更新容器运行环境

.gitignore

@@ -32,6 +32,7 @@
 !*.ini
 !*.xml
 !*.yml
+!*.yaml
 !*.json
 
 !*.h

environment/u20/Dockerfile

@@ -0,0 +1,3 @@
+# C++14
+FROM nvcr.io/nvidia/l4t-jetpack:r35.4.1
+

environment/u20/README-usage.bash

@@ -0,0 +1,21 @@
+## NOTE
+
+
+echo "执行：构建调试" \
+&& project_path="/media/nvidia/nvme0n1/server/repositories/repositories/SRI.vehicle-demo/environment/u20" \
+&& cd ${project_path} \
+&& sudo docker-compose --file compose.yml down \
+&& sudo docker-compose --file compose.yml up --detach --build \
+&& sudo docker exec -it devzhq bash
+
+echo "执行：停服调试" \
+&& project_path="/media/nvidia/nvme0n1/server/repositories/repositories/SRI.vehicle-demo/environment/u20" \
+&& cd ${project_path} \
+&& sudo docker-compose --file compose.yml down \
+&& sudo docker-compose --file compose.yml up --detach \
+&& sudo docker exec -it devzhq bash
+
+echo "执行：进入调试" \
+&& project_path="/media/nvidia/nvme0n1/server/repositories/repositories/SRI.vehicle-demo/environment/u20" \
+&& cd ${project_path} \
+&& sudo docker exec -it devzhq bash

environment/u20/compose.yml

@@ -0,0 +1,46 @@
+version: '3.5'
+services:
+
+    devzhq:
+
+        # --- building ---
+        image: devzhq:2024
+        build:
+            context: ./
+            dockerfile: ./Dockerfile
+        environment:
+            TZ: Asia/Shanghai
+            LC_ALL: C.UTF-8
+            LANG: C.UTF-8
+
+        # --- binding ---
+        runtime: nvidia
+        ipc: host
+        shm_size: 8g  # 共享内存 默认64m
+        volumes:
+            - /media/nvidia/nvme0n1:/media/nvidia/nvme0n1
+            - /dev:/dev
+        networks:
+            - sri_network
+        ports:
+            - "39999:39999"
+
+        # --- running ---
+        container_name: devzhq
+        cap_add:
+            - SYS_ADMIN
+        privileged: true
+
+        # --- for debug ---
+        working_dir: /media/nvidia/nvme0n1
+        stdin_open: true
+        tty: true
+
+        # --- for release ---
+#        working_dir: /media/nvidia/nvme0n1/server/repositories/repositories/sri-project.demo-cpp
+#        command: bash run.sh
+#        restart: always
+
+networks:
+    sri_network:
+        external: true

test/test-yolov5-deepsort/AIDetector_pytorch.py

@@ -0,0 +1,72 @@
+import cv2
+import torch
+import numpy as np
+import onnxruntime as ort
+from utils.general import non_max_suppression, scale_coords
+from utils.BaseDetector import baseDet
+from utils.datasets import letterbox
+
+
+class Detector(baseDet):
+    def __init__(self):
+        super(Detector, self).__init__()
+        self.device = None
+        self.weights = None
+        self.session = None
+        self.names = None
+        self.img_size = 640
+        self.init_model()
+        self.build_config()
+
+    def init_model(self):
+        self.weights = 'weights/yolov5s.onnx'
+        self.device = '0' if torch.cuda.is_available() else 'cpu'
+        self.session = ort.InferenceSession(self.weights, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        self.names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+                      'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+                      'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+                      'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+                      'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+                      'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+                      'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+                      'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+                      'hair drier', 'toothbrush']
+
+    def preprocess(self, img):
+        img0 = img.copy()
+        img = cv2.resize(img, (640, 640))
+        img = letterbox(img, new_shape=self.img_size)[0]
+        img = img[:, :, ::-1].transpose(2, 0, 1)
+        img = np.ascontiguousarray(img)
+        img = img.astype(np.float32)
+        img /= 255.0
+
+        if img.ndim == 3:
+            img = np.expand_dims(img, axis=0)
+
+        return img0, img
+
+    def detect(self, im):
+        im0, img = self.preprocess(im)
+
+        # Prepare input for ONNX model
+        input_name = self.session.get_inputs()[0].name
+        pred = self.session.run(None, {input_name: img})[0]  # Run inference
+
+        pred = pred.astype(np.float32)
+        pred = non_max_suppression(torch.from_numpy(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 lbl not in ['person', 'car', 'truck']:  # Filter unwanted labels
+                        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 im0, pred_boxes

test/test-yolov5-deepsort/README-install.bash

@@ -0,0 +1 @@
+## NOTE

test/test-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
+  

test/test-yolov5-deepsort/deep_sort/deep_sort/README.md

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

test/test-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)
+    
+
+
+
+
+
+
+
+
+

test/test-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)))
+
+

test/test-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)
+

test/test-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()
+
+

test/test-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()
+
+

test/test-yolov5-deepsort/deep_sort/deep_sort/deep_sort.py

@@ -0,0 +1,100 @@
+import numpy as np
+import torch
+
+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
+            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

test/test-yolov5-deepsort/deep_sort/deep_sort/sort/detection.py

@@ -0,0 +1,28 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+class Detection(object):
+
+    def __init__(self, tlwh, cls_, confidence, feature):
+        self.tlwh = np.asarray(tlwh, dtype=np.float)
+        self.cls_ = cls_
+        self.confidence = float(confidence)
+        self.feature = np.asarray(feature, dtype=np.float32)
+
+    def to_tlbr(self):
+        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
+        `(top left, bottom right)`.
+        """
+        ret = self.tlwh.copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    def to_xyah(self):
+        """Convert bounding box to format `(center x, center y, aspect ratio,
+        height)`, where the aspect ratio is `width / height`.
+        """
+        ret = self.tlwh.copy()
+        ret[:2] += ret[2:] / 2
+        ret[2] /= ret[3]
+        return ret

test/test-yolov5-deepsort/deep_sort/deep_sort/sort/iou_matching.py

@@ -0,0 +1,81 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from . import linear_assignment
+
+
+def iou(bbox, candidates):
+    """Computer intersection over union.
+
+    Parameters
+    ----------
+    bbox : ndarray
+        A bounding box in format `(top left x, top left y, width, height)`.
+    candidates : ndarray
+        A matrix of candidate bounding boxes (one per row) in the same format
+        as `bbox`.
+
+    Returns
+    -------
+    ndarray
+        The intersection over union in [0, 1] between the `bbox` and each
+        candidate. A higher score means a larger fraction of the `bbox` is
+        occluded by the candidate.
+
+    """
+    bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
+    candidates_tl = candidates[:, :2]
+    candidates_br = candidates[:, :2] + candidates[:, 2:]
+
+    tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],
+               np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]
+    br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],
+               np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]
+    wh = np.maximum(0., br - tl)
+
+    area_intersection = wh.prod(axis=1)
+    area_bbox = bbox[2:].prod()
+    area_candidates = candidates[:, 2:].prod(axis=1)
+    return area_intersection / (area_bbox + area_candidates - area_intersection)
+
+
+def iou_cost(tracks, detections, track_indices=None,
+             detection_indices=None):
+    """An intersection over union distance metric.
+
+    Parameters
+    ----------
+    tracks : List[deep_sort.track.Track]
+        A list of tracks.
+    detections : List[deep_sort.detection.Detection]
+        A list of detections.
+    track_indices : Optional[List[int]]
+        A list of indices to tracks that should be matched. Defaults to
+        all `tracks`.
+    detection_indices : Optional[List[int]]
+        A list of indices to detections that should be matched. Defaults
+        to all `detections`.
+
+    Returns
+    -------
+    ndarray
+        Returns a cost matrix of shape
+        len(track_indices), len(detection_indices) where entry (i, j) is
+        `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.
+
+    """
+    if track_indices is None:
+        track_indices = np.arange(len(tracks))
+    if detection_indices is None:
+        detection_indices = np.arange(len(detections))
+
+    cost_matrix = np.zeros((len(track_indices), len(detection_indices)))
+    for row, track_idx in enumerate(track_indices):
+        if tracks[track_idx].time_since_update > 1:
+            cost_matrix[row, :] = linear_assignment.INFTY_COST
+            continue
+
+        bbox = tracks[track_idx].to_tlwh()
+        candidates = np.asarray([detections[i].tlwh for i in detection_indices])
+        cost_matrix[row, :] = 1. - iou(bbox, candidates)
+    return cost_matrix

test/test-yolov5-deepsort/deep_sort/deep_sort/sort/kalman_filter.py

@@ -0,0 +1,229 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import scipy.linalg
+
+
+"""
+Table for the 0.95 quantile of the chi-square distribution with N degrees of
+freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
+function and used as Mahalanobis gating threshold.
+"""
+chi2inv95 = {
+    1: 3.8415,
+    2: 5.9915,
+    3: 7.8147,
+    4: 9.4877,
+    5: 11.070,
+    6: 12.592,
+    7: 14.067,
+    8: 15.507,
+    9: 16.919}
+
+
+class KalmanFilter(object):
+    """
+    A simple Kalman filter for tracking bounding boxes in image space.
+
+    The 8-dimensional state space
+
+        x, y, a, h, vx, vy, va, vh
+
+    contains the bounding box center position (x, y), aspect ratio a, height h,
+    and their respective velocities.
+
+    Object motion follows a constant velocity model. The bounding box location
+    (x, y, a, h) is taken as direct observation of the state space (linear
+    observation model).
+
+    """
+
+    def __init__(self):
+        ndim, dt = 4, 1.
+
+        # Create Kalman filter model matrices.
+        self._motion_mat = np.eye(2 * ndim, 2 * ndim)
+        for i in range(ndim):
+            self._motion_mat[i, ndim + i] = dt
+        self._update_mat = np.eye(ndim, 2 * ndim)
+
+        # Motion and observation uncertainty are chosen relative to the current
+        # state estimate. These weights control the amount of uncertainty in
+        # the model. This is a bit hacky.
+        self._std_weight_position = 1. / 20
+        self._std_weight_velocity = 1. / 160
+
+    def initiate(self, measurement):
+        """Create track from unassociated measurement.
+
+        Parameters
+        ----------
+        measurement : ndarray
+            Bounding box coordinates (x, y, a, h) with center position (x, y),
+            aspect ratio a, and height h.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector (8 dimensional) and covariance matrix (8x8
+            dimensional) of the new track. Unobserved velocities are initialized
+            to 0 mean.
+
+        """
+        mean_pos = measurement
+        mean_vel = np.zeros_like(mean_pos)
+        mean = np.r_[mean_pos, mean_vel]
+
+        std = [
+            2 * self._std_weight_position * measurement[3],
+            2 * self._std_weight_position * measurement[3],
+            1e-2,
+            2 * self._std_weight_position * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            1e-5,
+            10 * self._std_weight_velocity * measurement[3]]
+        covariance = np.diag(np.square(std))
+        return mean, covariance
+
+    def predict(self, mean, covariance):
+        """Run Kalman filter prediction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The 8 dimensional mean vector of the object state at the previous
+            time step.
+        covariance : ndarray
+            The 8x8 dimensional covariance matrix of the object state at the
+            previous time step.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector and covariance matrix of the predicted
+            state. Unobserved velocities are initialized to 0 mean.
+
+        """
+        std_pos = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-2,
+            self._std_weight_position * mean[3]]
+        std_vel = [
+            self._std_weight_velocity * mean[3],
+            self._std_weight_velocity * mean[3],
+            1e-5,
+            self._std_weight_velocity * mean[3]]
+        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
+
+        mean = np.dot(self._motion_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
+
+        return mean, covariance
+
+    def project(self, mean, covariance):
+        """Project state distribution to measurement space.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The state's mean vector (8 dimensional array).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the projected mean and covariance matrix of the given state
+            estimate.
+
+        """
+        std = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-1,
+            self._std_weight_position * mean[3]]
+        innovation_cov = np.diag(np.square(std))
+
+        mean = np.dot(self._update_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._update_mat, covariance, self._update_mat.T))
+        return mean, covariance + innovation_cov
+
+    def update(self, mean, covariance, measurement):
+        """Run Kalman filter correction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The predicted state's mean vector (8 dimensional).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+        measurement : ndarray
+            The 4 dimensional measurement vector (x, y, a, h), where (x, y)
+            is the center position, a the aspect ratio, and h the height of the
+            bounding box.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the measurement-corrected state distribution.
+
+        """
+        projected_mean, projected_cov = self.project(mean, covariance)
+
+        chol_factor, lower = scipy.linalg.cho_factor(
+            projected_cov, lower=True, check_finite=False)
+        kalman_gain = scipy.linalg.cho_solve(
+            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
+            check_finite=False).T
+        innovation = measurement - projected_mean
+
+        new_mean = mean + np.dot(innovation, kalman_gain.T)
+        new_covariance = covariance - np.linalg.multi_dot((
+            kalman_gain, projected_cov, kalman_gain.T))
+        return new_mean, new_covariance
+
+    def gating_distance(self, mean, covariance, measurements,
+                        only_position=False):
+        """Compute gating distance between state distribution and measurements.
+
+        A suitable distance threshold can be obtained from `chi2inv95`. If
+        `only_position` is False, the chi-square distribution has 4 degrees of
+        freedom, otherwise 2.
+
+        Parameters
+        ----------
+        mean : ndarray
+            Mean vector over the state distribution (8 dimensional).
+        covariance : ndarray
+            Covariance of the state distribution (8x8 dimensional).
+        measurements : ndarray
+            An Nx4 dimensional matrix of N measurements, each in
+            format (x, y, a, h) where (x, y) is the bounding box center
+            position, a the aspect ratio, and h the height.
+        only_position : Optional[bool]
+            If True, distance computation is done with respect to the bounding
+            box center position only.
+
+        Returns
+        -------
+        ndarray
+            Returns an array of length N, where the i-th element contains the
+            squared Mahalanobis distance between (mean, covariance) and
+            `measurements[i]`.
+
+        """
+        mean, covariance = self.project(mean, covariance)
+        if only_position:
+            mean, covariance = mean[:2], covariance[:2, :2]
+            measurements = measurements[:, :2]
+
+        cholesky_factor = np.linalg.cholesky(covariance)
+        d = measurements - mean
+        z = scipy.linalg.solve_triangular(
+            cholesky_factor, d.T, lower=True, check_finite=False,
+            overwrite_b=True)
+        squared_maha = np.sum(z * z, axis=0)
+        return squared_maha

test/test-yolov5-deepsort/deep_sort/deep_sort/sort/linear_assignment.py

@@ -0,0 +1,159 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+# from sklearn.utils.linear_assignment_ import linear_assignment
+from scipy.optimize import linear_sum_assignment as linear_assignment
+from . import kalman_filter
+
+
+INFTY_COST = 1e+5
+
+
+def min_cost_matching(
+        distance_metric, max_distance, tracks, detections, track_indices=None,
+        detection_indices=None):
+    if track_indices is None:
+        track_indices = np.arange(len(tracks))
+    if detection_indices is None:
+        detection_indices = np.arange(len(detections))
+
+    if len(detection_indices) == 0 or len(track_indices) == 0:
+        return [], track_indices, detection_indices  # Nothing to match.
+
+    cost_matrix = distance_metric(
+        tracks, detections, track_indices, detection_indices)
+    cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
+
+    row_indices, col_indices = linear_assignment(cost_matrix)
+
+    matches, unmatched_tracks, unmatched_detections = [], [], []
+    for col, detection_idx in enumerate(detection_indices):
+        if col not in col_indices:
+            unmatched_detections.append(detection_idx)
+    for row, track_idx in enumerate(track_indices):
+        if row not in row_indices:
+            unmatched_tracks.append(track_idx)
+    for row, col in zip(row_indices, col_indices):
+        track_idx = track_indices[row]
+        detection_idx = detection_indices[col]
+        if cost_matrix[row, col] > max_distance:
+            unmatched_tracks.append(track_idx)
+            unmatched_detections.append(detection_idx)
+        else:
+            matches.append((track_idx, detection_idx))
+    return matches, unmatched_tracks, unmatched_detections
+
+
+def matching_cascade(
+        distance_metric, max_distance, cascade_depth, tracks, detections,
+        track_indices=None, detection_indices=None):
+    """Run matching cascade.
+
+    Parameters
+    ----------
+    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
+        The distance metric is given a list of tracks and detections as well as
+        a list of N track indices and M detection indices. The metric should
+        return the NxM dimensional cost matrix, where element (i, j) is the
+        association cost between the i-th track in the given track indices and
+        the j-th detection in the given detection indices.
+    max_distance : float
+        Gating threshold. Associations with cost larger than this value are
+        disregarded.
+    cascade_depth: int
+        The cascade depth, should be se to the maximum track age.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : Optional[List[int]]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above). Defaults to all tracks.
+    detection_indices : Optional[List[int]]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above). Defaults to all
+        detections.
+
+    Returns
+    -------
+    (List[(int, int)], List[int], List[int])
+        Returns a tuple with the following three entries:
+        * A list of matched track and detection indices.
+        * A list of unmatched track indices.
+        * A list of unmatched detection indices.
+
+    """
+    if track_indices is None:
+        track_indices = list(range(len(tracks)))
+    if detection_indices is None:
+        detection_indices = list(range(len(detections)))
+
+    unmatched_detections = detection_indices
+    matches = []
+    for level in range(cascade_depth):
+        if len(unmatched_detections) == 0:  # No detections left
+            break
+
+        track_indices_l = [
+            k for k in track_indices
+            if tracks[k].time_since_update == 1 + level
+        ]
+        if len(track_indices_l) == 0:  # Nothing to match at this level
+            continue
+
+        matches_l, _, unmatched_detections = \
+            min_cost_matching(
+                distance_metric, max_distance, tracks, detections,
+                track_indices_l, unmatched_detections)
+        matches += matches_l
+    unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))
+    return matches, unmatched_tracks, unmatched_detections
+
+
+def gate_cost_matrix(
+        kf, cost_matrix, tracks, detections, track_indices, detection_indices,
+        gated_cost=INFTY_COST, only_position=False):
+    """Invalidate infeasible entries in cost matrix based on the state
+    distributions obtained by Kalman filtering.
+
+    Parameters
+    ----------
+    kf : The Kalman filter.
+    cost_matrix : ndarray
+        The NxM dimensional cost matrix, where N is the number of track indices
+        and M is the number of detection indices, such that entry (i, j) is the
+        association cost between `tracks[track_indices[i]]` and
+        `detections[detection_indices[j]]`.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : List[int]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above).
+    detection_indices : List[int]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above).
+    gated_cost : Optional[float]
+        Entries in the cost matrix corresponding to infeasible associations are
+        set this value. Defaults to a very large value.
+    only_position : Optional[bool]
+        If True, only the x, y position of the state distribution is considered
+        during gating. Defaults to False.
+
+    Returns
+    -------
+    ndarray
+        Returns the modified cost matrix.
+
+    """
+    gating_dim = 2 if only_position else 4
+    gating_threshold = kalman_filter.chi2inv95[gating_dim]
+    measurements = np.asarray(
+        [detections[i].to_xyah() for i in detection_indices])
+    for row, track_idx in enumerate(track_indices):
+        track = tracks[track_idx]
+        gating_distance = kf.gating_distance(
+            track.mean, track.covariance, measurements, only_position)
+        cost_matrix[row, gating_distance > gating_threshold] = gated_cost
+    return cost_matrix

test/test-yolov5-deepsort/deep_sort/deep_sort/sort/nn_matching.py

@@ -0,0 +1,177 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+def _pdist(a, b):
+    """Compute pair-wise squared distance between points in `a` and `b`.
+
+    Parameters
+    ----------
+    a : array_like
+        An NxM matrix of N samples of dimensionality M.
+    b : array_like
+        An LxM matrix of L samples of dimensionality M.
+
+    Returns
+    -------
+    ndarray
+        Returns a matrix of size len(a), len(b) such that eleement (i, j)
+        contains the squared distance between `a[i]` and `b[j]`.
+
+    """
+    a, b = np.asarray(a), np.asarray(b)
+    if len(a) == 0 or len(b) == 0:
+        return np.zeros((len(a), len(b)))
+    a2, b2 = np.square(a).sum(axis=1), np.square(b).sum(axis=1)
+    r2 = -2. * np.dot(a, b.T) + a2[:, None] + b2[None, :]
+    r2 = np.clip(r2, 0., float(np.inf))
+    return r2
+
+
+def _cosine_distance(a, b, data_is_normalized=False):
+    """Compute pair-wise cosine distance between points in `a` and `b`.
+
+    Parameters
+    ----------
+    a : array_like
+        An NxM matrix of N samples of dimensionality M.
+    b : array_like
+        An LxM matrix of L samples of dimensionality M.
+    data_is_normalized : Optional[bool]
+        If True, assumes rows in a and b are unit length vectors.
+        Otherwise, a and b are explicitly normalized to lenght 1.
+
+    Returns
+    -------
+    ndarray
+        Returns a matrix of size len(a), len(b) such that eleement (i, j)
+        contains the squared distance between `a[i]` and `b[j]`.
+
+    """
+    if not data_is_normalized:
+        a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
+        b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
+    return 1. - np.dot(a, b.T)
+
+
+def _nn_euclidean_distance(x, y):
+    """ Helper function for nearest neighbor distance metric (Euclidean).
+
+    Parameters
+    ----------
+    x : ndarray
+        A matrix of N row-vectors (sample points).
+    y : ndarray
+        A matrix of M row-vectors (query points).
+
+    Returns
+    -------
+    ndarray
+        A vector of length M that contains for each entry in `y` the
+        smallest Euclidean distance to a sample in `x`.
+
+    """
+    distances = _pdist(x, y)
+    return np.maximum(0.0, distances.min(axis=0))
+
+
+def _nn_cosine_distance(x, y):
+    """ Helper function for nearest neighbor distance metric (cosine).
+
+    Parameters
+    ----------
+    x : ndarray
+        A matrix of N row-vectors (sample points).
+    y : ndarray
+        A matrix of M row-vectors (query points).
+
+    Returns
+    -------
+    ndarray
+        A vector of length M that contains for each entry in `y` the
+        smallest cosine distance to a sample in `x`.
+
+    """
+    distances = _cosine_distance(x, y)
+    return distances.min(axis=0)
+
+
+class NearestNeighborDistanceMetric(object):
+    """
+    A nearest neighbor distance metric that, for each target, returns
+    the closest distance to any sample that has been observed so far.
+
+    Parameters
+    ----------
+    metric : str
+        Either "euclidean" or "cosine".
+    matching_threshold: float
+        The matching threshold. Samples with larger distance are considered an
+        invalid match.
+    budget : Optional[int]
+        If not None, fix samples per class to at most this number. Removes
+        the oldest samples when the budget is reached.
+
+    Attributes
+    ----------
+    samples : Dict[int -> List[ndarray]]
+        A dictionary that maps from target identities to the list of samples
+        that have been observed so far.
+
+    """
+
+    def __init__(self, metric, matching_threshold, budget=None):
+
+
+        if metric == "euclidean":
+            self._metric = _nn_euclidean_distance
+        elif metric == "cosine":
+            self._metric = _nn_cosine_distance
+        else:
+            raise ValueError(
+                "Invalid metric; must be either 'euclidean' or 'cosine'")
+        self.matching_threshold = matching_threshold
+        self.budget = budget
+        self.samples = {}
+
+    def partial_fit(self, features, targets, active_targets):
+        """Update the distance metric with new data.
+
+        Parameters
+        ----------
+        features : ndarray
+            An NxM matrix of N features of dimensionality M.
+        targets : ndarray
+            An integer array of associated target identities.
+        active_targets : List[int]
+            A list of targets that are currently present in the scene.
+
+        """
+        for feature, target in zip(features, targets):
+            self.samples.setdefault(target, []).append(feature)
+            if self.budget is not None:
+                self.samples[target] = self.samples[target][-self.budget:]
+        self.samples = {k: self.samples[k] for k in active_targets}
+
+    def distance(self, features, targets):
+        """Compute distance between features and targets.
+
+        Parameters
+        ----------
+        features : ndarray
+            An NxM matrix of N features of dimensionality M.
+        targets : List[int]
+            A list of targets to match the given `features` against.
+
+        Returns
+        -------
+        ndarray
+            Returns a cost matrix of shape len(targets), len(features), where
+            element (i, j) contains the closest squared distance between
+            `targets[i]` and `features[j]`.
+
+        """
+        cost_matrix = np.zeros((len(targets), len(features)))
+        for i, target in enumerate(targets):
+            cost_matrix[i, :] = self._metric(self.samples[target], features)
+        return cost_matrix

test/test-yolov5-deepsort/deep_sort/deep_sort/sort/preprocessing.py

@@ -0,0 +1,73 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import cv2
+
+
+def non_max_suppression(boxes, max_bbox_overlap, scores=None):
+    """Suppress overlapping detections.
+
+    Original code from [1]_ has been adapted to include confidence score.
+
+    .. [1] http://www.pyimagesearch.com/2015/02/16/
+           faster-non-maximum-suppression-python/
+
+    Examples
+    --------
+
+        >>> boxes = [d.roi for d in detections]
+        >>> scores = [d.confidence for d in detections]
+        >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores)
+        >>> detections = [detections[i] for i in indices]
+
+    Parameters
+    ----------
+    boxes : ndarray
+        Array of ROIs (x, y, width, height).
+    max_bbox_overlap : float
+        ROIs that overlap more than this values are suppressed.
+    scores : Optional[array_like]
+        Detector confidence score.
+
+    Returns
+    -------
+    List[int]
+        Returns indices of detections that have survived non-maxima suppression.
+
+    """
+    if len(boxes) == 0:
+        return []
+
+    boxes = boxes.astype(np.float)
+    pick = []
+
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2] + boxes[:, 0]
+    y2 = boxes[:, 3] + boxes[:, 1]
+
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    if scores is not None:
+        idxs = np.argsort(scores)
+    else:
+        idxs = np.argsort(y2)
+
+    while len(idxs) > 0:
+        last = len(idxs) - 1
+        i = idxs[last]
+        pick.append(i)
+
+        xx1 = np.maximum(x1[i], x1[idxs[:last]])
+        yy1 = np.maximum(y1[i], y1[idxs[:last]])
+        xx2 = np.minimum(x2[i], x2[idxs[:last]])
+        yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+        w = np.maximum(0, xx2 - xx1 + 1)
+        h = np.maximum(0, yy2 - yy1 + 1)
+
+        overlap = (w * h) / area[idxs[:last]]
+
+        idxs = np.delete(
+            idxs, np.concatenate(
+                ([last], np.where(overlap > max_bbox_overlap)[0])))
+
+    return pick

test/test-yolov5-deepsort/deep_sort/deep_sort/sort/track.py

@@ -0,0 +1,168 @@
+# vim: expandtab:ts=4:sw=4
+
+
+class TrackState:
+    """
+    Enumeration type for the single target track state. Newly created tracks are
+    classified as `tentative` until enough evidence has been collected. Then,
+    the track state is changed to `confirmed`. Tracks that are no longer alive
+    are classified as `deleted` to mark them for removal from the set of active
+    tracks.
+
+    """
+
+    Tentative = 1
+    Confirmed = 2
+    Deleted = 3
+
+
+class Track:
+    """
+    A single target track with state space `(x, y, a, h)` and associated
+    velocities, where `(x, y)` is the center of the bounding box, `a` is the
+    aspect ratio and `h` is the height.
+
+    Parameters
+    ----------
+    mean : ndarray
+        Mean vector of the initial state distribution.
+    covariance : ndarray
+        Covariance matrix of the initial state distribution.
+    track_id : int
+        A unique track identifier.
+    n_init : int
+        Number of consecutive detections before the track is confirmed. The
+        track state is set to `Deleted` if a miss occurs within the first
+        `n_init` frames.
+    max_age : int
+        The maximum number of consecutive misses before the track state is
+        set to `Deleted`.
+    feature : Optional[ndarray]
+        Feature vector of the detection this track originates from. If not None,
+        this feature is added to the `features` cache.
+
+    Attributes
+    ----------
+    mean : ndarray
+        Mean vector of the initial state distribution.
+    covariance : ndarray
+        Covariance matrix of the initial state distribution.
+    track_id : int
+        A unique track identifier.
+    hits : int
+        Total number of measurement updates.
+    age : int
+        Total number of frames since first occurance.
+    time_since_update : int
+        Total number of frames since last measurement update.
+    state : TrackState
+        The current track state.
+    features : List[ndarray]
+        A cache of features. On each measurement update, the associated feature
+        vector is added to this list.
+
+    """
+
+    def __init__(self, mean, cls_, covariance, track_id, n_init, max_age,
+                 feature=None):
+        self.mean = mean
+        self.cls_ = cls_
+        self.covariance = covariance
+        self.track_id = track_id
+        self.hits = 1
+        self.age = 1
+        self.time_since_update = 0
+
+        self.state = TrackState.Tentative
+        self.features = []
+        if feature is not None:
+            self.features.append(feature)
+
+        self._n_init = n_init
+        self._max_age = max_age
+
+    def to_tlwh(self):
+        """Get current position in bounding box format `(top left x, top left y,
+        width, height)`.
+
+        Returns
+        -------
+        ndarray
+            The bounding box.
+
+        """
+        ret = self.mean[:4].copy()
+        ret[2] *= ret[3]
+        ret[:2] -= ret[2:] / 2
+        return ret
+
+    def to_tlbr(self):
+        """Get current position in bounding box format `(min x, miny, max x,
+        max y)`.
+
+        Returns
+        -------
+        ndarray
+            The bounding box.
+
+        """
+        ret = self.to_tlwh()
+        ret[2:] = ret[:2] + ret[2:]
+        return ret
+
+    def predict(self, kf):
+        """Propagate the state distribution to the current time step using a
+        Kalman filter prediction step.
+
+        Parameters
+        ----------
+        kf : kalman_filter.KalmanFilter
+            The Kalman filter.
+
+        """
+        self.mean, self.covariance = kf.predict(self.mean, self.covariance)
+        self.age += 1
+        self.time_since_update += 1
+
+    def update(self, kf, detection):
+        """Perform Kalman filter measurement update step and update the feature
+        cache.
+
+        Parameters
+        ----------
+        kf : kalman_filter.KalmanFilter
+            The Kalman filter.
+        detection : Detection
+            The associated detection.
+
+        """
+        self.mean, self.covariance = kf.update(
+            self.mean, self.covariance, detection.to_xyah())
+        self.features.append(detection.feature)
+        self.cls_ = detection.cls_
+
+        self.hits += 1
+        self.time_since_update = 0
+        if self.state == TrackState.Tentative and self.hits >= self._n_init:
+            self.state = TrackState.Confirmed
+
+    def mark_missed(self):
+        """Mark this track as missed (no association at the current time step).
+        """
+        if self.state == TrackState.Tentative:
+            self.state = TrackState.Deleted
+        elif self.time_since_update > self._max_age:
+            self.state = TrackState.Deleted
+
+    def is_tentative(self):
+        """Returns True if this track is tentative (unconfirmed).
+        """
+        return self.state == TrackState.Tentative
+
+    def is_confirmed(self):
+        """Returns True if this track is confirmed."""
+        return self.state == TrackState.Confirmed
+
+    def is_deleted(self):
+        """Returns True if this track is dead and should be deleted."""
+        return self.state == TrackState.Deleted

test/test-yolov5-deepsort/deep_sort/deep_sort/sort/tracker.py

@@ -0,0 +1,109 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from . import kalman_filter
+from . import linear_assignment
+from . import iou_matching
+from .track import Track
+
+
+class Tracker:
+
+    def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3):
+        self.metric = metric
+        self.max_iou_distance = max_iou_distance
+        self.max_age = max_age
+        self.n_init = n_init
+
+        self.kf = kalman_filter.KalmanFilter()
+        self.tracks = []
+        self._next_id = 1
+
+    def predict(self):
+        """Propagate track state distributions one time step forward.
+
+        This function should be called once every time step, before `update`.
+        """
+        for track in self.tracks:
+            track.predict(self.kf)
+
+    def update(self, detections):
+        """Perform measurement update and track management.
+
+        Parameters
+        ----------
+        detections : List[deep_sort.detection.Detection]
+            A list of detections at the current time step.
+
+        """
+        # Run matching cascade.
+        matches, unmatched_tracks, unmatched_detections = \
+            self._match(detections)
+
+        # Update track set.
+        for track_idx, detection_idx in matches:
+            self.tracks[track_idx].update(
+                self.kf, detections[detection_idx])
+        for track_idx in unmatched_tracks:
+            self.tracks[track_idx].mark_missed()
+        for detection_idx in unmatched_detections:
+            self._initiate_track(detections[detection_idx])
+        self.tracks = [t for t in self.tracks if not t.is_deleted()]
+
+        # Update distance metric.
+        active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
+        features, targets = [], []
+        for track in self.tracks:
+            if not track.is_confirmed():
+                continue
+            features += track.features
+            targets += [track.track_id for _ in track.features]
+            track.features = []
+        self.metric.partial_fit(
+            np.asarray(features), np.asarray(targets), active_targets)
+
+    def _match(self, detections):
+
+        def gated_metric(tracks, dets, track_indices, detection_indices):
+            features = np.array([dets[i].feature for i in detection_indices])
+            targets = np.array([tracks[i].track_id for i in track_indices])
+            cost_matrix = self.metric.distance(features, targets)
+            cost_matrix = linear_assignment.gate_cost_matrix(
+                self.kf, cost_matrix, tracks, dets, track_indices,
+                detection_indices)
+
+            return cost_matrix
+
+        # Split track set into confirmed and unconfirmed tracks.
+        confirmed_tracks = [
+            i for i, t in enumerate(self.tracks) if t.is_confirmed()]
+        unconfirmed_tracks = [
+            i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
+
+        # Associate confirmed tracks using appearance features.
+        matches_a, unmatched_tracks_a, unmatched_detections = \
+            linear_assignment.matching_cascade(
+                gated_metric, self.metric.matching_threshold, self.max_age,
+                self.tracks, detections, confirmed_tracks)
+
+        # Associate remaining tracks together with unconfirmed tracks using IOU.
+        iou_track_candidates = unconfirmed_tracks + [
+            k for k in unmatched_tracks_a if
+            self.tracks[k].time_since_update == 1]
+        unmatched_tracks_a = [
+            k for k in unmatched_tracks_a if
+            self.tracks[k].time_since_update != 1]
+        matches_b, unmatched_tracks_b, unmatched_detections = \
+            linear_assignment.min_cost_matching(
+                iou_matching.iou_cost, self.max_iou_distance, self.tracks,
+                detections, iou_track_candidates, unmatched_detections)
+        matches = matches_a + matches_b
+        unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
+        return matches, unmatched_tracks, unmatched_detections
+
+    def _initiate_track(self, detection):
+        mean, covariance = self.kf.initiate(detection.to_xyah())
+        self.tracks.append(Track(
+            mean, detection.cls_, covariance, self._next_id, self.n_init, self.max_age,
+            detection.feature))
+        self._next_id += 1

test/test-yolov5-deepsort/deep_sort/utils/asserts.py

@@ -0,0 +1,13 @@
+from os import environ
+
+
+def assert_in(file, files_to_check):
+    if file not in files_to_check:
+        raise AssertionError("{} does not exist in the list".format(str(file)))
+    return True
+
+
+def assert_in_env(check_list: list):
+    for item in check_list:
+        assert_in(item, environ.keys())
+    return True

test/test-yolov5-deepsort/deep_sort/utils/draw.py

@@ -0,0 +1,36 @@
+import numpy as np
+import cv2
+
+palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
+
+
+def compute_color_for_labels(label):
+    """
+    Simple function that adds fixed color depending on the class
+    """
+    color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
+    return tuple(color)
+
+
+def draw_boxes(img, bbox, identities=None, offset=(0,0)):
+    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]
+        # box text and bar
+        id = int(identities[i]) if identities is not None else 0    
+        color = compute_color_for_labels(id)
+        label = '{}{:d}'.format("", id)
+        t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]
+        cv2.rectangle(img,(x1, y1),(x2,y2),color,3)
+        cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1)
+        cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 2, [255,255,255], 2)
+    return img
+
+
+
+if __name__ == '__main__':
+    for i in range(82):
+        print(compute_color_for_labels(i))

test/test-yolov5-deepsort/deep_sort/utils/evaluation.py

@@ -0,0 +1,103 @@
+import os
+import numpy as np
+import copy
+import motmetrics as mm
+mm.lap.default_solver = 'lap'
+from utils.io import read_results, unzip_objs
+
+
+class Evaluator(object):
+
+    def __init__(self, data_root, seq_name, data_type):
+        self.data_root = data_root
+        self.seq_name = seq_name
+        self.data_type = data_type
+
+        self.load_annotations()
+        self.reset_accumulator()
+
+    def load_annotations(self):
+        assert self.data_type == 'mot'
+
+        gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')
+        self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)
+        self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)
+
+    def reset_accumulator(self):
+        self.acc = mm.MOTAccumulator(auto_id=True)
+
+    def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
+        # results
+        trk_tlwhs = np.copy(trk_tlwhs)
+        trk_ids = np.copy(trk_ids)
+
+        # gts
+        gt_objs = self.gt_frame_dict.get(frame_id, [])
+        gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
+
+        # ignore boxes
+        ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
+        ignore_tlwhs = unzip_objs(ignore_objs)[0]
+
+
+        # remove ignored results
+        keep = np.ones(len(trk_tlwhs), dtype=bool)
+        iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)
+        if len(iou_distance) > 0:
+            match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
+            match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
+            match_ious = iou_distance[match_is, match_js]
+
+            match_js = np.asarray(match_js, dtype=int)
+            match_js = match_js[np.logical_not(np.isnan(match_ious))]
+            keep[match_js] = False
+            trk_tlwhs = trk_tlwhs[keep]
+            trk_ids = trk_ids[keep]
+
+        # get distance matrix
+        iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
+
+        # acc
+        self.acc.update(gt_ids, trk_ids, iou_distance)
+
+        if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):
+            events = self.acc.last_mot_events  # only supported by https://github.com/longcw/py-motmetrics
+        else:
+            events = None
+        return events
+
+    def eval_file(self, filename):
+        self.reset_accumulator()
+
+        result_frame_dict = read_results(filename, self.data_type, is_gt=False)
+        frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))
+        for frame_id in frames:
+            trk_objs = result_frame_dict.get(frame_id, [])
+            trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
+            self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
+
+        return self.acc
+
+    @staticmethod
+    def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):
+        names = copy.deepcopy(names)
+        if metrics is None:
+            metrics = mm.metrics.motchallenge_metrics
+        metrics = copy.deepcopy(metrics)
+
+        mh = mm.metrics.create()
+        summary = mh.compute_many(
+            accs,
+            metrics=metrics,
+            names=names,
+            generate_overall=True
+        )
+
+        return summary
+
+    @staticmethod
+    def save_summary(summary, filename):
+        import pandas as pd
+        writer = pd.ExcelWriter(filename)
+        summary.to_excel(writer)
+        writer.save()

test/test-yolov5-deepsort/deep_sort/utils/io.py

@@ -0,0 +1,133 @@
+import os
+from typing import Dict
+import numpy as np
+
+# from utils.log import get_logger
+
+
+def write_results(filename, results, data_type):
+    if data_type == 'mot':
+        save_format = '{frame},{id},{x1},{y1},{w},{h},-1,-1,-1,-1\n'
+    elif data_type == 'kitti':
+        save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
+    else:
+        raise ValueError(data_type)
+
+    with open(filename, 'w') as f:
+        for frame_id, tlwhs, track_ids in results:
+            if data_type == 'kitti':
+                frame_id -= 1
+            for tlwh, track_id in zip(tlwhs, track_ids):
+                if track_id < 0:
+                    continue
+                x1, y1, w, h = tlwh
+                x2, y2 = x1 + w, y1 + h
+                line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h)
+                f.write(line)
+
+
+# def write_results(filename, results_dict: Dict, data_type: str):
+#     if not filename:
+#         return
+#     path = os.path.dirname(filename)
+#     if not os.path.exists(path):
+#         os.makedirs(path)
+
+#     if data_type in ('mot', 'mcmot', 'lab'):
+#         save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
+#     elif data_type == 'kitti':
+#         save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n'
+#     else:
+#         raise ValueError(data_type)
+
+#     with open(filename, 'w') as f:
+#         for frame_id, frame_data in results_dict.items():
+#             if data_type == 'kitti':
+#                 frame_id -= 1
+#             for tlwh, track_id in frame_data:
+#                 if track_id < 0:
+#                     continue
+#                 x1, y1, w, h = tlwh
+#                 x2, y2 = x1 + w, y1 + h
+#                 line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0)
+#                 f.write(line)
+#     logger.info('Save results to {}'.format(filename))
+
+
+def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
+    if data_type in ('mot', 'lab'):
+        read_fun = read_mot_results
+    else:
+        raise ValueError('Unknown data type: {}'.format(data_type))
+
+    return read_fun(filename, is_gt, is_ignore)
+
+
+"""
+labels={'ped', ...			% 1
+'person_on_vhcl', ...	% 2
+'car', ...				% 3
+'bicycle', ...			% 4
+'mbike', ...			% 5
+'non_mot_vhcl', ...		% 6
+'static_person', ...	% 7
+'distractor', ...		% 8
+'occluder', ...			% 9
+'occluder_on_grnd', ...		%10
+'occluder_full', ...		% 11
+'reflection', ...		% 12
+'crowd' ...			% 13
+};
+"""
+
+
+def read_mot_results(filename, is_gt, is_ignore):
+    valid_labels = {1}
+    ignore_labels = {2, 7, 8, 12}
+    results_dict = dict()
+    if os.path.isfile(filename):
+        with open(filename, 'r') as f:
+            for line in f.readlines():
+                linelist = line.split(',')
+                if len(linelist) < 7:
+                    continue
+                fid = int(linelist[0])
+                if fid < 1:
+                    continue
+                results_dict.setdefault(fid, list())
+
+                if is_gt:
+                    if 'MOT16-' in filename or 'MOT17-' in filename:
+                        label = int(float(linelist[7]))
+                        mark = int(float(linelist[6]))
+                        if mark == 0 or label not in valid_labels:
+                            continue
+                    score = 1
+                elif is_ignore:
+                    if 'MOT16-' in filename or 'MOT17-' in filename:
+                        label = int(float(linelist[7]))
+                        vis_ratio = float(linelist[8])
+                        if label not in ignore_labels and vis_ratio >= 0:
+                            continue
+                    else:
+                        continue
+                    score = 1
+                else:
+                    score = float(linelist[6])
+
+                tlwh = tuple(map(float, linelist[2:6]))
+                target_id = int(linelist[1])
+
+                results_dict[fid].append((tlwh, target_id, score))
+
+    return results_dict
+
+
+def unzip_objs(objs):
+    if len(objs) > 0:
+        tlwhs, ids, scores = zip(*objs)
+    else:
+        tlwhs, ids, scores = [], [], []
+    tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
+
+    return tlwhs, ids, scores

test/test-yolov5-deepsort/deep_sort/utils/json_logger.py

@@ -0,0 +1,383 @@
+"""
+References:
+    https://medium.com/analytics-vidhya/creating-a-custom-logging-mechanism-for-real-time-object-detection-using-tdd-4ca2cfcd0a2f
+"""
+import json
+from os import makedirs
+from os.path import exists, join
+from datetime import datetime
+
+
+class JsonMeta(object):
+    HOURS = 3
+    MINUTES = 59
+    SECONDS = 59
+    PATH_TO_SAVE = 'LOGS'
+    DEFAULT_FILE_NAME = 'remaining'
+
+
+class BaseJsonLogger(object):
+    """
+    This is the base class that returns __dict__ of its own
+    it also returns the dicts of objects in the attributes that are list instances
+
+    """
+
+    def dic(self):
+        # returns dicts of objects
+        out = {}
+        for k, v in self.__dict__.items():
+            if hasattr(v, 'dic'):
+                out[k] = v.dic()
+            elif isinstance(v, list):
+                out[k] = self.list(v)
+            else:
+                out[k] = v
+        return out
+
+    @staticmethod
+    def list(values):
+        # applies the dic method on items in the list
+        return [v.dic() if hasattr(v, 'dic') else v for v in values]
+
+
+class Label(BaseJsonLogger):
+    """
+    For each bounding box there are various categories with confidences. Label class keeps track of that information.
+    """
+
+    def __init__(self, category: str, confidence: float):
+        self.category = category
+        self.confidence = confidence
+
+
+class Bbox(BaseJsonLogger):
+    """
+    This module stores the information for each frame and use them in JsonParser
+    Attributes:
+        labels (list): List of label module.
+        top (int):
+        left (int):
+        width (int):
+        height (int):
+
+    Args:
+        bbox_id (float):
+        top (int):
+        left (int):
+        width (int):
+        height (int):
+
+    References:
+        Check Label module for better understanding.
+
+
+    """
+
+    def __init__(self, bbox_id, top, left, width, height):
+        self.labels = []
+        self.bbox_id = bbox_id
+        self.top = top
+        self.left = left
+        self.width = width
+        self.height = height
+
+    def add_label(self, category, confidence):
+        # adds category and confidence only if top_k is not exceeded.
+        self.labels.append(Label(category, confidence))
+
+    def labels_full(self, value):
+        return len(self.labels) == value
+
+
+class Frame(BaseJsonLogger):
+    """
+    This module stores the information for each frame and use them in JsonParser
+    Attributes:
+        timestamp (float): The elapsed time of captured frame
+        frame_id (int): The frame number of the captured video
+        bboxes (list of Bbox objects): Stores the list of bbox objects.
+
+    References:
+        Check Bbox class for better information
+
+    Args:
+        timestamp (float):
+        frame_id (int):
+
+    """
+
+    def __init__(self, frame_id: int, timestamp: float = None):
+        self.frame_id = frame_id
+        self.timestamp = timestamp
+        self.bboxes = []
+
+    def add_bbox(self, bbox_id: int, top: int, left: int, width: int, height: int):
+        bboxes_ids = [bbox.bbox_id for bbox in self.bboxes]
+        if bbox_id not in bboxes_ids:
+            self.bboxes.append(Bbox(bbox_id, top, left, width, height))
+        else:
+            raise ValueError("Frame with id: {} already has a Bbox with id: {}".format(self.frame_id, bbox_id))
+
+    def add_label_to_bbox(self, bbox_id: int, category: str, confidence: float):
+        bboxes = {bbox.id: bbox for bbox in self.bboxes}
+        if bbox_id in bboxes.keys():
+            res = bboxes.get(bbox_id)
+            res.add_label(category, confidence)
+        else:
+            raise ValueError('the bbox with id: {} does not exists!'.format(bbox_id))
+
+
+class BboxToJsonLogger(BaseJsonLogger):
+    """
+    ُ This module is designed to automate the task of logging jsons. An example json is used
+    to show the contents of json file shortly
+    Example:
+          {
+          "video_details": {
+            "frame_width": 1920,
+            "frame_height": 1080,
+            "frame_rate": 20,
+            "video_name": "/home/gpu/codes/MSD/pedestrian_2/project/public/camera1.avi"
+          },
+          "frames": [
+            {
+              "frame_id": 329,
+              "timestamp": 3365.1254
+              "bboxes": [
+                {
+                  "labels": [
+                    {
+                      "category": "pedestrian",
+                      "confidence": 0.9
+                    }
+                  ],
+                  "bbox_id": 0,
+                  "top": 1257,
+                  "left": 138,
+                  "width": 68,
+                  "height": 109
+                }
+              ]
+            }],
+
+    Attributes:
+        frames (dict): It's a dictionary that maps each frame_id to json attributes.
+        video_details (dict): information about video file.
+        top_k_labels (int): shows the allowed number of labels
+        start_time (datetime object): we use it to automate the json output by time.
+
+    Args:
+        top_k_labels (int): shows the allowed number of labels
+
+    """
+
+    def __init__(self, top_k_labels: int = 1):
+        self.frames = {}
+        self.video_details = self.video_details = dict(frame_width=None, frame_height=None, frame_rate=None,
+                                                       video_name=None)
+        self.top_k_labels = top_k_labels
+        self.start_time = datetime.now()
+
+    def set_top_k(self, value):
+        self.top_k_labels = value
+
+    def frame_exists(self, frame_id: int) -> bool:
+        """
+        Args:
+            frame_id (int):
+
+        Returns:
+            bool: true if frame_id is recognized
+        """
+        return frame_id in self.frames.keys()
+
+    def add_frame(self, frame_id: int, timestamp: float = None) -> None:
+        """
+        Args:
+            frame_id (int):
+            timestamp (float): opencv captured frame time property
+
+        Raises:
+             ValueError: if frame_id would not exist in class frames attribute
+
+        Returns:
+            None
+
+        """
+        if not self.frame_exists(frame_id):
+            self.frames[frame_id] = Frame(frame_id, timestamp)
+        else:
+            raise ValueError("Frame id: {} already exists".format(frame_id))
+
+    def bbox_exists(self, frame_id: int, bbox_id: int) -> bool:
+        """
+        Args:
+            frame_id:
+            bbox_id:
+
+        Returns:
+            bool: if bbox exists in frame bboxes list
+        """
+        bboxes = []
+        if self.frame_exists(frame_id=frame_id):
+            bboxes = [bbox.bbox_id for bbox in self.frames[frame_id].bboxes]
+        return bbox_id in bboxes
+
+    def find_bbox(self, frame_id: int, bbox_id: int):
+        """
+
+        Args:
+            frame_id:
+            bbox_id:
+
+        Returns:
+            bbox_id (int):
+
+        Raises:
+            ValueError: if bbox_id does not exist in the bbox list of specific frame.
+        """
+        if not self.bbox_exists(frame_id, bbox_id):
+            raise ValueError("frame with id: {} does not contain bbox with id: {}".format(frame_id, bbox_id))
+        bboxes = {bbox.bbox_id: bbox for bbox in self.frames[frame_id].bboxes}
+        return bboxes.get(bbox_id)
+
+    def add_bbox_to_frame(self, frame_id: int, bbox_id: int, top: int, left: int, width: int, height: int) -> None:
+        """
+
+        Args:
+            frame_id (int):
+            bbox_id (int):
+            top (int):
+            left (int):
+            width (int):
+            height (int):
+
+        Returns:
+            None
+
+        Raises:
+            ValueError: if bbox_id already exist in frame information with frame_id
+            ValueError: if frame_id does not exist in frames attribute
+        """
+        if self.frame_exists(frame_id):
+            frame = self.frames[frame_id]
+            if not self.bbox_exists(frame_id, bbox_id):
+                frame.add_bbox(bbox_id, top, left, width, height)
+            else:
+                raise ValueError(
+                    "frame with frame_id: {} already contains the bbox with id: {} ".format(frame_id, bbox_id))
+        else:
+            raise ValueError("frame with frame_id: {} does not exist".format(frame_id))
+
+    def add_label_to_bbox(self, frame_id: int, bbox_id: int, category: str, confidence: float):
+        """
+        Args:
+            frame_id:
+            bbox_id:
+            category:
+            confidence: the confidence value returned from yolo detection
+
+        Returns:
+            None
+
+        Raises:
+            ValueError: if labels quota (top_k_labels) exceeds.
+        """
+        bbox = self.find_bbox(frame_id, bbox_id)
+        if not bbox.labels_full(self.top_k_labels):
+            bbox.add_label(category, confidence)
+        else:
+            raise ValueError("labels in frame_id: {}, bbox_id: {} is fulled".format(frame_id, bbox_id))
+
+    def add_video_details(self, frame_width: int = None, frame_height: int = None, frame_rate: int = None,
+                          video_name: str = None):
+        self.video_details['frame_width'] = frame_width
+        self.video_details['frame_height'] = frame_height
+        self.video_details['frame_rate'] = frame_rate
+        self.video_details['video_name'] = video_name
+
+    def output(self):
+        output = {'video_details': self.video_details}
+        result = list(self.frames.values())
+        output['frames'] = [item.dic() for item in result]
+        return output
+
+    def json_output(self, output_name):
+        """
+        Args:
+            output_name:
+
+        Returns:
+            None
+
+        Notes:
+            It creates the json output with `output_name` name.
+        """
+        if not output_name.endswith('.json'):
+            output_name += '.json'
+        with open(output_name, 'w') as file:
+            json.dump(self.output(), file)
+        file.close()
+
+    def set_start(self):
+        self.start_time = datetime.now()
+
+    def schedule_output_by_time(self, output_dir=JsonMeta.PATH_TO_SAVE, hours: int = 0, minutes: int = 0,
+                                seconds: int = 60) -> None:
+        """
+        Notes:
+            Creates folder and then periodically stores the jsons on that address.
+
+        Args:
+            output_dir (str): the directory where output files will be stored
+            hours (int):
+            minutes (int):
+            seconds (int):
+
+        Returns:
+            None
+
+        """
+        end = datetime.now()
+        interval = 0
+        interval += abs(min([hours, JsonMeta.HOURS]) * 3600)
+        interval += abs(min([minutes, JsonMeta.MINUTES]) * 60)
+        interval += abs(min([seconds, JsonMeta.SECONDS]))
+        diff = (end - self.start_time).seconds
+
+        if diff > interval:
+            output_name = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '.json'
+            if not exists(output_dir):
+                makedirs(output_dir)
+            output = join(output_dir, output_name)
+            self.json_output(output_name=output)
+            self.frames = {}
+            self.start_time = datetime.now()
+
+    def schedule_output_by_frames(self, frames_quota, frame_counter, output_dir=JsonMeta.PATH_TO_SAVE):
+        """
+        saves as the number of frames quota increases higher.
+        :param frames_quota:
+        :param frame_counter:
+        :param output_dir:
+        :return:
+        """
+        pass
+
+    def flush(self, output_dir):
+        """
+        Notes:
+            We use this function to output jsons whenever possible.
+            like the time that we exit the while loop of opencv.
+
+        Args:
+            output_dir:
+
+        Returns:
+            None
+
+        """
+        filename = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '-remaining.json'
+        output = join(output_dir, filename)
+        self.json_output(output_name=output)

test/test-yolov5-deepsort/deep_sort/utils/log.py

@@ -0,0 +1,17 @@
+import logging
+
+
+def get_logger(name='root'):
+    formatter = logging.Formatter(
+        # fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s')
+        fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+
+    handler = logging.StreamHandler()
+    handler.setFormatter(formatter)
+
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.INFO)
+    logger.addHandler(handler)
+    return logger
+
+

test/test-yolov5-deepsort/deep_sort/utils/parser.py

@@ -0,0 +1,39 @@
+import os
+import yaml
+from easydict import EasyDict as edict
+
+class YamlParser(edict):
+    """
+    This is yaml parser based on EasyDict.
+    """
+    def __init__(self, cfg_dict=None, config_file=None):
+        if cfg_dict is None:
+            cfg_dict = {}
+
+        if config_file is not None:
+            assert(os.path.isfile(config_file))
+            with open(config_file, 'r') as fo:
+                cfg_dict.update(yaml.load(fo.read()))
+
+        super(YamlParser, self).__init__(cfg_dict)
+
+    
+    def merge_from_file(self, config_file):
+        with open(config_file, 'r') as fo:
+            self.update(yaml.safe_load(fo.read()))
+            #self.update(yaml.load(fo.read()))
+
+    
+    def merge_from_dict(self, config_dict):
+        self.update(config_dict)
+
+
+def get_config(config_file=None):
+    return YamlParser(config_file=config_file)
+
+
+if __name__ == "__main__":
+    cfg = YamlParser(config_file="../configs/yolov3.yaml")
+    cfg.merge_from_file("../configs/deep_sort.yaml")
+
+    import ipdb; ipdb.set_trace()

test/test-yolov5-deepsort/deep_sort/utils/tools.py

@@ -0,0 +1,39 @@
+from functools import wraps
+from time import time
+
+
+def is_video(ext: str):
+    """
+    Returns true if ext exists in
+    allowed_exts for video files.
+
+    Args:
+        ext:
+
+    Returns:
+
+    """
+
+    allowed_exts = ('.mp4', '.webm', '.ogg', '.avi', '.wmv', '.mkv', '.3gp')
+    return any((ext.endswith(x) for x in allowed_exts))
+
+
+def tik_tok(func):
+    """
+    keep track of time for each process.
+    Args:
+        func:
+
+    Returns:
+
+    """
+    @wraps(func)
+    def _time_it(*args, **kwargs):
+        start = time()
+        try:
+            return func(*args, **kwargs)
+        finally:
+            end_ = time()
+            print("time: {:.03f}s, fps: {:.03f}".format(end_ - start, 1 / (end_ - start)))
+
+    return _time_it

test/test-yolov5-deepsort/demo.py

@@ -0,0 +1,62 @@
+import gc
+import cv2
+import imutils
+import threading
+from AIDetector_pytorch import Detector
+
+
+def start_camera_detector(camera_id, detector):
+    name = 'Demo Camera {}'.format(camera_id)
+    cap = cv2.VideoCapture(camera_id, cv2.CAP_V4L2)
+    if not cap.isOpened():
+        print('Error: Unable to open camera {}.'.format(camera_id))
+        return
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 640)
+
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    if fps <= 0:
+        fps = 30
+    t = int(1000 / fps)
+    print(f'{name} fps:', fps)
+
+    frame_count = 0
+
+    while True:
+        ret, im = cap.read()
+        if not ret or im is None:
+            break
+
+        if frame_count % 3 == 0:
+            result = detector.feedCap(im)
+            result = result['frame']
+            result = imutils.resize(result, height=500)
+
+            cv2.imshow(name, result)
+
+            if cv2.waitKey(t) & 0xFF == ord('q'):
+                break
+
+        frame_count += 1
+        if frame_count % 30 == 0:
+            gc.collect()
+
+    cap.release()
+    cv2.destroyWindow(name)
+
+
+def main():
+    detector = Detector()
+
+    threads = []
+    for i in range(6): # camera 数量
+        thread = threading.Thread(target=start_camera_detector, args=(i, detector))
+        thread.start()
+        threads.append(thread)
+
+    for thread in threads:
+        thread.join()
+
+
+if __name__ == "__main__":
+    main()

test/test-yolov5-deepsort/requirements.txt

@@ -0,0 +1,24 @@
+easydict==1.13
+imutils==0.5.4
+ipdb==0.13.13
+matplotlib==3.2.2
+motmetrics==1.4.0
+numpy==1.21.6
+onnxruntime==1.14.1
+onnxruntime_gpu==1.10.0
+opencv_python==4.1.2.30
+pafy==0.5.5
+pandas==1.1.5
+Pillow==9.5.0
+protobuf==4.24.2
+PyYAML==6.0.1
+requests==2.27.1
+scipy==1.4.1
+seaborn==0.11.0
+setuptools==58.0.4
+thop==0.1.1.post2209072238
+torch==1.10.1
+torchvision==0.11.2
+tqdm==4.41.0
+wandb==0.17.5
+python==3.7

test/test-yolov5-deepsort/tracker.py

@@ -0,0 +1,92 @@
+from deep_sort.utils.parser import get_config
+from deep_sort.deep_sort import DeepSort
+import torch
+import cv2
+
+palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
+cfg = get_config()
+cfg.merge_from_file("deep_sort/configs/deep_sort.yaml")
+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)
+
+
+def plot_bboxes(image, bboxes, line_thickness=None):
+    # Plots one bounding box on image img
+    tl = line_thickness or round(
+        0.002 * (image.shape[0] + image.shape[1]) / 2) + 1  # line/font thickness
+    for (x1, y1, x2, y2, cls_id, pos_id) in bboxes:
+        if cls_id in ['person']:
+            color = (0, 0, 255)
+        else:
+            color = (0, 255, 0)
+        c1, c2 = (x1, y1), (x2, y2)
+        cv2.rectangle(image, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
+        tf = max(tl - 1, 1)  # font thickness
+        t_size = cv2.getTextSize(cls_id, 0, fontScale=tl / 3, thickness=tf)[0]
+        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
+        cv2.rectangle(image, c1, c2, color, -1, cv2.LINE_AA)  # filled
+        cv2.putText(image, '{} ID-{}'.format(cls_id, pos_id), (c1[0], c1[1] - 2), 0, tl / 3,
+                    [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+
+    return image
+
+
+def update_tracker(target_detector, image):
+
+    new_faces = []
+    _, bboxes = target_detector.detect(image)
+
+    bbox_xywh = []
+    confs = []
+    clss = []
+
+    for x1, y1, x2, y2, cls_id, conf in bboxes:
+
+        obj = [
+            int((x1+x2)/2), int((y1+y2)/2),
+            x2-x1, y2-y1
+        ]
+        bbox_xywh.append(obj)
+        confs.append(conf)
+        clss.append(cls_id)
+
+    xywhs = torch.Tensor(bbox_xywh)
+    confss = torch.Tensor(confs)
+
+    outputs = deepsort.update(xywhs, confss, clss, image)
+
+    bboxes2draw = []
+    face_bboxes = []
+    current_ids = []
+    for value in list(outputs):
+        x1, y1, x2, y2, cls_, track_id = value
+        bboxes2draw.append(
+            (x1, y1, x2, y2, cls_, track_id)
+        )
+        current_ids.append(track_id)
+        if cls_ == 'face':
+            if not track_id in target_detector.faceTracker:
+                target_detector.faceTracker[track_id] = 0
+                face = image[y1:y2, x1:x2]
+                new_faces.append((face, track_id))
+            face_bboxes.append(
+                (x1, y1, x2, y2)
+            )
+
+    ids2delete = []
+    for history_id in target_detector.faceTracker:
+        if not history_id in current_ids:
+            target_detector.faceTracker[history_id] -= 1
+        if target_detector.faceTracker[history_id] < -5:
+            ids2delete.append(history_id)
+
+    for ids in ids2delete:
+        target_detector.faceTracker.pop(ids)
+        print('-[INFO] Delete track id:', ids)
+
+    image = plot_bboxes(image, bboxes2draw)
+
+    return image, new_faces, face_bboxes

test/test-yolov5-deepsort/utils/BaseDetector.py

@@ -0,0 +1,50 @@
+from tracker import update_tracker
+import cv2
+
+
+class baseDet(object):
+
+    def __init__(self):
+
+        self.img_size = 640
+        self.threshold = 0.3
+        self.stride = 1
+
+    def build_config(self):
+
+        self.faceTracker = {}
+        self.faceClasses = {}
+        self.faceLocation1 = {}
+        self.faceLocation2 = {}
+        self.frameCounter = 0
+        self.currentCarID = 0
+        self.recorded = []
+
+        self.font = cv2.FONT_HERSHEY_SIMPLEX
+
+    def feedCap(self, im):
+
+        retDict = {
+            'frame': None,
+            'faces': None,
+            'list_of_ids': None,
+            'face_bboxes': []
+        }
+        self.frameCounter += 1
+
+        im, faces, face_bboxes = update_tracker(self, im)
+
+        retDict['frame'] = im
+        retDict['faces'] = faces
+        retDict['face_bboxes'] = face_bboxes
+
+        return retDict
+
+    def init_model(self):
+        raise EOFError("Undefined model type.")
+
+    def preprocess(self):
+        raise EOFError("Undefined model type.")
+
+    def detect(self):
+        raise EOFError("Undefined model type.")

test/test-yolov5-deepsort/utils/activations.py

@@ -0,0 +1,98 @@
+# Activation functions
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
+class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
+    @staticmethod
+    def forward(x):
+        return x * torch.sigmoid(x)
+
+
+class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
+    @staticmethod
+    def forward(x):
+        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
+        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX
+
+
+# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
+class Mish(nn.Module):
+    @staticmethod
+    def forward(x):
+        return x * F.softplus(x).tanh()
+
+
+class MemoryEfficientMish(nn.Module):
+    class F(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_tensors[0]
+            sx = torch.sigmoid(x)
+            fx = F.softplus(x).tanh()
+            return grad_output * (fx + x * sx * (1 - fx * fx))
+
+    def forward(self, x):
+        return self.F.apply(x)
+
+
+# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
+class FReLU(nn.Module):
+    def __init__(self, c1, k=3):  # ch_in, kernel
+        super().__init__()
+        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
+        self.bn = nn.BatchNorm2d(c1)
+
+    def forward(self, x):
+        return torch.max(x, self.bn(self.conv(x)))
+
+
+# ACON https://arxiv.org/pdf/2009.04759.pdf ----------------------------------------------------------------------------
+class AconC(nn.Module):
+    r""" ACON activation (activate or not).
+    AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
+    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
+    """
+
+    def __init__(self, c1):
+        super().__init__()
+        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
+        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
+        self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
+
+    def forward(self, x):
+        dpx = (self.p1 - self.p2) * x
+        return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
+
+
+class MetaAconC(nn.Module):
+    r""" ACON activation (activate or not).
+    MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
+    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
+    """
+
+    def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
+        super().__init__()
+        c2 = max(r, c1 // r)
+        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
+        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
+        self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
+        self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
+        # self.bn1 = nn.BatchNorm2d(c2)
+        # self.bn2 = nn.BatchNorm2d(c1)
+
+    def forward(self, x):
+        y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
+        # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
+        # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y)))))  # bug/unstable
+        beta = torch.sigmoid(self.fc2(self.fc1(y)))  # bug patch BN layers removed
+        dpx = (self.p1 - self.p2) * x
+        return dpx * torch.sigmoid(beta * dpx) + self.p2 * x

test/test-yolov5-deepsort/utils/autoanchor.py

@@ -0,0 +1,161 @@
+# Auto-anchor utils
+
+import numpy as np
+import torch
+import yaml
+from tqdm import tqdm
+
+from utils.general import colorstr
+
+
+def check_anchor_order(m):
+    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
+    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
+    da = a[-1] - a[0]  # delta a
+    ds = m.stride[-1] - m.stride[0]  # delta s
+    if da.sign() != ds.sign():  # same order
+        print('Reversing anchor order')
+        m.anchors[:] = m.anchors.flip(0)
+        m.anchor_grid[:] = m.anchor_grid.flip(0)
+
+
+def check_anchors(dataset, model, thr=4.0, imgsz=640):
+    # Check anchor fit to data, recompute if necessary
+    prefix = colorstr('autoanchor: ')
+    print(f'\n{prefix}Analyzing anchors... ', end='')
+    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
+    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
+    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
+
+    def metric(k):  # compute metric
+        r = wh[:, None] / k[None]
+        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        best = x.max(1)[0]  # best_x
+        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
+        bpr = (best > 1. / thr).float().mean()  # best possible recall
+        return bpr, aat
+
+    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
+    bpr, aat = metric(anchors)
+    print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
+    if bpr < 0.98:  # threshold to recompute
+        print('. Attempting to improve anchors, please wait...')
+        na = m.anchor_grid.numel() // 2  # number of anchors
+        try:
+            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
+        except Exception as e:
+            print(f'{prefix}ERROR: {e}')
+        new_bpr = metric(anchors)[0]
+        if new_bpr > bpr:  # replace anchors
+            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
+            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
+            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
+            check_anchor_order(m)
+            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
+        else:
+            print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
+    print('')  # newline
+
+
+def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
+    """ Creates kmeans-evolved anchors from training dataset
+
+        Arguments:
+            path: path to dataset *.yaml, or a loaded dataset
+            n: number of anchors
+            img_size: image size used for training
+            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
+            gen: generations to evolve anchors using genetic algorithm
+            verbose: print all results
+
+        Return:
+            k: kmeans evolved anchors
+
+        Usage:
+            from utils.autoanchor import *; _ = kmean_anchors()
+    """
+    from scipy.cluster.vq import kmeans
+
+    thr = 1. / thr
+    prefix = colorstr('autoanchor: ')
+
+    def metric(k, wh):  # compute metrics
+        r = wh[:, None] / k[None]
+        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        # x = wh_iou(wh, torch.tensor(k))  # iou metric
+        return x, x.max(1)[0]  # x, best_x
+
+    def anchor_fitness(k):  # mutation fitness
+        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
+        return (best * (best > thr).float()).mean()  # fitness
+
+    def print_results(k):
+        k = k[np.argsort(k.prod(1))]  # sort small to large
+        x, best = metric(k, wh0)
+        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
+        print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
+        print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
+              f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')
+        for i, x in enumerate(k):
+            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
+        return k
+
+    if isinstance(path, str):  # *.yaml file
+        with open(path) as f:
+            data_dict = yaml.safe_load(f)  # model dict
+        from utils.datasets import LoadImagesAndLabels
+        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
+    else:
+        dataset = path  # dataset
+
+    # Get label wh
+    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
+
+    # Filter
+    i = (wh0 < 3.0).any(1).sum()
+    if i:
+        print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
+    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
+    # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
+
+    # Kmeans calculation
+    print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
+    s = wh.std(0)  # sigmas for whitening
+    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
+    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
+    k *= s
+    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
+    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
+    k = print_results(k)
+
+    # Plot
+    # k, d = [None] * 20, [None] * 20
+    # for i in tqdm(range(1, 21)):
+    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
+    # ax = ax.ravel()
+    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
+    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
+    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
+    # fig.savefig('wh.png', dpi=200)
+
+    # Evolve
+    npr = np.random
+    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
+    pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:')  # progress bar
+    for _ in pbar:
+        v = np.ones(sh)
+        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
+            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
+        kg = (k.copy() * v).clip(min=2.0)
+        fg = anchor_fitness(kg)
+        if fg > f:
+            f, k = fg, kg.copy()
+            pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
+            if verbose:
+                print_results(k)
+
+    return print_results(k)

test/test-yolov5-deepsort/utils/datasets.py

@@ -0,0 +1,1067 @@
+# Dataset utils and dataloaders
+
+import glob
+import logging
+import math
+import os
+import random
+import shutil
+import time
+from itertools import repeat
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+from threading import Thread
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image, ExifTags
+from torch.utils.data import Dataset
+from tqdm import tqdm
+
+from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \
+    resample_segments, clean_str
+from utils.torch_utils import torch_distributed_zero_first
+
+# Parameters
+help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
+img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo']  # acceptable image suffixes
+vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv']  # acceptable video suffixes
+logger = logging.getLogger(__name__)
+
+# Get orientation exif tag
+for orientation in ExifTags.TAGS.keys():
+    if ExifTags.TAGS[orientation] == 'Orientation':
+        break
+
+
+def get_hash(files):
+    # Returns a single hash value of a list of files
+    return sum(os.path.getsize(f) for f in files if os.path.isfile(f))
+
+
+def exif_size(img):
+    # Returns exif-corrected PIL size
+    s = img.size  # (width, height)
+    try:
+        rotation = dict(img._getexif().items())[orientation]
+        if rotation == 6:  # rotation 270
+            s = (s[1], s[0])
+        elif rotation == 8:  # rotation 90
+            s = (s[1], s[0])
+    except:
+        pass
+
+    return s
+
+
+def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
+                      rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''):
+    # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
+    with torch_distributed_zero_first(rank):
+        dataset = LoadImagesAndLabels(path, imgsz, batch_size,
+                                      augment=augment,  # augment images
+                                      hyp=hyp,  # augmentation hyperparameters
+                                      rect=rect,  # rectangular training
+                                      cache_images=cache,
+                                      single_cls=opt.single_cls,
+                                      stride=int(stride),
+                                      pad=pad,
+                                      image_weights=image_weights,
+                                      prefix=prefix)
+
+    batch_size = min(batch_size, len(dataset))
+    nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers])  # number of workers
+    sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
+    loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader
+    # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader()
+    dataloader = loader(dataset,
+                        batch_size=batch_size,
+                        num_workers=nw,
+                        sampler=sampler,
+                        pin_memory=True,
+                        collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
+    return dataloader, dataset
+
+
+class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
+    """ Dataloader that reuses workers
+
+    Uses same syntax as vanilla DataLoader
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
+        self.iterator = super().__iter__()
+
+    def __len__(self):
+        return len(self.batch_sampler.sampler)
+
+    def __iter__(self):
+        for i in range(len(self)):
+            yield next(self.iterator)
+
+
+class _RepeatSampler(object):
+    """ Sampler that repeats forever
+
+    Args:
+        sampler (Sampler)
+    """
+
+    def __init__(self, sampler):
+        self.sampler = sampler
+
+    def __iter__(self):
+        while True:
+            yield from iter(self.sampler)
+
+
+class LoadImages:  # for inference
+    def __init__(self, path, img_size=640, stride=32):
+        p = str(Path(path).absolute())  # os-agnostic absolute path
+        if '*' in p:
+            files = sorted(glob.glob(p, recursive=True))  # glob
+        elif os.path.isdir(p):
+            files = sorted(glob.glob(os.path.join(p, '*.*')))  # dir
+        elif os.path.isfile(p):
+            files = [p]  # files
+        else:
+            raise Exception(f'ERROR: {p} does not exist')
+
+        images = [x for x in files if x.split('.')[-1].lower() in img_formats]
+        videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
+        ni, nv = len(images), len(videos)
+
+        self.img_size = img_size
+        self.stride = stride
+        self.files = images + videos
+        self.nf = ni + nv  # number of files
+        self.video_flag = [False] * ni + [True] * nv
+        self.mode = 'image'
+        if any(videos):
+            self.new_video(videos[0])  # new video
+        else:
+            self.cap = None
+        assert self.nf > 0, f'No images or videos found in {p}. ' \
+                            f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}'
+
+    def __iter__(self):
+        self.count = 0
+        return self
+
+    def __next__(self):
+        if self.count == self.nf:
+            raise StopIteration
+        path = self.files[self.count]
+
+        if self.video_flag[self.count]:
+            # Read video
+            self.mode = 'video'
+            ret_val, img0 = self.cap.read()
+            if not ret_val:
+                self.count += 1
+                self.cap.release()
+                if self.count == self.nf:  # last video
+                    raise StopIteration
+                else:
+                    path = self.files[self.count]
+                    self.new_video(path)
+                    ret_val, img0 = self.cap.read()
+
+            self.frame += 1
+            print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ', end='')
+
+        else:
+            # Read image
+            self.count += 1
+            img0 = cv2.imread(path)  # BGR
+            assert img0 is not None, 'Image Not Found ' + path
+            print(f'image {self.count}/{self.nf} {path}: ', end='')
+
+        # Padded resize
+        img = letterbox(img0, self.img_size, stride=self.stride)[0]
+
+        # Convert
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+        img = np.ascontiguousarray(img)
+
+        return path, img, img0, self.cap
+
+    def new_video(self, path):
+        self.frame = 0
+        self.cap = cv2.VideoCapture(path)
+        self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    def __len__(self):
+        return self.nf  # number of files
+
+
+class LoadWebcam:  # for inference
+    def __init__(self, pipe='0', img_size=640, stride=32):
+        self.img_size = img_size
+        self.stride = stride
+
+        if pipe.isnumeric():
+            pipe = eval(pipe)  # local camera
+        # pipe = 'rtsp://192.168.1.64/1'  # IP camera
+        # pipe = 'rtsp://username:password@192.168.1.64/1'  # IP camera with login
+        # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg'  # IP golf camera
+
+        self.pipe = pipe
+        self.cap = cv2.VideoCapture(pipe)  # video capture object
+        self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)  # set buffer size
+
+    def __iter__(self):
+        self.count = -1
+        return self
+
+    def __next__(self):
+        self.count += 1
+        if cv2.waitKey(1) == ord('q'):  # q to quit
+            self.cap.release()
+            cv2.destroyAllWindows()
+            raise StopIteration
+
+        # Read frame
+        if self.pipe == 0:  # local camera
+            ret_val, img0 = self.cap.read()
+            img0 = cv2.flip(img0, 1)  # flip left-right
+        else:  # IP camera
+            n = 0
+            while True:
+                n += 1
+                self.cap.grab()
+                if n % 30 == 0:  # skip frames
+                    ret_val, img0 = self.cap.retrieve()
+                    if ret_val:
+                        break
+
+        # Print
+        assert ret_val, f'Camera Error {self.pipe}'
+        img_path = 'webcam.jpg'
+        print(f'webcam {self.count}: ', end='')
+
+        # Padded resize
+        img = letterbox(img0, self.img_size, stride=self.stride)[0]
+
+        # Convert
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+        img = np.ascontiguousarray(img)
+
+        return img_path, img, img0, None
+
+    def __len__(self):
+        return 0
+
+
+class LoadStreams:  # multiple IP or RTSP cameras
+    def __init__(self, sources='streams.txt', img_size=640, stride=32):
+        self.mode = 'stream'
+        self.img_size = img_size
+        self.stride = stride
+
+        if os.path.isfile(sources):
+            with open(sources, 'r') as f:
+                sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
+        else:
+            sources = [sources]
+
+        n = len(sources)
+        self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
+        self.sources = [clean_str(x) for x in sources]  # clean source names for later
+        for i, s in enumerate(sources):  # index, source
+            # Start thread to read frames from video stream
+            print(f'{i + 1}/{n}: {s}... ', end='')
+            if 'youtube.com/' in s or 'youtu.be/' in s:  # if source is YouTube video
+                check_requirements(('pafy', 'youtube_dl'))
+                import pafy
+                s = pafy.new(s).getbest(preftype="mp4").url  # YouTube URL
+            s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam
+            cap = cv2.VideoCapture(s)
+            assert cap.isOpened(), f'Failed to open {s}'
+            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            self.fps[i] = max(cap.get(cv2.CAP_PROP_FPS) % 100, 0) or 30.0  # 30 FPS fallback
+            self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf')  # infinite stream fallback
+
+            _, self.imgs[i] = cap.read()  # guarantee first frame
+            self.threads[i] = Thread(target=self.update, args=([i, cap]), daemon=True)
+            print(f" success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
+            self.threads[i].start()
+        print('')  # newline
+
+        # check for common shapes
+        s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0)  # shapes
+        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
+        if not self.rect:
+            print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
+
+    def update(self, i, cap):
+        # Read stream `i` frames in daemon thread
+        n, f = 0, self.frames[i]
+        while cap.isOpened() and n < f:
+            n += 1
+            # _, self.imgs[index] = cap.read()
+            cap.grab()
+            if n % 4:  # read every 4th frame
+                success, im = cap.retrieve()
+                self.imgs[i] = im if success else self.imgs[i] * 0
+            time.sleep(1 / self.fps[i])  # wait time
+
+    def __iter__(self):
+        self.count = -1
+        return self
+
+    def __next__(self):
+        self.count += 1
+        if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'):  # q to quit
+            cv2.destroyAllWindows()
+            raise StopIteration
+
+        # Letterbox
+        img0 = self.imgs.copy()
+        img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0]
+
+        # Stack
+        img = np.stack(img, 0)
+
+        # Convert
+        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB, to bsx3x416x416
+        img = np.ascontiguousarray(img)
+
+        return self.sources, img, img0, None
+
+    def __len__(self):
+        return 0  # 1E12 frames = 32 streams at 30 FPS for 30 years
+
+
+def img2label_paths(img_paths):
+    # Define label paths as a function of image paths
+    sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep  # /images/, /labels/ substrings
+    return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths]
+
+
+class LoadImagesAndLabels(Dataset):  # for training/testing
+    def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
+                 cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''):
+        self.img_size = img_size
+        self.augment = augment
+        self.hyp = hyp
+        self.image_weights = image_weights
+        self.rect = False if image_weights else rect
+        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
+        self.mosaic_border = [-img_size // 2, -img_size // 2]
+        self.stride = stride
+        self.path = path
+
+        try:
+            f = []  # image files
+            for p in path if isinstance(path, list) else [path]:
+                p = Path(p)  # os-agnostic
+                if p.is_dir():  # dir
+                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
+                    # f = list(p.rglob('**/*.*'))  # pathlib
+                elif p.is_file():  # file
+                    with open(p, 'r') as t:
+                        t = t.read().strip().splitlines()
+                        parent = str(p.parent) + os.sep
+                        f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
+                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
+                else:
+                    raise Exception(f'{prefix}{p} does not exist')
+            self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
+            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats])  # pathlib
+            assert self.img_files, f'{prefix}No images found'
+        except Exception as e:
+            raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}')
+
+        # Check cache
+        self.label_files = img2label_paths(self.img_files)  # labels
+        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')  # cached labels
+        if cache_path.is_file():
+            cache, exists = torch.load(cache_path), True  # load
+            if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache:  # changed
+                cache, exists = self.cache_labels(cache_path, prefix), False  # re-cache
+        else:
+            cache, exists = self.cache_labels(cache_path, prefix), False  # cache
+
+        # Display cache
+        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupted, total
+        if exists:
+            d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
+            tqdm(None, desc=prefix + d, total=n, initial=n)  # display cache results
+        assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'
+
+        # Read cache
+        cache.pop('hash')  # remove hash
+        cache.pop('version')  # remove version
+        labels, shapes, self.segments = zip(*cache.values())
+        self.labels = list(labels)
+        self.shapes = np.array(shapes, dtype=np.float64)
+        self.img_files = list(cache.keys())  # update
+        self.label_files = img2label_paths(cache.keys())  # update
+        if single_cls:
+            for x in self.labels:
+                x[:, 0] = 0
+
+        n = len(shapes)  # number of images
+        bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
+        nb = bi[-1] + 1  # number of batches
+        self.batch = bi  # batch index of image
+        self.n = n
+        self.indices = range(n)
+
+        # Rectangular Training
+        if self.rect:
+            # Sort by aspect ratio
+            s = self.shapes  # wh
+            ar = s[:, 1] / s[:, 0]  # aspect ratio
+            irect = ar.argsort()
+            self.img_files = [self.img_files[i] for i in irect]
+            self.label_files = [self.label_files[i] for i in irect]
+            self.labels = [self.labels[i] for i in irect]
+            self.shapes = s[irect]  # wh
+            ar = ar[irect]
+
+            # Set training image shapes
+            shapes = [[1, 1]] * nb
+            for i in range(nb):
+                ari = ar[bi == i]
+                mini, maxi = ari.min(), ari.max()
+                if maxi < 1:
+                    shapes[i] = [maxi, 1]
+                elif mini > 1:
+                    shapes[i] = [1, 1 / mini]
+
+            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
+
+        # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
+        self.imgs = [None] * n
+        if cache_images:
+            gb = 0  # Gigabytes of cached images
+            self.img_hw0, self.img_hw = [None] * n, [None] * n
+            results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n)))  # 8 threads
+            pbar = tqdm(enumerate(results), total=n)
+            for i, x in pbar:
+                self.imgs[i], self.img_hw0[i], self.img_hw[i] = x  # img, hw_original, hw_resized = load_image(self, i)
+                gb += self.imgs[i].nbytes
+                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)'
+            pbar.close()
+
+    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
+        # Cache dataset labels, check images and read shapes
+        x = {}  # dict
+        nm, nf, ne, nc = 0, 0, 0, 0  # number missing, found, empty, duplicate
+        pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files))
+        for i, (im_file, lb_file) in enumerate(pbar):
+            try:
+                # verify images
+                im = Image.open(im_file)
+                im.verify()  # PIL verify
+                shape = exif_size(im)  # image size
+                segments = []  # instance segments
+                assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
+                assert im.format.lower() in img_formats, f'invalid image format {im.format}'
+
+                # verify labels
+                if os.path.isfile(lb_file):
+                    nf += 1  # label found
+                    with open(lb_file, 'r') as f:
+                        l = [x.split() for x in f.read().strip().splitlines()]
+                        if any([len(x) > 8 for x in l]):  # is segment
+                            classes = np.array([x[0] for x in l], dtype=np.float32)
+                            segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l]  # (cls, xy1...)
+                            l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
+                        l = np.array(l, dtype=np.float32)
+                    if len(l):
+                        assert l.shape[1] == 5, 'labels require 5 columns each'
+                        assert (l >= 0).all(), 'negative labels'
+                        assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
+                        assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
+                    else:
+                        ne += 1  # label empty
+                        l = np.zeros((0, 5), dtype=np.float32)
+                else:
+                    nm += 1  # label missing
+                    l = np.zeros((0, 5), dtype=np.float32)
+                x[im_file] = [l, shape, segments]
+            except Exception as e:
+                nc += 1
+                logging.info(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}')
+
+            pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \
+                        f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted"
+        pbar.close()
+
+        if nf == 0:
+            logging.info(f'{prefix}WARNING: No labels found in {path}. See {help_url}')
+
+        x['hash'] = get_hash(self.label_files + self.img_files)
+        x['results'] = nf, nm, ne, nc, i + 1
+        x['version'] = 0.1  # cache version
+        try:
+            torch.save(x, path)  # save for next time
+            logging.info(f'{prefix}New cache created: {path}')
+        except Exception as e:
+            logging.info(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}')  # path not writeable
+        return x
+
+    def __len__(self):
+        return len(self.img_files)
+
+    # def __iter__(self):
+    #     self.count = -1
+    #     print('ran dataset iter')
+    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
+    #     return self
+
+    def __getitem__(self, index):
+        index = self.indices[index]  # linear, shuffled, or image_weights
+
+        hyp = self.hyp
+        mosaic = self.mosaic and random.random() < hyp['mosaic']
+        if mosaic:
+            # Load mosaic
+            img, labels = load_mosaic(self, index)
+            shapes = None
+
+            # MixUp https://arxiv.org/pdf/1710.09412.pdf
+            if random.random() < hyp['mixup']:
+                img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1))
+                r = np.random.beta(8.0, 8.0)  # mixup ratio, alpha=beta=8.0
+                img = (img * r + img2 * (1 - r)).astype(np.uint8)
+                labels = np.concatenate((labels, labels2), 0)
+
+        else:
+            # Load image
+            img, (h0, w0), (h, w) = load_image(self, index)
+
+            # Letterbox
+            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
+            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
+            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling
+
+            labels = self.labels[index].copy()
+            if labels.size:  # normalized xywh to pixel xyxy format
+                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
+
+        if self.augment:
+            # Augment imagespace
+            if not mosaic:
+                img, labels = random_perspective(img, labels,
+                                                 degrees=hyp['degrees'],
+                                                 translate=hyp['translate'],
+                                                 scale=hyp['scale'],
+                                                 shear=hyp['shear'],
+                                                 perspective=hyp['perspective'])
+
+            # Augment colorspace
+            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
+
+            # Apply cutouts
+            # if random.random() < 0.9:
+            #     labels = cutout(img, labels)
+
+        nL = len(labels)  # number of labels
+        if nL:
+            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])  # convert xyxy to xywh
+            labels[:, [2, 4]] /= img.shape[0]  # normalized height 0-1
+            labels[:, [1, 3]] /= img.shape[1]  # normalized width 0-1
+
+        if self.augment:
+            # flip up-down
+            if random.random() < hyp['flipud']:
+                img = np.flipud(img)
+                if nL:
+                    labels[:, 2] = 1 - labels[:, 2]
+
+            # flip left-right
+            if random.random() < hyp['fliplr']:
+                img = np.fliplr(img)
+                if nL:
+                    labels[:, 1] = 1 - labels[:, 1]
+
+        labels_out = torch.zeros((nL, 6))
+        if nL:
+            labels_out[:, 1:] = torch.from_numpy(labels)
+
+        # Convert
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+        img = np.ascontiguousarray(img)
+
+        return torch.from_numpy(img), labels_out, self.img_files[index], shapes
+
+    @staticmethod
+    def collate_fn(batch):
+        img, label, path, shapes = zip(*batch)  # transposed
+        for i, l in enumerate(label):
+            l[:, 0] = i  # add target image index for build_targets()
+        return torch.stack(img, 0), torch.cat(label, 0), path, shapes
+
+    @staticmethod
+    def collate_fn4(batch):
+        img, label, path, shapes = zip(*batch)  # transposed
+        n = len(shapes) // 4
+        img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
+
+        ho = torch.tensor([[0., 0, 0, 1, 0, 0]])
+        wo = torch.tensor([[0., 0, 1, 0, 0, 0]])
+        s = torch.tensor([[1, 1, .5, .5, .5, .5]])  # scale
+        for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW
+            i *= 4
+            if random.random() < 0.5:
+                im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[
+                    0].type(img[i].type())
+                l = label[i]
+            else:
+                im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
+                l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
+            img4.append(im)
+            label4.append(l)
+
+        for i, l in enumerate(label4):
+            l[:, 0] = i  # add target image index for build_targets()
+
+        return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4
+
+
+# Ancillary functions --------------------------------------------------------------------------------------------------
+def load_image(self, index):
+    # loads 1 image from dataset, returns img, original hw, resized hw
+    img = self.imgs[index]
+    if img is None:  # not cached
+        path = self.img_files[index]
+        img = cv2.imread(path)  # BGR
+        assert img is not None, 'Image Not Found ' + path
+        h0, w0 = img.shape[:2]  # orig hw
+        r = self.img_size / max(h0, w0)  # ratio
+        if r != 1:  # if sizes are not equal
+            img = cv2.resize(img, (int(w0 * r), int(h0 * r)),
+                             interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR)
+        return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
+    else:
+        return self.imgs[index], self.img_hw0[index], self.img_hw[index]  # img, hw_original, hw_resized
+
+
+def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
+    r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
+    hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
+    dtype = img.dtype  # uint8
+
+    x = np.arange(0, 256, dtype=np.int16)
+    lut_hue = ((x * r[0]) % 180).astype(dtype)
+    lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
+    lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
+
+    img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
+    cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed
+
+
+def hist_equalize(img, clahe=True, bgr=False):
+    # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255
+    yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
+    if clahe:
+        c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+        yuv[:, :, 0] = c.apply(yuv[:, :, 0])
+    else:
+        yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])  # equalize Y channel histogram
+    return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)  # convert YUV image to RGB
+
+
+def load_mosaic(self, index):
+    # loads images in a 4-mosaic
+
+    labels4, segments4 = [], []
+    s = self.img_size
+    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
+    indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
+    for i, index in enumerate(indices):
+        # Load image
+        img, _, (h, w) = load_image(self, index)
+
+        # place img in img4
+        if i == 0:  # top left
+            img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
+            x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
+            x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
+        elif i == 1:  # top right
+            x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+            x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+        elif i == 2:  # bottom left
+            x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
+            x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+        elif i == 3:  # bottom right
+            x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
+            x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+        img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
+        padw = x1a - x1b
+        padh = y1a - y1b
+
+        # Labels
+        labels, segments = self.labels[index].copy(), self.segments[index].copy()
+        if labels.size:
+            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
+            segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
+        labels4.append(labels)
+        segments4.extend(segments)
+
+    # Concat/clip labels
+    labels4 = np.concatenate(labels4, 0)
+    for x in (labels4[:, 1:], *segments4):
+        np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
+    # img4, labels4 = replicate(img4, labels4)  # replicate
+
+    # Augment
+    img4, labels4 = random_perspective(img4, labels4, segments4,
+                                       degrees=self.hyp['degrees'],
+                                       translate=self.hyp['translate'],
+                                       scale=self.hyp['scale'],
+                                       shear=self.hyp['shear'],
+                                       perspective=self.hyp['perspective'],
+                                       border=self.mosaic_border)  # border to remove
+
+    return img4, labels4
+
+
+def load_mosaic9(self, index):
+    # loads images in a 9-mosaic
+
+    labels9, segments9 = [], []
+    s = self.img_size
+    indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices
+    for i, index in enumerate(indices):
+        # Load image
+        img, _, (h, w) = load_image(self, index)
+
+        # place img in img9
+        if i == 0:  # center
+            img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
+            h0, w0 = h, w
+            c = s, s, s + w, s + h  # xmin, ymin, xmax, ymax (base) coordinates
+        elif i == 1:  # top
+            c = s, s - h, s + w, s
+        elif i == 2:  # top right
+            c = s + wp, s - h, s + wp + w, s
+        elif i == 3:  # right
+            c = s + w0, s, s + w0 + w, s + h
+        elif i == 4:  # bottom right
+            c = s + w0, s + hp, s + w0 + w, s + hp + h
+        elif i == 5:  # bottom
+            c = s + w0 - w, s + h0, s + w0, s + h0 + h
+        elif i == 6:  # bottom left
+            c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
+        elif i == 7:  # left
+            c = s - w, s + h0 - h, s, s + h0
+        elif i == 8:  # top left
+            c = s - w, s + h0 - hp - h, s, s + h0 - hp
+
+        padx, pady = c[:2]
+        x1, y1, x2, y2 = [max(x, 0) for x in c]  # allocate coords
+
+        # Labels
+        labels, segments = self.labels[index].copy(), self.segments[index].copy()
+        if labels.size:
+            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady)  # normalized xywh to pixel xyxy format
+            segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
+        labels9.append(labels)
+        segments9.extend(segments)
+
+        # Image
+        img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:]  # img9[ymin:ymax, xmin:xmax]
+        hp, wp = h, w  # height, width previous
+
+    # Offset
+    yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border]  # mosaic center x, y
+    img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
+
+    # Concat/clip labels
+    labels9 = np.concatenate(labels9, 0)
+    labels9[:, [1, 3]] -= xc
+    labels9[:, [2, 4]] -= yc
+    c = np.array([xc, yc])  # centers
+    segments9 = [x - c for x in segments9]
+
+    for x in (labels9[:, 1:], *segments9):
+        np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
+    # img9, labels9 = replicate(img9, labels9)  # replicate
+
+    # Augment
+    img9, labels9 = random_perspective(img9, labels9, segments9,
+                                       degrees=self.hyp['degrees'],
+                                       translate=self.hyp['translate'],
+                                       scale=self.hyp['scale'],
+                                       shear=self.hyp['shear'],
+                                       perspective=self.hyp['perspective'],
+                                       border=self.mosaic_border)  # border to remove
+
+    return img9, labels9
+
+
+def replicate(img, labels):
+    # Replicate labels
+    h, w = img.shape[:2]
+    boxes = labels[:, 1:].astype(int)
+    x1, y1, x2, y2 = boxes.T
+    s = ((x2 - x1) + (y2 - y1)) / 2  # side length (pixels)
+    for i in s.argsort()[:round(s.size * 0.5)]:  # smallest indices
+        x1b, y1b, x2b, y2b = boxes[i]
+        bh, bw = y2b - y1b, x2b - x1b
+        yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw))  # offset x, y
+        x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
+        img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
+        labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
+
+    return img, labels
+
+
+def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = img.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+    if not scaleup:  # only scale down, do not scale up (for better test mAP)
+        r = min(r, 1.0)
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+    if auto:  # minimum rectangle
+        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
+    elif scaleFill:  # stretch
+        dw, dh = 0.0, 0.0
+        new_unpad = (new_shape[1], new_shape[0])
+        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return img, ratio, (dw, dh)
+
+
+def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
+                       border=(0, 0)):
+    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
+    # targets = [cls, xyxy]
+
+    height = img.shape[0] + border[0] * 2  # shape(h,w,c)
+    width = img.shape[1] + border[1] * 2
+
+    # Center
+    C = np.eye(3)
+    C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
+    C[1, 2] = -img.shape[0] / 2  # y translation (pixels)
+
+    # Perspective
+    P = np.eye(3)
+    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)
+    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)
+
+    # Rotation and Scale
+    R = np.eye(3)
+    a = random.uniform(-degrees, degrees)
+    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
+    s = random.uniform(1 - scale, 1 + scale)
+    # s = 2 ** random.uniform(-scale, scale)
+    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
+
+    # Shear
+    S = np.eye(3)
+    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
+    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)
+
+    # Translation
+    T = np.eye(3)
+    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)
+    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)
+
+    # Combined rotation matrix
+    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
+    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
+        if perspective:
+            img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
+        else:  # affine
+            img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
+
+    # Visualize
+    # import matplotlib.pyplot as plt
+    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
+    # ax[0].imshow(img[:, :, ::-1])  # base
+    # ax[1].imshow(img2[:, :, ::-1])  # warped
+
+    # Transform label coordinates
+    n = len(targets)
+    if n:
+        use_segments = any(x.any() for x in segments)
+        new = np.zeros((n, 4))
+        if use_segments:  # warp segments
+            segments = resample_segments(segments)  # upsample
+            for i, segment in enumerate(segments):
+                xy = np.ones((len(segment), 3))
+                xy[:, :2] = segment
+                xy = xy @ M.T  # transform
+                xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]  # perspective rescale or affine
+
+                # clip
+                new[i] = segment2box(xy, width, height)
+
+        else:  # warp boxes
+            xy = np.ones((n * 4, 3))
+            xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
+            xy = xy @ M.T  # transform
+            xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine
+
+            # create new boxes
+            x = xy[:, [0, 2, 4, 6]]
+            y = xy[:, [1, 3, 5, 7]]
+            new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
+
+            # clip
+            new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
+            new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
+
+        # filter candidates
+        i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
+        targets = targets[i]
+        targets[:, 1:5] = new[i]
+
+    return img, targets
+
+
+def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
+    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
+    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
+    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
+    ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio
+    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates
+
+
+def cutout(image, labels):
+    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
+    h, w = image.shape[:2]
+
+    def bbox_ioa(box1, box2):
+        # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2
+        box2 = box2.transpose()
+
+        # Get the coordinates of bounding boxes
+        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
+        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
+
+        # Intersection area
+        inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
+                     (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
+
+        # box2 area
+        box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16
+
+        # Intersection over box2 area
+        return inter_area / box2_area
+
+    # create random masks
+    scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction
+    for s in scales:
+        mask_h = random.randint(1, int(h * s))
+        mask_w = random.randint(1, int(w * s))
+
+        # box
+        xmin = max(0, random.randint(0, w) - mask_w // 2)
+        ymin = max(0, random.randint(0, h) - mask_h // 2)
+        xmax = min(w, xmin + mask_w)
+        ymax = min(h, ymin + mask_h)
+
+        # apply random color mask
+        image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
+
+        # return unobscured labels
+        if len(labels) and s > 0.03:
+            box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
+            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
+            labels = labels[ioa < 0.60]  # remove >60% obscured labels
+
+    return labels
+
+
+def create_folder(path='./new'):
+    # Create folder
+    if os.path.exists(path):
+        shutil.rmtree(path)  # delete output folder
+    os.makedirs(path)  # make new output folder
+
+
+def flatten_recursive(path='../coco128'):
+    # Flatten a recursive directory by bringing all files to top level
+    new_path = Path(path + '_flat')
+    create_folder(new_path)
+    for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)):
+        shutil.copyfile(file, new_path / Path(file).name)
+
+
+def extract_boxes(path='../coco128/'):  # from utils.datasets import *; extract_boxes('../coco128')
+    # Convert detection dataset into classification dataset, with one directory per class
+
+    path = Path(path)  # images dir
+    shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None  # remove existing
+    files = list(path.rglob('*.*'))
+    n = len(files)  # number of files
+    for im_file in tqdm(files, total=n):
+        if im_file.suffix[1:] in img_formats:
+            # image
+            im = cv2.imread(str(im_file))[..., ::-1]  # BGR to RGB
+            h, w = im.shape[:2]
+
+            # labels
+            lb_file = Path(img2label_paths([str(im_file)])[0])
+            if Path(lb_file).exists():
+                with open(lb_file, 'r') as f:
+                    lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)  # labels
+
+                for j, x in enumerate(lb):
+                    c = int(x[0])  # class
+                    f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg'  # new filename
+                    if not f.parent.is_dir():
+                        f.parent.mkdir(parents=True)
+
+                    b = x[1:] * [w, h, w, h]  # box
+                    # b[2:] = b[2:].max()  # rectangle to square
+                    b[2:] = b[2:] * 1.2 + 3  # pad
+                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
+
+                    b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
+                    b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
+                    assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'
+
+
+def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0), annotated_only=False):
+    """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
+    Usage: from utils.datasets import *; autosplit('../coco128')
+    Arguments
+        path:           Path to images directory
+        weights:        Train, val, test weights (list)
+        annotated_only: Only use images with an annotated txt file
+    """
+    path = Path(path)  # images dir
+    files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], [])  # image files only
+    n = len(files)  # number of files
+    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split
+
+    txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt']  # 3 txt files
+    [(path / x).unlink() for x in txt if (path / x).exists()]  # remove existing
+
+    print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
+    for i, img in tqdm(zip(indices, files), total=n):
+        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label
+            with open(path / txt[i], 'a') as f:
+                f.write(str(img) + '\n')  # add image to txt file

test/test-yolov5-deepsort/utils/general.py

@@ -0,0 +1,692 @@
+# YOLOv5 general utils
+
+import glob
+import logging
+import math
+import os
+import platform
+import random
+import re
+import subprocess
+import time
+from itertools import repeat
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+
+import cv2
+import numpy as np
+import pandas as pd
+import pkg_resources as pkg
+import torch
+import torchvision
+import yaml
+
+from utils.google_utils import gsutil_getsize
+from utils.metrics import fitness
+from utils.torch_utils import init_torch_seeds
+
+# Settings
+torch.set_printoptions(linewidth=320, precision=5, profile='long')
+np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
+pd.options.display.max_columns = 10
+cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
+os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8))  # NumExpr max threads
+
+
+def set_logging(rank=-1, verbose=True):
+    logging.basicConfig(
+        format="%(message)s",
+        level=logging.INFO if (verbose and rank in [-1, 0]) else logging.WARN)
+
+
+def init_seeds(seed=0):
+    # Initialize random number generator (RNG) seeds
+    random.seed(seed)
+    np.random.seed(seed)
+    init_torch_seeds(seed)
+
+
+def get_latest_run(search_dir='.'):
+    # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
+    last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
+    return max(last_list, key=os.path.getctime) if last_list else ''
+
+
+def is_docker():
+    # Is environment a Docker container
+    return Path('/workspace').exists()  # or Path('/.dockerenv').exists()
+
+
+def is_colab():
+    # Is environment a Google Colab instance
+    try:
+        import google.colab
+        return True
+    except Exception as e:
+        return False
+
+
+def emojis(str=''):
+    # Return platform-dependent emoji-safe version of string
+    return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
+
+
+def file_size(file):
+    # Return file size in MB
+    return Path(file).stat().st_size / 1e6
+
+
+def check_online():
+    # Check internet connectivity
+    import socket
+    try:
+        socket.create_connection(("1.1.1.1", 443), 5)  # check host accesability
+        return True
+    except OSError:
+        return False
+
+
+def check_git_status():
+    # Recommend 'git pull' if code is out of date
+    print(colorstr('github: '), end='')
+    try:
+        assert Path('.git').exists(), 'skipping check (not a git repository)'
+        assert not is_docker(), 'skipping check (Docker image)'
+        assert check_online(), 'skipping check (offline)'
+
+        cmd = 'git fetch && git config --get remote.origin.url'
+        url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git')  # github repo url
+        branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip()  # checked out
+        n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True))  # commits behind
+        if n > 0:
+            s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \
+                f"Use 'git pull' to update or 'git clone {url}' to download latest."
+        else:
+            s = f'up to date with {url} ✅'
+        print(emojis(s))  # emoji-safe
+    except Exception as e:
+        print(e)
+
+
+def check_python(minimum='3.7.0', required=True):
+    # Check current python version vs. required python version
+    current = platform.python_version()
+    result = pkg.parse_version(current) >= pkg.parse_version(minimum)
+    if required:
+        assert result, f'Python {minimum} required by YOLOv5, but Python {current} is currently installed'
+    return result
+
+
+def check_requirements(requirements='requirements.txt', exclude=()):
+    # Check installed dependencies meet requirements (pass *.txt file or list of packages)
+    prefix = colorstr('red', 'bold', 'requirements:')
+    check_python()  # check python version
+    if isinstance(requirements, (str, Path)):  # requirements.txt file
+        file = Path(requirements)
+        if not file.exists():
+            print(f"{prefix} {file.resolve()} not found, check failed.")
+            return
+        requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude]
+    else:  # list or tuple of packages
+        requirements = [x for x in requirements if x not in exclude]
+
+    n = 0  # number of packages updates
+    for r in requirements:
+        try:
+            pkg.require(r)
+        except Exception as e:  # DistributionNotFound or VersionConflict if requirements not met
+            n += 1
+            print(f"{prefix} {r} not found and is required by YOLOv5, attempting auto-update...")
+            try:
+                print(subprocess.check_output(f"pip install '{r}'", shell=True).decode())
+            except Exception as e:
+                print(f'{prefix} {e}')
+
+    if n:  # if packages updated
+        source = file.resolve() if 'file' in locals() else requirements
+        s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
+            f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
+        print(emojis(s))  # emoji-safe
+
+
+def check_img_size(img_size, s=32):
+    # Verify img_size is a multiple of stride s
+    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
+    if new_size != img_size:
+        print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
+    return new_size
+
+
+def check_imshow():
+    # Check if environment supports image displays
+    try:
+        assert not is_docker(), 'cv2.imshow() is disabled in Docker environments'
+        assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments'
+        cv2.imshow('test', np.zeros((1, 1, 3)))
+        cv2.waitKey(1)
+        cv2.destroyAllWindows()
+        cv2.waitKey(1)
+        return True
+    except Exception as e:
+        print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
+        return False
+
+
+def check_file(file):
+    # Search for file if not found
+    if Path(file).is_file() or file == '':
+        return file
+    else:
+        files = glob.glob('./**/' + file, recursive=True)  # find file
+        assert len(files), f'File Not Found: {file}'  # assert file was found
+        assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}"  # assert unique
+        return files[0]  # return file
+
+
+def check_dataset(dict):
+    # Download dataset if not found locally
+    val, s = dict.get('val'), dict.get('download')
+    if val and len(val):
+        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
+        if not all(x.exists() for x in val):
+            print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
+            if s and len(s):  # download script
+                if s.startswith('http') and s.endswith('.zip'):  # URL
+                    f = Path(s).name  # filename
+                    print(f'Downloading {s} ...')
+                    torch.hub.download_url_to_file(s, f)
+                    r = os.system(f'unzip -q {f} -d ../ && rm {f}')  # unzip
+                elif s.startswith('bash '):  # bash script
+                    print(f'Running {s} ...')
+                    r = os.system(s)
+                else:  # python script
+                    r = exec(s)  # return None
+                print('Dataset autodownload %s\n' % ('success' if r in (0, None) else 'failure'))  # print result
+            else:
+                raise Exception('Dataset not found.')
+
+
+def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
+    # Multi-threaded file download and unzip function
+    def download_one(url, dir):
+        # Download 1 file
+        f = dir / Path(url).name  # filename
+        if not f.exists():
+            print(f'Downloading {url} to {f}...')
+            if curl:
+                os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -")  # curl download, retry and resume on fail
+            else:
+                torch.hub.download_url_to_file(url, f, progress=True)  # torch download
+        if unzip and f.suffix in ('.zip', '.gz'):
+            print(f'Unzipping {f}...')
+            if f.suffix == '.zip':
+                s = f'unzip -qo {f} -d {dir} && rm {f}'  # unzip -quiet -overwrite
+            elif f.suffix == '.gz':
+                s = f'tar xfz {f} --directory {f.parent}'  # unzip
+            if delete:  # delete zip file after unzip
+                s += f' && rm {f}'
+            os.system(s)
+
+    dir = Path(dir)
+    dir.mkdir(parents=True, exist_ok=True)  # make directory
+    if threads > 1:
+        pool = ThreadPool(threads)
+        pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multi-threaded
+        pool.close()
+        pool.join()
+    else:
+        for u in tuple(url) if isinstance(url, str) else url:
+            download_one(u, dir)
+
+
+def make_divisible(x, divisor):
+    # Returns x evenly divisible by divisor
+    return math.ceil(x / divisor) * divisor
+
+
+def clean_str(s):
+    # Cleans a string by replacing special characters with underscore _
+    return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
+
+
+def one_cycle(y1=0.0, y2=1.0, steps=100):
+    # lambda function for sinusoidal ramp from y1 to y2
+    return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
+
+
+def colorstr(*input):
+    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
+    *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string
+    colors = {'black': '\033[30m',  # basic colors
+              'red': '\033[31m',
+              'green': '\033[32m',
+              'yellow': '\033[33m',
+              'blue': '\033[34m',
+              'magenta': '\033[35m',
+              'cyan': '\033[36m',
+              'white': '\033[37m',
+              'bright_black': '\033[90m',  # bright colors
+              'bright_red': '\033[91m',
+              'bright_green': '\033[92m',
+              'bright_yellow': '\033[93m',
+              'bright_blue': '\033[94m',
+              'bright_magenta': '\033[95m',
+              'bright_cyan': '\033[96m',
+              'bright_white': '\033[97m',
+              'end': '\033[0m',  # misc
+              'bold': '\033[1m',
+              'underline': '\033[4m'}
+    return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
+
+
+def labels_to_class_weights(labels, nc=80):
+    # Get class weights (inverse frequency) from training labels
+    if labels[0] is None:  # no labels loaded
+        return torch.Tensor()
+
+    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
+    classes = labels[:, 0].astype(np.int)  # labels = [class xywh]
+    weights = np.bincount(classes, minlength=nc)  # occurrences per class
+
+    # Prepend gridpoint count (for uCE training)
+    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
+    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start
+
+    weights[weights == 0] = 1  # replace empty bins with 1
+    weights = 1 / weights  # number of targets per class
+    weights /= weights.sum()  # normalize
+    return torch.from_numpy(weights)
+
+
+def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
+    # Produces image weights based on class_weights and image contents
+    class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
+    image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
+    # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
+    return image_weights
+
+
+def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
+    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
+    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
+    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
+    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
+    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
+    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
+         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
+         64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
+    return x
+
+
+def xyxy2xywh(x):
+    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
+    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
+    y[:, 2] = x[:, 2] - x[:, 0]  # width
+    y[:, 3] = x[:, 3] - x[:, 1]  # height
+    return y
+
+
+def xywh2xyxy(x):
+    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
+    # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw  # top left x
+    y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh  # top left y
+    y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw  # bottom right x
+    y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh  # bottom right y
+    return y
+
+
+def xyn2xy(x, w=640, h=640, padw=0, padh=0):
+    # Convert normalized segments into pixel segments, shape (n,2)
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = w * x[:, 0] + padw  # top left x
+    y[:, 1] = h * x[:, 1] + padh  # top left y
+    return y
+
+
+def segment2box(segment, width=640, height=640):
+    # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
+    x, y = segment.T  # segment xy
+    inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
+    x, y, = x[inside], y[inside]
+    return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4))  # xyxy
+
+
+def segments2boxes(segments):
+    # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
+    boxes = []
+    for s in segments:
+        x, y = s.T  # segment xy
+        boxes.append([x.min(), y.min(), x.max(), y.max()])  # cls, xyxy
+    return xyxy2xywh(np.array(boxes))  # cls, xywh
+
+
+def resample_segments(segments, n=1000):
+    # Up-sample an (n,2) segment
+    for i, s in enumerate(segments):
+        x = np.linspace(0, len(s) - 1, n)
+        xp = np.arange(len(s))
+        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T  # segment xy
+    return segments
+
+
+def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
+    # Rescale coords (xyxy) from img1_shape to img0_shape
+    if ratio_pad is None:  # calculate from img0_shape
+        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
+        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
+    else:
+        gain = ratio_pad[0][0]
+        pad = ratio_pad[1]
+
+    coords[:, [0, 2]] -= pad[0]  # x padding
+    coords[:, [1, 3]] -= pad[1]  # y padding
+    coords[:, :4] /= gain
+    clip_coords(coords, img0_shape)
+    return coords
+
+
+def clip_coords(boxes, img_shape):
+    # Clip bounding xyxy bounding boxes to image shape (height, width)
+    boxes[:, 0].clamp_(0, img_shape[1])  # x1
+    boxes[:, 1].clamp_(0, img_shape[0])  # y1
+    boxes[:, 2].clamp_(0, img_shape[1])  # x2
+    boxes[:, 3].clamp_(0, img_shape[0])  # y2
+
+
+def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
+    # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
+    box2 = box2.T
+
+    # Get the coordinates of bounding boxes
+    if x1y1x2y2:  # x1, y1, x2, y2 = box1
+        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
+        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
+    else:  # transform from xywh to xyxy
+        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
+        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
+        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
+        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
+
+    # Intersection area
+    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
+            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
+
+    # Union Area
+    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
+    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
+    union = w1 * h1 + w2 * h2 - inter + eps
+
+    iou = inter / union
+    if GIoU or DIoU or CIoU:
+        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
+        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
+        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
+            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
+            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
+                    (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center distance squared
+            if DIoU:
+                return iou - rho2 / c2  # DIoU
+            elif CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
+                with torch.no_grad():
+                    alpha = v / (v - iou + (1 + eps))
+                return iou - (rho2 / c2 + v * alpha)  # CIoU
+        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
+            c_area = cw * ch + eps  # convex area
+            return iou - (c_area - union) / c_area  # GIoU
+    else:
+        return iou  # IoU
+
+
+def box_iou(box1, box2):
+    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+    """
+    Return intersection-over-union (Jaccard index) of boxes.
+    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+    Arguments:
+        box1 (Tensor[N, 4])
+        box2 (Tensor[M, 4])
+    Returns:
+        iou (Tensor[N, M]): the NxM matrix containing the pairwise
+            IoU values for every element in boxes1 and boxes2
+    """
+
+    def box_area(box):
+        # box = 4xn
+        return (box[2] - box[0]) * (box[3] - box[1])
+
+    area1 = box_area(box1.T)
+    area2 = box_area(box2.T)
+
+    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
+    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)
+
+
+def wh_iou(wh1, wh2):
+    # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
+    wh1 = wh1[:, None]  # [N,1,2]
+    wh2 = wh2[None]  # [1,M,2]
+    inter = torch.min(wh1, wh2).prod(2)  # [N,M]
+    return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)
+
+
+def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
+                        labels=(), max_det=300):
+    """Runs Non-Maximum Suppression (NMS) on inference results
+
+    Returns:
+         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
+    """
+
+    nc = prediction.shape[2] - 5  # number of classes
+    xc = prediction[..., 4] > conf_thres  # candidates
+
+    # Checks
+    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
+    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
+
+    # Settings
+    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
+    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
+    time_limit = 10.0  # seconds to quit after
+    redundant = True  # require redundant detections
+    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
+    merge = False  # use merge-NMS
+
+    t = time.time()
+    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
+    for xi, x in enumerate(prediction):  # image index, image inference
+        # Apply constraints
+        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
+        x = x[xc[xi]]  # confidence
+
+        # Cat apriori labels if autolabelling
+        if labels and len(labels[xi]):
+            l = labels[xi]
+            v = torch.zeros((len(l), nc + 5), device=x.device)
+            v[:, :4] = l[:, 1:5]  # box
+            v[:, 4] = 1.0  # conf
+            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
+            x = torch.cat((x, v), 0)
+
+        # If none remain process next image
+        if not x.shape[0]:
+            continue
+
+        # Compute conf
+        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
+
+        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+        box = xywh2xyxy(x[:, :4])
+
+        # Detections matrix nx6 (xyxy, conf, cls)
+        if multi_label:
+            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
+            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
+        else:  # best class only
+            conf, j = x[:, 5:].max(1, keepdim=True)
+            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
+
+        # Filter by class
+        if classes is not None:
+            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+        # Apply finite constraint
+        # if not torch.isfinite(x).all():
+        #     x = x[torch.isfinite(x).all(1)]
+
+        # Check shape
+        n = x.shape[0]  # number of boxes
+        if not n:  # no boxes
+            continue
+        elif n > max_nms:  # excess boxes
+            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
+
+        # Batched NMS
+        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
+        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
+        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
+        if i.shape[0] > max_det:  # limit detections
+            i = i[:max_det]
+        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
+            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
+            weights = iou * scores[None]  # box weights
+            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
+            if redundant:
+                i = i[iou.sum(1) > 1]  # require redundancy
+
+        output[xi] = x[i]
+        if (time.time() - t) > time_limit:
+            print(f'WARNING: NMS time limit {time_limit}s exceeded')
+            break  # time limit exceeded
+
+    return output
+
+
+def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_optimizer()
+    # Strip optimizer from 'f' to finalize training, optionally save as 's'
+    x = torch.load(f, map_location=torch.device('cpu'))
+    if x.get('ema'):
+        x['model'] = x['ema']  # replace model with ema
+    for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates':  # keys
+        x[k] = None
+    x['epoch'] = -1
+    x['model'].half()  # to FP16
+    for p in x['model'].parameters():
+        p.requires_grad = False
+    torch.save(x, s or f)
+    mb = os.path.getsize(s or f) / 1E6  # filesize
+    print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
+
+
+def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
+    # Print mutation results to evolve.txt (for use with train.py --evolve)
+    a = '%10s' * len(hyp) % tuple(hyp.keys())  # hyperparam keys
+    b = '%10.3g' * len(hyp) % tuple(hyp.values())  # hyperparam values
+    c = '%10.4g' * len(results) % results  # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
+    print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
+
+    if bucket:
+        url = 'gs://%s/evolve.txt' % bucket
+        if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0):
+            os.system('gsutil cp %s .' % url)  # download evolve.txt if larger than local
+
+    with open('evolve.txt', 'a') as f:  # append result
+        f.write(c + b + '\n')
+    x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0)  # load unique rows
+    x = x[np.argsort(-fitness(x))]  # sort
+    np.savetxt('evolve.txt', x, '%10.3g')  # save sort by fitness
+
+    # Save yaml
+    for i, k in enumerate(hyp.keys()):
+        hyp[k] = float(x[0, i + 7])
+    with open(yaml_file, 'w') as f:
+        results = tuple(x[0, :7])
+        c = '%10.4g' * len(results) % results  # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
+        f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
+        yaml.safe_dump(hyp, f, sort_keys=False)
+
+    if bucket:
+        os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket))  # upload
+
+
+def apply_classifier(x, model, img, im0):
+    # Apply a second stage classifier to yolo outputs
+    im0 = [im0] if isinstance(im0, np.ndarray) else im0
+    for i, d in enumerate(x):  # per image
+        if d is not None and len(d):
+            d = d.clone()
+
+            # Reshape and pad cutouts
+            b = xyxy2xywh(d[:, :4])  # boxes
+            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
+            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
+            d[:, :4] = xywh2xyxy(b).long()
+
+            # Rescale boxes from img_size to im0 size
+            scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
+
+            # Classes
+            pred_cls1 = d[:, 5].long()
+            ims = []
+            for j, a in enumerate(d):  # per item
+                cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
+                im = cv2.resize(cutout, (224, 224))  # BGR
+                # cv2.imwrite('test%i.jpg' % j, cutout)
+
+                im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+                im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
+                im /= 255.0  # 0 - 255 to 0.0 - 1.0
+                ims.append(im)
+
+            pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1)  # classifier prediction
+            x[i] = x[i][pred_cls1 == pred_cls2]  # retain matching class detections
+
+    return x
+
+
+def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True):
+    # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
+    xyxy = torch.tensor(xyxy).view(-1, 4)
+    b = xyxy2xywh(xyxy)  # boxes
+    if square:
+        b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # attempt rectangle to square
+    b[:, 2:] = b[:, 2:] * gain + pad  # box wh * gain + pad
+    xyxy = xywh2xyxy(b).long()
+    clip_coords(xyxy, im.shape)
+    crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
+    if save:
+        cv2.imwrite(str(increment_path(file, mkdir=True).with_suffix('.jpg')), crop)
+    return crop
+
+
+def increment_path(path, exist_ok=False, sep='', mkdir=False):
+    # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
+    path = Path(path)  # os-agnostic
+    if path.exists() and not exist_ok:
+        suffix = path.suffix
+        path = path.with_suffix('')
+        dirs = glob.glob(f"{path}{sep}*")  # similar paths
+        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
+        i = [int(m.groups()[0]) for m in matches if m]  # indices
+        n = max(i) + 1 if i else 2  # increment number
+        path = Path(f"{path}{sep}{n}{suffix}")  # update path
+    dir = path if path.suffix == '' else path.parent  # directory
+    if not dir.exists() and mkdir:
+        dir.mkdir(parents=True, exist_ok=True)  # make directory
+    return path

test/test-yolov5-deepsort/utils/google_utils.py

@@ -0,0 +1,127 @@
+# Google utils: https://cloud.google.com/storage/docs/reference/libraries
+
+import os
+import platform
+import subprocess
+import time
+from pathlib import Path
+
+import requests
+import torch
+
+
+def gsutil_getsize(url=''):
+    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
+    s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
+    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
+
+
+def attempt_download(file, repo='ultralytics/yolov5'):
+    # Attempt file download if does not exist
+    file = Path(str(file).strip().replace("'", ''))
+
+    if not file.exists():
+        file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)
+        try:
+            response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json()  # github api
+            assets = [x['name'] for x in response['assets']]  # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...]
+            tag = response['tag_name']  # i.e. 'v1.0'
+        except:  # fallback plan
+            assets = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt',
+                      'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt']
+            try:
+                tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
+            except:
+                tag = 'v5.0'  # current release
+
+        name = file.name
+        if name in assets:
+            msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/'
+            redundant = False  # second download option
+            try:  # GitHub
+                url = f'https://github.com/{repo}/releases/download/{tag}/{name}'
+                print(f'Downloading {url} to {file}...')
+                torch.hub.download_url_to_file(url, file)
+                assert file.exists() and file.stat().st_size > 1E6  # check
+            except Exception as e:  # GCP
+                print(f'Download error: {e}')
+                assert redundant, 'No secondary mirror'
+                url = f'https://storage.googleapis.com/{repo}/ckpt/{name}'
+                print(f'Downloading {url} to {file}...')
+                os.system(f"curl -L '{url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
+            finally:
+                if not file.exists() or file.stat().st_size < 1E6:  # check
+                    file.unlink(missing_ok=True)  # remove partial downloads
+                    print(f'ERROR: Download failure: {msg}')
+                print('')
+                return
+
+
+def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'):
+    # Downloads a file from Google Drive. from yolov5.utils.google_utils import *; gdrive_download()
+    t = time.time()
+    file = Path(file)
+    cookie = Path('cookie')  # gdrive cookie
+    print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='')
+    file.unlink(missing_ok=True)  # remove existing file
+    cookie.unlink(missing_ok=True)  # remove existing cookie
+
+    # Attempt file download
+    out = "NUL" if platform.system() == "Windows" else "/dev/null"
+    os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}')
+    if os.path.exists('cookie'):  # large file
+        s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}'
+    else:  # small file
+        s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"'
+    r = os.system(s)  # execute, capture return
+    cookie.unlink(missing_ok=True)  # remove existing cookie
+
+    # Error check
+    if r != 0:
+        file.unlink(missing_ok=True)  # remove partial
+        print('Download error ')  # raise Exception('Download error')
+        return r
+
+    # Unzip if archive
+    if file.suffix == '.zip':
+        print('unzipping... ', end='')
+        os.system(f'unzip -q {file}')  # unzip
+        file.unlink()  # remove zip to free space
+
+    print(f'Done ({time.time() - t:.1f}s)')
+    return r
+
+
+def get_token(cookie="./cookie"):
+    with open(cookie) as f:
+        for line in f:
+            if "download" in line:
+                return line.split()[-1]
+    return ""
+
+# def upload_blob(bucket_name, source_file_name, destination_blob_name):
+#     # Uploads a file to a bucket
+#     # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
+#
+#     storage_client = storage.Client()
+#     bucket = storage_client.get_bucket(bucket_name)
+#     blob = bucket.blob(destination_blob_name)
+#
+#     blob.upload_from_filename(source_file_name)
+#
+#     print('File {} uploaded to {}.'.format(
+#         source_file_name,
+#         destination_blob_name))
+#
+#
+# def download_blob(bucket_name, source_blob_name, destination_file_name):
+#     # Uploads a blob from a bucket
+#     storage_client = storage.Client()
+#     bucket = storage_client.get_bucket(bucket_name)
+#     blob = bucket.blob(source_blob_name)
+#
+#     blob.download_to_filename(destination_file_name)
+#
+#     print('Blob {} downloaded to {}.'.format(
+#         source_blob_name,
+#         destination_file_name))

test/test-yolov5-deepsort/utils/loss.py

@@ -0,0 +1,216 @@
+# Loss functions
+
+import torch
+import torch.nn as nn
+
+from utils.general import bbox_iou
+from utils.torch_utils import is_parallel
+
+
+def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
+    # return positive, negative label smoothing BCE targets
+    return 1.0 - 0.5 * eps, 0.5 * eps
+
+
+class BCEBlurWithLogitsLoss(nn.Module):
+    # BCEwithLogitLoss() with reduced missing label effects.
+    def __init__(self, alpha=0.05):
+        super(BCEBlurWithLogitsLoss, self).__init__()
+        self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')  # must be nn.BCEWithLogitsLoss()
+        self.alpha = alpha
+
+    def forward(self, pred, true):
+        loss = self.loss_fcn(pred, true)
+        pred = torch.sigmoid(pred)  # prob from logits
+        dx = pred - true  # reduce only missing label effects
+        # dx = (pred - true).abs()  # reduce missing label and false label effects
+        alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
+        loss *= alpha_factor
+        return loss.mean()
+
+
+class FocalLoss(nn.Module):
+    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
+        super(FocalLoss, self).__init__()
+        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
+        self.gamma = gamma
+        self.alpha = alpha
+        self.reduction = loss_fcn.reduction
+        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
+
+    def forward(self, pred, true):
+        loss = self.loss_fcn(pred, true)
+        # p_t = torch.exp(-loss)
+        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
+
+        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
+        pred_prob = torch.sigmoid(pred)  # prob from logits
+        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
+        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
+        modulating_factor = (1.0 - p_t) ** self.gamma
+        loss *= alpha_factor * modulating_factor
+
+        if self.reduction == 'mean':
+            return loss.mean()
+        elif self.reduction == 'sum':
+            return loss.sum()
+        else:  # 'none'
+            return loss
+
+
+class QFocalLoss(nn.Module):
+    # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
+        super(QFocalLoss, self).__init__()
+        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
+        self.gamma = gamma
+        self.alpha = alpha
+        self.reduction = loss_fcn.reduction
+        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
+
+    def forward(self, pred, true):
+        loss = self.loss_fcn(pred, true)
+
+        pred_prob = torch.sigmoid(pred)  # prob from logits
+        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
+        modulating_factor = torch.abs(true - pred_prob) ** self.gamma
+        loss *= alpha_factor * modulating_factor
+
+        if self.reduction == 'mean':
+            return loss.mean()
+        elif self.reduction == 'sum':
+            return loss.sum()
+        else:  # 'none'
+            return loss
+
+
+class ComputeLoss:
+    # Compute losses
+    def __init__(self, model, autobalance=False):
+        super(ComputeLoss, self).__init__()
+        device = next(model.parameters()).device  # get model device
+        h = model.hyp  # hyperparameters
+
+        # Define criteria
+        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
+        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
+
+        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
+        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
+
+        # Focal loss
+        g = h['fl_gamma']  # focal loss gamma
+        if g > 0:
+            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
+
+        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
+        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
+        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
+        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
+        for k in 'na', 'nc', 'nl', 'anchors':
+            setattr(self, k, getattr(det, k))
+
+    def __call__(self, p, targets):  # predictions, targets, model
+        device = targets.device
+        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
+        tcls, tbox, indices, anchors = self.build_targets(p, targets)  # targets
+
+        # Losses
+        for i, pi in enumerate(p):  # layer index, layer predictions
+            b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
+            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
+
+            n = b.shape[0]  # number of targets
+            if n:
+                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
+
+                # Regression
+                pxy = ps[:, :2].sigmoid() * 2. - 0.5
+                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
+                pbox = torch.cat((pxy, pwh), 1)  # predicted box
+                iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
+                lbox += (1.0 - iou).mean()  # iou loss
+
+                # Objectness
+                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
+
+                # Classification
+                if self.nc > 1:  # cls loss (only if multiple classes)
+                    t = torch.full_like(ps[:, 5:], self.cn, device=device)  # targets
+                    t[range(n), tcls[i]] = self.cp
+                    lcls += self.BCEcls(ps[:, 5:], t)  # BCE
+
+                # Append targets to text file
+                # with open('targets.txt', 'a') as file:
+                #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
+
+            obji = self.BCEobj(pi[..., 4], tobj)
+            lobj += obji * self.balance[i]  # obj loss
+            if self.autobalance:
+                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
+
+        if self.autobalance:
+            self.balance = [x / self.balance[self.ssi] for x in self.balance]
+        lbox *= self.hyp['box']
+        lobj *= self.hyp['obj']
+        lcls *= self.hyp['cls']
+        bs = tobj.shape[0]  # batch size
+
+        loss = lbox + lobj + lcls
+        return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
+
+    def build_targets(self, p, targets):
+        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
+        na, nt = self.na, targets.shape[0]  # number of anchors, targets
+        tcls, tbox, indices, anch = [], [], [], []
+        gain = torch.ones(7, device=targets.device)  # normalized to gridspace gain
+        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
+        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices
+
+        g = 0.5  # bias
+        off = torch.tensor([[0, 0],
+                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
+                            # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
+                            ], device=targets.device).float() * g  # offsets
+
+        for i in range(self.nl):
+            anchors = self.anchors[i]
+            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
+
+            # Match targets to anchors
+            t = targets * gain
+            if nt:
+                # Matches
+                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
+                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
+                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
+                t = t[j]  # filter
+
+                # Offsets
+                gxy = t[:, 2:4]  # grid xy
+                gxi = gain[[2, 3]] - gxy  # inverse
+                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
+                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
+                j = torch.stack((torch.ones_like(j), j, k, l, m))
+                t = t.repeat((5, 1, 1))[j]
+                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
+            else:
+                t = targets[0]
+                offsets = 0
+
+            # Define
+            b, c = t[:, :2].long().T  # image, class
+            gxy = t[:, 2:4]  # grid xy
+            gwh = t[:, 4:6]  # grid wh
+            gij = (gxy - offsets).long()
+            gi, gj = gij.T  # grid xy indices
+
+            # Append
+            a = t[:, 6].long()  # anchor indices
+            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
+            tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
+            anch.append(anchors[a])  # anchors
+            tcls.append(c)  # class
+
+        return tcls, tbox, indices, anch

test/test-yolov5-deepsort/utils/metrics.py

@@ -0,0 +1,223 @@
+# Model validation metrics
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from . import general
+
+
+def fitness(x):
+    # Model fitness as a weighted combination of metrics
+    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+    return (x[:, :4] * w).sum(1)
+
+
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+    # Arguments
+        tp:  True positives (nparray, nx1 or nx10).
+        conf:  Objectness value from 0-1 (nparray).
+        pred_cls:  Predicted object classes (nparray).
+        target_cls:  True object classes (nparray).
+        plot:  Plot precision-recall curve at mAP@0.5
+        save_dir:  Plot save directory
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Sort by objectness
+    i = np.argsort(-conf)
+    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+    # Find unique classes
+    unique_classes = np.unique(target_cls)
+    nc = unique_classes.shape[0]  # number of classes, number of detections
+
+    # Create Precision-Recall curve and compute AP for each class
+    px, py = np.linspace(0, 1, 1000), []  # for plotting
+    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
+    for ci, c in enumerate(unique_classes):
+        i = pred_cls == c
+        n_l = (target_cls == c).sum()  # number of labels
+        n_p = i.sum()  # number of predictions
+
+        if n_p == 0 or n_l == 0:
+            continue
+        else:
+            # Accumulate FPs and TPs
+            fpc = (1 - tp[i]).cumsum(0)
+            tpc = tp[i].cumsum(0)
+
+            # Recall
+            recall = tpc / (n_l + 1e-16)  # recall curve
+            r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
+
+            # Precision
+            precision = tpc / (tpc + fpc)  # precision curve
+            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
+
+            # AP from recall-precision curve
+            for j in range(tp.shape[1]):
+                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+                if plot and j == 0:
+                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
+
+    # Compute F1 (harmonic mean of precision and recall)
+    f1 = 2 * p * r / (p + r + 1e-16)
+    if plot:
+        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
+        plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
+        plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
+        plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
+
+    i = f1.mean(0).argmax()  # max F1 index
+    return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')
+
+
+def compute_ap(recall, precision):
+    """ Compute the average precision, given the recall and precision curves
+    # Arguments
+        recall:    The recall curve (list)
+        precision: The precision curve (list)
+    # Returns
+        Average precision, precision curve, recall curve
+    """
+
+    # Append sentinel values to beginning and end
+    mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01]))
+    mpre = np.concatenate(([1.], precision, [0.]))
+
+    # Compute the precision envelope
+    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+    # Integrate area under curve
+    method = 'interp'  # methods: 'continuous', 'interp'
+    if method == 'interp':
+        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
+    else:  # 'continuous'
+        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
+
+    return ap, mpre, mrec
+
+
+class ConfusionMatrix:
+    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
+    def __init__(self, nc, conf=0.25, iou_thres=0.45):
+        self.matrix = np.zeros((nc + 1, nc + 1))
+        self.nc = nc  # number of classes
+        self.conf = conf
+        self.iou_thres = iou_thres
+
+    def process_batch(self, detections, labels):
+        """
+        Return intersection-over-union (Jaccard index) of boxes.
+        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+        Arguments:
+            detections (Array[N, 6]), x1, y1, x2, y2, conf, class
+            labels (Array[M, 5]), class, x1, y1, x2, y2
+        Returns:
+            None, updates confusion matrix accordingly
+        """
+        detections = detections[detections[:, 4] > self.conf]
+        gt_classes = labels[:, 0].int()
+        detection_classes = detections[:, 5].int()
+        iou = general.box_iou(labels[:, 1:], detections[:, :4])
+
+        x = torch.where(iou > self.iou_thres)
+        if x[0].shape[0]:
+            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
+            if x[0].shape[0] > 1:
+                matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+        else:
+            matches = np.zeros((0, 3))
+
+        n = matches.shape[0] > 0
+        m0, m1, _ = matches.transpose().astype(np.int16)
+        for i, gc in enumerate(gt_classes):
+            j = m0 == i
+            if n and sum(j) == 1:
+                self.matrix[detection_classes[m1[j]], gc] += 1  # correct
+            else:
+                self.matrix[self.nc, gc] += 1  # background FP
+
+        if n:
+            for i, dc in enumerate(detection_classes):
+                if not any(m1 == i):
+                    self.matrix[dc, self.nc] += 1  # background FN
+
+    def matrix(self):
+        return self.matrix
+
+    def plot(self, save_dir='', names=()):
+        try:
+            import seaborn as sn
+
+            array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6)  # normalize
+            array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
+
+            fig = plt.figure(figsize=(12, 9), tight_layout=True)
+            sn.set(font_scale=1.0 if self.nc < 50 else 0.8)  # for label size
+            labels = (0 < len(names) < 99) and len(names) == self.nc  # apply names to ticklabels
+            sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,
+                       xticklabels=names + ['background FP'] if labels else "auto",
+                       yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
+            fig.axes[0].set_xlabel('True')
+            fig.axes[0].set_ylabel('Predicted')
+            fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
+        except Exception as e:
+            pass
+
+    def print(self):
+        for i in range(self.nc + 1):
+            print(' '.join(map(str, self.matrix[i])))
+
+
+# Plots ----------------------------------------------------------------------------------------------------------------
+
+def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
+    # Precision-recall curve
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+    py = np.stack(py, axis=1)
+
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
+        for i, y in enumerate(py.T):
+            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)
+    else:
+        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
+
+    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
+    ax.set_xlabel('Recall')
+    ax.set_ylabel('Precision')
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    fig.savefig(Path(save_dir), dpi=250)
+
+
+def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):
+    # Metric-confidence curve
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
+        for i, y in enumerate(py):
+            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)
+    else:
+        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)
+
+    y = py.mean(0)
+    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    fig.savefig(Path(save_dir), dpi=250)

test/test-yolov5-deepsort/utils/plots.py

@@ -0,0 +1,446 @@
+# Plotting utils
+
+import glob
+import math
+import os
+import random
+from copy import copy
+from pathlib import Path
+
+import cv2
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import torch
+import yaml
+from PIL import Image, ImageDraw, ImageFont
+
+from utils.general import xywh2xyxy, xyxy2xywh
+from utils.metrics import fitness
+
+# Settings
+matplotlib.rc('font', **{'size': 11})
+matplotlib.use('Agg')  # for writing to files only
+
+
+class Colors:
+    # Ultralytics color palette https://ultralytics.com/
+    def __init__(self):
+        # hex = matplotlib.colors.TABLEAU_COLORS.values()
+        hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
+               '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
+        self.palette = [self.hex2rgb('#' + c) for c in hex]
+        self.n = len(self.palette)
+
+    def __call__(self, i, bgr=False):
+        c = self.palette[int(i) % self.n]
+        return (c[2], c[1], c[0]) if bgr else c
+
+    @staticmethod
+    def hex2rgb(h):  # rgb order (PIL)
+        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
+
+
+colors = Colors()  # create instance for 'from utils.plots import colors'
+
+
+def hist2d(x, y, n=100):
+    # 2d histogram used in labels.png and evolve.png
+    xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
+    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
+    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
+    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
+    return np.log(hist[xidx, yidx])
+
+
+def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
+    from scipy.signal import butter, filtfilt
+
+    # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
+    def butter_lowpass(cutoff, fs, order):
+        nyq = 0.5 * fs
+        normal_cutoff = cutoff / nyq
+        return butter(order, normal_cutoff, btype='low', analog=False)
+
+    b, a = butter_lowpass(cutoff, fs, order=order)
+    return filtfilt(b, a, data)  # forward-backward filter
+
+
+def plot_one_box(x, im, color=(128, 128, 128), label=None, line_thickness=3):
+    # Plots one bounding box on image 'im' using OpenCV
+    assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'
+    tl = line_thickness or round(0.002 * (im.shape[0] + im.shape[1]) / 2) + 1  # line/font thickness
+    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
+    cv2.rectangle(im, 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(im, c1, c2, color, -1, cv2.LINE_AA)  # filled
+        cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+
+
+def plot_one_box_PIL(box, im, color=(128, 128, 128), label=None, line_thickness=None):
+    # Plots one bounding box on image 'im' using PIL
+    im = Image.fromarray(im)
+    draw = ImageDraw.Draw(im)
+    line_thickness = line_thickness or max(int(min(im.size) / 200), 2)
+    draw.rectangle(box, width=line_thickness, outline=color)  # plot
+    if label:
+        font = ImageFont.truetype("Arial.ttf", size=max(round(max(im.size) / 40), 12))
+        txt_width, txt_height = font.getsize(label)
+        draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=color)
+        draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font)
+    return np.asarray(im)
+
+
+def plot_wh_methods():  # from utils.plots import *; plot_wh_methods()
+    # Compares the two methods for width-height anchor multiplication
+    # https://github.com/ultralytics/yolov3/issues/168
+    x = np.arange(-4.0, 4.0, .1)
+    ya = np.exp(x)
+    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
+
+    fig = plt.figure(figsize=(6, 3), tight_layout=True)
+    plt.plot(x, ya, '.-', label='YOLOv3')
+    plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2')
+    plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6')
+    plt.xlim(left=-4, right=4)
+    plt.ylim(bottom=0, top=6)
+    plt.xlabel('input')
+    plt.ylabel('output')
+    plt.grid()
+    plt.legend()
+    fig.savefig('comparison.png', dpi=200)
+
+
+def output_to_target(output):
+    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
+    targets = []
+    for i, o in enumerate(output):
+        for *box, conf, cls in o.cpu().numpy():
+            targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])
+    return np.array(targets)
+
+
+def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
+    # Plot image grid with labels
+
+    if isinstance(images, torch.Tensor):
+        images = images.cpu().float().numpy()
+    if isinstance(targets, torch.Tensor):
+        targets = targets.cpu().numpy()
+
+    # un-normalise
+    if np.max(images[0]) <= 1:
+        images *= 255
+
+    tl = 3  # line thickness
+    tf = max(tl - 1, 1)  # font thickness
+    bs, _, h, w = images.shape  # batch size, _, height, width
+    bs = min(bs, max_subplots)  # limit plot images
+    ns = np.ceil(bs ** 0.5)  # number of subplots (square)
+
+    # Check if we should resize
+    scale_factor = max_size / max(h, w)
+    if scale_factor < 1:
+        h = math.ceil(scale_factor * h)
+        w = math.ceil(scale_factor * w)
+
+    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init
+    for i, img in enumerate(images):
+        if i == max_subplots:  # if last batch has fewer images than we expect
+            break
+
+        block_x = int(w * (i // ns))
+        block_y = int(h * (i % ns))
+
+        img = img.transpose(1, 2, 0)
+        if scale_factor < 1:
+            img = cv2.resize(img, (w, h))
+
+        mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
+        if len(targets) > 0:
+            image_targets = targets[targets[:, 0] == i]
+            boxes = xywh2xyxy(image_targets[:, 2:6]).T
+            classes = image_targets[:, 1].astype('int')
+            labels = image_targets.shape[1] == 6  # labels if no conf column
+            conf = None if labels else image_targets[:, 6]  # check for confidence presence (label vs pred)
+
+            if boxes.shape[1]:
+                if boxes.max() <= 1.01:  # if normalized with tolerance 0.01
+                    boxes[[0, 2]] *= w  # scale to pixels
+                    boxes[[1, 3]] *= h
+                elif scale_factor < 1:  # absolute coords need scale if image scales
+                    boxes *= scale_factor
+            boxes[[0, 2]] += block_x
+            boxes[[1, 3]] += block_y
+            for j, box in enumerate(boxes.T):
+                cls = int(classes[j])
+                color = colors(cls)
+                cls = names[cls] if names else cls
+                if labels or conf[j] > 0.25:  # 0.25 conf thresh
+                    label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j])
+                    plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)
+
+        # Draw image filename labels
+        if paths:
+            label = Path(paths[i]).name[:40]  # trim to 40 char
+            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
+            cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
+                        lineType=cv2.LINE_AA)
+
+        # Image border
+        cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3)
+
+    if fname:
+        r = min(1280. / max(h, w) / ns, 1.0)  # ratio to limit image size
+        mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)
+        # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))  # cv2 save
+        Image.fromarray(mosaic).save(fname)  # PIL save
+    return mosaic
+
+
+def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
+    # Plot LR simulating training for full epochs
+    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals
+    y = []
+    for _ in range(epochs):
+        scheduler.step()
+        y.append(optimizer.param_groups[0]['lr'])
+    plt.plot(y, '.-', label='LR')
+    plt.xlabel('epoch')
+    plt.ylabel('LR')
+    plt.grid()
+    plt.xlim(0, epochs)
+    plt.ylim(0)
+    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
+    plt.close()
+
+
+def plot_test_txt():  # from utils.plots import *; plot_test()
+    # Plot test.txt histograms
+    x = np.loadtxt('test.txt', dtype=np.float32)
+    box = xyxy2xywh(x[:, :4])
+    cx, cy = box[:, 0], box[:, 1]
+
+    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
+    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
+    ax.set_aspect('equal')
+    plt.savefig('hist2d.png', dpi=300)
+
+    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
+    ax[0].hist(cx, bins=600)
+    ax[1].hist(cy, bins=600)
+    plt.savefig('hist1d.png', dpi=200)
+
+
+def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
+    # Plot targets.txt histograms
+    x = np.loadtxt('targets.txt', dtype=np.float32).T
+    s = ['x targets', 'y targets', 'width targets', 'height targets']
+    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
+    ax = ax.ravel()
+    for i in range(4):
+        ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
+        ax[i].legend()
+        ax[i].set_title(s[i])
+    plt.savefig('targets.jpg', dpi=200)
+
+
+def plot_study_txt(path='', x=None):  # from utils.plots import *; plot_study_txt()
+    # Plot study.txt generated by test.py
+    fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
+    # ax = ax.ravel()
+
+    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
+    # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
+    for f in sorted(Path(path).glob('study*.txt')):
+        y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
+        x = np.arange(y.shape[1]) if x is None else np.array(x)
+        s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
+        # for i in range(7):
+        #     ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
+        #     ax[i].set_title(s[i])
+
+        j = y[3].argmax() + 1
+        ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,
+                 label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
+
+    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
+             'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
+
+    ax2.grid(alpha=0.2)
+    ax2.set_yticks(np.arange(20, 60, 5))
+    ax2.set_xlim(0, 57)
+    ax2.set_ylim(30, 55)
+    ax2.set_xlabel('GPU Speed (ms/img)')
+    ax2.set_ylabel('COCO AP val')
+    ax2.legend(loc='lower right')
+    plt.savefig(str(Path(path).name) + '.png', dpi=300)
+
+
+def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
+    # plot dataset labels
+    print('Plotting labels... ')
+    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
+    nc = int(c.max() + 1)  # number of classes
+    x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
+
+    # seaborn correlogram
+    sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
+    plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
+    plt.close()
+
+    # matplotlib labels
+    matplotlib.use('svg')  # faster
+    ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
+    y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
+    # [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # update colors bug #3195 
+    ax[0].set_ylabel('instances')
+    if 0 < len(names) < 30:
+        ax[0].set_xticks(range(len(names)))
+        ax[0].set_xticklabels(names, rotation=90, fontsize=10)
+    else:
+        ax[0].set_xlabel('classes')
+    sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
+    sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
+
+    # rectangles
+    labels[:, 1:3] = 0.5  # center
+    labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
+    img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
+    for cls, *box in labels[:1000]:
+        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls))  # plot
+    ax[1].imshow(img)
+    ax[1].axis('off')
+
+    for a in [0, 1, 2, 3]:
+        for s in ['top', 'right', 'left', 'bottom']:
+            ax[a].spines[s].set_visible(False)
+
+    plt.savefig(save_dir / 'labels.jpg', dpi=200)
+    matplotlib.use('Agg')
+    plt.close()
+
+    # loggers
+    for k, v in loggers.items() or {}:
+        if k == 'wandb' and v:
+            v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False)
+
+
+def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.plots import *; plot_evolution()
+    # Plot hyperparameter evolution results in evolve.txt
+    with open(yaml_file) as f:
+        hyp = yaml.safe_load(f)
+    x = np.loadtxt('evolve.txt', ndmin=2)
+    f = fitness(x)
+    # weights = (f - f.min()) ** 2  # for weighted results
+    plt.figure(figsize=(10, 12), tight_layout=True)
+    matplotlib.rc('font', **{'size': 8})
+    for i, (k, v) in enumerate(hyp.items()):
+        y = x[:, i + 7]
+        # mu = (y * weights).sum() / weights.sum()  # best weighted result
+        mu = y[f.argmax()]  # best single result
+        plt.subplot(6, 5, i + 1)
+        plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
+        plt.plot(mu, f.max(), 'k+', markersize=15)
+        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
+        if i % 5 != 0:
+            plt.yticks([])
+        print('%15s: %.3g' % (k, mu))
+    plt.savefig('evolve.png', dpi=200)
+    print('\nPlot saved as evolve.png')
+
+
+def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
+    # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
+    ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
+    s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
+    files = list(Path(save_dir).glob('frames*.txt'))
+    for fi, f in enumerate(files):
+        try:
+            results = np.loadtxt(f, ndmin=2).T[:, 90:-30]  # clip first and last rows
+            n = results.shape[1]  # number of rows
+            x = np.arange(start, min(stop, n) if stop else n)
+            results = results[:, x]
+            t = (results[0] - results[0].min())  # set t0=0s
+            results[0] = x
+            for i, a in enumerate(ax):
+                if i < len(results):
+                    label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
+                    a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
+                    a.set_title(s[i])
+                    a.set_xlabel('time (s)')
+                    # if fi == len(files) - 1:
+                    #     a.set_ylim(bottom=0)
+                    for side in ['top', 'right']:
+                        a.spines[side].set_visible(False)
+                else:
+                    a.remove()
+        except Exception as e:
+            print('Warning: Plotting error for %s; %s' % (f, e))
+
+    ax[1].legend()
+    plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
+
+
+def plot_results_overlay(start=0, stop=0):  # from utils.plots import *; plot_results_overlay()
+    # Plot training 'results*.txt', overlaying train and val losses
+    s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
+    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
+    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
+        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
+        n = results.shape[1]  # number of rows
+        x = range(start, min(stop, n) if stop else n)
+        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
+        ax = ax.ravel()
+        for i in range(5):
+            for j in [i, i + 5]:
+                y = results[j, x]
+                ax[i].plot(x, y, marker='.', label=s[j])
+                # y_smooth = butter_lowpass_filtfilt(y)
+                # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
+
+            ax[i].set_title(t[i])
+            ax[i].legend()
+            ax[i].set_ylabel(f) if i == 0 else None  # add filename
+        fig.savefig(f.replace('.txt', '.png'), dpi=200)
+
+
+def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
+    # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp')
+    fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
+    ax = ax.ravel()
+    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
+         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
+    if bucket:
+        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
+        files = ['results%g.txt' % x for x in id]
+        c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)
+        os.system(c)
+    else:
+        files = list(Path(save_dir).glob('results*.txt'))
+    assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
+    for fi, f in enumerate(files):
+        try:
+            results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
+            n = results.shape[1]  # number of rows
+            x = range(start, min(stop, n) if stop else n)
+            for i in range(10):
+                y = results[i, x]
+                if i in [0, 1, 2, 5, 6, 7]:
+                    y[y == 0] = np.nan  # don't show zero loss values
+                    # y /= y[0]  # normalize
+                label = labels[fi] if len(labels) else f.stem
+                ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8)
+                ax[i].set_title(s[i])
+                # if i in [5, 6, 7]:  # share train and val loss y axes
+                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
+        except Exception as e:
+            print('Warning: Plotting error for %s; %s' % (f, e))
+
+    ax[1].legend()
+    fig.savefig(Path(save_dir) / 'results.png', dpi=200)

test/test-yolov5-deepsort/utils/torch_utils.py

@@ -0,0 +1,304 @@
+# YOLOv5 PyTorch utils
+
+import datetime
+import logging
+import math
+import os
+import platform
+import subprocess
+import time
+from contextlib import contextmanager
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+import torch.backends.cudnn as cudnn
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+try:
+    import thop  # for FLOPS computation
+except ImportError:
+    thop = None
+logger = logging.getLogger(__name__)
+
+
+@contextmanager
+def torch_distributed_zero_first(local_rank: int):
+    """
+    Decorator to make all processes in distributed training wait for each local_master to do something.
+    """
+    if local_rank not in [-1, 0]:
+        torch.distributed.barrier()
+    yield
+    if local_rank == 0:
+        torch.distributed.barrier()
+
+
+def init_torch_seeds(seed=0):
+    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
+    torch.manual_seed(seed)
+    if seed == 0:  # slower, more reproducible
+        cudnn.benchmark, cudnn.deterministic = False, True
+    else:  # faster, less reproducible
+        cudnn.benchmark, cudnn.deterministic = True, False
+
+
+def date_modified(path=__file__):
+    # return human-readable file modification date, i.e. '2021-3-26'
+    t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)
+    return f'{t.year}-{t.month}-{t.day}'
+
+
+def git_describe(path=Path(__file__).parent):  # path must be a directory
+    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
+    s = f'git -C {path} describe --tags --long --always'
+    try:
+        return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
+    except subprocess.CalledProcessError as e:
+        return ''  # not a git repository
+
+
+def select_device(device='', batch_size=None):
+    # device = 'cpu' or '0' or '0,1,2,3'
+    s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} '  # string
+    cpu = device.lower() == 'cpu'
+    if cpu:
+        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
+    elif device:  # non-cpu device requested
+        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
+        assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'  # check availability
+
+    cuda = not cpu and torch.cuda.is_available()
+    if cuda:
+        devices = device.split(',') if device else range(torch.cuda.device_count())  # i.e. 0,1,6,7
+        n = len(devices)  # device count
+        if n > 1 and batch_size:  # check batch_size is divisible by device_count
+            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
+        space = ' ' * len(s)
+        for i, d in enumerate(devices):
+            p = torch.cuda.get_device_properties(i)
+            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
+    else:
+        s += 'CPU\n'
+
+    logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
+    return torch.device('cuda:0' if cuda else 'cpu')
+
+
+def time_synchronized():
+    # pytorch-accurate time
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+    return time.time()
+
+
+def profile(x, ops, n=100, device=None):
+    # profile a pytorch module or list of modules. Example usage:
+    #     x = torch.randn(16, 3, 640, 640)  # input
+    #     m1 = lambda x: x * torch.sigmoid(x)
+    #     m2 = nn.SiLU()
+    #     profile(x, [m1, m2], n=100)  # profile speed over 100 iterations
+
+    device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    x = x.to(device)
+    x.requires_grad = True
+    print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
+    print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
+    for m in ops if isinstance(ops, list) else [ops]:
+        m = m.to(device) if hasattr(m, 'to') else m  # device
+        m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m  # type
+        dtf, dtb, t = 0., 0., [0., 0., 0.]  # dt forward, backward
+        try:
+            flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPS
+        except:
+            flops = 0
+
+        for _ in range(n):
+            t[0] = time_synchronized()
+            y = m(x)
+            t[1] = time_synchronized()
+            try:
+                _ = y.sum().backward()
+                t[2] = time_synchronized()
+            except:  # no backward method
+                t[2] = float('nan')
+            dtf += (t[1] - t[0]) * 1000 / n  # ms per op forward
+            dtb += (t[2] - t[1]) * 1000 / n  # ms per op backward
+
+        s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list'
+        s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list'
+        p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0  # parameters
+        print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}')
+
+
+def is_parallel(model):
+    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
+
+
+def intersect_dicts(da, db, exclude=()):
+    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
+    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
+
+
+def initialize_weights(model):
+    for m in model.modules():
+        t = type(m)
+        if t is nn.Conv2d:
+            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+        elif t is nn.BatchNorm2d:
+            m.eps = 1e-3
+            m.momentum = 0.03
+        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
+            m.inplace = True
+
+
+def find_modules(model, mclass=nn.Conv2d):
+    # Finds layer indices matching module class 'mclass'
+    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
+
+
+def sparsity(model):
+    # Return global model sparsity
+    a, b = 0., 0.
+    for p in model.parameters():
+        a += p.numel()
+        b += (p == 0).sum()
+    return b / a
+
+
+def prune(model, amount=0.3):
+    # Prune model to requested global sparsity
+    import torch.nn.utils.prune as prune
+    print('Pruning model... ', end='')
+    for name, m in model.named_modules():
+        if isinstance(m, nn.Conv2d):
+            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
+            prune.remove(m, 'weight')  # make permanent
+    print(' %.3g global sparsity' % sparsity(model))
+
+
+def fuse_conv_and_bn(conv, bn):
+    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+    fusedconv = nn.Conv2d(conv.in_channels,
+                          conv.out_channels,
+                          kernel_size=conv.kernel_size,
+                          stride=conv.stride,
+                          padding=conv.padding,
+                          groups=conv.groups,
+                          bias=True).requires_grad_(False).to(conv.weight.device)
+
+    # prepare filters
+    w_conv = conv.weight.clone().view(conv.out_channels, -1)
+    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
+
+    # prepare spatial bias
+    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+    return fusedconv
+
+
+def model_info(model, verbose=False, img_size=640):
+    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
+    n_p = sum(x.numel() for x in model.parameters())  # number parameters
+    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
+    if verbose:
+        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
+        for i, (name, p) in enumerate(model.named_parameters()):
+            name = name.replace('module_list.', '')
+            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
+                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
+
+    try:  # FLOPS
+        from thop import profile
+        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32
+        img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device)  # input
+        flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride GFLOPS
+        img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float
+        fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPS
+    except (ImportError, Exception):
+        fs = ''
+
+    logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
+
+
+def load_classifier(name='resnet101', n=2):
+    # Loads a pretrained model reshaped to n-class output
+    model = torchvision.models.__dict__[name](pretrained=True)
+
+    # ResNet model properties
+    # input_size = [3, 224, 224]
+    # input_space = 'RGB'
+    # input_range = [0, 1]
+    # mean = [0.485, 0.456, 0.406]
+    # std = [0.229, 0.224, 0.225]
+
+    # Reshape output to n classes
+    filters = model.fc.weight.shape[1]
+    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
+    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
+    model.fc.out_features = n
+    return model
+
+
+def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
+    # scales img(bs,3,y,x) by ratio constrained to gs-multiple
+    if ratio == 1.0:
+        return img
+    else:
+        h, w = img.shape[2:]
+        s = (int(h * ratio), int(w * ratio))  # new size
+        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
+        if not same_shape:  # pad/crop img
+            h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
+        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
+
+
+def copy_attr(a, b, include=(), exclude=()):
+    # Copy attributes from b to a, options to only include [...] and to exclude [...]
+    for k, v in b.__dict__.items():
+        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
+            continue
+        else:
+            setattr(a, k, v)
+
+
+class ModelEMA:
+    """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
+    Keep a moving average of everything in the model state_dict (parameters and buffers).
+    This is intended to allow functionality like
+    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+    A smoothed version of the weights is necessary for some training schemes to perform well.
+    This class is sensitive where it is initialized in the sequence of model init,
+    GPU assignment and distributed training wrappers.
+    """
+
+    def __init__(self, model, decay=0.9999, updates=0):
+        # Create EMA
+        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
+        # if next(model.parameters()).device.type != 'cpu':
+        #     self.ema.half()  # FP16 EMA
+        self.updates = updates  # number of EMA updates
+        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
+        for p in self.ema.parameters():
+            p.requires_grad_(False)
+
+    def update(self, model):
+        # Update EMA parameters
+        with torch.no_grad():
+            self.updates += 1
+            d = self.decay(self.updates)
+
+            msd = model.module.state_dict() if is_parallel(model) else model.state_dict()  # model state_dict
+            for k, v in self.ema.state_dict().items():
+                if v.dtype.is_floating_point:
+                    v *= d
+                    v += (1. - d) * msd[k].detach()
+
+    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
+        # Update EMA attributes
+        copy_attr(self.ema, model, include, exclude)

test/test-yolov5-deepsort/utils/wandb_logging/log_dataset.py

@@ -0,0 +1,24 @@
+import argparse
+
+import yaml
+
+from wandb_utils import WandbLogger
+
+WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
+
+
+def create_dataset_artifact(opt):
+    with open(opt.data) as f:
+        data = yaml.safe_load(f)  # data dict
+    logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
+    parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
+    parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
+    opt = parser.parse_args()
+    opt.resume = False  # Explicitly disallow resume check for dataset upload job
+
+    create_dataset_artifact(opt)

test/test-yolov5-deepsort/utils/wandb_logging/wandb_utils.py

@@ -0,0 +1,318 @@
+"""Utilities and tools for tracking runs with Weights & Biases."""
+import json
+import sys
+from pathlib import Path
+
+import torch
+import yaml
+from tqdm import tqdm
+
+sys.path.append(str(Path(__file__).parent.parent.parent))  # add utils/ to path
+from utils.datasets import LoadImagesAndLabels
+from utils.datasets import img2label_paths
+from utils.general import colorstr, xywh2xyxy, check_dataset, check_file
+
+try:
+    import wandb
+    from wandb import init, finish
+except ImportError:
+    wandb = None
+
+WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
+
+
+def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
+    return from_string[len(prefix):]
+
+
+def check_wandb_config_file(data_config_file):
+    wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1))  # updated data.yaml path
+    if Path(wandb_config).is_file():
+        return wandb_config
+    return data_config_file
+
+
+def get_run_info(run_path):
+    run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
+    run_id = run_path.stem
+    project = run_path.parent.stem
+    entity = run_path.parent.parent.stem
+    model_artifact_name = 'run_' + run_id + '_model'
+    return entity, project, run_id, model_artifact_name
+
+
+def check_wandb_resume(opt):
+    process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None
+    if isinstance(opt.resume, str):
+        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
+            if opt.global_rank not in [-1, 0]:  # For resuming DDP runs
+                entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
+                api = wandb.Api()
+                artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')
+                modeldir = artifact.download()
+                opt.weights = str(Path(modeldir) / "last.pt")
+            return True
+    return None
+
+
+def process_wandb_config_ddp_mode(opt):
+    with open(check_file(opt.data)) as f:
+        data_dict = yaml.safe_load(f)  # data dict
+    train_dir, val_dir = None, None
+    if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
+        api = wandb.Api()
+        train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
+        train_dir = train_artifact.download()
+        train_path = Path(train_dir) / 'data/images/'
+        data_dict['train'] = str(train_path)
+
+    if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
+        api = wandb.Api()
+        val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
+        val_dir = val_artifact.download()
+        val_path = Path(val_dir) / 'data/images/'
+        data_dict['val'] = str(val_path)
+    if train_dir or val_dir:
+        ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
+        with open(ddp_data_path, 'w') as f:
+            yaml.safe_dump(data_dict, f)
+        opt.data = ddp_data_path
+
+
+class WandbLogger():
+    """Log training runs, datasets, models, and predictions to Weights & Biases.
+
+    This logger sends information to W&B at wandb.ai. By default, this information
+    includes hyperparameters, system configuration and metrics, model metrics,
+    and basic data metrics and analyses.
+
+    By providing additional command line arguments to train.py, datasets,
+    models and predictions can also be logged.
+
+    For more on how this logger is used, see the Weights & Biases documentation:
+    https://docs.wandb.com/guides/integrations/yolov5
+    """
+    def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
+        # Pre-training routine --
+        self.job_type = job_type
+        self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict
+        # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call
+        if isinstance(opt.resume, str):  # checks resume from artifact
+            if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
+                entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
+                model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
+                assert wandb, 'install wandb to resume wandb runs'
+                # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
+                self.wandb_run = wandb.init(id=run_id, project=project, entity=entity, resume='allow')
+                opt.resume = model_artifact_name
+        elif self.wandb:
+            self.wandb_run = wandb.init(config=opt,
+                                        resume="allow",
+                                        project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
+                                        entity=opt.entity,
+                                        name=name,
+                                        job_type=job_type,
+                                        id=run_id) if not wandb.run else wandb.run
+        if self.wandb_run:
+            if self.job_type == 'Training':
+                if not opt.resume:
+                    wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict
+                    # Info useful for resuming from artifacts
+                    self.wandb_run.config.opt = vars(opt)
+                    self.wandb_run.config.data_dict = wandb_data_dict
+                self.data_dict = self.setup_training(opt, data_dict)
+            if self.job_type == 'Dataset Creation':
+                self.data_dict = self.check_and_upload_dataset(opt)
+        else:
+            prefix = colorstr('wandb: ')
+            print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
+
+    def check_and_upload_dataset(self, opt):
+        assert wandb, 'Install wandb to upload dataset'
+        check_dataset(self.data_dict)
+        config_path = self.log_dataset_artifact(check_file(opt.data),
+                                                opt.single_cls,
+                                                'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
+        print("Created dataset config file ", config_path)
+        with open(config_path) as f:
+            wandb_data_dict = yaml.safe_load(f)
+        return wandb_data_dict
+
+    def setup_training(self, opt, data_dict):
+        self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16  # Logging Constants
+        self.bbox_interval = opt.bbox_interval
+        if isinstance(opt.resume, str):
+            modeldir, _ = self.download_model_artifact(opt)
+            if modeldir:
+                self.weights = Path(modeldir) / "last.pt"
+                config = self.wandb_run.config
+                opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(
+                    self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \
+                                                                                                       config.opt['hyp']
+            data_dict = dict(self.wandb_run.config.data_dict)  # eliminates the need for config file to resume
+        if 'val_artifact' not in self.__dict__:  # If --upload_dataset is set, use the existing artifact, don't download
+            self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
+                                                                                           opt.artifact_alias)
+            self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
+                                                                                       opt.artifact_alias)
+            self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None
+            if self.train_artifact_path is not None:
+                train_path = Path(self.train_artifact_path) / 'data/images/'
+                data_dict['train'] = str(train_path)
+            if self.val_artifact_path is not None:
+                val_path = Path(self.val_artifact_path) / 'data/images/'
+                data_dict['val'] = str(val_path)
+                self.val_table = self.val_artifact.get("val")
+                self.map_val_table_path()
+        if self.val_artifact is not None:
+            self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
+            self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
+        if opt.bbox_interval == -1:
+            self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
+        return data_dict
+
+    def download_dataset_artifact(self, path, alias):
+        if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
+            artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
+            dataset_artifact = wandb.use_artifact(artifact_path.as_posix())
+            assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
+            datadir = dataset_artifact.download()
+            return datadir, dataset_artifact
+        return None, None
+
+    def download_model_artifact(self, opt):
+        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
+            model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
+            assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
+            modeldir = model_artifact.download()
+            epochs_trained = model_artifact.metadata.get('epochs_trained')
+            total_epochs = model_artifact.metadata.get('total_epochs')
+            is_finished = total_epochs is None
+            assert not is_finished, 'training is finished, can only resume incomplete runs.'
+            return modeldir, model_artifact
+        return None, None
+
+    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
+        model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
+            'original_url': str(path),
+            'epochs_trained': epoch + 1,
+            'save period': opt.save_period,
+            'project': opt.project,
+            'total_epochs': opt.epochs,
+            'fitness_score': fitness_score
+        })
+        model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
+        wandb.log_artifact(model_artifact,
+                           aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
+        print("Saving model artifact on epoch ", epoch + 1)
+
+    def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
+        with open(data_file) as f:
+            data = yaml.safe_load(f)  # data dict
+        nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
+        names = {k: v for k, v in enumerate(names)}  # to index dictionary
+        self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
+            data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None
+        self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
+            data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None
+        if data.get('train'):
+            data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
+        if data.get('val'):
+            data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
+        path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1))  # updated data.yaml path
+        data.pop('download', None)
+        with open(path, 'w') as f:
+            yaml.safe_dump(data, f)
+
+        if self.job_type == 'Training':  # builds correct artifact pipeline graph
+            self.wandb_run.use_artifact(self.val_artifact)
+            self.wandb_run.use_artifact(self.train_artifact)
+            self.val_artifact.wait()
+            self.val_table = self.val_artifact.get('val')
+            self.map_val_table_path()
+        else:
+            self.wandb_run.log_artifact(self.train_artifact)
+            self.wandb_run.log_artifact(self.val_artifact)
+        return path
+
+    def map_val_table_path(self):
+        self.val_table_map = {}
+        print("Mapping dataset")
+        for i, data in enumerate(tqdm(self.val_table.data)):
+            self.val_table_map[data[3]] = data[0]
+
+    def create_dataset_table(self, dataset, class_to_id, name='dataset'):
+        # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
+        artifact = wandb.Artifact(name=name, type="dataset")
+        img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
+        img_files = tqdm(dataset.img_files) if not img_files else img_files
+        for img_file in img_files:
+            if Path(img_file).is_dir():
+                artifact.add_dir(img_file, name='data/images')
+                labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
+                artifact.add_dir(labels_path, name='data/labels')
+            else:
+                artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
+                label_file = Path(img2label_paths([img_file])[0])
+                artifact.add_file(str(label_file),
+                                  name='data/labels/' + label_file.name) if label_file.exists() else None
+        table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
+        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
+        for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
+            box_data, img_classes = [], {}
+            for cls, *xywh in labels[:, 1:].tolist():
+                cls = int(cls)
+                box_data.append({"position": {"middle": [xywh[0], xywh[1]], "width": xywh[2], "height": xywh[3]},
+                                 "class_id": cls,
+                                 "box_caption": "%s" % (class_to_id[cls])})
+                img_classes[cls] = class_to_id[cls]
+            boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}}  # inference-space
+            table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes),
+                           Path(paths).name)
+        artifact.add(table, name)
+        return artifact
+
+    def log_training_progress(self, predn, path, names):
+        if self.val_table and self.result_table:
+            class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
+            box_data = []
+            total_conf = 0
+            for *xyxy, conf, cls in predn.tolist():
+                if conf >= 0.25:
+                    box_data.append(
+                        {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
+                         "class_id": int(cls),
+                         "box_caption": "%s %.3f" % (names[cls], conf),
+                         "scores": {"class_score": conf},
+                         "domain": "pixel"})
+                    total_conf = total_conf + conf
+            boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
+            id = self.val_table_map[Path(path).name]
+            self.result_table.add_data(self.current_epoch,
+                                       id,
+                                       wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
+                                       total_conf / max(1, len(box_data))
+                                       )
+
+    def log(self, log_dict):
+        if self.wandb_run:
+            for key, value in log_dict.items():
+                self.log_dict[key] = value
+
+    def end_epoch(self, best_result=False):
+        if self.wandb_run:
+            wandb.log(self.log_dict)
+            self.log_dict = {}
+            if self.result_artifact:
+                train_results = wandb.JoinedTable(self.val_table, self.result_table, "id")
+                self.result_artifact.add(train_results, 'result')
+                wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),
+                                                                  ('best' if best_result else '')])
+                self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
+                self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
+
+    def finish_run(self):
+        if self.wandb_run:
+            if self.log_dict:
+                wandb.log(self.log_dict)
+            wandb.run.finish()