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 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()