python里面len的作用是什么

在Python中，`len()`是一个内置函数，用于返回对象（如字符串、列表、元组、字典等）的长度或元素个数。例如，如果你有一个字符串，你可以使用`len()`来获取字符串中字符的数量。如果你有一个列表或元组，你可以使用`len()`来获取列表或元组中元素的数量。示例：

string = "Hello, World!"
print(len(string))  # 输出结果为 13

my_list = ['apple', 'banana', 'orange']
print(len(my_list))  # 输出结果为 3

注意：`len()`函数只能用于序列类型的对象，否则会抛出`TypeError`。

相关问题

python里面str是干什么的

在Python中，`str`是用来表示字符串类型的数据的。字符串是一种序列类型，表示一个或多个字符组成的有序序列。字符串可以使用单引号或双引号来表示，例如：

string1 = 'Hello World!'
string2 = "Python is awesome!"

字符串可以进行很多操作，例如拼接、截取、查找、替换等。同时，Python中也内置了许多字符串处理函数，如`len()`、`upper()`、`lower()`、`split()`等。字符串在Python中是不可变的，这意味着一旦一个字符串被创建，它的内容就不能再被更改了。

python里面piqe

### Python PIQE Module Usage and Definition

The Perceptual Image Quality Evaluator (PIQE) is a no-reference image quality assessment metric designed to evaluate the perceptual quality of images without requiring an original reference image. This method evaluates various distortions present in the image, such as blurring and noise.

In Python, one can use libraries like `skimage` or custom implementations based on research papers for calculating PIQE scores. However, specific dedicated modules might not be directly available under standard library distributions but can often be found within specialized packages or implemented from scratch using NumPy and SciPy.

For implementing PIQE calculation manually:

import numpy as np
from scipy.signal import convolve2d
from skimage.color import rgb2gray


def calculate_piqe(image):
    """
    Calculate the Perceptual Image Quality Evaluation score.
    
    Parameters:
        image : ndarray
            Input color or grayscale image with values normalized between 0 and 1
    
    Returns:
        float
            The calculated PIQE value indicating perceived visual quality
    """

    if len(image.shape) == 3:
        img_gray = rgb2gray(image)
    else:
        img_gray = image.copy()

    # Ensure pixel intensity range is [0, 1]
    img_normalized = img_gray / 255.0

    # Define parameters according to literature review
    block_size = 8
    overlap_ratio = 0.75
    mu_pris_param = 0.9
    sigma_sqrd_nuis_param = 0.025 ** 2

    height, width = img_normalized.shape[:2]

    num_blocks_h = int(np.floor((height - block_size * (1 - overlap_ratio)) / (block_size * overlap_ratio)))
    num_blocks_w = int(np.floor((width - block_size * (1 - overlap_ratio)) / (block_size * overlap_ratio)))

    piqe_scores = []

    for i in range(num_blocks_h):
        for j in range(num_blocks_w):

            start_row = int(i * block_size * overlap_ratio)
            end_row = min(start_row + block_size, height)

            start_col = int(j * block_size * overlap_ratio)
            end_col = min(start_col + block_size, width)

            block = img_normalized[start_row:end_row, start_col:end_col]

            mean_block = np.mean(block)
            var_block = np.var(block)

            piqe_score = ((mean_block - mu_pris_param)**2 +
                          (var_block - sigma_sqrd_nuis_param)**2)
            
            piqe_scores.append(piqe_score)

    final_piqe_value = np.median(piqe_scores)

    return final_piqe_value

This function calculates the PIQE by dividing the input image into overlapping blocks, computing statistics over these regions, and then determining how much they deviate from expected pristine conditions[^1].