BATMAN

Count.py

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+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)