324 lines
16 KiB
Python
324 lines
16 KiB
Python
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import argparse
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import os
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# limit the number of cpus used by high performance libraries
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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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import torch.backends.cudnn as cudnn
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0] # yolov5 strongsort root directory
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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)) # add ROOT to PATH
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if str(ROOT / 'yolov5') not in sys.path:
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sys.path.append(str(ROOT / 'yolov5')) # add yolov5 ROOT to PATH
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if str(ROOT / 'strong_sort') not in sys.path:
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sys.path.append(str(ROOT / 'strong_sort')) # add strong_sort ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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import logging
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from yolov5.models.common import DetectMultiBackend
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from yolov5.utils.dataloaders import VID_FORMATS, LoadImages, LoadStreams
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from yolov5.utils.general import (LOGGER, check_img_size, non_max_suppression, scale_boxes, check_requirements, cv2,
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check_imshow, xyxy2xywh, increment_path, strip_optimizer, colorstr, print_args, check_file)
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from yolov5.utils.torch_utils import select_device, time_sync
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from yolov5.utils.plots import Annotator, colors, save_one_box
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from strong_sort.utils.parser import get_config
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from strong_sort.strong_sort import StrongSORT
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# remove duplicated stream handler to avoid duplicated logging
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# logging.getLogger().removeHandler(logging.getLogger().handlers[0])
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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 / 'yolov5m.pt', # model.pt path(s),
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strong_sort_weights=WEIGHTS / 'osnet_x0_25_msmt17.pt', # model.pt path,
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config_strongsort=ROOT / 'strong_sort/configs/strong_sort.yaml',
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imgsz=(640, 640), # inference size (height, width)
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conf_thres=0.25, # confidence threshold
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iou_thres=0.45, # NMS IOU threshold
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max_det=1000, # maximum detections per image
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device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
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show_vid=False, # show results
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save_txt=False, # save results to *.txt
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save_conf=False, # save confidences in --save-txt labels
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save_crop=False, # save cropped prediction boxes
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save_vid=False, # save confidences in --save-txt labels
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nosave=False, # do not save images/videos
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classes=None, # filter by class: --class 0, or --class 0 2 3
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agnostic_nms=False, # class-agnostic NMS
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augment=False, # augmented inference
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visualize=False, # visualize features
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update=False, # update all models
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project=ROOT / 'runs/track', # save results to project/name
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name='exp', # save results to project/name
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exist_ok=False, # existing project/name ok, do not increment
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line_thickness=3, # bounding box thickness (pixels)
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hide_labels=False, # hide labels
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hide_conf=False, # hide confidences
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hide_class=False, # hide IDs
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half=False, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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):
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source = str(source)
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save_img = not nosave and not source.endswith('.txt') # save inference images
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is_file = Path(source).suffix[1:] in (VID_FORMATS)
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is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
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webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
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if is_url and is_file:
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source = check_file(source) # download
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# Directories
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if not isinstance(yolo_weights, list): # single yolo model
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exp_name = yolo_weights.stem
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elif type(yolo_weights) is list and len(yolo_weights) == 1: # single models after --yolo_weights
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exp_name = Path(yolo_weights[0]).stem
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else: # multiple models after --yolo_weights
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exp_name = 'ensemble'
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exp_name = name if name else exp_name + "_" + strong_sort_weights.stem
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save_dir = increment_path(Path(project) / exp_name, exist_ok=exist_ok) # increment run
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(save_dir / 'tracks' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Load model
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device = select_device(device)
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model = DetectMultiBackend(yolo_weights, device=device, dnn=dnn, data=None, fp16=half)
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stride, names, pt = model.stride, model.names, model.pt
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imgsz = check_img_size(imgsz, s=stride) # check image size
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# Dataloader
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if webcam:
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show_vid = check_imshow()
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cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
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nr_sources = len(dataset)
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
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nr_sources = 1
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vid_path, vid_writer, txt_path = [None] * nr_sources, [None] * nr_sources, [None] * nr_sources
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# initialize StrongSORT
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cfg = get_config()
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cfg.merge_from_file(opt.config_strongsort)
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# Create as many strong sort instances as there are video sources
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strongsort_list = []
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for i in range(nr_sources):
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strongsort_list.append(
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StrongSORT(
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strong_sort_weights,
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device,
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max_dist=cfg.STRONGSORT.MAX_DIST,
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max_iou_distance=cfg.STRONGSORT.MAX_IOU_DISTANCE,
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max_age=cfg.STRONGSORT.MAX_AGE,
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n_init=cfg.STRONGSORT.N_INIT,
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nn_budget=cfg.STRONGSORT.NN_BUDGET,
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mc_lambda=cfg.STRONGSORT.MC_LAMBDA,
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ema_alpha=cfg.STRONGSORT.EMA_ALPHA,
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)
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)
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outputs = [None] * nr_sources
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# Run tracking
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model.warmup(imgsz=(1 if pt else nr_sources, 3, *imgsz)) # warmup
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dt, seen = [0.0, 0.0, 0.0, 0.0], 0
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print('nr_sources', nr_sources)
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curr_frames, prev_frames = [None] * nr_sources, [None] * nr_sources
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for frame_idx, (path, im, im0s, vid_cap, s) in enumerate(dataset):
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t1 = time_sync()
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im = torch.from_numpy(im).to(device)
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im = im.half() if half else im.float() # uint8 to fp16/32
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im /= 255.0 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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t2 = time_sync()
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dt[0] += t2 - t1
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# Inference
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visualize = increment_path(save_dir / Path(path[0]).stem, mkdir=True) if visualize else False
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pred = model(im, augment=augment, visualize=visualize)
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t3 = time_sync()
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dt[1] += t3 - t2
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# Apply NMS
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pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
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dt[2] += time_sync() - t3
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# Process detections
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for i, det in enumerate(pred): # detections per image
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seen += 1
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if webcam: # nr_sources >= 1
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p, im0, _ = path[i], im0s[i].copy(), dataset.count
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p = Path(p) # to Path
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s += f'{i}: '
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txt_file_name = p.name
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save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ...
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else:
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p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)
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p = Path(p) # to Path
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# video file
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if source.endswith(VID_FORMATS):
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txt_file_name = p.stem
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save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ...
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# folder with imgs
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else:
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txt_file_name = p.parent.name # get folder name containing current img
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save_path = str(save_dir / p.parent.name) # im.jpg, vid.mp4, ...
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curr_frames[i] = im0
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txt_path = str(save_dir / 'tracks' / txt_file_name) # im.txt
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s += '%gx%g ' % im.shape[2:] # print string
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imc = im0.copy() if save_crop else im0 # for save_crop
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annotator = Annotator(im0, line_width=2, pil=not ascii)
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if cfg.STRONGSORT.ECC: # camera motion compensation
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strongsort_list[i].tracker.camera_update(prev_frames[i], curr_frames[i])
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if det is not None and len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum() # detections per class
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
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xywhs = xyxy2xywh(det[:, 0:4])
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confs = det[:, 4]
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clss = det[:, 5]
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# pass detections to strongsort
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t4 = time_sync()
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outputs[i] = strongsort_list[i].update(xywhs.cpu(), confs.cpu(), clss.cpu(), im0)
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t5 = time_sync()
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dt[3] += t5 - t4
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# draw boxes for visualization
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if len(outputs[i]) > 0:
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for j, (output, conf) in enumerate(zip(outputs[i], confs)):
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bboxes = output[0:4]
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id = output[4]
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cls = output[5]
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if save_txt:
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# to MOT format
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bbox_left = output[0]
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bbox_top = output[1]
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bbox_w = output[2] - output[0]
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bbox_h = output[3] - output[1]
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# Write MOT compliant results to file
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with open(txt_path + '.txt', 'a') as f:
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f.write(('%g ' * 10 + '\n') % (frame_idx + 1, id, bbox_left, # MOT format
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bbox_top, bbox_w, bbox_h, -1, -1, -1, i))
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if save_vid or save_crop or show_vid: # Add bbox to image
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c = int(cls) # integer class
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id = int(id) # integer id
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label = None if hide_labels else (f'{id} {names[c]}' if hide_conf else \
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(f'{id} {conf:.2f}' if hide_class else f'{id} {names[c]} {conf:.2f}'))
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annotator.box_label(bboxes, label, color=colors(c, True))
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if save_crop:
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txt_file_name = txt_file_name if (isinstance(path, list) and len(path) > 1) else ''
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save_one_box(bboxes, imc, file=save_dir / 'crops' / txt_file_name / names[c] / f'{id}' / f'{p.stem}.jpg', BGR=True)
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LOGGER.info(f'{s}Done. YOLO:({t3 - t2:.3f}s), StrongSORT:({t5 - t4:.3f}s)')
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else:
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strongsort_list[i].increment_ages()
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LOGGER.info('No detections')
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# Stream results
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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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cv2.waitKey(1) # 1 millisecond
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# Save results (image with detections)
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if save_vid:
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if vid_path[i] != save_path: # new video
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vid_path[i] = save_path
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if isinstance(vid_writer[i], cv2.VideoWriter):
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vid_writer[i].release() # release previous video writer
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if vid_cap: # video
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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: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
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vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer[i].write(im0)
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prev_frames[i] = curr_frames[i]
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# Print results
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t = tuple(x / seen * 1E3 for x in dt) # speeds per image
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS, %.1fms strong sort update per image at shape {(1, 3, *imgsz)}' % t)
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if save_txt or save_vid:
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s = f"\n{len(list(save_dir.glob('tracks/*.txt')))} tracks saved to {save_dir / 'tracks'}" if save_txt else ''
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
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if update:
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strip_optimizer(yolo_weights) # update model (to fix SourceChangeWarning)
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def parse_opt():
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parser = argparse.ArgumentParser()
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parser.add_argument('--yolo-weights', nargs='+', type=str, default=WEIGHTS / 'yolov5m.pt', help='model.pt path(s)')
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parser.add_argument('--strong-sort-weights', type=str, default=WEIGHTS / 'osnet_x0_25_msmt17.pt')
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parser.add_argument('--config-strongsort', type=str, default='strong_sort/configs/strong_sort.yaml')
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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('--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=0.5, help='confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.5, 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 tracking video results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
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parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
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parser.add_argument('--save-vid', action='store_true', help='save video tracking results')
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parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
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# class 0 is person, 1 is bycicle, 2 is car... 79 is oven
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parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
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parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--visualize', action='store_true', help='visualize features')
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parser.add_argument('--update', action='store_true', help='update all models')
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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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|
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parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
|
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|
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parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
|
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|
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parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
|
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|
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parser.add_argument('--hide-class', default=False, action='store_true', help='hide IDs')
|
||
|
|
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')
|
||
|
|
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(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
|
||
|
|
run(**vars(opt))
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
opt = parse_opt()
|
||
|
|
main(opt)
|