SAR/predict_qt.py

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.

Usage - sources:
    $ python segment/predict.py --weights yolov5s-seg.pt --source 0                               # webcam
                                                                  img.jpg                         # image
                                                                  vid.mp4                         # video
                                                                  screen                          # screenshot
                                                                  path/                           # directory
                                                                  list.txt                        # list of images
                                                                  list.streams                    # list of streams
                                                                  'path/*.jpg'                    # glob
                                                                  'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                                  'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Usage - formats:
    $ python segment/predict.py --weights yolov5s-seg.pt                 # PyTorch
                                          yolov5s-seg.torchscript        # TorchScript
                                          yolov5s-seg.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                          yolov5s-seg_openvino_model     # OpenVINO
                                          yolov5s-seg.engine             # TensorRT
                                          yolov5s-seg.mlmodel            # CoreML (macOS-only)
                                          yolov5s-seg_saved_model        # TensorFlow SavedModel
                                          yolov5s-seg.pb                 # TensorFlow GraphDef
                                          yolov5s-seg.tflite             # TensorFlow Lite
                                          yolov5s-seg_edgetpu.tflite     # TensorFlow Edge TPU
                                          yolov5s-seg_paddle_model       # PaddlePaddle
"""

import argparse
import os
import platform
import sys
from pathlib import Path

import torch

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams, LoadImages_batch
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                           increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
                           strip_optimizer)
from utils.plots import Annotator, colors, save_one_box
from utils.segment.general import masks2segments, process_mask, process_mask_native
from utils.segment.dataloaders import polygons2masks
from utils.torch_utils import select_device, smart_inference_mode
import numpy as np


@smart_inference_mode()
def run(
        weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
        source=ROOT / 'data/HRSID_YOLO/val',  # file/dir/URL/glob/screen/0(webcam)
        data=ROOT / 'data/HRSID_YOLO/dataset.yaml',  # dataset.yaml path
        imgsz=(640, 640),  # inference size (height, width)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=False,  # save results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_crop=False,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project=ROOT / 'runs/predict-seg',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        line_thickness=3,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        half=False,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference
        vid_stride=1,  # video frame-rate stride
        retina_masks=False,
        batch_size=16,
):
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
    screenshot = source.lower().startswith('screen')
    if is_url and is_file:
        source = check_file(source)  # download

    # Directories
    save_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
    (save_dir / 'jsons' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    logFile = save_dir / 'detect.txt'
    import logging
    LOGGER = logging.getLogger('yolov5')
    logging.basicConfig(level=logging.INFO)
    # 创建一个FileHandler,并将日志写入指定的日志文件中
    fileHandler = logging.FileHandler(logFile, mode='a', encoding="utf-8")
    fileHandler.setLevel(logging.INFO)

    # 定义Handler的日志输出格式
    # formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    formatter = logging.Formatter('%(asctime)s - %(message)s')
    fileHandler.setFormatter(formatter)

    # 添加Handler
    LOGGER.addHandler(fileHandler)

    # Load model
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
    stride, names, pt = model.stride, model.names, model.pt
    nmm = len(list(names.keys()))
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Dataloader
    bs = batch_size  # batch_size
    if webcam:
        view_img = check_imshow(warn=True)
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
        bs = len(dataset)
    elif screenshot:
        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
    else:
        if bs == 1:
            dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
        else:
            from torch.utils.data import DataLoader
            dataset = LoadImages_batch(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
            bs = min(bs, len(dataset))
            dataloader = DataLoader(dataset,
                                    batch_size=bs,
                                    shuffle=True,
                                    num_workers=4,
                                    pin_memory=True,
                                    collate_fn=LoadImages_batch.collate_fn)

    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
    if bs == 1:
        for path, im, im0s, vid_cap, s in dataset:
            with dt[0]:
                im = torch.from_numpy(im).to(model.device)
                im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
                im /= 255  # 0 - 255 to 0.0 - 1.0
                if len(im.shape) == 3:
                    im = im[None]  # expand for batch dim
            # Inference
            with dt[1]:
                visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
                pred, proto = model(im, augment=augment, visualize=visualize)[:2]

            # NMS
            with dt[2]:
                # if pred.numel() > 0:  # numel() 返回张量中元素的数量
                #     pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms,
                #                                    max_det=max_det, nm=nm)
                # else:
                #     print("No valid predictions, skipping non_max_suppression.")
                #     pred = torch.empty((0, 6))  # 返回一个空的张量以避免后续错误
                # print(pred)
                pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=nmm)

            # Second-stage classifier (optional)
            # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)

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

                p = Path(p)  # to Path
                save_path = str(save_dir / p.name)  # im.jpg
                txt_path = str(save_dir / 'labels' / p.stem) + (
                    '' if dataset.mode == 'image' else f'_{frame}')  # im.txt

                labelme_path = str(save_dir / 'jsons' / p.stem) + (
                    '' if dataset.mode == 'image' else f'_{frame}')  # im.json

                label_dict = {}
                label_dict['version'] = '5.0.1'
                label_dict['flags'] = {}
                label_dict['imageData'] = None
                label_dict['imagePath'] = p.name
                label_dict['imageHeight'] = im0.shape[0]
                label_dict['imageWidth'] = im0.shape[1]
                label_dict['shapes'] = []

                s += '%gx%g ' % im.shape[2:]  # print string
                imc = im0.copy() if save_crop else im0  # for save_crop
                annotator = Annotator(im0, line_width=line_thickness, example=str(names))
                if len(det):
                    if retina_masks:
                        # scale bbox first the crop masks
                        det[:, :4] = scale_boxes(im.shape[2:], det[:, :4],
                                                 im0.shape).round()  # rescale boxes to im0 size
                        masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2])  # HWC
                    else:
                        masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True)  # HWC
                        det[:, :4] = scale_boxes(im.shape[2:], det[:, :4],
                                                 im0.shape).round()  # rescale boxes to im0 size

                    # Segments
                    if save_txt:
                        segments = [
                            scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=False)
                            for x in reversed(masks2segments(masks))]  # ,strategy='concat'

                    # Print results
                    for c in det[:, 5].unique():
                        n = (det[:, 5] == c).sum()  # detections per class
                        s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                    # Mask plotting
                    annotator.masks(
                        masks,
                        colors=[colors(x, True) for x in det[:, 5]],
                        im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(
                            0).contiguous() /
                               255 if retina_masks else im[i])

                    # Write results
                    for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
                        if save_txt:  # Write to file
                            seg = segments[j].reshape(-1)  # (n,2) to (n*2)
                            line = (cls, *seg, conf) if save_conf else (cls, *seg)  # label format
                            with open(f'{txt_path}.txt', 'a') as f:
                                f.write(('%g ' * len(line)).rstrip() % line + '\n')
                            # add save_labeme format
                            tmp = {}
                            tmp['label'] = names[int(cls)]
                            # tmp['label_id'] = int(cls)
                            tmp['points'] = segments[
                                j].tolist()  # [[float(xyxy[0].detach().item()), float(xyxy[1].detach().item())], [float(xyxy[2].detach().item()), float(xyxy[3].detach().item())]]

                            tmp['group_id'] = None
                            tmp['shape_type'] = 'polygon'  # 注意修改

                            tmp['flags'] = {}
                            # tmp['bbox'] = [xyxy[0].tolist(),xyxy[1].tolist()]
                            label_dict['shapes'].append(tmp)

                        if save_img or save_crop or view_img:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = 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), flag=False)
                        #     # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
                        if save_crop:
                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

                    if save_txt:  # Write to file
                        import json
                        with open(f'{labelme_path}.json', 'w') as f:
                            json.dump(label_dict, f)

                # Stream results
                im0 = annotator.result()
                if view_img:
                    if platform.system() == 'Linux' and p not in windows:
                        windows.append(p)
                        cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                        cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                    cv2.imshow(str(p), im0)
                    if cv2.waitKey(1) == ord('q'):  # 1 millisecond
                        exit()

                # 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 video
                            vid_path[i] = save_path
                            if isinstance(vid_writer[i], cv2.VideoWriter):
                                vid_writer[i].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 = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
                            vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                        vid_writer[i].write(im0)

            # Print time (inference-only)
            LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
            f_for_val = open('x.excel', 'w')
            f_for_val.writelines(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
    else:
        for batch_i, (paths, im, im0s, s) in enumerate(dataloader):
            with dt[0]:
                im = im.to(device, non_blocking=True)
                im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
                im /= 255  # 0 - 255 to 0.0 - 1.0

            # Inference
            with dt[1]:
                # visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
                pred, proto = model(im, augment=augment, visualize=False)[:2]

            # NMS
            with dt[2]:
                if pred.numel() > 0:  # numel() 返回张量中元素的数量
                    pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms,
                                                   max_det=max_det, nm=nm)
                else:
                    print("No valid predictions, skipping non_max_suppression.")
                    pred = torch.empty((0, 6))  # 返回一个空的张量以避免后续错误
                pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=nmm)

            # Second-stage classifier (optional)
            # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)

            # Process predictions
            for i, det in enumerate(pred):  # per image
                seen += 1
                if webcam:  # batch_size >= 1
                    p, im0, frame = paths[i], im0s[i].copy(), dataset.count
                    s += f'{i}: '
                else:
                    p, im0, frame = paths[i], im0s[i].numpy().copy(), getattr(dataset, 'frame', 0)
                    s[i] += f'{i}: '
                p = Path(p)  # to Path
                save_path = str(save_dir / p.name)  # im.jpg
                txt_path = str(save_dir / 'labels' / p.stem) + (
                    '' if dataset.mode == 'image' else f'_{frame}')  # im.txt

                labelme_path = str(save_dir / 'jsons' / p.stem) + (
                    '' if dataset.mode == 'image' else f'_{frame}')  # im.json
                label_dict = {}
                label_dict['version'] = '5.0.1'
                label_dict['flags'] = {}
                label_dict['imageData'] = None
                label_dict['imagePath'] = p.name
                label_dict['imageHeight'] = im0.shape[0]
                label_dict['imageWidth'] = im0.shape[1]
                label_dict['shapes'] = []

                s += '%gx%g ' % im.shape[2:]  # print string
                imc = im0.copy() if save_crop else im0  # for save_crop
                annotator = Annotator(im0, line_width=line_thickness, example=str(names))
                if len(det):
                    if retina_masks:
                        # scale bbox first the crop masks
                        det[:, :4] = scale_boxes(im.shape[2:], det[:, :4],
                                                 im0.shape).round()  # rescale boxes to im0 size
                        masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2])  # HWC
                    else:
                        masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True)  # HWC
                        det[:, :4] = scale_boxes(im.shape[2:], det[:, :4],
                                                 im0.shape).round()  # rescale boxes to im0 size

                    # Segments
                    if save_txt:
                        segments = [
                            scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=False)
                            for x in reversed(masks2segments(masks))]

                    # Print results
                    for c in det[:, 5].unique():
                        n = (det[:, 5] == c).sum()  # detections per class
                        s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                    # Mask plotting
                    annotator.masks(
                        masks,
                        colors=[colors(x, True) for x in det[:, 5]],
                        im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(
                            0).contiguous() /
                               255 if retina_masks else im[i])

                    # Write results
                    for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
                        if save_txt:  # Write to file
                            seg = segments[j].reshape(-1)  # (n,2) to (n*2)
                            line = (cls, *seg, conf) if save_conf else (cls, *seg)  # label format
                            with open(f'{txt_path}.txt', 'a') as f:
                                f.write(('%g ' * len(line)).rstrip() % line + '\n')

                            # add save_labeme format
                            tmp = {}
                            tmp['label'] = names[int(cls)]

                            tmp['points'] = segments[
                                j].tolist()  # E[float(xyxy[0].detach().item()), float(xyxy[1].detach().item())], [float(xyxy[2].detach().item()), float(xyxy[3].detach().item())]
                            tmp['group_id'] = None
                            tmp['shape_type'] = 'polygon'  # 注意修改
                            # area = component_polygon_area(segments[j])
                            # tmp['area'] = area
                            # tmp['lenth'] = component_polygon_Circle(segments[j])# 宽度
                            # tmp['width'] = component_polygon_Circle(segments[j])
                            tmp['flags'] = {}
                            label_dict['shapes'].append(tmp)

                        if save_img or save_crop or view_img:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = 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), flag=False)
                            # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
                        if save_crop:
                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

                    if save_txt:  # Write to file
                        import json
                        with open(f'{labelme_path}.json', 'w') as f:
                            json.dump(label_dict, f)
                        # # copy file
                        # import shutil
                        # #shutil.copyfile(f'{labelme_path}.json', os.path.dirname(source) + f'{labelme_path}.json')
                        # if  os.path.isdir(source):
                        #     shutil.copyfile(f'{labelme_path}.json',os.path.join(source,f'{p.stem}.json'))
                        # else:
                        #     shutil.copyfile(f'{labelme_path}.json',os.path.join(os.path.dirname(source),f'{p.stem}.json'))
                else:
                    # 不存在 检测结果
                    if save_txt:  # Write to file
                        # add save_labeme format
                        tmp = {}
                        tmp['label'] = ""  # names[int(cls)]

                        tmp['points'] = [
                            []]  # segments[j].tolist() #E[float(xyxy[0].detach().item()), float(xyxy[1].detach().item())], [float(xyxy[2].detach().item()), float(xyxy[3].detach().item())]
                        tmp['group_id'] = None
                        tmp['shape_type'] = 'polygon'  # 注意修改
                        # area = component_polygon_area(segments[j])
                        # tmp['area'] = area
                        # tmp['lenth'] = component_polygon_Circle(segments[j])# 宽度
                        # tmp['width'] = component_polygon_Circle(segments[j])
                        tmp['flags'] = {}
                        label_dict['shapes'].append(tmp)
                        import json
                        with open(f'{labelme_path}.json', 'w') as f:
                            json.dump(label_dict, f)

                # Stream results
                im0 = annotator.result()
                if view_img:
                    if platform.system() == 'Linux' and p not in windows:
                        windows.append(p)
                        cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                        cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                    cv2.imshow(str(p), im0)
                    if cv2.waitKey(1) == ord('q'):  # 1 millisecond
                        exit()

                # 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 video
                            vid_path[i] = save_path
                            if isinstance(vid_writer[i], cv2.VideoWriter):
                                vid_writer[i].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 = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
                            vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                        vid_writer[i].write(im0)

            # Print time (inference-only)
            LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
            f_for_val.writelines(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
    f_for_val.close()
    # Print results
    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
    LOGGER.info(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 ''
        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
    if update:
        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'weights\\best.pt', help='model path(s)')
    parser.add_argument('--source', type=str, default=ROOT / 'data\\HRSID_YOLO\\test\\images', help='file/dir/URL/glob/screen/0(webcam)')
    parser.add_argument('--data', type=str, default=ROOT / 'data\HRSID_YOLO\dataset.yaml', help='(optional) dataset.yaml path')
    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='0', 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', default=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', default=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=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, 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')
    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
    parser.add_argument('--retina-masks', action='store_true', help='whether to plot masks in native resolution')
    parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs, -1 for autobatch')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(vars(opt))
    return opt


def main(opt):
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)