def extract_features(self): """Extracts features from the image""" """从图像中提取特征 """ if self.feature_type == 'sift': detector = cv2.xfeatures2d.SIFT_create() elif self.feature_type == 'surf': detector = cv2.xfeatures2d.SURF_create() elif self.feature_type == 'orb': detector = cv2.ORB_create(nfeatures=1500) else: logging.error("Admitted feature types are SIFT, SURF or ORB") sys.exit(0)
时间: 2024-04-28 12:19:37 浏览: 16
这是一个Python方法,它的作用是从图像中提取特征。具体解释如下:
- 方法名为extract_features,它属于一个类,通过self可以访问该类的属性和方法。
- 如果self对象的feature_type属性为'sift',则创建一个SIFT特征检测器,赋值给detector变量。
- 如果self对象的feature_type属性为'surf',则创建一个SURF特征检测器,赋值给detector变量。
- 如果self对象的feature_type属性为'orb',则创建一个ORB特征检测器,其中nfeatures参数指定了最大特征点数为1500,赋值给detector变量。
- 如果self对象的feature_type属性既不是'sift'、'surf'、'orb'中的任何一种,则打印错误信息并退出程序。
- 最后返回detector变量,它是一个特征检测器对象,用于后续的特征提取。
相关问题
def extract_tuple(input_tuple, start_index, end_index): return input_tuple[start_index : end_index+1] In the terminal, run function call of extract_tuple with different arguments. For example, try extract_tuple((100,200,300,400,500,600), 1, 4) . What does it output?
When running the function call `extract_tuple((100, 200, 300, 400, 500, 600), 1, 4)`, the output will be:
```
(200, 300, 400, 500)
```
This is because the function `extract_tuple` takes three arguments: `input_tuple`, `start_index`, and `end_index`. It returns a new tuple that contains the elements of `input_tuple` starting from `start_index` up to `end_index` (inclusive).
In this case, `(100, 200, 300, 400, 500, 600)` is the `input_tuple`, `1` is the `start_index`, and `4` is the `end_index`. Therefore, it extracts the elements from index 1 to index 4 (inclusive), resulting in the tuple `(200, 300, 400, 500)`.
kp, desc = sift.detectAndCompute(img_gray, None) kp = np.array([p.pt for p in kp]).T
The first line of code detects and computes SIFT (Scale-Invariant Feature Transform) keypoints and descriptors from the grayscale image "img_gray".
The second line of code extracts the keypoint locations from the "kp" variable and stores them as a numpy array. The ".pt" attribute of each keypoint object represents the (x,y) coordinates of the keypoint.
Finally, the ".T" method transposes the numpy array so that each row represents a single keypoint location, with the first row containing all the x-coordinates and the second row containing all the y-coordinates.