├── GUI_picture ├── 1.gif ├── 2.gif └── window.jpg ├── LICENSE ├── MouseLabel.py ├── README.md ├── __init__.py ├── __pycache__ ├── MouseLabel.cpython-38.pyc ├── MouseLabel.cpython-39.pyc ├── __init__.cpython-38.pyc ├── apprcc.cpython-39.pyc ├── apprcc_rc.cpython-38.pyc ├── apprcc_rc.cpython-39.pyc ├── detector.cpython-38.pyc ├── detector.cpython-39.pyc ├── main.cpython-38.pyc ├── main0.cpython-38.pyc ├── mywin.cpython-38.pyc ├── show_license_plate.cpython-38.pyc ├── tracker.cpython-38.pyc ├── tracker.cpython-39.pyc ├── tracker.cpython-39.pyc.1645821883424 ├── win.cpython-38.pyc └── win.cpython-39.pyc ├── apprcc.qrc ├── apprcc_rc.py ├── config ├── fold.json ├── ip.json └── setting.json ├── cover.jpg ├── data ├── Argoverse.yaml ├── Argoverse_HD.yaml ├── GlobalWheat2020.yaml ├── Objects365.yaml ├── SKU-110K.yaml ├── VOC.yaml ├── VisDrone.yaml ├── coco.yaml ├── coco128.yaml ├── hyps │ ├── hyp.Objects365.yaml │ ├── hyp.VOC.yaml │ ├── hyp.finetune.yaml │ ├── hyp.finetune_objects365.yaml │ ├── hyp.scratch-high.yaml │ ├── hyp.scratch-low.yaml │ ├── hyp.scratch-med.yaml │ ├── hyp.scratch-p6.yaml │ └── hyp.scratch.yaml ├── icon │ ├── 停止.png │ ├── 图片1.png │ ├── 图片2.png │ ├── 开始.png │ ├── 打开.png │ ├── 摄像头关.png │ ├── 摄像头开.png │ ├── 数据探索.png │ ├── 模.png │ ├── 模型中心.png │ └── 赞停.png ├── images │ ├── bus.jpg │ ├── zidane.jpg │ └── zidane.png ├── scripts │ ├── download_weights.sh │ ├── get_coco.sh │ └── get_coco128.sh └── xView.yaml ├── detect.py ├── detector.py ├── dialog ├── __pycache__ │ ├── rtsp_dialog.cpython-38.pyc │ ├── rtsp_dialog.cpython-39.pyc │ └── rtsp_win.cpython-38.pyc ├── rtsp_dialog.py ├── rtsp_dialog.ui └── rtsp_win.py ├── export.py ├── hubconf.py ├── icon ├── SYSU.jpg ├── SYSU0.png ├── background.jpg ├── button-off.png ├── button-on.png ├── doctor.png ├── evil.png ├── 下拉_白色.png ├── 中大.jpg ├── 停止.png ├── 关闭.png ├── 圆.png ├── 实时视频流解析.png ├── 打开.png ├── 摄像头关.png ├── 摄像头开.png ├── 数据探索.png ├── 暂停.png ├── 最大化.png ├── 最小化.png ├── 正方形.png ├── 笑脸.png ├── 箭头_列表展开.png ├── 箭头_列表收起.png ├── 终止.png ├── 背景.png ├── 表情.png ├── 赞停.png ├── 运行.png ├── 还原.png └── 鸭.jpg ├── images └── bus.jpg ├── main.py ├── main0.py ├── models ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── __init__.cpython-39.pyc │ ├── common.cpython-38.pyc │ ├── common.cpython-39.pyc │ ├── experimental.cpython-38.pyc │ ├── experimental.cpython-39.pyc │ ├── yolo.cpython-38.pyc │ └── yolo.cpython-39.pyc ├── common.py ├── experimental.py ├── export.py ├── hub │ ├── anchors.yaml │ ├── yolov3-spp.yaml │ ├── yolov3-tiny.yaml │ ├── yolov3.yaml │ ├── yolov5-bifpn.yaml │ ├── yolov5-fpn.yaml │ ├── yolov5-p2.yaml │ ├── yolov5-p34.yaml │ ├── yolov5-p6.yaml │ ├── yolov5-p7.yaml │ ├── yolov5-panet.yaml │ ├── yolov5l6.yaml │ ├── yolov5m6.yaml │ ├── yolov5n6.yaml │ ├── yolov5s-ghost.yaml │ ├── yolov5s-transformer.yaml │ ├── yolov5s6.yaml │ └── yolov5x6.yaml ├── tf.py ├── yolo.py ├── yolov5l.yaml ├── yolov5m.yaml ├── yolov5n.yaml ├── yolov5s.yaml └── yolov5x.yaml ├── models0 ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── __init__.cpython-39.pyc │ ├── common.cpython-38.pyc │ ├── common.cpython-39.pyc │ ├── experimental.cpython-38.pyc │ ├── experimental.cpython-39.pyc │ ├── yolo.cpython-38.pyc │ └── yolo.cpython-39.pyc ├── common.py ├── experimental.py ├── export.py ├── yolo.py ├── yolov5l.yaml ├── yolov5m.yaml ├── yolov5s.yaml └── yolov5x.yaml ├── mymain.py ├── mywin.py ├── mywin.ui ├── platech.ttf ├── pt └── yolov5s.pt ├── push.sh ├── requirements.txt ├── show_license_plate.py ├── te.py ├── tracker.py ├── utils0 ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── __init__.cpython-39.pyc │ ├── autoanchor.cpython-38.pyc │ ├── autoanchor.cpython-39.pyc │ ├── datasets.cpython-38.pyc │ ├── datasets.cpython-39.pyc │ ├── general.cpython-38.pyc │ ├── general.cpython-39.pyc │ ├── google_utils.cpython-38.pyc │ ├── google_utils.cpython-39.pyc │ ├── metrics.cpython-38.pyc │ ├── metrics.cpython-39.pyc │ ├── plots.cpython-38.pyc │ ├── plots.cpython-39.pyc │ ├── torch_utils.cpython-38.pyc │ └── torch_utils.cpython-39.pyc ├── activations.py ├── autoanchor.py ├── aws │ ├── __init__.py │ ├── mime.sh │ ├── resume.py │ └── userdata.sh ├── datasets.py ├── general.py ├── google_app_engine │ ├── Dockerfile │ ├── additional_requirements.txt │ └── app.yaml ├── google_utils.py ├── loss.py ├── metrics.py ├── plots.py ├── torch_utils.py └── wandb_logging │ ├── __init__.py │ ├── log_dataset.py │ └── wandb_utils.py ├── val.py ├── win.py └── win.ui /GUI_picture/1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/GUI_picture/1.gif -------------------------------------------------------------------------------- /GUI_picture/2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/GUI_picture/2.gif -------------------------------------------------------------------------------- /GUI_picture/window.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/GUI_picture/window.jpg -------------------------------------------------------------------------------- /MouseLabel.py: -------------------------------------------------------------------------------- 1 | from PyQt5.QtWidgets import QLabel 2 | from PyQt5.QtCore import pyqtSignal 3 | 4 | 5 | class LabelMouse(QLabel): 6 | double_clicked = pyqtSignal() 7 | 8 | # 鼠标双击事件 9 | def mouseDoubleClickEvent(self, event): 10 | self.double_clicked.emit() 11 | 12 | def mouseMoveEvent(self): 13 | """ 14 | 当鼠标划过标签label2时触发事件 15 | :return: 16 | """ 17 | print('当鼠标划过标签label2时触发事件') 18 | 19 | 20 | class Label_click_Mouse(QLabel): 21 | clicked = pyqtSignal() 22 | 23 | # 鼠标点击事件 24 | def mousePressEvent(self, event): 25 | self.clicked.emit() -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 中山大学深度学习大作业--yolov5+lpr3+deepsort交通识别检测系统 2 | 3 | 本次大作业，我们小组完成了基于[Yolov5](https://github.com/ultralytics/yolov5) 的车型、行人、车牌检测，基于[lpr3](https://github.com/szad670401/HyperLPR)的车牌号码识别以及基于[deepsort](https://github.com/dyh/unbox_yolov5_deepsort_counting)的车流量计数任务，并将所有功能集成为一体，完成了一个交互式的交通识别检测GUI系统。 4 | 5 | ![](https://github.com/donahowe/traffic-detect-GUI/blob/main/GUI_picture/window.jpg) 6 | 7 | ![](https://github.com/donahowe/traffic-detect-GUI/blob/main/GUI_picture/1.gif) 8 | 9 | ![](https://github.com/donahowe/traffic-detect-GUI/blob/main/GUI_picture/2.gif) 10 | 11 | ## Our Idea 12 | 13 | 我们首先选用了速度更快的Yolov5作为基模型，完成10种车型，行人的目标检测任务。在此基础上，我们使用了封装好的lpr3库完成了车牌的检测任务，并以deepsort为基础完成了4种类别的目标流量计数。最后我们将所有的功能封装进了该GUI系统中，以用于车辆实时计数。 14 | 15 | ## Installation 16 | 17 | **！！！注意！！！** 由于本项目过大，在此仅展示核心代码，请点击[此链接](https://drive.google.com/file/d/1U3-Bq-sWQ0DcAHOL3HjDh71Z-cffA2OS/view?usp=sharing)手动下载完整的项目 18 | 19 | 环境配置： 20 | ``` 21 | cd traffic-detect-GUI 22 | pip install -r requirements.txt 23 | ``` 24 | 25 | 若要在本地电脑运行GUI系统，还需要安装[QT Designer](https://blog.csdn.net/qq_32892383/article/details/108867482)。请依据连接中给出的参考下载步骤安装。 26 | 27 | ## How To Run? 28 | 29 | ### Quick Start 30 | 31 | 运行我们的GUI界面： 32 | ``` 33 | python mymain.py 34 | ``` 35 | 如果视频较长或视频中车流量较慢，在不使用GPU加速的情况下检测需要一定时间，待界面左下角提示 **Ready!** 时，便可点击 **开始** 查看结果。 36 | 37 | ### Pre-trained Models 38 | 39 | 您可以在[此处](https://drive.google.com/file/d/1qMw3ofK_nJauSrvDFTfoThprqvynLgB7/view?usp=sharing)下载我们的yolov5车型+行人识别预训练模型。 40 | -------------------------------------------------------------------------------- /__init__.py: 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-------------------------------------------------------------------------------- /config/fold.json: -------------------------------------------------------------------------------- 1 | { 2 | "open_fold": "E:/images" 3 | } -------------------------------------------------------------------------------- /config/ip.json: -------------------------------------------------------------------------------- 1 | { 2 | "ip": "rtsp://admin:admin888@192.168.1.67:555" 3 | } 4 | -------------------------------------------------------------------------------- /config/setting.json: -------------------------------------------------------------------------------- 1 | { 2 | "iou": 0.32, 3 | "conf": 0.2, 4 | "rate": 6, 5 | "check": 2, 6 | "savecheck": 0 7 | } -------------------------------------------------------------------------------- /cover.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/cover.jpg -------------------------------------------------------------------------------- /data/Argoverse.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI 3 | # Example usage: python train.py --data Argoverse.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── Argoverse ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/Argoverse # dataset root dir 12 | train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images 13 | val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images 14 | test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview 15 | 16 | # Classes 17 | nc: 8 # number of classes 18 | names: ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign'] # class names 19 | 20 | 21 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 22 | download: | 23 | import json 24 | 25 | from tqdm import tqdm 26 | from utils.general import download, Path 27 | 28 | 29 | def argoverse2yolo(set): 30 | labels = {} 31 | a = json.load(open(set, "rb")) 32 | for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): 33 | img_id = annot['image_id'] 34 | img_name = a['images'][img_id]['name'] 35 | img_label_name = img_name[:-3] + "txt" 36 | 37 | cls = annot['category_id'] # instance class id 38 | x_center, y_center, width, height = annot['bbox'] 39 | x_center = (x_center + width / 2) / 1920.0 # offset and scale 40 | y_center = (y_center + height / 2) / 1200.0 # offset and scale 41 | width /= 1920.0 # scale 42 | height /= 1200.0 # scale 43 | 44 | img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] 45 | if not img_dir.exists(): 46 | img_dir.mkdir(parents=True, exist_ok=True) 47 | 48 | k = str(img_dir / img_label_name) 49 | if k not in labels: 50 | labels[k] = [] 51 | labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") 52 | 53 | for k in labels: 54 | with open(k, "w") as f: 55 | f.writelines(labels[k]) 56 | 57 | 58 | # Download 59 | dir = Path('../datasets/Argoverse') # dataset root dir 60 | urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] 61 | download(urls, dir=dir, delete=False) 62 | 63 | # Convert 64 | annotations_dir = 'Argoverse-HD/annotations/' 65 | (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' 66 | for d in "train.json", "val.json": 67 | argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels 68 | -------------------------------------------------------------------------------- /data/Argoverse_HD.yaml: -------------------------------------------------------------------------------- 1 | # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ 2 | # Train command: python train.py --data Argoverse_HD.yaml 3 | # Default dataset location is next to YOLOv5: 4 | # /parent 5 | # /datasets/Argoverse 6 | # /yolov5 7 | 8 | 9 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 10 | path: ../datasets/Argoverse # dataset root dir 11 | train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images 12 | val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images 13 | test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview 14 | 15 | # Classes 16 | nc: 8 # number of classes 17 | names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] # class names 18 | 19 | 20 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 21 | download: | 22 | import json 23 | 24 | from tqdm import tqdm 25 | from utils.general import download, Path 26 | 27 | 28 | def argoverse2yolo(set): 29 | labels = {} 30 | a = json.load(open(set, "rb")) 31 | for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): 32 | img_id = annot['image_id'] 33 | img_name = a['images'][img_id]['name'] 34 | img_label_name = img_name[:-3] + "txt" 35 | 36 | cls = annot['category_id'] # instance class id 37 | x_center, y_center, width, height = annot['bbox'] 38 | x_center = (x_center + width / 2) / 1920.0 # offset and scale 39 | y_center = (y_center + height / 2) / 1200.0 # offset and scale 40 | width /= 1920.0 # scale 41 | height /= 1200.0 # scale 42 | 43 | img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] 44 | if not img_dir.exists(): 45 | img_dir.mkdir(parents=True, exist_ok=True) 46 | 47 | k = str(img_dir / img_label_name) 48 | if k not in labels: 49 | labels[k] = [] 50 | labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") 51 | 52 | for k in labels: 53 | with open(k, "w") as f: 54 | f.writelines(labels[k]) 55 | 56 | 57 | # Download 58 | dir = Path('../datasets/Argoverse') # dataset root dir 59 | urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] 60 | download(urls, dir=dir, delete=False) 61 | 62 | # Convert 63 | annotations_dir = 'Argoverse-HD/annotations/' 64 | (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' 65 | for d in "train.json", "val.json": 66 | argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels 67 | -------------------------------------------------------------------------------- /data/GlobalWheat2020.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan 3 | # Example usage: python train.py --data GlobalWheat2020.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── GlobalWheat2020 ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/GlobalWheat2020 # dataset root dir 12 | train: # train images (relative to 'path') 3422 images 13 | - images/arvalis_1 14 | - images/arvalis_2 15 | - images/arvalis_3 16 | - images/ethz_1 17 | - images/rres_1 18 | - images/inrae_1 19 | - images/usask_1 20 | val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1) 21 | - images/ethz_1 22 | test: # test images (optional) 1276 images 23 | - images/utokyo_1 24 | - images/utokyo_2 25 | - images/nau_1 26 | - images/uq_1 27 | 28 | # Classes 29 | nc: 1 # number of classes 30 | names: ['wheat_head'] # class names 31 | 32 | 33 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 34 | download: | 35 | from utils.general import download, Path 36 | 37 | # Download 38 | dir = Path(yaml['path']) # dataset root dir 39 | urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', 40 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] 41 | download(urls, dir=dir) 42 | 43 | # Make Directories 44 | for p in 'annotations', 'images', 'labels': 45 | (dir / p).mkdir(parents=True, exist_ok=True) 46 | 47 | # Move 48 | for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ 49 | 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': 50 | (dir / p).rename(dir / 'images' / p) # move to /images 51 | f = (dir / p).with_suffix('.json') # json file 52 | if f.exists(): 53 | f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations 54 | -------------------------------------------------------------------------------- /data/Objects365.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Objects365 dataset https://www.objects365.org/ by Megvii 3 | # Example usage: python train.py --data Objects365.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── Objects365 ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/Objects365 # dataset root dir 12 | train: images/train # train images (relative to 'path') 1742289 images 13 | val: images/val # val images (relative to 'path') 80000 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | nc: 365 # number of classes 18 | names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup', 19 | 'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book', 20 | 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', 21 | 'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV', 22 | 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', 23 | 'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird', 24 | 'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck', 25 | 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning', 26 | 'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife', 27 | 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock', 28 | 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish', 29 | 'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan', 30 | 'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard', 31 | 'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign', 32 | 'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat', 33 | 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard', 34 | 'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry', 35 | 'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks', 36 | 'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors', 37 | 'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape', 38 | 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck', 39 | 'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette', 40 | 'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket', 41 | 'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine', 42 | 'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine', 43 | 'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon', 44 | 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse', 45 | 'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball', 46 | 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', 47 | 'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts', 48 | 'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit', 49 | 'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD', 50 | 'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder', 51 | 'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', 52 | 'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab', 53 | 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', 54 | 'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart', 55 | 'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French', 56 | 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', 57 | 'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil', 58 | 'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis'] 59 | 60 | 61 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 62 | download: | 63 | from pycocotools.coco import COCO 64 | from tqdm import tqdm 65 | 66 | from utils.general import Path, download, np, xyxy2xywhn 67 | 68 | # Make Directories 69 | dir = Path(yaml['path']) # dataset root dir 70 | for p in 'images', 'labels': 71 | (dir / p).mkdir(parents=True, exist_ok=True) 72 | for q in 'train', 'val': 73 | (dir / p / q).mkdir(parents=True, exist_ok=True) 74 | 75 | # Train, Val Splits 76 | for split, patches in [('train', 50 + 1), ('val', 43 + 1)]: 77 | print(f"Processing {split} in {patches} patches ...") 78 | images, labels = dir / 'images' / split, dir / 'labels' / split 79 | 80 | # Download 81 | url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/" 82 | if split == 'train': 83 | download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json 84 | download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8) 85 | elif split == 'val': 86 | download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json 87 | download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8) 88 | download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8) 89 | 90 | # Move 91 | for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'): 92 | f.rename(images / f.name) # move to /images/{split} 93 | 94 | # Labels 95 | coco = COCO(dir / f'zhiyuan_objv2_{split}.json') 96 | names = [x["name"] for x in coco.loadCats(coco.getCatIds())] 97 | for cid, cat in enumerate(names): 98 | catIds = coco.getCatIds(catNms=[cat]) 99 | imgIds = coco.getImgIds(catIds=catIds) 100 | for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'): 101 | width, height = im["width"], im["height"] 102 | path = Path(im["file_name"]) # image filename 103 | try: 104 | with open(labels / path.with_suffix('.txt').name, 'a') as file: 105 | annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None) 106 | for a in coco.loadAnns(annIds): 107 | x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner) 108 | xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4) 109 | x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped 110 | file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n") 111 | except Exception as e: 112 | print(e) 113 | -------------------------------------------------------------------------------- /data/SKU-110K.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail 3 | # Example usage: python train.py --data SKU-110K.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── SKU-110K ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/SKU-110K # dataset root dir 12 | train: train.txt # train images (relative to 'path') 8219 images 13 | val: val.txt # val images (relative to 'path') 588 images 14 | test: test.txt # test images (optional) 2936 images 15 | 16 | # Classes 17 | nc: 1 # number of classes 18 | names: ['object'] # class names 19 | 20 | 21 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 22 | download: | 23 | import shutil 24 | from tqdm import tqdm 25 | from utils.general import np, pd, Path, download, xyxy2xywh 26 | 27 | # Download 28 | dir = Path(yaml['path']) # dataset root dir 29 | parent = Path(dir.parent) # download dir 30 | urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz'] 31 | download(urls, dir=parent, delete=False) 32 | 33 | # Rename directories 34 | if dir.exists(): 35 | shutil.rmtree(dir) 36 | (parent / 'SKU110K_fixed').rename(dir) # rename dir 37 | (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir 38 | 39 | # Convert labels 40 | names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names 41 | for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv': 42 | x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations 43 | images, unique_images = x[:, 0], np.unique(x[:, 0]) 44 | with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f: 45 | f.writelines(f'./images/{s}\n' for s in unique_images) 46 | for im in tqdm(unique_images, desc=f'Converting {dir / d}'): 47 | cls = 0 # single-class dataset 48 | with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f: 49 | for r in x[images == im]: 50 | w, h = r[6], r[7] # image width, height 51 | xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance 52 | f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label 53 | -------------------------------------------------------------------------------- /data/VOC.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford 3 | # Example usage: python train.py --data VOC.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── VOC ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/VOC 12 | train: # train images (relative to 'path') 16551 images 13 | - images/train2012 14 | - images/train2007 15 | - images/val2012 16 | - images/val2007 17 | val: # val images (relative to 'path') 4952 images 18 | - images/test2007 19 | test: # test images (optional) 20 | - images/test2007 21 | 22 | # Classes 23 | nc: 20 # number of classes 24 | names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 25 | 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names 26 | 27 | 28 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 29 | download: | 30 | import xml.etree.ElementTree as ET 31 | 32 | from tqdm import tqdm 33 | from utils.general import download, Path 34 | 35 | 36 | def convert_label(path, lb_path, year, image_id): 37 | def convert_box(size, box): 38 | dw, dh = 1. / size[0], 1. / size[1] 39 | x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2] 40 | return x * dw, y * dh, w * dw, h * dh 41 | 42 | in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml') 43 | out_file = open(lb_path, 'w') 44 | tree = ET.parse(in_file) 45 | root = tree.getroot() 46 | size = root.find('size') 47 | w = int(size.find('width').text) 48 | h = int(size.find('height').text) 49 | 50 | for obj in root.iter('object'): 51 | cls = obj.find('name').text 52 | if cls in yaml['names'] and not int(obj.find('difficult').text) == 1: 53 | xmlbox = obj.find('bndbox') 54 | bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')]) 55 | cls_id = yaml['names'].index(cls) # class id 56 | out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n') 57 | 58 | 59 | # Download 60 | dir = Path(yaml['path']) # dataset root dir 61 | url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' 62 | urls = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images 63 | url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images 64 | url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images 65 | download(urls, dir=dir / 'images', delete=False) 66 | 67 | # Convert 68 | path = dir / f'images/VOCdevkit' 69 | for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'): 70 | imgs_path = dir / 'images' / f'{image_set}{year}' 71 | lbs_path = dir / 'labels' / f'{image_set}{year}' 72 | imgs_path.mkdir(exist_ok=True, parents=True) 73 | lbs_path.mkdir(exist_ok=True, parents=True) 74 | 75 | image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').read().strip().split() 76 | for id in tqdm(image_ids, desc=f'{image_set}{year}'): 77 | f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path 78 | lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path 79 | f.rename(imgs_path / f.name) # move image 80 | convert_label(path, lb_path, year, id) # convert labels to YOLO format 81 | -------------------------------------------------------------------------------- /data/VisDrone.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University 3 | # Example usage: python train.py --data VisDrone.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── VisDrone ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/VisDrone # dataset root dir 12 | train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images 13 | val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images 14 | test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images 15 | 16 | # Classes 17 | nc: 10 # number of classes 18 | names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor'] 19 | 20 | 21 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 22 | download: | 23 | from utils.general import download, os, Path 24 | 25 | def visdrone2yolo(dir): 26 | from PIL import Image 27 | from tqdm import tqdm 28 | 29 | def convert_box(size, box): 30 | # Convert VisDrone box to YOLO xywh box 31 | dw = 1. / size[0] 32 | dh = 1. / size[1] 33 | return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh 34 | 35 | (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory 36 | pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}') 37 | for f in pbar: 38 | img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size 39 | lines = [] 40 | with open(f, 'r') as file: # read annotation.txt 41 | for row in [x.split(',') for x in file.read().strip().splitlines()]: 42 | if row[4] == '0': # VisDrone 'ignored regions' class 0 43 | continue 44 | cls = int(row[5]) - 1 45 | box = convert_box(img_size, tuple(map(int, row[:4]))) 46 | lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n") 47 | with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl: 48 | fl.writelines(lines) # write label.txt 49 | 50 | 51 | # Download 52 | dir = Path(yaml['path']) # dataset root dir 53 | urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip', 54 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip', 55 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip', 56 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip'] 57 | download(urls, dir=dir) 58 | 59 | # Convert 60 | for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev': 61 | visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels 62 | -------------------------------------------------------------------------------- /data/coco.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # COCO 2017 dataset http://cocodataset.org by Microsoft 3 | # Example usage: python train.py --data coco.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── coco ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco # dataset root dir 12 | train: train2017.txt # train images (relative to 'path') 118287 images 13 | val: val2017.txt # val images (relative to 'path') 5000 images 14 | test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 15 | 16 | # Classes 17 | nc: 80 # number of classes 18 | names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 19 | 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 20 | 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 21 | 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 22 | 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 23 | 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 24 | 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 25 | 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 26 | 'hair drier', 'toothbrush'] # class names 27 | 28 | 29 | # Download script/URL (optional) 30 | download: | 31 | from utils.general import download, Path 32 | 33 | # Download labels 34 | segments = False # segment or box labels 35 | dir = Path(yaml['path']) # dataset root dir 36 | url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' 37 | urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels 38 | download(urls, dir=dir.parent) 39 | 40 | # Download data 41 | urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images 42 | 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images 43 | 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) 44 | download(urls, dir=dir / 'images', threads=3) 45 | -------------------------------------------------------------------------------- /data/coco128.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics 3 | # Example usage: python train.py --data coco128.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── coco128 ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco128 # dataset root dir 12 | train: images/train2017 # train images (relative to 'path') 128 images 13 | val: images/train2017 # val images (relative to 'path') 128 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | nc: 80 # number of classes 18 | names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 19 | 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 20 | 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 21 | 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 22 | 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 23 | 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 24 | 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 25 | 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 26 | 'hair drier', 'toothbrush'] # class names 27 | 28 | 29 | # Download script/URL (optional) 30 | download: https://ultralytics.com/assets/coco128.zip 31 | -------------------------------------------------------------------------------- /data/hyps/hyp.Objects365.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for Objects365 training 3 | # python train.py --weights yolov5m.pt --data Objects365.yaml --evolve 4 | # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.00258 7 | lrf: 0.17 8 | momentum: 0.779 9 | weight_decay: 0.00058 10 | warmup_epochs: 1.33 11 | warmup_momentum: 0.86 12 | warmup_bias_lr: 0.0711 13 | box: 0.0539 14 | cls: 0.299 15 | cls_pw: 0.825 16 | obj: 0.632 17 | obj_pw: 1.0 18 | iou_t: 0.2 19 | anchor_t: 3.44 20 | anchors: 3.2 21 | fl_gamma: 0.0 22 | hsv_h: 0.0188 23 | hsv_s: 0.704 24 | hsv_v: 0.36 25 | degrees: 0.0 26 | translate: 0.0902 27 | scale: 0.491 28 | shear: 0.0 29 | perspective: 0.0 30 | flipud: 0.0 31 | fliplr: 0.5 32 | mosaic: 1.0 33 | mixup: 0.0 34 | copy_paste: 0.0 35 | -------------------------------------------------------------------------------- /data/hyps/hyp.VOC.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for VOC training 3 | # python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve 4 | # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials 5 | 6 | # YOLOv5 Hyperparameter Evolution Results 7 | # Best generation: 319 8 | # Last generation: 434 9 | # metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss 10 | # 0.86236, 0.86184, 0.91274, 0.72647, 0.0077056, 0.0042449, 0.0013846 11 | 12 | lr0: 0.0033 13 | lrf: 0.15184 14 | momentum: 0.74747 15 | weight_decay: 0.00025 16 | warmup_epochs: 3.4278 17 | warmup_momentum: 0.59032 18 | warmup_bias_lr: 0.18742 19 | box: 0.02 20 | cls: 0.21563 21 | cls_pw: 0.5 22 | obj: 0.50843 23 | obj_pw: 0.6729 24 | iou_t: 0.2 25 | anchor_t: 3.4172 26 | fl_gamma: 0.0 27 | hsv_h: 0.01032 28 | hsv_s: 0.5562 29 | hsv_v: 0.28255 30 | degrees: 0.0 31 | translate: 0.04575 32 | scale: 0.73711 33 | shear: 0.0 34 | perspective: 0.0 35 | flipud: 0.0 36 | fliplr: 0.5 37 | mosaic: 0.87158 38 | mixup: 0.04294 39 | copy_paste: 0.0 40 | anchors: 3.3556 41 | -------------------------------------------------------------------------------- /data/hyps/hyp.finetune.yaml: -------------------------------------------------------------------------------- 1 | # Hyperparameters for VOC finetuning 2 | # python train.py --batch 64 --weights yolov5m.pt --data VOC.yaml --img 512 --epochs 50 3 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 4 | 5 | 6 | # Hyperparameter Evolution Results 7 | # Generations: 306 8 | # P R mAP.5 mAP.5:.95 box obj cls 9 | # Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146 10 | 11 | lr0: 0.0032 12 | lrf: 0.12 13 | momentum: 0.843 14 | weight_decay: 0.00036 15 | warmup_epochs: 2.0 16 | warmup_momentum: 0.5 17 | warmup_bias_lr: 0.05 18 | box: 0.0296 19 | cls: 0.243 20 | cls_pw: 0.631 21 | obj: 0.301 22 | obj_pw: 0.911 23 | iou_t: 0.2 24 | anchor_t: 2.91 25 | # anchors: 3.63 26 | fl_gamma: 0.0 27 | hsv_h: 0.0138 28 | hsv_s: 0.664 29 | hsv_v: 0.464 30 | degrees: 0.373 31 | translate: 0.245 32 | scale: 0.898 33 | shear: 0.602 34 | perspective: 0.0 35 | flipud: 0.00856 36 | fliplr: 0.5 37 | mosaic: 1.0 38 | mixup: 0.243 39 | copy_paste: 0.0 40 | -------------------------------------------------------------------------------- /data/hyps/hyp.finetune_objects365.yaml: -------------------------------------------------------------------------------- 1 | lr0: 0.00258 2 | lrf: 0.17 3 | momentum: 0.779 4 | weight_decay: 0.00058 5 | warmup_epochs: 1.33 6 | warmup_momentum: 0.86 7 | warmup_bias_lr: 0.0711 8 | box: 0.0539 9 | cls: 0.299 10 | cls_pw: 0.825 11 | obj: 0.632 12 | obj_pw: 1.0 13 | iou_t: 0.2 14 | anchor_t: 3.44 15 | anchors: 3.2 16 | fl_gamma: 0.0 17 | hsv_h: 0.0188 18 | hsv_s: 0.704 19 | hsv_v: 0.36 20 | degrees: 0.0 21 | translate: 0.0902 22 | scale: 0.491 23 | shear: 0.0 24 | perspective: 0.0 25 | flipud: 0.0 26 | fliplr: 0.5 27 | mosaic: 1.0 28 | mixup: 0.0 29 | copy_paste: 0.0 30 | -------------------------------------------------------------------------------- /data/hyps/hyp.scratch-high.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for high-augmentation COCO training from scratch 3 | # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 4 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.3 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 0.7 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.9 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.1 # image mixup (probability) 34 | copy_paste: 0.1 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /data/hyps/hyp.scratch-low.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for low-augmentation COCO training from scratch 3 | # python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear 4 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.5 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 1.0 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.5 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.0 # image mixup (probability) 34 | copy_paste: 0.0 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /data/hyps/hyp.scratch-med.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for medium-augmentation COCO training from scratch 3 | # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 4 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.3 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 0.7 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.9 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.1 # image mixup (probability) 34 | copy_paste: 0.0 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /data/hyps/hyp.scratch-p6.yaml: -------------------------------------------------------------------------------- 1 | # Hyperparameters for COCO training from scratch 2 | # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 3 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 4 | 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.3 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 0.7 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.9 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.0 # image mixup (probability) 34 | copy_paste: 0.0 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /data/hyps/hyp.scratch.yaml: -------------------------------------------------------------------------------- 1 | # Hyperparameters for COCO training from scratch 2 | # python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300 3 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 4 | 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.5 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 1.0 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.5 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.0 # image mixup (probability) 34 | copy_paste: 0.0 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /data/icon/停止.png: -------------------------------------------------------------------------------- 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74 | 75 | # Write YOLO label 76 | if id not in shapes: 77 | shapes[id] = Image.open(file).size 78 | box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True) 79 | with open((labels / id).with_suffix('.txt'), 'a') as f: 80 | f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt 81 | except Exception as e: 82 | print(f'WARNING: skipping one label for {file}: {e}') 83 | 84 | 85 | # Download manually from https://challenge.xviewdataset.org 86 | dir = Path(yaml['path']) # dataset root dir 87 | # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels 88 | # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images 89 | # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels) 90 | # download(urls, dir=dir, delete=False) 91 | 92 | # Convert labels 93 | convert_labels(dir / 'xView_train.geojson') 94 | 95 | # Move images 96 | images = Path(dir / 'images') 97 | images.mkdir(parents=True, exist_ok=True) 98 | Path(dir / 'train_images').rename(dir / 'images' / 'train') 99 | Path(dir / 'val_images').rename(dir / 'images' / 'val') 100 | 101 | # Split 102 | autosplit(dir / 'images' / 'train') 103 | -------------------------------------------------------------------------------- /detector.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | 4 | from models.experimental import attempt_load 5 | from utils.datasets import letterbox 6 | from utils.general import non_max_suppression, scale_coords 7 | from utils.torch_utils import select_device 8 | 9 | 10 | class Detector: 11 | 12 | def __init__(self): 13 | self.img_size = 640 14 | self.threshold = 0.3 15 | self.stride = 1 16 | 17 | self.weights = './weights/yolov5m.pt' 18 | 19 | self.device = '0' if torch.cuda.is_available() else 'cpu' 20 | self.device = select_device(self.device) 21 | model = attempt_load(self.weights, map_location=self.device) 22 | model.to(self.device).eval() 23 | model.float() 24 | 25 | self.m = model 26 | self.names = model.module.names if hasattr( 27 | model, 'module') else model.names 28 | 29 | def preprocess(self, img): 30 | 31 | img0 = img.copy() 32 | img = letterbox(img, new_shape=self.img_size)[0] 33 | img = img[:, :, ::-1].transpose(2, 0, 1) 34 | img = np.ascontiguousarray(img) 35 | img = torch.from_numpy(img).to(self.device) 36 | img = img.float() 37 | img /= 255.0 38 | if img.ndimension() == 3: 39 | img = img.unsqueeze(0) 40 | 41 | return img0, img 42 | 43 | def detect(self, im): 44 | 45 | im0, img = self.preprocess(im) 46 | 47 | pred = self.m(img, augment=False)[0] 48 | pred = pred.float() 49 | pred = non_max_suppression(pred, self.threshold, 0.4) 50 | 51 | boxes = [] 52 | for det in pred: 53 | 54 | if det is not None and len(det): 55 | det[:, :4] = scale_coords( 56 | img.shape[2:], det[:, :4], im0.shape).round() 57 | 58 | for *x, conf, cls_id in det: 59 | lbl = self.names[int(cls_id)] 60 | if lbl not in ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck']: 61 | continue 62 | pass 63 | x1, y1 = int(x[0]), int(x[1]) 64 | x2, y2 = int(x[2]), int(x[3]) 65 | boxes.append( 66 | (x1, y1, x2, y2, lbl, conf)) 67 | 68 | return boxes -------------------------------------------------------------------------------- /dialog/__pycache__/rtsp_dialog.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/dialog/__pycache__/rtsp_dialog.cpython-38.pyc -------------------------------------------------------------------------------- /dialog/__pycache__/rtsp_dialog.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/dialog/__pycache__/rtsp_dialog.cpython-39.pyc -------------------------------------------------------------------------------- /dialog/__pycache__/rtsp_win.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/dialog/__pycache__/rtsp_win.cpython-38.pyc -------------------------------------------------------------------------------- /dialog/rtsp_dialog.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Form implementation generated from reading ui file 'rtsp_dialog.ui' 4 | # 5 | # Created by: PyQt5 UI code generator 5.15.2 6 | # 7 | # WARNING: Any manual changes made to this file will be lost when pyuic5 is 8 | # run again. Do not edit this file unless you know what you are doing. 9 | 10 | 11 | from PyQt5 import QtCore, QtGui, QtWidgets 12 | 13 | 14 | class Ui_Form(object): 15 | def setupUi(self, Form): 16 | Form.setObjectName("Form") 17 | Form.resize(783, 40) 18 | Form.setMinimumSize(QtCore.QSize(0, 40)) 19 | Form.setMaximumSize(QtCore.QSize(16777215, 41)) 20 | icon = QtGui.QIcon() 21 | icon.addPixmap(QtGui.QPixmap(":/img/icon/实时视频流解析.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) 22 | Form.setWindowIcon(icon) 23 | Form.setStyleSheet("#Form{background:rgba(120,120,120,255)}") 24 | self.horizontalLayout = QtWidgets.QHBoxLayout(Form) 25 | self.horizontalLayout.setContentsMargins(-1, 5, -1, 5) 26 | self.horizontalLayout.setObjectName("horizontalLayout") 27 | self.label = QtWidgets.QLabel(Form) 28 | self.label.setMinimumSize(QtCore.QSize(0, 30)) 29 | self.label.setMaximumSize(QtCore.QSize(16777215, 30)) 30 | self.label.setStyleSheet("QLabel{font-family: \"Microsoft YaHei\";\n" 31 | "font-size: 18px;\n" 32 | "font-weight: bold;\n" 33 | "color:white;}") 34 | self.label.setObjectName("label") 35 | self.horizontalLayout.addWidget(self.label) 36 | self.rtspEdit = QtWidgets.QLineEdit(Form) 37 | self.rtspEdit.setMinimumSize(QtCore.QSize(0, 31)) 38 | self.rtspEdit.setStyleSheet("background-color: rgb(207, 207, 207);") 39 | self.rtspEdit.setObjectName("rtspEdit") 40 | self.horizontalLayout.addWidget(self.rtspEdit) 41 | self.rtspButton = QtWidgets.QPushButton(Form) 42 | self.rtspButton.setStyleSheet("QPushButton{font-family: \"Microsoft YaHei\";\n" 43 | "font-size: 18px;\n" 44 | "font-weight: bold;\n" 45 | "color:white;\n" 46 | "text-align: center center;\n" 47 | "padding-left: 5px;\n" 48 | "padding-right: 5px;\n" 49 | "padding-top: 4px;\n" 50 | "padding-bottom: 4px;\n" 51 | "border-style: solid;\n" 52 | "border-width: 0px;\n" 53 | "border-color: rgba(255, 255, 255, 255);\n" 54 | "border-radius: 3px;\n" 55 | "background-color: rgba(255,255,255,30);}\n" 56 | "\n" 57 | "QPushButton:focus{outline: none;}\n" 58 | "\n" 59 | "QPushButton::pressed{font-family: \"Microsoft YaHei\";\n" 60 | " font-size: 16px;\n" 61 | " font-weight: bold;\n" 62 | " color:rgb(200,200,200);\n" 63 | " text-align: center center;\n" 64 | " padding-left: 5px;\n" 65 | " padding-right: 5px;\n" 66 | " padding-top: 4px;\n" 67 | " padding-bottom: 4px;\n" 68 | " border-style: solid;\n" 69 | " border-width: 0px;\n" 70 | " border-color: rgba(255, 255, 255, 255);\n" 71 | " border-radius: 3px;\n" 72 | " background-color: rgba(255,255,255,150);}\n" 73 | "\n" 74 | "QPushButton::hover {\n" 75 | "border-style: solid;\n" 76 | "border-width: 0px;\n" 77 | "border-radius: 0px;\n" 78 | "background-color: rgba(255,255,255,50);}") 79 | self.rtspButton.setObjectName("rtspButton") 80 | self.horizontalLayout.addWidget(self.rtspButton) 81 | 82 | self.retranslateUi(Form) 83 | QtCore.QMetaObject.connectSlotsByName(Form) 84 | 85 | def retranslateUi(self, Form): 86 | _translate = QtCore.QCoreApplication.translate 87 | Form.setWindowTitle(_translate("Form", "Form")) 88 | self.label.setText(_translate("Form", "rtsp address:")) 89 | self.rtspButton.setText(_translate("Form", "confirm")) 90 | import apprcc_rc 91 | -------------------------------------------------------------------------------- /dialog/rtsp_dialog.ui: -------------------------------------------------------------------------------- 1 | 2 | 3 | Form 4 | 5 | 6 | 7 | 0 8 | 0 9 | 783 10 | 40 11 | 12 | 13 | 14 | 15 | 0 16 | 40 17 | 18 | 19 | 20 | 21 | 16777215 22 | 41 23 | 24 | 25 | 26 | Form 27 | 28 | 29 | 30 | :/img/icon/实时视频流解析.png:/img/icon/实时视频流解析.png 31 | 32 | 33 | #Form{background:rgba(120,120,120,255)} 34 | 35 | 36 | 37 | 5 38 | 39 | 40 | 5 41 | 42 | 43 | 44 | 45 | 46 | 0 47 | 30 48 | 49 | 50 | 51 | 52 | 16777215 53 | 30 54 | 55 | 56 | 57 | QLabel{font-family: "Microsoft YaHei"; 58 | font-size: 18px; 59 | font-weight: bold; 60 | color:white;} 61 | 62 | 63 | rtsp address: 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 0 72 | 31 73 | 74 | 75 | 76 | background-color: rgb(207, 207, 207); 77 | 78 | 79 | 80 | 81 | 82 | 83 | QPushButton{font-family: "Microsoft YaHei"; 84 | font-size: 18px; 85 | font-weight: bold; 86 | color:white; 87 | text-align: center center; 88 | padding-left: 5px; 89 | padding-right: 5px; 90 | padding-top: 4px; 91 | padding-bottom: 4px; 92 | border-style: solid; 93 | border-width: 0px; 94 | border-color: rgba(255, 255, 255, 255); 95 | border-radius: 3px; 96 | background-color: rgba(255,255,255,30);} 97 | 98 | QPushButton:focus{outline: none;} 99 | 100 | QPushButton::pressed{font-family: "Microsoft YaHei"; 101 | font-size: 16px; 102 | font-weight: bold; 103 | color:rgb(200,200,200); 104 | text-align: center center; 105 | padding-left: 5px; 106 | padding-right: 5px; 107 | padding-top: 4px; 108 | padding-bottom: 4px; 109 | border-style: solid; 110 | border-width: 0px; 111 | border-color: rgba(255, 255, 255, 255); 112 | border-radius: 3px; 113 | background-color: rgba(255,255,255,150);} 114 | 115 | QPushButton::hover { 116 | border-style: solid; 117 | border-width: 0px; 118 | border-radius: 0px; 119 | background-color: rgba(255,255,255,50);} 120 | 121 | 122 | confirm 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | -------------------------------------------------------------------------------- /dialog/rtsp_win.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from PyQt5.QtWidgets import QApplication, QWidget 3 | from dialog.rtsp_dialog import Ui_Form 4 | 5 | 6 | class Window(QWidget, Ui_Form): 7 | def __init__(self): 8 | super(Window, self).__init__() 9 | self.setupUi(self) 10 | 11 | 12 | if __name__ == '__main__': 13 | app = QApplication(sys.argv) 14 | window = Window() 15 | window.show() 16 | sys.exit(app.exec_()) 17 | -------------------------------------------------------------------------------- /export.py: -------------------------------------------------------------------------------- 1 | """Export a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats 2 | 3 | Usage: 4 | $ python path/to/export.py --weights yolov5s.pt --img 640 --batch 1 5 | """ 6 | 7 | import argparse 8 | import sys 9 | import time 10 | from pathlib import Path 11 | 12 | import torch 13 | import torch.nn as nn 14 | from torch.utils.mobile_optimizer import optimize_for_mobile 15 | 16 | FILE = Path(__file__).absolute() 17 | sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path 18 | 19 | from models.common import Conv 20 | from models.yolo import Detect 21 | from models.experimental import attempt_load 22 | from utils.activations import Hardswish, SiLU 23 | from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging 24 | from utils.torch_utils import select_device 25 | 26 | 27 | def run(weights='./yolov5s.pt', # weights path 28 | img_size=(640, 640), # image (height, width) 29 | batch_size=1, # batch size 30 | device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu 31 | include=('torchscript', 'onnx', 'coreml'), # include formats 32 | half=False, # FP16 half-precision export 33 | inplace=False, # set YOLOv5 Detect() inplace=True 34 | train=False, # model.train() mode 35 | optimize=False, # TorchScript: optimize for mobile 36 | dynamic=False, # ONNX: dynamic axes 37 | simplify=False, # ONNX: simplify model 38 | opset_version=12, # ONNX: opset version 39 | ): 40 | t = time.time() 41 | include = [x.lower() for x in include] 42 | img_size *= 2 if len(img_size) == 1 else 1 # expand 43 | 44 | # Load PyTorch model 45 | device = select_device(device) 46 | assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0' 47 | model = attempt_load(weights, map_location=device) # load FP32 model 48 | labels = model.names 49 | 50 | # Input 51 | gs = int(max(model.stride)) # grid size (max stride) 52 | img_size = [check_img_size(x, gs) for x in img_size] # verify img_size are gs-multiples 53 | img = torch.zeros(batch_size, 3, *img_size).to(device) # image size(1,3,320,192) iDetection 54 | 55 | # Update model 56 | if half: 57 | img, model = img.half(), model.half() # to FP16 58 | model.train() if train else model.eval() # training mode = no Detect() layer grid construction 59 | for k, m in model.named_modules(): 60 | m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility 61 | if isinstance(m, Conv): # assign export-friendly activations 62 | if isinstance(m.act, nn.Hardswish): 63 | m.act = Hardswish() 64 | elif isinstance(m.act, nn.SiLU): 65 | m.act = SiLU() 66 | elif isinstance(m, Detect): 67 | m.inplace = inplace 68 | m.onnx_dynamic = dynamic 69 | # m.forward = m.forward_export # assign forward (optional) 70 | 71 | for _ in range(2): 72 | y = model(img) # dry runs 73 | print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)") 74 | 75 | # TorchScript export ----------------------------------------------------------------------------------------------- 76 | if 'torchscript' in include or 'coreml' in include: 77 | prefix = colorstr('TorchScript:') 78 | try: 79 | print(f'\n{prefix} starting export with torch {torch.__version__}...') 80 | f = weights.replace('.pt', '.torchscript.pt') # filename 81 | ts = torch.jit.trace(model, img, strict=False) 82 | (optimize_for_mobile(ts) if optimize else ts).save(f) 83 | print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') 84 | except Exception as e: 85 | print(f'{prefix} export failure: {e}') 86 | 87 | # ONNX export ------------------------------------------------------------------------------------------------------ 88 | if 'onnx' in include: 89 | prefix = colorstr('ONNX:') 90 | try: 91 | import onnx 92 | 93 | print(f'{prefix} starting export with onnx {onnx.__version__}...') 94 | f = weights.replace('.pt', '.onnx') # filename 95 | torch.onnx.export(model, img, f, verbose=False, opset_version=opset_version, 96 | training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL, 97 | do_constant_folding=not train, 98 | input_names=['images'], 99 | output_names=['output'], 100 | dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640) 101 | 'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85) 102 | } if dynamic else None) 103 | 104 | # Checks 105 | model_onnx = onnx.load(f) # load onnx model 106 | onnx.checker.check_model(model_onnx) # check onnx model 107 | # print(onnx.helper.printable_graph(model_onnx.graph)) # print 108 | 109 | # Simplify 110 | if simplify: 111 | try: 112 | check_requirements(['onnx-simplifier']) 113 | import onnxsim 114 | 115 | print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') 116 | model_onnx, check = onnxsim.simplify( 117 | model_onnx, 118 | dynamic_input_shape=dynamic, 119 | input_shapes={'images': list(img.shape)} if dynamic else None) 120 | assert check, 'assert check failed' 121 | onnx.save(model_onnx, f) 122 | except Exception as e: 123 | print(f'{prefix} simplifier failure: {e}') 124 | print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') 125 | except Exception as e: 126 | print(f'{prefix} export failure: {e}') 127 | 128 | # CoreML export ---------------------------------------------------------------------------------------------------- 129 | if 'coreml' in include: 130 | prefix = colorstr('CoreML:') 131 | try: 132 | import coremltools as ct 133 | 134 | print(f'{prefix} starting export with coremltools {ct.__version__}...') 135 | assert train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`' 136 | model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) 137 | f = weights.replace('.pt', '.mlmodel') # filename 138 | model.save(f) 139 | print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') 140 | except Exception as e: 141 | print(f'{prefix} export failure: {e}') 142 | 143 | # Finish 144 | print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.') 145 | 146 | 147 | def parse_opt(): 148 | parser = argparse.ArgumentParser() 149 | parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') 150 | parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image (height, width)') 151 | parser.add_argument('--batch-size', type=int, default=1, help='batch size') 152 | parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 153 | parser.add_argument('--include', nargs='+', default=['torchscript', 'onnx', 'coreml'], help='include formats') 154 | parser.add_argument('--half', action='store_true', help='FP16 half-precision export') 155 | parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True') 156 | parser.add_argument('--train', action='store_true', help='model.train() mode') 157 | parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile') 158 | parser.add_argument('--dynamic', action='store_true', help='ONNX: dynamic axes') 159 | parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model') 160 | parser.add_argument('--opset-version', type=int, default=12, help='ONNX: opset version') 161 | opt = parser.parse_args() 162 | return opt 163 | 164 | 165 | def main(opt): 166 | set_logging() 167 | print(colorstr('export: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items())) 168 | run(**vars(opt)) 169 | 170 | 171 | if __name__ == "__main__": 172 | opt = parse_opt() 173 | main(opt) 174 | -------------------------------------------------------------------------------- /hubconf.py: -------------------------------------------------------------------------------- 1 | """YOLOv5 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/ 2 | 3 | Usage: 4 | import torch 5 | model = torch.hub.load('ultralytics/yolov5', 'yolov5s') 6 | """ 7 | 8 | import torch 9 | 10 | 11 | def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): 12 | """Creates a specified YOLOv5 model 13 | 14 | Arguments: 15 | name (str): name of model, i.e. 'yolov5s' 16 | pretrained (bool): load pretrained weights into the model 17 | channels (int): number of input channels 18 | classes (int): number of model classes 19 | autoshape (bool): apply YOLOv5 .autoshape() wrapper to model 20 | verbose (bool): print all information to screen 21 | device (str, torch.device, None): device to use for model parameters 22 | 23 | Returns: 24 | YOLOv5 pytorch model 25 | """ 26 | from pathlib import Path 27 | 28 | from models.yolo import Model, attempt_load 29 | from utils.general import check_requirements, set_logging 30 | from utils.google_utils import attempt_download 31 | from utils.torch_utils import select_device 32 | 33 | file = Path(__file__).absolute() 34 | check_requirements(requirements=file.parent / 'requirements.txt', exclude=('tensorboard', 'thop', 'opencv-python')) 35 | set_logging(verbose=verbose) 36 | 37 | save_dir = Path('') if str(name).endswith('.pt') else file.parent 38 | path = (save_dir / name).with_suffix('.pt') # checkpoint path 39 | try: 40 | device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device) 41 | 42 | if pretrained and channels == 3 and classes == 80: 43 | model = attempt_load(path, map_location=device) # download/load FP32 model 44 | else: 45 | cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path 46 | model = Model(cfg, channels, classes) # create model 47 | if pretrained: 48 | ckpt = torch.load(attempt_download(path), map_location=device) # load 49 | msd = model.state_dict() # model state_dict 50 | csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 51 | csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter 52 | model.load_state_dict(csd, strict=False) # load 53 | if len(ckpt['model'].names) == classes: 54 | model.names = ckpt['model'].names # set class names attribute 55 | if autoshape: 56 | model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS 57 | return model.to(device) 58 | 59 | except Exception as e: 60 | help_url = 'https://github.com/ultralytics/yolov5/issues/36' 61 | s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url 62 | raise Exception(s) from e 63 | 64 | 65 | def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None): 66 | # YOLOv5 custom or local model 67 | return _create(path, autoshape=autoshape, verbose=verbose, device=device) 68 | 69 | 70 | def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): 71 | # YOLOv5-small model https://github.com/ultralytics/yolov5 72 | return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device) 73 | 74 | 75 | def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): 76 | # YOLOv5-medium model https://github.com/ultralytics/yolov5 77 | return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device) 78 | 79 | 80 | def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): 81 | # YOLOv5-large model https://github.com/ultralytics/yolov5 82 | return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device) 83 | 84 | 85 | def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): 86 | # YOLOv5-xlarge model https://github.com/ultralytics/yolov5 87 | return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device) 88 | 89 | 90 | def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): 91 | # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5 92 | return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device) 93 | 94 | 95 | def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): 96 | # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5 97 | return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device) 98 | 99 | 100 | def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): 101 | # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5 102 | return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device) 103 | 104 | 105 | def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): 106 | # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5 107 | return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device) 108 | 109 | 110 | if __name__ == '__main__': 111 | model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained 112 | # model = custom(path='path/to/model.pt') # custom 113 | 114 | # Verify inference 115 | import cv2 116 | import numpy as np 117 | from PIL import Image 118 | 119 | imgs = ['data/images/zidane.jpg', # filename 120 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg', # URI 121 | cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV 122 | Image.open('data/images/bus.jpg'), # PIL 123 | np.zeros((320, 640, 3))] # numpy 124 | 125 | results = model(imgs) # batched inference 126 | results.print() 127 | results.save() 128 | -------------------------------------------------------------------------------- /icon/SYSU.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/icon/SYSU.jpg -------------------------------------------------------------------------------- /icon/SYSU0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/icon/SYSU0.png -------------------------------------------------------------------------------- /icon/background.jpg: -------------------------------------------------------------------------------- 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expansion, shortcut 19 | super().__init__() 20 | c_ = int(c2 * e) # hidden channels 21 | self.cv1 = Conv(c1, c_, (1, k), (1, s)) 22 | self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) 23 | self.add = shortcut and c1 == c2 24 | 25 | def forward(self, x): 26 | return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) 27 | 28 | 29 | class Sum(nn.Module): 30 | # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 31 | def __init__(self, n, weight=False): # n: number of inputs 32 | super().__init__() 33 | self.weight = weight # apply weights boolean 34 | self.iter = range(n - 1) # iter object 35 | if weight: 36 | self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights 37 | 38 | def forward(self, x): 39 | y = x[0] # no weight 40 | if self.weight: 41 | w = torch.sigmoid(self.w) * 2 42 | for i in self.iter: 43 | y = y + x[i + 1] * w[i] 44 | else: 45 | for i in self.iter: 46 | y = y + x[i + 1] 47 | return y 48 | 49 | 50 | class MixConv2d(nn.Module): 51 | # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595 52 | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy 53 | super().__init__() 54 | n = len(k) # number of convolutions 55 | if equal_ch: # equal c_ per group 56 | i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices 57 | c_ = [(i == g).sum() for g in range(n)] # intermediate channels 58 | else: # equal weight.numel() per group 59 | b = [c2] + [0] * n 60 | a = np.eye(n + 1, n, k=-1) 61 | a -= np.roll(a, 1, axis=1) 62 | a *= np.array(k) ** 2 63 | a[0] = 1 64 | c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b 65 | 66 | self.m = nn.ModuleList( 67 | [nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]) 68 | self.bn = nn.BatchNorm2d(c2) 69 | self.act = nn.SiLU() 70 | 71 | def forward(self, x): 72 | return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) 73 | 74 | 75 | class Ensemble(nn.ModuleList): 76 | # Ensemble of models 77 | def __init__(self): 78 | super().__init__() 79 | 80 | def forward(self, x, augment=False, profile=False, visualize=False): 81 | y = [] 82 | for module in self: 83 | y.append(module(x, augment, profile, visualize)[0]) 84 | # y = torch.stack(y).max(0)[0] # max ensemble 85 | # y = torch.stack(y).mean(0) # mean ensemble 86 | y = torch.cat(y, 1) # nms ensemble 87 | return y, None # inference, train output 88 | 89 | 90 | def attempt_load(weights, map_location=None, inplace=True, fuse=True): 91 | from models.yolo import Detect, Model 92 | 93 | # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a 94 | model = Ensemble() 95 | for w in weights if isinstance(weights, list) else [weights]: 96 | ckpt = torch.load(attempt_download(w), map_location=map_location) # load 97 | if fuse: 98 | model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model 99 | else: 100 | model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse 101 | 102 | # Compatibility updates 103 | for m in model.modules(): 104 | if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]: 105 | m.inplace = inplace # pytorch 1.7.0 compatibility 106 | if type(m) is Detect: 107 | if not isinstance(m.anchor_grid, list): # new Detect Layer compatibility 108 | delattr(m, 'anchor_grid') 109 | setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) 110 | elif type(m) is Conv: 111 | m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility 112 | 113 | if len(model) == 1: 114 | return model[-1] # return model 115 | else: 116 | print(f'Ensemble created with {weights}\n') 117 | for k in ['names']: 118 | setattr(model, k, getattr(model[-1], k)) 119 | model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride 120 | return model # return ensemble 121 | -------------------------------------------------------------------------------- /models/export.py: -------------------------------------------------------------------------------- 1 | """Exports a YOLOv5 *.pt model to ONNX and TorchScript formats 2 | 3 | Usage: 4 | $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1 5 | """ 6 | 7 | import argparse 8 | import sys 9 | import time 10 | 11 | sys.path.append('./') # to run '$ python *.py' files in subdirectories 12 | 13 | import torch 14 | import torch.nn as nn 15 | 16 | import models 17 | from models.experimental import attempt_load 18 | from utils.activations import Hardswish, SiLU 19 | from utils.general import set_logging, check_img_size 20 | from utils.torch_utils import select_device 21 | 22 | if __name__ == '__main__': 23 | parser = argparse.ArgumentParser() 24 | parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/ 25 | parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width 26 | parser.add_argument('--batch-size', type=int, default=1, help='batch size') 27 | parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') 28 | parser.add_argument('--grid', action='store_true', help='export Detect() layer grid') 29 | parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 30 | opt = parser.parse_args() 31 | opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand 32 | print(opt) 33 | set_logging() 34 | t = time.time() 35 | 36 | # Load PyTorch model 37 | device = select_device(opt.device) 38 | model = attempt_load(opt.weights, map_location=device) # load FP32 model 39 | labels = model.names 40 | 41 | # Checks 42 | gs = int(max(model.stride)) # grid size (max stride) 43 | opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples 44 | 45 | # Input 46 | img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection 47 | 48 | # Update model 49 | for k, m in model.named_modules(): 50 | m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility 51 | if isinstance(m, models.common.Conv): # assign export-friendly activations 52 | if isinstance(m.act, nn.Hardswish): 53 | m.act = Hardswish() 54 | elif isinstance(m.act, nn.SiLU): 55 | m.act = SiLU() 56 | # elif isinstance(m, models.yolo.Detect): 57 | # m.forward = m.forward_export # assign forward (optional) 58 | model.model[-1].export = not opt.grid # set Detect() layer grid export 59 | y = model(img) # dry run 60 | 61 | # TorchScript export 62 | try: 63 | print('\nStarting TorchScript export with torch %s...' % torch.__version__) 64 | f = opt.weights.replace('.pt', '.torchscript.pt') # filename 65 | ts = torch.jit.trace(model, img, strict=False) 66 | ts.save(f) 67 | print('TorchScript export success, saved as %s' % f) 68 | except Exception as e: 69 | print('TorchScript export failure: %s' % e) 70 | 71 | # ONNX export 72 | try: 73 | import onnx 74 | 75 | print('\nStarting ONNX export with onnx %s...' % onnx.__version__) 76 | f = opt.weights.replace('.pt', '.onnx') # filename 77 | torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], 78 | output_names=['classes', 'boxes'] if y is None else ['output'], 79 | dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640) 80 | 'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None) 81 | 82 | # Checks 83 | onnx_model = onnx.load(f) # load onnx model 84 | onnx.checker.check_model(onnx_model) # check onnx model 85 | # print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model 86 | print('ONNX export success, saved as %s' % f) 87 | except Exception as e: 88 | print('ONNX export failure: %s' % e) 89 | 90 | # CoreML export 91 | try: 92 | import coremltools as ct 93 | 94 | print('\nStarting CoreML export with coremltools %s...' % ct.__version__) 95 | # convert model from torchscript and apply pixel scaling as per detect.py 96 | model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) 97 | f = opt.weights.replace('.pt', '.mlmodel') # filename 98 | model.save(f) 99 | print('CoreML export success, saved as %s' % f) 100 | except Exception as e: 101 | print('CoreML export failure: %s' % e) 102 | 103 | # Finish 104 | print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t)) 105 | -------------------------------------------------------------------------------- /models/hub/anchors.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Default anchors for COCO data 3 | 4 | 5 | # P5 ------------------------------------------------------------------------------------------------------------------- 6 | # P5-640: 7 | anchors_p5_640: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | 13 | # P6 ------------------------------------------------------------------------------------------------------------------- 14 | # P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 15 | anchors_p6_640: 16 | - [9,11, 21,19, 17,41] # P3/8 17 | - [43,32, 39,70, 86,64] # P4/16 18 | - [65,131, 134,130, 120,265] # P5/32 19 | - [282,180, 247,354, 512,387] # P6/64 20 | 21 | # P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 22 | anchors_p6_1280: 23 | - [19,27, 44,40, 38,94] # P3/8 24 | - [96,68, 86,152, 180,137] # P4/16 25 | - [140,301, 303,264, 238,542] # P5/32 26 | - [436,615, 739,380, 925,792] # P6/64 27 | 28 | # P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 29 | anchors_p6_1920: 30 | - [28,41, 67,59, 57,141] # P3/8 31 | - [144,103, 129,227, 270,205] # P4/16 32 | - [209,452, 455,396, 358,812] # P5/32 33 | - [653,922, 1109,570, 1387,1187] # P6/64 34 | 35 | 36 | # P7 ------------------------------------------------------------------------------------------------------------------- 37 | # P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 38 | anchors_p7_640: 39 | - [11,11, 13,30, 29,20] # P3/8 40 | - [30,46, 61,38, 39,92] # P4/16 41 | - [78,80, 146,66, 79,163] # P5/32 42 | - [149,150, 321,143, 157,303] # P6/64 43 | - [257,402, 359,290, 524,372] # P7/128 44 | 45 | # P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 46 | anchors_p7_1280: 47 | - [19,22, 54,36, 32,77] # P3/8 48 | - [70,83, 138,71, 75,173] # P4/16 49 | - [165,159, 148,334, 375,151] # P5/32 50 | - [334,317, 251,626, 499,474] # P6/64 51 | - [750,326, 534,814, 1079,818] # P7/128 52 | 53 | # P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 54 | anchors_p7_1920: 55 | - [29,34, 81,55, 47,115] # P3/8 56 | - [105,124, 207,107, 113,259] # P4/16 57 | - [247,238, 222,500, 563,227] # P5/32 58 | - [501,476, 376,939, 749,711] # P6/64 59 | - [1126,489, 801,1222, 1618,1227] # P7/128 60 | -------------------------------------------------------------------------------- /models/hub/yolov3-spp.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 17 | [-1, 1, Bottleneck, [64]], 18 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 19 | [-1, 2, Bottleneck, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 21 | [-1, 8, Bottleneck, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 23 | [-1, 8, Bottleneck, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 25 | [-1, 4, Bottleneck, [1024]], # 10 26 | ] 27 | 28 | # YOLOv3-SPP head 29 | head: 30 | [[-1, 1, Bottleneck, [1024, False]], 31 | [-1, 1, SPP, [512, [5, 9, 13]]], 32 | [-1, 1, Conv, [1024, 3, 1]], 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [256, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Bottleneck, [512, False]], 40 | [-1, 1, Bottleneck, [512, False]], 41 | [-1, 1, Conv, [256, 1, 1]], 42 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 43 | 44 | [-2, 1, Conv, [128, 1, 1]], 45 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 46 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 47 | [-1, 1, Bottleneck, [256, False]], 48 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 49 | 50 | [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 51 | ] 52 | -------------------------------------------------------------------------------- /models/hub/yolov3-tiny.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,14, 23,27, 37,58] # P4/16 9 | - [81,82, 135,169, 344,319] # P5/32 10 | 11 | # YOLOv3-tiny backbone 12 | backbone: 13 | # [from, number, module, args] 14 | [[-1, 1, Conv, [16, 3, 1]], # 0 15 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 16 | [-1, 1, Conv, [32, 3, 1]], 17 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 18 | [-1, 1, Conv, [64, 3, 1]], 19 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 20 | [-1, 1, Conv, [128, 3, 1]], 21 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 22 | [-1, 1, Conv, [256, 3, 1]], 23 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 24 | [-1, 1, Conv, [512, 3, 1]], 25 | [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 26 | [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 27 | ] 28 | 29 | # YOLOv3-tiny head 30 | head: 31 | [[-1, 1, Conv, [1024, 3, 1]], 32 | [-1, 1, Conv, [256, 1, 1]], 33 | [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) 34 | 35 | [-2, 1, Conv, [128, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 38 | [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) 39 | 40 | [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) 41 | ] 42 | -------------------------------------------------------------------------------- /models/hub/yolov3.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 17 | [-1, 1, Bottleneck, [64]], 18 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 19 | [-1, 2, Bottleneck, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 21 | [-1, 8, Bottleneck, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 23 | [-1, 8, Bottleneck, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 25 | [-1, 4, Bottleneck, [1024]], # 10 26 | ] 27 | 28 | # YOLOv3 head 29 | head: 30 | [[-1, 1, Bottleneck, [1024, False]], 31 | [-1, 1, Conv, [512, 1, 1]], 32 | [-1, 1, Conv, [1024, 3, 1]], 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [256, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Bottleneck, [512, False]], 40 | [-1, 1, Bottleneck, [512, False]], 41 | [-1, 1, Conv, [256, 1, 1]], 42 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 43 | 44 | [-2, 1, Conv, [128, 1, 1]], 45 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 46 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 47 | [-1, 1, Bottleneck, [256, False]], 48 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 49 | 50 | [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 51 | ] 52 | -------------------------------------------------------------------------------- /models/hub/yolov5-bifpn.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 BiFPN head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/hub/yolov5-fpn.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 FPN head 28 | head: 29 | [[-1, 3, C3, [1024, False]], # 10 (P5/32-large) 30 | 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 3, C3, [512, False]], # 14 (P4/16-medium) 35 | 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 38 | [-1, 1, Conv, [256, 1, 1]], 39 | [-1, 3, C3, [256, False]], # 18 (P3/8-small) 40 | 41 | [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 42 | ] 43 | -------------------------------------------------------------------------------- /models/hub/yolov5-p2.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 3 # AutoAnchor evolves 3 anchors per P output layer 8 | 9 | # YOLOv5 v6.0 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 13 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 14 | [-1, 3, C3, [128]], 15 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 16 | [-1, 6, C3, [256]], 17 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 18 | [-1, 9, C3, [512]], 19 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 20 | [-1, 3, C3, [1024]], 21 | [-1, 1, SPPF, [1024, 5]], # 9 22 | ] 23 | 24 | # YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs 25 | head: 26 | [[-1, 1, Conv, [512, 1, 1]], 27 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 28 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 29 | [-1, 3, C3, [512, False]], # 13 30 | 31 | [-1, 1, Conv, [256, 1, 1]], 32 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 33 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 34 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 35 | 36 | [-1, 1, Conv, [128, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 2], 1, Concat, [1]], # cat backbone P2 39 | [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) 40 | 41 | [-1, 1, Conv, [128, 3, 2]], 42 | [[-1, 18], 1, Concat, [1]], # cat head P3 43 | [-1, 3, C3, [256, False]], # 24 (P3/8-small) 44 | 45 | [-1, 1, Conv, [256, 3, 2]], 46 | [[-1, 14], 1, Concat, [1]], # cat head P4 47 | [-1, 3, C3, [512, False]], # 27 (P4/16-medium) 48 | 49 | [-1, 1, Conv, [512, 3, 2]], 50 | [[-1, 10], 1, Concat, [1]], # cat head P5 51 | [-1, 3, C3, [1024, False]], # 30 (P5/32-large) 52 | 53 | [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) 54 | ] 55 | -------------------------------------------------------------------------------- /models/hub/yolov5-p34.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 3 # AutoAnchor evolves 3 anchors per P output layer 8 | 9 | # YOLOv5 v6.0 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ], # 0-P1/2 13 | [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 14 | [ -1, 3, C3, [ 128 ] ], 15 | [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 16 | [ -1, 6, C3, [ 256 ] ], 17 | [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 18 | [ -1, 9, C3, [ 512 ] ], 19 | [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 20 | [ -1, 3, C3, [ 1024 ] ], 21 | [ -1, 1, SPPF, [ 1024, 5 ] ], # 9 22 | ] 23 | 24 | # YOLOv5 v6.0 head with (P3, P4) outputs 25 | head: 26 | [ [ -1, 1, Conv, [ 512, 1, 1 ] ], 27 | [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], 28 | [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 29 | [ -1, 3, C3, [ 512, False ] ], # 13 30 | 31 | [ -1, 1, Conv, [ 256, 1, 1 ] ], 32 | [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], 33 | [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 34 | [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) 35 | 36 | [ -1, 1, Conv, [ 256, 3, 2 ] ], 37 | [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 38 | [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) 39 | 40 | [ [ 17, 20 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4) 41 | ] 42 | -------------------------------------------------------------------------------- /models/hub/yolov5-p6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 3 # AutoAnchor evolves 3 anchors per P output layer 8 | 9 | # YOLOv5 v6.0 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 13 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 14 | [-1, 3, C3, [128]], 15 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 16 | [-1, 6, C3, [256]], 17 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 18 | [-1, 9, C3, [512]], 19 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 20 | [-1, 3, C3, [768]], 21 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 22 | [-1, 3, C3, [1024]], 23 | [-1, 1, SPPF, [1024, 5]], # 11 24 | ] 25 | 26 | # YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs 27 | head: 28 | [[-1, 1, Conv, [768, 1, 1]], 29 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 30 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 31 | [-1, 3, C3, [768, False]], # 15 32 | 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 35 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 36 | [-1, 3, C3, [512, False]], # 19 37 | 38 | [-1, 1, Conv, [256, 1, 1]], 39 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 40 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 41 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 42 | 43 | [-1, 1, Conv, [256, 3, 2]], 44 | [[-1, 20], 1, Concat, [1]], # cat head P4 45 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 46 | 47 | [-1, 1, Conv, [512, 3, 2]], 48 | [[-1, 16], 1, Concat, [1]], # cat head P5 49 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 50 | 51 | [-1, 1, Conv, [768, 3, 2]], 52 | [[-1, 12], 1, Concat, [1]], # cat head P6 53 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 54 | 55 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 56 | ] 57 | -------------------------------------------------------------------------------- /models/hub/yolov5-p7.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 3 # AutoAnchor evolves 3 anchors per P output layer 8 | 9 | # YOLOv5 v6.0 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 13 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 14 | [-1, 3, C3, [128]], 15 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 16 | [-1, 6, C3, [256]], 17 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 18 | [-1, 9, C3, [512]], 19 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 20 | [-1, 3, C3, [768]], 21 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 22 | [-1, 3, C3, [1024]], 23 | [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 24 | [-1, 3, C3, [1280]], 25 | [-1, 1, SPPF, [1280, 5]], # 13 26 | ] 27 | 28 | # YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs 29 | head: 30 | [[-1, 1, Conv, [1024, 1, 1]], 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 10], 1, Concat, [1]], # cat backbone P6 33 | [-1, 3, C3, [1024, False]], # 17 34 | 35 | [-1, 1, Conv, [768, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 38 | [-1, 3, C3, [768, False]], # 21 39 | 40 | [-1, 1, Conv, [512, 1, 1]], 41 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 42 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 43 | [-1, 3, C3, [512, False]], # 25 44 | 45 | [-1, 1, Conv, [256, 1, 1]], 46 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 47 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 48 | [-1, 3, C3, [256, False]], # 29 (P3/8-small) 49 | 50 | [-1, 1, Conv, [256, 3, 2]], 51 | [[-1, 26], 1, Concat, [1]], # cat head P4 52 | [-1, 3, C3, [512, False]], # 32 (P4/16-medium) 53 | 54 | [-1, 1, Conv, [512, 3, 2]], 55 | [[-1, 22], 1, Concat, [1]], # cat head P5 56 | [-1, 3, C3, [768, False]], # 35 (P5/32-large) 57 | 58 | [-1, 1, Conv, [768, 3, 2]], 59 | [[-1, 18], 1, Concat, [1]], # cat head P6 60 | [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) 61 | 62 | [-1, 1, Conv, [1024, 3, 2]], 63 | [[-1, 14], 1, Concat, [1]], # cat head P7 64 | [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) 65 | 66 | [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) 67 | ] 68 | -------------------------------------------------------------------------------- /models/hub/yolov5-panet.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 PANet head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/hub/yolov5l6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/hub/yolov5m6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/hub/yolov5n6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.25 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/hub/yolov5s-ghost.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3Ghost, [128]], 18 | [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3Ghost, [256]], 20 | [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3Ghost, [512]], 22 | [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3Ghost, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, GhostConv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3Ghost, [512, False]], # 13 33 | 34 | [-1, 1, GhostConv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, GhostConv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, GhostConv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/hub/yolov5s-transformer.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/hub/yolov5s6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/hub/yolov5x6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.33 # model depth multiple 6 | width_multiple: 1.25 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/yolov5l.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5m.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5n.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.25 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5s.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5x.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.33 # model depth multiple 6 | width_multiple: 1.25 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models0/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/models0/__init__.py -------------------------------------------------------------------------------- /models0/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/models0/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /models0/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/models0/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /models0/__pycache__/common.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/models0/__pycache__/common.cpython-38.pyc -------------------------------------------------------------------------------- /models0/__pycache__/common.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/models0/__pycache__/common.cpython-39.pyc -------------------------------------------------------------------------------- /models0/__pycache__/experimental.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/models0/__pycache__/experimental.cpython-38.pyc -------------------------------------------------------------------------------- /models0/__pycache__/experimental.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/models0/__pycache__/experimental.cpython-39.pyc -------------------------------------------------------------------------------- /models0/__pycache__/yolo.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/models0/__pycache__/yolo.cpython-38.pyc -------------------------------------------------------------------------------- /models0/__pycache__/yolo.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/models0/__pycache__/yolo.cpython-39.pyc -------------------------------------------------------------------------------- /models0/experimental.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 experimental modules 2 | 3 | import numpy as np 4 | import torch 5 | import torch.nn as nn 6 | 7 | from models.common import Conv, DWConv 8 | from utils.google_utils import attempt_download 9 | 10 | 11 | class CrossConv(nn.Module): 12 | # Cross Convolution Downsample 13 | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): 14 | # ch_in, ch_out, kernel, stride, groups, expansion, shortcut 15 | super(CrossConv, self).__init__() 16 | c_ = int(c2 * e) # hidden channels 17 | self.cv1 = Conv(c1, c_, (1, k), (1, s)) 18 | self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) 19 | self.add = shortcut and c1 == c2 20 | 21 | def forward(self, x): 22 | return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) 23 | 24 | 25 | class Sum(nn.Module): 26 | # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 27 | def __init__(self, n, weight=False): # n: number of inputs 28 | super(Sum, self).__init__() 29 | self.weight = weight # apply weights boolean 30 | self.iter = range(n - 1) # iter object 31 | if weight: 32 | self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights 33 | 34 | def forward(self, x): 35 | y = x[0] # no weight 36 | if self.weight: 37 | w = torch.sigmoid(self.w) * 2 38 | for i in self.iter: 39 | y = y + x[i + 1] * w[i] 40 | else: 41 | for i in self.iter: 42 | y = y + x[i + 1] 43 | return y 44 | 45 | 46 | class GhostConv(nn.Module): 47 | # Ghost Convolution https://github.com/huawei-noah/ghostnet 48 | def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups 49 | super(GhostConv, self).__init__() 50 | c_ = c2 // 2 # hidden channels 51 | self.cv1 = Conv(c1, c_, k, s, None, g, act) 52 | self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) 53 | 54 | def forward(self, x): 55 | y = self.cv1(x) 56 | return torch.cat([y, self.cv2(y)], 1) 57 | 58 | 59 | class GhostBottleneck(nn.Module): 60 | # Ghost Bottleneck https://github.com/huawei-noah/ghostnet 61 | def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride 62 | super(GhostBottleneck, self).__init__() 63 | c_ = c2 // 2 64 | self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw 65 | DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw 66 | GhostConv(c_, c2, 1, 1, act=False)) # pw-linear 67 | self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), 68 | Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() 69 | 70 | def forward(self, x): 71 | return self.conv(x) + self.shortcut(x) 72 | 73 | 74 | class MixConv2d(nn.Module): 75 | # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595 76 | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): 77 | super(MixConv2d, self).__init__() 78 | groups = len(k) 79 | if equal_ch: # equal c_ per group 80 | i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices 81 | c_ = [(i == g).sum() for g in range(groups)] # intermediate channels 82 | else: # equal weight.numel() per group 83 | b = [c2] + [0] * groups 84 | a = np.eye(groups + 1, groups, k=-1) 85 | a -= np.roll(a, 1, axis=1) 86 | a *= np.array(k) ** 2 87 | a[0] = 1 88 | c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b 89 | 90 | self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)]) 91 | self.bn = nn.BatchNorm2d(c2) 92 | self.act = nn.LeakyReLU(0.1, inplace=True) 93 | 94 | def forward(self, x): 95 | return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) 96 | 97 | 98 | class Ensemble(nn.ModuleList): 99 | # Ensemble of models 100 | def __init__(self): 101 | super(Ensemble, self).__init__() 102 | 103 | def forward(self, x, augment=False): 104 | y = [] 105 | for module in self: 106 | y.append(module(x, augment)[0]) 107 | # y = torch.stack(y).max(0)[0] # max ensemble 108 | # y = torch.stack(y).mean(0) # mean ensemble 109 | y = torch.cat(y, 1) # nms ensemble 110 | return y, None # inference, train output 111 | 112 | 113 | def attempt_load(weights, map_location=None): 114 | # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a 115 | model = Ensemble() 116 | for w in weights if isinstance(weights, list) else [weights]: 117 | attempt_download(w) 118 | ckpt = torch.load(w, map_location=map_location) # load 119 | model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model 120 | 121 | # Compatibility updates 122 | for m in model.modules(): 123 | if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: 124 | m.inplace = True # pytorch 1.7.0 compatibility 125 | elif type(m) is Conv: 126 | m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility 127 | 128 | if len(model) == 1: 129 | return model[-1] # return model 130 | else: 131 | print('Ensemble created with %s\n' % weights) 132 | for k in ['names', 'stride']: 133 | setattr(model, k, getattr(model[-1], k)) 134 | return model # return ensemble 135 | -------------------------------------------------------------------------------- /models0/export.py: -------------------------------------------------------------------------------- 1 | """Exports a YOLOv5 *.pt model to ONNX and TorchScript formats 2 | 3 | Usage: 4 | $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1 5 | """ 6 | 7 | import argparse 8 | import sys 9 | import time 10 | 11 | sys.path.append('./') # to run '$ python *.py' files in subdirectories 12 | 13 | import torch 14 | import torch.nn as nn 15 | 16 | import models 17 | from models.experimental import attempt_load 18 | from utils.activations import Hardswish, SiLU 19 | from utils.general import set_logging, check_img_size 20 | from utils.torch_utils import select_device 21 | 22 | if __name__ == '__main__': 23 | parser = argparse.ArgumentParser() 24 | parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/ 25 | parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width 26 | parser.add_argument('--batch-size', type=int, default=1, help='batch size') 27 | parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') 28 | parser.add_argument('--grid', action='store_true', help='export Detect() layer grid') 29 | parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 30 | opt = parser.parse_args() 31 | opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand 32 | print(opt) 33 | set_logging() 34 | t = time.time() 35 | 36 | # Load PyTorch model 37 | device = select_device(opt.device) 38 | model = attempt_load(opt.weights, map_location=device) # load FP32 model 39 | labels = model.names 40 | 41 | # Checks 42 | gs = int(max(model.stride)) # grid size (max stride) 43 | opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples 44 | 45 | # Input 46 | img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection 47 | 48 | # Update model 49 | for k, m in model.named_modules(): 50 | m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility 51 | if isinstance(m, models.common.Conv): # assign export-friendly activations 52 | if isinstance(m.act, nn.Hardswish): 53 | m.act = Hardswish() 54 | elif isinstance(m.act, nn.SiLU): 55 | m.act = SiLU() 56 | # elif isinstance(m, models.yolo.Detect): 57 | # m.forward = m.forward_export # assign forward (optional) 58 | model.model[-1].export = not opt.grid # set Detect() layer grid export 59 | y = model(img) # dry run 60 | 61 | # TorchScript export 62 | try: 63 | print('\nStarting TorchScript export with torch %s...' % torch.__version__) 64 | f = opt.weights.replace('.pt', '.torchscript.pt') # filename 65 | ts = torch.jit.trace(model, img, strict=False) 66 | ts.save(f) 67 | print('TorchScript export success, saved as %s' % f) 68 | except Exception as e: 69 | print('TorchScript export failure: %s' % e) 70 | 71 | # ONNX export 72 | try: 73 | import onnx 74 | 75 | print('\nStarting ONNX export with onnx %s...' % onnx.__version__) 76 | f = opt.weights.replace('.pt', '.onnx') # filename 77 | torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], 78 | output_names=['classes', 'boxes'] if y is None else ['output'], 79 | dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640) 80 | 'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None) 81 | 82 | # Checks 83 | onnx_model = onnx.load(f) # load onnx model 84 | onnx.checker.check_model(onnx_model) # check onnx model 85 | # print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model 86 | print('ONNX export success, saved as %s' % f) 87 | except Exception as e: 88 | print('ONNX export failure: %s' % e) 89 | 90 | # CoreML export 91 | try: 92 | import coremltools as ct 93 | 94 | print('\nStarting CoreML export with coremltools %s...' % ct.__version__) 95 | # convert model from torchscript and apply pixel scaling as per detect.py 96 | model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) 97 | f = opt.weights.replace('.pt', '.mlmodel') # filename 98 | model.save(f) 99 | print('CoreML export success, saved as %s' % f) 100 | except Exception as e: 101 | print('CoreML export failure: %s' % e) 102 | 103 | # Finish 104 | print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t)) 105 | -------------------------------------------------------------------------------- /models0/yolov5l.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Focus, [64, 3]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 9, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 1, SPP, [1024, [5, 9, 13]]], 24 | [-1, 3, C3, [1024, False]], # 9 25 | ] 26 | 27 | # YOLOv5 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models0/yolov5m.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 0.67 # model depth multiple 4 | width_multiple: 0.75 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Focus, [64, 3]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 9, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 1, SPP, [1024, [5, 9, 13]]], 24 | [-1, 3, C3, [1024, False]], # 9 25 | ] 26 | 27 | # YOLOv5 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models0/yolov5s.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 0.33 # model depth multiple 4 | width_multiple: 0.50 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Focus, [64, 3]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 9, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 1, SPP, [1024, [5, 9, 13]]], 24 | [-1, 3, C3, [1024, False]], # 9 25 | ] 26 | 27 | # YOLOv5 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models0/yolov5x.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.33 # model depth multiple 4 | width_multiple: 1.25 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Focus, [64, 3]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 9, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 1, SPP, [1024, [5, 9, 13]]], 24 | [-1, 3, C3, [1024, False]], # 9 25 | ] 26 | 27 | # YOLOv5 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /platech.ttf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/platech.ttf -------------------------------------------------------------------------------- /pt/yolov5s.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/pt/yolov5s.pt -------------------------------------------------------------------------------- /push.sh: -------------------------------------------------------------------------------- 1 | echo "#################### config global user name & email ####################" 2 | git config --global user.email "1358366+dyh@users.noreply.github.com" 3 | git config --global user.name "dyh" 4 | 5 | echo "#################### git add . ####################" 6 | git add . 7 | 8 | echo "#################### git pull ####################" 9 | git pull 10 | 11 | echo "#################### git commit -m \"daily\" ####################" 12 | git commit -m "unbox" 13 | 14 | echo "#################### git push -u origin main ####################" 15 | git push -u origin main 16 | 17 | echo "#################### done ####################" 18 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | chardet 2 | dataclasses 3 | pkg-resources 4 | typing-extensions 5 | absl-py 0.15.0 6 | anyio 3.7.0 7 | appdirs 1.4.4 8 | asttokens 2.2.1 9 | astunparse 1.6.3 10 | backcall 0.2.0 11 | bleach 1.5.0 12 | cachetools 5.3.1 13 | certifi 2023.5.7 14 | cffi 1.15.1 15 | charset-normalizer 3.1.0 16 | click 8.1.3 17 | cmake 3.26.3 18 | coloredlogs 15.0.1 19 | contourpy 1.0.7 20 | cycler 0.11.0 21 | decorator 5.1.1 22 | DNN-printer 0.0.2 23 | docker-pycreds 0.4.0 24 | easydict 1.10 25 | exceptiongroup 1.1.1 26 | executing 1.2.0 27 | fastapi 0.97.0 28 | filelock 3.12.0 29 | flatbuffers 1.12 30 | fonttools 4.39.4 31 | gast 0.3.3 32 | gitdb 4.0.10 33 | GitPython 3.1.31 34 | google-auth 2.19.1 35 | google-auth-oauthlib 1.0.0 36 | google-pasta 0.2.0 37 | grpcio 1.54.2 38 | h11 0.14.0 39 | h5py 2.10.0 40 | html5lib 0.9999999 41 | humanfriendly 10.0 42 | hyperlpr3 0.1.3 43 | idna 3.4 44 | importlib-metadata 6.6.0 45 | importlib-resources 5.12.0 46 | imutils 0.5.4 47 | ipython 8.12.2 48 | jedi 0.18.2 49 | Jinja2 3.1.2 50 | Keras-Preprocessing 1.1.2 51 | kiwisolver 1.4.4 52 | lit 16.0.5.post0 53 | loguru 0.7.0 54 | Markdown 3.4.3 55 | MarkupSafe 2.1.3 56 | matplotlib 3.7.1 57 | matplotlib-inline 0.1.6 58 | mpmath 1.3.0 59 | networkx 3.1 60 | numpy 1.22.3 61 | nvidia-cublas-cu11 11.10.3.66 62 | nvidia-cuda-cupti-cu11 11.7.101 63 | nvidia-cuda-nvrtc-cu11 11.7.99 64 | nvidia-cuda-runtime-cu11 11.7.99 65 | nvidia-cudnn-cu11 8.5.0.96 66 | nvidia-cufft-cu11 10.9.0.58 67 | nvidia-curand-cu11 10.2.10.91 68 | nvidia-cusolver-cu11 11.4.0.1 69 | nvidia-cusparse-cu11 11.7.4.91 70 | nvidia-nccl-cu11 2.14.3 71 | nvidia-nvtx-cu11 11.7.91 72 | oauthlib 3.2.2 73 | onnxruntime 1.15.1 74 | opencv-python 4.1.2.30 75 | opt-einsum 3.3.0 76 | packaging 23.1 77 | pandas 2.0.2 78 | parso 0.8.3 79 | pathtools 0.1.2 80 | pexpect 4.8.0 81 | pickleshare 0.7.5 82 | Pillow 9.5.0 83 | pip 23.0.1 84 | prompt-toolkit 3.0.38 85 | protobuf 3.20.3 86 | psutil 5.9.5 87 | ptyprocess 0.7.0 88 | pure-eval 0.2.2 89 | pyasn1 0.5.0 90 | pyasn1-modules 0.3.0 91 | pycparser 2.21 92 | pydantic 1.10.9 93 | Pygments 2.15.1 94 | pyparsing 3.0.9 95 | python-dateutil 2.8.2 96 | python-multipart 0.0.6 97 | pytz 2023.3 98 | PyYAML 6.0 99 | requests 2.31.0 100 | requests-oauthlib 1.3.1 101 | rsa 4.9 102 | scipy 1.10.1 103 | seaborn 0.12.2 104 | sentry-sdk 1.25.1 105 | setproctitle 1.3.2 106 | setuptools 67.8.0 107 | six 1.15.0 108 | smmap 5.0.0 109 | sniffio 1.3.0 110 | soundfile 0.12.1 111 | stack-data 0.6.2 112 | starlette 0.27.0 113 | sympy 1.12 114 | tensorboard 2.13.0 115 | tensorboard-data-server 0.7.1 116 | tensorboard-plugin-wit 1.8.1 117 | tensorflow-estimator 2.4.0 118 | termcolor 1.1.0 119 | thop 0.1.1.post2209072238 120 | torch 1.13.1+cu116 121 | torchaudio 0.13.1+cu116 122 | torchvision 0.14.1+cu116 123 | tqdm 4.65.0 124 | traitlets 5.9.0 125 | triton 2.0.0 126 | typing_extensions 4.6.3 127 | tzdata 2023.3 128 | ultralytics 8.0.114 129 | urllib3 1.26.16 130 | utils 1.0.1 131 | uvicorn 0.22.0 132 | wcwidth 0.2.6 133 | Werkzeug 2.3.6 134 | wheel 0.38.4 135 | wrapt 1.12.1 136 | zipp 3.15.0 -------------------------------------------------------------------------------- /show_license_plate.py: -------------------------------------------------------------------------------- 1 | # 导入cv相关库 2 | import cv2 3 | import numpy as np 4 | from PIL import Image 5 | from PIL import ImageDraw 6 | 7 | 8 | def draw_plate_on_image(img, box, text, font): 9 | x1, y1, x2, y2 = box 10 | cv2.rectangle(img, (x1, y1), (x2, y2), (139, 139, 102), 2, cv2.LINE_AA) 11 | cv2.rectangle(img, (x1, y1 - 20), (x2, y1), (139, 139, 102), -1) 12 | data = Image.fromarray(img) 13 | draw = ImageDraw.Draw(data) 14 | draw.text((x1 + 1, y1 - 18), text, (255, 255, 255), font=font) 15 | res = np.asarray(data) 16 | # print("车牌",res) 17 | return res 18 | -------------------------------------------------------------------------------- /te.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | 4 | def on_mouse_click(event, x, y, flags, param): 5 | if event == cv2.EVENT_LBUTTONDOWN: 6 | print(f'鼠标坐标:({x},{y})') 7 | 8 | # 创建一个空白的窗口，并设置回调函数 9 | cv2.namedWindow('image') 10 | cv2.setMouseCallback('image', on_mouse_click) 11 | 12 | # 在窗口中显示一张图片 13 | img = np.zeros((300, 300, 3), np.uint8) 14 | while(True): 15 | cv2.imshow('image', img) 16 | if cv2.waitKey(1) & 0xFF == ord('q'): 17 | break 18 | 19 | # 释放窗口和资源 20 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /tracker.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import torch 3 | import numpy as np 4 | import math 5 | 6 | from deep_sort.utils.parser import get_config 7 | from deep_sort.deep_sort import DeepSort 8 | import hyperlpr3 as lpr3 9 | 10 | cfg = get_config() 11 | cfg.merge_from_file("../GUI/deep_sort/configs/deep_sort.yaml") 12 | deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT, 13 | max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, 14 | nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, 15 | max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, 16 | use_cuda=True) 17 | 18 | 19 | def draw_bboxes(i, image, bboxes, speed, line_thickness): 20 | catcher = lpr3.LicensePlateCatcher(detect_level=lpr3.DETECT_LEVEL_HIGH) 21 | with open(r"./result/num_" + str(i) + ".txt", 'w') as f: 22 | f.write('Num:\t' + str(len(bboxes)) + '\n') 23 | line_thickness = line_thickness or round( 24 | 0.002 * (image.shape[0] + image.shape[1]) * 0.5) + 1 25 | 26 | list_pts = [] 27 | point_radius = 4 28 | index = 0 29 | for (x1, y1, x2, y2, cls_id, pos_id) in bboxes: 30 | 31 | if len(speed) > 0: 32 | speed_id = speed[index] 33 | else: 34 | speed_id = 0 35 | # print("speed: ", speed) 36 | # print("speed_id: ", speed_id) 37 | index += 1 38 | if index >= len(speed): 39 | index -= 1 40 | color = (0, 255, 0) 41 | 42 | # 撞线的点 43 | check_point_x = x1 44 | check_point_y = int(y1 + ((y2 - y1) * 0.6)) 45 | 46 | c1, c2 = (x1, y1), (x2, y2) 47 | cv2.rectangle(image, c1, c2, color, thickness=line_thickness, lineType=cv2.LINE_AA) 48 | 49 | font_thickness = max(line_thickness - 1, 1) 50 | t_size = cv2.getTextSize(cls_id, 0, fontScale=line_thickness / 3, thickness=font_thickness)[0] 51 | c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 52 | cv2.rectangle(image, c1, c2, color, -1, cv2.LINE_AA) # filled 53 | if cls_id == 'person' or cls_id == 'bicycle' or cls_id == 'motorbicycle': 54 | cv2.putText(image, '{} ID-{}'.format(cls_id, pos_id), (c1[0], c1[1] - 2), 0, 55 | line_thickness / 3, 56 | [225, 255, 255], thickness=font_thickness, lineType=cv2.LINE_AA) 57 | else: 58 | cv2.putText(image, '{} ID-{},{}km/h'.format(cls_id, pos_id, speed_id), (c1[0], c1[1] - 2), 0, line_thickness / 3, 59 | [225, 255, 255], thickness=font_thickness, lineType=cv2.LINE_AA) 60 | # print("ssss: ", pos_id, speed_id) 61 | # results = catcher(image[x1:y1,x2:y2]) 62 | # print('MMMMM: ',x1,y1,x2,y2) 63 | # cv2.imshow('car',image[y1:y2,x1:x2]) 64 | results = catcher(image[y1:y2,x1:x2]) 65 | # print('SSSS: ',results) 66 | 67 | if results != []: 68 | with open(r"./result/num_" + str(i) + ".txt", 'a') as f: 69 | f.write('ID' + str(pos_id) + ':\t' + cls_id + '\t' + str(speed_id) + '\t' + results[0][0] + '\t' + str(results[0][1]) + '\n') 70 | else: 71 | with open(r"./result/num_" + str(i) + ".txt", 'a') as f: 72 | f.write('ID' + str(pos_id) + ':\t' + cls_id + '\t' + str(speed_id) + '\t' + '0' + '\t' + '0' + '\n') 73 | # with open(r"./result/num_" + str(i) + ".txt", 'a') as f: 74 | # f.write('ID' + str(pos_id) + '\n') 75 | 76 | list_pts.append([check_point_x - point_radius, check_point_y - point_radius]) 77 | list_pts.append([check_point_x - point_radius, check_point_y + point_radius]) 78 | list_pts.append([check_point_x + point_radius, check_point_y + point_radius]) 79 | list_pts.append([check_point_x + point_radius, check_point_y - point_radius]) 80 | 81 | ndarray_pts = np.array(list_pts, np.int32) 82 | 83 | cv2.fillPoly(image, [ndarray_pts], color=(0, 0, 255)) 84 | 85 | list_pts.clear() 86 | 87 | return image 88 | 89 | 90 | def update(bboxes, image): 91 | bbox_xywh = [] 92 | confs = [] 93 | bboxes2draw = [] 94 | 95 | if len(bboxes) > 0: 96 | for x1, y1, x2, y2, lbl, conf in bboxes: 97 | obj = [ 98 | int((x1 + x2) * 0.5), int((y1 + y2) * 0.5), 99 | x2 - x1, y2 - y1 100 | ] 101 | bbox_xywh.append(obj) 102 | confs.append(conf) 103 | 104 | xywhs = torch.Tensor(bbox_xywh) 105 | confss = torch.Tensor(confs) 106 | 107 | outputs = deepsort.update(xywhs, confss, image) 108 | 109 | for x1, y1, x2, y2, track_id in list(outputs): 110 | # x1, y1, x2, y2, track_id = value 111 | center_x = (x1 + x2) * 0.5 112 | center_y = (y1 + y2) * 0.5 113 | 114 | label = search_label(center_x=center_x, center_y=center_y, 115 | bboxes_xyxy=bboxes, max_dist_threshold=20.0) 116 | 117 | bboxes2draw.append((x1, y1, x2, y2, label, track_id)) 118 | pass 119 | pass 120 | 121 | return bboxes2draw 122 | 123 | 124 | def search_label(center_x, center_y, bboxes_xyxy, max_dist_threshold): 125 | """ 126 | 在 yolov5 的 bbox 中搜索中心点最接近的label 127 | :param center_x: 128 | :param center_y: 129 | :param bboxes_xyxy: 130 | :param max_dist_threshold: 131 | :return: 字符串 132 | """ 133 | label = '' 134 | # min_label = '' 135 | min_dist = -1.0 136 | 137 | for x1, y1, x2, y2, lbl, conf in bboxes_xyxy: 138 | center_x2 = (x1 + x2) * 0.5 139 | center_y2 = (y1 + y2) * 0.5 140 | 141 | # 横纵距离都小于 max_dist 142 | min_x = abs(center_x2 - center_x) 143 | min_y = abs(center_y2 - center_y) 144 | 145 | if min_x < max_dist_threshold and min_y < max_dist_threshold: 146 | # 距离阈值，判断是否在允许误差范围内 147 | # 取 x, y 方向上的距离平均值 148 | avg_dist = (min_x + min_y) * 0.5 149 | if min_dist == -1.0: 150 | # 第一次赋值 151 | min_dist = avg_dist 152 | # 赋值label 153 | label = lbl 154 | pass 155 | else: 156 | # 若不是第一次，则距离小的优先 157 | if avg_dist < min_dist: 158 | min_dist = avg_dist 159 | # label 160 | label = lbl 161 | pass 162 | pass 163 | pass 164 | 165 | return label 166 | 167 | def Estimated_speed(locations, fps, width): # 基于 deepssort 的车速检测 168 | print("loca ", locations) 169 | present_IDs = [] 170 | prev_IDs = [] 171 | work_IDs = [] 172 | work_IDs_index = [] 173 | work_IDs_prev_index = [] 174 | work_locations = [] # 当前帧数据：中心点x坐标、中心点y坐标、目标序号、车辆类别、车辆像素宽度 175 | work_prev_locations = [] # 上一帧数据，数据格式相同 176 | speed = [] 177 | for i in range(len(locations[1])): 178 | present_IDs.append(locations[1][i][5]) # 获得当前帧中跟踪到车辆的ID locations[1][i][1] label locations[1][i][2] ID 179 | for i in range(len(locations[0])): 180 | prev_IDs.append(locations[0][i][5]) # 获得前一帧中跟踪到车辆的ID 181 | for m, n in enumerate(present_IDs): 182 | if n in prev_IDs: # 进行筛选，找到在两帧图像中均被检测到的有效车辆ID，存入work_IDs中 183 | work_IDs.append(n) 184 | work_IDs_index.append(m) 185 | for x in work_IDs_index: # 将当前帧有效检测车辆的信息存入work_locations中 186 | work_locations.append(locations[1][x]) 187 | for y, z in enumerate(prev_IDs): 188 | if z in work_IDs: # 将前一帧有效检测车辆的ID索引存入work_IDs_prev_index中 189 | work_IDs_prev_index.append(y) 190 | for x in work_IDs_prev_index: # 将前一帧有效检测车辆的信息存入work_prev_locations中 191 | work_prev_locations.append(locations[0][x]) 192 | print("work_locations: ", work_locations) 193 | print("work_prev_locations: ", work_prev_locations) 194 | for i in range(len(work_IDs)): 195 | ss = ((math.sqrt((work_locations[i][0] - work_prev_locations[i][0]) ** 2 + # 计算有效检测车辆的速度，采用线性的从像素距离到真实空间距离的映射 196 | (work_locations[i][1] - work_prev_locations[i][1]) ** 2) * # 当视频拍摄视角并不垂直于车辆移动轨迹时，测算出来的速度将比实际速度低 197 | 500 / (work_locations[i][3]) * fps / 5 * 3.6 * 2)) 198 | speed.append(math.sqrt(ss)*1.5) # [work_locations[i][3]] 199 | for i in range(len(speed)): 200 | speed[i] = round(speed[i], 1) #work_locations[i][2]] # 将保留一位小数的单位为km/h的车辆速度及其ID存入speed二维列表中 201 | return speed 202 | -------------------------------------------------------------------------------- /utils0/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/utils0/__init__.py -------------------------------------------------------------------------------- /utils0/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- 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---------------------------------------------------------------------------- 9 | class SiLU(nn.Module): # export-friendly version of nn.SiLU() 10 | @staticmethod 11 | def forward(x): 12 | return x * torch.sigmoid(x) 13 | 14 | 15 | class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() 16 | @staticmethod 17 | def forward(x): 18 | # return x * F.hardsigmoid(x) # for torchscript and CoreML 19 | return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX 20 | 21 | 22 | class MemoryEfficientSwish(nn.Module): 23 | class F(torch.autograd.Function): 24 | @staticmethod 25 | def forward(ctx, x): 26 | ctx.save_for_backward(x) 27 | return x * torch.sigmoid(x) 28 | 29 | @staticmethod 30 | def backward(ctx, grad_output): 31 | x = ctx.saved_tensors[0] 32 | sx = torch.sigmoid(x) 33 | return grad_output * (sx * (1 + x * (1 - sx))) 34 | 35 | def forward(self, x): 36 | return self.F.apply(x) 37 | 38 | 39 | # Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- 40 | class Mish(nn.Module): 41 | @staticmethod 42 | def forward(x): 43 | return x * F.softplus(x).tanh() 44 | 45 | 46 | class MemoryEfficientMish(nn.Module): 47 | class F(torch.autograd.Function): 48 | @staticmethod 49 | def forward(ctx, x): 50 | ctx.save_for_backward(x) 51 | return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) 52 | 53 | @staticmethod 54 | def backward(ctx, grad_output): 55 | x = ctx.saved_tensors[0] 56 | sx = torch.sigmoid(x) 57 | fx = F.softplus(x).tanh() 58 | return grad_output * (fx + x * sx * (1 - fx * fx)) 59 | 60 | def forward(self, x): 61 | return self.F.apply(x) 62 | 63 | 64 | # FReLU https://arxiv.org/abs/2007.11824 ------------------------------------------------------------------------------- 65 | class FReLU(nn.Module): 66 | def __init__(self, c1, k=3): # ch_in, kernel 67 | super().__init__() 68 | self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) 69 | self.bn = nn.BatchNorm2d(c1) 70 | 71 | def forward(self, x): 72 | return torch.max(x, self.bn(self.conv(x))) 73 | -------------------------------------------------------------------------------- /utils0/autoanchor.py: -------------------------------------------------------------------------------- 1 | # Auto-anchor utils 2 | 3 | import numpy as np 4 | import torch 5 | import yaml 6 | from scipy.cluster.vq import kmeans 7 | from tqdm import tqdm 8 | 9 | from utils.general import colorstr 10 | 11 | 12 | def check_anchor_order(m): 13 | # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary 14 | a = m.anchor_grid.prod(-1).view(-1) # anchor area 15 | da = a[-1] - a[0] # delta a 16 | ds = m.stride[-1] - m.stride[0] # delta s 17 | if da.sign() != ds.sign(): # same order 18 | print('Reversing anchor order') 19 | m.anchors[:] = m.anchors.flip(0) 20 | m.anchor_grid[:] = m.anchor_grid.flip(0) 21 | 22 | 23 | def check_anchors(dataset, model, thr=4.0, imgsz=640): 24 | # Check anchor fit to data, recompute if necessary 25 | prefix = colorstr('autoanchor: ') 26 | print(f'\n{prefix}Analyzing anchors... ', end='') 27 | m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() 28 | shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) 29 | scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale 30 | wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh 31 | 32 | def metric(k): # compute metric 33 | r = wh[:, None] / k[None] 34 | x = torch.min(r, 1. / r).min(2)[0] # ratio metric 35 | best = x.max(1)[0] # best_x 36 | aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold 37 | bpr = (best > 1. / thr).float().mean() # best possible recall 38 | return bpr, aat 39 | 40 | anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors 41 | bpr, aat = metric(anchors) 42 | print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') 43 | if bpr < 0.98: # threshold to recompute 44 | print('. Attempting to improve anchors, please wait...') 45 | na = m.anchor_grid.numel() // 2 # number of anchors 46 | try: 47 | anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) 48 | except Exception as e: 49 | print(f'{prefix}ERROR: {e}') 50 | new_bpr = metric(anchors)[0] 51 | if new_bpr > bpr: # replace anchors 52 | anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) 53 | m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference 54 | m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss 55 | check_anchor_order(m) 56 | print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') 57 | else: 58 | print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.') 59 | print('') # newline 60 | 61 | 62 | def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): 63 | """ Creates kmeans-evolved anchors from training dataset 64 | 65 | Arguments: 66 | path: path to dataset *.yaml, or a loaded dataset 67 | n: number of anchors 68 | img_size: image size used for training 69 | thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 70 | gen: generations to evolve anchors using genetic algorithm 71 | verbose: print all results 72 | 73 | Return: 74 | k: kmeans evolved anchors 75 | 76 | Usage: 77 | from utils.autoanchor import *; _ = kmean_anchors() 78 | """ 79 | thr = 1. / thr 80 | prefix = colorstr('autoanchor: ') 81 | 82 | def metric(k, wh): # compute metrics 83 | r = wh[:, None] / k[None] 84 | x = torch.min(r, 1. / r).min(2)[0] # ratio metric 85 | # x = wh_iou(wh, torch.tensor(k)) # iou metric 86 | return x, x.max(1)[0] # x, best_x 87 | 88 | def anchor_fitness(k): # mutation fitness 89 | _, best = metric(torch.tensor(k, dtype=torch.float32), wh) 90 | return (best * (best > thr).float()).mean() # fitness 91 | 92 | def print_results(k): 93 | k = k[np.argsort(k.prod(1))] # sort small to large 94 | x, best = metric(k, wh0) 95 | bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr 96 | print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr') 97 | print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' 98 | f'past_thr={x[x > thr].mean():.3f}-mean: ', end='') 99 | for i, x in enumerate(k): 100 | print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg 101 | return k 102 | 103 | if isinstance(path, str): # *.yaml file 104 | with open(path) as f: 105 | data_dict = yaml.load(f, Loader=yaml.SafeLoader) # model dict 106 | from utils.datasets import LoadImagesAndLabels 107 | dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) 108 | else: 109 | dataset = path # dataset 110 | 111 | # Get label wh 112 | shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) 113 | wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh 114 | 115 | # Filter 116 | i = (wh0 < 3.0).any(1).sum() 117 | if i: 118 | print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') 119 | wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels 120 | # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 121 | 122 | # Kmeans calculation 123 | print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') 124 | s = wh.std(0) # sigmas for whitening 125 | k, dist = kmeans(wh / s, n, iter=30) # points, mean distance 126 | assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}') 127 | k *= s 128 | wh = torch.tensor(wh, dtype=torch.float32) # filtered 129 | wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered 130 | k = print_results(k) 131 | 132 | # Plot 133 | # k, d = [None] * 20, [None] * 20 134 | # for i in tqdm(range(1, 21)): 135 | # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance 136 | # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) 137 | # ax = ax.ravel() 138 | # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') 139 | # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh 140 | # ax[0].hist(wh[wh[:, 0]<100, 0],400) 141 | # ax[1].hist(wh[wh[:, 1]<100, 1],400) 142 | # fig.savefig('wh.png', dpi=200) 143 | 144 | # Evolve 145 | npr = np.random 146 | f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma 147 | pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:') # progress bar 148 | for _ in pbar: 149 | v = np.ones(sh) 150 | while (v == 1).all(): # mutate until a change occurs (prevent duplicates) 151 | v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) 152 | kg = (k.copy() * v).clip(min=2.0) 153 | fg = anchor_fitness(kg) 154 | if fg > f: 155 | f, k = fg, kg.copy() 156 | pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' 157 | if verbose: 158 | print_results(k) 159 | 160 | return print_results(k) 161 | -------------------------------------------------------------------------------- /utils0/aws/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/utils0/aws/__init__.py -------------------------------------------------------------------------------- /utils0/aws/mime.sh: -------------------------------------------------------------------------------- 1 | # AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ 2 | # This script will run on every instance restart, not only on first start 3 | # --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- 4 | 5 | Content-Type: multipart/mixed; boundary="//" 6 | MIME-Version: 1.0 7 | 8 | --// 9 | Content-Type: text/cloud-config; charset="us-ascii" 10 | MIME-Version: 1.0 11 | Content-Transfer-Encoding: 7bit 12 | Content-Disposition: attachment; filename="cloud-config.txt" 13 | 14 | #cloud-config 15 | cloud_final_modules: 16 | - [scripts-user, always] 17 | 18 | --// 19 | Content-Type: text/x-shellscript; charset="us-ascii" 20 | MIME-Version: 1.0 21 | Content-Transfer-Encoding: 7bit 22 | Content-Disposition: attachment; filename="userdata.txt" 23 | 24 | #!/bin/bash 25 | # --- paste contents of userdata.sh here --- 26 | --// 27 | -------------------------------------------------------------------------------- /utils0/aws/resume.py: -------------------------------------------------------------------------------- 1 | # Resume all interrupted trainings in yolov5/ dir including DDP trainings 2 | # Usage: $ python utils/aws/resume.py 3 | 4 | import os 5 | import sys 6 | from pathlib import Path 7 | 8 | import torch 9 | import yaml 10 | 11 | sys.path.append('./') # to run '$ python *.py' files in subdirectories 12 | 13 | port = 0 # --master_port 14 | path = Path('').resolve() 15 | for last in path.rglob('*/**/last.pt'): 16 | ckpt = torch.load(last) 17 | if ckpt['optimizer'] is None: 18 | continue 19 | 20 | # Load opt.yaml 21 | with open(last.parent.parent / 'opt.yaml') as f: 22 | opt = yaml.load(f, Loader=yaml.SafeLoader) 23 | 24 | # Get device count 25 | d = opt['device'].split(',') # devices 26 | nd = len(d) # number of devices 27 | ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel 28 | 29 | if ddp: # multi-GPU 30 | port += 1 31 | cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' 32 | else: # single-GPU 33 | cmd = f'python train.py --resume {last}' 34 | 35 | cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread 36 | print(cmd) 37 | os.system(cmd) 38 | -------------------------------------------------------------------------------- /utils0/aws/userdata.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html 3 | # This script will run only once on first instance start (for a re-start script see mime.sh) 4 | # /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir 5 | # Use >300 GB SSD 6 | 7 | cd home/ubuntu 8 | if [ ! -d yolov5 ]; then 9 | echo "Running first-time script." # install dependencies, download COCO, pull Docker 10 | git clone https://github.com/ultralytics/yolov5 && sudo chmod -R 777 yolov5 11 | cd yolov5 12 | bash data/scripts/get_coco.sh && echo "Data done." & 13 | sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & 14 | python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & 15 | wait && echo "All tasks done." # finish background tasks 16 | else 17 | echo "Running re-start script." # resume interrupted runs 18 | i=0 19 | list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' 20 | while IFS= read -r id; do 21 | ((i++)) 22 | echo "restarting container $i: $id" 23 | sudo docker start $id 24 | # sudo docker exec -it $id python train.py --resume # single-GPU 25 | sudo docker exec -d $id python utils/aws/resume.py # multi-scenario 26 | done <<<"$list" 27 | fi 28 | -------------------------------------------------------------------------------- /utils0/google_app_engine/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM gcr.io/google-appengine/python 2 | 3 | # Create a virtualenv for dependencies. This isolates these packages from 4 | # system-level packages. 5 | # Use -p python3 or -p python3.7 to select python version. Default is version 2. 6 | RUN virtualenv /env -p python3 7 | 8 | # Setting these environment variables are the same as running 9 | # source /env/bin/activate. 10 | ENV VIRTUAL_ENV /env 11 | ENV PATH /env/bin:$PATH 12 | 13 | RUN apt-get update && apt-get install -y python-opencv 14 | 15 | # Copy the application's requirements.txt and run pip to install all 16 | # dependencies into the virtualenv. 17 | ADD requirements.txt /app/requirements.txt 18 | RUN pip install -r /app/requirements.txt 19 | 20 | # Add the application source code. 21 | ADD . /app 22 | 23 | # Run a WSGI server to serve the application. gunicorn must be declared as 24 | # a dependency in requirements.txt. 25 | CMD gunicorn -b :$PORT main:app 26 | -------------------------------------------------------------------------------- /utils0/google_app_engine/additional_requirements.txt: -------------------------------------------------------------------------------- 1 | # add these requirements in your app on top of the existing ones 2 | pip==18.1 3 | Flask==1.0.2 4 | gunicorn==19.9.0 5 | -------------------------------------------------------------------------------- /utils0/google_app_engine/app.yaml: -------------------------------------------------------------------------------- 1 | runtime: custom 2 | env: flex 3 | 4 | service: yolov5app 5 | 6 | liveness_check: 7 | initial_delay_sec: 600 8 | 9 | manual_scaling: 10 | instances: 1 11 | resources: 12 | cpu: 1 13 | memory_gb: 4 14 | disk_size_gb: 20 -------------------------------------------------------------------------------- /utils0/google_utils.py: -------------------------------------------------------------------------------- 1 | # Google utils: https://cloud.google.com/storage/docs/reference/libraries 2 | 3 | import os 4 | import platform 5 | import subprocess 6 | import time 7 | from pathlib import Path 8 | 9 | import requests 10 | import torch 11 | 12 | 13 | def gsutil_getsize(url=''): 14 | # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du 15 | s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') 16 | return eval(s.split(' ')[0]) if len(s) else 0 # bytes 17 | 18 | 19 | def attempt_download(file, repo='ultralytics/yolov5'): 20 | # Attempt file download if does not exist 21 | file = Path(str(file).strip().replace("'", '').lower()) 22 | 23 | if not file.exists(): 24 | try: 25 | response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api 26 | assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] 27 | tag = response['tag_name'] # i.e. 'v1.0' 28 | except: # fallback plan 29 | assets = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt'] 30 | tag = subprocess.check_output('git tag', shell=True).decode().split()[-1] 31 | 32 | name = file.name 33 | if name in assets: 34 | msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/' 35 | redundant = False # second download option 36 | try: # GitHub 37 | url = f'https://github.com/{repo}/releases/download/{tag}/{name}' 38 | print(f'Downloading {url} to {file}...') 39 | torch.hub.download_url_to_file(url, file) 40 | assert file.exists() and file.stat().st_size > 1E6 # check 41 | except Exception as e: # GCP 42 | print(f'Download error: {e}') 43 | assert redundant, 'No secondary mirror' 44 | url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' 45 | print(f'Downloading {url} to {file}...') 46 | os.system(f'curl -L {url} -o {file}') # torch.hub.download_url_to_file(url, weights) 47 | finally: 48 | if not file.exists() or file.stat().st_size < 1E6: # check 49 | file.unlink(missing_ok=True) # remove partial downloads 50 | print(f'ERROR: Download failure: {msg}') 51 | print('') 52 | return 53 | 54 | 55 | def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'): 56 | # Downloads a file from Google Drive. from yolov5.utils.google_utils import *; gdrive_download() 57 | t = time.time() 58 | file = Path(file) 59 | cookie = Path('cookie') # gdrive cookie 60 | print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') 61 | file.unlink(missing_ok=True) # remove existing file 62 | cookie.unlink(missing_ok=True) # remove existing cookie 63 | 64 | # Attempt file download 65 | out = "NUL" if platform.system() == "Windows" else "/dev/null" 66 | os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') 67 | if os.path.exists('cookie'): # large file 68 | s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' 69 | else: # small file 70 | s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' 71 | r = os.system(s) # execute, capture return 72 | cookie.unlink(missing_ok=True) # remove existing cookie 73 | 74 | # Error check 75 | if r != 0: 76 | file.unlink(missing_ok=True) # remove partial 77 | print('Download error ') # raise Exception('Download error') 78 | return r 79 | 80 | # Unzip if archive 81 | if file.suffix == '.zip': 82 | print('unzipping... ', end='') 83 | os.system(f'unzip -q {file}') # unzip 84 | file.unlink() # remove zip to free space 85 | 86 | print(f'Done ({time.time() - t:.1f}s)') 87 | return r 88 | 89 | 90 | def get_token(cookie="./cookie"): 91 | with open(cookie) as f: 92 | for line in f: 93 | if "download" in line: 94 | return line.split()[-1] 95 | return "" 96 | 97 | # def upload_blob(bucket_name, source_file_name, destination_blob_name): 98 | # # Uploads a file to a bucket 99 | # # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python 100 | # 101 | # storage_client = storage.Client() 102 | # bucket = storage_client.get_bucket(bucket_name) 103 | # blob = bucket.blob(destination_blob_name) 104 | # 105 | # blob.upload_from_filename(source_file_name) 106 | # 107 | # print('File {} uploaded to {}.'.format( 108 | # source_file_name, 109 | # destination_blob_name)) 110 | # 111 | # 112 | # def download_blob(bucket_name, source_blob_name, destination_file_name): 113 | # # Uploads a blob from a bucket 114 | # storage_client = storage.Client() 115 | # bucket = storage_client.get_bucket(bucket_name) 116 | # blob = bucket.blob(source_blob_name) 117 | # 118 | # blob.download_to_filename(destination_file_name) 119 | # 120 | # print('Blob {} downloaded to {}.'.format( 121 | # source_blob_name, 122 | # destination_file_name)) 123 | -------------------------------------------------------------------------------- /utils0/wandb_logging/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donahowe/traffic-detect-GUI/539724440508c46cad409ec602aae26e35f002b6/utils0/wandb_logging/__init__.py -------------------------------------------------------------------------------- /utils0/wandb_logging/log_dataset.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import yaml 4 | 5 | from wandb_utils import WandbLogger 6 | 7 | WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' 8 | 9 | 10 | def create_dataset_artifact(opt): 11 | with open(opt.data) as f: 12 | data = yaml.load(f, Loader=yaml.SafeLoader) # data dict 13 | logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') 14 | 15 | 16 | if __name__ == '__main__': 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') 19 | parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') 20 | parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') 21 | opt = parser.parse_args() 22 | opt.resume = False # Explicitly disallow resume check for dataset upload job 23 | 24 | create_dataset_artifact(opt) 25 | --------------------------------------------------------------------------------