"""
    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.detclass = 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.detclass = 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_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_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 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,:], np.array([0]))))
            #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((0,6))

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")
