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 18:19:37 浏览: 10
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)`.
Online test questions: search, extract, and process data from a list
I apologize for misunderstanding your question earlier. Here is an example implementation in Python to search, extract, and process data from a list:
```
# Example list of dictionaries
data = [
{'name': 'Alice', 'age': 25, 'gender': 'F'},
{'name': 'Bob', 'age': 30, 'gender': 'M'},
{'name': 'Charlie', 'age': 35, 'gender': 'M'},
{'name': 'David', 'age': 40, 'gender': 'M'},
{'name': 'Eve', 'age': 45, 'gender': 'F'}
]
# Search for all males in the list
males = [d for d in data if d['gender'] == 'M']
# Extract the names of all females in the list and store in a new list
female_names = [d['name'] for d in data if d['gender'] == 'F']
# Process the ages of all individuals in the list and compute the average age
ages = [d['age'] for d in data]
average_age = sum(ages) / len(ages)
# Print the results
print('Males in the list:', males)
print('Names of females in the list:', female_names)
print('Average age of individuals in the list:', average_age)
```
This example uses a list of dictionaries to represent data, but the same concepts can be applied to other types of data structures. The code searches for a specific gender, extracts names, and processes ages to compute the average age.