├── KDTree.py ├── LICENSE ├── README.md ├── Selectdir.py ├── a.gif ├── ap.py ├── chengyuan.png ├── extract_feature.py ├── featureCNN1.h5 ├── index.py ├── one_ap.py ├── query.py ├── resize.py ├── root.py ├── settings.py ├── sousuo.png ├── test.py └── tupian.png /KDTree.py: -------------------------------------------------------------------------------- 1 | """ 2 | K-D Tree 3 | 参考资料：https://blog.csdn.net/zhanghao632045/article/details/78447159 4 | https://blog.csdn.net/silangquan/article/details/41483689 5 | https://github.com/stefankoegl/kdtree 6 | """ 7 | from __future__ import print_function 8 | import heapq 9 | import math 10 | 11 | 12 | class KDNode(object): 13 | """节点""" 14 | def __init__(self, data=None, left=None, right=None, axis=None, 15 | sel_axis=None, dimensions=None): 16 | """ 节点 """ 17 | self.data = data # 值. 列表 18 | self.left = left # 左叶子节点 19 | self.right = right # 右叶子节点 20 | # sel_axis(axis)在创建当前节点的子节点中将被使用，输入为父节点的axis，输出为子节点的axis 21 | self.axis = axis 22 | self.sel_axis = sel_axis 23 | self.dimensions = dimensions 24 | 25 | def __nonzero__(self): 26 | return self.data is not None 27 | 28 | __bool__ = __nonzero__ 29 | 30 | def dist(self, point): 31 | """计算当前点和指定点的平方距离""" 32 | return sum([math.pow(self.data[i] - point[i], 2) for i in range(self.dimensions)]) 33 | 34 | def search_knn(self, point, k, dist=None): 35 | """ 寻找和给定值最近的k个节点，输出为元组(node, distance) """ 36 | if dist is None: 37 | get_dist = (lambda n: n.dist(point)) 38 | else: 39 | get_dist = (lambda n: dist(n.data, point)) 40 | 41 | results = [] 42 | self.search_node(point, k, results, get_dist) 43 | 44 | return [(node, -d) for d, node in sorted(results, reverse=True)] # (, distance) 排序后输出 45 | 46 | def search_node(self, point, k, results, get_dist): 47 | """ 48 | :param point: 给定点 49 | :param k: 查找数目 50 | :param results: 结果，类型是列表 51 | :param get_dist: 计算距离 52 | """ 53 | if not self: 54 | return 55 | 56 | nodeDist = get_dist(self) 57 | item = (-nodeDist, self) 58 | if len(results) >= k: # 如果堆满了，就替换掉最远的那个值 59 | if -nodeDist > results[0][0]: 60 | heapq.heapreplace(results, item) 61 | else: # 堆没满就都加入堆中 62 | heapq.heappush(results, item) 63 | # 得到分界面 64 | split_plane = self.data[self.axis] 65 | # 指定点和分界面的平方距离 66 | plane_dist = pow(point[self.axis] - split_plane, 2) 67 | 68 | # 从根节点递归向下访问，若point的axis维小于且分点坐标 69 | # 则移动到左子节点，否则移动到右子节点 70 | if point[self.axis] < split_plane: 71 | if self.left is not None: 72 | self.left.search_node(point, k, results, get_dist) 73 | else: 74 | if self.right is not None: 75 | self.right.search_node(point, k, results, get_dist) 76 | 77 | # 检查父节点的另一子节点是否存在比当前子节点更近的点 78 | # 判断另一区域是否与当前最近邻的圆相交 79 | if -plane_dist > results[0][0] or len(results) < k: 80 | if point[self.axis] < self.data[self.axis]: 81 | if self.right is not None: 82 | self.right.search_node(point, k, results, get_dist) 83 | else: 84 | if self.left is not None: 85 | self.left.search_node(point, k, results, get_dist) 86 | 87 | 88 | def create(point_list=None, dimensions=None, axis=0): 89 | """ 由列表创建一个K-D Tree """ 90 | sel_axis = (lambda prev_axis: (prev_axis+1) % dimensions) # 用来计算sel_axis 91 | 92 | if not point_list: 93 | return KDNode(sel_axis=sel_axis, axis=axis, dimensions=dimensions) 94 | 95 | # 排序，选择中心 96 | point_list = list(point_list) 97 | point_list.sort(key=lambda point: point[axis]) 98 | median = len(point_list) // 2 99 | 100 | loc = point_list[median] 101 | left = create(point_list[:median], dimensions, sel_axis(axis)) # sel_axis表示下一次需要切分的维度 102 | right = create(point_list[median + 1:], dimensions, sel_axis(axis)) 103 | return KDNode(loc, left, right, axis=axis, sel_axis=sel_axis, dimensions=dimensions) 104 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## 一、环境配置 2 | 3 | Python 3.7.6 4 | 5 | 第三方库：PIL,tkinter,time,xlwt,numpy,keras=2.4.3,TensorFlow=2.3.1,h5py,heapq,math 6 | 建议大家直接运行root.py，看缺少哪个库就pip安装哪一个。面向报错修改就可以了。 7 | 8 | OS:Windows10 9 | 10 | ## 二、使用方法 11 | 12 | **将5063张图片放入settings中对应路径文件夹。** 13 | 14 | 1、下载后直接运行root.py即可进入默认的主界面，通过预先生成的索引文件对默认数据库《The Oxford Buildings Dataset》进行检索。 15 | 16 | 2、通过更改settings.py中的classes，运行one_ap.py即可计算对应样例的ap。 17 | 18 | 3、通过更改ap.py文件中的rootdir和magins_path再运行ap.py即可运算所有样例的ap并输出在excel文件中。 19 | 20 | 4、通过在query.py对应位置进行注释和解注释即可选择是否进行特征降维，以及选择检索方法（包括K-D Tree，线性搜索，np.argsort()）。 21 | 22 | 5、通过更改settings.py中的pic_file_path和h5即可选择自己的图形库和运行index.py生成自己的特征文件。 23 | 24 | 任何路径都可在settings.py中修改。 25 | 26 | 27 | 百度网盘链接：https://pan.baidu.com/s/1YzFRFTY_bDCXVxICA3r69g 提取码：7oaf 28 | 百度网盘里包括图片等运行程序的所有内容，下载解压后即可直接运行root.py文件进入主界面。 29 | 30 | ## 视频演示地址 31 | [【CBIR】基于内容的图形检索系统——Python实现](https://www.bilibili.com/video/BV1Jf4y15795) 32 | 33 | 有问题可以提issue 34 | 不过提问之前最好先看看有没有人已经提出了类似的问题，以及这篇文章[提问的智慧](https://github.com/ryanhanwu/How-To-Ask-Questions-The-Smart-Way/blob/main/README-zh_CN.md) 35 | -------------------------------------------------------------------------------- /Selectdir.py: -------------------------------------------------------------------------------- 1 | """ 2 | 创建界面，选择文件，创建检索结果界面 3 | """ 4 | 5 | from PIL import ImageTk 6 | import tkinter as tk 7 | from tkinter import filedialog 8 | import os 9 | from query import query_picture 10 | from resize import * 11 | import time 12 | from settings import find_num 13 | 14 | 15 | class Selectdir(object): 16 | def __init__(self, master=None): 17 | self.root = master 18 | self.root.geometry("1280x720") 19 | self.Createpage() 20 | 21 | # 创建界面 22 | def Createpage(self): 23 | inp1 = tk.Entry(self.root, font=("宋体", 18)) 24 | inp1.pack() 25 | inp1.place(relx=0.2, rely=0.3, relwidth=0.6, relheight=0.1) 26 | 27 | # 添加一些小图标 28 | imgs = Image.open('sousuo.png') 29 | imgs_resized = Resize(0.05 * 1280, 0.1 * 720, imgs) 30 | self.imgs = ImageTk.PhotoImage(imgs_resized) 31 | lbs = tk.Label(self.root, imag=self.imgs, compound=tk.CENTER, bg='white') 32 | lbs.place(relx=0.15, rely=0.3, relwidth=0.05, relheight=0.1) 33 | 34 | imgt = Image.open('tupian.png') 35 | imgt_resized = Resize(0.05 * 1280, 0.1 * 720, imgt) 36 | self.imgt = ImageTk.PhotoImage(imgt_resized) 37 | lbt = tk.Label(self.root, imag=self.imgt, compound=tk.CENTER, bg='lightskyblue') 38 | lbt.place(relx=0.05, rely=0.05, relwidth=0.05, relheight=0.1) 39 | 40 | imgc = Image.open('chengyuan.png') 41 | imgc_resized = Resize(0.05 * 1280, 0.1 * 720, imgc) 42 | self.imgc = ImageTk.PhotoImage(imgc_resized) 43 | lbc = tk.Label(self.root, imag=self.imgc, compound=tk.CENTER, bg='white') 44 | lbc.place(relx=0.7, rely=0.9, relwidth=0.05, relheight=0.1) 45 | 46 | # 标注作者姓名 47 | lbname = tk.Label(self.root, text="李佳乐 冯威", font=("宋体", 20), bg='white') 48 | lbname.place(relx=0.75, rely=0.9, relwidth=0.15, relheight=0.1) 49 | # 本地上传图标 50 | databutton = tk.Button(self.root, text="本地上传",font=("宋体", 20), command=lambda: Choosedatadir(inp1)) 51 | databutton.place(relx=0.8, rely=0.3, relwidth=0.1, relheight=0.1) 52 | # 开始检索图标 53 | enterbutton = tk.Button(self.root, text="开始检索",font=("宋体", 20), command=lambda: Enter()) 54 | enterbutton.place(relx=0.7, rely=0.3, relwidth=0.1, relheight=0.1) 55 | 56 | # 选择图片 57 | def Choosedatadir(inp1): 58 | self.root.picture = filedialog.askopenfilename(parent=self.root, initialdir=os.getcwd(), title="本地上传") 59 | inp1.insert(0, self.root.picture) 60 | 61 | # 输入框地址为图片地址 62 | def Enter(): 63 | var = inp1.get() 64 | 65 | start = time.perf_counter() 66 | im_ls = query_picture(var, find_num) 67 | end = time.perf_counter() 68 | run_time = end - start 69 | print('run_time:', run_time) 70 | 71 | # 关闭主页面，创建结果界面 72 | self.root.destroy() 73 | result = tk.Tk() 74 | result.geometry("1280x720") 75 | result.title('查询结果') 76 | photo = tk.PhotoImage(file="a.gif") # 背景图片 77 | 78 | background = tk.Label(result, image=photo, compound=tk.CENTER) 79 | background.place(relx=0, rely=0, relwidth=1, relheight=1) 80 | 81 | backbutton = tk.Button(result, text="返回", font=("宋体", 25), command=lambda: Back(result)) 82 | backbutton.place(relx=0.8, rely=0.1, relwidth=0.08, relheight=0.08) 83 | 84 | word1 = tk.Label(result, text='将要检索的图片如下', font=("宋体", 25), image=photo, compound=tk.CENTER) 85 | word1.place(relx=0.1, rely=0, relwidth=0.3, relheight=0.07) 86 | 87 | word2 = tk.Label(result, text='检索结果如下', font=("宋体", 25), image=photo, compound=tk.CENTER) 88 | word2.place(relx=0.15, rely=0.4, relwidth=0.2, relheight=0.07) 89 | 90 | # 上传的图片 91 | img0 = Image.open(var) 92 | img0_resized = Resize(0.3 * 1280, 0.3 * 720, img0) 93 | img0 = ImageTk.PhotoImage(img0_resized) 94 | lb0 = tk.Label(result, imag=img0, compound=tk.CENTER) 95 | lb0.place(relx=0.1, rely=0.1, relwidth=0.3, relheight=0.3) 96 | # 十张检索结果图 97 | img1 = Image.open(im_ls[0]) 98 | img1_resized = Resize(0.19 * 1280, 0.2 * 720, img1) 99 | img1 = ImageTk.PhotoImage(img1_resized) 100 | lb1 = tk.Label(result, imag=img1, compound=tk.CENTER) 101 | lb1.place(relx=0, rely=0.5, relwidth=0.19, relheight=0.2) 102 | 103 | img2 = Image.open(im_ls[1]) 104 | img2_resized = Resize(0.19 * 1280, 0.2 * 720, img2) 105 | img2 = ImageTk.PhotoImage(img2_resized) 106 | lb2 = tk.Label(result, imag=img2, compound=tk.CENTER) 107 | lb2.place(relx=0.2, rely=0.5, relwidth=0.19, relheight=0.2) 108 | 109 | img3 = Image.open(im_ls[2]) 110 | img3_resized = Resize(0.19 * 1280, 0.2 * 720, img3) 111 | img3 = ImageTk.PhotoImage(img3_resized) 112 | lb3 = tk.Label(result, imag=img3, compound=tk.CENTER) 113 | lb3.place(relx=0.4, rely=0.5, relwidth=0.19, relheight=0.2) 114 | 115 | img4 = Image.open(im_ls[3]) 116 | img4_resized = Resize(0.19 * 1280, 0.2 * 720, img4) 117 | img4 = ImageTk.PhotoImage(img4_resized) 118 | lb4 = tk.Label(result, imag=img4, compound=tk.CENTER) 119 | lb4.place(relx=0.6, rely=0.5, relwidth=0.19, relheight=0.2) 120 | 121 | img5 = Image.open(im_ls[4]) 122 | img5_resized = Resize(0.19 * 1280, 0.2 * 720, img5) 123 | img5 = ImageTk.PhotoImage(img5_resized) 124 | lb5 = tk.Label(result, imag=img5, compound=tk.CENTER) 125 | lb5.place(relx=0.8, rely=0.5, relwidth=0.19, relheight=0.2) 126 | 127 | img6 = Image.open(im_ls[5]) 128 | img6_resized = Resize(0.19 * 1280, 0.2 * 720, img6) 129 | img6 = ImageTk.PhotoImage(img6_resized) 130 | lb6 = tk.Label(result, imag=img6, compound=tk.CENTER) 131 | lb6.place(relx=0, rely=0.75, relwidth=0.19, relheight=0.2) 132 | 133 | img7 = Image.open(im_ls[6]) 134 | img7_resized = Resize(0.19 * 1280, 0.2 * 720, img7) 135 | img7 = ImageTk.PhotoImage(img7_resized) 136 | lb7 = tk.Label(result, imag=img7, compound=tk.CENTER) 137 | lb7.place(relx=0.2, rely=0.75, relwidth=0.19, relheight=0.2) 138 | 139 | img8 = Image.open(im_ls[7]) 140 | img8_resized = Resize(0.19 * 1280, 0.2 * 720, img8) 141 | img8 = ImageTk.PhotoImage(img8_resized) 142 | lb8 = tk.Label(result, imag=img8, compound=tk.CENTER) 143 | lb8.place(relx=0.4, rely=0.75, relwidth=0.19, relheight=0.2) 144 | 145 | img9 = Image.open(im_ls[8]) 146 | img9_resized = Resize(0.19 * 1280, 0.2 * 720, img9) 147 | img9 = ImageTk.PhotoImage(img9_resized) 148 | lb9 = tk.Label(result, imag=img9, compound=tk.CENTER) 149 | lb9.place(relx=0.6, rely=0.75, relwidth=0.19, relheight=0.2) 150 | 151 | img10 = Image.open(im_ls[9]) 152 | img10_resized = Resize(0.19 * 1280, 0.2 * 720, img10) 153 | img10 = ImageTk.PhotoImage(img10_resized) 154 | lb10 = tk.Label(result, imag=img10, compound=tk.CENTER) 155 | lb10.place(relx=0.8, rely=0.75, relwidth=0.19, relheight=0.2) 156 | 157 | result.mainloop() 158 | 159 | # 返回按键 160 | def Back(result): 161 | result.destroy() 162 | 163 | root = tk.Tk() 164 | root.title('CBIR') 165 | photo = tk.PhotoImage(file="a.gif") # 背景图片 166 | 167 | lb0 = tk.Label(root, image=photo, compound=tk.CENTER) 168 | lb0.place(relx=0, rely=0, relwidth=1, relheight=1) 169 | lb1 = tk.Label(root, text='请输入需要检索的图片', font=("宋体", 25), image=photo, compound=tk.CENTER) 170 | lb1.place(relx=0.35, rely=0.2, relwidth=0.3, relheight=0.1) 171 | 172 | Selectdir(root) 173 | root.mainloop() 174 | -------------------------------------------------------------------------------- /a.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FengWei2000/CBIR/c57e5f67e676692420e79bf5ed5a79930035c2e6/a.gif -------------------------------------------------------------------------------- /ap.py: -------------------------------------------------------------------------------- 1 | """ 2 | 计算ap 3 | """ 4 | from settings import gt_path, pic_file_path 5 | import os 6 | from query import query_picture 7 | import xlwt 8 | 9 | 10 | def compute_one_ap(classes): 11 | ranked_list = load_list('rank_list.txt') 12 | good_ls = load_list(gt_path + '\\' + classes+'good.txt') 13 | ok_ls = load_list(gt_path + '\\' + classes + 'ok.txt') 14 | junk_ls = load_list(gt_path + '\\' + classes + 'junk.txt') 15 | 16 | pos_ls = good_ls + ok_ls 17 | 18 | return compute_ap(pos_ls, junk_ls, ranked_list) 19 | 20 | 21 | def compute_ap(pos, amb, ranked_list): 22 | ap = 0 23 | ap_ls = [] 24 | for j in range(10): 25 | for i in range(j+1): 26 | if ranked_list[i] in amb: 27 | continue 28 | elif ranked_list[i] in pos: 29 | ap += 1 30 | ap_ls.append(ap*100/(j+1)) 31 | print('top', j+1, ':', ap*100/(j+1), sep='') 32 | ap = 0 33 | return ap_ls 34 | 35 | 36 | def load_list(path): 37 | with open(path, 'r') as f: 38 | data = f.readlines() 39 | return data 40 | 41 | 42 | def main(): 43 | rootdir = gt_path # gt文件夹路径 44 | magins_path = pic_file_path # 图形库文件夹路径 45 | excelf = xlwt.Workbook() 46 | sheet1 = excelf.add_sheet(u'sheet1', cell_overwrite_ok=True) 47 | n = 0 48 | for dirpath, dirnames, filenames in os.walk(rootdir): 49 | for filename in filenames: 50 | if filename.split('.')[0][-5:] == 'query': 51 | classes = filename.split('.')[0][:-5] 52 | with open(rootdir + '\\' + filename) as f: 53 | pic_name = f.read().split(' ')[0][5:] 54 | pic_path = magins_path + '\\' + pic_name + '.jpg' 55 | query_picture(pic_path, 10) 56 | ap_ls = compute_one_ap(classes) 57 | for i in range(len(ap_ls)): 58 | sheet1.write(n, i, ap_ls[i]) 59 | n += 1 60 | excelf.save('ap.xls') 61 | 62 | 63 | if __name__ == '__main__': 64 | main() 65 | -------------------------------------------------------------------------------- /chengyuan.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FengWei2000/CBIR/c57e5f67e676692420e79bf5ed5a79930035c2e6/chengyuan.png -------------------------------------------------------------------------------- /extract_feature.py: -------------------------------------------------------------------------------- 1 | """ 2 | 功能：通过keras中预训练好的vgg模型提取特征 3 | 日期：2020/12/25 4 | """ 5 | import numpy as np 6 | from numpy import linalg as la 7 | from keras.applications.vgg16 import VGG16 8 | from keras.preprocessing import image 9 | from keras.applications.vgg16 import preprocess_input 10 | 11 | 12 | class VGGNet: 13 | def __init__(self): 14 | self.input_shape = (224, 224, 3) 15 | self.weight = 'imagenet' # 选择权重 16 | self.pooling = 'max' # 最大池化 17 | self.model = VGG16(weights=self.weight, input_shape=(self.input_shape[0], self.input_shape[1], self.input_shape[2]), pooling=self.pooling, include_top=False) 18 | # print(self.model.summary()) 19 | 20 | ''' 21 | 使用训练好的VGG16模型来提取特征，输出归一化的特征向量 22 | ''' 23 | def extract_feat(self, img_path): 24 | img = image.load_img(img_path, target_size=(self.input_shape[0], self.input_shape[1])) 25 | img = image.img_to_array(img) 26 | img = np.expand_dims(img, axis=0) 27 | img = preprocess_input(img) 28 | feat = self.model.predict(img) # 输出为最后一个最大池化层 512 29 | # print('feat', feat) 30 | norm_feat = feat[0]/la.norm(feat[0]) # 相当于归一化 31 | # print('norm_feat', norm_feat) 32 | return norm_feat 33 | 34 | 35 | if __name__ == '__main__': 36 | model = VGGNet() 37 | print(model.extract_feat(r'D:\working_fold\软件课程设计\ruanjiankeshe\database\all_souls_000001.jpg')) 38 | -------------------------------------------------------------------------------- /featureCNN1.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FengWei2000/CBIR/c57e5f67e676692420e79bf5ed5a79930035c2e6/featureCNN1.h5 -------------------------------------------------------------------------------- /index.py: -------------------------------------------------------------------------------- 1 | """ 2 | 创建索引文件，存储在h5中 3 | """ 4 | import os 5 | import h5py 6 | import numpy as np 7 | from extract_feature import VGGNet 8 | from settings import pic_file_path 9 | from settings import h5 10 | 11 | 12 | def creat_h5(path, output=os.getcwd() + h5): 13 | """ 14 | :param path: 图片库路径 15 | :param output: 生成h5文件路径 16 | :return: 生成h5文件路径 17 | """ 18 | img_list = get_imlist(path) 19 | print('图片列表已读取完毕') 20 | 21 | feats = [] 22 | names = [] 23 | model = VGGNet() 24 | 25 | for i, img_path in enumerate(img_list): 26 | try: 27 | norm_feat = model.extract_feat(img_path) 28 | img_name = os.path.split(img_path)[1] 29 | feats.append(norm_feat) 30 | names.append(img_name) 31 | print("extracting feature from image No. {} , {} images in total".format(i+1, len(img_list))) 32 | except Exception as e: 33 | print('出错了！', img_path) 34 | print(e) 35 | 36 | feats = np.array(feats) 37 | 38 | print("--------------------------------------------------") 39 | print(" 正在写入特征提取结果 ...") 40 | print("--------------------------------------------------") 41 | 42 | h5f = h5py.File(output, 'w') 43 | h5f.create_dataset('dataset_1', data=feats) 44 | h5f.create_dataset('dataset_2', data=np.string_(names)) 45 | h5f.close() 46 | 47 | return output 48 | 49 | 50 | def get_imlist(path): 51 | return [os.path.join(path, f) for f in os.listdir(path) if (f.endswith('.jpg') or f.endswith('.png'))] 52 | 53 | 54 | if __name__ == '__main__': 55 | creat_h5(pic_file_path) 56 | -------------------------------------------------------------------------------- /one_ap.py: -------------------------------------------------------------------------------- 1 | """ 2 | 计算ap 3 | """ 4 | from settings import gt_path 5 | from settings import classes 6 | 7 | 8 | def main(): 9 | ranked_list = load_list('rank_list.txt') 10 | good_ls = load_list(gt_path + '\\' + classes+'_good.txt') 11 | ok_ls = load_list(gt_path + '\\' + classes + '_ok.txt') 12 | junk_ls = load_list(gt_path + '\\' + classes + '_junk.txt') 13 | 14 | pos_ls = good_ls + ok_ls 15 | 16 | compute_ap(pos_ls, junk_ls, ranked_list) 17 | 18 | 19 | def compute_ap(pos, amb, ranked_list): 20 | ap = 0 21 | for j in range(10): 22 | for i in range(j+1): 23 | if ranked_list[i] in amb: 24 | continue 25 | elif ranked_list[i] in pos: 26 | ap += 1 27 | print('top', j+1, ':', ap*100/(j+1), sep='') 28 | ap = 0 29 | 30 | 31 | def load_list(path): 32 | with open(path, 'r') as f: 33 | data = f.readlines() 34 | 35 | return data 36 | 37 | 38 | if __name__ == '__main__': 39 | main() 40 | -------------------------------------------------------------------------------- /query.py: -------------------------------------------------------------------------------- 1 | """ 2 | 匹配图片 3 | 有3中搜索方法，分别为K-D树，冒泡排序，np.argsort()。使用时注释掉另外两种即可，其中除K-D树外两种方法均可选择是否进行PCA特征降维。 4 | """ 5 | from extract_feature import VGGNet 6 | import os 7 | import h5py 8 | from settings import pic_file_path 9 | import time 10 | import KDTree 11 | import numpy as np 12 | from sklearn.decomposition import PCA 13 | 14 | 15 | def query_picture(pic, nums=10, database=pic_file_path, index=os.getcwd() + r'\featureCNN1.h5'): 16 | """ 17 | :param pic: 要查询的图片 18 | :param nums: 想要匹配的数量 19 | :param database: 图片库路径 20 | :param index: h5文件路径 21 | :return: 查询所得文件路径列表 22 | """ 23 | h5f = h5py.File(index, 'r') 24 | feats = h5f['dataset_1'][:] 25 | # print(feats) 26 | imgNames = h5f['dataset_2'][:] 27 | # print(imgNames) 28 | h5f.close() 29 | fe_ls = [list(feat.reshape(512)) for feat in feats] 30 | im_ls = [imgNmae for imgNmae in imgNames] 31 | 32 | model = VGGNet() # 初始化VGG16模型 33 | 34 | queryVec = model.extract_feat(pic) # 提取要搜索的图片的特征 35 | 36 | # # 特征降维 37 | # queryVec_ls = list(queryVec.reshape(512)) 38 | # fe_ls.append(queryVec_ls) 39 | # pca = PCA(n_components=0.9) 40 | # fe_ls = list(pca.fit_transform(fe_ls)) 41 | # queryVec = fe_ls.pop() 42 | # feats = np.array(fe_ls) 43 | # # 44 | 45 | # # 使用K-D Tree 46 | # kd_tree = KDTree.create(fe_ls, len(fe_ls[0])) 47 | # start = time.perf_counter() 48 | # nearest_points = kd_tree.search_knn(queryVec, nums) 49 | # end = time.perf_counter() 50 | # imlist = [database + '\\' + str(im_ls[fe_ls.index(index[0].data)], 'utf-8') for index in nearest_points] 51 | # # 52 | 53 | # # 使用冒泡排序线性搜索 54 | # start = time.perf_counter() 55 | # scores = np.dot(queryVec, feats.T) # T转置,类似numpy.transpose 矩阵的点积 56 | # scores2 = list(scores.copy()) 57 | # for k in range(len(scores)): 58 | # for j in range(0, len(scores)-k-1): 59 | # if scores[j] < scores[j+1]: 60 | # scores[j], scores[j+1] = scores[j+1], scores[j] 61 | # rank_ID = [scores2.index(s) for s in scores] 62 | # end = time.perf_counter() 63 | # imlist = [database + '\\' + str(imgNames[index], 'utf-8') for i, index in enumerate(rank_ID[0:nums])] 64 | # # 65 | 66 | # # 使用np.argsort()进行线性搜索 67 | start = time.perf_counter() 68 | scores = np.dot(queryVec, feats.T) # T转置,类似numpy.transpose 矩阵的点积 69 | rank_ID = np.argsort(scores)[::-1] 70 | end = time.perf_counter() 71 | imlist = [database + '\\' + str(imgNames[index], 'utf-8') for i, index in enumerate(rank_ID[0:nums])] 72 | # # 73 | 74 | print("top {} images in order are: {}".format(nums, imlist)) 75 | print('time', (end-start)/len(imlist)) 76 | 77 | with open('rank_list.txt', 'w') as f: 78 | for im in imlist: 79 | f.write(im.split('\\')[-1][:-4] + '\n') 80 | 81 | return imlist 82 | 83 | 84 | if __name__ == '__main__': 85 | query_picture(r'D:\working_fold\software\oxbuild_images\bodleian_000132.jpg') 86 | -------------------------------------------------------------------------------- /resize.py: -------------------------------------------------------------------------------- 1 | """ 2 | 使图片大小适应窗口 3 | """ 4 | from PIL import Image 5 | 6 | 7 | def Resize(w_box, h_box, pil_image): # 参数是：要适应的窗口宽、高、Image.open后的图片 8 | print(pil_image) 9 | w, h = pil_image.size # 获取图像的原始大小 10 | f1 = 1.0 * w_box / w 11 | f2 = 1.0 * h_box / h 12 | factor = min([f1, f2]) 13 | width = int(w * factor) 14 | height = int(h * factor) 15 | return pil_image.resize((width, height), Image.ANTIALIAS) 16 | -------------------------------------------------------------------------------- /root.py: -------------------------------------------------------------------------------- 1 | """ 2 | 主界面 3 | """ 4 | from Selectdir import * 5 | 6 | # 创建主界面 7 | root = tk.Tk() 8 | root.title('CBIR') 9 | photo = tk.PhotoImage(file="a.gif") # 背景图片 10 | 11 | # 添加背景和标题 12 | lb0 = tk.Label(root, image=photo, compound=tk.CENTER) 13 | lb0.place(relx=0, rely=0, relwidth=1, relheight=1) 14 | lb1 = tk.Label(root, text='请输入需要检索的图片', font=("宋体", 25), image=photo, compound=tk.CENTER) 15 | lb1.place(relx=0.35, rely=0.2, relwidth=0.3, relheight=0.1) 16 | 17 | Selectdir(root) 18 | root.mainloop() 19 | -------------------------------------------------------------------------------- /settings.py: -------------------------------------------------------------------------------- 1 | """ 2 | 设置 3 | """ 4 | pic_file_path = r'oxbuild_images' 5 | find_num = 10 6 | classes = 'all_souls_5' 7 | h5 = r'\featureCNN1.h5' 8 | gt_path = r'gt_files_170407' 9 | -------------------------------------------------------------------------------- /sousuo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FengWei2000/CBIR/c57e5f67e676692420e79bf5ed5a79930035c2e6/sousuo.png -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.decomposition import PCA 3 | X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) 4 | pca = PCA(n_components=1) 5 | 6 | Xr = pca.fit_transform(X) 7 | print(Xr) 8 | 9 | -------------------------------------------------------------------------------- /tupian.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FengWei2000/CBIR/c57e5f67e676692420e79bf5ed5a79930035c2e6/tupian.png --------------------------------------------------------------------------------