#!/usr/bin/python3.6
# -*- coding: utf-8 -*-
'''
@ Copyright (C) 2019
@
@ env stetup:pip3 install opencv-python
@
@ 免费知识星球:[一番码客-积累交流](https://t.zsxq.com/NRVBURr)
@ 微信公众号:一番码客
@ 微信:Efon-fighting
@ 网站:http://efonfighting.imwork.net
'''
import cv2
import numpy, os, sys
def isPicChanged(dividePar, pointDelta, judgeTh):
'''
通过前后帧对比,判断画面是否改变
:param dividePar = 4 # 对比隔点,减少计算量
:param pointDelta = 50 # 像素点的差异大于该值认为是差异点
:param judgeTh = 64 # 判断变化画面大小的阈值:画面(1/judgeTh)
'''
capIdx = 0 # 截图命名
camIdx = -1
while (int(camIdx) < 0 or int(camIdx) > 10) :
print("enter camera index in 0 and 10:")
camIdx = int(input())
if not (os.path.isdir('cap')): # 创建存放截图的文件夹
os.system('mkdir -p {}'.format("cap"))
cap = cv2.VideoCapture(camIdx) #调整参数实现读取视频或调用摄像头
ret, frameBak = cap.read()
for i in range(10): #刚打开相机时,曝光不稳定,清理10张
ret, frameBak = cap.read()
frame = frameBak
frameWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frameHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print("frameWidth:{},frameHeight:{}".format(frameWidth,frameHeight))
if frameWidth == 0:
exit("camera is not available.")
while True:
absCnt = 0
frameBak = frame
ret, frame = cap.read()
for wIdx in range(int(frameWidth/dividePar)) :
for hIdx in range(int(frameHeight/dividePar)) :
if abs(int(frameBak[hIdx*dividePar][wIdx*dividePar][2]) - int(frame[hIdx*dividePar][wIdx*dividePar][2])) > pointDelta :
absCnt += 1
cv2.imshow("cap", frame)
if absCnt > ( frameWidth * frameHeight ) / (dividePar * dividePar) / (judgeTh * judgeTh) :
capIdx += 2
cv2.imwrite('cap/cap_{}.jpg'.format(capIdx), frame)
cv2.imwrite('cap/cap_{}.jpg'.format(capIdx+1), frameBak)
print("get a pic:{}".format(capIdx/2))
if cv2.waitKey(1) & 0xff == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__": #这里可以判断,当前文件是否是直接被python调用执行
isPicChanged(4, 50, 64)
import cv2
import time
import os
# 定义摄像头对象,其参数0表示第一个摄像头
camera = cv2.VideoCapture(0)
# 测试用,查看视频size
width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
size = width,height
#打印一下分辨率
print(repr(size))
print("ObjectTrack is running!")
#设置一下帧数和前背景
fps = 5
pre_frame = None
while (1):
time.sleep(0.5)
start = time.time()
# 读取视频流
ret, frame = camera.read()
# 转灰度图
gray_pic = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if not ret:
print("打开摄像头失败")
break
end = time.time()
#查看视频窗口
#cv2.imshow("capture", frame)
# 运动检测部分,看看是不是5FPS
seconds = end - start
if seconds < 1.0 / fps:
time.sleep(1.0 / fps - seconds)
gray_pic = cv2.resize(gray_pic, (480, 480))
# 用高斯滤波进行模糊处理
gray_pic = cv2.GaussianBlur(gray_pic, (21, 21), 0)
# 如果没有背景图像就将当前帧当作背景图片
if pre_frame is None:
pre_frame = gray_pic
else:
# absdiff把两幅图的差的绝对值输出到另一幅图上面来
img_delta = cv2.absdiff(pre_frame, gray_pic)
# threshold阈值函数(原图像应该是灰度图,对像素值进行分类的阈值,当像素值高于(有时是小于)阈值时应该被赋予的新的像素值,阈值方法)
thresh = cv2.threshold(img_delta, 30, 255, cv2.THRESH_BINARY)[1]
# 用一下腐蚀与膨胀
thresh = cv2.dilate(thresh, None, iterations=2)
# findContours检测物体轮廓(寻找轮廓的图像,轮廓的检索模式,轮廓的近似办法)
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
# 设置敏感度
# contourArea计算轮廓面积
if cv2.contourArea(c) < 1000:
continue
else:
print("Get It!!!")
# 保存图像
TFile1 = time.strftime('%Y-%m-%d', time.localtime(time.time()))
TFile2 = time.strftime('%H.%M', time.localtime(time.time()))
path="G:/test/objectTrack/"+TFile1+"/"+TFile2
isExists=os.path.exists(path)
if not isExists:
# 如果不存在则创建目录创建目录操作函数
os.makedirs(path)
print (path+' Saved!')
TI = time.strftime('%m%d-%H.%M.%S', time.localtime(time.time()))
cv2.imwrite(path+ "/"+TI+ '.jpg', frame)
print(TI)
break
pre_frame = gray_pic
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# release()释放摄像头
camera.release()
# destroyAllWindows()关闭所有图像窗口
cv2.destroyAllWindows()
import cv2
import time
import os
# 定义摄像头对象,其参数0表示第一个摄像头
camera = cv2.VideoCapture(0)
# 测试用,查看视频size
width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
size = width,height
#打印一下分辨率
print(repr(size))
print("ObjectTrack is running!")
#设置一下帧数和前背景
fps = 5
pre_frame = None
while (1):
time.sleep(0.5)
start = time.time()
# 读取视频流
ret, frame = camera.read()
# 转灰度图
gray_pic = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if not ret:
print("打开摄像头失败")
break
end = time.time()
#查看视频窗口
#cv2.imshow("capture", frame)
# 运动检测部分,看看是不是5FPS
seconds = end - start
if seconds < 1.0 / fps:
time.sleep(1.0 / fps - seconds)
gray_pic = cv2.resize(gray_pic, (480, 480))
# 用高斯滤波进行模糊处理
gray_pic = cv2.GaussianBlur(gray_pic, (21, 21), 0)
# 如果没有背景图像就将当前帧当作背景图片
if pre_frame is None:
pre_frame = gray_pic
else:
# absdiff把两幅图的差的绝对值输出到另一幅图上面来
img_delta = cv2.absdiff(pre_frame, gray_pic)
# threshold阈值函数(原图像应该是灰度图,对像素值进行分类的阈值,当像素值高于(有时是小于)阈值时应该被赋予的新的像素值,阈值方法)
thresh = cv2.threshold(img_delta, 30, 255, cv2.THRESH_BINARY)[1]
# 用一下腐蚀与膨胀
thresh = cv2.dilate(thresh, None, iterations=2)
# findContours检测物体轮廓(寻找轮廓的图像,轮廓的检索模式,轮廓的近似办法)
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
# 设置敏感度
# contourArea计算轮廓面积
if cv2.contourArea(c) < 1000:
continue
else:
print("Get It!!!")
# 保存图像
TFile1 = time.strftime('%Y-%m-%d', time.localtime(time.time()))
TFile2 = time.strftime('%H.%M', time.localtime(time.time()))
path="G:/test/objectTrack/"+TFile1+"/"+TFile2
isExists=os.path.exists(path)
if not isExists:
# 如果不存在则创建目录创建目录操作函数
os.makedirs(path)
print (path+' Saved!')
TI = time.strftime('%m%d-%H.%M.%S', time.localtime(time.time()))
cv2.imwrite(path+ "/"+TI+ '.jpg', frame)
print(TI)
break
pre_frame = gray_pic
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# release()释放摄像头
camera.release()
# destroyAllWindows()关闭所有图像窗口
cv2.destroyAllWindows()