def echo(self, mat):
'''
'''
request = pt.matrix_np2proto(mat)
# Run the computation on the server
dist_mat = self.service_stub.echo(request, None)
return pt.matrix_proto2np(dist_mat)
def score(self, probe, gallery):
'''
'''
request = fsd.ScoreRequest()
# Copy the templates into the request
for face_rec in probe:
request.template_probes.templates.add().CopyFrom(face_rec.template)
for face_rec in gallery:
request.template_gallery.templates.add().CopyFrom(
face_rec.template)
# Run the computation on the server
dist_mat = self.service_stub.score(request, None)
return pt.matrix_proto2np(dist_mat)
def generateMatchDistribution(self, gallery_name):
request = fsd.EnrollmentListRequest()
request.gallery_name = gallery_name
dist_mat = self.service_stub.generateMatchDistribution(request)
return pt.matrix_proto2np(dist_mat)
def search(self, request, context):
'''
Search a gallery for a face.
request - should be a SearchRequest protobuf.
returns: SearchResponse protobuf
'''
#global STORAGE
try:
start = time.time()
#result = fsd.SearchResponse()
#result.probes.CopyFrom(request.probes)
# Collect the options
search_gallery = request.search_gallery
probes = request.probes
max_results = request.max_results
threshold = request.threshold
matched = 0
if len(probes.face_records
) > 0: # if there are no probes then skip the search
if self.gallery_worker.isSearchable():
self.gallery_worker.generateIndex(search_gallery)
probes = self.gallery_worker.search(
search_gallery, probes, max_results, threshold)
else:
gallery = self.gallery_worker.getAllFaceRecords(
search_gallery)
score_request = fsd.ScoreRequest()
score_request.face_probes.CopyFrom(probes)
score_request.face_gallery.CopyFrom(gallery)
# Compute the scores matrix
scores = self.score(score_request, context)
scores = pt.matrix_proto2np(scores)
for p in range(scores.shape[0]):
#probe = probes.face_records[p]
#out = result.probes.face_records[p].search_results
matches = []
for g in range(scores.shape[1]):
score = scores[p, g]
if score > threshold:
continue
matches.append([score, gallery.face_records[g]])
matches.sort(key=lambda x: x[0])
if max_results > 0:
matches = matches[:max_results]
matched += 1
for score, face in matches:
probes.face_records[
p].search_results.face_records.add().CopyFrom(
face)
probes.face_records[p].search_results.face_records[
-1].score = score
# Count the matches
count = len(probes.face_records)
# Compute speed
stop = time.time()
gcount = self.gallery_worker.size(search_gallery)
total_faces = count * gcount
speed = total_faces / (stop - start)
notes = "Processed %d probes. Searched %0.1f faces per second." % (
count, speed)
print(
(LOG_FORMAT % (pv.timestamp(), stop - start, "search()", notes,
context.peer())))
return probes
except:
traceback.print_exc()
raise
def search(self, request, context):
'''
Search a gallery for a face.
request - should be a SearchRequest protobuf.
returns: SearchResponse protobuf
'''
try:
start = time.time()
#result = fsd.SearchResponse()
#result.probes.CopyFrom(request.probes)
# Collect the options
search_gallery = request.search_gallery
probes = request.probes
max_results = request.max_results
threshold = request.threshold
if search_gallery not in GALLERIES:
raise ValueError("Unknown gallery: " + search_gallery)
gallery = fsd.FaceRecordList()
for key in GALLERIES[search_gallery]:
face = GALLERIES[search_gallery][key]
gallery.face_records.add().CopyFrom(face)
score_request = fsd.ScoreRequest()
score_request.face_probes.CopyFrom(probes)
score_request.face_gallery.CopyFrom(gallery)
# Compute the scores matrix
scores = self.score(score_request, None)
scores = pt.matrix_proto2np(scores)
matched = 0
for p in range(scores.shape[0]):
#probe = probes.face_records[p]
#out = result.probes.face_records[p].search_results
matches = []
for g in range(scores.shape[1]):
score = scores[p, g]
if score > threshold:
continue
matches.append([score, gallery.face_records[g]])
matches.sort(key=lambda x: x[0])
if max_results > 0:
matches = matches[:max_results]
matched += 1
for score, face in matches:
probes.face_records[p].search_results.face_records.add(
).CopyFrom(face)
probes.face_records[p].search_results.face_records[
-1].score = score
# Count the matches
count = len(probes.face_records)
notes = "Processed %d probes. %d matched." % (count, matched)
stop = time.time()
print((LOG_FORMAT %
(pv.timestamp(), stop - start, "search()", notes)))
return probes
except:
traceback.print_exc()
raise
def search(self, request, context):
'''
Search a gallery for a face.
request - should be a SearchRequest protobuf.
returns: SearchResponse protobuf
'''
try:
start = time.time()
result = fsd.SearchResponse()
#print('search',request)
name = request.gallery_name
probes = request.probes
max_results = request.max_results
threshold = request.threshold
if name not in GALLERIES:
raise ValueError("Unknown gallery: "+name)
gallery = fsd.FaceRecordList()
for key in GALLERIES[name]:
face = GALLERIES[name][key]
gallery.face_records.add().CopyFrom(face)
#print('gallery size',len(gallery.face_records))
score_request = fsd.ScoreRequest()
score_request.face_probes.CopyFrom(probes)
score_request.face_gallery.CopyFrom(gallery)
scores = self.score(score_request,None)
#print('-------------- scores ------------')
#print(threshold)
#print(scores)
scores = pt.matrix_proto2np(scores)
for row in range(scores.shape[0]):
out = result.matches.add()
matches = []
for col in range(scores.shape[1]):
score = scores[row,col]
if score > threshold:
continue
matches.append( [ score, gallery.face_records[col] ] )
matches.sort(key=lambda x: x[0])
#print('matches',matches)
for score,face in matches:
out.face_records.add().CopyFrom(face)
out.scores.append(score)
#for each in request.face_records:
# self.galleries[name].addTemplate( each )
notes = "Returned %d Results"%(len(result.matches))
stop = time.time()
print(( LOG_FORMAT%(pv.timestamp(),stop-start,"search()",notes)))
return result
except:
traceback.print_exc()
raise
