python 图像对比度 opencv python 对比度

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数据科学探索者 2023-08-09 14:12:27

文章标签 python 图像对比度灰度直方图 desktop 坐标轴 文章分类 Python 后端开发

直方图

%matplotlib inline
import numpy as np
import cv2
import matplotlib.pyplot as plt
def calcGrayHist(image):
    #灰度图像矩阵的高和宽
    rows,cols = image.shape
    #存储灰度直方图
    grayHist = np.zeros([256],np.uint64)
    for r in range(rows):
        for c in range(cols):
            grayHist[image[r][c]]+=1
    return grayHist
if __name__ == "__main__":
    image = cv2.imread("c:/users/76973/desktop/picture/output_image2.jpg",cv2.IMREAD_GRAYSCALE)
    #计算灰度直方图
    grayHist = calcGrayHist(image)
    cv2.imshow("src",image)
    cv2.waitKey(0)
    #画出灰度直方图
    x_range = range(256)
    plt.plot(x_range,grayHist,'r',linewidth = 2, c = 'black')
    #设置坐标轴范围
    y_maxValue = np.max(grayHist)
    plt.axis([0,255,0,y_maxValue])
    #设置坐标轴标签
    plt.xlabel('gray Level')
    plt.ylabel('number of pixels')
    #显示灰度直方图
    plt.show()

python 图像对比度 opencv python 对比度_desktop

%matplotlib inline
import numpy as np
import cv2
import matplotlib.pyplot as plt
if __name__ == "__main__":
    image = cv2.imread("c:/users/76973/desktop/picture/output_image2.jpg",cv2.IMREAD_GRAYSCALE)
    #得到了图像矩阵的高和宽
    rows,cols = image.shape
    #将二维图像矩阵变为一维数组，便于计算灰度直方图
    pixelSequence = image.reshape([rows * cols,])
    #组数
    numberBins = 256
    #计算灰度直方图
    histogram, bins, patch = plt.hist(pixelSequence,numberBins,facecolor = 'black', histtype = 'bar')
    #设置坐标轴的标签
    plt.xlabel(u"gray Level")
    plt.ylabel(u"number of pixels")
    #设置坐标轴的范围
    y_maxValue = np.max(histogram)
    plt.axis([0,255,0,y_maxValue])
    plt.show()

python 图像对比度 opencv python 对比度_python 图像对比度_02

线性变换

import numpy as np
I = np.array([[0,200],[23,4]],np.uint8)
O = 2*I
print(O)
O = 2.0*I#常数数据影响输出类型
print(O)

[[  0 144]
 [ 46   8]]
[[  0. 400.]
 [ 46.   8.]]

%matplotlib inline
import numpy as np
import cv2
import matplotlib.pyplot as plt
def plotHist(image):
     #得到了图像矩阵的高和宽
    rows,cols = image.shape
    #将二维图像矩阵变为一维数组，便于计算灰度直方图
    pixelSequence = image.reshape([rows * cols,])
    #组数
    numberBins = 256
    #计算灰度直方图
    histogram, bins, patch = plt.hist(pixelSequence,numberBins,facecolor = 'black', histtype = 'bar')
    #设置坐标轴的标签
    plt.xlabel(u"gray Level")
    plt.ylabel(u"number of pixels")
    #设置坐标轴的范围
    y_maxValue = np.max(histogram)
    plt.axis([0,255,0,y_maxValue])
    plt.show()
#主函数
if __name__ == "__main__":
    I = cv2.imread("c:/users/76973/desktop/picture/output_image1.jpg",cv2.IMREAD_GRAYSCALE)
    #线性变换
    a = 2
    O = float(a)*I
#     O = I.dot(float(a)) 
    #进行数据截断，大于255的值截断为255
    O[O > 255] = 255
    #数据类型转换
    O = np.round(O)
    O = O.astype(np.uint8)
    plotHist(I)
    plotHist(O)
    #显示原图和线性变换的结果
    cv2.imshow("I",I)
    cv2.imshow("O",O)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

python 图像对比度 opencv python 对比度_灰度直方图_03

python 图像对比度 opencv python 对比度_desktop_04

import numpy as np
import cv2
import matplotlib.pyplot as plt
def plotHist(image):
     #得到了图像矩阵的高和宽
    rows,cols = image.shape
    #将二维图像矩阵变为一维数组，便于计算灰度直方图
    pixelSequence = image.reshape([rows * cols,])
    #组数
    numberBins = 256
    #计算灰度直方图
    histogram, bins, patch = plt.hist(pixelSequence,numberBins,facecolor = 'black', histtype = 'bar')
    #设置坐标轴的标签
    plt.xlabel(u"gray Level")
    plt.ylabel(u"number of pixels")
    #设置坐标轴的范围
    y_maxValue = np.max(histogram)
    plt.axis([0,255,0,y_maxValue])
    plt.show()
#主函数
if __name__ == "__main__":
    I = cv2.imread("c:/users/76973/desktop/picture/output_image4.jpg",cv2.IMREAD_GRAYSCALE)
    #线性变换
    #得到了图像矩阵的高和宽
    rows,cols = I.shape
    #print(rows, cols)
    a = 2
    O = np.zeros((rows,cols),np.uint8)
    for r in range(rows):
        for c in range(cols):
            if I[r,c] <170 and I[r,c]>100:
#                 print(I[r,c])
                if 2.0*I[r,c]>255.0:
                    O[r,c] =  255
                else:
                    O[r,c] =  2.0 * I[r,c]
            else:
                if 1.0*I[r,c]>255.0:
                    O[r,c] =  255
                else:
                    O[r,c] =  1.0*I[r,c]

    #进行数据截断，大于255的值截断为255
#     O[O > 255] = 255
    #数据类型转换
#     O = np.round(O)
    O = O.astype(np.uint8)
    plotHist(I)
    plotHist(O)
    #显示原图和线性变换的结果
    cv2.imshow("I",I)
    cv2.imshow("O",O)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

在这里插入图片描述

直方图正规化

import cv2 
import numpy as np
import sys
#主函数
if __name__ == "__main__":
    I = cv2.imread("c:/users/76973/desktop/picture/output_image2.jpg",cv2.IMREAD_GRAYSCALE)
    # 求 I 的最大值、最小值
    Imax = np.max(I)
    Imin = np.min(I)
    # 要输出的最小灰度级和最大灰度级
    Omin,Omax = 0, 255
    #计算a 和 b 的值
    a = float(Omax - Omin)/(Imax-Imin)
    print(a)
    b = Omin - a*Imin
    print(b)
    #矩阵的线性变换
    O = a * I + b
    #数据类型转换
    O = O.astype(np.uint8)
    #显示原图和直方图正规化的效果
    cv2.imshow("I",I)
    cv2.imshow("O",O)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

1.0
0.0

使用normalize 函数

import cv2
src = cv2.imread("c:/users/76973/desktop/picture/output_image2.jpg",cv2.IMREAD_GRAYSCALE)
#直方图正规化
dst  = src.copy()
cv2.normalize(src,dst,255,0,cv2.NORM_MINMAX,cv2.CV_8U)
#显示原图与正规化效果
cv2.imshow("src",src)
cv2.imshow("dst",dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

伽马变换

import cv2
import numpy as np
# 主函数
if __name__ == "__main__":
    I = cv2.imread("c:/users/76973/desktop/picture/output_image2.jpg",cv2.IMREAD_GRAYSCALE)
    #图像归一化
    fI = I/255.0
    #伽马变换
    gamma = 0.5
    O = np.power(fI,gamma)
     #显示原图和伽马变换后的效果
    cv2.imshow("I",I)
    cv2.imshow("O",O)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

全局直方图均衡化

import cv2
import numpy as np
import math
def calcGrayHist(image):
    #灰度图像矩阵的高和宽
    rows,cols = image.shape
    #存储灰度直方图
    grayHist = np.zeros([256],np.uint64)
    for r in range(rows):
        for c in range(cols):
            grayHist[image[r][c]]+=1
    return grayHist
def equalHist(image):
    # 灰度图像矩阵的高、宽
    rows, cols = image.shape
    # 第一步：计算灰度直方图
    grayHist = calcGrayHist(image)
    # 第二步：计算累加灰度直方图
    zeroCumuMoment = np.zeros([256],np.uint32)
    for p in range(256):
        if p == 0:
            zeroCumuMoment[p] = grayHist[0]
        else:
            zeroCumuMoment[p] = zeroCumuMoment[p-1] + grayHist[p]
    # 第三步: 根据累加灰度直方图得到输入灰度级和输出灰度级之间的映射关系
    outPut_q = np.zeros([256],np.uint8)
    cofficient = 256.0/(rows*cols)
    for p in range(256):
        q = cofficient * float(zeroCumuMoment[p]) - 1
        if q >=0:
            outPut_q[p] = math.floor(q)
        else:
            outPut_q[p] = 0;
    # 第四步：得到直方图均衡化后的图像
    equalHistImage = np.zeros(image.shape,np.uint8)
    for r in range(rows):
        for c in range(cols):
            equalHistImage[r][c] = outPut_q[image[r][c]]
    return equalHistImage
if __name__ == "__main__":
    I = cv2.imread("c:/users/76973/desktop/picture/output_image2.jpg",cv2.IMREAD_GRAYSCALE)
    I  = equalHist(I)
     #显示原图和伽马变换后的效果
    cv2.imshow("I",I)
    cv2.imshow("O",O)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

限制对比度的自适应直方图均值化

import cv2
import numpy as np
if __name__ == "__main__":
    I = cv2.imread("c:/users/76973/desktop/picture/output_image2.jpg",cv2.IMREAD_GRAYSCALE)
    #创建CLAHE对象
    clahe = cv2.createCLAHE(clipLimit = 2.0, tileGridSize = (8, 8))
    #限制对比度的自适应阈值均衡化
    O = clahe.apply(I)
     #显示原图和伽马变换后的效果
    cv2.imshow("I",I)
    cv2.imshow("O",O)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

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