YOLOv5如何进行区域目标检测（手把手教学）

提示：本项目的源码是基于yolov5 6.0版本修改

文章目录

  • YOLOv5如何进行区域目标检测（手把手教学）
  • 效果展示
  • 一、确定检测范围
  • 二、detect.py代码修改
    • 1.确定区域检测范围
    • 2.画检测区域线（若不想像效果图一样显示出检测区域可不添加）
  • 总结
  • 整体detect.py修改代码

效果展示

在使用YOLOv5的有些时候，我们会遇到一些具体的目标检测要求，比如要求不检测全图，只在规定的区域内才检测。所以为了满足这个需求，可以用一个mask覆盖掉不想检测的区域，使得YOLOv5在检测的时候，该覆盖区域就不纳入检测范围。

话不多说，直接上检测效果，可以很直观的看到目标在进入规定的检测区域才得到检测。

一、确定检测范围

快捷查询方法：

  1. 用windows自带画图打开图片
  2. 鼠标移到想要框选检测区域的四个顶点，查询点的像素坐标
  3. 分别计算点的像素坐标与图片总像素坐标的比例（后面要用）
    查询方法如下图所示：

    提示：以下是计算的举例说明，仅供参考
    例如：图中所标注的点（1010，174）代表（x，y）坐标
    hl1 = 174 / 768 （可约分）监测区域纵坐标距离图片***顶部*** 比例
    wl1 = 1010 / 1614 （可约分）监测区域横坐标距离图片***左部*** 比例
    这里只举例了一个点，如检测范围是四边形，需要计算左上，右上，右下，左下四个顶点。

二、detect.py代码修改

1.确定区域检测范围

在下面代码位置填上计算好的比例：

 # mask for certain region#1,2,3,4 分别对应左上，右上，右下，左下四个点hl1 = 1.4 / 10 #监测区域高度距离图片顶部比例wl1 = 6.4 / 10 #监测区域高度距离图片左部比例hl2 = 1.4 / 10# 监测区域高度距离图片顶部比例wl2 = 6.8 / 10# 监测区域高度距离图片左部比例hl3 = 4.5 / 10# 监测区域高度距离图片顶部比例wl3 = 7.6 / 10# 监测区域高度距离图片左部比例hl4 = 4.5 / 10# 监测区域高度距离图片顶部比例wl4 = 5.5 / 10# 监测区域高度距离图片左部比例

在135行：for path, img, im0s, vid_cap in dataset: 下插入代码：

# mask for certain region#1,2,3,4 分别对应左上，右上，右下，左下四个点hl1 = 1.6 / 10 #监测区域高度距离图片顶部比例wl1 = 6.4 / 10 #监测区域高度距离图片左部比例hl2 = 1.6 / 10# 监测区域高度距离图片顶部比例wl2 = 6.8 / 10# 监测区域高度距离图片左部比例hl3 = 4.5 / 10# 监测区域高度距离图片顶部比例wl3 = 7.6 / 10# 监测区域高度距离图片左部比例hl4 = 4.5 / 10# 监测区域高度距离图片顶部比例wl4 = 5.5 / 10# 监测区域高度距离图片左部比例if webcam:for b in range(0,img.shape[0]):mask = np.zeros([img[b].shape[1], img[b].shape[2]], dtype=np.uint8)#mask[round(img[b].shape[1] * hl1):img[b].shape[1], round(img[b].shape[2] * wl1):img[b].shape[2]] = 255pts = np.array([[int(img[b].shape[2] * wl1), int(img[b].shape[1] * hl1)],# pts1[int(img[b].shape[2] * wl2), int(img[b].shape[1] * hl2)],# pts2[int(img[b].shape[2] * wl3), int(img[b].shape[1] * hl3)],# pts3[int(img[b].shape[2] * wl4), int(img[b].shape[1] * hl4)]], np.int32)mask = cv2.fillPoly(mask,[pts],(255,255,255))imgc = img[b].transpose((1, 2, 0))imgc = cv2.add(imgc, np.zeros(np.shape(imgc), dtype=np.uint8), mask=mask)#cv2.imshow('1',imgc)img[b] = imgc.transpose((2, 0, 1))else:mask = np.zeros([img.shape[1], img.shape[2]], dtype=np.uint8)#mask[round(img.shape[1] * hl1):img.shape[1], round(img.shape[2] * wl1):img.shape[2]] = 255pts = np.array([[int(img.shape[2] * wl1), int(img.shape[1] * hl1)],# pts1[int(img.shape[2] * wl2), int(img.shape[1] * hl2)],# pts2[int(img.shape[2] * wl3), int(img.shape[1] * hl3)],# pts3[int(img.shape[2] * wl4), int(img.shape[1] * hl4)]], np.int32)mask = cv2.fillPoly(mask, [pts], (255,255,255))img = img.transpose((1, 2, 0))img = cv2.add(img, np.zeros(np.shape(img), dtype=np.uint8), mask=mask)img = img.transpose((2, 0, 1))

2.画检测区域线（若不想像效果图一样显示出检测区域可不添加）

在196行： p, s, im0, frame = path, ‘’, im0s.copy(), getattr(dataset, ‘frame’, 0) 后添加

if webcam:# batch_size >= 1p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.countcv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),cv2.FONT_HERSHEY_SIMPLEX,1.0, (255, 255, 0), 2, cv2.LINE_AA)pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)],# pts1[int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)],# pts2[int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)],# pts3[int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32)# pts4# pts = pts.reshape((-1, 1, 2))zeros = np.zeros((im0.shape), dtype=np.uint8)mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)cv2.polylines(im0, [pts], True, (255, 255, 0), 3)# plot_one_box(dr, im0, label='Detection_Region', color=(0, 255, 0), line_thickness=2)else:p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)cv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),cv2.FONT_HERSHEY_SIMPLEX,1.0, (255, 255, 0), 2, cv2.LINE_AA)pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)],# pts1[int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)],# pts2[int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)],# pts3[int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32)# pts4# pts = pts.reshape((-1, 1, 2))zeros = np.zeros((im0.shape), dtype=np.uint8)mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)cv2.polylines(im0, [pts], True, (255, 255, 0), 3)

总结

基于yolov5的区域目标检测实质上就是在图片选定检测区域做一个遮掩mask，检测区域不一定为四边形，也可是其他形状。该方法可检测图片/视频/摄像头。
提示：使用该方法要先确定数据集图像是否像监控图像一样，画面主体保持不变
原理展现如图所示：（图片参考http://t.csdn.cn/lgyO1）


整体detect.py修改代码

# YOLOv5by Ultralytics, GPL-3.0 license"""Run inference on images, videos, directories, streams, etc.Usage:$ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640"""import argparseimport osimport sysfrom pathlib import Pathimport cv2import numpy as npimport torchimport torch.backends.cudnn as cudnnFILE = Path(__file__).resolve()ROOT = FILE.parents[0]# YOLOv5 root directoryif str(ROOT) not in sys.path:sys.path.append(str(ROOT))# add ROOT to PATHROOT = Path(os.path.relpath(ROOT, Path.cwd()))# relativefrom models.experimental import attempt_loadfrom utils.datasets import LoadImages, LoadStreamsfrom utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \strip_optimizer, xyxy2xywhfrom utils.plots import Annotator, colorsfrom utils.torch_utils import load_classifier, select_device, time_sync@torch.no_grad()def run(weights=ROOT / 'yolov5s.pt',# model.pt path(s)source=ROOT / 'data/images',# file/dir/URL/glob, 0 for webcamimgsz=640,# inference size (pixels)conf_thres=0.25,# confidence thresholdiou_thres=0.45,# NMS IOU thresholdmax_det=1000,# maximum detections per imagedevice='',# cuda device, i.e. 0 or 0,1,2,3 or cpuview_img=False,# show resultssave_txt=False,# save results to *.txtsave_conf=False,# save confidences in --save-txt labelssave_crop=False,# save cropped prediction boxesnosave=False,# do not save images/videosclasses=None,# filter by class: --class 0, or --class 0 2 3agnostic_nms=False,# class-agnostic NMSaugment=False,# augmented inferencevisualize=False,# visualize featuresupdate=False,# update all modelsproject=ROOT / 'runs/detect',# save results to project/namename='exp',# save results to project/nameexist_ok=False,# existing project/name ok, do not incrementline_thickness=3,# bounding box thickness (pixels)hide_labels=False,# hide labelshide_conf=False,# hide confidenceshalf=False,# use FP16 half-precision inferencednn=False,# use OpenCV DNN for ONNX inference):source = str(source)save_img = not nosave and not source.endswith('.txt')# save inference imageswebcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))# Directoriessave_dir = 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# Initializeset_logging()device = select_device(device)half &= device.type != 'cpu'# half precision only supported on CUDA# Load modelw = str(weights[0] if isinstance(weights, list) else weights)classify, suffix, suffixes = False, Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '']check_suffix(w, suffixes)# check weights have acceptable suffixpt, onnx, tflite, pb, saved_model = (suffix == x for x in suffixes)# backend booleansstride, names = 64, [f'class{i}' for i in range(1000)]# assign defaultsif pt:model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device)stride = int(model.stride.max())# model stridenames = model.module.names if hasattr(model, 'module') else model.names# get class namesif half:model.half()# to FP16if classify:# second-stage classifiermodelc = load_classifier(name='resnet50', n=2)# initializemodelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()elif onnx:if dnn:# check_requirements(('opencv-python>=4.5.4',))net = cv2.dnn.readNetFromONNX(w)else:check_requirements(('onnx', 'onnxruntime'))import onnxruntimesession = onnxruntime.InferenceSession(w, None)else:# TensorFlow modelscheck_requirements(('tensorflow>=2.4.1',))import tensorflow as tfif pb:# https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxtdef wrap_frozen_graph(gd, inputs, outputs):x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])# wrapped importreturn x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs), tf.nest.map_structure(x.graph.as_graph_element, outputs))graph_def = tf.Graph().as_graph_def()graph_def.ParseFromString(open(w, 'rb').read())frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")elif saved_model:model = tf.keras.models.load_model(w)elif tflite:interpreter = tf.lite.Interpreter(model_path=w)# load TFLite modelinterpreter.allocate_tensors()# allocateinput_details = interpreter.get_input_details()# inputsoutput_details = interpreter.get_output_details()# outputsint8 = input_details[0]['dtype'] == np.uint8# is TFLite quantized uint8 modelimgsz = check_img_size(imgsz, s=stride)# check image size# Dataloaderif webcam:view_img = check_imshow()cudnn.benchmark = True# set True to speed up constant image size inferencedataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)bs = len(dataset)# batch_sizeelse:dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)bs = 1# batch_sizevid_path, vid_writer = [None] * bs, [None] * bs# Run inferenceif pt and device.type != 'cpu':model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters())))# run oncedt, seen = [0.0, 0.0, 0.0], 0for path, img, im0s, vid_cap in dataset:# mask for certain region#1,2,3,4 分别对应左上，右上，右下，左下四个点hl1 = 1.6 / 10 #监测区域高度距离图片顶部比例wl1 = 6.4 / 10 #监测区域高度距离图片左部比例hl2 = 1.6 / 10# 监测区域高度距离图片顶部比例wl2 = 6.8 / 10# 监测区域高度距离图片左部比例hl3 = 4.5 / 10# 监测区域高度距离图片顶部比例wl3 = 7.6 / 10# 监测区域高度距离图片左部比例hl4 = 4.5 / 10# 监测区域高度距离图片顶部比例wl4 = 5.5 / 10# 监测区域高度距离图片左部比例if webcam:for b in range(0,img.shape[0]):mask = np.zeros([img[b].shape[1], img[b].shape[2]], dtype=np.uint8)#mask[round(img[b].shape[1] * hl1):img[b].shape[1], round(img[b].shape[2] * wl1):img[b].shape[2]] = 255pts = np.array([[int(img[b].shape[2] * wl1), int(img[b].shape[1] * hl1)],# pts1[int(img[b].shape[2] * wl2), int(img[b].shape[1] * hl2)],# pts2[int(img[b].shape[2] * wl3), int(img[b].shape[1] * hl3)],# pts3[int(img[b].shape[2] * wl4), int(img[b].shape[1] * hl4)]], np.int32)mask = cv2.fillPoly(mask,[pts],(255,255,255))imgc = img[b].transpose((1, 2, 0))imgc = cv2.add(imgc, np.zeros(np.shape(imgc), dtype=np.uint8), mask=mask)#cv2.imshow('1',imgc)img[b] = imgc.transpose((2, 0, 1))else:mask = np.zeros([img.shape[1], img.shape[2]], dtype=np.uint8)#mask[round(img.shape[1] * hl1):img.shape[1], round(img.shape[2] * wl1):img.shape[2]] = 255pts = np.array([[int(img.shape[2] * wl1), int(img.shape[1] * hl1)],# pts1[int(img.shape[2] * wl2), int(img.shape[1] * hl2)],# pts2[int(img.shape[2] * wl3), int(img.shape[1] * hl3)],# pts3[int(img.shape[2] * wl4), int(img.shape[1] * hl4)]], np.int32)mask = cv2.fillPoly(mask, [pts], (255,255,255))img = img.transpose((1, 2, 0))img = cv2.add(img, np.zeros(np.shape(img), dtype=np.uint8), mask=mask)img = img.transpose((2, 0, 1))t1 = time_sync()if onnx:img = img.astype('float32')else:img = torch.from_numpy(img).to(device)img = img.half() if half else img.float()# uint8 to fp16/32img = img / 255.0# 0 - 255 to 0.0 - 1.0if len(img.shape) == 3:img = img[None]# expand for batch dimt2 = time_sync()dt[0] += t2 - t1# Inferenceif pt:visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else Falsepred = model(img, augment=augment, visualize=visualize)[0]elif onnx:if dnn:net.setInput(img)pred = torch.tensor(net.forward())else:pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))else:# tensorflow model (tflite, pb, saved_model)imn = img.permute(0, 2, 3, 1).cpu().numpy()# image in numpyif pb:pred = frozen_func(x=tf.constant(imn)).numpy()elif saved_model:pred = model(imn, training=False).numpy()elif tflite:if int8:scale, zero_point = input_details[0]['quantization']imn = (imn / scale + zero_point).astype(np.uint8)# de-scaleinterpreter.set_tensor(input_details[0]['index'], imn)interpreter.invoke()pred = interpreter.get_tensor(output_details[0]['index'])if int8:scale, zero_point = output_details[0]['quantization']pred = (pred.astype(np.float32) - zero_point) * scale# re-scalepred[..., 0] *= imgsz[1]# xpred[..., 1] *= imgsz[0]# ypred[..., 2] *= imgsz[1]# wpred[..., 3] *= imgsz[0]# hpred = torch.tensor(pred)t3 = time_sync()dt[1] += t3 - t2# NMSpred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)dt[2] += time_sync() - t3# Second-stage classifier (optional)if classify:pred = apply_classifier(pred, modelc, img, im0s)# Process predictionsfor i, det in enumerate(pred):# per imageseen += 1# if webcam:# batch_size >= 1# p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count# else:# p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)if webcam:# batch_size >= 1p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.countcv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),cv2.FONT_HERSHEY_SIMPLEX,1.0, (255, 255, 0), 2, cv2.LINE_AA)pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)],# pts1[int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)],# pts2[int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)],# pts3[int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32)# pts4# pts = pts.reshape((-1, 1, 2))zeros = np.zeros((im0.shape), dtype=np.uint8)mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)cv2.polylines(im0, [pts], True, (255, 255, 0), 3)# plot_one_box(dr, im0, label='Detection_Region', color=(0, 255, 0), line_thickness=2)else:p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)cv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),cv2.FONT_HERSHEY_SIMPLEX,1.0, (255, 255, 0), 2, cv2.LINE_AA)pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)],# pts1[int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)],# pts2[int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)],# pts3[int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32)# pts4# pts = pts.reshape((-1, 1, 2))zeros = np.zeros((im0.shape), dtype=np.uint8)mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)cv2.polylines(im0, [pts], True, (255, 255, 0), 3)p = Path(p)# to Pathsave_path = str(save_dir / p.name)# img.jpgtxt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')# img.txts += '%gx%g ' % img.shape[2:]# print stringgn = torch.tensor(im0.shape)[[1, 0, 1, 0]]# normalization gain whwhimc = im0.copy() if save_crop else im0# for save_cropannotator = Annotator(im0, line_width=line_thickness, example=str(names))if len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, -1].unique():n = (det[:, -1] == c).sum()# detections per classs += f"{n} {names[int(c)]}{'s' * (n > 1)}, "# add to string# Write resultsfor *xyxy, conf, cls in reversed(det):if save_txt:# Write to filexywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()# normalized xywhline = (cls, *xywh, conf) if save_conf else (cls, *xywh)# label formatwith open(txt_path + '.txt', 'a') as f:f.write(('%g ' * len(line)).rstrip() % line + '\n')if save_img or save_crop or view_img:# Add bbox to imagec = int(cls)# integer classlabel = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')annotator.box_label(xyxy, label, color=colors(c, True))if save_crop:save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)# Print time (inference-only)print(f'{s}Done. ({t3 - t2:.3f}s)')# Stream resultsim0 = annotator.result()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[i] != save_path:# new videovid_path[i] = save_pathif isinstance(vid_writer[i], cv2.VideoWriter):vid_writer[i].release()# release previous video writerif vid_cap:# videofps = 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:# streamfps, w, h = 30, im0.shape[1], im0.shape[0]save_path += '.mp4'vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))vid_writer[i].write(im0)# Print resultst = tuple(x / seen * 1E3 for x in dt)# speeds per imageprint(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)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 {colorstr('bold', save_dir)}{s}")if update:strip_optimizer(weights)# update model (to fix SourceChangeWarning)def parse_opt():parser = argparse.ArgumentParser()parser.add_argument('--weights', nargs='+', type=str, default=ROOT / '权重文件', help='model path(s)')parser.add_argument('--source', type=str, default=ROOT / '检测图片', help='file/dir/URL/glob, 0 for webcam')parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')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='show 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('--save-crop', action='store_true', help='save cropped prediction boxes')parser.add_argument('--nosave', action='store_true', help='do not save images/videos')parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 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('--visualize', action='store_true', help='visualize features')parser.add_argument('--update', action='store_true', help='update all models')parser.add_argument('--project', default=ROOT / '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('--line-thickness', default=1, type=int, help='bounding box thickness (pixels)')parser.add_argument('--hide-labels', default=True, action='store_true', help='hide labels')parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')opt = parser.parse_args()opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1# expandprint_args(FILE.stem, opt)return optdef main(opt):check_requirements(exclude=('tensorboard', 'thop'))run(**vars(opt))if __name__ == "__main__":opt = parse_opt()main(opt)