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
This guide covers the technical architecture, setup procedures, stream optimization techniques, and automation scripts required to deploy an IP camera system featuring QR-assisted configuration and Telegram alert integration. 1. System Architecture and Hardware Selection
When streaming high-definition video directly to a messaging platform, hardware and bandwidth limits can cause dropped frames or fragmented videos. Use these optimization tips to maintain an extra-quality feed: ip camera qr telegram extra quality upd
Paste the Telegram Bot API Token and your personal Chat ID into the webhook configuration fields.
To bridge your camera's motion detection triggers with the Telegram notification daemon, employ an optimized Python script that monitors your network video recorder (NVR) directories or directly captures frames via an RTSP loop. Production Alert Script import cv2 import time import requests import json
Input your configuration string into your firmware's QR utility. This tool compiles the network settings, Telegram authentication, and extra-quality video parameters into a dense QR code matrix.
Copy the provided by BotFather (e.g., 123456789:ABCdefGhIJKlmNoPQRsTUVwxyZ ). Security Hardening and Optimization This guide covers the
This guide will walk you through setting up, optimizing, and securing a next-generation Telegram-integrated IP camera system.
Access your camera's status from a phone, tablet, or desktop simultaneously. Step-by-Step Setup: QR Code & Telegram
Manually typing long RTSP URLs, bot tokens, and chat IDs into headless servers can cause formatting errors. Creating a configuration QR code streamlines deployment. Format your configuration details into a clean JSON string: