return (X_test, y_test)
else:
return
#data_format = conv_utils.normalize_data_format(data_format)
pic_dir_set = eachFile(pic_dir_data)
X_train = []
y_train = []
X_test = []
y_test = []
label = 0
for pic_dir in pic_dir_set:
#print (pic_dir_data+pic_dir)
if not os.path.isdir(os.path.join(pic_dir_data,pic_dir)):
continue
pic_set = eachFile(os.path.join(pic_dir_data,pic_dir))
pic_index = 0
train_count = int(len(pic_set)*train_all)
train_l = int(len(pic_set)*train_left)
train_r = int(len(pic_set)*train_right)
for pic_name in pic_set:
if (pic_index < train_count):
if t=='train':
if not os.path.isfile(os.path.join(pic_dir_data,pic_dir,pic_name)):
continue
img = cv2.imread(os.path.join(pic_dir_data,pic_dir,pic_name))
if img is None:
continue
if (resize):
img = cv2.resize(img,(Width,Height))
img = img.reshape(-1,Width,Height,3)
if (pic_index >= train_l and pic_index < train_r):
X_train.append(img)
y_train.append(label)
else:
if t=='test':
if not os.path.isfile(os.path.join(pic_dir_data,pic_dir,pic_name)):
continue
img = cv2.imread(os.path.join(pic_dir_data,pic_dir,pic_name))
if img is None:
continue
if (resize):
img = cv2.resize(img,(Width,Height))
img = img.reshape(-1,Width,Height,3)
X_test.append(img)
y_test.append(label)
pic_index += 1
if len(pic_set) != 0:
label += 1
f = h5py.File(file_name,'w')
if t=='train':
X_train = np.concatenate(X_train,axis=0)
y_train = np.array(y_train)
f.create_dataset('X_train', data = X_train)
f.create_dataset('y_train', data = y_train)
f.close()
return (X_train, y_train)
elif t=='test':
X_test = np.concatenate(X_test,axis=0)
y_test = np.array(y_test)
f.create_dataset('X_test', data = X_test)
f.create_dataset('y_test', data = y_test)
f.close()
return (X_test, y_test)
else:
return
def main():
global Width, Height, pic_dir_out, pic_dir_data
Width = 224
Height = 224
num_classes = 71 #71 #102 #Caltech101为102 cifar10为10
pic_dir_out = './pic_out2/'#'./pic_out2/'
pic_dir_data = './pic_dataset/'#'./pic_dataset2/'
sub_dir = '224_resnet50/'
if not os.path.isdir(os.path.join(pic_dir_out,sub_dir)):
os.mkdir(os.path.join(pic_dir_out,sub_dir))
pic_dir_mine = os.path.join(pic_dir_out,sub_dir)
(X_train, y_train) = get_data("Kaggle_data_",0.0,0.7,data_format='channels_last',t='train')
y_train = np_utils.to_categorical(y_train, num_classes)
input_tensor = Input(shape=(224, 224, 3))
base_model = ResNet50(input_tensor=input_tensor,include_top=False,weights='imagenet')#,pooling = 'avg') #pic_out1
#base_model = InceptionResNetV2(input_tensor=input_tensor,include_top=False,weights='imagenet')#,pooling = 'avg')
#base_model = VGG19(input_tensor=input_tensor,include_top=False,weights='imagenet',pooling = 'avg')
#base_model = InceptionV3(input_tensor=input_tensor,include_top=False,weights='imagenet',pooling = 'avg')
#base_model = DenseNet201(input_tensor=input_tensor,include_top=False,weights='imagenet')#,pooling = 'avg')#'imagenet'
get_resnet50_output = K.function([base_model.layers[0].input, K.learning_phase()],
[base_model.layers[-1].output])
file_name = os.path.join(pic_dir_mine,'resnet50_train_output'+'.h5')
if os.path.exists(file_name):
f = h5py.File(file_name,'r')
resnet50_train_output = f['resnet50_train_output'][:]
f.close()
else:
resnet50_train_output = []
delta = 10
for i in range(0,len(X_train),delta):
#print (i)
one_resnet50_train_output = get_resnet50_output([X_train[i:i+delta], 0])[0]
resnet50_train_output.append(one_resnet50_train_output)
resnet50_train_output = np.concatenate(resnet50_train_output,axis=0)
f = h5py.File(file_name,'w')
f.create_dataset('resnet50_train_output', data = resnet50_train_output)
f.close()
print('kankan'+str(resnet50_train_output.shape))
input_tensor = Input(shape=(7,7,2048))#shape=(7, 7, 2048)(5,5,1536)(7,7,1920)
x = AveragePooling2D((7, 7), name='avg_pool')(input_tensor)
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=input_tensor, outputs=predictions)
model.compile(optimizer=Adam(), loss='categorical_crossentropy',metrics=['accuracy'])
print('\nTraining ------------') #从文件中提取参数，训练后存在新的文件中
cm = 0 #修改这个参数可以多次训练
cm_str = '' if cm==0 else str(cm)
cm2_str = '' if (cm+1)==0 else str(cm+1)
#if cm >= 1:
# model.load_weights(os.path.join(pic_dir_mine,'cnn_model_Kaggle_resnet50_'+cm_str+'.h5'))
model.fit(resnet50_train_output, y_train, epochs=20, batch_size=128,) #batch_size有没有影响
model.save_weights(os.path.join(pic_dir_mine,'cnn_model_Kaggle_resnet50_'+cm2_str+'.h5'))
(X_test, y_test) = get_data("Kaggle_data_",0.0,0.7,data_format='channels_last',t='test')
y_test = np_utils.to_categorical(y_test, num_classes)
file_name = os.path.join(pic_dir_mine,'resnet50_test_output'+'.h5')
if os.path.exists(file_name):
f = h5py.File(file_name,'r')
resnet50_test_output = f['resnet50_test_output'][:]
f.close()
else:
resnet50_test_output = []
delta = 10
for i in range(0,len(X_test),delta):
#print(i)
one_resnet50_test_output = get_resnet50_output([X_test[i:i+delta], 0])[0]
resnet50_test_output.append(one_resnet50_test_output)
resnet50_test_output = np.concatenate(resnet50_test_output,axis=0)
f = h5py.File(file_name,'w')
f.create_dataset('resnet50_test_output', data = resnet50_test_output)
f.close()
print('\nTesting ------------') #对测试集进行评估
class_name_list = get_name_list(pic_dir_data) #获取top-N的每类的准确率
pred = model.predict(resnet50_test_output, batch_size=32)
f = h5py.File(os.path.join(pic_dir_mine,'pred_'+cm2_str+'.h5'),'w')
f.create_dataset('pred', data = pred)
f.close()
N = 1
pred_list = []
for row in pred:
pred_list.append(row.argsort()[-N:][::-1]) #获取最大的N个值的下标
pred_array = np.array(pred_list)
test_arg = np.argmax(y_test,axis=1)
class_count = [0 for _ in range(num_classes)]
class_acc = [0 for _ in range(num_classes)]
for i in range(len(test_arg)):
class_count[test_arg[i]] += 1
if test_arg[i] in pred_array[i]:
class_acc[test_arg[i]] += 1
print('top-'+str(N)+' all acc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))
#for i in range(num_classes):
# print (i, class_name_list[i], 'acc: '+str(class_acc[i])+'/'+str(class_count[i]))
'''print('----------------------------------------------------')
N = 5
pred_list = []
for row in pred:
pred_list.append(row.argsort()[-N:][::-1]) #获取最大的N个值的下标
pred_array = np.array(pred_list)
test_arg = np.argmax(y_test,axis=1)
class_count = [0 for _ in range(num_classes)]
class_acc = [0 for _ in range(num_classes)]
for i in range(len(test_arg)):
class_count[test_arg[i]] += 1
if test_arg[i] in pred_array[i]:
class_acc[test_arg[i]] += 1
print('top-'+str(N)+' all acc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))
for i in range(num_classes):
print (i, class_name_list[i], 'acc: '+str(class_acc[i])+'/'+str(class_count[i]))'''
if __name__ == '__main__':
main()