Python图像运算之图像掩膜直方图和HS直方图详解

# -*- coding: utf-8 -*-
# By：Eastmount
import cv2  
import numpy as np
import matplotlib.pyplot as plt
import matplotlib

#读取图像
img = cv2.imread('luo.png')

#转换为RGB图像
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

#设置掩膜
mask = np.zeros(img.shape[:2], np.uint8)
mask[100:300, 100:300] = 255
masked_img = cv2.bitwise_and(img, img, mask=mask)

#图像直方图计算
hist_full = cv2.calcHist([img], [0], None, [256], [0,256]) #通道[0]-灰度图

#图像直方图计算(含掩膜)
hist_mask = cv2.calcHist([img], [0], mask, [256], [0,256])

plt.figure(figsize=(8, 6))

#设置字体
matplotlib.rcParams['font.sans-serif']=['SimHei']

#原始图像
plt.subplot(221)
plt.imshow(img_rgb, 'gray')
plt.axis('off')
plt.title("(a)原始图像")

#绘制掩膜
plt.subplot(222)
plt.imshow(mask, 'gray')
plt.axis('off')
plt.title("(b)掩膜")

#绘制掩膜设置后的图像
plt.subplot(223)
plt.imshow(masked_img, 'gray')
plt.axis('off')
plt.title("(c)图像掩膜处理")

#绘制直方图
plt.subplot(224)
plt.plot(hist_full)
plt.plot(hist_mask)
plt.title("(d)直方图曲线")
plt.xlabel("x")
plt.ylabel("y")
plt.show()

# -*- coding: utf-8 -*-
# By：Eastmount
import cv2  
import numpy as np
import matplotlib.pyplot as plt

#读取图像
img = cv2.imread('luo.png')

#转换为RGB图像
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

#图像HSV转换
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

#计算H-S直方图
hist = cv2.calcHist(hsv, [0,1], None, [180,256], [0,180,0,256])

#原始图像
plt.figure(figsize=(8, 6))
plt.subplot(121), plt.imshow(img_rgb, 'gray'), plt.title("(a)"), plt.axis('off')

#绘制H-S直方图
plt.subplot(122), plt.imshow(hist, interpolation='nearest'), plt.title("(b)")
plt.xlabel("x"), plt.ylabel("y")
plt.show()

# -*- coding: utf-8 -*-
# By：Eastmount
import cv2  
import numpy as np
import matplotlib.pyplot as plt

#函数: 获取图像的灰度平均值
def fun_mean(img, height, width):
    sum_img = 0
    for i in range(height):
        for j in range(width):
            sum_img = sum_img + int(img[i,j])
    mean = sum_img / (height * width)
    return mean

#函数: 获取中位数
def fun_median(data):
    length = len(data)
    data.sort()
    if (length % 2)== 1: 
        z = length // 2
        y = data[z]
    else:
        y = (int(data[length//2]) + int(data[length//2-1])) / 2
    return y

#读取图像
img = cv2.imread('lena-hd.png')

#图像灰度转换
grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#获取图像高度和宽度
height = grayImage.shape[0]
width = grayImage.shape[1]

#计算图像的灰度平均值
mean = fun_mean(grayImage, height, width)
print("灰度平均值：", mean)

#计算图像的灰度中位数
value = grayImage.ravel() #获取所有像素值
median = fun_median(value)
print("灰度中值：", median)

#计算图像的灰度标准差
std = np.std(value, ddof = 1)
print("灰度标准差", std)

# -*- coding: utf-8 -*-
# By：Eastmount
import cv2  
import numpy as np
import matplotlib.pyplot as plt

#函数: 判断黑夜或白天
def func_judge(img):
    #获取图像高度和宽度
    height = grayImage.shape[0]
    width = grayImage.shape[1]
    piexs_sum = height * width
    dark_sum = 0  #偏暗像素个数
    dark_prop = 0 #偏暗像素所占比例
    
    for i in range(height):
        for j in range(width):
            if img[i, j] < 50: #阈值为50
                dark_sum += 1

    #计算比例
    print(dark_sum)
    print(piexs_sum)
    dark_prop = dark_sum * 1.0 / piexs_sum 
    if dark_prop >=0.8:
        print("This picture is dark!", dark_prop)
    else:
        print("This picture is bright!", dark_prop)
               
#读取图像
img = cv2.imread('day.png')

#转换为RGB图像
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

#图像灰度转换
grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#计算256灰度级的图像直方图
hist = cv2.calcHist([grayImage], [0], None, [256], [0,255])

#判断黑夜或白天
func_judge(grayImage)

#显示原始图像和绘制的直方图
plt.subplot(121), plt.imshow(img_rgb, 'gray'), plt.axis('off'), plt.title("(a)")
plt.subplot(122), plt.plot(hist, color='r'), plt.xlabel("x"), plt.ylabel("y"), plt.title("(b)")

plt.show()