Python plot_results Examples

Example #1

File: ch07_test_candidates.py Project: jianfengMan/my-python-computer-vision

import pickle
from PCV.imagesearch import imagesearch
from PCV.localdescriptors import sift
from sqlite3 import dbapi2 as sqlite
from PCV.tools.imtools import get_imlist

# 获取图像列表
imlist = get_imlist('./first500/')
nbr_images = len(imlist)
# 获取特征列表
featlist = [imlist[i][:-3] + 'sift' for i in range(nbr_images)]

# 载入词汇
f = open('./first500/vocabulary.pkl', 'rb')
voc = pickle.load(f)
f.close()

src = imagesearch.Searcher('testImaAdd.db', voc)
locs, descr = sift.read_features_from_file(featlist[0])
iw = voc.project(descr)

print('ask using a histogram...')
print(src.candidates_from_histogram(iw)[:10])

src = imagesearch.Searcher('testImaAdd.db', voc)
print('try a query...')

nbr_results = 12
res = [w[1] for w in src.query(imlist[0])[:nbr_results]]
imagesearch.plot_results(src, res)

Example #2

File: candidates.py Project: CBIR-LL/py-cbir-image-search-engine

import pickle
from PCV.imagesearch import imagesearch
from PCV.localdescriptors import sift
from sqlite3 import dbapi2 as sqlite
from PCV.tools.imtools import get_imlist

imlist = get_imlist('./first500/')
nbr_images = len(imlist)
featlist = [imlist[i][:-3]+'sift' for i in range(nbr_images)]


f = open('./first500/vocabulary.pkl', 'rb')
voc = pickle.load(f)
f.close()

src = imagesearch.Searcher('web.db',voc)
locs,descr = sift.read_features_from_file(featlist[0])
iw = voc.project(descr)

print 'ask using a histogram...'
print src.candidates_from_histogram(iw)[:10]

src = imagesearch.Searcher('web.db',voc)
print 'try a query...'

nbr_results = 12
res = [w[1] for w in src.query(imlist[39])[:nbr_results]]
imagesearch.plot_results(src,res)

Example #3

File: 03.py Project: VRER1997/python_CV

res_reg = [w[1] for w in src.query(imlist[q_ind])[:nbr_results]]
print 'top matches (regular) : ', res_reg

q_locs, q_descr = sift.read_features_from_file(featlist[q_ind])
fp = homography.make_homog(q_locs[:, :2].T)

model = homography.RansacModel()
rank = {}

for ndx in res_reg[1:]:
    locs, descr = sift.read_features_from_file(featlist[ndx])
    matches = sift.match(q_descr, descr)
    ind = matches.nonzero()[0]
    ind2 = matches[ind]
    tp = homography.make_homog(locs[:, :2].T)
    try:
        H, inliners = homography.H_from_ransac(fp[:, ind],
                                               tp[:, ind2],
                                               model,
                                               match_theshold=4)
    except:
        inliners = []
    rank[ndx] = len(inliners)

sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res_reg[0]] + [s[0] for s in sorted_rank]
# 显示查询结果
imagesearch.plot_results(src, res_reg[:8])  #常规查询
imagesearch.plot_results(src, res_geom[:8])  #重排后的结果

Example #4

File: reranking.py Project: CBIR-LL/py-cbir-image-search-engine

# RANSAC model for homography fitting
model = homography.RansacModel()

rank = {}
# load image features for result
for ndx in res_reg[1:]:
    locs,descr = sift.read_features_from_file(featlist[ndx-1])  # because 'ndx' is a rowid of the DB that starts at 1.
    # locs,descr = sift.read_features_from_file(featlist[ndx])
    # get matches
    matches = sift.match(q_descr,descr)
    ind = matches.nonzero()[0]
    ind2 = matches[ind]
    tp = homography.make_homog(locs[:,:2].T)
    # compute homography, count inliers. if not enough matches return empty list
    try:
        H,inliers = homography.H_from_ransac(fp[:,ind],tp[:,ind2],model,match_theshold=4)
    except:
        inliers = []
    # store inlier count
    rank[ndx] = len(inliers)

# sort dictionary to get the most inliers first
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res_reg[0]]+[s[0] for s in sorted_rank]
print 'top matches (homography):', res_geom


# plot the top results
imagesearch.plot_results(src,res_reg[:8])
imagesearch.plot_results(src,res_geom[:8])

Example #5

rank = {}
# load image features for result
for ndx in res_reg[1:]:
    locs, descr = sift.read_features_from_file(
        featlist[ndx -
                 1])  # because 'ndx' is a rowid of the DB that starts at 1.
    # locs,descr = sift.read_features_from_file(featlist[ndx])
    # get matches
    matches = sift.match(q_descr, descr)
    ind = matches.nonzero()[0]
    ind2 = matches[ind]
    tp = homography.make_homog(locs[:, :2].T)
    # compute homography, count inliers. if not enough matches return empty list
    try:
        H, inliers = homography.H_from_ransac(fp[:, ind],
                                              tp[:, ind2],
                                              model,
                                              match_theshold=4)
    except:
        inliers = []
    # store inlier count
    rank[ndx] = len(inliers)

# sort dictionary to get the most inliers first
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res_reg[0]] + [s[0] for s in sorted_rank]
print 'top matches (homography):', res_geom

# plot the top results
imagesearch.plot_results(src, res_reg[:8])
imagesearch.plot_results(src, res_geom[:8])

Example #6

File: ch07_reranking.py Project: CBIR-LL/pcv-book-code

# RANSAC model for homography fitting
#用单应性进行拟合建立RANSAC模型
model = homography.RansacModel()
rank = {}

# load image features for result
#载入候选图像的特征
for ndx in res_reg[1:]:
    locs,descr = sift.read_features_from_file(featlist[ndx])  # because 'ndx' is a rowid of the DB that starts at 1
    # get matches
    matches = sift.match(q_descr,descr)
    ind = matches.nonzero()[0]
    ind2 = matches[ind]
    tp = homography.make_homog(locs[:,:2].T)
    # compute homography, count inliers. if not enough matches return empty list
    try:
        H,inliers = homography.H_from_ransac(fp[:,ind],tp[:,ind2],model,match_theshold=4)
    except:
        inliers = []
    # store inlier count
    rank[ndx] = len(inliers)

# sort dictionary to get the most inliers first
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res_reg[0]]+[s[0] for s in sorted_rank]
print 'top matches (homography):', res_geom

# 显示查询结果
imagesearch.plot_results(src,res_reg[:8]) #常规查询
imagesearch.plot_results(src,res_geom[:8]) #重排后的结果