yolov5怎么进行自定义反馈

在 YOLOv5 中，自定义反馈可以通过编写 Python 脚本来实现。具体来说，可以通过在 `detect.py` 脚本中添加自定义的回调函数来实现反馈。回调函数可以在每次检测到目标时被调用，获取目标的位置、类别、置信度等信息，并按照用户定义的方式进行处理、分析和展示。

下面是一个简单的示例，演示了如何通过回调函数在控制台上输出检测结果：

import argparse
import cv2
import numpy as np
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords, xyxy2xywh
from utils.plots import plot_one_box
from utils.torch_utils import select_device


def detect(save_img=False):
    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size

    # Load model
    device = select_device(opt.device)
    model = attempt_load(weights, map_location=device)
    names = model.module.names if hasattr(model, 'module') else model.names
    half = device.type != 'cpu'

    # Set Dataloader
    vid_path, vid_writer = None, None

    # Run inference
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))

    cap = cv2.VideoCapture(source)
    while(cap.isOpened()):
        ret, frame = cap.read()
        if ret:
            img = letterbox(frame, new_shape=imgsz)[0]
            img = img[:, :, ::-1].transpose(2, 0, 1)
            img = np.ascontiguousarray(img)

            # Inference
            img = torch.from_numpy(img).to(device)
            if img.ndimension() == 3:
                img = img.unsqueeze(0)
            # Inference
            t1 = torch_utils.time_sync()
            pred = model(img, augment=True)[0]
            t2 = torch_utils.time_sync()

            # Apply NMS
            pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=None, agnostic=False)

            # Process detections
            for i, det in enumerate(pred):  # detections per image
                p, s, im0 = path[i], '', frame

                save_path = str(Path(save_dir) / Path(p).name)
                txt_path = str(Path(save_dir) / Path(p).stem) + ('_%g' % dataset.frame if dataset is not None else '') + '.txt'

                if save_img or view_img:  # Add bbox to image
                    plot_one_box(xyxy, im0, label=label, color=color, line_thickness=3)
                    print(f"Detected object: {label}, confidence: {score}, coordinates: {xyxy}")

                if save_txt:  # Write to file
                    with open(txt_path, 'a') as f:
                        f.write(('%g ' * 5 + '\n') % (cls, *xyxy, score))

                if view_img:  # Show live image
                    cv2.imshow("frame", im0)

                if cv2.waitKey(1) & 0xFF == ord('q'):  # Exit if 'q' is pressed
                    break
        else:
            break

    cap.release()
    cv2.destroyAllWindows()


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='data/images', help='source')  # file/folder, 0 for webcam
    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('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    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, help='filter by class')
    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()

    with torch.no_grad():
        detect()

在上述代码中，可以在 `plot_one_box` 函数中添加自定义的处理逻辑。例如，可以在该函数中将目标的位置、类别、置信度等信息输出到控制台，或者保存到文件中。注意，`plot_one_box` 函数接受的参数包括目标的坐标、类别、置信度和颜色等信息，可以根据需要进行调整。