毕业设计题目：基于步态识别的多目标跨镜头跟踪算法研究 主算法：基于yoloV5-deepsort框架进行目标检测和跟踪+GaitSet算法

效果演示：

介绍

此存储库包含一个高度可配置的两阶段跟踪器，可根据不同的部署场景进行调整。YOLOv5是一系列在COCO数据集上预训练的对象检测架构和模型，它生成的检测结果被传递给跟踪对象的深度排序算法。它可以跟踪Yolov5模型训练用来检测的任何对象。

教程

Yolov5自定义数据培训（链接到外部存储库）

DeepSort深度描述符训练（链接到外部存储库）

Yolov5-pytorch评估

在运行跟踪器之前

如果您已经克隆并忘记使用--recurse子模块，则可以运行git-submodule update--init

确保满足所有要求：Python 3.8或更高版本，安装了所有requirements.txt依赖项，包括torch>=1.7。要安装，请运行：

pip install-r requirements.txt

追踪源

视频推理

$ python track.py --source 0 # webcam

img.jpg # image

vid.mp4 # video

path/ # directory

path/*.jpg # glob

'https://youtu.be/Zgi9g1ksQHc' # YouTube

'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream

选择对象检测和ReID模型

Yolov5

模型推理速度和准确性之间存在明显的权衡。为了满足您的推理速度/准确性需求，您可以选择Yolov5模型进行自动下载

python track.py --source 0 --yolo_model yolov5n.pt --img 640

yolov5s.pt

yolov5m.pt

yolov5l.pt

yolov5x.pt --img 1280

...

DeepSort

以上内容也适用于DeepSort模型。根据您的需求从ReID模型动物园选择ReID模型

python track.py --source 0 --deep_sort_model osnet_x0_5_market1501

resnet50_MSMT17

mobilenetv2_x1_4_dukemtmcreid

...

筛选跟踪的类

默认情况下，跟踪器跟踪所有MS COCO类。

如果你只想跟踪别人，我建议你使用这些重量来提高表现

python3 track.py --source 0 --yolo_model yolov5/weights/crowdhuman_yolov5m.pt --classes 0 # tracks persons, only

如果要跟踪MS COCO类的子集，请在classes标志后面添加它们相应的索引

python3 track.py --source 0 --yolo_model yolov5s.pt --classes 16 17 # tracks cats and dogs, only

以下是在MS COCO上训练的Yolov5模型可以检测到的所有可能对象的列表。请注意，此repo中类的索引从零开始。

符合MOT的结果

可以通过保存到您的实验文件夹track/exN

python3 track.py--source --save-txt

核心代码track.py

<code># limit the number of cpus used by high performance libraries

import os

os.environ["OMP_NUM_THREADS"] = "1"

os.environ["OPENBLAS_NUM_THREADS"] = "1"

os.environ["MKL_NUM_THREADS"] = "1"

os.environ["VECLIB_MAXIMUM_THREADS"] = "1"

os.environ["NUMEXPR_NUM_THREADS"] = "1"

import sys

sys.path.insert(0, './yolov5')

import argparse

import os

import platform

import shutil

import time

from pathlib import Path

import cv2

import torch

import torch.backends.cudnn as cudnn

from yolov5.models.experimental import attempt_load

from yolov5.utils.downloads import attempt_download

from yolov5.models.common import DetectMultiBackend

from yolov5.utils.datasets import LoadImages, LoadStreams

from yolov5.utils.general import (LOGGER, check_img_size, non_max_suppression, scale_coords,

check_imshow, xyxy2xywh, increment_path)

from yolov5.utils.torch_utils import select_device, time_sync

from yolov5.utils.plots import Annotator, colors, save_one_box,save_one_boxmod

from deep_sort.utils.parser import get_config

from deep_sort.deep_sort import DeepSort

FILE = Path(__file__).resolve()

ROOT = FILE.parents[0] # yolov5 deepsort 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

def detect(opt):

out, source, yolo_model, deep_sort_model, show_vid, save_vid, save_txt, imgsz, evaluate, half, project, name, exist_ok= \

opt.output, opt.source, opt.yolo_model, opt.deep_sort_model, opt.show_vid, opt.save_vid, \

opt.save_txt, opt.imgsz, opt.evaluate, opt.half, opt.project, opt.name, opt.exist_ok

webcam = source == '0' or source.startswith(

'rtsp') or source.startswith('http') or source.endswith('.txt')

device = select_device(opt.device)

# initialize deepsort

cfg = get_config()

cfg.merge_from_file(opt.config_deepsort)

deepsort = DeepSort(deep_sort_model,

device,

max_dist=cfg.DEEPSORT.MAX_DIST,

max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,

max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,

)

# Initialize

half &= device.type != 'cpu' # half precision only supported on CUDA

# The MOT16 evaluation runs multiple inference streams in parallel, each one writing to

# its own .txt file. Hence, in that case, the output folder is not restored

if not evaluate:

if os.path.exists(out):

pass

shutil.rmtree(out) # delete output folder

os.makedirs(out) # make new output folder

# Directories

if type(yolo_model) is str:

exp_name = yolo_model.split(".")[0]

elif type(yolo_model) is list and len(yolo_model) == 1:

exp_name = yolo_model[0].split(".")[0]

else:

exp_name = "ensemble"

exp_name = exp_name + "_" + deep_sort_model.split('/')[-1].split('.')[0]

save_dir = increment_path(Path(project) / "out", exist_ok=exist_ok) # increment run if project name exists

save_dir.mkdir(parents=True, exist_ok=True) # make dir

# Load model

device = select_device(device)

model = DetectMultiBackend(yolo_model, device=device, dnn=opt.dnn)

stride, names, pt, jit, _ = model.stride, model.names, model.pt, model.jit, model.onnx

imgsz = check_img_size(imgsz, s=stride) # check image size

# Half

half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA

if pt:

model.model.half() if half else model.model.float()

# Set Dataloader

vid_path, vid_writer = None, None

# Check if environment supports image displays

if show_vid:

show_vid = check_imshow()

# Dataloader

if webcam:

show_vid = check_imshow()

cudnn.benchmark = True # set True to speed up constant image size inference

dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)

bs = len(dataset) # batch_size

else:

dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)

bs = 1 # batch_size

vid_path, vid_writer = [None] * bs, [None] * bs

# Get names and colors

names = model.module.names if hasattr(model, 'module') else model.names

# extract what is in between the last '/' and last '.'

txt_file_name = source.split('/')[-1].split('.')[0]

txt_path = str(Path(save_dir)) + '/' + txt_file_name + '.txt'

if pt and device.type != 'cpu':

model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters()))) # warmup

dt, seen = [0.0, 0.0, 0.0, 0.0], 0

index = 1

for frame_idx, (path, img, im0s, vid_cap, s) in enumerate(dataset):

t1 = time_sync()

img = torch.from_numpy(img).to(device)

img = img.half() if half else img.float() # uint8 to fp16/32

img /= 255.0 # 0 - 255 to 0.0 - 1.0

if img.ndimension() == 3:

img = img.unsqueeze(0)

t2 = time_sync()

dt[0] += t2 - t1

# Inference yolo处理

visualize = increment_path(save_dir / Path(path[0]).stem, mkdir=True) if opt.visualize else False

pred = model(img, augment=opt.augment, visualize=visualize)

t3 = time_sync()

dt[1] += t3 - t2

# Apply NMS

pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, opt.classes, opt.agnostic_nms, max_det=opt.max_det)

dt[2] += time_sync() - t3

# Process detections

for i, det in enumerate(pred): # detections per image

seen += 1

if webcam: # batch_size >= 1

p, im0, _ = path[i], im0s[i].copy(), dataset.count

s += f'{i}: '

else:

p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)

p = Path(p) # to Path

save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ...

s += '%gx%g ' % img.shape[2:] # print string

imc = im0.copy()

# 画框

annotator = Annotator(im0, line_width=1, pil=not ascii)

if det is not None and len(det):

# Rescale boxes from img_size to im0 size

det[:, :4] = scale_coords(

img.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]

# Deepsort 处理 pass detections to deepsort

t4 = time_sync()

outputs = deepsort.update(xywhs.cpu(), confs.cpu(), clss.cpu(), im0)

t5 = time_sync()

dt[3] += t5 - t4

# index = 0

# for *xyxy, conf, cls in reversed(det):

# save_one_box(xyxy, imc, file=save_dir / 'person' /str(output[4])/ f'{index}.jpg', BGR=True)

# index = index + 1

# draw boxes for visualization

if len(outputs) > 0:

for j, (output, conf) in enumerate(zip(outputs, confs)):

bboxes = output[0:4]

# 这里的ID就是画面中任务头上的ID

id = output[4]

cls = output[5]

c = int(cls) # integer class

label = f'{id} {names[c]} {conf:.2f}'

# Todo 保存每一帧检测框中的图片

bgrImg = cv2.imread('./data/back.jpg')

# print(bgrImg)

# save_one_box(bboxes, imc,file=save_dir / 'person' / str(id)/ f'{index}.jpg', BGR=True)

save_one_boxmod(bboxes, imc,bgrImg,file=save_dir / 'person' / str(id)/ 'temp'/f'{index}.jpg',

lasted=save_dir / 'person' / str(id)/f'{index}.jpg', BGR=True)

index = index+1

# 画标记

annotator.box_label(bboxes, label, color=colors(c, True))

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]

# Write MOT compliant results to file

with open(txt_path, 'a') as f:

f.write(('%g ' * 10 + '\n') % (frame_idx + 1, id, bbox_left, # MOT format

bbox_top, bbox_w, bbox_h, -1, -1, -1, -1))

LOGGER.info(f'{s}Done. YOLO:({t3 - t2:.3f}s), DeepSort:({t5 - t4:.3f}s)')

else:

deepsort.increment_ages()

LOGGER.info('No detections')

# Stream results

im0 = annotator.result()

if show_vid:

cv2.imshow(str(p), im0)

if cv2.waitKey(1) == ord('q'): # q to quit

raise StopIteration

# Save results (image with detections)

if save_vid:

if vid_path != save_path: # new video

vid_path = save_path

if isinstance(vid_writer, cv2.VideoWriter):

vid_writer.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]

vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))

vid_writer.write(im0)

# Print results

t = tuple(x / seen * 1E3 for x in dt) # speeds per image

LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS, %.1fms deep sort update \

per image at shape {(1, 3, *imgsz)}' % t)

if save_txt or save_vid:

print('Results saved to %s' % save_path)

if platform == 'darwin': # MacOS

os.system('open ' + save_path)

if __name__ == '__main__':

parser = argparse.ArgumentParser()

parser.add_argument('--yolo_model', nargs='+', type=str, default='yolov5m.pt', help='model.pt path(s)')code>

parser.add_argument('--deep_sort_model', type=str, default='osnet_ibn_x1_0_MSMT17')code>

parser.add_argument('--source', type=str, default='0', help='source') # file/folder, 0 for webcamcode>

parser.add_argument('--output', type=str, default='inference/output', help='output folder') # output foldercode>

parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')code>

parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')code>

parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')code>

parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')code>

parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')code>

parser.add_argument('--show-vid', action='store_true', help='display tracking video results')code>

parser.add_argument('--save-vid', action='store_true', help='save video tracking results')code>

parser.add_argument('--save-txt', action='store_true', help='save MOT compliant results to *.txt')code>

# class 0 is person, 1 is bycicle, 2 is car... 79 is oven

parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 16 17')code>

parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')code>

parser.add_argument('--augment', action='store_true', help='augmented inference')code>

parser.add_argument('--evaluate', action='store_true', help='augmented inference')code>

parser.add_argument("--config_deepsort", type=str, default="deep_sort/configs/deep_sort.yaml")code>

parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")code>

parser.add_argument('--visualize', action='store_true', help='visualize features')code>

parser.add_argument('--max-det', type=int, default=1000, help='maximum detection per image')code>

parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')code>

parser.add_argument('--project', default=ROOT / 'runs/track', help='save results to project/name')code>

parser.add_argument('--name', default='exp', help='save results to project/name')code>

parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')code>

opt = parser.parse_args()

opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand

with torch.no_grad():

detect(opt)

完整代码下载地址：https://download.csdn.net/download/FL1768317420/89232934