def subjectDelete(self, request, context):
''' Delete subjects from this service. '''
try:
start = time.time()
response = fsd.EnrollmentDeleteResponse()
gallery_name = request.gallery_name
subject_id = request.subject_id
#gallery_key = request.gallery_key
delete_count = self.gallery_worker.subjectDelete(
gallery_name, subject_id)
stop = time.time()
notes = "%d records deleted." % (delete_count)
global LOG_FORMAT
print((LOG_FORMAT % (pv.timestamp(), stop - start,
"subjectDelete()", notes, context.peer())))
response.delete_count = delete_count
return response
except:
traceback.print_exc()
raise
def echo(self, request, context):
try:
start = time.time()
#face_records = request.records
#worker_result = self.workers.apply_async(worker_score,[request])
#dist_mat = worker_result.get()
stop = time.time()
size = "?X?"
#speed = "? per second"
try:
size = "%dX%d" % (len(request.rows), len(request.rows[0].data))
#speed = "%0.1f per second"%(len(dist_mat.rows)*len(dist_mat.rows[0].data)/(stop-start))
except:
pass
notes = ""
notes += "Matrix %s" % (size)
global LOG_FORMAT
print((LOG_FORMAT % (pv.timestamp(), stop - start, "echo()", notes,
context.peer())))
return request
except:
traceback.print_exc()
raise
def galleryDelete(self, request, context):
''' Delete a gallery. '''
result = self.passthrough(inspect.currentframe().f_code.co_name,
request)
if result:
return Empty()
try:
start = time.time()
result = GalleryList()
gallery_name = request.gallery_name
deleted_faces = self.gallery_worker.deleteGallery(gallery_name)
stop = time.time()
notes = "%d faces removed." % (deleted_faces, )
global LOG_FORMAT
print((LOG_FORMAT % (pv.timestamp(), stop - start,
"galleryDelete()", notes, context.peer())))
return result
except:
traceback.print_exc()
raise
def extract(self, request, context):
''' Extracts faces from the image for the given face detections. '''
try:
start = time.time()
#display = False
img = pt.image_proto2np(request.image)
face_records = request.records
worker_result = self.workers.apply_async(worker_extract,
[img, face_records])
face_records_list = worker_result.get()
notes = "Image Size %s" % (img.shape, )
notes += ", Faces %s" % (len(face_records_list.face_records), )
stop = time.time()
global LOG_FORMAT
print((LOG_FORMAT % (pv.timestamp(), stop - start, "extract()",
notes, context.peer())))
return face_records_list
except:
traceback.print_exc()
raise
def galleryList(self, request, context):
''' List the galleries hosted by this service. '''
result = self.passthrough(inspect.currentframe().f_code.co_name,
request)
if result:
return result
try:
start = time.time()
result = GalleryList()
#gallery_name = request.enroll_gallery
count = 0
for gallery_name in self.gallery_worker.galleryNames():
item = result.galleries.add()
item.gallery_name = gallery_name
item.face_count = self.gallery_worker.size(gallery_name)
#print("Name:",gallery_name)
#print("Size:",len(GALLERIES[gallery_name]))
#print()
count += 1
stop = time.time()
notes = "%d galleries returned." % (count, )
global LOG_FORMAT
print((LOG_FORMAT % (pv.timestamp(), stop - start, "galleryList()",
notes, context.peer())))
return result
except:
traceback.print_exc()
raise
def detect(self, request, context):
''' Runs a face detector and return rectangles. '''
try:
start = time.time()
mat = pt.image_proto2np(request.image)
#skimage.io.imsave("test.png",mat)
options = request.detect_options
notes = "Image Size %s" % (mat.shape, )
# scale down for speed
for _ in range(options.downsample):
mat = cv2.pyrDown(mat)
worker_result = self.workers.apply_async(worker_detect,
[mat, options])
face_records_list = worker_result.get()
# scale up the results
for face in face_records_list.face_records:
for _ in range(options.downsample):
face.detection.location.x *= 2
face.detection.location.y *= 2
face.detection.location.width *= 2
face.detection.location.height *= 2
notes += ", Detections %s" % (len(
face_records_list.face_records), )
for face in face_records_list.face_records:
face.source = request.source
face.frame = request.frame
face.subject_id = request.subject_id
face.name = request.subject_name
#date = request.image.date
#time_ = request.image.time
#module = request.image.module
#camera = request.image.camera
#event = request.image.event
#image_num = request.image.image_num
stop = time.time()
global LOG_FORMAT
print(
(LOG_FORMAT % (pv.timestamp(), stop - start, "detect()", notes,
context.peer())))
#print (face_records_list)
return face_records_list
except:
traceback.print_exc()
raise
def enrollmentList(self, request, context):
''' List the faces enrolled in this '''
start = time.time()
gallery_name = request.gallery_name
result = self.gallery_worker.enrollmentList(gallery_name)
stop = time.time()
notes = "%d records returned." % (len(result.face_records), )
global LOG_FORMAT
print((LOG_FORMAT % (pv.timestamp(), stop - start, "enrollmentList()",
notes, context.peer())))
return result
def enroll(self, request, context):
''' Enrolls the faces in the gallery. '''
try:
start = time.time()
gallery_name = request.enroll_gallery
global GALLERIES, STORAGE
if gallery_name not in GALLERIES:
print("Creating gallery", gallery_name)
GALLERIES[gallery_name] = {}
path = os.path.join(self.gallery_storage, gallery_name + '.h5')
STORAGE[gallery_name] = h5py.File(path)
count = 0
for face in request.records.face_records:
face_id = faro.generateFaceId(face)
face.gallery_key = face_id
#face_id = "-".join(face_id.split('/'))
#print('face_id:',face_id)
count += 1
GALLERIES[gallery_name][face_id] = face
if face_id in STORAGE[gallery_name]:
del STORAGE[gallery_name][
face_id] # delete so it can be replaced.
STORAGE[gallery_name][face_id] = np.bytes_(
face.SerializeToString())
STORAGE[gallery_name].flush()
#response = fsd.ErrorMessage()
stop = time.time()
notes = "Enrolled %d faces into gallery '%s'. Gallery size = %d." % (
count, gallery_name, len(GALLERIES[gallery_name]))
global LOG_FORMAT
print((LOG_FORMAT %
(pv.timestamp(), stop - start, "enroll()", notes)))
return request.records
except:
traceback.print_exc()
raise
def detect(self, request, context):
''' Runs a face detector and return rectangles. '''
try:
start = time.time()
mat = pt.image_proto2np(request.image)
options = request.detect_options
notes = "Image Size %s" % (mat.shape, )
#print('time_check AA:',time.time()-start)
worker_result = self.workers.apply_async(worker_detect,
[mat, options])
#print('time_check BB:',time.time()-start)
face_records_list = worker_result.get()
notes += ", Detections %s" % (len(
face_records_list.face_records), )
#print('time_check CC:',time.time()-start)
for face in face_records_list.face_records:
face.source = request.source
face.frame = request.frame
face.subject_id = request.subject_id
face.name = request.subject_name
#date = request.image.date
#time_ = request.image.time
#module = request.image.module
#camera = request.image.camera
#event = request.image.event
#image_num = request.image.image_num
stop = time.time()
global LOG_FORMAT
print((LOG_FORMAT %
(pv.timestamp(), stop - start, "detect()", notes)))
#print (face_records_list)
return face_records_list
except:
traceback.print_exc()
raise
def status(self, request, context):
''' Returns the status of the service. '''
try:
start = time.time()
worker_result = self.workers.apply_async(worker_status, [])
status_message = worker_result.get()
status_message.worker_count = len(self.workers._pool)
# print('Status Request', '<',status_message,'>')
# print(context.peer())
notes = None
stop = time.time()
global LOG_FORMAT
print(
(LOG_FORMAT % (pv.timestamp(), stop - start, "status()", notes,
context.peer())))
return status_message
except:
traceback.print_exc()
raise
def enroll(self, request, context):
''' Enrolls the faces in the gallery. '''
result = self.passthrough(inspect.currentframe().f_code.co_name,
request)
if result:
return request.records
else:
try:
start = time.time()
gallery_name = request.enroll_gallery
replacements = 0
count = 0
for face in request.records.face_records:
face_id = faro.generateFaceId(face)
face.gallery_key = face_id
enrolled, replaced = self.gallery_worker.addFaceToGallery(
gallery_name, face_id, face)
count += enrolled
replacements += replaced
stop = time.time()
notes = "Enrolled %d faces into gallery '%s' with %d replacements. Gallery size = %d." % (
count, gallery_name, replacements,
self.gallery_worker.size(gallery_name))
global LOG_FORMAT
print((LOG_FORMAT % (pv.timestamp(), stop - start, "enroll()",
notes, context.peer())))
return request.records
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
'''
#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
