פיתוח בינה מלאוכתית – חיבור מצלמת WIFI פשוטה לרשת – רובוטרוניקס: מפתחת מעגלים ואלקטרוניקה ,מיקרובקרים , תוכנה ורובוטיקה: התקשר עכשיו 0506399001

בפרוייקט זה נחבר כ 24 מצלמות וואפי פי ישירות לרשת שלנו של הבינה מלאכותית וניתן לה ללמוד

Tp-link Tapo TC65 Outdoor Security Wi-Fi Camera Purchase

This summer, we decided it was time to look again at putting an external camera on our roof to use on the Virtual Swanage website. After looking at various options, we ordered a tp-link TC65 Outdoor Security Wi-Fi Camera, which cost £45.

The camera specifications are:

Sensor

Image Sensor

1/2.7 “

Resolution

3 MP (2304 × 1296)

Lens

F/NO: 2.2; Focal Length: 3.89mm

Night Vision

850 nm IR LED up to 98 ft (30m)

Video

Frame Rate

15fps

Video Streaming

3MP

Video Compression

H.264

Network

Security

128 bit AES encryption with SSL/TLS

Wireless Rate

11Mbps(802.11b)

54Mbps(802.11g)

150Mbps(802.11n)

Frequency

2.4 GHz

Wireless Security

WPA/WPA2-PSK

The camera does not have the option to upload an image directly to our server but supports the Real Time Streaming Protocol (RTSP), which allows the capture of the video stream by another device on the network.

The camera has connections for power and an RJ45 network connection. We do not want to connect another network device on the roof after the lightning strike a few years ago, which destroyed all of our networked devices and computers.

We mounted the new camera to the bracket which holds our TV antenna, and the power cable is routed into the loft space via one of the window frames. This goes into the supplied PSU adapter to power the camera.

Setting up the Tapo Camera for RTSP streaming

To use the RTSP protocol, you must set a username and password in the camera and find its IP address.

The camera uses the following URL format to access the video stream:

rtsp://username:password@IP-Address/stream1

Your internet router assigns the camera’s IP address; you can find it on your router’s DHCP list or in the Tapo app.

Finding the IP address in the Tapo app

On the app’s home page, select your camera model/name.
Tap the settings icon (cog) at the top-right of the app.
Tap the camera name at the top of the Camera Settings screen.
The network settings will be shown, including the IP address and MAC address of the device.

Setting the username and password

On the app’s home page, select your camera model/name.
Tap the settings icon (cog) at the top-right of the app.
Tap Advanced Settings, then select Device Account
Enter your new username and password and save.

Capturing the Images

OpenCV Logo

We have a small Linux server running 24/7 in our network cupboard, and with help from Chat GPT, we created a Python script to access the video feed via its RTSP URL and OpenCV.

OpenCV is a library of programming functions mainly for real-time computer vision and processing.

The Python script uses a threading timer to capture a new frame every 30 seconds.

The capture function first opens the video stream and captures the current frame. If this succeeds, it crops the image to a 1920 x 1080 size.

There are windows visible in the houses directly in front of the camera, which are blurred using the GaussianBlur function in OpenCV.

The image is then saved to a folder on the server with a timestamp, and a separate script uploads the latest image to the server every 30 seconds.

In the future if our internet provider decides to upgrade us to fibre broadband we will look at setting up a video stream and upgrading to a better quality 4K camera.

  #!/usr/bin/python3

from __future__ import absolute_import, division, print_function, \
                                                    unicode_literals
import time
import datetime as dt
import os
import cv2
import traceback
import threading

# Example usage
rtsp_url = "rtsp://username:password@ipaddress/stream1"
image_folder = "/home/user/webcam/images"
video_folder = "/home/user/webcam/videos"
capture_interval = 30  # seconds


error_log_file = "/home/user/webcam/webcamerror.log"

blur1_x = 0  # X-coordinate of the top-left corner of the blur region
blur1_y = 1000  # Y-coordinate of the top-left corner of the blur region
blur1_width = 200  # Width of the blur region
blur1_height = 80  # Height of the blur region

blur2_x = 254  # X-coordinate of the top-left corner of the blur region
blur2_y = 936  # Y-coordinate of the top-left corner of the blur region
blur2_width = 30  # Width of the blur region
blur2_height = 50  # Height of the blur region

blur3_x = 519  # X-coordinate of the top-left corner of the blur region
blur3_y = 936  # Y-coordinate of the top-left corner of the blur region
blur3_width = 52  # Width of the blur region
blur3_height = 80  # Height of the blur region

blur4_x = 598  # X-coordinate of the top-left corner of the blur region
blur4_y = 936  # Y-coordinate of the top-left corner of the blur region
blur4_width = 52  # Width of the blur region
blur4_height = 80  # Height of the blur region

blur5_x = 684  # X-coordinate of the top-left corner of the blur region
blur5_y = 936  # Y-coordinate of the top-left corner of the blur region
blur5_width = 52  # Width of the blur region
blur5_height = 80  # Height of the blur region

cap = None

def blur_region(frame, x, y, width, height):
    # Create a region of interest (ROI) for blurring
    roi = frame[y:y+height, x:x+width]

    # Apply Gaussian blur to the ROI
    blurred_roi = cv2.GaussianBlur(roi, (99, 99), 0)

    # Replace the ROI with the blurred version
    frame[y:y+height, x:x+width] = blurred_roi

    return frame

def get_image():

    print("Opening video stream")
    cap = cv2.VideoCapture(rtsp_url)

    # Check if the video stream is opened successfully
    if cap.isOpened():       
    
        # Get the original frame size
        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

        # Calculate the crop coordinates
        crop_x = 0
        crop_y = 0
        crop_width = min(frame_width, 1920)
        crop_height = min(frame_height, 1080)

        # Read the current frame from the video stream
        ret, frame = cap.read()

        # Check if the frame was read successfully
        if ret:
            # Crop the frame
            frame = frame[crop_y:crop_y + crop_height, crop_x:crop_x + crop_width]

            # Blur the specified region of the frame
            frame = blur_region(frame, blur1_x, blur1_y, blur1_width, blur1_height)
            frame = blur_region(frame, blur2_x, blur2_y, blur2_width, blur2_height) 
            frame = blur_region(frame, blur3_x, blur3_y, blur3_width, blur3_height) 
            frame = blur_region(frame, blur4_x, blur4_y, blur4_width, blur4_height)
            frame = blur_region(frame, blur5_x, blur5_y, blur5_width, blur5_height) 

            # Save the frame as a JPEG image
            timestamp = time.strftime("%Y%m%d%H%M%S")

            filename = f"{image_folder}/webcam-{timestamp}.jpeg"
            cv2.imwrite(filename, frame, [cv2.IMWRITE_JPEG_QUALITY, 95])
            print(f"Saved frame {filename}")

        else:
            print("Error reading frame")

        cap.release()
    else:
        print("Error opening video stream")

    restart_thread()

    

def restart_thread():
    st = threading.Timer(capture_interval, get_image)
    st.daemon = True
    st.start()

def main():
    '''
    Main program function
    '''
    global cap

    get_image()

    while (True):
        time.sleep(1)


if __name__ == "__main__":
    main()

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#!/usr/bin/python3

from __future__ import absolute_import, division, print_function, \

unicode_literals

import time

import datetime as dt

import os

import cv2

import traceback

import threading

# Example usage

rtsp_url = "rtsp://username:password@ipaddress/stream1"

image_folder = "/home/user/webcam/images"

video_folder = "/home/user/webcam/videos"

capture_interval = 30 # seconds

error_log_file = "/home/user/webcam/webcamerror.log"

blur1_x = 0 # X-coordinate of the top-left corner of the blur region

blur1_y = 1000 # Y-coordinate of the top-left corner of the blur region

blur1_width = 200 # Width of the blur region

blur1_height = 80 # Height of the blur region

blur2_x = 254 # X-coordinate of the top-left corner of the blur region

blur2_y = 936 # Y-coordinate of the top-left corner of the blur region

blur2_width = 30 # Width of the blur region

blur2_height = 50 # Height of the blur region

blur3_x = 519 # X-coordinate of the top-left corner of the blur region

blur3_y = 936 # Y-coordinate of the top-left corner of the blur region

blur3_width = 52 # Width of the blur region

blur3_height = 80 # Height of the blur region

blur4_x = 598 # X-coordinate of the top-left corner of the blur region

blur4_y = 936 # Y-coordinate of the top-left corner of the blur region

blur4_width = 52 # Width of the blur region

blur4_height = 80 # Height of the blur region

blur5_x = 684 # X-coordinate of the top-left corner of the blur region

blur5_y = 936 # Y-coordinate of the top-left corner of the blur region

blur5_width = 52 # Width of the blur region

blur5_height = 80 # Height of the blur region

cap = None

def blur_region(frame, x, y, width, height):

# Create a region of interest (ROI) for blurring

roi = frame[y:y+height, x:x+width]

# Apply Gaussian blur to the ROI

blurred_roi = cv2.GaussianBlur(roi, (99, 99), 0)

# Replace the ROI with the blurred version

frame[y:y+height, x:x+width] = blurred_roi

return frame

def get_image():

print("Opening video stream")

cap = cv2.VideoCapture(rtsp_url)

# Check if the video stream is opened successfully

if cap.isOpened():

# Get the original frame size

frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))

frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

# Calculate the crop coordinates

crop_x = 0

crop_y = 0

crop_width = min(frame_width, 1920)

crop_height = min(frame_height, 1080)

# Read the current frame from the video stream

ret, frame = cap.read()

# Check if the frame was read successfully

if ret:

# Crop the frame

frame = frame[crop_y:crop_y + crop_height, crop_x:crop_x + crop_width]

# Blur the specified region of the frame

frame = blur_region(frame, blur1_x, blur1_y, blur1_width, blur1_height)

frame = blur_region(frame, blur2_x, blur2_y, blur2_width, blur2_height)

frame = blur_region(frame, blur3_x, blur3_y, blur3_width, blur3_height)

frame = blur_region(frame, blur4_x, blur4_y, blur4_width, blur4_height)

frame = blur_region(frame, blur5_x, blur5_y, blur5_width, blur5_height)

# Save the frame as a JPEG image

timestamp = time.strftime("%Y%m%d%H%M%S")

filename = f"{image_folder}/webcam-{timestamp}.jpeg"

cv2.imwrite(filename, frame, [cv2.IMWRITE_JPEG_QUALITY, 95])

print(f"Saved frame {filename}")

else:

print("Error reading frame")

cap.release()

else:

print("Error opening video stream")

restart_thread()

def restart_thread():

st = threading.Timer(capture_interval, get_image)

st.daemon = True

st.start()

def main():

'''

Main program function

'''

global cap

get_image()

while (True):

time.sleep(1)

if __name__ == "__main__":

main()

Tp-link Tapo TC65 Outdoor Security Wi-Fi Camera Purchase

Setting up the Tapo Camera for RTSP streaming

Capturing the Images

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