add StrongSORT Tacker
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Pongsatorn Kanjanasantisak
parent
ffc2e99678
commit
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93 changed files with 20230 additions and 6 deletions
245
feeder/simple_track.py
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245
feeder/simple_track.py
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import argparse
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import cv2
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import os
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os.environ["OMP_NUM_THREADS"] = "1"
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os.environ["OPENBLAS_NUM_THREADS"] = "1"
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os.environ["MKL_NUM_THREADS"] = "1"
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os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
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os.environ["NUMEXPR_NUM_THREADS"] = "1"
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import sys
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import numpy as np
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from pathlib import Path
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import torch
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0]
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WEIGHTS = ROOT / 'weights'
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT))
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if str(ROOT / 'trackers' / 'strongsort') not in sys.path:
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sys.path.append(str(ROOT / 'trackers' / 'strongsort'))
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from ultralytics.nn.autobackend import AutoBackend
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
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from ultralytics.yolo.data.utils import VID_FORMATS
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from ultralytics.yolo.utils import LOGGER, colorstr
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from ultralytics.yolo.utils.checks import check_file, check_imgsz
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from ultralytics.yolo.utils.files import increment_path
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from ultralytics.yolo.utils.torch_utils import select_device
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from ultralytics.yolo.utils.ops import Profile, non_max_suppression, scale_boxes
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from ultralytics.yolo.utils.plotting import Annotator, colors
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from trackers.multi_tracker_zoo import create_tracker
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from sender.jsonlogger import JsonLogger
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from sender.szmq import ZmqLogger
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@torch.no_grad()
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def run(
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source='0',
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yolo_weights=WEIGHTS / 'yolov8n.pt',
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reid_weights=WEIGHTS / 'osnet_x0_25_msmt17.pt',
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imgsz=(640, 640),
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conf_thres=0.7,
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iou_thres=0.45,
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max_det=1000,
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device='',
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show_vid=True,
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save_vid=True,
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project=ROOT / 'runs' / 'track',
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name='exp',
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exist_ok=False,
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line_thickness=2,
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hide_labels=False,
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hide_conf=False,
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half=False,
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vid_stride=1,
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enable_json_log=False,
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enable_zmq=False,
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zmq_ip='localhost',
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zmq_port=5555,
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):
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source = str(source)
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is_file = Path(source).suffix[1:] in (VID_FORMATS)
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if is_file:
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source = check_file(source)
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device = select_device(device)
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model = AutoBackend(yolo_weights, device=device, dnn=False, fp16=half)
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stride, names, pt = model.stride, model.names, model.pt
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imgsz = check_imgsz(imgsz, stride=stride)
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dataset = LoadImages(
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source,
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imgsz=imgsz,
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stride=stride,
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auto=pt,
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transforms=getattr(model.model, 'transforms', None),
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vid_stride=vid_stride
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)
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bs = len(dataset)
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tracking_config = ROOT / 'trackers' / 'strongsort' / 'configs' / 'strongsort.yaml'
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tracker = create_tracker('strongsort', tracking_config, reid_weights, device, half)
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)
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(save_dir / 'tracks').mkdir(parents=True, exist_ok=True)
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# Initialize loggers
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json_logger = JsonLogger(f"{source}-strongsort.log") if enable_json_log else None
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zmq_logger = ZmqLogger(zmq_ip, zmq_port) if enable_zmq else None
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vid_path, vid_writer = [None] * bs, [None] * bs
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dt = (Profile(), Profile(), Profile())
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for frame_idx, (path, im, im0s, vid_cap, s) in enumerate(dataset):
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with dt[0]:
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im = torch.from_numpy(im).to(model.device)
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im = im.half() if model.fp16 else im.float()
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im /= 255.0
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if len(im.shape) == 3:
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im = im[None]
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with dt[1]:
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pred = model(im, augment=False, visualize=False)
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with dt[2]:
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pred = non_max_suppression(pred, conf_thres, iou_thres, None, False, max_det=max_det)
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for i, det in enumerate(pred):
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seen = 0
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p, im0, _ = path, im0s.copy(), dataset.count
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p = Path(p)
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annotator = Annotator(im0, line_width=line_thickness, example=str(names))
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if len(det):
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# Filter detections for 'car' class only (class 2 in COCO dataset)
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car_mask = det[:, 5] == 2 # car class index is 2
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det = det[car_mask]
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if len(det):
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det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
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for *xyxy, conf, cls in reversed(det):
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c = int(cls)
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id = f'{c}'
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label = None if hide_labels else (f'{id} {names[c]}' if hide_conf else f'{id} {names[c]} {conf:.2f}')
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annotator.box_label(xyxy, label, color=colors(c, True))
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t_outputs = tracker.update(det.cpu(), im0)
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if len(t_outputs) > 0:
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for j, (output) in enumerate(t_outputs):
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bbox = output[0:4]
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id = output[4]
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cls = output[5]
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conf = output[6]
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# Log tracking data
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if json_logger or zmq_logger:
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track_data = {
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'bbox': bbox.tolist() if hasattr(bbox, 'tolist') else list(bbox),
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'id': int(id),
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'cls': int(cls),
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'conf': float(conf),
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'frame_idx': frame_idx,
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'source': source,
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'class_name': names[int(cls)]
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}
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if json_logger:
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json_logger.send(track_data)
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if zmq_logger:
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zmq_logger.send(track_data)
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if save_vid or show_vid:
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c = int(cls)
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id = int(id)
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label = f'{id} {names[c]}' if not hide_labels else f'{id}'
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if not hide_conf:
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label += f' {conf:.2f}'
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annotator.box_label(bbox, label, color=colors(c, True))
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im0 = annotator.result()
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if show_vid:
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cv2.imshow(str(p), im0)
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if cv2.waitKey(1) == ord('q'):
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break
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if save_vid:
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if vid_path[i] != str(save_dir / p.name):
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vid_path[i] = str(save_dir / p.name)
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if isinstance(vid_writer[i], cv2.VideoWriter):
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vid_writer[i].release()
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if vid_cap:
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else:
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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vid_writer[i] = cv2.VideoWriter(vid_path[i], cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer[i].write(im0)
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LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
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for i, vid_writer_obj in enumerate(vid_writer):
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if isinstance(vid_writer_obj, cv2.VideoWriter):
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vid_writer_obj.release()
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cv2.destroyAllWindows()
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
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def xyxy2xywh(x):
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# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
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y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
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y[:, 2] = x[:, 2] - x[:, 0] # width
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y[:, 3] = x[:, 3] - x[:, 1] # height
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return y
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def parse_opt():
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parser = argparse.ArgumentParser()
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parser.add_argument('--source', type=str, default='0', help='file/dir/URL/glob, 0 for webcam')
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parser.add_argument('--yolo-weights', nargs='+', type=str, default=WEIGHTS / 'yolov8n.pt', help='model path')
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parser.add_argument('--reid-weights', type=str, default=WEIGHTS / 'osnet_x0_25_msmt17.pt')
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parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
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parser.add_argument('--conf-thres', type=float, default=0.7, help='confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
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parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--show-vid', action='store_true', help='display results')
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parser.add_argument('--save-vid', action='store_true', help='save video tracking results')
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parser.add_argument('--project', default=ROOT / 'runs' / 'track', help='save results to project/name')
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parser.add_argument('--name', default='exp', help='save results to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--line-thickness', default=2, type=int, help='bounding box thickness (pixels)')
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parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
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parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
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parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
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parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
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parser.add_argument('--enable-json-log', action='store_true', help='enable JSON file logging')
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parser.add_argument('--enable-zmq', action='store_true', help='enable ZMQ messaging')
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parser.add_argument('--zmq-ip', type=str, default='localhost', help='ZMQ server IP')
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parser.add_argument('--zmq-port', type=int, default=5555, help='ZMQ server port')
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opt = parser.parse_args()
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opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1
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return opt
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def main(opt):
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run(**vars(opt))
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if __name__ == "__main__":
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opt = parse_opt()
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main(opt)
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