检索出与指定图片相似度最高的n张图片_resnet50图片特征 不准确

给定一张指定图片“22.png”，在指定图片库“database目录”中检索出与其相似度最高的3张图片。

1. 使用深度神经网络提取图片特征

1.1 vgg16提取图片特征

# -*- coding: UTF-8 -*-
import numpy as np
import h5py
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input as preprocess_input_vgg
from keras.preprocessing import image
 
class VGGNet:
    def __init__(self):
        self.input_shape = (224, 224, 3)
        self.weight = 'imagenet'   # None代表随机初始化，即不加载预训练权重
        self.pooling = 'max'  # avg
        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( Use vgg16/Resnet model to extract features Output normalized feature vector)
    def vgg_extract_feat(self, img_path):
        img = image.load_img(img_path, 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] / np.linalg.norm(feat[0])
        return norm_feat

1.2 resnet50提取图片特征

from keras.applications.resnet50 import ResNet50
from keras.applications.resnet50 import preprocess_input as preprocess_input_resnet
 
self.model_resnet = ResNet50(weights = self.weight,
    input_shape = (self.input_shape[0], self.input_shape[1], self.input_shape[2]),
    pooling = self.pooling,
    include_top = False)
 
# 提取resnet50最后一层卷积特征
def resnet_extract_feat(self, img_path):
    img = preprocess_input_resnet(img)
    feat = self.model_resnet.predict(img)

1.3 densenet121提取图片特征

from keras.applications.densenet import DenseNet121
from keras.applications.densenet import preprocess_input as preprocess_input_densenet
 
self.model_densenet = DenseNet121(weights = self.weight,
    input_shape = (self.input_shape[0], self.input_shape[1], self.input_shape[2]),
    pooling = self.pooling,
    include_top = False)
 
# 提取densenet121最后一层卷积特征
def densenet_extract_feat(self, img_path):
    img = preprocess_input_densenet(img)
    feat = self.model_densenet.predict(img)

2. 从图像库抽取特征

使用深度神经网络从database目录提取每张图片的name和feature。

def save_features():
    database = 'database'
    # directory for storing extracted features
    index = 'models/vgg_featureCNN.h5'
    # Returns a list of filenames for all jpg images in a directory.
    img_list = [os.path.join(database, f) for f in os.listdir(database) if f.endswith('.jpg')]
    feats, names = extract_features_and_images_index(img_list)
 
    # writing feature extraction results
    h5f = h5py.File(index, 'w')
    h5f.create_dataset('dataset_1', data=np.array(feats))
    h5f.create_dataset('dataset_2', data=np.string_(names))
    h5f.close()
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结果写入hdf5

def extract_features_and_images_index(img_path_list):
    feats = []
    names = []
    model = VGGNet()
    for i, img_path in enumerate(img_path_list):
        norm_feat = model.vgg_extract_feat(img_path)  # 修改此处改变提取特征的网络
        img_name = os.path.split(img_path)[1]
        feats.append(norm_feat)
        names.append(img_name)
        print("extracting feature from image No. %d , %d images in total" % ((i + 1), len(img_list)))
        return feats, names
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3. 加载特征检索相似图片

检索出三张相似度最高的图片

def get_similarity_top3_picture(image_path='22.png', maxres=3):
    path = 'models/vgg_featureCNN.h5'
    feats, names = get_feature_from_hdf5(path)
    # init VGGNet16 model
    model = VGGNet()
 
    # extract query image's feature, compute simlarity score and sort
    img_feat = model.vgg_extract_feat(image_path)  # 修改此处改变提取特征的网络
    scores = np.dot(img_feat, feats.T)
    # scores = np.dot(img_feat, feats.T)/(np.linalg.norm(img_feat)*np.linalg.norm(feats.T))
    rank_ID = np.argsort(scores)[::-1]
    rank_score = scores[rank_ID]
    print (rank_ID)
    # [0 3 1 2]
    print(rank_score)
    # [0.5255763 0.5209291 0.4861027 0.4736392]
 
    # number of top retrieved images to show
    imlist = []
    for i, index in enumerate(rank_ID[0:maxres]):
        imlist.append(names[index])
        print("image names: " + str(names[i]) + " scores: %f" % rank_score[i])
        # show top #maxres retrieved result one by one
        plot_img(i, index)
 
    top_1_score = rank_score[0]
    top_1_md5 = str(imlist[0]).split(".")[0].split("'")[1].strip()
    return [top_1_md5, top_1_score]
    # ['bf43ddd28d6a2544b4ba8f95002674ed', '0.5255763']
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从hdf5读取特征

def get_feature_from_hdf5(path):
    # read in indexed images' feature vectors and corresponding image names
    path = 'models/vgg_featureCNN.h5'
    h5f = h5py.File(path, 'r')
    feats = h5f['dataset_1'][:]
    names = h5f['dataset_2'][:]
    h5f.close()
    return feats, names
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显示图片

import matplotlib.image as mpimg
 
def plot_img(i, index):
    image = mpimg.imread('database/' + str(index, 'utf-8'))
    plt.title("search output %d" % (i + 1))
    plt.imshow(image)
    plt.show()
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