predict.py

import hydra
import torch
import argparse
import time
from pathlib import Path
import math
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops
from ultralytics.yolo.utils.checks import check_imgsz
from ultralytics.yolo.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)
cars_deque = {}


deepsort = None

object_counter = {}

speed_line_queue = {}
def estimatespeed(Location1, Location2):
    #Euclidean Distance Formula
    d_pixel = math.sqrt(math.pow(Location2[0] - Location1[0], 2) + math.pow(Location2[1] - Location1[1], 2))
    # defining thr pixels per meter
    ppm = 8
    d_meters = d_pixel/ppm
    time_constant = 15*3.6
    #distance = speed/time
    speed = d_meters * time_constant

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


def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)):
    #cv2.line(img, line[0], line[1], (46,162,112), 3)
    cv2.putText(img, f'Number of cars: {len(cars_deque)}', (11, 35), 0, 1, [0, 255, 0], thickness=2, lineType=cv2.LINE_AA)
    height, width, _ = img.shape
    # remove tracked point from buffer if object is lost
    for key in list(cars_deque):
      if key not in identities:
        cars_deque.pop(key)
    
    for i, box in enumerate(bbox):
        obj_name = names[object_id[i]]
        if obj_name == 'car':
            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 cars_deque:  
              cars_deque[id] = deque(maxlen= 64)
              speed_line_queue[id] = []
            color = compute_color_for_labels(object_id[i])
        
        
            label = '{}{:d}'.format("", id) + ":"+ '%s' % (obj_name)
        

            # add center to buffer
            cars_deque[id].appendleft(center)
            if len(cars_deque[id]) >= 2:
              object_speed = estimatespeed(cars_deque[id][1], cars_deque[id][0])
              speed_line_queue[id].append(object_speed)
              if obj_name not in object_counter:
                        object_counter[obj_name] = 1
        

            try:
                label = label + " " + str(sum(speed_line_queue[id])//len(speed_line_queue[id])) + "km/h"
            except:
                pass
            UI_box(box, img, label=label, color=color, line_thickness=2)
    
    
    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()