文章目录

示例视频

fire-detect

下载数据集

BoWFireDataset数据集
官方网站：下载链接
天翼云盘：下载链接 (访问码:0aod)

或者下载火灾图片存放到一个文件夹里。

配置环境

conda create -n fire-detect python=3.8
conda activate fire-detect
conda install opencv


运行程序


python main.py




图片检测代码




import cv2 as cv
import numpy as np
import glob

def contrast_brightness_demo(image, c, b):  #其中c为对比度，b为每个像素加上的值（调节亮度）
    blank = np.zeros(image.shape, image.dtype)   #创建一张与原图像大小及通道数都相同的黑色图像
    dst = cv.addWeighted(image, c, blank, 1-c, b) #c为加权值，b为每个像素所加的像素值
    ret, dst = cv.threshold(dst, 25, 255, cv.THRESH_BINARY)
    return dst

pic_list = glob.glob('./img/*')

for pic in pic_list:
    frame = cv.imread(pic)
    cv.imshow("frame", frame)
    B = frame[:, :, 0]
    G = frame[:, :, 1]
    R = frame[:, :, 2]
    R_mean = np.mean(R)
    val1 = G /(R + 1)
    val2 = B /(R + 1)
    val3 = B /(G + 1) 
    fireImg = np.array(np.where(R > R_mean, np.where(R >= G, np.where(G >= B, np.where(val1 >= 0.25, np.where(val1 <=0.65, np.where(val2 >= 0.05,np.where(val2 <=0.45,np.where(val3 >=0.2 ,np.where(val3 <= 0.6, 255, 0), 0), 0), 0), 0), 0), 0), 0), 0))
    
    gray_fireImg = np.zeros([fireImg.shape[0], fireImg.shape[1], 1], np.uint8)
    gray_fireImg[:, :, 0] = fireImg
    gray_fireImg = cv.GaussianBlur(gray_fireImg, (7, 7), 0)
    gray_fireImg = contrast_brightness_demo(gray_fireImg, 5.0, 25)
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
    gray_fireImg = cv.morphologyEx(gray_fireImg, cv.MORPH_CLOSE, kernel)
    dst = cv.bitwise_and(frame, frame, mask=gray_fireImg)
    cv.namedWindow("fire",0)
    cv.imshow("fire", dst)
    cv.namedWindow("gray_fireImg",0)
    cv.imshow("gray_fireImg", gray_fireImg)
    c = cv.waitKey(400)
    if c == 27:
        break




视频检测代码




import cv2 as cv
import numpy as np

def contrast_brightness_demo(image, c, b):  #其中c为对比度，b为每个像素加上的值（调节亮度）
    blank = np.zeros(image.shape, image.dtype)   #创建一张与原图像大小及通道数都相同的黑色图像
    dst = cv.addWeighted(image, c, blank, 1-c, b) #c为加权值，b为每个像素所加的像素值
    ret, dst = cv.threshold(dst, 25, 255, cv.THRESH_BINARY)
    return dst


capture = cv.VideoCapture("./test1.mp4")

while(True):
    ret, frame = capture.read()
    cv.imshow("frame", frame)
    B = frame[:, :, 0]
    G = frame[:, :, 1]
    R = frame[:, :, 2]
    R_mean = np.mean(R)
    val1 = G /(R + 1)
    val2 = B /(R + 1)
    val3 = B /(G + 1) 
    fireImg = np.array(np.where(R > R_mean, np.where(R >= G, np.where(G >= B, np.where(val1 >= 0.25, np.where(val1 <=0.65, np.where(val2 >= 0.05,np.where(val2 <=0.45,np.where(val3 >=0.2 ,np.where(val3 <= 0.6, 255, 0), 0), 0), 0), 0), 0), 0), 0), 0))
    
    gray_fireImg = np.zeros([fireImg.shape[0], fireImg.shape[1], 1], np.uint8)
    gray_fireImg[:, :, 0] = fireImg
    gray_fireImg = cv.GaussianBlur(gray_fireImg, (7, 7), 0)
    gray_fireImg = contrast_brightness_demo(gray_fireImg, 5.0, 25)
    kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
    gray_fireImg = cv.morphologyEx(gray_fireImg, cv.MORPH_CLOSE, kernel)
    dst = cv.bitwise_and(frame, frame, mask=gray_fireImg)
    cv.namedWindow("fire",0)
    cv.imshow("fire", dst)
    cv.namedWindow("gray_fireImg",0)
    cv.imshow("gray_fireImg", gray_fireImg)
    c = cv.waitKey(40)
    if c == 27:
        break