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countdistanceCS.py

+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+import imageio
+
+from PIL import Image
+import os
+from pathlib import Path
+import json
+import csv
+from jsonpath import jsonpath
+import math
+import cv2
+import imageio
+#滤波操作
+class MovAvg(object):
+    def __init__(self, window_size=5):
+        self.window_size = window_size
+        self.data_queue = []
+    def update(self, data):
+        if len(self.data_queue) == self.window_size:
+            del self.data_queue[0]
+        #if data>0:
+        self.data_queue.append(data)
+        return sum(self.data_queue)/len(self.data_queue)
+
+def changejoint(pose,jointlist):
+    for jointindex in jointlist:
+        if (jointindex+3) not in jointlist:
+            continue
+        if pose[jointindex,0]>pose[jointindex+3,0]:
+            pose[jointindex, 0],pose[jointindex + 3, 0]=pose[jointindex + 3, 0],pose[jointindex, 0]
+            pose[jointindex, 1], pose[jointindex + 3, 1] = pose[jointindex + 3, 1], pose[jointindex, 1]
+
+def selectjointdistance(PoseX,PoseY,jointlist):
+    newPoseX=[]
+    newPoseY=[]
+    for jointindex in jointlist:
+        PoseX[jointindex]=updateArray(deleterror(searcherror(PoseX[jointindex]), PoseX[jointindex]))
+        PoseY[jointindex] = updateArray(deleterror(searcherror(PoseY[jointindex]), PoseY[jointindex]))
+        newPoseX.append(PoseX[jointindex])
+        newPoseY.append(PoseY[jointindex])
+    return computedistance(np.array(newPoseX),np.array(newPoseY))
+
+def selectjointdistancestd(PoseX,PoseY,jointlist):
+    newPoseX=[]
+    newPoseY=[]
+    for jointindex in jointlist:
+        PoseX[jointindex]=updateArray(deleterror(searcherror(PoseX[jointindex]), PoseX[jointindex]))
+        PoseY[jointindex] = updateArray(deleterror(searcherror(PoseY[jointindex]), PoseY[jointindex]))
+        newPoseX.append(PoseX[jointindex])
+        newPoseY.append(PoseY[jointindex])
+    return computedistancestd(np.array(newPoseX),np.array(newPoseY))
+
+def deletezero(Array):
+    newArray=[]
+    for array in Array:
+        if array!=0:
+            newArray.append(array)
+    return newArray
+def computedistance(X,Y):
+    inDistanceList=[]
+    for i_1 in range(0,len(X[0])-1):
+        innerdistance=0
+        for j_1 in range(0,len(X[:,])):
+            distance=math.sqrt(math.pow((X[j_1,i_1]-X[j_1,i_1+1]),2)+math.pow((Y[j_1,i_1]-Y[j_1,i_1+1]),2))
+            #print('distance:',distance)
+            innerdistance=innerdistance+distance
+        inDistanceList.append(innerdistance)
+    return np.mean(inDistanceList)
+
+def multi(N):
+    result=1
+    for n in N:
+        result=result*n
+    return result
+
+
+def computedistancestd(X,Y):
+    inDistanceList=[]
+    for i_1 in range(0,len(X[0])-1):
+        innerdistance=0
+        for j_1 in range(0,len(X[:,])):
+            distance=math.sqrt(math.pow((X[j_1,i_1]-X[j_1,i_1+1]),2)+math.pow((Y[j_1,i_1]-Y[j_1,i_1+1]),2))
+            innerdistance=innerdistance+distance
+        inDistanceList.append(innerdistance)
+    return np.std(inDistanceList,ddof=1)
+
+def updateArray(Prearray):
+    Newarray=[]
+    ma=MovAvg()
+    for prearray in Prearray:
+        Newarray.append(ma.update(prearray))
+    return Newarray
+
+def searcherror(Array):
+    newArray=[]
+    for i_1 in range(1,len(Array)):
+        if abs(Array[i_1-1]-Array[i_1])>30:
+            newArray.append(i_1-1)
+    return newArray
+
+def deleterror(index,Array):
+    begin=[]
+    end=[]
+    newArray=[]
+    for i_1 in range(0,len(index)):
+        if i_1%2!=0:
+            begin.append(index[i_1-1])
+            end.append(index[i_1])
+
+    for i_2 in range(0,len(begin)):
+        be=begin[i_2]
+        en=end[i_2]
+        for j_1 in range(be+1,en+1):
+            Array[j_1]=Array[be]
+    return Array
+
+def str2sec(x):
+    h, m, s = x.strip().split(':')
+    return (int(h) * 3600 + int(m) * 60 + int(s)) * 30
+
+
+def dict_generator(indict, pre=None):
+    pre = pre[:] if pre else []
+    if isinstance(indict, dict):
+        for key, value in indict.items():
+            if isinstance(value, dict):
+                if len(value) == 0:
+                    yield pre + [key, '{}']
+                else:
+                    for d in dict_generator(value, pre + [key]):
+                        yield d
+            elif isinstance(value, list):
+                if len(value) == 0:
+                    yield pre + [key, '[]']
+                else:
+                    for v in value:
+                        for d in dict_generator(v, pre + [key]):
+                            yield d
+            elif isinstance(value, tuple):
+                if len(value) == 0:
+                    yield pre + [key, '()']
+                else:
+                    for v in value:
+                        for d in dict_generator(v, pre + [key]):
+                            yield d
+            else:
+                yield pre + [key, value]
+    else:
+        yield indict
+
+def get_file_path(root_path, file_list, dir_list):
+    dir_or_files = os.listdir(root_path)
+    for dir_file in dir_or_files:
+
+        dir_file_path = os.path.join(root_path, dir_file)
+
+        if os.path.isdir(dir_file_path):
+            dir_list.append(dir_file_path)
+
+        else:
+            file_list.append(dir_file_path)
+
+def countdistance(pre,next):
+    Distance=0
+    for i in range(0,4):
+        #print('前一时刻第',i,'个点的坐标：','(',pre[i,0],',',pre[i,1],')')
+        #print('后一时刻第', i, '个点的坐标：', '(', next[i, 0], ',', next[i,1], ')')
+        distance=math.sqrt(math.pow((pre[i,0]-next[i,0]),2)+math.pow((pre[i,1]-next[i,1]),2))
+        Distance=Distance+distance
+    return Distance
+
+def getclip(mp4_list):
+    clip_list=[]
+    for mp4 in mp4_list:
+        clip_list.append(int(mp4[-6:-4]))
+    #clip_list.sort()
+    return clip_list
+
+
+point_path = r"/mnt/data1/zhanghongyi/non_normlize_CS_PR/CS/"
+point_file_list=[]
+point_dir_list=[]
+get_file_path(point_path, point_file_list, point_dir_list)
+point_dir_list.sort()
+point_file_list.sort()
+for point_file in point_file_list:
+    print('point_file:',point_file)
+    Value = json.load(open(point_file))
+    firstpose = jsonpath(Value, "$..data")[0][0:150]
+    secondpose = jsonpath(Value, "$..data")[0][150:300]
+    Raw_x_first=[]
+    Raw_y_first = []
+    Raw_x_second = []
+    Raw_y_second = []
+
+
+    #print('pose:',np.array(firstpose).shape)
+    #print('pose:',np.array(secondpose).shape)
+    for i in range(0,150):
+        changejoint(np.array(firstpose)[i],[11,14])
+        Raw_x_first.append(np.array(firstpose)[i][:,0])
+        Raw_y_first.append(np.array(firstpose)[i][:, 1])
+    firstDistance = selectjointdistance(np.transpose(np.array(Raw_x_first)), np.transpose(np.array(Raw_y_first)), [11,14])
+    print('firstDistance:',firstDistance)
+    for i in range(0,150):
+        changejoint(np.array(secondpose)[i],[11,14])
+        Raw_x_second.append(np.array(secondpose)[i][:, 0])
+        Raw_y_second.append(np.array(secondpose)[i][:, 1])
+    secondDistance = selectjointdistance(np.transpose(np.array(Raw_x_second)), np.transpose(np.array(Raw_y_second)), [11,14])
+    print('secondDistance:',secondDistance)
+