#! /usr/bin/env python3
# from https://docs.ultralytics.com/modes/track/
import sys, argparse
from copy import copy
from collections import Counter
import numpy as np
from scipy.optimize import linear_sum_assignment
from ultralytics import YOLO
from torch import cat
from torchvision.ops import box_iou
import cv2

from trafficintelligence import cvutils, moving, storage, utils

parser = argparse.ArgumentParser(description='The program tracks objects using the ultralytics models and trakcers.')
parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file', required = True)
parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
parser.add_argument('-m', dest = 'detectorFilename', help = 'name of the detection model file', required = True)
parser.add_argument('-t', dest = 'trackerFilename', help = 'name of the tracker file', required = True)
parser.add_argument('-o', dest = 'homographyFilename', help = 'filename of the homography matrix', default = 'homography.txt')
parser.add_argument('-k', dest = 'maskFilename', help = 'name of the mask file')
parser.add_argument('--undistort', dest = 'undistort', help = 'undistort the video', action = 'store_true')
parser.add_argument('--intrinsic', dest = 'intrinsicCameraMatrixFilename', help = 'name of the intrinsic camera file')
parser.add_argument('--distortion-coefficients', dest = 'distortionCoefficients', help = 'distortion coefficients', nargs = '*', type = float)
parser.add_argument('--display', dest = 'display', help = 'show the raw detection and tracking results', action = 'store_true')
parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to process', type = int, default = 0)
parser.add_argument('-l', dest = 'lastFrameNum', help = 'number of last frame number to process', type = int, default = float('Inf'))
parser.add_argument('--bike-prop', dest = 'bikeProportion', help = 'minimum proportion of time a person classified as bike or motorbike to be classified as cyclist', type = float, default = 0.2)
parser.add_argument('--cyclist-iou', dest = 'cyclistIou', help = 'IoU threshold to associate a bike and ped bounding box', type = float, default = 0.15)
parser.add_argument('--cyclist-match-prop', dest = 'cyclistMatchingProportion', help = 'minimum proportion of time a bike exists and is associated with a pedestrian to be merged as cyclist', type = float, default = 0.3)
parser.add_argument('--max-temp-overal', dest = 'maxTemporalOverlap', help = 'maximum proportion of time to merge 2 bikes associated with same pedestrian', type = float, default = 0.05)
args = parser.parse_args()

# TODO add option to refine position with mask for vehicles

# use 2 x bytetrack track buffer to remove objects from existing ones

# Load a model
model = YOLO(args.detectorFilename) # seg yolov8x-seg.pt
# seg could be used on cropped image... if can be loaded and kept in memory
# model = YOLO('/home/nicolas/Research/Data/classification-models/yolo_nas_l.pt ') # AttributeError: 'YoloNAS_L' object has no attribute 'get'

# Track with the model
if args.display:
    windowName = 'frame'
    cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)

capture = cv2.VideoCapture(args.videoFilename)
objects = {}
featureNum = 1
frameNum = args.firstFrameNum
capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
lastFrameNum = args.lastFrameNum

success, frame = capture.read()
if not success:
    print('Input {} could not be read. Exiting'.format(args.videoFilename))
    import sys; sys.exit()
results = model.track(frame, tracker=args.trackerFilename, classes=list(moving.cocoTypeNames.keys()), persist=True, verbose=False)
# create object with user type and list of 3 features (bottom ones and middle) + projection
while capture.isOpened() and success and frameNum <= lastFrameNum:
#for frameNum, result in enumerate(results):
    result = results[0]
    if frameNum %10 == 0:
        print(frameNum, len(result.boxes), 'objects')
    for box in result.boxes:
        #print(box.cls, box.id, box.xyxy)
        if box.id is not None: # None are objects with low confidence
            num = int(box.id.item())
            #xyxy = box.xyxy[0].tolist()
            if num in objects:
                objects[num].timeInterval.last = frameNum
                objects[num].features[0].timeInterval.last = frameNum
                objects[num].features[1].timeInterval.last = frameNum
                objects[num].bboxes[frameNum] = copy(box.xyxy)
                objects[num].userTypes.append(moving.coco2Types[int(box.cls.item())])
                objects[num].features[0].tmpPositions[frameNum] = moving.Point(box.xyxy[0,0].item(), box.xyxy[0,1].item())
                objects[num].features[1].tmpPositions[frameNum] = moving.Point(box.xyxy[0,2].item(), box.xyxy[0,3].item())
            else:
                inter = moving.TimeInterval(frameNum, frameNum)
                objects[num] = moving.MovingObject(num, inter)
                objects[num].bboxes = {frameNum: copy(box.xyxy)}
                objects[num].userTypes = [moving.coco2Types[int(box.cls.item())]]
                objects[num].features = [moving.MovingObject(featureNum, copy(inter)), moving.MovingObject(featureNum+1, copy(inter))]
                objects[num].featureNumbers = [featureNum, featureNum+1]
                objects[num].features[0].tmpPositions = {frameNum: moving.Point(box.xyxy[0,0].item(), box.xyxy[0,1].item())}
                objects[num].features[1].tmpPositions = {frameNum: moving.Point(box.xyxy[0,2].item(), box.xyxy[0,3].item())}
                featureNum += 2
    if args.display:
        cvutils.cvImshow(windowName, result.plot()) # original image in orig_img
        key = cv2.waitKey()
        if cvutils.quitKey(key):
            break
    frameNum += 1
    success, frame = capture.read()
    results = model.track(frame, persist=True)

# classification
for num, obj in objects.items():
    obj.setUserType(utils.mostCommon(obj.userTypes)) # improve? mix with speed?

# add quality control: avoid U-turns
    
# merge bikes and people
twowheels = [num for num, obj in objects.items() if obj.getUserType() in (3,4)]
pedestrians = [num for num, obj in objects.items() if obj.getUserType() == 2]

def mergeObjects(obj1, obj2):
    obj1.features = obj1.features+obj2.features
    obj1.featureNumbers = obj1.featureNumbers+obj2.featureNumbers
    obj1.timeInterval = moving.TimeInterval(min(obj1.getFirstInstant(), obj2.getFirstInstant()), max(obj1.getLastInstant(), obj2.getLastInstant()))    
    
costs = []
for twInd in twowheels:
    tw = objects[twInd]
    tw.nBBoxes = len(tw.bboxes)
    twCost = []
    for pedInd in pedestrians:
        ped = objects[pedInd]
        nmatches = 0
        for t in tw.bboxes:
            if t in ped.bboxes:
                #print(tw.num, ped.num, t, box_iou(tw.bboxes[t], ped.bboxes[t]))
                if not tw.commonTimeInterval(ped).empty() and box_iou(tw.bboxes[t], ped.bboxes[t]).item() > args.cyclistIou:
                    nmatches += 1
        twCost.append(nmatches/tw.nBBoxes)
    costs.append(twCost)

costs = -np.array(costs)

# before matching, scan for pedestrians with good non-overlapping temporal match with different bikes
for pedInd in range(costs.shape[1]):
    nMatchedBikes = (costs[:,pedInd] < -args.cyclistMatchingProportion).sum()
    if nMatchedBikes == 0: # peds that have no bike matching: see if they have been classified as bikes sometimes
        userTypeStats = Counter(obj.userTypes)
        if (4 in userTypeStats or (3 in userTypeStats and 4 in userTypeStats and userTypeStats[3]<=userTypeStats[4])) and userTypeStats[3]+userTypeStats[4] > args.bikeProportion*userTypeStats.total(): # 3 is motorcycle and 4 is cyclist (verif if not turning all motorbike into cyclists)
            obj.setUserType(4)
    elif nMatchedBikes > 1: # try to merge bikes first
        twIndices = np.nonzero(costs[:,pedInd] < -args.cyclistMatchingProportion)[0]
        # we have to compute temporal overlaps of all 2 wheels among themselves, then remove the ones with the most overlap (sum over column) one by one until there is little left
        nTwoWheels = len(twIndices)
        twTemporalOverlaps = np.zeros((nTwoWheels,nTwoWheels))
        for i in range(nTwoWheels):
            for j in range(i):
                twi = objects[twowheels[twIndices[i]]]
                twj = objects[twowheels[twIndices[j]]]
                twTemporalOverlaps[i,j] = len(set(twi.bboxes).intersection(set(twj.bboxes)))/max(len(twi.bboxes), len(twj.bboxes))
                #twTemporalOverlaps[j,i] = twTemporalOverlaps[i,j]
        tw2merge = list(range(nTwoWheels))
        while len(tw2merge)>0 and (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).max() >= 2:
            i = (twTemporalOverlaps[np.ix_(tw2merge, tw2merge)] > args.maxTemporalOverlap).sum(0).argmax()
            del tw2merge[i]
        twIndices = [twIndices[i] for i in tw2merge]
        tw1 = objects[twowheels[twIndices[0]]]
        twCost = costs[twIndices[0],:]*tw1.nBBoxes
        nBBoxes = tw1.nBBoxes
        for twInd in twIndices[1:]:
            mergeObjects(tw1, objects[twowheels[twInd]])
            twCost = twCost + costs[twInd,:]*objects[twowheels[twInd]].nBBoxes
            nBBoxes += objects[twowheels[twInd]].nBBoxes
        twIndicesToKeep = list(range(costs.shape[0]))
        for twInd in twIndices[1:]:
            twIndicesToKeep.remove(twInd)
            del objects[twowheels[twInd]]
        twowheels = [twowheels[i] for i in twIndicesToKeep]
        costs = costs[twIndicesToKeep,:]

twIndices, matchingPedIndices = linear_sum_assignment(costs)
for twInd, pedInd in zip(twIndices, matchingPedIndices): # caution indices in the cost matrix
    if -costs[twInd, pedInd] >= args.cyclistMatchingProportion:
        tw = objects[twowheels[twInd]]
        ped = objects[pedestrians[pedInd]]
        mergeObjects(tw, ped)
        del objects[pedestrians[pedInd]]
        #TODO Verif overlap piéton vélo : si long hors overlap, changement mode (trouver exemples)

# interpolate and generate velocity (?) for the features (bboxes) before saving
for num, obj in objects.items():
    #obj.features[1].timeInterval = copy(obj.getTimeInterval())
    for f in obj.getFeatures():
        if f.length() != len(f.tmpPositions): # interpolate
            f.positions = moving.Trajectory.fromPointDict(f.tmpPositions)
            #obj.features[1].positions = moving.Trajectory.fromPointDict(obj.features[1].tmpPositions)
        else:
            f.positions = moving.Trajectory.fromPointList(list(f.tmpPositions.values()))
            #obj.features[1].positions = moving.Trajectory.fromPointList(list(obj.features[1].tmpPositions.values()))

storage.saveTrajectoriesToSqlite(args.databaseFilename, list(objects.values()), 'object')

# todo save bbox and mask to study localization / representation
# apply quality checks deviation and acceleration bounds?

# def mergeBBoxes(tw, ped):
#     'merges ped into tw (2nd obj into first obj)'
#     timeInstants = set(tw.bboxes).union(set(ped.bboxes))
#     for t in timeInstants:
#         if t in tw.bboxes and t in ped.bboxes:
#             tw.features[0].tmpPositions[t] = moving.Point(min(tw.features[0].tmpPositions[t].x, ped.features[0].tmpPositions[t].x),
#                                                           min(tw.features[0].tmpPositions[t].y, ped.features[0].tmpPositions[t].y))
#             tw.features[1].tmpPositions[t] = moving.Point(max(tw.features[1].tmpPositions[t].x, ped.features[1].tmpPositions[t].x),
#                                                           max(tw.features[1].tmpPositions[t].y, ped.features[1].tmpPositions[t].y))
#         elif t in ped.bboxes:
#             tw.features[0].tmpPositions[t] = ped.features[0].tmpPositions[t]
#             tw.features[1].tmpPositions[t] = ped.features[1].tmpPositions[t]
#     tw.timeInterval = moving.TimeInterval(min(tw.getFirstInstant(), ped.getFirstInstant()), max(tw.getLastInstant(), ped.getLastInstant()))
