一、代码简单修改

      Yolov7的速度以快出名，目前处于入门状态的小伙伴儿，可以先试着跑起来，但是对于去训练周期太长了，我在学习中，发现项目工程的detect.py里边放入自己的照片，运行来进行目标检测，我使用的权重参数是项目默认的那一个yolov7.pt

如果想使用别的权重参数，可以在主函数里边进行更改，在主函数代码：

parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')

这里边的默认是default='yolov7.pt',使用别的在这儿更改就好

      由于在项目的readme中所说明的运行detect.py的方法使利用命令行的形式，每次都要进行传参，我们在初始阶段很多参数不需要去更改，所以让其默认就好，我们将deteced.py的代码做简单的修改，代码附在最后。

说明：所修改是将主函数main中的传参过程改为了默认的值，如需修改可在detect（）函数中就行传入参数即可，例如:

想要修改con_fthres=0.25的值，可以这样修改：

def detect(source,save_img=False,conf_thres1):
    # 将命令行传参，改为了使用主函数将参数传入
    agnostic_nms=False
    augment=False
    classes=None
    conf_thres=conf_thres1

            ......

           .......

           ......

在主函数中调用时只需要把相应的参数值传入即可

if __name__ == '__main__':

     source='inference/images/test5.jpg'
     detect(source，conf_thres1=0.20)

二、检测图片

       将自己所要检测的图片放入主目录inference/images目录下，并将其地址在source中进行修改，即可运行进行此照片的检测，放入的图片不用管其具体尺寸大小

       运行结束后会在runs/detect中新产生的一个文件夹exp中保存识别的结果，结果图如下：

测试图片放入地址

产生结果的位置

三、我自己的图片测试展示

测试前图片

测试后图片:

       本篇到此结束，本人才疏学浅，有错误的地方望大佬指正，谢谢，有问题请留言，将第一时间回复！！！！！

detect.py的代码附下：

import argparse
import time
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel



# 运训测试： python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/test2.jpg

def detect(source,save_img=False):
    # 将命令行传参，改为了使用主函数将参数传入
    agnostic_nms=False
    augment=False
    classes=None
    conf_thres=0.25
    device=''
    exist_ok=False
    img_size=640
    iou_thres=0.45
    name='exp'
    nosave=False
    project='runs/detect'
    save_conf=False
    save_txt=False
    source=source
    trace=False
    update=False
    view_img=False
    weights=['yolov7.pt']  #参数使用
    source, weights, view_img, save_txt, imgsz, trace = source,weights, view_img, save_txt, img_size, trace


    save_img = not nosave and not source.endswith('.txt')  # save inference images
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))

    # Directories
    save_dir = Path(increment_path(Path(project) / name, exist_ok=exist_ok))  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Initialize
    set_logging()
    device = select_device(device)
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_size

    if trace:
        model = TracedModel(model, device, img_size)

    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()

    # 预处理
    ###############################################################################

    # 推理  ——正在处理
    for path, img, im0s, vid_cap in dataset:#利用opcv提取画面里边的每一帧
        # 把每一帧图片放到模型里表
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=augment)[0]


        #######################
        # 对每一帧图片作输出处理


        #后处理
        # Apply NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes=classes, agnostic=agnostic_nms)
        t2 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if 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' * (n > 1)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or view_img:  # Add bbox to image
                        label = f'{names[int(cls)]} {conf:.2f}'
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

            # Print time (inference + NMS)
            #print(f'{s}Done. ({t2 - t1:.3f}s)')

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                            save_path += '.mp4'
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer.write(im0)

    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        #print(f"Results saved to {save_dir}{s}")

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

# 主函数
if __name__ == '__main__':
    # parser = argparse.ArgumentParser()
    # parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')
    # parser.add_argument('--source', type=str, default='inference/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('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    # parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    # parser.add_argument('--classes', nargs='+', type=int, 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')
    # parser.add_argument('--trace', action='store_true', help='trace model')
    # opt = parser.parse_args()
    # print(opt)
    # #check_requirements(exclude=('pycocotools', 'thop'))
    #
    # with torch.no_grad():
    #     if opt.update:  # update all models (to fix SourceChangeWarning)
    #         for opt.weights in ['yolov7.pt']:
    #             detect()
    #             strip_optimizer(opt.weights)
    #     else:
    #         detect()

    source='inference/images/test5.jpg'
    detect(source)