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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Yolov5-Deepsort/deep_sort/deep_sort/deep/checkpoint/ckpt.t7 filter=lfs diff=lfs merge=lfs -text
+Yolov5-Deepsort/mot.mp4 filter=lfs diff=lfs merge=lfs -text
+Yolov5-Deepsort/niuzi.mp4 filter=lfs diff=lfs merge=lfs -text
+Yolov5-Deepsort/result.mp4 filter=lfs diff=lfs merge=lfs -text

Yolov5-Deepsort/AIDetector_pytorch.py

@@ -0,0 +1,74 @@
+import torch
+import numpy as np
+from models.experimental import attempt_load
+from utils.general import non_max_suppression, scale_coords
+from utils.BaseDetector import baseDet
+from utils.torch_utils import select_device
+from utils.datasets import letterbox
+import rich
+
+class Detector(baseDet):
+
+    def __init__(self):
+        super(Detector, self).__init__()
+        self.init_model()
+        self.build_config()
+
+    def init_model(self):
+
+        self.weights = 'weights/yolov5s.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]
+        #rich.print(pred.shape)
+        pred = pred.float()
+
+        
+        pred = non_max_suppression(pred, self.threshold, 0.4)
+        #rich.print((pred))
+        
+
+        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
+

Yolov5-Deepsort/DDM_DeepSort/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Yolov5-Deepsort/DDM_DeepSort/README.md

@@ -0,0 +1,13 @@
+---
+title: DDM DeepSort
+emoji: 📚
+colorFrom: yellow
+colorTo: gray
+sdk: gradio
+sdk_version: 5.6.0
+app_file: app.py
+pinned: false
+short_description: 将Drift Diffusion Model 应用于 yolov5 + deepsort
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Yolov5-Deepsort/DDM_DeepSort/app.py

@@ -0,0 +1,7 @@
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch()

Yolov5-Deepsort/LICENSE

@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+
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+    it under the terms of the GNU General Public License as published by
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+    (at your option) any later version.
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
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+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
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+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
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+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

Yolov5-Deepsort/README.md

@@ -0,0 +1,139 @@
+# 本文禁止转载!
+
+
+本文地址：[https://blog.csdn.net/weixin_44936889/article/details/112002152](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/](https://github.com/Sharpiless/yolov5-deepsort/)
+
+最终效果：
+![在这里插入图片描述](https://github.com/Sharpiless/Yolov5-Deepsort/blob/main/image.png)
+# YOLOv5检测器：
+
+```python
+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：
+
+```python
+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：
+
+```bash
+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）](https://blog.csdn.net/weixin_44936889/article/details/110661862)
+
+训练好后放到 weights 文件夹下
+
+# 调用接口：
+
+## 创建检测器：
+
+```python
+from AIDetector_pytorch import Detector
+
+det = Detector()
+```
+
+## 调用检测接口：
+
+```python
+result = det.feedCap(im)
+```
+
+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
+
+# 联系作者：
+
+> B站：[https://space.bilibili.com/470550823](https://space.bilibili.com/470550823)
+
+> CSDN：[https://blog.csdn.net/weixin_44936889](https://blog.csdn.net/weixin_44936889)
+
+> AI Studio：[https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156)
+
+> Github：[https://github.com/Sharpiless](https://github.com/Sharpiless)
+
+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/
+
+

Yolov5-Deepsort/deep_sort/configs/deep_sort.yaml

@@ -0,0 +1,10 @@
+DEEPSORT:
+  REID_CKPT: "deep_sort/deep_sort/deep/checkpoint/ckpt.t7"
+  MAX_DIST: 0.2
+  MIN_CONFIDENCE: 0.3
+  NMS_MAX_OVERLAP: 0.5
+  MAX_IOU_DISTANCE: 0.7
+  MAX_AGE: 70
+  N_INIT: 3
+  NN_BUDGET: 100
+  

Yolov5-Deepsort/deep_sort/deep_sort/README.md

@@ -0,0 +1,3 @@
+# Deep Sort 
+
+This is the implemention of deep sort with pytorch.

Yolov5-Deepsort/deep_sort/deep_sort/__init__.py

@@ -0,0 +1,21 @@
+from .deep_sort import DeepSort
+
+
+__all__ = ['DeepSort', 'build_tracker']
+
+
+def build_tracker(cfg, use_cuda):
+    return 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=use_cuda)
+    
+
+
+
+
+
+
+
+
+

Yolov5-Deepsort/deep_sort/deep_sort/deep/checkpoint/ckpt.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df75ddef42c3d1bda67bc94b093e7ce61de7f75a89f36a8f868a428462198316
+size 46034619

Yolov5-Deepsort/deep_sort/deep_sort/deep/evaluate.py

@@ -0,0 +1,15 @@
+import torch
+
+features = torch.load("features.pth")
+qf = features["qf"]
+ql = features["ql"]
+gf = features["gf"]
+gl = features["gl"]
+
+scores = qf.mm(gf.t())
+res = scores.topk(5, dim=1)[1][:,0]
+top1correct = gl[res].eq(ql).sum().item()
+
+print("Acc top1:{:.3f}".format(top1correct/ql.size(0)))
+
+

Yolov5-Deepsort/deep_sort/deep_sort/deep/feature_extractor.py

@@ -0,0 +1,55 @@
+import torch
+import torchvision.transforms as transforms
+import numpy as np
+import cv2
+import logging
+
+from .model import Net
+
+class Extractor(object):
+    def __init__(self, model_path, use_cuda=True):
+        self.net = Net(reid=True)
+        self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
+        state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)['net_dict']
+        self.net.load_state_dict(state_dict)
+        logger = logging.getLogger("root.tracker")
+        logger.info("Loading weights from {}... Done!".format(model_path))
+        self.net.to(self.device)
+        self.size = (64, 128)
+        self.norm = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ])
+        
+
+
+    def _preprocess(self, im_crops):
+        """
+        TODO:
+            1. to float with scale from 0 to 1
+            2. resize to (64, 128) as Market1501 dataset did
+            3. concatenate to a numpy array
+            3. to torch Tensor
+            4. normalize
+        """
+        def _resize(im, size):
+            return cv2.resize(im.astype(np.float32)/255., size)
+
+        im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float()
+        return im_batch
+
+
+    def __call__(self, im_crops):
+        im_batch = self._preprocess(im_crops)
+        with torch.no_grad():
+            im_batch = im_batch.to(self.device)
+            features = self.net(im_batch)
+        return features.cpu().numpy()
+
+
+if __name__ == '__main__':
+    img = cv2.imread("demo.jpg")[:,:,(2,1,0)]
+    extr = Extractor("checkpoint/ckpt.t7")
+    feature = extr(img)
+    print(feature.shape)
+

Yolov5-Deepsort/deep_sort/deep_sort/deep/model.py

@@ -0,0 +1,104 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class BasicBlock(nn.Module):
+    def __init__(self, c_in, c_out,is_downsample=False):
+        super(BasicBlock,self).__init__()
+        self.is_downsample = is_downsample
+        if is_downsample:
+            self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=2, padding=1, bias=False)
+        else:
+            self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(c_out)
+        self.relu = nn.ReLU(True)
+        self.conv2 = nn.Conv2d(c_out,c_out,3,stride=1,padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(c_out)
+        if is_downsample:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=2, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+        elif c_in != c_out:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=1, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+            self.is_downsample = True
+
+    def forward(self,x):
+        y = self.conv1(x)
+        y = self.bn1(y)
+        y = self.relu(y)
+        y = self.conv2(y)
+        y = self.bn2(y)
+        if self.is_downsample:
+            x = self.downsample(x)
+        return F.relu(x.add(y),True)
+
+def make_layers(c_in,c_out,repeat_times, is_downsample=False):
+    blocks = []
+    for i in range(repeat_times):
+        if i ==0:
+            blocks += [BasicBlock(c_in,c_out, is_downsample=is_downsample),]
+        else:
+            blocks += [BasicBlock(c_out,c_out),]
+    return nn.Sequential(*blocks)
+
+class Net(nn.Module):
+    def __init__(self, num_classes=751 ,reid=False):
+        super(Net,self).__init__()
+        # 3 128 64
+        self.conv = nn.Sequential(
+            nn.Conv2d(3,64,3,stride=1,padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+            # nn.Conv2d(32,32,3,stride=1,padding=1),
+            # nn.BatchNorm2d(32),
+            # nn.ReLU(inplace=True),
+            nn.MaxPool2d(3,2,padding=1),
+        )
+        # 32 64 32
+        self.layer1 = make_layers(64,64,2,False)
+        # 32 64 32
+        self.layer2 = make_layers(64,128,2,True)
+        # 64 32 16
+        self.layer3 = make_layers(128,256,2,True)
+        # 128 16 8
+        self.layer4 = make_layers(256,512,2,True)
+        # 256 8 4
+        self.avgpool = nn.AvgPool2d((8,4),1)
+        # 256 1 1 
+        self.reid = reid
+        self.classifier = nn.Sequential(
+            nn.Linear(512, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(256, num_classes),
+        )
+    
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.avgpool(x)
+        x = x.view(x.size(0),-1)
+        # B x 128
+        if self.reid:
+            x = x.div(x.norm(p=2,dim=1,keepdim=True))
+            return x
+        # classifier
+        x = self.classifier(x)
+        return x
+
+
+if __name__ == '__main__':
+    net = Net()
+    x = torch.randn(4,3,128,64)
+    y = net(x)
+    import ipdb; ipdb.set_trace()
+
+

Yolov5-Deepsort/deep_sort/deep_sort/deep/original_model.py

@@ -0,0 +1,106 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class BasicBlock(nn.Module):
+    def __init__(self, c_in, c_out,is_downsample=False):
+        super(BasicBlock,self).__init__()
+        self.is_downsample = is_downsample
+        if is_downsample:
+            self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=2, padding=1, bias=False)
+        else:
+            self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(c_out)
+        self.relu = nn.ReLU(True)
+        self.conv2 = nn.Conv2d(c_out,c_out,3,stride=1,padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(c_out)
+        if is_downsample:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=2, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+        elif c_in != c_out:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=1, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+            self.is_downsample = True
+
+    def forward(self,x):
+        y = self.conv1(x)
+        y = self.bn1(y)
+        y = self.relu(y)
+        y = self.conv2(y)
+        y = self.bn2(y)
+        if self.is_downsample:
+            x = self.downsample(x)
+        return F.relu(x.add(y),True)
+
+def make_layers(c_in,c_out,repeat_times, is_downsample=False):
+    blocks = []
+    for i in range(repeat_times):
+        if i ==0:
+            blocks += [BasicBlock(c_in,c_out, is_downsample=is_downsample),]
+        else:
+            blocks += [BasicBlock(c_out,c_out),]
+    return nn.Sequential(*blocks)
+
+class Net(nn.Module):
+    def __init__(self, num_classes=625 ,reid=False):
+        super(Net,self).__init__()
+        # 3 128 64
+        self.conv = nn.Sequential(
+            nn.Conv2d(3,32,3,stride=1,padding=1),
+            nn.BatchNorm2d(32),
+            nn.ELU(inplace=True),
+            nn.Conv2d(32,32,3,stride=1,padding=1),
+            nn.BatchNorm2d(32),
+            nn.ELU(inplace=True),
+            nn.MaxPool2d(3,2,padding=1),
+        )
+        # 32 64 32
+        self.layer1 = make_layers(32,32,2,False)
+        # 32 64 32
+        self.layer2 = make_layers(32,64,2,True)
+        # 64 32 16
+        self.layer3 = make_layers(64,128,2,True)
+        # 128 16 8
+        self.dense = nn.Sequential(
+            nn.Dropout(p=0.6),
+            nn.Linear(128*16*8, 128),
+            nn.BatchNorm1d(128),
+            nn.ELU(inplace=True)
+        )
+        # 256 1 1 
+        self.reid = reid
+        self.batch_norm = nn.BatchNorm1d(128)
+        self.classifier = nn.Sequential(
+            nn.Linear(128, num_classes),
+        )
+    
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+
+        x = x.view(x.size(0),-1)
+        if self.reid:
+            x = self.dense[0](x)
+            x = self.dense[1](x)
+            x = x.div(x.norm(p=2,dim=1,keepdim=True))
+            return x
+        x = self.dense(x)
+        # B x 128
+        # classifier
+        x = self.classifier(x)
+        return x
+
+
+if __name__ == '__main__':
+    net = Net(reid=True)
+    x = torch.randn(4,3,128,64)
+    y = net(x)
+    import ipdb; ipdb.set_trace()
+
+

Yolov5-Deepsort/deep_sort/deep_sort/deep/test.py

@@ -0,0 +1,77 @@
+import torch
+import torch.backends.cudnn as cudnn
+import torchvision
+
+import argparse
+import os
+
+from model import Net
+
+parser = argparse.ArgumentParser(description="Train on market1501")
+parser.add_argument("--data-dir",default='data',type=str)
+parser.add_argument("--no-cuda",action="store_true")
+parser.add_argument("--gpu-id",default=0,type=int)
+args = parser.parse_args()
+
+# device
+device = "cuda:{}".format(args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu"
+if torch.cuda.is_available() and not args.no_cuda:
+    cudnn.benchmark = True
+
+# data loader
+root = args.data_dir
+query_dir = os.path.join(root,"query")
+gallery_dir = os.path.join(root,"gallery")
+transform = torchvision.transforms.Compose([
+    torchvision.transforms.Resize((128,64)),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+queryloader = torch.utils.data.DataLoader(
+    torchvision.datasets.ImageFolder(query_dir, transform=transform),
+    batch_size=64, shuffle=False
+)
+galleryloader = torch.utils.data.DataLoader(
+    torchvision.datasets.ImageFolder(gallery_dir, transform=transform),
+    batch_size=64, shuffle=False
+)
+
+# net definition
+net = Net(reid=True)
+assert os.path.isfile("./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
+print('Loading from checkpoint/ckpt.t7')
+checkpoint = torch.load("./checkpoint/ckpt.t7")
+net_dict = checkpoint['net_dict']
+net.load_state_dict(net_dict, strict=False)
+net.eval()
+net.to(device)
+
+# compute features
+query_features = torch.tensor([]).float()
+query_labels = torch.tensor([]).long()
+gallery_features = torch.tensor([]).float()
+gallery_labels = torch.tensor([]).long()
+
+with torch.no_grad():
+    for idx,(inputs,labels) in enumerate(queryloader):
+        inputs = inputs.to(device)
+        features = net(inputs).cpu()
+        query_features = torch.cat((query_features, features), dim=0)
+        query_labels = torch.cat((query_labels, labels))
+
+    for idx,(inputs,labels) in enumerate(galleryloader):
+        inputs = inputs.to(device)
+        features = net(inputs).cpu()
+        gallery_features = torch.cat((gallery_features, features), dim=0)
+        gallery_labels = torch.cat((gallery_labels, labels))
+
+gallery_labels -= 2
+
+# save features
+features = {
+    "qf": query_features,
+    "ql": query_labels,
+    "gf": gallery_features,
+    "gl": gallery_labels
+}
+torch.save(features,"features.pth")

Yolov5-Deepsort/deep_sort/deep_sort/deep/train.py

@@ -0,0 +1,189 @@
+import argparse
+import os
+import time
+
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.backends.cudnn as cudnn
+import torchvision
+
+from model import Net
+
+parser = argparse.ArgumentParser(description="Train on market1501")
+parser.add_argument("--data-dir",default='data',type=str)
+parser.add_argument("--no-cuda",action="store_true")
+parser.add_argument("--gpu-id",default=0,type=int)
+parser.add_argument("--lr",default=0.1, type=float)
+parser.add_argument("--interval",'-i',default=20,type=int)
+parser.add_argument('--resume', '-r',action='store_true')
+args = parser.parse_args()
+
+# device
+device = "cuda:{}".format(args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu"
+if torch.cuda.is_available() and not args.no_cuda:
+    cudnn.benchmark = True
+
+# data loading
+root = args.data_dir
+train_dir = os.path.join(root,"train")
+test_dir = os.path.join(root,"test")
+transform_train = torchvision.transforms.Compose([
+    torchvision.transforms.RandomCrop((128,64),padding=4),
+    torchvision.transforms.RandomHorizontalFlip(),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+transform_test = torchvision.transforms.Compose([
+    torchvision.transforms.Resize((128,64)),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+trainloader = torch.utils.data.DataLoader(
+    torchvision.datasets.ImageFolder(train_dir, transform=transform_train),
+    batch_size=64,shuffle=True
+)
+testloader = torch.utils.data.DataLoader(
+    torchvision.datasets.ImageFolder(test_dir, transform=transform_test),
+    batch_size=64,shuffle=True
+)
+num_classes = max(len(trainloader.dataset.classes), len(testloader.dataset.classes))
+
+# net definition
+start_epoch = 0
+net = Net(num_classes=num_classes)
+if args.resume:
+    assert os.path.isfile("./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
+    print('Loading from checkpoint/ckpt.t7')
+    checkpoint = torch.load("./checkpoint/ckpt.t7")
+    # import ipdb; ipdb.set_trace()
+    net_dict = checkpoint['net_dict']
+    net.load_state_dict(net_dict)
+    best_acc = checkpoint['acc']
+    start_epoch = checkpoint['epoch']
+net.to(device)
+
+# loss and optimizer
+criterion = torch.nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(net.parameters(), args.lr, momentum=0.9, weight_decay=5e-4)
+best_acc = 0.
+
+# train function for each epoch
+def train(epoch):
+    print("\nEpoch : %d"%(epoch+1))
+    net.train()
+    training_loss = 0.
+    train_loss = 0.
+    correct = 0
+    total = 0
+    interval = args.interval
+    start = time.time()
+    for idx, (inputs, labels) in enumerate(trainloader):
+        # forward
+        inputs,labels = inputs.to(device),labels.to(device)
+        outputs = net(inputs)
+        loss = criterion(outputs, labels)
+
+        # backward
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        # accumurating
+        training_loss += loss.item()
+        train_loss += loss.item()
+        correct += outputs.max(dim=1)[1].eq(labels).sum().item()
+        total += labels.size(0)
+
+        # print 
+        if (idx+1)%interval == 0:
+            end = time.time()
+            print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format(
+                100.*(idx+1)/len(trainloader), end-start, training_loss/interval, correct, total, 100.*correct/total
+            ))
+            training_loss = 0.
+            start = time.time()
+    
+    return train_loss/len(trainloader), 1.- correct/total
+
+def test(epoch):
+    global best_acc
+    net.eval()
+    test_loss = 0.
+    correct = 0
+    total = 0
+    start = time.time()
+    with torch.no_grad():
+        for idx, (inputs, labels) in enumerate(testloader):
+            inputs, labels = inputs.to(device), labels.to(device)
+            outputs = net(inputs)
+            loss = criterion(outputs, labels)
+
+            test_loss += loss.item()
+            correct += outputs.max(dim=1)[1].eq(labels).sum().item()
+            total += labels.size(0)
+        
+        print("Testing ...")
+        end = time.time()
+        print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format(
+                100.*(idx+1)/len(testloader), end-start, test_loss/len(testloader), correct, total, 100.*correct/total
+            ))
+
+    # saving checkpoint
+    acc = 100.*correct/total
+    if acc > best_acc:
+        best_acc = acc
+        print("Saving parameters to checkpoint/ckpt.t7")
+        checkpoint = {
+            'net_dict':net.state_dict(),
+            'acc':acc,
+            'epoch':epoch,
+        }
+        if not os.path.isdir('checkpoint'):
+            os.mkdir('checkpoint')
+        torch.save(checkpoint, './checkpoint/ckpt.t7')
+
+    return test_loss/len(testloader), 1.- correct/total
+
+# plot figure
+x_epoch = []
+record = {'train_loss':[], 'train_err':[], 'test_loss':[], 'test_err':[]}
+fig = plt.figure()
+ax0 = fig.add_subplot(121, title="loss")
+ax1 = fig.add_subplot(122, title="top1err")
+def draw_curve(epoch, train_loss, train_err, test_loss, test_err):
+    global record
+    record['train_loss'].append(train_loss)
+    record['train_err'].append(train_err)
+    record['test_loss'].append(test_loss)
+    record['test_err'].append(test_err)
+
+    x_epoch.append(epoch)
+    ax0.plot(x_epoch, record['train_loss'], 'bo-', label='train')
+    ax0.plot(x_epoch, record['test_loss'], 'ro-', label='val')
+    ax1.plot(x_epoch, record['train_err'], 'bo-', label='train')
+    ax1.plot(x_epoch, record['test_err'], 'ro-', label='val')
+    if epoch == 0:
+        ax0.legend()
+        ax1.legend()
+    fig.savefig("train.jpg")
+
+# lr decay
+def lr_decay():
+    global optimizer
+    for params in optimizer.param_groups:
+        params['lr'] *= 0.1
+        lr = params['lr']
+        print("Learning rate adjusted to {}".format(lr))
+
+def main():
+    for epoch in range(start_epoch, start_epoch+40):
+        train_loss, train_err = train(epoch)
+        test_loss, test_err = test(epoch)
+        draw_curve(epoch, train_loss, train_err, test_loss, test_err)
+        if (epoch+1)%20==0:
+            lr_decay()
+
+
+if __name__ == '__main__':
+    main()

Yolov5-Deepsort/deep_sort/deep_sort/deep_sort.py

@@ -0,0 +1,101 @@
+import numpy as np
+import torch
+import rich
+from .deep.feature_extractor import Extractor
+from .sort.nn_matching import NearestNeighborDistanceMetric
+from .sort.preprocessing import non_max_suppression
+from .sort.detection import Detection
+from .sort.tracker import Tracker
+
+
+__all__ = ['DeepSort']
+
+
+class DeepSort(object):
+    def __init__(self, model_path, max_dist=0.2, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True):
+        self.min_confidence = min_confidence
+        self.nms_max_overlap = nms_max_overlap
+
+        self.extractor = Extractor(model_path, use_cuda=use_cuda)
+
+        max_cosine_distance = max_dist
+        nn_budget = 100
+        metric = NearestNeighborDistanceMetric(
+            "cosine", max_cosine_distance, nn_budget)
+        self.tracker = Tracker(
+            metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init)
+
+    def update(self, bbox_xywh, confidences, clss, ori_img):
+        self.height, self.width = ori_img.shape[:2]
+        # generate detections
+        features = self._get_features(bbox_xywh, ori_img)
+        bbox_tlwh = self._xywh_to_tlwh(bbox_xywh)
+        detections = [Detection(bbox_tlwh[i], clss[i], conf, features[i]) for i, conf in enumerate(
+            confidences) if conf > self.min_confidence]
+        # update tracker
+        self.tracker.predict()
+        self.tracker.update(detections)
+
+        # output bbox identities
+        outputs = []
+        for track in self.tracker.tracks:
+            if not track.is_confirmed() or track.time_since_update > 1:
+                continue
+            #rich.print(track)
+            box = track.to_tlwh()
+            x1, y1, x2, y2 = self._tlwh_to_xyxy(box)
+            outputs.append((x1, y1, x2, y2, track.cls_, track.track_id))
+        return outputs
+
+    @staticmethod
+    def _xywh_to_tlwh(bbox_xywh):
+        if isinstance(bbox_xywh, np.ndarray):
+            bbox_tlwh = bbox_xywh.copy()
+        elif isinstance(bbox_xywh, torch.Tensor):
+            bbox_tlwh = bbox_xywh.clone()
+        if bbox_tlwh.size(0):
+            bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2]/2.
+            bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3]/2.
+        return bbox_tlwh
+
+    def _xywh_to_xyxy(self, bbox_xywh):
+        x, y, w, h = bbox_xywh
+        x1 = max(int(x-w/2), 0)
+        x2 = min(int(x+w/2), self.width-1)
+        y1 = max(int(y-h/2), 0)
+        y2 = min(int(y+h/2), self.height-1)
+        return x1, y1, x2, y2
+
+    def _tlwh_to_xyxy(self, bbox_tlwh):
+        """
+        TODO:
+            Convert bbox from xtl_ytl_w_h to xc_yc_w_h
+        Thanks JieChen91@github.com for reporting this bug!
+        """
+        x, y, w, h = bbox_tlwh
+        x1 = max(int(x), 0)
+        x2 = min(int(x+w), self.width-1)
+        y1 = max(int(y), 0)
+        y2 = min(int(y+h), self.height-1)
+        return x1, y1, x2, y2
+
+    def _xyxy_to_tlwh(self, bbox_xyxy):
+        x1, y1, x2, y2 = bbox_xyxy
+
+        t = x1
+        l = y1
+        w = int(x2-x1)
+        h = int(y2-y1)
+        return t, l, w, h
+
+    def _get_features(self, bbox_xywh, ori_img):
+        im_crops = []
+        for box in bbox_xywh:
+            x1, y1, x2, y2 = self._xywh_to_xyxy(box)
+            im = ori_img[y1:y2, x1:x2]
+            im_crops.append(im)
+        if im_crops:
+            features = self.extractor(im_crops)
+        else:
+            features = np.array([])
+        return features