hist = np.histogram(image, bins=256, range=(0,255))[0]含义

这行代码是使用numpy库中的histogram函数对image进行直方图统计。其中，bins参数表示将整个范围划分为256个区间，range参数表示统计的范围为0到255。最后使用[0]取得直方图统计数据。

相关问题

def get_lbp(self, image): """ 获取给定图片的LBP，划分成几个区域后 :param rgb: :return: """ gridx = 6 gridy = 6 widx = 8 widy = 8 hists = [] for i in range(gridx): for j in range(gridy): mat = image[i * widx: (i + 1) * widx, j * widy: (j + 1) * widy] lbp = skif.local_binary_pattern(mat, 8, 1, 'uniform') max_bins = 10 hist, _ = np.histogram(lbp, normed=True, bins=max_bins, range=(0, max_bins)) hists.append(hist) out = np.array(hists).reshape(-1, 1) return out

这是一个函数，用于获取给定图片的局部二值模式（Local Binary Pattern，LBP）特征。函数需要一个参数image，表示要提取特征的图片。

该函数将图片划分为gridx*gridy个区域，每个区域的大小为widx*widy，然后对每个区域提取LBP特征。LBP是一种常用的图像特征提取方法，它通过对每个像素与周围8个像素的灰度进行比较，得到一个二进制数，将这些二进制数连起来形成一个LBP图像。然后，将LBP图像中的像素值分为若干个区间，统计每个区间中像素的数量，得到一个直方图。该直方图即为该区域的LBP特征。

最终，将所有区域的LBP特征组合成一个向量，并返回该向量。

Implement a function that equalizes grey-scale images based on their histogram. The input is a given grey level image; the output is the derived image with uniform intensity distribution.

Sure, I can help you with that! Here is a Python implementation of the function:

import numpy as np
import cv2

def hist_equalization(img):
    # Calculate histogram
    hist, bins = np.histogram(img.flatten(), 256, [0, 256])
    # Calculate cumulative distribution function (CDF)
    cdf = hist.cumsum()
    # Normalize CDF
    cdf_normalized = cdf * hist.max() / cdf.max()
    # Create lookup table
    lut = np.interp(np.arange(256), bins[:-1], cdf_normalized)
    # Apply lookup table to image
    equalized_img = cv2.LUT(img, lut)
    return equalized_img

To use this function, simply pass in a grayscale image as the argument and it will return the equalized image. Here's an example of how to use it:

# Load grayscale image
img = cv2.imread('image.jpg', 0)
# Apply histogram equalization
equalized_img = hist_equalization(img)
# Display original and equalized images
cv2.imshow('Original Image', img)
cv2.imshow('Equalized Image', equalized_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

I hope this helps! Let me know if you have any questions.