181 lines
6.5 KiB
Python
181 lines
6.5 KiB
Python
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import cv2
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import numpy as np
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from numpy import genfromtxt
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from KalmanFilter import KalmanFilter
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import math
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import os
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import glob
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datasets="2DMOT2015Labels/train"
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dataset="PETS09-S2L1"
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distance_mini=500
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rectangle=0
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trace=0
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dir_images=datasets+"/"+dataset+"/img1/"
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fichier_label=datasets+"/"+dataset+"/gt/gt.txt"
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if not os.path.exists(dir_images):
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print("Le repertoire n'existe pas ...", dir_images)
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quit()
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if not os.path.exists(fichier_label):
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print("Le fichier de label n'existe pas ...", fichier)
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quit()
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objets_points=[]
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objets_id=[]
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objets_KF=[]
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objets_historique=[]
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def distance(point, liste_points):
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distances=[]
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for p in liste_points:
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distances.append(np.sum(np.power(p-np.expand_dims(point, axis=-1), 2)))
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return distances
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def trace_historique(tab_points, longueur, couleur=(0, 255, 255)):
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historique=np.array(tab_points)
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nbr_point=len(historique)
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longueur=min(nbr_point, longueur)
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for i in range(nbr_point-1, nbr_point-longueur, -1):
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cv2.line(frame,
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(historique[i-1, 0], historique[i-1, 1]),
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(historique[i, 0], historique[i, 1]),
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couleur,
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2)
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data=genfromtxt(fichier_label, delimiter=',')
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id_frame=0
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id_objet=0
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start=0
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for image in glob.glob(dir_images+"*.jpg"):
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frame=cv2.imread(image)
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# Prediction de l'ensemble des objets + affichage
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for id_obj in range(len(objets_points)):
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etat=objets_KF[id_obj].predict()
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etat=np.array(etat, dtype=np.int32)
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objets_points[id_obj]=np.array([etat[0], etat[1], etat[4], etat[5]])
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objets_historique[id_obj].append([etat[0], etat[1]])
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cv2.circle(frame, (etat[0], etat[1]), 5, (0, 0, 255), 2)
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if rectangle:
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cv2.rectangle(frame,
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(int(etat[0]-etat[4]/2), int(etat[1]-etat[5]/2)),
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(int(etat[0]+etat[4]/2), int(etat[1]+etat[5]/2)),
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(0, 0, 255),
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2)
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cv2.arrowedLine(frame,
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(etat[0], etat[1]),
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(etat[0]+3*etat[2], etat[1]+3*etat[3]),
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color=(0, 0, 255),
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thickness=2,
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tipLength=0.2)
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cv2.putText(frame,
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"ID{:d}".format(objets_id[id_obj]),
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(int(etat[0]-etat[4]/2), int(etat[1]-etat[5]/2)),
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cv2.FONT_HERSHEY_PLAIN,
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1.5,
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(255, 0, 0),
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2)
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if trace:
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trace_historique(objets_historique[id_obj], 42)
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# Permet de suivre l'ID 0 avec une flèche
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if objets_id[id_obj]==0:
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cv2.arrowedLine(frame,
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(etat[0], int(etat[1]-etat[5]/2-80)),
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(etat[0], int(etat[1]-etat[5]/2-30)),
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color=(0, 0, 255),
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thickness=5,
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tipLength=0.2)
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# Récupération des objets de la frame concernée
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mask=data[:, 0]==id_frame
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# Affichage des données (rectangle) du detecteur
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points=[]
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for d in data[mask, :]:
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#if np.random.randint(2):
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if rectangle:
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cv2.rectangle(frame, (int(d[2]), int(d[3])), (int(d[2]+d[4]), int(d[3]+d[5])), (0, 255, 0), 2)
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xm=int(d[2]+d[4]/2)
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ym=int(d[3]+d[5]/2)
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cv2.circle(frame, (xm, ym), 2, (0, 255, 0), 2)
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points.append([xm, ym, int(d[4]), int(d[5])])
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# calcul des distances
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nouveaux_objets=np.ones((len(points)))
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tab_distances=[]
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if len(objets_points):
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for point_id in range(len(points)):
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distances=distance(points[point_id], objets_points)
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tab_distances.append(distances)
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tab_distances=np.array(tab_distances)
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sorted_distances=np.sort(tab_distances, axis=None)
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for d in sorted_distances:
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if d>distance_mini:
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break
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id1, id2=np.where(tab_distances==d)
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if not len(id1) or not len(id2):
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continue
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tab_distances[id1, :]=distance_mini+1
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tab_distances[:, id2]=distance_mini+1
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objets_KF[id2[0]].update(np.expand_dims(points[id1[0]], axis=-1))
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nouveaux_objets[id1]=0
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# Création du filtre de Kalman pour les nouveaux objets
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for point_id in range(len(points)):
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if nouveaux_objets[point_id]:
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print("NOUVEAU", points[point_id])
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objets_points.append(points[point_id])
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objets_KF.append(KalmanFilter(0.5, [points[point_id][0], points[point_id][1]], [points[point_id][2], points[point_id][3]]))
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objets_id.append(id_objet)
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objets_historique.append([])
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id_objet+=1
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# Nettoyage ...
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tab_id=[]
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for id_point in range(len(objets_points)):
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if int(objets_points[id_point][0])<-100 or \
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int(objets_points[id_point][1])<-100 or \
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objets_points[id_point][0]>frame.shape[1]+100 or \
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objets_points[id_point][1]>frame.shape[0]+100:
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print("SUPPRESSION", objets_points[id_point])
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tab_id.append(id_point)
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for index in sorted(tab_id, reverse=True):
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del objets_points[index]
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del objets_KF[index]
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del objets_id[index]
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del objets_historique[index]
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cv2.rectangle(frame, (0, 0), (frame.shape[1], 30), (100, 100, 100), cv2.FILLED)
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message="Frame: {:03d} Nbr personne: {:d} nbr filtre: {:d} [r]Rectangle: {:3} [t]Trace: {:3}".format(id_frame,
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len(points),
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len(objets_points),
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"ON" if rectangle else "OFF",
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"ON" if trace else "OFF")
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cv2.putText(frame,
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message,
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(20, 20),
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cv2.FONT_HERSHEY_PLAIN,
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1,
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(255, 255, 255),
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1)
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cv2.imshow("frame", frame)
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key=cv2.waitKey(70)&0xFF
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if key==ord('r'):
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rectangle=not rectangle
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if key==ord('t'):
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trace=not trace
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if key==ord('q'):
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quit()
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id_frame+=1
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