def search_SIFT_BOVW(returnCount=100,
mySIFTmodel=mySIFTmodel,
mySIFTtree=mySIFTtree,
mydataSIFT=mydataSIFT,
txt='',
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,
ACCURRACY_RANGE)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_sift_tree' + txt] = a
row_dict['index_sift_tree' + txt] = ind
row_dict['Count_sift_tree' + txt] = cnt
row_dict['quality_sift_tree' + txt] = d
row_dict['time_sift_tree' + txt] = searchtime
return imagematches, searchtime
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_SIFT_FLANN(returnCount=100, mydataSIFT=mydataSIFT, write=False):
imagepredictionsFLANN , searchtimesift = ImageSearch_Algo_SIFT.SIFT_SEARCH(mydataSIFT, q_path, sift_features_limit=SIFT_FEATURES_LIMIT , lowe_ratio=LOWE_RATIO, predictions_count=returnCount)
if write:
a ,d, ind, cnt = accuracy.accuracy_matches(q_path, imagepredictionsFLANN, ACCURRACY_RANGE)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_sift_Flann'] = a
row_dict['index_sift_Flann'] = ind
row_dict['Count_sift_Flann'] = cnt
row_dict['quality_sift_Flann'] = d
row_dict['time_sift_Flann'] = searchtimesift
return imagepredictionsFLANN, searchtimesift
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
# --------------- IMAGES ----------------------#
imagepaths = sorted (list(paths.list_images(IMGDIR)))
myDataFiles = pd.DataFrame( {'file' : imagepaths })
# ----------- GENERATE ALL FEATURES & SAVE ------------ #
# GEN SIFT
sift_features_limit = SIFT_FEATURES_LIMIT
lowe_ratio = LOWE_RATIO
predictions_count = SIFT_PREDICTIONS_COUNT
mydataSIFT, mytime1 = ImageSearch_Algo_SIFT.gen_sift_features(
imagepaths, sift_features_limit)
print("SIFT Feature Generation time :", mytime1)
savefile = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp'+ str(sift_features_limit)
ImageSearch_Algo_SIFT.SIFT_SAVE_FEATURES (mydataSIFT, savefile)
print("SIFT Feature saved to : ", savefile)
# -- END
# GEN ORB
orb_features_limit = ORB_FEATURES_LIMIT
mydataORB, mytime1 = ImageSearch_Algo_ORB.GEN_ORB_FEATURES(imagepaths, orb_features_limit)
print("ORB Feature Generation time :", mytime1)
savefile = 'data/' + TESTNAME + '_PandasDF_ORB_Features_kp'+ str(orb_features_limit)
ImageSearch_Algo_ORB.ORB_SAVE_FEATURES (mydataORB, savefile)
print("ORB Feature saved to : ", savefile)
# -- END
# from sklearn.neighbors import NearestNeighbors # neighbor = NearestNeighbors(n_neighbors = 30) # neighbor.fit(feature_vectors) from sklearn.neighbors import KDTree SIFTtree = KDTree(feature_vectors) # ----------- search q_path = random.sample(imagepaths, 1)[0] print(q_path) import ImageSearch_Algo_SIFT # get the feature q_kp, q_des = ImageSearch_Algo_SIFT.FEATURE(q_path) predict_kmeans = kmeans.predict(q_des) #calculates the histogram hist1, bin_edges1 = np.histogram(predict_kmeans, bins=n_bins) #histogram is the feature vector q_feature_vector = hist1 # ------- Using KD TREE # reshape - something wrong in this implementation F = q_feature_vector.reshape(1, -1) dist, result = SIFTtree.query(F, k=50) print(result) flist = list(mydataSIFT.iloc[result[0].tolist()]['file']) slist = list(dist[0]) matches = tuple(zip(slist, flist)) # create a list of tuples from 2 lists
from kneed import KneeLocator
import random
import matplotlib.pyplot as plt
q_path = random.sample(q_paths, 1)[0]
# Features
TESTNAME = 'Data519'
file_HSV_Feature = 'data/' + TESTNAME + '_PandasDF_HSV_Features'
file_HSV_Tree = 'data/' + TESTNAME + '_HSV_Tree'
file_RGB_Feature = 'data/' + TESTNAME + '_PandasDF_RGB_Features'
file_RGB_Tree = 'data/' + TESTNAME + '_RGB_Tree'
file_SIFT_Feature = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp100'
mydataRGB = ImageSearch_Algo_RGB.RGB_LOAD_FEATURES(file_RGB_Feature)
mydataHSV = ImageSearch_Algo_HSV.HSV_LOAD_FEATURES(file_HSV_Feature)
mydataSIFT = ImageSearch_Algo_SIFT.SIFT_LOAD_FEATURES(file_SIFT_Feature)
# Tree & Clusters
myRGBtree = ImageSearch_Algo_RGB.RGB_Load_Tree(file_RGB_Tree)
myHSVtree = ImageSearch_Algo_HSV.HSV_Load_Tree(file_HSV_Tree)
# imagematches , searchtimergb = ImageSearch_Algo_RGB.RGB_SEARCH_TREE (myRGBtree, mydataRGB, q_path, 100)
# imagematches , searchtimergb = ImageSearch_Algo_RGB.RGB_SEARCH(mydataRGB, q_path, 0.5)
imagematches, searchtimehsv = ImageSearch_Algo_HSV.HSV_SEARCH_TREE(
myHSVtree, mydataHSV, q_path, 100)
# imagematches , searchtimehsv = ImageSearch_Algo_HSV.HSV_SEARCH(mydataHSV, q_path)
# to reload module: uncomment use the following
# %load_ext autoreload
# %autoreload 2
def SEARCH_TESTS(TESTNAME, IMGDIR):
# --------------- CONFIG PARAMETERS ----------------------#
ORB_FEATURES_LIMIT = 100
ORB_FEATURES_LIMIT2 = 500
ORB_N_CLUSTERS = 500
ORB_N_CLUSTERS2 = 50 # 500 # (option2)
SIFT_FEATURES_LIMIT = 100
SIFT_FEATURES_LIMIT2 = 300
SIFT_N_CLUSTERS = 500
SIFT_N_CLUSTERS2 = 50 # 100 # (option2)
LOWE_RATIO = 0.7
SIFT_PREDICTIONS_COUNT = 100
RGB_PARAMETERCORRELATIONTHRESHOLD = 0.70 # not needed for generation
kneeHSV = 2
kneeRGB = 2
kneeORB = 2
kneeSIFT = 2
HASHLENGTH = 16
ACCURRACY_RANGE = 20
accuracy = AccuracyGlobal.AccuracyGlobal() # empty class genrated
accuracy.read(IMGDIR)
# --------------- IMAGES ----------------------#
imagepaths = (list(paths.list_images(IMGDIR)))
myDataFiles = pd.DataFrame({'file': imagepaths})
print('Testname: ', TESTNAME)
print("Searching Features for ", len(imagepaths), "images in", IMGDIR)
# ----------- LOAD FEATUTES AND TREES from file ------------ #
HybridAlgoList = ['whash', 'ahash']
AlgoGenList = ['whash', 'phash', 'dhash', 'ahash']
# Files
file_HASH_Feature = 'data/' + TESTNAME + '_PandasDF_HASH_Features'
file_HASH_HybridTree = 'data/' + TESTNAME + '_HASH_Hybrid_Tree_' + str(
('_').join(HybridAlgoList))
file_HSV_Cluster = 'data/' + 'test' + '_HSV_Cluster' + str(kneeHSV)
file_HSV_Feature = 'data/' + TESTNAME + '_PandasDF_HSV_Features'
file_HSV_Tree = 'data/' + TESTNAME + '_HSV_Tree'
file_ORB_Cluster = 'data/' + 'test' + '_ORB_Cluster' + str(kneeORB)
file_ORB_Feature = 'data/' + TESTNAME + '_PandasDF_ORB_Features_kp' + str(
ORB_FEATURES_LIMIT)
file_ORB_TreeCluster = 'data/' + TESTNAME + '_ORB_Tree_Cluster' + str(
ORB_N_CLUSTERS)
file_Results = 'data/' + TESTNAME + '_Results'
file_RGB_Cluster = 'data/' + 'test' + '_RGB_Cluster' + str(kneeRGB)
file_RGB_Feature = 'data/' + TESTNAME + '_PandasDF_RGB_Features'
file_RGB_Tree = 'data/' + TESTNAME + '_RGB_Tree'
file_SIFT_Cluster = 'data/' + 'test' + '_SIFT_Cluster' + str(kneeSIFT)
file_SIFT_Feature = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp' + str(
SIFT_FEATURES_LIMIT)
file_SIFT_TreeCluster = 'data/' + TESTNAME + '_SIFT_Tree_Cluster' + str(
SIFT_N_CLUSTERS)
file_SIFT_Feature2 = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp' + str(
SIFT_FEATURES_LIMIT2)
file_SIFT_TreeCluster2 = 'data/' + TESTNAME + '_SIFT_Tree_Cluster' + str(
SIFT_N_CLUSTERS2) + 'kp' + str(SIFT_FEATURES_LIMIT2)
file_ORB_Feature2 = 'data/' + TESTNAME + '_PandasDF_ORB_Features_kp' + str(
ORB_FEATURES_LIMIT2)
file_ORB_TreeCluster2 = 'data/' + TESTNAME + '_ORB_Tree_Cluster' + str(
ORB_N_CLUSTERS2) + 'kp' + str(ORB_FEATURES_LIMIT2)
# Features
mydataRGB = ImageSearch_Algo_RGB.RGB_LOAD_FEATURES(file_RGB_Feature)
mydataHSV = ImageSearch_Algo_HSV.HSV_LOAD_FEATURES(file_HSV_Feature)
mydataSIFT = ImageSearch_Algo_SIFT.SIFT_LOAD_FEATURES(file_SIFT_Feature)
mydataSIFT2 = ImageSearch_Algo_SIFT.SIFT_LOAD_FEATURES(file_SIFT_Feature2)
mydataORB = ImageSearch_Algo_ORB.ORB_LOAD_FEATURES(file_ORB_Feature)
mydataORB2 = ImageSearch_Algo_ORB.ORB_LOAD_FEATURES(file_ORB_Feature2)
mydataHASH = ImageSearch_Algo_Hash.HASH_LOAD_FEATURES(file_HASH_Feature)
# Tree & Clusters
myRGBtree = ImageSearch_Algo_RGB.RGB_Load_Tree(file_RGB_Tree)
myHSVtree = ImageSearch_Algo_HSV.HSV_Load_Tree(file_HSV_Tree)
mySIFTtree, mySIFTmodel, mySIFTFVHist = ImageSearch_Algo_SIFT.SIFT_Load_Tree_Model(
file_SIFT_TreeCluster)
mySIFTtree2, mySIFTmodel2, mySIFTFVHist2 = ImageSearch_Algo_SIFT.SIFT_Load_Tree_Model(
file_SIFT_TreeCluster2)
myORBtree, myORBmodel, myORBFVHist = ImageSearch_Algo_ORB.ORB_Load_Tree_Model(
file_ORB_TreeCluster)
myORBtree2, myORBmodel2, myORBFVHist2 = ImageSearch_Algo_ORB.ORB_Load_Tree_Model(
file_ORB_TreeCluster2)
myHybridtree = ImageSearch_Algo_Hash.HASH_Load_Tree(file_HASH_HybridTree)
# Hash Algo load all TREES
myHASH_Trees = {}
for algo in AlgoGenList:
savefile = 'data/' + TESTNAME + '_HASH_Tree_' + str(algo)
myHASHTree = ImageSearch_Algo_Hash.HASH_Load_Tree(savefile)
myHASH_Trees[algo] = myHASHTree
################################################################################
# ALGO CALLS #
################################################################################
# ---------- search HSV Tree
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, ACCURRACY_RANGE)
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
# # ---------- search RGB Tree
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,
ACCURRACY_RANGE)
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
# # ---------- search RGB Correlation
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,
ACCURRACY_RANGE)
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
# # ---------- search SIFT FLANN
def search_SIFT_FLANN(returnCount=100, mydataSIFT=mydataSIFT, write=False):
imagepredictionsFLANN, searchtimesift = ImageSearch_Algo_SIFT.SIFT_SEARCH(
mydataSIFT,
q_path,
sift_features_limit=SIFT_FEATURES_LIMIT,
lowe_ratio=LOWE_RATIO,
predictions_count=returnCount)
if write:
a, d, ind, cnt = accuracy.accuracy_matches(q_path,
imagepredictionsFLANN,
ACCURRACY_RANGE)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_sift_Flann'] = a
row_dict['index_sift_Flann'] = ind
row_dict['Count_sift_Flann'] = cnt
row_dict['quality_sift_Flann'] = d
row_dict['time_sift_Flann'] = searchtimesift
return imagepredictionsFLANN, searchtimesift
# # ---------- search SIFT BF
def search_SIFT_BF(returnCount=100,
mydataSIFT=mydataSIFT,
txt='',
write=False):
imagepredictionsBF, searchtimesift = ImageSearch_Algo_SIFT.SIFT_SEARCH_BF(
mydataSIFT,
q_path,
sift_features_limit=SIFT_FEATURES_LIMIT,
lowe_ratio=LOWE_RATIO,
predictions_count=returnCount)
if write:
a, d, ind, cnt = accuracy.accuracy_matches(q_path,
imagepredictionsBF,
ACCURRACY_RANGE)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_sift_BF' + txt] = a
row_dict['index_sift_BF' + txt] = ind
row_dict['Count_sift_BF' + txt] = cnt
row_dict['quality_sift_BF' + txt] = d
row_dict['time_sift_BF' + txt] = searchtimesift
return imagepredictionsBF, searchtimesift
# # ---------- search SIFT BOVW Tree
def search_SIFT_BOVW(returnCount=100,
mySIFTmodel=mySIFTmodel,
mySIFTtree=mySIFTtree,
mydataSIFT=mydataSIFT,
txt='',
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,
ACCURRACY_RANGE)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_sift_tree' + txt] = a
row_dict['index_sift_tree' + txt] = ind
row_dict['Count_sift_tree' + txt] = cnt
row_dict['quality_sift_tree' + txt] = d
row_dict['time_sift_tree' + txt] = searchtime
return imagematches, searchtime
# # ---------- search ORB FLANN-LSH
def search_ORB_FLANN(returnCount=100,
mydataORB=mydataORB,
txt='',
write=False):
imagematches, searchtime = ImageSearch_Algo_ORB.ORB_SEARCH_FLANN(
mydataORB,
q_path,
ORB_features_limit=ORB_FEATURES_LIMIT,
lowe_ratio=LOWE_RATIO,
predictions_count=returnCount)
if write:
a, d, ind, cnt = accuracy.accuracy_matches(q_path, imagematches,
ACCURRACY_RANGE)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_orb_Flann' + txt] = a
row_dict['index_orb_Flann' + txt] = ind
row_dict['Count_orb_Flann' + txt] = cnt
row_dict['quality_orb_Flann' + txt] = d
row_dict['time_orb_Flann' + txt] = searchtime
return imagematches, searchtime
# # ---------- search ORB BF
def search_ORB_BF(returnCount=100, mydataORB=mydataORB, write=False):
imagematches, searchtime = ImageSearch_Algo_ORB.ORB_SEARCH_BF(
mydataORB,
q_path,
ORB_features_limit=ORB_FEATURES_LIMIT,
lowe_ratio=LOWE_RATIO,
predictions_count=returnCount)
if write:
a, d, ind, cnt = accuracy.accuracy_matches(q_path, imagematches,
ACCURRACY_RANGE)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_orb_BF'] = a
row_dict['index_orb_BF'] = ind
row_dict['Count_orb_BF'] = cnt
row_dict['quality_orb_BF'] = d
row_dict['time_orb_BF'] = searchtime
return imagematches, searchtime
# # ---------- search ORB BF NEW
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,
ACCURRACY_RANGE)
# 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
# # ---------- search ORB BOVW Tree
def search_ORB_BOVW(returnCount=100,
myORBmodel=myORBmodel,
myORBtree=myORBtree,
mydataORB=mydataORB,
txt='',
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,
ACCURRACY_RANGE)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_orb_tree' + txt] = a
row_dict['index_orb_tree' + txt] = ind
row_dict['Count_orb_tree' + txt] = cnt
row_dict['quality_orb_tree' + txt] = d
row_dict['time_orb_tree' + txt] = searchtime
return imagematches, searchtime
# # ---------- search HASH All
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, ACCURRACY_RANGE)
# 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
# # ---------- search HASH specific Algo
def search_HASH(algo='whash', returnCount=100, write=False):
# AlgoGenList = ['whash', 'phash', 'dhash', 'ahash']
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,
ACCURRACY_RANGE)
# 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
return imagematches, searchtime
# # ---------- search Hybrid HASH
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,
ACCURRACY_RANGE)
# 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
# ---------- Algo A = ( HSV(100) + RGB (100) => SIFT BF )
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,
ACCURRACY_RANGE)
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,
ACCURRACY_RANGE)
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,
ACCURRACY_RANGE)
print('index RGB : ', ind)
# get thresholded accurracy of RGB
a, d, ind, cnt = accuracy.accuracy_from_list(
q_path, final_RGB_List, ACCURRACY_RANGE)
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,
ACCURRACY_RANGE)
print('index HSV : ', ind)
# get thresholded accurracy for HSV
a, d, ind, cnt = accuracy.accuracy_from_list(
q_path, final_HSV_List, ACCURRACY_RANGE)
print('index HSV Th: ', ind)
# update candidates
row_dict['index_Algo_A_cHSV'] = ind
return imagepredictions, t
def algo_selector(algo, return_count=100):
if algo == "search_AlgoA": # Tree
imagepredictions, searchtime = search_AlgoA(
candidates=return_count, verbose=False)
elif algo == "search_HASH": # Tree
imagepredictions, searchtime = search_HASH(
returnCount=return_count)
elif algo == "search_HASH_HYBRID": # Tree
imagepredictions, searchtime = search_HASH_HYBRID(
returnCount=return_count)
elif algo == "search_ORB_BF": # Full
imagepredictions, searchtime = search_ORB_BF(
returnCount=return_count)
elif algo == "search_ORB_BF2": # Full
imagepredictions, searchtime = search_ORB_BF2(
returnCount=return_count)
elif algo == "search_ORB_BOVW": # Tree
imagepredictions, searchtime = search_ORB_BOVW(
returnCount=return_count)
elif algo == "search_ORB_BOVW2": # Tree
imagepredictions, searchtime = search_ORB_BOVW(
returnCount=return_count,
myORBmodel=myORBmodel2,
myORBtree=myORBtree2,
mydataORB=mydataORB2,
txt='2')
elif algo == "search_ORB_FLANN": # Full
imagepredictions, searchtime = search_ORB_FLANN(
returnCount=return_count)
elif algo == "search_ORB_FLANN2": # Full
imagepredictions, searchtime = search_ORB_FLANN(
returnCount=return_count, mydataORB=mydataORB2, txt='2')
elif algo == "search_RGB": # Tree
imagepredictions, searchtime = search_RGB(returnCount=return_count)
elif algo == "search_RGB_Corr": # Full
imagepredictions, searchtime = search_RGB_Corr(
returnCount=return_count)
elif algo == "search_SIFT_BF": # Full
imagepredictions, searchtime = search_SIFT_BF(
returnCount=return_count)
elif algo == "search_SIFT_BF2": # Full
imagepredictions, searchtime = search_SIFT_BF(
returnCount=return_count, mydataSIFT=mydataSIFT2, txt='2')
elif algo == "search_SIFT_BOVW": # Tree
imagepredictions, searchtime = search_SIFT_BOVW(
returnCount=return_count)
elif algo == "search_SIFT_BOVW2": # Tree
imagepredictions, searchtime = search_SIFT_BOVW(
returnCount=return_count,
mySIFTmodel=mySIFTmodel2,
mySIFTtree=mySIFTtree2,
mydataSIFT=mydataSIFT2,
txt='2')
elif algo == "search_SIFT_FLANN": # Full
imagepredictions, searchtime = search_SIFT_FLANN(
returnCount=return_count)
elif algo == "search_HSV": # Tree
imagepredictions, searchtime = search_HSV(returnCount=return_count)
else:
print("No Algo Found")
return 0, 0
return (imagepredictions, searchtime)
def algo_selector_final(algo, algoFrame, return_count=100):
if algo == "search_ORB_BF": # Full
imagepredictions, searchtime = search_ORB_BF(
returnCount=return_count, mydataORB=algoFrame)
elif algo == "search_ORB_BF2": # Full
imagepredictions, searchtime = search_ORB_BF2(
returnCount=return_count, mydataORB=algoFrame)
elif algo == "search_ORB_FLANN": # Full
imagepredictions, searchtime = search_ORB_FLANN(
returnCount=return_count, mydataORB=algoFrame)
elif algo == "search_RGB_Corr": # Full
imagepredictions, searchtime = search_RGB_Corr(
returnCount=return_count, mydataRGB=algoFrame)
elif algo == "search_SIFT_BF": # Full
imagepredictions, searchtime = search_SIFT_BF(
returnCount=return_count, mydataSIFT=algoFrame)
elif algo == "search_SIFT_FLANN": # Full
imagepredictions, searchtime = search_SIFT_FLANN(
returnCount=return_count, mydataSIFT=algoFrame)
else:
print("No Algo Found")
return 0, 0
return (imagepredictions, searchtime)
def algomixerAppend(algos, return_count, algoname='NewAlgo'):
# myAlgos = [ search_RGB, search_SIFT_BF ]
# algomixerFunnel (myAlgos)
start = time.time()
algoResults = []
algoTimes = []
for algo in algos:
thisResult, thisTime = algo_selector(algo,
return_count=return_count)
algoResults.append(thisResult)
algoTimes.append(thisTime)
# generate algo uniques: apply threshold for each Result (imagematches)
unique_final_list = []
for result in algoResults:
unique_final_list.append(Thresholding.autothreshold_knee(result))
# MERGE OPEARATION FROM ALL RESULTS
# algo uniques + final algo detections results
toplist = unique_final_list.copy()
# retaining all the individual results as well (P.S: note difference from algomixerFunnel)
for result in algoResults:
toplist.append(Thresholding.imagepredictions_to_list(result))
# merge lists of algo results and remove duplicates order[[commons], [algo1], [algo2]...]
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,
ACCURRACY_RANGE)
# 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 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,
ACCURRACY_RANGE)
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,
ACCURRACY_RANGE)
# 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
#######################################################################
# ----------- DATA COLLECTION START ------------ #
gt = accuracy.check_ground_truth()
# image dirs path
imagepaths = (list(paths.list_images(IMGDIR)))
# imagepaths = ['./imagesbooks/ukbench09622.jpg','./imagesbooks/ukbench10066.jpg','./imagesbooks/ukbench03864.jpg','./imagesbooks/ukbench06696.jpg','./imagesbooks/ukbench08546.jpg','./imagesbooks/ukbench05988.jpg','./imagesbooks/ukbench02718.jpg','./imagesbooks/ukbench05945.jpg','./imagesbooks/ukbench05779.jpg','./imagesbooks/ukbench08054.jpg','./imagesbooks/ukbench10166.jpg','./imagesbooks/ukbench05776.jpg','./imagesbooks/ukbench03865.jpg','./imagesbooks/ukbench06004.jpg','./imagesbooks/ukbench08048.jpg','./imagesbooks/ukbench05874.jpg','./imagesbooks/ukbench03098.jpg','./imagesbooks/ukbench05600.jpg','./imagesbooks/ukbench06047.jpg','./imagesbooks/ukbench10065.jpg']
# ************************* CUSTOM ALGO DATA **************************** #
# AlgoMixerAppend :
# runs specified algos by text calls and merges result
# AlgoMixerFunnel :
# runs specified algosm generates candidates and then runs final algo
# thresholds candidates for top list
# merges all the finals to results
# initialize
Results = pd.DataFrame(columns=['file'])
# for q_path in imagepaths[30:35]:
# for q_path in imagepaths[100:201]:
for q_path in imagepaths:
row_dict = {'file': q_path}
# ------------Generic Algo Full Sample
search_HSV(write=True)
search_RGB(write=True)
# search_RGB_Corr(write=True)
search_SIFT_BF(txt='100', write=True)
# search_SIFT_BF(mydataSIFT=mydataSIFT2, txt='300', write=True)
# search_SIFT_FLANN(write=True)
search_SIFT_BOVW(write=True)
search_SIFT_BOVW(mySIFTmodel=mySIFTmodel2,
mySIFTtree=mySIFTtree2,
mydataSIFT=mydataSIFT2,
txt='kp300n50',
write=True)
# search_ORB_FLANN(txt='100', write=True)
# search_ORB_FLANN(mydataORB=mydataORB2, txt='500', write=True)
# search_ORB_BF(write=True)
# search_ORB_BF2(write=True)
# search_ORB_BOVW(write=True)
# search_ORB_BOVW(myORBmodel=myORBmodel2, myORBtree=myORBtree2, mydataORB=mydataORB2, txt='kp500n50',write=True)
search_HASH_All(write=True)
search_HASH_HYBRID(write=True)
# ---------- ALGO SELECTOR
# algo_selector('search_HSV', 100)
# search_RGB()
# ----------- generate custom combination algos w/ adaptive thresholding
# algomixerAppend(['search_HSV', 'search_RGB'], 100, 'AlgoS')
# algomixerAppend(['search_ORB_BOVW', 'search_SIFT_BOVW'], 100, 'AlgoF')
# algomixerAppend(['search_ORB_BOVW2', 'search_SIFT_BOVW2'], 100, 'AlgoFX')
algomixerAppend(['search_HSV', 'search_RGB', 'search_SIFT_BOVW2'], 100,
'AlgoX')
# ----------- generate custom Funnel algos
algomixerFunnel(['search_HSV', 'search_RGB'],
100,
'search_SIFT_BF',
mydataSIFT,
'AlgoA',
write=True)
# algomixerFunnel(['search_HSV', 'search_RGB'], 100, 'search_SIFT_BF', mydataSIFT2, 'AlgoA2', write=True)
# algomixerFunnel(['search_HSV', 'search_RGB'], 100, 'search_SIFT_FLANN', mydataSIFT, 'F_SIFT2')
algomixerFunnel(['search_HSV', 'search_RGB', 'search_SIFT_BOVW'],
100,
'search_SIFT_BF',
mydataSIFT,
'AlgoB_100',
write=True)
algomixerFunnel(['search_HSV', 'search_RGB', 'search_SIFT_BOVW'],
50,
'search_SIFT_BF',
mydataSIFT,
'AlgoB_50',
write=True)
# algomixerFunnel(['search_HSV', 'search_RGB', 'search_ORB_BOVW'], 100, 'search_SIFT_BF', mydataSIFT, 'AlgoC', write=True)
# algomixerFunnel(['search_HSV', 'search_RGB', 'search_ORB_BOVW2'], 100, 'search_SIFT_BF', mydataSIFT, 'AlgoC2', write=True)
algomixerFunnel(['search_HSV', 'search_RGB', 'search_SIFT_BOVW2'],
100,
'search_SIFT_BF',
mydataSIFT,
'AlgoB2_100',
write=True)
algomixerFunnel(['search_HSV', 'search_RGB', 'search_SIFT_BOVW2'],
50,
'search_SIFT_BF',
mydataSIFT,
'AlgoB2_50',
write=True)
Results = Results.append(row_dict, ignore_index=True)
print('Completed ', imagepaths.index(q_path), q_path)
# ---------- SAVE ALL FILES TO DISK
# Save Frame to csv
Results.to_csv('data/' + TESTNAME + '_RESULTS_FINAL.csv')
print("Data Collection Completed ")
# Save Frame to pickle
savefile = 'data/' + TESTNAME + '_RESULTS_FINAL' # + str(int(time.time()))
outfile = open(savefile + '.pickle', 'wb')
pickle.dump(Results, outfile)
# ---------- SAVED
# Calculate statistics and save to a file
stats = Results.describe()
stats.to_csv('data/' + TESTNAME + '_RESULTS_FINAL_stats.csv')
print("Data Analysis Completed.")
# from sklearn.neighbors import NearestNeighbors # neighbor = NearestNeighbors(n_neighbors = 30) # neighbor.fit(feature_vectors) from sklearn.neighbors import KDTree SIFTtree = KDTree(feature_vectors) # ----------- search q_path = random.sample(imagepaths, 1)[0] print (q_path) import ImageSearch_Algo_SIFT # get the feature q_kp, q_des = ImageSearch_Algo_SIFT.FEATURE (q_path) predict_kmeans=kmeans.predict(q_des) #calculates the histogram hist1, bin_edges1=np.histogram(predict_kmeans, bins=n_bins) #histogram is the feature vector q_feature_vector = hist1 # ------- Using KD TREE # reshape - something wrong in this implementation F = q_feature_vector.reshape (1, -1) dist, result = SIFTtree.query(F, k=50) print (result) flist = list (mydataSIFT.iloc[ result[0].tolist()]['file']) slist = list (dist[0])
# --------------- IMAGES ----------------------#
imagepaths = sorted (list(paths.list_images(IMGDIR)))
myDataFiles = pd.DataFrame( {'file' : imagepaths })
# ----------- GENERATE ALL FEATURES & SAVE ------------ #
# kps = [50]
kps = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]
# GEN SIFT
for kp in kps:
sift_features_limit = kp
lowe_ratio = LOWE_RATIO
predictions_count = SIFT_PREDICTIONS_COUNT
mydataSIFT, mytime1 = ImageSearch_Algo_SIFT.gen_sift_features(
imagepaths, sift_features_limit)
print("SIFT Feature Generation time :", mytime1)
savefile = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp'+ str(sift_features_limit)
ImageSearch_Algo_SIFT.SIFT_SAVE_FEATURES (mydataSIFT, savefile)
print("SIFT Feature saved to : ", savefile)
# -- END
# # ---------- search SIFT BF
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)
# Hyper-Parameters for SIFT comparison
sift_features_limit = 1000
lowe_ratio = 0.75
predictions_count = 50
IMGDIR = "./imagesbooks/"
imagepaths = list(paths.list_images(IMGDIR))
mydata1, mytime1 = gen_sift_features(imagepaths, 1000)
# ------------------ 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
def GENERATE(TESTNAME, IMGDIR):
'''Sequesnce of tests
TESTNAME : name of the test (suffix to all the pickles, csv dumped)
IMGDIR : DIRECTORY PATH CONTAINING IMAGES
'''
# --------------- CONFIG PARAMETERS ----------------------#
ORB_FEATURES_LIMIT = 100
ORB_FEATURES_LIMIT2 = 500
ORB_N_CLUSTERS = 500
ORB_N_CLUSTERS2 = 50 # 500 # (option2) #optimal = 50 for 500 features
SIFT_FEATURES_LIMIT = 100
SIFT_FEATURES_LIMIT2 = 300
SIFT_N_CLUSTERS = 500
SIFT_N_CLUSTERS2 = 50 # 100 # (option2) #optimal = 50 for 300 features
LOWE_RATIO = 0.7
SIFT_PREDICTIONS_COUNT = 100
RGB_PARAMETERCORRELATIONTHRESHOLD = 0.70 # not needed for generation
kneeHSV = 2
kneeRGB = 2
kneeORB = 2
kneeSIFT = 2
HASHLENGTH = 16
# --------------- IMAGES ----------------------#
imagepaths = (list(paths.list_images(IMGDIR)))
myDataFiles = pd.DataFrame({'file': imagepaths})
# ----------- GENERATE ALL FEATURES & SAVE ------------ #
print('Testname: ', TESTNAME)
print("Generating Features for ", len(imagepaths), "images in", IMGDIR)
# GEN SIFT
sift_features_limit = SIFT_FEATURES_LIMIT
lowe_ratio = LOWE_RATIO
predictions_count = SIFT_PREDICTIONS_COUNT
mydataSIFT, mytime1 = ImageSearch_Algo_SIFT.gen_sift_features(
imagepaths, sift_features_limit)
print("SIFT Feature Generation time :", mytime1)
savefile = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp' + str(
sift_features_limit)
ImageSearch_Algo_SIFT.SIFT_SAVE_FEATURES(mydataSIFT, savefile)
print("SIFT Feature saved to : ", savefile)
# -- END
# GEN SIFT 2 # for TREE kp=300, n_cluster=50
sift_features_limit = SIFT_FEATURES_LIMIT2
lowe_ratio = LOWE_RATIO
predictions_count = SIFT_PREDICTIONS_COUNT
mydataSIFT2, mytime1 = ImageSearch_Algo_SIFT.gen_sift_features(
imagepaths, sift_features_limit)
print("SIFT Feature Generation time :", mytime1)
savefile = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp' + str(
sift_features_limit)
ImageSearch_Algo_SIFT.SIFT_SAVE_FEATURES(mydataSIFT2, savefile)
print("SIFT Feature saved to : ", savefile)
# -- END
# GEN ORB
orb_features_limit = ORB_FEATURES_LIMIT
mydataORB, mytime1 = ImageSearch_Algo_ORB.GEN_ORB_FEATURES(
imagepaths, orb_features_limit)
print("ORB Feature Generation time :", mytime1)
savefile = 'data/' + TESTNAME + '_PandasDF_ORB_Features_kp' + str(
orb_features_limit)
ImageSearch_Algo_ORB.ORB_SAVE_FEATURES(mydataORB, savefile)
print("ORB Feature saved to : ", savefile)
# -- END
# GEN ORB 2 # for TREE kp=500, n_cluster=50
orb_features_limit = ORB_FEATURES_LIMIT2
mydataORB2, mytime1 = ImageSearch_Algo_ORB.GEN_ORB_FEATURES(
imagepaths, orb_features_limit)
print("ORB Feature Generation time :", mytime1)
savefile = 'data/' + TESTNAME + '_PandasDF_ORB_Features_kp' + str(
orb_features_limit)
ImageSearch_Algo_ORB.ORB_SAVE_FEATURES(mydataORB2, savefile)
print("ORB Feature saved to : ", savefile)
# -- END
# GEN RGB
parametercorrelationthreshold = 0.70 # not needed for generation
mydataRGB, mytime = ImageSearch_Algo_RGB.RGB_GEN(imagepaths)
print('RGB Feature Generation time', mytime)
savefile = 'data/' + TESTNAME + '_PandasDF_RGB_Features'
ImageSearch_Algo_RGB.RGB_SAVE_FEATURES(mydataRGB, savefile)
print("RGB Feature saved to : ", savefile)
# -- END
# GEN HSV
mydataHSV, mytime = ImageSearch_Algo_HSV.HSV_GEN(imagepaths)
print('HSV Feature Generation time', mytime)
savefile = 'data/' + TESTNAME + '_PandasDF_HSV_Features'
ImageSearch_Algo_HSV.HSV_SAVE_FEATURES(mydataHSV, savefile)
print("HSV Feature saved to : ", savefile)
# -- END
# GEN HASH
mydataHASH, mytime = ImageSearch_Algo_Hash.HASH_GEN(imagepaths, HASHLENGTH)
print("HASH Features Generation time :", mytime)
savefile = 'data/' + TESTNAME + '_PandasDF_HASH_Features'
ImageSearch_Algo_Hash.HASH_SAVE_FEATURES(mydataHASH, savefile)
# -- END
print("## Feature Generation Complete.")
# ----------- GENERATE ALL TREES ------------ #
# RGB TREE
savefile = 'data/' + TESTNAME + '_RGB_Tree'
myRGBtree = ImageSearch_Algo_RGB.RGB_Create_Tree(mydataRGB,
savefile=savefile)
# HSV TREE
savefile = 'data/' + TESTNAME + '_HSV_Tree'
myHSVtree = ImageSearch_Algo_HSV.HSV_Create_Tree(mydataHSV,
savefile=savefile)
# HASH TREE
AlgoGenList = ['whash', 'phash', 'dhash', 'ahash']
for algo in AlgoGenList:
savefile = 'data/' + TESTNAME + '_HASH_Tree_' + str(algo)
myHASHTree = ImageSearch_Algo_Hash.HASH_Create_Tree(mydataHASH,
savefile=savefile,
hashAlgo=algo)
# HASH TREE USE HYBRID HASH
HybridAlgoList = ['whash', 'ahash']
savefile = 'data/' + TESTNAME + '_HASH_Hybrid_Tree_' + str(
('_').join(HybridAlgoList))
myHybridtree = ImageSearch_Algo_Hash.HASH_CREATE_HYBRIDTREE(
mydataHASH, savefile, HybridAlgoList)
# SIFT FV Tree and Cluster
n_clusters = SIFT_N_CLUSTERS
savefile = 'data/' + TESTNAME + '_SIFT_Tree_Cluster' + str(n_clusters)
mySIFTtree, mySIFTmodel, mySIFTFVHist = ImageSearch_Algo_SIFT.SIFT_CREATE_TREE_MODEL(
mydataSIFT, savefile, n_clusters)
# SIFT FV Tree and Cluster with kp=300, n_cluster=50
n_clusters = SIFT_N_CLUSTERS2
savefile = 'data/' + TESTNAME + '_SIFT_Tree_Cluster' + str(
n_clusters) + 'kp' + str(SIFT_FEATURES_LIMIT2)
mySIFTtree2, mySIFTmodel2, mySIFTFVHist2 = ImageSearch_Algo_SIFT.SIFT_CREATE_TREE_MODEL(
mydataSIFT2, savefile, n_clusters)
# ORB FV Tree and Cluster
n_clusters = ORB_N_CLUSTERS
savefile = 'data/' + TESTNAME + '_ORB_Tree_Cluster' + str(n_clusters)
myORBtree, myORBmodel, myORBFVHist = ImageSearch_Algo_ORB.ORB_CREATE_TREE_MODEL(
mydataORB, savefile, n_clusters)
# ORB FV Tree and Cluster with kp=500, n_cluster=50
n_clusters = ORB_N_CLUSTERS2
savefile = 'data/' + TESTNAME + '_ORB_Tree_Cluster' + str(
n_clusters) + 'kp' + str(ORB_FEATURES_LIMIT2)
myORBtree2, myORBmodel2, myORBFVHist2 = ImageSearch_Algo_ORB.ORB_CREATE_TREE_MODEL(
mydataORB2, savefile, n_clusters)
print("## Tree Generation Complete.")
file_SIFT_Cluster = 'data/' + 'test' + '_SIFT_Cluster' + str(kneeSIFT) file_SIFT_Feature = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp'+ str(SIFT_FEATURES_LIMIT) file_SIFT_TreeCluster = 'data/' + TESTNAME + '_SIFT_Tree_Cluster' + str(SIFT_N_CLUSTERS) file_SIFT_Feature2 = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp'+ str(SIFT_FEATURES_LIMIT2) file_SIFT_TreeCluster2 = 'data/' + TESTNAME + '_SIFT_Tree_Cluster' + str(SIFT_N_CLUSTERS2) + 'kp'+str(SIFT_FEATURES_LIMIT2) file_ORB_Feature2 = 'data/' + TESTNAME + '_PandasDF_ORB_Features_kp'+ str(ORB_FEATURES_LIMIT2) file_ORB_TreeCluster2 = 'data/' + TESTNAME + '_ORB_Tree_Cluster' + str(ORB_N_CLUSTERS2) + 'kp'+str(ORB_FEATURES_LIMIT2) # Features mydataRGB = ImageSearch_Algo_RGB.RGB_LOAD_FEATURES (file_RGB_Feature) mydataHSV = ImageSearch_Algo_HSV.HSV_LOAD_FEATURES (file_HSV_Feature) mydataSIFT = ImageSearch_Algo_SIFT.SIFT_LOAD_FEATURES (file_SIFT_Feature) mydataSIFT2 = ImageSearch_Algo_SIFT.SIFT_LOAD_FEATURES (file_SIFT_Feature2) mydataORB = ImageSearch_Algo_ORB.ORB_LOAD_FEATURES(file_ORB_Feature) mydataORB2 = ImageSearch_Algo_ORB.ORB_LOAD_FEATURES(file_ORB_Feature2) mydataHASH = ImageSearch_Algo_Hash.HASH_LOAD_FEATURES(file_HASH_Feature) # Tree & Clusters myRGBtree = ImageSearch_Algo_RGB.RGB_Load_Tree (file_RGB_Tree) myHSVtree = ImageSearch_Algo_HSV.HSV_Load_Tree (file_HSV_Tree) mySIFTtree, mySIFTmodel, mySIFTFVHist = ImageSearch_Algo_SIFT.SIFT_Load_Tree_Model (file_SIFT_TreeCluster) mySIFTtree2, mySIFTmodel2, mySIFTFVHist2 = ImageSearch_Algo_SIFT.SIFT_Load_Tree_Model (file_SIFT_TreeCluster2) myORBtree, myORBmodel, myORBFVHist = ImageSearch_Algo_ORB.ORB_Load_Tree_Model(file_ORB_TreeCluster) myORBtree2, myORBmodel2, myORBFVHist2 = ImageSearch_Algo_ORB.ORB_Load_Tree_Model(file_ORB_TreeCluster2) myHybridtree =ImageSearch_Algo_Hash.HASH_Load_Tree (file_HASH_HybridTree) # Hash Algo load all TREES
# to reload module: uncomment use the following
%load_ext autoreload
%autoreload 2
IMGDIR = "./imagesbooks/"
imagepaths = list(paths.list_images(IMGDIR))
# print (imagepathss)
mydataHSV, mytimeHSV = ImageSearch_Algo_HSV.HSV_GEN(imagepaths)
print ('HSV Feature Generation time', mytimeHSV)
mydataRGB, mytimeRGB = ImageSearch_Algo_RGB.RGB_GEN(imagepaths)
print ('RGB Feature Generation time', mytimeRGB)
#Loading kp100 sift features
mydatasift = ImageSearch_Algo_SIFT.SIFT_LOAD_FEATURES('data/SIFT_features_519set_kp100_pandas')
# to create a new tree from dataframe features 'mydataHSV'
mytreeHSV = ImageSearch_Algo_HSV.HSV_Create_Tree (mydataHSV, savefile='HSV_Tree')
mytreeRGB = ImageSearch_Algo_RGB.RGB_Create_Tree (mydataRGB, savefile='RGB_Tree')
# q_paths = random.sample(imagepaths, 5) # random sample 100 items in list
# q_paths = imagepaths
# 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', './imagesbooks/ukbench06113.jpg', './imagesbooks/ukbench05964.jpg', './imagesbooks/ukbench10164.jpg', './imagesbooks/ukbench02750.jpg', './imagesbooks/ukbench05951.jpg', './imagesbooks/ukbench05983.jpg', './imagesbooks/ukbench03867.jpg', './imagesbooks/ukbench05883.jpg', './imagesbooks/ukbench06049.jpg', './imagesbooks/ukbench06017.jpg', './imagesbooks/ukbench06150.jpg', './imagesbooks/ukbench06151.jpg', './imagesbooks/ukbench02749.jpg', './imagesbooks/ukbench02721.jpg', './imagesbooks/ukbench05879.jpg', './imagesbooks/ukbench06148.jpg', './imagesbooks/ukbench05880.jpg', './imagesbooks/ukbench05929.jpg', './imagesbooks/ukbench06048.jpg', './imagesbooks/ukbench08544.jpg', './imagesbooks/ukbench03058.jpg', './imagesbooks/ukbench10154.jpg', './imagesbooks/ukbench00000.jpg', './imagesbooks/ukbench05972.jpg', './imagesbooks/ukbench05872.jpg', './imagesbooks/ukbench08542.jpg', './imagesbooks/ukbench06004.jpg', './imagesbooks/ukbench05993.jpg', './imagesbooks/ukbench05988.jpg', './imagesbooks/ukbench00483.jpg', './imagesbooks/ukbench08546.jpg', './imagesbooks/ukbench06539.jpg', './imagesbooks/ukbench02748.jpg', './imagesbooks/ukbench05980.jpg', './imagesbooks/ukbench08001.jpg', './imagesbooks/ukbench03890.jpg', './imagesbooks/ukbench03059.jpg', './imagesbooks/ukbench10081.jpg', './imagesbooks/ukbench06519.jpg', './imagesbooks/ukbench05787.jpg']
# 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", "./imagesbooks/ukbench08594.jpg","./imagesbooks/ukbench08595.jpg","./imagesbooks/ukbench08609.jpg","./imagesbooks/ukbench09364.jpg","./imagesbooks/ukbench09365.jpg","./imagesbooks/ukbench09366.jpg","./imagesbooks/ukbench10061.jpg","./imagesbooks/ukbench10065.jpg","./imagesbooks/ukbench10066.jpg","./imagesbooks/ukbench10085.jpg","./imagesbooks/ukbench10087.jpg","./imagesbooks/ukbench10108.jpg","./imagesbooks/ukbench10109.jpg","./imagesbooks/ukbench10110.jpg","./imagesbooks/ukbench10112.jpg","./imagesbooks/ukbench10113.jpg","./imagesbooks/ukbench10114.jpg","./imagesbooks/ukbench10116.jpg","./imagesbooks/ukbench10118.jpg","./imagesbooks/ukbench10119.jpg","./imagesbooks/ukbench10124.jpg","./imagesbooks/ukbench10125.jpg","./imagesbooks/ukbench10126.jpg","./imagesbooks/ukbench10128.jpg","./imagesbooks/ukbench10129.jpg","./imagesbooks/ukbench10130.jpg","./imagesbooks/ukbench10131.jpg","./imagesbooks/ukbench10152.jpg","./imagesbooks/ukbench10153.jpg","./imagesbooks/ukbench10154.jpg","./imagesbooks/ukbench10164.jpg","./imagesbooks/ukbench10165.jpg","./imagesbooks/ukbench10166.jpg","./imagesbooks/ukbench10167.jpg"]
print("ORB BF Search time :", searchtime)
a, q, pos, cnt = accuracy.accuracy_matches(q_path, imagematches, 20)
print('Accuracy =', a, '%', '| Quality:', q)
print('Count', cnt, ' | position', pos)
# ----- END Alternative
# compiled run over 100 samples
q_paths = random.sample(imagepaths, 100) # random sample 100 items in list
accStatssift = pd.DataFrame(columns=['file'])
for q_path in q_paths:
row_dict = {'file': q_path}
imagepredictions, searchtime = ImageSearch_Algo_SIFT.SIFT_SEARCH(
mydataSIFT, q_path, sift_features_limit, 0.75, 50)
a, d, i, cnt = accuracy.accuracy_matches(q_path, imagepredictions, 20)
# adding to dict
row_dict['kp_' + str(sift_features_limit) + '_predict10'] = a
row_dict['kp_' + str(sift_features_limit) + '_quality'] = d
row_dict['kp_' + str(sift_features_limit) + '_time'] = searchtime
row_dict['kp_' + str(sift_features_limit) + 'matchposition'] = i
row_dict['kp_' + str(sift_features_limit) + 'PCount'] = cnt
accStatssift = accStatssift.append(row_dict, ignore_index=True)
print("Processing, time", q_paths.index(q_path), searchtime)
# plt.plot(accStats['Acc'])
# plt.plot (accStats['PCount'])
# plt.hlines(accStats['kp_100_time'].mean(), 0, 100, 'r')
myDataFiles = pd.DataFrame( {'file' : imagepaths })
# ----------- GENERATE ALL FEATURES & SAVE ------------ #
print ('Testname: ', TESTNAME)
print ("Generating Features for ", len(imagepaths), "images in", IMGDIR)
# GEN SIFT
sift_features_limit = SIFT_FEATURES_LIMIT
lowe_ratio = LOWE_RATIO
predictions_count = SIFT_PREDICTIONS_COUNT
mydataSIFT, mytime1 = ImageSearch_Algo_SIFT.gen_sift_features(
imagepaths, sift_features_limit)
print("SIFT Feature Generation time :", mytime1)
savefile = 'data/' + TESTNAME + '_PandasDF_SIFT_Features_kp'+ str(sift_features_limit)
ImageSearch_Algo_SIFT.SIFT_SAVE_FEATURES (mydataSIFT, savefile)
print("SIFT Feature saved to : ", savefile)
# -- END
# GEN SIFT 2 # for TREE kp=300, n_cluster=50
sift_features_limit = SIFT_FEATURES_LIMIT2
lowe_ratio = LOWE_RATIO
predictions_count = SIFT_PREDICTIONS_COUNT
mydataSIFT2, mytime1 = ImageSearch_Algo_SIFT.gen_sift_features(
imagepaths, sift_features_limit)
print("SIFT Feature Generation time :", mytime1)