import cv2 import time import requests import json from datetime import datetime # Load configuration generated by the QR code setup with open("config.json", "r") as f: config = json.load(f) RTSP_URL = config["rtsp_url"] BOT_TOKEN = config["telegram_bot_token"] CHAT_ID = config["telegram_chat_id"] CAM_ID = config["cam_id"] def send_telegram_alert(image_path): """Sends high-quality uncompressed image alerts to Telegram API.""" url = f"https://telegram.orgBOT_TOKEN/sendDocument" caption = f"🚨 **Motion Detected**\n📷 Camera: CAM_ID\n📅 Time: datetime.now().strftime('%Y-%m-%d %H:%M:%S')" with open(image_path, 'rb') as photo: files = 'document': photo data = 'chat_id': CHAT_ID, 'caption': caption, 'parse_mode': 'Markdown' response = requests.post(url, files=files, data=data) return response.json() def monitor_stream(): """Processes RTSP frames and analyzes structural changes for motion detection.""" cap = cv2.VideoCapture(RTSP_URL) # Configure buffer size to lower stream processing latency cap.set(cv2.CAP_PROP_BUFFERSIZE, 2) ret, frame1 = cap.read() ret, frame2 = cap.read() last_alert_time = 0 cooldown_period = 15 # seconds to prevent notification flooding while cap.isOpened(): if not ret: # Reconnect logic if RTSP stream drops cap = cv2.VideoCapture(RTSP_URL) ret, frame1 = cap.read() ret, frame2 = cap.read() continue # Calculate structural absolute differences between consecutive frames diff = cv2.absdiff(frame1, frame2) gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(gray, (5, 5), 0) _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY) dilated = cv2.dilate(thresh, None, iterations=3) contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) current_time = time.time() for contour in contours: # Adjust contour area threshold based on camera distance and targets if cv2.contourArea(contour) < 5000: continue if current_time - last_alert_time > cooldown_period: filename = f"alert_int(current_time).png" # Save frame with high-quality PNG compression parameters cv2.imwrite(filename, frame1, [cv2.IMWRITE_PNG_COMPRESSION, 0]) print(f"[ALERT] Triggered on CAM_ID. Dispatching high-fidelity media...") send_telegram_alert(filename) last_alert_time = current_time break # Break loop to prioritize next frame alignment frame1 = frame2 ret, frame2 = cap.read() cap.release() if __name__ == "__main__": monitor_stream() Use code with caution. 5. Security Hardening and Optimization
Using the secure, lightning-fast messaging app as the front-end interface for receiving alerts, snapshots, and live streams.
| Component | Function | | :--- | :--- | | | Simplified, plug-and-play Wi-Fi setup for anyone. | | Telegram Bot | Free, instant, and secure cloud-based messaging. | | Raspberry Pi | Central, low-power, and private automation hub. | | Motion Detection | Smart, customizable alerts that filter out false events. | | OpenIPC | Advanced customization, streaming, and full control. | ip camera qr telegram extra quality upd
Accessing IP cameras without authorization is illegal in most jurisdictions. Even if the "extra quality upd" sounds like a technical curiosity, such packages are frequently used for unauthorized surveillance or distributing access to private cameras.
: Enter your Wi-Fi credentials into the camera’s proprietary app (e.g., Hik-Connect, Yi Home, or SriHome) to generate a setup QR code. Scan with Camera import cv2 import time import requests import json
Access your feed on your phone, desktop, or tablet instantly. Setting Up Your IP Camera via Telegram (Step-by-Step)
in the app's video settings before linking to third-party services. 2. Setting Up the Telegram Bot | | Raspberry Pi | Central, low-power, and
Here is everything you need to know about the new update and how to set it up.