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"""
提取图片特征信息的模型：vgg16
"""
import io
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from numpy import linalg as LA
from keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input as preprocess_input_vgg
class VGGNet:
def __init__(self):
self.input_shape = (224, 224, 3)
self.weight = 'imagenet'
self.pooling = 'max'
self.model_vgg = VGG16(weights=self.weight,
input_shape=(self.input_shape[0], self.input_shape[1], self.input_shape[2]),
pooling=self.pooling, include_top=False)
# self.model_vgg.predict(np.zeros((1, 224, 224, 3)))
# 提取vgg16最后一层卷积特征
def vgg_extract_feat(self, file, file_type='file'):
# if file_type == 'bytes':
# img = image.load_img(io.BytesIO(file.read()), target_size=(self.input_shape[0], self.input_shape[1]))
# else:
# # file_type = 'file'
# img = image.load_img(file, target_size=(self.input_shape[0], self.input_shape[1]))
img = image.load_img(file, target_size=(self.input_shape[0], self.input_shape[1]))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = preprocess_input_vgg(img)
feat = self.model_vgg.predict(img)
# print(feat.shape)
norm_feat = feat[0] / LA.norm(feat[0])
# print("norm_feat:", norm_feat)
return list(norm_feat)
# # 提取vgg16最后一层卷积特征
# def vgg_extract_feat(self, image_bytes):
# try:
# img = image.load_img(io.BytesIO(image_bytes), target_size=(self.input_shape[0], self.input_shape[1]))
# img = image.img_to_array(img)
# img = np.expand_dims(img, axis=0)
# img = preprocess_input_vgg(img)
# feat = self.model_vgg.predict(img)
# norm_feat = feat[0] / LA.norm(feat[0])
# return norm_feat.tolist()
# except Exception as e:
# print(e, traceback.format_exc())
# return list(np.zeros(shape=(512,)))
if __name__ == '__main__':
handle_obj = VGGNet()
handle_obj.vgg_extract_feat(file='')