OpenCV实现图像搜索引擎(Image Search Engine)

雨点打透心脏的1/2处 2022-09-15 00:08 307阅读 0赞

##### 一.原理 #####

1. 图像搜索原理
    图像搜索算法基本可以分为如下步骤：
    
    提取图像特征。如采用SIFT、指纹算法函数、哈希函数、bundling features算法等。当然如知乎中所言，也可以针对特定的图像集群采用特定的模式设计算法，从而提高匹配的精度。如已知所有图像的中间部分在颜色空间或构图上有显著的区别，就可以加强对中间部分的分析，从而更加高效地提取图像特征。
    
    图像特征的存储。一般将图像特征量化为数据存放于索引表中，并存储在外部存储介质中，搜索图片时仅搜索索引表中的图像特征，按匹配程度从高到低查找类似图像。对于图像尺寸分辩率不同的情况可以采用降低采样或归一化方法。
    
    相似度匹配。如存储的是特征向量，则比较特征向量之间的加权后的平方距离。如存储的是散列码，则比较Hamming距离。初筛后，还可以进一步筛选最佳图像集。
    
    2. 图片搜索引擎算法及框架设计
    基本步骤
    采用颜色空间特征提取器和构图空间特征提取器提取图像特征。
    图像索引表构建驱动程序生成待搜索图像库的图像特征索引表。
    图像搜索引擎驱动程序执行搜索命令，生成原图图像特征并传入图片搜索匹配器。
    图片搜索匹配内核执行搜索匹配任务。返回前limit个最佳匹配图像。

color\_descriptor.py

# -*- coding: utf-8 -*-
    # @Time : 2021/10/9 9:44
    # @Author : 
    import cv2
    import numpy
    """ 颜色空间特征提取器ColorDescriptor 类成员bins。记录HSV色彩空间生成的色相、饱和度及明度分布直方图的最佳bins分配。bins分配过多则可能导致程序效率低下，匹配难度和匹配要求过分苛严；bins分配过少则会导致匹配精度不足，不能表证图像特征。 成员函数describe(self, image)。将图像从BGR色彩空间转为HSV色彩空间（此处应注意OpenCV读入图像的色彩空间为BGR而非RGB）。生成左上、右上、左下、右下、中心部分的掩模。中心部分掩模的形状为椭圆形。这样能够有效区分中心部分和边缘部分，从而在getHistogram()方法中对不同部位的色彩特征做加权处理。 """
    
    class ColorDescriptor:
        __slot__ = ["bins"]
    
        def __init__(self, bins):
            self.bins = bins
    
        def getHistogram(self, image, mask, isCenter):
            """ :param image: :param mask: :param isCenter: :return: """
            # get histogram
            imageHistogram = cv2.calcHist([image], [0, 1, 2], mask, self.bins, [0, 180, 0, 256, 0, 256])
            # normalize
            imageHistogram = cv2.normalize(imageHistogram, imageHistogram).flatten()
            if isCenter:
                weight = 5.0
                for index in range(len(imageHistogram)):
                    imageHistogram[index] *= weight
            return imageHistogram
    
        def describe(self, image):
            image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
            features = []
            # get dimension and center
            height, width = image.shape[0], image.shape[1]
            centerX, centerY = int(width * 0.5), int(height * 0.5)
            # initialize mask dimension
            segments = [(0, centerX, 0, centerY), (0, centerX, centerY, height), (centerX, width, 0, centerY),
                        (centerX, width, centerY, height)]
            # initialize center part
            axesX, axesY = int(width * 0.75) / 2, int(height * 0.75) / 2
            ellipseMask = numpy.zeros([height, width], dtype="uint8")
            # img, center, axes, angle, startAngle, endAngle, color, thickness=None, lineType=None, shift=None
            cv2.ellipse(ellipseMask, (centerX, centerY), (int(axesX), int(axesY)), 0, 0, 360, 255, -1)
            # initialize corner part
            for startX, endX, startY, endY in segments:
                cornerMask = numpy.zeros([height, width], dtype="uint8")
                cv2.rectangle(cornerMask, (startX, startY), (endX, endY), 255, -1)
                cornerMask = cv2.subtract(cornerMask, ellipseMask)
                # get histogram of corner part
                imageHistogram = self.getHistogram(image, cornerMask, False)
                features.append(imageHistogram)
            # get histogram of center part
            imageHistogram = self.getHistogram(image, ellipseMask, True)
            features.append(imageHistogram)
            # return
            return features

index.py

# -*- coding: utf-8 -*-
    # @Time : 2021/10/9 9:45
    # @Author : 
    import color_descriptor
    import structure_descriptor
    import glob
    import argparse
    import cv2
    
    """ 图像索引表构建驱动index.py。 引入color_descriptor和structure_descriptor。用于解析图片库图像，获得色彩空间特征向量和构图空间特征向量。 用argparse设置命令行参数。参数包括图片库路径、色彩空间特征索引表路径、构图空间特征索引表路径。 用glob获得图片库路径。 生成索引表文本并写入csv文件。 可采用如下命令行形式启动驱动程序: python index.py --dataset dataset --colorindex color_index.csv --structure structure_index.csv """
    searchArgParser = argparse.ArgumentParser()
    searchArgParser.add_argument("-d", "--dataset", required=True,
                                 help="Path to the directory that contains the images to be indexed")
    searchArgParser.add_argument("-c", "--colorindex", required=True,
                                 help="Path to where the computed color index will be stored")
    searchArgParser.add_argument("-s", "--structureindex", required=True,
                                 help="Path to where the computed structure index will be stored")
    arguments = vars(searchArgParser.parse_args())
    
    idealBins = (8, 12, 3)
    colorDesriptor = color_descriptor.ColorDescriptor(idealBins)
    
    output = open(arguments["colorindex"], "w")
    
    for imagePath in glob.glob(arguments["dataset"] + "/*.jpg"):
        imageName = imagePath[imagePath.rfind("/") + 1:]
        image = cv2.imread(imagePath)
        features = colorDesriptor.describe(image)
        # write features to file
        features = [str(feature).replace("\n", "") for feature in features]
        output.write("%s,%s\n" % (imageName, ",".join(features)))
    # close index file
    output.close()
    
    idealDimension = (16, 16)
    structureDescriptor = structure_descriptor.StructureDescriptor(idealDimension)
    
    output = open(arguments["structureindex"], "w")
    
    for imagePath in glob.glob("dataset" + "/*.jpg"):
        imageName = imagePath[imagePath.rfind("/") + 1:]
        image = cv2.imread(imagePath)
        structures = structureDescriptor.describe(image)
        # write structures to file
        structures = [str(structure).replace("\n", "") for structure in structures]
        output.write("%s,%s\n" % (imageName, ",".join(structures)))
    # close index file
    output.close()

searchEngine.py

# -*- coding: utf-8 -*-
    # @Time : 2021/10/9 9:45
    # @Author : 
    import color_descriptor
    import structure_descriptor
    import searcher
    import argparse
    import cv2
    """ 图像搜索引擎驱动searchEngine.py。 引入color_descriptor和structure_descriptor。用于解析待匹配（搜索）的图像，获得色彩空间特征向量和构图空间特征向量。 用argparse设置命令行参数。参数包括图片库路径、色彩空间特征索引表路径、构图空间特征索引表路径、待搜索图片路径。 生成索引表文本并写入csv文件。 可采用如下命令行形式启动驱动程序: python searchEngine.py -c color_index.csv -s structure_index.csv -r dataset -q query/pyramid.jpg dataset为图片库路径。color_index.csv为色彩空间特征索引表路径。structure_index.csv为构图空间特征索引表路径，query/pyramid.jpg为待搜索图片路径。 """
    
    searchArgParser = argparse.ArgumentParser()
    searchArgParser.add_argument("-c", "--colorindex", required = True, help = "Path to where the computed color index will be stored")
    searchArgParser.add_argument("-s", "--structureindex", required = True, help = "Path to where the computed structure index will be stored")
    searchArgParser.add_argument("-q", "--query", required = True, help = "Path to the query image")
    searchArgParser.add_argument("-r", "--resultpath", required = True, help = "Path to the result path")
    searchArguments = vars(searchArgParser.parse_args())
    
    idealBins = (8, 12, 3)
    idealDimension = (16, 16)
    
    colorDescriptor = color_descriptor.ColorDescriptor(idealBins)
    structureDescriptor = structure_descriptor.StructureDescriptor(idealDimension)
    queryImage = cv2.imread(searchArguments["query"])
    colorIndexPath = searchArguments["colorindex"]
    structureIndexPath = searchArguments["structureindex"]
    resultPath = searchArguments["resultpath"]
    
    queryFeatures = colorDescriptor.describe(queryImage)
    queryStructures = structureDescriptor.describe(queryImage)
    
    imageSearcher = searcher.Searcher(colorIndexPath, structureIndexPath)
    searchResults = imageSearcher.search(queryFeatures, queryStructures)
    print(searchResults)
    for imageName, score in searchResults:
        queryResult = cv2.imread( imageName)
        cv2.imshow("Result Score: " + str(int(score)) + " (lower is better)", queryResult)
    cv2.imshow("Query", queryImage)
    cv2.waitKey(0)

searcher.py

# -*- coding: utf-8 -*-
    # @Time : 2021/10/9 9:45
    # @Author : 
    import numpy
    import csv
    import re
    
    """ 图片搜索匹配内核Searcher。 类成员colorIndexPath和structureIndexPath。记录色彩空间特征索引表路径和结构特征索引表路径。 成员函数solveColorDistance(self, features, queryFeatures, eps = 1e-5)。求features和queryFeatures特征向量的二范数。eps是为了避免除零错误。 成员函数solveStructureDistance(self, structures, queryStructures, eps = 1e-5)。同样是求特征向量的二范数。eps是为了避免除零错误。需作统一化处理，color和structure特征向量距离相对比例适中，不可过分偏颇。 成员函数searchByColor(self, queryFeatures)。使用csv模块的reader方法读入索引表数据。采用re的split方法解析数据格式。用字典searchResults存储query图像与库中图像的距离，键为图库内图像名imageName，值为距离distance。 成员函数transformRawQuery(self, rawQueryStructures)。将未处理的query图像矩阵转为用于匹配的特征向量形式。 成员函数searchByStructure(self, rawQueryStructures)。类似4。 成员函数search(self, queryFeatures, rawQueryStructures, limit = 3)。将searchByColor方法和searchByStructure的结果汇总，获得总匹配分值，分值越低代表综合距离越小，匹配程度越高。返回前limit个最佳匹配图像。 """
    
    
    class Searcher:
        __slot__ = ["colorIndexPath", "structureIndexPath"]
    
        def __init__(self, colorIndexPath, structureIndexPath):
            self.colorIndexPath, self.structureIndexPath = colorIndexPath, structureIndexPath
    
        def solveColorDistance(self, features, queryFeatures, eps=1e-5):
            distance = 0.5 * numpy.sum([((a - b) ** 2) / (a + b + eps) for a, b in zip(features, queryFeatures)])
            return distance
    
        def solveStructureDistance(self, structures, queryStructures, eps=1e-5):
            distance = 0
            normalizeRatio = 5e3
            for index in range(len(queryStructures)):
                for subIndex in range(len(queryStructures[index])):
                    a = structures[index][subIndex]
                    b = queryStructures[index][subIndex]
                    distance += (a - b) ** 2 / (a + b + eps)
            return distance / normalizeRatio
    
        def searchByColor(self, queryFeatures):
            searchResults = { }
            with open(self.colorIndexPath) as indexFile:
                reader = csv.reader(indexFile)
                for line in reader:
                    features = []
                    for feature in line[1:]:
                        feature = feature.replace("[", "").replace("]", "")
                        findStartPosition = 0
                        feature = re.split("\s+", feature)
                        rmlist = []
                        for index, strValue in enumerate(feature):
                            if strValue == "":
                                rmlist.append(index)
                        for _ in range(len(rmlist)):
                            currentIndex = rmlist[-1]
                            rmlist.pop()
                            del feature[currentIndex]
                        feature = [float(eachValue) for eachValue in feature]
                        features.append(feature)
                    distance = self.solveColorDistance(features, queryFeatures)
                    searchResults[line[0]] = distance
                indexFile.close()
            # print "feature", sorted(searchResults.iteritems(), key = lambda item: item[1], reverse = False)
            return searchResults
    
        def transformRawQuery(self, rawQueryStructures):
            queryStructures = []
            for substructure in rawQueryStructures:
                structure = []
                for line in substructure:
                    for tripleColor in line:
                        structure.append(float(tripleColor))
                queryStructures.append(structure)
            return queryStructures
    
        def searchByStructure(self, rawQueryStructures):
            searchResults = { }
            queryStructures = self.transformRawQuery(rawQueryStructures)
            with open(self.structureIndexPath) as indexFile:
                reader = csv.reader(indexFile)
                for line in reader:
                    structures = []
                    for structure in line[1:]:
                        structure = structure.replace("[", "").replace("]", "")
                        structure = re.split("\s+", structure)
                        if structure[0] == "":
                            structure = structure[1:]
                        structure = [float(eachValue) for eachValue in structure]
                        structures.append(structure)
                    distance = self.solveStructureDistance(structures, queryStructures)
                    searchResults[line[0]] = distance
                indexFile.close()
            # print "structure", sorted(searchResults.iteritems(), key = lambda item: item[1], reverse = False)
            return searchResults
    
        def search(self, queryFeatures, rawQueryStructures, limit=3):
            featureResults = self.searchByColor(queryFeatures)
            structureResults = self.searchByStructure(rawQueryStructures)
            results = { }
            for key, value in featureResults.items():
                results[key] = value + structureResults[key]
            results = sorted(results.items(), key=lambda item: item[1], reverse=False)
            return results[: limit]

structure\_descriptor.py

# -*- coding: utf-8 -*-
    # @Time : 2021/10/9 9:45
    # @Author : 
    import cv2
    """ 构图空间特征提取器StructureDescriptor。 类成员dimension。将所有图片归一化（降低采样）为dimension所规定的尺寸。由此才能够用于统一的匹配和构图空间特征的生成。 成员函数describe(self, image)。将图像从BGR色彩空间转为HSV色彩空间（此处应注意OpenCV读入图像的色彩空间为BGR而非RGB）。返回HSV色彩空间的矩阵，等待在搜索引擎核心中的下一步处理。 """
    
    
    class StructureDescriptor:
        __slot__ = ["dimension"]
    
        def __init__(self, dimension):
            self.dimension = dimension
    
        def describe(self, image):
            image = cv2.resize(image, self.dimension, interpolation=cv2.INTER_CUBIC)
            # image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
            return image

##### 二.执行 #####

# 构建索引
    python index.py --dataset dataset --colorindex color_index.csv --structure structure_index.csv
    
    # 搜图
    python searchEngine.py -c color_index.csv -s structure_index.csv -r dataset -q query/pyramid.jpg

![在这里插入图片描述][6315c5afaab44adbbc9e6faf0bd26035.png]  
![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAQ29ja3RhaWxfcHk_size_20_color_FFFFFF_t_70_g_se_x_16]

参考:https://blog.csdn.net/coderhuhy/article/details/46575667

[6315c5afaab44adbbc9e6faf0bd26035.png]: /images/20220828/923ff62787774097bd0c7d29ad5c42cb.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAQ29ja3RhaWxfcHk_size_20_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/f27681175b2742d39fcccddff029548b.png