上述问题，在文件里函数如下，请指出在哪里修改路径load_image(self, index): # loads 1 image from dataset, returns img, original hw, resized hw img = self.imgs[index] if img is None: # not cached path = self.img_files[index] img = cv2.imread(path) # BGR assert img is not None, 'Image Not Found ' + path h0, w0 = img.shape[:2] # orig hw r = self.img_size / max(h0, w0) # resize image to img_size if r != 1: # always resize down, only resize up if training with augmentation interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized else: return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized

在上述代码中，你可以在 `load_image()` 函数中修改图像路径。具体来说，你需要修改 `path = self.img_files[index]` 这一行的代码。将其中的 `self.img_files[index]` 更改为数据集中实际的图像路径。

请确保修改后的路径与数据集中实际的图像路径一致，以确保正确加载数据集中的图像文件。

以下是修改后的代码示例：

# loads 1 image from dataset, returns img, original hw, resized hw
img = self.imgs[index]
if img is None:  # not cached
    path = "数据集中的实际图像路径"  # 修改为数据集中实际的图像路径
    img = cv2.imread(path)  # BGR
    assert img is not None, 'Image Not Found ' + path
    h0, w0 = img.shape[:2]  # orig hw
    r = self.img_size / max(h0, w0)  # resize image to img_size
    if r != 1:  # always resize down, only resize up if training with augmentation
        interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR
        img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
    return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
else:
    return self.imgs[index], self.img_hw0[index], self.img_hw[index]  # img, hw_original, hw_resized

请将 "数据集中的实际图像路径" 替换为数据集中对应图像的路径。保存修改后的文件，并重新运行 train.py 文件。