F - Count the Colors

本文介绍线段树的基本概念及其实现细节,通过具体示例讲解如何利用线段树解决区间更新和查询问题,并针对特定场景提出优化方案。

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http://acm.hust.edu.cn/vjudge/contest/view.action?cid=66989#problem/F

线段树入门,起初,设置数组sum=-1表示无颜色,sum=-2表示下方无统一颜色;sum>=0表示颜色为sum的占据了整个子树

li ri数组==-1,-1表示左右无统一颜色其他表示区间rt左边颜色为li右边为ri,这样既可实现本题

看代码

#include <cstdio>
#include <cstring>
#include <algorithm>
#include <queue>
#include <map>
#include <set>
#include <stack>
#include <string>
#define SI(T)int T;scanf("%d",&T)
#define lson l,m,rt<<1
#define rson m+1,r,rt<<1|1
#define LL long long
using namespace std;
const int SIZE=5;
const int maxn=1<<30;
int sum[SIZE<<2],li[SIZE<<2],ri[SIZE<<2];
int cnt[SIZE];
void pushup(int rt){
    if(sum[rt<<1]!=-1||sum[rt<<1|1]!=-1){
        li[rt]=ri[rt]=-1;
        sum[rt]=-2;
        return;
    }
    if(ri[rt<<1]==li[rt<<1|1]){
        sum[rt]=li[rt]=ri[rt]=ri[rt<<1];
    }
}
void pushdown(int rt){
    if(sum[rt]>0){
        sum[rt<<1]=sum[rt<<1|1]=sum[rt];
        sum[rt]=-2;
    }
}
void build(int l,int r,int rt){
    memset(sum,-1,sizeof(sum));
    memset(li,-1,sizeof(li));
    memset(ri,-1,sizeof(ri));
}
void update(int L,int R,int c,int l,int r,int rt){
    if(L<=l&&r<=R){
        sum[rt]=li[rt]=ri[rt]=c;
        return;
    }
    pushdown(rt);
    int m=(l+r)>>1;
    if(L<=m)update(L,R,c,lson);
    if(R>m)update(L,R,c,rson);
    pushup(rt);
}
void query(int l,int r,int rt){
    if(sum[rt]>0){
        cnt[sum[rt]]++;
        return ;
    }
    if(sum[rt]==-1)return;
    int m=(l+r)>>1;
    if(l<=m)query(lson);
    if(r>m)query(rson);
}
int main()
{
    int n,x1,x2,c;
    while(scanf("%d",&n)!=EOF){
        memset(cnt,0,sizeof(cnt));
        build(1,SIZE,1);
        for(int i=0;i<n;i++){
            scanf("%d%d%d",&x1,&x2,&c);
            update(x1+1,x2,c,1,SIZE,1);
        }
        query(1,SIZE,1);
        for(int i=0;i<SIZE<<1;i++)
            printf("%d %d\n",sum[i],i);
        for(int i=0;i<SIZE;i++){
            if(cnt[i]){
                printf("%d %d\n",i,cnt[i]);
            }
        }
    }
    return 0;
}
但是要是更新的是3跟4区间从二叉树上看3跟4区间分隔在了两颗子树,虽然他们在线上是链接的,但是在线段树上却是断开的,导致了错误。

吃个饭回来好像明白了些什么了,rili数组干嘛的。。。明显上面的代码没有用,然后根据后序遍历树个特点(神奇的地方)设置一个前驱temp,刚刚好解决了上面的问题,4的前驱刚好是3。代码贴上

#include <cstdio>
#include <cstring>
#include <algorithm>
#include <queue>
#include <map>
#include <set>
#include <stack>
#include <string>
#define SI(T)int T;scanf("%d",&T)
#define lson l,m,rt<<1
#define rson m+1,r,rt<<1|1
#define LL long long
using namespace std;
const int SIZE=8080;
const int maxn=1<<30;
int sum[SIZE<<2];
int cnt[SIZE];
int temp;
void pushup(int rt){
    if(sum[rt<<1]==-2||sum[rt<<1|1]==-2){
        sum[rt]=-2;
        return;
    }
    if(sum[rt<<1]==sum[rt<<1|1])sum[rt]=sum[rt<<1];
    else sum[rt]=-2;
}
void pushdown(int rt){
    if(sum[rt]>=0){
        sum[rt<<1]=sum[rt<<1|1]=sum[rt];
        sum[rt]=-2;
    }
}
void build(int l,int r,int rt){
    sum[rt]=-1;
    if(l==r)return;
    int m=(l+r)>>1;
    build(lson);
    build(rson);
}
void update(int L,int R,int c,int l,int r,int rt){
    if(L<=l&&r<=R){
        sum[rt]=c;
        return;
    }
    pushdown(rt);
    int m=(l+r)>>1;
    if(L<=m)update(L,R,c,lson);
    if(R>m)update(L,R,c,rson);
    pushup(rt);
}
void query(int l,int r,int rt){
    if(sum[rt]>=0){
        if(temp==sum[rt])return;
        temp=sum[rt];
        cnt[sum[rt]]++;
        return ;
    }
    if(sum[rt]==-1){temp=-1;return;}
    int m=(l+r)>>1;
    if(l<=m)query(lson);
    if(r>m)query(rson);
}
int main()
{
    int n,x1,x2,c;
    while(scanf("%d",&n)!=EOF){
        memset(cnt,0,sizeof(cnt));
        build(1,SIZE,1);
        for(int i=0;i<n;i++){
            scanf("%d%d%d",&x1,&x2,&c);
            update(x1+1,x2,c,1,SIZE,1);
        }
        temp=-1;
        query(1,SIZE,1);
        for(int i=0;i<SIZE;i++){
            if(cnt[i]){
                printf("%d %d\n",i,cnt[i]);
            }
        }
        printf("\n");
    }
    return 0;
}


# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. Usage - sources: $ python detect.py --weights yolov5s.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/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: $ python detect.py --weights yolov5s.pt # PyTorch yolov5s.torchscript # TorchScript yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s_openvino_model # OpenVINO yolov5s.engine # TensorRT yolov5s.mlpackage # CoreML (macOS-only) yolov5s_saved_model # TensorFlow SavedModel yolov5s.pb # TensorFlow GraphDef yolov5s.tflite # TensorFlow Lite yolov5s_edgetpu.tflite # TensorFlow Edge TPU yolov5s_paddle_model # PaddlePaddle """ import argparse import csv 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 ultralytics.utils.plotting import Annotator, colors, save_one_box from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams 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, strip_optimizer, xyxy2xywh, ) from utils.torch_utils import select_device, smart_inference_mode # 新增:计算IOU函数 def calculate_iou(box1, box2): """计算两个边界框的IOU""" x1, y1, x2, y2 = box1 x1g, y1g, x2g, y2g = box2 # 计算交集区域 xA = max(x1, x1g) yA = max(y1, y1g) xB = min(x2, x2g) yB = min(y2, y2g) # 计算交集面积 inter_area = max(0, xB - xA + 1) * max(0, yB - yA + 1) # 计算并集面积 box1_area = (x2 - x1 + 1) * (y2 - y1 + 1) box2_area = (x2g - x1g + 1) * (y2g - y1g + 1) union_area = float(box1_area + box2_area - inter_area) # 计算IOU iou = inter_area / union_area return iou # 新增:计算准确率函数 def calculate_accuracy(gt_labels, pred_detections, iou_threshold=0.5): """计算目标检测的准确率""" correct_predictions = 0 total_gt_objects = 0 total_pred_objects = 0 for img_name in gt_labels: if img_name not in pred_detections: continue gt_boxes = gt_labels[img_name] pred_boxes = pred_detections[img_name] total_gt_objects += len(gt_boxes) total_pred_objects += len(pred_boxes) # 标记已匹配的真实标签 gt_matched = [False] * len(gt_boxes) for pred_box in pred_boxes: pred_class, pred_bbox, pred_conf = pred_box best_iou = 0 best_gt_idx = -1 # 寻找最佳匹配的真实标签 for i, gt_box in enumerate(gt_boxes): gt_class, gt_bbox = gt_box if gt_matched[i]: continue iou = calculate_iou(pred_bbox, gt_bbox) if iou > best_iou and pred_class == gt_class: best_iou = iou best_gt_idx = i # 如果IOU超过阈值且类别正确,则计为正确预测 if best_gt_idx != -1 and best_iou >= iou_threshold: correct_predictions += 1 gt_matched[best_gt_idx] = True # 避免除零错误 if total_gt_objects == 0: return 0.0 # 计算准确率 return correct_predictions / total_gt_objects @smart_inference_mode() def run( weights=ROOT / "yolov5s.pt", # model path or triton URL source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) data=ROOT / "data/coco128.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_format=0, # save boxes coordinates in YOLO format or Pascal-VOC format (0 for YOLO and 1 for Pascal-VOC) save_csv=False, # save results in CSV format 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/detect", # 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 gt_dir="", # 新增:真实标签目录 eval_interval=10, # 新增:评估间隔帧数 ): """ Runs YOLOv5 detection inference on various sources like images, videos, directories, streams, etc. Args: weights (str | Path): Path to the model weights file or a Triton URL. Default is 'yolov5s.pt'. source (str | Path): Input source, which can be a file, directory, URL, glob pattern, screen capture, or webcam index. Default is 'data/images'. data (str | Path): Path to the dataset YAML file. Default is 'data/coco128.yaml'. imgsz (tuple[int, int]): Inference image size as a tuple (height, width). Default is (640, 640). conf_thres (float): Confidence threshold for detections. Default is 0.25. iou_thres (float): Intersection Over Union (IOU) threshold for non-max suppression. Default is 0.45. max_det (int): Maximum number of detections per image. Default is 1000. device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu'. Default is an empty string, which uses the best available device. view_img (bool): If True, display inference results using OpenCV. Default is False. save_txt (bool): If True, save results in a text file. Default is False. save_format (int): Whether to save boxes coordinates in YOLO format or Pascal-VOC format. Default is 0. save_csv (bool): If True, save results in a CSV file. Default is False. save_conf (bool): If True, include confidence scores in the saved results. Default is False. save_crop (bool): If True, save cropped prediction boxes. Default is False. nosave (bool): If True, do not save inference images or videos. Default is False. classes (list[int]): List of classes to filter detections by. Default is None. agnostic_nms (bool): If True, perform class-agnostic non-max suppression. Default is False. augment (bool): If True, use augmented inference. Default is False. visualize (bool): If True, visualize feature maps. Default is False. update (bool): If True, update all models' weights. Default is False. project (str | Path): Directory to save results. Default is 'runs/detect'. name (str): Name of the current experiment; used to create a subdirectory within 'project'. Default is 'exp'. exist_ok (bool): If True, existing directories with the same name are reused instead of being incremented. Default is False. line_thickness (int): Thickness of bounding box lines in pixels. Default is 3. hide_labels (bool): If True, do not display labels on bounding boxes. Default is False. hide_conf (bool): If True, do not display confidence scores on bounding boxes. Default is False. half (bool): If True, use FP16 half-precision inference. Default is False. dnn (bool): If True, use OpenCV DNN backend for ONNX inference. Default is False. vid_stride (int): Stride for processing video frames, to skip frames between processing. Default is 1. gt_dir (str): 新增:真实标签目录路径 eval_interval (int): 新增:每隔多少帧计算一次准确率 Returns: None """ 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 # 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 imgsz = check_img_size(imgsz, s=stride) # check image size # Dataloader bs = 1 # 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: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) vid_path, vid_writer = [None] * bs, [None] * bs # 新增:加载真实标签数据 gt_labels = {} if gt_dir: gt_dir = Path(gt_dir) for txt_file in gt_dir.glob("*.txt"): img_name = txt_file.stem gt_labels[img_name] = [] with open(txt_file, "r") as f: for line in f: parts = line.strip().split() if len(parts) >= 5: cls = int(parts[0]) # 将YOLO格式转换为xyxy格式 x, y, w, h = map(float, parts[1:5]) # 假设真实标签对应的图像尺寸与输入图像一致 x1 = (x - w/2) * imgsz[1] y1 = (y - h/2) * imgsz[0] x2 = (x + w/2) * imgsz[1] y2 = (y + h/2) * imgsz[0] gt_labels[img_name].append((cls, (x1, y1, x2, y2))) # 新增:收集预测结果 pred_detections = {} frame_count = 0 accuracy = 0.0 # 初始化准确率 # Run inference model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) 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 if model.xml and im.shape[0] > 1: ims = torch.chunk(im, im.shape[0], 0) # Inference with dt[1]: visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False if model.xml and im.shape[0] > 1: pred = None for image in ims: if pred is None: pred = model(image, augment=augment, visualize=visualize).unsqueeze(0) else: pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0) pred = [pred, None] else: pred = model(im, augment=augment, visualize=visualize) # NMS with dt[2]: pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) # Second-stage classifier (optional) # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) # Define the path for the CSV file csv_path = save_dir / "predictions.csv" # Create or append to the CSV file def write_to_csv(image_name, prediction, confidence): """Writes prediction data for an image to a CSV file, appending if the file exists.""" data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence} file_exists = os.path.isfile(csv_path) with open(csv_path, mode="a", newline="") as f: writer = csv.DictWriter(f, fieldnames=data.keys()) if not file_exists: writer.writeheader() writer.writerow(data) # 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 s += "{:g}x{:g} ".format(*im.shape[2:]) # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # 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 # Write results for *xyxy, conf, cls in reversed(det): c = int(cls) # integer class label = names[c] if hide_conf else f"{names[c]}" confidence = float(conf) confidence_str = f"{confidence:.2f}" if save_csv: write_to_csv(p.name, label, confidence_str) if save_txt: # Write to file if save_format == 0: coords = ( (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() ) # normalized xywh else: coords = (torch.tensor(xyxy).view(1, 4) / gn).view(-1).tolist() # xyxy line = (cls, *coords, conf) if save_conf else (cls, *coords) # label format with open(f"{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 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)) if save_crop: save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True) # 新增:收集预测结果 img_name = p.stem pred_detections[img_name] = [] if len(det): for *xyxy, conf, cls in det: c = int(cls) x1, y1, x2, y2 = map(int, xyxy) pred_detections[img_name].append((c, (x1, y1, x2, y2), float(conf))) # 新增:定期计算准确率并显示 frame_count += 1 if gt_dir and frame_count % eval_interval == 0: accuracy = calculate_accuracy(gt_labels, pred_detections) if save_img or view_img: accuracy_text = f"Accuracy: {accuracy:.2f}" annotator.text((10, 30), accuracy_text, txt_color=(255, 255, 255)) im0 = annotator.result() # 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) 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 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") # 新增:在终端输出最终准确率 if gt_dir: accuracy = calculate_accuracy(gt_labels, pred_detections) LOGGER.info(f"Overall Accuracy: {accuracy:.4f}") # 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(): """ Parse command-line arguments for YOLOv5 detection, allowing custom inference options and model configurations. Args: --weights (str | list[str], optional): Model path or triton URL. Defaults to ROOT / 'yolov5s.pt'. --source (str, optional): File/dir/URL/glob/screen/0(webcam). Defaults to ROOT / 'data/images'. --data (str, optional): Dataset YAML path. Provides dataset configuration information. --imgsz (list[int], optional): Inference size (height, width). Defaults to [640]. --conf-thres (float, optional): Confidence threshold. Defaults to 0.25. --iou-thres (float, optional): NMS IoU threshold. Defaults to 0.45. --max-det (int, optional): Maximum number of detections per image. Defaults to 1000. --device (str, optional): CUDA device, i.e. 0 or 0,1,2,3 or cpu. Defaults to "". --view-img (bool, optional): Flag to display results. Default is False. --save-txt (bool, optional): Flag to save results to *.txt files. Default is False. --save-format (int, optional): Whether to save boxes coordinates in YOLO format or Pascal-VOC format. Default is 0. --save-csv (bool, optional): Flag to save results in CSV format. Default is False. --save-conf (bool, optional): Flag to save confidences in labels saved via --save-txt. Default is False. --save-crop (bool, optional): Flag to save cropped prediction boxes. Default is False. --nosave (bool, optional): Flag to prevent saving images/videos. Default is False. --classes (list[int], optional): List of classes to filter results by. Default is None. --agnostic-nms (bool, optional): Flag for class-agnostic NMS. Default is False. --augment (bool, optional): Flag for augmented inference. Default is False. --visualize (bool, optional): Flag for visualizing features. Default is False. --update (bool, optional): Flag to update all models in the model directory. Default is False. --project (str, optional): Directory to save results. Default is ROOT / 'runs/detect'. --name (str, optional): Sub-directory name for saving results within --project. Default is 'exp'. --exist-ok (bool, optional): Flag to allow overwriting if the project/name already exists. Default is False. --line-thickness (int, optional): Thickness (in pixels) of bounding boxes. Default is 3. --hide-labels (bool, optional): Flag to hide labels in the output. Default is False. --hide-conf (bool, optional): Flag to hide confidences in the output. Default is False. --half (bool, optional): Flag to use FP16 half-precision inference. Default is False. --dnn (bool, optional): Flag to use OpenCV DNN for ONNX inference. Default is False. --vid-stride (int, optional): Video frame-rate stride. Default is 1. --gt-dir (str, optional): 新增:真实标签目录路径 --eval-interval (int, optional): 新增:每隔多少帧计算一次准确率 Returns: argparse.Namespace: Parsed command-line arguments as an argparse.Namespace object. """ parser = argparse.ArgumentParser() parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL") parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") parser.add_argument("--data", type=str, default=ROOT / "data/coco128.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="", 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-format", type=int, default=0, help="whether to save boxes coordinates in YOLO format or Pascal-VOC format when save-txt is True, 0 for YOLO and 1 for Pascal-VOC", ) parser.add_argument("--save-csv", action="store_true", help="save results in CSV format") 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=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") # 新增参数 parser.add_argument("--gt-dir", type=str, default="", help="ground truth labels directory") parser.add_argument("--eval-interval", type=int, default=10, help="evaluate accuracy every N frames") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) return opt def main(opt): """ Executes YOLOv5 model inference based on provided command-line arguments, validating dependencies before running. Args: opt (argparse.Namespace): Command-line arguments for YOLOv5 detection. Returns: None """ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt)代码如上。yolov5在detect.py得到有类别和置信度标注的视频和图片,请问我如何操作,才能在有类别和置信度标注的视频和图片的基础上,在视频或图片中显示识别准确率Accuracy。请给出修改后的完整代码(尽量少修改,不要改变代码的其他地方),要求直接在vscode点击运行即可生成显示识别准确率Accuracy的视频或图片
07-07
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