详细说明如何被其他函数如何调用深度学习中训练生成的detect.py

如果你已经训练好了一个目标检测模型，可以使用 `detect.py` 脚本来进行对象检测。可以将该脚本作为一个函数来调用，以便在其他代码中使用。

以下是一个示例代码，展示如何在 Python 中调用 `detect.py` 脚本：

import argparse
import torch
from models import *
from utils.datasets import *
from utils.utils import *

def detect():
    parser = argparse.ArgumentParser()
    parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='*.cfg path')
    parser.add_argument('--names', type=str, default='data/coco.names', help='*.names path')
    parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='weights path')
    parser.add_argument('--source', type=str, default='data/samples', help='source')  # input file/folder, 0 for webcam
    parser.add_argument('--output', type=str, default='output', help='output folder')  # output folder
    parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.6, 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')
    opt = parser.parse_args()

    with torch.no_grad():
        if opt.classes:
            opt.filter_classes = True
        device = torch_utils.select_device(opt.device)
        print(f'Using {device}')

        # Initialize model
        model = Darknet(opt.cfg, img_size=opt.img_size)
        attempt_download(opt.weights)
        if opt.weights.endswith('.pt'):  # pytorch format
            model.load_state_dict(torch.load(opt.weights, map_location=device)['model'])
        else:  # darknet format
            load_darknet_weights(model, opt.weights)

        model.to(device).eval()

        # Get dataloader
        dataset = LoadImages(opt.source, img_size=opt.img_size)

        # Run inference
        t0 = time.time()
        for path, img, im0s, vid_cap in dataset:
            img = torch.from_numpy(img).to(device)
            img = img.float() / 255.0
            if img.ndimension() == 3:
                img = img.unsqueeze(0)

            # Inference
            pred = model(img)[0]

            # Apply NMS
            pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)

            # Process detections
            for i, det in enumerate(pred):  # detections for image i
                p, s, im0 = path[i], f'{i}: ', im0s[i].copy()

                save_path = str(Path(opt.output) / Path(p).name)
                txt_path = str(Path(opt.output) / Path(p).stem) + ('' if opt.save_txt else '.nobak') + '.txt'
                s += '%gx%g ' % img.shape[2:]  # print string
                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
                if det is not None and len(det):
                    # Rescale boxes from img_size to im0 size
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                    # Print results
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()  # detections per class
                        s += f'{n} {names[int(c)]}s, '  # add to string

                    # Write results
                    for *xyxy, conf, cls in det:
                        if opt.save_txt:  # Write to file
                            xyxy = torch.tensor(xyxy).view(1, 4)  # convert to tensor
                            xyxy = xyxy / gn[:2] + torch.tensor([0, 0, 1, 1]).float().cuda()  # normalized to img0 0-1
                            line = (cls, *xyxy[0], conf) if opt.save_txt else (cls, conf)
                            with open(txt_path, 'a') as f:
                                f.write(('%g ' * len(line)).rstrip() % line + '\n')

                        if opt.view_img:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
                            plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness)

                # Print time (inference + NMS)
                print(f'{s}Done. ({time.time() - t0:.3f}s)')

                # Save results (image with detections)
                if opt.save_img:
                    cv2.imwrite(save_path, im0)

        print(f'Done. ({time.time() - t0:.3f}s)')

上述代码中 `detect()` 函数中包含了 `detect.py` 的全部代码。在这个函数中，我们使用 `argparse` 模块来处理命令行参数，并在模型加载后对传入的图片进行目标检测。最后，我们可以选择将结果保存到文件或显示在屏幕上。

你可以在你自己的代码中调用此函数，以便在其他应用程序中使用目标检测模型。