Yathrakal ishtam

 dsgdsg sudo apt install python3-dev python3-pip python3-setuptools python3-wheel gfortran libopenblas-dev liblapack-dev -y pip3 install scipy

  from ultralytics import YOLO import cv2 import winsound import time # Load pretrained YOLO model model = YOLO( "yolov8n.pt" )   # You can replace with yolov9 if needed # Animal + Human classes we want to detect TARGET_CLASSES = {     0 : "person" ,     14 : "bird" ,     15 : "cat" ,     16 : "dog" ,     17 : "horse" ,     18 : "sheep" ,     19 : "cow" ,     20 : "elephant" ,     21 : "bear" ,     22 : "zebra" ,     23 : "giraffe" } # Alert sound file ALERT_SOUND = "alert.wav" def play_alert ():     try :         winsound . PlaySound ( ALERT_SOUND , winsound . SND_FILENAME )     except :         print ( "Error playing alert sound!" ) def main ():     cap = cv2 .VideoCapture( 0 )   # 0 = default webcam     last_alert_time = 0     while True :     ...

from ultralytics import YOLO import cv2 import BlynkLib import time from datetime import datetime import requests # Blynk Configuration BLYNK_AUTH = "YOUR_BLYNK_AUTH_TOKEN"  # Replace with your Blynk auth token BLYNK_SERVER = "blynk.cloud" # Initialize Blynk blynk = BlynkLib.Blynk(BLYNK_AUTH) # Virtual Pins VPIN_ALERT = 0          # V0 - Alert text display VPIN_DETECTION_LED = 1  # V1 - LED indicator VPIN_LAST_SEEN = 2      # V2 - Last detection timestamp VPIN_ANIMAL_COUNT = 3   # V3 - Number of animals detected VPIN_IMAGE = 4          # V4 - Image widget for detection snapshots # Load YOLO model model = YOLO("yolov8n.pt") # Animal classes to detect animal_classes = {     0: "person",     14: "bird",     15: "cat",     16: "dog",     17: "horse",     18: "sheep",     19: "cow",     20: "elephant",     21: "bear" } ...