Create core_pipeline.py

core_pipeline.py

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+import cv2
+import os
+import tempfile
+import numpy as np
+from ultralytics import YOLO
+import matplotlib.pyplot as plt
+
+# Load YOLOv8 model (you can use your own tree-detection model)
+model = YOLO("yolov8n.pt")  # Replace with tree-trained model
+
+def extract_frames(video_path, interval=30):
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    idx = 0
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if idx % interval == 0:
+            frames.append(frame)
+        idx += 1
+    cap.release()
+    return frames
+
+def detect_trees(frame):
+    results = model(frame)
+    bboxes = results[0].boxes.xyxy.cpu().numpy()
+    confs = results[0].boxes.conf.cpu().numpy()
+    labels = results[0].boxes.cls.cpu().numpy()
+    return frame, bboxes, confs, labels
+
+def plot_detections(frame, bboxes):
+    for box in bboxes:
+        x1, y1, x2, y2 = box.astype(int)
+        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+    return frame