detect_deepsort.py

import argparse
import os
import platform
import sys
from pathlib import Path
import math
import torch
import numpy as np
from deep_sort_pytorch.utils.parser import get_config
from deep_sort_pytorch.deep_sort import DeepSort
from collections import deque
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLO root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode

# def initialize_deepsort():
#     # Create the Deep SORT configuration object and load settings from the YAML file
#     cfg_deep = get_config()
#     cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml")

#     # Initialize the DeepSort tracker
#     deepsort = DeepSort(cfg_deep.DEEPSORT.REID_CKPT,
#                         max_dist=cfg_deep.DEEPSORT.MAX_DIST,
#                         # min_confidence  parameter sets the minimum tracking confidence required for an object detection to be considered in the tracking process
#                         min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE,
#                         #nms_max_overlap specifies the maximum allowed overlap between bounding boxes during non-maximum suppression (NMS)
#                         nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP,
#                         #max_iou_distance parameter defines the maximum intersection-over-union (IoU) distance between object detections
#                         max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE,
#                         # Max_age: If an object's tracking ID is lost (i.e., the object is no longer detected), this parameter determines how many frames the tracker should wait before assigning a new id
#                         max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT,
#                         #nn_budget: It sets the budget for the nearest-neighbor search.
#                         nn_budget=cfg_deep.DEEPSORT.NN_BUDGET,
#                         use_cuda=False
#         )

#     return deepsort

#deepsort = initialize_deepsort()
data_deque = {}
def classNames():
    cocoClassNames = ["Bus", "Bike", "Car", "Pedestrian", "Truck"
                  ]
    return cocoClassNames
className = classNames()

def colorLabels(classid):
    if classid == 0: #Bus
        color = (0, 0, 255)
    elif classid == 1: #Bike  250, 247, 0
        color = (0,148,255)
    elif classid == 2: #Car 
        color = (0, 255, 10)
    elif classid == 3: #Pedestrian
        color = (250,247,0) 
    else: #Truck
        color = (235,0,255)   
    return tuple(color)

def draw_boxes(frame, bbox_xyxy, draw_trails, identities=None, categories=None, offset=(0,0)):
    height, width, _ = frame.shape
    for key in list(data_deque):
      if key not in identities:
        data_deque.pop(key)

    for i, box in enumerate(bbox_xyxy):
        x1, y1, x2, y2 = [int(i) for i in box]
        x1 += offset[0]
        y1 += offset[0]
        x2 += offset[0]
        y2 += offset[0]
        #Find the center point of the bounding box
        center = int((x1+x2)/2), int((y1+y2)/2)
        cat = int(categories[i]) if categories is not None else 0
        color = colorLabels(cat)
        #color = [255,0,0]#compute_color_labels(cat)
        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)
        data_deque[id].appendleft(center)
        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
        name = className[cat]
        label = str(id) + ":" + name
        text_size = cv2.getTextSize(label, 0, fontScale=0.5, thickness=2)[0]
        c2 = x1 + text_size[0], y1 - text_size[1] - 3
        cv2.rectangle(frame, (x1, y1), c2, color, -1)
        cv2.putText(frame, label, (x1, y1 - 2), 0, 0.5, [255, 255, 255], thickness=1, lineType=cv2.LINE_AA)
        cv2.circle(frame,center, 2, (0,255,0), cv2.FILLED)
        if draw_trails:
              # 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(frame, data_deque[id][i - 1], data_deque[id][i], color, thickness)    
    return frame

@smart_inference_mode()
def run_deepsort(
        weights=ROOT / 'yolo.pt',  # model path or triton URL
        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
        data=ROOT / 'data/coco.yaml',  # dataset.yaml path
        imgsz=(640, 640),  # inference size (height, width)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project=ROOT / 'runs/detect',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        half=False,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference
        vid_stride=1,  # video frame-rate stride
        draw_trails = False,
):
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
    screenshot = source.lower().startswith('screen')
    if is_url and is_file:
        source = check_file(source)  # download

    # Directories
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    save_dir.mkdir(parents=True, exist_ok=True)  # make dir

    #Initalize deepsort
    # Create the Deep SORT configuration object and load settings from the YAML file
    cfg_deep = get_config()
    cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml")

    # Initialize the DeepSort tracker
    deepsort = DeepSort(cfg_deep.DEEPSORT.REID_CKPT,
                        max_dist=cfg_deep.DEEPSORT.MAX_DIST,
                        # min_confidence  parameter sets the minimum tracking confidence required for an object detection to be considered in the tracking process
                        min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE,
                        #nms_max_overlap specifies the maximum allowed overlap between bounding boxes during non-maximum suppression (NMS)
                        nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP,
                        #max_iou_distance parameter defines the maximum intersection-over-union (IoU) distance between object detections
                        max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE,
                        # Max_age: If an object's tracking ID is lost (i.e., the object is no longer detected), this parameter determines how many frames the tracker should wait before assigning a new id
                        max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT,
                        #nn_budget: It sets the budget for the nearest-neighbor search.
                        nn_budget=cfg_deep.DEEPSORT.NN_BUDGET,
                        use_cuda=True
        )
    
    # Load model
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
    stride, names, pt = model.stride, model.names, model.pt
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Dataloader
    bs = 1  # batch_size
    if webcam:
        view_img = check_imshow(warn=True)
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
        bs = len(dataset)
    elif screenshot:
        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
    for path, im, im0s, vid_cap, s in dataset:
        with dt[0]:
            im = torch.from_numpy(im).to(model.device)
            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
            im /= 255  # 0 - 255 to 0.0 - 1.0
            if len(im.shape) == 3:
                im = im[None]  # expand for batch dim

        # Inference
        with dt[1]:
            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
            pred = model(im, augment=augment, visualize=visualize)
            # pred = pred[0][1]

        # NMS
        with dt[2]:
            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

        # Second-stage classifier (optional)
        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)

        # Process predictions
        for i, det in enumerate(pred):  # per image
            seen += 1
            if webcam:  # batch_size >= 1
                p, im0, frame = path[i], im0s[i].copy(), dataset.count
                s += f'{i}: '
            else:
                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # im.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
            s += '%gx%g ' % im.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            ims = im0.copy()
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, 5].unique():
                    n = (det[:, 5] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
                xywh_bboxs = []
                confs = []
                oids = []
                outputs = []
                # Write results
                for *xyxy, conf, cls in reversed(det):
                    x1, y1, x2, y2 = xyxy
                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
                    #Find the Center Coordinates for each of the detected object
                    cx, cy = int((x1+x2)/2), int((y1+y2)/2)
                    #Find the Width and Height of the Boundng box
                    bbox_width = abs(x1-x2)
                    bbox_height = abs(y1-y2)
                    xcycwh = [cx, cy, bbox_width, bbox_height]
                    xywh_bboxs.append(xcycwh)
                    conf = math.ceil(conf*100)/100
                    confs.append(conf)
                    classNameInt = int(cls)
                    oids.append(classNameInt)
                xywhs = torch.tensor(xywh_bboxs)
                confss = torch.tensor(confs)
                outputs = deepsort.update(xywhs, confss, oids, ims)
                if len(outputs) > 0:
                    bbox_xyxy = outputs[:, :4]
                    identities = outputs[:, -2]
                    object_id = outputs[:, -1]
                    draw_boxes(ims, bbox_xyxy, draw_trails, identities, object_id)

            # Stream results
            if view_img:
                if platform.system() == 'Linux' and p not in windows:
                    windows.append(p)
                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                    cv2.resizeWindow(str(p), ims.shape[1], ims.shape[0])
                cv2.imshow(str(p), ims)
                cv2.waitKey(1)  # 1 millisecond
            # Save results (image with detections)
            if save_img:
                if vid_path[i] != save_path:  # new video
                    vid_path[i] = save_path
                    if isinstance(vid_writer[i], cv2.VideoWriter):
                        vid_writer[i].release()  # release previous video writer
                    if vid_cap:  # video
                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                    else:  # stream
                        fps, w, h = 30, ims.shape[1], ims.shape[0]
                    save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
                    vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc('m','p','4','v'), fps, (w, h))
                vid_writer[i].write(ims)

        # Print time (inference-only)
        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
    if update:
        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
    return save_path 

def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolo.pt', help='model path or triton URL')
    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='show results')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--draw-trails', action='store_true', help='do not drawtrails')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--visualize', action='store_true', help='visualize features')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(vars(opt))
    return opt


def main(opt):
    # check_requirements(exclude=('tensorboard', 'thop'))
    run_deepsort(**vars(opt))



if __name__ == "__main__":
    opt = parse_opt()
    main(opt)