├── Annotation └── 1066405,1b8000ef60354f.xml ├── README.md ├── coco128.yaml ├── detect_auto.py ├── detector_classes.txt ├── images ├── 1066405,100a000985ef070.jpg ├── 1066405,138f000e0722fb6.jpg ├── 1066405,145000347015bf.jpg ├── 1066405,19c000252c07c1.jpg ├── 1066405,1b8000ef60354f.jpg ├── 1066405,203e100058ea258d.jpg ├── 1066405,20860000ee0e092d.jpg ├── 1066405,208d6000f2df55e2.jpg ├── 1066405,218a700048baf6a5.jpg ├── 1066405,21a7d000f2a62a47.jpg ├── 1066405,21a91000dadf00db.jpg ├── 1066405,21cb2000146af46d.jpg ├── 1066405,21d8c000adfab713.jpg ├── 1066405,2285400037042458.jpg ├── 1066405,22995000cde69f78.jpg ├── 1066405,230d5000e420f416.jpg ├── 1066405,237810002ffe79ac.jpg ├── 1066405,239eb000e5646ae4.jpg ├── 1066405,23cba00002ff14cc.jpg ├── 1066405,241240008ab43ec5.jpg ├── 1066405,242210007467cafb.jpg ├── 1066405,245c90002f8426d4.jpg ├── 1066405,2465d0009e9e6ac5.jpg ├── 1066405,247ba00091c96751.jpg ├── 1066405,248180001b360287.jpg ├── 1066405,25122000e3016665.jpg ├── 1066405,2536200096ff33dd.jpg ├── 1066405,26e6300093359464.jpg ├── 1066405,2767d000c415f4dd.jpg ├── 1066405,278c500023fc022b.jpg ├── 1066405,27c2000039cf5bc.jpg ├── 1066405,291b00006e60dcd4.jpg ├── 1066405,2928e000517d7fdd.jpg ├── 1066405,292cf00075ce55d9.jpg ├── 1066405,295ee0009ddbbec8.jpg ├── 1066405,29d100049e5bdb1.jpg ├── 1066405,29dad0002f46a6ba.jpg ├── 1066405,2a72f000f214d26a.jpg ├── 1066405,2ac2400079a6d80f.jpg ├── 1066405,2b6000ffe20c07.jpg ├── 1066405,2bf6e00075455d3c.jpg ├── 1066405,2bfbf000c47880b7.jpg ├── 1066405,2c8c7000530eb0e7.jpg ├── 1066405,2cdca0006185e7eb.jpg ├── 1066405,2d2c6000adf6f6f4.jpg ├── 1066405,2d33a000c7763864.jpg ├── 1066405,2d6f2000fec9dcab.jpg ├── 1066405,2d7ff000e71fdf94.jpg ├── 1066405,2d8cc000744a9d77.jpg ├── 1066405,2eaf00010697e58.jpg ├── 1066405,2f53000bf95ec86.jpg ├── 1066405,2fabf0005345b98f.jpg ├── 1066405,2ff1d00066a506a2.jpg ├── 1066405,3160004fdd79b1.jpg ├── 1066405,31f20000361a07b.jpg ├── 1066405,3331000369bf491.jpg ├── 1066405,44b1a000cfdd13b4.jpg ├── 1066405,451e2000c914a83f.jpg ├── 1066405,45222000bb360294.jpg ├── 1066405,4546c00060322a60.jpg ├── 1066405,4555b0004fb0e3cd.jpg ├── 1066405,4558c000e8616649.jpg ├── 1066405,464270001967ddfc.jpg ├── 1066405,4698c00024435919.jpg ├── 1066405,46c0f000fddbfabd.jpg ├── 1066405,46d8f000f000c3ba.jpg ├── 1066405,48e09000df8221ea.jpg ├── 1066405,49e44000a5111f69.jpg ├── 1066405,4a3400068afccd0.jpg ├── 1066405,4a401000f1028c5f.jpg ├── 1066405,4a5e00003fbd5ba9.jpg ├── 1066405,4a61c000dac3be80.jpg ├── 1066405,4a641000c285435c.jpg ├── 1066405,4a84e000b5993cba.jpg ├── 1066405,4a8b90009c5a4db8.jpg ├── 1066405,4b1ff0007e9d5ad6.jpg ├── 1066405,4b24e000859e8a86.jpg ├── 1066405,4b2860001a557210.jpg ├── 1066405,4b353000430772fd.jpg ├── 1066405,4b3d80004c3edf0b.jpg ├── 1066405,4b4120002d5006f5.jpg ├── 1066405,4b60b00012556ead.jpg ├── 1066405,4b854000ce22bfb7.jpg ├── 1066405,4b8ca0004e29ce23.jpg ├── 1066405,4c18d000b6a45a2a.jpg ├── 1066405,4c5e4000e48bf542.jpg ├── 1066405,4c733000d555deae.jpg ├── 1066405,4cb0e0000c41dec3.jpg ├── 1066405,4d250002d1e67c6.jpg ├── 1066405,4d5f00006b786e70.jpg ├── 1066405,4da3a00020d163d9.jpg ├── 1066405,4e6b00035f8be38.jpg ├── 1066405,4f2c700020f77ada.jpg ├── 1066405,4f88e000a2d11de0.jpg ├── 1066405,4fc4000419a695b.jpg ├── 1066405,4fe96000ee383c47.jpg ├── 1066405,503120005f7979b0.jpg ├── 1066405,5034000853ef5de.jpg ├── 1066405,50791000dde5fe22.jpg ├── 1066405,507df00095766c27.jpg ├── 1066405,50fa000784c9aff.jpg ├── 1066405,514d0002ac65e6b.jpg ├── 1066405,5187000bbd15ae1.jpg ├── 1066405,51e5c0008bfaa124.jpg ├── 1066405,52207000a7cc01ff.jpg ├── 1066405,52830009acc3ec1.jpg ├── 1066405,529c90001be74b7e.jpg ├── 1066405,529f50005e0a8fa4.jpg ├── 1066405,52ec60007b42dca1.jpg ├── 1066405,54700040a004d8.jpg ├── 1066405,54cd00050d1b112.jpg ├── 1066405,671c7000f1b7dca2.jpg ├── 1066405,686000d3439407.jpg ├── 1066405,69bb0000a909151c.jpg ├── 1066405,6a14900017ccb93b.jpg ├── 1066405,6a2410005ecf901a.jpg ├── 1066405,6ca0a0002262e42d.jpg ├── 1066405,6d64000d33afc87.jpg ├── 1066405,6e022000cc185c34.jpg ├── 1066405,6e40e0008b74d952.jpg ├── 1066405,6e5100009efa61ad.jpg ├── 1066405,6e7d5000d6ef8cff.jpg ├── 1066405,6ee400026fe448f.jpg ├── 1066405,6f16d000a3a517dc.jpg ├── 1066405,6f225000bedd6161.jpg ├── 1066405,6f825000cc772540.jpg ├── 1066405,6fc2300064014e7f.jpg ├── 1066405,6fd3600066067955.jpg ├── 1066405,6fe470007c9dedc1.jpg ├── 1066405,70310006f6c91b7.jpg ├── 1066405,705b00008c8fdd3a.jpg ├── 1066405,706160009237d189.jpg ├── 1066405,706df00037ae3e6a.jpg ├── 1066405,7077000fb795082.jpg ├── 1066405,71068000f2c21869.jpg ├── 1066405,710b600031203a42.jpg ├── 1066405,710f30004bea5a24.jpg ├── 1066405,715b30007dd6c447.jpg ├── 1066405,71a32000c3e9ba22.jpg ├── 1066405,725e3000c3415d44.jpg ├── 1066405,72bff0004c142313.jpg ├── 1066405,72c25000a152a560.jpg ├── 1066405,72c5b000a93c68c2.jpg ├── 1066405,72e8f000ed2394b1.jpg ├── 1066405,72eca000e4676c4e.jpg ├── 1066405,732550007eb6925d.jpg ├── 1066405,7332a000c2295bea.jpg ├── 1066405,7335000f59e06f2.jpg ├── 1066405,73a5000854d1480.jpg ├── 1066405,73c64000cc0bc4a5.jpg ├── 1066405,73fb6000b7c5496b.jpg ├── 1066405,74121000a9fa77b3.jpg ├── 1066405,741520000a9b50ea.jpg ├── 1066405,745e0005bcb359a.jpg ├── 1066405,7fe0000e3ca0d99.jpg ├── 1066405,818000b6422dfb.jpg ├── 1066405,85b73000eb8f2299.jpg ├── 1066405,85c5000049aa0896.jpg ├── 1066405,86538000b3df1028.jpg ├── 1066405,86c8d0005855a116.jpg ├── 1066405,86e15000068aff2a.jpg ├── 1066405,86fea00057317d40.jpg ├── 1066405,878570000703c779.jpg ├── 1066405,8788c000723bb59d.jpg ├── 1066405,87b070009e3dbcdf.jpg ├── 1066405,87b46000d11fb755.jpg ├── 1066405,87b72000edfb4aca.jpg ├── 1066405,87f9b000804d3fed.jpg ├── 1066405,88d4a000aebdc25b.jpg ├── 1066405,8921c00054293f10.jpg ├── 1066405,89267000c3aef9ad.jpg ├── 1066405,893f50008514abbe.jpg ├── 1066405,8942700056e50a58.jpg ├── 1066405,89850000e5119ada.jpg ├── 1066405,8b8a80005173d099.jpg ├── 1066405,8bcc100045592450.jpg ├── 1066405,8c180003c36bda1.jpg ├── 1066405,8cd2400045fa24ea.jpg ├── 1066405,8cf08000511e0d59.jpg ├── 1066405,8cfa700082ea238a.jpg ├── 1066405,8cfc60007c6bb794.jpg ├── 1066405,8dee0000e0b383d.jpg ├── 1066405,8e322000ae4594bf.jpg ├── 1066405,8e5b3000229b162b.jpg ├── 1066405,8ecc5000e4c28cd8.jpg ├── 1066405,8ee220001c076f2c.jpg ├── 1066405,8f54e000aedf38da.jpg ├── 1066405,9006e0007cd7a2ab.jpg ├── 1066405,904c50002eeb8556.jpg ├── 1066405,90a000054d818ff.jpg ├── 1066405,918f000073d3db15.jpg ├── 1066405,91a000d120a109.jpg ├── 1066405,91c07000feb183c9.jpg ├── 1066405,91c170005398a4a9.jpg ├── 1066405,926c0000be642850.jpg ├── 1066405,928b1000d9d2fdaf.jpg ├── 1066405,9a50005e1fdd43.jpg ├── 1066405,a4aed0004b26dfec.jpg ├── 1066405,a7aab0002682db1b.jpg └── 1066405,a7e7c000d4ea782a.jpg └── yolov5s.pt /Annotation/1066405,1b8000ef60354f.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,1b8000ef60354f.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,1b8000ef60354f.jpg 5 | 6 | Unknown 7 | 8 | 9 | 1024 10 | 683 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 698 21 | 419 22 | 723 23 | 490 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 724 33 | 418 34 | 746 35 | 469 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 528 45 | 448 46 | 580 47 | 514 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 639 57 | 422 58 | 654 59 | 462 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 596 69 | 426 70 | 614 71 | 462 72 | 73 | 74 | 75 | person 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 96 81 | 442 82 | 116 83 | 468 84 | 85 | 86 | 87 | person 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 729 93 | 208 94 | 750 95 | 266 96 | 97 | 98 | 99 | person 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 658 105 | 427 106 | 678 107 | 479 108 | 109 | 110 | 111 | person 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 675 117 | 426 118 | 693 119 | 482 120 | 121 | 122 | 123 | person 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 687 129 | 422 130 | 713 131 | 495 132 | 133 | 134 | 135 | person 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 400 141 | 478 142 | 442 143 | 584 144 | 145 | 146 | 147 | person 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 124 153 | 460 154 | 199 155 | 561 156 | 157 | 158 | 159 | person 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 0 165 | 468 166 | 43 167 | 557 168 | 169 | 170 | 171 | person 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 321 177 | 466 178 | 365 179 | 556 180 | 181 | 182 | 183 | person 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 755 189 | 420 190 | 810 191 | 532 192 | 193 | 194 | 195 | person 196 | Unspecified 197 | 0 198 | 0 199 | 200 | 967 201 | 221 202 | 1022 203 | 369 204 | 205 | 206 | 207 | person 208 | Unspecified 209 | 0 210 | 0 211 | 212 | 85 213 | 456 214 | 152 215 | 565 216 | 217 | 218 | 219 | person 220 | Unspecified 221 | 0 222 | 0 223 | 224 | 198 225 | 460 226 | 258 227 | 560 228 | 229 | 230 | 231 | person 232 | Unspecified 233 | 0 234 | 0 235 | 236 | 428 237 | 417 238 | 486 239 | 596 240 | 241 | 242 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # detectAuto 2 | 自动标注 3 | 哔哩哔哩 4 | https://www.bilibili.com/video/BV1TZ4y1S76W?p=1 5 | 需要yolov5_6.0的朋友可到这里下载 6 | https://github.com/mohenghui/yolov5_6.0 7 | yolov7自动标注，操作与该仓库类似 8 | https://github.com/mohenghui/detectAuto_v7.git 9 | yolov8自动标注，操作与该仓库类似 10 | https://github.com/mohenghui/detectAuto_v8/tree/main 11 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /detect_auto.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from utils.torch_utils import select_device 3 | from models.common import DetectMultiBackend 4 | from utils.datasets import * 5 | import torch 6 | from utils.general import (check_img_size, non_max_suppression, scale_coords) 7 | from tkinter import * 8 | from tkinter import filedialog 9 | import natsort 10 | # FILE = Path(__file__).resolve() 11 | # ROOT = FILE.parents[0] # YOLOv5 root directory 12 | # if str(ROOT) not in sys.path: 13 | # sys.path.append(str(ROOT)) # add ROOT to PATH 14 | # ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative 15 | 16 | import os 17 | from os import getcwd 18 | from xml.etree import ElementTree as ET 19 | 20 | 21 | class Detector(): 22 | 23 | def __init__(self): 24 | self.objectList = [] 25 | self.weights = r"" # 可去官方下载v5 6.0的预训练权重模型 26 | self.dnn = False 27 | self.data = r"" # 选择你的配置文件(一般为.yaml) 28 | self.device = select_device(device='0') 29 | self.half = self.device.type != 'cpu' # 半精度化 30 | self.predefined_classes = [] 31 | self.imgdir = r"" # 你需要标注的图片文件夹 32 | self.outdir = r"" # 你需要保存的xml文件夹 33 | self.detect_class = r"" # 你需要自动标注的类型 34 | self.root_window = None 35 | self.flag = False 36 | 37 | @torch.no_grad() 38 | def run(self, 39 | frame, 40 | model, 41 | device, 42 | half=False, 43 | img_size=None, 44 | augment=False, 45 | visualize=False, 46 | max_det=1000): 47 | if img_size is None: 48 | img_size = [640, 640] 49 | img0 = frame 50 | stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine 51 | img_size = check_img_size(img_size, s=stride) 52 | # Half 53 | half &= (pt or jit or engine) and device.type != 'cpu' # half precision only supported by PyTorch on CUDA 54 | if pt or jit: 55 | model.model.half() if half else model.model.float() 56 | 57 | img = letterbox(img0, auto=True, new_shape=img_size, stride=32)[0] 58 | img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB 59 | img = np.ascontiguousarray(img) 60 | # model.warmup(imgsz=(1, 3, *img_size), half=half) # warmup 61 | img = torch.from_numpy(img).to(device) 62 | img = img.half() if half else img.float() # uint8 to fp16/32 63 | img /= 255 # 0 - 255 to 0.0 - 1.0 64 | if len(img.shape) == 3: 65 | img = img[None] # expand for batch dim 66 | 67 | pred = model(img, augment=augment, visualize=visualize) 68 | 69 | pred = non_max_suppression(pred, max_det=max_det) 70 | for i, det in enumerate(pred): 71 | if det is not None and len(det): 72 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round() 73 | info_list = [] 74 | for *xyxy, conf, cls in reversed(det): 75 | xyxy = torch.tensor(xyxy).view(-1).tolist() 76 | info = [xyxy[0], xyxy[1], xyxy[2], xyxy[3], int(cls)] 77 | info_list.append(info) 78 | return info_list 79 | else: 80 | return None 81 | 82 | def create_annotation(self, xn): 83 | global annotation 84 | tree = ET.ElementTree() 85 | tree.parse(xn) 86 | annotation = tree.getroot() 87 | 88 | # 遍历xml里面每个object的值如果相同就不插入 89 | def traverse_object(self, AnotPath): 90 | tree = ET.ElementTree(file=AnotPath) 91 | root = tree.getroot() 92 | ObjectSet = root.findall('object') 93 | for Object in ObjectSet: 94 | ObjName = Object.find('name').text 95 | BndBox = Object.find('bndbox') 96 | x1 = int(BndBox.find('xmin').text) 97 | y1 = int(BndBox.find('ymin').text) 98 | x2 = int(BndBox.find('xmax').text) 99 | y2 = int(BndBox.find('ymax').text) 100 | self.objectList.append([x1, y1, x2, y2, ObjName]) 101 | 102 | # 定义一个创建一级分支object的函数 103 | def create_object(self, root, objl): # 参数依次，树根，xmin，ymin，xmax，ymax 104 | # 创建一级分支object 105 | _object = ET.SubElement(root, 'object') 106 | # 创建二级分支 107 | name = ET.SubElement(_object, 'name') 108 | # print(obj_name) 109 | name.text = str(objl[4]) 110 | pose = ET.SubElement(_object, 'pose') 111 | pose.text = 'Unspecified' 112 | truncated = ET.SubElement(_object, 'truncated') 113 | truncated.text = '0' 114 | difficult = ET.SubElement(_object, 'difficult') 115 | difficult.text = '0' 116 | # 创建bndbox 117 | bndbox = ET.SubElement(_object, 'bndbox') 118 | xmin = ET.SubElement(bndbox, 'xmin') 119 | xmin.text = '%s' % objl[0] 120 | ymin = ET.SubElement(bndbox, 'ymin') 121 | ymin.text = '%s' % objl[1] 122 | xmax = ET.SubElement(bndbox, 'xmax') 123 | xmax.text = '%s' % objl[2] 124 | ymax = ET.SubElement(bndbox, 'ymax') 125 | ymax.text = '%s' % objl[3] 126 | 127 | # 创建xml文件的函数 128 | def create_tree(self, image_name, h, w, imgdir): 129 | global annotation 130 | # 创建树根annotation 131 | annotation = ET.Element('annotation') 132 | # 创建一级分支folder 133 | folder = ET.SubElement(annotation, 'folder') 134 | # 添加folder标签内容 135 | folder.text = (imgdir) 136 | 137 | # 创建一级分支filename 138 | filename = ET.SubElement(annotation, 'filename') 139 | filename.text = image_name 140 | 141 | # 创建一级分支path 142 | path = ET.SubElement(annotation, 'path') 143 | 144 | # path.text = getcwd() + '\{}\{}'.format(imgdir, image_name) # 用于返回当前工作目录 145 | path.text ='{}/{}'.format(imgdir, image_name) # 用于返回当前工作目录 146 | 147 | # 创建一级分支source 148 | source = ET.SubElement(annotation, 'source') 149 | # 创建source下的二级分支database 150 | database = ET.SubElement(source, 'database') 151 | database.text = 'Unknown' 152 | 153 | # 创建一级分支size 154 | size = ET.SubElement(annotation, 'size') 155 | # 创建size下的二级分支图像的宽、高及depth 156 | width = ET.SubElement(size, 'width') 157 | width.text = str(w) 158 | height = ET.SubElement(size, 'height') 159 | height.text = str(h) 160 | depth = ET.SubElement(size, 'depth') 161 | depth.text = '3' 162 | 163 | # 创建一级分支segmented 164 | segmented = ET.SubElement(annotation, 'segmented') 165 | segmented.text = '0' 166 | 167 | def pretty_xml(self, element, indent, newline, level=0): # elemnt为传进来的Elment类，参数indent用于缩进，newline用于换行 168 | if element: # 判断element是否有子元素 169 | if (element.text is None) or element.text.isspace(): # 如果element的text没有内容 170 | element.text = newline + indent * (level + 1) 171 | else: 172 | element.text = newline + indent * (level + 1) + element.text.strip() + newline + indent * (level + 1) 173 | # else: # 此处两行如果把注释去掉，Element的text也会另起一行 174 | # element.text = newline + indent * (level + 1) + element.text.strip() + newline + indent * level 175 | temp = list(element) # 将element转成list 176 | for subelement in temp: 177 | if temp.index(subelement) < (len(temp) - 1): # 如果不是list的最后一个元素，说明下一个行是同级别元素的起始，缩进应一致 178 | subelement.tail = newline + indent * (level + 1) 179 | else: # 如果是list的最后一个元素， 说明下一行是母元素的结束，缩进应该少一个 180 | subelement.tail = newline + indent * level 181 | self.pretty_xml(subelement, indent, newline, level=level + 1) # 对子元素进行递归操作 182 | 183 | def work(self): 184 | with open(self.detect_class, "r") as f: # 打开文件 185 | for line in f.readlines(): 186 | line = line.strip('\n') # 去掉列表中每一个元素的换行符 187 | self.predefined_classes.append(line) 188 | model = DetectMultiBackend(self.weights, device=self.device, dnn=self.dnn, data=self.data) 189 | model.to(self.device).eval() 190 | names = model.module.names if hasattr(model, 'module') else model.names 191 | IMAGES_LIST = os.listdir(self.imgdir) 192 | for image_name in natsort.natsorted(IMAGES_LIST): 193 | # print(image_name) 194 | # 判断后缀只处理图片文件 195 | if image_name.endswith(('.jpg', '.png', '.jpeg', '.bmp')): 196 | image = cv2.imread(os.path.join(self.imgdir, image_name)) 197 | if image is None: 198 | print(image_name+"图像为空请删除") 199 | continue 200 | file_tail = os.path.splitext(image_name)[1] 201 | coordinates_list = self.run(image, model, self.device, self.half) 202 | (h, w) = image.shape[:2] 203 | # xml_name = ('.\{}\{}.xml'.format(outdir, image_name.strip('.jpg'))) 204 | xml_name = ('{}\{}.xml'.format(self.outdir, image_name.strip('.jpg'))) 205 | if (os.path.exists(xml_name)): 206 | self.create_annotation(xml_name) 207 | self.traverse_object(xml_name) 208 | else: 209 | self.create_tree(image_name, h, w, self.imgdir) 210 | if coordinates_list: 211 | print(image_name+"已标注完成") 212 | for coordinate in coordinates_list: 213 | label_id = coordinate[4] 214 | if (self.predefined_classes.count(names[label_id]) > 0): 215 | object_information = [int(coordinate[0]), int(coordinate[1]), int(coordinate[2]), 216 | int(coordinate[3]), names[label_id]] 217 | if (self.objectList.count(object_information) == 0): 218 | self.create_object(annotation, object_information) 219 | self.objectList = [] 220 | # 将树模型写入xml文件 221 | tree = ET.ElementTree(annotation) 222 | root = tree.getroot() 223 | self.pretty_xml(root, '\t', '\n') 224 | # tree.write('.\{}\{}.xml'.format(outdir, image_name.strip('.jpg')), encoding='utf-8') 225 | tree.write('{}\{}.xml'.format(self.outdir, image_name.strip(file_tail)), encoding='utf-8') 226 | else: 227 | print(image_name) 228 | 229 | # 客户端 230 | def client(self): 231 | def creatWindow(): 232 | self.root_window.destroy() 233 | window() 234 | 235 | def judge(str): 236 | if (str): 237 | text = "你已选择" + str 238 | else: 239 | text = "你还未选择文件夹，请选择" 240 | return text 241 | 242 | def test01(): 243 | self.imgdir = r"" 244 | self.imgdir += filedialog.askdirectory() 245 | creatWindow() 246 | 247 | def test02(): 248 | self.outdir = r"" 249 | self.outdir += filedialog.askdirectory() 250 | creatWindow() 251 | 252 | def test03(): 253 | self.data = r"" 254 | self.data += filedialog.askopenfilename() 255 | creatWindow() 256 | 257 | def test04(): 258 | self.weights = r"" 259 | self.weights += filedialog.askopenfilename() 260 | creatWindow() 261 | 262 | def test05(): 263 | self.detect_class = r"" 264 | self.detect_class += filedialog.askopenfilename() 265 | creatWindow() 266 | 267 | def tes06(): 268 | self.work() 269 | self.flag=True 270 | creatWindow() 271 | 272 | def window(): 273 | self.root_window = Tk() 274 | self.root_window.title("") 275 | screenWidth = self.root_window.winfo_screenwidth() # 获取显示区域的宽度 276 | screenHeight = self.root_window.winfo_screenheight() # 获取显示区域的高度 277 | tk_width = 500 # 设定窗口宽度 278 | tk_height = 400 # 设定窗口高度 279 | tk_left = int((screenWidth - tk_width) / 2) 280 | tk_top = int((screenHeight - tk_width) / 2) 281 | self.root_window.geometry('%dx%d+%d+%d' % (tk_width, tk_height, tk_left, tk_top)) 282 | self.root_window.minsize(tk_width, tk_height) # 最小尺寸 283 | self.root_window.maxsize(tk_width, tk_height) # 最大尺寸 284 | self.root_window.resizable(width=False, height=False) 285 | btn_1 = Button(self.root_window, text='请选择你要标注的图片文件夹', command=test01, 286 | height=0) 287 | btn_1.place(x=169, y=40, anchor='w') 288 | 289 | text = judge(self.imgdir) 290 | text_label = Label(self.root_window, text=text) 291 | text_label.place(x=160, y=70, anchor='w') 292 | btn_2 = Button(self.root_window, text='请选择你要保存的xml文件夹(.xml)', command=test02, 293 | height=0) 294 | btn_2.place(x=169, y=100, anchor='w') 295 | text = judge(self.outdir) 296 | text_label = Label(self.root_window, text=text) 297 | text_label.place(x=160, y=130, anchor='w') 298 | btn_3 = Button(self.root_window, text='请选择你的配置文件(.yaml)', command=test03, 299 | height=0) 300 | btn_3.place(x=169, y=160, anchor='w') 301 | text = judge(self.data) 302 | text_label = Label(self.root_window, text=text) 303 | text_label.place(x=160, y=190, anchor='w') 304 | 305 | # if(self.outdir and self.imgdir and self.data): 306 | btn_4 = Button(self.root_window, text='请选择使用的模型(.pt)', command=test04, 307 | height=0) 308 | btn_4.place(x=169, y=220, anchor='w') 309 | text = judge(self.weights) 310 | text_label = Label(self.root_window, text=text) 311 | text_label.place(x=160, y=250, anchor='w') 312 | 313 | btn_5 = Button(self.root_window, text='请选择需要自动标注的类别文件(.txt)', command=test05, 314 | height=0) 315 | btn_5.place(x=169, y=280, anchor='w') 316 | text = judge(self.detect_class) 317 | text_label = Label(self.root_window, text=text) 318 | text_label.place(x=160, y=310, anchor='w') 319 | 320 | btn_6 = Button(self.root_window, text='开始自动标注', command=tes06, 321 | height=0) 322 | btn_6.place(x=169, y=340, anchor='w') 323 | if (self.flag): 324 | text = "标注完成" 325 | else: 326 | text = "等待标注" 327 | text_label = Label(self.root_window, text=text) 328 | text_label.place(x=160, y=370, anchor='w') 329 | self.root_window.mainloop() 330 | 331 | window() 332 | 333 | 334 | if __name__ == '__main__': 335 | detector = Detector() 336 | detector.client() 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