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, 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 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
row_dict['totaltime'] = t
print ('total processing time = ', t, 'seconds')
# print( toplist)
# append top 20 results from SIFT to end of TOPLIST
for myitem in imagepredictions[:20]:
x, y = myitem
toplist.append(y)
# print( toplist)
# print sanitize_List(toplist))
toplist = sanitize_List(toplist)
a ,d, i_toplist, cnt = accuracy.accuracy_from_list(q_path, toplist, 20 )
print (q_paths.index(q_path), q_path)
print ('Final Accuracy =', a, '%', '| Quality:', d )
print ('Final Count', cnt, ' | position', i_toplist)
row_dict['toplistmerge_acc'] = a
row_dict['toplistmerge_matchindex'] = i_toplist
row_dict['toplistmerge_Count'] = cnt
row_dict['toplistmerge_quality'] = d
# row_dict['toplistmerge_time'] = searchtimetoplist
# print ("toplistmerge processing time", searchtimetoplist, 'sec')
print ("-------------------------------------------")