def image_ready():
"""Same as image_raw but no face detection"""
try:
image_name = request.args.get('name')
if not os.path.exists(image_name):
return "Path doesn't exists"
img = cv2.imread(image_name)
frame = transform_frame(img)
embedding = get_feature(model, frame)
most_similar_embedding = check_against_embedding_db(yale_faces, embedding)
add_to_scores(most_similar_embedding)
except Exception as e:
print(e)
return "Exception: Not found"
return str(most_similar_embedding)
def add_to_db():
global detector_mtcnn
global yale_faces
try:
image_name = request.args.get('name')
if not os.path.exists(image_name):
return "Path doesn't exists"
img = cv2.imread(image_name)
img = dlib_hog_face_detection.get_frontal_dlib(img, detector, (112,112))
frame = transform_frame(img)
embedding = get_feature(model, frame)
yale_faces[image_name] = embedding
yale_faces_data[image_name] = img
except Exception as e:
print(e)
return "Failure"
return "Success"
def image_raw():
global yale_faces
try:
image_name = request.args.get('name')
if not os.path.exists(image_name):
return "Path doesn't exists"
img = cv2.imread(image_name)
img = dlib_hog_face_detection.get_frontal_dlib(img, detector, (112,112))
frame = transform_frame(img)
embedding = get_feature(model, frame)
most_similar_embedding = check_against_embedding_db(yale_faces, embedding)
add_to_scores(most_similar_embedding)
except Exception as e:
print(e)
return "Exception: Not found"
return str(most_similar_embedding)
def add_to_db_queue():
global q_send, q_return
try:
image_name = request.args.get('name')
if not os.path.exists(image_name):
return "Path doesn't exists"
q_send.put(image_name)
img = q_return.get()
if img is None:
return "Face detection failed"
frame = transform_frame(img)
embedding = get_feature(model, frame)
yale_faces[image_name] = embedding
yale_faces_data[image_name] = img
except Exception as e:
print(e)
return "Failure"
return "Success"
def image_raw_mtcnn():
global q_send, q_return
global yale_faces
try:
image_name = request.args.get('name')
if not os.path.exists(image_name):
return "Path doesn't exists"
q_send.put(image_name)
frame = q_return.get()
if frame is None:
return "Face detection failed"
frame = transform_frame(frame)
embedding = get_feature(model, frame)
most_similar_embedding = check_against_embedding_db(yale_faces, embedding)
add_to_scores(most_similar_embedding)
except Exception as e:
print(e)
return "Exception: Not found"
return str(most_similar_embedding)
def index():
video_name = request.args.get('name')
cap = cv2.VideoCapture(video_name)
most_likely = dict()
if not cap.isOpened():
print("Error opening video stream or file")
while(cap.isOpened()):
ret, frame = cap.read()
if ret:
frame = transform_frame(frame)
embedding = get_feature(model, frame)
most_similar_embedding = check_against_embedding_db(yale_faces, embedding)
if most_similar_embedding[0] in most_likely:
most_likely[most_similar_embedding[0]] += 1
else:
most_likely[most_similar_embedding[0]] = 1
else:
break
cap.release()
__import__('pprint').pprint(most_likely)
return "Sucess"