def face_comp(str_relative_path,str_img_save):
str_query_top = "SELECT * FROM face_capture2 where realtive_path='" + str_relative_path + "' and " \
"is_proccessed=0 "
result_fetch_last_rec = db_opr.fetch_result(str_query_top)
for row_fetch_last_rec in result_fetch_last_rec:
comp_result = recognize.compare_dat_in_out(row_fetch_last_rec["id"])
print(comp_result)
if comp_result is False:
os.remove(str_img_save)
str_delete_query = "DELETE FROM face_capture2 where reative_path='" + str_relative_path + "'"
db_opr.execute_insert(str_delete_query)
print("GARBAGE REMOVED")
def train():
str_reinit = "UPDATE person_details2 set data_set=0"
db_opr.execute_insert(str_reinit)
global train_face_encode_list
query_fetch_faces = "SELECT * from person_details2 where is_train=1"
result_fetch_faces = db_opr.fetch_result(query_fetch_faces)
for row_fetch_faces in result_fetch_faces:
train_face_fetch = None
train_image = None
train_face_encode = None
print(row_fetch_faces)
# person_face_name,train_face_encode = fast_encode(row_fetch_faces)
with ThreadPoolExecutor(max_workers=3) as executor:
data = executor.submit(fast_encode, row_fetch_faces)
print("ALL ENCODINGS DONE")
with open('DATASET/dataset_faces_full.dat', 'wb') as f:
pickle.dump(train_face_encode_list, f)
def train_stud():
str_reinit = "UPDATE student set data_set=0"
db_opr.execute_insert(str_reinit)
global train_face_encode_list
query_fetch_faces = "SELECT * from student where is_train=1"
result_fetch_faces = db_opr.fetch_result(query_fetch_faces)
print("DB DONE")
for row_fetch_faces in result_fetch_faces:
train_face_fetch = None
train_image = None
train_face_encode = None
print(row_fetch_faces)
person_face_name = str(row_fetch_faces["id"])
train_face_fetch = str(config_obj.str_views_dir) + str("\\assets\\uploads\\files\\") + str(
row_fetch_faces["img_file_path"])
train_image = face_recognition.load_image_file(train_face_fetch)
train_face_encode = face_recognition.face_encodings(train_image)[0]
train_face_encode_list[person_face_name] = train_face_encode
str_query_update = "UPDATE student set data_set=1 where id='" + person_face_name + ""
db_opr.execute_insert(str_query_update)
with open('TRAINDATASET/dataset_faces_full_students.dat', 'wb') as f:
pickle.dump(train_face_encode_list, f)
# loop over the face detections
for i, d in enumerate(face_rects):
x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
crop = overlay[d.top():d.bottom(), d.left():d.right()]
face_found(crop,0)
draw_border(overlay, (x1, y1), (x2, y2), (162, 255, 0), 2, 10, 10)
# make semi-transparent bounding box
cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
# show the frame
cv2.imshow(frame_title, output)
key = cv2.waitKey(1)
# press q to break out of the loop
if key == ord("q"):
break
# cleanup
cv2.destroyAllWindows()
stream.release()
if __name__ == '__main__':
from multiprocessing import Process
str_query_camera_master = "SELECT * FROM camera_master"
result_camera_master = db_opr.fetch_result(str_query_camera_master)
processes = []
count = 0
for row_camera_master in result_camera_master:
if row_camera_master is not None:
processes.append(Process(target=main, args=(row_camera_master['id'], row_camera_master['source_URL'],row_camera_master['cam_nam'])).start())