def show_map(img): for i in range(0,10): for j in range(0, 10): if (cube_map[i][j]&0x08) == 0x08: cv2.line(img,(i*step+zero_px,j*step+zero_py),(i*step+zero_px,j*step+zero_py+step),(0,255,0),2) if (cube_map[i][j]&0x04) == 0x04: cv2.line(img,(i*step+zero_px,j*step+zero_py),(i*step+zero_px+step,j*step+zero_py),(0,255,0),2) if (cube_map[i][j] & 0x02) == 0x02: cv2.line(img, (i * step + zero_px+step, j * step + zero_py),(i * step + zero_px+step, j * step + zero_py+ step), (0, 255, 0), 2) if (cube_map[i][j] & 0x01) == 0x01: cv2.line(img, (i * step + zero_px, j * step + zero_py+step), (i * step + zero_px+step, j * step + zero_py + step),(0, 255, 0), 2)
时间: 2024-04-18 09:24:11 浏览: 18
这段代码定义了一个名为show_map的函数,用于在图像上显示地图的边界线。函数接受一张图像作为输入参数。
在函数内部使用嵌套的for循环遍历cube_map二维列表,即遍历地图上的每个方块。通过按位与运算(&)和判断条件,判断方块的边界线信息。
如果方块的上边界线信息(cube_map[i][j]&0x08)为0x08,则在图像上绘制一条从方块的左上角点到右上角点的线段,颜色为绿色(0, 255, 0),线宽为2。
同理,如果方块的左、右、下边界线信息满足条件,也分别绘制相应的线段。
最终,将绘制好边界线的图像返回。
相关问题
这是对单个文件进行预测“import os import json import torch from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt from model import convnext_tiny as create_model def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(f"using {device} device.") num_classes = 5 img_size = 224 data_transform = transforms.Compose( [transforms.Resize(int(img_size * 1.14)), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # load image img_path = "../tulip.jpg" assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path) img = Image.open(img_path) plt.imshow(img) # [N, C, H, W] img = data_transform(img) # expand batch dimension img = torch.unsqueeze(img, dim=0) # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path) with open(json_path, "r") as f: class_indict = json.load(f) # create model model = create_model(num_classes=num_classes).to(device) # load model weights model_weight_path = "./weights/best_model.pth" model.load_state_dict(torch.load(model_weight_path, map_location=device)) model.eval() with torch.no_grad(): # predict class output = torch.squeeze(model(img.to(device))).cpu() predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)], predict[predict_cla].numpy()) plt.title(print_res) for i in range(len(predict)): print("class: {:10} prob: {:.3}".format(class_indict[str(i)], predict[i].numpy())) plt.show() if __name__ == '__main__': main()”,改为对指定文件夹下的左右文件进行预测,并绘制混淆矩阵
好的,你的问题是如何将上述代码修改为对指定文件夹下的所有文件进行预测,并绘制混淆矩阵。
要实现这个功能,你需要进行以下几个步骤:
1. 导入必要的包:
```python
import os
import json
import torch
from PIL import Image
from torchvision import transforms
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
from model import convnext_tiny as create_model
```
2. 定义函数 `predict_folder`,用于对指定文件夹下的所有文件进行预测:
```python
def predict_folder(folder_path, model_weight_path, json_path, img_size, num_classes, device):
data_transform = transforms.Compose([
transforms.Resize(int(img_size * 1.14)),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# read class_indict
with open(json_path, "r") as f:
class_indict = json.load(f)
# create model
model = create_model(num_classes=num_classes).to(device)
# load model weights
model.load_state_dict(torch.load(model_weight_path, map_location=device))
model.eval()
y_true = []
y_pred = []
for root, dirs, files in os.walk(folder_path):
for file in files:
img_path = os.path.join(root, file)
assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
img = Image.open(img_path)
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)
with torch.no_grad():
# predict class
output = torch.squeeze(model(img.to(device))).cpu()
predict = torch.softmax(output, dim=0)
predict_cla = torch.argmax(predict).numpy()
y_true.append(class_indict[os.path.basename(root)])
y_pred.append(predict_cla)
return y_true, y_pred
```
这个函数接受五个参数:
- `folder_path`:要预测的文件夹路径。
- `model_weight_path`:模型权重文件路径。
- `json_path`:类别标签文件路径。
- `img_size`:输入图片的大小。
- `num_classes`:分类器的类别数。
- `device`:设备类型。
函数会返回两个列表 `y_true` 和 `y_pred`,分别代表真实标签和预测标签。
3. 加载类别标签:
```python
json_path = './class_indices.json'
assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
with open(json_path, "r") as f:
class_indict = json.load(f)
```
4. 调用 `predict_folder` 函数进行预测:
```python
folder_path = './test'
assert os.path.exists(folder_path), "folder: '{}' dose not exist.".format(folder_path)
y_true, y_pred = predict_folder(folder_path, "./weights/best_model.pth", json_path, 224, 5, device)
```
这里假设要预测的文件夹路径为 `./test`,模型权重文件路径为 `./weights/best_model.pth`,输入图片大小为 224,分类器的类别数为 5。
5. 绘制混淆矩阵:
```python
cm = confusion_matrix(y_true, y_pred)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
xticklabels=list(class_indict.values()), yticklabels=list(class_indict.values()),
title='Confusion matrix',
ylabel='True label',
xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
fmt = 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
plt.show()
```
这里使用了 `sklearn.metrics` 中的 `confusion_matrix` 函数进行混淆矩阵的计算。然后使用 `matplotlib` 绘制混淆矩阵图像。
import torch import torch.nn.functional as F from skimage.segmentation import slic, mark_boundaries import torchvision.transforms as transforms import numpy as np from PIL import Image import matplotlib.pyplot as plt # 加载图像 image = Image.open('3.jpg') # 转换为 PyTorch 张量 transform = transforms.ToTensor() img_tensor = transform(image).unsqueeze(0) # 将 PyTorch 张量转换为 Numpy 数组 img_np = img_tensor.numpy().transpose(0, 2, 3, 1)[0] # 使用 SLIC 算法生成超像素标记图 segments = slic(img_np, n_segments=60, compactness=10) # 可视化超像素索引映射 plt.imshow(segments, cmap='gray') plt.show() # 将超像素索引映射可视化 segment_img = mark_boundaries(img_np, segments) # 将 Numpy 数组转换为 PIL 图像 segment_img = Image.fromarray((segment_img * 255).astype(np.uint8)) # 保存超像素索引映射可视化 segment_img.save('segment_map.jpg') # 定义超像素池化函数 def superpixel_pooling(feature_map, segments): # 获取超像素数量和特征维度 n_segments = np.unique(segments).size n_channels = feature_map.shape[0] # 初始化超像素特征 pooled_features = torch.zeros((n_segments, n_channels)) # 对每个超像素内的像素特征进行聚合 for segment_id in range(n_segments): mask = (segments == segment_id).reshape(-1, 1, 1) mask = torch.from_numpy(mask).float() # 转换为 PyTorch 张量并进行类型转换 pooled_feature = (feature_map * mask).sum(dim=(1, 2)) / mask.sum() pooled_features[segment_id] = pooled_feature return pooled_features # 进行超像素池化 pooled_features = superpixel_pooling(img_tensor, segments) # 可视化超像素特征图 plt.imshow(pooled_features.transpose(0, 1), cmap='gray') plt.show(),上述代码出现问题:pooled_feature = (feature_map * mask).sum(dim=(1, 2)) / mask.sum() RuntimeError: The size of tensor a (3) must match the size of tensor b (262144) at non-singleton dimension 1,如何 修改
这个错误表明,在进行张量相乘时,两个张量的形状不匹配。具体来说,feature_map张量的第1维大小为3,而mask张量的第1维大小为262144。这可能是因为segments张量的形状不正确导致的。你可以尝试将segments张量的形状更改为与img_tensor张量的形状相同,即(1, H, W),其中H和W分别为图像的高度和宽度。可以使用img_tensor的形状信息来创建一个与之相同的全零张量,然后将segments张量的值复制到新张量的正确位置。修改后的代码如下:
```
import torch
import torch.nn.functional as F
from skimage.segmentation import slic, mark_boundaries
import torchvision.transforms as transforms
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
# 加载图像
image = Image.open('3.jpg')
# 转换为 PyTorch 张量
transform = transforms.ToTensor()
img_tensor = transform(image).unsqueeze(0)
# 将 PyTorch 张量转换为 Numpy 数组
img_np = img_tensor.numpy().transpose(0, 2, 3, 1)[0]
# 使用 SLIC 算法生成超像素标记图
segments = slic(img_np, n_segments=60, compactness=10)
# 将 segments 张量形状更改为与 img_tensor 相同
new_segments = torch.zeros((1, img_tensor.shape[2], img_tensor.shape[3]))
for i in range(segments.shape[0]):
for j in range(segments.shape[1]):
new_segments[0, i, j] = segments[i, j]
segments = new_segments.long()
# 可视化超像素索引映射
plt.imshow(segments, cmap='gray')
plt.show()
# 将超像素索引映射可视化
segment_img = mark_boundaries(img_np, segments)
# 将 Numpy 数组转换为 PIL 图像
segment_img = Image.fromarray((segment_img * 255).astype(np.uint8))
# 保存超像素索引映射可视化
segment_img.save('segment_map.jpg')
# 定义超像素池化函数
def superpixel_pooling(feature_map, segments):
# 获取超像素数量和特征维度
n_segments = np.unique(segments).size
n_channels = feature_map.shape[0]
# 初始化超像素特征
pooled_features = torch.zeros((n_segments, n_channels))
# 对每个超像素内的像素特征进行聚合
for segment_id in range(n_segments):
mask = (segments == segment_id).reshape(1, -1, 1, 1)
mask = torch.from_numpy(mask).float()
# 转换为 PyTorch 张量并进行类型转换
pooled_feature = (feature_map * mask).sum(dim=(2, 3)) / mask.sum()
pooled_features[segment_id] = pooled_feature
return pooled_features
# 进行超像素池化
pooled_features = superpixel_pooling(img_tensor, segments)
# 可视化超像素特征图
plt.imshow(pooled_features.transpose(0, 1), cmap='gray')
plt.show()
```
这个修改应该可以解决这个问题。