def main_ui():
####################################
# Basic Info
# 처음 시작 UI
# Feature
# 3가지의 시나리오를 분리합니다.
# Todo
# Scene no.2, no.3 완성
# Advanced Usage를 위한 경우를 마련해야 합니다.
####################################
print("""
==================================================================
CHOOSE WHAT SCENARIO YOU WANT TO USE
1. Register Trainer
2. Get Advice about Exercise Captured from Video
3. Analyze Physical Data and Get Advice from it
------------------------------------------------------------------
FOR DEVELOPERS
4. Initialize DataBase (Init data folder in project root directory)
==================================================================
""")
main_option = int(input("Enter: "))
if main_option == 1:
trainer_ui()
elif main_option == 2:
diff_videos_ui()
elif main_option == 3:
physical_ui()
elif main_option == 4:
DB.initialize()
def __init__(self, user_id):
super().__init__()
self.setupUi(self)
self.show()
self.user_id = user_id
self.setuserinfo()
self.yourlist.clear()
self.comboBox.clear()
self.comparison.clear()
rows = DB.get_input_list(self.user_id)
if rows != []:
self.input_id_list = rows[:]
for row in rows:
self.yourlist.addItem(f'{row[0]}.{row[1]}')
rows = DB.get_user_list('standard')
if rows != []:
self.trainer_id_list = list(zip(*rows))[0]
for row in rows:
self.comboBox.addItem(f'{row[1]}')
# 무조건 트레이너 한명은 있다고 가정한다.
rows = DB.get_math_info_list(self.trainer_id_list[0])
if rows != None:
self.extraction_id_list = rows[:]
for row in rows:
self.comparison.addItem(f'{row[0]}.{row[1]}')
self.extraction_id = 0
self.input_id = 0
self.connectFunction()
def get_feedback(standard_id, exercise_id, input_video_loc):
# openpose를 통해서 분석
result_numpy = Method.parse_person(input_video_loc)
# 초기자세를 분석
skeleton = Method.find_initial_skeleton(result_numpy)
# 초기에 서 있는 자세를 찾을 수 없는 경우
if skeleton == False:
print("Error: Couldn't find initial pose. Find another one")
return False
# Load Math_info extraction info
rows = DB.get_math_info(extraction_id)
vectors = np.load(rows[0])
# Resizing
resized_numpy = Method.resize(exercise_id, skeleton, vectors)
# Issue : 운동 분석을 통한 보정
# sample, math_info = Method.analyze_exercise(result_numpy, exercise_id, skeleton)
# Issue: 운동 분석을 통한 보정 결과물도 얻을 수 있도록 수정
# if sample_numpy == False:
# print("Error: You can't use it")
# return False
# 파일이름으로 사용할 값 생성
file_name = id_generator() + '.npy'
# Issue: Store 구현
return True
def setuserinfo(self):
user = DB.get_user_info(self.user_id)
if user[0][0] == 'standard':
text = f'Hello, Trainer {user[0][1]}'
else:
text = f'Hello, {user[0][1]}'
self.userinfo.setText(text)
def change_extraction_list(self, idx):
trainer_id = self.trainer_id_list[idx]
self.comparison.clear()
rows = DB.get_math_info_list(trainer_id)
self.extraction_id_list = rows
if rows != None:
for row in rows:
self.comparison.addItem(f'{row[0]}.{row[1]}')
def __init__(self, user_id):
super().__init__()
self.setupUi(self)
self.show()
self.user_id = user_id
self.connectFunction()
rows = DB.get_input_list(self.user_id)
if rows != []:
self.input_id_list = rows[:]
for row in rows:
self.user.addItem(f'{row[0]}.{row[1]}')
rows = DB.load_data_list(self.user_id, 2)
if rows != []:
self.applied_trainer_list = rows[:]
for row in rows:
self.applied_trainer.addItem(f'{row[0]}.{row[1]}-{row[2]}')
self.input_id = 0
self.target_id = 0
def checkUnique(self):
is_unique = DB.checkUnique(self.username.text())
if is_unique == False:
self.user_unique.setText("Duplicated!")
self.user_unique.setStyleSheet('color: red')
self.conditions[0] = False
else:
self.user_unique.setText("Unique!")
self.user_unique.setStyleSheet('color: green')
self.conditions[0] = True
def __init__(self, user_id):
super().__init__()
self.setupUi(self)
font = QtGui.QFont()
font.setFamily("MS Gothic")
font.setPointSize(26)
self.user_id = user_id
self.comboBox.clear()
for name in DB.get_exercise_names():
self.comboBox.addItems(name)
self.show()
self.connectFunction()
def regist_trainer(trainer_id, exercise_id, input_video_loc):
# openpose를 통해서 분석
result_numpy = Method.parse_person(input_video_loc)
# 초기자세를 분석
skeleton = Method.find_initial_skeleton(result_numpy)
# 초기에 서 있는 자세를 찾을 수 없는 경우
if skeleton == False:
print("Error: Couldn't find initial pose. Find another one")
return False
# 보정 및 운동 분석
math_info = Method.analyze_exercise(result_numpy, exercise_id, skeleton)
# Issue: 운동 분석을 통한 보정 결과물도 얻을 수 있도록 수정
# if sample_numpy == False:
# print("Error: You can't use it")
# return False
if math_info == False:
print("Error: ") # Issue: math_info Extraction 오류 잡기
return False
# 파일이름으로 사용할 값 생성
file_name = id_generator() + '.npy'
# Store
sample_id = DB.save_sample(trainer_id, sample_numpy, file_name,
exercise_id)
skeleton_id = DB.save_skeleton(trainer_id, skeleton, file_name)
math_info_names = [
os.path.split(file_name)[0] + str(i) + os.path.split(file_name)[1]
for i in range(len(math_info))
]
extraction_id = DB.save_math_info_extraction(skeleton_id, exercise_id,
sample_id, math_info,
math_info_names)
return True
def __init__(self, user_id):
super().__init__()
self.setupUi(self)
self.show()
self.user_id = user_id
self.connectFunction()
self.setuserinfo()
rows = DB.get_input_list(self.user_id)
if rows != []:
for row in rows:
self.yourlist.addItem(f'{row[0]}.{row[1]}')
self.input_id_list = rows[:]
rows = DB.get_math_info_list(self.user_id)
if rows != []:
self.extraction_id_list = rows[:]
for row in rows:
self.registeredlist.addItem(f'{row[0]}.{row[1]}')
self.target_type = 0
self.target_id = 0
def physical_ui():
indent = " " * 4
# (exercise_name)
# (applied_sample_id, exercise_id, sample_location, user_name, applied_sample_location, trainer_name)
exercise_list = DB.get_exercise_names()
applied_samples = DB.load_applied_skeleton_list()
print(exercise_list)
print("""
==================================================================
CHOOSE EXERCISE DATA WHERE YOU WANT TO GET REPORT(USER)""")
for idx, row in enumerate(applied_samples):
print(indent + "%d. %s: %s by %s" %
(idx + 1, exercise_list[row[1] - 1][0], row[3], row[5]))
print(indent +
"""==================================================================
""")
option = int(input("Enter: "))
args = applied_samples[option]
ret_val = PoseSystem.Analyze_Physical_Data(*args)
if ret_val == True:
print("""
==================================================================
END OF ANALYZE PHYSICAL DATA PROCESS
RESULT : SUCCESS
==================================================================
""")
else:
print("""
==================================================================
END OF ANALYZE PHYSICAL DATA PROCESS
RESULT : FAIL
==================================================================
""")
return
def main_function(option, *args):
base_folder = 'temp'
if os.path.exists(base_folder):
shutil.rmtree(base_folder)
time.sleep(1)
os.mkdir(base_folder)
# args = (address_init, address_ex, user_id, exercise_id)
if option == 1:
# Usage - store blob data into table
# file_naming - ./temp/column_name+적절한 확장자
# Store file in temp
# insert_input_list(1, 0, "./temp/init_numpy.py", "./temp/init_video.avi", "./temp/exercise_numpy.py", "./temp/exercise_video.avi")
# delete temp folder
# Usage - read blob data from table
# make temp folder
# readBlobData(1, 1, 'temp')
print(args)
input_init = args[0]
input_exercise = args[1]
output_init_numpy = os.path.join(base_folder, 'init_numpy.npy')
output_init_video = os.path.join(base_folder, 'init_video.avi')
output_ex_numpy = os.path.join(base_folder, 'exercise_numpy.npy')
output_ex_video = os.path.join(base_folder, 'exercise_video.avi')
Method.parse_person(input_init, output_init_numpy, output_init_video)
Method.parse_person(input_exercise, output_ex_numpy, output_ex_video)
DB.insert_input_list(args[2], args[3], output_init_numpy,
output_init_video, output_ex_numpy,
output_ex_video)
elif option == 2:
DB.read_from_input_list(args[0], base_folder)
numpy = np.load(os.path.join(base_folder, 'init_numpy.npy'))
skeleton_numpy = 'skeleon.npy'
graph_numpy = 'graph.npy'
(res1, res2) = Method.find_initial_skeleton(numpy, base_folder,
args[1])
# print(res1, res2)
np.save(os.path.join(base_folder, skeleton_numpy), [res1, res2])
pk = DB.save_skeleton(args[0], os.path.join(base_folder,
skeleton_numpy),
os.path.join(base_folder, graph_numpy))
return (res1, res2, pk)
elif option == 3:
DB.read_from_input_list(args[1], base_folder)
numpy = np.load(os.path.join(base_folder, 'exercise_numpy.npy'))
(res1, res2) = Method.find_initial_skeleton(numpy, base_folder)
DB.load_skeleton(args[0], base_folder)
ex_type = 2
time.sleep(0.5)
numpy_array = np.load(os.path.join(base_folder, 'exercise_numpy.npy'))
skeleton = np.load(os.path.join(base_folder, 'skeleton.npy'))[0]
target_skeleton = skeleton
common = bc_common.Common()
accuracy, body_part = common.check_accuracy(numpy_array, ex_type, 0)
input_vector = common.calculate_trainer(ex_type, skeleton,
body_part[0], body_part[1])
resized = common.apply_vector(ex_type, target_skeleton, input_vector)
np.save(os.path.join(base_folder, 'math_info.npy'), input_vector)
np.save(os.path.join(base_folder, 'resized.npy'), resized)
screen = run.human_pic(resized,
os.path.join(base_folder, 'math_info.avi'))
DB.save_math_info_extraction(
args[0], os.path.join(base_folder, 'math_info.npy'),
os.path.join(base_folder, 'math_info.avi'))
elif option == 4:
exercise_id = DB.get_exercise_id(args[2])[0][0]
DB.load_skeleton(args[0], base_folder)
DB.load_math_info_extraction(args[1], base_folder)
ex_type = 2
time.sleep(0.5)
math_info = np.load(os.path.join(base_folder, 'math_info.npy'))
skeleton = np.load(os.path.join(base_folder, 'skeleton.npy'))[0]
common = bc_common.Common()
resized = common.apply_vector(ex_type, skeleton, math_info)
(res1, res2) = Method.find_initial_skeleton(resized, base_folder)
np.save(os.path.join(base_folder, 'resized.npy'), resized)
screen = run.human_pic(resized, os.path.join(base_folder,
'resized.avi'))
DB.save_applied_sample(args[0], args[1], exercise_id,
os.path.join(base_folder, 'resized.npy'),
os.path.join(base_folder, 'resized.avi'))
elif option == 5:
pass
# args = (input_id, sample_id)
elif option == 6:
video_name = os.path.join(base_folder, 'output.avi')
numpy_name = os.path.join(base_folder, 'graph.npy')
DB.load_applied_skeleton_file(args[1], base_folder)
DB.read_from_input_list(args[0], base_folder)
input1 = os.path.join(base_folder, 'upgraded.npy')
input2 = os.path.join(base_folder, 'exercise_numpy.npy')
run.Video(input1, input2, video_name)
DB.save_diff(args[1], args[0], video_name, numpy_name)
elif option == 7:
input2 = 'data/result.avi'
input1 = 'data/user/exercise/raw/output_video/result.avi'
selected = 'user'
file_names = [
'data/%s/exercise/exercise_left_elbow.npy' % (selected, ),
'data/%s/exercise/exercise_right_elbow.npy' % (selected, ),
'data/%s/exercise/exercise_left_knee.npy' % (selected, ),
'data/%s/exercise/exercise_right_knee.npy' % (selected, )
]
plot_titles = [
'left_elbow angle', "right_elbow angle", "left_knee angle",
"right_knee angle"
]
get_result.debugger(0,
isImage=False,
video=input1,
video2=input2,
file_name=file_names,
plot_title=plot_titles,
title1='pose difference algorithm',
title='original user exercise',
title2='graph data for main angle')
elif option == 8:
# DB.load_applied_skeleton_file(args[1], base_folder)
# DB.read_from_input_list(args[0], base_folder)
# input1 = np.load(os.path.join(base_folder, 'upgraded.npy'))
# input2 = np.load(os.path.join(base_folder, 'exercise_numpy.npy'))
user_info = DB.get_user_info_full(args[0])
other_info = DB.get_diff_info(args[2])
DB.load_skeleton(args[1], base_folder)
DB.load_diff(args[2], base_folder)
input = os.path.join(base_folder, 'graph.npy')
input2 = np.load(os.path.join(base_folder, 'skeleton.npy'))[0]
input3 = os.path.join(base_folder, 'skeleton.png')
info = user_info[0] + other_info[0]
run.make_skeleton_image(input2, input3, 2)
report.make_graph(input, base_folder)
paragraph = report.make_paragraph(input)
report.insert_image_and_pictures(info, paragraph)
elif option == 10:
PoseDifference.main_ui()
def change_ucs(self, i):
self.ids.clear()
self.data = []
for row in DB.load_data_list(self.user_id, i):
self.ids.addItem(str(row[0]) + "-" + row[1])
self.data.append(row)
def loading(self):
if self.cpt != None or self.cpt2 != None:
self.cpt.release()
self.cpt2.release()
self.play = False
index = self.ucs.currentIndex()
datas = self.data[self.ids.currentIndex()]
self.graph_title = self.graph_titles[index]
base_folder = 'temp'
if os.path.exists(base_folder):
shutil.rmtree(base_folder)
time.sleep(1)
os.mkdir(base_folder)
if index == 0:
# file load
DB.read_from_input_list(datas[2], base_folder)
DB.load_skeleton(datas[0], base_folder)
# video
self.origin_file = 'temp/init_video.avi'
self.copy_file = 'temp/init_video.avi'
elif index == 1:
# file load
DB.load_skeleton(datas[3], base_folder) # N
DB.load_math_info_extraction(datas[2], base_folder)
DB.load_applied_skeleton_file(datas[0], base_folder)
# video
self.origin_file = 'temp/math_info.avi'
self.copy_file = 'temp/upgraded.avi'
elif index == 2:
# file load
DB.read_from_input_list(datas[2], base_folder)
DB.load_diff(datas[0], base_folder)
# video
self.origin_file = 'temp/exercise_video.avi'
self.copy_file = 'temp/diff.avi'
elif index == 3:
# file load
DB.load_skeleton(datas[3], base_folder) # N
DB.read_from_input_list(datas[2], base_folder)
DB.load_math_info_extraction(datas[0], base_folder)
# video
self.origin_file = 'temp/exercise_video.avi'
self.copy_file = 'temp/math_info.avi'
self.graph()
self.Video()
def diff_videos_ui():
####################################
# Basic Info
# Scene No.2 유저가 비디오 분석을 받기 위한 UI이다.
# Console Based UI가 기본이고 INPUT에 대한 exception을 처리한다.
# Feature
# 유저로부터 Input을 받고 적절한 함수를 호출합니다.
# Todo
# 결과창 띄우기 (A+)
# 비디오 분석 건너 뛰기 옵션 적용 (A)
# Video 조건 영어로 번역
####################################
# For Print Format
indent = " " * 4
# Get the number of math_info for exercise
exercise_list = DB.get_exercise_list()
for idx, exercise in enumerate(exercise_list):
exercise_list[idx] = list(exercise) + [
len(DB.get_math_info_list_per_exercise(exercise[0]))
]
# Select Exercise
print("""
==================================================================
CHOOSE WHICH EXERCISE YOU WANT TO GET ADVICE FOR""")
for idx, row in enumerate(exercise_list):
print(indent + "%d. %s [%d]" % (idx + 1, row[1], row[2]))
print(indent +
"""==================================================================
""")
option = int(input("Enter: "))
if exercise_list[option - 1][2] == 0:
print("""
==================================================================
END OF GET ADVICE PROCESS
RESULT : FAIL
ERROR : Select Exercise Which Trainer Registered Before
==================================================================
""")
return
exercise_id = exercise_list[option - 1][0]
# Select Math Info
rows = DB.get_math_info_list_per_exercise(exercise_id)
print("""
==================================================================
CHOOSE TRAINER WHO YOU WANT TO GET ADVICE FROM""")
for idx, row in enumerate(rows):
print(indent + "%d. %s: %s" % (idx + 1, row[3], row[2]))
print(indent +
"""==================================================================
""")
option = int(input("Enter: "))
standard_id = rows[option - 1][0]
print("""
==================================================================
ENTER LOCATION OF INPUT VIDEO FILE (PLEASE CHECK VIDEO CONDITION)
1. Stand Upright Still at Least 1 Sec
2. 카메라로부터 운동하는 사람과의 거리는 해당 사람이 똑바로 서있을때와 동일해야 합니다.
3. The Maximum number of people in Video is 1
4. Cam should not Move
==================================================================
""")
input_video_loc = input("Enter (relative path): ")
ret_val = False
try:
if os.path.exists(input_video_loc):
ret_val = PoseSystem.get_feedback(standard_id, exercise_id,
input_video_loc)
else:
raise Exception()
except Exception:
traceback.print_exc()
return
if ret_val == True:
print("""
==================================================================
END OF GET ADVICE PROCESS
RESULT : SUCCESS
==================================================================
""")
else:
print("""
==================================================================
END OF GET ADVICE PROCESS
RESULT : FAIL
==================================================================
""")
return
def trainer_ui():
####################################
# Basic Info
# Scene No.1 트레이너 등록을 위한 UI 이다.
# Console Based UI가 기본이고 INPUT에 대한 exception을 처리한다.
# Feature
# 유저로부터 Input을 받고 적절한 함수를 호출합니다.
# Todo
# 결과창 띄우기 (A+)
# 비디오 분석을 건너뛰고 등록된 sample을 이용할 수 있는 옵션 추가(A)
# Exercise 추가 기능 (B)
# Video 조건 영어로 번역 (A)
####################################
# For Print Format
indent = " " * 4
# Select Exercise
rows = DB.get_user_list('standard')
rows.append([])
print("""
==================================================================
CHOOSE WHICH TRAINER YOU WANT TO REGISTER EXERCISE FOR""")
for idx, row in enumerate(rows):
if idx != len(rows) - 1:
print(indent + "%d. %s" % (idx + 1, row[1]))
else:
print(indent + "%d. Register New Trainer" % (idx + 1, ))
print(indent +
"""==================================================================
""")
option = int(input("Enter: "))
# Get PK for table of user_list
trainer_id = 0
if option == len(rows):
print("""
==================================================================
Register New Trainer
1. Name (English)
2. Weight (kg)
3. Stature (cm)
4. Other Info
5. Sex (men or women)
==================================================================
""")
# Regist New Trainer
register_form = ['standard']
try:
register_form.append(input("Enter Name: "))
register_form.append(int(input("Enter Weight(kg): ")))
register_form.append(int(input("Enter Height(cm): ")))
register_form.append(input("Enter Other Info: "))
register_form.append(input("Enter Sex: "))
if register_form[5] not in ('men', 'women', ''):
raise Exception()
except ValueError:
print('Please enter an integer')
return
except Exception:
print("Please enter men or women for your sex")
return
trainer_id = DB.create_user(*register_form)
else:
trainer_id = rows[option - 1][0]
# Get the number of Exercise for trainer
exercise_list = DB.get_exercise_list()
for idx, exercise in enumerate(exercise_list):
exercise_list[idx] = list(exercise) + [
len(
DB.get_registered_exercise_for_one_user(
trainer_id, exercise[0]))
]
# Select Exercise Type
print("""
==================================================================
CHOOSE WHICH EXERCISE YOU WANT TO REGISTER EXERCISE FOR""")
for idx, row in enumerate(exercise_list):
print(indent + "%d. %s [%d]" % (idx + 1, row[1], row[2]))
print(indent +
"""==================================================================
""")
option = int(input("Enter: "))
exercise_id = exercise_list[option - 1][0]
print("""
==================================================================
ENTER LOCATION OF INPUT VIDEO FILE (PLEASE CHECK VIDEO CONDITION)
1. Stand Upright Still at Least 1 Sec
2. 카메라로부터 운동하는 사람과의 거리는 해당 사람이 똑바로 서있을때와 동일해야 합니다.
3. The Maximum number of people in Video is 1
4. Cam should not Move
==================================================================
""")
input_video_loc = input("Enter (relative path): ")
ret_val = False
try:
if os.path.exists(input_video_loc):
ret_val = PoseSystem.regist_trainer(trainer_id, exercise_id,
input_video_loc)
else:
raise Exception()
except Exception:
traceback.print_exc()
return
if ret_val == True:
print("""
==================================================================
END OF TRAINER REGISTER PROCESS
RESULT : SUCCESS
==================================================================
""")
else:
print("""
==================================================================
END OF TRAINER REGISTER PROCESS
RESULT : FAIL
==================================================================
""")
return