import cv2
import numpy as np
from ultralytics import YOLO
import cvzone

device = 'cuda'

# Load the YOLO11 model
model = YOLO("yolo11m-pecan.pt")

# Open the video file (use video file or webcam, here using webcam)
cap = cv2.VideoCapture(0)
cy1=550
offset=30
idDict={}
pecanCount = 0

while True:
    ret,frame = cap.read()
    if not ret:
        break
    
    # Run YOLO11 tracking on the frame, persisting tracks between frames
    results = model.track(frame, persist=True,classes=0,device = device)

    # Check if there are any boxes in the results
    if results[0].boxes is not None and results[0].boxes.id is not None:
        # Get the boxes (x, y, w, h), class IDs, track IDs, and confidences
        boxes = results[0].boxes.xyxy.int().cpu().tolist()  # Bounding boxes
        class_ids = results[0].boxes.cls.int().cpu().tolist()  # Class IDs
        track_ids = results[0].boxes.id.int().cpu().tolist()  # Track IDs
        confidences = results[0].boxes.conf.cpu().tolist()  # Confidence score
       
        for box, class_id, track_id, conf in zip(boxes, class_ids, track_ids, confidences):
            x1, y1, x2, y2 = box
            cy = int(y1+y2)//2
            if cy<(cy1+offset) and cy>(cy1-offset) and track_id not in idDict.keys():
                pecanCount += 1
                idDict[track_id] = pecanCount

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()

print(pecanCount)