sort_yoloV5.py

"""
    MINOR MODIFICATION FOR ClassySORT:
    In the original implementation of SORT, 
    it threw away the object classification category information 
    For example, (0: person, 1: bike, etc.)
    
    I needed to keep that information for use in `Watchout`, 
    so I added a `detclass` attribute to the `KalmanBoxTracker` object
    which stores YOLO detection object class information.
    With this modification, SORT returns data in the format:
    `[x_left_top, y_left_top, x_right_bottom, y_right_bottom, object_category, object_identification]`
    ==========================================================================
    
    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 os
import numpy as np
import matplotlib
#matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from skimage import io

import glob
import time
import argparse
from filterpy.kalman import KalmanFilter

np.random.seed(0)

def linear_assignment(cost_matrix):
    try:
        import lap #linear assignment problem solver
        _, 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))) 

def iou_batch(bb_test, bb_gt):
    """
    From SORT: Computes IOU between two boxes in the form [x1,y1,x2,y2]
    """
    bb_gt = np.expand_dims(bb_gt, 0)
    bb_test = np.expand_dims(bb_test, 1)
    
    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 center 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):
        """
        Initialize a tracker using initial bounding box
        
        Parameter 'bbox' must have 'detected class' int number at the -1 position.
        """
        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. # R: Covariance matrix of measurement noise (set to high for noisy inputs -> more 'inertia' of boxes')
        self.kf.P[4:,4:] *= 1000. #give high uncertainty to the unobservable initial velocities
        self.kf.P *= 10.
        self.kf.Q[-1,-1] *= 0.5 # Q: Covariance matrix of process noise (set to high for erratically moving things)
        self.kf.Q[4:,4:] *= 0.5

        self.kf.x[:4] = convert_bbox_to_z(bbox) # STATE VECTOR
        self.time_since_update = 0
        self.id = KalmanBoxTracker.count
        KalmanBoxTracker.count += 1
        self.history = []
        self.hits = 0
        self.hit_streak = 0
        self.age = 0
        
        # keep yolov5 detected class information
        self.score = bbox[4]
        self.detclass = int(bbox[5])
        
    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.score = bbox[4]
        self.detclass = int(bbox[5])
        
        
    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
        # test
        arr1 = np.array([[1,2,3,4]])
        arr2 = np.array([0])
        arr3 = np.expand_dims(arr2, 0)
        np.concatenate((arr1,arr3), axis=1)
        """
        arr_score = np.expand_dims(np.array([self.score]), 0)
        arr_detclass = np.expand_dims(np.array([self.detclass]), 0)
        
        arr_u_dot = np.expand_dims(self.kf.x[4],0)
        arr_v_dot = np.expand_dims(self.kf.x[5],0)
        arr_s_dot = np.expand_dims(self.kf.x[6],0)
        
        return np.concatenate((convert_x_to_bbox(self.kf.x), arr_score, arr_detclass, arr_u_dot, arr_v_dot, arr_s_dot), axis=1)
    
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 
    1. matches,
    2. unmatched_detections
    3. 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 = iou_batch(detections, trackers)
    
    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=1, min_hits=3, iou_threshold=0.3):
        """
        Parameters for SORT
        """
        self.max_age = max_age
        self.min_hits = min_hits
        self.iou_threshold = iou_threshold
        self.trackers = []
        self.frame_count = 0
    
    def reset_count(self):
        KalmanBoxTracker.count = 0
  
    def update(self, dets=np.empty((0,6))):
        """
        Parameters:
        'dets' - a numpy array of detection in the format [[x1, y1, x2, y2, score], [x1,y1,x2,y2,score],...]
        
        Ensure to call this method even frame has no detections. (pass np.empty((0,5)))
        
        Returns a similar array, where the last column is object ID (replacing confidence score)
        
        NOTE: The number of objects returned may differ from the number of objects provided.
        """
        self.frame_count += 1
        
        # Get predicted locations from existing trackers
        trks = np.zeros((len(self.trackers), 6))
        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, 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 initialize new trackers for unmatched detections
        for i in unmatched_dets:
            trk = KalmanBoxTracker(np.hstack(dets[i,:]))
            #trk = KalmanBoxTracker(np.hstack(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'd because MOT benchmark requires positive value
            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((0,7))

def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='SORT demo')
    parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]',action='store_true')
    parser.add_argument("--seq_path", help="Path to detections.", type=str, default='data')
    parser.add_argument("--phase", help="Subdirectory in seq_path.", type=str, default='train')
    parser.add_argument("--max_age", 
                        help="Maximum number of frames to keep alive a track without associated detections.", 
                        type=int, default=1)
    parser.add_argument("--min_hits", 
                        help="Minimum number of associated detections before track is initialised.", 
                        type=int, default=3)
    parser.add_argument("--iou_threshold", help="Minimum IOU for match.", type=float, default=0.3)
    args = parser.parse_args()
    return args

if __name__ == '__main__':
    # all train
    args = parse_args()
    display = args.display
    phase = args.phase
    total_time = 0.0
    total_frames = 0
    colours = np.random.rand(32, 3) #used only for display
    if(display):
        if not os.path.exists('mot_benchmark'):
            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()
    plt.ion()
    fig = plt.figure()
    ax1 = fig.add_subplot(111, aspect='equal')

    if not os.path.exists('output'):
        os.makedirs('output')
    pattern = os.path.join(args.seq_path, phase, '*', 'det', 'det.txt')
    for seq_dets_fn in glob.glob(pattern):
        mot_tracker = Sort(max_age=args.max_age, 
                   min_hits=args.min_hits,
                   iou_threshold=args.iou_threshold) #create instance of the SORT tracker
    seq_dets = np.loadtxt(seq_dets_fn, delimiter=',')
    seq = seq_dets_fn[pattern.find('*'):].split(os.path.sep)[0]
    
    with open(os.path.join('output', '%s.txt'%(seq)),'w') as out_file:
        print("Processing %s."%(seq))
        for frame in range(int(seq_dets[:,0].max())):
            frame += 1 #detection and frame numbers begin at 1
            dets = seq_dets[seq_dets[:, 0]==frame, 2:7]
            dets[:, 2:4] += dets[:, 0:2] #convert to [x1,y1,w,h] to [x1,y1,x2,y2]
            total_frames += 1

        if(display):
            fn = os.path.join('mot_benchmark', phase, seq, 'img1', '%06d.jpg'%(frame))
            im =io.imread(fn)
            ax1.imshow(im)
            plt.title(seq + ' Tracked Targets')

        start_time = time.time()
        trackers = mot_tracker.update(dets)
        cycle_time = time.time() - start_time
        total_time += cycle_time

        for d in trackers:
            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)
            if(display):
                d = d.astype(np.int32)
                ax1.add_patch(patches.Rectangle((d[0],d[1]),d[2]-d[0],d[3]-d[1],fill=False,lw=3,ec=colours[d[4]%32,:]))

        if(display):
            fig.canvas.flush_events()
            plt.draw()
            ax1.cla()

    print("Total Tracking took: %.3f seconds 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")