from utils.datasets import exif_transpose, letterbox ModuleNotFoundError: No module named 'utils.datasets'

出现这个 `ModuleNotFoundError` 是因为 Python 解释器无法找到名为 'utils.datasets' 的模块。这通常意味着你在尝试运行的代码中引用了一个还未导入或不在当前工作目录、Python 环境路径中的模块。

"exif_transpose" 和 "letterbox" 可能是某个特定项目或库中的函数，它们可能来自于一个叫做 'utils.datasets' 的子包或文件。为了解决这个问题，你可以按照以下步骤操作：

1. 首先，确认 'utils.datasets' 是否是你正在使用的项目的官方名称，或者是你本地的一个自定义模块。如果不是内置模块，那么它应该位于你的项目源代码文件夹内。

2. 如果是项目内的模块，检查你是否已经正确安装了该项目，并且在使用之前是否通过 `import` 命令引入了这个模块。例如，如果你在一个名为 'my_project' 的目录下，你可能需要像这样导入：`import my_project.utils.datasets`.

3. 如果模块是一个第三方库，你需要先安装它。使用 `pip install [package_name]`（替换 `[package_name]` 为实际的库名）来安装。

4. 确保安装后，再次尝试导入，如果问题依然存在，检查你的 PYTHONPATH 环境变量，确保包含了模块所在的路径。

相关问题

AssertionError: train: No labels found in D:\YOLO\datasets\Armor\labels\1.cache, can not start training.

这个错误提示表明在训练YOLO模型时，找不到训练数据集中的标签文件。解决这个问题的方法如下：

1. 确认标签文件是否存在，标签文件应该与图像文件在同一目录下，且文件名相同，只是扩展名不同。标签文件的扩展名通常为.txt，每个文件应包含与其对应的图像文件中所有对象的标签信息。

2. 确认标签文件的格式是否正确。YOLO模型要求标签文件的格式为每行一个对象，每行包含对象的类别和位置信息。位置信息应该是相对于图像宽度和高度的归一化坐标，即左上角和右下角的坐标值应该在0到1之间。

3. 确认训练脚本中的数据集路径和标签文件路径是否正确。如果数据集路径或标签文件路径不正确，就会导致找不到标签文件的错误。

4. 修改datasets.py文件。在该文件中，需要将标签文件的路径替换为正确的路径。具体来说，需要将datasets.py文件中的JPEGImages替换为标签文件所在的目录。

以下是修改后的datasets.py文件的示例代码：

import glob
import os

import numpy as np
import torch
from PIL import Image

from torch.utils.data import Dataset


class LoadImagesAndLabels(Dataset):  # for training/testing
    def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
                 cache_images=False, single_cls=False):
        path = str(Path(path))  # os-agnostic
        assert os.path.isfile(path), f'File not found {path}'
        with open(path, 'r') as f:
            self.img_files = [x.replace('\n', '') for x in f.readlines() if os.path.isfile(x.replace('\n', ''))]
        assert self.img_files, f'No images found in {path}'
        self.label_files = [x.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt')
                            .replace('.jpeg', '.txt') for x in self.img_files]
        self.img_size = img_size
        self.batch_size = batch_size
        self.augment = augment
        self.hyp = hyp
        self.rect = rect
        self.image_weights = image_weights
        self.cache_images = cache_images
        self.single_cls = single_cls

    def __len__(self):
        return len(self.img_files)

    def __getitem__(self, index):
        img_path = self.img_files[index % len(self.img_files)].rstrip()
        label_path = self.label_files[index % len(self.img_files)].rstrip()

        # Load image
        img = None
        if self.cache_images:  # option 1 - caches small/medium images
            img = self.imgs[index % len(self.imgs)]
        if img is None:  # option 2 - loads large images on-the-fly
            img = Image.open(img_path).convert('RGB')
            if self.cache_images:
                if img.size[0] < 640 or img.size[1] < 640:  # if one side is < 640
                    img = img.resize((640, 640))  # resize
                self.imgs[index % len(self.imgs)] = img  # save
        assert img.size[0] > 9, f'Width must be >9 pixels {img_path}'
        assert img.size[1] > 9, f'Height must be >9 pixels {img_path}'

        # Load labels
        targets = None
        if os.path.isfile(label_path):
            with open(label_path, 'r') as f:
                x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
            # Normalized xywh to pixel xyxy format
            labels = x.copy()
            if x.size > 0:
                labels[:, 1] = x[:, 1] * img.width  # xmin
                labels[:, 2] = x[:, 2] * img.height  # ymin
                labels[:, 3] = x[:, 3] * img.width  # xmax
                labels[:, 4] = x[:, 4] * img.height  # ymax
                labels[:, 1:5] = xywh2xyxy(labels[:, 1:5])  # xywh to xyxy
                targets = torch.zeros((len(labels), 6))
                targets[:, 1:] = torch.from_numpy(labels)

        # Apply augmentations
        if self.augment:
            img, targets = random_affine(img, targets,
                                         degrees=self.hyp['degrees'],
                                         translate=self.hyp['translate'],
                                         scale=self.hyp['scale'],
                                         shear=self.hyp['shear'],
                                         border=self.img_size // 2)  # border to remove

        # Letterbox
        img, ratio, pad = letterbox(img, new_shape=self.img_size, auto=self.rect, scaleup=self.augment,
                                    stride=self.hyp['stride'])
        targets[:, 2:6] = xyxy2xywh(targets[:, 2:6]) / self.img_size / ratio  # normalized xywh (to grid cell)

        # Load into tensor
        img = np.array(img).transpose(2, 0, 1)  # HWC to CHW
        img = torch.from_numpy(img).to(torch.float32)  # uint8 to fp16/32
        targets = targets[torch.where(targets[:, 0] == index % len(self.img_files))]  # filter by image index
        return img, targets, index, img_path

    def coco_index(self, index):
        """Map dataset index to COCO index (minus 1)"""
        return int(Path(self.img_files[index]).stem) - 1

    @staticmethod
    def collate_fn(batch):
        img, label, _, path = zip(*batch)  # transposed
        for i, l in enumerate(label):
            l[:, 0] = i  # add target image index for build_targets()
        return torch.stack(img, 0), torch.cat(label, 0), path


class LoadImages(Dataset):  # for inference
    def __init__(self, path, img_size=640, stride=32, auto=True):
        path = str(Path(path))  # os-agnostic
        if os.path.isdir(path):
            files = sorted(glob.glob('%s/*.*' % path))
        elif os.path.isfile(path):
            files = [path]
        else:
            raise Exception(f'Error: {path} does not exist')
        images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
        videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
        ni, nv = len(images), len(videos)

        self.img_size = img_size
        self.stride = stride
        self.auto = auto
        self.video_flag = [False] * ni + [True] * nv
        self.img_files = images + videos
        self.cap = [cv2.VideoCapture(x) for x in videos]
        self.frame = [None] * nv
        self.ret = [False] * nv
        self.path = path

    def __len__(self):
        return len(self.img_files)

    def __getitem__(self, index):
        if self.video_flag[index]:
            return self.load_video(index)
        else:
            return self.load_image(index)

    def load_image(self, index):
        img_path = self.img_files[index]
        img = cv2.imread(img_path)  # BGR
        assert img is not None, 'Image Not Found ' + img_path
        h0, w0 = img.shape[:2]  # orig hw
        img = letterbox(img, new_shape=self.img_size, auto=self.auto)[0]
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        img = np.ascontiguousarray(img)

        return torch.from_numpy(img), index, img_path, (h0, w0)

    def load_video(self, index):
        cap = self.cap[index]
        while True:
            self.ret[index], frame = cap.read()
            if not self.ret[index]:
                break
            if self.frame[index] is None:
                self.frame[index] = letterbox(frame, new_shape=self.img_size, auto=self.auto)[0]
                self.frame[index] = self.frame[index][:, :, ::-1].transpose(2, 0, 1)
                self.frame[index] = np.ascontiguousarray(self.frame[index])
            else:
                self.frame[index] = torch.cat((self.frame[index][self.stride:], letterbox(frame, new_shape=self.img_size,
                                                                                          auto=self.auto)[0]), 0)
            if self.ret[index]:
                return self.frame[index], index, self.img_files[index], frame.shape[:2]

    def __del__(self):
        if hasattr(self, 'cap'):
            for c in self.cap:
                c.release()


def letterbox(img, new_shape=640, color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
    # Resize and pad image while meeting stride-multiple constraints
    shape = img.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        ratio = float(new_shape) / max(shape)
    else:
        ratio = min(float(new_shape[0]) / shape[0], float(new_shape[1]) / shape[1])
    if ratio != 1:  # always resize down, only resize up if shape < new_shape * 1.5
        if scaleup or (ratio < 1 and max(shape) * ratio > stride * 1.5):
            interp = cv2.INTER_LINEAR
            if ratio < 1:
                img = cv2.resize(img, (int(round(shape[1] * ratio)), int(round(shape[0] * ratio))), interpolation=interp)
            else:
                img = cv2.resize(img, (int(round(shape[1] * ratio)), int(round(shape[0] * ratio))), interpolation=interp)
        else:
            interp = cv2.INTER_AREA
            img = cv2.resize(img, (int(round(shape[1] * ratio)), int(round(shape[0] * ratio))), interpolation=interp)
    new_shape = [round(shape[1] * ratio), round(shape[0] * ratio)]

    # Compute stride-aligned boxes
    if auto:
        stride = int(np.ceil(new_shape[0] / stride) * stride)
        top_pad = (stride - new_shape[0]) % stride  # add top-padding (integer pixels only)
        left_pad = (stride - new_shape[1]) % stride  # add left-padding (integer pixels only)
        if top_pad or left_pad:
            img = cv2.copyMakeBorder(img, top_pad // 2, top_pad - top_pad // 2, left_pad // 2, left_pad - left_pad // 2,
                                     cv2.BORDER_CONSTANT, value=color)  # add border
    else:
        stride = 32
        top_pad, left_pad = 0, 0

    # Pad to rectangular shape divisible by stride
    h, w = img.shape[:2]
    if scaleFill or new_shape == (w, h):  # scale-up width and height
        new_img = np.zeros((new_shape[1], new_shape[0], 3), dtype=np.uint8) + color  # whole image
        nh, nw = h, w
    else:  # scale width OR height
        nh = new_shape[1] - top_pad
        nw = new_shape[0] - left_pad
        assert nh > 0 and nw > 0, 'image size < new_size'
        new_img = np.zeros((new_shape[1], new_shape[0], 3), dtype=np.uint8) + color  # whole image
        if nw / w <= nh / h:  # resize by width, then pad height
            new_w = new_shape[0]
            new_h = int(nh * new_w / nw)
            assert new_h > 0, 'image size < new_size'
            img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
            top = top_pad // 2
            bottom = top_pad - top
            left = left_pad // 2
            right = left_pad - left
            new_img[top:top + new_h, left:left + new_w] = img
        else:  # resize by height, then pad width
            new_h = new_shape[1]
            new_w = int(nw * new_h / nh)
            assert new_w > 0, 'image size < new_size'
            img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
            top = top_pad // 2
            bottom = top_pad - top
            left = left_pad // 2
            right = left_pad - left
            new_img[top:top + new_h, left:left + new_w] = img

    return new_img, ratio, (top_pad, left_pad)


def xywh2xyxy(x):
    # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
    y = x.copy() if isinstance(x, np.ndarray) else np.array(x)
    y[..., 0] = x[..., 0] - x[..., 2] / 2
    y[..., 1] = x[..., 1] - x[..., 3] / 2
    y[..., 2] = x[..., 0] + x[..., 2] / 2
    y[..., 3] = x[..., 1] + x[..., 3] / 2
    return y


def xyxy2xywh(x):
    # Convert bounding

上述表述和我下载的官方代码不一样，以下是我截取的部分源代码，请重新整理if name == 'main': parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') 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='display 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')

根据您提供的代码，我重新整理了Detect.py的代码，并添加了时间记录和报警规避功能的注释说明。以下是修改后的代码：

import cv2
import time
import argparse
from models.experimental import attempt_load
from utils.datasets import letterbox
from utils.general import non_max_suppression, scale_coords, xyxy2xywh
from utils.torch_utils import select_device

# 解析命令行参数
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='data/images', help='source')  # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
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='display 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')
args = parser.parse_args()

# 加载模型权重
device = select_device(args.device)  # 使用指定设备
model = attempt_load(args.weights, map_location=device)  # 加载模型

# 初始化时间记录和报警规避相关变量
last_alarm_time = time.time()  # 上次报警时间
alarm_interval = 10  # 报警时间间隔（秒）

# 打开摄像头
cap = cv2.VideoCapture(args.source)

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    # 图像预处理
    img = letterbox(frame, new_shape=args.img_size)[0]
    img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB
    img = np.ascontiguousarray(img)

    # 将图像转换为Tensor并进行推理
    img = torch.from_numpy(img).to(device)
    img = img.float() / 255.0
    if img.ndimension() == 3:
        img = img.unsqueeze(0)

    # 推理并进行非极大值抑制
    pred = model(img, augment=False)[0]
    pred = non_max_suppression(pred, args.conf_thres, args.iou_thres)

    # 处理检测结果
    for det in pred[0]:
        if det is not None:
            # 获取检测框的坐标和置信度
            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], frame.shape).round()
            for *xyxy, conf, cls in reversed(det):
                x, y, w, h = map(int, xyxy2xywh(xyxy))
                label = f'{names[int(cls)]} {conf:.2f}'
                
                # 判断是否检测到不带安全帽的人
                if label == 'no_hat':
                    # 检查是否达到报警时间间隔
                    current_time = time.time()
                    if current_time - last_alarm_time > alarm_interval:
                        # 记录报警时间并执行报警操作
                        last_alarm_time = current_time
                        print("Alarm: No helmet detected!")
                        # 添加报警操作的代码

                # 在图像上绘制检测结果
                cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
                cv2.putText(frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,
                            0.9, (255, 0, 0), 2)

    # 显示检测结果
    cv2.imshow('Detection', frame)
    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

请注意，上述代码仅修改了与您提供的代码不一致的部分，并添加了时间记录和报警规避功能的注释说明。您可能需要根据您的实际需求对其进行进一步的修改和优化。