new

import hydra
import torch
import argparse
import time
from pathlib import Path

import cv2
import torch
from omegaconf import DictConfig
from numpy import random
import contextlib
import inspect
import logging.config
import os
import platform
import subprocess
import sys
import tempfile
import threading
import uuid
from pathlib import Path

import cv2
import numpy as np
import pandas as pd
import torch
import yaml

from ultralytics.engine.predictor import BasePredictor 

from ultralytics.utils.checks import check_imgsz
from ultralytics.utils.plotting import Annotator, colors, save_one_box

import cv2
from deep_sort_pytorch.utils.parser import get_config
from deep_sort_pytorch.deep_sort import DeepSort
from collections import deque
import numpy as np
palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
data_deque = {}

deepsort = None

object_counter = {}

object_counter1 = {}

line = [(100, 500), (1050, 500)]

def init_tracker():
    global deepsort
    cfg_deep = get_config()
    cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml")

    deepsort= DeepSort(cfg_deep.DEEPSORT.REID_CKPT,
                            max_dist=cfg_deep.DEEPSORT.MAX_DIST, min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE,
                            nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE,
                            max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, nn_budget=cfg_deep.DEEPSORT.NN_BUDGET,
                            use_cuda=True)
    
# Constants
FILE = Path(__file__).resolve()
ROOT = FILE.parents[2]  # YOLO
DEFAULT_CONFIG = ROOT / "yolo/configs/default.yaml"
RANK = int(os.getenv('RANK', -1))
NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv5 multiprocessing threads
AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true'  # global auto-install mode
FONT = 'Arial.ttf'  # https://ultralytics.com/assets/Arial.ttf
VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true'  # global verbose mode
TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}'  # tqdm bar format
LOGGING_NAME = 'yolov5'
HELP_MSG = \
    """
    Usage examples for running YOLOv8:

    1. Install the ultralytics package:

        pip install ultralytics

    2. Use the Python SDK:

        from ultralytics import YOLO

        model = YOLO('yolov8n.yaml')                # build a new model from scratch
        model = YOLO('yolov8n.pt')                  # load a pretrained model (recommended for best training results)
        results = model.train(data='coco128.yaml')  # train the model
        results = model.val()                       # evaluate model performance on the validation set
        results = model.predict(source='bus.jpg')   # predict on an image
        success = model.export(format='onnx')       # export the model to ONNX format

    3. Use the command line interface (CLI):

        yolo task=detect    mode=train    model=yolov8n.yaml      args...
                  classify       predict        yolov8n-cls.yaml  args...
                  segment        val            yolov8n-seg.yaml  args...
                                 export         yolov8n.pt        format=onnx  args...

    Docs: https://docs.ultralytics.com
    Community: https://community.ultralytics.com
    GitHub: https://github.com/ultralytics/ultralytics
   
     """
# Settings
torch.set_printoptions(linewidth=320, precision=5, profile='long')
np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
pd.options.display.max_columns = 10
cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS)  # NumExpr max threads
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'  # for deterministic training

# Default config dictionary


def is_colab():
    """
    Check if the current script is running inside a Google Colab notebook.

    Returns:
        bool: True if running inside a Colab notebook, False otherwise.
    """
    # Check if the google.colab module is present in sys.modules
    return 'google.colab' in sys.modules

def is_kaggle():
    """
    Check if the current script is running inside a Kaggle kernel.

    Returns:
        bool: True if running inside a Kaggle kernel, False otherwise.
    """
    return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com'

def is_jupyter_notebook():
    """
    Check if the current script is running inside a Jupyter Notebook.
    Verified on Colab, Jupyterlab, Kaggle, Paperspace.

    Returns:
        bool: True if running inside a Jupyter Notebook, False otherwise.
    """
    # Check if the get_ipython function exists
    # (it does not exist when running as a standalone script)
    try:
        from IPython import get_ipython
        return get_ipython() is not None
    except ImportError:
        return False

def is_docker() -> bool:
    """
    Determine if the script is running inside a Docker container.

    Returns:
        bool: True if the script is running inside a Docker container, False otherwise.
    """
    file = Path('/proc/self/cgroup')
    if file.exists():
        with open(file) as f:
            return 'docker' in f.read()
    else:
        return False

def is_git_directory() -> bool:
    """
    Check if the current working directory is inside a git repository.

    Returns:
        bool: True if the current working directory is inside a git repository, False otherwise.
    """
    from git import Repo
    try:
        # Check if the current working directory is a git repository
        Repo(search_parent_directories=True)
        return True
    except Exception:
        return False

def is_pip_package(filepath: str = __name__) -> bool:
    """
    Determines if the file at the given filepath is part of a pip package.

    Args:
        filepath (str): The filepath to check.

    Returns:
        bool: True if the file is part of a pip package, False otherwise.
    """
    import importlib.util

    # Get the spec for the module
    spec = importlib.util.find_spec(filepath)

    # Return whether the spec is not None and the origin is not None (indicating it is a package)
    return spec is not None and spec.origin is not None

def is_dir_writeable(dir_path: str) -> bool:
    """
    Check if a directory is writeable.

    Args:
        dir_path (str): The path to the directory.

    Returns:
        bool: True if the directory is writeable, False otherwise.
    """
    try:
        with tempfile.TemporaryFile(dir=dir_path):
            pass
        return True
    except OSError:
        return False

def get_git_root_dir():
    """
    Determines whether the current file is part of a git repository and if so, returns the repository root directory.
    If the current file is not part of a git repository, returns None.
    """
    try:
        output = subprocess.run(["git", "rev-parse", "--git-dir"], capture_output=True, check=True)
        return Path(output.stdout.strip().decode('utf-8')).parent  # parent/.git
    except subprocess.CalledProcessError:
        return None

def get_default_args(func):
    # Get func() default arguments
    signature = inspect.signature(func)
    return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}

def get_user_config_dir(sub_dir='Ultralytics'):
    """
    Get the user config directory.

    Args:
        sub_dir (str): The name of the subdirectory to create.

    Returns:
        Path: The path to the user config directory.
    """
    # Get the operating system name
    os_name = platform.system()

    # Return the appropriate config directory for each operating system
    if os_name == 'Windows':
        path = Path.home() / 'AppData' / 'Roaming' / sub_dir
    elif os_name == 'Darwin':  # macOS
        path = Path.home() / 'Library' / 'Application Support' / sub_dir
    elif os_name == 'Linux':
        path = Path.home() / '.config' / sub_dir
    else:
        raise ValueError(f'Unsupported operating system: {os_name}')

    # GCP and AWS lambda fix, only /tmp is writeable
    if not is_dir_writeable(str(path.parent)):
        path = Path('/tmp') / sub_dir

    # Create the subdirectory if it does not exist
    path.mkdir(parents=True, exist_ok=True)

    return path

USER_CONFIG_DIR = get_user_config_dir()  # Ultralytics settings dir

def emojis(string=''):
    # Return platform-dependent emoji-safe version of string
    return string.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else string

def colorstr(*input):
    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
    *args, string = input if len(input) > 1 else ("blue", "bold", input[0])  # color arguments, string
    colors = {
        "black": "\033[30m",  # basic colors
        "red": "\033[31m",
        "green": "\033[32m",
        "yellow": "\033[33m",
        "blue": "\033[34m",
        "magenta": "\033[35m",
        "cyan": "\033[36m",
        "white": "\033[37m",
        "bright_black": "\033[90m",  # bright colors
        "bright_red": "\033[91m",
        "bright_green": "\033[92m",
        "bright_yellow": "\033[93m",
        "bright_blue": "\033[94m",
        "bright_magenta": "\033[95m",
        "bright_cyan": "\033[96m",
        "bright_white": "\033[97m",
        "end": "\033[0m",  # misc
        "bold": "\033[1m",
        "underline": "\033[4m",}
    return "".join(colors[x] for x in args) + f"{string}" + colors["end"]

def set_logging(name=LOGGING_NAME, verbose=True):
    # sets up logging for the given name
    rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings
    level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
    logging.config.dictConfig({
        "version": 1,
        "disable_existing_loggers": False,
        "formatters": {
            name: {
                "format": "%(message)s"}},
        "handlers": {
            name: {
                "class": "logging.StreamHandler",
                "formatter": name,
                "level": level,}},
        "loggers": {
            name: {
                "level": level,
                "handlers": [name],
                "propagate": False,}}})

class TryExcept(contextlib.ContextDecorator):
    # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
    def __init__(self, msg=''):
        self.msg = msg

    def __enter__(self):
        pass

    def __exit__(self, exc_type, value, traceback):
        if value:
            print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
        return True

def threaded(func):
    # Multi-threads a target function and returns thread. Usage: @threaded decorator
    def wrapper(*args, **kwargs):
        thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
        thread.start()
        return thread

    return wrapper

def yaml_save(file='data.yaml', data=None):
    """
    Save YAML data to a file.

    Args:
        file (str, optional): File name. Default is 'data.yaml'.
        data (dict, optional): Data to save in YAML format. Default is None.

    Returns:
        None: Data is saved to the specified file.
    """
    file = Path(file)
    if not file.parent.exists():
        # Create parent directories if they don't exist
        file.parent.mkdir(parents=True, exist_ok=True)

    with open(file, 'w') as f:
        # Dump data to file in YAML format, converting Path objects to strings
        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)

def yaml_load(file='data.yaml', append_filename=False):
    """
    Load YAML data from a file.

    Args:
        file (str, optional): File name. Default is 'data.yaml'.
        append_filename (bool): Add the YAML filename to the YAML dictionary. Default is False.

    Returns:
        dict: YAML data and file name.
    """
    with open(file, errors='ignore') as f:
        # Add YAML filename to dict and return
        return {**yaml.safe_load(f), 'yaml_file': str(file)} if append_filename else yaml.safe_load(f)

def get_settings(file=USER_CONFIG_DIR / 'settings.yaml'):
    """
    Loads a global settings YAML file or creates one with default values if it does not exist.

    Args:
        file (Path): Path to the settings YAML file. Defaults to 'settings.yaml' in the USER_CONFIG_DIR.

    Returns:
        dict: Dictionary of settings key-value pairs.
    """
    from ultralytics.utils.torch_utils import torch_distributed_zero_first

    root = get_git_root_dir() or Path('')  # not is_pip_package()
    defaults = {
        'datasets_dir': str(root / 'datasets'),  # default datasets directory.
        'weights_dir': str(root / 'weights'),  # default weights directory.
        'runs_dir': str(root / 'runs'),  # default runs directory.
        'sync': True,  # sync analytics to help with YOLO development
        'uuid': uuid.getnode()}  # device UUID to align analytics

    with torch_distributed_zero_first(RANK):
        if not file.exists():
            yaml_save(file, defaults)

        settings = yaml_load(file)

        # Check that settings keys and types match defaults
        correct = settings.keys() == defaults.keys() and \
                  all(type(a) == type(b) for a, b in zip(settings.values(), defaults.values()))
        if not correct:
            LOGGER.warning('WARNING ⚠️ Different global settings detected, resetting to defaults. '
                           'This may be due to an ultralytics package update. '
                           f'View and update your global settings directly in {file}')
            settings = defaults  # merge **defaults with **settings (prefer **settings)
            yaml_save(file, settings)  # save updated defaults

        return settings

# Run below code on utils init -----------------------------------------------------------------------------------------

# Set logger
set_logging(LOGGING_NAME)  # run before defining LOGGER
LOGGER = logging.getLogger(LOGGING_NAME)  # define globally (used in train.py, val.py, detect.py, etc.)
if platform.system() == 'Windows':
    for fn in LOGGER.info, LOGGER.warning:
        setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x)))  # emoji safe logging

# Check first-install steps
SETTINGS = get_settings()
DATASETS_DIR = Path(SETTINGS['datasets_dir'])  # global datasets directory

def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):
    """
    Function that runs on a first-time ultralytics package installation to set up global settings and create necessary
    directories.
    """
    SETTINGS.update(kwargs)

    yaml_save(file, SETTINGS)

##########################################################################################
def xyxy_to_xywh(*xyxy):
    """" Calculates the relative bounding box from absolute pixel values. """
    bbox_left = min([xyxy[0].item(), xyxy[2].item()])
    bbox_top = min([xyxy[1].item(), xyxy[3].item()])
    bbox_w = abs(xyxy[0].item() - xyxy[2].item())
    bbox_h = abs(xyxy[1].item() - xyxy[3].item())
    x_c = (bbox_left + bbox_w / 2)
    y_c = (bbox_top + bbox_h / 2)
    w = bbox_w
    h = bbox_h
    return x_c, y_c, w, h

def xyxy_to_tlwh(bbox_xyxy):
    tlwh_bboxs = []
    for i, box in enumerate(bbox_xyxy):
        x1, y1, x2, y2 = [int(i) for i in box]
        top = x1
        left = y1
        w = int(x2 - x1)
        h = int(y2 - y1)
        tlwh_obj = [top, left, w, h]
        tlwh_bboxs.append(tlwh_obj)
    return tlwh_bboxs

def compute_color_for_labels(label):
    """
    Simple function that adds fixed color depending on the class
    """
    if label == 0: #person
        color = (85,45,255)
    elif label == 2: # Car
        color = (222,82,175)
    elif label == 3:  # Motobike
        color = (0, 204, 255)
    elif label == 5:  # Bus
        color = (0, 149, 255)
    else:
        color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
    return tuple(color)

def draw_border(img, pt1, pt2, color, thickness, r, d):
    x1,y1 = pt1
    x2,y2 = pt2
    # Top left
    cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness)
    cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness)
    cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness)
    # Top right
    cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness)
    cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness)
    cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness)
    # Bottom left
    cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness)
    cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness)
    cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness)
    # Bottom right
    cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness)
    cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness)
    cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness)

    cv2.rectangle(img, (x1 + r, y1), (x2 - r, y2), color, -1, cv2.LINE_AA)
    cv2.rectangle(img, (x1, y1 + r), (x2, y2 - r - d), color, -1, cv2.LINE_AA)
    
    cv2.circle(img, (x1 +r, y1+r), 2, color, 12)
    cv2.circle(img, (x2 -r, y1+r), 2, color, 12)
    cv2.circle(img, (x1 +r, y2-r), 2, color, 12)
    cv2.circle(img, (x2 -r, y2-r), 2, color, 12)
    
    return img

def UI_box(x, img, color=None, label=None, line_thickness=None):
    # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]

        img = draw_border(img, (c1[0], c1[1] - t_size[1] -3), (c1[0] + t_size[0], c1[1]+3), color, 1, 8, 2)

        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)

def intersect(A,B,C,D):
    return ccw(A,C,D) != ccw(B,C,D) and ccw(A,B,C) != ccw(A,B,D)

def ccw(A,B,C):
    return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])

def get_direction(point1, point2):
    direction_str = ""

    # calculate y axis direction
    if point1[1] > point2[1]:
        direction_str += "South"
    elif point1[1] < point2[1]:
        direction_str += "North"
    else:
        direction_str += ""

    # calculate x axis direction
    if point1[0] > point2[0]:
        direction_str += "East"
    elif point1[0] < point2[0]:
        direction_str += "West"
    else:
        direction_str += ""

    return direction_str
def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)):
    cv2.line(img, line[0], line[1], (46,162,112), 3)

    height, width, _ = img.shape
    # remove tracked point from buffer if object is lost
    for key in list(data_deque):
      if key not in identities:
        data_deque.pop(key)

    for i, box in enumerate(bbox):
        x1, y1, x2, y2 = [int(i) for i in box]
        x1 += offset[0]
        x2 += offset[0]
        y1 += offset[1]
        y2 += offset[1]

        # code to find center of bottom edge
        center = (int((x2+x1)/ 2), int((y2+y2)/2))

        # get ID of object
        id = int(identities[i]) if identities is not None else 0

        # create new buffer for new object
        if id not in data_deque:  
          data_deque[id] = deque(maxlen= 64)
        color = compute_color_for_labels(object_id[i])
        obj_name = names[object_id[i]]
        label = '{}{:d}'.format("", id) + ":"+ '%s' % (obj_name)

        # add center to buffer
        data_deque[id].appendleft(center)
        if len(data_deque[id]) >= 2:
          direction = get_direction(data_deque[id][0], data_deque[id][1])
          if intersect(data_deque[id][0], data_deque[id][1], line[0], line[1]):
              cv2.line(img, line[0], line[1], (255, 255, 255), 3)
              if "South" in direction:
                if obj_name not in object_counter:
                    object_counter[obj_name] = 1
                else:
                    object_counter[obj_name] += 1
              if "North" in direction:
                if obj_name not in object_counter1:
                    object_counter1[obj_name] = 1
                else:
                    object_counter1[obj_name] += 1
        UI_box(box, img, label=label, color=color, line_thickness=2)
        # draw trail
        for i in range(1, len(data_deque[id])):
            # check if on buffer value is none
            if data_deque[id][i - 1] is None or data_deque[id][i] is None:
                continue
            # generate dynamic thickness of trails
            thickness = int(np.sqrt(64 / float(i + i)) * 1.5)
            # draw trails
            cv2.line(img, data_deque[id][i - 1], data_deque[id][i], color, thickness)
    
    #4. Display Count in top right corner
        for idx, (key, value) in enumerate(object_counter1.items()):
            cnt_str = str(key) + ":" +str(value)
            cv2.line(img, (width - 500,25), (width,25), [85,45,255], 40)
            cv2.putText(img, f'Number of Vehicles Entering', (width - 500, 35), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA)
            cv2.line(img, (width - 150, 65 + (idx*40)), (width, 65 + (idx*40)), [85, 45, 255], 30)
            cv2.putText(img, cnt_str, (width - 150, 75 + (idx*40)), 0, 1, [255, 255, 255], thickness = 2, lineType = cv2.LINE_AA)

        for idx, (key, value) in enumerate(object_counter.items()):
            cnt_str1 = str(key) + ":" +str(value)
            cv2.line(img, (20,25), (500,25), [85,45,255], 40)
            cv2.putText(img, f'Numbers of Vehicles Leaving', (11, 35), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA)    
            cv2.line(img, (20,65+ (idx*40)), (127,65+ (idx*40)), [85,45,255], 30)
            cv2.putText(img, cnt_str1, (11, 75+ (idx*40)), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA)
    
    
    
    return img

class DetectionPredictor(BasePredictor):

    def get_annotator(self, img):
        return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))

    def preprocess(self, img):
        img = torch.from_numpy(img).to(self.model.device)
        img = img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32
        img /= 255  # 0 - 255 to 0.0 - 1.0
        return img

    def postprocess(self, preds, img, orig_img):
        preds = ops.non_max_suppression(preds,
                                        self.args.conf,
                                        self.args.iou,
                                        agnostic=self.args.agnostic_nms,
                                        max_det=self.args.max_det)

        for i, pred in enumerate(preds):
            shape = orig_img[i].shape if self.webcam else orig_img.shape
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()

        return preds

    def write_results(self, idx, preds, batch):
        p, im, im0 = batch
        all_outputs = []
        log_string = ""
        if len(im.shape) == 3:
            im = im[None]  # expand for batch dim
        self.seen += 1
        im0 = im0.copy()
        if self.webcam:  # batch_size >= 1
            log_string += f'{idx}: '
            frame = self.dataset.count
        else:
            frame = getattr(self.dataset, 'frame', 0)

        self.data_path = p
        save_path = str(self.save_dir / p.name)  # im.jpg
        self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
        log_string += '%gx%g ' % im.shape[2:]  # print string
        self.annotator = self.get_annotator(im0)

        det = preds[idx]
        all_outputs.append(det)
        if len(det) == 0:
            return log_string
        for c in det[:, 5].unique():
            n = (det[:, 5] == c).sum()  # detections per class
            log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
        # write
        gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
        xywh_bboxs = []
        confs = []
        oids = []
        outputs = []
        for *xyxy, conf, cls in reversed(det):
            x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy)
            xywh_obj = [x_c, y_c, bbox_w, bbox_h]
            xywh_bboxs.append(xywh_obj)
            confs.append([conf.item()])
            oids.append(int(cls))
        xywhs = torch.Tensor(xywh_bboxs)
        confss = torch.Tensor(confs)
          
        outputs = deepsort.update(xywhs, confss, oids, im0)
        if len(outputs) > 0:
            bbox_xyxy = outputs[:, :4]
            identities = outputs[:, -2]
            object_id = outputs[:, -1]
            
            draw_boxes(im0, bbox_xyxy, self.model.names, object_id,identities)

        return log_string

@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def predict(cfg):
    init_tracker()
    cfg.model = cfg.model or "yolov8n.pt"
    cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2)  # check image size
    cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
    predictor = DetectionPredictor(cfg)
    predictor()

if __name__ == "__main__":
    predict()