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步驟:

第 1 步:手動均衡

第 2 步:通過使用 OpenCV 函數(shù)

什么是圖像直方圖?

它是圖像強度分布的圖形表示。它量化了所考慮的每個強度值的像素數(shù)。

第 1 步:手動均衡

%matplotlib inline

from IPython．display import display, Math, Latex

import numpy as np

import matplotlib．pyplot as plt

from PIL import Image

img = Image．open('DATA/einstein．jpg')

plt．imshow(img)

輸出:

顯示彩色圖像

將圖像轉換為 numpy 數(shù)組,以便 OpenCV 可以使用:

img = np．a(chǎn)sanyarray(img)

img．shape

輸出:

(2354, 2560, 3)

將 RGB 轉換為灰度:

import cv2

img = cv2．cvtColor(img, cv2．COLOR_BGR2GRAY)

img．shape

輸出:

(2354, 2560)

顯示圖像:

plt．imshow(img, cmap='gray')

輸出:

我們現(xiàn)在知道如何處理直方圖了

img．max()

輸出:

255

img．min()

輸出:

0

img．shape

輸出:

(2354, 2560)

把它展平:

flat = img．flatten()

# 1 row 2354 x 2560 = 6．026．240

flat．shape

輸出:

(6026240,)

顯示直方圖

plt．hist(flat, bins=50)

請注意,灰度值在某個值周圍分布很差

什么是直方圖均衡?

為了更清楚,從上圖中,你可以看到像素似乎聚集在可用強度范圍的中間。直方圖均衡所做的就是擴大這個范圍。

# formula for creating the histogram

display(Math(r'P_x(j) = sum_{i=0}^{j} P_x(i)'))

# create our own histogram function

def get_histogram(image, bins):

   # array with size of bins, set to zeros

   histogram = np．zeros(bins)

   # loop through pixels and sum up counts of pixels

   for pixel in image:

       histogram[pixel] += 1

       # return our final result

       return histogram

hist = get_histogram(flat, 256)

plt．plot(hist)

[]

# create our cumulative sum function

def cumsum(a):

  a = iter(a)

  b = [next(a)]

  for i in a:

   b．a(chǎn)ppend(b[-1] + i)

   return np．a(chǎn)rray(b)
   

# execute the fn

cs = cumsum(hist)

# display the result

plt．plot(cs)

[]

# formula to calculate cumulation sum

display(Math(r's_k = sum_{j=0}^{k} {rac{n_j}{N}}'))

# re-normalize cumsum values to be between 0-255

# numerator & denomenator

nj = (cs - cs．min()) * 255

N = cs．max() - cs．min()

# re-normalize the cdf

cs = nj / N

plt．plot(cs)

[]

Casting:

# cast it back to uint8 since we can't use floating point values in imagescs =

cs．a(chǎn)stype('uint8')

plt．plot(cs)

輸出:

[]

獲取 CDF:

# get the value from cumulative sum for every index in flat, and set that as img_new

img_new = cs[flat]

# we see a much more evenly distributed histogram

plt．hist(img_new, bins=50)

它是如何工作的?

均衡意味著將一個分布(給定的直方圖)映射到另一個分布(強度值的更廣泛和更均勻的分布),因此強度值分布在整個范圍內(nèi)。

# get the value from cumulative sum for every index in flat, and set that as img_new

img_new = cs[flat]

# we see a much more evenly distributed histogram

plt．hist(img_new, bins=50)

# put array back into original shape since we flattened it

img_new = np．reshape(img_new, img．shape)

img_new

輸出:

array([[233, 231, 228, ．．．, 216, 216, 215],

      [233, 230, 228, ．．．, 215, 215, 214],

      [233, 231, 229, ．．．, 213, 213, 212],

      ．．．,

      [115, 107,  96, ．．．, 180, 187, 194],

      [111, 103,  93, ．．．, 187, 189, 192],

      [111, 103,  93, ．．．, 187, 189, 192]], dtype=uint8)

一探究竟：

# set up side-by-side image display

fig = plt．figure()

fig．set_figheight(15)

fig．set_figwidth(15)

fig．a(chǎn)dd_subplot(1,2,1)

plt．imshow(img, cmap='gray')

# display the new image

fig．a(chǎn)dd_subplot(1,2,2)

plt．imshow(img_new, cmap='gray')

plt．show(block=True)

使用 OpenCV equalizeHist(img) 方法

第 2 步:通過使用 OpenCV 函數(shù)

# Reading image via OpenCV and Equalize it right away!

img = cv2．imread('DATA/einstein．jpg',0)

equ = cv2．equalizeHist(img)

準備好!這就是你需要做的!

fig = plt．figure()

fig．set_figheight(15)

fig．set_figwidth(15)

fig．a(chǎn)dd_subplot(1,2,1)

plt．imshow(img, cmap='gray')

# display the Equalized (equ) image

fig．a(chǎn)dd_subplot(1,2,2)

plt．imshow(equ, cmap='gray')

plt．show(block=True)

print("That?s it! Thank you once again!I hope will be helpful．")

輸出:

That?s it! Thank you once again!

I hope will be helpful．

       原文標題 : OpenCV：直方圖均衡