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import argparse
import os
# limit the number of cpus used by high performance libraries
# os.environ["OMP_NUM_THREADS"] = "8"
# os.environ["OPENBLAS_NUM_THREADS"] = "8"
# os.environ["MKL_NUM_THREADS"] = "8"
# os.environ["VECLIB_MAXIMUM_THREADS"] = "8"
# os.environ["NUMEXPR_NUM_THREADS"] = "8"
import platform
import sys
import numpy as np
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from time import time
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # yolov5 strongsort root directory
WEIGHTS = ROOT / 'weights'
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
if str(ROOT / 'yolov9') not in sys.path:
sys.path.append(str(ROOT / 'yolov9')) # add yolov5 ROOT to PATH
if str(ROOT / 'strong_sort') not in sys.path:
sys.path.append(str(ROOT / 'strong_sort')) # add strong_sort ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.experimental import attempt_load
from models.common import DetectMultiBackend
from utils.dataloaders import LoadImages, LoadStreams, LoadScreenshots
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.torch_utils import select_device, time_sync, smart_inference_mode
from utils.plots import Annotator, colors, save_one_box
from strong_sort.utils.parser import get_config
from strong_sort.strong_sort import StrongSORT
VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes
def plot_one_box(x, img, color=None, label=None, line_thickness=3):
# 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]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
@smart_inference_mode()
def run_strongsort(
source='0',
data = ROOT / 'data/coco.yaml', # data.yaml path
yolo_weights=WEIGHTS / 'yolo.pt', # model.pt path(s),
strong_sort_weights=WEIGHTS / 'osnet_x0_25_msmt17.pt', # model.pt path,
config_strongsort=ROOT / 'strong_sort/configs/strong_sort.yaml',
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
save_txt=False, # save results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
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/track', # save results to project/name
name='exp', # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
vid_stride=1, # video frame-rate stride
):
source = str(source)
save_img = not nosave and not source.endswith('.txt') # save inference images
is_file = Path(source).suffix[1:] in (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
if not isinstance(yolo_weights, list): # single yolo model
exp_name = Path(yolo_weights).stem
elif type(yolo_weights) is list and len(yolo_weights) == 1: # single models after --yolo_weights
exp_name = Path(yolo_weights[0]).stem
yolo_weights = Path(yolo_weights[0])
else: # multiple models after --yolo_weights
exp_name = 'ensemble'
exp_name = name if name else exp_name + "_" + Path(strong_sort_weights).stem
save_dir = increment_path(Path(project) / exp_name, exist_ok=exist_ok) # increment run
save_dir = Path(save_dir)
(save_dir / 'tracks' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
device = select_device(device)
model = DetectMultiBackend(yolo_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
# 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,txt_path = [None] * bs, [None] * bs, [None] * bs
# initialize StrongSORT
cfg = get_config()
cfg.merge_from_file(config_strongsort)
# Create as many strong sort instances as there are video sources
gpu = '0'
strongsort_list = []
for i in range(bs):
strongsort_list.append(
StrongSORT(
strong_sort_weights,
gpu,
half,
max_dist=cfg.STRONGSORT.MAX_DIST,
max_iou_distance=cfg.STRONGSORT.MAX_IOU_DISTANCE,
max_age=cfg.STRONGSORT.MAX_AGE,
n_init=cfg.STRONGSORT.N_INIT,
nn_budget=cfg.STRONGSORT.NN_BUDGET,
mc_lambda=cfg.STRONGSORT.MC_LAMBDA,
ema_alpha=cfg.STRONGSORT.EMA_ALPHA,
)
)
strongsort_list[i].model.warmup()
outputs = [None] * bs
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run tracking
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
seen, windows, dt,sdt = 0, [], (Profile(), Profile(), Profile(), Profile()),[0.0, 0.0, 0.0, 0.0]
curr_frames, prev_frames = [None] * bs, [None] * bs
for frame_idx, (path, im, im0s, vid_cap, s) in enumerate(dataset):
# s = ''
t1 = time_sync()
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
t2 = time_sync()
sdt[0] += t2 - t1
# 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]
t3 = time_sync()
sdt[1] += t3 - t2
# Apply NMS
with dt[2]:
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
sdt[2] += time_sync() - t3
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
seen += 1
if webcam: # bs >= 1
p, im0, _ = path[i], im0s[i].copy(), dataset.count
p = Path(p) # to Path
s += f'{i}: '
# txt_file_name = p.name
txt_file_name = p.stem + f'_{i}' # Unique text file name
# save_path = str(save_dir / p.name) + str(i) # im.jpg, vid.mp4, ...
save_path = str(save_dir / p.stem) + f'_{i}' # Unique video file name
else:
p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
# video file
if source.endswith(VID_FORMATS):
txt_file_name = p.stem
save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ...
# folder with imgs
else:
txt_file_name = p.parent.name # get folder name containing current img
save_path = str(save_dir / p.parent.name) # im.jpg, vid.mp4, ...
curr_frames[i] = im0
txt_path = str(save_dir / 'tracks' / txt_file_name) # im.txt
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if cfg.STRONGSORT.ECC: # camera motion compensation
strongsort_list[i].tracker.camera_update(prev_frames[i], curr_frames[i])
if det is not None and 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[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
xywhs = xyxy2xywh(det[:, 0:4])
confs = det[:, 4]
clss = det[:, 5]
# pass detections to strongsort
t4 = time_sync()
outputs[i] = strongsort_list[i].update(xywhs.cpu(), confs.cpu(), clss.cpu(), im0)
t5 = time_sync()
sdt[3] += t5 - t4
# Write results
for j, (output, conf) in enumerate(zip(outputs[i], confs)):
xyxy = output[0:4]
id = output[4]
cls = output[5]
# for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
# line = (id , cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
line = ( int(p.stem), frame_idx, id , cls, *xywh, conf) if save_conf else ( p.stem, frame_idx, cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as file:
file.write(('%g ' * len(line) + '\n') % line)
if save_img or save_crop or view_img: # Add bbox to image
c = int(cls) # integer class
label = None if hide_labels else ( str(id) + ' ' + names[c] if hide_conf else f' { id } {names[c]} {conf:.2f}')
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=2)
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# # draw boxes for visualization
# if len(outputs[i]) > 0:
# for j, (output, conf) in enumerate(zip(outputs[i], confs)):
# bboxes = output[0:4]
# id = output[4]
# cls = output[5]
# if save_txt:
# # to MOT format
# bbox_left = output[0]
# bbox_top = output[1]
# bbox_w = output[2] - output[0]
# bbox_h = output[3] - output[1]
# # format video_name frame id xmin ymin width height score class
# with open(txt_path + '.txt', 'a') as file:
# file.write(f'{p.stem} {frame_idx} {id} {bbox_left} {bbox_top} {bbox_w} {bbox_h} {conf:.2f} {cls}\n')
# if save_img or save_crop or view_img: # Add bbox to image
# c = int(cls) # integer class
# id = int(id) # integer id
# label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
# plot_one_box(bboxes, im0, label=label, color=colors[int(cls)], line_thickness=2)
# if save_crop:
# txt_file_name = txt_file_name if (isinstance(path, list) and len(path) > 1) else ''
# save_one_box(bboxes, imc, file=save_dir / 'crops' / txt_file_name / names[c] / f'{id}' / f'{p.stem}.jpg', BGR=True)
print(f'{s}Done. YOLO:({t3 - t2:.3f}s), StrongSORT:({t5 - t4:.3f}s)')
else:
strongsort_list[i].increment_ages()
print('No detections')
# Stream results
im0 = annotator.result()
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), im0.shape[1], im0.shape[0])
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
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, im0.shape[1], im0.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(im0)
prev_frames[i] = curr_frames[i]
# Print time (inference-only)
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
# Print results
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape, %.1fms StrongSORT' % tuple(1E3 * x / seen for x in sdt))
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
if update:
strip_optimizer(yolo_weights[0]) # update model (to fix SourceChangeWarning)
return save_path
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--yolo-weights', nargs='+', type=str, default=WEIGHTS / 'yolov9.pt', help='model.pt path(s)')
parser.add_argument('--strong-sort-weights', type=str, default=WEIGHTS / 'osnet_x0_25_msmt17.pt')
parser.add_argument('--config-strongsort', type=str, default='strong_sort/configs/strong_sort.yaml')
parser.add_argument('--source', type=str, default='0', help='file/dir/URL/glob, 0 for 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=0.5, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, 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('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
# class 0 is person, 1 is bycicle, 2 is car... 79 is oven
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/track', 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('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
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
return opt
def main(opt):
# check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
run_strongsort(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt) |