Get超炫技能：如何使用 Python 执行运动检测？initialState = None List of all t

initialState = None

List of all the tracks when there is any detected of motion in the frames

motionTrackList= [ None, None ]

A new list ‘time’ for storing the time when movement detected

motionTime = []

Initialising DataFrame variable ‘dataFrame’ using pandas libraries panda with Initial and Final column

dataFrame = panda.DataFrame(columns = ["Initial", "Final"])


#### 主要捕获过程


在本节中，我们将执行主要的运动检测步骤。让我们逐步理解它们：


1. 首先，我们将开始使用 cv2 模块捕获视频并将其存储在 video 变量中。
2. 然后我们将使用无限循环来捕获视频中的每一帧。
3. 我们将使用 read() 方法读取每一帧并将它们存储到各自的变量中。
4. 我们定义了一个可变运动并将其初始化为零。
5. 我们使用 cv2 函数 cvtColor 和 GaussianBlur 创建了另外两个变量 grayImage 和 grayFrame 来查找运动的变化。
6. 如果我们的 initialState 是 None 那么我们将当前的 grayFrame 分配给 initialState 否则并使用“continue”关键字停止下一个进程。
7. 在下一节中，我们计算了我们在当前迭代中创建的初始帧和灰度帧之间的差异。
8. 然后我们将使用 cv2 阈值和扩张函数突出显示初始帧和当前帧之间的变化。
9. 我们将从当前图像或帧中的移动对象中找到轮廓，并通过使用矩形函数在其周围创建绿色边界来指示移动对象。
10. 在此之后，我们将通过添加当前检测到的元素来附加我们的motionTrackList。
11. 我们使用imshow方法显示了所有的帧，如灰度和原始帧等。
12. 此外，我们使用 cv2 模块的 witkey() 方法创建了一个键来结束进程，我们可以使用“m”键来结束我们的进程。

starting the webCam to capture the video using cv2 module

video = cv2.VideoCapture(0)

using infinite loop to capture the frames from the video

while True:

Reading each image or frame from the video using read function

check, cur_frame = video.read()

Defining 'motion' variable equal to zero as initial frame

var_motion = 0

From colour images creating a gray frame

gray_image = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2GRAY)

To find the changes creating a GaussianBlur from the gray scale image

gray_frame = cv2.GaussianBlur(gray_image, (21, 21), 0)

For the first iteration checking the condition

we will assign grayFrame to initalState if is none

if initialState is None:

   initialState = gray_frame  

   continue

Calculation of difference between static or initial and gray frame we created

differ_frame = cv2.absdiff(initialState, gray_frame)

the change between static or initial background and current gray frame are highlighted

thresh_frame = cv2.threshold(differ_frame, 30, 255, cv2.THRESH_BINARY)[1]

thresh_frame = cv2.dilate(thresh_frame, None, iterations = 2)

For the moving object in the frame finding the coutours

cont,_ = cv2.findContours(thresh_frame.copy(),

                  cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

for cur in cont:

   if cv2.contourArea(cur) < 10000:  

       continue  

   var_motion = 1  

   (cur_x, cur_y,cur_w, cur_h) = cv2.boundingRect(cur)  

   

   # To create a rectangle of green color around the moving object 

   cv2.rectangle(cur_frame, (cur_x, cur_y), (cur_x + cur_w, cur_y + cur_h), (0, 255, 0), 3)

from the frame adding the motion status

motionTrackList.append(var_motion)

motionTrackList = motionTrackList[-2:]

Adding the Start time of the motion

if motionTrackList[-1] == 1 and motionTrackList[-2] == 0:

   motionTime.append(datetime.now())

Adding the End time of the motion

if motionTrackList[-1] == 0 and motionTrackList[-2] == 1:

   motionTime.append(datetime.now())

In the gray scale displaying the captured image

cv2.imshow("The image captured in the Gray Frame is shown below: ", gray_frame)

To display the difference between inital static frame and the current frame

cv2.imshow("Difference between the inital static frame and the current frame: ", differ_frame)

To display on the frame screen the black and white images from the video

cv2.imshow("Threshold Frame created from the PC or Laptop Webcam is: ", thresh_frame)

Through the colour frame displaying the contour of the object

cv2.imshow("From the PC or Laptop webcam, this is one example of the Colour Frame:", cur_frame)

Creating a key to wait

wait_key = cv2.waitKey(1)

With the help of the 'm' key ending the whole process of our system

if wait_key == ord('m'):

   # adding the motion variable value to motiontime list when something is moving on the screen 

   if var_motion == 1:  

       motionTime.append(datetime.now())  

   break


#### 完成代码


关闭循环后，我们会将 dataFrame 和 motionTime 列表中的数据添加到 CSV 文件中，最后关闭视频。

At last we are adding the time of motion or var_motion inside the data frame

for a in range(0, len(motionTime), 2):

dataFrame = dataFrame.append({"Initial" : time[a], "Final" : motionTime[a + 1]}, ignore_index = True)

To record all the movements, creating a CSV file

dataFrame.to_csv("EachMovement.csv")

Releasing the video

video.release()

Now, Closing or destroying all the open windows with the help of openCV

cv2.destroyAllWindows()


### **流程总结**


我们已经创建了代码；现在让我们再次简要讨论该过程。


首先，我们使用设备的网络摄像头拍摄视频，然后将输入视频的初始帧作为参考，并不时检查下一帧。如果找到与第一个不同的帧，则存在运动。这将在绿色矩形中标记。


### **组合代码**


我们已经在不同的部分看到了代码。现在，让我们结合起来：

Importing the Pandas libraries

import pandas as panda

Importing the OpenCV libraries

import cv2

Importing the time module

import time

Importing the datetime function of the datetime module

from datetime import datetime

Assigning our initial state in the form of variable initialState as None for initial frames