def search_SIFT_BF(returnCount=100, mydataSIFT=mydataSIFT,SIFT_FEATURES_LIMIT=SIFT_FEATURES_LIMIT):
imagepredictions , searchtimesift = ImageSearch_Algo_SIFT.SIFT_SEARCH_BF(mydataSIFT, q_path, sift_features_limit=SIFT_FEATURES_LIMIT, lowe_ratio=LOWE_RATIO, predictions_count=returnCount)
a ,d, ind, cnt = accuracy.accuracy_matches(q_path, imagepredictions, 20 )
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_sift_BF'] = a
row_dict['index_sift_BF'] = ind
row_dict['Count_sift_BF'] = cnt
row_dict['quality_sift_BF'] = d
row_dict['time_sift_BF'] = searchtimesift
return imagepredictions, searchtimesift
def search_ORB_BOVW (returnCount=100, write=False) :
imagematches, searchtime = ImageSearch_Algo_ORB.ORB_SEARCH_TREE(q_path, myORBmodel, myORBtree, mydataORB, returnCount=100, kp=ORB_FEATURES_LIMIT)
if write:
a ,d, ind, cnt = accuracy.accuracy_matches(q_path, imagematches, 20 )
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_orb_tree'] = a
row_dict['index_orb_tree'] = ind
row_dict['Count_orb_tree'] = cnt
row_dict['quality_orb_tree'] = d
row_dict['time_orb_tree'] = searchtime
return imagematches, searchtime
def search_SIFT_BOVW(returnCount=100, write=False):
imagematches, searchtime = ImageSearch_Algo_SIFT.SIFT_SEARCH_TREE(q_path, mySIFTmodel, mySIFTtree, mydataSIFT, returnCount=returnCount, kp=100)
if write:
a ,d, ind, cnt = accuracy.accuracy_matches(q_path, imagematches, 20 )
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_sift_tree'] = a
row_dict['index_sift_tree'] = ind
row_dict['Count_sift_tree'] = cnt
row_dict['quality_sift_tree'] = d
row_dict['time_sift_tree'] = searchtime
return imagematches, searchtime
def search_ORB_BF2(returnCount=100, mydataORB=mydataORB, write=False) :
imagematches, searchtime = ImageSearch_Algo_ORB.ORB_SEARCH_MODBF(mydataORB, q_path, ORB_FEATURES_LIMIT , lowe_ratio=LOWE_RATIO, predictions_count=returnCount )
if write:
a ,d, ind, cnt = accuracy.accuracy_matches(q_path, imagematches, 20 )
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_orb_BF2'] = a
row_dict['index_orb_BF2'] = ind
row_dict['Count_orb_BF2'] = cnt
row_dict['quality_orb_BF2'] = d
row_dict['time_orb_BF2'] = searchtime
return imagematches, searchtime
def search_HASH_All(returnCount=100, write=False):
# AlgoGenList = ['whash', 'phash', 'dhash', 'ahash']
for algo in AlgoGenList :
imagematches, searchtime = ImageSearch_Algo_Hash.HASH_SEARCH_TREE(myHASH_Trees[algo], mydataHASH, q_path, hashAlgo=algo, hashsize=16, returnCount=returnCount)
if write:
a ,d, ind, cnt = accuracy.accuracy_matches(q_path, imagematches, 20 )
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_HASH_'+str(algo)] = a
row_dict['index_HASH_'+str(algo)] = ind
row_dict['Count_HASH_'+str(algo)] = cnt
row_dict['quality_HASH_'+str(algo)] = d
row_dict['time_HASH_'+str(algo)] = searchtime
def search_HASH_HYBRID (returnCount=100, write=False):
# HybridAlgoList = ['whash', 'ahash']
imagematches, searchtime = ImageSearch_Algo_Hash.HASH_SEARCH_HYBRIDTREE( myHybridtree, mydataHASH, q_path,hashAlgoList=HybridAlgoList, hashsize=16, returnCount=returnCount)
if write:
a ,d, ind, cnt = accuracy.accuracy_matches(q_path, imagematches, 20 )
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_HASH_Hybrid'] = a
row_dict['index_HASH_Hybrid'] = ind
row_dict['Count_HASH_Hybrid'] = cnt
row_dict['quality_HASH_Hybrid'] = d
row_dict['time_HASH_Hybrid'] = searchtime
return imagematches, searchtime
def search_RGB_Corr(returnCount=100, mydataRGB=mydataRGB, write=False):
imagematchesrgb , searchtimergb = ImageSearch_Algo_RGB.RGB_SEARCH(mydataRGB, q_path, correl_threshold=RGB_PARAMETERCORRELATIONTHRESHOLD)
# y= autothreshold (imagematchesrgb)
# toplist = toplist + y
# print (y)
if write:
a, d, ind, cnt = accuracy.accuracy_matches(q_path, imagematchesrgb, 20)
row_dict['acc_rgb_corr'] = a
row_dict['index_rgb_corr'] = ind
row_dict['Count_rgb_corr'] = cnt
row_dict['quality_rgb_corr'] = d
row_dict['time_rgb_corr'] = searchtimergb
# print ('RGB Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
return imagematchesrgb , searchtimergb
def search_RGB(returnCount=100, mydataRGB=mydataRGB, write=False) :
imagematchesrgb , searchtimergb = ImageSearch_Algo_RGB.RGB_SEARCH_TREE (myRGBtree, mydataRGB, q_path, returnCount=returnCount)
# y= autothreshold (imagematchesrgb)
# toplist = toplist + y
# print (y)
if write:
a, d, ind, cnt = accuracy.accuracy_matches(q_path, imagematchesrgb, 20)
row_dict['acc_rgb'] = a
row_dict['index_rgb'] = ind
row_dict['Count_rgb'] = cnt
row_dict['quality_rgb'] = d
row_dict['time_rgb'] = searchtimergb
# print ('RGB Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
return imagematchesrgb , searchtimergb
def search_HSV(returnCount=100, write=False):
imagematcheshsv , searchtimehsv = ImageSearch_Algo_HSV.HSV_SEARCH_TREE ( myHSVtree, mydataHSV, q_path, returnCount=returnCount)
if write:
a, d, i_hsv, cnt = accuracy.accuracy_matches(q_path, imagematcheshsv, 20)
row_dict['acc_hsv'] = a
row_dict['index_hsv'] = i_hsv
row_dict['Count_hsv'] = cnt
row_dict['quality_hsv'] = d
row_dict['time_hsv'] = searchtimehsv
# print ('HSV Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', i_hsv)
# x = autothreshold (imagematcheshsv)
# toplist = toplist + x
# # print (x)
return imagematcheshsv , searchtimehsv
scores, ind = HSVtree.query(F, k=100)
t = time.time() - start
print (ind)
print (scores)
# get the index of searchimage
print (mydataHSV.index[mydataHSV['file'] == q_path])
print (q_path)
print ( "Search took ", t, ' secs')
# Zip results into a list of tuples (score , file) & calculate score
flist = list (mydataHSV.iloc[ ind[0].tolist()]['file'])
slist = list (scores[0])
result = tuple(zip( slist, flist)) # create a list of tuples from 2 lists
a , q, pos, cnt = accuracy.accuracy_matches(q_path, result, 100)
print ('Accuracy =', a, '%', '| Quality:', q )
print ('Count', cnt, ' | position', pos)
# -------------------- KD Tree HASH (phash, ahash, dhash, whash) ---------------------
# Avg. Time per search: 0.033 s
from sklearn.neighbors import KDTree
import imagehash
import numpy as np
import time
# YD = np.array(mydataHASH['phash'].apply(imagehash.ImageHash.__hash__))
YD = list(mydataHASH['ahash'])
# ------------------ SEARCH TEST
# q_path = random.sample(fileterd_file, 1)[0]
# q_path = './imagesbooks/ukbench00481.jpg'
# imagepredictions , searchtime = SIFT_SEARCH(mydata1, q_path, 50 ,0.75, 20)
imagepredictions, searchtime = ImageSearch_Algo_SIFT(mydatasift, q_path, 100,
0.75, 20)
# to reload module: uncomment use the following
# %load_ext autoreload
# %autoreload 2
import Accuracy as accuracy
a, b, c, d = accuracy.accuracy_matches(q_path, imagepredictions, 50)
print('Accuracy =', a, '%', d, searchtime)
# import ImageSearch_Plots as myplots
# myplots.plot_predictions(imagepredictions[:20], q_path)
#---------------- Compile data and plot results
# q_paths = random.sample(imagepaths, 50) # random sample 100 items in list
# q_paths = ['./imagesbooks/ukbench05960.jpg', './imagesbooks/ukbench00459.jpg', './imagesbooks/ukbench06010.jpg', './imagesbooks/ukbench06104.jpg', './imagesbooks/ukbench00458.jpg']
q_paths = [
'./imagesbooks/ukbench05960.jpg', './imagesbooks/ukbench00459.jpg',
'./imagesbooks/ukbench06010.jpg', './imagesbooks/ukbench06104.jpg',
'./imagesbooks/ukbench00458.jpg', './imagesbooks/ukbench00248.jpg',
'./imagesbooks/ukbench06408.jpg', './imagesbooks/ukbench00303.jpg',
'./imagesbooks/ukbench03124.jpg', './imagesbooks/ukbench05776.jpg',
def algomixerFunnel (algos, return_count, finalalgo, finalalgoDataframe, algoname='NewAlgo', write=False) :
'''
algos [list]: list of candidare algos
return_count (int) : number of candidates to be generated
finalalgo (str) : list of candidare algos
finalalgoDataframe (pd.Dataframe): dataframe of the finalAlgo to be filtered and used
algoname (str) : 'column name of datframe for the final reported accuracy, time etc.
write (bool): True / False -> whether to report funnel accurracy, time before merge with thresholded candidates
'''
# myAlgos = [ search_RGB, search_SIFT_BF ]
# algomixerFunnel (myAlgos)
start = time.time()
algoResults = []
algoTimes = []
for algo in algos:
algoResult, algoTime = algo_selector (algo, return_count=return_count)
algoResults.append(algoResult)
algoTimes.append(algoTime)
# generate candidates (short listing)
filteredFeatureData = Thresholding.filter_candidates( algoResults, finalalgoDataframe)
# run Final Algo (detection)
imagepredictions,searchtimesift = algo_selector_final(finalalgo,filteredFeatureData,return_count=return_count)
if write:
# find accuracy and append to dict
a ,d, ind, cnt = accuracy.accuracy_matches(q_path, imagepredictions, 20)
t = time.time() - start
row_dict['acc_'+ algoname + '_BM'] = a
row_dict['index_'+ algoname + '_BM'] = ind
row_dict['Count_'+ algoname + '_BM'] = cnt
row_dict['quality_'+ algoname + '_BM'] = d
row_dict['time_'+ algoname + '_BM'] = t
# generate algo uniques: apply threshold for each Result (imagematches)
unique_final_list = []
for Result in algoResults :
unique_final_list.append(Thresholding.autothreshold_knee(Result))
# print (unique_final_list)
# MERGE OPEARATION FROM ALL RESULTS
# algo uniques + final algo detections results
final_algo_List = Thresholding.imagepredictions_to_list(imagepredictions)
toplist = unique_final_list.copy()
# copy all commons from candidates threshold list to front (2 lists)
toplist = [Thresholding.merge_results( toplist, False)]
# Add final algo derivatives to toplist
toplist.append(final_algo_List)
# merge lists of algo results: HSV Thresh, RGB Thresh, SIFT
toplist = Thresholding.merge_results( toplist, False)
t = time.time() - start
# find accuracy and append to dict
a ,d, ind, cnt = accuracy.accuracy_from_list(q_path, toplist, 20 )
print ('index F_'+algoname+': ', ind)
row_dict['acc_'+ algoname] = a
row_dict['index_'+ algoname] = ind
row_dict['Count_'+ algoname] = cnt
row_dict['quality_'+ algoname] = d
row_dict['time_'+ algoname] = t
def search_AlgoA ( candidates=100, verbose=False ):
toplist = []
start = time.time()
# run RGB
imagematchesrgb , searchtimergb = ImageSearch_Algo_RGB.RGB_SEARCH_TREE (myRGBtree, mydataRGB, q_path, returnCount=candidates)
# run HSV
imagematcheshsv , searchtimehsv = ImageSearch_Algo_HSV.HSV_SEARCH_TREE ( myHSVtree, mydataHSV, q_path, returnCount=candidates)
# create shortlist for SIFT
filteredSIFTData = Thresholding.filter_sift_candidates( [imagematcheshsv, imagematchesrgb], mydataSIFT)
# run SIFT
imagepredictions , searchtimesift = ImageSearch_Algo_SIFT.SIFT_SEARCH_BF(filteredSIFTData, q_path, sift_features_limit=100 , lowe_ratio=LOWE_RATIO, predictions_count=SIFT_PREDICTIONS_COUNT)
# threshold RGB
final_RGB_List = Thresholding.autothreshold_knee(imagematchesrgb)
# thresold HSV
final_HSV_List = Thresholding.autothreshold_knee(imagematcheshsv)
# MERGE OPEARATION FROM ALL RESULTS
# SIFT LIST
final_SIFT_List = Thresholding.imagepredictions_to_list(imagepredictions)
# merge lists of algo results: HSV Thresh, RGB Thresh, SIFT
toplist = Thresholding.merge_results([final_HSV_List, final_RGB_List, final_SIFT_List], False)
# find accuracy and append to dict
a ,d, ind, cnt = accuracy.accuracy_from_list(q_path, toplist, 20 )
t = time.time() - start
row_dict['acc_algo_A'] = a
row_dict['index_algo_A'] = ind
row_dict['Count_algo_A'] = cnt
row_dict['quality_algo_A'] = d
row_dict['time_algo_A'] = t
# run if verbose enabled; DEBUGGING
if verbose:
print ('index FINAL AlgoA: ', ind)
# append SIFT Results
a ,d, ind, cnt = accuracy.accuracy_matches(q_path, imagepredictions, 20 )
print ('SIFT-A Accuracy =', a, '%', '| Quality:', d )
print ('SIFT-A Count', cnt, ' | position', ind)
row_dict['acc_Algo_A_SIFT'] = a
row_dict['index_Algo_A_SIFT'] = ind
row_dict['Count_Algo_A_SIFT'] = cnt
row_dict['quality_Algo_A_SIFT'] = d
# row_dict['time_Algo_A_SIFT'] = searchtime
# get current accurracy of RGB
a, d, ind, cnt = accuracy.accuracy_matches(q_path, imagematchesrgb, 20)
print ('index RGB : ', ind)
# get thresholded accurracy of RGB
a ,d, ind, cnt = accuracy.accuracy_from_list(q_path, final_RGB_List, 20 )
print ('index RGB Th:', ind)
# update candidates RGB
row_dict['index_Algo_A_cRGB'] = ind
# get current accurracy for HSV
a, d, ind, cnt = accuracy.accuracy_matches(q_path, imagematcheshsv, 20)
print ('index HSV : ', ind)
# get thresholded accurracy for HSV
a ,d, ind, cnt = accuracy.accuracy_from_list(q_path, final_HSV_List, 20 )
print ('index HSV Th: ', ind)
# update candidates
row_dict['index_Algo_A_cHSV'] = ind
return imagepredictions, t
matcheshsv = []
matchesrgb = []
for q_path in q_paths:
toplist = []
start = time.time()
row_dict = {'file':q_path }
imagematcheshsv , searchtimehsv = ImageSearch_Algo_HSV.HSV_SEARCH_TREE (mytreeHSV, mydataHSV, q_path, 100)
x = autothreshold (imagematcheshsv)
toplist = toplist + x
# print (x)
a, d, i_hsv, cnt = accuracy.accuracy_matches(q_path, imagematcheshsv, 20)
row_dict['hsv_acc'] = a
row_dict['hsv_matchindex'] = i_hsv
row_dict['hsv_Count'] = cnt
row_dict['hsv_time'] = searchtimehsv
print ('HSV Accuracy =', a, '%', '| Quality:', d )
print ('Count', cnt, ' | position', i_hsv)
imagematchesrgb , searchtimergb = ImageSearch_Algo_RGB.RGB_SEARCH_TREE (mytreeRGB, mydataRGB, q_path, 100)
y= autothreshold (imagematchesrgb)
toplist = toplist + y
# print (y)
a, d, i_rgb, cnt = accuracy.accuracy_matches(q_path, imagematchesrgb, 20)
row_dict['rgb_acc'] = a
row_dict['rgb_matchindex'] = i_rgb
start = time.time()
# get the feature for this image
q_kp, q_des = ImageSearch_Algo_SIFT.FEATURE (q_path)
# get bow cluster
q_clustered_words = cluster_model.predict(q_des)
# get FV histogram
q_bow_hist = np.array([np.bincount(q_clustered_words, minlength=n_clusters)])
# search the KDTree for nearest match
dist, result = SIFTtree2.query(q_bow_hist, k=100)
t= time.time() - start
print (result)
print ('SIFT Search Tree: ', t , ' secs')
flist = list (mydataSIFT.iloc[ result[0].tolist()]['file'])
slist = list (dist[0])
matches = tuple(zip( slist, flist)) # create a list of tuples frm 2 lists
a, q, pos, cnt = accuracy.accuracy_matches(q_path, matches, 20)
print('Accuracy =', a, '%', '| Quality:', q)
print('Count', cnt, ' | position', pos)
################# Unsupervised Clustering ###################
# --------------------------------------------------------- #
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture
# --------- find unsupervised cluster ID with KMEANS
X = img_bow_hist
km = KMeans(n_clusters=200)
km.fit(X)
km.predict(X)
labels = km.labels_
print (labels)
# # update labels to original dataframe
q_paths = random.sample(imagepaths, 20) # random sample 100 items in list
# q_paths = ["./imagesbooks/ukbench00196.jpg", "./imagesbooks/ukbench00199.jpg", "./imagesbooks/ukbench00296.jpg", "./imagesbooks/ukbench00298.jpg", "./imagesbooks/ukbench00299.jpg", "./imagesbooks/ukbench00300.jpg", "./imagesbooks/ukbench00302.jpg", "./imagesbooks/ukbench00303.jpg", "./imagesbooks/ukbench02730.jpg", "./imagesbooks/ukbench02740.jpg", "./imagesbooks/ukbench02743.jpg", "./imagesbooks/ukbench05608.jpg", "./imagesbooks/ukbench05932.jpg", "./imagesbooks/ukbench05933.jpg", "./imagesbooks/ukbench05934.jpg", "./imagesbooks/ukbench05935.jpg", "./imagesbooks/ukbench05952.jpg", "./imagesbooks/ukbench05953.jpg", "./imagesbooks/ukbench05954.jpg", "./imagesbooks/ukbench05955.jpg", "./imagesbooks/ukbench05956.jpg", "./imagesbooks/ukbench05957.jpg", "./imagesbooks/ukbench05958.jpg", "./imagesbooks/ukbench05959.jpg", "./imagesbooks/ukbench06148.jpg", "./imagesbooks/ukbench06149.jpg", "./imagesbooks/ukbench06150.jpg", "./imagesbooks/ukbench06151.jpg", "./imagesbooks/ukbench06558.jpg", "./imagesbooks/ukbench06559.jpg", "./imagesbooks/ukbench07285.jpg", "./imagesbooks/ukbench07588.jpg", "./imagesbooks/ukbench07589.jpg", "./imagesbooks/ukbench07590.jpg", "./imagesbooks/ukbench08540.jpg", "./imagesbooks/ukbench08542.jpg", "./imagesbooks/ukbench08592.jpg"]
counter = 1
for q_path in q_paths:
imagematchesrgb, searchtimergb = ImageSearch_Algo_RGB.RGB_SEARCH_TREE(
myRGBtree, mydataRGB, q_path, 100)
# imagematchesrgb , searchtimergb = ImageSearch_Algo_RGB.RGB_SEARCH(mydataRGB, q_path, 0.7)
# to reload module: uncomment use the following
# %load_ext autoreload
# %autoreload 2
import Accuracy as accuracy
a, d, i_rgb, cnt = accuracy.accuracy_matches(q_path, imagematchesrgb, 20)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', i_rgb)
# import ImageSearch_Plots as myplots
# myplots.plot_predictions(imagematchesrgb, q_path)
score = []
matchesposition = [0] * len(imagematchesrgb)
for i in i_rgb:
matchesposition[i] = 1
# score, file = item
for item in imagematchesrgb:
x, y = item
score.append(x)
mydataHSV, mytime = ImageSearch_Algo_HSV.HSV_GEN(imagepaths)
print('HSV Feature Generation time', mytime)
#------------ HSV SEARCH TEST------------------------------#
q_path = random.sample(imagepaths, 1)[0]
# imagematches , searchtime = ImageSearch_Algo_HSV.HSV_SEARCH(mydataHSV, q_path)
imagematches, searchtime = ImageSearch_Algo_HSV.HSV_SEARCH(mydataHSV, q_path)
print('HSV Search time', searchtime)
# to reload module: uncomment use the following
# %load_ext autoreload
# %autoreload 2
a, m, pos, cnt = accuracy.accuracy_matches(q_path, imagematches, 20)
print('Accuracy =', a, '%', ' | Quality: ', m)
# ----- Alternative tree search code [Optimized search time ]
# test TREE SEARCH code
# to create a new tree from dataframe features 'mydataHSV'
mytree = ImageSearch_Algo_HSV.HSV_Create_Tree(mydataHSV, savefile='HSV_Tree')
# to load an existing tree
thistree = ImageSearch_Algo_HSV.HSV_Load_Tree('HSV_Tree')
# sample 1 image
q_path = random.sample(imagepaths, 1)[0]