FFormationsDetectionAndAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33fbc6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "import cv2\n",
    "import matplotlib as mpl\n",
    "import nbconvert\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import scipy.signal\n",
    "import sys\n",
    "from scipy.spatial import distance\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import itertools \n",
    "import csv\n",
    "import scipy.signal\n",
    "from matplotlib.pyplot import figure\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e1bd89e",
   "metadata": {},
   "outputs": [],
   "source": [
    "DAYINT=1\n",
    "cameranumint=1\n",
    "datasetPath='C:/Users/Lucrezia/Desktop/Internship/MNM_version2-Revision17/Mingle/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9116284",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converts a CSV file into an array, returns any array, the array encoded in the CSV file\n",
    "def openCSV(filename):\n",
    "    rows = []\n",
    "    with open(filename) as csvfile:\n",
    "        reader = csv.reader(csvfile)\n",
    "        for row in reader:\n",
    "            rows.append(row)\n",
    "    arr = np.array(rows)\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32adc37e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the file, datasetPath is defined, name is the specific folder\n",
    "def reading(name):\n",
    "    try:\n",
    "        filename = datasetPath+name\n",
    "        label = openCSV(filename)\n",
    "    except FileNotFoundError:\n",
    "        filename = datasetPath+name\n",
    "        label = openCSV(filename).astype(np.int64)\n",
    "    except PermissionError:\n",
    "        label=datasetPath+name\n",
    "    \n",
    "    return(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0207f6ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#some values in the dataset has strange format, with this function we are sure to transform them in to int\n",
    "def posiz(stringa):\n",
    "    try:\n",
    "        s=int(stringa)\n",
    "    except ValueError:\n",
    "        s=float(stringa)\n",
    "        s=round(s)\n",
    "        s=int(s)\n",
    "    return(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b40754e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If you don't want the text,just comment scv2.putText\n",
    "#it shows the frame postion of the participants, if their data are NaN or the camera is filming them is different,\n",
    "#we will append 'out'. \n",
    "def showframe(frame,label,camera_num,name,counter2,lista):\n",
    "    if (label[counter2][5] != 'NaN' or label[counter2][6] != 'NaN') and str(label[counter2][2]) == camera_num :\n",
    "        cv2.putText(frame, name, (posiz(label[counter2][5])-90,posiz(label[counter2][6])-20), 0, 0.8, (255, 255, 255), 1, cv2.LINE_AA)\n",
    "        frame = cv2.circle(frame, (posiz(label[counter2][5]),posiz(label[counter2][6])), radius=2, color=(255, 255, 255), thickness=10)\n",
    "        lista.append((float(label[counter2][5]),float(label[counter2][6]),name))\n",
    "    else:\n",
    "        lista.append('out') \n",
    "        pass\n",
    "    return(frame,lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0df5c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#showing the o-spaces\n",
    "def centrf(frame,counter2,cen):\n",
    "    for i in cen:\n",
    "        frame = cv2.circle(frame, (int(i[0]),int(i[1])), radius=60, color=(52, 151, 255), thickness=5)\n",
    "    return(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e564f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If you don't want the text,just comment scv2.putText\n",
    "#Showing the center of attentions\n",
    "def showatt(frame,Cfin,lista):\n",
    "    lun=len(lista)\n",
    "    for i in range(0,lun):\n",
    "        if lista[i]!='out':\n",
    "            frame = cv2.circle(frame, (int(cbodies[i][0][0]),int(cbodies[i][0][1])), radius=2, color=(128,114,250), thickness=10)\n",
    "            #frame= cv2.putText(frame, 'CA'+lista[i][1], (int(cbodies[i][0][0])-100,int(cbodies[i][0][1])+4), 0, 0.8, (128,114,250), 1, cv2.LINE_AA)\n",
    "    return(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1f9a5e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transforming degree to rad, since we will use mainly rad\n",
    "def degreerad(n):\n",
    "    return(float(n*np.pi/180.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "acfe6216",
   "metadata": {},
   "outputs": [],
   "source": [
    "#final angle calculation considering also some specific cases, indeed, we don't have negative angles in the dataset, so \n",
    "#if we end up with a negative angle we will consider degreerad(360)-angleatot, same if our final angle is bigger then 360, we\n",
    "#will consider degreerad(360)-(angleatot-degreerad(360))\n",
    "def anglecalc(wind,li):\n",
    "    sin=0\n",
    "    cos=0\n",
    "    for i in range(0,wind+1):#+1\n",
    "        sin+=np.sin(float(li[i]))\n",
    "        cos+=np.cos(float(li[i]))\n",
    "    angleatot= np.arctan2(sin,cos)\n",
    "    if angleatot<0:\n",
    "        angleatot=degreerad(360)-angleatot\n",
    "    if angleatot>degreerad(360):\n",
    "        angleatot=degreerad(360)-(angleatot-degreerad(360))\n",
    "    return(angleatot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a7fbbba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We create the final list of angles that depend on the average angle of the body, the average angle of the head and the frequences \n",
    "#of torsion in a wind->time window\n",
    "def finallistangle(a,b,s,wind):\n",
    "    lis=[]\n",
    "    for i in range(0,s):\n",
    "        lis.append(a)\n",
    "    for i in range(0,(wind+1)-s):\n",
    "        lis.append(b)\n",
    "    lisf=anglecalc(wind,np.array(lis))\n",
    "    return(lisf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50264a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculation of the average angle of the body and angle of the head, only if our participants are not 'out', so if we\n",
    "#have all the data for the last wind-> time window  frames\n",
    "def anglestory(i,counter2,anglemem,anglebodymem,angleheadmem,anglebody,anglehead,actual_st,wind):\n",
    "    if counter2>=wind+1: #and checkf==0 :\n",
    "        check1=np.count_nonzero(anglemem[-wind:,i]=='out')\n",
    "        check2=np.count_nonzero(anglebodymem[-wind:,i]=='out')\n",
    "        check3=np.count_nonzero(angleheadmem[-wind:,i]=='out')\n",
    "        checkf=check1 or check2 or check3\n",
    "        if checkf==0 :\n",
    "            s=np.count_nonzero(anglemem[-wind:,i]=='0')\n",
    "            a=anglebodymem[-wind:,i]\n",
    "            a=np.append(a,anglebody)\n",
    "            b=angleheadmem[-wind:,i]\n",
    "            b=np.append(b,anglehead)\n",
    "            atot=anglecalc(wind,a)\n",
    "            btot=anglecalc(wind,b)\n",
    "            if actual_st==0:\n",
    "                s=s\n",
    "            else: \n",
    "                s+=1\n",
    "            Anglefin=finallistangle(atot,btot,s,wind)\n",
    "        else:\n",
    "            Anglefin=anglebody \n",
    "    else:\n",
    "        Anglefin=anglebody  \n",
    "    return(Anglefin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1011798d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculation of the center of attention, depending on the average angle of the body, the average angle of the head and the frequences \n",
    "#of torsion in a wind->time window\n",
    "#if we have 'out' or if the alghortim does not has a time window of timewindow , we will use the actual angle of the body\n",
    "def attentionbody(frame,lista,counter2,anglemem,anglebodymem,angleheadmem):\n",
    "    timewindow=5 #we need to be aware that in the videos of day one, we don't have data after frame 100, that is second 5. so if we\n",
    "    #give as input 6, the alghoritm have to wait all the empty frames to pass. This creates many problem, both to the alghortim and \n",
    "    #both logically, because after all the NaN the situation is different. For this reason in day one, 5 is the biggest \n",
    "    #time window we can reach. If the error in the dataset will be correct, this value may be increased.\n",
    "    maxtorsion=5\n",
    "    lun=len(lista)\n",
    "    cbodies=[]\n",
    "    r=100 #90\n",
    "    anglebodyit=[]\n",
    "    angleheadit=[]\n",
    "    angleit=[]\n",
    "    anglemem_arr=np.array(anglemem)\n",
    "    anglebodymem_arr=np.array(anglebodymem)\n",
    "    angleheadmem_arr=np.array(angleheadmem)\n",
    "    for i in range(0,lun):\n",
    "        if lista[i]!='out':\n",
    "            anglebody=degreerad(posiz(listalabels[i][counter2][12]))#11 head 12 body\n",
    "            anglehead=degreerad(posiz(listalabels[i][counter2][11]))#11 head 12 body\n",
    "            Ctot=[]\n",
    "            angleheadit.append(anglehead)\n",
    "            anglebodyit.append(anglebody)\n",
    "            a = np.abs(anglehead - anglebody)\n",
    "            if (np.abs((a + degreerad(180)) % degreerad(360) - degreerad(180)))<degreerad(maxtorsion):\n",
    "                angleit.append(0) \n",
    "                anglefin=anglestory(i,counter2,anglemem_arr,anglebodymem_arr,angleheadmem_arr,anglebody,anglehead,0,timewindow-1)\n",
    "\n",
    "            else:\n",
    "                angleit.append(1)\n",
    "                anglefin=anglestory(i,counter2,anglemem_arr,anglebodymem_arr,angleheadmem_arr,anglebody,anglehead,1,timewindow-1)\n",
    "\n",
    "            Cfin=[posiz(listalabels[i][counter2][5])+r*np.cos(anglefin),posiz(listalabels[i][counter2][6])+r*np.sin(anglefin)]\n",
    "            cbodies.append([Cfin,lista[i][2]])\n",
    "        else:\n",
    "            angleit.append('out')\n",
    "            angleheadit.append('out')\n",
    "            anglebodyit.append('out')\n",
    "    \n",
    "    anglemem.append(angleit)\n",
    "    anglebodymem.append(anglebodyit)\n",
    "    angleheadmem.append(angleheadit)\n",
    "    if counter2>=timewindow:#cleaning the memory\n",
    "        anglemem.pop(0)\n",
    "        anglebodymem.pop(0)\n",
    "        angleheadmem.pop(0)\n",
    "    if len(cbodies)==0:\n",
    "        checkactsit=1\n",
    "    else:\n",
    "        checkactsit=0\n",
    "    return(frame,cbodies,anglemem,anglebodymem,angleheadmem,checkactsit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d56992d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#clustering alghoritm\n",
    "def clust(orient,counter2,cont,ncl,pl=False):\n",
    "    mpl.style.use('seaborn')\n",
    "    lst2 = [item[0] for item in orient]\n",
    "    X = np.array(lst2)#position of centers of attention\n",
    "    met=[]\n",
    "    colori=['red','green','yellow','orange','black','purple','brown','pink','gray','olive','cyan','springgreen','lightsalmon','plum']\n",
    "    if cont==0:\n",
    "        for i in range(2,len(X)):#first iteration between 2 and the total number of people identified in that moment in the room\n",
    "            kmeans = KMeans(n_clusters=i, random_state=0).fit(X)\n",
    "            labels=kmeans.labels_\n",
    "            met.append(metrics.silhouette_score(X, labels, metric='euclidean'))#we append all results\n",
    "        massimo=max(met)#find the highest one\n",
    "        counter=2\n",
    "        s=0\n",
    "        for j in met:\n",
    "            if j==massimo:\n",
    "                s=counter #find in which iteration it was\n",
    "                counter+=1\n",
    "            else:\n",
    "                counter+=1\n",
    "    else: #after the first frame, we only  between  ncl-1 and ncl+2, if our actual class is 2, we will iterate between 2 and ncl+2. \n",
    "        if ncl==2:\n",
    "            ncl1=ncl\n",
    "        else:\n",
    "            ncl1=ncl-1\n",
    "        for i in range(ncl1,ncl+2):\n",
    "            kmeans = KMeans(n_clusters=i, random_state=0).fit(X)\n",
    "            labels=kmeans.labels_\n",
    "            if (labels.size - np.count_nonzero(labels))==labels.size:\n",
    "                met.append(0)\n",
    "            else:\n",
    "                met.append(metrics.silhouette_score(X, labels, metric='euclidean'))     \n",
    "        massimo=max(met)\n",
    "        counter=ncl-1#ncl-1\n",
    "        s=0\n",
    "        for j in met:\n",
    "            if j==massimo:\n",
    "                s=counter\n",
    "                counter+=1\n",
    "            else:\n",
    "                counter+=1\n",
    "    \n",
    "    kmeans = KMeans(n_clusters=s, random_state=0).fit(X)\n",
    "    labels=kmeans.labels_\n",
    "    peopleclust=[]\n",
    "    for j in range(0,len(X)):\n",
    "        peopleclust.append([orient[j][1],labels[j]]) \n",
    "        if pl==True:\n",
    "            plt.scatter(X[kmeans.labels_==j,0],X[kmeans.labels_==j,1],c=colori[j],s=31)    \n",
    "    if pl==True:\n",
    "        plt.gca().invert_yaxis()\n",
    "        plt.xlabel('frame width')\n",
    "        plt.ylabel('frame height')\n",
    "        plt.savefig('clusteringprova'+str(counter2)+'.png')\n",
    "        plt.show()\n",
    "    return(kmeans.cluster_centers_,s,peopleclust)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8fe54cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving interaction each frame, do in momentanea, we have the nuber of cluster and the participants of one f-form,\n",
    "# in totale for all the interaction find in one frame\n",
    "def data(counter2,ncl,peopleclust):\n",
    "    totale=[]\n",
    "    totale.append(counter2)\n",
    "    for m in range(0,ncl):\n",
    "        momentanea=[]\n",
    "        momentanea.append(m)\n",
    "        f_fcer=[]\n",
    "        for i in peopleclust:\n",
    "            if i[1]==m:\n",
    "                f_fcer.append(i[0])\n",
    "        momentanea.append(f_fcer)\n",
    "        totale.append(momentanea)\n",
    "    return(totale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cf990aa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#construction of the actual memory matrix, based on actual f-formation, 1 if present 0 if not\n",
    "def matrice(mom):\n",
    "    mat=np.zeros((32,32))\n",
    "    for i in range(1,len(mom)):\n",
    "        x=len(mom[i][1])\n",
    "        li=[]\n",
    "        for j in range(0,x):\n",
    "            if 'person' in mom[i][1][j]:\n",
    "                li.append(int((mom[i][1][j].replace('person',''))))\n",
    "        c = list(itertools.combinations(li, 2))\n",
    "        for m in c:\n",
    "            mat[m[0]-1,m[1]-1]=1\n",
    "            mat[m[1]-1,m[0]-1]=1\n",
    "    return(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c9f29355",
   "metadata": {},
   "outputs": [],
   "source": [
    "#analysis of the final memory matrix\n",
    "def analysis(totale,a,prec,cont0,cont1):\n",
    "    t=len(totale)\n",
    "    temp=np.arange(0,t)\n",
    "    x=[]\n",
    "    for i in range(0,t):\n",
    "        c = list(itertools.combinations(a, 2))#in we want to check between a group of three\n",
    "        listamom=[]\n",
    "        for j in c:\n",
    "            if totale[i][j[0]-1][j[1]-1]==1:\n",
    "                listamom.append(1)\n",
    "            else:\n",
    "                listamom.append(0)\n",
    "        s0=listamom.count(0)\n",
    "        if s0!=0:\n",
    "            if prec==0:\n",
    "                if cont0>2:\n",
    "                    x.append(0) #precedent is 0 in for more than 60 frames->no more f-form \n",
    "                else:\n",
    "                    x.append(0.2) #precedent is 0 in for already 20-40 frames \n",
    "            else:\n",
    "                x.append(0.5) #precent is 1, maybe just an error \n",
    "            prec=0\n",
    "            cont0+=1\n",
    "            cont1-=1\n",
    "            if cont0>5:\n",
    "                cont0=5\n",
    "            if cont1<0:\n",
    "                cont1=0\n",
    "        else: \n",
    "            if prec==1:\n",
    "                if cont1>2:\n",
    "                    x.append(1) #precedent is  1 in for more than 60 frames-> f-form\n",
    "                else:\n",
    "                    x.append(0.8) #precedent is  1 in for already 20-40 frames \n",
    "            else:\n",
    "                x.append(0.5) #precent is 0, maybe just an error \n",
    "            prec=1\n",
    "            cont1+=1\n",
    "            cont0-=1\n",
    "            if cont1>5:\n",
    "                cont1=5\n",
    "            if cont0<0:\n",
    "                cont0=0\n",
    "        \n",
    "    #print(len(x))\n",
    "    figure(figsize=(10, 6))\n",
    "    plt.plot(temp,x)\n",
    "    tit=str(a)\n",
    "    plt.xlabel('Time',fontsize=20)\n",
    "    plt.ylabel('Engagement',fontsize=20)\n",
    "    plt.xticks(fontsize=17)\n",
    "    plt.yticks(fontsize=17)\n",
    "    plt.title('Engadgment in the F-formation between persons %s' %tit)\n",
    "    plt.show()\n",
    "    \n",
    "    return(temp,x,tit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f7406fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#analysis of the distances in each frame, if the data were not NaN, of a group or a couple. \n",
    "def analysisdist(totale,a,listalabels,pl=False):\n",
    "    t=len(totale)\n",
    "    x=[]\n",
    "    c = list(itertools.combinations(a, 2))#in we want to check between a group of three\n",
    "    for j in c:\n",
    "        temp=[]\n",
    "        num=0\n",
    "        dist=[]\n",
    "        for i in range(0,t-1):\n",
    "            if listalabels[j[0]-1][i+1][5]!= 'NaN' and listalabels[j[0]-1][i+1][6]!= 'NaN' and listalabels[j[1]-1][i+1][5]!= 'NaN' and listalabels[j[1]-1][i+1][6]!= 'NaN':\n",
    "                d=(posiz(listalabels[j[0]-1][i+1][5]),posiz(listalabels[j[0]-1][i+1][6]))\n",
    "                b=(posiz(listalabels[j[1]-1][i+1][5]),posiz(listalabels[j[1]-1][i+1][6]))\n",
    "                dist.append(distance.euclidean(d, b))\n",
    "                num+=1\n",
    "                temp.append(num) \n",
    "        fig=plt.figure()\n",
    "        if pl==True:\n",
    "             plt.figure(figsize=(10, 6))\n",
    "        plt.plot(temp,dist)\n",
    "        tit=str(a)\n",
    "        #plt.title('Distance in the F-formation between persons %s' %str(j))\n",
    "        ttl='Distanceepersons17000'+ str(j)+'.png'\n",
    "        plt.xlabel('Time',fontsize=20)\n",
    "        plt.ylabel('Distance',fontsize=20)\n",
    "        plt.xticks(fontsize=17)\n",
    "        plt.yticks(fontsize=17)\n",
    "        plt.savefig(ttl,bbox='tight_layout',dpi=100)\n",
    "        if pl==True:\n",
    "            plt.show()\n",
    "        else:\n",
    "            plt.close(fig)\n",
    "    return(0)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "41842158",
   "metadata": {},
   "outputs": [],
   "source": [
    "#analysis of the reciprocal angle in each frame, if the data were not NaN, of a group or a couple. \n",
    "def analysisangle(totale,a,listalabels,pl=False):\n",
    "    t=len(totale)\n",
    "    x=[]\n",
    "    distmed=[]\n",
    "    c = list(itertools.combinations(a, 2))#in we want to check between a group of three\n",
    "    for j in c:\n",
    "        temp=[]\n",
    "        angle=[]\n",
    "        num=0\n",
    "        for i in range(0,t-1):\n",
    "            if listalabels[j[0]-1][i+1][5]!= 'NaN' and listalabels[j[0]-1][i+1][6]!= 'NaN' and listalabels[j[1]-1][i+1][5]!= 'NaN' and listalabels[j[1]-1][i+1][6]!= 'NaN' and listalabels[j[1]-1][i+1][12]!= 'NaN' and listalabels[j[0]-1][i+1][12]!= 'NaN':\n",
    "                point1=(posiz(listalabels[j[0]-1][i+1][5]),posiz(listalabels[j[0]-1][i+1][6]))\n",
    "                point2=(posiz(listalabels[j[1]-1][i+1][5]),posiz(listalabels[j[1]-1][i+1][6]))\n",
    "                v=np.array([point2[0]-point1[0],point2[1]-point1[1]])\n",
    "                v=v/np.linalg.norm(v)\n",
    "                alpha=degreerad(float(listalabels[j[0]-1][i+1][12]))\n",
    "                beta=degreerad(float(listalabels[j[1]-1][i+1][12]))\n",
    "                u1=np.array([np.cos(alpha),np.sin(alpha)])\n",
    "                u2=np.array([np.cos(beta),np.sin(beta)])\n",
    "                angletot=np.arccos(np.dot(v,u1))+np.arccos(np.dot(-v,u2))\n",
    "                angle.append((angletot*180)/np.pi)\n",
    "                num+=1\n",
    "                temp.append(num) \n",
    "        \n",
    "        fig=plt.figure()\n",
    "        if pl==True:\n",
    "             plt.figure(figsize=(10, 6))\n",
    "        plt.plot(temp,angle)\n",
    "        tit=str(a)\n",
    "        #plt.title('Total rotation required in the F-formation to face each other %s' %str(j),fontsize=20)\n",
    "        ttl='Anglepersons17000'+ str(j)+'.png'\n",
    "        plt.xlabel('Time',fontsize=20)\n",
    "        plt.ylabel('Angle in degree',fontsize=20)\n",
    "        plt.xticks(fontsize=17)\n",
    "        plt.yticks(fontsize=17)\n",
    "        plt.savefig(ttl,bbox='tight_layout',dpi=100)\n",
    "        if pl==True:\n",
    "            plt.show()\n",
    "        else:\n",
    "            plt.close(fig)\n",
    "    return(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d318d7e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creation of a csv that save all the interaction of person p1.\n",
    "def fformwithwho(p1,totale):\n",
    "    spi=str(p1)\n",
    "    with open('fformof'+spi+'.csv', 'w',newline='') as csvfile:\n",
    "        writer = csv.writer(csvfile)\n",
    "        writer.writerow([\"Frame\", \"Person\"])\n",
    "        for i in range(0,len(totale)):\n",
    "            mom=[]\n",
    "            for j in range(0,len(totale[0])):\n",
    "                if totale[i][j][p1-1]==1:\n",
    "                    mom.append(j+1)  \n",
    "            writer.writerow([i,mom])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c577e493",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This function is used to see the difference in the result of the center of attention of person 'person', \n",
    "#with respect tot the one calculated with the time-averaged angle.\n",
    "def checkontheangle(frame,person,listalabels,count):\n",
    "    a=degreerad(posiz(listalabels[person-1][count][12]))\n",
    "    Cfin=[posiz(listalabels[person-1][count][5])+100*np.cos(a),posiz(listalabels[person-1][count][6])+100*np.sin(a)]\n",
    "    frame = cv2.circle(frame, (int(Cfin[0]),int(Cfin[1])), radius=2, color=(0, 255, 0), thickness=10)\n",
    "\n",
    "    return(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ba5f2cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File count: 32\n"
     ]
    }
   ],
   "source": [
    "DAY=str(DAYINT)\n",
    "cameranum=str(cameranumint)\n",
    "#1->32\n",
    "#2->30\n",
    "#3->30\n",
    "\n",
    "count=0\n",
    "datasetPath1=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY)\n",
    "for path in os.listdir(datasetPath1):\n",
    "    # check if current path is a file\n",
    "    if os.path.isfile(os.path.join(datasetPath1, path)):\n",
    "        count += 1\n",
    "print('File count:', count)\n",
    "#reading and saving all files of each person\n",
    "labels1=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person1.csv')\n",
    "labels2=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person2.csv')\n",
    "labels3=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person3.csv')\n",
    "labels4=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person4.csv')\n",
    "labels5=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person5.csv')\n",
    "labels6=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person6.csv')\n",
    "labels7=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person7.csv')\n",
    "labels8=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person8.csv')\n",
    "labels9=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person9.csv')\n",
    "labels10=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person10.csv')\n",
    "labels11=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person11.csv')\n",
    "labels12=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person12.csv')\n",
    "labels13=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person13.csv')\n",
    "labels14=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person14.csv')\n",
    "labels15=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person15.csv')\n",
    "labels16=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person16.csv')\n",
    "labels17=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person17.csv')\n",
    "labels18=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person18.csv')\n",
    "labels19=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person19.csv')\n",
    "labels20=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person20.csv')\n",
    "labels21=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person21.csv')\n",
    "labels22=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person22.csv')\n",
    "labels23=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person23.csv')\n",
    "labels24=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person24.csv')\n",
    "labels25=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person25.csv')\n",
    "labels26=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person26.csv')\n",
    "labels27=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person27.csv')\n",
    "labels28=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person28.csv')\n",
    "labels29=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person29.csv')\n",
    "labels30=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person30.csv')\n",
    "if count==32: \n",
    "    labels31=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person31.csv')\n",
    "    labels32=reading('manual_annotations/AdditionalAnnotations/keypointsAndOrientations/day'+DAY+'/person32.csv')\n",
    "#appending everything \n",
    "listalabels=[]\n",
    "listalabels.append(labels1)\n",
    "listalabels.append(labels2)\n",
    "listalabels.append(labels3)\n",
    "listalabels.append(labels4)\n",
    "listalabels.append(labels5)\n",
    "listalabels.append(labels6)\n",
    "listalabels.append(labels7)\n",
    "listalabels.append(labels8)\n",
    "listalabels.append(labels9)\n",
    "listalabels.append(labels10)\n",
    "listalabels.append(labels11)\n",
    "listalabels.append(labels12)\n",
    "listalabels.append(labels13)\n",
    "listalabels.append(labels14)\n",
    "listalabels.append(labels15)\n",
    "listalabels.append(labels16)\n",
    "listalabels.append(labels17)\n",
    "listalabels.append(labels18)\n",
    "listalabels.append(labels19)\n",
    "listalabels.append(labels20)\n",
    "listalabels.append(labels21)\n",
    "listalabels.append(labels22)\n",
    "listalabels.append(labels23)\n",
    "listalabels.append(labels24)\n",
    "listalabels.append(labels25)\n",
    "listalabels.append(labels26)\n",
    "listalabels.append(labels27)\n",
    "listalabels.append(labels28)\n",
    "listalabels.append(labels29)\n",
    "listalabels.append(labels30)\n",
    "if count==32:\n",
    "    listalabels.append(labels31)\n",
    "    listalabels.append(labels32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9658a664",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "960\n",
      "540\n",
      "total time:  402.9064450263977\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(datasetPath+'videos/downsample/30min_day'+DAY+'_cam'+cameranum+'_20fps_960x540.mp4')\n",
    "tempo=[]\n",
    "totale=[]\n",
    "totalnumberfform=[]\n",
    "allcenter=[]\n",
    "seconds = time.time()\n",
    "counter=0\n",
    "counter2=0\n",
    "memory=[]\n",
    "frame_w=int(cap.get(3))\n",
    "frame_h=int(cap.get(4))\n",
    "print(frame_w)\n",
    "print(frame_h)\n",
    "outvideo = cv2.VideoWriter('fformdetection.mp4',cv2.VideoWriter_fourcc('M','P','4','V'), 25, (frame_w,frame_h))\n",
    "#height=540, width=960\n",
    "conclu=0\n",
    "ncl=0\n",
    "prevcenter=[]\n",
    "anglemem=[]\n",
    "anglebodymem=[]\n",
    "angleheadmem=[]\n",
    "while cap.isOpened() and counter2<1800:\n",
    "    ret, frame = cap.read()\n",
    "    N,M=frame.shape[:2]\n",
    "    # if frame is read correctly ret is True\n",
    "    if not ret:\n",
    "        print(\"Can't receive frame (stream end?). Exiting ...\")\n",
    "        break\n",
    "    counter+=1\n",
    "    if (counter-1)%20==0:\n",
    "        counter2+=1 \n",
    "    lista=[]\n",
    "    #we save all the data for each frame \n",
    "    frame,lista=showframe(frame,labels1,cameranum,'person1',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels2,cameranum,'person2',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels3,cameranum,'person3',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels4,cameranum,'person4',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels5,cameranum,'person5',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels6,cameranum,'person6',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels7,cameranum,'person7',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels8,cameranum,'person8',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels9,cameranum,'person9',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels10,cameranum,'person10',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels11,cameranum,'person11',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels12,cameranum,'person12',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels13,cameranum,'person13',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels14,cameranum,'person14',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels15,cameranum,'person15',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels16,cameranum,'person16',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels17,cameranum,'person17',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels18,cameranum,'person18',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels19,cameranum,'person19',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels20,cameranum,'person20',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels21,cameranum,'person21',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels22,cameranum,'person22',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels23,cameranum,'person23',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels24,cameranum,'person24',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels25,cameranum,'person25',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels26,cameranum,'person26',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels27,cameranum,'person27',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels28,cameranum,'person28',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels29,cameranum,'person29',counter2,lista)\n",
    "    frame,lista=showframe(frame,labels30,cameranum,'person30',counter2,lista)\n",
    "    if count==32:\n",
    "        frame,lista=showframe(frame,labels31,cameranum,'person31',counter2,lista)\n",
    "        frame,lista=showframe(frame,labels32,cameranum,'person32',counter2,lista)\n",
    "    \n",
    "    cv2.putText(frame, 'frame: %d' % counter, (50, 50), 0, 0.8, (6, 0, 210), 2, cv2.LINE_AA)\n",
    "    #every 20 frame, cause the labels are given in this way\n",
    "    if (counter-1)%20==0:#since we have the data every 20 frame\n",
    "        frame,cbodies,anglemem,anglebodymem,angleheadmem,checkactualsit=attentionbody(frame,lista,counter2,anglemem,anglebodymem,angleheadmem)\n",
    "        if checkactualsit==0: # in attentionbody, if we don't have the data this will be 0, so we will use k-means with no time information\n",
    "            centr,ncl,peopleclust=clust(cbodies,counter2,conclu,ncl,False)#clustering, True if we want to show and save the plot, otherwise False\n",
    "            tempo.append(counter2)\n",
    "            prevcenter = centr\n",
    "            totalnumberfform.append(ncl)\n",
    "            mom=data(counter2,ncl,peopleclust)#saving the actual interaction\n",
    "            mat=matrice(mom) #creating the matrix of interaction in this frame\n",
    "            totale.append(mat) #adding the actual matrix in the final one\n",
    "            conclu=1\n",
    "        else:\n",
    "            conclu=0\n",
    "    if checkactualsit==0:\n",
    "        nan=1\n",
    "        frame=showatt(frame,lista,cbodies)\n",
    "        \n",
    "        #frame=checkontheangle(frame,10,listalabels,counter2) #you can check the center of attention of one persone using the torso angle\n",
    "        frame=centrf(frame,counter2,centr)#show the f-formation \n",
    "    else:\n",
    "        frame=cv2.putText(frame, 'All data are NaN', (390,270), 0, 0.8, (250, 250, 250), 4, cv2.LINE_AA)\n",
    "    cv2.imshow('frame', frame)\n",
    "    outvideo.write(frame)\n",
    "    if cv2.waitKey(10) == ord('q'):\n",
    "        break\n",
    "    if cv2.waitKey(10) == ord('p'):\n",
    "        cv2.waitKey(-1) \n",
    "    else: \n",
    "        #conclu=0 # if you scomment this you can see the result using no time information in the K-Means clustering \n",
    "        pass\n",
    "seconds2 = time.time()\n",
    "print('total time: ',seconds2-seconds)\n",
    "plt.plot(tempo,totalnumberfform)\n",
    "plt.show()\n",
    "cap.release()\n",
    "outvideo.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d9ec793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total number of F-Formations analysis\n",
    "figure(figsize=(10, 6))\n",
    "\n",
    "plt.xlabel('Time',fontsize=20)\n",
    "plt.ylabel('Number of F-formation',fontsize=20)\n",
    "plt.xticks(fontsize=17)\n",
    "plt.yticks(fontsize=17)\n",
    "plt.plot(tempo,totalnumberfform)\n",
    "plt.savefig('NumberofFform1700sb',bbox='tight_layout',dpi=100)\n",
    "plt.show()\n",
    "\n",
    "figure(figsize=(10, 6))\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Number of F-formation')\n",
    "plt.plot(tempo,totalnumberfform)\n",
    "plt.savefig('NumberofFform',bbox='tight_layout',dpi=100)\n",
    "yhat = scipy.signal.savgol_filter(totalnumberfform,21,3)# convolution, polynom of 3rd degree, window of 21 samples\n",
    "plt.plot(tempo, yhat, color='red')\n",
    "plt.xlabel('Time',fontsize=18)\n",
    "plt.ylabel('Number of F-formation Conv',fontsize=20)\n",
    "plt.xticks(fontsize=17)\n",
    "plt.yticks(fontsize=17)\n",
    "plt.legend(['Actual Value', 'Approximation'])\n",
    "plt.savefig('NumberofFforsb'+'conv',bbox='tight_layout',dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "03b1c4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Distance analysis, insert True to see and save the plots\n",
    "a=[10,25]#[13,8]#[10,25]#[9,31,28]#[2,14,18]#[13,8]#[10,25]\n",
    "analysisdist(totale,a,listalabels,False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ff8014bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Angle analysis, insert True to see and save the plots\n",
    "a=[9,28]#[2,14]#[9,31]#[10,25]#[13,8]#[9,31,28]#[2,14,18]\n",
    "analysisangle(totale,a,listalabels,False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "277d3235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Engadgment number of F-Formations analysis\n",
    "a= [9,31]\n",
    "#a=[10,25]\n",
    "#a=[28,9]\n",
    "#a=[2,14]\n",
    "prec=1\n",
    "cont1=0\n",
    "cont0=0\n",
    "figure(figsize=(10, 6))\n",
    "temp,x,tit=analysis(totale,a,prec,cont0,cont1) #no convolution\n",
    "yhat = scipy.signal.savgol_filter(x,21,2) # convolution, polynom of 2nd degree, window of 21 samples\n",
    "\n",
    "figure(figsize=(10, 6))\n",
    "#plt.title('Engagment in the F-formation between persons %s' %tit)\n",
    "plt.xlabel('Time',fontsize=20)\n",
    "plt.ylabel('Engagement',fontsize=20)\n",
    "plt.xticks(fontsize=17)\n",
    "plt.yticks(fontsize=17)\n",
    "plt.legend(['Actual Value', 'Approximation'])\n",
    "#plt.plot(temp,x)\n",
    "plt.plot(temp, yhat)# color='red'\n",
    "plt.savefig('Engagment'+str(a)+'onlyconv',bbox='tight_layout',dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "053717e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#if we want to check in which frame two persons were no in the same f-form, to check manually\n",
    "#in the video if it's true or it's a dataset error\n",
    "p1=25\n",
    "p2=10\n",
    "for i in range(len(totale)):\n",
    "    if totale[i][a-1][b-1]<1:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6f5071d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.88851351351352\n"
     ]
    }
   ],
   "source": [
    "#calculate average angle or distance in determined period of time \n",
    "sum1=0\n",
    "cont=0\n",
    "for i in range(700,t):\n",
    "    try:\n",
    "        an[i]=int(an[i])\n",
    "        sum1+=an[i]\n",
    "    except ValueError:\n",
    "        cont+=1\n",
    "final1=sum1/(t-700-cont) #(t-cont)\n",
    "print(final1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3e66da3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fformwithwho(9,totale)"
   ]
  }
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