def pushUpAnalysis(folder_path):
'''Open folder and process .json into a dict
input: The file path
ouput: The times of push-up
Judge by tendency!
'''
files = os.listdir(folder_path)
results = []
tendency = []
result = {}
r_elbow_angle_list = []
r_knee_angle_list = []
hip_angle_list = []
# l_elbow_angle_list = []
tick = []
cnt = 0
for i, file in enumerate(files):
coor = jp.getJson(folder_path + '\\' + file)
if not coor:
continue
r_elbow_angle = getValue.getElbowAngle(coor, 'R')
r_knee_angle = getValue.getKneeAngle(coor, 'R')
hip_angle = getValue.getHipAngle(coor, 'R')
if hip_angle:
hip_angle_list.append(hip_angle)
if r_knee_angle:
r_knee_angle_list.append(r_knee_angle)
if r_elbow_angle:
r_elbow_angle_list.append(r_elbow_angle)
tick.append(r_elbow_angle)
if len(tick) == 5:
tend = analysis.getTendency(tick, 20) # One tick
tick = []
if tend:
tendency.append(tend)
if 3 <= len(tendency):
if tendency[-1] == 'down' or tendency[-1] == 'stable':
if tendency[-2] == 'upper' and tendency[
-3] == 'upper': # a period
cnt += 1
result['Num'] = cnt
standard = analysis.pushUpPeriodJudge(
r_elbow_angle_list, hip_angle_list,
r_knee_angle_list)
result['IsRElbowStandard'], result[
'IsHipAngleStandard'], result[
'IsRKneeStandard'] = standard
result['Flag'] = i
r_elbow_angle_list = r_knee_angle_list = hip_angle_list = []
results.append(result)
result = {}
# print(tendency)
return results
def getPushUpInfos(folder_path, is_print, region=[]):
'''Open folder and process .json into a dict
input: The file path
ouput: The analysis of some infos of the whole process
'''
files = os.listdir(folder_path)
max_r_elbow_angle = 0
min_r_elbow_angle = 200
max_knee_angle = 0
min_knee_angle = 200
max_hip_angle = 0
min_hip_angle = 10000
max_hip_distance = 0
min_hip_distance = 10000
cnt = 0
cnt1 = total_elbow_angle = cnt2 = total_hip_distance = 0
cnt3 = total_knee_angle = cnt4 = total_hip_angle = 0
elb_ang = []
hip_dis = []
knee_ang = []
hip_ang = []
for i, file in enumerate(files):
coor = jp.getJson(folder_path + '\\' + file)
if not coor:
continue
cnt = i
r_elbow_angle = getValue.getElbowAngle(coor, 'R')
r_knee_angle = getValue.getKneeAngle(coor, 'R')
hip_angle = getValue.getHipAngle(coor, 'R')
hip_distance = getValue.getHipDistance(coor, 'R')
if r_elbow_angle:
elb_ang.append(r_elbow_angle)
total_elbow_angle += r_elbow_angle
cnt1 += 1
max_r_elbow_angle = max(max_r_elbow_angle, r_elbow_angle)
min_r_elbow_angle = min(min_r_elbow_angle, r_elbow_angle)
else:
elb_ang.append(0)
if hip_distance:
hip_dis.append(hip_distance)
total_hip_distance += hip_distance
cnt2 += 1
max_hip_distance = max(max_hip_distance, hip_distance)
min_hip_distance = min(min_hip_distance, hip_distance)
else:
hip_dis.append(0)
if r_knee_angle:
knee_ang.append(r_knee_angle)
total_knee_angle += r_knee_angle
cnt3 += 1
max_knee_angle = max(max_knee_angle, r_knee_angle)
min_knee_angle = min(min_knee_angle, r_knee_angle)
else:
knee_ang.append(0)
if hip_angle:
hip_ang.append(hip_angle)
total_hip_angle += hip_angle
cnt4 += 1
max_hip_angle = max(max_hip_angle, hip_angle)
min_hip_angle = min(min_hip_angle, hip_angle)
else:
hip_ang.append(0)
aver_r_elbow_angle = total_elbow_angle / cnt1
aver_hip_distance = total_hip_distance / cnt2
aver_knee_angle = total_knee_angle / cnt3
aver_hip_angle = total_hip_angle / cnt4
if is_print:
print('max elbow angle:', max_r_elbow_angle)
print('min elbow angle:', min_r_elbow_angle)
print('aver elbow angle:', aver_r_elbow_angle)
print('max hip distance:', max_hip_distance)
print('min hip distance:', min_hip_distance)
print('aver hip distance:', aver_hip_distance)
print('max knee angle:', max_knee_angle)
print('min knee angle:', min_knee_angle)
print('aver knee angle:', aver_knee_angle)
print('max hip angle:', max_hip_angle)
print('min hip angle:', min_hip_angle)
print('aver hip angle:', aver_hip_angle)
elb_ang_below_105 = []
for ans in elb_ang:
if ans < 105:
elb_ang_below_105.append(ans)
else:
elb_ang_below_105.append(None)
cnt += 1
ax1 = plt.subplot(2, 2, 1)
# plt.hlines(165, )
# ax1.scatter(range(cnt1), elb_ang, label='elbow angle', s=10)
# ax1.scatter(range(cnt1), elb_ang_below_105, label='elbow angle', color='r', s=10)
# actu = [89, 149, 238, 328, 418, 597, 686, 746, 836, 955, 985]
# region = 0
ax1.scatter(range(cnt), elb_ang, label='elbow angle', s=2)
ax1.scatter(range(cnt), elb_ang_below_105, label='elbow angle', color='r', s=2)
ax1.hlines(165, 0, cnt, colors='c')
ax1.vlines(region, 0, 180)
# ax1.vlines(actu, 0, 180, colors='r')
ax1.set_title('elbow_angle')
ax2 = plt.subplot(2, 2, 2)
ax2.scatter(range(cnt), hip_dis, label='hip distance', s=2)
ax2.set_title('hip_distance')
ax3 = plt.subplot(2, 2, 3)
ax3.scatter(range(cnt), knee_ang, label='knee angle', s=2)
ax3.vlines(region, 0, 180)
# ax3.vlines(actu, 0, 180, colors='r')
ax3.set_title('knee_angle')
ax4 = plt.subplot(2, 2, 4)
ax4.scatter(range(cnt), hip_ang, label='hip', s=2)
ax4.vlines(region, 0, 180)
# ax4.vlines(actu, 0, 180, colors='r')
ax4.set_title('hip_angle')
plt.show()
def getSitUpInfos(folder_path, is_print, region=[]):
'''Open folder and process .json into a dict
input: The file path
ouput: The analysis of some infos of the whole process
'''
files = os.listdir(folder_path)
max_r_waist_angle = 0
min_r_waist_angle = 200
max_r_s_knee_angle = 0
min_r_s_knee_angle = 200
s_knee_ang = []
wai_ang = []
cnt = 0
cnt1 = cnt2 = 0
cnt3 = 0
total_waist_angle = total_s_knee_angle = 0
for i, file in enumerate(files):
coor = jp.getJson(folder_path + '\\' + file)
if not coor:
continue
cnt = i
r_s_knee_angle = getValue.getKneeAngle(coor, 'R')
r_waist_angle = getValue.getWaistAngle(coor, 'R')
if r_s_knee_angle:
s_knee_ang.append(r_s_knee_angle)
total_s_knee_angle += r_s_knee_angle
cnt1 += 1
max_r_s_knee_angle = max(max_r_s_knee_angle, r_s_knee_angle)
min_r_s_knee_angle = min(min_r_s_knee_angle, r_s_knee_angle)
else:
s_knee_ang.append(0)
if r_waist_angle:
wai_ang.append(r_waist_angle)
total_waist_angle += r_waist_angle
cnt2 += 1
max_r_waist_angle = max(max_r_waist_angle, r_waist_angle)
min_r_waist_angle = min(min_r_waist_angle, r_waist_angle)
else:
wai_ang.append(0)
aver_r_s_knee_angle = total_s_knee_angle / cnt1
aver_r_waist_angle = total_waist_angle / cnt2
if is_print:
print('max sit-up knee angle:', max_r_s_knee_angle)
print('min sit-up knee angle:', min_r_s_knee_angle)
print('aver sit-up knee angle:', aver_r_s_knee_angle)
print('max sit-up waist angle:', max_r_waist_angle)
print('min sit-up waist angle:', min_r_waist_angle)
print('aver sit-up waist angle:', aver_r_waist_angle)
s_knee_ang_below_60 = []
for ans in s_knee_ang:
if ans < 105:
s_knee_ang_below_60.append(ans)
else:
s_knee_ang_below_60.append(None)
cnt += 1
wai_ang_above_140 = []
for wans in s_knee_ang:
if wans > 140:
wai_ang_above_140.append(wans)
else:
wai_ang_above_140.append(None)
cnt3 += 1
cnt = 0
for i in s_knee_ang:
if s_knee_ang[i] is not None:
cnt += 1
return cnt
ax1 = plt.subplot(1, 2, 1) # 第一行的左图 第一个参数代表子图的行数，第二个参数代表该行图像的列数，第三个参数代表每行的第几个图像
# plt.hlines(165, )
# ax1.scatter(range(cnt1), elb_ang, label='elbow angle', s=10)
# ax1.scatter(range(cnt1), elb_ang_below_105, label='elbow angle', color='r', s=10)
# actu = [89, 149, 238, 328, 418, 597, 686, 746, 836, 955, 985]
# region = 0
ax1.scatter(range(cnt), s_knee_ang, label='s_knee angle', s=2) # range(cnt)表示绘制散点图的数据点，s_knee_ang:图名，s:一个实数？
ax1.scatter(range(cnt), s_knee_ang_below_60, label='s_knee_angle', color='r', s=2) # color表示颜色
ax1.hlines(70, 0, cnt, colors='c') # 绘制水平辅助线 70：横坐标，0：ymin,cnt: ymax(辅助线纵坐标的最大最小值) colors:颜色
ax1.vlines(region, 0, 180) # 绘制竖直辅助线
# ax1.vlines(actu, 0, 180, colors='r')
ax1.set_title('s_knee_angle')
ax2 = plt.subplot(1, 2, 2)
ax2.scatter(range(cnt), wai_ang, label='waist angle', s=2)
ax1.scatter(range(cnt), wai_ang_above_140, label='waist_angle', color='r', s=2) # color表示颜色
ax1.hlines(140, 0, cnt, colors='c') # 绘制水平辅助线 140：横坐标，0：ymin,cnt: ymax(辅助线纵坐标的最大最小值) colors:颜色
ax2.set_title('waist_angle')
#fig, ax2 = plt.subplots(figsize=(12, 12))
#ax2.imshow(ax2, aspect='waist_angle') # im是要显示的图案
plt.savefig(r"C:\Users\Lenovo\Desktop\雏燕2019\后端部分所需资料\sit-up.png")
plt.show()
def sitUpDetect(length):
'''Open folder and process .json into a dict
input: The file path
ouput: The times of push-up
'''
params = dict()
params["model_folder"] = r"E:\Programming\Openpose\openpose\openposePython\models"
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# get video from webcam or video
# start = time.time()
cap_side = cv2.VideoCapture(0)
cap_front = cv2.VideoCapture(1)
# cap2 = cv2.VideoCapture(r'E:\University\科研创新\雏燕计划-体测\体测姿势素材\push-up\push-up-test-1.mp4')
# coorList = []
results = []
tendency = []
tick = []
result = {}
r_waist_angle_list = []
l_waist_angle_list = []
r_s_knee_angle_list = []
l_s_knee_angle_list = []
r_elbowtoneck_dist_list = []
l_elbowtoneck_dist_list = []
total_r_elbowtoneck_dist = 0
total_l_elbowtoneck_dist = 0
cnt = cnt_1 = cnt_2 = 0
sitUp_cnt = 0
start_time = datetime.now()
while True:
# Get images from cam
ret, imageToProcessFront = cap_front.read()
ret, imageToProcessSide = cap_side.read()
cv2.imshow('front', imageToProcessFront)
cv2.imshow('side', imageToProcessSide)
cur_time = datetime.now()
delta_time = (cur_time - start_time).seconds
print('\r倒计时：' + length - delta_time + '秒', end='')
if delta_time == length:
break
if cnt % 2 == 0:
datum1 = op.Datum()
datum2 = op.Datum()
datum1.cvInputData = imageToProcessFront
datum2.cvInputData = imageToProcessSide
opWrapper.emplaceAndPop([datum1])
opWrapper.emplaceAndPop([datum2])
if datum1.poseKeypoints.size != 1 and datum2.poseKeypoints.size != 1:
coor_front = kpp.getKeyPoints(datum1.poseKeypoints[0]) # 记得改参数
coor_side = kpp.getKeyPoints(datum2.poseKeypoints[0])
r_waist_angle = getValue.getWaistAngle(coor_side, 'R')
l_waist_angle = getValue.getWaistAngle(coor_side, 'L')
r_s_knee_angle = getValue.getKneeAngle(coor_side, 'R')
l_s_knee_angle = getValue.getKneeAngle(coor_side, 'L')
r_elbowtoneck_dist = getValue.getElbowToNeckDistance(coor_front, 'R')
l_elbowtoneck_dist = getValue.getElbowToNeckDistance(coor_front, 'L')
if l_waist_angle:
l_waist_angle_list.append(r_waist_angle)
tick.append(l_waist_angle)
# if l_waist_angle:
# l_waist_angle_list.append(l_waist_angle)
if r_s_knee_angle:
r_s_knee_angle_list.append(r_s_knee_angle)
if l_s_knee_angle:
l_s_knee_angle_list.append(l_s_knee_angle)
if r_elbowtoneck_dist:
r_elbowtoneck_dist_list.append(r_elbowtoneck_dist)
cnt_1 += 1
total_r_elbowtoneck_dist += r_elbowtoneck_dist
aver_r_elbowtoneck_dist = total_r_elbowtoneck_dist / cnt_1
if l_elbowtoneck_dist:
l_elbowtoneck_dist_list.append(l_elbowtoneck_dist)
cnt_2 += 1
total_l_elbowtoneck_dist += l_elbowtoneck_dist
aver_l_elbowtoneck_dist = total_l_elbowtoneck_dist / cnt_2
if len(tick) == 5:
tend = analysis.getTendency(tick, 8) # One tick
tick = []
if tend:
tendency.append(tend)
if 3 <= len(tendency):
if tendency[-1] == 'down':
if tendency[-2] == 'upper': # a period and tendency[-3] == 'upper'
sitUp_cnt += 1
result['Num'] = cnt
standard = analysis.sitUpPeriodJudge(
r_waist_angle_list, l_waist_angle_list,
r_s_knee_angle_list, l_s_knee_angle_list,
r_elbowtoneck_dist_list,
l_elbowtoneck_dist_list,
aver_r_elbowtoneck_dist,
aver_l_elbowtoneck_dist)
result['IsRWaistStandard'], result[
'IsLWaistStandard'], result[
'IsRKneeAngle'], result[
'IsLKneeAngle'], result[
'IsRElbowtoNeckStandard'], result[
'IsLElbowtoNeckStandard'], total = standard
# result['Flag'] = i
r_waist_angle_list = l_waist_angle_list = []
r_s_knee_angle_list = l_s_knee_angle_list = []
r_elbowtoneck_dist_list = l_elbowtoneck_dist_list = [] # 序列置空
cnt_1 = cnt_2 = 0
results.append(result)
print(result)
db.ret(delta_time, total, sitUp_cnt, 'sitUp', results, True)
result = {}
cnt += 1
cv2.imshow("OpenPose 1.5.1 - Tutorial Python API", datum1.cvOutputData)
cv2.imshow("OpenPose 1.5.1 - Tutorial Python API - front", datum2.cvOutputData)
if cv2.waitKey(1) == ord('q'):
break
db.ret(None, None, None, None, None, False)
def sitUpAnalysis(folder_path):
'''Open folder and process .json into a dict
input: The file path
ouput: The times of push-up
'''
files = os.listdir(folder_path)
results = []
tendency = []
tick = []
result = {}
r_waist_angle_list = []
l_waist_angle_list = []
r_s_knee_angle_list = []
l_s_knee_angle_list = []
r_elbowtoneck_dist_list = []
l_elbowtoneck_dist_list = []
total_r_elbowtoneck_dist = 0
total_l_elbowtoneck_dist = 0
cnt = cnt_1 = cnt_2 = 0
for i, file in enumerate(files):
coor = jp.getJson(folder_path + '\\' + file)
if not coor:
continue
r_waist_angle = getValue.getWaistAngle(coor, 'R')
l_waist_angle = getValue.getWaistAngle(coor, 'L')
r_s_knee_angle = getValue.getKneeAngle(coor, 'R')
l_s_knee_angle = getValue.getKneeAngle(coor, 'L')
r_elbowtoneck_dist = getValue.getElbowToNeckDistance(coor, 'R')
l_elbowtoneck_dist = getValue.getElbowToNeckDistance(coor, 'L')
tick.append(r_waist_angle)
if r_waist_angle:
r_waist_angle_list.append(r_waist_angle)
if l_waist_angle:
l_waist_angle_list.append(l_waist_angle)
if r_s_knee_angle:
r_s_knee_angle_list.append(r_s_knee_angle)
if l_s_knee_angle:
l_s_knee_angle_list.append(l_s_knee_angle)
if r_elbowtoneck_dist:
r_elbowtoneck_dist_list.append(r_elbowtoneck_dist)
cnt_1 += 1
total_r_elbowtoneck_dist += r_elbowtoneck_dist
aver_r_elbowtoneck_dist = total_r_elbowtoneck_dist / cnt_1
if l_elbowtoneck_dist:
l_elbowtoneck_dist_list.append(l_elbowtoneck_dist)
cnt_2 += 1
total_l_elbowtoneck_dist += l_elbowtoneck_dist
aver_l_elbowtoneck_dist = total_l_elbowtoneck_dist / cnt_2
if len(tick) == 5:
tend = analysis.getTendency(tick, 8) # One tick
tick = []
if tend:
tendency.append(tend)
if 3 <= len(tendency):
if tendency[-1] == 'down':
if tendency[
-2] == 'upper': # a period and tendency[-3] == 'upper'
cnt += 1
result['Num'] = cnt
standard = analysis.SitUpPeriodJudge(
r_waist_angle_list, l_waist_angle_list,
r_s_knee_angle_list, l_s_knee_angle_list,
r_elbowtoneck_dist_list,
l_elbowtoneck_dist_list,
aver_r_elbowtoneck_dist,
aver_l_elbowtoneck_dist)
result['IsRWaistStandard'], result[
'IsLWaistStandard'] = standard
result['IsRKneeAngle'], result[
'IsLKneeAngle'] = standard
result['IsRElbowtoNeckStandard'], result[
'IsLElbowtoNeckStandard'] = standard
result['Flag'] = i
r_waist_angle_list = l_waist_angle_list = []
r_s_knee_angle_list = l_s_knee_angle_list = []
r_elbowtoneck_dist_list = l_elbowtoneck_dist_list = [
] #序列置空
results.append(result)
result = {}
print(tendency)
return results
def pushUpDetect(length):
# Process
cnt = 0
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# get video from webcam or video
start = time.time()
cap_side = cv2.VideoCapture(0)
cap_front = cv2.VideoCapture(1)
# cap2 = cv2.VideoCapture(r'E:\University\科研创新\雏燕计划-体测\体测姿势素材\push-up\push-up-test-1.mp4')
coorList = []
results = []
tendency = []
result = {}
r_elbow_angle_list = []
l_elbow_angle_list = []
l_knee_angle_list = []
hip_angle_list = []
hip_distance_list = []
tick = []
pushUpCnt = 0
start_time = datetime.now()
while True:
# Get images from cam
ret, imageToProcessFront = cap_front.read()
ret, imageToProcessSide = cap_side.read()
cv2.imshow('front', imageToProcessFront)
cv2.imshow('side', imageToProcessSide)
cnt = 0
cur_time = datetime.now()
delta_time = (cur_time - start_time).seconds
print('\r倒计时：' + length - delta_time + '秒', end='')
if delta_time == length:
break
if cnt % 2 == 0:
datum_front = op.Datum()
datum_side = op.Datum()
datum_front.cvInputData = imageToProcessFront
datum_side.cvInputData = imageToProcessSide
opWrapper.emplaceAndPop([datum_front])
opWrapper.emplaceAndPop([datum_side])
# print("Body keypoints:")
if datum_front.poseKeypoints.size != 1 and datum_side.poseKeypoints.size != 1:
coor_front = kpp.getKeyPoints(
datum_front.poseKeypoints[0]) # 记得改参数
coor_side = kpp.getKeyPoints(datum_side.poseKeypoints[0])
r_elbow_angle = getValue.getElbowAngle(coor_front, 'R')
l_elbow_angle = getValue.getElbowAngle(coor_front, 'L')
l_knee_angle = getValue.getKneeAngle(coor_side, 'L')
hip_angle = getValue.getHipAngle(coor_side, 'L')
hip_distance = getValue.getHipDistance(coor_side, 'L')
if r_elbow_angle:
r_elbow_angle_list.append(r_elbow_angle)
tick.append(r_elbow_angle)
if hip_angle:
hip_angle_list.append(hip_angle)
if l_knee_angle:
l_knee_angle_list.append(l_knee_angle)
if l_elbow_angle:
l_elbow_angle_list.append(l_elbow_angle)
if hip_distance:
hip_distance_list.append(hip_distance)
if len(tick) == 5:
tend = analysis.getTendency(tick, 20) # One tick
print(tend)
tick = []
if tend:
tendency.append(tend)
if 3 <= len(tendency):
if tendency[-1] == 'down' or tendency[
-1] == 'stable':
if tendency[
-2] == 'upper': # a period and tendency[-3] == 'upper'
pushUpCnt += 1
result['Num'] = pushUpCnt
standard = analysis.pushUpPeriodJudgeTwoSides(
r_elbow_angle_list,
l_elbow_angle_list, hip_angle_list,
l_knee_angle_list,
hip_distance_list)
result['IsRElbowStandard'], result[
'IsLElbowStandard'], result[
'IsHipAngleStandard'], result[
'IsRKneeStandard'], result[
'IsHipDistanceStandard'], total = standard
r_elbow_angle_list = []
l_elbow_angle_list = []
l_knee_angle_list = []
hip_angle_list = []
hip_distance_list = []
pushUpCnt += 1
results.append(result)
print(result)
result = {}
db.ret(delta_time, total, pushUpCnt,
'pushUp', results, True)
cnt += 1
cv2.imshow("front", datum_front.cvOutputData)
cv2.imshow("side", datum_side.cvOutputData)
if cv2.waitKey(1) == ord('q'):
break
db.ret(None, None, None, None, None, False)
def pushUpVisualization(coor, i):
'''Read coordinates and display it
input: List of coordinates
ouput: The analysis of some infos of the whole process
'''
plt.ion()
# for i, coor in enumerate(coorList):
# if not coor:
# continue
cnt = i
r_elbow_angle = getValue.getElbowAngle(coor, 'R')
r_knee_angle = getValue.getKneeAngle(coor, 'R')
hip_angle = getValue.getHipAngle(coor, 'R')
hip_distance = getValue.getHipDistance(coor, 'R')
# if r_elbow_angle:
# elb_ang.append(r_elbow_angle)
# else:
# elb_ang.append(0)
# if hip_distance:
# hip_dis.append(hip_distance)
# else:
# hip_dis.append(0)
# if r_knee_angle:
# knee_ang.append(r_knee_angle)
# else:
# knee_ang.append(0)
# if hip_angle:
# hip_ang.append(hip_angle)
# else:
# hip_ang.append(0)
# elb_ang_below_105 = []
# for ans in elb_ang:
# if ans < 105:
# elb_ang_below_105.append(ans)
# else:
# elb_ang_below_105.append(None)
# cnt += 1
ax1 = plt.subplot(2, 2, 1)
# plt.hlines(165, )
# ax1.scatter(range(cnt1), elb_ang, label='elbow angle', s=10)
# ax1.scatter(range(cnt1), elb_ang_below_105, label='elbow angle', color='r', s=10)
# actu = [89, 149, 238, 328, 418, 597, 686, 746, 836, 955, 985]
# region = 0
ax1.scatter(i, r_elbow_angle, label='elbow angle', s=2)
# ax1.scatter(i, elb_ang_below_105, label='elbow angle', color='r', s=2)
ax1.hlines(165, 0, cnt, colors='c')
# ax1.vlines(actu, 0, 180, colors='r')
ax1.set_title('elbow_angle')
ax2 = plt.subplot(2, 2, 2)
ax2.scatter(i, hip_distance, label='hip distance', s=2)
ax2.set_title('hip_distance')
ax3 = plt.subplot(2, 2, 3)
ax3.scatter(i, r_knee_angle, label='knee angle', s=2)
# ax3.vlines(actu, 0, 180, colors='r')
ax3.set_title('knee_angle')
ax4 = plt.subplot(2, 2, 4)
ax4.scatter(i, hip_angle, label='hip', s=2)
# ax4.vlines(actu, 0, 180, colors='r')
ax4.set_title('hip_angle')
# plt.pause(0.01)
plt.plot()