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ag_processing.py
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ag_processing.py
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#coding=utf-8
from keras.models import Model
from keras.layers import Input,GlobalAveragePooling2D,GlobalMaxPooling2D
from keras.preprocessing.image import load_img,img_to_array
import pandas as pd
import numpy as np
import os
def get_model(type = 0):
if type==0:
input_x = Input((224,224,3))#tf
from keras.applications.resnet50 import ResNet50,preprocess_input
model = ResNet50(input_tensor=input_x,weights='imagenet',include_top=False)
func = preprocess_input
elif type == 1:
input_x = Input((299,299,3))#tf
from keras.applications.inception_v3 import InceptionV3,preprocess_input
model = InceptionV3(input_tensor=input_x,weights='imagenet',include_top=False)
func = preprocess_input
elif type == 2:
input_x = Input((299,299,3))#tf
from keras.applications.xception import Xception,preprocess_input
model = Xception(input_tensor=input_x,weights='imagenet',include_top=False)
func = preprocess_input
elif type == 3:
input_x = Input((299,299,3))#tf
from keras.applications.mobilenet import MobileNet,preprocess_input
model = MobileNet(input_tensor=input_x,weights='imagenet',include_top=False)
func = preprocess_input
elif type == 4:
input_x = Input((299,299,3))#tf
from keras.applications.vgg19 import VGG19,preprocess_input
model = VGG19(input_tensor=input_x,weights='imagenet',include_top=False)
func = preprocess_input
return model,preprocess_input
if __name__ == '__main__':
import json
import h5py
train_data_path = "./ai_challenger_scene_train_20170904/scene_train_images_20170904/"
val_data_path = "./ai_challenger_scene_validation_20170908/scene_validation_images_20170908/"
test_data_path = "./ai_challenger_scene_test_a_20170922/scene_test_a_images_20170922/"
train_data = {}
val_data = {}
test_data = {}
with open("./ai_challenger_scene_train_20170904/scene_train_annotations_20170904.json",'r') as f:
train_data = json.load(f)
# print(train_data)
# exit()
with open("./ai_challenger_scene_validation_20170908/scene_validation_annotations_20170908.json",'r') as f:
val_data = json.load(f)
images = []
labels = []
for i in train_data:
images.append(train_data_path + i['image_id'])
labels.append(i['label_id'])
for i in val_data:
images.append(val_data_path + i['image_id'])
labels.append(i['label_id'])
import pandas as pd
df = pd.DataFrame({'images_id':np.asarray(images),'labels':np.asarray(labels)})
df.to_csv('./train.csv')
base_model, preprocess_input = get_model(type=1)
target_size = (299, 299)
# target_size = (224,224)#resnet only#0
# save_name = 'res50_feature.h5'#0
save_name = 'inceptionv3_feature_ag.h5'#1
# save_name = "Xception_feature.h5"#2
# save_name = 'mobilenet_feature.h5'#3
# save_name = 'vgg19_feature.h5'#4
save_path = './feature_/'
model = Model(input=base_model.input, output=GlobalMaxPooling2D()(base_model.output))
BATCHSIZE = 64
from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest'
)
train_feature = np.array([0, ])
test_feature = np.array([0, ])
new_labels = []
for i in range(int(df.shape[0] / BATCHSIZE) + 1):
imgs = []
label = []
for j in range(BATCHSIZE):
imgs.append(img_to_array(load_img(df.iloc[i * BATCHSIZE + j, 0], target_size=target_size)))
label.append(df.iloc[i*BATCHSIZE+j,1])
if i * BATCHSIZE + j == df.shape[0] - 1:
break
imgs = np.asarray(imgs)
if imgs.ndim==4:
imgs = np.expand_dims(imgs, axis=1)
# print(imgs.shape)
ag_imgs = []
for k in range(imgs.shape[0]):
n=0
for ag in datagen.flow(imgs[k], batch_size=1):
ag_imgs.append(ag)
new_labels.append(label[k])
n+=1
if n>5:#增强数量5张
break
# for ag in datagen.flow(,batch_size=1):
ag_imgs = np.asarray(ag_imgs)
ag_imgs = ag_imgs.reshape(ag_imgs.shape[0],ag_imgs.shape[2],ag_imgs.shape[3],ag_imgs.shape[4])
if i == 0:
train_feature = model.predict(preprocess_input(ag_imgs))
else:
print(train_feature.shape,model.predict(preprocess_input(ag_imgs)).shape)
train_feature = np.vstack((train_feature, model.predict(preprocess_input(ag_imgs))))
print (train_feature.shape,len(new_labels))
new_labels = np.asarray(new_labels)
list_2 = [test_data_path + list for list in os.listdir(test_data_path)]
for i in range(int(len(list_2) / BATCHSIZE) + 1):
imgs = []
for j in range(BATCHSIZE):
if i * BATCHSIZE + j <len(list_2):
imgs.append(img_to_array(load_img(list_2[i * BATCHSIZE + j], target_size=target_size)))
if i * BATCHSIZE + j == len(list_2) - 1:
break
imgs = np.asarray(imgs)
if i == 0:
test_feature = model.predict(preprocess_input(imgs))
else:
test_feature = np.vstack([test_feature, model.predict(preprocess_input(imgs))])
print (test_feature.shape, len(list_2))
with h5py.File(save_name) as h:
h.create_dataset("train", data=train_feature)
h.create_dataset("test", data=test_feature)
h.create_dataset("labels",data=new_labels)