AI项目二十：基于YOLOv8实例分割的DeepSORT多目标跟踪

若该文为原创文章，转载请注明原文出处。

前面提及目标跟踪使用的方法有很多，更多的是Deepsort方法。

本篇博客记录YOLOv8的实例分割+deepsort视觉跟踪算法。结合YOLOv8的目标检测分割和deepsort的特征跟踪，该算法在复杂环境下确保了目标的准确与稳定跟踪。在计算机视觉中，这种跟踪技术在安全监控、无人驾驶等领域有着广泛应用。

源码地址：GitHub - MuhammadMoinFaisal/YOLOv8_Segmentation_DeepSORT_Object_Tracking: YOLOv8 Segmentation with DeepSORT Object Tracking (ID + Trails)

感谢Muhammad Moin

一、环境搭建教程

使用的是Anaconda3，环境自行安装，可以参考前面的文章搭建。

1、创建虚拟环境

conda create -n YOLOv8-Seg-Deepsort python=3.8

2、激活

conda activate YOLOv8-Seg-Deepsort

二、下载代码

代码可以使用源码，也可以使用我的，我把YOLOv8_Segmentation_DeepSORT_Object_Tracking和YOLOv8-DeepSORT-Object-Tracking整合在一起了。

下载地址：

Yinyifeng18/YOLOv8_Segmentation_DeepSORT_Object_Tracking (github.com)

git clone https://github.com/Yinyifeng18/YOLOv8_Segmentation_DeepSORT_Object_Tracking.git

三、、安装依赖项

pip install -e ".[dev]"

如果使用的是源码，会出现下面错误：

AttributeError: module 'numpy' has no attribute 'float'

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

出错错误的原因是所用的代码是依赖于旧版本的Numpy。您可以将你的Numpy版本降级到1.23.5。

pip install numpy==1.23.5

四、测试

1、转到检测或分割目录下

cd YOLOv8_Segmentation_DeepSORT_Object_Tracking\ultralytics\yolo\v8\detect

或

cd YOLOv8_Segmentation_DeepSORT_Object_Tracking\ultralytics\yolo\v8\segment

2、测试

python predict.py model=yolov8l.pt source="test3.mp4" show=True

或

python predict.py model=yolov8x-seg.pt source="test3.mp4" show=True

使用是实例分割测试，运行结果。

如果想保存视频，直接参数save=True

五、代码説明

DeepSort需要DeepSORT 文件，下载地址是：


https://drive.google.com/drive/folders/1kna8eWGrSfzaR6DtNJ8_GchGgPMv3VC8?usp=sharing

下载DeepSORT Zip文件后，将其解压缩到子文件夹中，然后将deep_sort_pytorch文件夹放入ultralytics/yolo/v8/segment文件夹中
目录结果如下

这里直接附predict.py代码

# Ultralytics YOLO 🚀, GPL-3.0 licenseimport hydra
import torchfrom ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops
from ultralytics.yolo.utils.checks import check_imgsz
from ultralytics.yolo.utils.plotting import colors, save_one_boxfrom ultralytics.yolo.v8.detect.predict import DetectionPredictor
from numpy import randomimport cv2
from deep_sort_pytorch.utils.parser import get_config
from deep_sort_pytorch.deep_sort import DeepSort
#Deque is basically a double ended queue in python, we prefer deque over list when we need to perform insertion or pop up operations
#at the same time
from collections import deque
import numpy as np
palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
data_deque = {}deepsort = Noneobject_counter = {}object_counter1 = {}line = [(100, 500), (1050, 500)]
def init_tracker():global deepsortcfg_deep = get_config()cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml")deepsort= DeepSort(cfg_deep.DEEPSORT.REID_CKPT,max_dist=cfg_deep.DEEPSORT.MAX_DIST, min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE,nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE,max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, nn_budget=cfg_deep.DEEPSORT.NN_BUDGET,use_cuda=True)
##########################################################################################
def xyxy_to_xywh(*xyxy):"""" Calculates the relative bounding box from absolute pixel values. """bbox_left = min([xyxy[0].item(), xyxy[2].item()])bbox_top = min([xyxy[1].item(), xyxy[3].item()])bbox_w = abs(xyxy[0].item() - xyxy[2].item())bbox_h = abs(xyxy[1].item() - xyxy[3].item())x_c = (bbox_left + bbox_w / 2)y_c = (bbox_top + bbox_h / 2)w = bbox_wh = bbox_hreturn x_c, y_c, w, hdef xyxy_to_tlwh(bbox_xyxy):tlwh_bboxs = []for i, box in enumerate(bbox_xyxy):x1, y1, x2, y2 = [int(i) for i in box]top = x1left = y1w = int(x2 - x1)h = int(y2 - y1)tlwh_obj = [top, left, w, h]tlwh_bboxs.append(tlwh_obj)return tlwh_bboxsdef compute_color_for_labels(label):"""Simple function that adds fixed color depending on the class"""if label == 0: #personcolor = (85,45,255)elif label == 2: # Carcolor = (222,82,175)elif label == 3:  # Motobikecolor = (0, 204, 255)elif label == 5:  # Buscolor = (0, 149, 255)else:color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]return tuple(color)def draw_border(img, pt1, pt2, color, thickness, r, d):x1,y1 = pt1x2,y2 = pt2# Top leftcv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness)cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness)cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness)# Top rightcv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness)cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness)cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness)# Bottom leftcv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness)cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness)cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness)# Bottom rightcv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness)cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness)cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness)cv2.rectangle(img, (x1 + r, y1), (x2 - r, y2), color, -1, cv2.LINE_AA)cv2.rectangle(img, (x1, y1 + r), (x2, y2 - r - d), color, -1, cv2.LINE_AA)cv2.circle(img, (x1 +r, y1+r), 2, color, 12)cv2.circle(img, (x2 -r, y1+r), 2, color, 12)cv2.circle(img, (x1 +r, y2-r), 2, color, 12)cv2.circle(img, (x2 -r, y2-r), 2, color, 12)return imgdef UI_box(x, img, color=None, label=None, line_thickness=None):# Plots one bounding box on image imgtl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thicknesscolor = 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 thicknesst_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]img = draw_border(img, (c1[0], c1[1] - t_size[1] -3), (c1[0] + t_size[0], c1[1]+3), color, 1, 8, 2)cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)def intersect(A,B,C,D):return ccw(A,C,D) != ccw(B,C,D) and ccw(A,B,C) != ccw(A,B,D)def ccw(A,B,C):return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])def get_direction(point1, point2):direction_str = ""# calculate y axis directionif point1[1] > point2[1]:direction_str += "South"elif point1[1] < point2[1]:direction_str += "North"else:direction_str += ""# calculate x axis directionif point1[0] > point2[0]:direction_str += "East"elif point1[0] < point2[0]:direction_str += "West"else:direction_str += ""return direction_str
def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)):cv2.line(img, line[0], line[1], (46,162,112), 3)height, width, _ = img.shape# remove tracked point from buffer if object is lostfor key in list(data_deque):if key not in identities:data_deque.pop(key)for i, box in enumerate(bbox):x1, y1, x2, y2 = [int(i) for i in box]x1 += offset[0]x2 += offset[0]y1 += offset[1]y2 += offset[1]# code to find center of bottom edgecenter = (int((x2+x1)/ 2), int((y2+y2)/2))# get ID of objectid = int(identities[i]) if identities is not None else 0# create new buffer for new objectif id not in data_deque:  data_deque[id] = deque(maxlen= 64)color = compute_color_for_labels(object_id[i])obj_name = names[object_id[i]]label = '{}{:d}'.format("", id) + ":"+ '%s' % (obj_name)# add center to bufferdata_deque[id].appendleft(center)if len(data_deque[id]) >= 2:direction = get_direction(data_deque[id][0], data_deque[id][1])if intersect(data_deque[id][0], data_deque[id][1], line[0], line[1]):cv2.line(img, line[0], line[1], (255, 255, 255), 3)if "South" in direction:if obj_name not in object_counter:object_counter[obj_name] = 1else:object_counter[obj_name] += 1if "North" in direction:if obj_name not in object_counter1:object_counter1[obj_name] = 1else:object_counter1[obj_name] += 1UI_box(box, img, label=label, color=color, line_thickness=2)# draw trailfor i in range(1, len(data_deque[id])):# check if on buffer value is noneif data_deque[id][i - 1] is None or data_deque[id][i] is None:continue# generate dynamic thickness of trailsthickness = int(np.sqrt(64 / float(i + i)) * 1.5)# draw trailscv2.line(img, data_deque[id][i - 1], data_deque[id][i], color, thickness)#4. Display Count in top right cornerfor idx, (key, value) in enumerate(object_counter1.items()):cnt_str = str(key) + ":" +str(value)cv2.line(img, (width - 500,25), (width,25), [85,45,255], 40)cv2.putText(img, f'Number of Vehicles Entering', (width - 500, 35), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA)cv2.line(img, (width - 150, 65 + (idx*40)), (width, 65 + (idx*40)), [85, 45, 255], 30)cv2.putText(img, cnt_str, (width - 150, 75 + (idx*40)), 0, 1, [255, 255, 255], thickness = 2, lineType = cv2.LINE_AA)for idx, (key, value) in enumerate(object_counter.items()):cnt_str1 = str(key) + ":" +str(value)cv2.line(img, (20,25), (500,25), [85,45,255], 40)cv2.putText(img, f'Numbers of Vehicles Leaving', (11, 35), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA)    cv2.line(img, (20,65+ (idx*40)), (127,65+ (idx*40)), [85,45,255], 30)cv2.putText(img, cnt_str1, (11, 75+ (idx*40)), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA)return imgclass SegmentationPredictor(DetectionPredictor):def postprocess(self, preds, img, orig_img):masks = []# TODO: filter by classesp = ops.non_max_suppression(preds[0],self.args.conf,self.args.iou,agnostic=self.args.agnostic_nms,max_det=self.args.max_det,nm=32)proto = preds[1][-1]for i, pred in enumerate(p):shape = orig_img[i].shape if self.webcam else orig_img.shapeif not len(pred):continueif self.args.retina_masks:pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()masks.append(ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2]))  # HWCelse:masks.append(ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True))  # HWCpred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()return (p, masks)def write_results(self, idx, preds, batch):p, im, im0 = batchlog_string = ""if len(im.shape) == 3:im = im[None]  # expand for batch dimself.seen += 1if self.webcam:  # batch_size >= 1log_string += f'{idx}: 'frame = self.dataset.countelse:frame = getattr(self.dataset, 'frame', 0)self.data_path = pself.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')log_string += '%gx%g ' % im.shape[2:]  # print stringself.annotator = self.get_annotator(im0)preds, masks = predsdet = preds[idx]if len(det) == 0:return log_string# Segmentsmask = masks[idx]if self.args.save_txt:segments = [ops.scale_segments(im0.shape if self.args.retina_masks else im.shape[2:], x, im0.shape, normalize=True)for x in reversed(ops.masks2segments(mask))]# Print resultsfor c in det[:, 5].unique():n = (det[:, 5] == c).sum()  # detections per classlog_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "  # add to string# Mask plottingself.annotator.masks(mask,colors=[colors(x, True) for x in det[:, 5]],im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(self.device).permute(2, 0, 1).flip(0).contiguous() /255 if self.args.retina_masks else im[idx])det = reversed(det[:, :6])self.all_outputs.append([det, mask])xywh_bboxs = []confs = []oids = []outputs = []# Write resultsfor j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy)xywh_obj = [x_c, y_c, bbox_w, bbox_h]xywh_bboxs.append(xywh_obj)confs.append([conf.item()])oids.append(int(cls))xywhs = torch.Tensor(xywh_bboxs)confss = torch.Tensor(confs)outputs = deepsort.update(xywhs, confss, oids, im0)if len(outputs) > 0:bbox_xyxy = outputs[:, :4]identities = outputs[:, -2]object_id = outputs[:, -1]draw_boxes(im0, bbox_xyxy, self.model.names, object_id,identities)return log_string@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def predict(cfg):init_tracker()cfg.model = cfg.model or "yolov8n-seg.pt"cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2)  # check image sizecfg.source = cfg.source if cfg.source is not None else ROOT / "assets"predictor = SegmentationPredictor(cfg)predictor()if __name__ == "__main__":predict()

这里给的是对象分割和 DeepSORT 跟踪（ID + 轨迹）和车辆计数

没有分割在detect目录下，自行测试。

测试结果