Python+Yolov8目标识别特征检测

Yolov8目标识别特征检测

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前言

这篇博客针对<<Yolov8目标识别特征检测>>编写代码，代码整洁，规则，易读。 学习与应用推荐首选。

文章目录

一、所需工具软件

二、使用步骤

1. 引入库

2. 识别图像特征

3. 参数设置

4. 运行结果

三、在线协助

一、所需工具软件

1. Pycharm, Python

2. Yolov8, OpenCV

二、使用步骤

1.引入库

代码如下（示例）：

import torchfrom ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box

2.识别图像特征

代码如下（示例）：

class DetectionPredictor(BasePredictor):def get_annotator(self, img):return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))def preprocess(self, img):img = torch.from_numpy(img).to(self.model.device)img = img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32img /= 255  # 0 - 255 to 0.0 - 1.0return imgdef postprocess(self, preds, img, orig_img):preds = ops.non_max_suppression(preds,self.args.conf,self.args.iou,agnostic=self.args.agnostic_nms,max_det=self.args.max_det,classes=self.args.classes)results = []for i, pred in enumerate(preds):orig_img = orig_img[i] if isinstance(orig_img, list) else orig_imgshape = orig_img.shapepred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()results.append(Results(boxes=pred, orig_img=orig_img, names=self.model.names))return resultsdef write_results(self, idx, results, batch):p, im, im0 = batchlog_string = ''if len(im.shape) == 3:im = im[None]  # expand for batch dimself.seen += 1imc = im0.copy() if self.args.save_crop else im0if self.source_type.webcam or self.source_type.from_img:  # batch_size >= 1log_string += f'{idx}: 'frame = self.dataset.countelse:frame = getattr(self.dataset, 'frame', 0)self.data_path = pself.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')log_string += '%gx%g ' % im.shape[2:]  # print stringself.annotator = self.get_annotator(im0)det = results[idx].boxes  # TODO: make boxes inherit from tensorsif len(det) == 0:return log_stringfor c in det.cls.unique():n = (det.cls == c).sum()  # detections per classlog_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "# writefor d in reversed(det):cls, conf = d.cls.squeeze(), d.conf.squeeze()if self.args.save_txt:  # Write to fileline = (cls, *(d.xywhn.view(-1).tolist()), conf) \if self.args.save_conf else (cls, *(d.xywhn.view(-1).tolist()))  # label formatwith open(f'{self.txt_path}.txt', 'a') as f:f.write(('%g ' * len(line)).rstrip() % line + '\n')if self.args.save or self.args.save_crop or self.args.show:  # Add bbox to imagec = int(cls)  # integer classname = f'id:{int(d.id.item())} {self.model.names[c]}' if d.id is not None else self.model.names[c]label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}')self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))if self.args.save_crop:save_one_box(d.xyxy,imc,file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg',BGR=True)return log_string

3.参数定义

代码如下（示例）：

if __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--weights', nargs='+', type=str, default='yolov5_best_road_crack_recog.pt', help='model.pt path(s)')parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')parser.add_argument('--view-img', action='store_true', help='display results')parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')parser.add_argument('--classes', nargs='+', type=int, default='0', help='filter by class: --class 0, or --class 0 2 3')parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')parser.add_argument('--augment', action='store_true', help='augmented inference')parser.add_argument('--update', action='store_true', help='update all models')parser.add_argument('--project', default='runs/detect', help='save results to project/name')parser.add_argument('--name', default='exp', help='save results to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')opt = parser.parse_args()

4.运行结果如下

三、在线协助：

如需安装运行环境或远程调试，见文章底部个人QQ名片，由专业技术人员远程协助！
1）远程安装运行环境，代码调试
2）Qt, C++, Python入门指导
3）界面美化
4）软件制作

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