WARNING Unable to automatically guess model task, assuming 'task=detect'. Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify', or 'pose'. from n params module arguments Traceback (most recent call last): File "d:/Python/ultralytics-main/val.py", line 6, in <module> data = YOLO('D:/Python/ultralytics-main/traindata3/data.yaml') File "d:\Python\ultralytics-main\ultralytics\yolo\engine\model.py", line 105, in __init__ self._new(model, task) File "d:\Python\ultralytics-main\ultralytics\yolo\engine\model.py", line 138, in _new self.model = TASK_MAP[self.task][0](cfg_dict, verbose=verbose and RANK == -1) # build model File "d:\Python\ultralytics-main\ultralytics\nn\tasks.py", line 195, in __init__ self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist File "d:\Python\ultralytics-main\ultralytics\nn\tasks.py", line 496, in parse_model for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args KeyError: 'backbone'

根据你提供的信息，看起来是一个YOLO模型的训练代码中出现了错误。错误信息中提到了缺少关键字'backbone'，你需要检查一下你的代码中是否正确设置了模型的backbone部分。此外，你也可以确认一下你的数据集配置文件data.yaml是否正确。如果问题仍然存在，可以提供更多的代码信息以及完整的错误信息，这样我可以更好地帮助你解决问题。

相关问题

Unable to find a @SpringBootConfiguration, you need to use @ContextConfiguration or @SpringBootTest(classes=...) with your test

If you are encountering the error message "Unable to find a @SpringBootConfiguration, you need to use @ContextConfiguration or @SpringBootTest(classes=...) with your test," it means that Spring Boot is unable to detect the configuration class for your test.

To resolve this issue, you have a few options:

1. Use @ContextConfiguration: Annotate your test class with @ContextConfiguration and specify the location of your configuration file(s). For example:

public class MyTest {
 // ...
}

2. Use @SpringBootTest(classes=...): Annotate your test class with @SpringBootTest and specify the configuration class(es) using the "classes" attribute. For example:

public class MyTest {
 // ...
}

Make sure to replace `MyConfiguration` with the actual class that contains your Spring Boot configuration.

3. Check your test package structure: Ensure that your test class is located in the same or a sub-package of your main application class. By default, Spring Boot will automatically scan for configuration classes within its package and sub-packages.

By applying one of these solutions, you should be able to resolve the error and properly configure Spring Boot for your tests.

import numpy as np import cv2 Load image img = cv2.imread("input.jpg") Convert to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) Detect edges edges = cv2.Canny(gray, 100, 200) Display image with edges cv2.imshow("Image with Edges", edges) Automatically select edge points using HoughLines method lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=100, minLineLength=50, maxLineGap=10) points = [] for line in lines: x1, y1, x2, y2 = line[0] points.append((x1, y1)) points.append((x2, y2)) Generate TSP art and tsplib dataset n = len(points) distances = np.zeros((n, n)) for i in range(n): for j in range(n): if i != j: distances[i][j] = np.sqrt((points[i][0] - points[j][0]) ** 2 + (points[i][1] - points[j][1]) ** 2) Write tsplib dataset with open("output.tsp", "w") as f: f.write("NAME: output\n") f.write("TYPE: TSP\n") f.write("DIMENSION: {}\n".format(n)) f.write("EDGE_WEIGHT_TYPE: EUC_2D\n") f.write("NODE_COORD_SECTION\n") for i in range(n): f.write("{} {} {}\n".format(i+1, points[i][0], points[i][1])) f.write("EOF\n") Display TSP art tsp_art = np.zeros_like(gray) path = list(range(n)) + [0] for i in range(n): cv2.line(tsp_art, points[path[i]], points[path[i+1]], (255, 255, 255), thickness=1) cv2.imshow("TSP Art", tsp_art) cv2.waitKey(0) cv2.destroyAllWindows()

这是一段使用OpenCV和NumPy库实现的图像处理代码，其目的是将一张图像转换成TSP艺术，并生成一个tsplib数据集。具体步骤如下：

1. 使用OpenCV的imread函数读取一张图像，将其转换为灰度图像。

2. 使用Canny函数检测边缘。

3. 使用HoughLinesP函数自动选择边缘点。

4. 使用NumPy计算两点之间的距离，生成距离矩阵。

5. 将距离矩阵写入tsplib数据集文件。

6. 使用OpenCV绘制TSP艺术，并显示。

值得注意的是，这段代码使用的是HoughLinesP函数自动选择边缘点，因此不一定能够得到最优的TSP艺术结果。如果需要更精确的结果，可以考虑手动选择边缘点，并使用更高效的TSP求解算法。