本文地址：https://blog.csdn.net/weixin_44936889/article/details/112002152
项目简介：
使用YOLOv5+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中。
代码地址（欢迎star）：
https://github.com/Sharpiless/Yolov5-deepsort-inference
最终效果：
YOLOv5检测器：
class Detector(baseDet):
def __init__(self):
super(Detector, self).__init__()
self.init_model()
self.build_config()
def init_model(self):
self.weights = 'weights/yolov5m.pt'
self.device = '0' if torch.cuda.is_available() else 'cpu'
self.device = select_device(self.device)
model = attempt_load(self.weights, map_location=self.device)
model.to(self.device).eval()
model.half()
# torch.save(model, 'test.pt')
self.m = model
self.names = model.module.names if hasattr(
model, 'module') else model.names
def preprocess(self, img):
img0 = img.copy()
img = letterbox(img, new_shape=self.img_size)[0]
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(self.device)
img = img.half() # 半精度
img /= 255.0 # 图像归一化
if img.ndimension() == 3:
img = img.unsqueeze(0)
return img0, img
def detect(self, im):
im0, img = self.preprocess(im)
pred = self.m(img, augment=False)[0]
pred = pred.float()
pred = non_max_suppression(pred, self.threshold, 0.4)
pred_boxes = []
for det in pred:
if det is not None and len(det):
det[:, :4] = scale_coords(
img.shape[2:], det[:, :4], im0.shape).round()
for *x, conf, cls_id in det:
lbl = self.names[int(cls_id)]
if not lbl in ['person', 'car', 'truck']:
continue
x1, y1 = int(x[0]), int(x[1])
x2, y2 = int(x[2]), int(x[3])
pred_boxes.append(
(x1, y1, x2, y2, lbl, conf))
return im, pred_boxes
调用 self.detect 方法返回图像和预测结果
DeepSort追踪器：
deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, 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,
use_cuda=True)
调用 self.update 方法更新追踪结果
运行demo：
python demo.py
训练自己的模型：
参考我的另一篇博客：
【小白CV】手把手教你用YOLOv5训练自己的数据集（从Windows环境配置到模型部署）
训练好后放到 weights 文件夹下
调用接口：
创建检测器：
from AIDetector_pytorch import Detector
det = Detector()
调用检测接口：
func_status = {}
func_status['headpose'] = None
result = det.feedCap(im, func_status)
其中 im 为 BGR 图像
返回的 result 是字典，result['frame'] 返回可视化后的图像
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遵循 GNU General Public License v3.0 协议，标明目标检测部分来源：https://github.com/ultralytics/yolov5/
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