Source code for livecellx.track.sort_tracker

"""
    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 matplotlib
import numpy as np

# matplotlib.use("TkAgg")
import argparse
import glob
import time

import matplotlib.patches as patches
import matplotlib.pyplot as plt
from filterpy.kalman import KalmanFilter
from skimage import io

np.random.seed(0)


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)))


def iou_batch(bb_test, bb_gt):
    """
    From SORT: Computes IOU between two bboxes 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(0.0, xx2 - xx1)
    h = np.maximum(0.0, 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 / 2.0
    y = bbox[1] + h / 2.0
    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 / 2.0, x[1] - h / 2.0, x[0] + w / 2.0, x[1] + h / 2.0]).reshape((1, 4))
    else:
        return np.array([x[0] - w / 2.0, x[1] - h / 2.0, x[0] + w / 2.0, x[1] + h / 2.0, 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.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:] *= 10.0
        self.kf.P[4:, 4:] *= 1000.0  # give high uncertainty to the unobservable initial velocities
        self.kf.P *= 10.0
        self.kf.Q[-1, -1] *= 0.01
        self.kf.Q[4:, 4:] *= 0.01

        self.origin_bbox = np.copy(np.array(bbox))
        self.origin_bboxes = [self.origin_bbox]
        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))
        self.origin_bbox = np.copy(np.array(bbox))
        self.origin_bboxes.append(self.origin_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 get_original_bbox(self):
        return self.origin_bbox


[docs]def associate_detections_with_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 = 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): """ Sets key 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 update(self, dets=np.empty((0, 5)), ret_origin_bbox=False): """ 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. if ret_origin_bbox is True, return [tracker_bb, original bbox, score, track_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_with_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): det_row = None if ret_origin_bbox: det_row = np.concatenate((d, trk.get_original_bbox(), [trk.id + 1])).reshape(1, -1) else: det_row = np.concatenate((d, [trk.id + 1])).reshape(1, -1) ret.append(det_row) # +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)) 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")