def identification():
## 1:N search
json_request = request.get_json()
if 'query' in json_request.keys():
# initialize
br = init_brpy(br_loc="/usr/local/lib")
br.br_initialize_default()
br.br_set_property('algorithm', 'FaceRecognition')
br.br_set_property('enrollAll', 'true')
# get query
image = BytesIO(b64decode(json_request.get('query'))).read()
# image template
imagetmpl1 = br.br_load_img(image, len(image))
# enroll
query = br.br_enroll_template(imagetmpl1)
# get filenames
files = [x[2] for x in walk('/images')]
# search
scores = []
for filename in files[0]:
# get target
image2 = open('/images/' + filename).read()
# image template
imagetmpl2 = br.br_load_img(image2, len(image2))
# enroll
target = br.br_enroll_template(imagetmpl2)
# score matrix
scoresmat = br.br_compare_template_lists(target, query)
# result
result = float(br.br_get_matrix_output_at(scoresmat, 0, 0))
# cache score
scores.append({'filename': filename, 'result': result})
# sort matches
scores.sort(key=lambda x: x['result'], reverse=True)
# retreive top 5 matches
result = scores[:5]
# clean up templates
br.br_free_template(imagetmpl1)
br.br_free_template_list(target)
br.br_free_template(imagetmpl2)
br.br_free_template_list(query)
# finalize
br.br_finalize()
return jsonify({
'mimetype': 'application/json',
'status': 200,
'request': request.url,
'response': [{
'scores': result
}]
})
else:
return jsonify({
'error': {
'message': 'Request must contain a query'
},
'status': 400,
'request': request.url
})
def verification():
## 1:1 verification
json_request = request.get_json()
if all(image in json_request.keys() for image in ['query', 'target']):
# initialize
br = init_brpy(br_loc="/usr/local/lib")
br.br_initialize_default()
br.br_set_property('algorithm', 'FaceRecognition')
# get images
image1 = BytesIO(b64decode(json_request.get('query'))).read()
image2 = BytesIO(b64decode(json_request.get('target'))).read()
# image templates
imagetmpl1 = br.br_load_img(image1, len(image1))
imagetmpl2 = br.br_load_img(image2, len(image2))
# enroll
query = br.br_enroll_template(imagetmpl1)
target = br.br_enroll_template(imagetmpl2)
# score matrix
scoresmat = br.br_compare_template_lists(target, query)
# result
result = float(br.br_get_matrix_output_at(scoresmat, 0, 0))
# clean up templates
br.br_free_template(imagetmpl1)
br.br_free_template_list(target)
br.br_free_template(imagetmpl2)
br.br_free_template_list(query)
# finalize
br.br_finalize()
return jsonify({
'mimetype': 'application/json',
'status': 200,
'request': request.url,
'response': [{
'score': result
}]
})
else:
return jsonify({
'error': {
'message': 'Request must contain a query and target'
},
'status': 400,
'request': request.url
})
def gender_estimation():
## Gender estimation
json_request = request.get_json()
if 'query' in json_request.keys():
# initialize
br = init_brpy(br_loc="/usr/local/lib")
br.br_initialize_default()
br.br_set_property('algorithm', 'GenderEstimation')
# get image
image = BytesIO(b64decode(json_request.get('query'))).read()
# write image to disk
with open('/tmp/image.bytes', 'wb') as fl:
fl.write(image)
# enroll image
br.br_enroll('/tmp/image.bytes', '/tmp/matrix.csv')
# get data
lines = open('/tmp/matrix.csv', 'r').readlines()
# result
result = 0 if lines[1].split(',')[10] == 'Male' else 1
# finalize
br.br_finalize()
return jsonify({
'mimetype': 'application/json',
'status': 200,
'request': request.url,
'response': [{
'score': result
}]
})
else:
return jsonify({
'error': {
'message': 'Request must contain a query'
},
'status': 400,
'request': request.url
})
from brpy import init_brpy
br = init_brpy()
br.br_initialize_default()
br.br_set_property('algorithm', 'FaceRecognition')
br.br_set_property('enrollAll', 'true')
def find_weight(main_person, rel_person, min_score):
mcomparision_list = []
mtmpl_list = []
rcomparision_list = []
rtmpl_list = []
pass_score = min_score
weight = 0
mimage_dir = imgreference_directory_path(main_person)
rimage_dir = imgreference_directory_path(rel_person)
downloads_dir = download_directory_path(main_person)
br = init_brpy(br_loc='/usr/local/lib') #Default openbr lib location.
br.br_initialize_default()
br.br_set_property('algorithm','FaceRecognition') #Algorithm to compare faces
br.br_set_property('enrollAll','false')
#Be sure directories exists
dir_exists(mimage_dir)
dir_exists(rimage_dir)
dir_exists(downloads_dir)
#Add images to br
directories = [mimage_dir]
br_add_images(directories,mtmpl_list,mcomparision_list,br)
#Add images to br
directories = [rimage_dir]
br_add_images(directories,rtmpl_list,rcomparision_list,br)
#Compare images
for root, dirs, files in os.walk(downloads_dir, topdown=False):
for name in files:
image = open(os.path.join(root, name)).read()
tmpl = br.br_load_img(image, len(image))
targets = br.br_enroll_template(tmpl)
ntargets = br.br_num_templates(targets)
# compare and collect scores
# compare with all images
scores = []
for query, nquery in mcomparision_list:
scoresmat = br.br_compare_template_lists(targets, query)
for r in range(ntargets):
for c in range(nquery):
scores.append(br.br_get_matrix_output_at(scoresmat, r, c))
if scores :
scores.sort()
maxscore = float("-inf")
for score in scores:
if(score > maxscore):
maxscore = score
#compare with pass score
if maxscore >= pass_score:
#if match, check if other person is also in photo
scores = []
for query, nquery in rcomparision_list:
scoresmat = br.br_compare_template_lists(targets, query)
for r in range(ntargets):
for c in range(nquery):
scores.append(br.br_get_matrix_output_at(scoresmat, r, c))
if scores:
scores.sort()
maxscore = float("-inf")
for score in scores:
if(score > maxscore):
maxscore = score
#compare with pass score
if maxscore >= pass_score:
#Both are in photo, so we add weight
weight = weight + 1
# clean up - no memory leaks
br.br_free_template(tmpl)
br.br_free_template_list(targets)
return weight
def main():
br = init_brpy()
br.br_initialize_default()
br.br_set_property('algorithm', 'FaceRecognition')
br.br_set_property('enrollAll', 'true')
enrollFaces(pictureDest)
#!/usr/bin/python
import sys
from os import listdir
from os.path import isfile, join
from subprocess import Popen, PIPE
from brpy import init_brpy
br = init_brpy(br_loc='/usr/local/lib')
br.br_initialize_default()
br.br_set_property('algorithm', 'FaceRecognition')
myfile = open("output.txt", "w")
picpaths = [f for f in listdir('.') if isfile(join('.', f))]
i = 0
for a in picpaths:
for b in picpaths:
# p = Popen(['br', '-algorithm','FaceRecognition', '-pairwiseCompare', a, b], stdin=None, stdout=PIPE, stderr=PIPE)
# output, err = p.communicate()
# rc = p.returncode
output = ""
myfile.write(a + ',' + b + ',')
br.br_compare(a, b, "output.txt")
myfile.write('\n')
i += 1
if i > 10:
break
if i > 10:
break
myfile.close()
#!/usr/bin/python
import sys
from os import listdir
from os.path import isfile, join
from subprocess import Popen, PIPE
from brpy import init_brpy
br = init_brpy(br_loc='/usr/local/lib')
br.br_initialize_default()
br.br_set_property('algorithm', 'FaceRecognition')
myfile = open("output.txt", "w")
picpaths = [f for f in listdir('.') if isfile(join('.', f))]
i = 0
for a in picpaths:
for b in picpaths:
# p = Popen(['br', '-algorithm','FaceRecognition', '-pairwiseCompare', a, b], stdin=None, stdout=PIPE, stderr=PIPE)
# output, err = p.communicate()
# rc = p.returncode
output = ""
myfile.write(a+','+b+',')
br.br_compare(a, b, "output.txt")
myfile.write('\n')
i += 1
if i > 10:
break
if i > 10:
break
myfile.close()
def main(argv):
br = init_brpy()
br.br_initialize_default()
br.br_set_property('algorithm','FaceRecognition') # also made up
# br.br_set_property('enrollAll','true')
inputfile = ''
outputfile = ''
dataLocation = ''
try:
opts, args = getopt.getopt(argv,"hi:d",["ifile=","dfile="])
except getopt.GetoptError:
print 'test.py -i <inputfile> -o <outputfile>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'test.py -i <inputfile> -o <outputfile>'
sys.exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-d", "--dfile"):
dataLocation = '~/openbr/data/LFW/img/'
lines = [line.rstrip('\n') for line in open(inputfile)]
lines = lines[1:]
scoreMat = []
trueFalseMat = []
numTrue = 0
numFalse = 0
i = 0
scoreOutput = 'file1,file2,score,true\n'
for line in lines :
components = line.split("\t");
im1Path = ''
im2Path = ''
isTrue = 0
if len(components) == 3 :
trueFalseMat.append(1)
isTrue = 1
numTrue +=1
folder = components[0]
im1num = components[1].zfill(4)
im2num = components[2].zfill(4)
im1Path = dataLocation + folder + '/' + folder + '_' + im1num + '.jpg'
im2Path = dataLocation + folder + '/' + folder + '_' + im2num + '.jpg'
elif len(components) == 4:
trueFalseMat.append(0)
isTrue = 0
numFalse += 1
folder1 = components[0]
folder2 = components[2]
im1num = components[1].zfill(4)
im2num = components[3].zfill(4)
im1Path = dataLocation + folder1 + '/' + folder1 + '_' + im1num + '.jpg'
im2Path = dataLocation + folder2 + '/' + folder2 + '_' + im2num + '.jpg'
im1 = open(im1Path, 'rb').read()
im2 = open(im2Path, 'rb').read()
p1 = subprocess.Popen(["br","-algorithm", "FaceRecognition" ,"-compare", im1Path, im2Path], stdout=subprocess.PIPE)
score = float(p1.communicate()[0])
if score < -100:
score = -10
elif score > 100:
score = 10
p1.stdout.close()
scoreOutput = scoreOutput+im1Path+','+im2Path+','+ str(score) + str(isTrue) + '\n'
# templ1 = br.br_load_img(im1, len(im1))
# templ2 = br.br_load_img(im2, len(im2))
# query1 = br.br_enroll_template(templ1)
# query2 = br.br_enroll_template(templ2)
# score = br.br_compare_template_lists(query1,query2)
# br.br_pairwise_compare(query1,query2,1)
scoreMat.append(score)
print i
i+=1
medianScore = numpy.median(scoreMat)
minScore = min(scoreMat)
maxScore = max(scoreMat)
outScoreFile = open('match_scores.csv','a');
outScoreFile.write(scoreOutput)
DET = []
outString = 'Thresh, True Accept,False Accept,True Reject,False Reject\n'
print 'found all scores'
sortedScores = sorted(scoreMat)
arr = numpy.arange((minScore),(maxScore),((maxScore)-(minScore))/1000)
for x in numpy.arange((minScore),(maxScore),((maxScore)-(minScore))/1000):
trueAccept = 0.0
falseAccept = 0.0
falseReject = 0.0
trueReject = 0.0
for i in xrange(0,len(scoreMat)):
if scoreMat[i] < x and trueFalseMat[i] == 1:
falseReject += 1.0
elif scoreMat[i] >= x and trueFalseMat[i] == 0:
falseAccept += 1.0
elif scoreMat[i] >= x and trueFalseMat[i] == 1:
trueAccept += 1.0
elif scoreMat[i] < x and trueFalseMat[i] == 0:
trueReject += 1.0
falseAccept /= (len(scoreMat)+0.0)
falseReject /= (len(scoreMat)+0.0)
trueAccept /= (len(scoreMat)+0.0)
trueReject /= (len(scoreMat)+0.0)
outString = outString + str(x)+ ','+ str(trueAccept) + ',' + str(falseAccept) + ',' + str(trueReject) + ',' + str(falseReject)+'\n'
DET.append([falseAccept,falseReject])
print DET
outFile = open('DETPoints.csv','a');
outFile.write(outString)