# -*- 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()