import cv2 import torch import matplotlib.pyplot as plt from skimage.segmentation import slic from matplotlib.patches import Rectangle # 定义超像素数目 n_segments = 25 # 加载输入图像 args = {"image": r"D:\Users\Administrator\PycharmProjects\pythonProject\heart_dataset\1_blur\img-00003-00007.jpg"} # load the image and apply SLIC and extract (approximately) # the supplied number of segments image = cv2.imread(args["image"]) # 使用SLIC算法进行超像素分割 segments = slic(image.transpose((1, 2, 0)), n_segments=n_segments) # 将超像素图像转换为掩膜 mask = torch.zeros_like(torch.from_numpy(image[0, :, :])) for i in range(n_segments): mask[segments == i] = i + 1 # 对掩膜进行处理,得到每个超像素块的区域 regions = [] for i in range(1, n_segments + 1): region = (mask == i).nonzero() if region.size(0) > 0: regions.append(region) # 绘制超像素块的区域 fig, ax = plt.subplots(1) ax.imshow(image.numpy().transpose((1, 2, 0))) for region in regions: x_min, y_min = region.min(dim=0)[0] x_max, y_max = region.max(dim=0)[0] rect = Rectangle((y_min, x_min), y_max - y_min, x_max - x_min, linewidth=1, edgecolor='r', facecolor='none') ax.add_patch(rect) plt.show(),上述代码出现问题: ax.imshow(image.numpy().transpose((1, 2, 0))) AttributeError: 'numpy.ndarray' object has no attribute 'numpy'
时间: 2024-03-08 07:46:55 浏览: 17
根据报错信息`AttributeError: 'numpy.ndarray' object has no attribute 'numpy'`,可以看出是因为在代码中使用了`image.numpy()`,而`numpy()`函数是tensor的方法,numpy数组没有该方法。
可以将以下代码:
```
ax.imshow(image.numpy().transpose((1, 2, 0)))
```
修改为:
```
ax.imshow(image.transpose((1, 2, 0)))
```
即可解决该问题。
相关问题
import torch import matplotlib.pyplot as plt
回答: 这段代码是用来比较使用matplotlib.pyplot和cv2读取图像的方法。其中,cv2.imread(imagepath)读取的图片默认为BGR模式的numpy格式,而matplotlib.pyplot.imread(imagepath)默认为RGB模式的numpy格式。通过这段代码可以看到,使用matplotlib.pyplot读取的图像是RGB格式的,而使用cv2读取的图像是BGR格式的。另外,还提到了使用PIL和cv2读取图片的对比,以及使用mxnet.image.imread和torchvision.transforms预处理图像的方法。\[2\]\[3\]
#### 引用[.reference_title]
- *1* [python进行图像处理中分别用到过matplotlib.pyplot、PIL、cv2三种库,这三种库图像读取和保存方法](https://blog.csdn.net/qq_43776757/article/details/123200004)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* *3* [cv2 和 matplotlib.pyplot 和 PIL.Image 读取图片方式对比【Python读取图片】](https://blog.csdn.net/sinat_28442665/article/details/120055469)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]
优化 import math from inspect import isfunction from functools import partial import matplotlib.pyplot as plt from tqdm.auto import tqdm from einops import rearrange import torch import torch.nn.functional as F import math from inspect import isfunction from functools import partial import matplotlib.pyplot as plt from tqdm.auto import tqdm from einops import rearrange import torch import torch.nn.functional as F import math from inspect import isfunction from functools import partial import matplotlib.pyplot as plt from tqdm.auto import tqdm from einops import rearrange import torch import torch.nn.functional as F
要优化这段代码,可以采取以下几个步骤:
1. 删除重复的导入语句,只保留一份即可。
2. 将相同模块的导入语句合并在一起,以提高代码的可读性。
下面是优化后的代码:
```python
import math
from inspect import isfunction
from functools import partial
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
from einops import rearrange
import torch
import torch.nn.functional as F
```
请注意,我已经假设您的代码中没有其他重要的部分,只有这些导入语句。如果还有其他需要优化的代码,请提供更多细节。