借用Ultralytics Yolo快速训练一个物体检测器
借用Ultralytics Yolo快速训练一个物体检测器
https://github.com/ultralytics/ultralytics
Step-1 准备数据集
你需要一些待检测物体比如安全帽, 把它从各个角度拍摄一下. 再找一些不相关的背景图片. 然后把安全帽给放大缩小旋转等等贴到背景图片上去, 生成一堆训练数据.
配置文件:
extract_cfg: output_dir: '/datasets/images' fps: 0.25 screen_images_path: '/datasets/待检测图片' max_scale: 1.0 min_scale: 0.1 manual_scale: [ {name: 'logo', min_scale: 0.05, max_scale: 0.3}, {name: 'logo', min_scale: 0.1, max_scale: 0.5}, {name: '箭头', min_scale: 0.1, max_scale: 0.5} ] data_cfgs: [ {id: 0, name: 'logo', min_scale: 0.05, max_scale: 0.3, gen_num: 2}, {id: 1, name: '截屏', min_scale: 0.1, max_scale: 1.0, gen_num: 3, need_full_screen: true}, {id: 2, name: '红包', min_scale: 0.1, max_scale: 0.5, gen_num: 2}, {id: 3, name: '箭头', min_scale: 0.1, max_scale: 0.5, gen_num: 2, rotate_aug: true}, ] save_oss_dir: /datasets/gen_datasets/ gen_num_per_image: 2 max_bg_img_sample: 1
数据集生成:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 | from pathlib import Path import io import random import cv2 import numpy as np from PIL import Image import hydra from omegaconf import DictConfig import json from tqdm import tqdm # 加载图片 def load_images(background_path, overlay_path): background = cv2.imread(background_path) overlay = cv2.imread(overlay_path, cv2.IMREAD_UNCHANGED) return background, overlay # 随机缩放和位置 def random_scale_and_position(bg_shape, overlay_shape, max_scale=1.0, min_scale=0.1): max_height, max_width = bg_shape[:2] overlay_height, overlay_width = overlay_shape[:2] base_scale = min(max_height / overlay_height, max_width / overlay_width) # 随机缩放 scale_factor = random.uniform( min_scale * base_scale, max_scale * base_scale) new_height, new_width = int ( overlay_height * scale_factor), int (overlay_width * scale_factor) # 随机位置 max_x = max_width - new_width - 1 max_y = max_height - new_height - 1 position_x = random.randint(0, max_x) position_y = random.randint(0, max_y) return scale_factor, (position_x, position_y) def get_resized_overlay(overlay, scale): overlay_resized = cv2.resize(overlay, (0, 0), fx=scale, fy=scale) return overlay_resized def rotate_image(img, angle): if isinstance(img, np.ndarray): img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGRA2RGBA)) # 确保图像具有alpha通道(透明度) img = img.convert( "RGBA" ) # 旋转原始图像并粘贴到新的透明图像框架中 rotated_img = img.rotate(angle, resample=Image.BICUBIC, expand=True) rotated_img = np.asarray(rotated_img) return cv2.cvtColor(rotated_img, cv2.COLOR_RGBA2BGRA) # 合成图片 def overlay_image(background, overlay_resized, position, scale): h, w = overlay_resized.shape[:2] x, y = position # 透明度处理 alpha_s = overlay_resized[:, :, 3] / 255.0 alpha_l = 1.0 - alpha_s for c in range(0, 3): background[y:y + h, x:x + w, c] = (alpha_s * overlay_resized[:, :, c] + alpha_l * background[y:y + h, x:x + w, c]) # 画出位置,调试使用 # print("position", x, y, w, h) # cv2.rectangle(background, (x, y), (x + w, y + h), (0, 255, 0), 2) background = cv2.cvtColor(background, cv2.COLOR_BGR2RGB) return Image.fromarray(background) class Box: def __init__(self, x, y, width, height, category_id, image_width, image_height): self.x = x self.y = y self.width = width self.height = height self.image_width = image_width self.image_height = image_height self.category_id = category_id def to_yolo_format(self): x_center = (self.x + self.width / 2) / self.image_width y_center = (self.y + self.height / 2) / self.image_height width = self.width / self.image_width height = self.height / self.image_height box_line = f "{self.category_id} {x_center} {y_center} {width} {height}" return box_line class SingleCategoryGen: def __init__(self, cfg, data_cfg, output_dir): self.output_dir = output_dir self.screen_png_images = [] self.coco_images = [] self.coco_annotations = [] screen_images_path = Path( cfg.screen_images_path.format(user_root=user_root)) self.manual_scale = {} self.data_cfg = data_cfg self.category_id = data_cfg.id self.category_name = self.data_cfg.name self.max_scale = self.data_cfg.max_scale self.min_scale = self.data_cfg.min_scale self.gen_num = self.data_cfg.gen_num self.rotate_aug = self.data_cfg. get ( "rotate_aug" , False) self.need_full_screen = self.data_cfg. get ( "need_full_screen" , False) self.category_num = 0 self.category_names = {} self.butcket = get_oss_bucket(cfg.bucket_name) output_dir = Path(output_dir) save_oss_dir = f "{cfg.save_oss_dir}/{output_dir.parent.name}/{output_dir.name}" self.save_oss_dir = save_oss_dir self.images_save_oss_dir = f "{save_oss_dir}/images" self.label_save_oss_dir = f "{save_oss_dir}/labels" self.annotations_save_oss_path = f "{save_oss_dir}/annotations.json" self.load_screen_png_images_and_category(screen_images_path) def load_screen_png_images_and_category(self, screen_images_dir): screen_images_dir = Path(screen_images_dir) category_id = self.category_id screen_images_path = screen_images_dir / self.category_name img_files = [p for p in screen_images_path.iterdir() if p.suffix in [ ".png" , ".jpg" ]] img_files.sort(key=lambda x: x.stem) for i, img_file in enumerate(img_files): self.screen_png_images.append( dict(id=i, name=img_file.stem, supercategory=None, path=str(img_file))) def add_new_images(self, bg_img_path: Path, gen_image_num=None, subset= "train" ): gen_image_num = gen_image_num or self.gen_num background_origin = cv2.imread(str(bg_img_path)) if background_origin is None: print(f "open image {bg_img_path} failed" ) return max_box_num = 1 for gen_id in range(gen_image_num): background = background_origin.copy() category_id = self.category_id overlay_img_path = self.sample_category_data() overlay = cv2.imread(overlay_img_path, cv2.IMREAD_UNCHANGED) if overlay.shape[2] == 3: overlay = cv2.cvtColor(overlay, cv2.COLOR_BGR2BGRA) if self.rotate_aug: overlay = rotate_image(overlay, random.uniform(-180, 180)) # # 随机裁剪图片 # if random.random() < 0.5: # origin_height = overlay.shape[0] # min_height = origin_height // 4 # new_height = random.randint(min_height, origin_height) # new_top = random.randint(0, origin_height - new_height) # overlay = overlay[new_top:new_top+new_height, :, :] box_num = random.randint(1, max_box_num) # 获取随机缩放和位置 max_scale = self.max_scale min_scale = self.min_scale scale, position = random_scale_and_position( background.shape, overlay.shape, max_scale, min_scale) # 缩放overlay图片 overlay_resized = get_resized_overlay(overlay, scale) # 合成后的图片 merged_img = overlay_image(background, overlay_resized, position, scale) # 保存合成后的图片 filename = f "{bg_img_path.stem}_{category_id}_{gen_id:02d}.png" merged_img.save(f '{output_dir}/{filename}' ) # 生成COCO格式的标注数据 box = Box(*position, overlay_resized.shape[1], overlay_resized.shape[0], category_id, background.shape[1], background.shape[0]) self.upload_image_to_oss(merged_img, filename, subset, [box]) def sample_category_data(self): return random.choice(self.screen_png_images)[ "path" ] image_id = self.gen_image_id() image_json = { "id" : image_id, "width" : image.width, "height" : image.height, "file_name" : image_name, } self.coco_images.append(image_json) annotation_json = { "id" : image_id, "image_id" : image_id, "category_id" : 0, "segmentation" : None, "area" : bbox[2] * bbox[3], "bbox" : bbox, "iscrowd" : 0 } self.coco_annotations.append(annotation_json) def upload_image_to_oss(self, image, image_name, subset, box_list=None): image_bytesio = io.BytesIO() image.save(image_bytesio, format= "PNG" ) self.butcket.put_object( f "{self.images_save_oss_dir}/{subset}/{image_name}" , image_bytesio.getvalue()) if box_list: label_str = "\n" . join ([box.to_yolo_format() for box in box_list]) label_name = image_name.split( "." )[0] + ".txt" self.butcket.put_object( f "{self.label_save_oss_dir}/{subset}/{label_name}" , label_str) def upload_full_screen_image(self): if not self.need_full_screen: return name = self.category_name category_id = self.category_id image_list = self.screen_png_images subset_list = [ "train" if i % 10 <= 7 else "val" if i % 10 <= 8 else "test" for i in range(len(image_list))] for i in range(len(image_list)): image_data = image_list[i] subset = subset_list[i] overlay_img_path = image_data[ "path" ] image = Image.open(overlay_img_path) if random.random() < 0.5: origin_height = image.height min_height = origin_height // 4 new_height = random.randint(min_height, origin_height) new_top = random.randint(0, origin_height - new_height) image = image.crop( (0, new_top, image.width, new_top + new_height)) filename = f "{name}_{category_id}_{i:05}.png" box = Box(0, 0, image.width, image.height, category_id, image.width, image.height) self.upload_image_to_oss(image, filename, subset, [box]) class ScreenDatasetGen: def __init__(self, cfg, output_dir): self.output_dir = output_dir self.screen_png_images = {} self.coco_images = [] self.coco_annotations = [] screen_images_path = Path( cfg.screen_images_path.format(user_root=user_root)) self.max_scale = cfg.max_scale self.min_scale = cfg.min_scale self.manual_scale = {} for info in cfg.manual_scale: self.manual_scale[info.name] = dict( max_scale=info.max_scale, min_scale=info.min_scale) self.category_num = 0 self.category_names = {} self.category_id_loop = -1 self.butcket = get_oss_bucket(cfg.bucket_name) output_dir = Path(output_dir) save_oss_dir = f "{cfg.save_oss_dir}/{output_dir.parent.name}/{output_dir.name}" self.save_oss_dir = save_oss_dir self.images_save_oss_dir = f "{save_oss_dir}/images" self.label_save_oss_dir = f "{save_oss_dir}/labels" self.annotations_save_oss_path = f "{save_oss_dir}/annotations.json" self.load_screen_png_images_and_category(screen_images_path) def add_new_images(self, bg_img_path: Path, gen_image_num=1, subset= "train" ): background_origin = cv2.imread(str(bg_img_path)) if background_origin is None: print(f "open image {bg_img_path} failed" ) return max_box_num = 1 for gen_id in range(gen_image_num): background = background_origin.copy() category_id = self.get_category_id_loop() overlay_img_path = self.sample_category_data( category_id, subset=subset) overlay = cv2.imread(overlay_img_path, cv2.IMREAD_UNCHANGED) if overlay.shape[2] == 3: overlay = cv2.cvtColor(overlay, cv2.COLOR_BGR2BGRA) # # 随机裁剪图片 # if random.random() < 0.5: # origin_height = overlay.shape[0] # min_height = origin_height // 4 # new_height = random.randint(min_height, origin_height) # new_top = random.randint(0, origin_height - new_height) # overlay = overlay[new_top:new_top+new_height, :, :] box_num = random.randint(1, max_box_num) # 获取随机缩放和位置 category_name = self.category_names[category_id] if category_name in self.manual_scale: max_scale = self.manual_scale[category_name][ "max_scale" ] min_scale = self.manual_scale[category_name][ "min_scale" ] else : max_scale = self.max_scale min_scale = self.min_scale scale, position = random_scale_and_position( background.shape, overlay.shape, max_scale, min_scale) # 缩放overlay图片 overlay_resized = get_resized_overlay(overlay, scale) # 合成后的图片 merged_img = overlay_image( background, overlay_resized, position, scale) # 保存合成后的图片 filename = f "{bg_img_path.stem}_{category_id}_{gen_id:02d}.png" # merged_img.save(f'{output_dir}/{filename}') # 生成COCO格式的标注数据 box = Box(*position, overlay_resized.shape[1], overlay_resized.shape[0], category_id, background.shape[1], background.shape[0]) self.upload_image_to_oss(merged_img, filename, subset, [box]) # self.add_image_annotion_to_coco(box, merged_img, filename) def upload_full_screen_image(self, category_name=None): if category_name is None: return if not isinstance(category_name, list): category_name = [category_name] for category_id in range(self.category_num): name = self.category_names[category_id] if name not in category_name: continue image_list = self.screen_png_images[category_id] subset_list = [ "train" if i % 10 <= 7 else "val" if i % 10 <= 8 else "test" for i in range(len(image_list))] for i in range(len(image_list)): image_data = image_list[i] subset = subset_list[i] overlay_img_path = image_data[ "path" ] image = Image.open(overlay_img_path) if random.random() < 0.5: origin_height = image.height min_height = origin_height // 4 new_height = random.randint(min_height, origin_height) new_top = random.randint(0, origin_height - new_height) image = image.crop( (0, new_top, image.width, new_top + new_height)) filename = f "{name}_{category_id}_{i:05}.png" box = Box(0, 0, image.width, image.height, category_id, image.width, image.height) self.upload_image_to_oss(image, filename, subset, [box]) def load_screen_png_images_and_category(self, screen_images_dir): screen_images_dir = Path(screen_images_dir) screen_images_paths = [ f for f in screen_images_dir.iterdir() if f.is_dir()] screen_images_paths.sort(key=lambda x: x.stem) for category_id, screen_images_path in enumerate(screen_images_paths): img_files = [p for p in screen_images_path.iterdir() if p.suffix in [ ".png" , ".jpg" ]] img_files.sort(key=lambda x: x.stem) self.screen_png_images[category_id] = [] self.category_names[category_id] = screen_images_path.stem print(f "{category_id}: {self.category_names[category_id]}" ) for i, img_file in enumerate(img_files): self.screen_png_images[category_id].append( dict(id=i, name=img_file.stem, supercategory=None, path=str(img_file))) self.category_num = len(screen_images_paths) print(f "category_num: {self.category_num}" ) def get_category_id_loop(self): # self.category_id_loop = (self.category_id_loop + 1) % self.category_num self.category_id_loop = random.randint(0, self.category_num - 1) return self.category_id_loop def sample_category_data(self, category_id, subset): image_data = self.screen_png_images[category_id] # valid_id = [] # if subset == "train": # valid_id = [i for i in range(len(image_data)) if i % 10 <= 7] # elif subset == "val": # valid_id = [i for i in range(len(image_data)) if i % 10 == 8] # elif subset == "test": # valid_id = [i for i in range(len(image_data)) if i % 10 == 9] # image_data = [image_data[i] for i in valid_id] return random.choice(image_data)[ "path" ] def gen_image_id(self): return len(self.coco_images) + 1 def add_image_annotion_to_coco(self, bbox, image: Image.Image, image_name): image_id = self.gen_image_id() image_json = { "id" : image_id, "width" : image.width, "height" : image.height, "file_name" : image_name, } self.coco_images.append(image_json) annotation_json = { "id" : image_id, "image_id" : image_id, "category_id" : 0, "segmentation" : None, "area" : bbox[2] * bbox[3], "bbox" : bbox, "iscrowd" : 0 } self.coco_annotations.append(annotation_json) def upload_image_to_oss(self, image, image_name, subset, box_list=None): image_bytesio = io.BytesIO() image.save(image_bytesio, format= "PNG" ) self.butcket.put_object( f "{self.images_save_oss_dir}/{subset}/{image_name}" , image_bytesio.getvalue()) if box_list: label_str = "\n" . join ([box.to_yolo_format() for box in box_list]) label_name = image_name.split( "." )[0] + ".txt" self.butcket.put_object( f "{self.label_save_oss_dir}/{subset}/{label_name}" , label_str) def dump_coco_json(self): categories = [{key: item[key] for key in ( "id" , "name" , "supercategory" )} for item in self.screen_png_images.values()] coco_json = { "images" : self.coco_images, "annotations" : self.coco_annotations, "categories" : categories } self.butcket.put_object( self.annotations_save_oss_path, json.dumps(coco_json, indent=2)) # with open(f"{self.output_dir}/coco.json", "w") as fp: # json.dump(coco_json, fp, indent=2) @hydra.main(version_base=None, config_path= "." , config_name= "conf" ) def main(cfg: DictConfig): output_dir = hydra.core.hydra_config.HydraConfig. get ().runtime.output_dir # get_image_and_annotation(output_dir) # screen_dataset_gen = ScreenDatasetGen(cfg, output_dir) category_generators = [] for data_cfg in cfg.data_cfgs: category_generators.append(SingleCategoryGen(cfg, data_cfg, output_dir)) bg_img_files = [f for f in Path(cfg.extract_cfg.output_dir.format(user_root=user_root)).iterdir() if f.suffix in [ ".png" , ".jpg" ]] if cfg. get ( "max_bg_img_sample" ): bg_img_files = random.sample(bg_img_files, cfg.max_bg_img_sample) img_index = 0 for bg_img_file in tqdm(bg_img_files): subset = "train" if img_index % 10 <= 7 else "val" if img_index % 10 == 8 else "test" img_index += 1 for category_generator in category_generators: category_generator.add_new_images(bg_img_path=bg_img_file, subset=subset) for category_generator in category_generators: category_generator.upload_full_screen_image() if __name__ == '__main__' : main() |
运行后, 可以在outputs文件夹下生成符合要求的训练数据.
image 就是背景+检测物体
labels 中的内容就是这样的文件:
1 2 | 1 0.6701388888888888 0.289453125 0.5736111111111111 0.57421875 # 类型 box |
Step-2 训练模型
这个更简单, 在官网下载一个模型权重, 比如yolo8s.pt, 对付安全帽这种东西, 几M大的模型就够了.
训练配置文件:
1 2 3 4 5 6 7 8 | names: 0: logo 1: 截屏 2: 红包 path: /outputs test: images/test train: images/train val: images/val |
训练代码:
没错就这么一点
1 2 3 4 | from ultralytics import YOLO model = YOLO( './yolo8s.pt' ) model.train(data= 'dataset.yaml' , epochs=100, imgsz=1280) |
然后就可以自动化训练了, 结束后会自动保存模型与评估检测效果.
Step-3 检测
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | class Special_Obj_Detect( object ): def __init__(self, cfg) -> None: model_path = cfg.model_path self.model = YOLO(model_path) self.model.requires_grad_ = False self.cls_names = {0: 'logo' , 1: '截屏' , 2: '红包' } # 单帧图像检测 def detect_image(self, img_path): results = self.model(img_path) objects = [] objects_cnt = dict() objects_area_pct = dict() for result in results: result = result.cpu() boxes = list(result.boxes) for box in boxes: if box.conf < 0.8: continue boxcls = box.cls[0].item() objects.append(self.cls_names[boxcls]) objects_cnt[self.cls_names[boxcls]] = objects_cnt. get (self.cls_names[boxcls], 0) + 1 area_p = sum([ (xywh[2]*xywh[3]).item() for xywh in box.xywhn]) area_p = min(1, area_p) objects_area_pct[self.cls_names[boxcls]] = area_p objects = list( set (objects)) return objects, objects_cnt, objects_area_pct |
收工.
本文作者:JiangOil
本文链接: https://www.codebonobo.tech/post/14
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