yolov5预测时img = Image.open(args.img) # 在图像上运行YOLOv5模型，获取预测结果 results = model(img,32) 参数要怎么写

yolov5模型，图像训练

yolov5推理路标的模型，包含示例图片、训练好的权重、预测代码和预测结果，可以直接用于预测图片中的路标，适合计算机视觉毕业设计

yolov5推理路标的模型，包含示例图片、训练好的权重、预测代码和预测结果，可以直接用于预测图片中的路标，适合计算机视觉毕业设计。要使用YOLOv5训练路标检测模型，你需要按照以下步骤进行操作： 1. 数据准备：收集包含路标的图像数据集，并进行标注。确保每个标注文件中包含每个路标的边界框坐标。 2. 安装环境：在本地计算机或服务器上安装PyTorch、OpenCV和其他必要的Python库。可以参考YOLOv5的官方文档进行环境安装。 3. 下载YOLOv5：从YOLOv5的GitHub存储库中下载最新版本的代码。 4. 配置数据集：将收集的数据集分成训练集和验证集，并将其转换成YOLOv5所需的格式。在YOLOv5的数据文件夹中创建一个新的数据集配置文件，指定训练集和验证集的路径、类别数量等。 5. 模型配置：在YOLOv5的models文件夹中选择适合你的任务的模型配置文件。根据需要调整模型的超参数，如输入图像尺寸、训练时的学习率等。 6. 开始训练：运行YOLOv5的train.py脚本，指定数据集配置文件和模型配置文件。训练过程中会自动保存模型和训练日志。

yolov5s模型，yolov5学习

yolov5s模型，yolov5学习，基础yolov5模型

如果我想加一个图片映射，根据类别不同划分不同颜色的边界框，例：如果是孔洞，希望能画一个红色的框，如果是裂痕，希望能画一个绿色的框。请在如下代码中进行添加，要求整个完整rt cv2 import numpy as np from ultralytics import YOLO from PIL import Image def split_image(image, tile_size=640): """ 将大图分割为指定尺寸的切片 Args: image: 输入图像(numpy数组) tile_size: 切片尺寸，默认640x640 Returns: tiles: 切片列表，每个元素为(image_tile, x_offset, y_offset) """ height, width = image.shape[:2] tiles = [] # 遍历图像网格 for y in range(0, height, tile_size): for x in range(0, width, tile_size): # 计算当前切片坐标 y1 = y y2 = min(y + tile_size, height) x1 = x x2 = min(x + tile_size, width) # 提取切片并记录偏移量 tile = image[y1:y2, x1:x2] tiles.append((tile, x1, y1)) return tiles # 加载YOLOv8分割模型 model = YOLO('yolov8n-seg.pt') # 请确认模型路径正确 # 读取输入图像 image = cv2.imread('large_image.jpg') # 替换为你的图像路径 original_image = image.copy() # 存储所有检测结果 all_boxes = [] all_scores = [] all_class_ids = [] all_masks = [] # 分割图像为640x640切片 tiles = split_image(image) for tile, x_offset, y_offset in tiles: # 转换为RGB格式 tile_rgb = cv2.cvtColor(tile, cv2.COLOR_BGR2RGB) # 执行推理 results = model(tile_rgb) # 解析当前切片结果 for result in results: if result.masks is not None: # 获取检测结果 boxes = result.boxes.xyxy.cpu().numpy() scores = result.boxes.conf.cpu().numpy() class_ids = result.boxes.cls.cpu().numpy().astype(int) masks = result.masks.data.cpu().numpy() # 调整坐标到原始图像坐标系 boxes[:, [0, 2]] += x_offset boxes[:, [1, 3]] += y_offset # 调整掩码坐标 for mask in masks: # 创建与原图大小相同的全零矩阵 full_mask = np.zeros(original_image.shape[:2], dtype=np.uint8) # 将切片区域的掩码放入正确位置 full_mask[y_offset:y_offset+tile.shape[0], x_off

通常，YOLO模型输出的结果中，每个检测到的对象都有一个类别索引，可以通过这个索引获取类别名称。例如，如果类别列表是classes = ['孔洞', '裂痕', ...]，那么检测结果的class_id对应的就是类别名称。用户可能...

import os import argparse import yaml import torch import torch.nn.functional as F import torch.nn as nn import numpy as np from tqdm import tqdm from natsort import natsorted from glob import glob from skimage import img_as_ubyte import utils from basicsr.models.archs.kbnet_l_arch import KBNet_l try: from yaml import CLoader as Loader except ImportError: from yaml import Loader parser = argparse.ArgumentParser(description='Image Deraining using Restormer') parser.add_argument('--input_dir', default='./Datasets/', type=str, help='Directory of validation images') parser.add_argument('--result_dir', default='./results/', type=str, help='Directory for results') parser.add_argument('--yml', default='Deraining/Options/kbnet_l.yml', type=str) args = parser.parse_args() ####### Load yaml ####### yaml_file = args.yml name = os.path.basename(yaml_file).split('.')[0] x = yaml.load(open(yaml_file, mode='r'), Loader=Loader) s = x['network_g'].pop('type') pth_path = x['path']['pretrain_network_g'] print('', yaml_file, pth_path) ########################## model_restoration = eval(s)(x['network_g']) checkpoint = torch.load(pth_path) model_restoration.load_state_dict(checkpoint['params']) print("===>Testing using weights: ", pth_path) model_restoration.cuda() model_restoration = nn.DataParallel(model_restoration) model_restoration.eval() factor = 8 datasets = ['Test1200', 'Test2800'] for dataset in datasets: result_dir = os.path.join(args.result_dir, dataset) os.makedirs(result_dir, exist_ok=True) inp_dir = os.path.join(args.input_dir, 'test', dataset, 'input') files = natsorted(glob(os.path.join(inp_dir, '.png')) + glob(os.path.join(inp_dir, '.jpg'))) with torch.no_grad(): for file_ in tqdm(files): torch.cuda.ipc_collect() torch.cuda.empty_cache() img = np.float32(utils.load_img(file_)) / 255. img = torch.from_numpy(img).permute(2, 0, 1) input_ = img.unsqueeze(0).c

好的，我现在需要帮助用户解决关于使用Restormer模型进行图像去雨处理的问题，并且审查相关的Python代码，特别是涉及KBNet_l架构、PyTorch和命令行参数解析的部分。首先，我得确保自己了解Restormer模型的基本原理和...

检测出来的目标点在目标框不禁止的时候会抖动，怎么高效且简单的解决这个问题，确保是我按住鼠标右键后瞄准的是处理过的点。请你根据我的代码风格，然后给我完整代码。绘制的点也是处理后的import tkinter as tk import cv2 import time import torch from ultralytics import YOLO from PIL import Image, ImageTk import threading import queue import dxcam import traceback import ctypes from tkinter import ttk, messagebox import os import glob import sys import logitech.lg from pynput import mouse class PIDController: """PID控制器实现""" def init(self, kp=0.0, ki=0.0, kd=0.0): self.kp = kp self.ki = ki self.kd = kd self.prev_error = (0, 0) self.integral = (0, 0) self.last_time = time.time() def update(self, error): current_time = time.time() dt = current_time - self.last_time self.last_time = current_time if dt <= 0: dt = 0.01 dx, dy = error px = self.kp * dx py = self.kp * dy self.integral = ( self.integral[0] + dx * dt, self.integral[1] + dy * dt ) ix = self.ki * self.integral[0] iy = self.ki * self.integral[1] dx_dt = (dx - self.prev_error[0]) / dt dy_dt = (dy - self.prev_error[1]) / dt ddx = self.kd * dx_dt ddy = self.kd * dy_dt self.prev_error = (dx, dy) output_x = px + ix + ddx output_y = py + iy + ddy return (output_x, output_y) def reset(self): self.prev_error = (0, 0) self.integral = (0, 0) self.last_time = time.time() class ScreenDetector: def init(self, config_path): # 解析配置文件 self._parse_config(config_path) # 设备检测与模型加载 self.device = self._determine_device() self.model = YOLO(self.model_path).to(self.device) # 屏幕信息初始化 self._init_screen_info() # 控制参数初始化 self._init_control_params() # 状态管理 self.stop_event = threading.Event() self.camera_lock = threading.Lock() self.target_lock = threading.Lock() self.offset_lock = threading.Lock() self.button_lock = threading.Lock() # 初始化相机 self._init_camera() # 初始化鼠标监听器 self._init_mouse_listener() def _parse_config(self, config_path): """解析并存储配置参数""" self.cfg = self._parse_txt_config(config_path) # 存储常用参数 self.model_path = self.cfg['model_path'] self.model_device = self.cfg['model_device'] self.screen_target_size = int(self.cfg['screen_target_size']) self.detection_conf_thres = float(self.cfg['detection_conf_thres']) self.detection_iou_thres = float(self.cfg['detection_iou_thres']) self.detection_classes = [int(x) for x in self.cfg['detection_classes'].split(',')] self.visualization_color = tuple(map(int, self.cfg['visualization_color'].split(','))) self.visualization_line_width = int(self.cfg['visualization_line_width']) self.visualization_font_scale = float(self.cfg['visualization_font_scale']) self.visualization_show_conf = bool(self.cfg['visualization_show_conf']) self.fov_horizontal = float(self.cfg.get('move_fov_horizontal', '90')) self.mouse_dpi = int(self.cfg.get('move_mouse_dpi', '400')) self.pid_kp = float(self.cfg.get('pid_kp', '0.5')) self.pid_ki = float(self.cfg.get('pid_ki', '0.0')) self.pid_kd = float(self.cfg.get('pid_kd', '0.1')) self.target_offset_x_percent = float(self.cfg.get('target_offset_x', '50')) self.target_offset_y_percent = 100 - float(self.cfg.get('target_offset_y', '50')) def _parse_txt_config(self, path): """解析TXT格式的配置文件""" config = {} with open(path, 'r', encoding='utf-8') as f: for line in f: line = line.strip() if not line or line.startswith('#'): continue if '=' in line: key, value = line.split('=', 1) config[key.strip()] = value.strip() return config def _determine_device(self): """确定运行设备""" if self.model_device == 'auto': return 'cuda' if torch.cuda.is_available() and torch.cuda.device_count() > 0 else 'cpu' return self.model_device def _init_screen_info(self): """初始化屏幕信息""" user32 = ctypes.windll.user32 self.screen_width, self.screen_height = user32.GetSystemMetrics(0), user32.GetSystemMetrics(1) self.screen_center = (self.screen_width // 2, self.screen_height // 2) # 计算截图区域 left = (self.screen_width - self.screen_target_size) // 2 top = (self.screen_height - self.screen_target_size) // 2 self.region = ( max(0, int(left)), max(0, int(top)), min(self.screen_width, int(left + self.screen_target_size)), min(self.screen_height, int(top + self.screen_target_size)) ) def _init_control_params(self): """初始化控制参数""" self.pid_controller = PIDController( kp=self.pid_kp, ki=self.pid_ki, kd=self.pid_kd ) self.previous_target_info = None self.closest_target_absolute = None self.target_offset = None self.right_button_pressed = False # 改为鼠标右键状态 def _init_camera(self): """初始化相机""" try: with self.camera_lock: self.camera = dxcam.create( output_idx=0, output_color="BGR", region=self.region ) self.camera.start(target_fps=120, video_mode=True) except Exception as e: print(f"相机初始化失败: {str(e)}") try: # 降级模式 with self.camera_lock: self.camera = dxcam.create() self.camera.start(target_fps=60, video_mode=True) except Exception as fallback_e: print(f"降级模式初始化失败: {str(fallback_e)}") self.camera = None def _init_mouse_listener(self): """初始化鼠标监听器""" self.mouse_listener = mouse.Listener( on_click=self.on_mouse_click # 监听鼠标点击事件 ) self.mouse_listener.daemon = True self.mouse_listener.start() def on_mouse_click(self, x, y, button, pressed): """处理鼠标点击事件""" try: if button == mouse.Button.right: # 监听鼠标右键 with self.button_lock: self.right_button_pressed = pressed # 更新状态 if pressed: # 当右键按下时重置PID self.pid_controller.reset() except Exception as e: print(f"鼠标事件处理错误: {str(e)}") def calculate_fov_movement(self, dx, dy): """基于FOV算法计算鼠标移动量""" # 计算屏幕对角线长度 screen_diagonal = (self.screen_width 2 + self.screen_height 2) ** 0.5 # 计算垂直FOV aspect_ratio = self.screen_width / self.screen_height fov_vertical = self.fov_horizontal / aspect_ratio # 计算每像素对应角度 angle_per_pixel_x = self.fov_horizontal / self.screen_width angle_per_pixel_y = fov_vertical / self.screen_height # 计算角度偏移 angle_offset_x = dx * angle_per_pixel_x angle_offset_y = dy * angle_per_pixel_y # 转换为鼠标移动量 move_x = (angle_offset_x / 360) * self.mouse_dpi move_y = (angle_offset_y / 360) * self.mouse_dpi return move_x, move_y def move_mouse_to_target(self): """移动鼠标使准心对准目标点""" if not self.target_offset: return try: # 获取目标点与屏幕中心的偏移量 with self.offset_lock: dx, dy = self.target_offset # 使用FOV算法计算鼠标移动量 move_x, move_y = self.calculate_fov_movement(dx, dy) # 使用PID控制器平滑移动 pid_output = self.pid_controller.update((move_x, move_y)) move_x_pid, move_y_pid = pid_output # 使用罗技API移动鼠标 if move_x_pid != 0 or move_y_pid != 0: logitech.lg.mouse_xy(int(move_x_pid), int(move_y_pid)) except Exception as e: print(f"移动鼠标时出错: {str(e)}") def run(self, frame_queue): """主检测循环""" while not self.stop_event.is_set(): try: # 截图 grab_start = time.perf_counter() screenshot = self._grab_screenshot() grab_time = (time.perf_counter() - grab_start) * 1000 # ms if screenshot is None: time.sleep(0.001) continue # 推理 inference_start = time.perf_counter() results = self._inference(screenshot) inference_time = (time.perf_counter() - inference_start) * 1000 # ms # 处理检测结果 target_info, closest_target_relative, closest_offset = self._process_detection_results(results) # 更新目标信息 self._update_target_info(target_info, closest_offset) # 移动鼠标 self._move_mouse_if_needed() # 可视化处理 annotated_frame = self._visualize_results(results, closest_target_relative) if frame_queue else None # 放入队列 if frame_queue: try: frame_queue.put( (annotated_frame, len(target_info), inference_time, grab_time, target_info), timeout=0.01 ) except queue.Full: pass except Exception as e: print(f"检测循环异常: {str(e)}") traceback.print_exc() self._reset_camera() time.sleep(0.5) def _grab_screenshot(self): """安全获取截图""" with self.camera_lock: if self.camera: return self.camera.grab() return None def _inference(self, screenshot): """执行模型推理""" return self.model.predict( screenshot, conf=self.detection_conf_thres, iou=self.detection_iou_thres, classes=self.detection_classes, device=self.device, verbose=False ) def _process_detection_results(self, results): """处理检测结果""" target_info = [] min_distance = float('inf') closest_target_relative = None closest_target_absolute = None closest_offset = None for box in results[0].boxes: # 获取边界框坐标 x1, y1, x2, y2 = map(int, box.xyxy[0]) # 计算绝对坐标 x1_abs = x1 + self.region[0] y1_abs = y1 + self.region[1] x2_abs = x2 + self.region[0] y2_abs = y2 + self.region[1] # 计算边界框尺寸 width = x2_abs - x1_abs height = y2_abs - y1_abs # 应用偏移百分比计算目标点 target_x = x1_abs + int(width * (self.target_offset_x_percent / 100)) target_y = y1_abs + int(height * (self.target_offset_y_percent / 100)) # 计算偏移量 dx = target_x - self.screen_center[0] dy = target_y - self.screen_center[1] distance = (dx 2 + dy 2) ** 0.5 # 更新最近目标 if distance < min_distance: min_distance = distance # 计算相对坐标（用于可视化） closest_target_relative = ( x1 + int(width * (self.target_offset_x_percent / 100)), y1 + int(height * (self.target_offset_y_percent / 100)) ) closest_target_absolute = (target_x, target_y) closest_offset = (dx, dy) # 保存目标信息 class_id = int(box.cls) class_name = self.model.names[class_id] target_info.append(f"{class_name}:{x1_abs},{y1_abs},{x2_abs},{y2_abs}") return target_info, closest_target_relative, closest_offset def _update_target_info(self, target_info, closest_offset): """更新目标信息""" # 检查目标信息是否有变化 if target_info != self.previous_target_info: self.previous_target_info = target_info.copy() print(f"{len(target_info)}|{'|'.join(target_info)}") # 更新目标偏移量 with self.offset_lock: self.target_offset = closest_offset def _visualize_results(self, results, closest_target): """可视化处理结果""" frame = results[0].plot( line_width=self.visualization_line_width, font_size=self.visualization_font_scale, conf=self.visualization_show_conf ) # 绘制最近目标 if closest_target: # 绘制目标中心点 cv2.circle( frame, (int(closest_target[0]), int(closest_target[1])), 3, (0, 0, 255), -1 ) # 计算屏幕中心在截图区域内的相对坐标 screen_center_x = self.screen_center[0] - self.region[0] screen_center_y = self.screen_center[1] - self.region[1] # 绘制中心到目标的连线 cv2.line( frame, (int(screen_center_x), int(screen_center_y)), (int(closest_target[0]), int(closest_target[1])), (0, 255, 0), 1 ) return frame def _move_mouse_if_needed(self): """如果需要则移动鼠标""" with self.button_lock: if self.right_button_pressed and self.target_offset: # 使用right_button_pressed self.move_mouse_to_target() def _reset_camera(self): """重置相机""" print("正在重置相机...") try: self._init_camera() except Exception as e: print(f"相机重置失败: {str(e)}") traceback.print_exc() def stop(self): """安全停止检测器""" self.stop_event.set() self._safe_stop() if hasattr(self, 'mouse_listener') and self.mouse_listener.running: # 改为停止鼠标监听器 self.mouse_listener.stop() def _safe_stop(self): """同步释放资源""" print("正在安全停止相机...") try: with self.camera_lock: if self.camera: self.camera.stop() print("相机已停止") except Exception as e: print(f"停止相机时发生错误: {str(e)}") print("屏幕检测器已停止") class App: def init(self, root, detector): self.root = root self.detector = detector self.root.title("DXcam Detection") self.root.geometry(f"{detector.region[2] - detector.region[0]}x{detector.region[3] - detector.region[1] + 50}") self.root.wm_attributes('-topmost', 1) # 界面组件 self.canvas = tk.Canvas(root, highlightthickness=0) self.canvas.pack(fill=tk.BOTH, expand=True) # 性能监控队列 self.frame_queue = queue.Queue(maxsize=3) # 控制面板 self.control_frame = tk.Frame(root) self.control_frame.pack(side=tk.BOTTOM, fill=tk.X) # 性能信息显示 self.info_label = tk.Label(self.control_frame, text="初始化中...", font=("Consolas", 10)) self.info_label.pack(side=tk.TOP, fill=tk.X, padx=5) # 按钮区域 self.toggle_btn = tk.Button(self.control_frame, text="切换可视化", command=self.toggle_visualization) self.toggle_btn.pack(side=tk.LEFT, padx=5) self.settings_btn = tk.Button(self.control_frame, text="设置", command=self.open_settings) self.settings_btn.pack(side=tk.LEFT, padx=5) # 鼠标右键状态显示（替换Shift状态） self.button_status = tk.Label(self.control_frame, text="鼠标右键状态: 未按下", fg="red", font=("Consolas", 10)) self.button_status.pack(side=tk.LEFT, padx=10) # 重命名为button_status # 启动检测线程 self.detection_thread = threading.Thread(target=self.detector.run, args=(self.frame_queue,)) self.detection_thread.daemon = True self.detection_thread.start() # 界面更新 self.visualization_enabled = True self.update_interval = 1 # 1ms更新一次界面 self.update_image() # 窗口关闭处理 self.root.protocol("WM_DELETE_WINDOW", self.safe_exit) # 添加鼠标事件绑定 self.root.bind('<Button-3>', self.update_button_status) # 绑定鼠标右键按下 self.root.bind('<ButtonRelease-3>', self.update_button_status) # 绑定鼠标右键释放 def update_button_status(self, event=None): """更新鼠标右键状态显示""" with self.detector.button_lock: if self.detector.right_button_pressed: self.button_status.config(text="鼠标右键状态: 按下", fg="green") else: self.button_status.config(text="鼠标右键状态: 未按下", fg="red") def toggle_visualization(self): """切换可视化状态""" self.visualization_enabled = not self.visualization_enabled state = "启用" if self.visualization_enabled else "禁用" self.info_label.config(text=f"可视化状态: {state}") self.canvas.delete("all") if not self.visualization_enabled: self.canvas.config(bg="black") def open_settings(self): """打开设置窗口""" SettingsWindow(self.root, self.detector.cfg) def display_target_info(self, target_info): """在画布上显示目标信息""" # 显示标题 title = "目标类别与坐标" self.canvas.create_text(10, 10, text=title, anchor=tk.NW, fill="#00FF00", font=("Consolas", 11, "bold")) # 显示目标信息 y_offset = 40 line_height = 20 if target_info: for i, data in enumerate(target_info): try: parts = data.split(":", 1) if len(parts) == 2: class_name, coords_str = parts coords = list(map(int, coords_str.split(','))) if len(coords) == 4: display_text = f"{class_name}: [{coords[0]}, {coords[1]}, {coords[2]}, {coords[3]}]" else: display_text = f"坐标格式错误: {data}" else: display_text = f"数据格式错误: {data}" except: display_text = f"解析错误: {data}" self.canvas.create_text(15, y_offset, text=display_text, anchor=tk.NW, fill="#00FFFF", font=("Consolas", 10)) y_offset += line_height else: self.canvas.create_text(15, y_offset, text="无检测目标", anchor=tk.NW, fill="#FF0000", font=("Consolas", 10)) def update_image(self): """更新界面显示""" try: # 获取最新数据 latest_data = None while not self.frame_queue.empty(): latest_data = self.frame_queue.get_nowait() if latest_data: # 解包数据 frame, targets_count, inference_time, grab_time, target_info = latest_data # 单位转换 inference_sec = inference_time / 1000 grab_sec = grab_time / 1000 # 更新显示 if self.visualization_enabled and frame is not None: # 显示图像 img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) img = Image.fromarray(img) self.tk_image = ImageTk.PhotoImage(image=img) self.canvas.delete("all") self.canvas.create_image(0, 0, image=self.tk_image, anchor=tk.NW) else: # 显示坐标文本 self.canvas.delete("all") self.display_target_info(target_info) # 更新性能信息 info_text = (f"目标: {targets_count} | " f"推理: {inference_sec:.3f}s | " f"截图: {grab_sec:.3f}s") self.info_label.config(text=info_text) except Exception as e: print(f"更新图像时出错: {str(e)}") finally: # 更新鼠标右键状态 self.update_button_status() # 调度下一次更新 self.root.after(self.update_interval, self.update_image) def safe_exit(self): """安全退出程序""" self.detector.stop() self.root.after(100, self.root.destroy) class SettingsWindow(tk.Toplevel): def init(self, parent, config): super().init(parent) self.title("设置") self.geometry("400x500") self.config = config self.transient(parent) self.grab_set() self.create_widgets() def create_widgets(self): """创建设置窗口界面""" notebook = ttk.Notebook(self) notebook.pack(fill=tk.BOTH, expand=True, padx=10, pady=10) # 模型设置 model_frame = ttk.Frame(notebook) notebook.add(model_frame, text="模型设置") self.create_model_settings(model_frame) # 屏幕设置 screen_frame = ttk.Frame(notebook) notebook.add(screen_frame, text="屏幕设置") self.create_screen_settings(screen_frame) # 检测设置 detection_frame = ttk.Frame(notebook) notebook.add(detection_frame, text="检测设置") self.create_detection_settings(detection_frame) # 移动设置 move_frame = ttk.Frame(notebook) notebook.add(move_frame, text="移动设置") self.create_move_settings(move_frame) # 目标点设置 target_frame = ttk.Frame(notebook) notebook.add(target_frame, text="目标点设置") self.create_target_settings(target_frame) # 按钮区域 btn_frame = ttk.Frame(self) btn_frame.pack(fill=tk.X, padx=10, pady=10) save_btn = tk.Button(btn_frame, text="保存配置", command=self.save_config) save_btn.pack(side=tk.RIGHT, padx=5) cancel_btn = tk.Button(btn_frame, text="取消", command=self.destroy) cancel_btn.pack(side=tk.RIGHT, padx=5) def create_model_settings(self, parent): """创建模型设置页面""" # 获取基础路径 if getattr(sys, 'frozen', False): base_path = sys._MEIPASS else: base_path = os.path.dirname(os.path.abspath(file)) # 获取模型文件列表 models_dir = os.path.join(base_path, 'models') model_files = [] if os.path.exists(models_dir): model_files = glob.glob(os.path.join(models_dir, '.pt')) # 处理模型显示名称 model_display_names = [os.path.basename(f) for f in model_files] if model_files else ["未找到模型文件"] self.model_name_to_path = {os.path.basename(f): f for f in model_files} # 当前配置的模型处理 current_model_path = self.config['model_path'] current_model_name = os.path.basename(current_model_path) # 确保当前模型在列表中 if current_model_name not in model_display_names: model_display_names.append(current_model_name) self.model_name_to_path[current_model_name] = current_model_path # 创建UI组件 ttk.Label(parent, text="选择模型:").grid(row=0, column=0, padx=5, pady=5, sticky=tk.W) self.model_name = tk.StringVar(value=current_model_name) model_combo = ttk.Combobox(parent, textvariable=self.model_name, state="readonly", width=30) model_combo['values'] = model_display_names model_combo.grid(row=0, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="运行设备:").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.device_var = tk.StringVar(value=self.config['model_device']) device_combo = ttk.Combobox(parent, textvariable=self.device_var, state="readonly", width=30) device_combo['values'] = ('auto', 'cuda', 'cpu') device_combo.grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) def create_screen_settings(self, parent): """创建屏幕设置页面""" ttk.Label(parent, text="显示器编号:").grid(row=0, column=0, padx=5, pady=5, sticky=tk.W) self.monitor_var = tk.StringVar(value=self.config.get('screen_monitor', '0')) ttk.Entry(parent, textvariable=self.monitor_var, width=10).grid(row=0, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="截屏尺寸:").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.target_size_var = tk.StringVar(value=self.config['screen_target_size']) ttk.Entry(parent, textvariable=self.target_size_var, width=10).grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) def create_detection_settings(self, parent): """创建检测设置页面""" ttk.Label(parent, text="置信度阈值:").grid(row=0, column=0, padx=5, pady=5, sticky=tk.W) self.conf_thres_var = tk.DoubleVar(value=float(self.config['detection_conf_thres'])) conf_scale = ttk.Scale(parent, from_=0.1, to=1.0, variable=self.conf_thres_var, orient=tk.HORIZONTAL, length=200) conf_scale.grid(row=0, column=1, padx=5, pady=5, sticky=tk.W) self.conf_thres_display = tk.StringVar() self.conf_thres_display.set(f"{self.conf_thres_var.get():.2f}") ttk.Label(parent, textvariable=self.conf_thres_display).grid(row=0, column=2, padx=5, pady=5) self.conf_thres_var.trace_add("write", lambda args: self.conf_thres_display.set(f"{self.conf_thres_var.get():.2f}")) ttk.Label(parent, text="IOU阈值:").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.iou_thres_var = tk.DoubleVar(value=float(self.config['detection_iou_thres'])) iou_scale = ttk.Scale(parent, from_=0.1, to=1.0, variable=self.iou_thres_var, orient=tk.HORIZONTAL, length=200) iou_scale.grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) self.iou_thres_display = tk.StringVar() self.iou_thres_display.set(f"{self.iou_thres_var.get():.2f}") ttk.Label(parent, textvariable=self.iou_thres_display).grid(row=1, column=2, padx=5, pady=5) self.iou_thres_var.trace_add("write", lambda args: self.iou_thres_display.set(f"{self.iou_thres_var.get():.2f}")) ttk.Label(parent, text="检测类别:").grid(row=2, column=0, padx=5, pady=5, sticky=tk.W) self.classes_var = tk.StringVar(value=self.config['detection_classes']) ttk.Entry(parent, textvariable=self.classes_var, width=20).grid(row=2, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="(逗号分隔)").grid(row=2, column=2, padx=5, pady=5, sticky=tk.W) def create_move_settings(self, parent): """创建移动设置页面""" ttk.Label(parent, text="横向FOV(度):").grid(row=0, column=0, padx=5, pady=5, sticky=tk.W) self.fov_horizontal_var = tk.StringVar(value=self.config.get('move_fov_horizontal', '90')) fov_entry = ttk.Entry(parent, textvariable=self.fov_horizontal_var, width=10) fov_entry.grid(row=0, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="鼠标DPI:").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.mouse_dpi_var = tk.StringVar(value=self.config.get('move_mouse_dpi', '400')) dpi_entry = ttk.Entry(parent, textvariable=self.mouse_dpi_var, width=10) dpi_entry.grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) # PID参数设置 ttk.Label(parent, text="PID参数设置", font=("Arial", 10, "bold")).grid(row=2, column=0, columnspan=3, pady=10, sticky=tk.W) ttk.Label(parent, text="比例系数(P):").grid(row=3, column=0, padx=5, pady=5, sticky=tk.W) self.pid_kp_var = tk.StringVar(value=self.config.get('pid_kp', '0.5')) kp_entry = ttk.Entry(parent, textvariable=self.pid_kp_var, width=10) kp_entry.grid(row=3, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="积分系数(I):").grid(row=4, column=0, padx=5, pady=5, sticky=tk.W) self.pid_ki_var = tk.StringVar(value=self.config.get('pid_ki', '0.0')) ki_entry = ttk.Entry(parent, textvariable=self.pid_ki_var, width=10) ki_entry.grid(row=4, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="微分系数(D):").grid(row=5, column=0, padx=5, pady=5, sticky=tk.W) self.pid_kd_var = tk.StringVar(value=self.config.get('pid_kd', '0.1')) kd_entry = ttk.Entry(parent, textvariable=self.pid_kd_var, width=10) kd_entry.grid(row=5, column=1, padx=5, pady=5, sticky=tk.W) def create_target_settings(self, parent): """创建目标点设置页面 (新增)""" ttk.Label(parent, text="目标点偏移设置", font=("Arial", 10, "bold")).grid( row=0, column=0, columnspan=3, pady=10, sticky=tk.W ) # X轴偏移设置 ttk.Label(parent, text="X轴偏移(%):").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.target_offset_x_var = tk.DoubleVar(value=float(self.config.get('target_offset_x', '50'))) offset_x_scale = ttk.Scale(parent, from_=0, to=100, variable=self.target_offset_x_var, orient=tk.HORIZONTAL, length=200) offset_x_scale.grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) self.offset_x_display = tk.StringVar(value=f"{self.target_offset_x_var.get():.0f}") ttk.Label(parent, textvariable=self.offset_x_display).grid(row=1, column=2, padx=5, pady=5) self.target_offset_x_var.trace_add("write", lambda args: self.offset_x_display.set( f"{self.target_offset_x_var.get():.0f}")) # Y轴偏移设置 ttk.Label(parent, text="Y轴偏移(%):").grid(row=2, column=0, padx=5, pady=5, sticky=tk.W) self.target_offset_y_var = tk.DoubleVar(value=float(self.config.get('target_offset_y', '50'))) offset_y_scale = ttk.Scale(parent, from_=0, to=100, variable=self.target_offset_y_var, orient=tk.HORIZONTAL, length=200) offset_y_scale.grid(row=2, column=1, padx=5, pady=5, sticky=tk.W) self.offset_y_display = tk.StringVar(value=f"{self.target_offset_y_var.get():.0f}") ttk.Label(parent, textvariable=self.offset_y_display).grid(row=2, column=2, padx=5, pady=5) self.target_offset_y_var.trace_add("write", lambda *args: self.offset_y_display.set( f"{self.target_offset_y_var.get():.0f}")) # 添加说明标签 ttk.Label(parent, text="(0% = 左上角, 50% = 中心, 100% = 右下角)").grid( row=3, column=0, columnspan=3, padx=5, pady=5, sticky=tk.W ) def save_config(self): """保存配置到文件""" try: model_name = self.model_name.get() model_path = self.model_name_to_path.get(model_name, model_name) self.config['model_path'] = model_path self.config['model_device'] = self.device_var.get() self.config['screen_monitor'] = self.monitor_var.get() self.config['screen_target_size'] = self.target_size_var.get() self.config['detection_conf_thres'] = str(self.conf_thres_var.get()) self.config['detection_iou_thres'] = str(self.iou_thres_var.get()) self.config['detection_classes'] = self.classes_var.get() # 保存移动设置 self.config['move_fov_horizontal'] = self.fov_horizontal_var.get() self.config['move_mouse_dpi'] = self.mouse_dpi_var.get() # 保存PID参数 self.config['pid_kp'] = self.pid_kp_var.get() self.config['pid_ki'] = self.pid_ki_var.get() self.config['pid_kd'] = self.pid_kd_var.get() # 保存目标点偏移设置 self.config['target_offset_x'] = str(self.target_offset_x_var.get()) self.config['target_offset_y'] = str(self.target_offset_y_var.get()) # 保存为TXT格式 with open('detection_config.txt', 'w', encoding='utf-8') as f: for key, value in self.config.items(): f.write(f"{key} = {value}\n") messagebox.showinfo("成功", "配置已保存！重启后生效") self.destroy() except Exception as e: messagebox.showerror("错误", f"保存配置失败: {str(e)}") if name == "main": detector = ScreenDetector('detection_config.txt') print(f"\nDXcam检测器初始化完成 | 设备: {detector.device.upper()}") root = tk.Tk() app = App(root, detector) root.mainloop()

这通常是因为目标检测模型对同一目标的检测结果在不同帧之间可能有微小的位置变化，导致计算出的目标点（例如头部中心）在相邻帧之间跳跃。要求：在按住鼠标右键时，瞄准的是经过处理（平滑）的点，而不是原始检测...

请作为资深开发工程师，解释我给出的代码。请逐行分析我的代码并给出你对这段代码的理解。我给出的代码是：【import tkinter as tk import cv2 import time import torch from ultralytics import YOLO from PIL import Image, ImageTk import threading import queue import dxcam import traceback import ctypes from tkinter import ttk, messagebox import os import glob import sys import logitech.lg from pynput import mouse class PIDController: """PID控制器实现""" def init(self, kp=0.0, ki=0.0, kd=0.0): self.kp = kp self.ki = ki self.kd = kd self.prev_error = (0, 0) self.integral = (0, 0) self.last_time = time.time() def update(self, error): current_time = time.time() dt = current_time - self.last_time self.last_time = current_time if dt <= 0: dt = 0.01 dx, dy = error px = self.kp * dx py = self.kp * dy self.integral = ( self.integral[0] + dx * dt, self.integral[1] + dy * dt ) ix = self.ki * self.integral[0] iy = self.ki * self.integral[1] dx_dt = (dx - self.prev_error[0]) / dt dy_dt = (dy - self.prev_error[1]) / dt ddx = self.kd * dx_dt ddy = self.kd * dy_dt self.prev_error = (dx, dy) output_x = px + ix + ddx output_y = py + iy + ddy return (output_x, output_y) def reset(self): self.prev_error = (0, 0) self.integral = (0, 0) self.last_time = time.time() class ScreenDetector: def init(self, config_path): # 解析配置文件 self._parse_config(config_path) # 设备检测与模型加载 self.device = self._determine_device() self.model = YOLO(self.model_path).to(self.device) # 屏幕信息初始化 self._init_screen_info() # 控制参数初始化 self._init_control_params() # 状态管理 self.stop_event = threading.Event() self.camera_lock = threading.Lock() self.target_lock = threading.Lock() self.offset_lock = threading.Lock() self.button_lock = threading.Lock() # 初始化相机 self._init_camera() # 初始化鼠标监听器 self._init_mouse_listener() def _parse_config(self, config_path): """解析并存储配置参数""" self.cfg = self._parse_txt_config(config_path) # 存储常用参数 self.model_path = self.cfg['model_path'] self.model_device = self.cfg['model_device'] self.screen_target_size = int(self.cfg['screen_target_size']) self.detection_conf_thres = float(self.cfg['detection_conf_thres']) self.detection_iou_thres = float(self.cfg['detection_iou_thres']) self.detection_classes = [int(x) for x in self.cfg['detection_classes'].split(',')] self.visualization_color = tuple(map(int, self.cfg['visualization_color'].split(','))) self.visualization_line_width = int(self.cfg['visualization_line_width']) self.visualization_font_scale = float(self.cfg['visualization_font_scale']) self.visualization_show_conf = bool(self.cfg['visualization_show_conf']) self.fov_horizontal = float(self.cfg.get('move_fov_horizontal', '90')) self.mouse_dpi = int(self.cfg.get('move_mouse_dpi', '400')) self.pid_kp = float(self.cfg.get('pid_kp', '0.5')) self.pid_ki = float(self.cfg.get('pid_ki', '0.0')) self.pid_kd = float(self.cfg.get('pid_kd', '0.1')) self.target_offset_x_percent = float(self.cfg.get('target_offset_x', '50')) self.target_offset_y_percent = 100 - float(self.cfg.get('target_offset_y', '50')) def _parse_txt_config(self, path): """解析TXT格式的配置文件""" config = {} with open(path, 'r', encoding='utf-8') as f: for line in f: line = line.strip() if not line or line.startswith('#'): continue if '=' in line: key, value = line.split('=', 1) config[key.strip()] = value.strip() return config def _determine_device(self): """确定运行设备""" if self.model_device == 'auto': return 'cuda' if torch.cuda.is_available() and torch.cuda.device_count() > 0 else 'cpu' return self.model_device def _init_screen_info(self): """初始化屏幕信息""" user32 = ctypes.windll.user32 self.screen_width, self.screen_height = user32.GetSystemMetrics(0), user32.GetSystemMetrics(1) self.screen_center = (self.screen_width // 2, self.screen_height // 2) # 计算截图区域 left = (self.screen_width - self.screen_target_size) // 2 top = (self.screen_height - self.screen_target_size) // 2 self.region = ( max(0, int(left)), max(0, int(top)), min(self.screen_width, int(left + self.screen_target_size)), min(self.screen_height, int(top + self.screen_target_size)) ) def _init_control_params(self): """初始化控制参数""" self.pid_controller = PIDController( kp=self.pid_kp, ki=self.pid_ki, kd=self.pid_kd ) self.previous_target_info = None self.closest_target_absolute = None self.target_offset = None self.right_button_pressed = False # 改为鼠标右键状态 def _init_camera(self): """初始化相机""" try: with self.camera_lock: self.camera = dxcam.create( output_idx=0, output_color="BGR", region=self.region ) self.camera.start(target_fps=120, video_mode=True) except Exception as e: print(f"相机初始化失败: {str(e)}") try: # 降级模式 with self.camera_lock: self.camera = dxcam.create() self.camera.start(target_fps=60, video_mode=True) except Exception as fallback_e: print(f"降级模式初始化失败: {str(fallback_e)}") self.camera = None def _init_mouse_listener(self): """初始化鼠标监听器""" self.mouse_listener = mouse.Listener( on_click=self.on_mouse_click # 监听鼠标点击事件 ) self.mouse_listener.daemon = True self.mouse_listener.start() def on_mouse_click(self, x, y, button, pressed): """处理鼠标点击事件""" try: if button == mouse.Button.right: # 监听鼠标右键 with self.button_lock: self.right_button_pressed = pressed # 更新状态 if pressed: # 当右键按下时重置PID self.pid_controller.reset() except Exception as e: print(f"鼠标事件处理错误: {str(e)}") def calculate_fov_movement(self, dx, dy): """基于FOV算法计算鼠标移动量""" # 计算屏幕对角线长度 screen_diagonal = (self.screen_width 2 + self.screen_height 2) ** 0.5 # 计算垂直FOV aspect_ratio = self.screen_width / self.screen_height fov_vertical = self.fov_horizontal / aspect_ratio # 计算每像素对应角度 angle_per_pixel_x = self.fov_horizontal / self.screen_width angle_per_pixel_y = fov_vertical / self.screen_height # 计算角度偏移 angle_offset_x = dx * angle_per_pixel_x angle_offset_y = dy * angle_per_pixel_y # 转换为鼠标移动量 move_x = (angle_offset_x / 360) * self.mouse_dpi move_y = (angle_offset_y / 360) * self.mouse_dpi return move_x, move_y def move_mouse_to_target(self): """移动鼠标使准心对准目标点""" if not self.target_offset: return try: # 获取目标点与屏幕中心的偏移量 with self.offset_lock: dx, dy = self.target_offset # 使用FOV算法计算鼠标移动量 move_x, move_y = self.calculate_fov_movement(dx, dy) # 使用PID控制器平滑移动 pid_output = self.pid_controller.update((move_x, move_y)) move_x_pid, move_y_pid = pid_output # 使用罗技API移动鼠标 if move_x_pid != 0 or move_y_pid != 0: logitech.lg.mouse_xy(int(move_x_pid), int(move_y_pid)) except Exception as e: print(f"移动鼠标时出错: {str(e)}") def run(self, frame_queue): """主检测循环""" while not self.stop_event.is_set(): try: # 截图 grab_start = time.perf_counter() screenshot = self._grab_screenshot() grab_time = (time.perf_counter() - grab_start) * 1000 # ms if screenshot is None: time.sleep(0.001) continue # 推理 inference_start = time.perf_counter() results = self._inference(screenshot) inference_time = (time.perf_counter() - inference_start) * 1000 # ms # 处理检测结果 target_info, closest_target_relative, closest_offset = self._process_detection_results(results) # 更新目标信息 self._update_target_info(target_info, closest_offset) # 移动鼠标 self._move_mouse_if_needed() # 可视化处理 annotated_frame = self._visualize_results(results, closest_target_relative) if frame_queue else None # 放入队列 if frame_queue: try: frame_queue.put( (annotated_frame, len(target_info), inference_time, grab_time, target_info), timeout=0.01 ) except queue.Full: pass except Exception as e: print(f"检测循环异常: {str(e)}") traceback.print_exc() self._reset_camera() time.sleep(0.5) def _grab_screenshot(self): """安全获取截图""" with self.camera_lock: if self.camera: return self.camera.grab() return None def _inference(self, screenshot): """执行模型推理""" return self.model.predict( screenshot, conf=self.detection_conf_thres, iou=self.detection_iou_thres, classes=self.detection_classes, device=self.device, verbose=False ) def _process_detection_results(self, results): """处理检测结果""" target_info = [] min_distance = float('inf') closest_target_relative = None closest_target_absolute = None closest_offset = None for box in results[0].boxes: # 获取边界框坐标 x1, y1, x2, y2 = map(int, box.xyxy[0]) # 计算绝对坐标 x1_abs = x1 + self.region[0] y1_abs = y1 + self.region[1] x2_abs = x2 + self.region[0] y2_abs = y2 + self.region[1] # 计算边界框尺寸 width = x2_abs - x1_abs height = y2_abs - y1_abs # 应用偏移百分比计算目标点 target_x = x1_abs + int(width * (self.target_offset_x_percent / 100)) target_y = y1_abs + int(height * (self.target_offset_y_percent / 100)) # 计算偏移量 dx = target_x - self.screen_center[0] dy = target_y - self.screen_center[1] distance = (dx 2 + dy 2) ** 0.5 # 更新最近目标 if distance < min_distance: min_distance = distance # 计算相对坐标（用于可视化） closest_target_relative = ( x1 + int(width * (self.target_offset_x_percent / 100)), y1 + int(height * (self.target_offset_y_percent / 100)) ) closest_target_absolute = (target_x, target_y) closest_offset = (dx, dy) # 保存目标信息 class_id = int(box.cls) class_name = self.model.names[class_id] target_info.append(f"{class_name}:{x1_abs},{y1_abs},{x2_abs},{y2_abs}") return target_info, closest_target_relative, closest_offset def _update_target_info(self, target_info, closest_offset): """更新目标信息""" # 检查目标信息是否有变化 if target_info != self.previous_target_info: self.previous_target_info = target_info.copy() print(f"{len(target_info)}|{'|'.join(target_info)}") # 更新目标偏移量 with self.offset_lock: self.target_offset = closest_offset def _visualize_results(self, results, closest_target): """可视化处理结果""" frame = results[0].plot( line_width=self.visualization_line_width, font_size=self.visualization_font_scale, conf=self.visualization_show_conf ) # 绘制最近目标 if closest_target: # 绘制目标中心点 cv2.circle( frame, (int(closest_target[0]), int(closest_target[1])), 3, (0, 0, 255), -1 ) # 计算屏幕中心在截图区域内的相对坐标 screen_center_x = self.screen_center[0] - self.region[0] screen_center_y = self.screen_center[1] - self.region[1] # 绘制中心到目标的连线 cv2.line( frame, (int(screen_center_x), int(screen_center_y)), (int(closest_target[0]), int(closest_target[1])), (0, 255, 0), 1 ) return frame def _move_mouse_if_needed(self): """如果需要则移动鼠标""" with self.button_lock: if self.right_button_pressed and self.target_offset: # 使用right_button_pressed self.move_mouse_to_target() def _reset_camera(self): """重置相机""" print("正在重置相机...") try: self._init_camera() except Exception as e: print(f"相机重置失败: {str(e)}") traceback.print_exc() def stop(self): """安全停止检测器""" self.stop_event.set() self._safe_stop() if hasattr(self, 'mouse_listener') and self.mouse_listener.running: # 改为停止鼠标监听器 self.mouse_listener.stop() def _safe_stop(self): """同步释放资源""" print("正在安全停止相机...") try: with self.camera_lock: if self.camera: self.camera.stop() print("相机已停止") except Exception as e: print(f"停止相机时发生错误: {str(e)}") print("屏幕检测器已停止") class App: def init(self, root, detector): self.root = root self.detector = detector self.root.title("DXcam Detection") self.root.geometry(f"{detector.region[2] - detector.region[0]}x{detector.region[3] - detector.region[1] + 50}") self.root.wm_attributes('-topmost', 1) # 界面组件 self.canvas = tk.Canvas(root, highlightthickness=0) self.canvas.pack(fill=tk.BOTH, expand=True) # 性能监控队列 self.frame_queue = queue.Queue(maxsize=3) # 控制面板 self.control_frame = tk.Frame(root) self.control_frame.pack(side=tk.BOTTOM, fill=tk.X) # 性能信息显示 self.info_label = tk.Label(self.control_frame, text="初始化中...", font=("Consolas", 10)) self.info_label.pack(side=tk.TOP, fill=tk.X, padx=5) # 按钮区域 self.toggle_btn = tk.Button(self.control_frame, text="切换可视化", command=self.toggle_visualization) self.toggle_btn.pack(side=tk.LEFT, padx=5) self.settings_btn = tk.Button(self.control_frame, text="设置", command=self.open_settings) self.settings_btn.pack(side=tk.LEFT, padx=5) # 鼠标右键状态显示（替换Shift状态） self.button_status = tk.Label(self.control_frame, text="鼠标右键状态: 未按下", fg="red", font=("Consolas", 10)) self.button_status.pack(side=tk.LEFT, padx=10) # 重命名为button_status # 启动检测线程 self.detection_thread = threading.Thread(target=self.detector.run, args=(self.frame_queue,)) self.detection_thread.daemon = True self.detection_thread.start() # 界面更新 self.visualization_enabled = True self.update_interval = 1 # 1ms更新一次界面 self.update_image() # 窗口关闭处理 self.root.protocol("WM_DELETE_WINDOW", self.safe_exit) # 添加鼠标事件绑定 self.root.bind('<Button-3>', self.update_button_status) # 绑定鼠标右键按下 self.root.bind('<ButtonRelease-3>', self.update_button_status) # 绑定鼠标右键释放 def update_button_status(self, event=None): """更新鼠标右键状态显示""" with self.detector.button_lock: if self.detector.right_button_pressed: self.button_status.config(text="鼠标右键状态: 按下", fg="green") else: self.button_status.config(text="鼠标右键状态: 未按下", fg="red") def toggle_visualization(self): """切换可视化状态""" self.visualization_enabled = not self.visualization_enabled state = "启用" if self.visualization_enabled else "禁用" self.info_label.config(text=f"可视化状态: {state}") self.canvas.delete("all") if not self.visualization_enabled: self.canvas.config(bg="black") def open_settings(self): """打开设置窗口""" SettingsWindow(self.root, self.detector.cfg) def display_target_info(self, target_info): """在画布上显示目标信息""" # 显示标题 title = "目标类别与坐标" self.canvas.create_text(10, 10, text=title, anchor=tk.NW, fill="#00FF00", font=("Consolas", 11, "bold")) # 显示目标信息 y_offset = 40 line_height = 20 if target_info: for i, data in enumerate(target_info): try: parts = data.split(":", 1) if len(parts) == 2: class_name, coords_str = parts coords = list(map(int, coords_str.split(','))) if len(coords) == 4: display_text = f"{class_name}: [{coords[0]}, {coords[1]}, {coords[2]}, {coords[3]}]" else: display_text = f"坐标格式错误: {data}" else: display_text = f"数据格式错误: {data}" except: display_text = f"解析错误: {data}" self.canvas.create_text(15, y_offset, text=display_text, anchor=tk.NW, fill="#00FFFF", font=("Consolas", 10)) y_offset += line_height else: self.canvas.create_text(15, y_offset, text="无检测目标", anchor=tk.NW, fill="#FF0000", font=("Consolas", 10)) def update_image(self): """更新界面显示""" try: # 获取最新数据 latest_data = None while not self.frame_queue.empty(): latest_data = self.frame_queue.get_nowait() if latest_data: # 解包数据 frame, targets_count, inference_time, grab_time, target_info = latest_data # 单位转换 inference_sec = inference_time / 1000 grab_sec = grab_time / 1000 # 更新显示 if self.visualization_enabled and frame is not None: # 显示图像 img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) img = Image.fromarray(img) self.tk_image = ImageTk.PhotoImage(image=img) self.canvas.delete("all") self.canvas.create_image(0, 0, image=self.tk_image, anchor=tk.NW) else: # 显示坐标文本 self.canvas.delete("all") self.display_target_info(target_info) # 更新性能信息 info_text = (f"目标: {targets_count} | " f"推理: {inference_sec:.3f}s | " f"截图: {grab_sec:.3f}s") self.info_label.config(text=info_text) except Exception as e: print(f"更新图像时出错: {str(e)}") finally: # 更新鼠标右键状态 self.update_button_status() # 调度下一次更新 self.root.after(self.update_interval, self.update_image) def safe_exit(self): """安全退出程序""" self.detector.stop() self.root.after(100, self.root.destroy) class SettingsWindow(tk.Toplevel): def init(self, parent, config): super().init(parent) self.title("设置") self.geometry("400x500") self.config = config self.transient(parent) self.grab_set() self.create_widgets() def create_widgets(self): """创建设置窗口界面""" notebook = ttk.Notebook(self) notebook.pack(fill=tk.BOTH, expand=True, padx=10, pady=10) # 模型设置 model_frame = ttk.Frame(notebook) notebook.add(model_frame, text="模型设置") self.create_model_settings(model_frame) # 屏幕设置 screen_frame = ttk.Frame(notebook) notebook.add(screen_frame, text="屏幕设置") self.create_screen_settings(screen_frame) # 检测设置 detection_frame = ttk.Frame(notebook) notebook.add(detection_frame, text="检测设置") self.create_detection_settings(detection_frame) # 移动设置 move_frame = ttk.Frame(notebook) notebook.add(move_frame, text="移动设置") self.create_move_settings(move_frame) # 目标点设置 target_frame = ttk.Frame(notebook) notebook.add(target_frame, text="目标点设置") self.create_target_settings(target_frame) # 按钮区域 btn_frame = ttk.Frame(self) btn_frame.pack(fill=tk.X, padx=10, pady=10) save_btn = tk.Button(btn_frame, text="保存配置", command=self.save_config) save_btn.pack(side=tk.RIGHT, padx=5) cancel_btn = tk.Button(btn_frame, text="取消", command=self.destroy) cancel_btn.pack(side=tk.RIGHT, padx=5) def create_model_settings(self, parent): """创建模型设置页面""" # 获取基础路径 if getattr(sys, 'frozen', False): base_path = sys._MEIPASS else: base_path = os.path.dirname(os.path.abspath(file)) # 获取模型文件列表 models_dir = os.path.join(base_path, 'models') model_files = [] if os.path.exists(models_dir): model_files = glob.glob(os.path.join(models_dir, '.pt')) # 处理模型显示名称 model_display_names = [os.path.basename(f) for f in model_files] if model_files else ["未找到模型文件"] self.model_name_to_path = {os.path.basename(f): f for f in model_files} # 当前配置的模型处理 current_model_path = self.config['model_path'] current_model_name = os.path.basename(current_model_path) # 确保当前模型在列表中 if current_model_name not in model_display_names: model_display_names.append(current_model_name) self.model_name_to_path[current_model_name] = current_model_path # 创建UI组件 ttk.Label(parent, text="选择模型:").grid(row=0, column=0, padx=5, pady=5, sticky=tk.W) self.model_name = tk.StringVar(value=current_model_name) model_combo = ttk.Combobox(parent, textvariable=self.model_name, state="readonly", width=30) model_combo['values'] = model_display_names model_combo.grid(row=0, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="运行设备:").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.device_var = tk.StringVar(value=self.config['model_device']) device_combo = ttk.Combobox(parent, textvariable=self.device_var, state="readonly", width=30) device_combo['values'] = ('auto', 'cuda', 'cpu') device_combo.grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) def create_screen_settings(self, parent): """创建屏幕设置页面""" ttk.Label(parent, text="显示器编号:").grid(row=0, column=0, padx=5, pady=5, sticky=tk.W) self.monitor_var = tk.StringVar(value=self.config.get('screen_monitor', '0')) ttk.Entry(parent, textvariable=self.monitor_var, width=10).grid(row=0, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="截屏尺寸:").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.target_size_var = tk.StringVar(value=self.config['screen_target_size']) ttk.Entry(parent, textvariable=self.target_size_var, width=10).grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) def create_detection_settings(self, parent): """创建检测设置页面""" ttk.Label(parent, text="置信度阈值:").grid(row=0, column=0, padx=5, pady=5, sticky=tk.W) self.conf_thres_var = tk.DoubleVar(value=float(self.config['detection_conf_thres'])) conf_scale = ttk.Scale(parent, from_=0.1, to=1.0, variable=self.conf_thres_var, orient=tk.HORIZONTAL, length=200) conf_scale.grid(row=0, column=1, padx=5, pady=5, sticky=tk.W) self.conf_thres_display = tk.StringVar() self.conf_thres_display.set(f"{self.conf_thres_var.get():.2f}") ttk.Label(parent, textvariable=self.conf_thres_display).grid(row=0, column=2, padx=5, pady=5) self.conf_thres_var.trace_add("write", lambda args: self.conf_thres_display.set(f"{self.conf_thres_var.get():.2f}")) ttk.Label(parent, text="IOU阈值:").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.iou_thres_var = tk.DoubleVar(value=float(self.config['detection_iou_thres'])) iou_scale = ttk.Scale(parent, from_=0.1, to=1.0, variable=self.iou_thres_var, orient=tk.HORIZONTAL, length=200) iou_scale.grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) self.iou_thres_display = tk.StringVar() self.iou_thres_display.set(f"{self.iou_thres_var.get():.2f}") ttk.Label(parent, textvariable=self.iou_thres_display).grid(row=1, column=2, padx=5, pady=5) self.iou_thres_var.trace_add("write", lambda args: self.iou_thres_display.set(f"{self.iou_thres_var.get():.2f}")) ttk.Label(parent, text="检测类别:").grid(row=2, column=0, padx=5, pady=5, sticky=tk.W) self.classes_var = tk.StringVar(value=self.config['detection_classes']) ttk.Entry(parent, textvariable=self.classes_var, width=20).grid(row=2, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="(逗号分隔)").grid(row=2, column=2, padx=5, pady=5, sticky=tk.W) def create_move_settings(self, parent): """创建移动设置页面""" ttk.Label(parent, text="横向FOV(度):").grid(row=0, column=0, padx=5, pady=5, sticky=tk.W) self.fov_horizontal_var = tk.StringVar(value=self.config.get('move_fov_horizontal', '90')) fov_entry = ttk.Entry(parent, textvariable=self.fov_horizontal_var, width=10) fov_entry.grid(row=0, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="鼠标DPI:").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.mouse_dpi_var = tk.StringVar(value=self.config.get('move_mouse_dpi', '400')) dpi_entry = ttk.Entry(parent, textvariable=self.mouse_dpi_var, width=10) dpi_entry.grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) # PID参数设置 ttk.Label(parent, text="PID参数设置", font=("Arial", 10, "bold")).grid(row=2, column=0, columnspan=3, pady=10, sticky=tk.W) ttk.Label(parent, text="比例系数(P):").grid(row=3, column=0, padx=5, pady=5, sticky=tk.W) self.pid_kp_var = tk.StringVar(value=self.config.get('pid_kp', '0.5')) kp_entry = ttk.Entry(parent, textvariable=self.pid_kp_var, width=10) kp_entry.grid(row=3, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="积分系数(I):").grid(row=4, column=0, padx=5, pady=5, sticky=tk.W) self.pid_ki_var = tk.StringVar(value=self.config.get('pid_ki', '0.0')) ki_entry = ttk.Entry(parent, textvariable=self.pid_ki_var, width=10) ki_entry.grid(row=4, column=1, padx=5, pady=5, sticky=tk.W) ttk.Label(parent, text="微分系数(D):").grid(row=5, column=0, padx=5, pady=5, sticky=tk.W) self.pid_kd_var = tk.StringVar(value=self.config.get('pid_kd', '0.1')) kd_entry = ttk.Entry(parent, textvariable=self.pid_kd_var, width=10) kd_entry.grid(row=5, column=1, padx=5, pady=5, sticky=tk.W) def create_target_settings(self, parent): """创建目标点设置页面 (新增)""" ttk.Label(parent, text="目标点偏移设置", font=("Arial", 10, "bold")).grid( row=0, column=0, columnspan=3, pady=10, sticky=tk.W ) # X轴偏移设置 ttk.Label(parent, text="X轴偏移(%):").grid(row=1, column=0, padx=5, pady=5, sticky=tk.W) self.target_offset_x_var = tk.DoubleVar(value=float(self.config.get('target_offset_x', '50'))) offset_x_scale = ttk.Scale(parent, from_=0, to=100, variable=self.target_offset_x_var, orient=tk.HORIZONTAL, length=200) offset_x_scale.grid(row=1, column=1, padx=5, pady=5, sticky=tk.W) self.offset_x_display = tk.StringVar(value=f"{self.target_offset_x_var.get():.0f}") ttk.Label(parent, textvariable=self.offset_x_display).grid(row=1, column=2, padx=5, pady=5) self.target_offset_x_var.trace_add("write", lambda args: self.offset_x_display.set( f"{self.target_offset_x_var.get():.0f}")) # Y轴偏移设置 ttk.Label(parent, text="Y轴偏移(%):").grid(row=2, column=0, padx=5, pady=5, sticky=tk.W) self.target_offset_y_var = tk.DoubleVar(value=float(self.config.get('target_offset_y', '50'))) offset_y_scale = ttk.Scale(parent, from_=0, to=100, variable=self.target_offset_y_var, orient=tk.HORIZONTAL, length=200) offset_y_scale.grid(row=2, column=1, padx=5, pady=5, sticky=tk.W) self.offset_y_display = tk.StringVar(value=f"{self.target_offset_y_var.get():.0f}") ttk.Label(parent, textvariable=self.offset_y_display).grid(row=2, column=2, padx=5, pady=5) self.target_offset_y_var.trace_add("write", lambda *args: self.offset_y_display.set( f"{self.target_offset_y_var.get():.0f}")) # 添加说明标签 ttk.Label(parent, text="(0% = 左上角, 50% = 中心, 100% = 右下角)").grid( row=3, column=0, columnspan=3, padx=5, pady=5, sticky=tk.W ) def save_config(self): """保存配置到文件""" try: model_name = self.model_name.get() model_path = self.model_name_to_path.get(model_name, model_name) self.config['model_path'] = model_path self.config['model_device'] = self.device_var.get() self.config['screen_monitor'] = self.monitor_var.get() self.config['screen_target_size'] = self.target_size_var.get() self.config['detection_conf_thres'] = str(self.conf_thres_var.get()) self.config['detection_iou_thres'] = str(self.iou_thres_var.get()) self.config['detection_classes'] = self.classes_var.get() # 保存移动设置 self.config['move_fov_horizontal'] = self.fov_horizontal_var.get() self.config['move_mouse_dpi'] = self.mouse_dpi_var.get() # 保存PID参数 self.config['pid_kp'] = self.pid_kp_var.get() self.config['pid_ki'] = self.pid_ki_var.get() self.config['pid_kd'] = self.pid_kd_var.get() # 保存目标点偏移设置 self.config['target_offset_x'] = str(self.target_offset_x_var.get()) self.config['target_offset_y'] = str(self.target_offset_y_var.get()) # 保存为TXT格式 with open('detection_config.txt', 'w', encoding='utf-8') as f: for key, value in self.config.items(): f.write(f"{key} = {value}\n") messagebox.showinfo("成功", "配置已保存！重启后生效") self.destroy() except Exception as e: messagebox.showerror("错误", f"保存配置失败: {str(e)}") if name == "main": detector = ScreenDetector('detection_config.txt') print(f"\nDXcam检测器初始化完成 | 设备: {detector.device.upper()}") root = tk.Tk() app = App(root, detector) root.mainloop()】

我们正在分析用户提供的代码。用户要求逐行解释代码功能。注意：用户没有提供具体的代码，但根据历史引用，用户可能关注的是SQL注入修复示例或优化代码性能的示例。然而，用户本次请求是“解释我给出的Python...

from torch.utils.data import dataset from tqdm import tqdm import network import utils import os import random import argparse import numpy as np from torch.utils import data from datasets import VOCSegmentation, Cityscapes, cityscapes from torchvision import transforms as T from metrics import StreamSegMetrics import torch import torch.nn as nn from PIL import Image import matplotlib import matplotlib.pyplot as plt from glob import glob def get_argparser(): parser = argparse.ArgumentParser() # Datset Options parser.add_argument("--input", type=str, required=True, help="path to a single image or image directory") parser.add_argument("--dataset", type=str, default='voc', choices=['voc', 'cityscapes'], help='Name of training set') # Deeplab Options available_models = sorted(name for name in network.modeling.dict if name.islower() and \ not (name.startswith("__") or name.startswith('_')) and callable( network.modeling.dict[name]) ) parser.add_argument("--model", type=str, default='deeplabv3plus_mobilenet', choices=available_models, help='model name') parser.add_argument("--separable_conv", action='store_true', default=False, help="apply separable conv to decoder and aspp") parser.add_argument("--output_stride", type=int, default=16, choices=[8, 16]) # Train Options parser.add_argument("--save_val_results_to", default=None, help="save segmentation results to the specified dir") parser.add_argument("--crop_val", action='store_true', default=False, help='crop validation (default: False)') parser.add_argument("--val_batch_size", type=int, default=4, help='batch size for validation (default: 4)') parser.add_argument("--crop_size", type=int, default=513) parser.add_argument("--ckpt", default=None, type=str, help="resume from checkpoint") parser.add_argument("--gpu_id", type=str, default='0', help="GPU ID") return parser def main(): opts = get_argparser().parse_args() if opts.dataset.lower() == 'voc': opts.num_classes = 21 decode_fn = VOCSegmentation.decode_target elif opts.dataset.lower() == 'cityscapes': opts.num_classes = 19 decode_fn = Cityscapes.decode_target os.environ['CUDA_VISIBLE_DEVICES'] = opts.gpu_id device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print("Device: %s" % device) # Setup dataloader image_files = [] if os.path.isdir(opts.input): for ext in ['png', 'jpeg', 'jpg', 'JPEG']: files = glob(os.path.join(opts.input, '**/*.%s'%(ext)), recursive=True) if len(files)>0: image_files.extend(files) elif os.path.isfile(opts.input): image_files.append(opts.input) # Set up model (all models are 'constructed at network.modeling) model = network.modeling.dict[opts.model](num_classes=opts.num_classes, output_stride=opts.output_stride) if opts.separable_conv and 'plus' in opts.model: network.convert_to_separable_conv(model.classifier) utils.set_bn_momentum(model.backbone, momentum=0.01) if opts.ckpt is not None and os.path.isfile(opts.ckpt): # https://github.com/VainF/DeepLabV3Plus-Pytorch/issues/8#issuecomment-605601402, @PytaichukBohdan checkpoint = torch.load(opts.ckpt, map_location=torch.device('cpu')) model.load_state_dict(checkpoint["model_state"]) model = nn.DataParallel(model) model.to(device) print("Resume model from %s" % opts.ckpt) del checkpoint else: print("[!] Retrain") model = nn.DataParallel(model) model.to(device) #denorm = utils.Denormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # denormalization for ori images if opts.crop_val: transform = T.Compose([ T.Resize(opts.crop_size), T.CenterCrop(opts.crop_size), T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) else: transform = T.Compose([ T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) if opts.save_val_results_to is not None: os.makedirs(opts.save_val_results_to, exist_ok=True) with torch.no_grad(): model = model.eval() for img_path in tqdm(image_files): ext = os.path.basename(img_path).split('.')[-1] img_name = os.path.basename(img_path)[:-len(ext)-1] img = Image.open(img_path).convert('RGB') img = transform(img).unsqueeze(0) # To tensor of NCHW img = img.to(device) pred = model(img).max(1)[1].cpu().numpy()[0] # HW colorized_preds = decode_fn(pred).astype('uint8') colorized_preds = Image.fromarray(colorized_preds) if opts.save_val_results_to: colorized_preds.save(os.path.join(opts.save_val_results_to, img_name+'.png')) if name == 'main': main() 这个代码运行后的文件存储在哪

如果用户运行脚本时提供了这个参数，比如--save_val_results_to ./results，那么结果就会保存在./results目录下。如果用户没有提供这个参数，则不会保存结果（因为代码中只有在opts.save_val_results_to is ...

import cv2 import torch import argparse import logging from pathlib import Path import yaml # 配置日志 logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler('detection.log'), logging.StreamHandler() ] ) logger = logging.getLogger(name) def load_config(config_path='config.yaml'): """加载配置文件""" with open(config_path) as f: return yaml.safe_load(f) def detect_objects(image_path, output_dir, model, confidence_threshold=0.5): """核心检测函数""" # 读取并转换图像 img = cv2.imread(image_path) if img is None: logger.error(f"无法读取图像: {image_path}") return None img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 执行推理 results = model(img_rgb) # 应用置信度阈值过滤 results = results.pandas().xyxy[0] results = results[results['confidence'] >= confidence_threshold] return results def process_video(input_path, output_path, model, config): """处理视频文件""" cap = cv2.VideoCapture(input_path) if not cap.isOpened(): logger.error(f"无法打开视频文件: {input_path}") return # 获取视频参数 width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = cap.get(cv2.CAP_PROP_FPS) # 初始化视频写入器 fourcc = cv2.VideoWriter_fourcc(*'mp4v') out = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) while cap.isOpened(): ret, frame = cap.read() if not ret: break # 执行检测 results = model(frame) rendered_frame = results.render()[0] out.write(cv2.cvtColor(rendered_frame, cv2.COLOR_RGB2BGR)) cap.release() out.release() 完整代码，怎么分块

可能在分块时，可以将模型加载单独作为一个函数，避免重复代码。另外，代码中使用了YAML配置文件，可能需要检查load_config是否正确处理配置项，比如模型路径、置信度阈值等，这些配置参数应该在各个功能函数中被...

# 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://", "https://", "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

我们希望在检测过程中，在图像或视频上显示当前帧的准确率（Accuracy）。为了计算准确率，我们需要有真实标签（ground truth）来与检测结果进行比较。但是，在原始的detect.py中并没有提供真实标签的输入和准确率...

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release. Usage - Single-GPU training: $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch Usage - Multi-GPU DDP training: $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3 Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data """ import argparse import math import os os.environ["GIT_PYTHON_REFRESH"] = "quiet" # add there import random import sys import time from copy import deepcopy from datetime import datetime from pathlib import Path import numpy as np import torch import torch.distributed as dist import torch.nn as nn import yaml from torch.optim import lr_scheduler from tqdm import tqdm # import numpy # import torch.serialization # torch.serialization.add_safe_globals([numpy._core.multiarray._reconstruct]) 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 import val as validate # for end-of-epoch mAP from models.experimental import attempt_load from models.yolo import Model from utils.autoanchor import check_anchors from utils.autobatch import check_train_batch_size from utils.callbacks import Callbacks from utils.dataloaders import create_dataloader from utils.downloads import attempt_download, is_url from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save) from utils.loggers import Loggers from utils.loggers.comet.comet_utils import check_comet_resume from utils.loss import ComputeLoss from utils.metrics import fitness from utils.plots import plot_evolve from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, smart_resume, torch_distributed_zero_first) LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv('RANK', -1)) WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) GIT_INFO = check_git_info() def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \ Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze callbacks.run('on_pretrain_routine_start') # Directories w = save_dir / 'weights' # weights dir (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir last, best = w / 'last.pt', w / 'best.pt' # Hyperparameters if isinstance(hyp, str): with open(hyp, errors='ignore') as f: hyp = yaml.safe_load(f) # load hyps dict LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) opt.hyp = hyp.copy() # for saving hyps to checkpoints # Save run settings if not evolve: yaml_save(save_dir / 'hyp.yaml', hyp) yaml_save(save_dir / 'opt.yaml', vars(opt)) # Loggers data_dict = None if RANK in {-1, 0}: loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance # Register actions for k in methods(loggers): callbacks.register_action(k, callback=getattr(loggers, k)) # Process custom dataset artifact link data_dict = loggers.remote_dataset if resume: # If resuming runs from remote artifact weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size # Config plots = not evolve and not opt.noplots # create plots cuda = device.type != 'cpu' init_seeds(opt.seed + 1 + RANK, deterministic=True) with torch_distributed_zero_first(LOCAL_RANK): data_dict = data_dict or check_dataset(data) # check if None train_path, val_path = data_dict['train'], data_dict['val'] nc = 1 if single_cls else int(data_dict['nc']) # number of classes names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset # Model check_suffix(weights, '.pt') # check weights pretrained = weights.endswith('.pt') if pretrained: with torch_distributed_zero_first(LOCAL_RANK): weights = attempt_download(weights) # download if not found locally ckpt = torch.load(weights, map_location='cpu', weights_only=False) # load checkpoint to CPU to avoid CUDA memory leak model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report else: model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create amp = check_amp(model) # check AMP # Freeze freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze for k, v in model.named_parameters(): v.requires_grad = True # train all layers # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) if any(x in k for x in freeze): LOGGER.info(f'freezing {k}') v.requires_grad = False # Image size gs = max(int(model.stride.max()), 32) # grid size (max stride) imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple # Batch size if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size batch_size = check_train_batch_size(model, imgsz, amp) loggers.on_params_update({"batch_size": batch_size}) # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing hyp['weight_decay'] = batch_size accumulate / nbs # scale weight_decay optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) # Scheduler if opt.cos_lr: lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] else: lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) # EMA ema = ModelEMA(model) if RANK in {-1, 0} else None # Resume best_fitness, start_epoch = 0.0, 0 if pretrained: if resume: best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) del ckpt, csd # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') model = torch.nn.DataParallel(model) # SyncBatchNorm if opt.sync_bn and cuda and RANK != -1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) LOGGER.info('Using SyncBatchNorm()') # Trainloader train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, hyp=hyp, augment=True, cache=None if opt.cache == 'val' else opt.cache, rect=opt.rect, rank=LOCAL_RANK, workers=workers, image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '), shuffle=True) labels = np.concatenate(dataset.labels, 0) mlc = int(labels[:, 0].max()) # max label class assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' # Process 0 if RANK in {-1, 0}: val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls, hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1, workers=workers * 2, pad=0.5, prefix=colorstr('val: '))[0] if not resume: if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor model.half().float() # pre-reduce anchor precision callbacks.run('on_pretrain_routine_end', labels, names) # DDP mode if cuda and RANK != -1: model = smart_DDP(model) # Model attributes nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) hyp['box'] = 3 / nl # scale to layers hyp['cls'] = nc / 80 * 3 / nl # scale to classes and layers hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers hyp['label_smoothing'] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights model.names = names # Start training t0 = time.time() nb = len(train_loader) # number of batches nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training last_opt_step = -1 maps = np.zeros(nc) # mAP per class results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls) scheduler.last_epoch = start_epoch - 1 # do not move scaler = torch.cuda.amp.GradScaler(enabled=amp) stopper, stop = EarlyStopping(patience=opt.patience), False compute_loss = ComputeLoss(model) # init loss class callbacks.run('on_train_start') LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' f"Logging results to {colorstr('bold', save_dir)}\n" f'Starting training for {epochs} epochs...') for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ callbacks.run('on_train_epoch_start') model.train() # Update image weights (optional, single-GPU only) if opt.image_weights: cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx # Update mosaic border (optional) # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) # dataset.mosaic_border = [b - imgsz, -b] # height, width borders mloss = torch.zeros(3, device=device) # mean losses if RANK != -1: train_loader.sampler.set_epoch(epoch) pbar = enumerate(train_loader) LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size')) if RANK in {-1, 0}: pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- callbacks.run('on_train_batch_start') ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 # Warmup if ni <= nw: xi = [0, nw] # x interp # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) for j, x in enumerate(optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)]) if 'momentum' in x: x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) # Multi-scale if opt.multi_scale: sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size sf = sz / max(imgs.shape[2:]) # scale factor if sf != 1: ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) # Forward # with torch.cuda.amp.autocast(amp): with torch.amp.autocast(device_type='cuda'): pred = model(imgs) # forward loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size if RANK != -1: loss = WORLD_SIZE # gradient averaged between devices in DDP mode if opt.quad: loss = 4. # Backward scaler.scale(loss).backward() # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html if ni - last_opt_step >= accumulate: scaler.unscale_(optimizer) # unscale gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients scaler.step(optimizer) # optimizer.step scaler.update() optimizer.zero_grad() if ema: ema.update(model) last_opt_step = ni # Log if RANK in {-1, 0}: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) pbar.set_description(('%11s' * 2 + '%11.4g' * 5) % (f'{epoch}/{epochs - 1}', mem, mloss, targets.shape[0], imgs.shape[-1])) callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss)) if callbacks.stop_training: return # end batch ------------------------------------------------------------------------------------------------ # Scheduler lr = [x['lr'] for x in optimizer.param_groups] # for loggers scheduler.step() if RANK in {-1, 0}: # mAP callbacks.run('on_train_epoch_end', epoch=epoch) ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) final_epoch = (epoch + 1 == epochs) or stopper.possible_stop if not noval or final_epoch: # Calculate mAP results, maps, _ = validate.run(data_dict, batch_size=batch_size // WORLD_SIZE 2, imgsz=imgsz, half=amp, model=ema.ema, single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, plots=False, callbacks=callbacks, compute_loss=compute_loss) # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]] stop = stopper(epoch=epoch, fitness=fi) # early stop check if fi > best_fitness: best_fitness = fi log_vals = list(mloss) + list(results) + lr callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) # Save model if (not nosave) or (final_epoch and not evolve): # if save ckpt = { 'epoch': epoch, 'best_fitness': best_fitness, 'model': deepcopy(de_parallel(model)).half(), 'ema': deepcopy(ema.ema).half(), 'updates': ema.updates, 'optimizer': optimizer.state_dict(), 'opt': vars(opt), 'git': GIT_INFO, # {remote, branch, commit} if a git repo 'date': datetime.now().isoformat()} # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if opt.save_period > 0 and epoch % opt.save_period == 0: torch.save(ckpt, w / f'epoch{epoch}.pt') del ckpt callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) # EarlyStopping if RANK != -1: # if DDP training broadcast_list = [stop if RANK == 0 else None] dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks if RANK != 0: stop = broadcast_list[0] if stop: break # must break all DDP ranks # end epoch ---------------------------------------------------------------------------------------------------- # end training ----------------------------------------------------------------------------------------------------- if RANK in {-1, 0}: LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if f is best: LOGGER.info(f'\nValidating {f}...') results, _, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, model=attempt_load(f, device).half(), iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, save_json=is_coco, verbose=True, plots=plots, callbacks=callbacks, compute_loss=compute_loss) # val best model with plots if is_coco: callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) callbacks.run('on_train_end', last, best, epoch, results) torch.cuda.empty_cache() return results def parse_opt(known=False): parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='model.yaml path') parser.add_argument('--data', type=str, default=r'C:data/AAAA.yaml', help='data.yaml path') parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=100, help='total training epochs') parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs, -1 for autobatch') parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--noval', action='store_true', help='only validate final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') parser.add_argument('--noplots', action='store_true', help='save no plot files') parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') parser.add_argument('--name', default='welding_defect_yolov5s_20241101_300', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--quad', action='store_true', help='quad dataloader') parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') parser.add_argument('--save-period', type=int, default=5, help='Save checkpoint every x epochs (disabled if < 1)') parser.add_argument('--seed', type=int, default=0, help='Global training seed') parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') # Logger arguments parser.add_argument('--entity', default=None, help='Entity') parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option') parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval') parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use') return parser.parse_known_args()[0] if known else parser.parse_args() def main(opt, callbacks=Callbacks()): # Checks if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() check_requirements() # Resume (from specified or most recent last.pt) if opt.resume and not check_comet_resume(opt) and not opt.evolve: last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml opt_data = opt.data # original dataset if opt_yaml.is_file(): with open(opt_yaml, errors='ignore') as f: d = yaml.safe_load(f) else: d = torch.load(last, map_location='cpu')['opt'] opt = argparse.Namespace(**d) # replace opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate if is_url(opt_data): opt.data = check_file(opt_data) # avoid HUB resume auth timeout else: opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' if opt.evolve: if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve opt.project = str(ROOT / 'runs/evolve') opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume if opt.name == 'cfg': opt.name = Path(opt.cfg).stem # use model.yaml as name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: msg = 'is not compatible with YOLOv5 Multi-GPU DDP training' assert not opt.image_weights, f'--image-weights {msg}' assert not opt.evolve, f'--evolve {msg}' assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' torch.cuda.set_device(LOCAL_RANK) device = torch.device('cuda', LOCAL_RANK) dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") # Train if not opt.evolve: train(opt.hyp, opt, device, callbacks) # Evolve hyperparameters (optional) else: # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) meta = { 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr 'box': (1, 0.02, 0.2), # box loss gain 'cls': (1, 0.2, 4.0), # cls loss gain 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight 'iou_t': (0, 0.1, 0.7), # IoU training threshold 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) 'scale': (1, 0.0, 0.9), # image scale (+/- gain) 'shear': (1, 0.0, 10.0), # image shear (+/- deg) 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) 'mosaic': (1, 0.0, 1.0), # image mixup (probability) 'mixup': (1, 0.0, 1.0), # image mixup (probability) 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) with open(opt.hyp, errors='ignore') as f: hyp = yaml.safe_load(f) # load hyps dict if 'anchors' not in hyp: # anchors commented in hyp.yaml hyp['anchors'] = 3 if opt.noautoanchor: del hyp['anchors'], meta['anchors'] opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' if opt.bucket: os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}') # download evolve.csv if exists for _ in range(opt.evolve): # generations to evolve if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate # Select parent(s) parent = 'single' # parent selection method: 'single' or 'weighted' x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) n = min(5, len(x)) # number of previous results to consider x = x[np.argsort(-fitness(x))][:n] # top n mutations w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0) if parent == 'single' or len(x) == 1: # x = x[random.randint(0, n - 1)] # random selection x = x[random.choices(range(n), weights=w)[0]] # weighted selection elif parent == 'weighted': x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination # Mutate mp, s = 0.8, 0.2 # mutation probability, sigma npr = np.random npr.seed(int(time.time())) g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 ng = len(meta) v = np.ones(ng) while all(v == 1): # mutate until a change occurs (prevent duplicates) v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) hyp[k] = float(x[i + 7] * v[i]) # mutate # Constrain to limits for k, v in meta.items(): hyp[k] = max(hyp[k], v[1]) # lower limit hyp[k] = min(hyp[k], v[2]) # upper limit hyp[k] = round(hyp[k], 5) # significant digits # Train mutation results = train(hyp.copy(), opt, device, callbacks) callbacks = Callbacks() # Write mutation results keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss', 'val/obj_loss', 'val/cls_loss') print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) # Plot results plot_evolve(evolve_csv) LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' f"Results saved to {colorstr('bold', save_dir)}\n" f'Usage example: $ python train.py --hyp {evolve_yaml}') def run(**kwargs): # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') opt = parse_opt(True) for k, v in kwargs.items(): setattr(opt, k, v) main(opt) return opt if name == "main": opt = parse_opt() main(opt) 为什么训练之后，他的runs里面并没有显示best.pt跟last.pt 请查找原因

根据用户提供的错误信息，训练在100个epoch完成后，在保存模型时出现了_pickle.UnpicklingError错误，导致进程退出代码为1（非正常退出）。因此，训练过程在保存权重文件之前就失败了，所以不会生成best.pt和last....

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 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.plots import Annotator, colors, save_one_box from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( weights=ROOT / 'runs/train/exp2/weights/best.pt', # model path or triton URL source=ROOT / 'datasets/binren', # 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_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 ): 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('.txt') 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 # Run inference model.warmup(imgsz=(1 if pt or model.triton else bs, 3, imgsz)) # warmup seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) 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 = 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) # 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 += '%gx%g ' % 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): 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(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) # 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") # 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 / 'runs/train/exp2/weights/best.pt', help='model path or triton URL') parser.add_argument('--source', type=str, default=ROOT / 'datasets/binren', 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-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') 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)我想将终端里面的输出结果改成中文的

2.**修改日志信息**：YOLOv5在运行过程中会输出各种日志信息（如“Fusinglayers...”），这些字符串硬编码在源代码中，需要找到并修改为中文。###步骤####1.修改类别标签为中文YOLOv5使用data/*.yaml文件（如data...

import os # os.environ["CUDA_VISIBLE_DEVICES"] = "0" import argparse import time import cv2 import json import numpy as np from tqdm import tqdm from model import build_model from dataset import build_dataset from utils.func import split_list, get_chunk from utils.prompt import Prompter import random, pickle, torch import torch.backends.cudnn as cudnn def get_model_activtion(args, data, model, answer_file): all_results = [] for ins in tqdm(data): img_path = ins['img_path'] img_id = img_path.split("/")[-1] prompt = ins['question'] answer = ins['answer'] label = ins["label"] hidden_states, mlp_residual, attn_residual, attn_heads, vit_attn_heads = model.get_activations(img_path, prompt, answer) # outputs = model._basic_forward(img_path, prompt, answer, return_dict=True) out = { "image": img_id, "img_path": img_path, "model_name": args.model_name, "question": prompt, "answer": answer, "label": label, "attn_residual": attn_residual[:, -1].cpu(), "hidden_states": hidden_states[:, -1].cpu(), "mlp_residual": mlp_residual[:, -1].cpu(), "attn_heads": attn_heads[:, -1].cpu(), "vit_attn_heads": vit_attn_heads.mean(1).cpu(), "hidden_states_mean": hidden_states.mean(1).cpu(), # "scenario": ins['scenario'] } all_results.append(out) with open(answer_file, 'wb') as file: pickle.dump(all_results, file) def setup_seeds(seed=42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) cudnn.benchmark = False cudnn.deterministic = True def main(args): setup_seeds() model = build_model(args) prompter = Prompter(args.prompt, args.theme) pos_data, neg_data = build_dataset(args.dataset, args.split, prompter) data = pos_data + neg_data if not os.path.exists(f"./output/{args.model_name}/"): os.makedirs(f"./output/{args.model_name}/") saved_file = f"./output/{args.model_name}/{args.model_status}_{args.dataset}_{args.split}_{args.answer_split}_{args.prompt}_activations.pkl" results = get_model_activtion(args, data, model, saved_file) print(f'Saved activations to {saved_file}.') if name == "main": parser = argparse.ArgumentParser(prog='Run a model') parser.add_argument("--model_name", default="LLaVA-7B") parser.add_argument("--model_path", default="./llava-v1.5-7b") parser.add_argument("--num_samples", type=int, default=None) parser.add_argument("--sampling", choices=['first', 'random', 'class'], default='first') parser.add_argument("--split", default="test") # SD_TYPO parser.add_argument("--dataset", default="mmsafety") parser.add_argument("--prompt", default='oe') parser.add_argument("--theme", default='mad') parser.add_argument("--activation_file", type=str, default="./output/test.pkl") parser.add_argument("--answer_file", type=str, default="./output/test.jsonl") parser.add_argument("--answer_split", default="all") parser.add_argument("--model_status", default="raw") parser.add_argument("--num_chunks", type=int, default=1) parser.add_argument("--chunk_idx", type=int, default=0) parser.add_argument("--temperature", type=float, default=0.0) parser.add_argument("--top_p", type=float, default=None) parser.add_argument("--num_beams", type=int, default=1) parser.add_argument("--token_id", type=int, default=0) parser.add_argument("--load-8bit", action="store_true") parser.add_argument("--load-4bit", action="store_true") # parser.add_argument("--max-length", type=int, default=64) main(parser.parse_args()) 我想在juptyer notebook内运行这段代码

嗯，用户想在Jupyter Notebook里运行他们提供的Python代码。首先，我需要仔细看看这段代码的结构和功能。看起来这是一个用于获取模型激活值的脚本，可能涉及深度学习模型的内部中间结果分析。首先，代码中有很多...

import os os.environ[“CUDA_VISIBLE_DEVICES”] = “0” import argparse import time import cv2 import json import numpy as np from tqdm import tqdm from model import build_model from dataset import build_dataset from utils.func import split_list, get_chunk from utils.prompt import Prompter import random, pickle, torch import torch.backends.cudnn as cudnn def get_model_activtion(args, data, model, answer_file): all_results = [] for ins in tqdm(data): img_path = ins['img_path'] img_id = img_path.split("/")[-1] prompt = ins['question'] answer = ins['answer'] label = ins["label"] hidden_states, mlp_residual, attn_residual, attn_heads, vit_attn_heads = model.get_activations(img_path, prompt, answer) # outputs = model._basic_forward(img_path, prompt, answer, return_dict=True) out = { "image": img_id, "img_path": img_path, "model_name": args.model_name, "question": prompt, "answer": answer, "label": label, "attn_residual": attn_residual[:, -1].cpu(), "hidden_states": hidden_states[:, -1].cpu(), "mlp_residual": mlp_residual[:, -1].cpu(), "attn_heads": attn_heads[:, -1].cpu(), "vit_attn_heads": vit_attn_heads.mean(1).cpu(), "hidden_states_mean": hidden_states.mean(1).cpu(), # "scenario": ins['scenario'] } all_results.append(out) with open(answer_file, 'wb') as file: pickle.dump(all_results, file) def setup_seeds(seed=42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) cudnn.benchmark = False cudnn.deterministic = True def main(args): setup_seeds() model = build_model(args) prompter = Prompter(args.prompt, args.theme) pos_data, neg_data = build_dataset(args.dataset, args.split, prompter) data = pos_data + neg_data if not os.path.exists(f"./output/{args.model_name}/"): os.makedirs(f"./output/{args.model_name}/") saved_file = f"./output/{args.model_name}/{args.model_status}_{args.dataset}_{args.split}_{args.answer_split}_{args.prompt}_activations.pkl" results = get_model_activtion(args, data, model, saved_file) print(f'Saved activations to {saved_file}.') if name == “main”: parser = argparse.ArgumentParser(prog=‘Run a model’) parser.add_argument(“–model_name”, default=“LLaVA-7B”) parser.add_argument(“–model_path”, default=“./llava-v1.5-7b”) parser.add_argument(“–num_samples”, type=int, default=None) parser.add_argument(“–sampling”, choices=[‘first’, ‘random’, ‘class’], default=‘first’) parser.add_argument(“–split”, default=“test”) # SD_TYPO parser.add_argument(“–dataset”, default=“mmsafety”) parser.add_argument(“–prompt”, default=‘oe’) parser.add_argument(“–theme”, default=‘mad’) parser.add_argument(“–activation_file”, type=str, default=“./output/test.pkl”) parser.add_argument("--answer_file", type=str, default="./output/test.jsonl") parser.add_argument("--answer_split", default="all") parser.add_argument("--model_status", default="raw") parser.add_argument("--num_chunks", type=int, default=1) parser.add_argument("--chunk_idx", type=int, default=0) parser.add_argument("--temperature", type=float, default=0.0) parser.add_argument("--top_p", type=float, default=None) parser.add_argument("--num_beams", type=int, default=1) parser.add_argument("--token_id", type=int, default=0) parser.add_argument("--load-8bit", action="store_true") parser.add_argument("--load-4bit", action="store_true") # parser.add_argument("--max-length", type=int, default=64) main(parser.parse_args()) 我已经安装好vscode,想要运行这段代码，需要配置什么环境，请详细讲解

嗯，用户想在VS Code里运行一段Python代码，但需要配置什么环境呢？首先，我得仔细看看用户提供的代码。看起来这段代码涉及深度学习模型，特别是LLaVA（Large Language and Vision Assistant）的7B版本。代码里用了...

import os # os.environ["CUDA_VISIBLE_DEVICES"] = "0" import argparse import time import cv2 import json import numpy as np from tqdm import tqdm from model import build_model from dataset import build_dataset from utils.func import split_list, get_chunk from utils.prompt import Prompter import random, pickle, torch import torch.backends.cudnn as cudnn def get_model_activtion(args, data, model, answer_file): all_results = [] for ins in tqdm(data): img_path = ins['img_path'] img_id = img_path.split("/")[-1] prompt = ins['question'] answer = ins['answer'] label = ins["label"] hidden_states, mlp_residual, attn_residual, attn_heads, vit_attn_heads = model.get_activations(img_path, prompt, answer) # outputs = model._basic_forward(img_path, prompt, answer, return_dict=True) out = { "image": img_id, "img_path": img_path, "model_name": args.model_name, "question": prompt, "answer": answer, "label": label, "attn_residual": attn_residual[:, -1].cpu(), "hidden_states": hidden_states[:, -1].cpu(), "mlp_residual": mlp_residual[:, -1].cpu(), "attn_heads": attn_heads[:, -1].cpu(), "vit_attn_heads": vit_attn_heads.mean(1).cpu(), "hidden_states_mean": hidden_states.mean(1).cpu(), # "scenario": ins['scenario'] } all_results.append(out) with open(answer_file, 'wb') as file: pickle.dump(all_results, file) def setup_seeds(seed=42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) cudnn.benchmark = False cudnn.deterministic = True def main(args): setup_seeds() model = build_model(args) prompter = Prompter(args.prompt, args.theme) pos_data, neg_data = build_dataset(args.dataset, args.split, prompter) data = pos_data + neg_data if not os.path.exists(f"./output/{args.model_name}/"): os.makedirs(f"./output/{args.model_name}/") saved_file = f"./output/{args.model_name}/{args.model_status}_{args.dataset}_{args.split}_{args.answer_split}_{args.prompt}_activations.pkl" results = get_model_activtion(args, data, model, saved_file) print(f'Saved activations to {saved_file}.') if name == "main": parser = argparse.ArgumentParser(prog='Run a model') parser.add_argument("--model_name", default="LLaVA-7B") parser.add_argument("--model_path", default="./llava-v1.5-7b") parser.add_argument("--num_samples", type=int, default=None) parser.add_argument("--sampling", choices=['first', 'random', 'class'], default='first') parser.add_argument("--split", default="test") # SD_TYPO parser.add_argument("--dataset", default="mmsafety") parser.add_argument("--prompt", default='oe') parser.add_argument("--theme", default='mad') parser.add_argument("--activation_file", type=str, default="./output/test.pkl") parser.add_argument("--answer_file", type=str, default="./output/test.jsonl") parser.add_argument("--answer_split", default="all") parser.add_argument("--model_status", default="raw") parser.add_argument("--num_chunks", type=int, default=1) parser.add_argument("--chunk_idx", type=int, default=0) parser.add_argument("--temperature", type=float, default=0.0) parser.add_argument("--top_p", type=float, default=None) parser.add_argument("--num_beams", type=int, default=1) parser.add_argument("--token_id", type=int, default=0) parser.add_argument("--load-8bit", action="store_true") parser.add_argument("--load-4bit", action="store_true") # parser.add_argument("--max-length", type=int, default=64) main(parser.parse_args()) 这是run_model.py文件，请逐行解释

此外，参数如--model_name、--dataset等，用户可能在运行时通过命令行指定，影响模型和数据集的加载方式。在解释过程中，需要明确各个参数的作用，例如--model_status可能指模型的训练状态（如原始或微调后...

import os import uuid from werkzeug.datastructures import FileStorage from flask import jsonify from flask_restx import Resource from flask_restx.reqparse import RequestParser from flask_restx.reqparse import Argument from apps.api.utils.image_process import image_filtering, image_split, images_scan from configs import TEMP_DIR class ImgMarkHandler(Resource): def init(self, api=None, *args, **kwargs): self.parser = RequestParser(argument_class=Argument) self.parser.add_argument('images', type=FileStorage, required=True, location='files', action='append') self.parser.add_argument('image_type', type=int, default=1) super(ImgMarkHandler, self).init(api=api, args=args, kwargs=kwargs) def post(self): try: args_data = self.parser.parse_args() except Exception as e: return jsonify(code=5000, msg="参数错误") images = args_data["images"] image_type = args_data['image_type'] images_path_list = [] results = [] for img in images: image_path = '%s/%s.png' % (TEMP_DIR, str(uuid.uuid4())) img.save(image_path) images_path_list.append(image_path) if image_type == 2: new_image_path = image_filtering(image_path, image_type) image_list = image_split(new_image_path, image_type) coordinates = images_scan(image_list, new_image_path) # os.remove(image_path) # os.remove(new_image_path) else: image_list = image_split(image_path) coordinates = images_scan(image_list) os.remove(image_path) results.append({"coordinates": coordinates, "img_name": img.filename}) return jsonify(code=1000, msg="success", results=results) import os import time import uuid import cv2 import numpy as np import onnxruntime as rt from torchvision import transforms from configs import BASE_D

with Image.open(temp_path) as img: # 图像处理逻辑 enhanced_img = img.convert('L') # 示例：转灰度图 output_path = f"/processed/{filename}" enhanced_img.save(output_path) return {"url": output_...

yolov5预测时img = Image.open(args.img) # 在图像上运行YOLOv5模型，获取预测结果 results = model(img,32) 参数要怎么写

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