怎么把yolov5的detect.py中的关于摄像头的代码提取出来

如果您想提取 YOLOv5 6.0 版本的 detect.py 中关于摄像头的代码，可以按照以下步骤进行： 1. 打开 detect.py 文件，找到以下代码： ```python # Camera inference elif source.isnumeric(): source = int(source) # Start streaming cap = cv2.VideoCapture(source) assert cap.isOpened(), f'Failed to open {source}' cap.set(cv2.CAP_PROP_FRAME_WIDTH, width) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height) fps = cap.get(cv2.CAP_PROP_FPS) print(f'Using {source} with FPS {fps:.3f} [OK]') for path, img, im0s, vid_cap in dataset: if vid_path != path: vid_path, frames = path, [] if isinstance(vid_cap, cv2.VideoCapture): frames = vid_cap.get(cv2.CAP_PROP_FRAME_COUNT) elif isinstance(vid_cap, int): frames = vid_cap else: assert False, f'Invalid video {path}' t1 = time_synchronized() # Get frames ret, frame = cap.read() if not ret: break assert frame is not None, 'Image Not Found ' # Padded resize frame = letterbox(frame, new_shape=inp_shape)[0] # Normalize RGB frame = frame[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 frame = np.ascontiguousarray(frame) # Inference img = torch.from_numpy(frame).to(device) img = img.half() if half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t2 = time_synchronized() pred = model(img, augment=opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) t3 = time_synchronized() # Process detections for i, det in enumerate(pred): # detections per image p, s, im0 = path, '', im0s.copy() gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if det is not None and len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += f'{n} {names[int(c)]}s, ' # add to string # Write results for *xyxy, conf, cls in det: if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format if save_img or save_crop or save_patch: # 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}') plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=3, **plot_kwargs) if save_crop or save_patch: # Save crop #crop = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]):int(xyxy[2])] save_file = str(Path(save_dir) / Path(p).stem) + f'_{i}_{names[c]}.jpg' cv2.imwrite(save_file, crop) # Print results print(f'{s}Done. ({t3 - t1:.3f}s)') # Stream results if view_img: cv2.imshow(p, im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration if save_txt or save_img: print(f"Results saved to {save_dir}") if platform == 'darwin': # MacOS os.system(f'open {save_dir}') cap.release() ``` 2. 将以上代码复制到一个新文件中 3. 删除以下代码： ```python elif source.isnumeric(): source = int(source) # Start streaming cap = cv2.VideoCapture(source) assert cap.isOpened(), f'Failed to open {source}' cap.set(cv2.CAP_PROP_FRAME_WIDTH, width) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height) fps = cap.get(cv2.CAP_PROP_FPS) print(f'Using {source} with FPS {fps:.3f} [OK]') ``` 4. 将下面的代码留下来： ```python for path, img, im0s, vid_cap in dataset: if vid_path != path: vid_path, frames = path, [] if isinstance(vid_cap, cv2.VideoCapture): frames = vid_cap.get(cv2.CAP_PROP_FRAME_COUNT) elif isinstance(vid_cap, int): frames = vid_cap else: assert False, f'Invalid video {path}' t1 = time_synchronized() # Get frames ret, frame = cap.read() if not ret: break assert frame is not None, 'Image Not Found ' # Padded resize frame = letterbox(frame, new_shape=inp_shape)[0] # Normalize RGB frame = frame[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 frame = np.ascontiguousarray(frame) # Inference img = torch.from_numpy(frame).to(device) img = img.half() if half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t2 = time_synchronized() pred = model(img, augment=opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) t3 = time_synchronized() # Process detections for i, det in enumerate(pred): # detections per image p, s, im0 = path, '', im0s.copy() gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if det is not None and len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += f'{n} {names[int(c)]}s, ' # add to string # Write results for *xyxy, conf, cls in det: if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format if save_img or save_crop or save_patch: # 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}') plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=3, **plot_kwargs) if save_crop or save_patch: # Save crop #crop = im0[int(xyxy[1]):int(xyxy[3]), int(xyxy[0]):int(xyxy[2])] save_file = str(Path(save_dir) / Path(p).stem) + f'_{i}_{names[c]}.jpg' cv2.imwrite(save_file, crop) # Print results print(f'{s}Done. ({t3 - t1:.3f}s)') # Stream results if view_img: cv2.imshow(p, im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration ``` 这部分代码处理摄像头输入并进行推理。 5. 将提取出的代码保存到一个新文件中，例如 camera_detect.py。 现在您可以使用 camera_detect.py 进行摄像头推理。请确保在运行代码之前正确设置 YOLOv5 模型、类别和权重路径。

### YOLOv5 7.0 中 `detect.py` 的源码解析及功能说明 #### 功能概述 YOLOv5 是一种高效的实时目标检测框架，其核心文件之一是 `detect.py`。该文件的主要目的是加载预训练模型并执行推理操作，从而完成图像或视频中的对象检测任务。尽管当前引用主要针对的是 YOLOv5-6.x 版本[^1]，但可以推测 YOLOv5 7.0 的 `detect.py` 文件在结构上与其相似。 以下是基于现有知识和引用内容对 YOLOv5 7.0 中 `detect.py` 的详细解析： --- #### 主要模块与逻辑分解 ##### 1. **导入必要的库** `detect.py` 开始部分会引入一系列 Python 库以及 YOLOv5 自定义的工具类。这些库用于处理数据输入、模型加载、推理计算等功能。 ```python import argparse import os from pathlib import Path import cv2 import torch import numpy as np from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams from utils.general import (LOGGER, Profile, check_file, check_img_size, increment_path, non_max_suppression, scale_boxes, strip_optimizer) from utils.plots import Annotator, colors, save_one_box from utils.torch_utils import select_device, smart_inference_mode ``` 这部分代码的功能在于初始化所需的依赖项，并确保程序能够正常运行[^2]。 --- ##### 2. **参数解析** 通过命令行传递参数来控制脚本的行为是一个常见的设计模式。`argparse` 被用来接收用户输入的各种选项，例如权重路径、图片大小等。 ```python parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model path(s)') parser.add_argument('--source', type=str, default='data/images', help='file/dir/URL/glob') parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') opt = parser.parse_args() ``` 上述代码片段允许用户指定自定义配置，比如使用的模型权重文件位置 (`--weights`) 和待检测的数据来源 (`--source`) 等[^3]。 --- ##### 3. **设备选择** 为了支持多平台部署（CPU 或 GPU），`detect.py` 提供了一个自动化的设备分配机制： ```python device = select_device(opt.device) ``` 此函数会优先尝试使用 NVIDIA CUDA 加速硬件资源；如果不可用，则回退到 CPU 进行运算。 --- ##### 4. **模型加载** 利用 `DetectMultiBackend` 类实例化一个检测器对象，它负责管理不同类型的后端引擎（如 ONNX、TensorRT）。这一步骤对于实际推断至关重要。 ```python model = DetectMultiBackend(weights=opt.weights, device=device, dnn=False, data=None, fp16=True) stride, names, pt = model.stride, model.names, model.pt imgsz = check_img_size(imgsz=imgsz, s=stride) ``` 此处还包含了尺寸校验环节以匹配网络需求。 --- ##### 5. **数据流准备** 根据传入的目标媒介形式（单张静态图还是动态影像序列），分别调用了不同的加载方法——要么逐帧读取本地磁盘上的素材，要么捕捉来自摄像头或其他直播渠道的内容。 ```python if webcam: view_img = True dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit) else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit) ``` --- ##### 6. **前向传播与后处理** 当每一批次样本准备好之后便进入预测阶段，在这里应用了非极大抑制算法去除冗余框体只保留最优解集。 ```python for path, im, im0s, vid_cap, s in dataset: im = torch.from_numpy(im).to(device) im = im.half() if half else im.float() # uint8 to fp16/32 im /= 255 # normalize to [0, 1] pred = model(im) det = non_max_suppression(pred, conf_thres=conf_thres, iou_thres=iou_thres)[0] annotator = Annotator(im0.copy(), line_width=line_thickness, example=str(names)) if len(det): det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round() for *xyxy, conf, cls in reversed(det): c = int(cls) # integer class label = f'{names[c]} {conf:.2f}' annotator.box_label(xyxy, label, color=colors(c, True)) cv2.imshow('Detection Result', im0) cv2.waitKey(1) # 1 millisecond delay per frame update. ``` 以上展示了如何提取边界框坐标及其对应的类别标签信息，并将其可视化叠加至原始画面上显示给最终使用者观看效果。 --- #### 总结 综上所述，`detect.py` 实现了一套完整的从输入获取直至输出展示的工作流程，涵盖了多个关键技术要点如跨平台兼容性考虑、高效内存管理策略等方面的设计理念均体现了作者团队深厚的技术功底。 ---