資料結構
網路
關係型資料庫管理系統
作業系統
Java
MS Excel
iOS
HTML
CSS
Android
Python
C 程式設計
C++
C#
MongoDB
MySQL
JavaScript
PHP
物理
化學
生物
數學
英語
經濟學
心理學
社會學
時尚研究
法律研究Python 中的網路攝像頭運動檢測程式 ?
在這項中，我們準備編寫一個 Python 程式，它將分析從網路攝像頭獲取的影像，嘗試檢測運動並以 CSV 檔案的形式儲存網路攝像頭影片的時間間隔。
所需庫
我們將使用 OpenCV 和 pandas 庫。如果尚未安裝，可以使用 pip 安裝它，如下所示
$pip install opencv2, pandas
程式碼示例
#Import required libraries
import cv2
import pandas as pd
import time
from datetime import datetime
#Initialise variables
stillImage = None
motionImage = [ None, None ]
time = []
# Initializing the DataFrame with start and end time
df = pd.DataFrame(columns = ["start", "end"])
# Capturing video
video = cv2.VideoCapture(0)
while True:
# Start reading image from video
check, frame = video.read()
motion = 0
# Convert color image to gray_scale image
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
if stillImage is None:
stillImage = gray
continue
# Still Image and current image.
diff_frame = cv2.absdiff(stillImage, gray)
# change the image to white if static background and current frame is greater than 25.
thresh_frame = cv2.threshold(diff_frame, 25, 255, cv2.THRESH_BINARY)[1]
thresh_frame = cv2.dilate(thresh_frame, None, iterations = 2)
# Finding contour and hierarchy from a moving object.
contours,hierachy = cv2.findContours(thresh_frame.copy(),
cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
if cv2.contourArea(contour) < 10000:
continue
motion = 1
(x, y, w, h) = cv2.boundingRect(contour)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# Append current status of motion
motionImage.append(motion)
motionImage = motionImage[-2:]
# Append Start time of motion
if motionImage[-1] == 1 and motionImage[-2] == 0:
time.append(datetime.now())
# Append End time of motion
if motionImage[-1] == 0 and motionImage[-2] == 1:
time.append(datetime.now())
# Displaying image in gray_scale
cv2.imshow("Gray_Frame", gray)
# Display black and white frame & if the intensity difference is > 25, it turns white
cv2.imshow("Threshold Frame", thresh_frame)
# Display colored frame
cv2.imshow("Colored_Frame", frame)
key = cv2.waitKey(1)
# Press q to stop the process
if key == ord('q'):
if motion == 1:
time.append(datetime.now())
break
# Append time of motion
for i in range(0, len(time), 2):
df = df.append({"Start":time[i], "End":time[i + 1]}, ignore_index = True)
# Creating a csv file in which time of movements will be saved
df.to_csv("FrameInMotion_time.csv")
video.release()
# close window
cv2.destroyAllWindows()輸出
我們可以看到，我們將獲得 3 個不同的視窗，它們將以 3 種不同的模式（灰度、彩色和黑白）顯示我們網路攝像頭的當前運動。
它還將在 CSV 檔案中儲存網路攝像頭運動的時間，而且 CSV 的輸出類似於
FrameMotion_time.csv（輸出）
start end End Start 0 2019-02-21 18:10:59.718005 2019-02-21 18:08:35.791487
更新於： 2019 年 7 月 30 日
290 次瀏覽
廣告