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 torch

from 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 fp16/32
        img /= 255  # 0 - 255 to 0.0 - 1.0
        return img

    def 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_img
            shape = orig_img.shape
            pred[:, :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 results

    def write_results(self, idx, results, batch):
        p, im, im0 = batch
        log_string = ''
        if len(im.shape) == 3:
            im = im[None]  # expand for batch dim
        self.seen += 1
        imc = im0.copy() if self.args.save_crop else im0
        if self.source_type.webcam or self.source_type.from_img:  # batch_size >= 1
            log_string += f'{idx}: '
            frame = self.dataset.count
        else:
            frame = getattr(self.dataset, 'frame', 0)
        self.data_path = p
        self.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 string
        self.annotator = self.get_annotator(im0)

        det = results[idx].boxes  # TODO: make boxes inherit from tensors
        if len(det) == 0:
            return log_string
        for c in det.cls.unique():
            n = (det.cls == c).sum()  # detections per class
            log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "

        # write
        for d in reversed(det):
            cls, conf = d.cls.squeeze(), d.conf.squeeze()
            if self.args.save_txt:  # Write to file
                line = (cls, *(d.xywhn.view(-1).tolist()), conf) \
                    if self.args.save_conf else (cls, *(d.xywhn.view(-1).tolist()))  # label format
                with 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 image
                c = int(cls)  # integer class
                name = 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()