Merge branch 'master' of http://gogs.seec.seecoder.cn/ZhaoFengShan/PoseCorrection

 Conflicts:

	backend/classify.py

	backend/video_to_photo.py

backend/advice.py

@@ -0,0 +1,80 @@
+def ad_jump(result):
+    ret=""
+    if(result["jump_power"]["level"]==3):
+        ret=ret+"Need to do more to promote leg muscle strength. "
+    elif(result["jump_power"]["level"]==4):
+        ret="The leg muscle is weak and the position needs to be improved. "
+
+    if(result["core_strength"]["level"]==3):
+        ret=ret+"The core strength need to be improved. "
+    elif result["core_strength"]["level"]==4:
+        ret=ret+"More practice to do to promote the core strength. "
+
+    if(result["landing_position"]["level"]==3):
+        ret=ret+"The landing position is not standard enough. Take some practice on the leg. "
+    elif result["landing_position"]["level"]==4:
+        ret=ret+"The landing position is far from standard, see some relavant video to correct posture. "
+    if(ret==""):
+        ret="The jump is good and keep it!"
+    return ret
+
+def ad_juanfu(result):
+    ret=""
+    if(result["head_power"]["level"]==3):
+        ret=ret+"Need to do more to promote head muscle strength. "
+    elif(result["head_power"]["level"]==4):
+        ret="The neck and abdominal muscles are weak and the position needs to be improved. "
+
+    if(result["core_strength"]["level"]==3):
+        ret=ret+"The core strength need to be improved.See some relavent video to promote it. "
+    elif result["core_strength"]["level"]==4:
+        ret=ret+"More practice to do to promote the core strength. "
+
+    if(result["body_stability"]["level"]==3):
+        ret=ret+"The body_stability is not enough. Take some practice on the leg. "
+    elif result["body_stability"]["level"]==4:
+        ret=ret+"The body_stability is far from standard, see some relavant video to correct posture . "
+    if(ret==""):
+        ret="The abdominal campaign is good and keep it!"
+    return ret
+
+def ad_pingban(result):
+    ret=""
+    if(result["arm_strength"]["level"]==3):
+        ret=ret+"Need to do more to promote arm muscle strength. "
+    elif(result["arm_strength"]["level"]==4):
+        ret="The forearm strength are inadequate and the position needs to be improved. "
+
+    if(result["stability"]["level"]==3):
+        ret=ret+"The core strength need to be improved.Try to lengthen duration time and frequency. "
+    elif result["stability"]["level"]==4:
+        ret=ret+"More practice to do to promote the stability . "
+
+    if(result["standard"]["level"]==3):
+        ret=ret+"Plank movements are less than standard and require more straight leg support. "
+    elif result["standard"]["level"]==4:
+        ret=ret+"The body posture is far from standard ,see the standard video and improve leg strength. "
+    if(ret==""):
+        ret="Plank moves are fine, and your goal is to maximize your endurance!"
+    return ret
+
+def ad_shendun(result):
+    if(result["leg_strength"]["level"]==1):
+        return "The posture is good and remember to enhance endurance. "
+    elif result["leg_strength"]["level"]==2:
+        return "Try a wider squat."
+
+    elif result["leg_strength"]["level"]==3:
+        return "Squat range is far from enough, more practice thigh strength. "
+    else:
+        return "Check out some video to learn about standard postures and work on your thigh and calf muscles"
+
+def ad_gaotaitui(result):
+    if(result["standard"]["level"]==1):
+        return "The posture is good and remember to enhance endurance. "
+    elif result["standard"]["level"]==2:
+        return "Try to elevate your thighs to increase intensity. "
+    elif result["standard"]["level"]==3:
+        return "High leg lift is not enough, more practice thigh strength. "
+    else:
+        return "Check out some video to learn about standard postures and work on your thigh and calf muscles"

backend/classify.py

@@ -1,233 +1,237 @@
 from video_to_photo import run
 import os
+import advice
 
+def classify_jump(name,re):
+    q="./capture_image/"+name
+    qqq=os.path.abspath(q)
 
-def classify_jump(name, re):
-    q = "./capture_image/" + name
-    qqq = os.path.abspath(q)
 
-    result = {}
-    result["jump_power"] = {}
+    result={}
+    result["jump_power"]={}
 
-    if (float(re["1"]["val"]) > 140.0):
-        result["jump_power"]["level"] = 1
-    elif (float(re["1"]["val"]) > 115.0):
-        result["jump_power"]["level"] = 2
-    elif (float(re["1"]["val"]) > 90.0):
-        result["jump_power"]["level"] = 3
+    if(float(re["1"]["val"])>140.0):
+        result["jump_power"]["level"]=1
+    elif (float(re["1"]["val"])>115.0):
+        result["jump_power"]["level"]=2
+    elif (float(re["1"]["val"])>90.0):
+        result["jump_power"]["level"]=3
     else:
-        result["jump_power"]["level"] = 4
-    r = "capture_image" + str(re["1"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["jump_power"]["path"] = a
-    result["jump_power"]["val"] = re["1"]["val"]
-
-    result["core_strength"] = {}
-    if (float(re["2"]["val"]) < 8.0):
-        result["core_strength"]["level"] = 1
-    elif (float(re["2"]["val"]) < 15.0):
-        result["core_strength"]["level"] = 2
-    elif (float(re["2"]["val"]) < 30.0):
-        result["core_strength"]["level"] = 3
+        result["jump_power"]["level"]=4
+    r="capture_image"+str(re["1"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["jump_power"]["path"]=a
+    result["jump_power"]["val"]=re["1"]["val"]
+
+    result["core_strength"]={}
+    if(float(re["2"]["val"])<8.0):
+        result["core_strength"]["level"]=1
+    elif (float(re["2"]["val"])<15.0):
+        result["core_strength"]["level"]=2
+    elif (float(re["2"]["val"])<30.0):
+        result["core_strength"]["level"]=3
     else:
-        result["core_strength"]["level"] = 4
-    r = "capture_image" + str(re["2"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["core_strength"]["path"] = a
-
-    result["landing_position"] = {}
-    if (float(re["3"]["val"]) > 140.0):
-        result["landing_position"]["level"] = 1
-    elif (float(re["3"]["val"]) > 115.0):
-        result["landing_position"]["level"] = 2
-    elif (float(re["3"]["val"]) > 90.0):
-        result["landing_position"]["level"] = 3
+        result["core_strength"]["level"]=4
+    r="capture_image"+str(re["2"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["core_strength"]["path"]=a
+
+    result["landing_position"]={}
+    if(float(re["3"]["val"])>140.0):
+        result["landing_position"]["level"]=1
+    elif (float(re["3"]["val"])>115.0):
+        result["landing_position"]["level"]=2
+    elif (float(re["3"]["val"])>90.0):
+        result["landing_position"]["level"]=3
     else:
-        result["landing_position"]["level"] = 4
-    r = "capture_image" + str(re["3"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["landing_position"]["path"] = a
-    result["landing_position"]["val"] = re["3"]["val"]
+        result["landing_position"]["level"]=4
+    r="capture_image"+str(re["3"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["landing_position"]["path"]=a
+    result["landing_position"]["val"]=re["3"]["val"]
+    result["advice"]= advice.ad_jump(result)
 
     return result
+def classify_juanfu(name,re):
+    q="./capture_image/"+name
+    qqq=os.path.abspath(q)
 
 
-def classify_juanfu(name, re):
-    q = "./capture_image/" + name
-    qqq = os.path.abspath(q)
-
-    result = {}
-    result["head_power"] = {}
+    result={}
+    result["head_power"]={}
 
-    if float(re["1"]["val"]) > 100.0:
-        result["head_power"]["level"] = 1
-    elif float(re["1"]["val"]) > 90.0:
-        result["head_power"]["level"] = 2
-    elif float(re["1"]["val"]) > 80.0:
-        result["head_power"]["level"] = 3
+    if(float(re["1"]["val"])>100.0):
+        result["head_power"]["level"]=1
+    elif (float(re["1"]["val"])>90.0):
+        result["head_power"]["level"]=2
+    elif (float(re["1"]["val"])>80.0):
+        result["head_power"]["level"]=3
     else:
-        result["head_power"]["level"] = 4
-    r = "capture_image" + str(re["1"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["head_power"]["path"] = a
-    result["head_power"]["val"] = re["1"]["val"]
-
-    result["core_strength"] = {}
-    if (float(re["2"]["val"]) > 75):
-        result["core_strength"]["level"] = 1
-    elif (float(re["2"]["val"]) > 70):
-        result["core_strength"]["level"] = 2
-    elif (float(re["2"]["val"]) > 60):
-        result["core_strength"]["level"] = 3
+        result["head_power"]["level"]=4
+    r="capture_image"+str(re["1"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["head_power"]["path"]=a
+    result["head_power"]["val"]=re["1"]["val"]
+
+    result["core_strength"]={}
+    if(float(re["2"]["val"])>90):
+        result["core_strength"]["level"]=1
+    elif (float(re["2"]["val"])>80):
+        result["core_strength"]["level"]=2
+    elif (float(re["2"]["val"])>70):
+        result["core_strength"]["level"]=3
     else:
-        result["core_strength"]["level"] = 4
-    r = "capture_image" + str(re["2"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["core_strength"]["path"] = a
-
-    result["body_stability"] = {}
-    if (float(re["4"]["num"]) < 15):
-        result["body_stability"]["level"] = 1
-    elif (float(re["4"]["val"]) < 20):
-        result["body_stability"]["level"] = 2
-    elif (float(re["4"]["val"]) < 30):
-        result["body_stability"]["level"] = 3
+        result["core_strength"]["level"]=4
+    r="capture_image"+str(re["2"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["core_strength"]["path"]=a
+
+    result["body_stability"]={}
+    if(float(re["4"]["num"])<15):
+        result["body_stability"]["level"]=1
+    elif (float(re["4"]["num"])<20):
+        result["body_stability"]["level"]=2
+    elif (float(re["4"]["num"])<30):
+        result["body_stability"]["level"]=3
     else:
-        result["body_stability"]["level"] = 4
+        result["body_stability"]["level"]=4
 
-    result["body_stability"]["val"] = re["4"]["num"]
+
+    result["body_stability"]["val"]=re["4"]["num"]
+    result["advice"]=advice.ad_juanfu(result)
     return result
 
+def classify_pingban(name,re):
+    q="./capture_image/"+name
+    qqq=os.path.abspath(q)
 
-def classify_pingban(name, re):
-    q = "./capture_image/" + name
-    qqq = os.path.abspath(q)
 
-    result = {}
-    result["arm_strength"] = {}
+    result={}
+    result["arm_strength"]={}
 
-    dis = abs(90 - float(re["1"]["val"]))
-    if (dis < 5):
-        result["arm_strength"]["level"] = 1
-    elif (dis < 10):
-        result["arm_strength"]["level"] = 2
-    elif (dis < 20):
-        result["arm_strength"]["level"] = 3
+    dis=abs(90-float(re["1"]["val"]))
+    if(dis<5):
+        result["arm_strength"]["level"]=1
+    elif (dis<10):
+        result["arm_strength"]["level"]=2
+    elif (dis<20):
+        result["arm_strength"]["level"]=3
     else:
-        result["arm_strength"]["level"] = 4
-    r = "capture_image" + str(re["1"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["arm_strength"]["path"] = a
-    result["arm_strength"]["val"] = re["1"]["val"]
-
-    result["stability"] = {}
-    if (float(re["2"]["val"]) < 5):
-        result["stability"]["level"] = 1
-    elif (float(re["2"]["val"]) < 10):
-        result["stability"]["level"] = 2
-    elif (float(re["2"]["val"]) < 15):
-        result["stability"]["level"] = 3
+        result["arm_strength"]["level"]=4
+    r="capture_image"+str(re["1"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["arm_strength"]["path"]=a
+    result["arm_strength"]["val"]=re["1"]["val"]
+
+    result["stability"]={}
+    if(float(re["2"]["val"])<5):
+        result["stability"]["level"]=1
+    elif (float(re["2"]["val"])<10):
+        result["stability"]["level"]=2
+    elif (float(re["2"]["val"])<15):
+        result["stability"]["level"]=3
     else:
-        result["stability"]["level"] = 4
-    r = "capture_image" + str(re["2"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["stability"]["path"] = a
-    result["stability"]["val"] = re["2"]["val"]
-
-    result["standard"] = {}
-    if (float(re["3"]["val"]) < 5):
-        result["standard"]["level"] = 1
-    elif (float(re["3"]["val"]) < 10):
-        result["standard"]["level"] = 2
-    elif (float(re["3"]["val"]) < 15):
-        result["standard"]["level"] = 3
+        result["stability"]["level"]=4
+    r="capture_image"+str(re["2"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["stability"]["path"]=a
+    result["stability"]["val"]=re["2"]["val"]
+
+    result["standard"]={}
+    if(float(re["3"]["val"])<5):
+        result["standard"]["level"]=1
+    elif (float(re["3"]["val"])<10):
+        result["standard"]["level"]=2
+    elif (float(re["3"]["val"])<15):
+        result["standard"]["level"]=3
     else:
-        result["standard"]["level"] = 4
-    r = "capture_image" + str(re["3"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["standard"]["path"] = a
-    result["standard"]["val"] = re["3"]["val"]
+        result["standard"]["level"]=4
+    r="capture_image"+str(re["3"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["standard"]["path"]=a
+    result["standard"]["val"]=re["3"]["val"]
+    result["advice"]=advice.ad_pingban(result)
 
     return result
-
-
-def classify_shendun(name, re):
-    q = "./capture_image/" + name
-    qqq = os.path.abspath(q)
-    result = {}
-    result["leg_strength"] = {}
-    if (float(re["1"]["val"]) < 70):
-        result["leg_strength"]["level"] = 1
-    elif (float(re["1"]["val"]) < 90):
-        result["leg_strength"]["level"] = 2
-    elif (float(re["1"]["val"]) < 100):
-        result["leg_strength"]["level"] = 3
+def classify_shendun(name,re):
+    q="./capture_image/"+name
+    qqq=os.path.abspath(q)
+    result={}
+    result["leg_strength"]={}
+    if(float(re["1"]["val"])<70):
+        result["leg_strength"]["level"]=1
+    elif(float(re["1"]["val"])<90):
+        result["leg_strength"]["level"]=2
+    elif(float(re["1"]["val"])<100):
+        result["leg_strength"]["level"]=3
     else:
-        result["leg_strength"]["level"] = 4
+        result["leg_strength"]["level"]=4
 
-    r = "capture_image" + str(re["1"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["leg_strength"]["path"] = a
+    r="capture_image"+str(re["1"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["leg_strength"]["path"]=a
+    result["advice"]=advice.ad_shendun(result)
     return result
-
-
-def classify_gaotaitui(name, re):
-    q = "./capture_image/" + name
-    qqq = os.path.abspath(q)
-    result = {}
-    result["standard"] = {}
-    if (float(re["1"]["val"]) < 40):
-        result["standard"]["level"] = 1
-    elif (float(re["1"]["val"]) < 70):
-        result["standard"]["level"] = 2
-    elif (float(re["1"]["val"]) < 100):
-        result["standard"]["level"] = 3
+def classify_gaotaitui(name,re):
+    q="./capture_image/"+name
+    qqq=os.path.abspath(q)
+    result={}
+    result["standard"]={}
+    if(float(re["1"]["val"])<40):
+        result["standard"]["level"]=1
+    elif(float(re["1"]["val"])<70):
+        result["standard"]["level"]=2
+    elif(float(re["1"]["val"])<100):
+        result["standard"]["level"]=3
     else:
-        result["standard"]["level"] = 4
+        result["standard"]["level"]=4
 
-    r = "capture_image" + str(re["1"]["num"]) + ".png"
-    a = os.path.join(qqq, r)
-    result["standard"]["path"] = a
+    r="capture_image"+str(re["1"]["num"])+".png"
+    a=os.path.join(qqq,r)
+    result["standard"]["path"]=a
+    result["advice"]=advice.ad_gaotaitui(result)
     return result
 
 
-def classify_result(video_path, re, mode):
-    # video_path = "./video/demo1.mp4"
-    path = video_path
+def classify_result(video_path,re,mode):
+    #video_path = "./video/demo1.mp4"
+    path=video_path
     os.path.split(path)
-    tur = os.path.split(path)
-    name = tur[len(tur) - 1].split('.')[0]
+    tur=os.path.split(path)
+    name=tur[len(tur)-1].split('.')[0]
+
 
     switch = {'jump': classify_jump,
-              'pingban': classify_pingban,
+              'pingban':classify_pingban,
               'juanfu': classify_juanfu,
-              'shendun': classify_shendun,
-              'gaotaitui': classify_gaotaitui,
+              'shendun':classify_shendun,
+              'gaotaitui':classify_gaotaitui,
               }
-    choice = mode
-    power = switch.get(choice, classify_jump)(video_path, re)
+    choice=mode
+    power=switch.get(choice,classify_jump)(name,re)
+    print(power)
     return power
 
+def api_dyna(path,mode,ana):
+    return classify_result(path,run(path,mode,ana),mode)
 
-def api_dyna(video_path, mode, ana=1):
-    return classify_result(path, run(path, mode, ana), mode)
+if __name__ == '__main__':
 
 
-if __name__ == '__main__':
-    path = "./video/demo1.mp4"
-    api_dyna(path, "jump", 0)
-    # classify_result(path,run(path,"jump",0),"jump")
-    path = "./video/juanfu.mp4"
-    api_dyna(path, "juanfu", 0)
-    # classify_result(path,run(path,"juanfu",0),"juanfu")
-    path = "./video/pingban.mp4"
-    api_dyna(path, "pingban", 0)
-    # classify_result(path,run(path,"pingban",0),"pingban")
-    path = "./video/gaotaitui.mp4"
-    api_dyna(path, "gaotaitui", 0)
-    # classify_result(path,run(path,"gaotaitui",0),"gaotaitui")
-    path = "./video/shendun.mp4"
-    api_dyna(path, "shendun", 0)
+    #path="./video/demo1.mp4"
+    #api_dyna(path,"jump",0)
+    #classify_result(path,run(path,"jump",0),"jump")
+    #path="./video/juanfu.mp4"
+    #api_dyna(path,"juanfu",0)
+    #classify_result(path,run(path,"juanfu",0),"juanfu")
+    path="./video/pingban.mp4"
+    api_dyna(path,"pingban",0)
+    classify_result(path,run(path,"pingban",0),"pingban")
+    path="./video/gaotaitui.mp4"
+    api_dyna(path,"gaotaitui",0)
+    classify_result(path,run(path,"gaotaitui",0),"gaotaitui")
+    path="./video/shendun.mp4"
+    api_dyna(path,"shendun",0)
     '''
     os.path.split(path)
     tur=os.path.split(path)
@@ -239,4 +243,4 @@ if __name__ == '__main__':
     #print(qqq)
     r="capture_image"+"48"+".png"
     a=os.path.join(qqq,r)
-'''
+'''

backend/video_to_photo.py

@@ -1,38 +1,39 @@
 import os
 import math
 
-import cv2
+import  cv2
 from demo_func import run_openpose_for_normal
 from PIL import Image
-from analyse_jump import analyse_npy_side_jump, analyse_npy_side_pingban, analyse_npy_side_juanfu, \
-    analyse_npy_side_gaotaitui, analyse_npy_side_shendun
+from analyse_jump import  analyse_npy_side_jump,analyse_npy_side_pingban,analyse_npy_side_juanfu,analyse_npy_side_gaotaitui,analyse_npy_side_shendun
 import glob
 import time
 import os
 
 
+
+
 def split_video(path):
     video_path = path
     os.path.split(path)
-    tur = os.path.split(path)
-    print(tur[len(tur) - 1].split('.')[0])
-    name = tur[len(tur) - 1].split('.')[0]
-    if os.path.exists("./capture_image/" + name) == False:
-        os.mkdir("./capture_image/" + name)
+    tur=os.path.split(path)
+    print(tur[len(tur)-1].split('.')[0])
+    name=tur[len(tur)-1].split('.')[0]
+    if os.path.exists("./capture_image/"+name)==False:
+        os.mkdir("./capture_image/"+name)
     cap = cv2.VideoCapture(video_path)
     FPS = cap.get(5)
     print(FPS)
     c = 1
     frameRate = 1
-    if os.path.exists("./capture_image/" + name) == False:
-        os.mkdir("./capture_image/" + name)
+    if os.path.exists("./capture_image/"+name)==False:
+        os.mkdir("./capture_image/"+name)
 
     while (True):
 
         ret, frame = cap.read()
         if ret:
             if (c % frameRate == 0):
-                cv2.imwrite("./capture_image/" + name + "/capture_image" + str(c) + '.png', frame)
+                cv2.imwrite("./capture_image/"+name+"/capture_image" + str(c) + '.png', frame)
 
             c += 1
             cv2.waitKey(0)
@@ -41,41 +42,39 @@ def split_video(path):
     cap.release()
     return c
 
-
-def process_photo(name, c=334):
+def process_photo(name,c=334):
     print(name)
-    for i in range(1, c):
-        run_openpose_for_normal(i, name)
-
+    for i in range(1,c):
+        run_openpose_for_normal(i,name)
 
 def gather_video(path):
     video_path = path
 
     os.path.split(path)
-    tur = os.path.split(path)
-    print(tur[len(tur) - 1].split('.')[0])
-    name = tur[len(tur) - 1].split('.')[0]
+    tur=os.path.split(path)
+    print(tur[len(tur)-1].split('.')[0])
+    name=tur[len(tur)-1].split('.')[0]
 
     cap = cv2.VideoCapture(video_path)
     fps = cap.get(5)
-    num_frames = len(os.listdir(r'./capture_image/' + name))
+    num_frames = len(os.listdir(r'./capture_image/'+name))
     print(num_frames)
     img_array = []
-    im = Image.open("./capture_image/" + name + "/capture_image_result1.png")
+    im=Image.open("./capture_image/"+name+"/capture_image_result1.png")
 
-    # img_width = 720
-    # img_height = 1280
-    k = 0
+    #img_width = 720
+    #img_height = 1280
+    k=0
     for i in range(num_frames + 1):
-        filename = "./capture_image/" + name + "/capture_image_result" + str(i) + ".png"
-        k += 1
+        filename = "./capture_image/"+name+"/capture_image_result" + str(i) + ".png"
+        k+=1
         img = cv2.imread(filename)
 
         if img is None:
             continue
         img_array.append(img)
     print(k)
-    save_name = "./video/video_a/" + name + ".mp4"
+    save_name="./video/video_a/"+name+".mp4"
     out = cv2.VideoWriter(save_name, cv2.VideoWriter_fourcc(*"mp4v"), fps, im.size)
 
     for i in range(len(img_array)):
@@ -84,408 +83,413 @@ def gather_video(path):
     im.close()
 
 
-def do_analysis(a_path, video_path, mode):
+def do_analysis(a_path,video_path,mode):
     path = video_path
     os.path.split(path)
-    tur = os.path.split(path)
-    name = tur[len(tur) - 1].split('.')[0]
-
-    if os.path.exists("./cap_file/" + name) == False:
-        os.mkdir("./cap_file/" + name)
-        # ticks=time.time()
-    source_npy_side = a_path
-    output = open("./cap_file/" + name + "/file2" + ".txt", "w")
+    tur=os.path.split(path)
+    name=tur[len(tur)-1].split('.')[0]
+
+    if os.path.exists("./cap_file/"+name)==False:
+        os.mkdir("./cap_file/"+name)
+        #ticks=time.time()
+    source_npy_side=a_path
+    output=open("./cap_file/"+name+"/file2"+".txt","w")
     output.close()
-    result = {}
-    p = './capture_image/' + name + "/"
-    lenF = int(len(glob.glob(p + '*.png')) / 2)
-    for i in range(1, lenF):  # run_openpose_for_normal(i)
-        if (mode == "jump"):
-            result_side = analyse_npy_side_jump(name, i, source_npy_side.format(i))
-        elif (mode == "pingban"):
-            result_side = analyse_npy_side_pingban(name, i, source_npy_side.format(i))
-        elif (mode == "juanfu"):
-            result_side = analyse_npy_side_juanfu(name, i, source_npy_side.format(i))
-        elif (mode == "gaotaitui"):
-            result_side = analyse_npy_side_gaotaitui(name, i, source_npy_side.format(i))
-        elif (mode == "shendun"):
-            result_side = analyse_npy_side_shendun(name, i, source_npy_side.format(i))
-
-        if (result_side != False):
-            result[i] = result_side
-
-    output = open("./cap_file/" + name + "/file1.txt", "w")
+    result={}
+    p='./capture_image/'+name+"/"
+    lenF=int(len(glob.glob(p+'*.png'))/2)
+    for i in range(1,lenF):#run_openpose_for_normal(i)
+        if(mode=="jump"):
+            result_side = analyse_npy_side_jump(name,i,source_npy_side.format(i))
+        elif (mode=="pingban"):
+            result_side = analyse_npy_side_pingban(name,i,source_npy_side.format(i))
+        elif(mode=="juanfu"):
+            result_side = analyse_npy_side_juanfu(name,i,source_npy_side.format(i))
+        elif(mode=="gaotaitui"):
+            result_side =analyse_npy_side_gaotaitui(name,i,source_npy_side.format(i))
+        elif(mode=="shendun"):
+            result_side =analyse_npy_side_shendun(name,i,source_npy_side.format(i))
+
+        if(result_side!=False):
+            result[i]=result_side
+
+
+    output=open("./cap_file/"+name+"/file1.txt","w")
     for re in result:
-        print(re, file=output)
-        for (name, value) in result[re].items():
-            print(name, value, sep=',', file=output)
-        print("kv-over", file=output)
+        print(re,file=output)
+        for(name,value) in result[re].items():
+            print(name,value,sep=',',file=output)
+        print("kv-over",file=output)
     output.close()
 
 
 def read2(fileName="./cap_file/file1.txt"):
-    file = open(fileName)
-    db = {}
-    key = file.readline().strip()
+    file=open(fileName)
+    db={}
+    key=file.readline().strip()
 
-    while (key != "kv-over\n"):
+    while(key  != "kv-over\n"):
         if not key:
             break
-        person = {}
-        field = file.readline().strip()
+        person={}
+        field=file.readline().strip()
         while field != "kv-over":
-            name, value = field.split(",")
-            person[name] = value.strip()
-            field = file.readline().strip()
-            if (field == "kv-over\n"):
+            name, value= field.split(",")
+            person[name]=value.strip()
+            field=file.readline().strip()
+            if(field=="kv-over\n"):
                 break
-        db[key] = person
+        db[key]=person
         key = file.readline().strip()
-    print("db==== ", db)
+    print("db==== " , db)
     return db
 
 
+
 def ignore_data_pingban(result):
-    result_ig = result
-    k = "0"
-    last = {}
-    max_index = 1
+    result_ig=result
+    k="0"
+    last={}
+    max_index=1
     for re in result:
-        if k == "0":
-            k = re
+        if k=="0":
+            k=re
         else:
-            if (max_index < int(re)):
-                max_index = int(re)
+            if(max_index<int(re)):
+                max_index=int(re)
+
+    max_angle=abs(float(result_ig[k]["s_e_angle"])-float(result_ig[k]["e_w_angle"]))
+    max_num=int(k)
 
-    max_angle = abs(float(result_ig[k]["s_e_angle"]) - float(result_ig[k]["e_w_angle"]))
-    max_num = int(k)
+    less_angle=100
+    less_num=1
 
-    less_angle = 100
-    less_num = 1
+    max_less_a=1
+    max_less_num=k
 
-    max_less_a = 1
-    max_less_num = k
 
     for i in result_ig:
-        if (int(i) >= max_index / 3 and int(i) <= max_index / 3 * 2):
-            val = math.fabs(
-                math.fabs(float(result_ig[i]["hip_knee_angle"])) - math.fabs(float(result_ig[i]["knee_ankle_angle"])))
-            an_ = abs(float(result_ig[i]["s_e_angle"]) - float(result_ig[i]["e_w_angle"]))
-            if (math.fabs(math.fabs(float(result_ig[i]["hip_knee_angle"])) - math.fabs(
-                    float(result_ig[i]["knee_ankle_angle"]))) < less_angle):
-                less_angle = val
-                less_num = int(i)
-            if (an_ < max_angle):
-                max_angle = an_
-                max_num = i
-            if (math.fabs(math.fabs(float(result_ig[i]["hip_knee_angle"])) - math.fabs(
-                    float(result_ig[i]["knee_ankle_angle"]))) > max_less_a):
-                max_less_an = val
-                max_less_num = int(i)
-    re = {}
-    re["1"] = {}
-    re["1"]["num"] = max_num
-    re["1"]["val"] = max_angle
-    re["2"] = {}
-    re["2"]["num"] = less_num
-    re["2"]["val"] = less_angle
-    re["3"] = {}
-    re["3"]["num"] = max_less_num
-    re["3"]["val"] = max_less_a
-    # 1表示手肘和肩膀最小角度
-    # 2表示最小的身体角度差
-    # 3表示最大的身体角度差
-    # 1用来说明手部力量 3-2说明稳定性 2说明最好的状态 3说明最差的状态
+        if (int(i)>=max_index/3 and int(i)<=max_index/3*2):
+            val=math.fabs( math.fabs(float(result_ig[i]["hip_knee_angle"]))-math.fabs(float(result_ig[i]["knee_ankle_angle"])))
+            an_=abs(float(result_ig[i]["s_e_angle"])-float(result_ig[i]["e_w_angle"]))
+            if(math.fabs( math.fabs(float(result_ig[i]["hip_knee_angle"]))-math.fabs(float(result_ig[i]["knee_ankle_angle"])))<less_angle):
+                less_angle=val
+                less_num=int(i)
+            if(an_<max_angle):
+                max_angle=an_
+                max_num=i
+            if(math.fabs( math.fabs(float(result_ig[i]["hip_knee_angle"]))-math.fabs(float(result_ig[i]["knee_ankle_angle"])))>max_less_a):
+                max_less_an=val
+                max_less_num=int(i)
+    re={}
+    re["1"]={}
+    re["1"]["num"]=max_num
+    re["1"]["val"]=max_angle
+    re["2"]={}
+    re["2"]["num"]=less_num
+    re["2"]["val"]=less_angle
+    re["3"]={}
+    re["3"]["num"]=max_less_num
+    re["3"]["val"]=max_less_a
+    #1表示手肘和肩膀最小角度
+    #2表示最小的身体角度差
+    #3表示最大的身体角度差
+    #1用来说明手部力量 3-2说明稳定性 2说明最好的状态 3说明最差的状态
     print(re)
-    return result_ig, re
-
+    return result_ig,re
 
 def ignore_data_juanfu(result):
-    result_ig = result
-    k = "0"
-    last = {}
-    max_index = 1
+    result_ig=result
+    k="0"
+    last={}
+    max_index=1
     for re in result:
-        if k == "0":
-            k = re
+        if k=="0":
+            k=re
         else:
-            if (max_index < int(re)):
-                max_index = int(re)
+            if(max_index<int(re)):
+                max_index=int(re)
 
-    max_angle = result[k]["nose_neck_angle"]
-    max_num = int(k)
+    max_angle=result[k]["nose_neck_angle"]
+    max_num=int(k)
 
-    less_h_k_a = result[k]["hip_knee_angle"]
-    less_h_k_num = k
+    less_h_k_a=result[k]["hip_knee_angle"]
+    less_h_k_num=k
 
-    max_h_k_a = result[k]["hip_knee_angle"]
-    max_h_k_num = k
+    max_h_k_a=result[k]["hip_knee_angle"]
+    max_h_k_num=k
 
-    less_k_a_a = result[k]["knee_ankle_angle"]
-    less_k_a_num = k
+    less_k_a_a=result[k]["knee_ankle_angle"]
+    less_k_a_num=k
 
-    max_k_a_a = result[k]["knee_ankle_angle"]
-    max_k_a_num = k
+    max_k_a_a=result[k]["knee_ankle_angle"]
+    max_k_a_num=k
 
-    max_neck_hip_a = result[k]["neck_hip_angle"]
-    max_neck_hip_num = k
+    max_neck_hip_a=result[k]["neck_hip_angle"]
+    max_neck_hip_num=k
 
     for i in result_ig:
-        if (float(result_ig[i]["nose_neck_angle"]) > float(max_angle)):
-            max_num = int(i)
-            max_angle = result_ig[i]["nose_neck_angle"]
-
-        if (float(result_ig[i]["hip_knee_angle"]) > float(max_h_k_a)):
-            max_h_k_num = int(i)
-            max_h_k_a = result_ig[i]["hip_knee_angle"]
-
-        if (float(result_ig[i]["knee_ankle_angle"]) > float(max_k_a_a)):
-            max_k_a_num = int(i)
-            max_k_a_a = result_ig[i]["knee_ankle_angle"]
-
-        if (float(result_ig[i]["neck_hip_angle"]) > float(max_neck_hip_a)):
-            max_neck_hip_num = int(i)
-            max_neck_hip_a = result_ig[i]["neck_hip_angle"]
-
-        if (float(result_ig[i]["hip_knee_angle"]) < float(less_h_k_a)):
-            less_h_k_num = int(i)
-            less_h_k_a = result_ig[i]["hip_knee_angle"]
-
-        if (float(result_ig[i]["knee_ankle_angle"]) < float(less_k_a_a)):
-            less_k_a_num = int(i)
-            less_k_a_a = result_ig[i]["knee_ankle_angle"]
-
-    re = {}
-    re["1"] = {}
-    re["1"]["num"] = max_num
-    re["1"]["val"] = max_angle
-    re["2"] = {}
-    re["2"]["num"] = max_neck_hip_num
-    re["2"]["val"] = max_neck_hip_a
-    re["3"] = {}
-    re["3"]["num"] = float(max_h_k_a) - float(less_h_k_a)
-    re["3"]["num1"] = max_h_k_num
-    re["3"]["num2"] = less_h_k_num
-    re["3"]["val1"] = max_h_k_a
-    re["3"]["val2"] = less_h_k_a
-    re["4"] = {}
-    re["4"]["num"] = float(max_k_a_a) - float(less_k_a_a)
-    re["4"]["num1"] = max_k_a_num
-    re["4"]["num2"] = less_k_a_num
-    re["4"]["val1"] = max_k_a_a
-    re["4"]["val2"] = less_k_a_a
-    # 1表示头部和脖子角度
-    # 2表示脖子和臀部角度
-    # 3表示hip-knee的极差 稳定性
-    # 4表示knee-ankle的极差 稳定性
-    # 3的数据误差太大 待用性待考察
+        if(float(result_ig[i]["nose_neck_angle"])>float(max_angle)):
+            max_num=int(i)
+            max_angle=result_ig[i]["nose_neck_angle"]
+
+        if(float(result_ig[i]["hip_knee_angle"])>float(max_h_k_a)):
+            max_h_k_num=int(i)
+            max_h_k_a=result_ig[i]["hip_knee_angle"]
+
+        if(float(result_ig[i]["knee_ankle_angle"])>float(max_k_a_a)):
+            max_k_a_num=int(i)
+            max_k_a_a=result_ig[i]["knee_ankle_angle"]
+
+        if(float(result_ig[i]["neck_hip_angle"])>float(max_neck_hip_a)):
+            max_neck_hip_num=int(i)
+            max_neck_hip_a=result_ig[i]["neck_hip_angle"]
+
+
+        if(float(result_ig[i]["hip_knee_angle"])<float(less_h_k_a)):
+            less_h_k_num=int(i)
+            less_h_k_a=result_ig[i]["hip_knee_angle"]
+
+        if(float(result_ig[i]["knee_ankle_angle"])<float(less_k_a_a)):
+            less_k_a_num=int(i)
+            less_k_a_a=result_ig[i]["knee_ankle_angle"]
+
+
+    re={}
+    re["1"]={}
+    re["1"]["num"]=max_num
+    re["1"]["val"]=max_angle
+    re["2"]={}
+    re["2"]["num"]=max_neck_hip_num
+    re["2"]["val"]=max_neck_hip_a
+    re["3"]={}
+    re["3"]["num"]=float(max_h_k_a)-float(less_h_k_a)
+    re["3"]["num1"]=max_h_k_num
+    re["3"]["num2"]=less_h_k_num
+    re["3"]["val1"]=max_h_k_a
+    re["3"]["val2"]=less_h_k_a
+    re["4"]={}
+    re["4"]["num"]=float(max_k_a_a)-float(less_k_a_a)
+    re["4"]["num1"]=max_k_a_num
+    re["4"]["num2"]=less_k_a_num
+    re["4"]["val1"]=max_k_a_a
+    re["4"]["val2"]=less_k_a_a
+    #1表示头部和脖子角度
+    #2表示脖子和臀部角度
+    #3表示hip-knee的极差 稳定性
+    #4表示knee-ankle的极差 稳定性
+    #3的数据误差太大 待用性待考察
     print(re)
-    return result_ig, re
+    return result_ig,re
 
 
 def ignore_data_gaotaitui(result):
-    result_ig = result
-    k = "0"
-    last = {}
-    max_index = 1
+
+    result_ig=result
+    k="0"
+    last={}
+    max_index=1
     for re in result:
-        if k == "0":
-            k = re
+        if k=="0":
+            k=re
         else:
-            if (max_index < int(re)):
-                max_index = int(re)
+            if(max_index<int(re)):
+                max_index=int(re)
 
-    max_leg_knee_r_angle = result_ig[k]["knee_ankle_r_angle"]
-    max_leg_knee_l_angle = result_ig[k]["knee_ankle_l_angle"]
-    max_leg_knee_angle_num = int(k)
-    panduan = {}
+
+    max_leg_knee_r_angle=result_ig[k]["knee_ankle_r_angle"]
+    max_leg_knee_l_angle=result_ig[k]["knee_ankle_l_angle"]
+    max_leg_knee_angle_num=int(k)
+    panduan={}
     for i in result_ig:
-        if (float(result_ig[i]["l_d"]) < 30):
-            if (float(result_ig[i]["knee_ankle_r_angle"]) > 120 and float(result_ig[i]["hip_knee_r_angle"]) < 90):
-                panduan[i] = result_ig[i]["r_d"]
-    f = 0
-    ma = 0
-    key_k = 0
+        if(float(result_ig[i]["l_d"])<30):
+            if(float(result_ig[i]["knee_ankle_r_angle"])>120 and float(result_ig[i]["hip_knee_r_angle"])<90):
+                panduan[i]=result_ig[i]["r_d"]
+    f=0
+    #ma=0
+    ma=panduan[k]
+    key_k=k
     for key in panduan:
-        if f == 0:
-            ma = panduan[key]
-            f = 1
-            key_k = key
-        elif float(ma) > float(panduan[key]):
-            key_k = key
-            ma = panduan[key]
+        if f==0:
+            ma=panduan[key]
+            f=1
+            key_k=key
+        elif float(ma)>float(panduan[key]):
+            key_k=key
+            ma=panduan[key]
 
-    re = {}
-    re["1"] = {}
-    re["1"]["num"] = key_k
-    re["1"]["val"] = ma
-    re["1"]["hip_knee_r_angle"] = result_ig[key_k]["hip_knee_r_angle"]
-    re["1"]["knee_ankle_r_angle"] = result_ig[key_k]["knee_ankle_r_angle"]
+    re={}
+    re["1"]={}
+    re["1"]["num"]=key_k
+    re["1"]["val"]=ma
+    re["1"]["hip_knee_r_angle"]=result_ig[key_k]["hip_knee_r_angle"]
+    re["1"]["knee_ankle_r_angle"]=result_ig[key_k]["knee_ankle_r_angle"]
 
-    return result_ig, re
 
+    return result_ig,re
 
 def ignore_data_jump(result):
-    result_ig = result
-    k = "0"
-    last = {}
-    max_index = 1
+    result_ig=result
+    k="0"
+    last={}
+    max_index=1
     for re in result:
-        if k == "0":
-            k = re
+        if k=="0":
+            k=re
         else:
-            if (max_index < int(re)):
-                max_index = int(re)
+            if(max_index<int(re)):
+                max_index=int(re)
 
     print(result_ig)
-    max_leg_knee_angle = result_ig[k]["knee_ankle_angle"]
-    max_leg_knee_angle_num = int(k)
-    less_angle = 100
-    less_num = 1
-    ankle = result_ig[k]["leg_heigh"]
-    ankle_num = int(k)
-    max_leg_knee_hip_knee = math.fabs(float(result_ig[k]["hip_knee_angle"]) - float(result_ig[k]["knee_ankle_angle"]))
-    max_l_h_num = 1
-    hip_hei_init = float(result_ig[k]["hip_heigh"])
-    # print("max")
-    # print(max_index)
-    # print(hip_hei_init)
+    max_leg_knee_angle=result_ig[k]["knee_ankle_angle"]
+    max_leg_knee_angle_num=int(k)
+    less_angle=100
+    less_num=1
+    ankle=result_ig[k]["leg_heigh"]
+    ankle_num=int(k)
+    max_leg_knee_hip_knee=math.fabs(float(result_ig[k]["hip_knee_angle"])-float(result_ig[k]["knee_ankle_angle"]))
+    max_l_h_num=1
+    hip_hei_init=float(result_ig[k]["hip_heigh"])
+    #print("max")
+    #print(max_index)
+    #print(hip_hei_init)
 
     for i in result_ig:
-        if int(i) <= max_index / 5:
-            if float(max_leg_knee_angle) < float(result_ig[i]["knee_ankle_angle"]):
-                max_leg_knee_angle_num = int(i)
-                max_leg_knee_angle = result_ig[i]["knee_ankle_angle"]
-        if int(i) <= max_index / 2 and int(i) > max_index / 3:
-            val = math.fabs(
-                math.fabs(float(result_ig[i]["hip_knee_angle"])) - math.fabs(float(result_ig[i]["knee_ankle_angle"])))
-            if (math.fabs(math.fabs(float(result_ig[i]["hip_knee_angle"])) - math.fabs(
-                    float(result_ig[i]["knee_ankle_angle"]))) < less_angle):
-                less_angle = val
-                less_num = int(i)
-
-        if int(i) <= max_index and int(i) > (max_index / 3):
-
-            if (float(result_ig[i]["hip_heigh"])) < (float(hip_hei_init)) and max_l_h_num != 1:
+        if int(i)<=max_index/5:
+            if float(max_leg_knee_angle)<float(result_ig[i]["knee_ankle_angle"]):
+                max_leg_knee_angle_num=int(i)
+                max_leg_knee_angle=result_ig[i]["knee_ankle_angle"]
+        if int(i)<=max_index/2 and int(i)>max_index/3:
+            val=math.fabs( math.fabs(float(result_ig[i]["hip_knee_angle"]))-math.fabs(float(result_ig[i]["knee_ankle_angle"])))
+            if(math.fabs( math.fabs(float(result_ig[i]["hip_knee_angle"]))-math.fabs(float(result_ig[i]["knee_ankle_angle"])))<less_angle):
+                less_angle=val
+                less_num=int(i)
+
+        if int(i)<=max_index and int(i)>(max_index/3):
+
+            if(float(result_ig[i]["hip_heigh"]))<(float(hip_hei_init)) and max_l_h_num!=1:
                 break
-            if (math.fabs(math.fabs(float(result_ig[i]["hip_knee_angle"])) - math.fabs(
-                    float(result_ig[i]["knee_ankle_angle"])))) > max_leg_knee_hip_knee and math.fabs(
-                    float(result_ig[i]["leg_heigh"]) - float(ankle)) <= 20:
-                max_leg_knee_hip_knee = math.fabs(
-                    float(result_ig[i]["hip_knee_angle"]) - math.fabs(float(result_ig[i]["knee_ankle_angle"])))
-                max_l_h_num = int(i)
+            if (math.fabs(math.fabs(float(result_ig[i]["hip_knee_angle"]))-math.fabs(float(result_ig[i]["knee_ankle_angle"]))))>max_leg_knee_hip_knee and math.fabs(float(result_ig[i]["leg_heigh"])-float(ankle))<=20:
+                max_leg_knee_hip_knee=math.fabs(float(result_ig[i]["hip_knee_angle"])-math.fabs(float(result_ig[i]["knee_ankle_angle"])))
+                max_l_h_num=int(i)
 
             #    ankle=result_ig[i]["leg_heigh"]
             #    ankle_num=int(i)
-            # elif ankle!=1 and int(i)>=(le/3*2):
+            #elif ankle!=1 and int(i)>=(le/3*2):
             #    break
 
     print(max_leg_knee_angle)
     print(max_leg_knee_angle_num)
     print(less_num)
     print(less_angle)
-    # print(ankle)
-    # print(ankle_num)
+    #print(ankle)
+    #print(ankle_num)
     print(max_leg_knee_hip_knee)
     print(max_l_h_num)
-    re = {}
-    re["1"] = {}
-    re["1"]["num"] = max_leg_knee_angle_num
-    re["1"]["val"] = max_leg_knee_angle
-    re["2"] = {}
-    re["2"]["num"] = less_num
-    re["2"]["val"] = less_angle
-    re["3"] = {}
-    re["3"]["num"] = max_l_h_num
-    re["3"]["val"] = max_leg_knee_hip_knee
+    re={}
+    re["1"]={}
+    re["1"]["num"]=max_leg_knee_angle_num
+    re["1"]["val"]=max_leg_knee_angle
+    re["2"]={}
+    re["2"]["num"]=less_num
+    re["2"]["val"]=less_angle
+    re["3"]={}
+    re["3"]["num"]=max_l_h_num
+    re["3"]["val"]=max_leg_knee_hip_knee
     print(re)
 
-    return result_ig, re
+
+    return result_ig,re
 
 
 def ignore_data_shendun(result):
-    result_ig = result
-    k = "0"
-    last = {}
-    max_index = 1
+    result_ig=result
+    k="0"
+    last={}
+    max_index=1
     for re in result:
-        if k == "0":
-            k = re
+        if k=="0":
+            k=re
         else:
-            if (max_index < int(re)):
-                max_index = int(re)
+            if(max_index<int(re)):
+                max_index=int(re)
 
-    max_d_num = k
-    max_d = result[k]["r_d"]
+
+    max_d_num=k
+    max_d=result[k]["r_d"]
     for i in result_ig:
-        if (float(result_ig[i]["r_d"]) > float(max_d)):
-            max_d_num = int(i)
-            max_d = result_ig[i]["r_d"]
+        if(float(result_ig[i]["r_d"])>float(max_d)):
+            max_d_num=int(i)
+            max_d= result_ig[i]["r_d"]
 
-    re = {}
-    re["1"] = {}
-    re["1"]["num"] = max_d_num
-    re["1"]["val"] = max_d
 
-    # 1最大的深蹲角度
-    return result_ig, re
+    re={}
+    re["1"]={}
+    re["1"]["num"]=max_d_num
+    re["1"]["val"]=max_d
 
+    #1最大的深蹲角度
+    return result_ig,re
 
-def write1(result, name):
-    output = open(name, "w")
+def write1(result,name):
+    output=open(name,"w")
     for re in result:
-        print(re, file=output)
-        for (name, value) in result[re].items():
-            print(name, value, sep=',', file=output)
-        print("kv-over", file=output)
+        print(re,file=output)
+        for(name,value) in result[re].items():
+            print(name,value,sep=',',file=output)
+        print("kv-over",file=output)
     output.close()
     return name
 
 
-def run(video_path, mode, need_split=1):
-    path = video_path
+def run(video_path,mode,need_split):
+    path=video_path
     os.path.split(path)
-    tur = os.path.split(path)
-    name = tur[len(tur) - 1].split('.')[0]
-    if (need_split == 1):
-        c = split_video(video_path)
-        process_photo(name, c)
+    tur=os.path.split(path)
+    if(mode=="pingban"):
+        print(need_split)
+    name=tur[len(tur)-1].split('.')[0]
+    if(need_split==1):
+        c=split_video(video_path)
+        process_photo(name,c)
         gather_video(video_path)
-    do_analysis("./capture_image/" + name + "/capture_image{}-1.png.npy", video_path, mode)
+    do_analysis("./capture_image/"+name+"/capture_image{}-1.png.npy",video_path,mode)
 
-    switch = {'longjump': ignore_data_jump,  # 注意此处不要加括号
+    switch = {'jump': ignore_data_jump,                # 注意此处不要加括号
               'pingban': ignore_data_pingban,
               'juanfu': ignore_data_juanfu,
-              'shendun': ignore_data_shendun,
-              'gaotaitui': ignore_data_gaotaitui,
+              'shendun':ignore_data_shendun,
+              'gaotaitui':ignore_data_gaotaitui,
               }
-    choice = mode  # 获取选择
-    result, re = switch.get(choice, ignore_data_jump)(read2("./cap_file/" + name + "/file1.txt"))
+    choice = mode                        # 获取选择
+    result,re=switch.get(choice,  ignore_data_jump)(read2("./cap_file/"+name+"/file1.txt"))
     # 执行对应的函数，如果没有就执行默认的函数
-    # result,re=ignore_data_juanfu(read2("./cap_file/"+name+"/file1.txt"))
-    write1(result, "./cap_file/" + name + "/file3.txt")
-    write1(re, "./cap_file/" + name + "-file.txt")
-    # write1()
+    #result,re=ignore_data_juanfu(read2("./cap_file/"+name+"/file1.txt"))
+    write1(result,"./cap_file/"+name+"/file3.txt")
+    write1(re,"./cap_file/"+name+"-file.txt")
+    #write1()
     return re
 
-
 if __name__ == '__main__':
     video_path = "./video/shendun.mp4"
-    # path=video_path
-    # os.path.split(path)
-    # tur=os.path.split(path)
-    # name=tur[len(tur)-1].split('.')[0]
-
-    # c=split_video(video_path)
-    # process_photo(name,c)
-    # gather_video(video_path)
-    # do_analysis("./capture_image/"+name+"/capture_image{}-1.png.npy",video_path,"shendun")
-    # result,re=ignore_data_shendun(read2("./cap_file/"+name+"/file1.txt"))
-    # write1(result,"./cap_file/"+name+"/file3.txt")
-    # write1(re,"./cap_file/"+name+"-file.txt")
-    # write1()
-    # print(re)
-    re = run(video_path, "shendun", 0)
-    # 参数一 video_path
-    # 参数二 shendun jump gaotaitui juanfu pingbanzhicheng
-    # 第三个参数为1 表示需要视频切割
+    #path=video_path
+    #os.path.split(path)
+    #tur=os.path.split(path)
+    #name=tur[len(tur)-1].split('.')[0]
+
+
+    #c=split_video(video_path)
+    #process_photo(name,c)
+    #gather_video(video_path)
+    #do_analysis("./capture_image/"+name+"/capture_image{}-1.png.npy",video_path,"shendun")
+    #result,re=ignore_data_shendun(read2("./cap_file/"+name+"/file1.txt"))
+    #write1(result,"./cap_file/"+name+"/file3.txt")
+    #write1(re,"./cap_file/"+name+"-file.txt")
+    #write1()
+    #print(re)
+    re=run(video_path,"shendun",0)
+    #参数一 video_path
+    #参数二 shendun jump gaotaitui juanfu pingban
+    #第三个参数为1 表示需要视频切割
+
+