ros-sort_ekf.py

#!/usr/bin/env python3

"""
    SORT: A Simple, Online and Realtime Tracker
    Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
    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

import numpy as np
import cv2
from skimage import io
import csv  
import io
import math
import time
import argparse
from filterpy.kalman import KalmanFilter
from filterpy.kalman import ExtendedKalmanFilter #new
import rospy
from darknet_ros_msgs.msg import BoundingBoxes
from darknet_ros_msgs.msg import BoundingBox
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
from std_msgs.msg import Int16



import sys
import signal
def signal_handler(signal, frame): # ctrl + c -> exit program
        print('You pressed Ctrl+C!')
        sys.exit(0)
signal.signal(signal.SIGINT, signal_handler)


try:
  from numba import jit
except:
  def jit(func):
    return func
np.random.seed(0)

#==================
from geometry_msgs.msg import PoseArray
from geometry_msgs.msg import Pose, PoseStamped
from sensor_msgs.msg import PointCloud2
from sensor_msgs import point_cloud2

pc2_data = None
temp_drone_x = None
temp_drone_y = None
temp_drone_z = None
import tf
from tf import TransformListener
#==================


def linear_assignment(cost_matrix):
  try:
    import lap
    _, x, y = lap.lapjv(cost_matrix, extend_cost=True)
    return np.array([[y[i],i] for i in x if i >= 0]) #
  except ImportError:
    from scipy.optimize import linear_sum_assignment
    x, y = linear_sum_assignment(cost_matrix)
    return np.array(list(zip(x, y)))


@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 internal 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 = ExtendedKalmanFilter(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
    self.start_time = time.time()
    self.end_time = time.time()
    
  def H_of(x,y):
    # H = 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]])
    # H = np.array([[1,0,0,0],
    #             [0,1,0,0],
    #             [0,0,1,0],
    #             [0,0,0,1]])
    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]])
    return H
  def Hx(x,y):
    Hx = np.array([[0],
                [0],
                [0],
                [0]])
    # Hx = np.array([[0],
    #             [0],
    #             [0],
    #             [0],
    #             [0],
    #             [0],
    #             [0]])
    # Hx = np.array([[1,0,0,0],
    #             [0,1,0,0],
    #             [0,0,1,0],
    #             [0,0,0,1]])
    return Hx
      
  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))
    self.kf.update(convert_bbox_to_z(bbox), HJacobian=self.H_of, Hx=self.Hx)
    self.start_time = time.time()


  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
    # self.end_time = time.time()
    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))
    # print("age:", self.age, ", time_since_update:", self.time_since_update, ", elapsed_time:", str(round(self.end_time - self.start_time, 5)), " Sec")
    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)

  if min(iou_matrix.shape) > 0:
    a = (iou_matrix > iou_threshold).astype(np.int32)
    if a.sum(1).max() == 1 and a.sum(0).max() == 1:
        matched_indices = np.stack(np.where(a), axis=1)
    else:
      matched_indices = linear_assignment(-iou_matrix)
  else:
    matched_indices = np.empty(shape=(0,2))

  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):
  def __init__(self, max_age=5, min_hits=1):
    rospy.init_node('sort', anonymous=True)
    bbox_topic = rospy.get_param("/bbox_topic", 'darknet_ros/bounding_boxes/')
    self.subb = rospy.Subscriber(bbox_topic, BoundingBoxes, self.boxcallback)
    self.subb2 = rospy.Subscriber("/steps_done", Int16, step_callback)
    multi_array_sub = rospy.Subscriber('/multi_array', Float64MultiArray, multi_array_callback)
    self.pubb = rospy.Publisher('tracked_boxes', BoundingBoxes, queue_size=50)
    self.rate = rospy.Rate(30)
    display = rospy.get_param("/display", True)
    max_age = rospy.get_param("/max_age", max_age)
    min_hits = rospy.get_param("/min_hits", min_hits)
    self.iou_threshold = rospy.get_param("/iou_threshold", 0.3)
    rospy.Subscriber('/stereo_camera/points2', PointCloud2, callback_pointcloud)

    if display:
        img_topic = rospy.get_param("/img_topic", '/imx477/image_raw')
        self.display = display
        self.subimage = rospy.Subscriber(img_topic, Image, self.imgcallback)
        self.pubimage = rospy.Publisher('tracked_image', Image, queue_size=20)
        

    """
    Sets key parameters for SORT
    """
    self.max_age = max_age
    self.min_hits = min_hits
    self.trackers = []
    self.frame_count = 0
    #self.colours = np.random.rand(32, 3) #used only for display

    self.img_in = 0
    self.bbox_checkin = 0
    self.bridge = CvBridge()


  def imgcallback(self, msg):
    temp_image = msg
    if self.display:
        try : 
            self.img = self.bridge.imgmsg_to_cv2(temp_image, "bgr8")
        except CvBridgeError as e:
            pass
    self.img_in = 1
    return
  def boxcallback(self, msg):
    dets = []
    for i in range(len(msg.bounding_boxes)):
        bbox = msg.bounding_boxes[i]
        if bbox.Class == "aeroplane" or bbox.Class == "bird" or bbox.Class == "kite" or bbox.Class == "Drone":
          dets.append(np.array([bbox.xmin, bbox.ymin, bbox.xmax, bbox.ymax, bbox.probability]))
    self.dets = np.array(dets)
    self.bbox_checkin=1
    return

  def update(self, dets=np.empty((0, 5))):
    """
    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 (use np.empty((0, 5)) for frames without 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
    # get predicted locations from existing trackers.
    trks = np.zeros((len(self.trackers), 5))
    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,self.iou_threshold)

    # update matched trackers with assigned detections
    for m in matched:
      self.trackers[m[1]].update(dets[m[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)
          trk.end_time = time.time()
          print("age:", trk.age, ", time_since_update:", trk.time_since_update, ", elapsed_time:", str(round(trk.end_time - trk.start_time, 5)), " Sec")
          
    if(len(ret)>0):
      return np.concatenate(ret)
    return np.empty((0,5))

#===============
def callback_pointcloud(data):
    global pc2_data
    global temp_drone_x, temp_drone_y, temp_drone_z
    pc2_data = data
def get_z(x,y):
    global pc2_data
    assert isinstance(pc2_data, PointCloud2)
    # gen = point_cloud2.read_points(pc2_data, field_names='z', skip_nans=False, uvs=[(x, y)])
    gen = point_cloud2.read_points(pc2_data, field_names=("x", "y", "z"), skip_nans=False, uvs=[(x, y)])
    # print("gen:",list(gen), [e for e in gen])#, list(gen)[0])# type(list(gen)[0]))
    
    ##=== comment temporeliy
    # next_gen = next(gen)
    # temp_drone_x = next_gen[0]
    # temp_drone_y = next_gen[1]
    # temp_drone_z = next_gen[2]
    temp_drone_x, temp_drone_y, temp_drone_z=0,0,0
    
    # print(temp_drone_x, temp_drone_y, temp_drone_z)
    # temp_drone_x, temp_drone_y, temp_drone_z = gen[0], gen[1], gen[2]
    # print("Z:",next(gen)[0])
    # return float(next(gen)[0]) #To return value out of funtion
    return (temp_drone_x, temp_drone_y, temp_drone_z)
#===============

actual_steps = 0
previous_actual_steps = -1
def step_callback(data):
    global actual_steps
    # print("=============> data:",data.data)
    actual_steps = data.data
    
from std_msgs.msg import Float64
from std_msgs.msg import Float64MultiArray
multi_array = None
from sklearn.neighbors import KDTree
def multi_array_callback(data):
    global multi_array
    # print("=============> data:",data.data)
    multi_array = np.array(data.data).reshape(10,3)
    # print("multi_array", multi_array)

if __name__ == '__main__':

    colours = np.random.rand(32, 3) #used only for display
    # print("colours:",colours) #debug
    mot_tracker = Sort(max_age=200, min_hits=1) #create instance of the SORT tracker

    font = cv2.FONT_HERSHEY_SIMPLEX
    org = (00, 185) # org
    fontScale = 1 # fontScale
    color = (0, 0, 255) # Red color in BGR
    thickness = 2 # Line thickness of 2 px
    loopcount=0
    with io.open('/home/rajendra/swarm_ws/csv/Tracked.csv', 'w+', encoding='UTF8') as f:
      writer = csv.writer(f)
      # writer = csv.writer(f, quoting=csv.QUOTE_NONNUMERIC)
      # writer.writerow(['sq', 'id'].decode('utf-8'))
    #   writer.writerow('\n'.join([i.decode('utf-8', 'ignore') for i in ['sq', 'id']]))
      # writer.writerow(["sq","id","xmin","ymin","xmax","ymax"])
      writer.writerow(["steps","Drone_ID", "X", "Y", "Z", "Guessed_Agentpy_ID", 'GX', 'GY', 'GZ', 'GError'])
      # cr = csv.writer(f,delimiter=";",lineterminator="\n")
      # cr.writerow(["sq","id","xmin","ymin","xmax","ymax"])
      while True:
          try:
              start_time_cycle = time.time()
              if mot_tracker.bbox_checkin==1:
                  trackers = mot_tracker.update(mot_tracker.dets)
                  # mot_tracker.bbox_checkin=0 ##old <=========
                  mot_tracker.bbox_checkin=1 #new <========= (don't reset)
              else:
                  trackers = mot_tracker.update(np.empty((0,5)))
              # print("len(trackers):",len(trackers))
              loopcount=loopcount+1
              r = BoundingBoxes()
              for d in range(len(trackers)):
                  rb = BoundingBox()
                  rb.probability=1
                  rb.xmin = int(trackers[d,0])
                  rb.ymin = int(trackers[d,1])
                  rb.xmax = int(trackers[d,2])
                  rb.ymax = int(trackers[d,3])
                  rb.id = int(trackers[d,4])
                  rb.Class = 'tracked'
                  # print("Old_CSV(sq,id,xmin,ymin,xmax,ymax):",loopcount, rb.id,rb.xmin,rb.ymin,rb.xmax,rb.ymax)
                  # writer.writerow([loopcount, rb.id,rb.xmin,rb.ymin,rb.xmax,rb.ymax])
                  #===============
                  box = rb
                  if pc2_data == None:
                      temp_drone_x, temp_drone_y, temp_drone_z = None, None, None
                  else:
                      (temp_drone_x, temp_drone_y, temp_drone_z) = get_z(int(round((box.xmin+box.xmax)/2)),int(round((box.ymin+box.ymax)/2)))
                  
                  p1 = PoseStamped()
                  p1.header.frame_id = "stereo_camera2_link" #world
                  p1.pose.position.x,p1.pose.position.y, p1.pose.position.z  = temp_drone_x, temp_drone_y, temp_drone_z
                  # p1.pose.orientation.w = 1.0    # Neutral orientation
                  # p1.pose.orientation.z = 1.0    # Neutral orientation
                  p1.pose.orientation.z = -0.7071068
                  p1.pose.orientation.w = 0.7071068
                  listener = tf.TransformListener()
                  listener.waitForTransform("/world", "/stereo_camera2_link", rospy.Time(), rospy.Duration(4.0))
                  # (trans, rot) = listener.lookupTransform("/frame1", "/frame2", rospy.Time(0))
                  p_in_base = listener.transformPose("/world", p1)
                  # p_in_base = tf.TransformListener().transformPose("/world", p1)
                  # print("New_Yolo_Drone_ID: {}, X: {}, Y: {}, Z: {}".format(rb.id, round(p_in_base.pose.position.x,4)*100,round(p_in_base.pose.position.y,4)*100, round(p_in_base.pose.position.z,4)*100))
                  # writer.writerow([loopcount,rb.id, round(p_in_base.pose.position.x,4)*100,round(p_in_base.pose.position.y,4)*100, round(p_in_base.pose.position.z,4)*100])
                  if actual_steps != previous_actual_steps:
                    if math.isnan(temp_drone_z)!=1: 
                      Current_Tracked = [[p_in_base.pose.position.x, p_in_base.pose.position.y, p_in_base.pose.position.z]]
                      tree = KDTree(multi_array, leaf_size=2) 
                      dist, ind = tree.query(Current_Tracked, k=1)
                      # print("dist,ind:", dist,ind)
                      Guessed_Agentpy_ID = ind[0][0]
                      GX,GY,GZ = multi_array[ind[0][0]]*100 #convert to cm
                      GError=dist[0][0]*100 #convert to cm
                      print("Steps: {}, Tracked_ID: {}, X: {}, Y: {}, Z: {}, Guessed_Agentpy_ID: {}, GX: {}, GY: {}, GZ: {}, GError: {}".format(actual_steps, rb.id, round(p_in_base.pose.position.x,4)*100,round(p_in_base.pose.position.y,4)*100, round(p_in_base.pose.position.z,4)*100, Guessed_Agentpy_ID, GX, GY, GZ, GError))
                      writer.writerow([actual_steps,rb.id, round(p_in_base.pose.position.x,4)*100,round(p_in_base.pose.position.y,4)*100, round(p_in_base.pose.position.z,4)*100, Guessed_Agentpy_ID, GX, GY, GZ, GError])
                
                  #===============  

                  r.bounding_boxes.append(rb)
                  if mot_tracker.img_in==1 and mot_tracker.display:
                      res = trackers[d].astype(np.int32)
                      rgb=colours[res[4]%32,:]*255
                      cv2.rectangle(mot_tracker.img, (res[0],res[1]), (res[2],res[3]), (rgb[0],rgb[1],rgb[2]), 4)
                      # cv2.putText(mot_tracker.img, "ID : %d"%(res[4]), (res[0],res[1]), cv2.FONT_HERSHEY_SIMPLEX, 1.6, (200,85,200), 6)
                      # cv2.putText(mot_tracker.img, "Drone_%d"%(res[4]), (res[0],res[1] - 10), font, fontScale, color, thickness, cv2.LINE_AA, False)
                      cv2.putText(mot_tracker.img, "%d"%(res[4]), (res[0],res[1] - 10), font, fontScale, color, thickness, cv2.LINE_AA, False)
              if mot_tracker.img_in==1 and mot_tracker.display:
                  try : 
                      mot_tracker.image = mot_tracker.bridge.cv2_to_imgmsg(mot_tracker.img, "bgr8")
                      mot_tracker.image.header.stamp = rospy.Time.now()
                      mot_tracker.pubimage.publish(mot_tracker.image)
                  except CvBridgeError as e:
                      pass
                  
              cycle_time = time.time() - start_time_cycle
              if len(r.bounding_boxes)>0: #prevent empty box
                  r.header.stamp = rospy.Time.now()
                  mot_tracker.pubb.publish(r)
                  # print("cycle_time: ", cycle_time)
                  
              mot_tracker.rate.sleep()
              previous_actual_steps = actual_steps

          except (rospy.ROSInterruptException, SystemExit, KeyboardInterrupt):
              sys.exit(0)
  #        except:
  #            pass