sort2.py

"""
    SORT: A Simple, Online and Realtime Tracker
    Copyright (C) 2016 Alex Bewley alex@dynamicdetection.com

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
"""
from __future__ import print_function

from numba import jit
import os.path
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from skimage import io
from sklearn.utils.linear_assignment_ import linear_assignment
import glob
import time
import argparse
from KalmanFilter import KalmanFilter
import motmetrics as mm

@jit
def iou(bb_test,bb_gt):
  """
  Computes IUO between two bboxes in the form [x1,y1,x2,y2]
  """
  xx1 = np.maximum(bb_test[0], bb_gt[0])
  yy1 = np.maximum(bb_test[1], bb_gt[1])
  xx2 = np.minimum(bb_test[2], bb_gt[2])
  yy2 = np.minimum(bb_test[3], bb_gt[3])
  w = np.maximum(0., xx2 - xx1)
  h = np.maximum(0., yy2 - yy1)
  wh = w * h
  o = wh / ((bb_test[2]-bb_test[0])*(bb_test[3]-bb_test[1])
    + (bb_gt[2]-bb_gt[0])*(bb_gt[3]-bb_gt[1]) - wh)
  return(o)

def convert_bbox_to_z(bbox):
  """
  Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
    [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
    the aspect ratio
  """
  w = bbox[2]-bbox[0]
  h = bbox[3]-bbox[1]
  x = bbox[0]+w/2.
  y = bbox[1]+h/2.
  s = w*h    #scale is just area
  r = w/float(h)
  return np.array([x,y,s,r]).reshape((4,1))

def convert_x_to_bbox(x,score=None):
  """
  Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
    [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
  """
  w = np.sqrt(x[2]*x[3])
  h = x[2]/w
  if(score==None):
    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))
  else:
    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.,score]).reshape((1,5))


class KalmanBoxTracker(object):
  """
  This class represents the internel state of individual tracked objects observed as bbox.
  """
  count = 0
  def __init__(self,bbox):
    """
    Initialises a tracker using initial bounding box.
    """
    #define constant velocity model
    self.kf = KalmanFilter(dim_x=7, dim_z=4)
    self.kf.F = np.array([[1,0,0,0,1,0,0],[0,1,0,0,0,1,0],[0,0,1,0,0,0,1],[0,0,0,1,0,0,0],  [0,0,0,0,1,0,0],[0,0,0,0,0,1,0],[0,0,0,0,0,0,1]])
    self.kf.H = np.array([[1,0,0,0,0,0,0],[0,1,0,0,0,0,0],[0,0,1,0,0,0,0],[0,0,0,1,0,0,0]])

    self.kf.R[2:,2:] *= 10.
    self.kf.P[4:,4:] *= 1000. #give high uncertainty to the unobservable initial velocities
    self.kf.P *= 10.
    self.kf.Q[-1,-1] *= 0.01
    self.kf.Q[4:,4:] *= 0.01

    self.kf.x[:4] = convert_bbox_to_z(bbox)
    self.time_since_update = 0
    self.id = KalmanBoxTracker.count
    KalmanBoxTracker.count += 1
    self.history = []
    self.hits = 0
    self.hit_streak = 0
    self.age = 0

  def update(self,bbox):
    """
    Updates the state vector with observed bbox.
    """
    self.time_since_update = 0
    self.history = []
    self.hits += 1
    self.hit_streak += 1
    self.kf.update(convert_bbox_to_z(bbox))

  def predict(self):
    """
    Advances the state vector and returns the predicted bounding box estimate.
    """
    if((self.kf.x[6]+self.kf.x[2])<=0):
      self.kf.x[6] *= 0.0
    self.kf.predict()
    self.age += 1
    if(self.time_since_update>0):
      self.hit_streak = 0
    self.time_since_update += 1
    self.history.append(convert_x_to_bbox(self.kf.x))
    return self.history[-1]

  def get_state(self):
    """
    Returns the current bounding box estimate.
    """
    return convert_x_to_bbox(self.kf.x)

def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
  """
  Assigns detections to tracked object (both represented as bounding boxes)

  Returns 3 lists of matches, unmatched_detections and unmatched_trackers
  """
  if(len(trackers)==0):
    return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)
  iou_matrix = np.zeros((len(detections),len(trackers)),dtype=np.float32)

  for d,det in enumerate(detections):
    for t,trk in enumerate(trackers):
      iou_matrix[d,t] = iou(det,trk)
  matched_indices = linear_assignment(-iou_matrix)

  unmatched_detections = []
  for d,det in enumerate(detections):
    if(d not in matched_indices[:,0]):
      unmatched_detections.append(d)
  unmatched_trackers = []
  for t,trk in enumerate(trackers):
    if(t not in matched_indices[:,1]):
      unmatched_trackers.append(t)

  #filter out matched with low IOU
  matches = []
  for m in matched_indices:
    if(iou_matrix[m[0],m[1]]<iou_threshold):
      unmatched_detections.append(m[0])
      unmatched_trackers.append(m[1])
    else:
      matches.append(m.reshape(1,2))
  if(len(matches)==0):
    matches = np.empty((0,2),dtype=int)
  else:
    matches = np.concatenate(matches,axis=0)

  return matches, np.array(unmatched_detections), np.array(unmatched_trackers)


class Sort(object):
  #initializes the object
  def __init__(self,max_age=1,min_hits=3):
    """
    Sets key parameters for SORT
    """
    print("max_age",max_age)
    self.max_age = max_age #The number of frames till the sequence is recorded
    self.min_hits = min_hits
    self.trackers = []
    self.frame_count = 0


  #Called after start time is noted down
  def update(self,dets):
    """
    Params:
      dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
    Requires: this method must be called once for each frame even with empty detections.
    Returns the a similar array, where the last column is the object ID.

    NOTE: The number of objects returned may differ from the number of detections provided.
    """
    self.frame_count += 1 #Frame count for the object
    #get predicted locations from existing trackers.
    trks = np.zeros((len(self.trackers),5))
    #print(self.trackers)
    #print("trks",np.size(trks))
    to_del = []
    ret = []
    for t,trk in enumerate(trks):
      pos = self.trackers[t].predict()[0]
      trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
      if(np.any(np.isnan(pos))):
        to_del.append(t)
    trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
    for t in reversed(to_del):
      self.trackers.pop(t)
    matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets,trks)

    #update matched trackers with assigned detections
    for t,trk in enumerate(self.trackers):
      if(t not in unmatched_trks):
        d = matched[np.where(matched[:,1]==t)[0],0]
        trk.update(dets[d,:][0])

    #create and initialise new trackers for unmatched detections
    for i in unmatched_dets:
        trk = KalmanBoxTracker(dets[i,:]) 
        self.trackers.append(trk)
    i = len(self.trackers)
    for trk in reversed(self.trackers):
        d = trk.get_state()[0]
        if((trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits)):
          ret.append(np.concatenate((d,[trk.id+1])).reshape(1,-1)) # +1 as MOT benchmark requires positive
        i -= 1
        #remove dead tracklet
        if(trk.time_since_update > self.max_age):
          self.trackers.pop(i)
    if(len(ret)>0):
      return np.concatenate(ret)
    return np.empty((0,5))
    
#Parses the given arguments
#Seq_map presents the list of subdatasets available for the processing.
def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='SORT')
    parser.add_argument('-m', '--seqmap', type=str, required=True,
                        help="full path to the seqmap file to evaluate")
    parser.add_argument('-o', '--res_dir', type=str, required=True,
                        help="path to the results directory")
    parser.add_argument('-b', '--benchmark_dir', type=str, required=True,
                        help="path to the sequence directory")
    parser.add_argument('-dt', '--detection_threshold', type=float, required=True,
                        help="minimum threshold for detections")
    parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]',action='store_true')
    args = parser.parse_args()
    return args


###################################################
#CODE starts execution here
###################################################


if __name__ == '__main__':
  # all train
  #Parse the arguments to args
  args = parse_args()
  #this opens the sequence or the videos of trackings in sequences # SEQMAP must contain the map of all the 
  #subdatasets in the train
  with open(args.seqmap) as fd:
      sequences = [line.rstrip('\n') for line in fd][1:]
  print("sequences \n",sequences)
  print("benchmark_dir" ,args.benchmark_dir)
  #display holds true when --display is given as an argument while running the code and 
  #the code starts to display the results from the tracker
  display = args.display
  #Training phase is used only
  phase = 'train'
  #initializing
  total_time = 0.0
  total_frames = 0
  colours = np.random.rand(32,3) #used only for display
  #if resuts diplaying var is high 
  if(display):
    #If the symlink is not created succesfully, the code presents an error
    #if not os.path.exists(args.benchmark_dir):
    #  print('\n\tERROR: mot_benchmark link not found!\n\n    Create a symbolic link to the MOT benchmark\n    (https://motchallenge.net/data/2D_MOT_2015/#download). E.g.:\n\n    $ ln -s /path/to/MOT2015_challenge/2DMOT2015 mot_benchmark\n\n')
    #  exit()
      #code execution exits
    #if not, the tracking is visualized using plt.ion
    plt.ion()
    fig = plt.figure() 
  

#make results directory if it doesnt already exists
  if not os.path.exists(args.res_dir):
    os.makedirs(args.res_dir)
  

#each sequence is taken inividually
#These sequences refer to the subdatasets - ETH, KITTI, etc
  for j in range(1,20):
    for seq in sequences:
      try:
        #print(args.benchmark_dir)
        print("Sequence", seq)
        mot_tracker = Sort(max_age=5) #create instance of the SORT tracker
        print(os.path.join(args.benchmark_dir, '%s/%s/det/det.txt'%(j,seq)))
        seq_dets = np.loadtxt(os.path.join(args.benchmark_dir, '%s/%s/detno/det.txt'%(j,seq)),delimiter=',') #load detections/ground truth
        #print("seq_dets initial", seq_dets)
        #seq_dets = seq_dets[seq_dets[:, -1] > args.detection_threshold]#Only detections having confidence level 
        #above the threshold confidence/score are considered. The detections are ground truth results
        #print("seq_dets New", seq_dets)
        # try:
        #   moreThanThresh=seq_dets[0]
        #   print("Success")
        # except:
        #   print("unsuccessful")
        # if(seq_dets is not empty):
        #   print("Success")
        # else:
        #   print("unsuccessful")
        
        ##If folders are not already made

        # path3="C:/Users/asd/Desktop/sort-tracker/Indian/results"
        # for i in range(1,20):#person number
        # inpath=path3+"/"+str(i)
        #   for j in range(1,8):#video number
        #       try:
        #           foldPath=inpath+"/"+str(j)
        #           print(foldPath)
        #           if not os.path.exists(foldPath):
        #               os.makedirs(foldPath)
        #               print("folder made")
        #       except:
        #           print("Error Occurred",i,j)    
              #Results file is opened

        with open(os.path.join(args.res_dir, '%s/%s/%s-%s.txt'%(j,seq,j,seq)),'w') as out_file:
          print(out_file)
          #processing the sequence
          print("Processing %s."%(seq))
          #each frame is taken one by one-0th index in the result as well as det.txt and gt.txt is the frame number
          for frame in np.unique(seq_dets[:,0]):
            dets = seq_dets[seq_dets[:,0]==frame,2:7]
            #seq dets contain the bounding box info of each pedestrian in a frame - x1,y1,w,h
            dets[:,2:4] += dets[:,0:2] #convert to [x1,y1,w,h] to [x1,y1,x2,y2]
            total_frames += 1#total frames are counted
            #Displaying of tracking code
            if(display):
              ax1 = fig.add_subplot(111, aspect='equal')
              fn = os.path.join(args.benchmark_dir, '%s/%s/img1/%06d.jpg'%(j,seq,frame))
              im =io.imread(fn)
              ax1.imshow(im)
              plt.title(seq+' Tracked Targets')
            #For tracking time elapsed, start time is first noted down
            start_time = time.time()


            ###########################################
            #Tracking starts
            ###########################################

            #Tracker object's update method is called first with dets being the data for all pedestrians in the current frame
            #It contains frame no. and x1, y1, x2, y2 and scores (converted from x1,y1,w,h)
            # Params:
            # dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
            # Requires: this method must be called once for each frame even with empty detections.
            # Returns the a similar array, where the last column is the object ID.
            trackers = mot_tracker.update(dets)


            cycle_time = time.time() - start_time
            total_time += cycle_time
            #Results are printed to a text file in the output folder
            for d in trackers:
              #print(d)
              #1,-1,-1,-1 are default numbers required for the MOT challenge's format for results submission
              print('%d,%d,%.2f,%.2f,%.2f,%.2f,1,-1,-1,-1'%(frame,d[4],d[0],d[1],d[2]-d[0],d[3]-d[1]),file=out_file)
              #frame gives the frame number in first column, 
              #d[4] gives the pedestrian id
              #d[0] gives the x1 coordinate while d[2] gives the y1 coordinate in 3rd and 4th column
              #d[2]-d[0] is x2-x1 which gives the bounding box width and d[3]-d[1] is y2-y1 which gives the bounding box height
              #in the 5th and 6th column
              #If display is given as an argument, show the rectangles with the given x1,y1,w,h on the corresponding frames
              if(display):
                d = d.astype(np.int32)
                ax1.add_patch(patches.Rectangle((d[0],d[1]-(d[3]-d[1])),d[2]-d[0],d[3]-d[1],fill=False,lw=3,ec=colours[d[4]%32,:]))
                ax1.set_adjustable('box-forced')

            if(display):
              fig.canvas.flush_events()
              plt.draw()
              ax1.cla()
      except:
        print("Sequence not found",j,seq)      

  print("Total Tracking took: %.3f for %d frames or %.1f FPS"%(total_time,total_frames,total_frames/total_time))
  if(display):
    print("Note: to get real runtime results run without the option: --display")
  