营销型网站推广方案,英雄联盟网站源码,网络营销方式举个例子,顺义建站好的公司目录
一、实验内容
二、实验过程
2.1 准备数据集
2.2 SIFT特征提取
2.3 学习“视觉词典”#xff08;vision vocabulary#xff09;
2.4 建立图像索引并保存到数据库中
2.5 用一幅图像查询
三、实验小结 一、实验内容
实现基于颜色直方图、bag of word等方法的以图搜…目录
一、实验内容
二、实验过程
2.1 准备数据集
2.2 SIFT特征提取
2.3 学习“视觉词典”vision vocabulary
2.4 建立图像索引并保存到数据库中
2.5 用一幅图像查询
三、实验小结 一、实验内容
实现基于颜色直方图、bag of word等方法的以图搜图打印图片特征向量、图片相似度等打印视觉词典大小、可视化部分特征基元、可视化部分图片的频率直方图
二、实验过程
2.1 准备数据集
如图1所示这里准备的文件夹中含有100张实验图片。 图1 2.2 SIFT特征提取
2.2.1 实验代码
import os
import cv2
import numpy as np
from PCV.tools.imtools import get_imlist
#获取图像列表
imlist get_imlist(rD:\Computer vision\20241217dataset)
nbr_images len(imlist)
#生成特征文件路径
featlist [os.path.splitext(imlist[i])[0] .sift for i in range(nbr_images)]
#创建sift对象
sift cv2.SIFT_create()
#提取sift特征并保存
for i in range(nbr_images):try:img cv2.imread(imlist[i], cv2.IMREAD_GRAYSCALE)if img is None:raise ValueError(fImage {imlist[i]} could not be read)keypoints, descriptors sift.detectAndCompute(img, None)locs np.array([[kp.pt[0], kp.pt[1], kp.size, kp.angle] for kp in keypoints])np.savetxt(featlist[i], np.hstack((locs, descriptors)))print(fProcessed {imlist[i]} to {featlist[i]})except Exception as e:print(fFailed to process {imlist[i]}: {e})
#可视化sift特征
def visualize_sift_features(image_path, feature_path):img cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)if img is None:raise ValueError(fImage {image_path} could not be read)features np.loadtxt(feature_path)locs features[:, :4]descriptors features[:, 4:]img_with_keypoints cv2.drawKeypoints(img, [cv2.KeyPoint(xloc[0], yloc[1], _sizeloc[2], _angleloc[3]) for loc in locs], outImagenp.array([]))plt.figure(figsize(10, 10))plt.imshow(img_with_keypoints, cmapgray)plt.title(SIFT Features Visualization)plt.axis(off)plt.show()visualize_sift_features(imlist[0], featlist[0])
2.2.2 结果展示 如图2所示对文件夹中的所有图片使用sift特征描述子图3展示了对第一个图像的SIFT特征的可视化。 图2 图3 2.3 学习“视觉词典”vision vocabulary
2.3.1 实验代码
1vocabulary.py文件
from numpy import *
from scipy.cluster.vq import *
from PCV.localdescriptors import sift
#构建视觉词汇表
class Vocabulary(object):#初始化方法def __init__(self,name):self.name nameself.voc []self.idf []self.trainingdata []self.nbr_words 0#训练过程def train(self,featurefiles,k100,subsampling10):nbr_images len(featurefiles)descr []descr.append(sift.read_features_from_file(featurefiles[0])[1])descriptors descr[0] for i in arange(1,nbr_images):descr.append(sift.read_features_from_file(featurefiles[i])[1])descriptors vstack((descriptors,descr[i]))#使用K-means聚类生成视觉单词self.voc,distortion kmeans(descriptors[::subsampling,:],k,1)self.nbr_words self.voc.shape[0]#生成视觉单词的直方图imwords zeros((nbr_images,self.nbr_words))for i in range( nbr_images ):imwords[i] self.project(descr[i])nbr_occurences sum( (imwords 0)*1 ,axis0)#计算每个视觉单词的逆文档频率self.idf log( (1.0*nbr_images) / (1.0*nbr_occurences1) )self.trainingdata featurefiles#投影方法def project(self,descriptors):imhist zeros((self.nbr_words))words,distance vq(descriptors,self.voc)for w in words:imhist[w] 1return imhistdef get_words(self,descriptors):return vq(descriptors,self.voc)[0]
2visual vocabulary.py文件
import pickle
from PCV.imagesearch import vocabulary
from PCV.tools.imtools import get_imlistimlist get_imlist(rD:\Computer vision\20241217dataset)
nbr_images len(imlist)featlist [imlist[i][:-3] sift for i in range(nbr_images)]
voc vocabulary.Vocabulary(bof_test)
voc.train(featlist, 50, 10)with open(rD:\Computer vision\20241217dataset\vocabulary50.pkl, wb) as f:pickle.dump(voc, f)
print(vocabulary is:, voc.name, voc.nbr_words)
2.3.2 结果展示 如图4所示打印视觉词典的名称和长度 图4 2.4 建立图像索引并保存到数据库中
2.4.1 实验代码
1imagesearch.py文件
from numpy import *
import pickle
import sqlite3 as sqlite
from functools import cmp_to_keyclass Indexer(object):def __init__(self,db,voc):self.con sqlite.connect(db)self.voc vocdef __del__(self):self.con.close()def db_commit(self):self.con.commit()def get_id(self,imname):cur self.con.execute(select rowid from imlist where filename%s % imname)rescur.fetchone()if resNone:cur self.con.execute(insert into imlist(filename) values (%s) % imname)return cur.lastrowidelse:return res[0] def is_indexed(self,imname):im self.con.execute(select rowid from imlist where filename%s % imname).fetchone()return im ! Nonedef add_to_index(self,imname,descr):if self.is_indexed(imname): returnprint (indexing, imname)imid self.get_id(imname)imwords self.voc.project(descr)nbr_words imwords.shape[0]for i in range(nbr_words):word imwords[i]self.con.execute(insert into imwords(imid,wordid,vocname) values (?,?,?), (imid,word,self.voc.name))self.con.execute(insert into imhistograms(imid,histogram,vocname) values (?,?,?), (imid,pickle.dumps(imwords),self.voc.name))def create_tables(self): self.con.execute(create table imlist(filename))self.con.execute(create table imwords(imid,wordid,vocname))self.con.execute(create table imhistograms(imid,histogram,vocname)) self.con.execute(create index im_idx on imlist(filename))self.con.execute(create index wordid_idx on imwords(wordid))self.con.execute(create index imid_idx on imwords(imid))self.con.execute(create index imidhist_idx on imhistograms(imid))self.db_commit()def cmp_for_py3(a, b):return (a b) - (a b)class Searcher(object):def __init__(self,db,voc):self.con sqlite.connect(db)self.voc vocdef __del__(self):self.con.close()def get_imhistogram(self, imname):cursor self.con.execute(select rowid from imlist where filename%s % imname)row cursor.fetchone()if row is None:raise ValueError(fNo entry found in imlist for filename: {imname})im_id row[0]cursor self.con.execute(select histogram from imhistograms where rowid%d % im_id)s cursor.fetchone()if s is None:raise ValueError(fNo histogram found for rowid: {im_id})return pickle.loads(s[0])def candidates_from_word(self,imword):im_ids self.con.execute(select distinct imid from imwords where wordid%d % imword).fetchall()return [i[0] for i in im_ids]def candidates_from_histogram(self,imwords):words imwords.nonzero()[0]candidates []for word in words:c self.candidates_from_word(word)candidatesctmp [(w,candidates.count(w)) for w in set(candidates)]tmp.sort(keycmp_to_key(lambda x,y:cmp_for_py3(x[1],y[1])))tmp.reverse()return [w[0] for w in tmp] def query(self, imname):try:h self.get_imhistogram(imname)except ValueError as e:print(e)return []candidates self.candidates_from_histogram(h)matchscores []for imid in candidates:cand_name self.con.execute(select filename from imlist where rowid%d % imid).fetchone()cand_h self.get_imhistogram(cand_name)cand_dist sqrt(sum(self.voc.idf * (h - cand_h) ** 2))matchscores.append((cand_dist, imid))matchscores.sort()return matchscoresdef get_filename(self,imid):s self.con.execute(select filename from imlist where rowid%d % imid).fetchone()return s[0]def tf_idf_dist(voc,v1,v2):v1 / sum(v1)v2 / sum(v2)return sqrt( sum( voc.idf*(v1-v2)**2 ) )def compute_ukbench_score(src,imlist):nbr_images len(imlist)pos zeros((nbr_images,4))for i in range(nbr_images):pos[i] [w[1]-1 for w in src.query(imlist[i])[:4]]score array([ (pos[i]//4)(i//4) for i in range(nbr_images)])*1.0return sum(score) / (nbr_images)from PIL import Image
from pylab import *def plot_results(src,res):figure()nbr_results len(res)for i in range(nbr_results):imname src.get_filename(res[i])subplot(1,nbr_results,i1)imshow(array(Image.open(imname)))axis(off)show()
2quantizeSet.py文件
import pickle
from PCV.imagesearch import imagesearch
from PCV.localdescriptors import sift
import sqlite3
from PCV.tools.imtools import get_imlist
import cv2
import matplotlib.pyplot as pltimlist get_imlist(rD:\Computer vision\20241217dataset)
nbr_images len(imlist)featlist [imlist[i][:-3] sift for i in range(nbr_images)]with open(rD:\Computer vision\20241217dataset\vocabulary50.pkl, rb) as f:voc pickle.load(f)
indx imagesearch.Indexer(D:\\Computer vision\\20241217CODES\\testImaAdd.db, voc)
indx.create_tables()
for i in range(nbr_images)[:100]:locs, descr sift.read_features_from_file(featlist[i])indx.add_to_index(imlist[i], descr)
indx.db_commit()
con sqlite3.connect(D:\\Computer vision\\20241217CODES\\testImaAdd.db)
print(con.execute(select count (filename) from imlist).fetchone())
print(con.execute(select * from imlist).fetchone())
searcher imagesearch.Searcher(D:\\Computer vision\\20241217CODES\\testImaAdd.db, voc)image_files imlist[:5]for image_file in image_files:histogram searcher.get_imhistogram(image_file)plt.figure()plt.title(image_file)plt.bar(range(len(histogram)), histogram)plt.xlabel(Word ID)plt.ylabel(Frequency)plt.show()
2.4.2 结果展示
如图5所示对数据集中的所有图像进行量化为所有图像创建索引再遍历所有的图像将它们的特征投影到词汇上最终提交到数据库保存下来如图6。图7、图8展示了部分图像的频率直方图。 图5 图6 图7 图8 2.5 用一幅图像查询
2.5.1 实验代码
import pickle
from PCV.imagesearch import imagesearch
from PCV.geometry import homography
from PCV.tools.imtools import get_imlist
import numpy as np
import cv2
import warnings
warnings.filterwarnings(ignore)imlist get_imlist(rD:\Computer vision\20241217dataset)
nbr_images len(imlist)
featlist [imlist[i][:-3] sift for i in range(nbr_images)]with open(rD:\Computer vision\20241217dataset\vocabulary50.pkl, rb) as f:voc pickle.load(f, encodingiso-8859-1)src imagesearch.Searcher(rD:\Computer vision\20241217CODES\testImaAdd.db, voc)q_ind 3
nbr_results 10res_reg [w[1] for w in src.query(imlist[q_ind])[:nbr_results]]
print(top matches (regular):, res_reg)img cv2.imread(imlist[q_ind])
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
sift cv2.SIFT_create()
kp, q_descr sift.detectAndCompute(gray, None)
q_locs np.array([[kp[i].pt[0], kp[i].pt[1]] for i in range(len(kp))])
fp homography.make_homog(q_locs[:, :2].T)model homography.RansacModel()
rank {}bf cv2.BFMatcher()
for ndx in res_reg[1:]:if ndx len(imlist):print(fIndex {ndx} is out of range for imlist with length {len(imlist)})continueimg cv2.imread(imlist[ndx])gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)kp, descr sift.detectAndCompute(gray, None)locs np.array([[kp[i].pt[0], kp[i].pt[1]] for i in range(len(kp))])matches bf.knnMatch(q_descr, descr, k2)good_matches []for m, n in matches:if m.distance 0.75 * n.distance:good_matches.append(m)ind [m.queryIdx for m in good_matches]ind2 [m.trainIdx for m in good_matches]tp homography.make_homog(locs[:, :2].T)try:H, inliers homography.H_from_ransac(fp[:, ind], tp[:, ind2], model, match_theshold4)except:inliers []# store inlier countrank[ndx] len(inliers)sorted_rank sorted(rank.items(), keylambda t: t[1], reverseTrue)
res_geom [res_reg[0]] [s[0] for s in sorted_rank]
print(top matches (homography):, res_geom)def calculate_similarity(query_index, result_indices, imlist):similarities []for res_index in result_indices:if res_index len(imlist):print(fIndex {res_index} is out of range for imlist with length {len(imlist)})continueimg1 cv2.imread(imlist[query_index])img2 cv2.imread(imlist[res_index])gray1 cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)gray2 cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)sift cv2.SIFT_create()kp1, des1 sift.detectAndCompute(gray1, None)kp2, des2 sift.detectAndCompute(gray2, None)bf cv2.BFMatcher()matches bf.knnMatch(des1, des2, k2)good_matches []for m, n in matches:if m.distance 0.75 * n.distance:good_matches.append(m)similarity len(good_matches) / min(len(kp1), len(kp2))similarities.append((res_index, similarity))return similaritiesquery_index q_ind
result_indices res_reg res_geom
similarities calculate_similarity(query_index, result_indices, imlist)print(Similarity scores:)
for index, score in similarities:print(fImage {index}: {score:.4f})imagesearch.plot_results(src, res_reg)
imagesearch.plot_results(src, res_geom)
imagesearch.plot_results(src, res_reg[:6])
imagesearch.plot_results(src, res_geom[:6])
2.5.2 结果展示
如图9所示根据给定的查询图像索引q_ind执行常规查询返回前nbr_results个匹配结果并打印这些结果以及可视化图片。 图 9 维度50常规排序 如图10所示使用 RANSAC 模型拟合单应性找到与查询图像几何一致的候选图像。 图 10 维度50重排序 如图11所示计算查询图像与候选图像之间的相似度分数并返回一个包含相似度分数的列表。 图11 当生成不同维度视觉词典时常规排序结果如图12所示。 图 12 维度10常规排序 三、实验小结
图像检索是一项重要的计算机视觉任务它在根据用户的输入如图像或关键词从图像数据库中检索出最相关的图像。Bag of Feature 是一种图像特征提取方法参考Bag of Words的思路把每幅图像描述为一个局部区域/关键点(Patches/Key Points)特征的无序集合。同时从图像抽象出很多具有代表性的「关键词」形成一个字典再统计每张图片中出现的「关键词」数量得到图片的特征向量。
