import cv2
import numpy as np
import matplotlib.pyplot as plt

# 读取原始图像
img = cv2.imread('smoke1.png')

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

# 创建一幅图像
grayimg = np.zeros((height, width, 3), np.uint8)

# 图像最大值灰度处理
for i in range(height):
    for j in range(width):
        # 获取图像R G B最大值
        gray = max(img[i, j][0], img[i, j][1], img[i, j][2])
        # 灰度图像素赋值 gray=max(R,G,B)
        grayimg[i, j] = np.uint8(gray)

# 显示图像
cv2.imshow("src", img)
cv2.imshow("gray", grayimg)

# 等待显示
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np
import matplotlib.pyplot as plt


def Origin_histogram(img):
    # 建立原始图像各灰度级的灰度值与像素个数对应表
    histogram = {}
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            k = img[i][j]
            if k in histogram:
                histogram[k] += 1
            else:
                histogram[k] = 1

    sorted_histogram = {}  # 建立排好序的映射表
    sorted_list = sorted(histogram)  # 根据灰度值进行从低至高的排序

    for j in range(len(sorted_list)):
        sorted_histogram[sorted_list[j]] = histogram[sorted_list[j]]

    return sorted_histogram


def equalization_histogram(histogram, img):
    pr = {}  # 建立概率分布映射表

    for i in histogram.keys():
        pr[i] = histogram[i] / (img.shape[0] * img.shape[1])

    tmp = 0
    for m in pr.keys():
        tmp += pr[m]
        pr[m] = max(histogram) * tmp

    new_img = np.zeros(shape=(img.shape[0], img.shape[1]), dtype=np.uint8)

    for k in range(img.shape[0]):
        for l in range(img.shape[1]):
            new_img[k][l] = pr[img[k][l]]

    return new_img


def GrayHist(img):
    # 计算灰度直方图
    height, width = img.shape[:2]
    grayHist = np.zeros([256], np.uint64)
    for i in range(height):
        for j in range(width):
            grayHist[img[i][j]] += 1
    return grayHist


if __name__ == '__main__':
    # 读取原始图像
    img = cv2.imread('./40.png', cv2.IMREAD_GRAYSCALE)
    # 计算原图灰度直方图
    origin_histogram = Origin_histogram(img)
    # 直方图均衡化
    new_img = equalization_histogram(origin_histogram, img)

    origin_grayHist = GrayHist(img)
    equaliza_grayHist = GrayHist(new_img)
    x = np.arange(256)
    # 绘制灰度直方图
    plt.figure(num=1)
    plt.subplot(2, 2, 1)
    plt.plot(x, origin_grayHist, 'r', linewidth=2, c='black')
    plt.title("Origin")
    plt.ylabel("number of pixels")
    plt.subplot(2, 2, 2)
    plt.plot(x, equaliza_grayHist, 'r', linewidth=2, c='black')
    plt.title("Equalization")
    plt.ylabel("number of pixels")
    plt.subplot(2, 2, 3)
    plt.imshow(img, cmap=plt.cm.gray)
    plt.title('Origin')
    plt.subplot(2, 2, 4)
    plt.imshow(new_img, cmap=plt.cm.gray)
    plt.title('Equalization')
    plt.show()

import numpy as np
import cv2
import time
import datetime

colour=((0, 205, 205),(154, 250, 0),(34,34,178),(211, 0, 148),(255, 118, 72),(137, 137, 139))#定义矩形颜色

cap = cv2.VideoCapture("./2.avi") #参数为0是打开摄像头，文件名是打开视频

fgbg = cv2.createBackgroundSubtractorMOG2()#混合高斯背景建模算法

fourcc = cv2.VideoWriter_fourcc(*'XVID')#设置保存图片格式
out = cv2.VideoWriter(datetime.datetime.now().strftime("%A_%d_%B_%Y_%I_%M_%S%p")+'.avi',fourcc, 10.0, (768,576))#分辨率要和原视频对应


while True:
    ret, frame = cap.read()  #读取图片
    fgmask = fgbg.apply(frame)

    element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))  # 形态学去噪
    fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, element)  # 开运算去噪

    _ ,contours, hierarchy = cv2.findContours(fgmask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) #寻找前景

    count=0
    for cont in contours:
        Area = cv2.contourArea(cont)  # 计算轮廓面积
        if Area < 300:  # 过滤面积小于10的形状
            continue

        count += 1  # 计数加一

        print("{}-prospect:{}".format(count,Area),end="  ") #打印出每个前景的面积

        rect = cv2.boundingRect(cont) #提取矩形坐标

        print("x:{} y:{}".format(rect[0],rect[1]))#打印坐标

        cv2.rectangle(frame,(rect[0],rect[1]),(rect[0]+rect[2],rect[1]+rect[3]),colour[count%6],1)#原图上绘制矩形
        cv2.rectangle(fgmask,(rect[0],rect[1]),(rect[0]+rect[2],rect[1]+rect[3]),(0xff, 0xff, 0xff), 1)  #黑白前景上绘制矩形

        y = 10 if rect[1] < 10 else rect[1]  # 防止编号到图片之外
        cv2.putText(frame, str(count), (rect[0], y), cv2.FONT_HERSHEY_COMPLEX, 0.4, (0, 255, 0), 1)  # 在前景上写上编号



    cv2.putText(frame, "count:", (5, 20), cv2.FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 0), 1) #显示总数
    cv2.putText(frame, str(count), (75, 20), cv2.FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 0), 1)
    print("----------------------------")

    cv2.imshow('frame', frame)#在原图上标注
    cv2.imshow('frame2', fgmask)  # 以黑白的形式显示前景和背景
    out.write(frame)
    k = cv2.waitKey(30)&0xff  #按esc退出
    if k == 27:
        break


out.release()#释放文件
cap.release()
cv2.destoryAllWindows()#关闭所有窗口