步骤:
第 1 步:手动均衡
第 2 步:通过使用 OpenCV 函数
什么是图像直方图?
它是图像强度分布的图形表示。它量化了所考虑的每个强度值的像素数。
第 1 步:手动均衡
%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)
输出:
显示彩色图像
将图像转换为 numpy 数组,以便 OpenCV 可以使用:
img = np．asanyarray(img)
img．shape
输出:
(2354, 2560, 3)
将 RGB 转换为灰度:
import cv2
img = cv2．cvtColor(img, cv2．COLOR_BGR2GRAY)
img．shape
输出:
(2354, 2560)
显示图像:
plt．imshow(img, cmap='gray')
输出:
我们现在知道如何处理直方图了
img．max()
输出:
255
img．min()
输出:
0
img．shape
输出:
(2354, 2560)
把它展平:
flat = img．flatten()
# 1 row 2354 x 2560 = 6．026．240
flat．shape
输出:
(6026240,)
显示直方图
plt．hist(flat, bins=50)
请注意,灰度值在某个值周围分布很差
什么是直方图均衡?
为了更清楚,从上图中,你可以看到像素似乎聚集在可用强度范围的中间。直方图均衡所做的就是扩大这个范围。
# 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．append(b[-1] + i)
return np．array(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．astype('uint8')
plt．plot(cs)
输出:
[
获取 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?
均衡意味着将一个分布(给定的直方图)映射到另一个分布(强度值的更广泛和更均匀的分布),因此强度值分布在整个范围内。
# 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
输出:
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．add_subplot(1,2,1)
plt．imshow(img, cmap='gray')
# display the new image
fig．add_subplot(1,2,2)
plt．imshow(img_new, cmap='gray')
plt．show(block=True)
使用 OpenCV equalizeHist(img) 方法
第 2 步:通过使用 OpenCV 函数
# Reading image via OpenCV and Equalize it right away!
img = cv2．imread('DATA/einstein．jpg',0)
equ = cv2．equalizeHist(img)
准备好!这就是你需要做的!
fig = plt．figure()
fig．set_figheight(15)
fig．set_figwidth(15)
fig．add_subplot(1,2,1)
plt．imshow(img, cmap='gray')
# display the Equalized (equ) image
fig．add_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．")
输出:
That?s it! Thank you once again!
I hope will be helpful．
原文标题 : OpenCV：直方图均衡
